SPIRIT-CONSORT-TM: a corpus for assessing transparency of clinical trial protocol and results publications.

Randomized controlled trials (RCTs) can produce valid estimates of the benefits and harms of therapeutic interventions. However, incomplete reporting can undermine the validity of their conclusions. Reporting guidelines, such as SPIRIT for protocols and CONSORT for results, have been developed to improve transparency in RCT publications.

Deep-Learning-Assisted Digital Fluorescence Immunoassay on Magnetic Beads for Ultrasensitive Determination of Protein Biomarkers.

Digital fluorescence immunoassay (DFI) based on random dispersion magnetic beads (MBs) is one of the powerful methods for ultrasensitive determination of protein biomarkers. However, in the DFI, improving the limit of detection (LOD) is challenging since the ratio of signal-to-background and the speed of manual counting beads are low. Herein, we developed a deep-learning network (ATTBeadNet) by utilizing a new hybrid attention mechanism within a UNet3+ framework for accurately and fast counting the MBs and proposed a DFI using CdS quantum dots (QDs) with narrow peak and optical stability as reported at first time.

Hybrid Quality-Based Recommender Systems: A Systematic Literature Review.

As technology develops, consumer behavior and how people search for what they want are constantly evolving. Online shopping has fundamentally changed the e-commerce industry. Although there are more products available than ever before, only a small portion of them are noticed; as a result, a few items gain disproportionate attention.

Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females.

Objective: Pain is subjective, and self-reporting pain might be challenging. Studies conducted to detect pain using biological signals and real-time self-reports pain are limited. We evaluated the feasibility of collecting pain data on healthy females' menstrual pain and conducted preliminary analysis.

Persistent prey species in the Lotka-Volterra apparent competition system with a single shared predator.

We analyze the Lotka-Volterra n prey-1 predator system with no direct interspecific interaction between prey species, in which every prey species undergoes the effect of apparent competition via a single shared predator with all other prey species. We prove that the considered system necessarily has a globally asymptotically stable equilibrium, and we find the necessary and sufficient condition to determine which of feasible equilibria becomes asymptotically stable. Such an asymptotically stable equilibrium shows which prey species goes extinct or persists, and we investigate the composition of persistent prey species at the equilibrium apparent competition system.

Returning genetic risk information for hereditary cancers to participants in a population-based cohort study in Japan.

Large-scale population cohort studies that collect genomic information are tasked with returning an assessment of genetic risk for hereditary cancers to participants. While several studies have applied to return identified genetic risks to participants, comprehensive surveys of participants' understanding, feelings, and behaviors toward cancer risk remain to be conducted. Here, we report our experience and surveys of returning genetic risks to 100 carriers of pathogenic variants for hereditary cancers identified through whole genome sequencing of 50 000 individuals from the Tohoku Medical Megabank project, a population cohort study.

BiomedRAG: A retrieval augmented large language model for biomedicine.

Retrieval-augmented generation (RAG) involves a solution by retrieving knowledge from an established database to enhance the performance of large language models (LLM). , these models retrieve information at the sentence or paragraph level, potentially introducing noise and affecting the generation quality. To address these issues, we propose a novel BiomedRAG framework that directly feeds automatically retrieved chunk-based documents into the LLM.

Deep learning-based histopathological assessment of tubulo-interstitial injury in chronic kidney diseases.

Background: Chronic kidney disease (CKD) causes progressive and irreversible damage to the kidneys. Renal biopsies are essential for diagnosing the etiology and prognosis of CKD, while accurate quantification of tubulo-interstitial injuries from whole slide images (WSIs) of renal biopsy specimens is challenging with visual inspection alone.

Methods: We develop a deep learning-based method named DLRS to quantify interstitial fibrosis and inflammatory cell infiltration as tubulo-interstitial injury scores, from WSIs of renal biopsy specimens.

Directional intermodular coupling enriches functional complexity in biological neuronal networks.

Hierarchically modular organization is a canonical network topology that is evolutionarily conserved in the nervous systems of animals. Within the network, neurons form directional connections defined by the growth of their axonal terminals. However, this topology is dissimilar to the network formed by dissociated neurons in culture because they form randomly connected networks on homogeneous substrates.

Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients.

Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients' quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights.

Combining Group Contribution Method and Semisupervised Learning to Build Machine Learning Models for Predicting Hydroxyl Radical Rate Constants of Water Contaminants.

Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO). Previously reported models have achieved relatively good performance, but due to scarce data (<1400 records), the applicability domain (AD) has been significantly limited. To address this limitation, we curated a much larger experimental data set (Primary data set), which contains 2358 kinetic records.

Enhancing the European power system resilience with a recommendation system for voluntary demand response.

