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.

ObjectiveTaking Aurantii Fructus Immaturus juice（AFI）-processed Atractylodis Macrocephalae Rhizoma（AMR） as an example， this study aims to systematically compare the volatile and non-volatile components of AMR and its processed products， investigate the key differential components， evaluate their anti-inflammatory activities， and elucidate the synergistic mechanism of processing. MethodsThe chemical compositions of volatile and non-volatile components in AMR and AFI-processed AMR were systematically characterized using gas chromatography-mass spectrometry（GC-MS） and ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS）， with relative mass fractions and response values determined separately. Volatile components were identified through searches in the National Institute of Standards and Technology（NIST）17 database， comparison with retention index（RI） and fragmentation pattern matching. Non-volatile components were identified by searching Waters Traditional Chinese Medicine （TCM） spectral library， in conjunction with PubChem and MassBank， characteristic fragmentation patterns and response values were also used to support identification. Differential components were screened using principal component analysis（PCA）， orthogonal partial least squares-discriminant analysis（OPLS-DA）， with variable importance in the projection（VIP） value >1. Components with high log₂fold change（FC） among major differential groups were selected as those exhibiting significant changes before and after processing. The anti-inflammatory activity of the differential compounds was evaluated by assessing their effects on nitric oxide（NO） production in a lipopolysaccharide（LPS）-induced RAW264.7 macrophage model. Enzyme-linked immunosorbent assay（ELISA） was used to detect the effects of the differential components on tumor necrosis factor（TNF）-α， interleukin（IL）-1β， IL-6， and monocyte chemotactic protein（MCP）-1 levels， and immunofluorescence（IF） was employed to assess their effects on nuclear transcription factor（NF）-κB p65 translocation， thereby elucidating the underlying molecular mechanisms. ResultsA total of 36 compounds were identified in the volatile components of AMR and AFI-processed AMR， among which， sesquiterpenes and monoterpenes were significantly increased after processing. In the non-volatile components， 36 compounds were identified， and the main differential components were flavonoids， sesquiterpenoids， and triterpenoids. Flavonoids were the primary differential components distinguishing AMR from its processed products， representing compounds directly introduced during processing. Five compounds， including atractylenolide Ⅲ， tangeritin， nobiletin， hesperidin and narirutin， were selected as representatives of three classes based on their most prominent differential expression among different compound types for subsequent anti-inflammatory activity studies. The results showed that 100 μmol·L^-1 tangerine and narirutin could significantly inhibit LPS-induced NO production（P<0.01） in a concentration-dependent manner. Tangeritin was able to significantly inhibit the levels of TNF-α and MCP-1 secreted by RAW264.7（P<0.05）， while narirutin significantly inhibited the levels of TNF-α， IL-1β， MCP-1 and IL-6（P<0.01）. IF revealed that both tangeritin and narirutin significantly blocked the translocation of NF-κB p65 from the cytoplasm to the nucleus. ConclusionAFI-processed AMR significantly alters the chemical composition profile of AMR， and the newly introduced flavonoid components during processing may be key to its enhanced anti-inflammatory effects.

Food-derived bioactive peptides (FBPs), particularly those with ten or fewer amino acid residues and a molecular weight below 1300 Da, have gained increasing attention for their safe, diverse structures and specific biological activities. The development of FBP-based functional foods and potential medications depends on understanding their structure‒activity relationships (SARs), stability, and bioavailability properties. In this review, we provide an in-depth overview of the roles of FBPs in treating various diseases, including Alzheimer's disease, hypertension, type 2 diabetes mellitus, liver diseases, and inflammatory bowel diseases, based on the literature from July 2017 to Mar. 2023. Subsequently, attention is directed toward elucidating the associations between the bioactivities and structural characteristics (e.g., molecular weight and the presence of specific amino acids within sequences and compositions) of FBPs. We also discuss in silico approaches for FBP screening and their limitations. Finally, we summarize recent advancements in formulation techniques to improve the bioavailability of FBPs in the food industry, thereby contributing to healthcare applications.
Humans ; Peptides/therapeutic use* ; Structure-Activity Relationship ; Functional Food ; Diabetes Mellitus, Type 2/drug therapy* ; Biological Availability ; Alzheimer Disease/drug therapy* ; Inflammatory Bowel Diseases/drug therapy* ; Hypertension/drug therapy* ; Liver Diseases/drug therapy* ; Bioactive Peptides, Dietary

