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Clinicopathological spectrum of snake bite-induced acute kidney injury from India

World Journal of Nephrology 2017年第3期6卷 150-161页

作者： Sanjay Vikrant Ajay Jaryal anupam parashar Department of Nephrology Indira Gandhi Medical College Department of Community Medicine Indira Gandhi Medical College

AIM To study the clinico-pathological spectrum of snake bite-induced acute kidney injury(AKI).METHODS A retrospective study of patients admitted at Indira Gandhi Medical College Hospital,Shimla with snake bite-induced AKI from July 2003 to June *** records were evaluated for patient's information on demographic,clinical characteristics,complications and *** of duration of hospital stay,requirement for intensive care unit support,treatment with dialysis,survival and mortality were *** survival and non survival groups were compared to see the difference in the demographic factors,clinical characteristics,laboratory results,and *** patients subjected to kidney biopsy,the findings of histopathological examination of the kidney biopsies were also *** One hundred and twenty-one patients were diagnosed with snake bite-induced *** age was 42.2 ± 15.1 years and majority(58%) were *** details were available in 88 *** mean duration of arrival at hospital was 3.4 ± 3.7 d with a range of 1 to30 *** percent had oliguria and 55% had history of having passed red or brown colored *** defect was seen in 89% *** hematological and biochemical laboratory abnormalities were:Anemia(80.7%),leukocytosis(75%),thrombocytopenia(47.7%),hyperkalemia(25%),severe metabolic acidosis(39.8%),hepatic dysfunction(40.9%),hemolysis(85.2%) and rhabdomyolysis(68.2%).Main complications were:Gastrointestinal bleed(12.5%),seizure/encephalopathy(10.2%),hypertension,pneumonia/acute respiratory distress syndrome(ARDS) and disseminated intravascular coagulation(9.1% each),hypotension and multi organ failure(MOF)(4.5% each).Eighty-two percent patients required renal replacement *** hundred and ten(90.9%) patient survived and 11(9.1%) patients *** compared to the survival group,the white blood cell count(P = 0.023) and bilirubin levels(P = 0.006) were significant higher and albumin levels were s

关键词： Acute kidney injury Acute tubular necrosis Acute interstitial nephritis Envenomation Hemolysis Renal cortical necrosis Rhabdomyolysis Snake bite

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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Computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： anupam Garg Anshu parashar Dipto Barman Sahil Jain Divya Singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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