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.

ObjectiveTo systematically elucidate the accumulation characteristics of secondary metabolites in the stems and leaves of Panax ginseng with different stem colors. MethodsUltra-high-performance liquid chromatography-triple quadrupole-tandem mass spectrometry （UHPLC-QqQ-MS） was employed to determine the of 22 ginsenosides in the stems and leaves of 2- to 5-year-old P. ginseng plants with green and purple stems. Principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and one-way analysis of variance （ANOVA） were performed to explore the distribution patterns of ginsenosides from three dimensions： tissue distribution， growth duration， and stem color. Results① Tissue distribution： Leaves served as the primary organ for ginsenoside accumulation， exhibiting significantly higher total ginsenoside content than stems， which indicated distinct tissue-specific distribution of ginsenosides. ② Growth duration： Ginsenoside levels in stems generally peaked in the 2-year-old plants before stabilizing， while those in leaves displayed a declining-rebounding pattern， indicating that the 2-year-old plants （harvest stage） was a critical time point for the resurgence of leaf ginsenosides. ③ Stem color： A stage-specific correlation was observed between stem color and ginsenoside accumulation. In stems， the early-growth stage （2-year-old） of purple-stemmed plants exhibited higher content of specific ginsenosides （e.g.， ginsenoside Rk₁ and notoginsenoside R₁）， which may be associated with its stronger early-stage stress resistance. In leaves， the content of core ginsenosides （e.g.， ginsenosides Re and Rd） in 5-year-old plants with green stems showed a surpassing trend， demonstrating higher potential of resource development. ConclusionThe accumulation of ginsenosides in P. ginseng stems and leaves is multidimensionally regulated by tissue type， growth duration， and stem color phenotypes. Purple-stemmed plants exhibit early-stage specific ginsenoside accumulation and stress resistance potential， whereas green-stemmed plants are more advantageous for core leaf ginsenoside development at the harvest stage. The findings confirm that aboveground stem color can serve as a biomarker for directed breeding and efficient resource utilization of P. ginseng.

ObjectiveTo systematically elucidate the accumulation characteristics of secondary metabolites in the stems and leaves of Panax ginseng with different stem colors. MethodsUltra-high-performance liquid chromatography-triple quadrupole-tandem mass spectrometry （UHPLC-QqQ-MS） was employed to determine the of 22 ginsenosides in the stems and leaves of 2- to 5-year-old P. ginseng plants with green and purple stems. Principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and one-way analysis of variance （ANOVA） were performed to explore the distribution patterns of ginsenosides from three dimensions： tissue distribution， growth duration， and stem color. Results① Tissue distribution： Leaves served as the primary organ for ginsenoside accumulation， exhibiting significantly higher total ginsenoside content than stems， which indicated distinct tissue-specific distribution of ginsenosides. ② Growth duration： Ginsenoside levels in stems generally peaked in the 2-year-old plants before stabilizing， while those in leaves displayed a declining-rebounding pattern， indicating that the 2-year-old plants （harvest stage） was a critical time point for the resurgence of leaf ginsenosides. ③ Stem color： A stage-specific correlation was observed between stem color and ginsenoside accumulation. In stems， the early-growth stage （2-year-old） of purple-stemmed plants exhibited higher content of specific ginsenosides （e.g.， ginsenoside Rk₁ and notoginsenoside R₁）， which may be associated with its stronger early-stage stress resistance. In leaves， the content of core ginsenosides （e.g.， ginsenosides Re and Rd） in 5-year-old plants with green stems showed a surpassing trend， demonstrating higher potential of resource development. ConclusionThe accumulation of ginsenosides in P. ginseng stems and leaves is multidimensionally regulated by tissue type， growth duration， and stem color phenotypes. Purple-stemmed plants exhibit early-stage specific ginsenoside accumulation and stress resistance potential， whereas green-stemmed plants are more advantageous for core leaf ginsenoside development at the harvest stage. The findings confirm that aboveground stem color can serve as a biomarker for directed breeding and efficient resource utilization of P. ginseng.

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 observe the causal association between colorectal cancer and sepsis by means of bidirec-tional two-sample Mendelian randomization(MR).METHODS The Genome Wide Association Study(GWAS)datasets for colorectal cancer and sepsis were retrieved from the GWAS databases between its establishment and Feb.1,2024.MR was carried out for the colorectal cancer and sepsis interacting as exposure and outcome factors.The single nucleotide polymorhpism(SNPs)that were significantly associated with the exposure factors were screened out by setting P as less than 5.0× 10-8,r2 less than 0.001,the genetic distance 10,000 kb.The SNPs that were remarkably associated with the exposure factors were extracted from the GWAS datasets of the outcome variables,the instrumental variable were finally obtained,the inverse variance weighting(IVW)was taken as the main approach for the causal inference.The level pleiotropy was tested by using MR Egger method and MR-PRESSO,the heterogeneity was tested by IVW method and MR-Egger method,the sensitivity was analyzed by leave-one-out method,and the robustness of the result was tested.RESULTS A total of 30 SNPs were screed out by setting the colorectal cancer as exposure factor and the sepsis as outcome variable(F＞10);there was causal as-sociation between the colorectal cancer and the sepsis(OR=28.955,95％CI:1.215 to 690.052,P=0.037).Totally 14 SNPs were screened out by setting the sepsis as exposure factor and the colorectal cancer as treatment variable(F＞10),and there was no causal association between the colorectal cancer and the sepsis(OR=0.999,95％CI:0.997 to 1.002,P=0.674).There was no level pleiotropy in the instrumental variables during the two times of MR analysis;there was no heterogeneity in the instrumental variables,and the result of the MR analysis was robust.CONCLUSION There is causal association between the colorectal cancer and the increases of risk of sepsis.But there is no causal association between the sepsis and the increase of risk of colorectal cancer.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

Objective:To investigate the key targets and mechanisms of compound Huangbai liquid in promoting wound healing of perianal abscess using network pharmacology and machine learning.Methods:Active components of compound Huangbai liquid and their target genes were screened and corrected using the TCMSP and HERB databases.Target genes related to wound healing were collected from the GeneCard and GEO databases.Common targets were identified using SangerBox online tool,followed by KEGG and GO enrichment analyses to explore potential biological functions.A PPI network was constructed to analyze core gene interactions,and immune cell infiltration was evaluated using the CIBERSORT algorithm.Key genes were screened using machine learning methods such as Boruta,random forest,XGBoost,and SVM-RFE.Finally,the binding affinity between active components and target genes was validated using AutoDock Vina.Results:Four key target genes(CYP19A1,IL10RA,ALOXE3,EGFR)were significantly correlated with components such as quercetin and berberine.These genes were involved in PI3K-Akt signaling pathway,and closely related to immune response and cell proliferation.The PPI network showed that these genes played important roles in angiogenesis and cell adhesion.Immune infiltration analysis showed that key genes were strongly correlated with immune cells such as macrophages.Conclusion:Compound Huangbai liquid may promote wound healing in perianal abscess by regulating multiple biological pathways and immune responses.