Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Clear Cell Sarcoma of the Foot in an 18-Year-Old Female

We report a case of an 18-year-old female without a relevant medical history who presented with an 8-month history of a left foot mass. It started as a small nodule that progressively increased in size over time. The mass then became ulcerative with foul-smelling discharge. There was no palpable left inguinal or other lymph nodes upon physical examination. Histological examination of the biopsy confirmed a diagnosis of clear cell sarcoma. Clear cell sarcoma is a rare soft tissue neoplasm. However, early diagnosis is crucial to prevent metastasis and worsened prognosis. Clear cell sarcoma has an extremely poor prognosis once metastasis occurs, and to the best of our knowledge, only fewer than 100 cases have been reported in the literature

Clear Cell Sarcoma of the Foot in an 18-Year-Old Female

We report a case of an 18-year-old female without a relevant medical history who presented with an 8-month history of a left foot mass. It started as a small nodule that progressively increased in size over time. The mass then became ulcerative with foul-smelling discharge. There was no palpable left inguinal or other lymph nodes upon physical examination. Histological examination of the biopsy confirmed a diagnosis of clear cell sarcoma. Clear cell sarcoma is a rare soft tissue neoplasm. However, early diagnosis is crucial to prevent metastasis and worsened prognosis. Clear cell sarcoma has an extremely poor prognosis once metastasis occurs, and to the best of our knowledge, only fewer than 100 cases have been reported in the literature.</jats:p

PSYCHOLOGICAL MEMORY FORGETTING MODEL USING LINEAR DIFFERENTIAL EQUATION ANALYSIS

To solve the problem of insufficient combination of neural mechanism and psychological mechanism in the study of human brain cognitive memory activity, in this exploration, the memory forgetting model in psychology is analyzed and constructed by fractional order model of linear differential equation. The study of memory in cognitive psychology is combined with the neurophysiological mechanism of memory. At the same time, for the stability of long-term memory, a long-term memory neural network model (LTMNNS) is proposed. The validity and accuracy of the model are tested by the recognition of memory eigenvectors. Numerical test and simulation results show that when the difference between any initial values of the system is not more than [Formula: see text], the corresponding system solution difference is not more than [Formula: see text] in the interval [Formula: see text]. Reasoning correctness of fractional order computation model of memory neural network is verified. The LTMNNS algorithm in this exploration guarantees a high number of completed tasks, and the average number of failed tasks is less than MemNNs, DNC and LSTM algorithm. Compared with other models, the proposed LTMNNS has better generalization ability and higher stability. </jats:p

A Review of <i>Rhazya stricta</i> Decne Phytochemistry, Bioactivities, Pharmacological Activities, Toxicity, and Folkloric Medicinal Uses

The local medicinal plant Rhazya stricta Decne is reviewed for its folkloric medicinal, phytochemical, pharmacological, biological, and toxicological features. R. stricta has been used widely in different cultures for various medical disorders. The phytochemical studies performed on the R. stricta extract revealed many alkaloidal and fatty acid compounds. Moreover, several flavonoid and terpenoid compounds were also detected. Pharmacological activates of R. stricta extracts are approved to possess antimicrobial, antioxidant, anticancer, antidiabetic, and antihypertensive activities. Additionally, R. stricta extract was found to hold biological activates such as larvicidal and phytoremediation activates R. stricta extract was found to be toxic, genotoxic, and mutagenic. R. stricta contains novel phytochemical compounds that have not been investigated pharmacologically. Further research is needed through in vitro and in vivo experiments to pave the road for these compounds for medical, veterinary, and ecological uses

An Explainable Hybrid CNN&ndash;Transformer Architecture for Visual Malware Classification

Malware continues to develop, posing significant challenges for traditional signature-based detection systems. Visual malware classification, which transforms malware binaries into grayscale images, has emerged as a promising alternative for recognizing patterns in malicious code. This study presents a hybrid deep learning architecture that combines the local feature extraction capabilities of ConvNeXt-Tiny (a CNN-based model) with the global context modeling of the Swin Transformer. The proposed model is evaluated using three benchmark datasets&mdash;Malimg, MaleVis, VirusMNIST&mdash;encompassing 61 malware classes. Experimental results show that the hybrid model achieved a validation accuracy of 94.04%, outperforming both the ConvNeXt-Tiny-only model (92.45%) and the Swin Transformer-only model (90.44%). Additionally, we extended our validation dataset to two more datasets&mdash;Maldeb and Dumpware-10&mdash;to strengthen the empirical foundation of our work. The proposed hybrid model achieved competitive accuracy on both, with 98% on Maldeb and 97% on Dumpware-10. To enhance model interpretability, we employed Gradient-weighted Class Activation Mapping (Grad-CAM), which visualizes the learned representations and reveals the complementary nature of CNN and Transformer modules. The hybrid architecture, combined with explainable AI, offers an effective and interpretable approach for malware classification, facilitating better understanding and trust in automated detection systems. In addition, a real-time deployment scenario is demonstrated to validate the model&rsquo;s practical applicability in dynamic environments