This study aims to investigate the molecular mechanism by which naringin alleviates cerebral ischemia/reperfusion(CI/R) injury through DRP1/LRRK2/MCU signaling axis. A total of 60 SD rats were randomly divided into the sham group, the model group, the sodium Danshensu group, and low-, medium-, and high-dose(50, 100, and 200 mg·kg~(-1)) naringin groups, with 10 rats in each group. Except for the sham group, a transient middle cerebral artery occlusion/reperfusion(tMCAO/R) model was established in SD rats using the suture method. Longa 5-point scale was used to assess neurological deficits. 2,3,5-Triphenyl tetrazolium chloride(TTC) staining was used to detect the volume percentage of cerebral infarction in rats. Hematoxylin-eosin(HE) staining and Nissl staining were employed to assess neuronal structural alterations and the number of Nissl bodies in cortex, respectively. Western blot was used to determine the protein expression levels of B-cell lymphoma-2 gene(Bcl-2), Bcl-2-associated X protein(Bax), cleaved cysteine-aspartate protease-3(cleaved caspase-3), mitochondrial calcium uniporter(MCU), microtubule-associated protein 1 light chain 3(LC3), and P62. Mitochondrial structure and autophagy in cortical neurons were observed by transmission electron microscopy. Immunofluorescence assay was used to quantify the fluorescence intensities of MCU and mitochondrial calcium ion, as well as the co-localization of dynamin-related protein 1(DRP1) with leucine-rich repeat kinase 2(LRRK2) and translocase of outer mitochondrial membrane 20(TOMM20) with LC3 in cortical mitochondria. The results showed that compared with the model group, naringin significantly decreased the volume percentage of cerebral infarction and neurological deficit score in tMCAO/R rats, alleviated the structural damage and Nissl body loss of cortical neurons in tMCAO/R rats, inhibited autophagosomes in cortical neurons, and increased the average diameter of cortical mitochondria. The Western blot results showed that compared to the sham group, the model group exhibited increased levels of cleaved caspase-3, Bax, MCU, and the LC3Ⅱ/LC3Ⅰ ratio in the cortex and reduced protein levels of Bcl-2 and P62. However, naringin down-regulated the protein expression of cleaved caspase-3, Bax, MCU and the ratio of LC3Ⅱ/LC3Ⅰ ratio and up-regulated the expression of Bcl-2 and P62 proteins in cortical area. In addition, immunofluorescence analysis showed that compared with the model group, naringin and positive drug treatments significantly decreased the fluorescence intensities of MCU and mitochondrial calcium ion. Meanwhile, the co-localization of DRP1 with LRRK2 and TOMM20 with LC3 in cortical mitochondria was also decreased significantly after the intervention. These findings suggest that naringin can alleviate cortical neuronal damage in tMCAO/R rats by inhibiting DRP1/LRRK2/MCU-mediated mitochondrial fragmentation and the resultant excessive mitophagy.
Animals ; Rats, Sprague-Dawley ; Reperfusion Injury/genetics* ; Flavanones/administration & dosage* ; Rats ; Dynamins/genetics* ; Male ; Brain Ischemia/genetics* ; Protein Serine-Threonine Kinases/genetics* ; Signal Transduction/drug effects* ; Humans ; Drugs, Chinese Herbal/administration & dosage*

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
Deep Learning ; Biological Ontologies ; Humans ; Big Data ; Biomedical Research

OBJECTIVES: To investigate the prognostic value of molecular minimal residual disease (Mol-MRD) monitored before and after allogeneic hematopoietic stem cell transplantation (HSCT) in pediatric acute myeloid leukemia (AML). METHODS: Clinical data of 71 pediatric AML patients who underwent HSCT between August 2016 and December 2023 were analyzed. Mol-MRD levels were dynamically monitored in MRD-positive patients, and survival outcomes were evaluated. RESULTS: No significant difference in the 3-year overall survival (OS) rate was observed between patients with pre-HSCT Mol-MRD ≥0.01% and <0.01% (77.3% ± 8.9% vs 80.4% ± 7.9%, P=0.705). However, patients with pre-HSCT Mol-MRD <1.75% had a significantly higher 3-year OS rate than those with Mol-MRD ≥1.75% (86.6% ± 5.6% vs 44.4% ± 16.6%, P=0.020). The median Mol-MRD level in long-term survivors was significantly lower than in non-survivors [0.61% (range: 0.04%-51.58%)] vs 10.60% (range: 1.90%-19.75%), P=0.035]. Concurrent flow cytometry-based MRD positivity was significantly higher in non-survivors (80% vs 24%, P=0.039). There was no significant difference in the 3-year overall survival rate between patients with Mol-MRD ≥0.01% and those with <0.01% at 30 days post-HSCT (P=0.527). For children with Mol-MRD <0.22% at 30 days post-HSCT, the 3-year overall survival rate was 80.4% ± 5.9%, showing no significant difference compared to those with molecular negativity (87.0% ± 7.0%) (P=0.523). CONCLUSIONS Patients with pre-HSCT Mol-MRD <1.75% or post-HSCT Mol-MRD <0.22% may achieve long-term survival outcomes comparable to Mol-MRD-negative cases through HSCT and targeted interventions.
Humans ; Hematopoietic Stem Cell Transplantation ; Neoplasm, Residual ; Leukemia, Myeloid, Acute/genetics* ; Child ; Male ; Female ; Child, Preschool ; Prognosis ; Adolescent ; Infant ; Transplantation, Homologous

