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.

19 cinnamamide/ester-triazole compounds were designed, synthesized and evaluated for their anti-Alzheimer's disease (AD) activity. Among them, compound 4f displayed excellent anti-β-amyloid protein (Aβ)-mediated cytotoxicity (EC₅₀ = 2.03 ± 2.45 μmol·L^-1) in APPswe cells and acetylcholinesterase inhibiton (IC₅₀ = 4.88 ± 4.70 μmol·L^-1). Further study indicated that, at dosages of (1, 5 and 25 mg·kg^-1), compound 4f was effective in improving spatial learning and memory deficits in Aβ_1-42-impaired mice, which was achieved by promoting the nonamyloidogenic signaling and inhibiting the amyloidogenic pathway, along with the suppression of Aβ-induced Tau phosphorylation. All animal experiments in this study were approved by the Experimental Animal Care and Use Committee of the Institute of Medicinal Biotechnology (IMB-20220908D701). In conclusion, compound 4f holds promise as a lead candidate for AD treatment, and the present study lays the foundation for its subsequent development.

Objective: To investigate the positive rate of drug-induced antibodies produced by the clinical application of β-lactam antibiotics, and analyze the differences in the detection methods and related influencing factors. Methods: A total of 350 adult inpatients who developed anemia after using β-lactam antibiotics for 3 days or more in Inner Mongolia People's hospital were selected as the experimental group, and 240 adult inpatients treated with β-lactam antibiotics for 3 days or more who did not develop anemia as the control group. The drug-induced antibody tests, direct antiglobulin tests, and unexpected antibody screening were performed on both groups, and the influencing factors of drug-induced antibodies were analyzed. Results: The numbers of positive cases of drug-induced antibody detected by the drug-coated red blood cell method in the experimental group and the control group were 12(12/350, 3.43%) and 2(2/240, 0.83%) respectively, with statistically significant differences (P<0.05). No drug-induced antibodies were detected in either group using the drug addition method. In the experimental group, the red blood cell method detected β-lactam drug-induced antibodies in 12 cases (12/350, 3.43%), while the drug added method detected 0 cases (0/350, 0.00%), with statistically significant differences (P<0.05). In the control group, the detection rates of two methods showed no statistically significant difference (P>0.05). In the experimental group, 7 cases of β-lactam antibodies were detected in the cephalosporin group (7/293, 2.40%) and 5 cases in the non-cephalosporin group (5/58, 8.62%), with statistically significant differences (P<0.05). There was no statistically significant difference between the second-generation and third-generation cephalosporin drugs (P>0.05). When the experimental group was stratified according to the history of blood transfusion and the blood type of patients, no statistically significant differences were observed between subgroups (P>0.05). Conclusion: Anemia may be related to the production of drug-induced antibodies followingβ-lactam antibiotics treatment. Therefore, improving the clinical awareness of drug-induced antibodies to β-lactam antibiotics is of great significance to clarify the causes of anemia and reduce unnecessary blood transfusions.

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 investigate the positive rate of drug-induced antibodies produced by the clinical application of β-lactam antibiotics, and analyze the differences in the detection methods and related influencing factors. Methods: A total of 350 adult inpatients who developed anemia after using β-lactam antibiotics for 3 days or more in Inner Mongolia People's hospital were selected as the experimental group, and 240 adult inpatients treated with β-lactam antibiotics for 3 days or more who did not develop anemia as the control group. The drug-induced antibody tests, direct antiglobulin tests, and unexpected antibody screening were performed on both groups, and the influencing factors of drug-induced antibodies were analyzed. Results: The numbers of positive cases of drug-induced antibody detected by the drug-coated red blood cell method in the experimental group and the control group were 12(12/350, 3.43%) and 2(2/240, 0.83%) respectively, with statistically significant differences (P<0.05). No drug-induced antibodies were detected in either group using the drug addition method. In the experimental group, the red blood cell method detected β-lactam drug-induced antibodies in 12 cases (12/350, 3.43%), while the drug added method detected 0 cases (0/350, 0.00%), with statistically significant differences (P<0.05). In the control group, the detection rates of two methods showed no statistically significant difference (P>0.05). In the experimental group, 7 cases of β-lactam antibodies were detected in the cephalosporin group (7/293, 2.40%) and 5 cases in the non-cephalosporin group (5/58, 8.62%), with statistically significant differences (P<0.05). There was no statistically significant difference between the second-generation and third-generation cephalosporin drugs (P>0.05). When the experimental group was stratified according to the history of blood transfusion and the blood type of patients, no statistically significant differences were observed between subgroups (P>0.05). Conclusion: Anemia may be related to the production of drug-induced antibodies followingβ-lactam antibiotics treatment. Therefore, improving the clinical awareness of drug-induced antibodies to β-lactam antibiotics is of great significance to clarify the causes of anemia and reduce unnecessary blood transfusions.

