1,502 research outputs found

    “Vaai Ganam” - a screening program for early detection of oral potentially malignant disorders and oral cancer among truck drivers in Chennai – a cross-sectional survey

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    Introduction: Truck drivers, though forming an integral part of a vital trade link for the Indian population, lack basic life insurance and health care benefits offered by other organized sectors in Indian Industries. This paper aims to present the initial findings of the “VaaiGanam” program which proposes to identify tobacco use and the prevalence of Oral potentially malignant disorders (OPMDs) among truck drivers who are stationed or passing via Chennai and provide cessation services by behavioral therapy. Methods: This cross-sectional study was conducted by a dental screening team who were involved in data collection and screening of the 747 truck drivers who fulfilled the inclusion and exclusion criteria between Jan to Oct 2022. After data collection, oral examinations were done and suspicious lesions were sought for expert opinion. A standard punch biopsy was taken from those lesions requiring confirmation. Results: Among the 747 subjects who participated in this program, 704 (94.2%) were current users of various tobacco products, with 235 (31.4%) preferring smoking and the rest 469(62.8%) using smokeless tobacco products. Oral mucosal lesions were recorded in 49 (6.5%) of the study population, mostly among tobacco users. Punch/incisional biopsies were taken among 17 of the 49 subjects and oral dysplasia was histopathologically confirmed in 9 (mild epithelial dysplasia = 5; moderate epithelial dysplasia = 4) subjects.  Conclusion: Truck drivers with tobacco and substance abuse are at high risk of developing oral cancer and hence this study emphasizes the importance of periodic oral cancer screening programs for this vulnerable population to identify potentially malignant oral lesions at an early stage

    Estimating the costs of blindness and moderate to severe visual impairment among people with diabetes in India

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    Objectives This study provides an estimate of the annual cost of blindness and moderate to severe visual impairment (MSVI) among people with diabetes aged 40 years and above in India in the year 2019.Design A cost of illness study.Setting India.Participants People with diabetes aged 40 years and above in India in the year 2019.Primary and secondary outcome measures Estimates are provided for the total costs of screening for most common vision-threatening eye conditions, treatment of these conditions, economic activity lost by these people and their family carers whose ability to work is affected, and loss of quality of life experienced by people with diabetes and blindness or MSVI.Results It is estimated that for people with diabetes aged 40 years or above, annual screening followed by eye examination where required would cost around 42.3 billion Indian rupees (INR) (4230 crores) per year; treating sight problems around 2.87 billion INR (287 crores) per year if 20% of those needing treatment receive it; and lost economic activity around 472 billion INR (47 200 crores). Moreover, 2.86 million (0.286 crores) quality-adjusted life years (QALYs) are lost annually due to blindness and MSVI. The estimate of lost production is highly sensitive to the proportion of people with MSVI able to work and how their output compares with that of a person with no visual impairment.Conclusions This is the first study to estimate the cost of blindness and MSVI for people aged 40 years and over with diabetes in India. The annual cost to the Indian economy is substantial. This cost will be expected to fall if a successful screening and treatment plan is introduced in India. Further work is suggested using more robust data, when available, to estimate the loss of productivity and loss of QALYs, as this would be worthwhile

    Viral Infections and Neonatal Necrotizing Enterocolitis: A Meta-analysis.

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    CONTEXT: Necrotizing enterocolitis (NEC) is a devastating intestinal disease affecting preterm infants. Studies implicate viral infections in etiopathogenesis. OBJECTIVE: To summarize the association of viral infections with NEC by systematic review and meta-analysis. DATA SOURCES: We searched Ovid-Medline, Embase, Web of Science, and Cochrane databases in November 2022. STUDY SELECTION: We included observational studies that examined the association between viral infections and NEC in newborn infants. DATA EXTRACTION: We extracted data regarding the methodology, participant characteristics, and outcome measures. RESULTS: We included 29 and 24 studies in the qualitative review and meta-analysis, respectively. The meta-analysis demonstrated a significant association between viral infections and NEC (odds ratio [OR], 3.81, 95% confidence interval: 1.99-7.30, 24 studies). The association remained significant after excluding the outliers (OR, 2.89 [1.56-5.36], 22 studies) and studies with poor methodology (OR, 3.33 [1.73-6.43], 22 studies). In subgroup analysis based on participants\u27 birth weight, studies including very low birth weight infants only (OR, 3.62 [1.63-8.03], 8 studies) and non-very low birth weight infants only (OR, 5.28 [1.69-16.54], 6 studies) showed a significant association. In subgroup analysis based on specific viruses, infection with rotavirus (OR, 3.96 [1.12-13.95], 10 studies), cytomegalovirus (OR, 3.50 [1.60-7.65], 5 studies), norovirus (OR, 11.95 [2.05-69.84], 2 studies), and astrovirus (OR, 6.32 [2.49-16.02], 2 studies) was significantly associated with NEC. LIMITATIONS: Heterogeneity of the included studies. CONCLUSIONS: Viral infection is associated with an increased risk of NEC in newborn infants. We need methodologically sound prospective studies to assess the effect of preventing or treating viral infections on NEC incidence

