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*

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.

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*

This study was aimed at evaluating the characteristics of H9N2 subtype avian influenza viruses(AIVs)origina-ting from wild birds in major wetlands in Fujian.Five H9N2 subtype AIVs isolated from fecal samples from wild birds in wet-lands of the Minjiang River,Jiulong River,Sandu Bay,Xinghua Bay,and Quanzhou Bay in Fujian were sequenced.Sequence a-nalysis of the HA genes of the five H9N2 subtype AIVs indicated that the five isolates shared 89.8％-99.4％nucleotide se-quence identity.All five isolates belonged to the same h9.4.2.5c evolutionary branch.The cleavage site motifs of HA were all PSRSSR ↓ GLF,thus indicating molecular characteristics of AIVs with low pathogenicity.The HA proteins of the viruses orig-inating from wild birds bore eight identical potential glycosylation sites,among which the glycosylation site at position 313 was located near the HA protein cleavage site.The 226th amino acid of HA in the receptor binding site was leucine in each virus,thus indicating that HAs of the five H9N2 subtype AIVs had mammalian sialic acid α-2,6 receptor binding affinity.In conclu-sion,the five H9N2 subtype AIVs originating from wild birds in Fujian had low pathogenicity,and the HAs had mammalian sialic acid α-2,6 receptor binding affinity.

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.

This study was aimed at evaluating the characteristics of H9N2 subtype avian influenza viruses(AIVs)origina-ting from wild birds in major wetlands in Fujian.Five H9N2 subtype AIVs isolated from fecal samples from wild birds in wet-lands of the Minjiang River,Jiulong River,Sandu Bay,Xinghua Bay,and Quanzhou Bay in Fujian were sequenced.Sequence a-nalysis of the HA genes of the five H9N2 subtype AIVs indicated that the five isolates shared 89.8％-99.4％nucleotide se-quence identity.All five isolates belonged to the same h9.4.2.5c evolutionary branch.The cleavage site motifs of HA were all PSRSSR ↓ GLF,thus indicating molecular characteristics of AIVs with low pathogenicity.The HA proteins of the viruses orig-inating from wild birds bore eight identical potential glycosylation sites,among which the glycosylation site at position 313 was located near the HA protein cleavage site.The 226th amino acid of HA in the receptor binding site was leucine in each virus,thus indicating that HAs of the five H9N2 subtype AIVs had mammalian sialic acid α-2,6 receptor binding affinity.In conclu-sion,the five H9N2 subtype AIVs originating from wild birds in Fujian had low pathogenicity,and the HAs had mammalian sialic acid α-2,6 receptor binding affinity.

OBJECTIVE: To determine the role of Tripterygium wilfordii multiglycoside (TGW) in the treatment of psoriatic dermatitis from a cellular immunological perspective. METHODS: Mouse models of psoriatic dermatitis were established by imiquimod (IMQ). Twelve male BALB/c mice were assigned to IMQ or IMQ₊TGW groups according to a random number table. Histopathological changes in vivo were assessed by hematoxylin and eosin staining. Ratios of immune cells and cytokines in mice, as well as PAM212 cell proliferation in vitro were assessed by flow cytometry. Pro-inflammatory cytokine expression was determined using reverse transcription quantitative polymerase chain reaction. RESULTS: TGW significantly ameliorated the severity of IMQ-induced psoriasis-like mouse skin lesions and restrained the activation of CD45⁺ cells, neutrophils and T lymphocytes (all P<0.01). Moreover, TGW significantly attenuated keratinocytes (KCs) proliferation and downregulated the mRNA levels of inflammatory cytokines including interleukin (IL)-17A, IL-23, tumor necrosis factor α, and chemokine (C-X-C motif) ligand 1 (P<0.01 or P<0.05). Furthermore, it reduced the number of γ δ T17 cells in skin lesion of mice and draining lymph nodes (P<0.01). CONCLUSIONS TGW improved psoriasis-like inflammation by inhibiting KCs proliferation, as well as the associated immune cells and cytokine expression. It inhibited IL-17 secretion from γ δ T cells, which improved the immune-inflammatory microenvironment of psoriasis.
Male ; Animals ; Mice ; Tripterygium ; Psoriasis/drug therapy* ; Keratinocytes ; Skin Diseases/metabolism* ; Cytokines/metabolism* ; Imiquimod/metabolism* ; Dermatitis/pathology* ; Disease Models, Animal ; Mice, Inbred BALB C ; Skin/metabolism*

