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  • Multiple roles of palmitic acid in cardiovascular diseases

    Multiple roles of palmitic acid in cardiovascular diseases

    Introduction

    Palmitic acid (PA) is a 16-carbon long-chain saturated fatty acid (SFA),1,2 which is widely found in animals and plants.3 It is an essential constituent acid of adipose tissue and the most abundant SFAs in the body,4 accounting for approximately 44–52% of the body’s total fat content5 and 28–32% of the total serum fatty acid (FA).6

    Cardiovascular diseases (CVD) is one of the deadliest diseases worldwide.7 CVD mainly includes coronary heart disease, cerebrovascular disease, peripheral artery disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, and pulmonary embolism.8 In addition, atrial fibrillation is very closely linked to atherosclerosis (AS) and has largely the same pathophysiological basis as other CVD: endothelial dysfunction and inflammation, coronary artery disease is an important and clinically relevant risk factor of atrial fibrillation.9 According to the World Health Organization, 17.3 million people died from CVD in 2016, accounting for 31.5% of all deaths. This number is expected to increase to 23.6 million by 2030.8 The mortality rate of CVD has exceeded that of cancer, infectious diseases, maternal diseases, and neonatal diseases.10 Hyperlipidaemia (elevated total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and reduced high-density lipoprotein cholesterol (HDL-C)), Systemic inflammation, and oxidative stress play a crucial role in the development of CVD.11–14

    Emerging evidence indicates that elevated circulating FA levels correlate with CVD incidence, and free fatty acids show diagnostic potential as early biomarkers for AS.15–20 In vivo and in vitro experiments evidence suggests potential mechanisms linking PA intake with CVD pathogenesis.21,22 Epidemiological studies also indicate that high dietary PA exposure associates with increased CVD risk across diverse populations.23,24 Elevated serum PA concentrations have been proposed to heighten atrial fibrillation risk primarily through PA’s impact on endothelial dysfunction and inflammation.25 However, critical gaps persist in current research: the analysis of PA’s biosynthetic pathways remains incomplete, with insufficient mechanistic delineation specific to individual pathologies, particularly AS, ischemic heart disease (IHD), and ischemic stroke (IS); in addition, lack of translational research frameworks connecting PA-related molecular mechanisms to therapeutic strategies; finally, while substantial evidence supports PA’s detrimental cardiovascular effects, several studies report context-dependent outcomes (Table 1). To address these gaps, this review: Systematically synthesizes PA’s anabolic pathways and pathological mechanisms in AS, IHD, and IS; Identifies novel targetable nodes for CVD prevention/treatment by pinpointing therapeutically exploitable sites within key biological pathways.

    Table 1 Palmitic Acid Associations with Traditional Cardiovascular Risk Factors

    Methods

    A systematic search was performed across four electronic databases (PubMed, Scopus, Web of Science, and Google Scholar) to comprehensively identify literature examining the association between palmitic acid and specific cardiovascular diseases, namely atherosclerosis, ischemic heart disease, and ischemic stroke. Search results were merged and deduplicated. Initial study inclusion/exclusion was determined by screening titles and abstracts. The review encompassed literature published through December 2024.

    The Anabolic Pathways of Palmitic Acid

    Endogenous Synthesis and Exogenous Uptake of Palmitic Acid

    Palmitic acid is mainly synthesized in the liver. In the de novo synthesis, glucose and glutamine produce pyruvate by glycolysis, which undergoes the tricarboxylic acid cycle in the mitochondria to produce citrate. And then the citrate is cleaved in the cytoplasm by ATP-citrate lyase (ACLY) to Acetyl-CoA and oxaloacetate. Acetyl-CoA is then carboxylated to malonyl-CoA by acetyl-CoA carboxylase (ACC) and condensed by fatty acid synthase (FASN) in a repeated reactions to generate PA.30,31 The endogenous synthesis of PA is controlled precisely under normal circumstances. However, when carbohydrate intake is excessive, the carbohydrate response element-binding protein (ChREBP) is activated, upregulation of the transcription factor sterol regulatory element binding protein-1c (SREBP-1c) and resulting in insulin production, which subsequently increases PA production32–34 (Figure 1).

    Figure 1 Endogenous synthesis and exogenous uptake of palmitic acid. In the de novo synthesis, glucose and glutamine are enzymatically catalyzed to produce citrate, which is cleaved to acetyl-CoA and oxaloacetate. Acetyl-CoA is carboxylated to malonyl-CoA, which is condensed by the repeated actions of FASN to produce PA. In the process of exogenous uptake, dietary fat is digested into free fatty acids and monoglycerides through the emulsification of bile acids in the duodenum and upper jejunum, which are then absorbed and converted into TG by intestinal epithelial cells, and then combined with apolipoproteins to form chylous particles, which enter the lymphatic system and the ultimately the bloodstream.

    Exogenous Uptake Pathway of Palmitic Acid

    Palmitic acid is found in plant oils including palm oil, peanut oil, and coconut oil, as well as in animal fats like butter and cream. Therefore, the human body can also obtain PA through exogenous dietary intake.35,36 The primary sites for digestion and absorption of fats in the human body are the duodenum and the upper jejunum. When the body consumes fats containing PA from the diet, they are emulsified by bile acids to form hydrophobic fat globules, which are then further broken down into smaller droplets. These droplets are subsequently hydrolyzed by pancreatic lipase into free fatty acids and monoacylglycerol, which are absorbed by the intestinal epithelial cells. In the endoplasmic reticulum (ER) of the epithelial cells, free fatty acids are converted into TG, which then combine with apolipoproteins. These TG, together with apolipoproteins, are transported through chylomicrons to the lymphatic system and eventually enter the bloodstream37,38 (Figure 1).

    Metabolism of Palmitic Acid

    The distribution and metabolism of PA in tissues is strictly controlled by the organism, which normally regulates the de novo synthesis pathway according to the amount of exogenous PA consumed.39,40 First, PA as a kind of FA, have the capability of providing the body with energy through the process of oxidative catabolism.41,42 PA combines with carnitine to produce an acylcarnitine molecular, and then the acylcarnitine molecular is transported across the mitochondrial membrane to the mitochondrial matrix to generate a molecule of nicotinamide adenine dinucleotide (NADH), a molecule of flavin adenine dinucleotide, reduced (FADH2), and an acetyl-CoA, which is eventually consumed as energy for the body.43 Secondly, PA is elongated or desaturated for conversion to other FA or compounds,44 which are produced in the presence of FA elongases (elongation of very long-chain fatty acids 1–7 (ELOVL1-7)) to produce longer chain FA (eg, stearic acid (SA) and arachidonic acid).45 Moreover, PA synthesized endogenously in adipocytes is converted to other FA or compounds through elongation and desaturation in preference to exogenous PA, thus ensuring that the concentration of PA in tissues is within the normal range to maintain cell membrane fluidity and insulin sensitivity.46,47 Finally, PA itself can be transformed into an important component of biofilms (phospholipids), which plays an important role in biological processes (eg, cellular proliferation, reproductive processes, and intracellular transport). PA was found to generate phosphatidylcholine and phosphatidylethanolamine (PE) by deacylation in rat hepatocyte, the final synthesis of membrane phospholipids.48 This process is regulated by membrane-binding transcription factors and can further regulate lipid synthesis.49

    In obese subjects, the activity of stearoyl coenzyme a desaturase 1 (SCD1) was increased, and SCD1 was associated with insulin sensitivity.46 However, under pathological conditions including insulin resistance and chronic nutritional imbalance, this regulatory mechanism can be disrupted, leading to excessive PA deposition in the liver and eventually to a series of CVD.50,51 Several studies have measured plasma PA concentrations in healthy subjects, indicating a range of 100~409 µM. Nevertheless, patients with diabetes, hypertriglyceridemia, and CVD have elevated plasma PA levels (Table 2).

    Table 2 Plasma Palmitic Acid Levels

    Palmitic Acid and Cardiovascular Diseases

    Palmitic Acid and Atherosclerosis

    Atherosclerosis is the basis of most CVD and causes of death, for example, coronary heart disease and stroke.58 It is characterized by the endothelial dysfunction and inflammation, form cells formation from macrophage, atherosclerotic plaque formation in the intima of arteries and apoptosis,59–61 which may result in acute cardiovascular events due to plaque rupture and thrombosis.62 Studies have demonstrated that the high concentrations of PA in blood are involved in the formation of AS through a variety of biological processes, including hyperlipidaemia,63,64 inflammation,65 vascular endothelial damage,66 form cells formation,67 and downregulation of apolipoprotein M (APOM).68

    Palmitic Acid Induces Hyperlipidemia

    There is an increased risk of CVD associated with high levels of TC, LDL-C, and lower levels of HDL-C.69,70 PA can induce AS by altering blood cholesterol levels, particularly through elevating LDL-C levels.63,64 PA inhibits the expression of low density lipoprotein (LDL) receptors and accelerates the secretion of very low-density lipoprotein (VLDL) from the liver.51 Genes related to lipid transport, adipogenesis, lipid droplet formation, and glucose and FA metabolism were found to be upregulated after incubation with PA in human hepatocytes cultured in vitro, similar effects were observed in primary cultures of human pancreatic islets.71,72 Specifically, PA promoted lipid accumulation by upregulating the CCN1/integrin α5β1 pathway.73 Lipid accumulation and apoptosis were also observed in PA-treated human kidney-2 (HK2).74 Increased dietary levels of 18:2(n-6) FA lead to lower total and LDL-C levels, while at low dietary levels of 18:2(n-6) FA, increased PA content leads to a significant increase in total and LDL-C levels.75 Meanwhile, in a controlled metabolic feeding study, PA intake promotes elevated blood cholesterol levels, consistent with previous studies.76–78 In addition, PA also induces insulin resistance, leading to impaired lipid metabolism. Prolonged exposure of cultured human, rat or mouse islets to PA leads to reduced insulin transcription, impairment of glucose-induced insulin secretion, and finally to β-cell apoptosis.79–81 PA promotes β-cell apoptosis via mTOR-mediated downregulation of protein kinase B (AKT).82 In human umbilical cord endothelial cells, PA induces insulin resistance by upregulating human regulator of G protein signaling 2 (RGS2) expression, which inhibit insulin-mediated AKT phosphorylation83,84 (Figure 2).

    Figure 2 Overview of the mechanisms by which palmitic acid promotes atherosclerosis. PA promotes the progression of by inducing hyperlipidemia, vascular endothelial cell injury, foam cell formation, downregulation of APOM, and proinflammatory effects. Its proinflammatory effect is by activating TLR2 and TLR4, enhanced LPS production and synergistic interactions with LPS, promoting FABP4 expression, amplification of proinflammatory T-cell responses, and induction of ER stress and oxidative stress (↑: increase/activation; ↓: decrease/inhibition).

