Exploring the ability of plasma pTau217, pTau181 and beta-amyloid in mirroring cerebrospinal fluid biomarker profile of Mild Cognitive Impairment by the fully automated Lumipulse platform.

Background: The approval of new disease-modifying therapies by the U.S. Food and Drug Administration and the European Medicine Agency makes it necessary to optimize non-invasive and cost-effective tools for the identification of subjects at-risk of developing Alzheimer's Disease (AD).

Dynamic docking algorithm for UGV to UAV based on single planning under disturbed conditions.

This paper investigates the initial dynamic docking problem to mobile and trajectory-disturbed targets for tracking and recovering drones by Unmanned Ground Vehicles (UGVs). First, the target status is estimated by employing the Extended Kalman Filter (EKF). Then, the drone's perturbation is mapped to a dynamic docking point, quantifying the target motion deviation.

Evaluation of AI-based auto-contouring tools in radiotherapy: A single-institution study.

Background: Accurate delineation of organs at risk (OARs) is crucial yet time-consuming in the radiotherapy treatment planning workflow. Modern artificial intelligence (AI) technologies had made automation of OAR contouring feasible. This report details a single institution's experience in evaluating two commercial auto-contouring software tools and making well-informed decisions about their clinical adoption.

Influence of DOAC Levels and Thrombin Generation on Postoperative Bleeding [SONAR]: A Nested Case-Control Study.

A DOAC concentration threshold above which an impact on surgical hemostasis starts to occur is unknown. Thrombin generation assays (TGAs) provide a measure of the coagulation phenotype. This study aimed to determine whether preoperative TGA parameters are associated with postoperative bleeding, and whether this is partly due to residual DOAC levels.

Ultra-sensitive and accurate multi-ion detection in biofluids via organic electrochemical transistors.

As an emerging ionic sensor with low-voltage operation (<1 V), biocompatibility, and stable operation in aqueous environments, organic electrochemical transistors (OECTs) have attracted significant research interest for various biofluid-related ion detection, where minor ion concentration variations can effectively reflect health or pathology states. However, OECT-based ion sensors are currently limited by restricted device transconductance g and stabilites, which severely hinder their applications in actual ion sensing scenarios. Here, ultra-sensitive multi-ion sensors based on high-performance n-type vertical OECTs (accumulation mode, g = 58 mS) for Na, K, and Ca detection in a practical biofluid (effluent from continuous renal replacement therapy), are demonstrated with high accuracy and stability, which are comparable to conventional Roche method.

Benefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation.

Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance.

RAVE checklist: Recommendations for overcoming challenges in retrospective safety studies of automated driving systems.

Objective: The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion of ADS technology deployments is paving the way for early stage safety impact evaluations, whereby the observational data from both an ADS and a representative benchmark fleet are compared to quantify safety performance.

Methods: In January 2024; a working group of experts across academia, insurance, and industry came together in Washington, DC to discuss the current and future challenges in performing such evaluations.

Collaborative large language models for automated data extraction in living systematic reviews.

Objective: Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process.

Materials And Methods: A dataset of 10 trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data.

Generative Artificial Intelligence in Anatomic Pathology.

Context.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities.

Objective.

Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children.

The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child's well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods.

Fibroblast activation protein peptide-targeted NIR-I/II fluorescence imaging for stable and functional detection of hepatocellular carcinoma.

Purpose: Cancer-associated fibroblasts (CAFs) are the primary stromal component of the tumor microenvironment in hepatocellular carcinoma (HCC), affecting tumor progression and post-resection recurrence. Fibroblast activation protein (FAP) is a key biomarker of CAFs. However, there is limited evidence on using FAP as a target in near-infrared (NIR) fluorescence imaging for HCC.

[Analysis of the surveillance system for adverse events following Immunization (AEFI) related to COVID-19 vaccines in Colombia, 2021-2023Análise do sistema de vigilância de ESAVI por vacinas contra a COVID-19 na Colômbia, 2021-2023].

Objective: To describe the functioning of the surveillance system for adverse events following immunization (AEFI) in Colombia using the evaluation tools proposed by the World Health Organization (WHO).

