Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.

Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the performance of TotalSegmentator, a state-of-the-art adult-trained CT organ segmentation model, on a subset of organs in a pediatric CT dataset and explored optimization strategies to improve pediatric segmentation performance. TotalSegmentator was retrospectively evaluated on abdominal CT scans from an external adult dataset (n = 300) and an external pediatric data set (n = 359).

Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.

Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, ADC, and FLAIR MRI sequences provide for deep learning in infarct segmentation. Recent technologies in model self-configuration have promised greater performance and generalizability through automated optimization.

ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging.

As the adoption of artificial intelligence (AI) systems in radiology grows, the increase in demand for greater bandwidth and computational resources can lead to greater infrastructural costs for healthcare providers and AI vendors. To that end, we developed ISLE, an intelligent streaming framework to address inefficiencies in current imaging infrastructures. Our framework draws inspiration from video-on-demand platforms to intelligently stream medical images to AI vendors at an optimal resolution for inference from a single high-resolution copy using progressive encoding.

Efficacy of Low-Dose Streptokinase Infusion in Late-Onset Permanent Tunnel Catheter Dysfunction: A Single-Center Interventional Study.

Introduction Hemodialysis is a vital modality for patients with renal dysfunction, with venous access being a significant factor in its success. While arteriovenous fistulas are preferred, tunneled catheters serve as important alternatives, especially in challenging cases. Late-onset tunneled catheter dysfunction, often due to fibrin sheath formation, impedes hemodialysis efficiency.

Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors.

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India.

A Single-Center Study on the Clinical Profiles and Ultrasonographic Assessments of Living Kidney Donors in the Marathwada Region of Maharashtra.

Living donor kidney transplantation plays a vital role in renal replacement therapy, particularly in India, where a substantial increase in kidney transplants has been observed. Thorough assessments of living kidney donors are crucial, focusing on parameters such as kidney size and glomerular filtration rate (GFR). Despite the importance of GFR in donor assessments, there is a noticeable lack of data on normal GFR ranges in the Indian population.

Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence.

Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics systems. The cloud presents a potential solution for radiology, and we should weigh its economic and environmental implications. Recently, cloud technologies have become a cost-effective strategy by providing necessary infrastructure while reducing expenditures associated with hardware ownership, maintenance, and upgrades.

Coarse Race and Ethnicity Labels Mask Granular Underdiagnosis Disparities in Deep Learning Models for Chest Radiograph Diagnosis.

See also the editorial by Nikolic in this issue.

Overcoming Challenges and Innovations in Orthopedic Prosthesis Design: An Interdisciplinary Perspective.

Recent advances in the orthopedic prostheses design have significantly improved the quality of life for individuals with orthopedic disabilities. However, there are still critical challenges that need to be addressed to further enhance the functionality of orthopedic prostheses improving biocompatibility to promote better integration with natural tissues, enhancing durability to withstand the demands of daily use, and improving sensory feedback for better control of movement are the most pressing issues. To address these challenges, promising emerging solutions such as smart prosthetics, 3D printing, regenerative medicine, and artificial intelligence have been developed.

Associations between dairy farm performance indicators and culling rates under policy-driven herd size constraints.

Introduction: This article aimed to study cross-sectional associations between the performance of dairy farms and their corresponding culling proportions under the herd size constraint as imposed in 2018 by the new phosphate regulation in the Netherlands.

Methods: To this end, production data from 10,540 Dutch dairy farms were analyzed to capture the inflow and outflow of both primiparous and multiparous cows. Farm performance was measured by 10 indicators structured in four areas of longevity, production, reproduction, and udder health.

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review.

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions.

Annular illumination in 2D quantitative phase imaging: a systematic evaluation.

Quantitative phase imaging (QPI) is an invaluable microscopic technology for definitively imaging phase objects such as biological cells and optical fibers. Traditionally, the condenser lens in QPI produces disk illumination of the object. However, it has been realized by numerous investigators that annular illumination can produce higher-resolution images.

Speech and Language Practitioners' Experiences of Commercially Available Voice-Assisted Technology: Web-Based Survey Study.

Background: Speech and language therapy involves the identification, assessment, and treatment of children and adults who have difficulties with communication, eating, drinking, and swallowing. Globally, pressing needs outstrip the availability of qualified practitioners who, of necessity, focus on individuals with advanced needs. The potential of voice-assisted technology (VAT) to assist people with speech impairments is an emerging area of research but empirical work exploring its professional adoption is limited.