    Author Correction: Federated learning enables big data for rare cancer boundary detection.

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    10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

    Uncertainty quantification of a deep learning fuel property prediction model

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    Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community

    Author Correction: Federated learning enables big data for rare cancer boundary detection.

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    Economic impact, clinical features and outcomes of hospitalised patients with SLE in India

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    Background: Systemic lupus erythematosus (SLE), a rare multisystem disorder with a female preponderance, has a high cost for the care; however, there is no evidence relevant to the Indian setting. The primary objective of our study was to determine the financial burden of the index admission (IA) and subsequent costs during follow-up and ascertain the proportion with a catastrophic health expenditure (CHE). Methodology: This was an observational retrospective cohort study where inpatients of a general medicine ward were recruited from January 2019 to October 2020. Clinical details and costs were obtained from the hospital's electronic records and bills. Patients were telephonically interviewed for follow-up clinical details and costs incurred. A patient-family payer perspective was used. Linear regression analysis was used. Results: Of the 73 patients recruited during the study period, 96% were females and the majority (71%) were admitted through casualty, with 59% of patients having high disease activity (SLE Disease Activity Index >12). The hospital mortality was 9.6%. After a median follow-up of 12 months, there was good quality of life with no difference between the two severity groups. The total cost of the IA was 135,768 INR (94,053–223,954) and it was higher for the severe disease group (P = 0.038). The direct medical costs compromised 83% of admission costs. In the multivariate regression, the duration of hospital and intensive care unit stay were predictors of high cost. The median 6 months follow-up cost was 32,978 (14,240–80,940) and the total calculated annualized cost was 202,124 (136,188–331,508), which was not statistically different between the two groups. There was a CHE among 86% of patient-families. Conclusion: This study demonstrates that there is high morbidity and cost involved in the management of a flare of SLE. However, with appropriate care, there are reasonably good outcomes and quality of life beyond six months

    Nuclear modification of ΄\Upsilon states in pPb collisions at sNN\sqrt{s_\mathrm{NN}} = 5.02 TeV

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    Production cross sections of ΄\Upsilon(1S), ΄\Upsilon(2S), and ΄\Upsilon(3S) states decaying into \muplusmuminus in proton-lead (pPb) collisions are reported using data collected by the CMS experiment atsNN\sqrt{s_\mathrm{NN}} = 5.02 TeV. A comparison is made with corresponding cross sections obtained with pp data measured at the same collision energy and scaled by the Pb nucleus mass number. The nuclear modification factor for ΄\Upsilon(1S) is found to be RpPb(΄(1S))R_\mathrm{pPb}(\Upsilon(1S)) = 0.806 ±\pm 0.024 (stat) ±\pm 0.059 (syst). Similar results for the excited states indicate a sequential suppression pattern, such that RpPb(΄(1S))>RpPb(΄(2S))>RpPb(΄(3S))R_\mathrm{pPb}(\Upsilon(1S)) \gt R_\mathrm{pPb}(\Upsilon(2S)) \gt R_\mathrm{pPb}(\Upsilon(3S)). The suppression is much less pronounced in pPb than in PbPb collisions, and independent of transverse momentum pT΄p_\mathrm{T}^\Upsilon and center-of-mass rapidity yCM΄y_\mathrm{CM}^\Upsilon of the individual ΄\Upsilon state in the studied range pT΄<p_\mathrm{T}^\Upsilon \lt 30 GeV/c/c and ∣yCM΄∣<\vert y_\mathrm{CM}^\Upsilon\vert \lt 1.93. Models that incorporate sequential suppression of bottomonia in pPb collisions are in better agreement with the data than those which only assume initial-state modifications

    Search for CP violating top quark couplings in pp collisions at s \sqrt{s} = 13 TeV