    Palmitic Acid Mediates Inflammation

    Palmitic Acid Promotes the Production of Inflammatory Factors

    PA has been shown to directly increase levels of interleukin-6 (IL-6) in vivo and in vitro.85–87 PA upregulates the expression of C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), and inducible nitric oxide synthase (iNOS) in vascular smooth muscle cells (VSMCs), thereby triggering an inflammatory response in cardiac fibroblasts and inducing apoptosis in VSMCs.88 PA increases the level of the cysteine-rich angiogenic inducer 61 (CYR61) in endothelial cells, thereby stimulating the production of pro-inflammatory cytokines and pro-apoptotic factors.89 PA also induces the secretion of interleukin-1β (IL-1β), monocyte chemoattractant protein-1 (MCP-1), and TNF-α by peritoneal macrophages, which activated the inflammatory process in LDLr KO mice and ultimately induced AS formation.90 In microvascular endothelial cells (EOMA lineage), palmitate stimulates the activation of NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome.88 Further studies showed that PA treatment of mouse primary macrophages induced the formation of crystals within the macrophages, which activated the NLRP3 inflammasome, resulting in lysosomal dysfunction and increased IL-1β release91 (Figure 2).

    Palmitic Acid Activates Toll-Like Receptor 4 (TLR4) to Promote Inflammation

    During the inflammatory response, toll-like receptors (TLR) serve as receptors for lipopolysaccharide (LPS).92–94 Several studies have demonstrated that PA is a TLR agonist that activates TLR4 and TLR2, and induces dimerization among TLR2 and TLR1, TLR2 and TLR6, or TLR4 and TLR6.95,96 TLR4 translocates into lipid rafts after activation and recruits its downstream adapter molecules (MyD88 and TRIF) to the rafts. After dimerizing with MyD88 or TRIF, initiates pro-inflammatory cytokine and type I interferon production.84 In addition, activated TLR4 forms a complex with myeloid differentiation protein 2 (MD2), which triggers downstream signaling. However, it is uncertain whether PA is a direct agonist of TLR4-MD2.97 During the activation of TLR4, atypical protein kinase Czeta (PKCζ) is triggered by RhoA, next PKCζ activates transforming growth factor β-activated kinase 1 (TAK1), which then participates in the activation of NF-κB,98 which results in the production of inflammatory cytokines (eg, TNF-α and IL-6).90 PA promoted the TLR4/phosphorylated-NF-κB signaling pathway by inhibiting Krüppel-like factor 4 (KLF4), upregulated Galectin-3 expression, and improved insulin resistance in macrophage99 (Figure 2).

    Palmitic Acid Activates the Proinflammatory Function of T Cells

    T cells are an instrumental component of adaptive immunity and account for 10% of all cells in atherosclerotic plaques.100,101 Using single-cell sequencing techniques, T cells were found to account for approximately 30–65% of white blood cells in atherosclerotic plaques in humans and mice.102–104 CD4+ T cells are the predominant T cell subtype in AS and exacerbate atherogenesis in immunodeficient Apoe-/- mice.105 Researchers found that both CD4+ T cells and CD8+ T cells were increased at atherosclerotic lesion sites associated with acute coronary syndrome.106 PA activates the proinflammatory function of T cells in four ways: metabolism, activation, proliferation, and polarization.107 There is evidence that PA increases insulin receptors (IR), insulin-like growth factors 1 (IGF-1), glucose transporter type 4 (GLUT4), and insulin receptor substrate 1 (IRS1) on the surface of T cells, resulting in T cell activation. PA also stimulates the proliferation of T cells and induces the polarization of T cells into proinflammatory subpopulations (Th1 cells and Th17 cells), which then induce an inflammatory response.107 The addition of 1 mM PA to peripheral blood mononuclear cells activated with anti-CD3 and anti-CD28 increased the proportion of Th1 and Th17 cells, while decreasing that of TH 2 and Treg cells. After in vitro exposure to PA, CD4+ T cells or CD8+ T cells isolated from five healthy, non-diabetic, and glucose-tolerant individuals were found to be activated in a time and concentration-dependent manner108 (Figure 2).

    Palmitic Acid Promotes Inflammation in Synergy with LPS

    A high-fat diet increases the levels of short-chain FA by altering the gut microbiome, which leads to elevated levels of LPS and enhanced activation of TLR4.109 PA also increases ceramide production through de initio synthesis and sphingolipid hydrolysis, thereby enhancing IL-6 expression and TNF-α stimulation induced by LPS.110 Researchers fed mice both LPS and a high-fat diet rich in PA, which accelerated thoracic aortic atherosclerosis.111 In human aortic endothelial cells (HAECs) and cardiac microvascular endothelial cells (MICECs), co-treatment with LPS and PA increased IL-6 expression at 36 hours111 (Figure 2).

    Palmitic Acid Promotes the Expression of Fatty Acid‑binding Protein 4 (FABP4)

    As a cytoplasmic FA carrier protein, FABP4 regulates lipid transport and responses in cells, and is associated with metabolic and inflammatory pathways.112–115 FABP4 bind a long-chain FA, including PA, SA, oleic acid (OA), linoleic acid (LA), and facilitates the translocation of FAs to specific organelles in the cell (eg mitochondria, peroxisomes, ER, and nucleus), regulates enzymatic activity, and stores excess FAs as lipid droplets.116 The FABP4 protein has a high affinity for free monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) in cells under normal conditions, however, under oxidative stress conditions, the conformation of FABP4 changes, losing its affinity for most FA (except PA), and triggers an inflammatory response.116 PA increases FABP4 protein expression in macrophages via ER stress.117,118 The genetic ablation of FABP4 in macrophages showed inhibition of inflammatory signaling, reduced NF-κB pathway activation, and reduced ER stress, protecting mice from AS and dyslipidemia.119,120 In C2C12 skeletal muscle cells, overexpression of FABP4 protein decreases expression the expression of Sirtuin 3, uncoupling protein 2 (UCP2), and Peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α), ultimately leading to increased ROS production in mitochondria and inflammation121–123 (Figure 2).

    Palmitic Acid Activates ER Stress

    The ER is involved in the biosynthesis of cholesterol, steroids, and other lipids. A high concentration of free fatty acids (eg PA) may disrupt lipid metabolism, which triggers stress in the ER. When PA is transformed into phospholipids and diacylglycerol (DAG), it accumulates in the ER, causing disruptions in the structure of the ER and activation of the stress sensors.124–126 The extracellular signal-regulated kinase (ERK) pathway mediates translation of CCAAT/enhancer binding protein (C/EBP) homologous proteins and genes involved in autophagy that are dependent on activating transcription factor 4 (ATF4). Inositol-requiring enzyme 1α (IRE1-α) mediates the expression of tumor necrosis factor receptor-associated factor 2 (TRAF2) and apoptosis signal-regulated kinase 1 (ASK1)/C-jun N-terminal kinase (JNK). They contribute to the ability of stress cells to maintain autophagy, which ultimately triggers ER oxidative and inflammatory signaling pathways leading to apoptosis.127–131 Phosphorylated ERK, IRE1α, and JNK activation are elevated in both adipose tissue and liver of high fat diet fed mice, which triggers ER stress, eventually leads to apoptosis.132–135 By upregulating ATF4 and C/EBP homologous protein (CHOP) expression, decreasing cytoplasmic NAD+/NADH, and reducing Sirt1 activity, PA induced ER stress in H9c2 myogblasts.136 Heart-specific sirt1 knockout mice fed a high palmitate diet were found to express higher levels of CHOP and ATF4.136 In obese individuals and type 2 diabetes mellitus (T2DM) patients, chronic exposure of β-cells to FA results in ER stress and lipotoxicity137 (Figure 2).

    Palmitic Acid Induces Oxidative Stress

    Increased reactive oxygen species (ROS) are the primary cause of palmitate-induced oxidative stress. PA enhances ROS production by promoting lipid uptake in podocytes, and the activity calcium/protein kinase Cα/NADH oxidase 4 (NOX4) pathway in endothelial cells, inhibited mitochondrial respiratory chain complex I and complex III. And the activity of adenine nucleotide carrier protein (ADP/ATP carrier protein).138–141 Normal mouse hepatocytes AML12 treated with PA. Lipid accumulation, expression of total ROS, mitochondrial ROS, NOX4, inflammasomes, and IL-1β were detected in hepatocytes after 24 h142 (Figure 2).

    Palmitic Acid Induces Vascular Endothelial Injury

    Vascular endothelial injury is an important pathological process in the process of AS. Endothelial dysfunction, characterized by impaired vasodilation, inflammation, and thrombosis, triggers future CVD.143 Reduced endothelial progenitor cells are independent predictors of CVD morbidity and mortality.144 Lipotoxicity of PA decreases immune surveillance protein DDX58/Rig-1 expression and activity, leading to impaired autophagy and apoptosis;145 apoptosis in vascular endothelial cells induces endothelial injury and promotes AS progression.146,147 A member of the angiopoietin-like protein family involved in lipid metabolism promotes endothelial cell proliferation and inhibits PA-induced endothelial cell injury by increasing autophagy, which may inhibit AS.66 Also, activation of the interferon regulator 3 (IRF3) pathway causes endothelial inflammation.148 Nitric oxide (NO) from enzymatic NO synthases (NOS) system importantly contributes to vascular homeostasis, in addition to the classical NOS system, NO can also be generated via the nitrate-nitrite-NO pathway.149 The addition of PA to HAECs resulted in decreased cell viability, reduced intracellular NO production, increased migratory capacity of HAECs, and cellular oxidative stress, ultimately leading to endothelial-to-mesenchymal transition.150 In endothelial cells, PA upregulated the expression of phosphorylated p38, JNK, and caspase-3, thereby increasing endothelial apoptosis dose- and time-dependently.151,152 Patients with coronary artery disease showed significantly higher levels of phosphorylation of p38 and mitogen-activated protein kinase (MAPK) in endothelial progenitor cells than healthy individuals.153 Inhibition or knockout of p38 and MAPK significantly increases the number of circulating endothelial progenitor cells154 (Figure 2).

    Palmitic Acid Promotes Foam Cells Formation

    Form cells is one of the major causes of AS, which is due to the accumulation of oxidized LDL (oxLDL) in the arterial intima. Macrophages absorb accumulated oxLDL and form cells. The presence of high levels of PA in the blood enhances the ability of macrophages to take up oxLDL and produce more form cells. OxLDL is a dysfunctional lipid metabolite that is a major promoter of the prothrombotic state in both animal models and human patients.67,155 In macrophages, PA enhances lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) expression, promotes oxLDL uptake, a process mediated mainly through the ROS-p38 MAPK pathway.156 5-hydroxytryptamine (5-HT) takes part in platelet aggregation, vasoconstriction, proliferation of VSMCs, ER function, and macrophage foam cell formation, play a key role in the development of AS.157,158 In vitro experiments, exposure of macrophages and human umbilical vein endothelial Cells (HUVECs) to oxLDL or PA demonstrated that activation of 5-HT2A receptor regulates TG synthesis and oxLDL uptake by activating PKCε, resulting in the formation of lipid droplets and even foam cells.159 PA increases CD146 expression in macrophages, promoting foam cell formation and disrupting migration-related signaling,160 by activating JNK signaling and inhibiting STAT3 activation, CD146 (Gp130) promotes proinflammatory polarization of M1-like adipose tissue macrophages (ATMs)161 (Figure 2).

