PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Efficacy of targeted therapy in patients with non-small cell lung cancer harboring very rare mutations in EGFR exon 18.

Background: Somatic mutations in epidermal growth factor receptor (EGFR) exon 18 are classified as uncommon or rare mutations in non-small cell lung cancer (NSCLC), in this context, other than G719X or E709X exon 18 mutations are even more rare and heterogeneous. In such scenario, first line treatment options are still debated. The aim of this study was to investigate the response of NSCLC patients harboring very rare exon 18 mutations to EGFR tyrosine kinase inhibitors (EGFR-TKIs). Methods: This retrospective descriptive study included 105 patients with NSCLC harboring mutations in EGFR exon 18 diagnosed at West China Hospital. The clinical response to EGFR-TKIs was evaluated according to different classifications of mutations in 45 NSCLC patients: 39 harboring G719X or E709X mutations and 6 harboring very rare mutations in EGFR exon 18. Results: Among 105 patients, 84% (88/105) harbored rare mutations in EGFR exon 18, including G719X and E709X mutations. The remaining 16% (17/105) had very rare mutations in EGFR exon 18, including E709_710delinsX and G724S. For the subsequent efficacy analysis of EGFR-TKI in 45 NSCLC patients, patients harboring very rare mutations achieved a favorable disease control rate (DCR) of 100% and had a median progression-free survival (PFS) of 17.2 months, which was not significantly different compared to patients harboring G719X or E709X (P=0.59). Conclusions: EGFR-TKIs showed great efficacy in terms of responses and survival in patients harboring exon 18 EGFR rare mutations. This may justify the use of targeted therapies as a potential treatment strategy for these patients.

Early detection and prognosis evaluation for hepatocellular carcinoma by circulating tumour DNA methylation: A multicentre cohort study.

BACKGROUND: Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood-based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. METHODS: A three-phase multicentre study was conducted to screen, optimise and validate HCC-specific differentially methylated regions (DMRs) using next-generation sequencing and quantitative methylation-specific PCR (qMSP). RESULTS: Genome-wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre- and postoperative plasma samples from 103 HCC patients and correlated with 2-year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p < .001). CONCLUSIONS: HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC-specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at-risk populations.

ChatGPT's ability to generate realistic experimental images poses a new challenge to academic integrity.

The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT's writing capabilities, recent updates have integrated DALL-E 3's image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT's nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding "invisible watermarks" to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

Risk factors for Pneumocystis jirovecii pneumonia after kidney transplantation: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: Pneumocystis jirovecii pneumonia (PJP), an opportunistic infection, often leads to an increase in hospitalization time and mortality rates in kidney transplant (KT) recipients. However, the risk factors associated with PJP in KT recipients remain debatable. Therefore, we conducted this meta-analysis to identify risk factors for PJP, which could potentially help to reduce PJP incidence and improve outcome of KT recipients. METHODS: We systematically retrieved relevant studies in PubMed, EMBASE, and the Cochrane Library up to November 2023. Pooled odds ratios (ORs) or mean differences (MDs) and the corresponding 95% confidence intervals (CIs) were calculated to assess the impact of potential risk factors on the occurrence of PJP. RESULTS: 27 studies including 42383 KT recipients were included. In this meta-analysis, age at transplantation (MD = 3.48; 95% CI = .56-6.41; p = .02), cytomegalovirus (CMV) infection (OR = 4.00; 95% CI = 2.53-6.32; p = .001), BK viremia (OR = 3.38; 95% CI = 1.70-6.71; p = .001), acute rejection (OR = 3.66; 95% CI = 2.44-5.49; p = .001), ABO-incompatibility (OR = 2.51; 95% CI = 1.57-4.01; p = .001), estimated glomerular filtration rate (eGFR) (MD = -14.52; 95% CI = -25.37- (-3.67); p = .009), lymphocyte count (MD = -.54; 95% CI = -.92- (-.16); p = .006) and anti-PJP prophylaxis (OR = .53; 95% CI = .28-.98; p = .04) were significantly associated with PJP occurrence. CONCLUSION: Our findings suggest that transplantation age greater than 50 years old, CMV infection, BK viremia, acute rejection, ABO-incompatibility, decreased eGFR and lymphopenia were risk factors for PJP.

