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Fulltext Evaluation of two commercially available ELISAs for the diagnosis of Japanese encephalitis applied to field samples

Lewthwaite P, Shankar MV, Tio PH, Daly J, Last A, Ravikumar R, et al.

Trop Med Int Health, 2010 Jul;15(7):811-8.
PMID: 20487425 DOI: 10.1111/j.1365-3156.2010.02537.x

OBJECTIVE: To compare two commercially available kits, Japanese Encephalitis-Dengue IgM Combo ELISA (Panbio Diagnostics) and JEV-CheX IgM capture ELISA (XCyton Diagnostics Limited), to a reference standard (Universiti Malaysia Sarawak - Venture Technologies VT ELISA).
METHODS: Samples were obtained from 172/192 children presenting to a site in rural India with acute encephalitis syndrome.
RESULTS: Using the reference VT ELISA, infection with Japanese encephalitis virus (JEV) was confirmed in 44 (26%) patients, with central nervous system infection confirmed in 27 of these; seven patients were dengue seropositive. Of the 121 remaining patients, 37 (31%) were JEV negative and 84 (69%) were JEV unknown because timing of the last sample tested was <10 day of illness or unknown. For patient classification with XCyton, using cerebrospinal fluid alone (the recommended sample), sensitivity was 77.8% (59.2-89.4) with specificity of 97.3% (90.6-99.2). For Panbio ELISA, using serum alone (the recommended sample), sensitivity was 72.5% (57.2-83.9) with specificity of 97.5% (92.8-99.1). Using all available samples for patient classification, sensitivity and specificity were 63.6% (95% CI: 48.9-76.2) and 98.4% (94.5-99.6), respectively, for XCyton ELISA and 75.0% (59.3-85.4) and 97.7% (93.3-99.2) for Panbio ELISA.
CONCLUSION: The two commercially available ELISAs had reasonable sensitivities and excellent specificities for diagnosing JEV.
Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions

Bhatt P, Sethi A, Tasgaonkar V, Shroff J, Pendharkar I, Desai A, et al.

Brain Inform, 2023 Jul 31;10(1):18.
PMID: 37524933 DOI: 10.1186/s40708-023-00196-6

Human behaviour reflects cognitive abilities. Human cognition is fundamentally linked to the different experiences or characteristics of consciousness/emotions, such as joy, grief, anger, etc., which assists in effective communication with others. Detection and differentiation between thoughts, feelings, and behaviours are paramount in learning to control our emotions and respond more effectively in stressful circumstances. The ability to perceive, analyse, process, interpret, remember, and retrieve information while making judgments to respond correctly is referred to as Cognitive Behavior. After making a significant mark in emotion analysis, deception detection is one of the key areas to connect human behaviour, mainly in the forensic domain. Detection of lies, deception, malicious intent, abnormal behaviour, emotions, stress, etc., have significant roles in advanced stages of behavioral science. Artificial Intelligence and Machine learning (AI/ML) has helped a great deal in pattern recognition, data extraction and analysis, and interpretations. The goal of using AI and ML in behavioral sciences is to infer human behaviour, mainly for mental health or forensic investigations. The presented work provides an extensive review of the research on cognitive behaviour analysis. A parametric study is presented based on different physical characteristics, emotional behaviours, data collection sensing mechanisms, unimodal and multimodal datasets, modelling AI/ML methods, challenges, and future research directions.
Fulltext Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi Y, Patel N, Kaka N, Desai A, Kaiwan O, Sheth M, et al.

J Clin Med, 2022 Nov 29;11(23).
PMID: 36498651 DOI: 10.3390/jcm11237072

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.
Enterovirus 71 encephalomyelitis and Japanese encephalitis can be distinguished by topographic distribution of inflammation and specific intraneuronal detection of viral antigen and RNA

Wong KT, Ng KY, Ong KC, Ng WF, Shankar SK, Mahadevan A, et al.

Neuropathol Appl Neurobiol, 2012 Aug;38(5):443-53.
PMID: 22236252 DOI: 10.1111/j.1365-2990.2011.01247.x

To investigate if two important epidemic viral encephalitis in children, Enterovirus 71 (EV71) encephalomyelitis and Japanese encephalitis (JE) whose clinical and pathological features may be nonspecific and overlapping, could be distinguished.
Fulltext Frequency of Germline Mutations in Cancer Susceptibility Genes in Malignant Mesothelioma

Panou V, Gadiraju M, Wolin A, Weipert CM, Skarda E, Husain AN, et al.

J Clin Oncol, 2018 Oct 01;36(28):2863-2871.
PMID: 30113886 DOI: 10.1200/JCO.2018.78.5204

PURPOSE: The aim of the current study was to determine the prevalence and clinical predictors of germline cancer susceptibility mutations in patients with malignant mesothelioma (MM).
METHODS: We performed targeted capture and next-generation sequencing of 85 cancer susceptibility genes on germline DNA from 198 patients with pleural, peritoneal, and tunica vaginalis MM.
RESULTS: Twenty-four germline mutations were identified in 13 genes in 23 (12%) of 198 patients. BAP1 mutations were the most common (n = 6; 25%). The remaining were in genes involved in DNA damage sensing and repair (n = 14), oxygen sensing (n = 2), endosome trafficking (n = 1), and cell growth (n = 1). Pleural site (odds ratio [OR], 0.23; 95% CI, 0.10 to 0.58; P < .01), asbestos exposure (OR, 0.28; 95% CI, 0.11 to 0.72; P < .01), and older age (OR, 0.95; 95% CI, 0.92 to 0.99; P = .01) were associated with decreased odds of carrying a germline mutation, whereas having a second cancer diagnosis (OR, 3.33; 95% CI, 1.22 to 9.07; P = .02) significantly increased the odds. The odds of carrying a mutation in BAP1 (OR, 1,658; 95% CI, 199 to 76,224; P < .001), BRCA2 (OR, 5; 95% CI, 1.0 to 14.7; P = .03), CDKN2A (OR, 53; 95% CI, 6 to 249; P < .001), TMEM127 (OR, 88; 95% CI, 1.7 to 1,105; P = .01), VHL (OR, 51; 95% CI, 1.1 to 453; P = .02), and WT1 (OR, 20; 95% CI, 0.5 to 135; P = .049) were significantly higher in MM cases than in a noncancer control population. Tumor sequencing identified mutations in a homologous recombination pathway gene in 52% (n = 29 of 54).
CONCLUSION: A significant proportion of patients with MM carry germline mutations in cancer susceptibility genes, especially those with peritoneal MM, minimal asbestos exposure, young age, and a second cancer diagnosis. These data support clinical germline genetic testing for patients with MM and provide a rationale for additional investigation of the homologous recombination pathway in MM.