    Palmitic Acid Induces Apolipoprotein M Downregulation

    Palmitic acid can downregulate the expression of human APOM, promote the accumulation of cholesterol in the blood and induce the development of AS. APOM facilitates HDL metabolism and stabilization, which can reduce blood cholesterol levels, with anti-AS, anti-inflammatory and antioxidant effects.162 Generally, it is found in hepatocytes and renal tubular epithelial cells, and is weakly expressed in colorectal tissues.163,164 APOM has been shown to be a possible HDL-carrying receptor for sphingosine 1-phosphate, which enhances HDL-mediated antioxidant effects.165,166 APOM plays a role in the formation of preb-HDL,167,168 PA significantly inhibited APOM gene expression in HepG2 cells, and the peroxisome proliferator-activated receptor β/δ (PPAR β/δ) antagonist GSK3787 completely reversed PA-induced downregulation of APOM expression, indicating that PA-induced downregulation of APOM expression is mediated through the PPAR β/δ pathway.68 A key regulator of lipid metabolism, peroxisome proliferator-activated receptor (PPAR), is expressed in platelets. This receptor upregulates the transcription of lipid metabolizing enzymes, including carnitine palmitoyl coenzyme A transferase-I (GPT-I) and acyl-CoA oxidase, both of which are important to thrombosis and hemostasis169,170 (Figure 2).

    Palmitic Acid and Ischemic Heart Disease

    Ischemic heart disease is heart disease caused by narrowing/occlusion of the coronary arteries or by ischemia, hypoxia, or necrosis of the heart muscle due to spasm of the coronary arteries. Approximately 40–80% of the heart’s energy comes from FA, several cohort studies have revealed, compared with healthy young subjects, patients with chronic heart failure, myocardial ischemia, T2DM, and obese individuals elevated levels of free fatty acids (include PA) in the blood.171–177 Additionally, there are studies that indicate that PA levels in adipose tissue are related to IHD incidence. Insull et al found that SA (18:0), lauric acid (12:0), palmitoleic acid (16:1), myristic acid (14:0), and LA (18:2) acids were associated with coronary artery disease, and PA (16:0) content in adipose tissue was associated with plasma cholesterol levels.178 A study by Lee et al compared the FA composition of adipose tissue in two races with different prevalences of coronary heart disease and found significant differences in PA, palmitoleic, and OA (18:1).179 Thus, high concentrations of PA, both circulation and adipose tissue, are associated with the incidence of IHD. There was a significant increase in FA uptake and FA oxidation in the heart when the supply of free FA was increased, according to Lopaschuk GD.180 Replacing saturated FA (FA and SA) with plant-based proteins may reduce the risk of myocardial infarction.181

    Palmitic Acid Induces Apoptosis in Cardiomyocytes

    Palmitic acid induces cardiomyocyte apoptosis by promoting autophagy. Studies have shown that after treating rat cardiomyocytes with PA (0.25 and 0.5 mM) for 18 hours, the number of apoptotic cells and biochemical markers (caspase activation, DNA fragmentation), significantly increased.182 In cardiomyocytes, PA induces apoptosis by promoting the generation of ceramide and activating the mitochondrial apoptosis pathway, leading to the myofibril disintegration.183 In a cohort study involving 4249 participants, the correlation between plasma ceramide (Cer) and sphingomyelin (SM) levels and the risk of sudden heart failure was investigated. The results showed that high levels of PA were associated with a higher risk of heart failure during a median follow-up of 9.4 years.184 Ischemic events are believed to increase the flow of free fatty acids to cardiomyocytes, thereby increasing oxidative stress and causing cardiomyocyte damage.185–188 When the heart is exposed to excessive energy (eg, glucose, free fatty acids, and TG) and growth factors (eg, insulin and leptin) over a long period, it accelerates the development of cardiomyopathy, leading to cardiac hypertrophy and failure. These processes are driven by oxidative stress induced by glucolipotoxicity and become the main drivers of cell apoptosis189 (Figure 3).

    Figure 3 Overview of the mechanism by which palmitic acid promotes ischemic heart disease. PA accelerates progression of induces oxidative stress and autophagic dysregulation, and further triggers cardiomyocyte apoptosis. Additionally, PA promotes cardiomyocyte ferroptosis by reducing the protein expression of Heat Shock Factor 1 and Glutathione Peroxidase 4. These mechanisms collectively drive pathogenesis the onset and development of IHD (↑: increase/activation; ↓: decrease/inhibition).

    Palmitic Acid Promotes Cardiomyocyte Ferroptosis

    Ferroptosis is an iron-dependent form of programmed cell death.190 The primary mechanism of ferroptosis is the induction of cell death through the action of divalent iron or lipoxygenases. Additionally, the expression of the antioxidant systems glutathione and glutathione peroxidase 4 (GPX4) is also involved in the process.191 A large body of evidence has shown that ferroptosis is associated with CVD, particularly with ischemia-reperfusion injury and myocardial infarction.192 Using different ferroptosis inhibitors significantly reduced PA-induced death in both H9c2s and primary neonatal rat cardiomyocytes. Specifically, PA promotes ferroptosis by reducing the protein expression of heat shock factor 1 (HSF1) and GPX4, while overexpression of HSF1 and GPX4 effectively prevents PA-induced ferroptosis4 (Figure 3).

    Palmitic Acid and Ischemic Stroke

    Ischemic stroke has become a major cause of global disease burden due to its high incidence, prevalence, mortality, and disability rates.193 In 2013, an estimated 6.9 million new IS cases occurred globally, with only 18.25 million surviving in good health, 3.32 million deaths, and 65.54 million disabilities.194 Plasma levels of docosahexaenoic acid, LA, arachidonic acid, and PA were measured by gas chromatography in 943 participants from the Framingham Heart Study and 1406 participants from three cities of the Bordeaux Study. The results showed that PA is a risk factor for stroke.195 In a study conducted at the Minneapolis Community Atherosclerosis Risk Center, 3870 white men and women aged 45–64 years (1987–1989) were assessed for plasma cholesterol esters and phospholipid FA, revealing a significant positive correlation between plasma SFAs (particularly PA) and IS.196

    Palmitic Acid Promotes Neuroinflammation

    Palmitic acid can induce chronic inflammation in both peripheral tissues and the central nervous system, for example, hypothalamic neurons.197–200 In in vitro experiments, PA was found to induce dysfunction in human adipose tissue and soft meningeal artery endothelial cells.201 Researchers found that when Medin (a common amyloid protein) was combined with PA, there was upregulation of IL-6, IL-8, and PAI-1 gene expression in HUVECs, suggesting combined proinflammatory and prothrombotic effects in IS pathogenesis.201,202 Mechanistically, PA promotes TLR4 recruitment to lipid rafts in SH-SY5Y neuroblastoma cells, facilitating TLR4/MYD88/TIRAP complex formation a process potentiated by heme-dependent TLR4 activation.94 PA promoted the upregulation of IL-6 and TNF-α in primary hypothalamic cultures from rats.203 Further studies confirmed that mice fed a high PA diet showed increased hypothalamic cytokine levels, proinflammatory signaling, neuronal death, and impaired leptin and insulin signaling.198,204 Direct intraventricular injection of PA also led to hypothalamic inflammation and insulin resistance.203 PA induces the expression of proinflammatory cytokines in cultured hypothalamic neurons (N42) by increasing ceramide accumulation and lipotoxicity.92 Additionally, PA interacts with LPS to activate microglial cells, upregulating the expression of proinflammatory cytokines via MAPK, NF-κB, and AP-1 signaling pathways, inducing neuroinflammation in HMC3 cells205 (Figure 4).

    Figure 4 Overview of the mechanism by which palmitic acid promotes ischemic stroke. PA exacerbates IS through multi-target mechanisms: (1) Atherogenesis: Accelerates plaque formation via ceramide overproduction and proinflammatory cytokine induction. (2) Neuroinflammation: Triggers CNS inflammatory cascades through microglial TLR/NLRP3 activation and astrocytic metabolic reprogramming. (3) Neuronal Apoptosis: Induces ER stress-autophagy axis dysregulation in neurons. (4) Glial Activation: Directly stimulates microglial inflammatory signaling and astrocytic lipotoxicity. These interconnected pathways collectively drive neurovascular unit dysfunction, culminating in IS progression (↑: increase/activation).

    Palmitic Acid Promotes Apoptosis of Neuronal

    The lipotoxicity of PA triggers ER stress and autophagic impairment, leading to an increase in apoptosis and the regulation of neuronal plasticity. High concentrations of PA have been shown to induce ER stress in SH-SY5Y cells and mouse brain cells.206 In SH-SY5Y cells and human glioblastoma cells, PA-induced neurotoxicity and glial cell toxicity, as well as increased oxidative stress in neurons and astrocytes, further promoted cell apoptosis.207 Mechanistic studies reveal that PA upregulates fatty acid transport protein 1 (FATP1) expression, which enhances prefrontal cortical autophagy dysregulation and ER stress while downregulating neuroplasticity markers including synaptophysin (SYN), brain-derived neurotrophic factor (BDNF), and acetylcholine receptors (AChRs).208 High-fat diets containing PA activate the MST1/JNK/Caspase-3 signaling pathway in hippocampal HT22 cells, leading to neuronal apoptosis.209,210 In in vitro experiments, PA significantly increased the autophagic flux in hypothalamic neurons. After PA exposure, the autophagic flux in hypothalamic neurons was suppressed, leading to impaired neuronal autophagy. This autophagic dysfunction was accompanied by changes in lysosomal dynamics, increased Rab7 GTPase activity, ERK phosphorylation, elevated expression of NADPH oxidase 4, and higher levels of inflammation, oxidative stress, and apoptosis in DRG neurons211 (Figure 4).

    Palmitic Acid Activates Glial Cells

    Glial cells, primarily composed of microglia and astrocytes, PA can activate glial cells. Microglia are the principal FA sensors in the hypothalamus related to neuronal stress and inflammation and are key mediators of the inflammatory response after stroke and brain injury.212 PA promotes inflammation by activating TLR receptors distributed in microglia, and also activates NLRP3 inflammasome by increasing TLR4/MyD88/NF-κB p65 signaling, Long-term activation of hypothalamic microglia inhibits neurogenesis in the medial basal hypothalamus (MBH), and the occurrence of IS further activates microglia and exacerbates disease progression.213,214 Astrocytes are the primary cells responsible for FA oxidation in the brain and play an important role in chronic inflammatory responses associated with obesity and the development of secondary metabolic disorders.215 Although the brain’s energy is primarily provided by glucose PA accumulation in astrocytes activates mitochondrial β-oxidation pathways, generating ATP while inducing proinflammatory activation216 (Figure 4).

