Malignant tumors are a class of diseases that pose a serious threat to human health, and radiotherapy (RT) is one of the essential treatment modalities. Virtual reality (VR) technology, with its unique characteristics of immersion, interactivity, and imagination, demonstrates tremendous application potential in radiotherapy. Several randomized controlled trials have confirmed that VR, as a non-pharmacological intervention, can effectively alleviate anxiety in patients, especially children, and improve treatment adherence. In medical education and professional training, VR provides safe training platforms for radiation oncologists, medical physicists, and radiation therapists through the construction of high-fidelity virtual environments. These platforms can be used for training skills in target volume delineation, treatment plan evaluation, and equipment quality control, thereby significantly improving training efficiency and quality. Furthermore, VR technology enables three-dimensional visualization and intuitive evaluation of treatment plans, facilitates multidisciplinary team (MDT) collaboration, and provides new paradigms for the development of real-time guidance technologies that integrate with augmented reality. This article systematically reviews the current applications of VR technology in the clinical practice, medical education, radiation protection, and scientific research of radiotherapy. It also discusses future prospects for integrating VR with artificial intelligence (AI), augmented reality (AR), and haptic feedback technologies. This review provides a valuable reference for the standardized application of VR technology in radiotherapy and radiation protection in China.

OBJECTIVE To screen and quantitatively analyze the quality marker （Q-Marker） associated with anti-renal fibrosis in Shenqi shenshuai mixture （SQSS）， so as to provide references for the analysis of pharmacodynamic substances and new drug transformation of SQSS. METHODS The chemical components of SQSS were characterized by untargeted metabolomics. Combined with network pharmacology， gene expression omnibus （GEO） and connectivity map （CMAP） databases， the anti-renal fibrosis Q-Markers of SQSS were screened. HPLC-MS/MS was applied to determine the contents of Q-Markers in 10 batches of SQSS. RESULTS A total of 1 319 compounds were identified from SQSS via untargeted metabolomics，and 84 active ingredients with anti-renal fibrosis activity were further screened out， such as formononetin. Nine anti-renal fibrosis Q-Markers were obtained by network pharmacology and GEO database， including berberine， quercetin， honokiol， nicotinamide， daidzein， coumarin， kaempferol， formononetin and amygdalin. The quantitative results showed that the average contents of the above components （excluding amygdalin） in 10 batches of SQSS were 2.523， 1.942， 26.848， 1.415， 0.692， 0.171， 0.374， 7.401 μg/mL， respectively. CONCLUSIONS Nine anti-renal fibrosis Q-Markers of SQSS were screened in this study， and the contents of eight among them were determined. The results can provide a basis for elucidating the pharmacodynamic material basis and promoting the new drug transformation of SQSS.

OBJECTIVE To screen and quantitatively analyze the quality marker （Q-Marker） associated with anti-renal fibrosis in Shenqi shenshuai mixture （SQSS）， so as to provide references for the analysis of pharmacodynamic substances and new drug transformation of SQSS. METHODS The chemical components of SQSS were characterized by untargeted metabolomics. Combined with network pharmacology， gene expression omnibus （GEO） and connectivity map （CMAP） databases， the anti-renal fibrosis Q-Markers of SQSS were screened. HPLC-MS/MS was applied to determine the contents of Q-Markers in 10 batches of SQSS. RESULTS A total of 1 319 compounds were identified from SQSS via untargeted metabolomics，and 84 active ingredients with anti-renal fibrosis activity were further screened out， such as formononetin. Nine anti-renal fibrosis Q-Markers were obtained by network pharmacology and GEO database， including berberine， quercetin， honokiol， nicotinamide， daidzein， coumarin， kaempferol， formononetin and amygdalin. The quantitative results showed that the average contents of the above components （excluding amygdalin） in 10 batches of SQSS were 2.523， 1.942， 26.848， 1.415， 0.692， 0.171， 0.374， 7.401 μg/mL， respectively. CONCLUSIONS Nine anti-renal fibrosis Q-Markers of SQSS were screened in this study， and the contents of eight among them were determined. The results can provide a basis for elucidating the pharmacodynamic material basis and promoting the new drug transformation of SQSS.

Malignant tumors are a class of diseases that pose a serious threat to human health, and radiotherapy (RT) is one of the essential treatment modalities. Virtual reality (VR) technology, with its unique characteristics of immersion, interactivity, and imagination, demonstrates tremendous application potential in radiotherapy. Several randomized controlled trials have confirmed that VR, as a non-pharmacological intervention, can effectively alleviate anxiety in patients, especially children, and improve treatment adherence. In medical education and professional training, VR provides safe training platforms for radiation oncologists, medical physicists, and radiation therapists through the construction of high-fidelity virtual environments. These platforms can be used for training skills in target volume delineation, treatment plan evaluation, and equipment quality control, thereby significantly improving training efficiency and quality. Furthermore, VR technology enables three-dimensional visualization and intuitive evaluation of treatment plans, facilitates multidisciplinary team (MDT) collaboration, and provides new paradigms for the development of real-time guidance technologies that integrate with augmented reality. This article systematically reviews the current applications of VR technology in the clinical practice, medical education, radiation protection, and scientific research of radiotherapy. It also discusses future prospects for integrating VR with artificial intelligence (AI), augmented reality (AR), and haptic feedback technologies. This review provides a valuable reference for the standardized application of VR technology in radiotherapy and radiation protection in China.

