Objective: To compare the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) model, the Prophet model, and the Bayesian structural time series (BSTS) model in forecasting the incidence of hand, foot, and mouth disease (HFMD) , so as to provide a basis for optimizing the early warning system of this disease. Methods: Weekly incidence data of HFMD in Longgang District, Shenzhen City from 2014 to 2024 were collected. The HFMD incidence data from 2014-2019 and 2023 were used as the training set to construct SARIMA, Prophet, and BSTS models, while the data from 2024 were used as the test set to compare and evaluate the predictive performance of the three models. The technique for order preference by similarity to ideal solution (TOPSIS) method was employed to calculate the C-value. This approach integrates multiple evaluation metrics, such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE), to comprehensively assess model performance. Results: A total of 150 111 cases of HFMD were reported in Longgang District from 2014 to 2024, with an average annual incidence of 400.72/105. The weekly incidence fluctuated between 0 and 63.78/105, exhibiting a bimodal seasonal pattern characterized by a primary peak from May to July and a secondary peak from September to October. In the training set, all three models demonstrated a good fit to the bimodal epidemic trend of HFMD, with the BSTS model achieving the best fit. The BSTS model yielded performance metrics as follows: MAE=0.124, MSE=0.050, RMSE=0.223, SMAPE=0.021, and a C-value of 1.000. In the test set, all three models, including SARIMA, Prophet, and BSTS, performed well for short-term predictions (≤16 weeks), with the Prophet model showing relatively superior predictive performance. However, the prediction accuracy of all models declined as the forecast horizon extended. During the primary peak period (May-July), the Prophet model exhibited better predictive performance, whereas the BSTS model performed relatively better during the secondary peak period (September-October). Conclusions For the short-term forecasting of weekly HFMD incidence, the Prophet model outperformed both the SARIMA and BSTS models. During the primary peak period, the Prophet model demonstrated superior predictive performance, whereas the BSTS model exhibited better accuracy in forecasting the secondary peak period.

The production of high-quality oocytes requires precisely orchestrated intercellular communication. Extracellular vesicles (EVs) are cell-derived nanoparticles that play a vital role in the transfer of bioactive molecules, which has gained much attention in the field of diagnosis and treatment. Over the past ten years, the participation of EVs in the reproductive processes of oocytes has been broadly studied and has shown great potential for elucidating the intricacies of female reproductive health. This review provides an extensive discussion of the influence of EVs on oocytes, emphasizing their involvement in normal physiology and altered cargo under pathological conditions. In addition, the positive impact of therapeutic EVs on oocyte quality and their role in alleviating ovarian pathological conditions are summarized.
Humans ; Extracellular Vesicles/physiology* ; Oocytes/cytology* ; Female ; Animals ; Cell Communication/physiology*