Bone Transplantation/adverse effects* ; Transplantation, Autologous/adverse effects*

Objective:To explore the current situation of binary coping in patients with perimenopausal syndrome and analyze its influencing factors, in order to provide a basis for improving the level of binary coping.Methods:Using convenience sampling method, a total of 210 patients with perimenopausal syndrome and their spouses from the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine were cross-sectional surveyed by a general data questionnaire, the Binary Coping Scale, and the Modified Kupperman Score Scale. The influencing factors of binary coping level in patients with perimenopausal syndrome were explored by univariate analysis and variance decomposition model analysis.Results:A total of 200 valid questionnaires were retrieved.The patients aged (50.52 ± 2.89) years old. The binary coping score was (79.64 ± 22.74) points. The variance decomposition model analysis showed that marriage age, type of medical insurance, number of children, education level, family monthly income, spouse′s education level, presence of major comorbidities in spouse, modified Kupperman score, presence of generalized anxiety in spouse were the main influencing factors of binary coping in patients with perimenopausal syndrome (all P<0.05). Conclusions:The binary coping scores of patients with perimenopausal syndrome are lower than normal, and considering the influence and involvement of patients' spouses, nursing staff should pay special attention to patients who are married relatively early, have more children, have lower education levels, and have lower family monthly incomes. Additionally, attention should be given to spouses who experience widespread anxiety, have a lower level of education, and suffer from major chronic diseases. By developing and implementing comprehensive intervention measures aimed at improving the Kupperman score and the level of binary coping, both parties can be encouraged to support each other more effectively, thereby improving the marital relationships of patients during the perimenopausal period.

Objective To understand the respiratory protection competency of staff in hospitals.Methods Staff from six hospitals of different levels and characteristics in Beijing were selected,including doctors,nurses,medical technicians,and servicers,to conduct knowledge assessment on respiratory protection competency.According to exposure risks of respiratory infectious diseases,based on actual cases and daily work scenarios,content of respira-tory protection competency assessment was designed from three aspects:identification of respiratory infectious di-seases,transmission routes and corresponding protection requirements,as well as correct selection and use of masks.The assessment included 6,6,and 8 knowledge points respectively,with 20 knowledge points in total,all of which were choice questions.For multiple-choice questions,full marks,partial marks,and no mark were given respective-ly if all options were correct,partial options were correct and without incorrect options,and partial options were correct but with incorrect options.Difficulty and discrimination analyses on question of each knowledge point was conducted based on classical test theory.Results The respiratory protection competency knowledge assessment for 326 staff members at different risk levels in 6 hospitals showed that concerning the 20 knowledge points,more than 60％participants got full marks for 6 points,while the proportion of full marks for other questions was relatively low.Less than 10％participants got full marks for the following 5 knowledge points:types of airborne diseases,types of droplet-borne diseases,conventional measures for the prevention and control of healthcare-associated infec-tion with respiratory infectious diseases,indications for wearing respirators,and indications for wearing medical protective masks.Among the 20 knowledge questions,5,1,and 14 questions were relatively easy,medium,and difficult,respectively;6,1,4,and 9 questions were with discrimination levels of ≥0.4,0.30-0.39,0.20-0.29,and ≤0.19,respectively.Conclusion There is still much room for hospital staff to improve their respiratory protection competency,especially in the recognition of diseases with different transmission routes and the indications for wearing different types of masks.

