Background Platelet parameters are important indicators of cardiovascular risk, and environmental pollutants such as polychlorinated biphenyls (PCBs) may impair platelet function through oxidative stress. Objective To investigate the differential effects of single and mixed exposure to PCBs on platelet parameters among individuals with normal glucose tolerance (NGT), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM), and to evaluate the potential modifying role of a healthy lifestyle. Methods This study included 2249 participants (including 707 with NGT, 759 with IFG, and 783 with T2DM). Plasma PCB concentrations were measured using triple quadrupole gaschromatography-tandem mass spectrometry. Generalized linear regression was used to assess the associations between individual PCB congeners and platelet parameters. Quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR) models were used to evaluate the overall effects of PCBs mixture exposure on platelet parameters across different glycemic states, as well as its interaction with healthy lifestyle score (HLS). Results Generalized linear regression analyses showed significant differences in the effects of PCBs on platelet parameters across different glycemic states (P<0.05). After adjusting for confounders, PCBs mixture exposure was significantly associated with lower platelet counts (PLT) in individuals with NGT (b=−10.60, 95%CI: −16.48, −4.71) and IFG (b=−12.91, 95%CI: −18.90, −6.92), whereas no significant association was observed in individuals with T2DM (P=0.051). Mean platelet volume (MPV) and platelet-large cell ratio (P-LCR) increased significantly with higher PCBs exposure levels across all three groups (P<0.05). BKMR analysis showed a positive association between PCBs mixture exposure and P-LCR, with the strongest association observed in the NGT group. Furthermore, a significant interaction was observed between HLS and PCBs mixture exposure, and a higher HLS attenuated the effects of PCBs on P-LCR. Conclusion Glycemic glycemic states may modify the effects of PCBs on platelets. Individuals with NGT appear more sensitive to PCBs exposure, whereas the T2DM state may attenuate this effect. Moreover, healthy lifestyles, including not smoking, moderate alcohol consumption, maintaining moderate-to-high physical activity, a healthy diet, and an appropriate body mass index (BMI), may mitigate the adverse effects of most PCBs on platelet parameters.

Background Platelet parameters are important indicators of cardiovascular risk, and environmental pollutants such as polychlorinated biphenyls (PCBs) may impair platelet function through oxidative stress. Objective To investigate the differential effects of single and mixed exposure to PCBs on platelet parameters among individuals with normal glucose tolerance (NGT), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM), and to evaluate the potential modifying role of a healthy lifestyle. Methods This study included 2249 participants (including 707 with NGT, 759 with IFG, and 783 with T2DM). Plasma PCB concentrations were measured using triple quadrupole gaschromatography-tandem mass spectrometry. Generalized linear regression was used to assess the associations between individual PCB congeners and platelet parameters. Quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR) models were used to evaluate the overall effects of PCBs mixture exposure on platelet parameters across different glycemic states, as well as its interaction with healthy lifestyle score (HLS). Results Generalized linear regression analyses showed significant differences in the effects of PCBs on platelet parameters across different glycemic states (P<0.05). After adjusting for confounders, PCBs mixture exposure was significantly associated with lower platelet counts (PLT) in individuals with NGT (b=−10.60, 95%CI: −16.48, −4.71) and IFG (b=−12.91, 95%CI: −18.90, −6.92), whereas no significant association was observed in individuals with T2DM (P=0.051). Mean platelet volume (MPV) and platelet-large cell ratio (P-LCR) increased significantly with higher PCBs exposure levels across all three groups (P<0.05). BKMR analysis showed a positive association between PCBs mixture exposure and P-LCR, with the strongest association observed in the NGT group. Furthermore, a significant interaction was observed between HLS and PCBs mixture exposure, and a higher HLS attenuated the effects of PCBs on P-LCR. Conclusion Glycemic glycemic states may modify the effects of PCBs on platelets. Individuals with NGT appear more sensitive to PCBs exposure, whereas the T2DM state may attenuate this effect. Moreover, healthy lifestyles, including not smoking, moderate alcohol consumption, maintaining moderate-to-high physical activity, a healthy diet, and an appropriate body mass index (BMI), may mitigate the adverse effects of most PCBs on platelet parameters.

