Objective: To analyze the video sources, contents and quality of YouTube videos about the topic of medical professionalism.
Methods: A systematic search was accomplished on YouTube videos during the period between March 1, 2020 and March 27, 2020. The phrases as significant words used throughout YouTube web search were 'Professionalism in Medical Education', Professionalism in medicine', 'Professionalism of medical students', 'Professionalism in healthcare'. 'Teaching professionalism', 'Attributes of professionalism'. The basic information collected for each video included author's/publisher's name, total number of watchers, likes, dislikes and positive and undesirable remarks. The videos were categorized into educationally useful and useless established on the content, correctness of the knowledge and the advices. Different variables were measured and correlated for the data analysis.YouTube website was searched the using keywords 'Professionalism in Medical Education', Professionalism in medicine', 'Professionalism of medical students', 'Professionalism in healthcare'. 'Teaching professionalism', and 'Attributes of professionalism'.
Results: After 2 rounds of screening by the subject experts and critical analysis of all the 137 YouTube videos, only 41 (29.92%) were identified as pertinent to the subject matter, i.e., educational type. After on expert viewing these 41 videos established upon our pre-set inclusion/exclusion criteria, only 17 (41.46%) videos were found to be academically valuable in nature.
Conclusion: Medical professionalism multimedia videos uploaded by the healthcare specialists or organizations on YouTube provided reliable information for medical students, healthcare workers and other professional. We conclude that YouTube is a leading and free online source of videos meant for students or other healthcare workers yet the viewers need to be aware of the source prior to using it for training learning.
Methods: We applied a retrospective approach using a top-down costing method to estimate the cost of health care services. Clinical and Administrative departments divided into cost centres, and the unit cost was calculated by dividing the total cost of final care cost centres into the total number of patients discharged in one year. The average cost of inpatient services was calculated based on the average cost of each ward and the number of patients treated.
Results: The average cost per patient stayed in KFCH was SAR 19,034, with the highest cost of SAR 108,561 for patients in the Orthopedic ward. The average cost of the patient in the Surgery ward, Plastic surgery, Neurosurgery, Medical ward, Pediatric ward and Gynecology ward was SAR 33,033, SAR 29,425, SAR 23,444, SAR 20,450, SAR 9579 and SAR 8636 respectively.
Conclusion: This study provides necessary information about the cost of health care services in a tertiary care setting. This information can be used as a primary tool and reference for further studies in other regions of the country. Hence, this data can help to provide a better understanding of tertiary hospital costing in the region to achieve the privatization objective.
Methods: 21 day old male Sprague Dawley rats were assigned as Experiment-1 & 2 - PND rats were divided into 4 groups with interventions for 7 months (n = 8/group). NC- Normal control fed normal chow diet; OB- Obese group, fed high fat diet; OB + CHO + DHA- fed high fat diet and oral supplementation of choline, DHA. OB + EE- fed high fat diet along with exposure to enriched environment .Experiment-2 had similar groups and interventions as experiment 1 but for next 5 months were fed normal chow diet without any interventions. Body mass index was assessed and blood was analyzed for serum lipid profile. Common Carotid Artery (CCA) was processed for Haematoxylin and eosin, Verhoff Vangeison stains. Images of tissue sections were analyzed and quantified using image J and tissue quant software.
Results: In experiment.1, mean body mass index (p
Objective: To assess the cytotoxic effects of two synthesised compounds against HT-29 human colon adenocarcinoma cells and human CCD-18Co normal colon cells.
Materials and methods: Two successfully synthesised compounds were characterised using elemental (carbon, hydrogen, nitrogen, and sulphur) analysis, Fourier-Transform Infrared (FTIR), and 1H, 13C 119Sn Nucleus Magnetic Resonance (NMR) spectroscopies. The single-crystal structure of both compounds was determined by X-ray single-crystal analysis. The cytotoxicity of the compounds was assessed using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazholium bromide (MTT) assay upon 24 h of treatment. While the mode of cell death was determined based on the externalisation of phosphatidylserine using a flow cytometer.
Results: The elemental analysis data of the two compounds showed an agreement with the suggested formula of (C6H5)2Sn[S2CN(C3H5)2]2 for Compound 1 and (C6H5)3Sn[S2CN(C3H5)2] for Compound 2. The two major peaks of infrared absorbance, i.e., ν(C = N) and ν(C = S) were detected at the range of 1475-1479 cm-1 and 972-977 cm-1, respectively. The chemical shift of carbon in NCS2 group for Compound 1 and 2 were found at 200.82 and 197.79 ppm. The crystal structure of Compound 1 showed that it is six coordinated and crystallised in monoclinic, P21/c space group. While the crystal structure of Compound 2 is five coordinated and crystallised in monoclinic, P21/c space group. The cytotoxicity (IC50) of the two compounds against HT-29 cell were 2.36 μM and 0.39 μM. Meanwhile, the percentage of cell death modes between 60% and 75% for compound 1 and compound 2 were mainly due to apoptosis, suggesting that both compounds induced growth arrest.
Conclusion: Our study concluded that the synthesised compounds showed potent cytotoxicity towards HT-29 cell, with the triphenyltin(IV) compound showing the highest effect compared to diphenyltin(IV).
Methods: Multiple sequence alignment with the Clustal Omega method was used to identify conserved regions and Geneious Prime was used to produce a consensus sequence. T and B cell epitopes were predicted by various computational tools from the NetCTL and Immune Epitope Database (IEDB), respectively.
Results: Altogether, 6 HTL cells and 11 CTL epitopes were predicted. This vaccine's molecular docking is done with Patch Dock and LigPlot to verify interactions. The immune server (C-IMMSIM) was used to develop In silico immune response in order to assess the multi-epitope vaccine's immunogenic profile.
Conclusion: We designed universal vaccine against B. cereus responsible for food poisoning. The disease may be avoided with the aid of the proposed epitope-based vaccine.