Comparison between the Accuracy of Endoanal Ultrasonography and Body Coil MRI in Preoperative Assessment of Internal Opening of Perianal Fistula Complex

Abstract Background and Aim: Surgery for anal fistula is a commonly performed practice. Integral part of fistula surgery is identification of the internal opening if high recurrence rate is to be diminished. In this study, accuracy of endoanal ultrasound versus body coil M.R.I in detection of the internal fistula opening was evaluated by comparing both modalities with the intra operative findings as a standard reference

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

Development and Assessment of 1,5–Diarylpyrazole/Oxime Hybrids Targeting EGFR and JNK–2 as Antiproliferative Agents: A Comprehensive Study through Synthesis, Molecular Docking, and Evaluation

New 1,5-diarylpyrazole oxime hybrid derivatives (scaffolds A and B) were designed, synthesized, and then their purity was verified using a variety of spectroscopic methods. A panel of five cancer cell lines known to express EGFR and JNK-2, including human colorectal adenocarcinoma cell line DLD-1, human cervical cancer cell line Hela, human leukemia cell line K562, human pancreatic cell line SUIT-2, and human hepatocellular carcinoma cell line HepG2, were used to biologically evaluate for their in vitro cytotoxicity for all the synthesized compounds 7a–j, 8a–j, 9a–c, and 10a–c. The oxime containing compounds 8a–j and 10a–c were more active as antiproliferative agents than their non-oxime congeners 7a–j and 9a–c. Compounds 8d, 8g, 8i, and 10c inhibited EGFR with IC50 values ranging from 8 to 21 µM when compared with sorafenib. Compound 8i inhibited JNK-2 as effectively as sorafenib, with an IC50 of 1.0 µM. Furthermore, compound 8g showed cell cycle arrest at the G2/M phase in the cell cycle analysis of the Hela cell line, whereas compound 8i showed combined S phase and G2 phase arrest. According to docking studies, oxime hybrid compounds 8d, 8g, 8i, and 10c exhibited binding free energies ranging from −12.98 to 32.30 kcal/mol at the EGFR binding site whereas compounds 8d and 8i had binding free energies ranging from −9.16 to −12.00 kcal/mol at the JNK-2 binding site

Clinical Decision Support Systems to Predict Drug–Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder

Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug–drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.</jats:p