This study aims to integrate data mining techniques of single cell transcriptomics and spatial transcriptomics, along with animal experiment validation, so as to systematically characterize the protective effects of Jianpi Tongluo Formula(JTF) on the cartilage in knee osteoarthritis(KOA) and elucidate the underlying molecular mechanisms. Single cell transcriptomics and spatial transcriptomics datasets(GSE254844 and GSE255460) of the cartilage tissue obtained from KOA patients were analyzed to map the single cell-spatial heterogeneity and identify key pathogenic factors. After that, a KOA rat model was established via knee joint injection of papain. The intervention effects of JTF on the expression features of these key factors were assessed through real-time quantitative polymerase chain reaction(PCR), Western blot, and immunohistochemical staining. As a result, the integrated single cell and spatial transcriptomics data identified distinct cell subsets with different pathological changes in different regions of the inflamed cartilage tissue in KOA, and their differentiation trajectories were closely related to the inflammatory fibrosis-like pathological changes of chondrocytes. Accordingly, the expression levels of the two key effect targets, namely nuclear receptor coactivator 4(NCOA4) and high mobility group box 1(HMGB1) were significantly reduced in the articular surface and superficial zone of the inflamed joints when JTF effectively alleviated various pathological changes in KOA rats, thus reversing the abnormal chondrocyte autophagy level, relieving the inflammatory responses and fibrosis-like pathological changes, and promoting the repair of chondrocyte function. Collectively, this study revealed the heterogeneous characteristics and dynamic changes of inflamed cartilage tissue in different regions and different cell subsets in KOA patients. It is worth noting that NCOA4 and HMGB1 were crucial in regulating chondrocyte autophagy and inflammatory reaction, while JTF could reverse the regulation of NCOA4 and HMGB1 and correct the abnormal molecular signal axis in the target cells of the inflamed joints. The research can provide a new research idea and scientific basis for developing a personalized therapeutic schedule targeting the spatiotemporal heterogeneity characteristics of KOA.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Osteoarthritis, Knee/pathology* ; Humans ; Male ; Cartilage, Articular/metabolism* ; Chondrocytes/metabolism* ; Rats, Sprague-Dawley ; Female ; Protective Agents/administration & dosage* ; Single-Cell Analysis ; Middle Aged ; HMGB1 Protein/metabolism*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

As a serine protease,thrombin can convert soluble fibrinogen into insoluble fibrin and plays a pivotal role in the coagulation cascade.Therefore,the accurate quantitative assay of thrombin levels is of great value in the evaluation of coagulation function,clinical screening and prognostic monitoring of coagulation-related diseases,and screening of drugs for targeted therapy.Existing methods for thrombin detection can be divided into two categories,e.g.,the assay of concentration levels using nucleic acid aptamers as the affinity elements and the assay of activity levels based on the hydrolytic cleavage of substrate peptides.In recent years,electrochemical biosensors have attracted much attention in thrombin detection due to high sensitivity,high selectivity,simple instrument,fast response,and good portability.In this review,the latest research progress in methods for electrochemical detection of thrombin was summarized,focusing on the detection principles and the applied signal amplification strategies of related electrochemical biosensors.In addition,the challenges with respect to the practical use of electrochemical thrombin biosensors and the prospects were discussed.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Lithocarpus litseifolius is rich in the chalcones phloridzin and trilobatin, the biosynthesis pathways of which have not been fully demonstrated. Chalcone synthase(CHS) is the first key rate-limiting enzyme in the biosynthesis of flavonoids in plants. To explore the functions of CHS gene family in chalcone synthesis of L. litseifolius, this study screened out two CHS genes(LlCHS1 and LlCHS2) from the transcriptome data of this plant, and then bioinformatics analysis and functional characterization were performed for the two genes. The bioinformatics analysis showed that LlCHS1 and LlCHS2 were acidic hydrophilic stable proteins with no transmembrane domain, composed of 395 and 390 amino acid residues, respectively. Both of them contained the characteristic amino acid sequence "WGVLFGFGPGL" and highly conserved active sites(Cys-164, Phe-215, His-303, and Asn-336) of the CHS family. The phylogenetic tree showed that LlCHS1 shared the same clade with similar genes in Aquilaria sinensis, and LlCHS2 was closely related to similar genes in Malus domestica. Under exogenous addition of phloretic acid, co-expression of LlCHS1 or LlCHS2 with Aa4CL from Aromatoleum aromaticum in Escherichia coli catalyzed the production of phloretin from phloretic acid. This study laid a theoretical foundation for revealing the functions of CHS in plants and provided new enzymatic modules for producing phloretin by synthetic biology.
Acyltransferases/chemistry* ; Phylogeny ; Plant Proteins/chemistry* ; Cloning, Molecular ; Amino Acid Sequence

