ObjectiveTo develop an early - warning approach for influenza in Anhui province using the moving epidemic method （MEM）， offering a scientific basis for influenza prevention and control in the province. MethodsBased on the surveillance data reported weekly by influenza sentinel hospitals and network laboratories in the “China Influenza Surveillance Information System”， the weekly influenza virus detection positive rate （positive rate， PR） across the province was analyzed. The MEM was used to estimate the epidemic threshold and classification intensity threshold of influenza in Anhui province before and after the COVID-19 pandemic， respectively. The early warning effect of the MEM was evaluated using cross-validation. ResultsEpidemic and graded intensity thresholds of influenza were derived using PR and MEM. Prior to the COVID-19 pandemic， the starting， medium-intensity， high-intensity， extremely-high-intensity， and ending thresholds of influenza epidemics were 17.08%， 38.47%， 53.20%， 59.71%， and 12.34% respectively. At this point， the model exhibited sensitivity of 0.98， specificity of 0.86， Matthews correlation coefficient of 0.84， and Youden index of 0.84. After the COVID-19 pandemic， these thresholds were 25.61%， 43.81%， 63.29%， 74.46%， and 17.82% respectively. At this point， the model demonstrates sensitivity of 0.86， specificity of 0.92， Matthews correlation coefficient of 0.78， and Youden index of 0.77. ConclusionMEM shows good applicability in estimating the epidemic and graded intensity thresholds of influenza during the winter and spring in Anhui province. Both the influenza epidemic threshold and graded intensity thresholds after the COVID-19 pandemic are higher than those before the pandemic.

ObjectiveTo explore the impact of meteorological factors on influenza-like illness （ILI） cases in Anhui Province. MethodsILI surveillance data of Anhui Province from 2016 to 2023 were collected from the “China Influenza Surveillance Information System”， and meteorological data were obtained from Anhui Meteorological Center. R 4.2.1 was used to conduct Spearman rank correlation analysis between ILI cases and meteorological factors. A generalized additive model （GAM） was established to analyze the impact of meteorological factors on ILI cases. ResultsCorrelation analysis and GAM analysis showed that the average temperature and average relative humidity with a one-week lag had significant non-linear effects on ILI （P<0.05）. When the weekly average temperature was below 14 ℃， the number of ILI cases gradually increased as the temperature decreased. When the weekly average relative humidity was below 65%， the number of ILI cases gradually increased as the weekly average relative humidity decreased. The changes in ILI among the age groups 5-14， 15-24 and 25-59 years were influenced by meteorological factors， exhibiting a nonlinear relationship with weekly average temperature or weekly average relative humidity. ConclusionWeekly average temperature and weekly average relative humidity are the main factors affecting ILI in Anhui Province， and there is a lag effect， which can provide a reference for influenza surveillance and early warning.

Objective To investigate the effect of ATP synthase inhibitory factor 1 (ATPIF1) on the antitumor activity of chimeric antigen receptor (CAR)-NK92 cells. Methods HER2-targeted CAR-NK92 cells with ATPIF1 overexpression or knockdown were constructed. CAR-positive expression rate was detected by flow cytometry. Cell proliferation capacity was measured using CCK-8 assay. Glycolytic capacity was analyzed by Seahorse metabolic analyzer. Mitochondrial membrane potential levels were detected using JC-1 probe. Target cell lysis rate was evaluated by firefly luciferase reporter assay. Expression levels of CD107a, natural-killer group 2 member D (NKG2D), granzyme B (GzmB), perforin, and interleukin 2 (IL-2) were detected via flow cytometry. Quantitative real-time PCR was used to measure the expression of interferon-induced protein with tetratricopeptide repeats 1 (IFIT1), tumor necrosis factor α (TNF-α), ATPIF1, and hexokinase 1 (HK1). The impact of glycolytic inhibition by 2-Deoxy-D-glucose (2-DG) on CAR-NK92 antitumor capacity was examined. Results Successfully generated HER2-targeting control CAR-NK92 cells, as well as ATPIF1-overexpressing and ATPIF1 knockdown CAR-NK92 cells. The ATPIF1-overexpressing CAR-NK92 cells showed significantly enhanced target cell lysis rate, elevated expression levels of NKG2D and CD107a, increased secretion capacities of Granzyme B (GzmB) and IL-2, and upregulated mRNA expression levels of IFIT1 and TNF-α, while ATPIF1-knockdown cells exhibited opposite effects. ATPIF1 overexpression induced metabolic reprogramming in CAR-NK92 cells, manifested by significantly decreased mitochondrial membrane potential (δpsim), markedly upregulated HK1 mRNA expression, and enhanced basal glycolysis and glycolytic capacity. After glycolysis inhibition with 2-DG (5 μmol/L), both ATPIF1-overexpressing and knockdown CAR-NK92 cells showed no significant differences in NKG2D and CD107a expression levels compared to control cells. Conclusion ATPIF1 regulates the antitumor activity of CAR-NK92 cells through modulating glycolytic metabolism. Overexpression of ATPIF1 can enhance the antitumor efficacy of CAR-NK92 cells.
Humans ; Glycolysis ; Killer Cells, Natural/metabolism* ; Receptors, Chimeric Antigen/immunology* ; Granzymes/genetics* ; Hexokinase/metabolism* ; Cell Line, Tumor ; Interleukin-2/genetics* ; Cell Proliferation ; NK Cell Lectin-Like Receptor Subfamily K/genetics* ; Membrane Potential, Mitochondrial

