ObjectiveTo investigate the efficacy and underlying mechanisms of Gynostemma pentaphyllum alcohol extract in improving glucose and lipid metabolism disorders in db/db mice through transcriptomics and gut microbiota analysis. MethodsEighteen db/db mice were randomly assigned to the model（DM） group， metformin（MET） group， and G. pentaphyllum alcohol extract（GP） group， with six mice in each group， based on stratification of fasting blood glucose and body weight. An additional six db/m mice were selected as the normal control（NC） group. Mice in the NC and DM groups were administered deionized water （10 mL·kg^-1） daily. The MET group received metformin （0.195 g·kg^-1） by gavage. The GP group was treated with G. pentaphyllum alcohol extract （3.9 g·kg^-1） by gavage for six weeks. Fasting blood glucose was measured every two weeks. After six weeks of intervention， serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， creatinine （CREA）， and blood urea nitrogen （BUN） were assessed. Enzyme-linked immunosorbent assay （ELISA） was used to measure insulin （FINS）， adiponectin （ADP）， and tumor necrosis factor-α （TNF-α）. Hematoxylin-eosin （HE） staining was used to observe liver histomorphology， periodic acid-Schiff （PAS） staining was employed to assess hepatic glycogen synthesis， and Oil Red O staining was used to detect hepatic lipid deposition. Liver transcriptomic data were used to identify differentially expressed genes in the liver and conduct enrichment analysis. Real-time PCR was employed to verify the expression levels of adiponectin gene （Adipoq）， peroxisome proliferator-activated receptor gamma coactivator-1α （PGC-1α）， AMP-activated protein kinase （AMPK）， peroxisome proliferator-activated receptor α （PPARα）， glucokinase （GCK）， forkhead box （Fox）O1， FoxO3， phosphoenolpyruvate carboxykinase （PEPCK）， and glucose-6-phosphatase （G6PC）. Metagenomic sequencing was conducted to analyze changes in gut microbiota composition. ResultsCompared with the NC group， the DM group exhibited significantly elevated fasting blood glucose （P<0.01）， serum AST， ALT， TC， TG， LDL-C， and HDL-C （P<0.01）. FINS， homeostatic model assessment for insulin resistance （HOMA-IR）， and the inflammatory cytokine TNF-α were significantly increased （P<0.01）， while ADP was significantly decreased （P<0.05）. Histological analysis confirmed severe hepatic steatosis and excessive lipid accumulation in the DM group， along with markedly reduced glycogen synthesis. Compared with the DM group， the GP group showed significantly decreased fasting blood glucose （P<0.01）， reduced serum TC， LDL-C， and HDL-C levels （P<0.05）， significantly decreased serum TG and AST levels （P<0.01）， significantly reduced FINS， HOMA-IR， and TNF-α levels （P<0.01）， and significantly increased ADP （P<0.01）. Hepatic steatosis and lipid deposition were significantly alleviated， while glycogen synthesis was markedly enhanced. Transcriptomic differential and enrichment analyses suggested that the mechanisms by which G. pentaphyllum alcohol extract improved hepatic glucose and lipid metabolism in db/db mice may involve regulation of the AMPK and FoxO signaling pathways. Real-time PCR results confirmed that expression of PGC-1α， PEPCK， G6PC， FoxO1， and FoxO3 was significantly downregulated following treatment with G. pentaphyllum alcohol extract （P<0.05， P<0.01）， whereas mRNA expression of Adipoq， PPARα， GCK， and AMPK was significantly upregulated （P<0.05， P<0.01）. Metagenomic analysis showed that the relative abundance of Lactobacillus， Alistipes， and Akkermansia species was higher in the GP group than in the DM group. ConclusionG. pentaphyllum alcohol extract may improve glucose and lipid metabolism disorders in db/db mice by regulating the hepatic AMPK/PPARα pathway to suppress lipid deposition and alleviate hepatic steatosis， by inhibiting gluconeogenesis through the AMPK/PGC-1α and FoxO pathways to lower fasting blood glucose， and by increasing the abundance of beneficial gut bacteria such as Lactobacillus， Alistipes， and Akkermansia to restore gut microbiota balance.

