Assessment of depression and diabetes distress in type 2 diabetes mellitus patients in a tertiary care hospital of South India

Background: Depression is one of the many complications seen among diabetics. Depression leads to lack of self-care by the diabetic and endangers the therapeutic compliance, accounting for a derangement in metabolic control which in turn causes further diabetic complications and may even result in hospitalization. This leads to an increase in depressive symptoms and thus the vicious cycle continues.Methods: It is a Descriptive, cross sectional study conducted in the Medicine outpatient department. Depression was assessed by Hamilton depression rating scale. Diabetic distress was assessed by diabetic distress scale.Results: Out of the 250 study participants, 142 (56.8%) were found to be suffering from depression and 6 (2.4%) were found to have diabetes distress. The magnitude of depression was similar in both male and female. Depression was high among illiterates, unemployed (70%), single, separated individuals and patients with complications of diabetes. There was no significant association between religion and low economic status with depression. Treatment modalities, complications of diabetes, sociodemographic factors like age, sex, occupation, education, marital status, religion and socio-economic status had no significant correlation with diabetic distress. But there was a statistically significant association between diabetic distress and co-morbid conditions. 95.8% with depression had no distress and this association was found to be statistically significant (0.038).Conclusions: The magnitude of depression and distress is much high among diabetics. Early detection, counselling and treatment are required for all diabetics, especially those who have additional risk factors for the development of depression

Acute Liver and Renal Failure: A Rare Adverse Effect Exclusive to Intravenous Form of Amiodarone

Amiodarone is an antiarrhythmic drug which is highly effective against a wide spectrum of ventricular tachyarrhythmias making it irreplaceable in certain group of patients. We report an unusual case of acute liver and renal failure within 24 hours of initiation of intravenous (IV) amiodarone which resolved after stopping the medication. The mechanism of acute liver and renal toxicity is not clearly known but is believed to be secondary to amiodarone induced (relative) hypotension, idiosyncratic reaction to the drug, and toxicity of the vector that carries the medication, polysorbate-80. In this case review, we discuss the hyperacute drug toxicity caused by IV amiodarone being a distinctly different entity compared to the adverse effects shown by oral amiodarone and support the suggestion that oral amiodarone can be safely administered even in patients who manifest acute hepatitis with the IV form

Low Yield of Thyroid-Function Tests in Adult Hospitalized Patients -- A Retrospective Analysis

Background: In the US, serum thyroid-stimulating hormone (TSH) and thyroxine measurements are the fourth- and tenth-commonest laboratory tests ordered, respectively. Diagnosis of thyroid disorder requires clinical suspicion supported by laboratory values. However, in the setting of acute illness, both the clinical and laboratory pictures can be confounded. Objective: To study clinical outcomes and derangement patterns of inpatient thyroid-function tests. Design: This retrospective study was conducted at an academic center on admissions aged ≥18 years and TSH tests performed over a 1-year period. Admissions with active pregnancy and/or prior thyroid-related diagnosis were excluded. Main Outcomes: Clinical outcomes were divided based on new diagnosis of thyroid-related disorder, newly prescribed thyroxine replacement, or antithyroid drugs/ endocrinology referrals, or both. In order to analyze the derangement patterns of abnormal TSH, only the results of the first test ordered were considered (as some admissions had multiple TSH tests ordered). Results: A total of 7,204 admissions aged ≥18 years had TSH tests done. Of these, 1,912 were excluded. Of the 5,292 admissions with no prior thyroid disorder or active pregnancy, 183 (3.46%) were assigned a new diagnosis of thyroid-related disorder, 54 (1.02%) received treatment/referral, and 46 (0.87%) received both a new diagnosis and treatment/referral. Based on the TSH results (reference range 0.42-4.0 mIU/L) of the 5,292 admissions, 4,312 (81.5%) and 980 (18.5%) admissions were flagged normal and abnormal, respectively. Of the 980 admissions with one or more abnormal TSH results, 21 (2.14%) had first ordered TSH10 mIU/L. Conclusion: There is low value in testing inpatients for thyroid disorders, and testing comes at significant expense to the health-care system

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submissio

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features

Federated Learning for Breast Density Classification: A Real-World Implementation

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.Comment: Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig.

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Daratumumab (Anti-CD38) interference with serological testing: An emerging challenge for blood banks in developing countries

Daratumumab (DARA), a monoclonal anti-CD38 antibody, belongs to the new generation of immunotherapy in refractory relapsed multiple myeloma. CD38 is weakly expressed on human erythrocytes. By its intrinsic anti-CD38 activity, DARA also interferes in routine pretransfusion compatibility testing such as antibody screening for red blood cells (RBCs) alloantibodies and compatibility testing. Treating RBCs with dithiothreitol eliminates the DARA interference. We report two cases of serological interference of DARA in pretransfusion testing and how timely information before starting the second patient on DARA prevented the delay in pretransfusion compatibility testing and blood availability

Microbial Diversity and Adaptation under Salt-Affected Soils: A Review

The salinization of soil is responsible for the reduction in the growth and development of plants. As the global population increases day by day, there is a decrease in the cultivation of farmland due to the salinization of soil, which threatens food security. Salt-affected soils occur all over the world, especially in arid and semi-arid regions. The total area of global salt-affected soil is 1 billion ha, and in India, an area of nearly 6.74 million ha−1 is salt-stressed, out of which 2.95 million ha−1 are saline soil (including coastal) and 3.78 million ha−1 are alkali soil. The rectification and management of salt-stressed soils require specific approaches for sustainable crop production. Remediating salt-affected soil by chemical, physical and biological methods with available resources is recommended for agricultural purposes. Bioremediation is an eco-friendly approach compared to chemical and physical methods. The role of microorganisms has been documented by many workers for the bioremediation of such problematic soils. Halophilic Bacteria, Arbuscular mycorrhizal fungi, Cyanobacteria, plant growth-promoting rhizobacteria and microbial inoculation have been found to be effective for plant growth promotion under salt-stress conditions. The microbial mediated approaches can be adopted for the mitigation of salt-affected soil and help increase crop productivity. A microbial product consisting of beneficial halophiles maintains and enhances the soil health and the yield of the crop in salt-affected soil. This review will focus on the remediation of salt-affected soil by using microorganisms and their mechanisms in the soil and interaction with the plants