Abstract: This study explores optimization strategies for Non-Invasive Prenatal Testing (NIPT) timing and fetal anomaly detection through data-driven modeling. By analyzing over 1,600 NIPT cases, the research identifies maternal BMI and gestational age as major determinants of fetal fraction, a key factor influencing test accuracy. For male fetuses, Y chromosome concentration showed a positive correlation with gestational age (r=0.45) and a negative correlation with BMI (r=–0.32). An XGBoost regression model achieved robust performance (R²>0.7), highlighting weight and height as significant predictors. For female fetuses, a probabilistic model integrating Z-scores, GC content, read proportions, and BMI achieved >95% accuracy and <5% false-positive rate in detecting trisomies 21, 18, and 13. The study further proposes BMI-stratified testing windows—12, 14, and 16 weeks for low-, medium-, and high-BMI groups—to ensure sufficient fetal DNA concentration. These results emphasize the necessity of personalized NIPT timing and model-based optimization to reduce false negatives and enhance detection efficiency. Future integration of AI-driven prediction systems and hybrid cell-based testing may enable real-time, individualized prenatal screening with higher clinical applicability. Read More

Abstract: Based on big data analytics and machine learning methods, this study develops a series of prediction and prevention models for three major diseases: cardiovascular disease, stroke, and cirrhosis. The datasets underwent systematic preprocessing, including missing value imputation, outlier removal, and categorical variable encoding. Associations between features and diseases were thoroughly examined using chi-square tests and visualization tools such as boxplots and correlation heatmaps, identifying significant factors such as smoking status, ST segment slope, and presence of edema. Linear Discriminant Analysis (LDA) was employed for feature reduction, and a backpropagation neural network optimized by a genetic algorithm (GA-BP) was constructed for disease prediction. Test results demonstrated high predictive accuracy, reaching 95.3% for stroke, 69.9% for heart disease, and 68.5% for cirrhosis, indicating robust model performance. Furthermore, random forest algorithms were applied to analyze disease comorbidity probabilities, revealing mechanisms of shared risk factors. Sensitivity analysis was conducted to identify key features influencing model outputs. Based on the findings, several optimized prevention strategies are proposed to the World Health Organization, providing a theoretical foundation and methodological support for precise prediction and scientific management of major diseases. Read More

Abstract: To further enhance the accuracy of non-invasive prenatal testing (NIPT) technology, this study analysed the correlation between NIPT results and factors such as gestational age and maternal body mass index (BMI), based on real-world data from 1,082 male fetuses and 605 female fetuses in China. First, Spearman's correlation coefficient analysis was conducted, revealing strong correlations between Y chromosome concentration and gestational age, BMI, and X chromosome concentration. Subsequently, K-means clustering revealed a distribution relationship between the optimal NIPT testing time point and different BMI intervals among pregnant women carrying male fetuses. Furthermore, a comprehensive BMI grouping model was developed by integrating multivariate clustering with optimization of the mean Y chromosome concentration. Constrained by a post-clustering Y-chromosome concentration mean exceeding 0.65% within each group and inter-group mean differences below 0.1%, the comprehensive BMI grouping results were: (20.70, 30.21], (30.21, 31.81], (31.81, 33.93], and (33.93, 46.88]. The corresponding optimal NIPT timepoints were 12.14, 13.28, 14.35, and 14.60 weeks respectively. Read More

Abstract: To address the issue that unreasonable timing of non-invasive prenatal testing (NIPT) increases detection risks, this study constructed a risk optimization model to determine the optimal NIPT time window for male fetuses. Taking gestational age and BMI as optimization variables, the study integrated the time-related risk from exponential fitting and the accuracy-related risk from density estimation, determined weights using the Analytic Hierarchy Process (AHP), and further established a total risk function. Pregnant women were divided into four groups by BMI via K-means clustering, and the Particle Swarm Optimization (PSO) algorithm was employed to solve the model. The results showed that: the optimal gestational ages corresponding to BMI ranges [26.6, 30.2), [30.2, 33.8), [33.8, 37.5), and [37.5, 46.9] were 11.85 weeks, 13.22 weeks, 15.78 weeks, and 19.33 weeks, respectively, indicating that the higher the BMI, the later the optimal detection time. Finally, an analysis of ±5% and ±10% concentration errors revealed that negative errors delay the optimal time point and reduce risks, while positive errors advance the time point and increase risks—with the high BMI groups being more significantly affected. This study provides a basis for personalized clinical testing. Read More

