Neurodegenerative disorders (NDDs) are prevalent chronic conditions characterized by progressive synaptic loss and pathological protein alterations. Increasing evidence suggested that mitochondrial quality control (MQC) serves as the key cellular process responsible for clearing misfolded proteins and impaired mitochondria. Herein, we provided a comprehensive analysis of the mechanisms through which MQC mediates the onset and progression of NDDs, emphasizing mitochondrial dynamic stability, the clearance of damaged mitochondria, and the generation of new mitochondria. In addition, traditional Chinese medicines (TCMs) and their active monomers targeting MQC in NDD treatment have been demonstrated. Consequently, we compiled the TCMs that show great potential in the treatment of NDDs by targeting MQC, aiming to offer novel insights and a scientific foundation for the use of MQC stabilizers in NDD prevention and treatment.

Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL) techniques, have introduced innovative approaches to metabolism research, enabling more accurate predictions and insights. This paper emphasizes computational and AI-driven methodologies, highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models (GEMs) at the omics level. Challenges still remain, including data heterogeneity and model interpretability. This work aims to provide valuable insights and references for researchers in drug discovery and development, ultimately contributing to the advancement of precision medicine.

Objective To investigate the effect and mechanism of Efferocytosis Relatived LncRNA (EFRL) on homocysteine-induced atherosclerosis in macrophage efferocytosis. Methods RAW264.7 cells were cultured in vitro, and the Control group (0 μmol/L Hcy) and Hcy intervention group (100 μmol/L Hcy) were set up. After GapmeR transfection of macrophages with Hcy intervention, EFRL knockdown negative control group (Hcy combined with LNA-NC) and EFRL knockdown group (Hcy combined with LNA-EFRL) were set up. High-throughput sequencing was applied for different expression of LncRNA MSTRG. 88917.16 (EFRL), UCSC was used to analyze its conservation, CPC and CPAT were used to analyze its ability to encode proteins, and GO and KEGG were used to analyze related biological functions. The localization of LncRNA EFRL in macrophages was analyzed by nucleoplasmic separation and RNA-FISH. Quantitative real-time PCR was used to detect the expression levels of LncRNA EFRL and its target gene SPAST in Hcy-treated macrophages. The apoptosis rate of Jurkat cells induced by UV was detected by flow cytometry. In vitro efferocytosis assay combined with immunofluorescence technique was used to analyze macrophage efferocytosis. ELISA was used to detect the levels of interleukin 1β(IL-1β) and IL-18. Results The new LncRNA MSTRG.88917.16 was identified and named EFRL(Efferocytosis Relatived LncRNA). UCSC, CPC and CPAT analyses showed that LncEFRL is highly conserved and does not have the ability to encode proteins. GO and KEGG analyses suggested that LncEFRL may be involved in macrophage efferocytosis. LncRNA EFRL was localized in the nucleus of macrophages as determined by nucleoplasmic separation and RNA-FISH. In comparison to the Control group, the expression levels of LncRNA EFRL and its target gene SPAST in the Hcy group were increased. In comparison to the Control group (0 min), the apoptosis rate of the experimental group (15, 30 min) Annexin V is more than 85%. Compared with Hcy combined with LNA-NC group, Hcy combined with LNA-EFRL group had enhanced macrophage efferocytosis and reduced levels of inflammatory factors. Compared with Hcy combined with LNA-NC group, the expression level of SPAST in Hcy combined with LNA-EFRL group was decreased. Conclusion Inhibition of EFRL expression can alleviate the process of Hcy inhibiting macrophage efferocytosis, and the mechanism is related to the regulation of the downstream target gene SPAST by EFRL.
RNA, Long Noncoding/physiology* ; Animals ; Homocysteine ; Mice ; Macrophages/drug effects* ; Humans ; RAW 264.7 Cells ; Atherosclerosis/chemically induced* ; Apoptosis/genetics* ; Phagocytosis/genetics* ; Jurkat Cells ; Interleukin-1beta/genetics* ; Efferocytosis

