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  • Office of Tibet, London Hosts Special Screening of Wisdom of Happiness to Mark The Year of Compassion

    Office of Tibet, London Hosts Special Screening of Wisdom of Happiness to Mark The Year of Compassion


















    Office of Tibet, London Hosts Special Screening of Wisdom of Happiness to Mark The Year of Compassion – Central Tibetan Administration





































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  • The Take: Netanyahu meets Trump – Could a Gaza ceasefire deal emerge? | Gaza News

    The Take: Netanyahu meets Trump – Could a Gaza ceasefire deal emerge? | Gaza News

    Podcast, The Take

    What does Netanyahu’s latest US visit mean for Palestinians in Gaza?

    Could Israeli Prime Minister Benjamin Netanyahu’s third trip to the United States during President Donald Trump’s administration mean a ceasefire in Gaza is close at hand? As Netanyahu lands in Washington, DC, for a week of discussions on topics such as Gaza and Iran, what pressures is he facing at home?

    In this episode:

    • Daniel Levy, president of US/Middle East Project, former Israeli negotiator

    Episode credits:

    This episode was produced by Marcos Bartolomé and Amy Walters, with Leonidas Sofogiannis, Remas Alhawari, Kisaa Zehra, Melanie Marich, Marya Khan and our guest host, Kevin Hirten. It was edited by Kylene Kiang. 

    Our sound designer is Alex Roldan. Our video editors are Hisham Abu Salah and Mohannad al-Melhemm. Alexandra Locke is The Take’s executive producer. Ney Alvarez is Al Jazeera’s head of audio. 

    Connect with us:

    @AJEPodcasts on Instagram, X, Facebook, Threads and YouTube


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  • Exploring the effects of di-(2-ethylhexyl)-phthalate exposure on metab

    Exploring the effects of di-(2-ethylhexyl)-phthalate exposure on metab

    Introduction

    Plastics are one of the indispensable and fundamental materials in modern society and are widely used in all aspects of daily life.1 However, the chemical stability of plastics leads to their extensive accumulation in aquatic and terrestrial ecosystems, which in turn contributes to persistent environmental pollution.2,3 The global production of plastic waste has reached 353 million tons per year and increased to 367 million tons in 2020.4,5 As the consumption of plastics continues to rise, the future generation of plastic waste will show an increasing trend.6 Various additives are often added during the production of plastics to achieve the desired physical properties.7

    Used as a plasticizer for plastics such as polyvinyl chloride (PVC), Di-(2-ethylhexyl)-Phthalate (DEHP) has become a core chemical additive in the plastics industry due to its low cost and excellent flexibility.8 DEHP is mainly used in food packaging, children’s products, and medical devices, which are involved in various aspects of life.9,10 DEHP leaches out of the material over time and ultimately enters the environment, which has an undesirable effect on humans.2,11 Exposure of newborns to DEHP during medical care has been found to begin after birth based on quantitative measurements of urinary DEHP metabolites.12 PBPK (physiologically based pharmacokinetics) modelling has found that the cumulative distribution of DEHP in different organs and tissues may lead to various harmful health outcomes.13

    Metabolic syndrome (MetS) is a common metabolic disorder characterized by a series of interrelated cardiovascular risk factors, including abdominal obesity, insulin resistance, hypertension, dyslipidemia, and disorders of glucose metabolism.14,15 Recent studies have shown that environmental pollutants, especially plasticizer-like chemicals (eg, DEHP), may play an essential role in the development and progression of MetS.16–18 DEHP acts as an endocrine disruptor (EDC) that may induce MetS through multiple biochemical pathways. It may interfere with hormone action through anti-androgenic or estrogenic mechanisms, thereby triggering early puberty.19 Studies have also shown that DEHP can upregulate the expression of hepatic PPARγand SREBP-1c, promote lipid accumulation, reduce insulin sensitivity, and cause inflammation by activating the NF-κB pathway.20 Since DEHP is widely present in the environment and can be exposed to humans, primarily through plastic products, an in-depth understanding of its potential health effects is of great public health importance.

    Derived from Network Pharmacology, Network Toxicology is based on systems biology theory and analyzes biological systems using bioinformatics and network analysis methods.21 Although studies have preliminarily revealed the association between DEHP and MetS, its complex multi-target mechanism of action has not been systematically analyzed. Therefore, this study aimed to systematically explore how DEHP exposure may affect the occurrence and development of MetS through multiple biological pathways and molecular targets using cyber toxicology techniques.22 Establishing the molecular network between DEHP exposure and MetS will provide a theoretical basis for scientific research.

    Methods

    DEHP Composition and Target Acquisition

    We first searched the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) for the chemical structure of DEHP and the canonical SMILES representation. Using the obtained SMILES symbols, we searched the STITCH database (http://stitch.embl.de/), SwissTargetPrediction database (http://swisstargetprediction.ch/) (Probability >0), and ChEMBL database (https://www.ebi.ac.uk/chembl/), specifying “Homo sapiens” as the target species.

    MetS Related Targets Collection

    Using “Metabolic syndrome” as the search term, we searched and collected MetS-related target genes in the GeneCards database (https://www.genecards.org/), OMIM database (https://www.omim.org/), and TTD database (https://db.idrblab.net/ttd/). All results were integrated into Excel, and data were merged and deduplicated to obtain a list of disease targets.

    Protein–Protein Interaction (PPI) Network Analysis

    Enter the common predicted targets of DEHP and MetS in the program corresponding to the protein interaction platform STRING (https://string-db.org),23 The species was set as “Homo sapiens”, and the minimum interaction score was set as “high confidence (0.700)” to obtain the PPI protein interactions map, which was simultaneously visualized using the Cytoscape 3.7.1 software was used for visualization.24 Finally, topology analysis was performed using the Network Analyzer plug-in in Cytoscape 3.7.1 to assess the degree (DC), betweenness centrality (BC), and closeness centrality (CC) of the network nodes to filter out the core targets.

    Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment

    We entered the common targets into the DAVID database to elucidate the potential functions and enrichment pathways of MetS induced by DEHP exposure (https://david.ncifcrf.gov/).25 The identifier was set to “OFFICIAL GENE SYMBOL”, the species was selected as “Homo sapiens”, and other defaults were kept, and gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. After downloading the relevant data, we logged into the Microbiome Visualization Cloud Platform (https://www.bioinformatics.com.cn/) and plotted the bar and bubble plots of the enrichment results. Items with P < 0.05 were considered statistically significant.

    Molecular Docking

    To confirm the effect of DEHP on core genes, molecular docking explored potential binding interactions. The 3D structures of the proteins were downloaded from the PDB database (https://www.rcsb.org/), while the PubChem database was used to download the 3D structural information of the compounds. The protein structures were obtained by searching the protein structure database, and after removing redundant structures such as small molecules and water, they were converted to pdbqt files using MGLTools. Download the small molecule structure file and process it into a pdbqt file using MGLTools. Construct the docking box so that it contains the entire protein. Use Autodock Vina 1.1.2 to dock the small molecule and protein. Receptor molecules with higher negative molecular docking binding energies are more stable in the docked conformation.

    Molecular Dynamics Simulations

    This study selected one set of protein-ligand complexes with the lowest molecular docking binding energies for molecular dynamics simulations using GROMACS 2022 software. The CHARMM36 force field was used for the proteins, and the ligand topology was constructed from the GAFF2 force field. The system was solvated using the TIP3P water model in a cubic box with periodic boundary conditions.26 Particle mesh Ewald (PME) and Verlet algorithms were used to handle electrostatic interactions. Subsequently, 100,000 steps of isothermal isovolumic (NVT) equilibrium and isothermal isobaric (NPT) equilibrium were performed.27 The van der Waals and Coulomb cutoffs were set to 1.0 nm. Ultimately, the system was subjected to 100 ns of molecular dynamics simulations at constant temperature and pressure.

    Result

    DEHP Affects the Target Identification of MetS

    From the GeneCards database, we initially obtained 19,282 potential disease targets, and after four rounds of rigorous median screening, we finally screened 1205 disease targets (score>26.7). The OMIM database contains 650 disease-related gene targets. TTD contains nine disease targets. By querying the STITCH database, we identified potential targets for 10 compounds. Using the SwissTargetPrediction tool, we further predicted and obtained 100 potential compound targets. Finally, we collected 1165 compound targets from the ChEMBL database. After merging and de-weighting, we received 1772 MetS and 1177 DEHP targets. Venn diagram analysis was performed on DEHP and MetS targets, and 150 intersecting targets were finally obtained (Figure 1). A detailed list is provided in the Supplementary File.

    Figure 1 Venn diagram of common targets of DEHP and MetS.

    Potential Target Interaction Networks and Core Gene Acquisition

    PPI network analysis was performed on 150 potential action targets using the STRING database, and the results were imported into Cytoscape 3.7.1 software to construct a PPI network and obtain a PPI network graph. The network graph contains 127 nodes and 385 edges, and the darker color of the nodes indicates the more important the target is in the network (Figure 2).

    Figure 2 (A) PPI network of DEHP interfering with MetS target obtained by STRING. (B) PPI network processed by Cytoscape. The darker the color, the greater the degree value.

    To identify the core targets, we further refined the selection of core targets based on three key parameters: “degree”, “betweenness centrality”, and “closeness centrality”. After two rounds of median screening, the core targets of Tumor Protein p53 (TP53), Estrogen Receptor 1 (ESR1), Epidermal Growth Factor Receptor (EGFR), Tumor Necrosis Factor (TNF), Interleukin 6 (IL6) were finally obtained. These may be potential key targets for DEHP to interfere with human metabolism (Table 1).

