Mine Your Own Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels.

Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping (i.e., pulling positive samples closer and negative samples apart in the feature space). However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owNAnatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances-through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, we both empirically and theoretically, demonstrate the efficacy of our MONA on three benchmark datasets with different labeled settings, achieving new state-of-the-art under different labeled semi-supervised settings. MONA makes minimal assumptions on domain expertise, and hence constitutes a practical and versatile solution in medical image analysis. We provide the PyTorch-like pseudo-code in supplementary.

Finding the minimum number of retrieved lymph nodes and negative lymph nodes in gastric cancer surgery: a real-world study.

BACKGROUND: Lymph node retrieval deficiency can lead to understagement and postoperative cancer recurrence, it is crucial to establish the standard number of retrieved lymph nodes (rLNs) and negative lymph nodes (nLNs) for patients undergoing gastrectomy. METHODS: Patients who has gastric adenocarcinoma and underwent either radical subtotal gastrectomy (RSG) or radical total gastrectomy (RTG) between 2000 and 2022 were retrospectively included. The authors utilized restricted cubic spline (RCS) analysis to determine the ideal threshold for rLNs and nLNs. Survival analysis was conducted using Kaplan-Meier (KM) curves, log-rank tests and forest plots. Propensity score matching (PSM) was utilized to balance parameters between two groups. The median follow-up time for this study was 3095 days. RESULTS: Our study found that there are significant tumor characteristic differences between RSG and RTG. For patients with N0-N3a stage undergoing RSG, retrieving greater than or equal to 24 lymph nodes intraoperatively were associated with better prognosis both before and after PSM [overall survival (OS): P <0.001, P =0.019]; whereas for N3b stage, at least 32 rLNs were required (OS: P =0.006, P =0.023). Similarly, for patients with N0-N3a stage undergoing RTG, retrieving greater than or equal to 27 lymph nodes intraoperatively were associated with better prognosis both before and after PSM (OS: P <0.001, P =0.047); whereas for N3b stage, at least 34 rLNs were required (OS: P <0.001, P =0.003). Additionally, for patients undergoing RSG, having greater than or equal to 21 nLNs (OS: P <0.001, P =0.013), and for those undergoing RTG, having greater than or equal to 22 nLNs (OS: P <0.001, P <0.001), were also associated with better prognosis both before and after PSM. CONCLUSIONS: For patients receiving RSG, rLNs should reach 24 when lymph nodes are limited, and 32 when lymph node metastasis is more extensive, with a minimum number of nLNs ideally reaching 21. Similarly, for patients receiving RTG, rLNs should reach 27 when lymph nodes are limited, 34 when lymph node metastasis is more extensive, and a minimum number of nLNs ideally reaching 22.

Impacts of information quantity and display formats on driving behaviors in a connected vehicle environment.

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human-machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers' responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers' gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs.

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation.

Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly adopt a supervised approach and rely heavily on coupled data-to-text pairs. However, for many targeted scenarios and for non-English languages, sufficient quantities of labeled data are often not available. As a result, it is necessary to collect and label data-text pairs for training, which is both costly and time-consuming. To relax the dependency on labeled data of downstream tasks, we propose an intuitive and effective zero-shot learning framework, ZeroNLG, which can deal with multiple NLG tasks, including image-to-text (image captioning), video-to-text (video captioning), and text-to-text (neural machine translation), across English, Chinese, German, and French within a unified framework. ZeroNLG does not require any labeled downstream pairs for training. During training, ZeroNLG (i) projects different domains (across modalities and languages) to corresponding coordinates in a shared common latent space; (ii) bridges different domains by aligning their corresponding coordinates in this space; and (iii) builds an unsupervised multilingual auto-encoder to learn to generate text by reconstructing the input text given its coordinate in shared latent space. Consequently, during inference, based on the data-to-text pipeline, ZeroNLG can generate target sentences across different languages given the coordinate of input data in the common space. Within this unified framework, given visual (imaging or video) data as input, ZeroNLG can perform zero-shot visual captioning; given textual sentences as input, ZeroNLG can perform zero-shot machine translation. We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and "believable" outputs and significantly outperforms existing zero-shot methods.