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

BACKGROUND: Temozolomide (TMZ) resistance is a significant challenge in treating glioblastoma (GBM). Collagen remodeling has been shown to be a critical factor for therapy resistance in other cancers. This study aimed to investigate the mechanism of TMZ chemoresistance by GBM cells reprogramming collagens. METHODS: Key extracellular matrix components, including collagens, were examined in paired primary and recurrent GBM samples as well as in TMZ-treated spontaneous and grafted GBM murine models. Human GBM cell lines (U251, TS667) and mouse primary GBM cells were used for in vitro studies. RNA-sequencing analysis, chromatin immunoprecipitation, immunoprecipitation-mass spectrometry, and co-immunoprecipitation assays were conducted to explore the mechanisms involved in collagen accumulation. A series of in vitro and in vivo experiments were designed to assess the role of the collagen regulators prolyl 4-hydroxylase subunit alpha 1 (P4HA1) and yes-associated protein (YAP) in sensitizing GBM cells to TMZ. RESULTS: This study revealed that TMZ exposure significantly elevated collagen type I (COL I) expression in both GBM patients and murine models. Collagen accumulation sustained GBM cell survival under TMZ-induced stress, contributing to enhanced TMZ resistance. Mechanistically, P4HA1 directly binded to and hydroxylated YAP, preventing ubiquitination-mediated YAP degradation. Stabilized YAP robustly drove collagen type I alpha 1 ( COL1A1) transcription, leading to increased collagen deposition. Disruption of the P4HA1-YAP axis effectively reduced COL I deposition, sensitized GBM cells to TMZ, and significantly improved mouse survival. CONCLUSION P4HA1 maintained YAP-mediated COL1A1 transcription, leading to collagen accumulation and promoting chemoresistance in GBM.
Temozolomide ; Humans ; Glioblastoma/drug therapy* ; Animals ; Mice ; Cell Line, Tumor ; Drug Resistance, Neoplasm/genetics* ; YAP-Signaling Proteins ; Hydroxylation ; Dacarbazine/pharmacology* ; Adaptor Proteins, Signal Transducing/metabolism* ; Transcription Factors/metabolism* ; Collagen/biosynthesis* ; Collagen Type I/metabolism* ; Prolyl Hydroxylases/metabolism* ; Antineoplastic Agents, Alkylating/therapeutic use*

OBJECTIVE: To review the research progress of strontium (Sr) modified β-tricalcium phosphate composite biomaterials (SrTCP) promoting osteogenesis through immune regulation, and provides reference and theoretical support for the further development and research of SrTCP bone repair materials in bone tissue engineering in the future. METHODS: The literature about SrTCP promoting osteogenesis through immune regulation at home and abroad in recent years was extensively reviewed, and the preparation methods, immune mechanism and application of promoting osteogenesis were summarized and analyzed. RESULTS: The preparation methods of SrTCP include solid-state reaction sintering method, solution combustion quenching method, direct doping method, ion substitution method, etc. SrTCP has immune regulatory effects, which can play an immune regulatory role in inducing macrophage polarization, inducing angiogenesis and anti oxidative stress to promote osteogenesis. CONCLUSION At present, studies have shown that SrTCP can promote bone defect repair through immune regulation. Subsequent studies can start from the control of the optimal repair concentration and release rate of Sr, and further clarify the specific mechanism of SrTCP in promoting angiogenesis and anti oxidative stress, which is helpful to develop new materials for bone defect repair.
Calcium Phosphates/pharmacology* ; Strontium/pharmacology* ; Biocompatible Materials/pharmacology* ; Humans ; Osteogenesis/drug effects* ; Tissue Engineering/methods* ; Bone Substitutes/pharmacology* ; Bone Regeneration/drug effects* ; Animals ; Tissue Scaffolds/chemistry* ; Neovascularization, Physiologic/drug effects* ; Macrophages/immunology*

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Glaucoma is a multifactorial degenerative optic neuropathy, and its irreversible and blinding pathological characteristics mainly come from the damage to the optic nerve, namely glaucomatous optic neuropathy(GON). The difficulty in the treatment of GON lies in the early intervention, and currently there is no optic neuroprotective drug for the treatment of all types of GON. The death of retinal ganglion cells(RGCs)is the core pathological change caused by various pathogenic mechanisms of GON. Recent studies have found that the widespread second messenger cyclic adenosine 3', 5' -monophosphate(cAMP)and its downstream effector protein kinase A(PKA)signal cascade play an important role in the pathogenesis of GON. It can also inhibit the apoptosis of RGCs and play a protective and therapeutic role in glaucoma. Therefore, this article reviews the role of cAMP/PKA pathway in the pathophysiological development of GON, focusing on its effects on glaucoma intraocular pressure regulation, oxidative stress, neuroinflammation and optic nerve degeneration, in order to find a common central regulatory target for the optic nerve damage caused by different pathological mechanisms of GON and promote the further understanding and clinical treatment of this disease.

Objective To explore the diagnostic value of MRI radiomics analysis in mild carpal tunnel syndrome(CTS).Methods Seventy patients with mild CTS and 86 healthy volunteers who underwent wrist MRI examination were retrospectively selected.MRI fat-suppressed proton density weighted imaging(PDWI)were imported into 3D Slicer software,and the region of interest(ROI)delineation was performed by two radiologists independently.The 830 radiomics parameters were extracted,including first-order fea-tures,shape features,texture features,and wavelet-transform features.Radiomics parameter selection was performed through observer intraclass correlation coefficient(ICC),correlation analysis,and multivariate logistic regression.Five diagnostic models were estab-lished,including logistic regression,support vector machine,naive Bayes,decision tree,and random forest.Receiver operating charac-teristic(ROC)curve was used to analyze the diagnostic efficiency of the models.Results Seven radiomics features were selected for inclusion in the diagnostic models.The logistic regression model demonstrated the best performance,with an area under the curve(AUC)of 0.91[95%confidence interval(CI)0.86-0.96],a sensitivity of 88.63%,and a specificity of 89.00%in the training group.In the test group,the AUC was 0.92(95%CI 0.85-0.97),with a sensitivity of 90.48%and a specificity of 84.62%.Conclusion MRI radiomics analysis can be used to diagnose mild CTS,and the logistic regression model demonstrates superior diagnostic per-formance.