    Table 1 Core Targets Screened from PPI

    GO and KEGG Pathway Enrichment Analysis

    The 150 DEHP and MetS intersecting targets were entered into the DAVID database for GO and KEGG enrichment analysis. Among them, 53 entries were obtained for cellular components (CC), 158 entries for molecular functions (MF), and 514 entries for biological processes (BP) (Figure 3). Among the BPs, metabolism, transcriptional regulation, insulin-like growth factor, etc. were mainly involved. Cellular components, on the other hand, focus on membrane-related structures and organelles, such as the endoplasmic reticulum and mitochondria. Various redox reactions, steroid metabolism, and other functions are involved in molecular functions.

    Figure 3 DEHP exposure interferes with the GO of MetS target genes (BP, CC, MF).

    125 signalling pathways were obtained from KEGG analysis, from which the top 20 were selected for ranking (p < 0.05). Among the top 20 ranked signalling pathways, steroid hormone biosynthesis, AGE-RAGE signalling pathway in diabetic complications, FoxO signalling pathway, metabolic pathway, and insulin resistance showed significant enrichment (Figure 4).

    Figure 4 DEHP exposure interferes with the first 20 KEGG pathways of MetS target genes.

    Molecular Docking of DEHP with MetS Core Target Proteins

    Lower binding energy implies higher binding activity between large and small molecules. When the binding energy is less than −4.25 kcal/mol, it indicates a certain degree of affinity between the two. When the binding energy is further reduced to below −5 kcal/mol, it means a more significant affinity.28 We performed molecular docking of the core targets of the screen, TP53, ESR1, EGFR, IL6, and DEHP (CHEMBL1242017). The Vina scores of TP53, ESR1, EGFR, and IL6 were −5.6 kcal/mol, −6.1 kcal/mol, −5.4 kcal/mol, and −4.8 kcal/mol, respectively (Figure 5). This indicates that these targets are highly bound and conformationally stable for DEHP. The detailed results of molecular docking are shown in Table 2.

    Table 2 The Docking Results of Core Genes and DEHP Molecules

    Figure 5 Two-dimensional and three-dimensional maps of molecular docking of DEHP with central targets (A) TP53-DEHP, (B) ESR1-DEHP, (C) EGFR-DEHP, (D) IL6-DEHP.

    Molecular Dynamics Simulations Validation

    The equilibrium state of the simulated system was assessed using root mean square deviation (RMSD) (Figure 6A), and the ESR1-DEHP complex system reached equilibrium after about 65 ns and ultimately fluctuated above and below 2.1 Å, suggesting that the complex has a high structural stability. Further analysis showed that the radius of gyration (Rg) and solvent-accessible surface area (SASA) of the complex fluctuated less during the simulation (Figure 6B and C), suggesting that no apparent structural contraction or expansion occurred. The number of hydrogen bonds between small molecules and target proteins (Figure 6D) and between complex systems ranged from 0 to 2, suggesting the existence of stable hydrogen bonding interactions between the two. In addition (Figure 6E), the root-mean-square rise and fall (RMSF) values were relatively low (mostly below 3 Å), reflecting their overall low flexibility and high stability.

    Figure 6 Molecular dynamics simulations of protein-ligand complexes. (A) Root mean square deviation, (B) Radius of gyration, (C) Solvent accessible surface area, (D) Number of hydrogen bonds, (E) Root-mean-square fluctuation.

    Discussion

    The widespread use of DEHP in various plastic products has raised increasing concerns about environmental pollution and health risks, which continue to grow, as many studies have linked it to adverse health manifestations in humans.29 DEHP has been detected at high frequencies and concentrations in soil, air, and water.30,31 Plasticizers such as DEHP released from the slow degradation of discarded plastic products penetrate the soil,32 and various persistent organic pollutants (POPs) are introduced into the aquatic environment, resulting in water contamination.33 After contaminating the environment, DEHP enters the human body through the air, food, water, dermal contact, and medical devices and accumulates in the body, potentially causing endocrine disruption to reproductive and immune systems with long-term health effects on health.34

    In this study, we systematically explored the potential mechanisms by which DEHP exposure may contribute to the pathogenesis of MetS through network toxicology. By integrating DEHP and MetS-related genes, we identified 150 overlapping targets and revealed key biological pathways, providing new insights into the molecular interactions between environmental pollutants and metabolic disorders. PPI network and topological analyses highlighted key targets, such as TP53, ESR1, EGFR, TNF, and IL6, which may serve as central hubs for DEHP-induced metabolic disorders.

    The TP53 gene encodes the tumor suppressor protein p53 and is traditionally thought to play an essential role in apoptosis and cancer suppression.35 Emerging evidence emphasizes that certain activities are also involved in the homeostatic regulation of energy metabolism.36,37 For example, p53 enhances gluconeogenesis in human and mouse hepatocytes.38 DEHP activates p53 by inducing oxidative stress and inhibiting Mdm2, and the p53-dependent apoptotic pathway plays a key role in DEHP-induced hepatotoxicity.39 Estrogen Receptor Alpha (ERα), encoded by the ESR1 gene, is a nuclear hormone receptor that plays a key role in regulating gene expression, cell proliferation, and differentiation. The function of ESR1 is closely related to metabolic processes. ESR1 directly regulates the obesity disparity gene MMAA to improve the prognosis of patients with hepatocellular carcinoma in terms of liver metabolism and tumor suppression.40 ERα knockout mice have increased adipose tissue and insulin resistance, indicating that the E2/Erα signalling pathway is essential in adipose tissue.41 Abnormalities in its function may lead to metabolic disorders and related diseases. EGFR genes play a key role in cell proliferation, differentiation, and survival. Recently, mutations in the EGFR gene have been found to affect metabolic processes.42 EGFR-sensitive mutations cause metabolic reprogramming in tumor cells, such as enhancement of aerobic glycolysis and the pentose phosphate pathway, up-regulation of glutamine metabolism, and increased synthesis of lipids and adenosine, among many other metabolic pathways.43 EGFR-mediated activation of adipose tissue macrophages promotes obesity and insulin resistance and thus encourages a low-grade inflammatory state in the MetS.44 Animal studies suggest that EGFR may play an essential role in lipid metabolism in mice. EGFR inhibitors reduce serum lipid levels and hepatic steatosis in high-fat diet-induced obese mice.45,46 The pro-inflammatory cytokines TNF and IL6 are known mediators of chronic low-grade inflammation in the MetS.47 Metabolic inflammation is characterized by elevated serum levels of pro-inflammatory cytokines, predominantly IL-6 and TNF-α, which are derived from chronically inflamed adipose tissue and are associated with oxidative stress.48,49 Inhibition of IL-6 and TNF-α alleviates hypertension, hyperuricemia, dyslipidemia, and insulin resistance in MetS rats induced by a high-fat diet.50 DEHP exposure may exacerbate their expression, further contributing to oxidative stress and metabolic dysfunction. These findings suggest that DEHP disrupts metabolic homeostasis by targeting multifunctional nodes involved in inflammation, hormonal signalling, and cellular stress responses.

    Our KEGG pathway enrichment analysis showed that DEHP exposure disrupts multiple key metabolic homeostatic pathways, including steroid hormone biosynthesis, AGE-RAGE signalling, FoxO signalling, and insulin resistance. ESR1 is a steroid hormone receptor, and DEHP interferes with adipocyte differentiation and lipid storage by enhancing ESR1 activity, leading to abnormal adipose tissue distribution and inducing insulin resistance.51 EGFR, on the other hand, induces the expression of inflammatory factors TNF and IL6 through activation of the NF-κB signalling pathway.52 In the inflammatory response, the sustained activation of the AGE-RAGE pathway further induces pathological processes such as inflammation, oxidative stress, and insulin resistance, significantly increasing the risk of MetS-associated cardiovascular complications.53,54 The metabolic disruptions induced by DEHP are dependent on FoxO1. DEHP-induced metabolic disturbances depend on the overexpression of FoxO1, which drives hepatic gluconeogenesis and lipid accumulation.18 Abnormal enhancement of FoxO signalling further contributes to disturbed energy metabolism and exacerbates the phenotype of MetS, and inhibition of FoxO1 reverses the metabolic disturbances induced by DEHP.55 The disruption of these metabolic pathways is a direct driver of lipid accumulation and insulin resistance, which are central pathological features of MetS.

    Although the present study revealed the potential mechanism of action of DEHP exposure and MetS through network toxicology, molecular docking, and kinetic simulations, there are still some limitations. First, cyber toxicology analysis is highly dependent on data from public databases, which may have data bias or incompleteness and cannot fully reflect the complex biological processes in the human body. Second, the results of molecular docking and kinetic simulations are computer simulations, which are difficult to adequately model the complex metabolic environment and its dynamic changes in the body. Finally, DEHP’s metabolizing ability and sensitivity differ in different populations (eg, children, pregnant women, and the elderly). It is also impossible to clarify the effect of DEHP exposure dose or exposure route on MetS. This is likewise a direction in which future research needs to focus on breakthroughs. Our findings emphasize the urgent need to regulate the use of DEHP, especially in products with a high risk of human exposure (eg, medical devices and food packaging). Future work should prioritize in vivo and in vitro validation of key targets. Epidemiological studies should also be conducted in different populations to establish dose-response relationships between DEHP exposure and MetS, to clarify the differential effects of DEHP in specific populations, and to track the impact of long-term DEHP exposure to MetS so that these heterogeneities can be fully assessed. In addition, there is an urgent need to find safer and more environmentally friendly alternatives and to systematically evaluate the differences in environmental persistence, bioaccumulation, and health risks between alternatives and DEHP. These directions will deepen the mechanistic understanding of the association between DEHP and MetS and provide a scientific basis for environmental health policy and precision medicine.

    Conclusion

    In this study, we revealed through network toxicology that DEHP exposure may promote MetS by regulating key target proteins (eg, TP53, ESR1, EGFR) and interfering with lipid metabolism, insulin signalling pathway, and inflammatory response. These findings not only elucidate the metabolic toxicity mechanism of DEHP but also provide new perspectives for understanding the association between environmental pollutants and metabolic diseases. Identification of DEHP-associated biomarkers of metabolic disorders may be helpful for early diagnosis and personalized intervention, especially in populations chronically exposed to plasticizers. It points the way for subsequent toxicological studies and provides a scientific basis for improving public health policy and clinical practice.