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.

Immune checkpoint inhibitor plus chemotherapy as first-line treatment for non-small cell lung cancer with malignant pleural effusion: a retrospective multicenter study.

BACKGROUND: Immune checkpoint inhibitors (ICI) combined with chemotherapy are efficacious for treating advanced non-small cell lung cancer (NSCLC); however, the effectiveness of this approach in the malignant pleural effusion (MPE) population is unclear. This study evaluated ICI plus chemotherapy in NSCLC patients with MPE. METHODS: Patients from 3 centers in China with NSCLC and MPE who received ICI plus chemotherapy (ICI Plus Chemo) or chemotherapy alone (Chemo) between December 2014 and June 2023 were enrolled. Clinical outcomes and adverse events (AEs) were compared. RESULTS: Of 155 eligible patients, the median age was 61.0 years old. Males and never-smokers accounted for 73.5% and 39.4%, respectively. Fifty-seven and 98 patients received ICI Plus Chemo or Chemo, respectively. With a median study follow-up of 10.8 months, progression-free survival (PFS) was significantly longer with ICI Plus Chemo than with Chemo (median PFS: 7.4 versus 5.7 months; HR = 0.594 [95% CI: 0.403-0.874], P = 0.008). Median overall survival (OS) did not differ between groups (ICI Plus Chemo: 34.2 versus Chemo: 28.3 months; HR = 0.746 [95% CI: 0.420-1.325], P = 0.317). The most common grade 3 or worse AEs included decreased neutrophil count (3 [5.3%] patients in the ICI Plus Chemo group vs. 5 [5.1%] patients in the Chemo group) and decreased hemoglobin (3 [5.3%] versus 10 [10.2%]). CONCLUSIONS: In patients with untreated NSCLC with MPE, ICI plus chemotherapy resulted in significantly longer PFS than chemotherapy and had a manageable tolerability profile, but the effect on OS may be limited.

[Progress on Neoadjuvant Immunotherapy for Resectable Non-small Cell Lung Cancer].

In recent years, there has been a consensus regarding the enhancement of prognosis in patients with advanced non-small cell lung cancer (NSCLC) through the utilization of immune checkpoint inhibitors (ICIs). Numerous clinical studies have also demonstrated the substantial value of immunotherapy for resectable NSCLC patients. Nevertheless, there remain controversies surrounding the exploration of immune combination strategies, treatment-related side effects, prognostic biomarkers, as well as other issues in the neoadjuvant therapy setting. Consequently, this article presents a comprehensive overview of the recent advancements in neoadjuvant immunotherapy for resectable NSCLC, stimulating fresh perspectives and delving into its merits and challenges in clinical application.
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A Long Life Moisture-Enabled Electric Generator Based on Ionic Diode Rectification and Electrode Chemistry Regulation.

Considerable efforts have recently been made to augment the power density of moisture-enabled electric generators. However, due to the unsustainable ion/water molecule concentration gradients, the ion-directed transport gradually diminishes, which largely affects the operating lifetime and energy efficiency of generators. This work introduces an electrode chemistry regulation strategy into the ionic diode-type energy conversion structure, which demonstrates 1240 h power generation in ambient humidity. The electrode chemical regulation can be achieved by adding Cl-. The purpose is to destroy the passivation film on the electrode interface and provide a continuous path for ion-electron coupling conduction. Moreover, this device simultaneously satisfies the requirements of fast trapping of moisture molecules, high rectification ratio transport of ions, and sustained ion-to-electron current conversion. A single device can deliver an open-circuit voltage of about 1 V and a peak short-circuit current density of 350 µA cm-2. Finally, the first-principle calculations are carried out to reveal the mechanism by which the electrode surface chemistry affects the power generation performance.

Measuring Protein-Ligand Binding by Hyperpolarized Ultrafast NMR.

Protein-ligand interactions can be detected by observing changes in the transverse relaxation rates of the ligand upon binding. The ultrafast NMR technique, which correlates the chemical shift with the transverse relaxation rate, allows for the simultaneous acquisition of R2 for carbon spins at different positions. In combination with dissolution dynamic nuclear polarization (D-DNP), where the signal intensity is enhanced by thousands of times, the R2 values of several carbon signals from unlabeled benzylamine are observable within a single scan. The hyperpolarized ultrafast chemical shift-R2 correlated experiment separates chemical shift encoding from the readout phase in the NMR pulse sequence, which allows it to beat the fundamental limit on the spectral resolution otherwise imposed by the sampling theorem. Applications enabled by the ability to measure multiple relaxation rates in a single scan include the study of structural properties of protein-ligand interactions.