    Conclusion and Future Directions

    Cardiovascular impact of dietary fatty acids exhibits fundamental dichotomy: Saturated fatty acids, particularly PA, promote cardiovascular pathogenesis through pro-inflammatory, dyslipidemic, and endothelial dysfunction pathways. SA as one of the metabolic products of PA, that exhibits neutral metabolic effects. While monounsaturated (eg, oleic acid) and polyunsaturated fatty acids confer cardioprotection. As the most abundant endogenous and dietary SFA, PA serves as a pathophysiological pivot in atherosclerosis development and cerebrovascular complications. Translation of these mechanistic insights into balanced nutritional interventions represents an actionable strategy for global CVD burden reduction.

    However, current limitations must be addressed: current evidence exhibits heterogeneity in PA exposure quantification across studies; different organizations, races, and diseases should adopt specific quantitative standards, rather than simply using the same standard for measurement; moreover, most interventional data derive from preclinical models requiring human validation.

    To advance this field, future research should prioritize: establish specific quantitative standards for different organizations, races and diseases; elucidate tissue-specific signaling mechanisms (eg, endothelial vs glial PA sensing); develop targeted therapies disrupting PA-induced inflammatory cascades (eg, RGS2 inhibitors); conduct randomized trials testing precision and personalized nutrition approaches for high-risk populations; establish clinical biomarkers quantifying PA’s pathogenic contributions.

    Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

    During the preparation of this work, the authors used [deep seek] in order to [improve language and readability]. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.

    Abbreviations

    PA, palmitic acid; SFAs, saturated fatty acids; CVD, cardiovascular diseases; AS, atherosclerosis; IHD, ischemic heart disease; IS, ischemic stroke; FA, fatty acid; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; ACLY, ATP-citrate lyase; ACC, acetyl-CoA carboxylase; FASN, fatty acid synthase; ChREBP, carbohydrate response element-binding protein; SREBP-1c, sterol regulatory element binding protein-1c; ER, endoplasmic reticulum; NADH, nicotinamide adenine dinucleotide; FADH2, flavin adenine dinucleotide, reduced; ELOVL1-7, elongation of very long-chain fatty acids 1-7; SA, stearic acid; PE, phosphatidylethanolamine; SCD1, stearoyl coenzyme a desaturase 1; APOM, apolipoprotein M; LDL, low density lipoprotein; VLDL, very low-density lipoprotein; HK2, human kidney-2; AKT, protein kinase B; RGS2, human regulator of G protein signaling 2; IL-6,interleukin-6; CRP, C-reactive protein; TNF-α, tumor necrosis factor-α; Inos, nitric oxide synthase; VSMCs, vascular smooth muscle cells; CYR61, cysteine-rich angiogenic inducer 61; IL-1β, interleukin-1β; MCP-1, monocyte chemoattractant protein-1; NLRP3, NACHT, LRR and PYD domains-containing protein 3; TLR4, Toll-like receptor 4; TLR, toll-like receptors; LPS, lipopolysaccharide; MD2, myeloid differentiation protein 2; pkcζ, atypical protein kinase Czeta; TAK1, transforming growth factor β-activated kinase 1; KLF4, Krüppel-like factor 4; IR, insulin receptors; IGF-1, insulin-like growth factors 1; GLUT4, glucose transporter type 4; IRS1, insulin receptor substrate 1; HAECs, human aortic endothelial cells; MICECs, cardiac microvascular endothelial cells; FABP4, fatty acid‑binding protein 4; OA, oleic acid; LA, linoleic acid; MUFAs, monounsaturated fatty acids; PUFAs, polyunsaturated fatty acids; PGC-1α, Peroxisome proliferator-activated receptor gamma coactivator 1α; UCP2, uncoupling protein 2; DAG, diacylglycerol; ERK, extracellular signal-regulated kinase; C/EBP, CCAAT/enhancer binding protein; ATF4, activating transcription factor 4; IRE1-α, inositol-requiring enzyme 1α; TRAF2, tumor necrosis factor receptor-associated factor 2; ASK1, apoptosis signal-regulated kinase 1; JNK, C-jun N-terminal kinase; CHOP, C/EBP homologous protein; T2DM, type 2 diabetes mellitus; ROS, reactive oxygen species; NOX4, calcium/protein kinase Cα/NADH oxidase 4; IRF3, interferon regulator 3; NO, nitric oxide; MAPK, mitogen-activated protein kinase; oxLDL, oxidized LDL; LOX-1, lectin-like oxidized low-density lipoprotein receptor-1; 5-HT, 5-hydroxytryptamine; HUVECs, human umbilical vein endothelial Cells; ATMs, M1-like adipose tissue macrophages; PPAR, eroxisome proliferator-activated receptor; GPT-I, carnitine palmitoyl coenzyme A transferase-I; Cer, ceramide; SM, sphingomyelin; GPX4, glutathione peroxidase 4; HSF1, heat shock factor 1; FATP1, fatty acid transport protein 1; SYN, synaptophysin; BDNF, brain-derived neurotrophic factor, AChRs, acetylcholine receptors; MBH, medial basal hypothalamus.

    Author Contributions

    All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

    Funding

    This work was supported by the National Natural Science Foundation of China (32460138); Priority Union Foundation of Yunnan Provincial Science and Technology Department and Kunming Medical University (202101AC070461), and Basic Research Program of Yunnan Province Science and Technology Department (202301AT070083).

    Disclosure

    The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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  • Improved Glycine max productivity in saline–sodic soils: coupling the impacts of urea–phosphate and magnesium oxide nanoparticles on the nutrient contents and growth–physiological attributes | BMC Plant Biology

    Improved Glycine max productivity in saline–sodic soils: coupling the impacts of urea–phosphate and magnesium oxide nanoparticles on the nutrient contents and growth–physiological attributes | BMC Plant Biology

    Growth and physiological attributes

    Response of growth–physiological attributes in salt-stressed soybean plants to UP soil application

    The data pertaining to the effect of urea phosphorus (UP) fertilizer rates on growth and physiological parameters, such as relative chlorophyll content (SPAD reading), plant height (PH), leaf area (LA), and plant dry matter percentage (DrM%), of salt-stressed soybean plants in the 2022 and 2023 growing seasons are graphically presented in Fig. 2 (A-D). The results obtained indicate that the maximum values in PH (72.11 vs. 75.14 cm) and LA (34.22 vs. 39.72 cm2) in both growth seasons were recorded in plants fertilized with UP3; moreover, the maximum value in SPAD (53.67) was recorded in the second season. Meanwhile, the application of the UP1 significantly increased DM% compared to higher UP levels. So, the highest values in DrM% were recorded in both growing seasons (57.55 vs. 58.80% in 2022 and 2023, respectively). On the other hand, UP1 was the least influential variable on PH (54.11 vs. 52.41 cm) and LA (18.01 vs. 19.79 cm²) in the first and second seasons and the least influential on SPAD readings (43.67) in the second season. The lowest DrM% values (42.50 vs. 43.04%) in both seasons and the lowest SPAD reading (42.47) in the first season were produced by plants treated with UP2. The analysis of variance presented a significant effect (at p ≤ 0.01) on LA in the 2023 season and a significant impact (at p ≤ 0.05) on PH in the second season and on LA in the first season. In addition, a non-significant influence was demonstrated by the SPAD reading and DrM% in both growing seasons, as well as by pH in the first season.

    Fig. 2

    AD The individual effect of urea-phosphate fertilizer rates (UPs) on 1 A) SPAD reading, 1B) Plant height, 1 C) leaf area, and 1D) leaf dry matter percentage (DrM%) of soybean plants cultivated in saline soil in both growth seasons 2022 and 2023, respectively. UP1, UP2, UP3, and UP4 represent urea-phosphate at 85.0, 107.0, 127, and 150.0 kg ha−1, respectively. The data are means±SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p ≤ 0.05 according to Duncan’s multiple range test

    Response of leaf nutrient contents in salt-stressed soybean plants to mgonp foliar application

    Figure 3 (A-D) graphically display the maximum values recorded in the leaves treated with MgONP2 in terms of SPAD readings (47.00 vs. 51.00 in 2022 and 2023, respectively), PH (64.08 vs. 67.57 cm in 2022 and 2023, respectively), and %DrM (56.63 vs. 55.81% in 2022 and 2023, respectively) in the first and second seasons. The highest LA values were achieved in the plants sprayed with MgONP1 (28.68 vs. 31.51 cm² in the 2022 and 2023 growing seasons, respectively). In contrast, the minimum values in terms of the SPAD readings (43.72 vs. 47.25 in 2022 and 2023, respectively) and PH (57.50 vs. 54.32 cm in 2022 and 2023, respectively) were produced in the untreated plants (MONP0) in both seasons. In addition, the lowest LA (23.80 vs. 24.12 cm² in 2022 and 2023, respectively) and DrM% (46.40 vs. 46.35% in 2022 and 2023, respectively) values in the 2022 and 2023 growth seasons were obtained in the plants treated with MgONP2 and MgONP1, respectively. Statistically, highly significant increases were recorded in the SPAD readings in 2022 and in the PH and LA values in 2023 following all the treatments. These treatments had no significant impacts on the PH or LA values in the first season or on the SPAD readings in the second season.

    Fig. 3
    figure 3

    AD The individual effect of magnesium oxide nanoparticle doses (MgONPs) on (A) SPAD reading, B Plant height, C leaf area, and D dry matter percentage of soybean plants cultivated in saline soil in both growth seasons 2022 and 2023, respectively. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p ≤ 0.05 according to Duncan’s multiple range test

    Response of growth–physiological attributes in salt-stressed soybean plants to the interaction between soil-applied UP and foliar-applied MgONPs

    The results, as observed in Table 4, demonstrate the enhanced impact of interaction between UP rates and MgONP doses on some growth and physiological traits. Similar findings were obtained for PH, LA, and DrM% in plants fertilized with UP3 and sprayed with MgONP2 (T32), plants treated with UP3 and foliarly sprayed with MgONP1 (T31), and plants fertilized with UP1 combined with MgONP2 (T12) treatments produced the maximum values in PH (81.33 vs. 83.37 cm), LA (44.92 vs. 54.63 cm²), and DrM% (65.41 vs. 62.51%) in the first and second seasons, respectively. Dissimilar data were recorded regarding the highest values in SPAD readings (53.25 vs. 83.37); however, in both seasons, the plants treated with T12 and T32 produced the best results. On the hand, the minimum values in PH (44.00 cm) in the second seasons and in SPAD readings (39.46) in the first season were obtained in plants fertilized with UP1 combined with MgONP1 (T11). Meanwhile, plants treated with UP2 and MgONP1 (T21) produced the lowest %DrM (38.33 and 38.40%) in both seasons. In addition, the lowest values in LA (15.65 cm²) in the 2022 season and in SPAD readings (40.00) in the 2023 season were recorded in plants fertilized with UP1 only, without using MgONP (T10). Furthermore, applying UP4 with MgONP (T42) was the least impactful on LA in the second season. There were highly significant differences among the treatments in SPAD readings in the first season and in LA in the second season. Non-significant differences were observed in SPAD readings in the second season, in LA in the first season, as well as in PH and %DrM in both seasons.