ObjectiveTo investigate the efficacy and underlying mechanisms of Gynostemma pentaphyllum alcohol extract in improving glucose and lipid metabolism disorders in db/db mice through transcriptomics and gut microbiota analysis. MethodsEighteen db/db mice were randomly assigned to the model（DM） group， metformin（MET） group， and G. pentaphyllum alcohol extract（GP） group， with six mice in each group， based on stratification of fasting blood glucose and body weight. An additional six db/m mice were selected as the normal control（NC） group. Mice in the NC and DM groups were administered deionized water （10 mL·kg^-1） daily. The MET group received metformin （0.195 g·kg^-1） by gavage. The GP group was treated with G. pentaphyllum alcohol extract （3.9 g·kg^-1） by gavage for six weeks. Fasting blood glucose was measured every two weeks. After six weeks of intervention， serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， creatinine （CREA）， and blood urea nitrogen （BUN） were assessed. Enzyme-linked immunosorbent assay （ELISA） was used to measure insulin （FINS）， adiponectin （ADP）， and tumor necrosis factor-α （TNF-α）. Hematoxylin-eosin （HE） staining was used to observe liver histomorphology， periodic acid-Schiff （PAS） staining was employed to assess hepatic glycogen synthesis， and Oil Red O staining was used to detect hepatic lipid deposition. Liver transcriptomic data were used to identify differentially expressed genes in the liver and conduct enrichment analysis. Real-time PCR was employed to verify the expression levels of adiponectin gene （Adipoq）， peroxisome proliferator-activated receptor gamma coactivator-1α （PGC-1α）， AMP-activated protein kinase （AMPK）， peroxisome proliferator-activated receptor α （PPARα）， glucokinase （GCK）， forkhead box （Fox）O1， FoxO3， phosphoenolpyruvate carboxykinase （PEPCK）， and glucose-6-phosphatase （G6PC）. Metagenomic sequencing was conducted to analyze changes in gut microbiota composition. ResultsCompared with the NC group， the DM group exhibited significantly elevated fasting blood glucose （P<0.01）， serum AST， ALT， TC， TG， LDL-C， and HDL-C （P<0.01）. FINS， homeostatic model assessment for insulin resistance （HOMA-IR）， and the inflammatory cytokine TNF-α were significantly increased （P<0.01）， while ADP was significantly decreased （P<0.05）. Histological analysis confirmed severe hepatic steatosis and excessive lipid accumulation in the DM group， along with markedly reduced glycogen synthesis. Compared with the DM group， the GP group showed significantly decreased fasting blood glucose （P<0.01）， reduced serum TC， LDL-C， and HDL-C levels （P<0.05）， significantly decreased serum TG and AST levels （P<0.01）， significantly reduced FINS， HOMA-IR， and TNF-α levels （P<0.01）， and significantly increased ADP （P<0.01）. Hepatic steatosis and lipid deposition were significantly alleviated， while glycogen synthesis was markedly enhanced. Transcriptomic differential and enrichment analyses suggested that the mechanisms by which G. pentaphyllum alcohol extract improved hepatic glucose and lipid metabolism in db/db mice may involve regulation of the AMPK and FoxO signaling pathways. Real-time PCR results confirmed that expression of PGC-1α， PEPCK， G6PC， FoxO1， and FoxO3 was significantly downregulated following treatment with G. pentaphyllum alcohol extract （P<0.05， P<0.01）， whereas mRNA expression of Adipoq， PPARα， GCK， and AMPK was significantly upregulated （P<0.05， P<0.01）. Metagenomic analysis showed that the relative abundance of Lactobacillus， Alistipes， and Akkermansia species was higher in the GP group than in the DM group. ConclusionG. pentaphyllum alcohol extract may improve glucose and lipid metabolism disorders in db/db mice by regulating the hepatic AMPK/PPARα pathway to suppress lipid deposition and alleviate hepatic steatosis， by inhibiting gluconeogenesis through the AMPK/PGC-1α and FoxO pathways to lower fasting blood glucose， and by increasing the abundance of beneficial gut bacteria such as Lactobacillus， Alistipes， and Akkermansia to restore gut microbiota balance.

The diagnosis of hematological disorders is currently established from the combined results of different tests, including those assessing morphology (M), immunophenotype (I), cytogenetics (C), and molecular biology (M) (collectively known as the MICM classification). In this workflow, most of the results are interpreted manually (i.e., by a human, without automation), which is expertise-dependent, labor-intensive, time-consuming, and with inherent interobserver variability. Also, with advances in instruments and technologies, the data is gaining higher dimensionality and throughput, making additional challenges for manual analysis. Recently, artificial intelligence (AI) has emerged as a promising tool in clinical hematology to ensure timely diagnosis, precise risk stratification, and treatment success. In this review, we summarize the current advances, limitations, and challenges of AI models and raise potential strategies for improving their performance in each sector of the MICM pipeline. Finally, we share perspectives, highlight future directions, and call for extensive interdisciplinary cooperation to perfect AI with wise human-level strategies and promote its integration into the clinical workflow.