Experimental Study on the Explosion Parameters of n-Decane Aerosol under Saturated Vapor Pressure.

This research aims to conduct a comprehensive investigation and analysis of the effects of particle size and concentration on crucial explosion parameters in n-decane aerosols. Two sets of n-decane aerosols with different concentrations were initially measured, with Sauter mean diameters of 22 and 38 µm, respectively. These measurements served as the foundation for understanding the impact of the particle size on explosion parameters. Subsequently, experiments were performed on n-decane aerosols with various concentrations, utilizing an ignition energy of 40.32 J. The analysis focused on critical explosion parameters including the lower flammability limit, explosion pressure, explosion temperature, and flame propagation delay time. Through thorough examination of the data obtained from these experiments, the research elucidated the relationship between n-decane concentration, particle size, and these explosion parameters.

Development and validation of a deep-learning model for the detection of non-displaced femoral neck fractures with anteroposterior and lateral hip radiographs.

Background: Hip fractures, including femoral neck fractures, are a significant cause of morbidity and mortality in the elderly population and are typically diagnosed using plain radiography. However, diagnosing non-displaced femoral neck fractures can be challenging due to their subtle appearance on hip radiographs. Previous deep-learning models have shown low accuracy in identifying these fractures on anteroposterior (AP) radiographs; however, no studies have used lateral radiographs. This study aimed to evaluate the potential of using deep-learning with both AP and lateral hip radiographs to automatically identify non-displaced femoral neck fractures. Methods: We conducted a retrospective analysis of patients with femoral neck fractures at The First Affiliated Hospital of Xiamen University. All the hip radiographs were reviewed, and cases of non-displaced femoral neck fractures were included in the study. Additionally, 439 participants with normal hip radiographs were also included in the study. A vision transformer (Vit) model was developed using 1,536 AP and lateral hip radiograph. The model's performance was compared to the performance of two groups of human observers: an expert group comprising orthopedic surgeons and radiologists, and a non-expert group, including emergency physicians and general practice doctors. We also carried out the external validation using two additional data sets to assess the generalizability of the model. Results: The Vit model showed exceptional performance in detecting non-displaced femoral neck fractures on paired AP and lateral hip radiographs, achieving a binary accuracy of 95.8% [95% confidence interval (CI): 94.9%, 96.8%] and an area under the curve (AUC) of 0.988. Compared to the human observers, the model had a higher accuracy of 96.7% (95% CI: 93.9%, 99.5%) on the paired AP and lateral hip radiographs, while the accuracy of the expert group was 90.5% (95% CI: 85.7%, 95.2%). Further, the model maintained good performance during the external validation, with an AUC of 0.959 on the paired AP and lateral views. Conclusions: Our Vit model showed expert-level performance in identifying non-displaced femoral neck fractures on paired AP and lateral hip radiographs. This model has the potential to enhance diagnosis accuracy and improve patient outcomes by reducing the need for additional examinations and preoperative time.

The role of exosomal circular RNA ZNF292 in intermittent hypoxia-induced AC16 cardiomyocytes injury.

