Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

To review the randomized controlled trials (RCTs) at home and abroad on digital intelligence (DI)-driven rehabilitation in patients of neuromuscular disease, compare the effects of DI-driven rehabilitation with traditional rehabilitation, summarize the special needs and challenges faced by patients in rehabilitation of rare neuromuscular diseases, and provide evidence for the development and quality improvement of rehabilitation for rare neuromuscular diseases.

To provide references to and give suggestions to the development and optimiza-tion of Digital Intelligence (DI) technology in management of non-hospitalized patients by systematical review the application of digital technology in non-hospital settings.

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male

Mechanical pain is one of the most common causes of clinical pain, but there remains a lack of effective treatment for debilitating mechanical and chronic forms of neuropathic pain. Recently, neurotoxin GsMTx4, a selective mechanosensitive (MS) channel inhibitor, has been found to be effective, while the underlying mechanism remains elusive. Here, with multiple rodent pain models, we demonstrated that a GsMTx4-based 17-residue peptide, which we call P10581, was able to reduce mechanical hyperalgesia and neuropathic pain. The analgesic effects of P10581 can be as strong as morphine but is not toxic in animal models. The anti-hyperalgesic effect of the peptide was resistant to naloxone (an μ-opioid receptor antagonist) and showed no side effects of morphine, including tolerance, motor impairment, and conditioned place preference. Pharmacological inhibition of TRPV4 by P10581 in a heterogeneous expression system, combined with the use of Trpv4 knockout mice indicates that TRPV4 channels may act as the potential target for the analgesic effect of P10581. Our study identified a potential drug for curing mechanical pain and exposed its mechanism.

The ambiguity of etiology makes temporomandibular joint osteoarthritis (TMJOA) "difficult-to-treat". Emerging evidence underscores the therapeutic promise of exosomes in osteoarthritis management. Nonetheless, challenges such as low yields and insignificant efficacy of current exosome therapies necessitate significant advances. Addressing lower strontium (Sr) levels in arthritic synovial microenvironment, we studied the effect of Sr element on exosomes and miRNA selectively loading in synovial mesenchymal stem cells (SMSCs). Here, we developed an optimized system that boosts the yield of SMSC-derived exosomes (SMSC-EXOs) and improves their miRNA profiles with an elevated proportion of beneficial miRNAs, while reducing harmful ones by pretreating SMSCs with Sr. Compared to untreated SMSC-EXOs, Sr-pretreated SMSC-derived exosomes (Sr-SMSC-EXOs) demonstrated superior therapeutic efficacy by mitigating chondrocyte ferroptosis and reducing osteoclast-mediated joint pain in TMJOA. Our results illustrate Alix's crucial role in Sr-triggered miRNA loading, identifying miR-143-3p as a key anti-TMJOA exosomal component. Interestingly, this system is specifically oriented towards synovium-derived stem cells. The insight into trace element-driven, site-specific miRNA selectively loading in SMSC-EXOs proposes a promising therapeutic enhancement strategy for TMJOA.
MicroRNAs/metabolism* ; Mesenchymal Stem Cells/drug effects* ; Osteoarthritis/drug therapy* ; Exosomes/drug effects* ; Strontium/pharmacology* ; Synovial Membrane/cytology* ; Humans ; Animals ; Temporomandibular Joint Disorders/therapy* ; Temporomandibular Joint

Seven novel linear polyketides,talaketides A-G(1-7),were isolated from the rice media cultures of the mangrove sed-iment-derived fungus Talaromyces sp.SCSIO 41027.Among these,talaketides A-E(1-5)represented unprecedented unsaturated lin-ear polyketides with an epoxy ring structure.The structures,including absolute configurations of these compounds,were elucidated through detailed analyses of nuclear magnetic resonance(NMR)and high-resolution mass spectrometry(HR-MS)data,as well as elec-tronic custom distributors(ECD)calculations.In the cytotoxicity screening against prostate cancer cell lines,talaketide E(5)demon-strated a dose-dependent inhibitory effect on prostate cancer PC-3 cell lines,with an IC50 value of 14.44 μmol·L-1.Moreover,com-pound 5 significantly inhibited the cloning formation of PC-3 cell lines and arrested the cell cycle in S-phase,ultimately inducing ap-optosis.These findings indicate that compound 5 may serve as a promising lead compound for the development of a potential treat-ment for prostate cancer.

