OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers. METHODS: Based on the endoscopic appearance of peptic ulcers, color features were extracted to distinguish active bleeding (Forrest I) from non-bleeding ulcers (Forrest II and III). The edge and texture features were used to describe the morphology and appearance of the ulcers in different grades. By integrating deep features extracted from a deep learning network with manually extracted visual features, a multi-feature representation of endoscopic images was created to predict the risk of rebleeding of peptic ulcers. RESULTS: In a dataset consisting of 3573 images from 708 patients with Forrest classification, the proposed multi-feature fusion model achieved an accuracy of 74.94% in the 6-level rebleeding risk classification task, outperforming the experienced physicians who had a classification accuracy of 59.9% (P<0.05). The F1 scores of the model for identifying Forrest Ib, IIa, and III ulcers were 90.16%, 75.44%, and 77.13%, respectively, demonstrating particularly good performance of the model for Forrest Ib ulcers. Compared with the first model for peptic ulcer rebleeding classification, the proposed model had improved F1 scores by 5.8%. In the simplified 3-level risk (high-risk, low-risk, and non-endoscopic treatment) classification task, the model achieved F1 scores of 93.74%, 81.30%, and 73.59%, respectively. CONCLUSIONS The proposed multi-feature fusion model integrating deep features from CNNs with manually extracted visual features effectively improves the accuracy of rebleeding risk classification for peptic ulcers, thus providing an efficient diagnostic tool for clinical assessment of rebleeding risks of peptic ulcers.
Humans ; Deep Learning ; Peptic Ulcer ; Risk Assessment ; Peptic Ulcer Hemorrhage ; Recurrence

OBJECTIVES: To investigate the therapeutic mechanism of Huoxue Shufeng Granules (HXSFG) for alleviating central sensitization in a mouse model of chronic migraine (CM). METHODS: We analyzed the main chemical components of HXSFG through literature review and explored their pharmacological mechanisms by bioinformatics analyses. In a male C57BL/6J mouse model of CM established by intraperitoneal injections of nitroglycerin (10 mg/kg) every other day (5 injections), the effects of gavage with low, and high doses of HXSFG or intraperitoneal injections of topiramate for ameliorating central sensitization were evaluated using Von Frey test and a hot plate apparatus; the changes in expressions of inflammatory factors, the proteins in the TLR4/NF‑κB signaling pathway, and activation of c-Fos and CGRP were detected using RT-qPCR, Western blotting and immunofluorescence staining. RESULTS: Network pharmacology analysis suggested that the main active components in HXSFG for alleviating CM included formononetin, paeoniflorin, quercetin, and tanshinone. Gene Ontology (GO) enrichment analysis identified 492 GO entries, comprising 366 biological processes, 46 cellular components, and 80 molecular functions. KEGG pathway enrichment analysis indicated that the Toll-like receptor and NF‑κB signaling pathways were crucial in mediating the therapeutic effects of HXSFG on CM. In the mouse models of CM, both topiramate and HXSFG treatments alleviated the symptoms of central sensitization, evidenced by improved mechanical and thermal pain thresholds in the mice. HXSFG significantly reduced the expression of c-Fos and CGRP, improved inflammatory markers, and downregulated the expressions of TLR4, p-NF‑κB, IL-1β, and TNF‑α proteins in the mouse models. CONCLUSIONS HXSFG effectively alleviates central sensitization in CM mice by modulating the inflammatory pathways and inhibiting the TLR4/ NF-κB signaling pathway, suggesting its potential as a therapeutic option for CM.
Animals ; Toll-Like Receptor 4/metabolism* ; NF-kappa B/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Mice ; Male ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Migraine Disorders/metabolism* ; Disease Models, Animal ; Inflammation

OBJECTIVES: To investigate the expression of parathyroid hormone-like hormone (PTHLH) in hepatocellular carcinoma (HCC) and analyze its correlation with clinical prognosis, its regulatory effects on HCC cell behaviors, and the signaling pathways mediating its effects. METHODS: We analyzed the differential expression of PTHLH in HCC and adjacent tissues and its association with patient prognosis based on data from TCGA and GEO databases and from 70 HCC patients treated in our hospital. The effects of PTHLH knockdown and overexpression on proliferation, migration, and invasion of cultured HCC cells were investigated using CCK-8 assay, colony formation assay, Transwell migration and invasion assays, and the signaling pathways activated by PTHLH were detected using Western blotting. RESULTS: TCGA and GEO database analysis showed significant overexpression of PTHLH mRNA in HCC tissues, which was associated with poor prognosis of the patients (P<0.05). High PTHLH mRNA expression was a probable independent prognostic risk factor for HCC (P<0.05). In the clinical samples, PTHLH mRNA and protein expressions were significantly higher in HCC tissues than in the adjacent tissues (P<0.001 or 0.01). Univariate and multivariate Cox regression analyses suggested that high PTHLH mRNA expression was an independent risk factor to affect postoperative disease-free survival of HCC patients (P<0.05). The prognostic prediction model based on PTHLH mRNA expression showed an improved accuracy for predicting the risk of postoperative recurrence in HCC patients. In cultured HCC cells, PTHLH overexpression significantly promoted cell proliferation, colony formation, migration and invasion, and caused activation of the ERK/JNK signaling pathway in Huh7 and Hep3B cells. CONCLUSIONS High PTHLH expression promotes HCC progression and is associated with poor patient prognosis. Its pro-tumor effects may be mediated by activation of the ERK/JNK signaling pathway.
Humans ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Prognosis ; Cell Proliferation ; Parathyroid Hormone-Related Protein/genetics* ; Cell Line, Tumor ; Cell Movement ; Disease Progression ; Signal Transduction ; Male ; RNA, Messenger/genetics* ; Female