BACKGROUND: Exosomes are involved in cell-to-cell communication in numerous diseases including cardiovascular diseases, neurological diseases. Little attention has been dedicated to exosomal circular RNAs in obstructive sleep apnea (OSA)-related cardiovascular diseases. The aim of this study was to explore the role of exosomal circular RNA ZNF292 (circZNF292) on AC16 cells exposure to intermittent hypoxia (IH). METHODS: Exosome release inhibitor GW4869 was used to examine the effect of exosomes on IH-induced AC16 cells apoptosis. The expression of exosomal circZNF292 was detected by qRT-PCR in AC16 cells exposure to IH, and a luciferase reporter assay was conducted to confirm the connection between circZNF292 and miR-146a-5p. Exosomal circZNF292 was stably transfected with short hairpin RNAs (shRNAs) against circZNF292 and co-cultured with AC16 cells. The expression of miR-146a-5p and apoptosis-related protein was then measured to evaluate the effect of exosomal circZNF292. RESULTS: We found that IH contributed to the AC16 cells apoptosis, and the administration of GW4869 increased the apoptosis of cardiomyocytes when exposed to IH. The expression of exosomal circZNF292 decreased and miR-146a-5p increased significantly in AC16 cells exposed to IH compared to normoxic conditions. Bioinformatics analysis predicted a circZNF292/miR-146a-5p axis in IH-induced cardiomyocytes apoptosis. The dual-luciferase reporter system validated the direct interaction of circZNF292 and miR-146a-5p. Knockdown of circZNF292 increased the expressions of miR-146a-5p and accelerated the AC16 cardiomyocytes apoptosis. CONCLUSIONS: The findings of this study suggested a novel mechanism by which exosomes transmit intrinsic regulatory signals to the myocardium through the exosomal circZNF292/miR-146a-5p axis. This finding highlights the potential of targeting this pathway as a therapeutic approach for treating cardiovascular diseases associated with OSA.

Cross-domain attention-guided generative data augmentation for medical image analysis with limited data.

Data augmentation is widely applied to medical image analysis tasks in limited datasets with imbalanced classes and insufficient annotations. However, traditional augmentation techniques cannot supply extra information, making the performance of diagnosis unsatisfactory. GAN-based generative methods have thus been proposed to obtain additional useful information to realize more effective data augmentation; but existing generative data augmentation techniques mainly encounter two problems: (i) Current generative data augmentation lacks of the capability in using cross-domain differential information to extend limited datasets. (ii) The existing generative methods cannot provide effective supervised information in medical image segmentation tasks. To solve these problems, we propose an attention-guided cross-domain tumor image generation model (CDA-GAN) with an information enhancement strategy. The CDA-GAN can generate diverse samples to expand the scale of datasets, improving the performance of medical image diagnosis and treatment tasks. In particular, we incorporate channel attention into a CycleGAN-based cross-domain generation network that captures inter-domain information and generates positive or negative samples of brain tumors. In addition, we propose a semi-supervised spatial attention strategy to guide spatial information of features at the pixel level in tumor generation. Furthermore, we add spectral normalization to prevent the discriminator from mode collapse and stabilize the training procedure. Finally, to resolve an inapplicability problem in the segmentation task, we further propose an application strategy of using this data augmentation model to achieve more accurate medical image segmentation with limited data. Experimental studies on two public brain tumor datasets (BraTS and TCIA) show that the proposed CDA-GAN model greatly outperforms the state-of-the-art generative data augmentation in both practical medical image classification tasks and segmentation tasks; e.g. CDA-GAN is 0.50%, 1.72%, 2.05%, and 0.21% better than the best SOTA baseline in terms of ACC, AUC, Recall, and F1, respectively, in the classification task of BraTS, while its improvements w.r.t. the best SOTA baseline in terms of Dice, Sens, HD95, and mIOU, in the segmentation task of TCIA are 2.50%, 0.90%, 14.96%, and 4.18%, respectively.

Extramedullary relapsed acute lymphoblastic leukemia presenting as asymptomatic acute kidney injury after hematopoietic stem cell transplantation.

Acute kidney injury (AKI) is a common complication in children with hematological malignancies. Although AKI due to infiltration of tumor cells in children is rare, it negatively impacts treatment outcomes and increases the risk of mortality. We introduce a case of a child with acute lymphoblastic leukemia (ALL) who experienced kidney relapse resulting in asymptomatic AKI after remission from treatment, to remind clinicians not to overlook the primary disease in clinical judgment. In cases of unexplained AKI, kidney biopsy should be performed when feasible to get an accurate diagnosis and scientific treatment. In brief, children with leukemia who have achieved remission after treatment still need regular monitoring of urine routine and kidney function.

Bioinspired ion channel receptor based on hygroelectricity for precontact sensing of living organism.

