To summarize the distribution characteristics of human papillomavirus(HPV) infection types in patients with cervical squamous intraepithelial lesion(SIL) and cervical cancer(CC), and to explore the impact of HPV vaccination, HPV infection types, and general clinical data on different grades of cervical lesions.

Metabolic associated fatty liver disease (MAFLD) is fundamentally driven by an imbalance in hepatic fatty-acid flux: the influx of fatty acids exceeds the liver’s capacity for disposal, resulting in excessive hepatic lipid accumulation, predominantly in the form of triglycerides (TGs). The occurrence and progression of MAFLD depend on disordered regulation across multiple metabolic steps, including fatty-acid uptake, de novo lipogenesis (DNL), fatty-acid oxidation (FAO), and very low-density lipoprotein (VLDL) export. Forkhead box protein O1 (FOXO1) is a key transcriptional regulator within the hepatic network coordinating glucose and lipid metabolism. Under metabolic stress and insulin resistance (IR), FOXO1 expression is frequently increased, whereas its inhibitory phosphorylation is reduced. These changes enhance FOXO1 nuclear localization and transcriptional activity, thereby reprogramming the expression of genes related to metabolism in the liver. Because hepatic lipid deposition is the central pathological feature of MAFLD, the functional status of FOXO1 directly influences hepatic lipid homeostasis. Growing evidence suggests that FOXO1 can exert bidirectional, environment-dependent effects on hepatic lipid accumulation; however, the molecular basis for this functional switch remains incompletely understood. This review systematically summarizes the biological functions and regulatory mechanisms of FOXO1 and its roles in hepatic lipid metabolism, with a particular focus on its crosstalk with insulin signaling. FOXO1 expression is shaped by RNA modifications and epigenetic regulation mediated by non-coding RNAs. Its transcriptional output is precisely governed by post-translational modifications—such as phosphorylation and acetylation—as well as by coordinated nucleocytoplasmic shuttling. Notably, these regulatory patterns vary markedly across nutritional states, degrees of insulin resistance, and stages of disease. In the fed state, insulin/IGF-1 signaling activates the PI3K-AKT pathway, promoting the inhibitory phosphorylation of FOXO1 and facilitating additional modifications, including acetylation, methylation, and ubiquitination. Together, these events drive FOXO1 export from the nucleus and dampen its transcriptional activity, suppressing gluconeogenesis and constraining lipogenic programs. Conversely, during fasting or when insulin signaling is weakened, FOXO1 inhibition is relieved. FOXO1 accumulates in the nucleus, binds to DNA, and regulates the transcription of downstream target genes. Mechanistically, FOXO1 can aggravate hepatic lipid accumulation by activating genes involved in TG synthesis while repressing FAO-related pathways, thereby favoring storage over oxidation. However, under specific conditions, FOXO1 may also alleviate the hepatic lipid burden by promoting TG hydrolysis and enhancing VLDL secretion, thereby reducing the net hepatic lipid load. In addition, lipotoxic signals mediated by ceramides and diacylglycerols (Cer/DAG) activate atypical protein kinase C (aPKC), further exacerbating the disruption of the AKT-FOXO1 axis. This vicious cycle ultimately produces a metabolic paradox in which increased hepatic glucose output coexists with persistent, insulin-independent lipogenesis, accelerating MAFLD progression. Importantly, FOXO1 regulation is not uniform: during early metabolic overload, insulin-mediated suppression may remain effective, whereas in advanced insulin resistance, the loss of AKT control permits sustained FOXO1 activity. Such stage-dependent dynamics may help explain why FOXO1 can either promote steatosis or, in certain contexts, support programs that facilitate lipid turnover. Accordingly, interventions should be liver-specific and tuned to the disease stage, aiming to curb maladaptive FOXO1 signaling while preserving its capacity to promote triglyceride hydrolysis and VLDL secretion when advantageous. Overall, this review offers an important perspective on MAFLD pathogenesis, emphasizing FOXO1 as a potential therapeutic target and providing a theoretical basis for developing liver-specific, disease-course-dependent precision interventions.