Objective To investigate the expression and role of neutrophil extracellular traps(NETs)in oral squamous cell carcinoma(OSCC).Methods Immunofluorescence and immunohistochemistry were used to detect the expression of NETs in OSCC.The clinico-pathological characteristics and prognosis of patients with different NETs expression levels were analyzed.Density gradient centrifugation was used to isolate neutrophils from human peripheral blood.NETs were induced by phorbol 12-myristate 13-acetate(PMA)and co-cultured with OSCC cells.CCK-8 assay was used to detect changes in OSCC cell proliferation ability.OSCC cell migration ability was detected by Transwell cell migration assay and cell scratch assay.Western blotting(WB)assay was used to detect the effect of NETs on the index of epithelial-mesenchymal transition(EMT).Results NETs'expression in OSCC was higher than that in normal tissues(P＜0.001).The prognosis of patients with high NETs expression was worse than that of patients with low NETs expression(P＜0.05).The expression level of NETs was correlated with the clinical grade,invasion and recurrence degree of OSCC patients(P＜0.05).NETs pro-moted the proliferation and migration of Cal27 and HN6 cells(P＜0.05),and inhibited the protein expression level of epithelial marker and promoted the protein expression level of mesenchymal markers(P＜0.05),which could be reversed by the NETs inhibitor DNaseⅠ.Conclusion NETs are expressed at high levels in OSCC.NETs can promote the proliferation and migration of OSCC cells by regulating epithelial-mesenchymal transition and can affect the prognosis of OSCC patients.

Schistosomiasis was once hyper-endemic in Yunnan Province. Following concerted efforts for over 70 years, remarkable achievements have been made for schistosomiasis control in the province. In 2004, the Mid- and Long-term Plan for Schistosomiasis Prevention and Control in Yunnan Province was initiated in Yunnan Province, and the target for transmission control of schistosomiasis was achieved in the province in 2009. Following the subsequent implementation of the Outline for Key Projects in Integrated Schistosomiasis Control Program (2009—2015) and the 13th Five - year Plan for Schistosomiasis Control in Yunnan Province, no acute schistosomiasis had been identified in Yunnan Province for successive 12 years, and no local Schistosoma japonicum infections had been detected in humans, animals or Oncomelania hupensis snails for successive 6 years in the province by the end of 2020. The transmission of schistosomiasis was interrupted in Yunnan Province in 2020. This review summarizes the history of schistosomiasis, changes in schistosomiasis prevalence and progress of schistosomiasis control in Yunnan Province, and proposes the future priorities for schistosomiasis control in the province.

Quantum dots are an emerging semiconductor nanocrystalline material with optical and electronic properties. Quantum dots in the environment are transmitted into human body mainly via digestive system. Quantum dots in the workplace are transmitted into human body via various pathways such as the respiratory tract and mucocutaneous surface. They cross the blood brain barrier to affect the nervous system, eventually leading to irreversible damage. Quantum dots have more complex biological toxicity than ordinary nanomaterials and metal ions, due to their core-shell structure and surface modifiers. The physicochemical properties of quantum dots, such as particle size, surface modification, and charge, greatly influence their neurotoxic effects. Quantum dots can induce oxidative stress and inflammatory responses, causing neuronal damage and functional impairment, thereby affecting learning, memory, and synaptic plasticity. Its mechanisms may be the modulation of signaling pathways such as the mitogen-activated protein kinase/extracellular signal regulated kinase pathway. In the future, the neurotoxicity of different types of quantum dots should be systematically analyzed, and multi-omics methods should be used to elucidate the mechanism of quantum dots on neuronal damage to improve the safety of workers exposed to quantum dots.