OBJECTIVES: To investigate the mechanisms of Modified Chaihu Guizhi Decoction (MCGD) for ameliorating anxiety- and depression-like behaviors in a mouse model of chronic unpredictable mild stress (CUMS). METHODS: The main chemical constituents of MCGD were identified through literature review, and network pharmacology analysis was performed to predict the potential pharmacological mechanisms of MCGD. For in vivo validation, male C57BL/6J mice were randomized into control group, CUMS model group, fluoxetine (FLX) treatment group, and low- and high-dose MCGD treatment groups (n=15), and in all but the control group, CUMS models were established by daily exposure to two randomized stressors for 28 consecutive days. Starting from 3 days prior to modeling, MCGD and fluoxetine treatments were administered daily via gavage and intraperitoneal injection, respectively. Depression- and anxiety-like behaviors of the mice were assessed using sucrose preference test, forced swim test, open field test and elevated plus maze test. The changes in mRNA expressions of the clock genes and inflammatory markers and expressions of the JAK2/STAT3 signaling proteins were detected using RT-qPCR and Western blotting, and immunofluorescence staining was used to detect microglia activation in the mice. RESULTS: The key active compounds in MCGD identified by network pharmacology analysis included quercetin, acacetin, formononetin, nobiletin, and baicalein. GO analysis identified 607 enriched pathways, and KEGG pathway enrichment revealed significant involvement of the JAK2/STAT3 and NF-κB signaling pathways. In the mouse models of CUMS, treatment with both fluoxetine and MCGD significantly alleviated anxiety- and depression-like behaviors. MCGD treatment significantly reduced Iba1 expression, improved the inflammatory markers, reversed the decrease in clock gene circadian rhythm amplitude, and obviously downregulated the expressions of JAK2, p-STAT3, p-NF-κB, IL-1β, and IL-6 proteins. CONCLUSIONS MCGD effectively alleviates anxiety- and depression-like behaviors in CUMS mice by modulating the inflammatory pathways and inhibiting the JAK2/STAT3 signaling pathway.
Animals ; Janus Kinase 2/metabolism* ; STAT3 Transcription Factor/metabolism* ; Signal Transduction/drug effects* ; Depression/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Male ; Mice, Inbred C57BL ; Anxiety/drug therapy* ; Stress, Psychological ; Disease Models, Animal

The level of urinary albumin is a critical indicator for the early diagnosis and management of chronic kidney disease (CKD). However, existing methods for detecting albumin are not conducive to point-of-care testing due to the complexity of reagent addition and incubation processes. This study presents a smartphone-integrated handheld automated biochemical analyzer (sHABA) designed for point-of-care testing of urinary albumin. The sHABA features a pre-loaded, disposable reagent cassette with reagents for the albumin assay arranged in the order of their addition within a hose. The smartphone-integrated analyzer can drive the reagents following a preset program, to enable automatic sequential addition. The sHABA has a detection limit for albumin of 5.9 mg/L and a linear detection range from 7 to 450 mg/L. The consistency of albumin level detection in 931 urine samples using sHABA with clinical tests indicates good sensitivity (95.78%) and specificity (90.16%). This research advances the field by providing an automated detection method for albumin in a portable device, allowing even untrained individuals to monitor CKD in real time at the patient's bedside. In the context of promoting tiered diagnosis and treatment, the sHABA has the potential to become an essential tool for the early diagnosis and comprehensive management of CKD and other chronic conditions.