A phytochemical study of the root extract of Hemerocallis fulva var. kwanso Regel resulted in the isolation and structural characterization of four lignans (1‒4), three flavanones (5, 6, and 8), one chalcone (9), and two monoterpenes (7 and 10). Enzyme inhibition assays revealed that two flavanone glucosides (5 and 6) showed significant inhibitory effect against protein tyrosine phosphatase 1B (PTP1B), with IC50 values of 31.38 µM and 51.46 µM, respectively. Further enzyme kinetic studies demonstrated that these two compounds acted as noncompetitive PTP1B inhibitors, with Ki values of 30.88 µM and 50.38 µM, respectively.

AIM To establish LC-MS and GC-MS method and analyze the chemical constituents of Dendrobium huoshanense C.Z.Tang & S.J.Cheng flowers.METHODS LC-MS was performed on a Zorbax Eclipse C18 column(2.1 mm×100 mm,1.8 μm),with the mobile phase comprising of water(containing 0.1％formic acid)-acetonitrile flowing at 0.3 mL/min,and electrospray ionization was operated in both positive and negative ion modes.The GC-MS employed headspace solid-phase microextraction for sample preparation,and the analysis was performed on an HP-5MS column(30 m×0.25 mm,0.25 μm),with the following temperature program:initial temperature 50 ℃(held for 2 min),increased at 5 ℃/min to 180 ℃(held for 5 min),then raised at 10 ℃/min to 250 ℃(held for 5 min),and electron impact ion source was employed.RESULTS A total of 62 compounds were identified by LC-MS,including 35 flavonoids,4 coumarins,6 alkaloids,6 terpenoids,3 amino acids,2 polyphenols,2 ketones and 4 others.A total of 101 volatile components were identified by GC-MS,including ketones,aldehydes,alcohols,esters,ethers,and acid.CONCLUSION This method can comprehensively analyze the chemical constituents of D.huoshanense flowers,and provide a scientific basis for elucidating its pharmacodynamic material basis.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Naturally occurring compounds have widely been applied to treat diverse pharmacological effects, including asthma, allergic diseases, antioxidants, inflammation, antibiotics, and cancer. Recent research has revealed the essential role of the thymic stromal lymphopoietin (TSLP) in regulating inflammatory responses at mucosal barriers and maintaining immune homeostasis. Asthma, inflammation, and chronic obstructive pulmonary disease are allergic disorders in which TSLP plays a significant role. Although TSLP’s role in type 2 immune responses has undergone comprehensive investigation, its involvement in inflammatory diseases and cancer has also been found to be expanding. However, investigating how to block the TSLP pathway using natural products has been limited. This paper summarizes the roles of various medicinal plants and their chemical components that effectively inhibit the TSLP pathway. In addition, we also highlight the contributions of several plant-derived compounds to treat allergic diseases via targeting TSLP. This review intends to offer innovative concepts to scientists investigating the use of naturally produced compounds and extracts for the treatment of allergic illnesses.

Naturally occurring compounds have widely been applied to treat diverse pharmacological effects, including asthma, allergic diseases, antioxidants, inflammation, antibiotics, and cancer. Recent research has revealed the essential role of the thymic stromal lymphopoietin (TSLP) in regulating inflammatory responses at mucosal barriers and maintaining immune homeostasis. Asthma, inflammation, and chronic obstructive pulmonary disease are allergic disorders in which TSLP plays a significant role. Although TSLP’s role in type 2 immune responses has undergone comprehensive investigation, its involvement in inflammatory diseases and cancer has also been found to be expanding. However, investigating how to block the TSLP pathway using natural products has been limited. This paper summarizes the roles of various medicinal plants and their chemical components that effectively inhibit the TSLP pathway. In addition, we also highlight the contributions of several plant-derived compounds to treat allergic diseases via targeting TSLP. This review intends to offer innovative concepts to scientists investigating the use of naturally produced compounds and extracts for the treatment of allergic illnesses.

Naturally occurring compounds have widely been applied to treat diverse pharmacological effects, including asthma, allergic diseases, antioxidants, inflammation, antibiotics, and cancer. Recent research has revealed the essential role of the thymic stromal lymphopoietin (TSLP) in regulating inflammatory responses at mucosal barriers and maintaining immune homeostasis. Asthma, inflammation, and chronic obstructive pulmonary disease are allergic disorders in which TSLP plays a significant role. Although TSLP’s role in type 2 immune responses has undergone comprehensive investigation, its involvement in inflammatory diseases and cancer has also been found to be expanding. However, investigating how to block the TSLP pathway using natural products has been limited. This paper summarizes the roles of various medicinal plants and their chemical components that effectively inhibit the TSLP pathway. In addition, we also highlight the contributions of several plant-derived compounds to treat allergic diseases via targeting TSLP. This review intends to offer innovative concepts to scientists investigating the use of naturally produced compounds and extracts for the treatment of allergic illnesses.