    Table 4 Impact of the interaction between urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment on the growth-physiological attributes of soybean plants cultivated in saline-sodic soil during two consecutive seasons (2022 and 2023)

    Leaf nutrient content

    Response of leaf nutrient content in salt-stressed soybean plants to UP soil application

    The results, as graphically presented in Fig. 4(A-E), indicated that UP4 was the best application rate for soybean leaf nitrogen (LNC), phosphorus (LPC), potassium (LKC), calcium (LCaC), and magnesium (LMgC). However, this treatment produced the maximum values of 4.88 vs. 3.70% for LNC, 0.48 vs. 0.45% for LPC, 3.56 vs. 3.11% for LKC, 0.63 vs. 0.65% for LCaC, and 0.30 vs. 0.32% for LMgC in the first and second seasons, respectively. In addition, the plant leaves fertilized with UP1 and UP2 recorded the highest leaf sodium values (LNaC), recording 0.04% in both seasons, respectively. In contrast, the lowest leaf contents of N (4.18 vs. 3.00%), P (0.31 vs. 0.30%), Ca (0.49 vs. 0.50%), and Mg (0.22 vs. 0.20%) were recorded in plants fertilized with UP1 and UP2 in the 2022 and 2023 growth seasons, respectively. Moreover, UP2 and UP1 for LKC as well as UP4 and UP3 for LNaC, were the least impactful, demonstrating 2.75 vs. 2.30% and 0.03 vs. 0.02% for both elements in both seasons, respectively. For all the UP rates tested, highly significant differences were obtained for the above-mentioned nutrients, except for LNaC, which had a non-significant effect in both seasons.

    Fig. 4
    figure 4

    AE The individual impact of urea-phosphate types (UPs) on leaf macronutrients content; (3A) nitrogen (LNC), (3B) phosphorus (LPC), (3C) potassium (LKC), (3D) calcium (LCaC), and (1E) magnesium (LMgC) of soybean plants cultivated in saline soil in two growing seasons 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    As seen in Fig. 5(A-D), the results related to the impact of UP fertilizer rates on the content of leaf micronutrients, such as iron (LFeC), manganese (LMnC), zinc (LZnC), and copper (LCuC), demonstrated that the plants fertilized with UP4 produced the maximum content of Mn (70.42 vs. 72.43 mg kg−1) in both seasons and of LZnC (36.77 mg kg−1) in the second season. Meanwhile, the plants treated with UP2 produced the highest LCuC (25.00 vs. 23.65 mg kg−1) in both growing seasons and the highest LFeC in the second season (113.72 mg kg1). Furthermore, UP was the most influential on LFeC (114.33 mg kg−1) and LZnC (35.99 mg kg−1) in the first season. On the contrary, UP3 were the least impactful, demonstrating the minimum values in LMnC (47.33 vs. 48.22 mg kg−1 and LCuC (15.30 vs. 13.15 mg kg−1) in both growing seasons, respectively, and the minimum value in LFeC (93.30 mg kg−1) in the first season. The lowest LZnC (31.86 vs. 32.82 mg kg−1 in 2022 and 2023, respectively) was obtained in plants treated with UP2 in both growth seasons. Statistically, highly significant differences were found for LFeC, LMnC, and LCuC; moreover, non-significant effects were found for LZnC in both growth seasons.

    Fig. 5
    figure 5

    AD The individual impact of urea-phosphate rates (UPs) on leaf micronutrients content; (4A) iron (LFeC), (4B) manganese (LMnC), (4 C) zinc (LZnC), and (4D) copper (LCuC) of soybean plants cultivated in saline soil in the 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p ≤ 0.05 according to Duncan’s multiple range test

    Response of leaf nutrient content in salt-stressed soybean plants to MgONPs foliar application

    The impact of the application of MgONP doses on the leaf contents of the aforementioned macronutrients in the 2022 and 2023 seasons are graphically presented in Fig. 6(A-E). Similar findings were obtained for LPC, LCaC, and LMgC. The MgONP doses, ranked in descending order in terms of MgONP2 > MgONP1 > MgONP0, were 0.47 > 0.41 > 0.35 and 0.43 > 0.37 > 0.32 for LPC, 0.62 > 0.56 > 0.51 and 0.63 > 0.58 > 0.63 for LCaC, and 0.31 > 0.27 > 0.22 and 0.30 > 0.26 > 0.20 for LMgC in each growth season, respectively. With regard to LNaC, the doses of MgONPs were arranged in the following order (for MgONP0 > MgONP1 > MgONP2): 0.04 > 0.03 > 0.02 and 0.04 > 0.04 > 0.03 in the 2022 and 2023 growing seasons, respectively. Dissimilar results were achieved in both growth seasons for LNC and LKC. However, the results for treatment with MgONPs were ranked as, in descending order, MgONP2 (4.58%) > MgONP0 (4.47%) > MgONP1 (4.42%) and MgONP2 (3.55%) > MgONP1 (3.35%) > MgONP0 (3.15%) for LNC and as MgONP1 (3.23%) > MgONP2 (3.19%) > MgONP0 (3.09%) and MgONP2, (2.83%) > MgONP1 (273%) > MgONP0 (2.63%) for LKC in the 2022 and 2023 seasons, respectively.

    Fig. 6
    figure 6

    AE The individual impact of magnesium oxide nanoparticle doses (MgONPs) on leaf macronutrients content; (3A) nitrogen (LNC), (3B) phosphorus (LPC), (3C) potassium (LKC), (3D) calcium (LCaC), and (1E) magnesium (LMgC) of soybean plants cultivated in saline soil in two growing seasons 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    The results obtained from the ANOVA showed that MgONPs had highly significant influences on the leaf content of P, Ca, and Mg in both seasons, of LNC and LKC in the 2023 season, and of LNC in the 2022 season. Non-significant differences were found for LNC and LKC in the first season. The results depicted in Fig. 7(A-D) document the effect of treatment with different MgONPs on the micronutrient contents of soybean leaves during the 2022 and 2023 seasons.

    Fig. 7
    figure 7

    AD The individual impact of magnesium oxide nanoparticle doses (MgONPs) on leaf micronutrients content; (6A) iron (LFeC), (6B) manganese (LMnC), (6C) zinc (LZnC), and (6D) copper (LCuC) of soybean plants cultivated in saline soil in the 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    The statistical analysis revealed that MgONPs did not have a significant impact on LFeC and had highly significant influences on LMnC, LZnC, and LCuC in both growth seasons. The obtained results demonstrated that the highest LMnC values (63.84 vs. 64.75 mg kg−1) were produced in untreated plants. Meanwhile, the plants treated with MgONP2 and MgONP1 produced the maximum LFeC in two growing seasons. Regarding the highest values of LZnC and LCuC, our results noted that MgONP1 for LZnC and MgONP2 for LCuC were the most impactful, producing levels of 38.89 vs. 38.12 mg kg1 and 23.08 vs. 22.10 mg kg−1 in the 2022 and 2023 seasons, respectively. On the contrary, the lowest LFeC (100.64 vs. 103.04 mg kg−1) and LCuC (16.91 vs. 17.26 mg kg−1) were obtained in untreated plants. Furthermore, the least influence of fertilizer on LMnC and LZnC levels was found in plants fertilized with MgONP1 and MgONP2, respectively; the minimum values in both growing seasons were recorded in these plants.

    Response of leaf nutrient content in salt-stressed soybean plants to the interaction between soil-applied UP and foliar-applied MgONPs

    Despite the improvements obtained due to the interactive impact between UP and MgONP, the results obtained from the statistical analysis indicated that there were no significant effects on all the macronutrients studied. The finding obtained from our field study highlighted the pivotal influence of using maximum rates of both UP and MgONP5, as listed in Table 5. More clearly, the maximum contents of N (5.52 vs. 3.90% in the 2022 and 2023 seasons, respectively), P (0.54 vs. 6.50% in the 2022 and 2023 seasons, respectively), Ca (0.67 vs. 0.70% in the 2022 and 2023 seasons, respectively), and Mg (0.36 vs. 0.34% in the 2022 and 2023 seasons, respectively) were recorded in soybean plants fertilized with UP4 and foliarly sprayed with MgONP2. Similarly, plants treated T20 produced the highest values in Na (0.05 vs. 0.04% in 2022 vs. 2023) in both seasons. For LKC, dissimilar results were produced among both growth seasons, as the highest values of K were found in the leaves of plants treated with T41 (3.64%) in the first season and in those treated with T42 (3.20%) in the second season. As for the lowest values obtained, the results were completely different. However, the T20 treatment was the least influential for LCaC (0.44 vs. 0.45%), LMgC (0.20 vs. 0.19%), and LPC (0.29 vs. 0.25%) in both growing seasons and for LNC (2.80%) and LKC (2.20%) in the second season. Furthermore, the application of T21 produced the lowest values in LNC (3.70%) and LKC (2.66%) in the first season, while plants treated with T42 produced the minimum LNaC values (0.01 vs. 0.02% in the 2022 and 2023 growth seasons, respectively). It is clear from Table 6 that the interaction between UP and MgONP significantly affected the leaf micronutrient content in soybean plants. The obtained results indicated that the application of T20 and T10 treatments was the most influential on LFeC (140.01 vs. 144.71 mg kg−1) and LMnC (93.43 vs. 96.87 mg kg−1) in the 2022 and 2023 seasons, respectively. Dissimilar results were found for LZnC and LCuC during both growth seasons. However, the maximum values in LZnC (52.32 vs. 51.75 mg kg−1) were produced in plants treated with T31 and T11. Likewise, the plants treated with T12 and T11 demonstrated the highest LCuC values (30.00 vs. 29.42 mg kg−1) in both seasons. In spite of the clear variation in the best values obtained, the values associated with the lowest were similar to each other. However, the plants treated with T40, T32, and T31 demonstrated the lowest values in Fe (53.24 vs. 51.50 mg kg−1), Mn (22.80 vs. 21.68 mg kg−1), and Cu (19.20 vs. 8.20 mg kg−1) in the first and second seasons. The application of T30, and T12 treatments was the least impactful on LZnC levels of 22.27 and 21.37 mg kg−1 were recorded in the 2022 and 2023 seasons, respectively.

    Table 5 Impact of the interaction between urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment on the leaf macronutrient contents of soybean plants cultivated in saline-sodic soil during two consecutive seasons (2022 and 2023)
    Table 6 Impact of the interaction between urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment on the leaf micronutrient contents of soybean plants cultivated in saline-sodic soil during two consecutive seasons (2022 and 2023)

    Seeds’ mineral compositions

    Response of mineral seed composition in salt-stressed soybean plants to UP soil application

    Figure 8(A-E) present the influences of the application of the different rates of UP as a soil fertilizer on the macronutrient contents of the seeds in the 2022 and 2023 seasons. According to the results, there was no noticeable benefit from adding any particular treatment over the others. However, the plants fertilized at UP1 produced the maximum seed nitrogen (SNC) and calcium (SCaC) contents, recording 6.62% and 0.40%, respectively, in the 2022 season, as well as a seed potassium content (SKC) of 1.78% in the 2023 season. In addition, UP₃ was the most impactful rate for the seed magnesium content (SMgC), which was recorded as 0.43% in the first season, and for the seed phosphorus (SPC) (0.61%) and SCaC (0.34%) in the second season. Meanwhile, UP4 was the superior rate, producing the maximum SKC value (1.71%) in the first season, as well as the highest SNC (6.46%) and SMgC (0.42%) values in the second season.