Objective To develop and validate a model to predict the risk of radiation-induced lung injury (RILI) and assess its clinical feasibility. Methods Clinical data from 125 patients with non-small cell lung cancer (NSCLC) were included in the study. The patients were divided into training group (88 cases) and validation group (38 cases). Key predictive factors were identified using univariate and multivariate logistic regression analyses combined with least absolute shrinkage and selection operator (LASSO) regression. A predictive model was constructed and evaluated using a nomogram, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results The key variables identified by the model were tumor volume (P = 0.017), Eastern Cooperative Oncology Group performance status score (P = 0.035), 95% of the minimum dose to the target volume (P = 0.028), percentage of bilateral lung volume receiving 20 Gy of radiation (P < 0.001), and neutrophil-to-lymphocyte ratio (P = 0.021). The ROC curve showed that the areas under the curve (AUC) for the model in the training and validation groups were 0.987 and 0.992, respectively, indicating good predictive ability. The calibration curve and decision curve further confirmed the accuracy and clinical practicability of the model. Conclusion The predictive model proposed in this study can accurately assess the risk of developing RILI in patients with NSCLC who have undergone radiotherapy, demonstrating its potential value in clinical practice.

Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process.Recent advancements in artificial intelligence(AI),particularly in machine learning(ML)and deep learning(DL)techniques,have introduced innovative approaches to metabolism research,enabling more accurate predictions and insights.This paper emphasizes computational and AI-driven methodologies,highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models(GEMs)at the omics level.Challenges still remain,including data heterogeneity and model interpretability.This work aims to provide valuable insights and references for researchers in drug discovery and development,ultimately contributing to the advancement of precision medicine.

Neurodegenerative disorders(NDDs)are prevalent chronic conditions characterized by progressive synaptic loss and pathological protein alterations.Increasing evidence suggested that mitochondrial quality control(MQC)serves as the key cellular process responsible for clearing misfolded proteins and impaired mitochondria.Herein,we provided a comprehensive analysis of the mechanisms through which MQC mediates the onset and progression of NDDs,emphasizing mitochondrial dynamic stability,the clearance of damaged mitochondria,and the generation of new mitochondria.In addition,traditional Chinese medicines(TCMs)and their active monomers targeting MQC in NDD treatment have been demonstrated.Consequently,we compiled the TCMs that show great potential in the treatment of NDDs by targeting MQC,aiming to offer novel insights and a scientific foundation for the use of MQC stabilizers in NDD prevention and treatment.

Objective:To establish a rapid detection method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry(MALDI-TOF MS)for drug resistance of Carbapenem-resistant Enterobacteriaceae(CRE),so as to provide basis for enhancing clinically diagnostic effect.Methods:A total of 120 suspected CRE strains that were isolated from the clinical laboratory of Jiading District Central Hospital Affiliated to Shanghai University of Medicine&Health Sciences during October 2023 and September 2024 were collected,and the experimental detection of 44 strains was finally completed.The 44 suspected CRE bacterial strains were all from hospitalized patients.A rapid detection protocol based on the MALDI-TOF MS platform was initially established and improved,and experimental detection was carried out using the fully automatic rapid microbial mass spectrometry detection system(VITEK MS).The results of identification review and antimicrobial susceptibility test(AST)for VitEK-2 Compact strain was used as the reference standard.The screening results and detection efficacy of two kinds of detection methods were compared.Results:In the 44 suspected CRE strains,31 CRE strains were screened out by MALDI-TOF MS detection,and 31 CRE stains were screened out by VITEK-2 Compact recheck detection.In them,one strain of Enterobacter cloacae was confirmed as negativity by the MALDI-TOF MS test,but it was confirmed as CRE by the VITEK-2 Compact test.One strain of Klebsiella pneumoniae was confirmed as positivity by the MALDI-TOF MS test,but it was confirmed as mediator for carbapenem drugs by the VITEK-2 Compact test.The tested results of VITEK-2 Compact was used as standard,the results of four-grid table(2×2 contingency table)statistical method indicated that the sensitivity,specificity,accuracy rate and Youden index(R)of MALDI-TOF MS were respectively 96.7%,92.3%,100%and 89.1%.Conclusion:The rapid detection method based on the MALDI-TOF MS platform for CRE drug resistance has simple operation process and high accuracy rate,which can provide a basis for the application of targeted antibacterial drugs at early stage.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.