Category: 3. Business

Nissl staining assessed neuronal loss after ICH [27]. Specifically, 16 μm-thick coronal frozen brain sections were immersed in Nissl staining solution for 10 min, followed by 2 min of differentiation in graded ethanol. Sections were then mounted and observed under a microscope. Nissl-positive cells were quantified at 20× magnification using ImageJ software (ImageJ 1.5, NIH, USA). H&E staining was performed to evaluate hematoma volume [1]. The section containing the largest hematoma area—typically at the level of the needle track—was selected for staining. The stained sections were scanned using a Leica DM6 microscope, and the hematoma volume was measured using ImageJ software (ImageJ 1.5, NIH, USA).

TUNEL staining

To quantify neuronal apoptosis 72 h after ICH, TUNEL staining (green) was performed using an in situ apoptosis detection kit (C1088, Beyotime Biotechnology, Shanghai, China). TUNEL-positive neurons in the hematoma region were manually counted. Three random sections were analyzed for each brain, and the average number of TUNEL-positive neurons was calculated using ImageJ software (ImageJ 1.5, NIH, USA) at 200× magnification. The results were expressed as the percentage (%) of TUNEL-positive neurons.

Western blot (WB)

Protein samples were mixed with loading buffer and heated at 95 °C for 8–10 min, followed by electrophoresis on a 10% SDS-polyacrylamide gel (E303-01, Vazyme Biotech Co., Ltd., Nanjing, China). Proteins were transferred to methanol-activated PVDF membranes (IPVH00010, Millipore, Billerica, MA, USA). Membranes were blocked with 5% non-fat milk in TBST at room temperature for 2 h and then incubated overnight at 4 °C with primary antibodies (1:1000 dilution). After three washes with TBST (15 min each), membranes were incubated with HRP-conjugated secondary antibodies (1:3000) at room temperature for 2 h. Protein bands were visualized using enhanced chemiluminescence reagents (WBULP-100ML, Millipore, Billerica, MA, USA) and analyzed for band density using ImageJ software (ImageJ 1.5, NIH, USA). Antibody details are provided in Table S2.

RNA isolation and quantitative PCR (qPCR)

Total RNA was extracted from brain tissue 72 h after ICH using Trizol (79306, Qiagen, Hilden, Germany). RNA was reverse transcribed into cDNA using the 5× All-in-One RT MasterMix Kit (R333-01, Vazyme, Nanjing, China). qPCR was performed in triplicate using SYBR Green qPCR MasterMix (Q711-02, Vazyme, Nanjing, China). mRNA expression levels were normalized to β-actin and calculated using the 2^-ΔΔCt method. Primer sequences are listed in Table S3.

Immunofluorescence staining

Paraffin-embedded brain tissues were sectioned at a thickness of 4 μm for immunofluorescence staining. Sections were first deparaffinized and rehydrated in xylene and graded ethanol, followed by antigen retrieval in sodium citrate buffer at boiling temperature for 1 h. After preparation, the sections were blocked with 5% BSA for 2 h and then incubated overnight at 4 °C with primary antibodies (1:150 dilution). On the following day, sections were incubated at room temperature for 1.5 h with secondary antibodies—goat anti-rabbit Cy3 (1:200, ab6939, Abcam, USA) and goat anti-rabbit Alexa Fluor 488 (1:200, ab150077, Abcam, USA). Finally, the sections were counterstained with DAPI (C0065, Solarbio, Beijing, China) for 15 min, rinsed, and imaged using a Zeiss fluorescence microscope (Zeiss Axio Imager 2, Germany). Cell immunofluorescence was carried out according to established protocols [21]. Neuronal fibers were quantified by MAP2 immunofluorescence staining, and fiber length was analyzed by tracing with ImageJ software. RAW264.7 or HT-22 cells (3 × 10⁴ cells per well) were seeded on coverslips in 24-well plates and cultured overnight. After drug treatment, the culture medium was removed, and the cells were washed three times with cold PBS. Cells were then fixed with paraformaldehyde for 30 min, permeabilized with 0.5% Triton X-100 for 15 min, and blocked with 5% BSA for 2 h. Fluorescently labeled secondary antibodies were applied and incubated at room temperature for 1.5 h. Cells, stained with DAPI (C0065, Solarbio, Beijing, China) for 15 min, were thoroughly washed and mounted with coverslips. Fluorescence images were captured using a Zeiss Axio Imager 2 microscope (Germany).

Tissue distribution of RVG/FA-NPs@API in ICH model mice

ICH model mice were established and intravenously injected with Cy5.5-labeled RVG/FA-NPs@API (200 µL, 2 mg/mL) via the tail vein 24 h after ICH induction. In vivo fluorescence imaging was performed at 1, 12, and 24 h post-injection using the IVIS Spectrum imaging system (PerkinElmer, USA). Following euthanasia, major organs, including the brain, liver, spleen, lungs, heart, and kidneys, were harvested for ex vivo imaging. Fluorescence intensity was quantified using ImageJ software to analyze the tissue distribution pattern of the nanoparticles.