OBJECTIVE: This study was aimed at investigating the carrier rate of, and molecular variation in, α- and β-globin gene mutations in Hunan Province. METHODS: We recruited 25,946 individuals attending premarital screening from 42 districts and counties in all 14 cities of Hunan Province. Hematological screening was performed, and molecular parameters were assessed. RESULTS: The overall carrier rate of thalassemia was 7.1%, including 4.83% for α-thalassemia, 2.15% for β-thalassemia, and 0.12% for both α- and β-thalassemia. The highest carrier rate of thalassemia was in Yongzhou (14.57%). The most abundant genotype of α-thalassemia and β-thalassemia was -α ^3.7/αα (50.23%) and β ^IVS-II-654/β ^N (28.23%), respectively. Four α-globin mutations [CD108 (ACC>AAC), CAP +29 (G>C), Hb Agrinio and Hb Cervantes] and six β-globin mutations [CAP +8 (C>T), IVS-II-848 (C>T), -56 (G>C), beta nt-77 (G>C), codon 20/21 (-TGGA) and Hb Knossos] had not previously been identified in China. Furthermore, this study provides the first report of the carrier rates of abnormal hemoglobin variants and α-globin triplication in Hunan Province, which were 0.49% and 1.99%, respectively. CONCLUSION Our study demonstrates the high complexity and diversity of thalassemia gene mutations in the Hunan population. The results should facilitate genetic counselling and the prevention of severe thalassemia in this region.
Humans ; beta-Thalassemia/genetics* ; alpha-Thalassemia/genetics* ; Hemoglobinopathies/genetics* ; China/epidemiology* ; High-Throughput Nucleotide Sequencing

Abstract: Objective　To evaluate the potential transmission risk of schistosomiasis in Yunnan Province, and to provide strategic basis for the prevention and control. Methods　Based on the prevalence of schistosomiasis, the social and environmental factors that may lead to the epidemic, 1-3 villages from 3 provincial-level and 15 county-level counties (cities and districts) were selected as the evaluated villages in 2021. The risk of schistosomiasis spread was analyzed comprehensively by consulting, reviewing and collecting routine surveillance data of schistosomiasis in the villages, combined with snail and wild feces survey. The risk level was evaluated for the positive snails, positive wild feces, resident infection, average density of live snails and snail frame occurrence rate. Results　Totally 7 snail counties schistosomiasis transmission was blocked of 18 epidemic counties and the rest were eliminated counties. A total of 152 447 snail frames were investigated and 3 043 frames with snails, 15 895 snails were captured and included 15 727 live snails in the 32 evaluated villages. The total area of snail was 58.87 hm2 and the area of reoccurrence was 34.19 hm2 with snail frame occurrence rate of 2.00% and average density of live snails 0.103 2/0.11 m2, and no positive snails were found by loop-mediated isothermal amplification (LAMP) assay. A total of 1 374 wild feces were collected in 27 evaluated villages of 14 epidemic counties, mainly from cattle, dogs, sheep, equine animals, pigs and so on, all of which were negative. According to the risk assessment of epidemic spread, Yongle Village and Yongsheng Village in Eryuan County, Zhiming Village in Chuxiong City were Ⅱ risk, and the rest were Ⅲ risk. Conclusions　Although the risk of transmission is low in Yunnan Province, the risk of transmission and spread still exists. It is necessary to strengthen the risk monitoring, control of snail and effective management of livestock to prevent the rebound of the epidemic.

Currently, the gut-organ axis has become a hot research topic. As increasing attention has been paid to the role of gut microbiota in the health of organs, the complex and integrated dialogue mechanism between the gastrointestinal tract and the associated microbiota has been demonstrated in more and more studies. Skin as the largest organ in the human body serves as the primary barrier protecting the human body from damage. The proposal of the gut-skin axis has established a bidirectional link between the gut and the skin. The disturbance of gut microbiota can lead to the occurrence of skin diseases, the mechanism of which is complex and may involve multiple pathways in immunity, metabolism, and internal secretion. According to the theory of traditional Chinese medicine(TCM), the connection between the intestine and the skin can be established through the lung, and the interior disorders will definitely cause symptoms on the exterior. This paper reviews the research progress in the gut-skin axis and its correlation with TCM theory and provides ideas and a basis for cli-nical treatment and drug development of skin and intestinal diseases.
Humans ; Medicine, Chinese Traditional ; Gastrointestinal Tract ; Skin Diseases/drug therapy* ; Gastrointestinal Microbiome