Objective:To evaluate the efficacy and safety of rituximab (RTX) in the treatment of idiopathic membranous nephropathy (IMN), explore the influencing factors of the therapeutic effect and construct a nomogram model for predicting the therapeutic effect.Methods:A single retrospective study was conducted in IMN patients in the First Affiliated Hospital of Zhejiang University School of Medicine from January 2017 to December 2022. All patients received monotherapy with RTX and were followed up for at least 12 months. RTX regimen adopted a B-cell guided regimen to achieve 0 cells/μl of peripheral blood CD19+ B cells through multiple administrations, followed by monitoring every 2?3 months and adding doses as needed to maintain this state. The complete response rate, partial response rate, and composite response rate at 6 months, 12 months and the end of follow up were analyzed. Logistic stepwise regression and R language were applied to construct a nomogram model for efficacy prediction. The receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test were used to internally validate the nomogram model.Results:A total of 147 IMN patients were included in the study, with age of 56 (47, 65) years, 99 (67.4%) males. There were 69 (46.9%) newly treated patients, 78 (53.1%) retreatment patients. The follow-up time was 14.4 (12.0, 15.0) months. The total RTX dose was 1 800 (1 200, 2 400) mg. The composite response rates at 6 months, 12 months and the end of the follow-up were 36.7% (54/147), 59.9% (88/147) and 63.3% (93/147), respectively. The complete remission rates at 6 months, 12 months and the end of the follow-up were 6.1% (9/147), 13.6% (20/147) and 19.7% (29/147), respectively. Logistic stepwise regression analysis showed that age ≥ 65 years ( OR=0.335, 95% CI 0.135?0.833), retreatment ( OR=0.333, 95% CI 0.144?0.771), high cholesterol ( OR=0.716, 95% CI 0.577?0.888), high serum creatinine ( OR=0.978, 95% CI 0.963?0.993) and B-cell reconstruction within 6 months ( OR=0.273, 95% CI 0.115?0.645) were independent correlated factors affecting composite remission. Based on these factors, a nomogram model for predicting the therapeutic effect of RTX in IMN patients was constructed. The ROC curve indicated that the accuracy of this model in predicting composite remission was good ( AUC=0.814, 95% CI 0.744-0.883). The calibration curve showed that the predicted composite response rate had a good fit with the actual response rate (Hosmer-Lemeshow test χ2=11.917, P=0.155). Conclusions:RTX has good efficacy and safety as a monotherapy for IMN patients. The constructed nomogram prediction model has high discrimination and accuracy to predict the efficacy of RTX treatment for IMN.

