Objective To analyze the infection status and epidemic trends of soil-borne nematode infections in Yubei, Chongqing City, in 2010,2021, and 2022. Methods The local populations from four survey sites of four towns in 2010 and five sites of five towns in 2021 and 2022 were surveyed regarding their basic information using a unified form. Fecal samples of the participants were collected and tested for soil-borne nematode infections using the modified Kato-Katz thick smear method. Results In 2010, 2 049 participants were surveyed, followed by 1 000 participants in 2021 and 2022. The overall prevalence of parasitic infections declined significantly from 3.86% to 0.20%. In 2010, soil-transmitted nematode included hookworms(3.81%) and roundworms(0.29%). In 2021, the infection rates of roundworms and hookworms were 1.70% and 0.10% respectively. Notably, only Ascaris was identified in 2022(0.20%). The≥60 age group consistently exhibited the highest infection rates across all surveys, followed by the 40-59 age group. The infection rates of males in the three surveys were 3.31%,1.92%, and 0.20% respectively, and those of females were 4.37%, 1.46%, and 0.20% respectively. There was no statistically significant difference in the infection rates between males and females. Educational attainment was inversely associated with infection; in 2010, the highest prevalence was observed among those with primary education or below, whereas in 2021, illiterate or semi-literate individuals showed the highest susceptibility. The occupational distribution of infections in 2010 indicated that retirees (8.33%), farmers(4.86%), and homemakers or unemployed individuals(3.45%)were the most affected. However, in 2021 and 2022, farmers emerged as the predominant occupational group with soil-transmitted nematode infections. Conclusions The infection rate of soil-borne nematodes showed a decreasing trend in Yubei, and the infection species changed from hookworms in 2010 to Ascaris in 2022. Farmers, the elderly, and people with low education levels should continue to be the focus of preventive and control efforts.

With the continuous development of society, a large number of new chemicals are continuously emerging, which presents a challenge to current risk assessment and safety management of chemicals. Traditional toxicology research methods have certain limitations in quickly, efficiently, and accurately assessing the toxicity of many chemicals, and cannot meet the actual needs. In response to this challenge, computational toxicology that use mathematical and computer models to achieve the prediction of chemical toxicity has emerged. In the meantime, as researchers increasingly pay attention to understanding the interaction mechanisms between exogenous chemical substances and the body from the system level, and multiomics technologies develop rapidly such as genomics, transcriptomics, proteomics, and metabolomics, huge amounts of data have been generated, providing rich information resources for studying the interactions between chemical substances and biological molecules. System toxicology and network toxicology have also developed accordingly. Of these, network toxicology can integrate these multiomics data to construct biomolecular networks, and then quickly predict the key toxicological targets and pathways of chemicals at the molecular level. This paper outlined the concept and development of network toxicology, summarized the main methods and supporting tools of network toxicology research, expounded the application status of network toxicology in studying potential toxicity of exogenous chemicals such as agricultural chemicals, environmental pollutants, industrial chemicals, and foodborne chemicals, and analyzed the development prospects and limitations of network toxicology research. This paper aimed to provide a reference for the application of network toxicology in other fields.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To evaluate the efficacy and safety of blinatumomab in adult patients with relapsed/refractory(R/R)or measurable residual disease(MRD)positive B-cell acute lymphoblastic leukemia(B-ALL)in the real world.Methods:The clinical data of 30 B-ALL patients received at least 1 course of blinatumomab therapy in the Chinese PLA General Hospital from January 1st,2021 to December 31st,2023 were retrospectively analyzed,including pre-treatment baseline clinical feature,post-treatment complete response(CR),CR with partial hematologic recovery(CRh),CR with incomplete hematologic recovery(CRi),complete MRD response rate,MRD response rate(MRD＜10-4),overall survival(OS),and disease-free survival(DFS),as well as drug-related adverse reactions.Results:Among 5 patients who were not assessed 4 were MRD negative and 1 did not receive bone marrow biopsy.In the R/R B-ALL group(13 cases),11 patients achieved CR/CRh/CRi and 10 patients achieved complete MRD response.In MRD+group(12 cases),9 patients achieved overall MRD response and 7 patients achieved complete MRD response.The median follow-up time was 8.4(95％CI:6.3-10.4)months.The median OS was 15.5(95％CI:0.7-30.3)months in the R/R group,while not reached in the MRD+group.The median DFS of the two groups were not reached.Drug-related adverse reactions occurred in 22 patients,and pyrexia was the most common(13 cases).Grade ≥3 adverse reactions occurred in 15 patients,and neutropenia was the most common(9 cases).Cytokine release syndrome occurred in 6 patients,including 5 cases with grade 1 and 1 case with grade 3.No patients interrupted therapy or died due to drug-related adverse reactions.Conclusion:Blinatumomab is effective in the treatment of R/R or continuous MRD+B-ALL with acceptable adverse reactions.