Method: Descriptive study of the performance of the AEFI surveillance system for COVID-19 vaccines between 17 February 2021 and 30 September 2023. WHO indicators for structure, process, and results were adapted.

Prediction of Preeclampsia Using Machine Learning: A Systematic Review.

Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality. Early prediction is the need of the hour so that interventions like aspirin prophylaxis can be started. Nowadays, machine learning (ML) is increasingly being used to predict the disease and its prognosis.

Connectional differences between humans and macaques in the MT+ complex.

MT+ is pivotal in the dorsal visual stream, encoding tool-use characteristics such as motion speed and direction. Despite its conservation between humans and monkeys, differences in MT+ spatial location and organization may lead to divergent, yet unexplored, connectivity patterns and functional characteristics. Using diffusion tensor imaging, we examined the structural connectivity of MT+ subregions in macaques and humans.

Comparative analysis of solvent-based and solvent-free (melting) methods for fabricating 3D-printed polycaprolactone-hydroxyapatite composite bone scaffolds: physicochemical/mechanical analyses and cytocompatibility.

Purpose: The study conducts a comparative analysis between two prominent methods for fabricating composites for bone scaffolds-the (solid) solvent method and the solvent-free (melting) method. While previous research has explored these methods individually, this study provides a direct comparison of their outcomes in terms of physicochemical properties, cytocompatibility, and mechanical strength. We also analyse their workflow and scalability potentials.

CalciumZero: a toolbox for fluorescence calcium imaging on iPSC derived brain organoids.

Calcium plays an important role in regulating various neuronal activities in human brains. Investigating the dynamics of the calcium level in neurons is essential not just for understanding the pathophysiology of neuropsychiatric disorders but also as a quantitative gauge to evaluate the influence of drugs on neuron activities. Accessing human brain tissue to study neuron activities has historically been challenging due to ethical concerns.

UDA-seq: universal droplet microfluidics-based combinatorial indexing for massive-scale multimodal single-cell sequencing.

The use of single-cell combinatorial indexing sequencing via droplet microfluidics presents an attractive approach for balancing cost, scalability, robustness and accessibility. However, existing methods often require tailored protocols for individual modalities, limiting their automation potential and clinical applicability. To address this, we introduce UDA-seq, a universal workflow that integrates a post-indexing step to enhance throughput and systematically adapt existing droplet-based single-cell multimodal methods.

GLUT1 as a generic biomarker enables near-infrared fluorescence molecular imaging guided precise intraoperative tumor detection in breast cancer.

Purpose: Precise tumor excision is important but challenging in breast-conserving surgery (BCS). Tumor-specific fluorescence imaging may be used for intraoperative tumor detection and, therefore, to guide precise tumor excision. The aims of this study are to develop a glucose transporter 1 (GLUT1)-targeted near-infrared fluorescence tracer and evaluate its accuracy for breast cancer detection using fresh surgical breast specimens.

Predicting drug and target interaction with dilated reparameterize convolution.

Predicting drug-target interaction (DTI) stands as a pivotal and formidable challenge in pharmaceutical research. Many existing deep learning methods only learn the high-dimensional representation of ligands and targets on a small scale. However, it is difficult for the model to obtain the potential law of combining pockets or multiple binding sites on a large scale.

Robotic assisted TKA may allow for smaller polyethylene liner sizes compared to manual TKA with simultaneous removal of Hardware.

Total knee arthroplasty (TKA) with hardware around the knee is a challenge to preserve bone while boney landmarks are distorted. Robotic assisted (RA) TKA may assist in simultaneous hardware removal and TKA due to preoperative planning and retention of bone. The aim of this study is to identify if there are differences in component fixation, component constraint and functional outcomes dependent during simultaneous removal of hardware (ROH) around the knee and TKA comparing RA-TKA to manual.

A multi-modal dental dataset for semi-supervised deep learning image segmentation.

In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions.

Efficient recognition of Parkinson's disease mice on stepping characters with CNN.

Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients. This study aims to enhance the accuracy of early diagnosis of PD through non-invasive methods, with the goal of enabling earlier intervention in the disease process. To this end, we constructed an open-field environment using flexible sensors under dark conditions, conducting experiments on a mouse model of Parkinson's disease alongside a normal control group.