AIM To investigate the mechanism of Shaoyao Gancao Decoction in preventing meglumine diatrizoate-induced post-ERCP pancreatitis in rats through autophagy regulation.METHODS The rats were randomized into the normal group,the model group,the low-dose and high-dose Shaoyao Gancao Decoction(1.5,3.0 g/kg),and the indomethacin suppository group.A rat model of post-ERCP pancreatitis was induced by meglumine diatrizoate injection into the pancreatic duct under continuous pressure.The rats had their pancreatic tissues stained with HE to observe the pathological alterations,inflammatory cell infiltration,hemorrhage and necrosis;their serum levels of IL-1β,IL-6,IL-8,TNF-α,AMS,and IL-10 identified by ELISA;their autophagic vacuoles in pancreatic acinar cells observed by transmission electron microscopy;their pancreatic protein expressions of Beclin1,LC3B,p62,TRAF2 and p-JNK detected by IHC and Western blot;and their pancreatic mRNA expressions of Beclin1 and TRAF2 detected by RT-qPCR.RESULTS Compared with the model group,the high-dose Shaoyao Gancao Decoction group displayed no obvious hemorrhage;improvement in edema of acinar and interstitial cells;obviously less cellular inflammatory infiltration;substantially decreased serum levels of IL-1β,IL-6,TNF-α and AMS(P＜0.05,P＜0.01);drastically reduced amount of autophagosomes in acinar cells;and down-regulated expressions of autophagy-related proteins Beclin1,LC3,p62,TRAF2 and p-JNK(P＜0.05,P＜0.01).CONCLUSION Shaoyao Gancao Decoction can prevent post-ERCP pancreatitis by ameliorating pancreatic tissue injury,decreasing serum inflammatory response level,and interfering with abnormal autophagy of pancreatic acinar cells.Its molecular mechanism may involve inhibition of TRAF2 protein expression and modulation of p-JNK activation.

AIM To investigate the mechanism of Shaoyao Gancao Decoction in preventing meglumine diatrizoate-induced post-ERCP pancreatitis in rats through autophagy regulation.METHODS The rats were randomized into the normal group,the model group,the low-dose and high-dose Shaoyao Gancao Decoction(1.5,3.0 g/kg),and the indomethacin suppository group.A rat model of post-ERCP pancreatitis was induced by meglumine diatrizoate injection into the pancreatic duct under continuous pressure.The rats had their pancreatic tissues stained with HE to observe the pathological alterations,inflammatory cell infiltration,hemorrhage and necrosis;their serum levels of IL-1β,IL-6,IL-8,TNF-α,AMS,and IL-10 identified by ELISA;their autophagic vacuoles in pancreatic acinar cells observed by transmission electron microscopy;their pancreatic protein expressions of Beclin1,LC3B,p62,TRAF2 and p-JNK detected by IHC and Western blot;and their pancreatic mRNA expressions of Beclin1 and TRAF2 detected by RT-qPCR.RESULTS Compared with the model group,the high-dose Shaoyao Gancao Decoction group displayed no obvious hemorrhage;improvement in edema of acinar and interstitial cells;obviously less cellular inflammatory infiltration;substantially decreased serum levels of IL-1β,IL-6,TNF-α and AMS(P＜0.05,P＜0.01);drastically reduced amount of autophagosomes in acinar cells;and down-regulated expressions of autophagy-related proteins Beclin1,LC3,p62,TRAF2 and p-JNK(P＜0.05,P＜0.01).CONCLUSION Shaoyao Gancao Decoction can prevent post-ERCP pancreatitis by ameliorating pancreatic tissue injury,decreasing serum inflammatory response level,and interfering with abnormal autophagy of pancreatic acinar cells.Its molecular mechanism may involve inhibition of TRAF2 protein expression and modulation of p-JNK activation.