    Fig. 8
    figure 8

    AE The individual impact of urea-phosphate types (UPs) on seed macronutrients content; (8A) nitrogen (SNC), (8B) phosphorus (SPC), (8C) potassium (SKC), (8D) calcium (SCaC), and (8E) magnesium (SMgC) of soybean plants cultivated in saline soil in two growing seasons 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    In contrast, UP₂ was the least influential for the SMgC (0.35 vs. 0.33%) contents in both growth seasons, as well as for the SNC (5.66%) and SKC (1.50%) in the first season and the SPC (0.56%) in the second season. The lowest SNC (5.83%) and SCaC (0.22%) values were obtained in the plants treated at UP1 in the second season. Furthermore, the application of UP4 produced the minimum SPC (0.60%) and SCaC (0.29%) values in the first season. The results obtained from the ANOVA indicated that all the treatments had highly significant influences on the SNCs, SKCs, SCaCs, and SMgCs; in addition, significant effects on the SPCs were observed in both seasons. The results presented in Fig. 9(A-D) reveal the beneficial effect that UP₂ exerted on the micronutrient contents of the seeds. The highest iron (SFeC) and zinc (SZnC) contents were recorded in both growing seasons (78.39 vs. 77.48 mg kg⁻¹ for the SFeC and 36.44 vs. 34.82 mg kg⁻¹ for the SZnC in 2022 and 2023, respectively). Moreover, the maximum seed manganese contents (SMnCs) were recorded in the plants fertilized at UP4 (42.45 vs. 43.55 mg kg⁻¹ in 2022 and 2023, respectively). Dissimilar findings were obtained for the seed copper contents (SCuCs); however, the highest values were produced as a result of applying UP2 and UP3 in both seasons, 2022 and 2023, respectively. In contrast, the UP1 application was the least influential; the minimum SFeC (69.25 vs. 65.26 mg kg⁻¹), SMnC (26.56 vs. 25.03 mg kg⁻¹), and SCuC (10.71 vs. 10.98 mg kg⁻¹) values were recorded in the plants treated at UP1 in both growth seasons, respectively. Meanwhile, the lowest SZnC values were achieved in the plants treated at UP4 (25.00 vs. 25.69 mg kg⁻¹ in 2022 and 2023, respectively). Statistically, all the treatments had significant impacts (at p ≤ 0.01) for all the aforementioned micronutrients in the first and second seasons.

    Fig. 9
    figure 9

    AD The individual impact of urea-phosphate rates (UPs) on seed micronutrients content; (9A) iron (SFeC), (9B) manganese (SMnC), (9C) zinc (SZnC), and (9D) copper (SCuC) of soybean plants cultivated in saline soil in the 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    Response of mineral seed composition in salt-stressed soybean plants to MgONPs foliar application

    The results obtained from the statistical analysis indicated that all MgONPs treatments had significant impacts (at p ≤ 0.01) on all studied macronutrient content levels in both growth seasons. As graphically presented in Fig. 10(A-E), the greatest improvements in the macronutrient content of leaves were closely associated with the application of MgONP1 and MgONP2 treatments, whereas the maximum values in SNC (6.40 vs. 6.35%) in both seasons, as well as in SPC (0.66%) in the first season and in SKC (1.68%) and SMgC (0.38%) in the second season were achieved in soybean plants that were foliar applied with MgONP1. Furthermore, the highest values in SCaC (0.40 vs. 0.33%) in both growing seasons and the highest value in SMgC (0.42%) in the 2022 season and in SPC (0.66%) in the 2023 season were achieved in plants treated with MgONP₂. The highest SKC value (1.75) in the first season was obtained in untreated plants. Conversely, the lowest values in SNC (6.25 vs. 6.07%), SPC (0.56 vs. 051%), SCaC (0.33 vs. 0.26), and SMgC (0.36 vs. 6.34%) were obtained in untreated plants (MgONP0). Dissimilar findings were produced regarding SKC, as the minimum values were recorded as a result of MgONP1 in the first season and as a result of MgONP2 in the second season.

    Fig. 10
    figure 10

    AE The individual impact of magnesium oxide nanoparticle doses (MgONPs) on seed macronutrients content; (10A) nitrogen (SNC), (10B) phosphorus (SPC), (10C) potassium (SKC), (10D) calcium (SCaC), and (10E) magnesium (SMgC) of soybean plants cultivated in saline soil in two growing seasons 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    The results presented in Fig. 11(A-D) reveal that following the application of MgONPs as a foliar application, the levels of micronutrients in the leaves of all studied plants significantly improved. The results indicated that for SFeC and SMnC, the MgONP doses, in descending order, were ranked as follows: MgONP1 > MgONP0 > MgONP2, recording 84.41 > 69.41 > 65.68 mg kg−1 vs. 84.03 > 68.20 > 63.95 mg kg−1 for SFeC and 36.55 > 36.01 > 30.40 mg kg−1 vs. 36.98 > 35.64 > 31.56 mg kg−1 for SMnC in both seasons, respectively. Similarly, for SZnC, the MgONP doses were ranked in descending order as follows: MgONP2 (34.09 vs. 34.36 mg kg−1) > MgONP1 (30.21 vs. 30.13 mg kg−1) > MgONP0 (28.34 vs. 26.90 mg kg−1) in 2022 and 2023, respectively. Meanwhile, the MgONP doses can be arranged in descending order as MgONP1 (15.16 vs. 14.67 mg kg−1) > MgONP2 (12.33 vs. 12.76 mg kg−1) > MgONP0 (10.03 vs. 10.75 mg kg−1) for SCuC in both growth seasons, respectively. The statistical analysis identified highly significant influences of MgONP doses on all studied leaf contents of micronutrients.

    Fig. 11
    figure 11

    AThe individual impact of magnesium oxide nanoparticle doses (MgONPs) on seed micronutrients content; (11A) iron (SFeC), (11B) manganese (SMnC), (11C) zinc (SZnC), and (11D) copper (SCuC) of soybean plants cultivated in saline soil in the 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    Response of mineral seed composition of salt-stressed soybean plants to the interaction between soil-applied UP and foliar-applied MgONPs

    The data presented in Table 7 indicate that all studied leaf macronutrient content levels were markedly enhanced as a result of interaction between UP fertilizer rates and MgONP doses. Our investigation demonstrated that the highest values (6.92 vs. 6.96%) in SNC in both growing seasons and in SKC (2.01%) in the second season were recorded in plants fertilized with the T41 treatment. In addition, the maximum values in SPC (0.70%) and SMgC (0.50%) in the second season were associated with the application of T42. Furthermore, the plants treated with T21 produced the highest values in SPC (0.71%) and SCaC (0.47%) in the first season. Moreover, the use of the T30 treatment was the most significant for LKC (2.01%) and LMgC (0.46%) levels in the first season. Dissimilar results were obtained regarding the lowest values in seed macronutrient contents, as the plants treated with T40 demonstrated the minimum values in SNC (5.51%) and LPC (0.52%) in the first season, while the plants fertilized with T10 demonstrated the lowest mean values for SCaC (0.21%) and for SMgC (0.31%) in the second season. In addition, the application of T12, T30, and T31 produced the minimum values in SNC (5.55%), SPC (0.50%), and SKC (1.02%), respectively in the second season. The analysis of variance indicating that all interaction treatments had highly significant effects on all aforementioned levels of macronutrient content. According to the results listed in Table 8, the highest values in SFeC (95.02. vs. 98.88 mg kg−1) in both seasons and the highest levels of SMnC (50.00 mg kg−1) in the 2022 season and of SCuC (19.75 mg kg−1) in the 2023 season were achieved in the plants fertilized with T41. Furthermore, the maximum SZnC (38.91 mg kg−1) and SCuC (19.87 mg kg−1) was recorded during the first season in plants treated with T22. The application of T32 was the most impactful on SMnC (49.53 mg kg−1) and SZnC (42.24 mg kg−1) in the second season. On the other hand, the lowest values in SMnC (18.57 vs. 16.87 mg kg⁻¹) and SCuC (5.00 vs. 5.89 mg kg⁻¹) in 2022 and 2023, respectively were obtained in plants treated with T₁₂ and T₂₀. Meanwhile, the plants fertilized with T42 demonstrated the lowest value in SFeC (55.33 mg kg⁻¹) in the second season and in SZnC (20.72 mg kg⁻¹) in the first season. Moreover, the application of T30 and T₁₀ produced the lowest values in SFeC (54.49 mg kg⁻¹) in the first season and in SZnC (20.17 mg kg⁻¹) in the second season.

    Table 7 Impact of the interaction between urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment on the seed macronutrient contents of soybean plants cultivated in saline-sodic soil during two consecutive seasons (2022 and 2023)
    Table 8 Impact of the interaction between urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment on the seed micronutrient contents of soybean plants cultivated in saline-sodic soil during two consecutive seasons (2022 and 2023)

    Yield and its attributes

    Response of yield and its attributes of salt-stressed soybean plants to UP soil application

    The results obtained from the ANOVA clearly indicated that all UP treatments had highly significant effects on 100-seed weight (HSW), seed oil content (SOC), seed protein content (SPC), and total seed yield (TSY) in both growth seasons. As visually evident in Fig. 12(A-D), the highest values in SOC (20.09 vs. 20,18%) were produced in plants fertilized with UP3 in both growing seasons. Meanwhile, the maximum values in HSW (17.82 vs. 17.60 g) and TSY (4.66 vs. 4.87ton ha−1) in the 2022 and 2023 seasons, respectively were obtained in plants treated with UP4. Although UP4 had a profound impact on SPrC in the second season, the use of UP1 produced the highest value in the first season. Similarly, the lowest values in HSW (15.06 vs. 15.08 g) and TSY (3.82 vs. 4.01ton ha−1) were obtained in plants treated with UP2, while the application of UP1 produced the lowest SOC (19.26%) in the first season and the lowest SPC (36.45%) in the second season. Simultaneously, the use of UP2 produced the minimum values in SPrC (35.39%) in the first season and the minimum value in SOC (19.49%) in the second season.