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

OBJECTIVE: To investigate the predictive value of sphinor kinase 1 (sphk1), D-lactic acid, and intestinal fatty acid binding protein (I-FABP) for gastrointestinal injury in patients with sepsis. METHODS: A prospective observational study was conducted. Sixty-eight patients with sepsis and gastrointestinal dysfunction admitted to the department of critical care medicine of the First Affiliated Hospital of Baotou Medical College Inner Mongolia University of Science and Technology from May 2024 to March 2025 were enrolled (sepsis group), and they were divided into acute gastrointestinal injury (AGI) I-IV groups according to the definition and grading criteria of AGI proposed by the European Society of Intensive Care Medicine in 2012. Twenty non-sepsis patients without AGI admitted to the intensive care unit during the same period were enrolled as the control group (non-sepsis group). Within 30 minutes of patient enrollment, plasma sphk1, D-lactic acid, and I-FABP levels were determined by enzyme linked immunosorbent assay (ELISA). General data such as gender, age were recorded, and levels of procalcitonin (PCT), high-sensitivity C-reactive protein (hs-CRP), lactic acid (Lac), and acute physiology and chronic health evaluation II (APACHEII), sequential organ failure assessment (SOFA) were measured. Spearman method was used to analyze the correlation between sphk1, I-FABP, D-lactic acid and other indicators. The receiver operator characteristic curve (ROC curve) was used to evaluate the predictive value of sphk1, D-lactic acid, I-FABP, APACHEII score, and SOFA score for gastrointestinal injury in patients with sepsis. RESULTS: Among the 68 sepsis patients, 13 were classified as AGI grade I, 16 as AGI grade II, 23 as AGI grade III, and 16 had AGI grade IV. There were no statistically significant differences in gender, age, and abdominal infection rate among the groups. The SOFA score and APACHEII score of the sepsis group were significantly higher than those of the non-sepsis group; and the APACHEII score of the AGI IV group was significantly higher than that of the AGI I and AGI II groups. The levels of sphk1, D-lactic acid, I-FABP, PCT, Lac and hs-CRP in the sepsis group were significantly higher than those in the non-sepsis group, and each indicator gradually increased with the increase of AGI grade. Correlation analysis showed that plasma sphk1, D-lactic acid, and I-FABP in patients with sepsis-induced gastrointestinal injury were positively correlated with PCT, Lac, APACHEII score, and AGI grade (all P < 0.05), and sphk1 was positively correlated with I-FABP and D-lactic acid (r values were 0.773 and 0.782, respectively, both P < 0.05). ROC curve analysis showed that sphk1, D-lactic acid, I-FABP, APACHEII score, and SOFA score had high predictive value for gastrointestinal injury in patients with sepsis, with area under the curve (AUC) of 0.996, 0.987, 0.976, 0.901, and 0.934 (all P < 0.05). When the optimal cut-off value of sphk1 was 60.46 ng/L, the sensitivity and specificity were 95.6% and 100%, respectively; when the optimal cut-off value of D-lactic acid was 1 454.3 μg/L, the sensitivity and specificity were 95.6% and 100%, respectively; when the optimal cut-off value of I-FABP was 0.91 ng/L, the sensitivity and specificity were 95.6% and 100%, respectively; when the optimal cut-off value of APACHEII score was 14.5, the sensitivity and specificity were 80.9% and 85.0%, respectively; when the optimal cut-off value of SOFA score was 3.5, the sensitivity and specificity were 85.3% and 95.0%, respectively. CONCLUSIONS The levels of plasma sphk1, I-FABP, and D-lactic acid were significantly elevated in patients with sepsis and gastrointestinal injury. These indicators can serve as sensitive and relatively specific serological markers for early prediction of intestinal mucosal damage.
Humans ; Lactic Acid/blood* ; Fatty Acid-Binding Proteins/blood* ; Sepsis/complications* ; Prospective Studies ; Male ; Female ; Middle Aged ; Predictive Value of Tests ; Adult ; Aged ; Gastrointestinal Diseases/blood* ; Prognosis

Objective:To investigate the risk of falls in patients with Parkinson's disease and establish and validate a prediction model.Methods:We selected 372 patients with Parkinson's disease at Sichuan Nanchong Mental Health Centre from January 2022 to Septem-ber 2023.The patients were divided in a 7:3 ratio into model group(260 cases)and validation group(112 cases).According to previ-ous literature and suspected factors found in clinical practice,we collected general information(sex,age,etc.)and disease-related factors(the duration of Parkinson's disease,the type of medications taken,etc.)that may be associated with falls in patients with Parkinson's disease.In the model group,between patients with and those without falls within 1 year as reported by the patients or their family members,potential predictors for falls were determined through comparison of general information and disease-related factors,least absolute shrinkage and selection operator(LASSO)regression,and multivariable logistic regression.Based on the significant factors,a nomogram model was established and validated.Results:In the model group,81(31.15%)of the 260 patients experienced falls.According to the LASSO regression and multivariable logistic regression results,alcohol consumption,the type of medications,the score of the Unified Parkinson's Disease Rating Scale part Ⅲ(UPDRS-Ⅲ),the Berg Balance Scale score,the presence of arthritis,and the presence of osteoporosis were independent factors influenc-ing falls in patients with Parkinson's disease.The area under curve(AUC)of the receiver operator characteristic curve(ROC)for pre-dicting the risk of falls in patients with Parkinson's disease was 0.896(95%CI=0.856-0.935)in the model group and 0.883(95%CI=0.840-0.926)in the validation group.The calibration curve analysis results showed that the prediction curves of the model and valida-tion groups closely fitted the standard curves.The decision curve analysis results indicated that when the probability threshold for pre-dicting the fall risk in Parkinson's disease using the nomogram was 0.10-0.90,the net benefit rate of the patients was greater than 0.Conclusion:The risk of falls in patients with Parkinson's disease are mainly influenced by factors such as alcohol consumption and the type of medications.The nomogram model established in this study can be used to predict the fall risk in patients with Parkinson's disease.