Although global brain science research has progressed rapidly in recent decades, several fundamental questions in neuroscience remain unresolved. In particular, the physical mechanism underlying neural signal transmission remains controversial, and the carriers responsible for neural information storage and retrieval have not yet been fully clarified. These unresolved issues motivate us to re-examine the processes of neural information generation, transmission, integration, storage, and retrieval from multiple perspectives. A key observation is that neural electromagnetic activities are closely associated with time. Their duration, temporal structure, and dynamic evolution play crucial roles in neural information processing. In this work, we analyze neural electromagnetic activities from the perspective of temporal scales (referred to here as the “time course”). By reviewing and integrating findings from previous studies, we examine the characteristic time requirements and dynamic features of neural processes occurring at different stages of information processing. These stages include neural signal generation, signal transmission along axons, synaptic integration, synaptic plasticity, and memory formation and retrieval. Based on this temporal analysis, we outline a framework describing neural electromagnetic activities across a wide range of time scales, spanning from microseconds to minutes, hours, or even longer periods associated with long-term memory, which suggests that neural information processing involves multiple physical processes operating at different time levels. Rapid electromagnetic events may occur on microsecond scales, whereas electrophysiological phenomena such as action potentials typically last on the order of milliseconds. Longer time scales are associated with synaptic plasticity and memory-related processes. From this perspective, we propose that the physical carrier of neural information may be transient electromagnetic pulses with durations on the microsecond scale. In this framework, action potentials can be interpreted as the macroscopic electrophysiological manifestation of underlying electromagnetic processes triggered by ionic currents across neuronal membranes. Rather than being the fundamental neural signal itself, the action potential may represent a measurable membrane-level response associated with the successful activation of these electromagnetic events. Moreover, we discuss a possible mechanism for long-term memory storage. Considering the apparent temporal contradiction between the millisecond-scale excitation of neurons and the long-term persistence of memories, we believe that long-term memory information may be stored within neural network topologies formed by electrical synapse coupling. Such structures, referred to as electrically coupled memory networks (ECMNs), may enable neurons within the same network to respond rapidly and synchronously to stimuli, thereby facilitating efficient memory retrieval. Overall, this study emphasizes the importance of considering the temporal organization of neural electromagnetic activities when interpreting neural signaling mechanisms. It may provide new insights into the physical nature of neural information carriers and the mechanisms of memory storage and retrieval. Furthermore, highlighting the potential role of electromagnetic interactions in neural activity may contribute to the development of new theoretical frameworks and experimental approaches in neuroscience. Such perspectives may also offer valuable references for future research on neural coding, brain function mechanisms, and neuromodulation technologies.

Objective To investigate the early clinical efficacy of a 3D-printed external fixation guide combined with video-assisted thoracic surgery (VATS) in the treatment of flail chest, and to provide evidence for its clinical application. Methods Patients with flail chest admitted to Xiamen University Affiliated Chenggong Hospital between January 2010 and January 2023 were retrospectively selected as the study subjects. Based on the surgical methods, the patients were divided into two groups: patients treated with the 3D-printed external fixation guide combined with VATS were assigned to a 3D group, and those who underwent open reduction and internal fixation were assigned to an internal fixation group. The operative time, intraoperative blood loss, duration of chest tube drainage, recovery of thoracic volume, visual analogue scale (VAS) scores at 1 month postoperatively, and complications were compared between the two groups. Results A total of 40 patients were included, with 20 patients in each group. The 3D group consisted of 13 males and 7 females, with a mean age of (45.7±3.8) years; the internal fixation group consisted of 14 males and 6 females, with a mean age of (47.3±4.1) years. There were no statistical differences between the two groups in terms of gender, age, number of rib fractures, or preoperative VAS scores (P>0.05). The surgeries were performed successfully in both groups, primary wound healing was achieved in all patients, and pain symptoms were significantly alleviated compared to preoperation. No postoperative complications occurred in the 3D group, whereas 1 patient each of chronic postoperative pain, fracture malunion, and incision infection occurred in the internal fixation group, resulting in a complication rate of 15.0%. The operative time, intraoperative blood loss, and duration of chest tube drainage in the 3D group were significantly shorter or less than those in the internal fixation group (P<0.05). There were no statistical differences in the recovery of thoracic volume or the VAS scores at 1 month postoperatively between the two groups (P>0.05). Conclusion The early clinical efficacy of a 3D-printed external fixation guide combined with VATS in the treatment of flail chest is satisfactory. This technique offers the advantages of being minimally invasive, highly efficient, promoting rapid recovery, and resulting in fewer postoperative complications, and can effectively reconstruct the thoracic contour and restore thoracic volume.