Tactile sensors play an important role in human-machine interaction (HMI). Compared to contact tactile sensing, which leaves physical hardware vulnerable to wear and tear, proximity sensing is better at reacting to remote events before physical contact. The apteronotus albifrons possess ion channel receptors for remote surroundings perception. Inspired by the relevant ion channel structure and self-powered operation mode, we designed a new proximity sensor with ion rectification characteristics and self-powered capability. This bio-inspired ion channel receptor exploits the hygroelectric effect to convert the humidity information into a series of current signals when the living organism approaches, and it is insensitive to non-aquatic non-organisms. The sensor offers high sensitivity (2.3 mm-1), a suitable range (0-10 mm) for close object detection, fast response (0.3 s), and fast recovery (2.5 s). The unique combination of bio-sensitivity, non-contact detection characteristics, and humidity-based power generation capabilities enriches the functionality of future HMI electronics. As a proof of concept, the sensor has been successfully applied in different scenarios such as human health management, early warning systems, non-contact switches to prevent virus transmission, object recognition, and finger trajectory detection.

Hybrid Reinforced Medical Report Generation With M-Linear Attention and Repetition Penalty.

To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical reports automatically. However, these state-of-the-art methods mainly suffer from three shortcomings: 1) incomprehensive optimization; 2) low-order and unidimensional attention; and 3) repeated generation. In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems. Specifically, a hybrid reward with different weights is employed to remedy the limitations of single-metric-based rewards, and a local optimal weight search algorithm is proposed to significantly reduce the complexity of searching the weights of the rewards from exponential to linear. Furthermore, we use m-linear attention modules to learn multidimensional high-order feature interactions and to achieve multimodal reasoning, while a new repetition penalty is proposed to apply penalties to repeated terms adaptively during the model's training process. Extensive experimental studies on two public benchmark datasets show that HReMRG-MR greatly outperforms the state-of-the-art baselines in terms of all metrics. The effectiveness and necessity of all components in HReMRG-MR are also proved by ablation studies. Additional experiments are further conducted and the results demonstrate that our proposed local optimal weight search algorithm can significantly reduce the search time while maintaining superior medical report generation performances.

Workplace violence inflicted by patients or their family members/visitors and its relationship with suicidal ideation among undergraduate medical students during clinical training in China.

BACKGROUND: Workplace violence in healthcare settings is a significant public concern that profoundly impacts healthcare workers. However, there is a dearth of knowledge regarding the prevalence of workplace violence and its correlation with suicidal ideation among undergraduate medical students in China during their clinical training. The objective of this study was to evaluate the prevalence of workplace violence inflicted by patients or their family members/visitors and to assess its association with suicidal ideation among undergraduate medical students. METHOD: The snowballing sampling technique was used to recruit Chinese medical students. A question designed by the research team was used to ask medical students about their encounters with workplace violence. Students' basic demographic information and mental distresses (learning burnout, depression symptoms, anxiety symptoms, alcohol abuse/dependence, excessive daytime sleepiness and history of mental disorders) were also assessed. As appropriate, the data were analysed using descriptive statistics, chi-square tests, independent-sample t-tests and multiple logistic regression. RESULTS: Out of the 1402 undergraduate medical students who participated, 493 (35.2%) reported having experienced workplace violence inflicted by patients or their family members/visitors, of which 394 (28.1%) were verbal abuse, 14 (1.0%) were physical aggression, and 85 (6.1%) were suffered from both verbal abuse and physical aggression. Furthermore, students exposed to workplace violence were more likely to report suicidal ideation and had a higher prevalence of learning burnout, depression symptoms, anxiety symptoms, alcohol abuse/dependence and excessive daytime sleepiness. Depression symptoms, history of mental disorders, learning burnout and having a partner were significantly associated with suicidal ideation in this population. CONCLUSION: The prevalence of workplace violence inflicted by patients or their family members/visitors was high among undergraduate medical students in China. This may be associated with their mental distress and suicidal ideation. Consequently, it is crucial to strengthen workplace safety measures and promptly implement interventions to mitigate the potentially serious consequences.

Workplace violence was common among Chinese undergraduate medical students during clinical training and may be associated with suicidal ideation, learning burnout, depression symptoms, anxiety symptoms, alcohol abuse/dependence and excessive daytime sleepiness.Depression symptoms, history of mental disorders, learning burnout and having a partner were significantly associated with suicidal ideation among undergraduate medical students exposed to workplace violence.