- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data.

Background: Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health state evolution.

Metabolic Engineering of Corynebacterium glutamicum for High-Level Production of 1,5-Pentanediol, a C5 Diol Platform Chemical.

The biobased production of chemicals is essential for advancing a sustainable chemical industry. 1,5-Pentanediol (1,5-PDO), a five-carbon diol with considerable industrial relevance, has shown limited microbial production efficiency until now. This study presents the development and optimization of a microbial system to produce 1,5-PDO from glucose in Corynebacterium glutamicum via the l-lysine-derived pathway.

Mechanisms of low MHC I expression and strategies for targeting MHC I with small molecules in cancer immunotherapy.

Major histocompatibility complex (MHC) class I load antigens and present them on the cell surface, which transduces the tumor-associated antigens to CD8 T cells, activating the acquired immune system. However, many tumors downregulate MHC I expression to evade immune surveillance. The low expression of MHC I not only reduce recognition by- and cytotoxicity of CD8 T cells, but also seriously weakens the anti-tumor effect of immunotherapy by restoring CD8 T cells, such as immune checkpoint inhibitors (ICIs).

Generative Adversarial Networks for Neuroimage Translation.

Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class are limited. From the learning perspective, this process contributes to the data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features.

Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.

Background: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved.

Enhancing the reverse transcriptase function in Taq polymerase via AI-driven multiparametric rational design.

Introduction: Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5'- 3' exonuclease activity, effective deoxyuracyl incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates. Our objective was to use AI-driven rational design combined with multiparametric wet-lab analysis to identify and validate Taq polymerase mutants with an optimal combination of these properties.

Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model.

Background: Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC death. However, the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited.

Optimized Trajectory Unraveling for Classical Simulation of Noisy Quantum Dynamics.

The dynamics of open quantum systems can be simulated by unraveling it into an ensemble of pure state trajectories undergoing nonunitary monitored evolution, which has recently been shown to undergo measurement-induced entanglement phase transition. Here, we show that, for an arbitrary decoherence channel, one can optimize the unraveling scheme to lower the threshold for entanglement phase transition, thereby enabling efficient classical simulation of the open dynamics for a broader range of decoherence rates. Taking noisy random unitary circuits as a paradigmatic example, we analytically derive the optimum unraveling basis that on average minimizes the threshold.

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.

Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time.

Clinical and biochemical features of atherogenic hyperlipidemias with different genetic basis: A comprehensive comparative study.

Patients with genetically-based hyperlipidemias exhibit a wide phenotypic variability. Investigation of clinical and biochemical features is important for identifying genetically-based hyperlipidemias, determining disease prognosis, and initiating timely treatment. We analyzed genetic data from 3374 samples and compared clinical data, lipid levels (low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, triglycerides, and lipoprotein (a)), frequency, age at onset of coronary heart disease (CHD), and the severity of carotid and femoral atherosclerosis (plaque number, maximum stenosis, total stenosis, maximum plaque height, and plaque score) among patients with familial hypercholesterolemia (FH), familial dysbetalipoproteinemia (FD), polygenic hypercholesterolemia (HCL), severe HCL, and those without lipid disorders (n = 324).

Multi-Modal CLIP-Informed Protein Editing.

Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback.

SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL), a Snakemate workflow for rapid and bulk analysis of Illumina sequencing of SARS-CoV-2 genomes.

The incorporation of sequencing technologies in frontline and public health healthcare settings was vital in developing virus surveillance programs during the Coronavirus Disease 2019 (COVID-19) pandemic caused by transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, increased data acquisition poses challenges for both rapid and accurate analyses. To overcome these hurdles, we developed the SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL) for quick bulk analyses of Illumina short-read sequencing data.

Attacking medical images with minimal noise: exploiting vulnerabilities in medical deep-learning systems.

Background: A change in the output of deep neural networks (DNNs) via the perturbation of a few pixels of an image is referred to as an adversarial attack, and these perturbed images are known as adversarial samples. This study examined strategies for compromising the integrity of DNNs under stringent conditions, specifically by inducing the misclassification of medical images of disease with minimal pixel modifications.

Methods: This study used the following three publicly available datasets: the chest radiograph of emphysema (cxr) dataset, the melanocytic lesion (derm) dataset, and the Kaggle diabetic retinopathy (dr) dataset.