    Data Sharing Statement

    The data supporting the findings of this study are available from the corresponding author, Dr. Maoyuan Wang, upon reasonable request.

    Ethics Approval and Informed Consent

    The data used are de-identified public datasets that cannot be traced back to any individual and do not involve direct interaction with human subjects. According to Article 32, Item 1 of the Measures for Ethical Review of Human Life Science and Medical Research (February 18, 2023, China), this type of research meets the conditions for exemption from ethical review.

    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 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

    The project is funded by the Postgraduate Innovation Special Fund Project of Gannan Medical University (grant number YC2024-X014).

    Disclosure

    The authors declare that the study has no conflicts of interest.

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    44. Cao S, Pan Y, Tang J, et al. EGFR-mediated activation of adipose tissue macrophages promotes obesity and insulin resistance. Nat Commun. 2022;13(1):4684. doi:10.1038/s41467-022-32348-3

    45. Scheving LA, Zhang X, Garcia OA, et al. Epidermal growth factor receptor plays a role in the regulation of liver and plasma lipid levels in adult male mice. Am J Physiol Gastrointest Liver Physiol. 2014;306(5):G370–381. doi:10.1152/ajpgi.00116.2013

    46. Choung S, Kim JM, Joung KH, Lee ES, Kim HJ, Ku BJ. Epidermal growth factor receptor inhibition attenuates non-alcoholic fatty liver disease in diet-induced obese mice. PLoS One. 2019;14(2):e0210828. doi:10.1371/journal.pone.0210828

    47. Hotamisligil GS. Inflammation, metaflammation and immunometabolic disorders. Nature. 2017;542(7640):177–185. doi:10.1038/nature21363

    48. Silveira Rossi JL, Barbalho SM, Reverete de Araujo R, Bechara MD, Sloan KP, Sloan LA. Metabolic syndrome and cardiovascular diseases: going beyond traditional risk factors. Diabetes/Metab Res Rev. 2022;38(3):e3502. doi:10.1002/dmrr.3502

    49. Mohammadi M, Gozashti MH, Aghadavood M, Mehdizadeh MR, Hayatbakhsh MM. Clinical significance of serum IL-6 and TNF-α levels in patients with metabolic syndrome. Rep Biochem Mol Biol. 2017;6(1):74–79.

    50. Said MA, Nafeh NY, Abdallah HA. Spexin alleviates hypertension, hyperuricaemia, dyslipidemia and insulin resistance in high fructose diet induced metabolic syndrome in rats via enhancing PPAR-ɣ and AMPK and inhibiting IL-6 and TNF-α. Arch Physiol Biochem. 2023;129(5):1111–1116. doi:10.1080/13813455.2021.1899242

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    53. Zhou M, Zhang Y, Shi L, et al. Activation and modulation of the AGEs-RAGE axis: implications for inflammatory pathologies and therapeutic interventions – A review. Pharmacol Res. 2024;206:107282. doi:10.1016/j.phrs.2024.107282

    54. Vianello E, Beltrami AP, Aleksova A, et al. The Advanced Glycation End-Products (AGE)–Receptor for AGE System (RAGE): an inflammatory pathway linking obesity and cardiovascular diseases. Int J Mol Sci. 2025;26(8):3707. doi:10.3390/ijms26083707

    55. Wang M, Wang Y, Han J, et al. Gestational and lactational co-exposure to DEHP and BPA impairs hepatic function via PI3K/AKT/FOXO1 pathway in offspring. Toxics. 2023;11(3).

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  • India To Reject F-35A, Su-57E Offers From U.S. & Russia; Likely To Opt For Stealth Fighters Based On FGFA

    India To Reject F-35A, Su-57E Offers From U.S. & Russia; Likely To Opt For Stealth Fighters Based On FGFA



    Apropos of an unconfirmed news report suggesting that India may procure stealth fighters from an allied nation to meet the IAF’s immediate requirements:

    While the report lacks supporting evidence, it appears credible—because it makes logical sense. However, logic is not widely seen as the driving force behind decision-making in India’s Ministry of Defence.

    In recent times, defense procurement in India has increasingly been perceived as being guided more by geopolitical signaling than by economic or military imperatives.

    Geopolitical Imperatives

    Given India’s 360-degree geopolitical balancing act, it may find itself needing to procure, more likely lease, F-35A fighters to preserve U.S. goodwill and, in turn, secure continued Russian cooperation on the Su-30MKI upgrade program.

    Without such a balancing gesture, India could risk facing punitive tariffs and sanctions from “Shylock Sam.”

    However, an F-35A deal would likely come with strings attached—most notably, a U.S. demand that India abandon its Russian S-400 air defense systems in favor of the American THAAD.

    Unfortunately, India lacks the fiscal bandwidth to field THAAD batteries across its vast borders. Recently, while speaking at a convention and addressing delays in HAL’s delivery of the LCA Mk-1A, the IAF Chief quoted a dialogue from a Salman Khan film.

    “Ek baar jo humne commit kiya hai, fir main apne aap ki bhi nahi sunta.”

    (“Once I commit to something, I don’t even listen to myself after that.”)

    Asked to blow a gaping hole in India’s air defense coverage just to host leased F-35As, the IAF Chief would not be able to summon a Salman Khan quote to express his frustration.

    Now, let’s assume that sensing resistance from a cornered India, Washington has graciously recused itself from the stealth fighter equation and tacitly permitted New Delhi to procure Su-57s from Russia instead.

    Not The Su-57

    I doubt the IAF will go for the Su-57 outright.

    The IAF’s requirement isn’t just for a stealth fighter—it is specifically for a dual-seat, twin-engine stealth fighter!

    It’s worth recalling that the Fifth Generation Fighter Aircraft (FGFA), which India and Russia once planned to co-develop, was envisioned as a two-seat variant of the Su-57. However, India opted out of the FGFA project, citing critical shortcomings—namely, the aircraft’s lack of essential fifth-generation features, such as supercruise, and its unproven operational capability.

    That said, India never completely closed the door. While it suspended participation in the FGFA project, it left the option open to procure the aircraft at a later stage.

    In July 2018, then-Defence Minister Nirmala Sitharaman told Business Standard, “In February, it was conveyed to the Russians that they could go ahead with developing the fighter without us. But the option remains, and we could well go back at a later stage and ask to buy the fighter.”

    A year later, in July 2019, the Chief of Air Staff, Air Chief Marshal B.S. Dhanoa, told Krasnaya Zvezda, the official newspaper of the Russian Armed Forces, that India would make a decision on the Su-57 after seeing it in action and after Russia showcases the aircraft in India.

    Since then, the Su-57 has been operationally deployed in conflict zones, and according to both Russian and Western reports, its performance has been creditable. Furthermore, concerns over the lack of supercruise capability are being addressed. The aircraft’s second-stage (fifth-generation) engine, known as Izdeliye 30, is currently undergoing flight testing. Su-57s delivered from the mid-2020s onward are expected to be powered by this engine, while earlier units use an interim fourth-generation powerplant.

    Also, Russia showcased the aircraft in Bengaluru during Aero India 2025.

    In short, many of the IAF’s earlier concerns regarding the Su-57’s operational capability and performance envelope—particularly supercruise—are now being addressed.

    Sukhoi Su-57 fifth-generation fighter aircraft perform during the MAKS 2019 International Aviation and Space Salon opening ceremony in Zhukovsky outside Moscow on August 27, 2019. (Photo by Alexander NEMENOV / AFP)

    The Dual Seat Option

    In June 2021, Russia’s Deputy Prime Minister Yuri Borisov discussed plans to develop a dual-seat variant of the Su-57, which would enhance the aircraft’s versatility. Also, the aircraft would be more attractive to foreign customers, he said.

    In November 2023, Russia’s United Aircraft Corporation published a patent for a Multifunctional two-seat stealth aircraft, which was easily recognizable as the fifth-generation heavy fighter Su-57.

    It’s interesting to note that at no stage did the Russian Aerospace Forces show any interest in a two-seat variant of the Su-57. Russia considered developing a two-seat variant only to meet FGFA requirements.

    Why A Two-Seat Variant Makes A Difference?

    The announcement and patenting of a two-seat Su-57 variant marked a strategic pivot intended to appeal to the IAF for several reasons:

    Ease of Pilot Training: A two-seat configuration allows easier transition and training for pilots, especially critical for complex fifth-generation systems. The IAF has always valued twin-seat trainers like the Su-30MKI.

    Enhanced Mission Management: A second crew member would ease the operational workload by managing complex systems, data fusion, and electronic warfare operations. As roles for 5th-gen fighters expand into network-centric warfare, this becomes increasingly vital.

    Drone Mothership Capability: The Su-57 two-seater is reportedly designed to control UAVs like the S-70 Okhotnik. The ability to command unmanned wingmen while operating in contested airspace adds a force-multiplier dimension, aligning with the IAF’s future combat doctrine.

    Combat Command Post: According to the Russian patent, the two-seater Su-57 can act as an airborne command center for mixed aircraft groups—ideal for integrating Su-30MKIs, Rafales, and future indigenous drones in a combat network.

    Expanded Strike Capability: With one pilot focused on flying and the other on weapons systems, the Su-57 becomes a more capable deep strike aircraft. This could be crucial for the IAF’s need to penetrate hostile airspace protected by advanced SAM systems.

    The Overall Logic For Dual-Seat Su-57 Procurement

    India’s requirement for a fifth-generation fighter aircraft (FGFA) is both urgent and specific. The Indian Air Force (IAF), having withdrawn from the original Indo-Russian Fifth Generation Fighter Aircraft (FGFA) program in 2018, has since closely monitored the evolution of Russia’s Su-57.

    Recent disclosures and patents suggest Russia is developing a two-seat variant of the Su-57—a development that could realign India’s interest toward the platform.