Malignant tumors are a class of diseases that pose a serious threat to human health, and radiotherapy (RT) is one of the essential treatment modalities. Virtual reality (VR) technology, with its unique characteristics of immersion, interactivity, and imagination, demonstrates tremendous application potential in radiotherapy. Several randomized controlled trials have confirmed that VR, as a non-pharmacological intervention, can effectively alleviate anxiety in patients, especially children, and improve treatment adherence. In medical education and professional training, VR provides safe training platforms for radiation oncologists, medical physicists, and radiation therapists through the construction of high-fidelity virtual environments. These platforms can be used for training skills in target volume delineation, treatment plan evaluation, and equipment quality control, thereby significantly improving training efficiency and quality. Furthermore, VR technology enables three-dimensional visualization and intuitive evaluation of treatment plans, facilitates multidisciplinary team (MDT) collaboration, and provides new paradigms for the development of real-time guidance technologies that integrate with augmented reality. This article systematically reviews the current applications of VR technology in the clinical practice, medical education, radiation protection, and scientific research of radiotherapy. It also discusses future prospects for integrating VR with artificial intelligence (AI), augmented reality (AR), and haptic feedback technologies. This review provides a valuable reference for the standardized application of VR technology in radiotherapy and radiation protection in China.

ObjectiveTo systematically characterize the in vitro chemical constituents of Entada phaseoloides and its absorbed prototype compounds and metabolites in a neuropathic pain （NP） model， thereby elucidating the potential pharmacological material basis underlying its analgesic activity. MethodsUltra-high-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry （UHPLC-Q-Orbitrap HRMS） was used to analyze the constituents of the 80% methanol extract of E. phaseoloides. Thirty-six mice were divided into a blank group， low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， an aspirin group （400 mg·kg^-1）， and a morphine group （10 mg·kg^-1）， with six mice in each group. The tail-flick threshold was measured. Twenty-four mice were randomly divided into a blank group and low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， with six mice in each group， and motor coordination was evaluated. A spinal nerve ligation （SNL） model was established in mice. Twenty-four mice were randomly divided into a sham-operated （Sham） group， an SNL group， an SNL + E. phaseoloides extract group （750 mg·kg^-1）， and an SNL + pregabalin group （20 mg·kg^-1）， with 6 mice in each group. Mechanical allodynia and thermal hyperalgesia were evaluated. Behavioral assessments were combined to determine the optimal onset time of the analgesic effect， and plasma samples were collected at this time point. Absorbed components and metabolites were systematically identified based on accurate mass， MS/MS fragmentation patterns， retention time， and comparisons with literature data and reference standards. Meanwhile， network pharmacology， target similarity analysis， and molecular docking validation were integrated to evaluate the potential effects of absorbed components on disease intervention. ResultsCompared with the blank group， the high-dose E. phaseoloides extract group and morphine group showed significantly increased pain response thresholds （P<0.01）， without affecting motor coordination in normal mice. Compared with the Sham group， the SNL group exhibited significantly decreased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. Compared with the SNL group， the SNL + E. phaseoloides extract group and SNL + pregabalin group showed significantly increased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. The analgesic effect of E. phaseoloides was most significant at 2 h after administration. A total of 70 constituents were identified in vitro， and 22 prototype compounds and 29 metabolites were detected in plasma. The targets of the absorbed components were mainly enriched in biological processes associated with pain signal perception and transmission and showed a high degree of overlap with disease genes related to neuropathic pain. In addition， multiple components exhibited strong binding energies with core pain-related targets. Based on integrated analysis of in vitro content， plasma exposure levels， and target associations， ketanthamide A-β-D-pyranoside and ketengzide were identified as the most representative pharmacologically active substances responsible for the analgesic effect. ConclusionThis study systematically elucidates the material basis of E. phaseoloides intervention in neuropathic pain from three aspects， including chemical constituent characteristics， in vivo behavioral characteristics， and potential targets， providing important theoretical foundations and experimental support for further studies on its pharmacological mechanism and analgesic drug development.