In vivo targeting evaluation of RVG/FA-NPs@API

After euthanasia, brain tissue was harvested and dissociated into single cells using the Adult Brain Dissociation Kit (130-107−677, Miltenyi Biotec, Germany). CD11b-positive cells were then enriched using CD11b MicroBeads (130-049−601, Miltenyi Biotec, Germany). The enriched cells were subjected to flow cytometry analysis using PE-conjugated anti-mouse CD86 antibodies (105007, BioLegend, USA) at a dilution of 5 µL per 1 million cells in a 100 µL staining volume. Cy5.5 fluorescence was used to evaluate nanoparticle uptake. The proportion of Cy5.5⁺CD86⁺ double-positive cells was analyzed using a BD LSRFortessa™ X-20 flow cytometer (BD Biosciences, USA) to assess the enrichment efficiency of RVG/FA-NPs@API in M1-type macrophages.

Network pharmacology analysis

Network pharmacology analysis was conducted as previously described [28]. We used databases, including SwissTargetPrediction, PharmMapper, and DrugBank, to identify potential targets associated with API. In the SwissTargetPrediction database, targets with a probability score greater than 0.1 were selected as initial candidates. Targets related to ICH were retrieved from DisGeNET, OMIM, and GeneCards. For the GeneCards database, targets with a relevance score of ≥ 20 were included. The keywords used for the search included “intracerebral hemorrhage,” “basal ganglia hemorrhage,” “hemorrhagic stroke,” and “intracranial hemorrhage.” Drug-related and disease-related targets from each database were compared, and duplicate entries were removed. The intersection of the two target sets was identified and visualized using a Venn diagram from the online tool (https://bioinformatics.psb.ugent.be/).

Protein-protein interaction (PPI) network and enrichment analysis

The overlapping targets of the drug and disease were imported into the STRING database, with the organism set to Homo sapiens and the confidence score threshold set to medium (0.4) to generate a PPI network. The resulting interaction data were then imported into Cytoscape software (version 3.10.1), and network topology parameters, including degree, betweenness, and closeness centrality, were analyzed using the CytoHubba plugin. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, was performed using the Metascape database (https://metascape.org/). The screening criteria were set to Homo sapiens and p < 0.05. Final visualizations were generated using the WeChat online charting tool.

Molecular docking

Molecular docking between API and JAK1 protein—including the full-length protein and each of its four individual domains—was performed using AutoDock (version 4.2.6). The protein structure files in PDB format were obtained from the RCSB Protein Data Bank (https://www.rcsb.org/). After removing water molecules from solvents and ligands, hydrogen atoms were added. The PDBQT files of the JAK1 protein and its domains were prepared as receptor files, while the PDBQT file of API was used as the ligand for docking analysis. Docking results were analyzed using AutoDockTools (version 1.5.7), and visualizations were generated using PyMOL (version 2.4.1) and Liplot (version 2.2.8). The root-mean-square deviation (RMSD) between the docked conformations of API and its original structure was calculated using PyMOL. A successful docking result was one with an RMSD ≤ 2.0 Å (0.2 nm).

Molecular dynamics simulation

Molecular docking and dynamics simulations of API with the JAK1 protein and its kinase domain were performed using Discovery Studio (BIOVIA, Dassault Systèmes, Discovery Studio Modeling Environment, version 2019). The molecular dynamics simulation involved the following steps: (1) The protein-ligand complex was imported into Discovery Studio, and the “Prepare Protein” tool was used to preprocess the protein structure. (2) The ligand file was opened, and the CHARMM36 force field was applied for energy minimization of the protein and ligand before simulation. (3) The dynamics process was initiated using the “Standard Dynamics Cascade” protocol, with the solvent system window (Complex.dsv) set as the active window for the molecular dynamics simulation. (4) Hydrogen bond fluctuations within the protein-ligand complex, along with the RMSD and root-mean-square fluctuation (RMSF) values of the protein backbone and side chains, were analyzed using Discovery Studio.

In vitro kinase activity assay

Active human JAK2 protein (Cat# J62-53G, Signal Chem, Canada) was diluted to a final concentration of 0.1 µg/ml in Kinase Dilution Buffer III (Cat# K23-09, Signal Chem, Canada), and then incubated with His-tagged STAT3 substrate (3 µg), purified from E. coli M15 cells using Ni-NTA magnetic beads (Cat# 30210, Qiagen, Germany), together with 5 µl ATP. Different concentrations of apigenin (10 µM and 20 µM; Cat# A800500, Macklin, China) were added to the reaction system, which was maintained at 30 ℃ for 30 min. Reactions were terminated by adding 1× Laemmli buffer (Cat# 1610747, Bio-Rad, USA) and boiling at 95 ℃ for 5 min. Western blotting was performed using an anti-phospho-STAT3 antibody (Cat# 9145, Cell Signaling Technology, USA) to detect JAK2-mediated phosphorylation of STAT3 at Tyr705 [29].

Cell culture and cell viability assay

Cell viability was assessed as previously described [6]. RAW264.7 and HT-22 cell lines (CL-0190/CL-0595, Procell Life Science & Technology Co., Ltd., Wuhan, China) were seeded in 96-well plates at a density of 5 × 10³ cells per well in high-glucose DMEM supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells were treated with various concentrations (0, 12.5, 25, 50, 100, and 200 µM) of API or biotinylated API for 12, 24, and 48 h. After treatment, CCK-8 reagent (C0038, Beyotime Biotechnology, Shanghai, China) was added, and cells were incubated at 37 °C for 2 h in a 5% CO₂ incubator. Absorbance was measured at 450 nm using a spectrophotometer (Thermo Fisher Scientific, USA).