    India’s original involvement in the FGFA program, based on the Su-57 (then PAK FA), was driven by the desire for co-development, industrial participation, and access to fifth-generation technology.

    However, concerns about cost, capability gaps (especially stealth and supercruise), and lack of clarity over work share led to India’s exit in 2018. Despite this, Indian officials kept the door open for a future acquisition, contingent on the maturity of the platform.

    In the years since, Russia has continued to develop the Su-57, operationalizing it with its own air force and introducing export versions like the Su-57E. Recent advances, including the development of the second-stage Izdeliye 30 engine and reports of successful deployment in Syria and Ukraine, have improved the fighter’s credibility.

    Industrial & Strategic Rationale

    India’s own AMCA fifth-generation fighter is under development but not expected to enter service before 2035. The Su-57 two-seater offers a strategic interim solution without compromising long-term indigenous goals.

    Moreover, Russia may offer industrial participation as part of a larger defense cooperation framework. This could include licensed assembly, MRO facilities, and possible avionics customization, appealing to both the IAF and the Indian industry.

    The IAF is facing delays in acquiring 114 multi-role fighters under the MRFA program. Acquiring a small batch (approximately 18-24) of twin-seat Su-57s could serve as a stopgap, providing advanced capabilities while the MRFA and AMCA mature.

    • Vijainder K Thakur is a retired IAF Jaguar pilot, author, software architect, entrepreneur, and military analyst. 
    • VIEWS PERSONAL OF THE AUTHOR
    • Follow the author @vkthakur


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  • Brain stimulation and coaching help older adults increase physical activity

    Brain stimulation and coaching help older adults increase physical activity

    A study published in The Journals of Gerontology: Series A reports that a novel combination of brain stimulation and personalized coaching significantly increased physical activity in older adults and held steady for months. The results offer hopeful news for inactive older adults living in subsidized housing, who may experience several barriers to increased activity, including depression and a lack of motivation.

    Regular physical activity is an extremely effective, safe, and modifiable means to improve health, especially in older adults. But more than 85% of adults aged 65 and above regularly fail to meet federal physical activity guidelines, and insufficient physical activity remains a global health issue.

    In the randomized trial, researchers found that inactive older adults who received transcranial direct current stimulation (tDCS), a noninvasive technique that delivers low-level electrical currents to targeted areas of the brain, along with individualized behavioral coaching, increased their daily step count an average of 1,179 steps per day.

    That’s more than twice the increase seen in those who received coaching and placebo stimulation. Importantly, this boost persisted for three months after the program ended, and adherence to both the stimulation and coaching was exceptionally high.

    Participants in the tDCS group received 10 brief 20-minute sessions over two weeks, targeting the left dorsolateral prefrontal cortex, a region associated with motivation, planning, and goal-directed behavior. They also received a personalized behavioral program that continued for two months. The coaching, delivered by physical therapists, included regular phone check-ins, individualized step goals, and practical strategies – like marching during commercials or walking with friends – to help participants gradually increase their daily movement. Researchers tracked daily steps using Fitbits and continued monitoring participants for 12 weeks after the intervention ended.

    Adherence was significant: 97% of tDCS sessions and 93% of coaching sessions were completed, with Fitbit usage remaining strong throughout the intervention. Even during the no-contact retention phase, many participants maintained increased activity, suggesting the behavioral changes had taken hold.

    Beyond the physical gains, participants in the tDCS group also reported improved motivation and greater perceived walking ability. The study’s findings suggest that tDCS may enhance not just the drive to move but also the stickiness of behavior change, particularly when paired with accessible, goal-driven coaching.

    While tDCS has been explored in clinical and lab settings to improve mood, memory, and motor function, this study is among the first to test its use to support real-world health behavior change in older adults, particularly those with limited resources.

    Helping older adults build and maintain healthy habits is notoriously difficult, especially in underserved communities. This study provides early but exciting evidence that a short course of brain stimulation can ‘prime the pump’ – enhancing motivation and helping new behaviors stick – and is encouraging, especially given the setting. The program was delivered entirely within participants’ housing facilities, which removed barriers to access. That model could be a blueprint for future community-based interventions.”


    On-Yee (Amy) Lo, PhD, assistant scientist II at the Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife

    The authors caution that larger trials are needed to confirm the findings and explore how tDCS might be used to amplify other types of behavioral health programs. They also note the importance of exploring how cognitive function, baseline activity level, and social support might affect outcomes.

    Still, for a population at high risk for inactivity-related decline, this study offers hope – and a potential new approach – for getting and keeping older adults moving.

    The researchers included Levi Ask; Melike Kahya, PT, PhD, assistant professor of physical therapy at High Point University; Thomas Travison, PhD, senior scientist at the Marcus Institute; Lewis Lipsitz, MD, director of the Marcus Institute and chief academic officer of the Irving and Edyth S. Usen and Family Chair in Medical Research, Hebrew SeniorLife; and Brad Manor, PhD, senior scientist at the Marcus Institute.

    The study, Modulating Brain Activity to Improve Goal-directed Physical Activity in Older Adults: A Pilot Randomized Controlled Trial, is published in The Journals of Gerontology: Series A, Volume 80, Issue 6, June 2025, glaf039.

    Source:

    Hebrew SeniorLife Hinda and Arthur Marcus Institute for Aging Research

    Journal reference:

    Lo, O-Y., et al. (2025) Modulating Brain Activity to Improve Goal-directed Physical Activity in Older Adults: A Pilot Randomized Controlled Trial. The Journals of Gerontology Series A. doi.org/10.1093/gerona/glaf039.

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  • The prognostic value of combined systemic immune-inflammatory index (S

    The prognostic value of combined systemic immune-inflammatory index (S

    Introduction

    Cancer ranks as a leading cause of death and an important barrier to increasing life expectancy in every country of the world, according to Global Cancer Statistics 2022.1 With the diagnostic and curative level of cancer improved in recent years, the 5-year survival rate among cancer patients in China has increased to 40.5% from 30% 10 years ago.2 Despite the improved survival rate, a few of cancer patients in China still live less after treatment as real-world experience confirmed. This therefore reminds us of the importance to suggest personalized therapy strategies for different patients with different clinical characteristics. The established prognostic factors were tumor, node, tumor-node-metastasis (TNM) stage, pathological type, and so on.3,4 However, even the same cancer type patients with same stage may have distinct survival outcomes. It is critical to identify reliable biomarkers to predict patients’ prognosis and guide their individualized treatment.

    There is growing evidence that systematic immune inflammation plays a part in the mechanism of tumor initiation, progression, and metastasis.5,6 Though the concept of inflammation-based scores, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) has been revealed as negative prognostic factors in various types of solid tumors,7–9 no specific factors were recognized as reliable biomarkers. New available and non-invasive prognostic indicators were looked for. The systemic immune-inflammatory index (SII) is calculated based on peripheral blood neutrophils, platelets and lymphocytes. It is a novel indicator that can predict the clinical outcomes of cancer patients confirmed by a few of studies.10,11

    It is generally accepted that cancer patients with malnutrition have a lower tolerance to treatment, as well as worse prognosis and short life span. At the same time, nutritional status is also an essential part of the immune status of cancer patients.12,13 The prognostic nutritional index (PNI), which is calculated based on the serum albumin and circulating peripheral blood lymphocyte count, has been used to assess the immunonutritional status of cancer patients.14,15 It is now also used to predict the prognosis of various malignancies, including lung cancer,16 breast cancer17 and liver cancer.18 Our previous study has confirmed that PNI was an independent prognostic factor for gastric cancer.19

    Most of the studies focused on the value of either SII or PNI, but a single marker may not precisely predict the prognosis of cancer patients. We conducted this study to evaluate the combined effects between PNI, SII and clinical outcomes in cancer patients.

    Materials and Methods

    Patients

    Five hundred and eight cancer patients were enrolled from June 2013 to June 2022 in the Affiliated Kunshan Hospital of Jiangsu University. The following inclusion criteria were applied: histologically or cytologically confirmed stage I–IV cancer patients; more than 18 years old; the Eastern Cooperative Oncology Group (ECOG) activity status score of <2; the expected survival should more than 12 months. Patients with the second primary tumor or active concurrent infection as well as incomplete follow-up data were excluded. The study was conducted in accordance with the Declaration of Helsinki, and all the patients provided written informed consent. This observational study was reviewed and approved by the Institutional Review Board of Affiliated Kunshan Hospital of Jiangsu University (2013-03-020-H04).

    Data Collection and Follow-up

    The detailed clinical characteristics including age, sex, pathologic type, smoking history, body mass index (BMI), TNM stage (AJCC 8th ed., 2018), ECOG PS, peripheral blood count and liver function were got from the electronic medical record system, which was authentic and reliable. One of the researchers collected the survival time by phone contact, with a follow-up deadline of June 30, 2023. The PNI was calculated as albumin level (g/L) + 5×total lymphocyte count (109 /L). The SII was defined as platelet × neutrophil/ lymphocyte counts. The AGR was calculated using the following equations: AGR = ALB/(total protein-ALB). The overall survival (OS) was defined as time from the date of diagnosis to the date of death or last contact. The data were double-checked.

    Statistical Analysis

    SPSS 16.0 software (SPSS, Chicago, IL, USA) was utilized to perform statistical analyses. The receiver operating characteristic (ROC) curves were carried out to get the optimal cutoff values for AGR, SII and PNI. Comparisons between groups were performed using chi-squared test. Survival analysis was performed using the Kaplan–Meier method and comparisons between survival curves were performed by the Log rank test. Univariate and multivariable analyses were investigated by the Cox proportional hazards regression model. The Cox proportional hazards model was also used to check proportional hazard assumption. The hazard ratio (HR) and 95% confidence interval (CI) were used to assess relative risks. Statistically significance was defined as p values (two sides) <0.05.