As a long-standing research focus in electroanalytic chemistry,electrochemical biosensors have attracted immense interest in detection of biological targets such as small molecules,proteins,enzymes,nucleic acids,exosomes,circulating tumor cells (CTCs),bacteria,and viruses. etc. The highly sensitive and selective electrochemical detection of biological targets relies on the use of the specific biorecognition elements (BREs) and efficient signal amplification strategies. In this review,the typically used BREs and amplification strategies in electrochemical biosensing were summarized. On the basis of the interactions between BREs and targets,the enzyme/substrate-based and receptor/ligand-based electrochemical biosensors were first introduced,focusing on the BREs used in the electrochemical detection of nucleic acid targets and non-nucleic acid targets. Then,the representative use of various isothermal nucleic acid amplification technologies,catalytic reactions,functionalized nanocarriers,and polymer materials in amplified electrochemical detection of biological targets were introduced. The challenges with respect to the practical use of electrochemical biosensors and the prospect of electrochemical biosensing were discussed.

Objective:To investigate the expression of heterogeneous nuclear ribonucleoprotein A2B1 (HNRNPA2B1) in human esophageal cancer tissues and its clinical significance.Methods:Single-cell data for esophageal cancer were downloaded from the Gene Expression Omnibus (GEO) database (GSE160269 dataset, last updated on November 29, 2020) to analyze the expression of HNRNPA2B1. Transcriptional sequencing data for esophageal cancer from The Cancer Genome Atlas (TCGA) database, including the fragments per kilobase of transcript per million mapped reads (FPKM) quantitative data (173 samples, consisting of 162 esophageal cancer tissues and 11 adjacent normal tissues), and survival data in the phenotype category were downloaded. Analysis of FPKM quantitative data from the TCGA database for esophageal cancer was performed. The top 250 genes most correlated with HNRNPA2B1 were selected and the R4.3.0 clusterProfiler package was used to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the selected gene set. FPKM quantitative data from the TCGA database for esophageal cancer were imported into the CIBERSORTx website to obtain immune cell abundance scores, and the correlation between HNRNPA2B1 and the degree of immune cell infiltration was analyzed. The clinicopathological data of patients from esophageal cancer tissue microarrays including 114 cases of esophageal squamous cell carcinoma tissues and 66 cases of adjacent normal tissues were collected. The patients underwent surgery from January 2006 to December 2008, and the follow-up period extended until July 2015. Cytokeratin (CK) and HNRNPA2B1 expression in esophageal cancer tissue microarrays were detected by using multi-color immunohistochemical (mIHC) staining, and multispectral tissue imaging was conducted. The R4.3.0 survival package and survminer package in TCGA database were used to calculate the optimal cut-off value of HNRNPA2B1 expression and the proportion of CK + HNRNPA2B1 + cells in tissue microarrays was used to calculate the cut-off value of HNRNPA2B1 expression based on which patients were categorized into high and low expression groups. The overall survival (OS) of both groups was compared and the factors influencing OS were analyzed by using the Cox proportional hazards model. Results:In the GSE160269 dataset of single-cell data for esophageal cancer, the expression level of HNRNPA2B1 in tumor epithelial cells was higher than that in normal epithelial cells, and HNRNPA2B1 was highly expressed in various immune cell subtypes. The high expression level of HNRNPA2B1 was positively correlated with regulatory T cells, naive B cells and memory CD4 + T cells. GO enrichment analysis revealed that HNRNPA2B1 was primarily involved in the biological process of nuclear division, cellular components were mainly enriched in chromosomal regions, and molecular functions were mainly enriched in ATP hydrolysis activity. KEGG enrichment analysis indicated that HNRNPA2B1 was primarily involved in biological processes such as the cell cycle, spliceosome, and DNA replication. Results from mIHC and multispectral tissue imaging demonstrated that CK was predominantly expressed in the cell membranes of tumor cells and normal esophageal epithelial cells, while HNRNPA2B1 was primarily expressed in the nuclei of tumor cells and normal esophageal cells. The expression level of HNRNPA2B1 in esophageal cancer tissues was higher than that in the normal paracancerous tissues ( U = 2 984.00, P < 0.05). Results of tissue microarrays and the survival analysis on the data in the TCGA database indicated that esophageal cancer patients with low HNRNPA2B1 expression had a better OS compared to those with high expression (both P < 0.05). Cox multivariate regression analysis revealed that age ( HR = 1.919, 95% CI: 1.158-3.182, P = 0.011), TNM stage ( HR = 2.404, 95% CI: 1.374-4.207, P = 0.002), T stage ( HR = 2.349, 95% CI: 1.150-4.789, P = 0.019), and the expression of HNRNPA2B1 in tumor epithelial cells ( HR = 2.160, 95% CI: 1.280-3.647, P = 0.004) were independent factors influencing OS in esophageal cancer patients. Conclusions:The high expression of HNRNPA2B1 protein in esophageal cancer tissues may play a role in the developement and progression of esophageal cancer, serving as a crucial biological indicator for prognostic assessment of esophageal cancer.

