Brain-inspired wiring economics for artificial neural networks.

Wiring patterns of brain networks embody a trade-off between information transmission, geometric constraints, and metabolic cost, all of which must be balanced to meet functional needs. Geometry and wiring economy are crucial in the development of brains, but their impact on artificial neural networks (ANNs) remains little understood. Here, we adopt a wiring cost-controlled training framework that simultaneously optimizes wiring efficiency and task performance during structural evolution of sparse ANNs whose nodes are located at arbitrary but fixed positions.

Incorporating spatial and genetic competition into breeding pipelines with the R package gencomp.

Genetic competition can obscure the true merit of selection candidates, potentially leading to altered genotype rankings and a divergence between expected and actual genetic gains. Despite a wealth of literature on genetic competition in plant and animal breeding, the separation of genetic values into direct genetic effects (DGE, related to a genotype's merit) and indirect genetic effects (IGE, related to the effects of a genotype's alleles on its neighbor's phenotype) in linear mixed models is often overlooked, likely due to the complexity involved. To address this, we introduce gencomp, a new R package designed to simplify the use of (spatial-) genetic competition models in crop and tree breeding routines.

Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients.

Objective: The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy.

Methods: We applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD.

Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China.

Inland river runoff variability is pivotal for maintaining regional ecological stability. Daily flow forecasting in arid regions is crucial in understanding water body ecological processes and promoting healthy river ecology. Precise daily runoff forecasting serves as a cornerstone for ecological evaluation, management, and decision-making.

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis.

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database.

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy.

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids.

Bilateral hip exoskeleton assistance enables faster walking in individuals with chronic stroke-related gait impairments.

Millions of individuals surviving a stroke have lifelong gait impairments that reduce their personal independence and quality of life. Reduced walking speed is one of the major problems limiting community mobility and reintegration. Previous studies have shown positive effect of robot-assisted gait training utilizing hip exoskeletons for individuals with gait impairments due to a stroke, leading to increased walking speed in post-treatment compared to pre-treatment assessments.

A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification.

Unlabelled: This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation with quantitative computed tomography (QCT) results. The proposed models offer potential for screening patients at a high risk of osteoporosis and reducing unnecessary radiation and costs.

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High- Resolution Short Echo Time MR Spectroscopy Datasets.

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep-learning method for quantification of high-resolution short echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/GRAPPA scans from clinical trials for glioblastoma (Trial 1, May 2014-October 2018) and major-depressive-disorder (Trial 2, 2022- 2023). The training dataset included 685k spectra from 20 participants (60 scans) in Trial 1.

Enhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2.

The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.

Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease.

Background: The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases.

Results: Here, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis.

SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden.

Background: Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity.

Deep generative modeling of annotated bacterial biofilm images.

Biofilms are critical for understanding environmental processes, developing biotechnology applications, and progressing in medical treatments of various infections. Nowadays, a key limiting factor for biofilm analysis is the difficulty in obtaining large datasets with fully annotated images. This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN.

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, forming a significant area of service in Primary Health Care (PHC), which healthcare professionals often confront. It serves as a precursor to many critical illnesses and can lead to fatalities if not addressed promptly.

Therapeutic Potential of Social Chatbots in Alleviating Loneliness and Social Anxiety: Quasi-Experimental Mixed Methods Study.

Background: Artificial intelligence (AI) social chatbots represent a major advancement in merging technology with mental health, offering benefits through natural and emotional communication. Unlike task-oriented chatbots, social chatbots build relationships and provide social support, which can positively impact mental health outcomes like loneliness and social anxiety. However, the specific effects and mechanisms through which these chatbots influence mental health remain underexplored.

Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial.

Background: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence-driven adaptive trial-Vibe Up.

Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach.

Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.

New Directions for Ophthalmic OCT - Handhelds, Surgery, and Robotics.

The introduction of optical coherence tomography (OCT) in the 1990s revolutionized diagnostic ophthalmic imaging. Initially, OCT's role was primarily in the adult ambulatory ophthalmic clinics. Subsequent advances in handheld form factors, integration into surgical microscopes, and robotic assistance have expanded OCT's utility and impact outside of its initial environment in the adult outpatient ophthalmic clinic.

Nanotherapy for Neural Retinal Regeneration.

Retinal diseases can severely impair vision and even lead to blindness, posing significant threats to both physical and mental health. Physical retinal regenerative therapies are poised to revolutionize the treatment of various disorders associated with blindness. However, these therapies must overcome the challenges posed by the protective inner and outer blood‒retinal barriers.

Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.

Objectives: This study aimed to develop an automated skills assessment tool for surgical trainees using deep learning.

Background: Optimal surgical performance in robot-assisted surgery (RAS) is essential for ensuring good surgical outcomes. This requires effective training of new surgeons, which currently relies on supervision and skill assessment by experienced surgeons.

Neutrophil adhesion to vessel walls impairs pulmonary circulation in COVID-19 pathology.

Microthrombus formation is associated with COVID-19 severity; however, the detailed mechanism remains unclear. In this study, we investigated mouse models with severe pneumonia caused by SARS-CoV-2 infection by using our in vivo two-photon imaging system. In the lungs of SARS-CoV-2-infected mice, increased expression of adhesion molecules in intravascular neutrophils prolonged adhesion time to the vessel wall, resulting in platelet aggregation and impaired lung perfusion.

Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research.

This review explores the current understanding and recent advancements in neuroblastoma, one of the most common extracranial solid pediatric cancers, accounting for ~ 15% of childhood cancer-related mortality. The hallmarks of NBL, including angiogenesis, metastasis, apoptosis resistance, cell cycle dysregulation, drug resistance, and responses to hypoxia and ROS, underscore its complex biology. The tumor microenvironment's significance in disease progression is acknowledged in this study, along with the pivotal role of cancer stem cells in sustaining tumor growth and heterogeneity.

Machine learning-based blood pressure estimation using impedance cardiography data.

Objective: Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, cuff-based BP measurements suffer from inaccuracies and discomfort. This study is the first to access the feasibility of machine learning-based BP estimation using impedance cardiography (ICG) data.

Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning.

Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. Recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection.

Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer.

Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.