    Results

    Clinicopathologic Characteristics of the Patients

    The baseline characteristics of the 508 patients enrolled in the study are summarized in Table 1. There were 239 males (47.05%) and 269 females (52.95%). The median age of the patient was 61 years old, ranging from 25 to 89, of which 191 (37.6%) were ≥65 years old. The most common cancer type was lung cancer (44.69%). 167 (32.88%) patients with stage I–II and 341 (67.12%) patients were diagnosed at stage III–IV. One hundred and seventy-three cases (34.06%) had a history of smoking. There were 68 patients with BMI < 18.5kg/m2 (13.39%), 379 patients with 18.5 to 24.9 kg/m2 (74.61%), and 61 patients with BMI > 24.9 kg/m2 (12.0%). Two hundred and eighty-three (55.71%) patients had PS score of 0 and 225 (44.29%) had PS score of 1.

    Table 1 Association of the Patients’ Characteristics with the SII and PNI

    Relationships Between SII, PNI and Clinicopathological Features

    The optimal SII and PNI cutoff values were analyzed by ROC curves for the OS of patients. According to the ROC curve and the Youden index, the ideal preoperative PNI and SII cutoff values were 792.0 (Youden index is 0.214) and 49.825 (Youden index is 0.356), respectively. The SII level before treatment was elevated in 149 (29.33%) patients and a total of 238 (46.85%) patients had lower PNI levels. As shown in Table 1, increased SII level was significantly associated with smoking history (p = 0.02), ECGO PS (p = 0.004) and AGR level (p < 0.001). The high PNI and low PNI groups showed significant differences in gender, age, smoking history, BMI group, cancer type, TNM stage, ECGO PS and AGR. Consider both of SII and PNI, smoking history, ECGO PS and AGR level may be the major related factors.

    Univariate and Multivariable Analyses

    We performed Cox regression analyses for OS. Table 2 demonstrated the univariate and multivariable analyses for OS. After multivariable analyses, the tumor stage of III/IV (p < 0.001), BMI<18.5kg/m2 (p = 0.042), high SII (p = 0.001, Figure 1) and low AGR (p = 0.047, Figure 2) were independently negative prognostic markers for OS.

    Table 2 Univariate and Multivariate Analyses of Factors for the Prediction of Overall Survival

    Figure 1 Kaplan-Meier survival curves of overall survival according to SII (p=0.001).

    Figure 2 Kaplan-Meier survival curves of overall survival according to AGR (p=0.047).

    The patients were also divided into four groups based on both the SII and PNI levels: high SII and low PNI (n = 101); high SII and high PNI (n = 48); low SII and high PNI (n = 222); low SII and low PNI (n = 137). At the last follow-up in this study, 377 patients (74.21%) were still alive. The median OS for all patients were 24 months. We performed joint analysis and showed in Figure 3. The results presented that both high SII and low PNI group had the worst prognosis (p < 0.001). The median OS of high SII and low PNI group was 21 months.

    Figure 3 Kaplan-Meier survival curves of overall survival according to both SII and PNI (p<0.001).

    Discussion

    Despite significant advancements in cancer treatment in recent years, not all patients benefit equally, primarily due to variations in their baseline health status, such as nutrition status and inflammatory conditions. Many blood-derived markers were applied for their cost-effectiveness and prognostic reliability. A great number of research studies have found that the PNI and SII play an important role in the cancer development and prognosis.10,20–22 Our results have added evidence that SII is an independent influencing factor of overall survival. Moreover, the joint analysis showed both high SII and low PNI had the lowest OS rate. This adds to the growing body of evidence supporting the utility of SII and PNI as prognostic markers in cancer patients, underscoring the importance of considering both inflammatory and nutritional status in prognostic prediction models.

    SII derives from peripheral lymphocyte, neutrophil and platelet counts, which could provide a comprehensive reflection of the local immune status and systemic inflammation in the whole body at the same time.23 SII alone has been proven to predict prognosis of various malignant tumors.24–26 PNI, a simple and feasible nutritional factor, has been used to assess the immunonutritional status of cancer patients.27,28 However, single factor may not reflect the complicated mechanism of tumor micro-microenvironment. More and more studies have focused on the joint predictive value of SII and PNI. Fan et al evaluated the value of SII combined with PNI to predict outcomes in non-small cell lung cancer (NSCLC) patients treated with platinum-doublet chemotherapy, and they found that patients with a higher SII-PNI score had a worse prognosis.29 Another prospective study showed lower SII-PNI scores were associated with better efficacy of chemotherapy combined with immunotherapy in patients with locally advanced gastric cancer.10 Yang et al developed a nomogram that incorporated the PIIN score, which includes SII and PNI, for predicting overall survival in postoperative pancreatic cancer patients.30 These studies collectively demonstrate the significance of PNI and SII in cancer development and prognosis, highlighting their potential as valuable biomarkers in clinical practice. The results could be explained that chronic inflammation associated with malnutrition could paradoxically suppress activation of the adaptive immune system, which is a vicious cycle.31 We could also tell that the patients with severe inflammatory reaction and poor nutritional condition had poor response to treatment and the overall survival was short. As an accessible, simple, and cost-effective marker derived from blood tests, preliminary evidence supports the potential clinical utility of PNI and SII.

    Going forward, it will be important to comprehend the interaction of nutritional status, inflammatory reaction and cancer survival. Malnutrition usually occurs in patients with malignant tumors and gradually leads to cachexia.32 The incidence of cachexia is particularly high in patients with tumors and lung cancer. Patients with digestive tract tumors are naturally more prone to malnutrition and even cachexia due to the decline or loss of their own digestion and absorption function, coupled with the serious depletion of the body’s nutrient reserves by cancer.33 Cytokines secreted by tumors are one of the causes of cachexia.34 These cytokines including IL-6, TGF-β and heat shock proteins (HSPs) which directly causes the catabolism and metabolism of the target tissue. Some studies have confirmed that nutritional intervention can improve the quality of life of patients with cachexia, and even prolong the survival of patients.35,36 Our findings revealed that patients with cancers and low BMI (<18.5) have short overall survival. In clinical practice, individualized nutrition intervention can effectively improve the nutritional status, life quality and the survival prognosis of locally advanced carcinoma patients.

    A few limitations of current study also should be explained. First of all, this retrospective analysis was conducted in a single center. It means the sample size is relative small and selection bias is inevitable Another aspect should be pointed out was that we assessed only the pretreatment level of these factors but did not focus on the dynamic change of them. However, the serum levels of these factors were easily affected by nutritional status and side effects of chemotherapy and radiotherapy. Moreover, other factors related to nutrition, inflammation, and immunity, such as weight, waist-to-hip ratio, C-reactive protein (CRP), procalcitonin, even treatments were not included in the final analysis. Though we have tried our best to minimize the risk of bias and unmeasured confounders by applying strict inclusion criteria (complete medical records) and dual-data verification, further research should aim to address these limitations and explore the full potential of SII and PNI as valuable biomarkers in clinical practice.

    Conclusion

    In conclusion, cancer patients with both high SII and low PNI had poor survival outcome. Pretreatment level of SII may be an independent prognostic factor for cancer patients.

    Funding

    This work was supported by the National Natural Science Foundation (Grant numbers: 82403069), Jiangsu Province Natural Science Foundation (Grant numbers: BK20240488), Kushan Science and Technology project (KS2207), Suzhou Science and Technology project (SLT2023020).

    Disclosure

    The authors report no competing interests for this work.

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    13. Rovesti G, Valoriani F, Rimini M, et al. Clinical implications of malnutrition in the management of patients with pancreatic cancer: introducing the concept of the nutritional oncology board. Nutrients. 2021;13(10):3522. doi:10.3390/nu13103522

    14. Keskinkilic M, Semiz HS, Ataca E, Yavuzsen T. The prognostic value of immune-nutritional status in metastatic colorectal cancer: Prognostic Nutritional Index (PNI). Support Care Cancer. 2024;32(6):374. doi:10.1007/s00520-024-08572-6

    15. Wang N, Xi W, Lu S, et al. A novel inflammatory-nutritional prognostic scoring system for stage III gastric cancer patients with radical gastrectomy followed by adjuvant chemotherapy. Front Oncol. 2021;11:650562. doi:10.3389/fonc.2021.650562

    16. Matsubara T, Hirai F, Yamaguchi M, Hamatake M. Immunonutritional indices in non-small-cell lung cancer patients receiving adjuvant platinum-based chemotherapy. Anticancer Res. 2021;41(10):5157–5163. doi:10.21873/anticanres.15333

    17. Qu F, Luo Y, Peng Y, et al. Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: a two-center study of 1,170 patients. Front Immunol. 2023;14:1335546. doi:10.3389/fimmu.2023.1335546

    18. Liu C, Zhao H, Zhang R, Guo Z, Wang P, Qu Z. Prognostic value of nutritional and inflammatory markers in patients with hepatocellular carcinoma who receive immune checkpoint inhibitors. Oncol Lett. 2023;26(4):437. doi:10.3892/ol.2023.14024

    19. Zhang Y, Zhu JY, Zhou LN, Tang M, Chen MB, Tao M. Predicting the prognosis of gastric cancer by albumin/globulin ratio and the prognostic nutritional index. Nutr Cancer. 2020;72(4):635–644. doi:10.1080/01635581.2019.1651347

    20. Lv X, Xu B, Zou Q, Han S, Feng Y. Clinical application of common inflammatory and nutritional indicators before treatment in prognosis evaluation of non-small cell lung cancer: a retrospective real-world study. Front Med. 2023;10:1183886. doi:10.3389/fmed.2023.1183886

    21. Atasever Akkas E, Erdis E, Yucel B. Prognostic value of the systemic immune-inflammation index, systemic inflammation response index, and prognostic nutritional index in head and neck cancer. Eur Arch Otorhinolaryngol. 2023;280(8):3821–3830. doi:10.1007/s00405-023-07954-6