In vitro live/Dead cell staining

Cell apoptosis was assessed using a live/dead cell staining method [30]. According to the manufacturer’s instructions, a Calcein-AM/PI Cell Viability and Cytotoxicity Assay Kit (C2015M, Beyotime, Shanghai, China) was used. Briefly, cultured neurons were washed to remove residual esterases from the medium. A working solution containing Calcein-AM (for viable cells) and Propidium Iodide (PI, for dead cells) was added to the cultured cells, followed by incubation at 37 ℃ for 30 min in the dark. After staining, live/dead cell status was assessed under a fluorescence microscope (Zeiss Axio Imager 2, Germany). The ratio of PI-positive (dead) cells to total cells was quantified to evaluate cell membrane integrity and viability.

Pull-down assay for API-binding proteins

RAW264.7 cell lysates were incubated overnight at 4 °C with biotin, biotinylated API, or biotinylated API pre-incubated with free API. After incubation, streptavidin-conjugated magnetic beads (21115, Thermo Fisher Scientific, USA) were used to pull down proteins from the lysates at 4 °C for at least 6 h. The beads were thoroughly washed with PBS, and 5× loading buffer was added. Samples were boiled, and the supernatant was collected to detect JAK1 protein.

Surface plasmon resonance (SPR)

The binding affinity between API and JAK1 protein (HY-P700583, MedChemExpress, USA) was evaluated using the SPR-based Biacore T200 instrument (Cytiva, Sweden). A CM5 sensor chip was used for the experiment. The sensor surface was activated with a mixture of 50 mM NHS and 200 mM EDC for 7 min. JAK1 protein (420 µg/mL), diluted in 10 mM acetate buffer (pH 4.5), was immobilized on the chip surface at a 10 µL/min flow rate. The surface was then blocked with 1 M ethanolamine (pH 8.5). Multiple binding cycles were performed, and response signals were recorded with time on the x-axis and response units on the y-axis. The collected data were fitted using the Biacore T200 evaluation software with a 1:1 Langmuir binding model to determine kinetic parameters, including the association rate constant, dissociation rate constant, and equilibrium dissociation constant. The experimental procedures and data analysis were carried out by TGTMED Pharmaceutical Technology Co., Ltd. (Shanghai, China).

Cellular thermal shift assay (CETSA)

CETSA was performed as previously described [31]. Briefly, ICH mice were intraperitoneally injected with API (20 mg/kg), and RAW264.7 cells were treated with API (25 µM) for 8 h. After treatment, brain tissue from the hemorrhagic side of mice and protein lysates from RAW264.7 cells were collected. Protein stability was assessed at temperatures ranging from 50 °C to 71 °C. Following thermal denaturation, samples were subjected to three freeze-thaw cycles using liquid nitrogen, then centrifuged at 20,000 rpm for 20 min at 4 °C. The supernatants were collected for further analysis.

Preparation of PLGA nanoparticles and API loading

API-loaded PLGA nanoparticles were prepared using the solvent evaporation method. Briefly, 50 mg of PLGA-COOH (LA: GA = 50:50, Mw = 30,000–60,000; P2191, Sigma-Aldrich, USA) was dissolved in 4 mL of dichloromethane (DCM; D807825, Macklin, China), followed by the addition of 10 mg of API. The mixture was thoroughly stirred to form the organic phase. This solution was then added dropwise into 40 mL of an aqueous phase containing 1% polyvinyl alcohol (PVA, Mw = 30,000–70,000; P8136, Sigma-Aldrich, USA) under ice bath conditions. A probe-type sonicator (Scientz-IID, Ningbo Scientz Biotechnology Co., Ltd., China) was used to ultrasonicate the mixture at 200 W for 3 min (2-second pause, 1-second pulse). The resulting emulsion was stirred at 500 rpm under ventilation for 4 h to allow DCM evaporation. The formed nanoparticles were collected by centrifugation at 12,000 rpm for 10 min (Model: 5810R, Eppendorf, Germany), washed three times with deionized water, and freeze-dried for storage [32, 33].

Surface functionalization with RVG29 and FA

Dual modification was achieved by incorporating DSPE-PEG2000-RVG29 (HY-172705, MedChemExpress, USA) and DSPE-PEG2000-FA (PS2-DEFA, Pengshuo Biotech, China). Briefly, 5 mg of lyophilized PLGA nanoparticles were dispersed in 10 mL PBS (pH 7.4), followed by the addition of 0.5 mg DSPE-PEG2000-RVG29 and 0.5 mg DSPE-PEG2000-FA. The mixture was sonicated in a 37 °C water bath for 10 min and stirred at room temperature for 12 h. The modified nanoparticles were washed three times by centrifugation at 10,000 rpm, lyophilized, and designated as RVG/FA-NPs@API [34]. To evaluate the respective targeting roles of RVG29 and FA, single-modified nanoparticles were also prepared, namely RVG-NPs@API and FA-NPs@API. The procedure was identical to that of the dual-modified group, except that only 0.5 mg DSPE-PEG2000-RVG29 or 0.5 mg DSPE-PEG2000-FA was added, followed by lyophilization for storage.

