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.

The triterpenoid saponins of Centella asiatica, including asiaticoside, madecassoside, asiatic acid, and madecassic acid, are pivotal bioactive compounds of the plant. These constituents exhibit a spectrum of pharmacological activities, such as antioxidant, antitumor, and antidepressant effects, promotion of wound healing, and enhancement of microcirculation. Owing to these therapeutic properties, C. asiatica is widely employed in pharmaceutical and cosmetic industries. However, the escalating global demand for its extracts has led to potential supply shortages, prompting researchers to use multiple strategies such as multi-omics, molecular biology, and synthetic biology to conduct extensive studies. These studies encompass the elucidation of the biosynthetic pathways of triterpenoid saponins in C. asiatica, metabolic regulation, the hormonal induction of secondary metabolite synthesis, and the application of biotechnological strategies for natural product production to increase the yield of secondary metabolites in C. asiatica, or to produce active components via microbial chassis, thus satisfying market demands and promoting the sustainable exploitation of wild C. asiatica resources. This article first introduced the triterpenoid saponins of C. asiatica and their biological activities, then summarized the latest research advancements in their biosynthetic pathways, metabolic regulation, and heterologous biosynthesis, and provided an outlook on future development directions, with the aim of providing reference for comprehensive resource development and biotechnological synthesis of active components from C. asiatica.
Centella/genetics* ; Triterpenes/chemistry* ; Biosynthetic Pathways ; Humans ; Drugs, Chinese Herbal/chemistry* ; Plant Extracts

This study employs ultra-performance liquid chromatography(UPLC) to analyze the differences in alkaloid content of Commelina communis from various geographical origins, exploring its feasibility as a quality evaluation indicator. A total of 57 batches of C. communis samples from 23 provinces, autonomous regions, and municipalities in China were selected. The MicroPulite HSS T3(2.1 mm×50 mm, 1.8 μm)column was used with a mobile phase of acetonitrile-0.2% phosphoric acid aqueous solution(20∶80), detection wavelength at 254 nm, and a flow rate of 0.3 mL·min~(-1) to measure the content of 1-deoxynojirimycin(DNJ) and deoxymannojirimycin(DMJ). The MicroPulite XP tC_(18)(2.1 mm×100 mm, 1.7 μm)column was employed with a mobile phase of acetonitrile-0.2% phosphoric acid aqueous solution(4∶96), detection wavelength at 254 nm, and a flow rate of 0.4 mL·min~(-1) to measure the content of norharmine(NHM) and harmanme(HM). Chemometric methods were applied to study the relationships and differences among the 57 batches of C. communis. Significant differences in alkaloid content were observed among C. communis from different regions, with the average total content decreasing in the order of North China, Northeast China, Northwest China, East China, Southwest China, Central China, and South China. Cluster analysis(CA) and principal component analysis(PCA) further revealed the quality differences of C. communis from various origins, and partial least squares discriminant analysis(PLS-DA) identified DNJ as a marker compound to distinguish the quality differences between different geographical sources of C. communis. It is recommended that the content limit of DNJ be set at no less than 0.055 9%, providing a reference for the quality evaluation and clinical application of C. communis medicinal materials.
Alkaloids/analysis* ; Drugs, Chinese Herbal/chemistry* ; China ; Chromatography, High Pressure Liquid ; Chemometrics/methods* ; Quality Control

A 12-year-old boy was admitted with symptoms of cough and fever lasting over a month, accompanied by weight loss 2 kg. Prior anti-infective treatments proved ineffective in alleviating the symptoms. Chest imaging revealed diffuse interstitial pulmonary edema in the right lung with obstructed lymphatic drainage. Combined with histopathological examinations, the diagnosis was confirmed as lung cancer with pulmonary lymphangitic carcinomatosis. The patient underwent chemotherapy with docetaxel and carboplatin, yet the disease progressively worsened, resulting in death three months after diagnosis. This case highlights lung cancer should not be overlooked in patients with persistent respiratory symptoms of unknown etiology. Early imaging examinations, along with necessary pathological evaluations, are crucial for timely detection and diagnosis. The presence of pulmonary lymphangitic carcinomatosis often indicates an advanced-stage of cancer, associated with a poor prognosis.
Humans ; Male ; Lung Neoplasms/complications* ; Child ; Carcinoma/drug therapy*