    22. Yi J, Xue J, Yang L, Xia L, He W. Predictive value of prognostic nutritional and systemic immune-inflammation indices for patients with microsatellite instability-high metastatic colorectal cancer receiving immunotherapy. Front Nutr. 2023;10:1094189. doi:10.3389/fnut.2023.1094189

    23. Huang Y, Chen Y, Zhu Y, et al. Postoperative Systemic Immune-Inflammation Index (SII): a superior prognostic factor of endometrial cancer. Front Surg. 2021;8:704235. doi:10.3389/fsurg.2021.704235

    24. Tian BW, Yang YF, Yang CC, et al. Systemic immune-inflammation index predicts prognosis of cancer immunotherapy: systemic review and meta-analysis. Immunotherapy. 2022;14(18):1481–1496. doi:10.2217/imt-2022-0133

    25. Yan X, Li G. Preoperative systemic immune-inflammation index predicts prognosis and guides clinical treatment in patients with non-small cell lung cancer. Biosci Rep. 2020;40(3). doi:10.1042/BSR20200352

    26. Yang X, Wu C. Systemic immune inflammation index and gastric cancer prognosis: a systematic review and meta‑analysis. Exp Ther Med. 2024;27(3):122. doi:10.3892/etm.2024.12410

    27. Ishiguro T, Aoyama T, Ju M, et al. Prognostic nutritional index as a predictor of prognosis in postoperative patients with gastric cancer. In Vivo. 2023;37(3):1290–1296. doi:10.21873/invivo.13207

    28. Nagashima Y, Funahashi K, Kagami S, et al. Which preoperative immunonutritional index best predicts postoperative mortality after palliative surgery for malignant bowel obstruction in patients with late-stage cancer? A single-center study in Japan comparing the modified Glasgow prognostic score (mGPS), the prognostic nutritional index (PNI), and the controlling nutritional status (CONUT). Surg Today. 2023;53(1):22–30. doi:10.1007/s00595-022-02534-3

    29. Fan R, Chen Y, Xu G, Pan W, Lv Y, Zhang Z. Combined systemic immune-inflammatory index and prognostic nutritional index predict outcomes in advanced non-small cell lung cancer patients receiving platinum-doublet chemotherapy. Front Oncol. 2023;13:996312. doi:10.3389/fonc.2023.996312

    30. Yang J, Zhou H, Li H, Zhao F, Tong K. Nomogram incorporating prognostic immune-inflammatory-nutritional score for survival prediction in pancreatic cancer: a retrospective study. BMC Cancer. 2024;24(1):193. doi:10.1186/s12885-024-11948-w

    31. Arihara Y, Takada K, Murase K, et al. Inflammation and malnutrition as markers of poor outcomes in head and neck cancer patients treated with nivolumab. Acta Otolaryngol. 2023;143(8):714–720. doi:10.1080/00016489.2023.2240372

    32. Arends J. Malnutrition in cancer patients: causes, consequences and treatment options. Eur J Surg Oncol. 2024;50(5):107074. doi:10.1016/j.ejso.2023.107074

    33. Gliwska E, Glabska D, Zaczek Z, Sobocki J, Guzek D. Influence of enteral nutrition on quality of life in head and neck cancer and upper gastrointestinal tract cancer patients within a pair-matched sample. Nutrients. 2023;15(21):4698. doi:10.3390/nu15214698

    34. Kwon YY, Hui S. IL-6 is dispensable for causing cachexia in the colon carcinoma 26 model. bioRxiv. 2023.

    35. Gomez-Serna MI, Lopez D, Perez-Garcia YE, Montoya Restrepo ME. [Nutritional assessment of cancer patients in palliative care is a key element in comprehensive care and survival]. Nutr Hosp. 2022;39(4):814–823. Czech. doi:10.20960/nh.03828

    36. Sugiyama K, Shiraishi K, Motohashi T, et al. The impact of nutritional support on survival outcomes in patients with advanced gastric adenocarcinoma treated with chemotherapy. Nutr Cancer. 2023;75(3):867–875. doi:10.1080/01635581.2022.2162090

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  • U.S. further eases sanctions on Syria by lifting terrorist designation – The Washington Post

    1. U.S. further eases sanctions on Syria by lifting terrorist designation  The Washington Post
    2. US revokes ‘terrorist’ designation for Syrian president’s former group HTS  Al Jazeera
    3. US revokes foreign terrorist designation for Syria’s Hayat Tahrir Al-Sham  Dawn
    4. Revoking the Foreign Terrorist Organization Designation of Hay’at Tahrir al-Sham  U.S. Department of State (.gov)
    5. Moral Gymnastics  Daily Times

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  • Michelle Khare on YouTube challenges, content creation and Emmy goals

    Michelle Khare on YouTube challenges, content creation and Emmy goals

    Michelle Khare has done everything from Houdini’s deadliest trick to the Secret Service’s training academy all in the name of content on her “Challenge Accepted” YouTube channel. 

    Perhaps though, the content creator’s biggest challenge will be nabbing a Primetime Emmy Award this year after earning a place on the nomination ballet. Should the win come through, it’ll prove Khare and other YouTubers offer quality programming worthy of Hollywood (and the ad dollars that flow through it). 

    “It is a sign of a maturing industry. It’s an opportunity to attract talent who want to work on the show, as well as the audience who will continue to support the show, and the advertisers who are interested in spending their ad dollars on high quality projects that will be seen by millions of people,” Khare said.

    On this episode of the Digiday Podcast, Khare shares exactly what goes into “Challenge Accepted” episodes, YouTube’s maturity curve and what comes next in the creator economy. 

    Also on this episode: Paramount agrees to pay $16 million to settle its CBS lawsuit with the Trump administration, European publishers hit Google’s AI Overviews with an antitrust complaint, and TikTok is said to be building a new version of the app ahead of its expected U.S. sale. 

    Here are a few highlights from the conversation with Khare, which have been edited for length and clarity.

    ‘Wrangling our white whale’

    The process of taking an episode of “Challenge Accepted” from ideation to upload is a wild one. In many ways, we’re wrangling our white whale. In the beginning of the show, I was making tons of content, uploading weekly — or every other week — and we got to this point where our audience really latched onto longer, in-depth storytelling. The episodes where I would train like an Olympic figure skater for 60 days for one single video? That is what people latched onto and also what started performing better. 

    The content greenlighting process

    We have a huge spreadsheet of ideas, and these ideas come from lots of different places. Once we have a potential idea ruminating with us, it’s a heavy research process. Once we have a couple ideas, we pass them on to [our researcher], who does this big research document on the topic. She will go into the history, the pop culture, potential personnel we could reach out to to collaborate with them. From there, we continue to develop the idea. Last year, we did an episode where we simulated what it would be to be the president if nuclear missiles were inbound. We created this simulation with actors and role players playing the various heads of state. We brought in a professional from Harvard who studies all these types of conflicts in history to help us write the simulation. 

    Monetizing IP for the long term

    We’re going to be licensing our catalog. This has been announced on Samsung TV Plus, which is really cool. I just remain super honored and excited about syndication as a whole. Historically, it’s been a really special avenue for legacy television shows. To be in those adjacent and similar conversations is a dream come true. It’s a vote of confidence, and it’s special, because we are operating in a world where not everybody knows the quality of what’s happening in our space. I genuinely believe that there is just a crop of content creators who are making content that is so good it cannot be ignored any further.

    https://digiday.com/?p=582734

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  • Network analysis of depressive symptoms, social support, and diabetes

    Network analysis of depressive symptoms, social support, and diabetes

    Introduction

    Diabetes distress (DD) refers to the negative emotional impact of living with diabetes, including feelings of guilt, anxiety, and concerns about the self-management of the condition.1 A previous study established DD as a clinically significant risk factor for suboptimal health outcomes in patients with diabetes.2 Previous studies have demonstrated that elevated DD is associated with biological markers, including higher HbA1c3 and lower heart rate variability.4 Additionally, there is evidence that DD is associated with a higher mortality rate5,6 and that elevated DD is associated with delayed medical care,7 impaired diabetes self-management,8–10 and lower quality of life.11 While DD is known to complicate diabetes management, its connection to DS and SS is unclear.

    For the past few decades, researchers12,13 have focused on the association between DD and DS, which frequently occur together.14 According to survey results, 19.6% of adults with diabetes have experienced DD and DS.15 A longitudinal study demonstrated the persistent coexistence of DD and DS for 18 months.16 Gastrointestinal symptoms exhibited independent associations with DD and DS in individuals with type 2 diabetes (T2D).17 The coexistence of DD and DS increases the risk of death, poor disease management, diabetes-related complications, and a lower quality of life, which is a challenge to the care of patients with T2D.18 The American Diabetes Association and other researchers agree that routine screening for DD and DS should be performed in all adults with diabetes due to comorbidity, persistence over time, and impact on health outcomes.19–21 Therefore, establishing a link between DD and DS is critical for developing effective interventions.22 Ehrmann et al demonstrated that higher DD predicted more DS 6 months later. Conversely, a higher DS at baseline indicated an increase in DD at the 6-month follow-up date.23 Burns et al reported a bidirectional association between DD and DS in a follow-up study on a group of nearly 1700 patients with T2D living in the community.24 This indicates that DD was associated with concurrent and subsequent DS, and DS, in turn, was associated with concurrent and subsequent DD. These studies demonstrate an intricate reciprocal association between DD and DS. However, the exact mechanisms of the interaction are unclear.

    SS is another external factor closely associated with DD in individuals with T2D. SS is a multidimensional construct that refers to objective support, subjective support, and support utilization.25 Previous studies have confirmed that SS buffers the impacts of DD on health-related quality of life.26,27 A previous study has demonstrated the potential direct effects of SS in diabetes and reported that higher levels of SS were associated with lower DD, better adoption of diabetes self-management behaviors, and better diabetes-related clinical outcomes, including glycemic control.28 Moreover, effective patient-centered communication has been indicated to buffer the effects of diabetes burden on distress levels, highlighting the importance of supportive interactions in diabetes care.29 A previous study reported that perceived SS can alleviate feelings of distress, potentially reducing the risk of developing DS.30 There could be a negative correlation between DD and SS. However, the mechanisms through which SS influences DD are unclear.

