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Background: India experienced three coronavirus disease (COVID-19) waves, with the third attributed to the highly contagious Omicron variant. Before the national vaccination rollout for children above 6, understanding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) positivity in the pediatric population was essential. This study aims to assess the burden of Covid-19 infection and to estimate the seroprevalence in children aged 6 to 14 years in the state of Karnataka. Material and Methods: We surveyed 5,358 children aged 6-14 across Karnataka using 232 health facilities, from June 6 to 14, 2022. We determined the sample size using the PPS (Population Proportional to Size) technique and employed cluster sampling. We tested all participants for SARS-CoV-2 IgG with an enzyme-linked immunosorbent assay (ELISA) kit and SARS-CoV-2 RNA with reverse transcription-polymerase chain reaction (RT-PCR). We sequenced samples with a cycle threshold (CT) value below 25 using whole genomic sequencing (WGS). Result: We found an adjusted seroprevalence of IgG at 75.38% statewide, and we found 0.04% of children RT-PCR positive for COVID-19. We determined a case-to-infection ratio of 1:37 and identified the SARS-CoV-2 strains as Omicron, BA.5, and BA.2.10. Conclusion: The study showed a high seroprevalence of IgG among children with low active infection. Omicron, BA. 5, and BA. 2.10 variants were detected through WGS.

Background: Picture-naming tests (PNTs) evaluate linguistic impairment in dementia due to semantic memory impairment, impaired lexical retrieval or perceptual deficits. They also assess the decline in naming impairment at various stages of dementia and mild cognitive impairment (MCI) that occurs due to progressive cognitive impairment. With the increasing numbers of people with dementia globally, it is necessary to have validated naming tests and norms that are culturally and linguistically appropriate. Aims: In this cross-sectional study we harmonized a set of 30 images applicable to the Indian context across five languages and investigated the picture-naming performance in patients with MCI and dementia. Methods & Procedures: A multidisciplinary expert group formed by the Indian Council of Medical Research (ICMR) collaborated towards developing and adapting a picture naming test (PNT) known as the ICMR-PNT in five Indian languages: Hindi, Bengali, Telugu, Kannada and Malayalam. Based on cross-cultural adaptation guidelines and item-wise factor analysis and correlations established separately across five languages, the final version of the ICMR-PNT test was developed. A total of 368 controls, 123 dementia and 128 MCI patients were recruited for the study. Psychometric properties of the adapted version of the ICMR-PNT were examined, and sensitivity and specificity were examined. Outcomes & Results The ICMR-PNT scores in all languages combined were higher in controls compared with patients with dementia and MCI (F[subscript 2], [subscript 615] = 139.85; p < 0.001). Furthermore, PNT scores for MCI was higher in comparison with patients with dementia in all languages combined (p < 0.001). The area under the curve across the five languages ranged from 0.81 to 1.00 for detecting dementia. There was a negative correlation between Clinical Dementia Rating (CDR) and ICMR-PNT scores and a positive correlation between Addenbrooke's Cognitive Examination--III (ACE-III) and ICMR-PNT scores in control and patient groups. Conclusions & Implications: The ICMR-PNT was developed by following cross-cultural adaptation guidelines and establishing correlations using item-wise factor analysis across five languages. This adapted PNT was found to be a reliable tool when assessing naming abilities effectively in mild to moderate dementia in a linguistically diverse context.

One of the most common causes of blindness, particularly in the elderly, is cataracts. Nearly half of India's elderly population has cataracts by the age of 80 or has had surgery to treat them. According to surveys conducted by the WHO and NPCB, there are over 12 million blind persons in India, and 80.1% of them are blind due to cataracts. Early detection of cataract cases is necessary to prevent total blindness. The World Health Organization (WHO) states that cataracts are the most frequent cause of blindness and visual loss. The risk of blindness among cataract patients can be decreased with prompt detection and treatment. On the other hand, clinical cataract identification requires the expertise of ophthalmologists. Therefore, the broad adoption of cataract detection to avert blindness may be hampered by the prospective expenses. Researchers are becoming more and more interested in artificial intelligence assisted diagnosis based on medical imagery. This study suggests an automated cataract detection procedure built on image processing and machine learning methods. A set of fundus retinal images serves as the input for the suggested model. The image dataset includes two kinds of images: healthy and images with cataracts to train the algorithm. This research consists of three primary phases: pre-processing, feature extraction and classification. The initial step of the method involves pre-processing the images to make them easier to process by extract the gray scale from the input image, then contrast-limited adaptive histogram equalization (CLAHE) is applied to improve the image and minimize noise, Finally ROI has been extracted. The second phase is feature extraction; by extract four kinds of texture features: (I) grey-level co-occurrence matrix (GLCM) to extract 11 features: 1) difference variance, 2) inverse difference moment, 3,4) information1\2, 5) entropy, 6) difference entropy, 7) correlation, 8) sum entropy, 9) maximal correlation coefficient, 10) contrast, and 11) angular second moment. (II) First Order Statistics (FOS) to extract 5 features: 1) entropy, 2) maximal gray level, 3) kurtosis, 4) skewness, and 5) energy. (III) Statistical Feature Matrix (SFM) to extract 4 features: 1) coarseness, 2) periodicity, 3) contrast, and 4) roughness. And finally (IV) Neighborhood Gray Tone Difference Matrix (NGTDM) to extract 4 features: 1) complexity, 2) coarseness, 3) strength, and 4) contrast. In the last phases, the extracted 24 features are put as input to the classifier, the classification was done by using Random Forest, support vector machines (SVM), Logistic Regression, K Neighbors (KNN), Decision Tree, and Naive Base Classifier. Classifies the retinal fundus images into two classes Cataracts or normal image. When compared to other current approaches, the experimental results of the suggested method show that its accuracy is 95%. [ABSTRACT FROM AUTHOR]

Background: Diabetic ketoacidosis (DKA) is an acute, severe and life-threatening metabolic complication of diabetes. Objective: The objective of this study was to conduct a clinical audit of the management of DKA based on the hospital protocol in the selected secondary care hospital. Design: An observational retrospective longitudinal study design was used to review the data of the patients admitted with DKA. Settings: The study was conducted in a secondary care government hospital, which has 200 beds, including 48 beds in medical ward and 6 beds in the high dependency unit. Materials and Methods: A survey questionnaire was used based on local hospital protocol, and the data was collected from patients admission notes between January 2010 and December 2014, using electronic patients records. Outcome Measures: The study looked at outcomes such as how DKA protocol was followed, complications, adherence, causes, investigations carried out or not, severity, readmissions, and duration of the stay. Sample Size: The audit selected 49 patients from a total of 83 admission notes with DKA who fits the sampling criteria. Results: Of 49 patients, 38 patients were having type 1 diabetes mellitus and 11 patients having type 2 diabetes mellitus. The most common cause in both groups is omission of insulin. Intravenous 0.9% sodium chloride was initiated in the early first hour of diagnosis of DKA for most patients. Readmission rate was 25%. Insulin was commenced in less than 1h for two-thirds of the total patients. Poor adherence to the protocol such as monitoring serum sodium bicarbonate, serum potassium levels and replacement of potassium levels in the early period of management were observed. Conclusion: Continued evidence-based practice and education for medical and paramedical staffs is needed to reduce the complications of DKA and efficiently resolve DKA, improve patient outcomes, and reduce the length of hospital stay. New DKA protocol has been introduced in the hospital as the result of this audit.