The current research attempted to evaluate the impact of various thawing techniques (R 0 : control group, R 1 : water immersion thawing, R 2 : low-temperature thawing, R 3 : combined thawing, water thawing then low-temperature thawing, R 4 : combination thawing, low temperature thawing then water thawing, and R 5 : oven thawing) on the quality, microbiota, and organoleptic characteristics of chicken meat fillets.
Numerous areas of medical services, including as imaging diagnostics, advanced pathology, emergency clinic confirmation prediction, drug plan, grouping of cancer and stromal cells, specialist help, and so forth, have profited from the utilization of deep learning. Cancer prognosis involves predicting the course of the disease, the likelihood that it will spread and recur, and the likelihood that patients will survive. The clinical management of cancer patients will considerably benefit from the precision of cancer prognosis prediction. To better forecast cancer prognosis, modern statistical analysis and Deep learning techniques are being applied, as well as biomedical translational research being improved. In recent years, the processing capacity has significantly increased and the innovation of artificial insight, especially deep learning, has advanced quickly. Cancer is the leading reason for death in people. As a result, cancer detection is essential for early diagnosis and provides the best chance for treating cancer patients in a secure and efficient manner. It is, however, the trickiest way to increase the likelihood of the person surviving. RNA sequencing has significantly advanced in the last few decades and is now a crucial method for transcriptome profiling.
Objective: The purpose of this study is to calculate the predictive value of injury severity score in relation to morbidity; mortality and hospital stay of patients following road traffic accidents. Study Design: Retrospective cohort study Place and Duration: Study was conducted at the department of General Surgery, Saidu Teaching hospital Swat, Jinnah Hospital Lahore and Bakhtawar Amin Memorial Trust Hospital Multan for duration six months from June 2020 to December 2020. Methods: Total eighty patients of both genders were presented in this study. Patients with ages 18-70 years had injury because of road traffic accidents were included. Informed written consent was taken from all the patients for detailed demographics included age, sex, body mass index and type of injury. Patients were admitted in emergency ward for initial treatment. Outcomes were calculated in terms of predictive value of injury severity score in relation to morbidity, mortality and hospital stay of patients. Abbreviated injury scale (AIS) score was used to analyze severity score. SPSS 20.0 version was used to analyze complete data. Results: Majority of the patients 55 (68.8%) were males and 25 (31.2%) were females. Mean age of the patients was 30.17±3.55 years with mean BMI 24.13±6.71 kg/m2. 45 (56.3%) patients were literate and 60 (75%) cases were from urban areas. Motorcycle was the most common cause of trauma found in 48 (60%) patients followed by car accidents in 18 (22.5%) and 14 (17.5%) trauma were because of cycle or other traffic. Legs and arms fractures were the most common injuries found in 32 (40%) and 25 (31.3%) followed by head fractures in 23 (28.8%) cases. Mean injury severity score was 54.07±3.64. Mean hospital stay was 3.12±4.37 days. Rate of mortality was high found in 19 (23.8%) cases. Conclusion: We concluded in this study that the injury severity score among traumatic patients were significantly high with rate of mortality and limb fractures were the most common injury found. Injury severity score is becoming more widely accepted as a predictor of mortality. Keywords: Road Traffic Accident, Injury Severity Score, Mortality, Limb Fracture
The purpose of this paper is to provide an overview of the current state of quantum computing in the health sector and to explore its potential future applications. Quantum computing has the potential to revolutionize a wide range of industries, including healthcare, by greatly enhancing the speed and accuracy of various tasks such as drug discovery, personalized medicine, and medical imaging. A literature review of the existing literature on the use of quantum computing in the health sector is conducted, revealing the various applications of quantum computing in the health sector and the current state of research in this area. The paper concludes that while the technology is still in its early stages of development, quantum computing has the potential to revolutionize the health sector, however, further studies are needed to fully understand the implications of quantum computing in healthcare.
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