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To analyze the research status and hotspots in the field of astragalus polysaccharides;To provide references for further research.Methods Research literature about astragalus polysaccharides was retrieved from CNKI,Wanfang Data,VIP,PubMed,and Web of Science databases from 1st,Jan.2013 to 1st,July 2023.NoteExpress 3.7 software was used to manage the literature and ultimately establish a database.Excel 2019,CiteSpace 6.2.2R and VOSviewer 1.6.18 were used to visually analyze the publication volume,authors,institutions,and keywords of the included literature.Results A total of 2 462 articles were included,with 1 284 Chinese articles and 1 178 English articles.The main research institutions were Gansu University of Chinese Medicine,Shandong University of Traditional Chinese Medicine,and Beijing University of Chinese Medicine.The core authors of Chinese literature were Liu Yongqi,Wang Hongxin,Lu Meili,etc.The core authors of English literature included Zhang Wei,Li Ke,Yang Xiaojun,etc.High-frequency keywords of Chinese literature included Astragali Radix,rats,polysaccharides,cell apoptosis,and oxidative stress,etc.High frequency keywords in English literature included expression,in vitro,oxidative stress,apoptosis,etc.Conclusion The research on astragalus polysaccharides focuses on their pharmacological effects and mechanisms.Intestinal flora,immune regulation,autophagy and apoptosis are the hot action mechanisms in this field.The focus of disease research involves tumor and diabetes,and antiviral,anti infection and other pharmacological effects are the research trend.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

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 evaluate the predictive value of dose-surface histogram(DSH)for radiation proctitis(RP)in prostate cancer(PCa)patients undergoing radiotherapy.Methods This prospective randomized controlled clinical trial included 380 PCa patients who underwent image-guided radiotherapy in the First Affiliated Hospital of Hebei Northern University from January 2018 to January 2023.Patients were randomly divided into observation group(n=200)and control group(n=180).The rectal dose distribution of patients in the two groups was evaluated by using DSH and dose-volume histogram(DVH),respectively.The receiver operating characteristic(ROC)curve was utilized to evaluate the predictive value of DSH for acute RP,with DVH serving as a reference.Results The difference was not statistically significant in clinical information such as age,KPS score,and body mass index(BMI)between the observation and control groups(P＞0.05),as well as in acute RP incidence(P＞0.05).There were significant differences in S40 and V40,S50 and V50,S60 and V60,S70 and V70,and S78 and V78 between the two groups(P＜0.05).S40,S50,V40,and V50 showed low efficacy(P＜0.001)in predicting acute RP at each level,with AUC≤0.700.S60 and V60 showed moderate efficacy(P＜0.001)in predicting acute RP at each level,with AUC 0.700-0.900.S70,S78,V70 and V78 showed high efficacy(P＜0.001)in predicting acute RP at each level,with AUC＞0.900.Conclusions The predictive value of DSH for rectal toxicity in patients with PCa is basically consistent with that of DVH.It is expected to become a novel and valuable tool for evaluating radiotherapy plans in the future.

Objective To comparatively analyze the clinical characteristics of primary bilateral macronodular adrenal hyperplasia(PBMAH)and adrenal cortisol-producing Adenoma(CPA),and enhance the understanding of two diseases.Methods The clinical data of 85 PBMAH patients(PBMAH group)and 195 CPA patients(CPA group)diagnosed at Department of Endocrinology,the First Medical Center of Chinese PLA General Hospital,from September 2014 to August 2024 were retrospectively analyzed.The demographic characteristics,comorbidities,biochemical indicators,adrenocorticotropic hormone-cortisol(ACTH-F)levels,and adrenal imaging features and treatment conditions were compared between the two groups.Results(1)General characteristics:Compared with CPA group,PBMAH group had older age at diagnosis and a higher proportion of male patients.(2)Clinical characteristics:Compared with CPA group,PBMAH group had a longer disease duration,a higher proportion of subclinical Cushing's syndrome(CS),and a higher proportion of hypertension,impaired glucose tolerance/diabetes,bone mass reduction or osteoporosis,with higher serum potassium levels,and the differences were statistically significant(P<0.01).(3)Hormone levels:Both PBMAH and CPA groups showed ACTH-F rhythm disorder,significantly increased cortisol levels and suppressed ACTH.Compared with PBMAH group,CPA group had stronger autonomous cortisol secretion ability,manifested by increased midnight serum cortisol(F0:00),16:00 serum cortisol(F16:00),24-hour urinary free cortisol(24 h UFC)levels and lower 8:00 serum ACTH(ACTH8:00)and 16:00 serum ACTH(ACTH16:00)(P<0.01).After low-dose dexamethasone suppression test(LDDST),CPA group showed lower suppression rates of ACTH and cortisol,and higher proportions of paradoxical elevation in serum cortisol and 24 h UFC compared with PBMAH(P<0.01).Conclusions PBMAH has a longer disease course and higher proportions of comorbid metabolic disorders than CPA,mostly manifested as subclinical Cushing's syndrome.CPA has stronger autonomous cortisol secretion ability,with cortisol less likely to be suppressed after LDDST and more obvious paradoxical elevation of cortisol and 24 h UFC.