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Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art.

This compilation covers emergency medical management lessons from the February 6th Kahramanmaraş earthquakes. The objective is to review relevant literature on emergency services patient management, focusing on Koenig’s 1996 Simple Triage and Rapid Treatment (START) and Secondary Assessment of Victim Endpoint (SAVE) frameworks. Establishing a comprehensive seismic and mass casualty incident (MCI) protocol chain is the goal. The prehospital phase of seismic MCIs treats hypovolemia and gets patients to the nearest hospital. START-A plans to expedite emergency patient triage and pain management. The SAVE algorithm is crucial for the emergency patient secondary assessment. It advises using Glasgow Coma Scale, Mangled Extremity Severity Score, Burn Triage Score, and Safe Quake Score for admission, surgery, transfer, discharge, and outcomes. This compilation emphasizes the importance of using diagnostic tools like bedside blood gas analyzers and ultrasound devices during the assessment process, drawing from 6 February earthquake research. The findings create a solid framework for improving emergency medical response strategies, making them applicable in similar situations.

Introduction: Suppression of tumorigenicity 2 (ST2) plays a key role in the serious complications that may occur during Coronavirus disease-2019 (COVID-19), including systemic inflammatory condition, sepsis, Acute respiratory distress syndrome, and fibrosis. In this study, we evaluated the relationship between the serum ST2 concentrations and the mortality and need for mechanical ventilation. Materials and Methods: A prospective observational study was conducted in patients diagnosed with COVID-19 who presented to the emergency department (ED) between October 2021 and April 2022. Patients with Severe acute respiratory syndrome-Coronavirus-2 infection were consecutively enrolled according to the clinical spectrum defined in the guidelines. Admission to the intensive care unit (ICU), requirement of intubation, and 90-day mortality were the primary study outcomes. Clinical, imaging and laboratory data were assessed to determine the clinical spectrum and severity scores. ST2 was assessed using the micro ELISA method and Sandwich-ELISA principle. Results: Of the 64 (48.5%) patients admitted to the ICU, 43 (32.6%) required mechanical ventilation. During the 90-day follow-up period, 44 (33.3%) patients died due to clinical deterioration. The median ST2 concentration at admission was 272.5 ng/mL in patients who died and 55.95 ng/mL in patients who survived (p

Objective: Upper gastrointestinal system (UGIS) bleeding is a life-threatening abdominal emergency. Numerous scoring systems have been developed to identify patients who may develop mortality due to UGIS bleeding. We aimed to determine the effectiveness of the Glasgow Blatchford Score (GBS), Rockall score (RS), and AIMS 65 score in predicting the length of hospital stay, re-bleeding, and transfusion need. Material and Methods: It was carried out retrospectively by recording the parameters and clinical scoring systems collected from the archive files and epicrisis information of the patients with the pre-diagnosis of UGIS hemorrhage. Results: Sixty-three (67.7%) of 93 patients were male. Four patients (4.3%) needed intensive care, and in-hospital mortality occurred in 4 (4.3%) patients. Mortality was observed in 7 patients (7.5%), and recurrent UGIS bleeding was observed in six patients (6.5%). A statistically significant difference was found in AIMS 65 and Rockall scores in predicting -intensive care needs (p

A 27-year-old female patient, G2P1, presented to the emergency department (ED) with acute onset nausea, vomiting, and mild chronic abdominopelvic pain. Physical examination revealed bilateral lower quadrant tenderness without rebound, guarding, or rigidity, and vital signs were within normal limits. Electrolytes, complete blood count, and liver and kidney function tests were normal. A pregnancy test was negative, and urinalysis did not reveal any abnormalities. No free fluid was observed in the abdominal ultrasound, and the ovaries and other intra-abdominal structures were found to be normal. The patient underwent intravenous contrast-enhanced abdomiopelvic computed tomography (CT) scan.