Humans ; Female ; Fibrocystic Breast Disease/surgery* ; Carcinoma

Objective: To evaluate the efficacy and safety of fecal microbiota transplantation (FMT) in the treatment of autism spectrum disorder (ASD). Methods: A longitudinal study was conducted. Clinical data from ASD patients with gastrointestinal symptoms and who underwent FMT in the Tenth People's Hospital affiliated to Tongji University or Jinling Hospital between May 2012 to May 2021 were retrospectively collected. Scores derived from the autism behavior checklist (ABC), the childhood autism rating scale (CARS), the Bristol stool form scale (BSFS), and the gastrointestinal symptom rating scale (GSRS) were analyzed at baseline and at the 1st, 3rd, 6th, 12th, 24th, 36th, 48th and 60th month after FMT. Records of any adverse reactions were collected. Generalized estimating equations were used for analysis of data on time points before and after FMT. Results: A total of 328 patients met the inclusion criteria for this study. Their mean age was 6.1±3.4 years old. The cohort included 271 boys and 57 girls. The percentage of patients remaining in the study for post-treatment follow-up at the 1st, 3rd, 12th, 24th, 36th, 48th and 60th month were as follows: 303 (92.4%), 284 (86.7%), 213 (64.9%), 190 (57.9%), 143 (43.6%), 79 (24.1%), 46 (14.0%), 31 (9.5%). After FMT, the average ABC score was significantly improved in the first 36 months and remained improved at the 48th month. However, the average score was not significantly different from baseline by the 60th month (1st-36th month, P<0.001; 48th month, P=0.008; 60th month, P=0.108). The average CARS score improved significantly during the first 48 months and remained improved at the 60th month (1st-48th month, P<0.001; 60th month, P=0.010). The average BSFS score was also significantly improved in the first 36 months (with an accompanying stool morphology that resembled type 4). This improvement was maintained at the 48th month. However, the average score was similar to baseline at the 60th month (1st-36th month, P<0.001; 48th month, P=0.008; 60th month, P=0.109). The average GSRS score was significantly improved during the first 24 months, but not afterwards (1st-24th month, P<0.001; 36th month, P=0.209; 48th month, P=0.996; 60th month, P=0.668). The adverse events recorded during treatment included abdominal distension in 21 cases (6.4%), nausea in 14 cases (4.3%), vomiting in 9 cases (2.7%), abdominal pain in 15 cases (4.6%), diarrhea in 18 cases (5.5%), fever in 13 cases (4.0%), and excitement in 24 cases (7.3%). All adverse reactions were mild to moderate and improved immediately after suspension of FMT or on treatment of symptoms. No serious adverse reactions occurred. Conclusion: FMT has satisfactory long-term efficacy and safety for the treatment of ASD with gastrointestinal symptoms.
Autism Spectrum Disorder/therapy* ; Child ; Child, Preschool ; Fecal Microbiota Transplantation/adverse effects* ; Feces ; Female ; Gastrointestinal Diseases ; Humans ; Longitudinal Studies ; Male ; Retrospective Studies