Mechanism of gambogic acid repressing invasion and metastasis of colorectal cancer by regulating macrophage polarization via tumor cell-derived extracellular vesicle-shuttled miR-21.

Colorectal cancer (CRC) is a major cause of mortality and morbidity. Gambogic acid (GA) is a promising antitumor drug for treating CRC. We aimed to elucidate its mechanism in CRC invasion/metastasis via tumor cell-derived extracellular vesicle (EV)-carried miR-21. Nude mice peritoneal carcinomatosis (PC) model was subjected to GA treatment liver collection, followed by observation/counting of metastatic liver tissues/liver metastatic nodules by hematoxylin and eosin staining. miR-21 expression in metastatic liver tissues/CD68 + CD86, CD68 + CD206 cell percentages and M2 macrophage marker CD206 level in tumor tissues/interleukin (IL)-12 and IL-10 levels were determined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR)/flow cytometry/enzyme-linked immunosorbent assay. HT-29 cells were treated with GA/miR-21 mimics/negative control for 48 h. miR-21 expression/cell proliferation/migration/invasion/apoptosis were assessed by RT-qPCR/cell counting kit-8/scratch assay/transwell assay/flow cytometry. EVs were extracted from HT-29 cells and identified by transmission electron microscope/nanoparticle tracking analysis/Western blot. IL-4/IL-13-induced macrophages/PC nude mice were treated with GA and EVs, with the internalization of EVs by macrophages assessed through the uptake test. After intraperitoneal injection of GA, PC nude mice exhibited decreased tumor cell density/irregular cell number/liver metastatic nodule number/miR-21 expression, and CRC cells manifested reduced CD68 + CD206 cells/IL-10/miR-21/proliferation/migration/invasion and increased CD68 + CD86 cells/IL-12/apoptosis, while these trends were opposite after miR-21 overexpression, implying that GA curbed CRC/cell invasion/metastasis and macrophage polarization by diminishing miR-21 levels. miR-21 was encapsulated in HT-29 cell-derived EVs. M2 polarization elevated CD206 cells/IL-10, which were decreased by simultaneous GA treatment. EVs could be uptaken by macrophages. CRC cell-EV-miR-21 annulled the suppression effects of GA on macrophage M2 polarization. GA suppressed macrophage M2 polarization by lessening tumor cell derived-EV-shuttled miR-21, thereby weakening CRC invasion/metastasis.

Ubiquilin-4 induces immune escape in gastric cancer by activating the notch signaling pathway.

PURPOSE: We aimed to investigate the role of ubiquilin-4 in predicting the immunotherapy response in gastric cancer. METHODS: Retrospective RNA-sequencing and immunohistochemical analysis were performed for patients with gastric cancer who received programmed death-1 blockade therapy after recurrence. Multiplex immunohistochemistry identified immune cell types in gastric cancer tissues. We used immunocompetent 615 mice and immunodeficient nude mice to perform tumorigenic experiments. RESULTS: Ubiquilin-4 expression was significantly higher in responders (p < 0.05, false discovery rate > 2.5) and showed slight superiority over programmed death ligand 1 in predicting programmed death-1 inhibitor therapy response (area under the curve: 87.08 vs. 72.50). Ubiquilin-4-high patients exhibited increased CD4+ and CD8+ T cells, T follicular helper cells, monocytes, and macrophages. Ubiquilin-4-overexpressed mouse forestomach carcinoma cells showed significantly enhanced growth in immunocompetent mice but not in immunodeficient mice. Upregulation or downregulation of ubiquilin-4 synergistically affected programmed death ligand 1 at the protein and messenger RNA levels. Functional enrichment analysis revealed significant enrichment of the Notch, JAK-STAT, and WNT signaling pathways in ubiquilin-4-high gastric cancers. Ubiquilin-4 promoted Numb degaration, activating the Notch signaling pathway and upregulating programmed death ligand 1. CONCLUSIONS: Ubiquilin-4 may contribute to immune escape in gastric cancer by upregulating programmed death ligand 1 expression in tumor cells through Notch signaling activation. Thus, ubiquilin-4 could serve as a predictive marker for programmed death ligand 1 inhibitor therapy response in gastric cancer.