Objective To verify the efficacy of a domestic human DNA quantification kit based on real-time fluorescence quantitative PCR in detecting the total human DNA concentration,male DNA concen-tration in mixed male/female DNA samples,the degree of DNA degradation and inhibitor tolerance.Methods Samples with different concentrations,different male/female ratios,different concentrations of inhibitors,and different degradation degrees were tested using the domestic human DNA quantification kit based on real-time fluorescence quantitative PCR.This kit was compared with a similar product on the market and was applied to the detection of DNA from real cases.Results This human DNA quan-tification kit can effectively detect human DNA as low as 0.001 65 ng/μL,and 6.25 pg/μL of male DNA in mixed samples with a male-to-female ratio of 1∶15 000.Even when the sample contains as high as 400 ng/μL of humic acid or 1 000 μmol/L of hemin alone,the DNA concentration can still be accurately detected.The degradation index can effectively characterize the degradation degree of the sample.This kit has been successfully applied in forensic practice.Conclusion This human DNA quan-tification kit is accurate and reliable in detection.It can accurately reflect the degradation of DNA and inhibitor tolerance.It has good performance in quantitative accuracy,determination of the male/female ratio in mixed samples,and inhibitor tolerance.It has application potential in forensic case examination.

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

Objective:To evaluate the efficacy and safety of blinatumomab in adult patients with relapsed/refractory(R/R)or measurable residual disease(MRD)positive B-cell acute lymphoblastic leukemia(B-ALL)in the real world.Methods:The clinical data of 30 B-ALL patients received at least 1 course of blinatumomab therapy in the Chinese PLA General Hospital from January 1st,2021 to December 31st,2023 were retrospectively analyzed,including pre-treatment baseline clinical feature,post-treatment complete response(CR),CR with partial hematologic recovery(CRh),CR with incomplete hematologic recovery(CRi),complete MRD response rate,MRD response rate(MRD＜10-4),overall survival(OS),and disease-free survival(DFS),as well as drug-related adverse reactions.Results:Among 5 patients who were not assessed 4 were MRD negative and 1 did not receive bone marrow biopsy.In the R/R B-ALL group(13 cases),11 patients achieved CR/CRh/CRi and 10 patients achieved complete MRD response.In MRD+group(12 cases),9 patients achieved overall MRD response and 7 patients achieved complete MRD response.The median follow-up time was 8.4(95％CI:6.3-10.4)months.The median OS was 15.5(95％CI:0.7-30.3)months in the R/R group,while not reached in the MRD+group.The median DFS of the two groups were not reached.Drug-related adverse reactions occurred in 22 patients,and pyrexia was the most common(13 cases).Grade ≥3 adverse reactions occurred in 15 patients,and neutropenia was the most common(9 cases).Cytokine release syndrome occurred in 6 patients,including 5 cases with grade 1 and 1 case with grade 3.No patients interrupted therapy or died due to drug-related adverse reactions.Conclusion:Blinatumomab is effective in the treatment of R/R or continuous MRD+B-ALL with acceptable adverse reactions.

Aim To investigate the expression levels of cytoplasmic FMR1-interacting protein-1(CYFIP1)in colorectal cancer and assess the impact of CYFIP1 interaction on the proliferation and apoptosis of colorec-tal cancer cell HT29,along with its potential mecha-nisms.Methods Immunohistochemistry was em-ployed to assess CYFIP1 expression in 32 colorectal cancer tissues and adjacent tissues.Coexpressed genes were identified using the GEPIA2 website to predict potential correlations and binding sites.Following the construction of a siRNA-CYFIP1,alterations in cell proliferation,apoptosis,and levels of apoptosis-related proteins were evaluated through CCK-8 assay,Hoechst 33342/PI double staining assay,and Western blot a-nalysis,respectively.Results The immunohisto-chemical findings revealed a significantly elevated level of CYFIP1 expression in colorectal cancer tissues com-pared to paracancer tissues(P＜0.05).The expres-sion of CYFIP1 did not show any correlation with age and gender,but exhibited associations with TNM stage and lymph node metastasis(P＜0.05).A conserved TP53 binding site was predicted in the 3kbps DNA re-gion upstream of the CYFIP1 gene using GEPIA2,JASPAR databases,and rVista 2.0 promoter prediction software.Following transfection of HT29 cells with siRNA-CYFIP1,the clonogenesis and proliferation of cells significantly decreased(P＜0.05).Additional-ly,the levels of cleaved caspase-3 were elevated,while the expression levels of caspase-3 and Bcl-2 were reduced after transfection with siRNA-CYFIP1(P＜0.05),which might be related to the interaction be-tween CYFIP1 and TP53.Conclusions The upregu-lation of CYFIP1 in colorectal cancer is associated with TNM stage and lymph node metastasis.Upon silen-cing,CYFIP1 demonstrates the ability to suppress pro-liferation in HT29 cells and modulate the expression of apoptotic proteins.