Objective: To develop and compare multiple machine learning models for identifying high-risk college students exhibiting phubbing behavior and to determine key predictive factors, so as to provide evidence for precise screening and early intervention strategies. Methods: In December 2025, 1 828 undergraduate students, selected from three universities in Wuhan by using convenience sampling method, were surveyed via online qustionnaire for sociodemographic characteristics, phubbing behavior, family cohesion index, mature happiness, fear of negative evaluation, multidimensional state boredom, and psychological vulnerability. Four machine learning algorithms, including multilayer perceptron (MLP), extreme gradient booting (XGBoost), K-nearest neighbors (KNN), and gradient boosting decision tree (GBDT), were applied. The dataset was randomly split into a training set and a test set at a ratio of 7∶3. Model performance was evaluated using accuracy, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The best performing model was further interpreted using shapley additive explanation (SHAP) analysis to assess feature importance. Results: In the training set,955 participants were classified as having low level phubbing behavior, and 325 participants were classified as having high level phubbing behavior. Significant differences were observed in psychological vulnerability, multidimensional state boredom, family cohesion index, and mature happiness between groups ( Z =-10.29, -11.72, -8.17, -7.83, all P <0.05). Significant differences were also found in the detection rate of high level phubbing behavior according to family residence, family status, left behind experience,interpersonal relationships,exercise frequency,physical flexibility,and sleep status ( χ 2=12.22, 38.93, 16.90, 44.64, 58.17, 82.14, 37.89, all P <0.05). Among the four machine learning models (MLP, KNN, GBDT, XGBoost),all showed good discrimination ability, with XGBoost performing best (accuracy 79%, recall 71%, F1-score=0.70, AUC=0.78), while GBDT achieved the highest precision (78%). Feature importance analysis showed that multidimensional state boredom was the most important predictor of phubbing behavior, followed by psychological vulnerability, family cohesion, mature happiness, and fear of negative evaluation. SHAP analysis indicated that multidimensional state boredom, psychological vulnerability, and fear of negative evaluation had positive risk effects, while family cohesion and hedonic well being had protective effects for high risk of phubbing behavior. Conclusions Multidimensional state boredom is the core predictor of phubbing behavior among college students. The XGBoost model demonstrates good predictive performance and can provide a reference for identifying high risk individuals and implementing targeted interventions.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Objective::To report a fetus with ARCN1-related syndrome caused by a novel de novo heterozygous variant, highlighting the importance of early genetic diagnosis in prenatal care. Methods::The clinical and genetic data of a fetus with a complex combination of clinical signs and a novel de novo heterozygous variant were collected and have been summarized in this study. The potential pathogenic variant was identified throughout the whole exome sequencing and the effects of candidate variants were further validated by a minigene splicing assay. Results::Prenatal systematic ultrasound detected fetal growth restriction. Genetic analysis identified a novel de novo heterozygous variant within the ARCN1 gene—c.1241 +5G>A-located in intron 8. In vitro minigene splicing assays demonstrated that the variant led to two abnormal transcripts. The longer transcript retained 189 base pairs of intron 8, resulting in a truncated protein of 414 amino acids (p.Ser415*). The shorter transcript involved exon 8 skippings, producing a truncated protein of 407 amino acids (p.Ile378Serfs*31). Conclusion::A novel de novo heterozygous variant of the ARCN1 gene, namely NM_001655.5: c.1241 +5G>A, was discovered and identified in a fetus with rhizomelic short stature, microretrognathia, and developmental delays.

Objective::To evaluate the diagnostic efficacy and clinical application of the Obstetrical Chinese Disseminated Intravascular Coagulation (DIC) Scoring System (OCDSS).Methods::This study is a retrospective study that collected 1063 cases from Wuhan Union Hospital, Yichang Central People’s Hospital, and the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture between July 2017 and June 2024. These cases were divided into DIC and non-DIC groups based on score standard. Diagnosis of DIC, the rate of hysterectomy, neonatal mortality, and severe asphyxia are the main outcome measures. All the laboratory indicators are all determined by clinical laboratory department of the hospital. Data were expressed as mean ± standard deviation or median (interquartile range) and frequencies. Independent sample t-test or non-parametric test were used to compare measurement data, while the chi-square test was used for count data. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to test the predictive accuracy. Using univariate and multivariate logistic regression analysis to study the high-risk factors. P < 0.050 indicates a statistical significance. Results::Of 1063 participants in this study, 29 participants (2.73%) were diagnosed with obstetrical DIC by OCDSS score standard, and all the participants were diagnosed as DIC with underlying disease. When the Takao, Clark, and Erez score standard is the "gold standard", the OCDSS score standard always shows good sensitivity and specificity, with all the AUC over 0.75. OCDSS score standard also has better predictive of hysterectomy (68.18%, 91.07%, 0.872), severe neonatal asphyxia and death (79.17%, 75.07%, 0.842) than the other three score standards. All the indicators included in the OCDSS score standard contributed to the DIC diagnosis (all the P < 0.001). The indicators in the DIC group were more abnormal than the non-DIC group (all the P < 0.001). Conclusion::OCDSS is a first score standard, especially for pregnancies, it considers the underlying disease, clinical symptoms, and laboratory results. This score system shared a good diagnosis performance for DIC in the Chinese population and may help clinicians make timely decisions.