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review.

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid.

In the realm of modern medicine, medical imaging stands as an irreplaceable pillar for accurate diagnostics. The significance of precise segmentation in medical images cannot be overstated, especially considering the variability introduced by different practitioners. With the escalating volume of medical imaging data, the demand for automated and efficient segmentation methods has become imperative. This study introduces an innovative approach to heart image segmentation, embedding a multi-scale feature and attention mechanism within an inverted pyramid framework. Recognizing the intricacies of extracting contextual information from low-resolution medical images, our method adopts an inverted pyramid architecture. Through training with multi-scale images and integrating prediction outcomes, we enhance the network's contextual understanding. Acknowledging the consistent patterns in the relative positions of organs, we introduce an attention module enriched with positional encoding information. This module empowers the network to capture essential positional cues, thereby elevating segmentation accuracy. Our research resides at the intersection of medical imaging and sensor technology, emphasizing the foundational role of sensors in medical image analysis. The integration of sensor-generated data showcases the symbiotic relationship between sensor technology and advanced machine learning techniques. Evaluation on two heart datasets substantiates the superior performance of our approach. Metrics such as the Dice coefficient, Jaccard coefficient, recall, and F-measure demonstrate the method's efficacy compared to state-of-the-art techniques. In conclusion, our proposed heart image segmentation method addresses the challenges posed by diverse medical images, offering a promising solution for efficiently processing 2D/3D sensor data in contemporary medical imaging.

A medical multimodal large language model for future pandemics.

Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) for radiograph representation learning, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed and easily adapted to them with limited labels. Furthermore, our model supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for clinical tasks that involve both visual and textual data. We demonstrate the effectiveness of our Med-MLLM by showing how it would perform using the COVID-19 pandemic "in replay". In the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input data; 2) three kinds of downstream tasks, including disease reporting, diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different languages, including English, Chinese, and Spanish. All experiments show that our model can make accurate and robust COVID-19 decision-support with little labelled data.

Visible light-induced radical cascade acylmethylation/cyclization of 2-(allyloxy)arylaldehydes with α-bromo ketones: access to cyclic 1,5-dicarbonyl-containing chroman-4-one skeletons.

A novel photocatalytic protocol for effective and efficient synthesis of cyclic 1,5-diketones containing chroman-4-one skeletons in moderate to good yields via radical cascade acylmethylation/cyclization of 2-(allyloxy)arylaldehydes with α-bromo ketones has been described. This reaction features a broad substrate scope, good functional group tolerance, and metal- and oxidant-free conditions. An acylmethyl radical-triggered cascade cyclization was involved.

Epidemiology and Association Rules Analysis for Pulmonary Tuberculosis Cases with Extrapulmonary Tuberculosis from Age and Gender Perspective: A Large-Scale Retrospective Multicenter Observational Study in China.

Background: Tuberculosis (TB), a multisystemic disease with protean presentation, remains a major global health problem. Although concurrent pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) cases are commonly observed clinically, knowledge regarding concurrent PTB-EPTB is limited. Here, a large-scale multicenter observational study conducted in China aimed to study the epidemiology of concurrent PTB-EPTB cases by diagnostically defining TB types and then implementing association rules analysis. Methods: The retrospective study was conducted at 21 hospitals in 15 provinces in China and included all inpatients with confirmed TB diagnoses admitted from Jan 2011 to Dec 2017. Association rules analysis was conducted for cases with concurrent PTB and various types of EPTB using the Apriori algorithm. Results: Evaluation of 438,979TB inpatients indicated PTB was the most commonly diagnosed (82.05%) followed by tuberculous pleurisy (23.62%). Concurrent PTB-EPTB was found in 129,422 cases (29.48%) of which tuberculous pleurisy was the most common concurrent EPTB type observed. The multivariable logistic regression models demonstrated that odds ratios of concurrent PTB-EPTB cases varied by gender and age group. For PTB cases with concurrent EPTB, the strongest association was found between PTB and concurrent bronchial tuberculosis (lift = 1.09). For EPTB cases with concurrent PTB, the strongest association was found between pharyngeal/laryngeal tuberculosis and concurrent PTB (lift = 1.11). Confidence and lift values of concurrent PTB-EPTB cases varied with gender and age. Conclusions: Numerous concurrent PTB-EPTB case types were observed, with confidence and lift values varying with gender and age. Clinicians should screen for concurrent PTB-EPTB in order to improve treatment outcomes.