Climate change, geopolitical tensions, and decarbonization targets are bringing the resilience of the European electric power system to the forefront of discussion. Among various regulatory and technological solutions, voluntary demand response can help balance generation and demand during periods of energy scarcity or renewable energy generation surplus. This work presents an open data service called Interoperable Recommender that leverages publicly accessible data to calculate a country-specific operational balancing risk, providing actionable recommendations to empower citizens toward adaptive energy consumption, considering interconnections and local grid constraints.

A new near-infrared fluorescence probe for highly selective and sensitive detection and imaging of Butyrylcholinesterase in Alzheimer's disease mice.

Butylcholinesterase (BChE) is a key enzyme in living system, closely related to liver and neurological diseases. It is very challenge to develop near-infrared (NIR) fluorescence probe methods for highly selective and sensitive detection of BChE in vivo. Based on the differences in active sites and spatial pockets between acetylcholinesterase (AChE) and BChE, a new NIR BChE-responsive fluorescence probe Probe-BChE (λ/λ = 600 nm/676 nm) was designed and synthesized by introducing dimethyl carbamate group as recognizing moiety to a NIR fluorophore hemicyanine skeleton.

A wearable alcohol biosensor: Exploring the accuracy of transdermal drinking detection.

Background: Trace amounts of consumed alcohol are detectable within sweat and insensible perspiration. However, the relationship between ingested and transdermally emitted alcohol is complex, varying across environmental conditions and involving a degree of lag. As such, the feasibility of real-time drinking detection across diverse environments has been unclear.

Diffractometer for element-specific analysis on local structures using a combination of X-ray fluorescence holography and anomalous X-ray scattering.

To tackle disorder in crystals and short- and intermediate-range order in amorphous materials, such as glass, we developed a carry-in diffractometer to utilise X-ray fluorescence holography (XFH) and anomalous X-ray scattering (AXS), facilitating element-specific analyses with atomic resolution using the wavelength tunability of a synchrotron X-ray source. Our diffractometer unifies XFH and AXS configurations to determine the crystal orientation via diffractometry. In particular, XFH was realised even for a crystal with blurred emission lines by a standing wave in a hologram, and high-throughput AXS with sufficient count statistics and energy resolution was achieved using three multi-array detectors with crystal analysers.

Garment Recognition and Reconstruction Using Object Simultaneous Localization and Mapping.

The integration of robotics in the garment industry remains relatively limited, primarily due to the challenges in the highly deformable nature of garments. The objective of this study is thus to explore a vision-based garment recognition and environment reconstruction model to facilitate the application of robots in garment processing. Object SLAM (Simultaneous Localization and Mapping) was employed as the core methodology for real-time mapping and tracking.

Effect of Organic Anion Transporting Polypeptide 1B1 on Plasma Concentration Dynamics of Clozapine in Patients with Treatment-Resistant Schizophrenia.

The involvement of drug-metabolizing enzymes and transporters in plasma clozapine (CLZ) dynamics has not been well examined in Japanese patients with treatment-resistant schizophrenia (TRS). Therefore, this clinical study investigated the relationship between single nucleotide polymorphisms (SNPs) of various pharmacokinetic factors (drug-metabolizing enzymes and transporters) and dynamic changes in CLZ. Additionally, we aimed to determine whether CLZ acts as a substrate for pharmacokinetic factors using in vitro assays and molecular docking calculations.

MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagnosis is typically based on the differences in the architectural arrangement of squamous epithelial layers and the presence of keratinization.

Toward an Asian-based bodily movement database for emotional communication.

Most current databases for bodily emotion expression are created in Western countries, resulting in culturally skewed representations. To address the obvious risk this bias poses to academic comprehension, we attempted to expand the current repertoire of human bodily emotions by recruiting Asian professional performers to wear whole-body suits with 57 retroreflective markers attached to major joints and body segments, and express seven basic emotions with whole-body movements in a motion-capture lab. For each emotion, actors performed three self-created scenarios that covered a broad range of real-life events to elicit the target emotion within 2-5 seconds.

Evaluating Interleukin-2 and Its Receptors As Indicators of Acute Renal Graft Rejection.

Introduction Interleukin-2 (IL-2) is a cytokine that exerts its actions via binding to a variety of interleukin-2 receptors (IL-2R), thereby stimulating T-cell response. Acute renal graft rejection (AR) is known to be mediated by CD8+ T-cells, through the IL-2 pathway. The aim of this study was to determine whether IL-2 and IL-2R could work as prognostic biomarkers of AR.

An empirical study of object detection models for the detection of iron deficiency anemia using peripheral blood smear images.

Iron Deficiency Anemia (IDA) is the nutritional disorder that occurs when the body does not contain enough iron, an essential component of hemoglobin (Hb). The World Health Organization (WHO) estimated that IDA is the main cause of anemia in 1.62 billion cases worldwide [1].