Characterization of particle Size, zeta potential, and morphology

Nanoparticle morphology and size were characterized using transmission electron microscopy (TEM) and dynamic light scattering (DLS). TEM: Approximately 3 µL of nanoparticle suspension was dropped onto a carbon-coated 200-mesh copper grid, allowed to stand at room temperature for 5 min, excess liquid removed, and negatively stained with 3 µL of 1% (w/v) uranyl acetate for 5 min. After drying, samples were observed at 80 kV using a TEM (Tecnai-10, Philips, The Netherlands). DLS: Hydrodynamic diameter, polydispersity index (PDI), and ζ-potential were measured using a Zetasizer Nano ZS90 (Malvern Instruments, UK) with three independent replicates [35]. Structural Confirmation: RVG/FA-NPs@API (6 mg) was dissolved in DMSO-d6/D2O (3:2) and analyzed by 1 H NMR spectroscopy to verify conjugation [36].

X-ray diffraction (XRD)

XRD patterns were obtained using a diffractometer equipped with a Cu target and graphite monochromator (Rigaku D/max 2500/PC, Japan). Cu-Kα radiation (λ = 1.54 Å) was used as the incident beam, with a scanning range of 5°−90°, operating at 40 kV and 200 mA, and a scan rate of 5°/min with a 1-s counting time.

Determination of drug encapsulation efficiency (EE%) and loading efficiency (LE%)

To evaluate the drug EE%, 5 mg of freeze-dried RVG/FA-NPs@API was dissolved in 1 mL of acetonitrile by sonication. The concentration of API was then determined using a high-performance liquid chromatography (HPLC) system (Agilent 1260, USA). A C18 column (4.6 mm × 250 mm, 5 μm; Waters, USA) was used with a mobile phase of methanol: water (60:40, v/v) at a 1.0 mL/min flow rate. The detection wavelength was set at 340 nm. EE% and LE% were calculated based on the standard calibration curve [34].

Stability evaluation

To assess the stability of RVG/FA-NPs@API, the nanoparticles were incubated in PBS buffer and cell culture medium containing 10% fetal bovine serum (FBS) for 0 and 24 h. At the designated time points, aliquots of the nanoparticle suspension were collected, and particle size was measured using DLS. For long-term storage stability evaluation, freshly prepared RVG/FA-NPs@API were stored at 4 °C in the dark. Samples were taken on days 0, 15, 30, 45, and 60, and particle size was measured via DLS to assess changes over time.

In vitro drug release study

A total of 5 mg of RVG/FA-NPs@API was resuspended in 1 mL of PBS (containing 0.5% Tween-80, at either pH 7.4 or pH 5.5) and placed into a dialysis bag (MW cut-off 10,000 Da; Spectrum Labs, USA). The bag was then immersed in 50 mL of the corresponding buffer and incubated at 37 °C with constant shaking at 100 rpm. At 1, 2, 5, 7, 10, 25, and 50 h, 1 mL of the release medium was collected and replaced with an equal volume of fresh buffer. The concentration of API in the release samples was determined using HPLC.

In vitro biocompatibility assessment of nanoparticles

RAW264.7 cells were seeded in 6-well plates at a density of1 × 10⁵ cells/well and polarized into M1 macrophages by stimulation with LPS (1 µg/mL, L4391, Sigma-Aldrich, USA) for 12 h, or into M2 macrophages by stimulation with IL-4 (20 ng/mL, 200-04, PeproTech, USA) for 24 h [37]. Polarized RAW264.7 cells and HT22 cells were then seeded into 96-well plates (5 × 10³ cells/well) and cultured in high-glucose DMEM supplemented with 10% fetal bovine serum and 1% penicillin–streptomycin at 37 ℃ in a 5% CO₂ incubator. Cells were treated with RVG/FA-NPs@API at different concentrations (5–25 µg/mL) for 24 h, followed by the addition of 10 µL/well CCK-8 reagent (Dojindo, Japan). After incubation for 2 h at 37 °C, absorbance was measured at 450 nm [38].

In vitro cellular uptake of nanoparticles

RAW264.7 cells were seeded into 6-well plates at a density of 2 × 10⁵ cells per well and incubated at 37 °C in a 5% CO₂ atmosphere for 24 h. The cells were then transferred to a fresh DMEM medium containing FITC-labeled RVG/FA-NPs@API (FITC conjugation achieved via covalent grafting; Sigma-Aldrich, USA) and incubated for 6 h. After incubation, all cells were fixed with 4% paraformaldehyde for 20 min and washed with PBS. The nuclei were stained with DAPI for 15 min. Fluorescence images were captured using a confocal laser scanning microscope (CLSM), and cellular uptake of RVG/FA-NPs@API was quantitatively analyzed using a flow cytometer (BD FACSvantage SE, USA) [39].

Statistical analysis

All experiments were conducted in a randomized and blinded manner. Each in vitro experiment was independently repeated at least three times, and each In vivo group included more than three mice. Statistical analyses were performed using GraphPad Prism 10.0 software (GraphPad Software Inc.). For comparisons between two groups, Tukey’s multiple comparison test was used. For multiple group comparisons, one-way or two-way analysis of variance (ANOVA) was applied, followed by appropriate post hoc tests. A p-value < 0.05 was considered statistically significant. All data were expressed as mean ± standard deviation (SD).

During the study period (September 2023 to April 2024), 10,290 febrile participants who consented to inclusion in the study were tested by RDT and subsequently by microscopy across 12 different study locations (Fig. 1, Table 1). Of these, 4,237 (41.2%) participants were from four highly endemic sites (Lawngtlai, Dantewada, Lunglei, and West Singhbhum), 3,477 (33.8%) were from three moderately endemic sites (Kothagudem, South Tripura, and Balaghat), and 2,576 (25.0%) were from 5 sites with low malaria endemicity (North Goa, Lakhimpur, Mangalore, Uttara Kannada, and Bokaro). Overall, 5,349 (52%) participants were female and 4,941 (48%) male. A total of 1,022 (9.9%) participants were < 5 years old, 2,774 (27.0%) were between 5 and < 15 years old, and the remaining 6,494 (63.1%) were aged 15 years or older (See supplemental file S1).

Overall malaria positivity using RDT and microscopy

Of the 10,290 participants, 1,516 (14.7%, 95% confidence interval (CI) 7.7–21.8%) tested positive for malaria (any species; either mono-infection or a mixed infection) by RDT (Fig. 2). Paired microscopic slide results were not available in 36 (0.3%) participants; reasons included damage during transportation, staining issues, and poor-quality smear. Of the 36 with missing microscopic results, 34 was RDT negative (any malaria) and 2 were RDT positive for P. falciparum. Among 10,254 participants in whom microscopy slides were available, 1,436 (14.0%, 95% CI: 6.9%–21.1%) tested positive for malaria (any species).

Malaria positivity estimates across the study sites are presented in Table 1 and Fig. 2. In the high endemicity sites, the proportion of participants who tested positive for malaria by RDT and microscopy were 21.2% (225/1,059) and 18.2% (193/1,058) respectively in Dantewada, 23.0% (385/1,671) and 23.8% (397/1,668) in Lawngtlai, 13.9% (124/893) and 13.2% (118/891) in Lunglei, and 49.5% (304/614) and 48.4% (297/614) in West Singhbhum. The corresponding estimates in moderately endemic sites were 30.6% (272/890) and 28.8% (256/888) in Balaghat, 7.9% (131/1,681) and 6.5% (110/1,681) in Kothagudem, and 4.1% (37/906) and 3.1% (28/904) in South Tripura. The corresponding estimates in areas of low endemicities were: 0% (0/241) and 0% (0/215) in Bokaro, 0.5% (3/644) and 0.5% (3/644) in Lakhimpur, 3.4% (22/642) and 3.4% (22/642) in Mangalore, 1.7% (11/647) and 1.7% (11/647) in North Goa, and 0.2% (1/402) and 0.2% (1/402) in Uttara Kannada. Further details are presented in supplemental file S1.

Causative parasite species

Of the 1,516 RDT positives, 1,105 (72.9%) were P. falciparum mono-infection, 290 (19.1%) were P. vivax mono-infection, and the remaining 121 (8.0%) had P. falciparum and P. vivax mixed infections. Of the 1,436 who tested positive using microscopy, 1,025 (71.4%) had P. falciparum mono-infection, 304 (21.2%) had P. vivax mono-infection, 2 (0.1%) were identified as P. malariae mono-infection, 104 (7.2%) presented with a mixed P. falciparum and P. vivax, and 1 (0.1%) patient had a mixed P. falciparum and P. malariae. Species specific breakdown of the malaria status is presented in Table 1.

Performance of RDT for detecting P. falciparum mono-infection

The diagnostic accuracy of the RDTs in detecting malaria by causative parasite species is presented in Tables 2, 3, and 4. Among 10,254 participants for whom both the RDT and microscopy results were available, 8,987 (87.3%) tested negative by both methods i.e., microscopy and RDT, and 1,087 (10.6%) tested positive by both methods. Further 43 participants (0.4%) were RDT negative but were subsequently found to be microscopy positive, while the remaining 137 (1.3%) were RDT-positive but subsequently tested negative by microscopy. Overall, this resulted in sensitivity and specificity estimates for the RDT (compared to microscopy) of 95.0% [95% CI 93.0–97.0%] and 98.0% [95% CI 97.0–98.0%] respectively in high endemicity areas, 95.0% [95% CI 92.0–97.0%] and 99.0% [95% CI 98.0–99.0%] in the areas of moderate endemicities, and 100% sensitivity and specificity were observed in the areas of low endemicities (Table 3). Pooled across the study sites, the overall sensitivity and specificity estimates of the RDT was 95.0% [95% CI 94.0–96.0%] and 99.0% [95% CI 98.0–99.0%] respectively (Table 2). See Tables 2 and 3 for the predictive values and further accuracy measures.

Performance of RDT for detecting P. vivax mono-infection

The sensitivity and specificity of the RDT were respectively 81.0% [95% CI 76.0–86.0%] and 99.0% [95% CI 99.0–100%] in the areas of high endemicity, 81.0% [95% CI 58.0–95.0%] and 100% [95% CI 99.0–100%] in the areas of moderate endemicities, and 100% sensitivity and specificity were observed in the areas of low endemicities (Table 4). Pooled across the 12 study sites, the overall sensitivity and specificity of the RDT for detecting a P. vivax mono-infection were 83.0% [95% CI 78.0–87.0%] and 100% [95% CI 99.0–100%] (Table 2). See Tables 2 and 4 for the predictive values and further accuracy measures.

Performance of RDTs for detecting mixed P. falciparum and P. vivax infection

The overall sensitivity and specificity of the RDT for detecting a mixed P. vivax and P. falciparum infection were 88% [95% CI: 80%–93%] and 100% respectively (see Table 2). See Table 2 for the predictive values.