    Previous studies on DD were primarily focused on its prevalence, instruments, and consequences.31–33 Studies have investigated the association between DD and DS/SS, often utilizing traditional statistical methods, including regression or factor analysis.34–36 While these methods effectively assess the association between specific predictive and outcome variables, they fail to capture the interdependencies and complex interactions among multiple variables.37 This limitation is particularly pronounced when investigating complex phenomena, including DD in patients with T2D. Consequently, a more nuanced statistical approach is needed to investigate the association between them, including central and bridging symptoms, thereby enhancing the understanding of the complex psychopathological mechanisms associated with DD and DS/SS.

    The Network Theory of Mental Disorder (NTMD) suggests that the development and maintenance of mental disorders are influenced by dynamic causal relationships among various symptoms within the disorder.38 The network analysis, a cutting-edge approach for analyzing psychiatric disorders, aligns with the principles of NTMD and addresses this complexity by examining the correlation between specific symptoms.39 This method elucidates the relationships among individual symptoms and, through the centrality metrics of the network, facilitates the identification of core and bridge symptoms, providing a more comprehensive perspective on exploring the connection between DD and DS/SS.

    Incorporating emotional and social factors in diabetes management may lead to improved health outcomes and enhanced quality of life for patients with T2D.40 Further exploration of these associations is essential, as understanding the dynamics of DD and DS/SS could inform more effective interventions for individuals with T2D. This study employed a network analysis method to construct a symptom network among DD and DS/SS to investigate their interactions, aiming to establish a theoretical foundation for future interventions by identifying critical nodes with cascading effects within the network.

    Methods

    Design

    A cross-sectional design was employed in this research. Figure 1 illustrates the study flow chart.

    Figure 1 The flowchart of the research.

    Setting and Sample

    The study was conducted at two diabetes centers in densely populated areas of southwest China, where the prevalence of T2D is among the highest in the country.41 One of the centers is within a large general hospital that provides outpatient and inpatient care for adults with diabetes. The other center is in a primary care facility that mainly provides outpatient care and home visits. The two centers serve patients with T2D in various medical settings in southwest China, including outpatients, inpatients, community patients, and home care patients, ensuring the representativeness of our T2D samples. The inclusion criteria for participants were as follows: (a) Patients diagnosed with T2D, (b) patients ≥ 18 years of age, and (c) patients who had an average score > 2 points on the Diabetes Distress Scale (DDS). The exclusion criteria were as follows: (a) Patients with a history of severe dementia, psychosis, or serious neurologic disease, and (b) patients refusing to participate in the study. We invited 912 patients with T2D from the two diabetes centers to participate, and 886 consented to enroll.

    Variables and Measurements

    Demographic and Clinical Information

    The participants self-reported their information, including their age, gender, educational background, marital status, family history of diabetes, smoking, and alcohol consumption.

    Diabetes Distress

    Diabetes distress was assessed using the DDS, developed by Polonsky to evaluate the distress of patients with diabetes.42 Zhang et al43 translated the scale into Chinese and reported that the Cronbach’s alpha for the overall scale was 0.88, while the subscales ranged from 0.76 to 0.81 in Chinese adults with T2D. The Chinese DDS comprises 17 items that measure four dimensions: Emotional burden (EB, five items), physician-related distress (PD, four items), regimen-related distress (RD, five items), and diabetes-related interpersonal distress (ID, three items). These items employ a six-point Likert scale that ranges from 1 (no distress) to 6 (high distress). A total score was calculated by adding the 17 items. The higher the scores, the more significant the distress. According to the revised rating system developed by Fisher, a mean item score < 2 indicates little or no distress; 2.0–2.9 indicates moderate distress, and ≥ 3 indicates high distress.

    Depressive Symptoms

    Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9), a short questionnaire. The internal reliability of the PHQ-9 was excellent, with a Cronbach’s alpha of 0.870 among patients with T2D.44 The scale consists of 9 questions with response options: including “no problem” (0 points), “a few days, sometimes” (1 point), “more than 7 days” (2 points), or “almost every day” (3 points). The total score is calculated by adding the points for each response, resulting in a score range of 0 to 27. Scores from 0 to 4 indicate the absence of DS, 5 to 9 indicate mild DS (subsyndromal depression), and ≥ 10 indicate a high probability of a depressive episode, which can be classified as moderate (10 to 14), moderately severe (15 to 19), and severe depression (20 and above).

    Social Support

    Social support was assessed using the Social Support Rating Scale (SSRS), designed for the Chinese population by Xiao.25 SSRS comprises three dimensions: Objective support, subjective support, and utilization of support, and has been verified to have favorable reliability and validity in patients with T2D. Chen et al45 indicated that the Cronbach’s alpha coefficient of the SSRS was 0.79. A higher score on the SSRS indicates better SS and comprehensively reflects an individual’s SS status.

    Data Analysis

    All analyses were performed using R software (Version 4.2.3). We described continuous variables as mean (standard deviation, SD), and presented categorical variables as frequencies and percentages.

    Network Estimation

    We computed polychoric correlations between all nodes to examine the edges of the network. We estimated the Graphical Gaussian Model (GGM) using the graphical least absolute shrinkage and selection operator.46 This study aimed to estimate two network structures: The first was the network structure of DD, which will help us investigate its core symptoms; the second was the network structure of DD-DS-SS, which will help us identify the bridge symptoms between DS and DD, and between SS and DD. In the network model, each symptom is represented as a “node”, and the association between symptoms is defined as an “edge”.47 Thicker edges represent stronger correlations between two nodes.

    Centrality Estimation

    The importance of each node in the item network of DD was quantified using the centrality of strength, which is the sum of the absolute value of the edge weights attached to a node for each node. The strength indicates the network connectivity used to identify the central nodes.48 To investigate the interconnections between DS, SS, and DD, we categorized nodes into three distinct communities: The DS community (items from PHQ-9), the SS community (items from SSRS), and the DD community (items from DDS). The bridge expected influence (BEI) was calculated to identify bridge components. The BEI of a node is the sum of its edge weights from all other communities. A higher positive BEI indicates a greater activation capacity to other communities, while a higher negative BEI indicates a greater deactivation capacity to other communities.49

    Accuracy and Stability

    The accuracy of the edge weights was confirmed by calculating 95% confidence intervals (CIs) for all edges using a nonparametric bootstrap approach with 500 bootstrap samples.50 Additionally, the stability of the correlation (CS) coefficient for the strength/BEI was thoroughly assessed using a case-dropping subset bootstrap approach with 500 bootstrap samples. The CS coefficient must be greater than 0.25, ideally surpassing 0.5, to maintain the integrity and reliability of the results.

    Ethics Approval and Consent to Participate

    This study was approved by the Ethics Committee of the Chengdu Jinniu District People’s Hospital (QYYLL-2022-011), and all procedures followed relevant guidelines and regulations. Informed consent was obtained from all subjects. As stated on the information sheet in the questionnaire packet, consent to participate was obtained by participants returning a completed survey. Participants could decide whether or not to participate and could withdraw at any time without repercussions. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki.

    Results

    Characteristics of the Participants

    The final sample comprised 886 participants with T2D, ranging from 20 to 80 years at the time of assessment. There were 562 (62.4%) male and 324 (36.6%) female participants. Of the 886 participants, 519 (58.6%) reported a family history of diabetes, while 367 (41.4%) did not. More demographic details about the participants are presented in Table 1.

    Table 1 Summary of Participants’ Characteristics (N = 886)

    Score Results of DD, DS, and SS

    The means and standard deviations of all variables in the network are presented in Table 2 as indicated by the statistical description results.

    Table 2 Mean Scores and Standard Deviations for Items of DDS, PHQ-9, and SSRS

    Structure of the DD Network

    The structure of the DD network is depicted in Figure 2A. Centrality analysis was performed to examine the importance of each symptom within the DD network, with the results depicted in Figure 2B. Due to high intercorrelations and the more reliable estimation of strength centrality and closeness (the accuracy analyses below), we will focus our interpretation of the most relevant symptoms on node strength centrality for the rest of the report. The three nodes with the highest node strength centrality were PD4 (Do not have doctor I can see regularly), PD2 (Doctor does not give clear directions), and PD1 (Doctor does not know about diabetes).

    Figure 2 Network structure of the DD (A) and centrality index of the DD network (B).

    Structure of the DD-DS-SS Network

    We estimated the network structures of the DD, DS, and SS. The resulting network is displayed in Figure 3. The nodes between DD and DS were positively connected within the network, and particularly strong connections were between DDS1 (diabetes taking up too much energy)-PHQ4 (tired or little energy), DDS13 (not sticking closely enough to meal plan)-PHQ5 (poor appetite/ overeating), DDS16 (Friends/family do not appreciate difficulty of diabetes)-PHQ2 (feeling down, depressed, or hopeless), and DDS17 (friends/family do not give emotional support)-PHQ6 (Failure). These three dimensions of SS were inversely related to DD, especially between DDS17-SSRS2 (subjective support) and DDS17-SSRS3 (support utilization).

    Figure 3 Network structure of the DD-DS-SS.

    Accuracy and Stability of the Two Networks

    We assessed the accuracy and stability of the estimated networks. Figure 4 illustrates the accuracy of the bootstrap method in obtaining edge weights. The narrow confidence interval indicates that the edge weights possess sufficient accuracy. The subset bootstrap (Figure 5) indicates that the centrality of node strength and closeness had good stability, with a decrease in sample size. Meanwhile, coefficients of 0.7 signify adequate stability in centrality of strength and closeness.

    Figure 4 Bootstrapped confidence intervals of the edge weights in the DD network (A) and DD-DS-SS network (B).

    Figure 5 Subsetting bootstrap for DD network (A) and DD-DS-SS network (B).