Objective To observe the value of vector flow mapping(VFM)technique for assessing changes of left ventricular diastolic function in ovarian cancer(OC)patients who underwent postoperative chemotherapy.Methods Totally 37 OC patients who received postoperative chemotherapy were prospectively enrolled in chemotherapy group,while 40 healthy adults were taken as controls(control group).Routine echocardiography and VFM were performed for chemotherapy group before chemotherapy,after 3 and 6 cycles of chemotherapy,also for controls at enrollment,and comparison was performed between groups before chemotherapy,as well as among different time points within chemotherapy group,and the correlations of VFM results with hemoglobin and routine echocardiographic results in chemotherapy group were analyzed.Results No significant difference of age,body mass,body surface area(BSA),nor hemoglobin level,routine echocardiographic and VFM results before chemotherapy was found between groups(all P＞0.05).With the process of chemotherapy,hemoglobin level gradually decreased,the isovolumic relaxation period(IR),atrial systole period(AS)intraventricular pressure difference(IVPD)and intraventricular pressure gradient(IVPG)of the left ventricle gradually increased(adjusted P＜0.05),whereas routine echocardiography only showed that the left atrial volume index(LAVI)and the ratio of early mitral inflow velocity and the mean mitral annular early diastolic velocity(E/e')increased after 6 cycles of chemotherapy compared with those pre-chemotherapy(adjusted P＜0.05).In chemotherapy group,VFM results in all diastolic subphases were strongly correlated with hemoglobin levels(|r|=0.718 to 0.836,all P＜0.05),weakly to moderately correlated with LAVI(|r|=0.375 to 0.525,all P＜0.05)and moderately correlated with E/e'(|r|=0.424 to 0.537,all P＜0.05).Conclusion The diastolic function of left ventricle was probably damaged in early stage after postoperative chemotherapy in OC patients.VFM might detect slight changes of early diastolic function of left ventricle more sensitively than routine echocardiography.

Objective This study aims to predict the coronavirus disease 2019(COVID-19)epidemic in Xi'an based on SEAIQR model and Dropout-LSTM model,and to provide a scientific basis for evaluating the effectiveness of the"dynamic zero-COVID policy".Methods Considering a large number of asymptomatic infections,the changing parameters,and control procedures,we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-removed(SEAIQR)model with stage-specific interventions.Considering the time-series characteristics of COVID-19 data and the nonlinear relationship between them,we constructed a deep learning Dropout-LSTM model.The data of newly confirmed cases in Xi'an from December 9th,2021 to January 31st,2022 were used to fit the model,and the data from February 1st,2022 to February 7th,2022 were used to evaluate the model performance of forecasting.We then calculated the effective reproduction number(Rt)and analyzed the sensitivity of the different measurement scenarios.Results The peak of newly confirmed cases predicted by the SEAIQR model would appear on December 26th,2021,with 176 cases,and the"dynamic zero-COVID policy"may be achieved in January 24th,2022,with R2=0.849.The Dropout-LSTM model can reflect the time-series and nonlinear characteristics of the data,and the predicted newly confirmed cases were highly consistent with the actual situation,with R2=0.937.The MAE and RMSE of the Dropout-LSTM model were lower than those of the SEAIQR model,indicating that the predicted results were more ideal.At the beginning of the outbreak,R0 was 5.63.Since the implementation of comprehensive control,Rt has shown a gradual downward trend,dropping to below 1.0 on December 27th,2021.With the reduction of effective contact rate,the early implementation of control measures and the improvement of immunity threshold,the peak of newly confirmed cases will continue to decrease.Conclusion The proposed Dropout-LSTM model forecasts the epidemic well,which can provide a reference for decision-making of the"dynamic zero-COVID policy."

Objective To investigate the effects of allergens on the expressions of IL-18,IL-18BPa and IL-18Rα protein in peripheral blood CD4+Th1 cells of healthy control subjects(HC)and patients with allergic rhi-nitis(AR),and on the expressions of IL-18,IL-18BPa and IL-18Rα mRNA in the peripheral blood CD4+T cells.Methods Blood samples were collected from patients with rhinitis for negative skin prick test(AR-),rhinitis for positive skin prick test(AR+)and HC.Flow cytometry was used to evaluate the effects of allergens on the expres-sions of IL-18,IL-18BPa and IL-18Rα protein in CD4+Th1 cells.The expressions of IL-18,IL-18BPa and IL-18Rα mRNA in CD4+T cells were determined by qPCR.Results Compared with HC,increased IL-18 while de-creased IL-18BPa expressions in Th1 cells of AR-and AR+patients were observed,increased IL-18Rα expression in Th1 cells of AR+patients was also found.Additionally,allergens induced elevated expression of IL-18Rα pro-tein in Th1 cells of HC,and induced elevated mRNA expressions of IL-18,IL-18BPa and IL-18Rα in isolated blood CD4+T cells of AR+patients and HC.Conclusion Allergens may be involved in the pathogenesis of AR by inducing the expressions of IL-18 and IL-18Rα in blood CD4+Th1 cells.