Relationship between age, sex and malaria status

In a multivariable logistic regression containing age, sex, and transmission endemicity, the following variables were associated with increased odds of test positivity by RDTs: every 5 yearly increase in age was associated with 12% lower odds of testing positive (adjusted odds ratio (AOR): 0.88, 95% CI 0.86–0.90), being male (AOR: 1.24, 95% CI 1.10–1.39) (compared to females), and residing in the areas of high malaria endemicity (AOR: 17.33, 95% CI 12.55–24.68) or moderate endemicity (AOR: 7.81, 95% CI 5.62–11.21) (compared to areas of low transmission) (Fig. 3 and see supplemental Table 1 and supplemental Table 2).

Baker McKenzie Switzerland advised Commerzbank Aktiengesellschaft (Zurich branch) on a PLN 1,166,286,000 guarantee facility agreement covered mainly by Swiss Export Risk Insurance (“SERV”) for Calik Enerji Swiss AG (“Calik Enerji Swiss”). 

The facility, guaranteed by Çalık Enerji San. ve Tic. A.Ş. (“Çalık Enerji”), was provided in connection with Calik Enerji Swiss and Çalık Enerji’s EPC contract for the turnkey construction of a 1,336 MW combined-cycle power plant with H2-ready technology in Kozienice, Poland. Baker McKenzie acted as the sole legal counsel to Commerzbank on all Swiss and Turkish law aspects of the transaction.

Headquartered in Lucerne, Switzerland, Calik Enerji Swiss is one of the Çalık Enerji group’s anchor EPC subsidiaries, delivering international energy and infrastructure projects through the Swiss supply chain. Çalık Enerji, based in Istanbul, is a leading diversified energy company operating across EPC, power distribution and renewable energy in Türkiye, Central Asia, the Middle East, Africa and Eastern Europe.

Commerzbank Aktiengesellschaft (Zurich branch) is one of Europe’s leading banks in trade and export finance, with a strong footprint in the Swiss market, supporting Swiss export and capital market-oriented clients.

The Baker McKenzie team was led by Partner Markus Wolf (Banking & Finance, Switzerland) and further included Associate Sammy Guidoum (Banking & Finance, Switzerland) and Trainee Lawyer Valentina Biland (Banking & Finance, Switzerland), as well as Partner Muhsin Keskin and Associate Seray Karaalp from Esin Attorney Partnership Istanbul (Baker McKenzie Istanbul).

Electric-vehicle maker Seres Group Co raised HK$14.3 billion ($1.8 billion) after pricing its Hong Kong listing at the upper limit it had set and exercising an option to increase its deal size.

The Chongqing-based company, an EV partner of Huawei Technologies Co, sold about 108.6 million shares at HK$131.50 each, according to a statement Sunday. That includes 8.4 million additional shares that expanded the offering by about 8.4 percent.

The listing price reflects a 22 percent discount to the 155.19 yuan ($21.8) closing price on Friday in Shanghai, where Seres’ stock already trades.

Seres’ Hong Kong-listed shares are due to start trading on Wednesday.

The deal is Hong Kong’s eighth listing this year that raised more than $1 billion. Listing proceeds in the city have already topped the $26 billion Bloomberg Intelligence had forecast for 2025.

READ MORE: Huawei’s EV partner Seres said to gauge interest for $2b listing in HKSAR

Founded in 1986, the company initially produced springs and shock absorbers before expanding into motorcycles and later to EVs. The partnership with Huawei has been a boon for Seres, which is expected to see profits surge 72 percent to a record 10.2 billion yuan this year, according to the average analyst estimate compiled by Bloomberg.

China International Capital Corp and China Galaxy Securities Co are joint sponsors of the Seres listing.

SharpLink Gaming (SBET) is making waves after announcing a major collaboration to deploy $200 million in ETH on the Linea blockchain network. This bold treasury strategy underscores SharpLink’s commitment to finding new ways to unlock digital asset yield.

See our latest analysis for SharpLink Gaming.

SharpLink’s momentum is hard to ignore, with shares rallying an impressive 71.3% year-to-date and delivering a massive 89.9% total shareholder return over the past year. While the stock has seen some cooling off in recent weeks, recent strategic moves and leadership hires have kept enthusiasm alive around its long-term transformation and DeFi ambitions.

If SharpLink’s recent growth and bold treasury strategy have you curious about what else is making headlines, it could be the perfect time to broaden your search and discover fast growing stocks with high insider ownership

All this activity begs a timely question for investors: with SharpLink’s shares still well below analyst targets and trading at a discount to its Ethereum net asset value, is there an overlooked buying opportunity here, or has the market already priced in its future growth?

Sitting at a price-to-book ratio of 6x, SharpLink Gaming’s shares trade well above industry and peer averages. This valuation raises the question of whether the market is too optimistic or simply factoring in significant future growth, especially in light of recently cooling price action.

The price-to-book (P/B) ratio compares a company’s market value to its book value, providing a snapshot of how much investors are willing to pay for each dollar of net assets. For emerging or high-growth businesses like SharpLink, a higher P/B ratio can signal aggressive expectations for future profitability or asset growth.

In SharpLink’s case, its P/B multiple stands significantly above the peer average of 1.9x and the broader US Hospitality industry average of 2.6x. This suggests investors are either betting on strong upcoming results or potentially overlooking the near-term lack of meaningful revenue and ongoing unprofitability.

See what the numbers say about this price — find out in our valuation breakdown.