Objective:To explore the differences between the Phoenix sepsis scoring system including Phoenix sepsis score (PSS) and Phoenix-8 organ dysfunction score (hereinafter referred to as Phoenix-8) and the commonly used pediatric sepsis scores in evaluating clinical characteristics and prognostic analysis of pediatric patients with severe sepsis diagnosed under traditional standards, namely the diagnostic criteria from the 2005 International Pediatric Sepsis Consensus Conference.Methods:This study was a retrospective observational study. From December 2020 to March 2023, 202 pediatric patients with severe sepsis meeting the inclusion criteria were admitted to the Children's Hospital of Nanjing Medical University. Based on the sepsis diagnostic criteria outlined in the International Consensus Criteria for Pediatric Sepsis and Septic Shock (2024), the pediatric patients were categorized into a sepsis group and a non-sepsis group. Sepsis group was further subdivided into a death subgroup and a survival subgroup based on the outcomes. The age, hospitalization costs, disease outcome indicators (e.g., mortality rate and incidence of septic shock), major organ (e.g., heart, liver, lungs, and kidneys) damage and their correlations, as well as PSS, Phoenix-8 and commonly used pediatric sepsis scores (e.g., pediatric sequential organ failure assessment (pSOFA), pediatric risk of mortality score Ⅲ (PRISM Ⅲ), pediatric logistic organ dysfunction-2 score (PELOD-2), pediatric multiple organ dysfunction score (P-MODS), pediatric critical illness score (PCIS), and pediatric early warning score (PEWS)) were collected and compared. Receiver operating characteristic (ROC) curve and precision-recall curve were plotted to evaluate the predictive ability of PSS, Phoenix-8, and commonly used pediatric sepsis scores for mortality risk in pediatric patients with severe sepsis under traditional standards. Predictive performance was quantified using the area under the ROC curve (AUROC). Univariate logistic regression analysis was employed to quantify the odds ratios of PSS and Phoenix-8 for predicting mortality risk. Patients with severe sepsis under traditional standards were further stratified into subgroups based on complications and comorbidities, including central nervous system (CNS) diseases, multiple infections, cardiovascular system diseases, shock, and malignancies. The Hosmer-Lemeshow goodness-of-fit test was used to assess calibration of PSS and Phoenix-8, and the DeLong test was used to compare whether there were statistically significant differences in the AUROC of PSS and Phoenix-8 for predicting mortality risk among different subgroups of pediatric patients. Results:Compared with those in non-sepsis group, pediatric patients in sepsis group were significantly older ( Z=-2.92, P<0.05) with higher incidences of septic shock and mortality, hospitalization costs, PRISM Ⅲ, PEWS, pSOFA, PELOD-2, PSS, and Phoenix-8 (with χ2 values of 21.28 and 13.64, respectively, Z values of -1.99, -5.33, -5.10, -8.55, -6.91, -10.98, and -9.93, respectively, P<0.05), and lower PCIS ( Z=-3.34, P<0.05). Compared with those in survival subgroup, hospitalization costs, PSS, Phoenix-8, PRISM Ⅲ, PEWS, pSOFA, PELOD-2, and P-MODS of pediatric patients in death subgroup was significantly higher (with Z values of -2.50, -3.50, -2.47, -5.11, -3.84, -2.94, -3.61, and -3.04, respectively, P<0.05). Compared with those in survival subgroup, the incidences of lung damage and liver damage of pediatric patients in death subgroup were also significantly higher (with χ2 values of 6.20 and 10.94, respectively, P<0.05), and 64.7% (97/150) of patients exhibited two or more concurrent organ damage. For predicting mortality risk in pediatric patients with severe sepsis under traditional standards, the AUROC values for PRISM Ⅲ, PCIS, PEWS, pSOFA, PELOD-2, P-MODS, PSS, and Phoenix-8 were approximately 0.70, with optimal cutoff values of 17.5, 91.0, 5.5, 4.5, 2.5, 4.5, 3.5, and 4.5, respectively; PELOD-2 demonstrated the highest sensitivity (0.83); while PRISM Ⅲ, PSS, and Phoenix-8 showed high specificity (>0.80). Univariate logistic regression analysis showed that for every 1-point increase in the PSS within 24 hours of pediatric intensive care unit admission, the relative risk of mortality increased by 63.7% (with odds ratio of 1.64, 95% confidence interval of 1.34-1.99, P<0.05). Similarly, for every 1-point increase in the Phoenix-8, the relative risk of mortality increased by 37.5% (with odds ratio of 1.38, 95% confidence interval of 1.18-1.60, P<0.05). The AUROC values (around 0.80) of PSS and Phoenix-8 for predicting mortality risk in pediatric patients with severe sepsis combined with CNS diseases, multiple infections, and cardiovascular system diseases were relatively high. In contrast, the AUROC values (0.60-0.80) for predicting mortality risk in pediatric patients with severe sepsis combined with shock or malignant tumors were moderate. All models passed the Hosmer-Lemeshow goodness-of-fit test ( P>0.05). The DeLong test indicated no statistically significant differences in predictive ability between PSS and Phoenix-8 across subgroups of pediatric patients ( P>0.05). Conclusions:PSS and Phoenix-8 exhibited higher specificity than most of the commonly used pediatric sepsis scores in predicting mortality risk under traditional standards. Both scores performed much better in predicting the mortality risk in pediatric patients with severe sepsis combined with CNS diseases, multiple infections, and cardiovascular system diseases.