Objective:To investigate the influencing factors of recurrence after radical resection of middle and low rectal cancer, and to establish a prediction model based on magnetic resonance imaging (MRI) measurement of perirectal fat content and investigate its application value.Methods:The retrospective cohort study was constructed. The clinicopathological data of 254 patients with middle and low rectal cancer who were admitted to Tianjin Union Medical Center from December 2016 to December 2021 were collected. There were 188 males and 66 females, aged (61±9)years. All patients underwent radical resection of rectal cancer and routine pelvic MRI examina-tion. Observation indicators: (1) follow-up and quantitative measurement of perirectal fat content; (2) factors influencing tumor recurrence after radical resection of middle and low rectal cancer; (3) construction and evaluation of the nomogram prediction model of tumor recurrence after radical resection of middle and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M(rang) and M( Q1, Q2). Count data were described as absolute numbers. Univariate and multivariate analyses were conducted using the COX regression model. The rms software package (4.1.3 version) was used to construct the nomogram and calibration curve. The survival software package (4.1.3 version) was used to calculate the C-index. The ggDCA software package (4.1.3 version) was used for decision curve analysis. Results:(1) Follow-up and quantitative measurement of perirectal fat content. All 254 patients were followed up for 41.0(range, 1.0?59.0)months after surgery. During the follow-up period, there were 81 patients undergoing tumor recurrence with the time to tumor recurrence as 15.0(range, 1.0?43.0)months, and there were 173 patients without tumor recurrence. The preoperative rectal mesangial fascia envelope volume, preoperative rectal mesangial fat area, preoperative rectal posterior mesangial thickness were 159.1(68.6,266.5)cm3, 17.0(5.1,34.4)cm2, 1.2(0.4,3.2)cm in the 81 patients with tumor recurrence, and 178.5(100.1,310.1)cm3, 19.8(5.3,40.2)cm2 and 1.6(0.3,3.7)cm in the 173 patients without tumor recurrence. (2) Factors influencing tumor recurrence after radical resection of middle and low rectal cancer. Results of multivariate analysis showed that poorly differentiated tumor, tumor pathological N staging as N1?N2 stage, rectal posterior mesangial thickness ≤1.43 cm, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures were independent risk factors of tumor recurrence after radical resection of middle and low rectal cancer ( hazard ratio=1.64, 2.20, 3.19, 1.69, 4.20, 95% confidence interval as 1.03?2.61, 1.29?3.74, 1.78?5.71, 1.02?2.81, 2.05?8.63, P<0.05). (3) Construction and evaluation of the nomogram prediction model of tumor recurrence after radical resection of middle and low rectal cancer. Based on the results of multivariate analysis, the tumor differentiation, tumor pathological N staging, rectal posterior mesangial thickness, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures were included to construct the nomogram predic-tion model of tumor recurrence after radical resection of middle and low rectal cancer. The total score of these index in the nomogram prediction model corresponded to the probability of post-operative tumor recurrence. The C-index of the nomogram was 0.80, indicating that the prediction model with good prediction accuracy. Results of calibration curve showed that the nomogram prediction model with good prediction ability. Results of decision curve showed that the prediction probability threshold range was wide when the nomogram prediction model had obvious net benefit rate, and the model had good clinical practicability. Conclusions:Poorly differentiated tumor, tumor pathological N staging as N1?N2 stage, rectal posterior mesangial thickness ≤1.43 cm, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures are independent risk factors of tumor recurrence after radical resection of middle and low rectal cancer. Nomogram prediction model based on MRI measurement of perirectal fat content can effectively predict the probability of postoperative tumor recurrence.

Ulcerative colitis (UC) is one of types of inflammatory bowel disease with high recurrence. Recent studies have highlighted that microbial dysbiosis as well as abnormal gut immunity are crucial factors that initiate a series of inflammatory responses in the UC. Modulating the gut microbiota-intestinal immunity loop has been suggested as one of key strategies for relieving UC. Many Chinese herbal medicines including some of single herb, herbal formulas and the derived constituents have been reported with protective effect against UC through modulating gut microbiome and intestinal immunity. Some clinical trials have shown promising results. This review thus focused on the current knowledge on using Chinese herbal medicines for treating UC from the mechanism aspects of regulating intestinal homeostasis involving microbiota and gut immunity. The existing clinical trials are also summarized.