Abstract: This study aims to quantify the association between fetal Y chromosome concentration and key indicators such as maternal gestational age and body mass index to inform non-invasive prenatal testing strategies. First, fetal testing data underwent rigorous preprocessing, including exclusion of concentrations below 4%, removal of missing and outlier values, and conversion of gestational age formats to continuous weeks suitable for continuous numerical variables. Exploratory data analysis revealed a wide range of fetal Y chromosome concentration values at identical or similar gestational ages, indicating stratified clustering and nested structures—significant inter-individual variation among pregnant women. Consequently, a linear mixed-effects model with random intercepts was constructed, incorporating gestational age, BMI, and their interaction term as fixed effects, while treating individual variation as random intercepts. The model fitted 541 observations from 236 pregnant women. The random intercept variance was significantly non-zero, confirming substantial individual variation influencing concentration variability. Significance tests for fixed effects revealed a significant negative main effect of BMI on Y chromosome concentration, along with a significant interaction effect between gestational week and BMI, confirming that BMI amplifies the influence of gestational week on Y chromosome concentration. The model achieved a conditional coefficient of determination of 0.725, but the marginal coefficient of determination was only 0.117, suggesting the need to incorporate additional important fixed effect factors. Read More

Abstract: Addressing the risks faced by high-BMI pregnant women in non-invasive prenatal testing (NIPT)—namely delayed attainment of sufficient fetal cell-free DNA (cfDNA) concentration and missed optimal intervention windows—this study establishes a data-driven system for optimizing testing timepoints. The research first preprocesses raw samples by converting gestational age into continuous numerical variables and develops evaluation metrics encompassing clinical intervention risks and testing accuracy. To address the limitation of static grouping in distinguishing individual differences, the K-means clustering algorithm was employed to reclassify pregnant women into five mutually exclusive clusters based on BMI characteristics. Kaplan-Meier survival analysis was then applied to reveal the temporal evolution of Y chromosome concentration attainment rates across different BMI levels. Building upon this foundation, a nonlinear optimization model was constructed to balance sequencing failure risk and missed therapeutic window risk. By minimizing total risk and sequencing error, optimal detection timepoints were determined for each group. Validation results demonstrated that the optimal detection timepoint for the high-BMI group was significantly delayed compared to the low-BMI group, with a maximum deviation of up to 4 weeks. Finally, sensitivity analysis via Monte Carlo simulation confirmed the model's robustness to sequencing perturbations Read More

Abstract: With the continuous growth of the world's population, ensuring the health of the fetus has become a top priority. To optimize the timing selection of Non-Invasive Prenatal Testing (NIPT) and the strategy for determining fetal abnormalities, this paper constructs multiple models based on medical clinical knowledge and solves for the best NIPT under different circumstances. To explore whether the BMI of pregnant women affects the accuracy of NIPT, the correlation between the concentration of fetal Y chromosome, gestational age of pregnant women, and BMI is first analyzed visually through a correlation heatmap. Then, a generalized additive model is constructed, including single models of BMI, gestational age, and Y chromosome concentration, as well as an interaction model of BMI, gestational age, and Y chromosome concentration to obtain the correlation model. The model is solved using algorithms such as PIRLS. Through analysis, it is found that the concentration of Y chromosome is positively correlated with gestational age and weakly negatively correlated with BMI; both gestational age and BMI have statistical significance with Y chromosome concentration. To further focus on the BMI grouping of pregnant women carrying male fetuses and analyze the impact of different BMIs on the time to reach the Y chromosome concentration standard (>4%), avoiding subjective grouping errors and achieving the goal of "minimizing potential risks", the clustering analysis method is used to divide the BMI intervals with significant differences in the time to reach the Y chromosome concentration standard. Through the analysis of the interference of detection errors on the determination of the standard time, the best NIPT time points for each group are matched. The BMI grouping strategy based on clustering analysis and the matched best NIPT time points can significantly improve the detection accuracy, reduce the potential risks of pregnancy, and keep the detection errors controllable, … Read More

Abstract: Against the backdrop of the deepening of the national fitness strategy, the enthusiasm for participation in public fitness continues to rise, and the size of the sports population is expanding year by year. However, the problem of sports injuries is also becoming increasingly prominent, which not only affects the continuity of fitness plans, but some acute injuries may also cause irreversible joint function impairment, becoming an important obstacle to the high-quality development of national fitness. Based on the clinical practice results and authoritative monitoring data of sports medicine, this article clarifies the core principles of sports injury prevention in fitness activities, systematically sorts out the practical paths of key dimensions such as individual adaptation assessment, scientific exercise planning, and standardized technical execution, and addresses the differentiated protection needs of the ordinary fitness population and special populations such as the elderly and those with chronic diseases. Related studies have confirmed that strict implementation of scientific prevention measures can reduce the incidence of fitness-related sports injuries by over 40%. The research results of this article aim to provide practical preventive guidance for the fitness population and professional instructors, as well as provide reference for fitness institutions to develop safety training standards, and facilitate the safe and effective development of national fitness activities. Read More