A Salient Object Detection Method Based on Boundary Enhancement.

Visual saliency refers to the human's ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex visual problems. In this paper, we propose a salient object detection method based on boundary enhancement, which is applicable to both 2D and 3D sensors data. To address the problem of large-scale variation of salient objects, our method introduces a multi-level feature aggregation module that enhances the expressive ability of fixed-resolution features by utilizing adjacent features to complement each other. Additionally, we propose a multi-scale information extraction module to capture local contextual information at different scales for back-propagated level-by-level features, which allows for better measurement of the composition of the feature map after back-fusion. To tackle the low confidence issue of boundary pixels, we also introduce a boundary extraction module to extract the boundary information of salient regions. This information is then fused with salient target information to further refine the saliency prediction results. During the training process, our method uses a mixed loss function to constrain the model training from two levels: pixels and images. The experimental results demonstrate that our salient target detection method based on boundary enhancement shows good detection effects on targets of different scales, multi-targets, linear targets, and targets in complex scenes. We compare our method with the best method in four conventional datasets and achieve an average improvement of 6.2% on the mean absolute error (MAE) indicators. Overall, our approach shows promise for improving the accuracy and efficiency of salient object detection in a variety of settings, including those involving 2D/3D semantic analysis and reconstruction/inpainting of image/video/point cloud data.

[Discussion on the Authenticity Verification Method in the Verification of Medical Device Registration Quality Management System].

Authenticity verification is a very important aspect of medical device registration quality management system verification of medical device. How to verify the authenticity of samples is a problem worth discussing. This study analyzes the methods of authenticity verification from the aspects of product retention sample, registration inspection report, traceability of records, hardware facilities and equipment. In order to provide reference for relevant supervisors and inspectors in the verification of registration quality management system.

Is Pathologic Complete Response a Good Predictor for the Long-Term, Clinical Outcome in Patients with Gastric Cancer After Neoadjuvant Chemotherapy? A Retrospective, Multi-institution Study in China.

BACKGROUND: Many studies have used pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) as the primary endpoint for the short-term efficacy in gastric cancer, but whether it is a good indicator for overall survival is poorly understood. METHODS: This study reviewed a multi-institution database of patients who underwent radical gastrectomy and achieved pCR after NAC. Cox regression models were used to identify clinicopathologic predictors of overall survival (OS) and disease-free survival (DFS). Survival curves were calculated by using the Kaplan-Meier method and compared by means of the log-rank test. RESULTS: OS and DFS in patients with pCR were significantly higher than in those with non-pCR (both P < 0.001). Multivariable analysis confirmed pCR was an independent prognostic factor for OS and DFS (P = 0.009 and P = 0.002 for OS and DFS, respectively). However, the survival benefit for pCR was present only for ypN0 tumors (P = 0.004 and P = 0.001 for OS and DFS, respectively), and OS (P = 0.292) and DFS (P = 0.285) among patients with ypN+ gastric cancer could not be stratified by pCR. CONCLUSIONS: In our study, pCR is an independent prognostic factor for OS and DFS, but the survival benefit for pCR is present only for ypN0 tumors but not ypN+ tumors.

Palladium/Norbornene Cooperative-Catalyzed Cascade Decarboxylative Cyclization of 4-Iodoisoquinolin-1(2H)-ones with o-Bromobenzoic Acids: Access to Diverse Fused Phenanthridinones and Hepta[1,2-c]isoquinolinones.