OBJECTIVE To investigate the mechanisms of paeoniflorin （PF） in anti-hyperprolactinemia （HPRL）. METHODS Twenty-four female SD rats were divided into blank control group （intragastric administration of 5% gum arabic solution）， olanzapine group （model group， intragastric administration of 5 mg/kg olanzapine suspension）， and PF group （intragastric administration of 5 mg/kg olanzapine suspension， followed by gavaging with 50 mg/kg PF solution 2 hours later） with 8 rats in each group. Once a day， continuously model/administer until the plasma prolactin （PRL） levels in the olanzapine group were twice as high as those in the blank control group. PRL levels were measured. The changes in gut microbiota of rats were analyzed， including assessments of α-diversity （Simpson， Chao1， and Shannon indexes）， β-diversity， species composition analysis （at the phylum and genus levels）， and microbiome LEfSe analysis. Fecal untargeted metabolomics technology was employed to analyze the effects of PF on the fecal metabolomics of rats， including multivariate statistical analysis， screening of differential metabolites， and pathway enrichment analysis. Spearman correlation analysis was performed to examine the correlations between differential microbiota and differential fecal metabolites. RESULTS PF significantly reduced serum PRL levels of rats in olanzapine group （P＜0.05）. 16S rRNA sequencing revealed that PF improved the α-diversity and β-diversity of gut microbiota in HPRL rats （P＜0.05）， restoring them to levels similar to the blank control group. At the phylum level， PF significantly reduced the relative abundance of Firmicutes and Desulfobacterota， while increasing the relative abundance of Verrucomicrobiota in HPRL rats （all P＜0.05）. At the genus level， PF reversed the relative abundance of Desulfovibrio，Allobaculum， and Prevotellaceae_NK3B31_group， etc （all P＜0.05）. The results of LEfSe analysis revealed that PF significantly enriched microbial taxa such as Actinobacteriota，Staphylococcales， Corynebacteriales， etc （all P＜0.05）. Metabolomics analysis identified 51 differential metabolites， with key metabolic pathways enriched in steroid hormone biosynthesis， prostate cancer， ovarian steroidogenesis， etc. Correlation analysis showed that the relative abundance of gut microbiota such as Desulfovibrio and Aerococcus was significantly correlated with the levels of steroid hormone metabolites such as tetrahydrocortisol and adrenosterone （P＜0.05）. CONCLUSIONS PF alleviates PRL by modulating gut microbiota structure in HPRL rats （including significantly reducing the relative abundance of Desulfovibrio， Allobaculum and Aerococcus， as well as significantly increasing the relative abundance of Ruminococcaceae_UBA1819 and Muribaculum）， and regulating steroid hormone pathways， then exerting its anti-HPRL effect.

The level of urinary albumin is a critical indicator for the early diagnosis and management of chronic kidney disease(CKD).However,existing methods for detecting albumin are not conducive to point-of-care testing due to the complexity of reagent addition and incubation processes.This study presents a smartphone-integrated handheld automated biochemical analyzer(sHABA)designed for point-of-care testing of urinary albumin.The sHABA features a pre-loaded,disposable reagent cassette with re-agents for the albumin assay arranged in the order of their addition within a hose.The smartphone-integrated analyzer can drive the reagents following a preset program,to enable automatic sequential addition.The sHABA has a detection limit for albumin of 5.9 mg/L and a linear detection range from 7 to 450 mg/L.The consistency of albumin level detection in 931 urine samples using sHABA with clinical tests indicates good sensitivity(95.78％)and specificity(90.16％).This research advances the field by providing an automated detection method for albumin in a portable device,allowing even untrained individuals to monitor CKD in real time at the patient's bedside.In the context of promoting tiered diagnosis and treatment,the sHABA has the potential to become an essential tool for the early diagnosis and comprehensive management of CKD and other chronic conditions.

OBJECTIVE To develop a population pharmacokinetic （PPK） model for mycophenolate mofetil active metabolite mycophenolic acid （MPA） in children with primary IgA nephropathy， explore the factors affecting the pharmacokinetic parameters of MPA， and provide a basis for clinical individualized therapy. METHODS Retrospective collection was conducted on 636 concentrations and clinical data from 47 pediatric patients with primary IgA nephropathy. PPK analysis was carried out by using the nonlinear mixed-effects model； the covariates were tested with a stepwise method. Goodness-of-fit plots， Bootstrap and visual predictive check were employed to evaluate the final model. RESULTS The pharmacokinetics of MPA in children with IgA nephropathy in vivo conformed to the first-order absorption and elimination two-compartment model （objective function value of 3 276.31）. Covariate analysis suggested that body weight and albumin （ALB） levels were significant influencing factors on apparent clearance rate and apparent distribution volume. The typical values of PPK parameters of MPA in the final model were as follows： the central room had a distributed volume of 5.79 L， the clearance rate was 4.06 L/h， the volume of peripheral ventricular distribution was 430.93 L， the clearance rate between compartments was 15.40 L/h， the oral absorption rate constant was 1.29 h－1. After verification， most of the predicted corrected observed concentration points were within the 90% confidence interval of the predicted corrected simulated concentration， indicating that the MPA final model had good predictive performance. CONCLUSIONS The PPK model of MPA in children with primary IgA nephropathy is established in this study， identifying body weight and ALB levels are significant factors affecting MPA metabolism.