Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification.

Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions.

The Prevalence of Multidrug-Resistant Uropathogenic Bacterial Profile With Antibiotic Susceptibility Patterns Among the Community and Hospitalized Patients During COVID Waves.

Background and objective Urinary tract infections (UTIs) are a common infectious disease affecting people of various ages and genders and are prevalent in different geographical locations. However, the way Gram-positive and Gram-negative (UTI) germs react to antibiotic treatment varies significantly. The coronavirus disease 2019 (COVID-19) pandemic has increased the frequency of secondary bacterial superinfection, leading to a spike in ongoing recommendations for antibiotic treatment, both therapeutic and preventative.

Intrinsic Dual-State Emission Zinc-Based MOF Rodlike Nanostructures with Applications in Smartphone Readout Visual-Based Detection for Tetracycline: MOF-Based Color Tonality.

Dual-state emitters (DSEs) are entities that exhibit fluorescence in both the solution and solid state, which open up a wide range of possibilities for their utilization in various fields. The development of detection platforms using intrinsic luminescent metal-organic frameworks (LMOFs) is highly desirable for a variety of applications. DSE MOFs as a subclass of intrinsic LMOFs are highly attractive due to no need for encapsulation/functionalization by fluorophores and/or using luminescent linkers.