Result: Price-to-Book of 6x (OVERVALUED)

However, SharpLink’s limited revenue base and ongoing unprofitability could dampen bullish sentiment if growth or market adoption fails to materialize as investors hope.

Find out about the key risks to this SharpLink Gaming narrative.

While the price-to-book ratio paints SharpLink as overvalued compared to its peers, our DCF model offers a different perspective. At $13.84 per share, SharpLink is actually trading slightly above our fair value estimate of $13.14. This means the shares may not present a clear bargain, despite the recent hype and ambitious growth projections. So, should investors be cautious about buying at this level, or is the market still overlooking something?

Look into how the SWS DCF model arrives at its fair value.

Simply Wall St performs a discounted cash flow (DCF) on every stock in the world every day (check out SharpLink Gaming for example). We show the entire calculation in full. You can track the result in your watchlist or portfolio and be alerted when this changes, or use our stock screener to discover 840 undervalued stocks based on their cash flows. If you save a screener we even alert you when new companies match – so you never miss a potential opportunity.

If you have a different perspective on SharpLink or want a hands-on approach, you can dive into the numbers and shape your own insights in just a few minutes. Do it your way

A great starting point for your SharpLink Gaming research is our analysis highlighting 1 key reward and 4 important warning signs that could impact your investment decision.

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This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

Companies discussed in this article include SBET.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team@simplywallst.com

China’s recent announcement to further expand sectoral coverage and shift its national emissions trading system (ETS) from an intensity-based to an absolute cap approach by 2027 marks a pivotal moment for Asia’s largest carbon market. 

Evolving from pilot programs across eight provinces, China’s national ETS has been operational since 2021. It currently covers approximately 8 billion tonnes of carbon dioxide (CO₂) emissions — roughly 20% of total global emissions. Initially limited to the power sector, the ETS expanded this year to include steel, cement, and aluminum smelting, bringing 1,334 additional emitting entities under its scope and raising coverage of the country’s total carbon emissions from 40% to 60%. The expansion also broadened the system’s scope beyond CO₂ to include the regulation of tetrafluoromethane (CF₄) and hexafluoroethane (C₂F₆) emissions from the aluminum sector. The inclusion of other major emitting industries by 2027 is likely to further expand national coverage and regulation of gases.

The significance of absolute caps and higher carbon pricing 

The shift to absolute caps is an important step in aligning China’s ETS with international best practices. Absolute caps underpin mature systems, such as the European Union’s (EU) ETS, which helped reduce carbon dioxide equivalent (CO₂e) emissions from 4.6 billion tonnes in 2005, when it was first introduced, to 3.2 billion tonnes in 2024. The decline in emissions intensity is steeper, as the EU’s Gross Domestic Product (GDP) is significantly higher in 2024 compared to 2005. 

Unlike the intensity-based approach that allows for increasing emissions alongside output, an absolute cap imposes a fixed ceiling on permitted emissions, exerting stronger pressure to adopt clean technologies or bear higher compliance costs. Markets that initially favor intensity targets to safeguard economic growth and ease early adoption should therefore transition to an absolute caps system to achieve meaningful emission reductions. China will begin its transition with major industries that have stable emissions starting in 2027, with full implementation by 2030.

A recent report by the Institute for Energy Economics and Financial Analysis (IEEFA) assessed the current state of carbon pricing in Asia, highlighting that substantially higher carbon prices are needed to drive notable decarbonization. With regional prices still below USD20 per tonne of carbon dioxide equivalent (tCO₂e), a significant gap remains to reach the estimated USD50–USD100/tCO₂e required by 2030 to achieve meaningful decarbonization and meet the Paris Agreement targets. The report also emphasizes that marginal abatement costs, which reflect the cost of shifting from high to low-carbon technologies, are as high as USD800/tCO₂e. The currently low carbon prices risk allowing emitters to simply pay for ETS permits or carbon taxes rather than invest in emission reduction. 

Prioritizing fixed cap reduction rates and permit supply management

While details are still forthcoming, China’s planned adoption of absolute caps should be accompanied by a fixed reduction rate — similar to the EU ETS’s linear reduction factor (LRF) — to establish the pace at which emission allowances decrease annually. The rate of reduction should rise over time to effectively tighten supply, support prices, and enhance market certainty. The EU ETS’s LRF started at 1.74% in 2013 and is expected to increase to 4.4% from 2028. The only two Asian ETSs with absolute caps (South Korea and Kazakhstan) lack a strict, gradually increasing reduction rate. While this allows for cap adjustment flexibility in response to economic conditions, these modest and less predictable reductions limit the effectiveness of tightening supply. China should adopt a clear, progressively increasing cap reduction rate to enhance market certainty and strengthen price signals. 

Another priority is to reduce the number of free permits and introduce auctions to allow the market to determine carbon prices. Currently, China allocates all ETS permits for free, a practice that not only shields incumbent emitters from the actual cost of emissions but also deprives the government of auction revenues that could be reinvested to fund climate and other social and economic initiatives. Contrastingly, more than 50% of the EU ETS permits are distributed through auctions. 

The EU ETS also uses a Market Stability Reserve (MSR), which automatically adjusts the supply of permits for auctions based on predefined thresholds. When the number of allowances in circulation exceeds or falls below these levels, the MSR withdraws or releases allowances, thereby maintaining market stability and minimizing price volatility. South Korea’s system, by contrast, relies on government intervention to address excessive price fluctuations, such as adjusting allocations or trading rules. These discretionary measures are less predictable and often less effective in their impact. Similarly, China’s regional ETSs employ discretionary interventions that lack transparency. These aspects should also be taken into consideration when setting caps for China’s national ETS. 