Intrahepatic cholangiocarcinoma （ICC） is a highly malignant liver tumor， and due to the absence of symptoms in its early stage and the lack of effective treatment measures， patients tend to have an extremely low 5-year survival rate. The tumor stroma-immune microenvironment （TSIME） is a complex ecosystem that changes dynamically during tumorigenesis and evolution and consists of a variety of cellular and non-cellular components， and it plays an important role in the development， proliferation， invasion， and progression of ICC and determines the heterogeneity and malignancy of ICC to a certain degree. This article reviews the cellular components （such as T cells， B cells， natural killer cells， dendritic cells， neutrophils， macrophages， myeloid-derived suppressor cells） and non-cellular components （such as chemokines and cytokines） within the ICC TSIME， as well as the complex mechanisms of interaction between these components， and it also reviews the spatial interactions between immune cells and tumor cells， in order to provide potential research directions for ICC immunotherapy and new ideas for the effective and precise treatment of ICC in the future.

ObjectiveTo observe the effect of gene mutation on the effectiveness of arsenic-containing Chinese herbal compound formulas in the treatment of myelodysplastic syndromes （MDS） of different traditional Chinese medicine （TCM） patterns， so as to provide the basis for the clinical application. MethodsClinical data of 442 MDS patients who were treated with arsenic-containing herbal compound formulas were retrospectively collected， including the baseline demographic and clinical characteristics of the patients. Based on the TCM four examinations， the patients were divided into the spleen-kidney deficiency group as well as the qi-yin deficiency group， and according to the results of the next-generation sequencing （NGS） test， they were divided into the group with and without gene mutation respectively. The influence of gene mutation on the clinical effectiveness of patients with different TCM patterns was analyzed， the baseline demographic and clinical characteristics of the patients with different outcomes of the two TCM patterns were compared， and multivariate Logistic regression analysis was conducted on the influencing factors of the effective rate of MDS patients with gene mutation. ResultsA total of 190 cases were included in the spleen-kidney deficiency group （119 cases with gene mutation） and 43 cases in the qi-yin deficiency group （23 cases with gene mutation）. No statistically significant differences were noted in effectiveness assessment， total effective rate， and total response rate between the spleen-kidney deficiency group and the qi-yin deficiency group （P＞0.05）. In the spleen-kidney deficiency group， the total effective rate of MDS with gene mutation was 65.55% （78/119）， which was lower than 80.28% （57/71） of MDS without gene mutation， with statistical significance （P = 0.033）， while no statistical differences in effectiveness assessment and total response rate were noted （P＞0.05）. In the qi-yin deficiency group， no statistical differences were observed in effectiveness assessment， total effective rate， and total response rate of the patients in with or without gene mutation （P＞0.05）. In the spleen-kidney deficiency group with gene mutation， the rate of complex karyotype （P = 0.031） and the mutation rate of CBL gene （P = 0.032） in the ineffective population were higher than those in the effective population， while the mutation rate of DDX41 gene in the effective population was higher than that in the ineffective population （P = 0.033）. No statistically significant differences were found in other gene mutations， age， gender distribution， number of gene mutations， bone marrow hyperplasia degree， blast cell range， reticular fiber tissue proliferation or not， and prognosis of chromosomal abnormalities between the effective and ineffective populations （P＞0.05）. In the qi-yin deficiency group with gene mutation， no statistically significant differences were found in various items between populations with different outcomes （P＞0.05）. Multivariate Logistic regression analysis showed that complex karyotype， CBL mutation， and DDX41 mutation were independently associated with the effective rate of MDS with spleen-kidney deficiency and gene mutation （P＜0.05）. DDX41 mutation was an independent protective factor in the spleen-kidney deficiency group （OR＞1）， while complex karyotype and CBL mutation were independent risk factors （OR＜1）. ConclusionThe arsenic-containing TCM compound formulas exhibited better effectiveness in MDS with spleen-kidney deficiency pattern without mutation； and in MDS with spleen-kidney deficiency pattern without complex karyotypes， CBL mutation， and with DDX41 mutations. Furthermore， DDX41 mutation was an independent protective factor in the spleen-kidney deficiency group， while complex karyotype and CBL mutation were independent risk factors. In MDS with qi-yin deficiency pattern， gene mutation-related factors showed no significant impact on the effectiveness of arsenic-containing TCM compound formulas.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective To understand the disease spectrum of a natural village in an area with high incidence of esophageal cancer to provide a reference for precise prevention and control. Methods From 2008 to 2024, 711 asymptomatic people over the age of 35 years in a natural village with high incidence of esophageal cancer in China were surveyed, and 171 of them were subjected to gastroscopy, biopsy, and pathological examination. All participants were followed up for a long time, and their disease history was recorded. Results A total of 16 years of follow-up were performed, and 703 people were effectively followed up. In 2008, 171 people underwent gastroscopy, and 160 people had biopsy and pathological results in endoscopic screening. By 2024, 76 people had been diagnosed with malignant tumors of 12 different types, and among these people, 45 had esophageal cancer. Conclusion Esophageal cancer remains a significant cause of morbidity and mortality from malignant tumors in this region. Biopsy and pathological examination should be strengthened during gastroscopy, and follow-ups and regular check-ups should be given high importance to reduce the incidence and mortality rates of esophageal cancer.

Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

Objective:To analyze the differences in screening neonatal glucose-6-phosphate dehydrogenase (G6PD) deficiency in different birth seasons, establish screening thresholds for G6PD concentration in each season using indirect methods, and verify the reliability of the results.Methods:This was a cross-sectional study.A total of 140 823 newborns were collected from the Neonatal Screening Center of Shanghai Children′s Hospital from January 2020 to December 2023, including 41 029 cases, 35 796 cases, 33 969 cases and 30 029 cases in spring, summer, autumn and winter groups, respectively.The concentration of G6PD on the dried blood filter paper was determined using an automatic fluorescence analyzer.The distribution and statistical index of concentration values in four seasons were analyzed.The Kolmogorov-Smirnov test was used for normal distribution.The skewed distribution data was converted into approximately normal distribution using Box-Cox.Outliers were eliminated using the interquartile range (Turkey) method.The cumulative frequency distribution map was drawn through R language programming.The linear regression equation Y=B X+ A was fitted.The 0.5th percentile ( P0.5) was used as the screening threshold, which was compared with the reference value given by the manufacturer or laboratory and with the reference change value (RCV). Results:In the spring group, the positive rate was 4.02‰, 91 cases were confirmed, and the incidence was 1∶451.In the summer group, the positive rate was 7.18‰, 90 cases were confirmed, and the incidence was 1∶398.In the autumn group, the positive rate was 3.21‰, 86 cases were confirmed, and the incidence was 1∶395.In the winter group, the positive rate was 2.26‰, 61 cases were confirmed, and the incidence was 1∶492.The incidence rate did not change significantly in the four seasons ( P>0.05).The G6PD concentrations in the four seasons were compared in pairs, and the result was winter>autumn>spring>summer.The thresholds for G6PD screening were established indirectly: 25.08 U/dL, 22.83 U/dL, 26.63 U/dL and 38.01 U/dL in spring, summer, autumn and winter groups, respectively.The relative deviation in the threshold between the summer group and the laboratory was lower than RCV, while that between the other groups was higher than RCV.According to the screening threshold, the negative and positive conformity rates of 12 batches of 120 samples in the inter-laboratory evaluation program of Chinese Taiwan Preventive Medicine Foundation of China reached 100%. Conclusions:There is no difference in the incidence of G6PD deficiency between birth seasons.It is feasible to establish the screening threshold in each season using indirect methods, which is conducive to improving the efficiency of screening.