Musculoskeletal disorders (MSDs) are characterized by extensive pathological involvement and high prevalence and cause a significant disease burden. Long-term drug administration often causes by adverse effects with poor therapeutic efficacy. Low-intensity pulsed ultrasound (LIPUS), as a specialized therapeutic modality, delivers acoustic energy at a low intensity in a pulsed wave mode, thus ensuring stable energy transmission to the target tissues while minimizing thermal effects. This non-invasive approach has demonstrated significant potential for MSD treatment by delivering effective physical stimulations. Extensive animal and clinical studies have demonstrated the efficacy of LIPUS for accelerating the healing process of fresh fractures and nonunions, promoting soft tissue regeneration and suppressing inflammatory responses. Emerging evidence suggests promising applications of LIPUS in skeletal muscle injury treatment and promoting tissue regeneration and repair. This review outlines the recent advancements and mechanistic studies of LIPUS for treatment of common MSDs including fractures, nonunions, muscle injuries, and osteoarthritis, addressing also the technical parameters of commercially available LIPUS devices, current therapeutic approaches, the existing challenges, and future research directions.
Humans ; Ultrasonic Therapy/methods* ; Musculoskeletal Diseases/therapy* ; Ultrasonic Waves ; Osteoarthritis/therapy* ; Muscle, Skeletal/injuries*

Autoimmune hepatitis （AIH） is a chronic inflammatory liver disease. The pathogenesis of AIH remains unclear， but it is mainly autoimmune injury caused by the breakdown of autoimmune tolerance due to the abnormal activation of the immune system， while the specific molecular mechanism remains unknown. Recent studies have shown that abnormal amino acid metabolism plays an important role in the development and progression of AIH. This article reviews the research advances in amino acid metabolic reprogramming in AIH， in order to provide a theoretical basis for amino acid metabolism as a new target for the clinical diagnosis and treatment of AIH.

Objective To investigate the effects of hypoxia on the transcriptional phenotype and ultrastructure of tumor-associated macrophages(TAMs)in glioma.Methods CD14+monocytes were isolated from healthy human peripheral blood samples collected from the Blood Bank of the First Affiliated Hospital of Army Medical University,and the cells were induced to differentiate into TAMs through co-culture with glioma cell-conditioned medium.Hypoxic TAM models were established using varying concentrations of cobalt chloride hexahydrate(CoCl2,50～400 μmol/L)or hypoxic conditions(1%,5%,10%O2)for 48 h,while normoxic TAM models(21%O2)served as controls.RT-qPCR and transcriptome sequencing were employed to analyze transcriptional changes in TAMs under normoxic and hypoxic conditions.Gene set enrichment analysis(GSEA)was applied to compare the differences in angiogenesis,glycolysis and other hypoxia-responsive pathways between the 2 conditions.Transmission electron microscopy(TEM)or immunofluorescence staining was conducted to assess the ultrastructural alterations in cytoskeleton,endoplasmic reticulum(ER),and mitochondria in normoxic and hypoxic TAMs(1%O2).Results Hypoxic TAMs exhibited up-regulated transcription of hypoxia-responsive markers(oxygen transport,glycolysis,pro-angiogenesis),with the effects correlating with hypoxia severity(P<0.05).GSEA revealed significant up-regulation of hypoxia,angiogenesis regulation,glycolysis and gluconeogenesis,and starvation stress pathways,alongside down-regulation of innate immunity,macrophage activation,cytoskeleton,and protein maturation pathways in hypoxic TAMs(P<0.05).TEM and immunofluorescence staining demonstrated obvious ultrastructure changes,including disrupted cytoskeletal organization,shortened rough ER with reduced ribosomes,mitochondrial swelling with cristae damage,and diminished ER-mitochondria contacts in hypoxic TAMs.Conclusion CoCl2 and hypoxia induce a hypoxic transcriptional phenotype in TAMs,which may potentially associated with ultrastructural remodeling of the cytoskeleton,ER,and mitochondria.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1％of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9％)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5％)was lower than that in inpatient children(31.5％).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1％)than in inpatient children(59.4％).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14％,11.7％,47.8％in outpatients,but 24.2％,20.6％,and 52.8％in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.