A novel three-component Pd/norbornene cooperative catalysis cascade decarboxylative [2+2+2]/[2+2+3]cyclization of 4-iodoisoquinolin-1(2H)-ones and o-bromobenzoic acids or 8-bromo-1-naphthoic acid has been developed. The method affords a range of fused phenanthridinones and hepta[1,2-c]isoquinolinones and displays unique regioselectivity and broad substrate scope. Palladium/norbornene (Pd/NBE)-catalyzed C-H activation and subsequent decarboxylative coupling reactions were involved, and NBE acts as a building block for the construction of rigid nonplanar molecular architectures.

The effect of the subthreshold oscillation induced by the neurons' resonance upon the electrical stimulation-dependent instability.

Repetitive electrical nerve stimulation can induce a long-lasting perturbation of the axon's membrane potential, resulting in unstable stimulus-response relationships. Despite being observed in electrophysiology, the precise mechanism underlying electrical stimulation-dependent (ES-dependent) instability is still an open question. This study proposes a model to reveal a facet of this problem: how threshold fluctuation affects electrical nerve stimulations. This study proposes a new method based on a Circuit-Probability theory (C-P theory) to reveal the interlinkages between the subthreshold oscillation induced by neurons' resonance and ES-dependent instability of neural response. Supported by in-vivo studies, this new model predicts several key characteristics of ES-dependent instability and proposes a stimulation method to minimize the instability. This model provides a powerful tool to improve our understanding of the interaction between the external electric field and the complexity of the biophysical characteristics of axons.

Hybrid source translation scanning mode for interior tomography.

Interior tomography is a promising technique that can be used to image large objects with high acquisition efficiency. However, it suffers from truncation artifacts and attenuation value bias due to the contribution from the parts of the object outside the ROI, which compromises its ability of quantitative evaluation in material or biological studies. In this paper, we present a hybrid source translation scanning mode for interior tomography, called hySTCT-where the projections inside the ROI and outside the ROI are finely sampled and coarsely sampled respectively to mitigate truncation artifacts and value bias within the ROI. Inspired by our previous work-virtual projection-based filtered backprojection (V-FBP) algorithm, we develop two reconstruction methods-interpolation V-FBP (iV-FBP) and two-step V-FBP (tV-FBP)-based on the linearity property of the inverse Radon transform for hySTCT reconstruction. The experiments demonstrate that the proposed strategy can effectively suppress truncated artifacts and improve the reconstruction accuracy within the ROI.

Surgeons' mental distress and risks after severe complications following radical gastrectomy in China: a nationwide cross-sectional questionnaire.

BACKGROUND: This study was designed to investigate incidences of surgeons' mental distress following severe complications after radical gastrectomy. METHODS: A cross-sectional survey was conducted between 1 June 2021 and 30 September 2021 among Chinese general and/or gastrointestinal surgeons who experienced severe complications after radical gastrectomy. The clinical features collected in the questionnaire included: (i) feeling burnout, anxiety, or depression; (ii) avoiding radical gastrectomy or feeling stress, slowing down the process during radical gastrectomy operations; (iii) having physical reactions, including heart pounding, trouble breathing, or sweating while recalling; (iv) having urges to quit being a surgeon; (v) taking psychiatric medications; and (vi) seeking psychological counselling. Analyses were performed to identify risk factors of severe mental distress, which was defined as meeting three or more of the above-mentioned clinical features. RESULTS: A total of 1062 valid questionnaires were received. The survey showed that most of the participating surgeons (69.02%) had at least one clinical feature of mental distress following severe complications after radical gastrectomy, and more than 25% of the surgeons suffered from severe mental distress. Surgeons from non-university affiliated hospitals, the junior surgeons, and existing violent doctor-patient conflicts were recognized as independent risk factors for surgeons' severe mental distress related to the severe complications after radical gastrectomy. CONCLUSIONS: About 70% of surgeons had mental health problems following severe complications after radical gastrectomy, and more than 25% of the surgeons suffered from severe mental distress. More strategies and policies are needed to improve the mental well-being of these surgeons after such incidences.