OBJECTIVES: To investigate the prognostic value of molecular minimal residual disease (Mol-MRD) monitored before and after allogeneic hematopoietic stem cell transplantation (HSCT) in pediatric acute myeloid leukemia (AML). METHODS: Clinical data of 71 pediatric AML patients who underwent HSCT between August 2016 and December 2023 were analyzed. Mol-MRD levels were dynamically monitored in MRD-positive patients, and survival outcomes were evaluated. RESULTS: No significant difference in the 3-year overall survival (OS) rate was observed between patients with pre-HSCT Mol-MRD ≥0.01% and <0.01% (77.3% ± 8.9% vs 80.4% ± 7.9%, P=0.705). However, patients with pre-HSCT Mol-MRD <1.75% had a significantly higher 3-year OS rate than those with Mol-MRD ≥1.75% (86.6% ± 5.6% vs 44.4% ± 16.6%, P=0.020). The median Mol-MRD level in long-term survivors was significantly lower than in non-survivors [0.61% (range: 0.04%-51.58%)] vs 10.60% (range: 1.90%-19.75%), P=0.035]. Concurrent flow cytometry-based MRD positivity was significantly higher in non-survivors (80% vs 24%, P=0.039). There was no significant difference in the 3-year overall survival rate between patients with Mol-MRD ≥0.01% and those with <0.01% at 30 days post-HSCT (P=0.527). For children with Mol-MRD <0.22% at 30 days post-HSCT, the 3-year overall survival rate was 80.4% ± 5.9%, showing no significant difference compared to those with molecular negativity (87.0% ± 7.0%) (P=0.523). CONCLUSIONS Patients with pre-HSCT Mol-MRD <1.75% or post-HSCT Mol-MRD <0.22% may achieve long-term survival outcomes comparable to Mol-MRD-negative cases through HSCT and targeted interventions.
Humans ; Hematopoietic Stem Cell Transplantation ; Neoplasm, Residual ; Leukemia, Myeloid, Acute/genetics* ; Child ; Male ; Female ; Child, Preschool ; Prognosis ; Adolescent ; Infant ; Transplantation, Homologous

OBJECTIVE: To investigate the effect of zedoarondiol on neovascularization of atherosclerotic (AS) plaque by exosomes experiment. METHODS: ApoE^-/- mice were fed with high-fat diet to establish AS model and treated with high- and low-dose (10, 5 mg/kg daily) of zedoarondiol, respectively. After 14 weeks, the expressions of anti-angiogenic protein thrombospondin 1 (THBS-1) and its receptor CD36 in plaques, as well as platelet activation rate and exosome-derived miR-let-7a were detected. Then, zedoarondiol was used to intervene in platelets in vitro, and miR-let-7a was detected in platelet-derived exosomes (Pexo). Finally, human umbilical vein endothelial cells (HUVECs) were transfected with miR-let-7a mimics and treated with Pexo to observe the effect of miR-let-7a in Pexo on tube formation. RESULTS: Animal experiments showed that after treating with zedoarondiol, the neovascularization density in plaques of AS mice was significantly reduced, THBS-1 and CD36 increased, the platelet activation rate was markedly reduced, and the miR-let-7a level in Pexo was reduced (P<0.01). In vitro experiments, the platelet activation rate and miR-let-7a levels in Pexo were significantly reduced after zedoarondiol's intervention. Cell experiments showed that after Pexo's intervention, the tube length increased, and the transfection of miR-let-7a minics further increased the tube length of cells, while reducing the expressions of THBS-1 and CD36. CONCLUSION Zedoarondiol has the effect of inhibiting neovascularization within plaque in AS mice, and its mechanism may be potentially related to inhibiting platelet activation and reducing the Pexo-derived miRNA-let-7a level.
Animals ; MicroRNAs/genetics* ; Exosomes/drug effects* ; Plaque, Atherosclerotic/genetics* ; Neovascularization, Pathologic/genetics* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Blood Platelets/drug effects* ; Apolipoproteins E/deficiency* ; Thrombospondin 1/metabolism* ; CD36 Antigens/metabolism* ; Platelet Activation/drug effects* ; Male ; Mice ; Mice, Inbred C57BL