 

Protecting exports while retaining carbon revenues for domestic use

Another factor supporting the development of a functioning carbon market with prices high enough to drive decarbonization is the EU’s Carbon Border Adjustment Mechanism (CBAM), which will be implemented in 2026. The CBAM will impose carbon costs on imports from countries with weaker climate policies, as evidenced by lower carbon coverage and prices. This underscores the urgency for economies like China and other Asian countries to advance their carbon markets to a) maintain the competitiveness of their exports compared to countries with effective carbon pricing schemes, and b) ensure that the revenue leakage represented by CBAM taxes paid to the EU is replaced by revenue generation for domestic use. This is crucial as Asia accounted for EUR1.1 trillion, or 46%, of the EU’s total imports (excluding trade between EU member states) in 2024. China was the EU’s largest import partner that year, with imports totaling EUR519 billion. 

It was previously estimated that in the first phase of CBAM, China’s steel and aluminum sectors would need to pay around RMB2 billion to RMB2.8 billion annually. This would add approximate costs of RMB652–690 per tonne for steel and RMB4,295–4,909 per tonne for aluminum. With the recent inclusion of these sectors in the national ETS, the actual impact is expected to be lower than these earlier assessments. However, with EU ETS permits trading at an average of approximately USD80/tCO₂e in the first nine months of 2025 — compared with only about USD11/tCO₂e for China’s carbon credits — domestic carbon prices will need to increase significantly to retain carbon revenues within the country. 

Strengthening climate ambition and regional leadership

China accounted for 29% of global greenhouse gas (GHG) emissions in 2024. It aims to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. In its latest Nationally Determined Contributions (NDCs), China further pledged to reduce net GHG emissions by 7%–10% from peak levels and increase the share of non-fossil fuels in energy consumption to 30% by 2035. A well-designed carbon market can complement the country’s strategies and accelerate progress towards these goals. 

While the changes in China’s ETS are largely consistent with international best practices, strong enforcement will be critical to ensure credibility and effectiveness. If implemented successfully, an important precedent can be set for other Asian markets. While several countries in the region have already established carbon markets or are preparing to introduce them, these systems have yet to drive significant decarbonization due to low prices and weak market design. Early flexibility measures aimed at easing participation and reflecting national circumstances have kept prices too low to facilitate emission reductions. 

With significant untapped potential, governments should continue to refine market design, improve transparency, and strengthen enforcement to enhance credibility. This would improve the effectiveness of domestic systems and lay the foundation for stronger linkages and interoperability across markets. China has the opportunity to lead Asia by designing an ETS that is fully fit for purpose.

The most you can lose on any stock (assuming you don’t use leverage) is 100% of your money. But when you pick a company that is really flourishing, you can make more than 100%. For example, the SAM Engineering & Equipment (M) Berhad (KLSE:SAM) share price has soared 203% in the last half decade. Most would be very happy with that. On top of that, the share price is up 16% in about a quarter.

Since it’s been a strong week for SAM Engineering & Equipment (M) Berhad shareholders, let’s have a look at trend of the longer term fundamentals.

Trump has pledged to “unleash” American oil and gas and these 15 US stocks have developments that are poised to benefit.

While the efficient markets hypothesis continues to be taught by some, it has been proven that markets are over-reactive dynamic systems, and investors are not always rational. By comparing earnings per share (EPS) and share price changes over time, we can get a feel for how investor attitudes to a company have morphed over time.

During five years of share price growth, SAM Engineering & Equipment (M) Berhad achieved compound earnings per share (EPS) growth of 2.6% per year. This EPS growth is lower than the 25% average annual increase in the share price. So it’s fair to assume the market has a higher opinion of the business than it did five years ago. And that’s hardly shocking given the track record of growth.

The image below shows how EPS has tracked over time (if you click on the image you can see greater detail).

It might be well worthwhile taking a look at our free report on SAM Engineering & Equipment (M) Berhad’s earnings, revenue and cash flow.

As well as measuring the share price return, investors should also consider the total shareholder return (TSR). The TSR is a return calculation that accounts for the value of cash dividends (assuming that any dividend received was reinvested) and the calculated value of any discounted capital raisings and spin-offs. So for companies that pay a generous dividend, the TSR is often a lot higher than the share price return. In the case of SAM Engineering & Equipment (M) Berhad, it has a TSR of 222% for the last 5 years. That exceeds its share price return that we previously mentioned. And there’s no prize for guessing that the dividend payments largely explain the divergence!

It’s nice to see that SAM Engineering & Equipment (M) Berhad shareholders have received a total shareholder return of 15% over the last year. That’s including the dividend. However, the TSR over five years, coming in at 26% per year, is even more impressive. The pessimistic view would be that be that the stock has its best days behind it, but on the other hand the price might simply be moderating while the business itself continues to execute. Before deciding if you like the current share price, check how SAM Engineering & Equipment (M) Berhad scores on these 3 valuation metrics.

But note: SAM Engineering & Equipment (M) Berhad may not be the best stock to buy. So take a peek at this free list of interesting companies with past earnings growth (and further growth forecast).

Please note, the market returns quoted in this article reflect the market weighted average returns of stocks that currently trade on Malaysian exchanges.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) simplywallst.com.

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.