    Fig. 12
    figure 12

    AThe individual impact of urea-phosphate rates (UPs) on (12A) SOC, (12B) SPrC, (12C) HSW, and (12D) TSY of soybean plants cultivated in saline soil in the 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    Response of yield and its attributes of salt-stressed soybean plants to MgONPs foliar application

    The results obtained from the statistical analysis indicated that MgONPs treatments significantly (at p ≤ 0.01) affected yield and its components. As graphically demonstrated in Fig. 13(A-D), the plants foliarly sprayed with MgONP2 produced the maximum values in SOC (19.79 vs. 19.91%) and TSY (4.55 vs. 4.74ton ha−1) in the first and second seasons. Furthermore, the highest SPrC values (40.03 vs. 39.69%) were produced in plants treated with MgONP1. Moreover, the highest values in HSW (17.38 vs. 17.04 g) were recorded in untreated plants in both growth seasons. On the other hand, MgONP2 treatment was the least influential on HSW, producing HSW levels of 16.29 vs. 16.23 g in the two growing seasons. Meanwhile, the lowest values in SPrC (39.05 vs. 37.96%), TSY (3.89 vs. 4.00ton ha−1), and SOC (19.35 vs. 19.54%) in the 2022 and 2023 seasons, respectively were obtained in untreated plants.

    Fig. 13
    figure 13

    AThe individual impact of magnesium oxide nanoparticles doses (MgONPs) on (13A) SOC, (13B) SPrC, (13C) HSW, and (13D) TSY of soybean plants cultivated in saline soil in the 2022 and 2023 growing seasons. The data are means ± SE (Standard Error) for three replicates. Means value that have different lower-case letter in each season are significant at p≤0.05 according to Duncan’s multiple range test

    Response of yield and its attributes of salt-stressed soybean plants to the interaction between soil-applied UP and foliar-applied MgONPs

    The data explored in Table 9 indicate that plants treated with both UP as a soil application and MgONPs as a foliar spray, irrespective of their doses, markedly outperformed the control treatment, in terms of enhanced productivity and yield-related attributes, although the ANOVA data revealed that all treatments had significant influences (at p ≤ 0.01) on the HSW, SOC, and SPC. Conversely, there were no significant impacts on TSY in both growth seasons, respectively. We found that the co-application of UP4 and MgONP1 (T41) was the superior treatment; it produced the maximum values for HSW (18.77 vs. 18.53 g) and for SPrC (43.25 vs. 43.48%) in both growing seasons. Meanwhile, the T42 treatment was the most impactful on TSY, producing the highest values (5.00 vs. 5.19ton ha−1) in both seasons, respectively. Dissimilar results were obtained for SOC; however, T31 and T40 produced the best values (20.75 vs. 21.02%) in the 2022 and 2023 seasons, respectively. Conversely, the lowest values in HSW (13.27 vs. 13.00 g) and TSY (3.54 vs. 3.62 tan ha−1) in the first and second seasons, respectively were produced in plants treated with T22 and T20. Interestingly, the application of T40 and T12 for SPrC and T11 and T41 for SOC were the least impactful, producing the minimum values for SPrC (34.42 vs. 34.67%) and SOC (18.40 vs. 18.56%) in the two growing seasons, respectively.

    Table 9 Impact of the interaction between urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment on the yield and its components of soybean plants cultivated in saline-sodic soil during two consecutive seasons (2022 and 2023)

    Principal component, pearson’s correlation, and stepwise multiple regression analyses

    Principal component, Pearson’s correlation, and stepwise multiple regression analyses were performed on the physiological–growth attributes, leaf nutrient contents, and yield- and quality-related parameters of soybean plants cultivated in saline–sodic soil. Principal component analysis (PCA) was performed to evaluate the relations between the UP x MgNP interaction treatments and the abovementioned characteristics. As shown in Fig. 14, the PCA indicated that the first two main components, Dim 1 and Dim 2 (PCA-diminution 1 and -diminution 2, respectively), accounted for 48.7% of the total variation. PC1 interpreted 32.5% of the variation. The nearby vectors of the measured parameters presented a positive correlation with one another. However, the SPAD readings, PH, LA, LMgC, SPC, SCaC, SOC, and TSY fell under the same group, while the LNC, LPC, LKC, LCaC, LMnC, SNC, SMgC, SPrC, and HSW were in a separate group.

    Fig. 14
    figure 14

    Principal component analysis (PCA) of applied urea–phosphate (UP) and magnesium oxide nanoparticle (MgONP) treatments and studied parameters. Each black dot denotes a treatment. SPAD, PH, LA, and DrM indicate the relative chlorophyll content, plant height, leaf area, and dry matter percentage, respectively. LNC, LPC, LKC, LCaC, LMgC, LFeC, LMnc, LZnC, and LCuC indicate the leaf nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese, zinc, and copper contents, respectively. SNC, SPC, SKC, SCaC, SMgC, SFeC, SMnC, SZnC, and SCuC indicate the seed nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese, zinc, and copper contents, respectively. SOC, SPrC, and TSY indicate the seed oil content, protein content, and total seed yield, respectively. Values are based on averages of two consecutive seasons (2022 and 2023). T10, T11, and T12 represent the UP applied at 85.0 kg ha−1 with three doses of MgONPs: 0.00, 50.0, and 100.0 mg L−1, respectively. T20, T21, and T22 represent the UP applied at 107.0 kg ha−1 with three doses of MgONPs: 0.00, 50.0, and 100.0 mg L−1, respectively. T30, T31, and T32 represent the UP applied at 127.0 kg ha−1 with three doses of MgONPs: 0.00, 50.0, and 100.0 mg L−1, respectively. T40, T41, and T42 represent the UP applied at 85.0 kg ha−1 with three doses of MgONPs: 0.00, 50.0, and 100.0 mg L−1, respectively

    The PCA biplot in Fig. 13 shows that the SPAD, PH, LA, LMgC, SPC, SCaC, and SOC were improved by T12, T31, and T32. Moreover, the LNC, LPC, LKC, LCaC, LMnC, HSW, DrM, SNC, SMnC, and SPrC were also enhanced by T41 and T42. Therefore, the application of UP and MgONP interaction plays a crucial role in promoting most of the traits associated with the nutritional status, yield, and their components.

    The results provided in Table 10 indicate the correlations of various physiological attributes that were determined (SPAD reading, LA, PH, and DrM%) and of the nutrient content in leaves (LNC, LPC, LKC, LCaC, LMgC, LFeC, LMnC, LZnC, and LCuC), with the TSY and SOC in both growth seasons, respectively. Our results revealed that SPAD readings correlated (r = 0.419* vs. 0.589** in the first and second seasons, respectively) with TSY and (r = 0.437** vs. 0.349*) with SOC in the first and second seasons, respectively. The influence of PH was found to be more correlated with SOC, with correlation values of r = 0.354* and 0.368 in the 2022 and 2023 seasons, respectively. Similarly, TSY had highly significant positive correlations with LNC (r = 0.351* vs. 0.951*), LPC (r = 0.953** vs. 0.934**), LKC (r = 0.642** vs. 0.826**), LCaC (r = 0.801** vs. 0.788**), and LMgC (r = 0.711** vs. 0.697**) in 2022 and 2023, respectively. A highly significant negative correlation of SOC was found with LMgC (r = −0.432** vs. −0.461** in 2022 and 2023, respectively).

    Table 10 Pearson’s correlation coefficient between total seed yield (TSY) and seed oil content (SOC) with 13 selected attributes of soybean plants fertilized with urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment under saline-sodic soil during two consecutive seasons (2022 and 2023)

    As observed in Table 11, stepwise regression analysis clearly identified the relationship between TSY and SOC as a response variable with physiological attributes (SPAD reading and LA), leaves’ nutrient contents (LNC, LPC, LKC, LCaC, and LMnC), and yield-related attribute as predictor variables. The obtained results revealed that model 3 and model 2 were the most suitable in the 2022 and 2023 growth seasons, respectively. However, these models had high adjusted R2 0.931 (0.968) and 0.924 (0.964) and the lowest SEE (0.113 and 0.129). These results demonstrate that 93.1% of variations in TSY occurred because of variations in the combination of LPC, LA, and LCaC (TSY = 2.014 LPC + 4.842 LA + 0.004 LCaC in the first season). According to model 2, 92.4% of variations in TSY were due to variations in the combination of LNC and LKC (TSY = 0.627 LNC + 1.990 LKC). With regard to SOC, model 3 in the 2022 season and model 2 in the 2023 season were the best models owing to their maximum adjusted R2, which was recorded as 0.344 (0.618) in the first season and 0.278 (0.565) in the second season, and due to achieving the lowest SEE, which was recorded as 0.673 and 0.808 in the 2022 and 2023 season, respectively. The adjusted R2 demonstrated 34.4% and 27.8% of variations in the combination of SPAD readings, LMnC, and SOC (16.695 SPAD reading + 0.082 LMnC) in the first season and 27.8% of variations in the combinations of LMnC, HSW, and SOC (17.386 LMnC − 0.016 HSW) in the second season.

    Table 11 Proportional contribution in predicting total seed yield (TSY) and seed oil content (SOC) using Stepwise linear regression for salt-stressed soybean plants fertilized with urea-phosphate (UP) as a soil application and magnesium oxide nanoparticles (MgONPs) as a foliar nourishment under saline-sodic soil during two consecutive seasons (2022 and 2023)

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  • Are we nearing a quantum leap? – Hamburg Business

    Are we nearing a quantum leap? – Hamburg Business

    1. Are we nearing a quantum leap?  Hamburg Business
    2. Can engineering catch up with quantum physics and bring us useful quantum computing  TechRadar
    3. AI and quantum computing are converging. Both could get a boost  qz.com
    4. Scientists Gave a Quantum Computer a ‘Lie Detector Test’ and It Passed  ZME Science
    5. Hybrid Quantum  Brownstone Research

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  • Europe’s green steel hope Stegra races to avoid fate of sister group Northvolt

    Europe’s green steel hope Stegra races to avoid fate of sister group Northvolt

    Swedish start-up Stegra is battling to avoid becoming the second multibillion-euro European green industrial project to fall into insolvency in a year.

    The green steel company, which has raised $6.5bn in debt and equity, is on the ropes 11 months after battery start-up Northvolt, launched by the same Swedish financiers, went bankrupt despite raising $15bn.

    While Stegra executives have told its board that “we must avoid parallels with Northvolt”, according to people familiar with the discussions, the similarities are hard to ignore as the company struggles in the face of a sudden crisis.

    Stegra’s funding gap for its first green steel plant just below the Arctic Circle in Sweden has jumped to as much as €1.5bn from about €500mn as recently as July, executives told an emergency board meeting this month.

    Stegra is discussing outsourcing several parts of its green steel plant in Boden, northern Sweden © Jonathan Nackstrand/AFP via Getty Images

    Several equity investors and multiple creditors are getting twitchy. Stegra will hold a crunch meeting with its lenders on Tuesday, several people familiar with the matter said.

    Citibank is seen by Stegra, formerly known as H2 Green Steel, as particularly problematic as it has put its loans of about €29mn to the steel start-up in a workout group, according to people familiar with the matter who say some other banks share Citi’s concerns and have put Stegra into “special measures”.

    “This looks more and more like Northvolt. It is hard to see anything else than equity investors getting all but wiped out,” said one person familiar with Stegra’s financing.

    Lawyers from Mannheimer Swartling, one of Sweden’s leading law firms, told the emergency board meeting about the risk of insolvency and the various tests directors should apply to determine it.