The human oral microbiome harbors one of the most diverse microbial communities in the human body,playing critical roles in oral and systemic health.Recent technological innovations are propelling the characterization and manipulation of oral microbiota.High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes.New long-read platforms improve genome assembly from complex samples.Single-cell genomics provides insights into uncultured taxa.Advanced imaging modalities including fluorescence,mass spectrometry,and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution.Fluorescence techniques link phylogenetic identity with localization.Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification.Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches.Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly,gene expression,metabolites,microenvironments,virulence mechanisms,and microbe-host interfaces in the context of health and disease.However,significant knowledge gaps persist regarding community origins,developmental trajectories,homeostasis versus dysbiosis triggers,functional biomarkers,and strategies to deliberately reshape the oral microbiome for therapeutic benefit.The convergence of sequencing,imaging,cultureomics,synthetic systems,and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict,prevent,diagnose,and treat associated oral diseases.

The human oral microbiome harbors one of the most diverse microbial communities in the human body,playing critical roles in oral and systemic health.Recent technological innovations are propelling the characterization and manipulation of oral microbiota.High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes.New long-read platforms improve genome assembly from complex samples.Single-cell genomics provides insights into uncultured taxa.Advanced imaging modalities including fluorescence,mass spectrometry,and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution.Fluorescence techniques link phylogenetic identity with localization.Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification.Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches.Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly,gene expression,metabolites,microenvironments,virulence mechanisms,and microbe-host interfaces in the context of health and disease.However,significant knowledge gaps persist regarding community origins,developmental trajectories,homeostasis versus dysbiosis triggers,functional biomarkers,and strategies to deliberately reshape the oral microbiome for therapeutic benefit.The convergence of sequencing,imaging,cultureomics,synthetic systems,and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict,prevent,diagnose,and treat associated oral diseases.

The human oral microbiome harbors one of the most diverse microbial communities in the human body,playing critical roles in oral and systemic health.Recent technological innovations are propelling the characterization and manipulation of oral microbiota.High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes.New long-read platforms improve genome assembly from complex samples.Single-cell genomics provides insights into uncultured taxa.Advanced imaging modalities including fluorescence,mass spectrometry,and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution.Fluorescence techniques link phylogenetic identity with localization.Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification.Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches.Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly,gene expression,metabolites,microenvironments,virulence mechanisms,and microbe-host interfaces in the context of health and disease.However,significant knowledge gaps persist regarding community origins,developmental trajectories,homeostasis versus dysbiosis triggers,functional biomarkers,and strategies to deliberately reshape the oral microbiome for therapeutic benefit.The convergence of sequencing,imaging,cultureomics,synthetic systems,and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict,prevent,diagnose,and treat associated oral diseases.

The human oral microbiome harbors one of the most diverse microbial communities in the human body,playing critical roles in oral and systemic health.Recent technological innovations are propelling the characterization and manipulation of oral microbiota.High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes.New long-read platforms improve genome assembly from complex samples.Single-cell genomics provides insights into uncultured taxa.Advanced imaging modalities including fluorescence,mass spectrometry,and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution.Fluorescence techniques link phylogenetic identity with localization.Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification.Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches.Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly,gene expression,metabolites,microenvironments,virulence mechanisms,and microbe-host interfaces in the context of health and disease.However,significant knowledge gaps persist regarding community origins,developmental trajectories,homeostasis versus dysbiosis triggers,functional biomarkers,and strategies to deliberately reshape the oral microbiome for therapeutic benefit.The convergence of sequencing,imaging,cultureomics,synthetic systems,and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict,prevent,diagnose,and treat associated oral diseases.

Objective:To predict the short-term postoperative recurrence status of patients with refractory temporal lobe epilepsy (TLE) by analyzing preoperative 18F-FDG PET images and patients′ clinical characteristics based on deep residual neural network (ResNet). Methods:Retrospective analysis was conducted on preoperative 18F-FDG PET images and clinical data of 220 patients with refractory TLE (132 males and 88 females, age 23.0(20.0, 30.2) years)) in the First Affiliated Hospital of Jinan University between January 2014 and June 2020. ResNet was used to perform high-throughput feature extraction on preprocessed PET images and clinical features, and to perform a postoperative recurrence prediction task for differentiating patients with TLE. The predictive performance of ResNet model was evaluated by ROC curve analysis, and the AUC was compared with that of classical Cox proportional risk model using Delong test. Results:Based on PET images combined with clinical feature training, AUCs of the ResNet in predicting 12-, 24-, and 36-month postoperative recurrence were 0.895±0.073, 0.861±0.058 and 0.754±0.111, respectively, which were 0.717±0.093, 0.697±0.081 and 0.645±0.087 for Cox proportional hazards model respectively ( z values: -3.00, -2.98, -1.09, P values: 0.011, 0.018, 0.310). The ResNet showed best predictive effect for recurrence events within 12 months after surgery. Conclusion:The ResNet model is expected to be used in clinical practice for postoperative follow-up of patients with TLE, helping for risk stratification and individualized management of postoperative patients.