Risk factors of sitting cross-legged on low back pain of students at Islamic Boarding School

This study aimed to analyze the correct cross-legged sitting position to prevent low back pain in students at Islamic boarding schools. This study used an analytical design with a cross-sectional design. The study was conducted 30 males on Students at the Durrotu Aswaja Islamic Boarding School in Semarang. Participants also agreed to the procedure by filling out and signing the informed. The instrument in this study used a questionnaire and pain measurement of low back pain using the Visual Analog Scale. The results of this study indicate that 20 out of 30 Islamic boarding school students have experienced low back pain due to the cross-legged position. From test data A to C found the average results show 13% of respondents experiencing very severe pain, 9% of respondents experiencing severe pain, 73% of respondents experiencing moderate pain, and 6% of respondents experiencing mild pain. In conclusion, many things cause low back pain, one of which is the effect that sitting in one position for a long time has on low back pain. Some of the pain results in complaints such as low back pain, knee pain, and tingling. Due to the COVID-19 pandemic's social isolation, only students who signed informed consent were willing to engage in the study until it was finished, which resulted in a insignificant number of respondents. Many respondents were still in their homes or local communities at the time, which poses a limitation for this study.

Detector Face Mask using UAV-based CNN Transfer Learning of YOLOv5

Detection of the use of masks on someone is helpful in health protocols during the COVID-19 pandemic. All public services or places require people to wear masks during the pandemic. There are about three types of masks commonly used by the public today: surgical/medical masks, cloth masks, and scuba masks. This research aims to detect masks by monitoring a user using a mask through a camera. also detects the type of mask used by the community. So that it can provide convenience in implementing discipline in carrying out the COVID-19 health protocol using masks. In addition, this research proposes the detection of masks on the face by monitoring using a drone. The detection method used in this research is Transfer Learning CNN. This algorithm is a deep learning method that can classify and detect in digital image processing. The initial step of the research is to collect the types of masks on the market in the form of digital images, followed by the application before being modeled into mathematical calculations, which will later be processed using the Convolutional Neural Network method. This research compares two architectural transfer learning methods in deep learning, namely mobile net V2 with YOLOv5. The system testing process will be carried out by analyzing the recall value, precision, and accuracy. The testing process on drone camera-based devices uses the python programming language. Based on the results of the transfer learning method using YOLOv5, the results of the data training accuracy are 97% in detecting masks. © 2022 IEEE.

Multi Epitopes Potential on Surface SARS-CoV-2 Protein as a Covid-19 Vaccine Candidate

Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the etiology of an outbreak Covid-19. SARS-CoV-2 has a structural part consisting of spike glycoprotein, nucleoprotein N, membrane M and envelopes small membrane pentamer E. Immunoinformatic approach epitope analysis is developed to identify both weak and robust epitopes. Our study aims to identify several epitopes present in the spike glycoprotein, envelope, and membrane protein from the SARCoV-2 surface, with the help of insilico approach that highly potential as vaccine candidates. Analysis of antigeninicity was performed with the Kolaskar and Tongaonkar Antigenicity software. Epitope Mapping was analyzed using Linear Epitope Prediction Bepired. The structure of proteins with epitope regions was visualized by software Pyrex and PyMOL. Conserve analysis was performed using bio edit software. HLA mimicry was analyzed through HLAPred software. Molecular docking between the epitope with HLA I and HLA II was validated by Chimera and PyMOL software. The toxicity test for candidate vaccine peptides was carried out using ToxinPred software. Our study found seven potential epitope candidates as vaccine candidates. The seven epitopes were derived from spike proteins (5 epitopes), envelope proteins (1 epitope), and membrane proteins (1 epitope). All epitope codes are conserved and are not the same as HLA in Humans. The docking test results show a value with low affinity so that a strong bond can provide a high immune response. Toxicity tests show that all epitopes are non-toxic and safe to use as vaccine ingredients. Seven peptides from the spike, envelope, membrane protein that showed potential as vaccine candidates against Covid-19.

Treatment prioritization and risk stratification of head and neck cancer during COVID-19 pandemic: A systematic review

INTRODUCTION: Treatment of head and neck cancer patients amidst the COVID-19 pandemic is challenging, whereas prolonged treatment initiation in head and neck squamous cell carcinoma may increase mortality and likelihood of recurrence. Special attention is needed to ensure safe and appropriate care of these patients. This article aims to review and discuss existing research on treatment prioritization and risk stratification of head and neck cancer patients during the pandemic. MATERIALS AND METHODS: The authors conducted literature search in three databases (PubMed, Cochrane, and Clinical Key) on July 15th, 2020. The keywords were ("Head and Neck Mucosal Malignancy" OR "Head and Neck Cancer") AND ("Management" OR "Head and Neck Surgery") AND ("COVID-19" OR "Pandemic"). The inclusion criteria were cancer in adult patients, published from 2020 in English, and with available access to full text. The exclusion criteria were comments, letters, and case reports. The articles were critically appraised using the Centre of Evidence-based Medicine (CEBM), University of Oxford and Duke University. The literature search strategy is illustrated using Preferred Reporting Items for Systematic review and meta-analysis (PRISMA) flow diagram. RESULTS: A total of 150 articles were identified;21 articles were gathered from Clinical Key, 33 from Cochrane, and 96 from Pubmed. After screening s and reviewing the full text, the authors determined five articles met the inclusion criteria. There are several key points of head and neck cancer management in the COVID-19 pandemic. Head and neck cancer management is considered a high-risk procedure;the clinician should use proper personal protective equipment. Before operative treatment, all patients should undergo a PCR test 14 days before surgery. In diagnosing head and neck cancer, laryngoscopy should be considered carefully;and cytology should be preferred instead. Medically Necessary, Time-sensitive (MeNTS) score is recommended for risk stratification and surgery prioritization;it has three domains: procedure, disease, and patient. However, it is not specified to head and neck cancer;therefore, it should be combined with other references. Stanford University Head and Neck Surgery Division Department of Otolaryngology made surgery prioritization into three groups, urgent (should be operated immediately), can be postponed for 30 days, and can be postponed for 30- 90 days. Some urgent cases and should be operated on immediately include cancers involving the airways, decreased renal function, and metastases. For chemoradiation decision to delay or continue should refer to the goal of treatment, current oncologic status, and tolerance to radiation. In terms of patient's follow up, telephone consultation should be maximized. CONCLUSION: MeNTS scoring combined with Guideline from Department of Otolaryngology at Stanford University prioritizing criteria can be helpful in decision making of stratifying Risk and prioritizing surgery in head and neck cancer management.