Objective:To analyze the clinical application value of a novel magnetic navigation ultrasound (MNU) combined with digital subtraction angiography (DSA) dual-guided percutaneous transhepatic biliary drainage (PTCD) through the right hepatic duct for the treatment of malignant obstructive jaundice.Methods:Randomized controlled trial. The clinical data of 64 patients with malignant obstructive jaundice requiring PTCD through the right hepatic duct at the Hepatobiliary Center of the First Affiliated Hospital of Nanjing Medical University (Jiangsu Province People′s Hospital) from December 2018 to December 2021 were retrospectively analyzed. The MNU group ( n=32) underwent puncture guided by a novel domestic MNU combined with DSA, and the control group ( n=32) underwent puncture guided by traditional DSA. The operation time, number of punctures, X-ray dose after biliary stenting as shown by DSA, patients' tolerance of the operation, success rate of the operation, pre- and post-operative total bilirubin, and incidence of postoperative complications were compared between the two groups. Results:The operation time of the MNU group was significantly shorter than that of the control group [(17.8±7.3) vs. (31.6±9.9) min, t=-6.35, P=0.001]; the number of punctures in the MNU group was significantly lower [(1.7±0.6) vs. (6.3±3.9) times, t=-6.59, P=0.001]; and the X-ray dose after biliary stenting as shown by DSA in the MNU group was lower than that in the control group [(132±88) vs. (746±187) mGy, t=-16.81, P<0.001]; Five patients in the control group were unable to tolerate the operation, and two stopped the operation, however all patients in the MNU group could tolerate the operation, and all completed the operation, with a success rate of 100% (32/32) in the MNU group compared to 93.8%(30/32) in the control group; the common complications of PTCD were biliary bleeding and infection, and the incidence of biliary bleeding (25.0%, 8/32) and infection (18.8%, 6/32) in the MNU group was significantly lower than that in the control group, 53.1% (17/32) and 28.1% (9/32), respectively. Conclusion:Magnetic navigation ultrasound combined with DSA dual-guided PTCD through the right biliary system for the treatment of malignant obstructive jaundice is safe and feasible.

Objective: To investigate the influencing factors of depressive symptoms and to construct and verify the prediction model of depressive symptoms in middle school students, so as to provide risk assessment tools for effectively screening depressive symptom. Methods: Physical examination and questionnaire survey were conducted among middle school students in one city in Guangdong Province from September to October in 2021 ( n =2 376) and from September to October in 2022 ( n =4 344) by a multistage cluster sampling method, and a nomographic prediction model of depressive symptoms in middle school student was constructed. The questionnaire survey was conducted using the student health status and influencing factors questionnaire (secondary school version) and the Center for Epidemiological Studies Depression Scale (CES-D) to measure the lifestyle and depressive symptom of middle school students. Results: The detection rate of depressive symptoms in 2021 was 23.3%. Multivariate Logistic regression analysis showed that irregular breakfast ( OR =2.64), school bullying ( OR =4.28), being beaten by parents ( OR =2.86), using mobile devices for a long time ( OR =1.08) and sitting for a long time ( OR =1.05) were positively related to depressive symptoms in middle school students ( P <0.05). Long sleep duration ( OR =0.78) and outdoor activity durations of 1-<2, 2-<3 and ≥3 h/d (compared with <1 h/d, OR =0.63, 0.61, 0.49) were negatively related to depressive symptoms in middle school students ( P < 0.05 ). Multivariate Logistic regression analysis showed that 7 statistically signifucant predictive factors constructed a nomogram, and the AUC of the nomogram was 0.77, which had been verified internally and externally with good differentiation and reliability. Conclusions The nomogram prediction model of depressive symptoms provides a convenient and effective risk assessment tool for depressive symptoms among middle school students. The life behavior, diet behavior and injury behavior of middle school students play an important role in the formation of depressive symptoms. It should pay attention to the impact of the behavioral factors on the mental health of middle school students.