Enrichment of Methylocystis dominant mixed culture from rice field for PHB production.

Presence of methanotrophs in diverse environmental habitats helps to reduce emissions of greenhouse gas like methane. Isolation and culture of undiscovered wealth of methanotrophic organisms can help in exploitation of these organisms in value added products. The present study focuses on the enrichment of methanotroph dominated mixed microbial community by use of three stage strategy of revival, proliferation, and segregation.

Selection of methanotrophic platform for methanol production using methane and biogas.

To develop biotechnological process for methane to methanol conversion, selection of a suitable methanotrophic platform is an important aspect. Systematic approach based on literature and public databases was developed to select representative methanotrophs Methylotuvimicrobium alcaliphilum, Methylomonas methanica, Methylosinus trichosporium and Methylocella silvestris. Selected methanotrophs were further investigated for methanol tolerance and methanol production on pure methane as well as biogas along with key enzyme activities involved in methane utilization.

Survival analysis of dairy cows in the Netherlands under altering agricultural policy.

Culling of underperforming dairy cows by replacement heifers is a fundamental part of Dutch dairy farm management. Changes in national agricultural policies can influence farmers' culling decisions. The objective of this study was to analyse the relevancy of cow-level risk factors for survival of Dutch dairy cows under perturbations due to national policy changes related to the -milk quota abolishment of 2015 and the phosphate regulations since 2017.

On the origin of the genetic variation in infectious disease prevalence: Genetic analysis of disease status versus infections for Digital Dermatitis in Dutch dairy cattle.

The purpose of this study was to investigate the origin of the genetic variation in the prevalence of bovine digital dermatitis (DD) by comparing a genetic analysis of infection events to a genetic analysis of disease status. DD is an important endemic infectious disease affecting the claws of cattle. For disease status, we analysed binary data on individual disease status (0,1; indicating being free versus infected), whereas for infections, we analysed binary data on disease transmission events (1,0; indicating becoming infected or not).

Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

Background: Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits.

Challenges in the Setup of Large-scale Next-Generation Sequencing Analysis Workflows.

While Next-Generation Sequencing (NGS) can now be considered an established analysis technology for research applications across the life sciences, the analysis workflows still require substantial bioinformatics expertise. Typical challenges include the appropriate selection of analytical software tools, the speedup of the overall procedure using HPC parallelization and acceleration technology, the development of automation strategies, data storage solutions and finally the development of methods for full exploitation of the analysis results across multiple experimental conditions. Recently, NGS has begun to expand into clinical environments, where it facilitates diagnostics enabling personalized therapeutic approaches, but is also accompanied by new technological, legal and ethical challenges.

Semi-automated cancer genome analysis using high-performance computing.

Next-generation sequencing (NGS) has turned from a new and experimental technology into a standard procedure for cancer genome studies and clinical investigation. While a multitude of software packages for cancer genome data analysis have been made available, these need to be combined into efficient analytical workflows that cover multiple aspects relevant to a clinical environment and that deliver handy results within a reasonable time frame. Here, we introduce QuickNGS Cancer as a new suite of bioinformatics pipelines that is focused on cancer genomics and significantly reduces the analytical hurdles that still limit a broader applicability of NGS technology, particularly to clinically driven research.

Aging is associated with highly defined epigenetic changes in the human epidermis.

Background: Altered DNA methylation patterns represent an attractive mechanism for understanding the phenotypic changes associated with human aging. Several studies have described global and complex age-related methylation changes, but their structural and functional significance has remained largely unclear.

Results: We have used transcriptome sequencing to characterize age-related gene expression changes in the human epidermis.

Disinfection by-product formation following chlorination of drinking water: artificial neural network models and changes in speciation with treatment.

Artificial neural network (ANN) models were developed to predict disinfection by-product (DBP) formation during municipal drinking water treatment using the Information Collection Rule Treatment Studies database complied by the United States Environmental Protection Agency. The formation of trihalomethanes (THMs), haloacetic acids (HAAs), and total organic halide (TOX) upon chlorination of untreated water, and after conventional treatment, granular activated carbon treatment, and nanofiltration were quantified using ANNs. Highly accurate predictions of DBP concentrations were possible using physically meaningful water quality parameters as ANN inputs including dissolved organic carbon (DOC) concentration, ultraviolet absorbance at 254nm and one cm path length (UV(254)), bromide ion concentration (Br(-)), chlorine dose, chlorination pH, contact time, and reaction temperature.