Abstract: Knee osteoarthritis (OA) is a common disease, especially among the elderly, and poses a serious global health burden. This review aims to analyze the clinical efficacy of aquatic exercise and other physical therapies in the treatment of knee osteoarthritis, focusing on its mechanism of action, efficacy and future prospects. The review emphasizes the role of aquatic exercise. Due to the buoyancy and resistance of water, water exercise provides a low-impact but effective treatment that can reduce pain, improve function and enhance joint stability. This review compares water exercise with other non-drug treatments, such as land-based exercise, manual therapy and electrotherapy, to evaluate their biomechanical and physiological effects. Although clinical research results generally support the value of water therapy, its benefit mechanism and its impact on joint load, muscle activation and exercise efficiency are not fully clarified. Filling these research gaps is crucial to the development of more personalized and sustainable rehabilitation programs. This review further explores the potential role of emerging technologies, including wearable sensors and digital monitoring tools, in promoting precise rehabilitation. In addition, this article also discusses the future development direction, emphasizes the importance of long-term clinical trials, and the necessity of combining biomechanics and biomedical engineering to improve and optimize the physical therapy strategy for patients with knee osteoarthritis. Read More

Abstract: Objective: To express and purify the recombinant fusion protein CAT SYI of Streptococcus mutans, and to provide basic materials for subsequent immunogenicity research and the development of anti caries vaccines. Methods: The recombinant plasmid pET32a CAT SYI constructed previously in our laboratory was transformed into Escherichia coli DH5α for amplification. After PCR verification, the correctly identified plasmid was transformed into the expression strain BL21. The expression of the recombinant fusion protein was induced by IPTG, and the induction conditions including IPTG concentration (0.25-1.5mM), induction time (0-10h) and induction temperature (26℃,37℃) were optimized. The bacterial cells were disrupted by ultrasonication, and the recombinant protein was purified and refolded by urea gradient dialysis. The expression and purification efficiency were analyzed by SDS PAGE. Results: PCR identification confirmed that the recombinant plasmid pET32a CAT SYI was successfully constructed, with an amplified fragment of approximately 750bp. SDS PAGE analysis showed that the recombinant fusion protein was successfully expressed with a molecular weight of about 50kDa, which was consistent with the expected value. The optimal induction conditions were determined as follows: final IPTG concentration of 1.25mM, induction time of 6 h, and induction temperature of 37℃. Soluble target protein was obtained after dialysis and refolding of the purified recombinant protein. Conclusion: The high efficiency expression of the recombinant fusion protein CAT SYI from Streptococcus mutans in Escherichia coli was successfully achieved, and the corresponding induction optimization and purification methods were established. These results lay an experimental foundation for the subsequent evaluation of immune protective efficacy and the research and development of anti caries vaccines. Read More

Abstract: The core dilemma of cancer treatment is to achieve tumor targeted delivery of chemotherapy drugs, reduce toxic and side effects on normal tissues, and improve the enrichment and activation efficiency of drugs in tumor sites. As a kind of oxidoreductase with high specific expression in tumor tissues and low or no expression in normal tissues, it has become an ideal target for the design of prodrug systems. This paper systematically reviews the types, characteristics and expression mechanism of base reducing enzymes in tumor tissues, describes in detail the design principle, structural types and latest research progress of these prodrug systems, deeply analyzes their application advantages and potential in tumor treatment, and discusses the current research bottle neck, so as to provide comprehensive theoretical reference and practical guidance for follow-up research in this field. Read More

Abstract: Glaucoma is an irreversible disease that seriously threatens vision. Chronic disease management involves continuous treatment, regular follow-up, and lifestyle interventions, which place high demands on patient compliance and initiative. With the development of digital technology, digital technology represented by applications, wearable sensor technology and the Internet has been widely used in the behavior management and disease prevention of glaucoma patients, such as taking medicine, eye pressure control, education and doctor patient interaction. In summary, this article attempts to summarize the current research results on the application of digital health tools in the self-management of glaucoma patients at home and abroad, while elaborating on the concept and importance of patient self-management efficacy, in order to explore whether the application of digital health tools affects patients' self-management efficacy and promote glaucoma patients to better complete disease self-management work. However, the degree of correlation between the two variables varies due to various factors such as tool category, tool attributes, user characteristics, and medical service environment. Therefore, this article proposes improvement plans including personalized tool matching suggestions, tool design that focuses on user needs, creating a multi-party cooperation platform, and implementing continuous tracking feedback to enhance the effectiveness of mobile health management, ultimately improving the long-term management quality and quality of life of glaucoma patients. Read More