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.

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.

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.

Heat acclimation provides cardiovascular protection in high-temperature environments through multilevel mechanisms; however, the complete molecular basis of its effects remains unclear. In this paper, we systematically review the effects of heat acclimation on blood volume, vascular function, cardiac structure, energy metabolism, and anti-stress regulation, revealing their potential mechanisms in cardiovascular adaptive protection. We also summarizes the multilevel responses induced by heat stress and heat acclimation, including the modulatory effects of heat acclimation on heat shock proteins (HSPs), hypoxia inducible factor 1 (HIF-1), and apoptotic pathways. Additionally, we highlights the comprehensive protective effects of heat acclimation across various stressors (e.g., hypoxia, heat stress). This review provides a significant physiological basis for cardiovascular disease management and sports medicine, emphasizing the potential application of heat acclimation in response to multiple stressors and supporting its role as an effective tool in cardiovascular health management and stress protection interventions.
Humans ; Acclimatization/physiology* ; Hot Temperature ; Heat-Shock Proteins/metabolism* ; Animals ; Heat-Shock Response/physiology* ; Hypoxia-Inducible Factor 1/metabolism* ; Apoptosis/physiology*

To investigate the mechanism of action of Syngnathus extract in treating knee osteoarthritis of rats, forty-eight male SD rats were randomly divided into the blank group, model group, positive drug group, as well as low-dose, medium-dose, and high-dose groups of Syngnathus extract. The rat model of knee osteoarthritis was constructed by intra-articular injection of sodium iodoacetate. After successful modeling, celecoxib(18 mg·kg~(-1)·d~(-1)) and Syngnathus extract(0.4, 0.8, and 1.6 g·kg~(-1)·d~(-1)) were given in different groups by gavage intervention for two weeks. Hematoxylin-eosin(HE) staining was used to observe the histopathological changes of cartilage in knee joints, and enzyme-linked immunosorbent assay(ELISA) was used to detect the expression level of inflammatory factors in serum. Real-time fluorescence quantitative PCR, Western blot, and immunohistochemistry were used to detect the levels of phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt)/mammalian target protein of rapamycin(mTOR) pathway-related mRNA and protein expression. The results showed that, comparied with the blank group, the cartilage surface of the knee joints of rats in the model group was uneven, with disorganized levels and defective cartilage tissue. The serum levels of interleukin-1β(IL-1β), interleukin-6(IL-6), and tumor necrosis factor-α(TNF-α) and the mRNA levels of PI3K, Akt, and mTOR in cartilage tissue, as well as the protein expression levels of phosphorylated PI3K(p-PI3K)/PI3K, phosphorylated Akt(p-Akt)/Akt, phosphorylated mTOR(p-mTOR)/mTOR, and P62 were significantly increased. Beclin1 protein expression was decreased. Comparied with the model group, the number of chondrocytes in the knee joint of rats in each group of Syngnathus extract increased, and the arrangement of chondrocytes was relatively neat. The cartilage layer was restored, and the serum levels of IL-1β, IL-6, and TNF-α, as well as the mRNA expression levels of PI3K, Akt, and mTOR in cartilage tissue were significantly reduced. The protein expression levels of p-PI3K/PI3K, p-Akt/Akt, p-mTOR/mTOR, and P62 were significantly reduced in the rats in the middle-dose and high-dose groups of Syngnathus extract, and the Beclin1 protein expression was significantly increased. The protein expression levels of p-PI3K/PI3K, p-Akt/Akt, and P62 in rats in the low-dose group of Syngnathus extract were significantly reduced. In summary, Syngnathus extract may be used to treat knee osteoarthritis by inhibiting the expression of PI3K/Akt/mTOR signaling pathway, so as to alleviate the inflammatory response in the organism, enhance the autophagy activity of chondrocytes, and reduce the apoptosis of chondrocytes.
Animals ; TOR Serine-Threonine Kinases/genetics* ; Male ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Proto-Oncogene Proteins c-akt/genetics* ; Rats ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Phosphatidylinositol 3-Kinases/genetics* ; Humans

Eight amide alkaloids(1-8) were isolated from the 70% ethanol extract of Cannabis Fructus using silica gel column chromatography, MCI column chromatography, and semi-preparative high-performance liquid chromatography(HPLC). Their structures were identified as hempspiramide A(1), N-[(4-hydroxyphenyl)ethyl]formamide(2), N-acetyltyramide(3), N-trans-p-coumaroyltyramine(4), N-trans-caffeoyltyramine(5), N-trans-feruloyltyramine(6), N-cis-p-coumaroyltyramine(7), N-cis-feruloyltyramine(8) by using spectroscopic methods such as NMR and MS. Among these compounds, compound 1 was a new amide alkaloid, while compounds 2 and 3 were isolated from Cannabis Fructus for the first time. Some of the isolates were assayed for their α-glucosidase inhibitory activity. Compounds 5-7 displayed significant inhibitory activity against α-glucosidase with IC_(50) values ranging from 1.07 to 4.63 μmol·L~(-1).
Cannabis/chemistry* ; Alkaloids/pharmacology* ; Amides/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Fruit/chemistry* ; Molecular Structure ; alpha-Glucosidases/chemistry* ; Chromatography, High Pressure Liquid

This study predicts the potential mechanism of Hippocampus in the treatment of knee osteoarthritis(KOA) through network pharmacology, with preliminary verification using molecular docking and animal experiments. The database was used to screen the active chemical components of Hippocampus and the targets of KOA, and Gene Ontology(GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis, and molecular docking were performed on the relevant core targets to preliminarily explore the potential targets and mechanisms of Hippocampus in the treatment of KOA. A rat KOA model was constructed by intra-articular injection of sodium iodoacetate, and the rats were intervened with different doses of Hippocampus decoction and celecoxib. The expression of relevant targets was detected through hematoxylin-eosin(HE) staining, enzyme-linked immunosorbent assay(ELISA), RT-qPCR, and Western blot to further validate the network pharmacology results. A total of 23 drug-like components of the Hippocampus were screened, and 128 common targets with KOA were identified, involving interleukin-17(IL-17) signaling pathway, transcription factor(FoxO) signaling pathway, tumor necrosis factor(TNF) signaling pathway. Molecular docking results showed that the screened core chemical components exhibited good affinity with key targets. HE staining demonstrated that Hippocampus improved the morphology of the cartilage layer. ELISA confirmed that Hippocampus significantly reduced the levels of IL-6 and TNF-α in the serum of KOA rats. Western blot and RT-qPCR analysis showed that Hippocampus significantly reduced the expression of IL-6, TNF-α, matrix metalloproteinase(MMP) 13, IL-17A, nuclear factor κB activator 1(ACT1), tumor necrosis factor receptor-associated factor 6(TRAF6) and nuclear factor κB(NF-κB) in cartilage tissue. The results suggest that Hippocampus can alleviate the degree of joint damage in the KOA rat model induced by sodium iodoacetate. The mechanism of action is related to the inhibition of the IL-17 signaling pathway, reduction of inflammation, and inhibition of extracellular matrix(ECM) degradation.
Animals ; Molecular Docking Simulation ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Network Pharmacology ; Male ; Osteoarthritis, Knee/metabolism* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Humans ; Interleukin-17/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Hippocampus/chemistry*