    They said Stegra should hold board meetings more regularly — as often as each week — to monitor its financial situation and especially its liquidity, people familiar with the meeting told the Financial Times.

    The lawyers added that a board meeting should be held far enough in advance of the 12th of each month to decide whether social security fees should be paid, and also sufficiently before the 25th of each month to decide whether to pay wages, the people said.

    Henrik Henriksson, Stegra’s chief executive, told the FT last week that he did not recognise “the very one-sided picture conveyed”. Stegra said on Monday it was “confident that our ongoing financing round, including opportunities for outsourcing and selected strategic partnerships, will be secured in an orderly fashion”.

    It has started a new financing round aimed at raising almost €1bn and said that it had received “strong initial equity commitments from our founders and lead investors” including Altor, Just Climate, a Wallenberg family foundation and co-founder Harald Mix. “We have several avenues to pursue to manage our cash position,” it added.

    But behind the scenes, Stegra is fighting to survive. A decision this year to delay a galvanisation line reduced its funding needs by about €140mn but will also lead to later deliveries for 15 of its 21 long-term customers including Volvo, Porsche and Scania, people familiar with its financing said.

    People close to the company, however, said the delay would have no significant impact on customers.

    Henrik Henriksson, Stegra’s chief executive, has said he does not recognise ‘the very one-sided picture conveyed’ © David Kawai/Bloomberg

    Stegra is also discussing outsourcing several parts of its steel plant in Boden — which is about 60 per cent complete but has been subject to several delays — including its hydrogen and electricity plant assets, according to executives.

    Such plans — to sell, and lease back or buy them as a service — could save as much as €1.3bn in capital expenditure but are likely to take until next April or May to conclude, according to information shown to the board.

    It is far from clear that Stegra has that much time. The emergency board meeting two weeks ago was told that as the Boden project was consuming about €280mn a month in cash, the company only had about 1.7 months of liquidity left unless it could draw down more debt.

    People familiar with its financing said that to unlock that debt Stegra needed to raise more equity, and that some investors were balking at that. Stegra said it was in talks with both existing and new investors, and was optimistic of a successful outcome.

    Its funding gap — judged in July to be about €500mn — is now €1.2bn under its central scenario and €1.5bn under its worst-case scenario, according to information prepared for the board meeting.

    Stegra has in recent weeks hired restructuring specialists PJT, just as Northvolt did, people familiar with the appointment said.

    Stegra AB’s green steel factory under construction in Boden
    One backer suggested the best outcome would be for a bigger steel company to buy the assets such as the green steel factory ‘and run this properly’ © Erika Gerdemark/Bloomberg

    Both Northvolt and Stegra were started by Vargas, a Swedish private equity firm founded in 2014 by financiers Harald Mix and Carl-Erik Lagercrantz with a goal of decarbonising 1 per cent of global emissions through its projects.

    Stegra’s lead shareholders include Swedish private equity group Altor, French investor Hy24, Singaporean sovereign wealth fund GIC, and fund manager Just Climate as well as Mix and Vargas.

    Stegra announced on Monday that it would replace co-founder Mix as chair with Shaun Kingsbury, co-chief investment officer of Just Climate.

    The start-up’s biggest creditors include the Swedish Export Credit Corporation, investment managers AIP, the European Investment Bank, and European banks including ING, BNP Paribas and Santander, the people added.

    Another similarity with Northvolt appears to be an unwillingness from the Swedish government to help out. Stegra executives blame Sweden’s refusal to disburse €165mn in aid approved by Brussels for part of its predicament. Northvolt ended up in bankruptcy only weeks after the government explicitly ruled out stepping in to help.

    Northvolt’s assets in northern Sweden, about 125km from Stegra’s, may be revived after US battery start-up Lyten bought them out of bankruptcy at a steep discount.

    Among Stegra’s backers, there is debate about how its predicament compares with Northvolt. “Everybody is very quick to say it is Northvolt mark two. But if you have something of value, you can raise money off it. That is a fundamental difference to Northvolt,” said one.

    But another suggested that the best outcome would be for a bigger steel company to buy the assets “and run this properly”.

    Either way, the struggles of another great hope of sustainable European industry raise serious questions both for policymakers and investors about Europe’s green transition.

    “It does not look pretty,” said one Nordic minister.

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  • Amivantamab Shows 45% Response Rate in Pretreated Head and Neck Cancer: OrigAMI-4 Findings

    Amivantamab Shows 45% Response Rate in Pretreated Head and Neck Cancer: OrigAMI-4 Findings

    Q: Could you summarize the key efficacy findings from the OrigAMI-4 trial, particularly response rates, duration of response, and progression-free survival, in this heavily pretreated population with head and neck squamous cell carcinoma?
    Kevin Harrington, MD, PhD, FRCP, FRCR, FRSB: We presented data from the OrigAMI-4 phase 1b/2 study in patients with recurrent or metastatic head and neck cancer who had received prior treatment with an immune checkpoint blocker and a platinum-based chemotherapy.

    We reported data on two populations: the safety population of 86 patients who received at least 1 dose of the drug and the efficacy population of 38 patients who had at least 2 tumor assessments or had stopped treatment for any reason. The efficacy data relate only to that smaller subset of patients.

    In that group, we observed an overall response rate of 45%, with a further 45% showing disease stabilization. When we looked at the lesions themselves, 82% showed evidence of shrinkage—clear evidence of the drug’s efficacy. In the efficacy-evaluable population, the median duration of response was over seven months, median progression-free survival was 6.8 months, and median overall survival was not yet reached. These findings show that single-agent subcutaneous amivantamab, delivered on a 3-week schedule, is very efficacious in this disease.

    Q: Given that these patients had progressed on both checkpoint inhibition and platinum-based chemotherapy, how clinically meaningful are the responses seen with amivantamab in this setting?
    Harrington: This group of patients is very difficult to achieve responses in. Previously, single-agent chemotherapy or investigator’s choice therapies—such as cetuximab, another EGFR-targeted drug—typically achieve response rates in the single digits or low teens, usually between 5% and 15%.

    To see a response rate as high as 45% in this group of patients is, we believe, clinically meaningful and hopefully leads to patient benefit.

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  • Gold's record run pauses as investors book profits – Reuters

    1. Gold’s record run pauses as investors book profits  Reuters
    2. Silver prices fall after Diwali buying. Is it still a good bet?  India Today
    3. Time to buy gold? Next dip could be investors’ best entry point, say analysts  Khaleej Times
    4. Overheated gold and silver markets under serious strain, but high prices should cure high prices – Heraeus  KITCO
    5. Gold price prediction for Diwali week: What’s the gold rate outlook for October 20, 2025 week? Levels to  Times of India

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  • ST Engineering iDirect Announces General Availability of Intuition Ground System

    ST Engineering iDirect Announces General Availability of Intuition Ground System

    Cloud-native architecture and high-density baseband processing improves operational efficiency, performance and TCO

    Herndon, VA., 20 October 2025 – ST Engineering iDirect, a global leader in satellite communications, today announced the general availability of Intuition 1.1, its next-generation ground system. Built on cloud-native, multi-orbit architecture, this release introduces advanced capabilities designed to unify satellite operations, enhance adaptability and maximize efficiency.

    The foundation of Intuition is its efficient, cloud-native architecture, which reduces compute resource requirements and is further enhanced by high-density processing savings with the XBB baseband solution. Together, these elements can reduce hardware requirements by up to 70%, significantly lowering the total cost of ownership (TCO). Its modular, microservices-based design enables seamless, low-risk deployments, including rapid feature upgrades with minimal operational disruption, while flexible cloud deployment options ensure scalability, efficiency and performance for any operational need.

    “Our vision with Intuition is to redefine agility and intelligent operations across the satellite industry,” said Sridhar Kuppanna, CTO and SVP of Engineering at ST Engineering iDirect. “With its cutting-edge architecture, robust APIs to facilitate analytics, and seamless readiness for 5G NTN, Intuition empowers customers to stay ahead in the rapidly changing connectivity landscape. Our agile development practice, and accelerated release delivery cycles will ensure that our customers have access to new capabilities and ongoing innovation to drive growth.”

    Intuition 1.1 integrates ST Engineering iDirect’s award-winning Mx-DMA® MRC return waveform technology with global bandwidth management and advanced mobility, enabling dynamic multi-orbit bandwidth pooling, automated resource allocation and a faster response to network demands. This combination dramatically enhances performance, reduces costs and optimizes resource allocation to support growing connectivity requirements. Additionally, satellite network resource orchestration APIs further empower operators with comprehensive control over ground and space assets, enabling them to fully leverage advancements in software-defined satellites.

    Intuition, paired with ST Engineering iDirect’s AI-powered analytics platform, drives advanced network optimization and operational performance. Designed for the future, Intuition enables seamless adoption of emerging 3GPP standards and sets the stage for 5G NTN roaming in upcoming releases. By deploying Intuition today, satellite operators can maximize their return on investment, support high-density growth and maintain readiness for the next wave of industry innovation.

    *****

    Media contact:
    Martyn Gettings Tank PR
    Email: martyn.gettings@tank.co.uk


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  • AWS outage spotlights the global economy's fragile foundations – Axios

    1. AWS outage spotlights the global economy’s fragile foundations  Axios
    2. Amazon web services return to ‘normal operations’ after mass outage, tech giant says  BBC
    3. Updates: Amazon AWS struggles to recover as outage hits Snapchat, apps  Al Jazeera
    4. The internet just had another global outage. Why does this keep happening?  CNN
    5. Amazon says AWS cloud service is back to normal after outage disrupts businesses worldwide  Reuters

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  • US$187.5 million sale of shares in UltraGreen.ai Private Limited to 65 Equity Partners, Vitruvian Partners and August Global Partners: Allen & Gledhill

    US$187.5 million sale of shares in UltraGreen.ai Private Limited to 65 Equity Partners, Vitruvian Partners and August Global Partners: Allen & Gledhill










    21 October 2025

    Allen & Gledhill advised Renew Group Private Limited (“RGPL”) on its sale of approximately 14.42% of the ordinary shares in UltraGreen.ai Private Limited to, among others, Anchor VI Pte. Ltd. (“65 Equity Partners”), Verde Taano Pte. Ltd. (“Vitruvian Partners”) and AGP Healthcare Fund VCC (“August Global Partners”), at an equity valuation of US$1.3 billion.

    Advising RGPL was Allen & Gledhill Partner Rhys Goh.

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  • Getting ahead during retail’s peak advertising season

    Getting ahead during retail’s peak advertising season

    As consumers ready their carts for the year’s biggest sales events, brands and agencies across Southeast Asia gear up for their final, crucial push of the year.

    Navigate the hyper-competitive peak season with insights into ad spend trends, the retail industry’s top advertisers and competing creative strategies.

    Inside this report, you’ll discover:

    Crucial industry trends: Understand how retail and manufacturing advertisers in Southeast Asia are spending year on year.

    Industry insights into spend activity: See who the top players within the industry are.

    Competing creative strategies: Get a preview of in-market messaging and creative trends, alongside the promotions and price points of top competitors.

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