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IntroductionImage segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).MethodThe literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.ResultsThe majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85–0.9.DiscussionDeep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

Introduction: Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT). Method: The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review. Results: The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85–0.9. Discussion: Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

Background Vegetable or plant-based sources of protein may confer health benefits in children with progressive kidney disease. Our aims were to understand the effect of the proportion of vegetable protein intake on changes in estimated GFR and to understand the effect of the proportion of vegetable protein intake on serum levels of bicarbonate, phosphorus, and potassium. Methods Children with baseline eGFR between 30 and 90 mL/min/1.73 m.sup.2 were recruited from 59 centers across North America as part of the chronic kidney disease in children (CKiD) study. The percentage of dietary vegetable protein (VP%) was gathered from annual Food Frequency Questionnaires. We performed longitudinal linear mixed models to determine the effect of VP% on eGFR and longitudinal logistic mixed models to determine the effect of VP% on electrolyte balance (potassium, phosphorus, bicarbonate). Results Two thousand visits from 631 subjects. Across all dichotomized groups of children (sex, African American race, Hispanic ethnicity, glomerular etiology of CKD, hypertension, anemia, hyperkalemia, hyperphosphatemia, acidosis, BMI < 95th percentile), the median VP% was 32-35%. The longitudinal mixed model analysis did not show any effect of VP% on eGFR electrolyte (bicarbonate, phosphorus, and potassium) abnormalities (p > 0.1). Conclusions A diverse cohort of children with CKD has a narrow and homogeneous intake of vegetable protein. Due to the low variability of plant-based protein in the cohort, there were no associations between the percentage of plant protein intake and changes in eGFR nor electrolyte balance., Author(s): Lokesh N. Shah [sup.1] [sup.2] , Matthew B. Matheson [sup.3] , Susan L. Furth [sup.4] [sup.5] , George J. Schwartz [sup.6] , Bradley A. Warady [sup.7] [sup.8] , Cynthia [...]

Background: Studies have shown increase in health-risking behavior and a decline in health-promoting behavior among medical students during their stay in medical school. This study aims to determine the prevalence and reason for substance abuse among the undergraduate medical students in a selected medical college in Puducherry. Material and Methods: This was a facility-based explanatory mixed method study conducted from May 2019 to July 2019. Assessment of their substance abuse was done using ASSIST questionnaire. Substance use was summarized as proportions with 95% CI. Results: A total of 379 participants were included in the study. The mean age of the study participants was 20 years (± 1.34). The most prevalent substance use was alcohol (10.8%). About 1.9% and 1.6% of students surveyed consume tobacco and cannabis, respectively. Conclusion: Facilitating factors for substance use as perceived by the participants were stress, peer pressure, easy availability of substances, socialization, curiosity, and awareness knowledge about safe limits of alcohol and tobacco. [ABSTRACT FROM AUTHOR]

Background:Atrial fibrillation is the most common arrythmia and accounts for 1/3 of hospital admissions for cardiac rhythm disturbance. Recent epidemiological data have reaffirmed the fact that AF is a global epidemic and has adverse effect on long term morbidity and mortality. Aims:To determine clinical profile of patients admitted with AF and to determine etiology of atrial fibrillation.To determine the mortality and morbidity of patients admitted with atrial fibrillation.Material and Methods:A prospective observational study was carried out in 100 cases of atrial fibrillation admitted at Mandya institute of medical sciences during a time period of 12 months from January 2019 to January 2020. A detailed history and clinical examination were done according to prepared proforma. All the required investigation was collected and analysed.Results:Out of 100 cases 63 were female and male 37. Mean age was 54.98 years, most of the patients were in the age group 41-60 year (45%). Dyspnoea was the most common presentation in 71 %, followed by palpitation in 66%. Most common cause of AF was RHD 49% in our study followed by CAD in 20% and hypertension in 15%. Commonest valvular pathology in our study was mitral regurgitation (MR) in 51%. Most common valvular lesion in RHD was mitral stenosis (MS) 93.8%. Complications in the study were congestive cardiac failure in 68% and stroke in 9 %. Mortality in our study was 2%.Conclusion:In our study females were more than males. Majority of patients belonged to 41-60 years age group. Majority of the case were due to RHD, followed by coronary artery disease and hypertension. [ABSTRACT FROM AUTHOR]

This report presents an extremely rare case of papillary meningioma with prominent flow voids and increased perfusion parameters on MRI in a 28-year-old male presenting with headache. This knowledge helped the neurosurgeon to minimise intra-operative blood loss and achieve a favourable post-surgical outcome. Contribution: A rare case of papillary meningioma and its differentiating features from typical meningiomas have been discussed considering its implications for management as well as prognostication to reduce morbidity and mortality.

Glaucoma is a neurodegenerative disease of the retina and optic nerve with elevated intraocular pressure as one of the main contributing factors. The traditional in vitro methods to evaluate new gene delivery systems for neuroprotective treatments of glaucoma require testing in cell monolayers under static conditions. Therefore, a microfluidic technology that provides a dynamic simulation of cell exposure to nanoparticles (NPs) could be a useful model for rapid screening. In this study, CellASIC ONIX2 microfluidic platform was used to evaluate and optimize culture and transfection conditions to assess cell viability and transfection efficiency (TE) of gemini NPs carrying GFP-encoding plasmid in A7 astrocytes, one of the targeted retinal cells for glaucoma treatment. The average TE (inlet-middle-outlet of culture chambers) by gemini nanoplexes, 18-7NH-18 NPXs and 18-7Np3-18 NPXs was > 85% and > 25%, respectively, after 5 h of perfusion with a combined average FI (caFI) of about 600 and < 100, respectively, whereas TE after Lipofectamine 3000 was > 70% and caFI of < 100. The results have shown that comparatively to 2D monolayers, the microfluidics technique provides better exposure of cells to NPs, suitable for kinetic mapping of gene expression, and a useful tool for cell growth and live evaluation of gene delivery system interaction with cells.

The fauna of butterfly species was studied from in and around area of Karanja (Ghadge) tahsil of Wardha District from period of July 2019 -- June 2020. A total 41 species of butterflies belonging to 5 families were recorded. Maximum 18 species of butterflies were recorded from family Nymphalidae, followed by 10 species from family Lycaenidae, 7 species from family Pieridae and 3 species were recorded each from family Papilionidae and family Hesperiidae. In the above study 2 species of butterfly recorded comes under Wild Life Protection Act 1972 of India. The butterfly species Euchrysops cnejus comes under Schedule n and Euploea core comes under Schedule IV of the Indian Wild Life Protection Act 1972. [ABSTRACT FROM AUTHOR]