    Discussion

    This is the first study to investigate the interconnections among components of DD and the correlations between DS, SS, and DD constructs in patients with T2D using network analysis, to the best of our knowledge. We performed a network analysis of DD to identify its core symptoms, followed by another analysis that included DS and SS to uncover key connections between them. The principal findings of this study were systematically delineated in Figure 6, which graphically elucidates the core symptoms of diabetes DD and its bridge symptoms between DS/SS. By assessing the stability and accuracy of these networks, we gained insights into the complex association between DD and DS/SS, which helped to provide a focus for the psychological care of people with T2D.

    Figure 6 Summary of key findings.

    The observed clustering pattern of DD items, illustrated in Figure 1, corresponds closely with the four subscales of DDS-17: Emotional burden, physician-related distress, regimen-related distress, and interpersonal distress.51 In the DD item network, the three nodes with the highest node strength centrality were PD4 (Do not have doctor I can see regularly), PD2 (Doctor does not give clear directions), and PD1 (Doctor does not know about diabetes). Highly central nodes in a cross-sectional network were indicated to predict the correlation between changes in one node and other network symptoms.52 A Canadian cross-sectional survey identified physician-related distress as a core symptom of DD.53 The findings indicated that while diabetes management primarily falls on the patient, healthcare professionals play a crucial role. Previous studies indicate that the involvement of healthcare professionals—including doctors, nurses, and dietitians—enhances patient self-management and compliance and reduces the risk of complications, particularly cardiovascular ones.54,55 Moreover, medical personnel are instrumental in setting individualized treatment goals and monitoring progress, which is essential for achieving optimal glycemic control.56 Although there are several treatment options available, many patients struggle to manage their condition effectively due to factors including a lack of support and low health literacy.57 The findings underscored the need for medical professionals to engage in open communication with their patients, to help them understand their condition and the importance of adherence to treatment plans.58 This dependence on medical professionals has become the primary source of DD in T2D patients and an essential part of psychological care. Similar evidence was reported in other interventional studies. Psychological interventions provided by nursing staff,59 integrating nurse counseling with mobile health technologies,60 and nurse-administered mindfulness-based stress reduction programs61 have all demonstrated significant positive effects on self-efficacy, self-management capabilities, and DD in patients with T2D. A focus group interview revealed favorable responses from patients with T2D toward nurse-physician collaborative care, with participants expressing feelings of empowerment.62 Therefore, we recommend incorporating healthcare professional support into psychological interventions for patients with T2D to optimize disease management outcomes.

    We observed the link paths between DD and DS/SS in the second network. We analyzed the more microscopic relationship between DD and DS as depicted in Figure 3. Although a previous study suggested that DD and DS overlap with each other,53 the exact overlap is not fully reported. Through network analysis, this study found the exact part of DD and DS duplication, which is of significant help in understanding the differences and connections between DD and DS. A previous study suggested that the scientific debate about the overlap between DD and DS may stem from shared etiological pathways and symptoms,63 and our study demonstrated that DDS1-PHQ4, DDS13-PHQ5, DDS16-PHQ2, and DDS17-PHQ6 have strong positive bridges in terms of their network structure. The DDS1 item addresses the energy expenditure associated with diabetes, while the PHQ4 item addresses the fatigue caused by the disease.64 The two focused on the negative emotions associated with the long-term illness. It is therefore not difficult to understand that the diabetes management of DD-positive patients is generally poor. DDS13 and PHQ5 items were focused on understanding the impact of diabetes on the diet of patients.65 Diet is a key modifiable factor in the management and prevention of T2D.66 This result is consistent with a previous study in which DD and DS were independently associated with gastrointestinal symptoms in patients with T2D.17 The other pairs of bridging symptoms (DDS16-PHQ2 and DDS17-PHQ6) were associated with inadequate support from family or friends, whether emotional support or dietary help. The above analysis of bridging symptoms summarizes the connection between DD and DS into three aspects: Fatigue, diet, and social interaction. For patients with DD and DS comorbidity, these three aspects may serve as effective intervention targets to sever the connection and comorbidity of DD and DS, representing a significant finding of this study. Based on evidence that dietary management,67 peer support,68 and family-focused interventions69 have independently demonstrated significant benefits for psychological well-being in patients with T2D, we recommend developing a comprehensive intervention package that integrates these approaches to address DD and DS simultaneously.

    The second network structure demonstrated the relationship between DD and SS. A strong negative bridge appeared in SSRS2/SSRS3-DDS17. In contrast to the objective support represented by SSR1, the subjective support represented by SSRS2 indicated a negative association with DDS17. This indicates that emotional support from family or friends is more important for patients with T2D than material and financial support and may directly affect patients’ self-cognition. This is consistent with the results of several systematic reviews, where low SS was reported to increase the risk of depression among people with T2D,70 and increased SS was inversely associated with emotional distress.71 More importantly, SS is more linked to the self-management of people with T2D than T1D.72 Similarly, the support utilization represented by SSRS3 is equally significant for patients with T2D. This implies that even when subjective and objective support are sufficient, the failure of the patient to perceive or utilize this support may, however, impact the success of their disease management. Few studies have noted this, with only one qualitative study73 examining how adolescents with T2D understand and use SS, indicating that their use of SS is restricted to close friends and family due to fear of disclosing their diabetes to others. Several randomized controlled trials have demonstrated that different SS technologies, including mobile health-enhanced peer support intervention74 and peer-led diabetes self-management support intervention,75 effectively reduce DD among patients with T2D. Our findings revealed that effective SS must incorporate emotional support components and actively encourage patient engagement with available resources, as interventions limited to offering disease-specific knowledge and skill training are insufficient for comprehensive SS.

    Certain limitations must be addressed. First, using cross-sectional data made identifying direct effects between symptoms impossible. Consequently, it is unclear whether the most central symptoms activate other symptoms, are activated by other symptoms, or are the case for both. To examine this causal relationship, longitudinal study data are necessary to provide new insights into the dynamic relationship between DD-DS/SS. Second, our survey was conducted during the COVID-19 pandemic. Therefore, it is impossible to rule out the possibility that the prevalence of the virus influenced the psychological state of people with T2D. Finally, although the sample size of this study is sufficient for network analysis, it is inadequate to support network comparison tests between different subgroups.49 Future studies should expand the sample size to more comprehensively investigate the differences in the co-occurrence networks of DD, DS, and SS among different samples.

    Conclusions

    Our study investigated the interconnections between components of DD and the correlations between constructs of DS, SS, and DD in patients with T2D using network analysis. Our findings from the DD network indicated that physician-related distress may significantly contribute to the development and maintenance of DD. From the DD-DS-SS network, the first significant finding is that the complex link between DD and DS can be summarized in three aspects: Fatigue, diet, and social interaction. Another significant finding is that the subjective support and utilization of support in patients with T2D are closely related to managing their disease. The findings provided more targeted theoretical guidance and a scientific basis for psychological counseling and interventions aimed at alleviating DD in patients with T2D. However, all the above conclusions require more confirmatory studies in the future for validation.

    Data Sharing Statement

    The data that support the findings of this study are available from the corresponding author upon reasonable request.

    Ethics Approval and Consent to Participate

    This study was approved by the Ethics Committee of the Chengdu Jinniu District People’s Hospital (QYYLL-2022-011), and written informed consent was obtained from every participant.

    Acknowledgments

    We would like to thank the study participants, clinicians, and nurses for their unreserved help. We also gratefully acknowledge the financial supports from the Sichuan Province Grassroots Health Development Research Center (SWFZ23-Y-23) and the 2021 Xinglin Scholars Scientific Research Promotion Project of Chengdu University of Traditional Chinese Medicine (MPRC2021021). Meanwhile, we gratefully acknowledge Dr. Jingting Liao from Chengdu Jinniu District People’s Hospital for securing the ethical approvals critical to this study.

    Funding

    This study was supported by grants from the Sichuan Province Grassroots Health Development Research Center (SWFZ23-Y-23) and the 2021 Xinglin Scholars Scientific Research Promotion Project of Chengdu University of Traditional Chinese Medicine (MPRC2021021).

    Disclosure

    The authors report no conflicts of interest regarding this manuscript.

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    37. Guan M, Liu J, Li X, et al. The impact of depressive and anxious symptoms on non-suicidal self-injury behavior in adolescents: a network analysis. Bmc Psychiatry. 2024;24(1):229. doi:10.1186/s12888-024-05599-1

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  • Today’s Connections Hints and Answer for July 8, Puzzle #758

    Today’s Connections Hints and Answer for July 8, Puzzle #758

    Looking for a hint for today’s Connections puzzle? Below, we have clues to help you unlock whichever category has you stumped for the puzzle on July 8, 2025.

    Connections first launched on the New York Times in June 2023. The premise is deceptively simple: Players have to find the thematic connection of four groups of four words … without making more than four mistakes.

    Today’s Connections has categories about meeting up, logging in and more.

    Below are the hints, categories and answers for today’s Connections game, puzzle #758, on July 8.

    A hint for each Connections category today, July 8

    Yellow group hint: What motels might be used for

    Green group hint: What gyms also come with

    Blue group hint: What computers often require

    Purple group hint: What connects an actor, composer, musician and prime minister

    A word in each Connections category today, July 8

    Yellow group word: Fling

    Green group word: Locker

    Blue group word: Key

    Purple group word: Carpenter

    Connections categories today, July 8

    Yellow group category: Liaison

    Green group category: Seen in a locker room

    Blue group category: Something entered for access

    Purple group category: Johns

    Here are the answers to Connections today, July 8

    What are the yellow words in today’s Connections?

    Liaison: Affair, fling, relations, thing

    What are the green words in today’s Connections?

    Seen in a locker room: Bench, locker, mirror, scale

    What are the blue words in today’s Connections?

    Something entered for access: Code, key, password, pin

    What are the purple words in today’s Connections?

    Johns: Candy, Carpenter, Legend, Major

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