Computer modeling of bevacizumab drug distribution after intravitreal injection in rabbit and human eyes.

Age-related macular degeneration (AMD) is a progressive eye disease that causes loss of central vision and has no cure. Wet AMD is the late neovascular form treated with vascular endothelial growth factor (VEGF) inhibitors. VEGF is the critical driver of wet AMD.

Monitoring the safety of the adjuvanted human papillomavirus vaccine, HPV-16/18-AS04: Protocol for a cohort study using electronic health records in Yinzhou, China.

This protocol was developed to conduct population-wide surveillance of the bivalent HPV-16/18-AS04-adjuvanted human papillomavirus (HPV) vaccine in terms of uptake and safety outcomes including potential immune-mediated diseases (pIMDs) and pregnancy-related outcomes in China. The study will use electronic health records from 2010 to 2020 from the Yinzhou Regional Health Information Platform and include a population-based cohort of female permanent residents aged 9-45 years. Baseline and follow-up periods will be defined according to the 2017 introduction of HPV-16/18-AS04 in China.

The Burden of Type 2 Diabetes in Adolescents and Young Adults in China: A Secondary Analysis from the Global Burden of Disease Study 2021.

Early-onset type 2 diabetes (T2D) is an increasingly serious public health issue, particularly in China. This study aimed to analyze the characteristics of disease burden, secular trend, and attributable risk factors of early-onset T2D in China. Using data from the Global Burden of Disease (GBD) 2021, we analyzed the age-standardized rate (ASR) of incidence, disability-adjusted life years (DALYs), and mortality rates of T2D among individuals aged 15 to 39 years in China from 1990 to 2021.

Investigating Task-Free Functional Connectivity Patterns in Newborns Using Functional Near-Infrared Spectroscopy.

Background: Resting-state networks (RSNs), particularly the sensorimotor network, begin to strengthe in the third trimester of pregnancy and mature extensively by term age. The integrity and structure of these networks have been repeatedly linked to neurological health outcomes in neonates, highlighting the importance of understanding the normative variations in RSNs in healthy development. Specifically, robust bilateral functional connectivity in the sensorimotor RSN has been linked to optimal neurodevelopmental outcomes in neonates.

Fault-tolerant neural networks from biological error correction codes.

It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have been observed to protect states against neural spiking noise, but their role in information processing is unclear. Here, we use these biological error correction codes to develop a universal fault-tolerant neural network that achieves reliable computation if the faultiness of each neuron lies below a sharp threshold; remarkably, we find that noisy biological neurons fall below this threshold. The discovery of a phase transition from faulty to fault-tolerant neural computation suggests a mechanism for reliable computation in the cortex and opens a path towards understanding noisy analog systems relevant to artificial intelligence and neuromorphic computing.

Fate of wastewater trace organic chemicals in vegetated biofiltration systems.

Vegetated biofiltration system (VBS) is an effective green technology for urban stormwater and greywater treatment. However, VBS is yet to be optimised for effective treatment of wastewater, particularly if it contains trace organic chemicals (TrOCs). The effect of plant species has not been addressed under TrOC wastewater loading.

Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography.

Purpose: This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).

Methods: Retrospective analysis of a prospectively collected clinical registry of patients with sICH consecutively admitted at a single academic comprehensive stroke center between January-2016 and April-2018. We conducted an in-depth analysis of 105 radiomic features extracted from 105 patients.

Association of early-life factors with biological age acceleration and the mediating effect of social environment risks in middle-aged and older adults.

Background: Adverse early-life events influence the health with ageing throughout the life course. However, the effects of combined early-life risks on ageing acceleration in adults and the roles of social environment risks remain unknown.

Objective: To investigate associations of maternal smoking, breastfeeding and birth weight with accelerated biological age (BA), and to explore genetic-predicted effect and mediating effect of social environment risks.

Spectrum and Prevalence of Rare Variants and Their Association with Familial Dysbetalipoproteinemia.

Familial dysbetalipoproteinemia (FD) is a highly atherogenic, prevalent genetically based lipid disorder. About 10% of FD patients have rare variants associated with autosomal dominant FD. However, there are insufficient data on the relationship between rare variants and FD.