SSL-VQ: Vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.

Motivation: Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).

Re-evaluating prognostic factors for cervical cancer with lymph node metastasis: a Japanese multicenter cohort study based on FIGO 2018.

Background: In 2018, the International Federation of Gynecology and Obstetrics (FIGO) revised its cervical cancer staging system to enhance clinical relevance, notably by categorizing lymph node metastases (LNM) as an independent stage IIIC. This multicenter study evaluates the prognostic implications of the FIGO 2018 classification within a Japanese cohort.

Methods: This study included 1468 patients with cervical cancer.

generation of dual-target compounds using artificial intelligence.

Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compounds that interact with multiple therapeutic target proteins. The molecular structure generation is performed by a fragment-based approach using a genetic algorithm with chemical substructures and a deep learning approach using reinforcement learning with stochastic policy gradients in the framework of generative adversarial networks.

Cybernetic avatars: Teleoperation technologies from in-body monitoring to social interaction.

Cybernetic avatars integrate physical and virtual avatars to enhance human capabilities in diverse scales and contexts.

Pathway-based prediction of the therapeutic effects and mode of action of custom-made multiherbal medicines.

Multiherbal medicines are traditionally used as personalized medicines with custom combinations of crude drugs; however, the mechanisms of multiherbal medicines are unclear. In this study, we developed a novel pathway-based method to predict therapeutic effects and the mode of action of custom-made multiherbal medicines using machine learning. This method considers disease-related pathways as therapeutic targets and evaluates the comprehensive influence of constituent compounds on their potential target proteins in the disease-related pathways.

CSF Neopterin Levels Are Elevated in Various Neurological Diseases and Aging.

: Cerebrospinal fluid (CSF) neopterin reflects inflammation of the central nervous system (CNS) and is a potentially useful biomarker for neuroinflammatory assessment and differential diagnosis. However, its optimal cut-off level in adult patients with neurological disease has not been established and it has not been adequately studied in controls. We aimed to determine its usefulness as a biomarker of neuroinflammation and the effect of age on its level.

Spatiotemporally controlled microvortices provide advanced microfluidic components.

Microvortices are emerging components that impart functionality to microchannels by exploiting inertia effects such as high shear stress, effective fluid diffusion, and large pressure loss. Exploring the dynamic generation of vortices further expands the scope of microfluidic applications, including cell stimulation, fluid mixing, and transport. Despite the crucial role of vortices' development within sub-millisecond timescales, previous studies in microfluidics did not explore the modulation of the Reynolds number (Re) in the range of several hundred.

Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design.

Computational molecular generation methods that generate chemical structures from gene expression profiles have been actively developed for de novo drug design. However, most omics-based methods involve complex models consisting of multiple neural networks, which require pretraining. In this study, we propose a straightforward molecular generation method called GxRNN (gene expression profile-based recurrent neural network), employing a single recurrent neural network (RNN) that necessitates no pretraining for omics-based drug design.

Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning.

Foods possess a range of unexplored functionalities; however, fully identifying these functions through empirical means presents significant challenges. In this study, we have proposed an approach to comprehensively predict the functionalities of foods, encompassing even processed foods. This prediction is accomplished through the utilization of machine learning on biomedical big data.

Predicting therapeutic and side effects from drug binding affinities to human proteome structures.

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed.

Ligand-based virtual-screening identified a novel CFTR ligand which improves the defective cell surface expression of misfolded ABC transporters.

Cystic fibrosis (CF) is a monogenetic disease caused by the mutation of CFTR, a cAMP-regulated Cl channel expressing at the apical plasma membrane (PM) of epithelia. ∆F508-CFTR, the most common mutant in CF, fails to reach the PM due to its misfolding and premature degradation at the endoplasmic reticulum (ER). Recently, CFTR modulators have been developed to correct CFTR abnormalities, with some being used as therapeutic agents for CF treatment.

Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology.

Background: Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes.

Objective: We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs).

Methods: We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA.

Optimization of oral entacapone administration in patients undergoing levodopa-carbidopa intestinal gel treatment.

Background: Levodopa-carbidopa intestinal gel (LCIG) treatment markedly reduces motor fluctuations in patients with Parkinson's disease; however, some patients undergoing LCIG treatment may demonstrate clinical deterioration in the afternoon. Entacapone, a catechol-O-methyltransferase inhibitor, may be a promising adjunctive option for LCIG-treated patients; however, the optimal timing of oral entacapone administration to ameliorate clinical symptoms in the afternoon remains unexplored. This study aimed to investigate the optimal timing of oral entacapone administration in patients with Parkinson's disease undergoing LCIG treatment.

Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data.

The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data.

Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization.

Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability.

A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors.

Japan College of Rheumatology guidance for the use of methotrexate in patients with rheumatoid arthritis: Secondary publication.

Methotrexate (MTX), the anchor drug in the current treatment strategy for rheumatoid arthritis (RA), was first approved for the treatment of RA in Japan in 1999 at a recommended dose of 6-8 mg/week. The approved maximum dose of MTX has been 16 mg/week since February 2011 when MTX was approved as a first-line drug in the treatment of RA. Recent evidence of MTX-polyglutamate concentration in the red blood cells of Japanese patients with RA justifies the current daily use of MTX in Japan.

Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization.

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles.