Awareness about diabetes mellitus and DKA among medical students: an observational study

Background: Diabetes Mellitus (DM) is a global epidemic and a leading cause for increased mortality and morbidity. The prevalence of Diabetes is very high so the management of DM and its complication like diabetic ketoacidosis (DKA) is very crucial in today’s world. Medical students being the future pillars of our health care system were included in this study to know their knowledge towards diabetes and its acute complication DKA.Methods: This study was a questionnaire based observational study conducted in Adichunchanagiri Institute of Medical Sciences, B G Nagara. Final year medical students and interns were included in the study.Results: A total of 150 (75 from each group) questionnaires were collected and analysed. Most of the final year students and interns were aware about the classical symptoms of diabetes (73.33% and 84% respectively) and were aware about the endocrine gland related to diabetes (82.6% and 94.6% respectively) however there were differences about the meaning of PPBS among both the groups. Interns (76%) had better knowledge regarding the fluid replacement in the management of DKA in compared to final year students (41.33%). There was statistically significant difference in knowledge between the two groups regarding GDM.Conclusions: This study identifies that both final year students and interns need to improve the practical knowledge towards diagnostic parameters of DM and treatment of DKA. Continous medical education programmes and workshops should be organised to enhance the knowledge towards DM and its complications

Bile Acids Conjugation in Human Bile Is Not Random: New Insights from 1H-NMR Spectroscopy at 800 MHz

Bile acids constitute a group of structurally closely related molecules and represent the most abundant constituents of human bile. Investigations of bile acids have garnered increased interest owing to their recently discovered additional biological functions including their role as signaling molecules that govern glucose, fat and energy metabolism. Recent NMR methodological developments have enabled single-step analysis of several highly abundant and common glycine- and taurine- conjugated bile acids, such as glycocholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurocholic acid, taurodeoxycholic acid, and taurochenodeoxycholic acid. Investigation of these conjugated bile acids in human bile employing high field (800 MHz) (1)H-NMR spectroscopy reveals that the ratios between two glycine-conjugated bile acids and their taurine counterparts correlate positively (R2 = 0.83-0.97; p = 0.001 x 10(-2)-0.006 x 10(-7)) as do the ratios between a glycine-conjugated bile acid and its taurine counterpart (R2 = 0.92-0.95; p = 0.004 x 10(-3)-0.002 x 10(-10)). Using such correlations, concentration of individual bile acids in each sample could be predicted in good agreement with the experimentally determined values. These insights into the pattern of bile acid conjugation in human bile between glycine and taurine promise useful clues to the mechanism of bile acids' biosynthesis, conjugation and enterohepatic circulation, and may improve our understanding of the role of individual conjugated bile acids in health and disease

Master Plan of Water Distribution System for Srinagar and Hanumanth Nagar Wards - using EPANET

Every metropolitan city requires a master plan which provides a conceptual layout to guide future growth and development of the city. One of the reasons for water crisis in developing countries is due to inefficient and wasteful water management systems. In this research paper EPANET is used to build the water network model, optimize it, compute head-loss, velocity of flow in each node and flow in each pipe in the water distribution. It can be used to consider and understand the growth of the demand of city with the design period of 30 years. The above knowledge is used to identify the risks related to the growth and to suggest measures to overcome these risks. The design of new model will make the respective authorities aware of the new challenges arising from changing demands

Investigations in the rôle of organic matter in plant nutrition. Part IV. Chemical and biological transformations attendant on the application of cane molasses to swamp soil

This article does not have an abstract

JBiopest 7(2):144-150(2014) Management of Meloidogyne incognita JBiopest 5(1): 1-6 © 453 Management of Meloidogyne incognita infecting carrot by using bioagents

ABSTRACT Root-knot nematode (Meloidogyne incognita) is a serious pest of vegetables and major limiting factor in the commercial production of carrot in many parts of the world, including India. To avoid excess use of chemicals, an attempt was made to manage M. incognita infecting carrot by using bioagents viz., Trichoderma harzianum (indigenous and commercial) and Pseudomonas fluorescens (commercial) under field conditions. Among biocontrol agents, the lowest nematode population in soil (222.66/200 g) was recorded in isolated T. harzianum @ 25g/m 2 (2 × 10 6 Cfu/g) treated plot than by commercial T. harzianum @ 20g/m 2 . Maximum reduction of galls/rhizome (11.00), galls/5 g of root (36.00) and egg masses (11.33) per 5 gram of root was recorded in isolated T. harzianum @ 25g/m 2 treatment compared to other treatments. Maximum shoot height (49.66 cm), shoot weight (26.00 g/rhizome) and rhizome yield (8.20 q/ha) were also recorded in isolated T. harzianum @ 25g/m 2 followed by isolated T. harzianum and commercial T. harzianum @ 20g/m 2

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Image-based multiplex immune profiling of cancer tissues : translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer.Gilead Breast Cancer Research Grant; Breast Cancer Research Foundation; Susan G Komen Leadership; Interne Fondsen KU Leuven/Internal Funds KU Leuven; Swedish Society for Medical Research; Swedish Breast Cancer Association; Cancer Research Program; US Department of Defense; Mayo Clinic Breast Cancer; Marie Sklodowska Curie; NHMRC; National Institutes of Health; Cancer Research UK; Japan Society for the Promotion of Science; Horizon 2020 European Union Research and Innovation Programme National Cancer Institute; National Heart, Lung and Blood Institute; National Institute of Biomedical Imaging and Bioengineering; VA Merit Review Award; US Department of Veterans Affairs Biomedical Laboratory Research Breast Cancer Research Program; Prostate Cancer Research Program; Lung Cancer Research Program; Kidney Precision Medicine Project (KPMP) Glue Grant; EPSRC; Melbourne Research Scholarship; Peter MacCallum Cancer Centre; KWF Kankerbestrijding; Dutch Ministry of Health, Welfare and Sport the Breast Cancer Research Foundation; Agence Nationale de la Recherche; Q-Life; National Breast Cancer Foundation of Australia; National Health and Medical Council of Australia; All-Island Cancer Research Institute; Irish Cancer Society; Science Foundation Ireland Investigator Programme; Science Foundation Ireland Strategic Partnership Programme. Open access funding provided by IReL.https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896hj2024ImmunologySDG-03:Good heatlh and well-bein

Spatial analyses of immune cell infiltration in cancer : current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.http://www.thejournalofpathology.com/hj2024ImmunologySDG-03:Good heatlh and well-bein