Polysaccharide Hydrogel-Assisted Biosensing Platforms for Point-of-Care Use.

Point-of-care (POC) use is one of the essential goals of biosensing platforms. Because the increasing demand for testing cannot be met by a centralized laboratory-based strategy, rapid and frequent testing at the right time and place will be key to increasing health and safety. To date, however, there are still difficulties in developing a simple and affordable, as well as sensitive and effective, platform that enables POC use.

Risk factors for cerebral infarction and cerebrovascular stenosis in antiphospholipid antibody-positive patients: A retrospective single-center study with propensity score matching analysis.

Antiphospholipid syndrome (APS) is an autoimmune disorder characterized by the presence of antiphospholipid antibodies (aPLA), such as anticardiolipin (aCL), anti-β2-glycoprotein I (aβ2GPI), or lupus anticoagulant (LA). Although cerebrovascular events are commonly associated with APS, comprehensive studies on risk factors for cerebral infarction in aPLA-positive patients remain sparse. In this retrospective single-center study, data from 9844 patients tested for aPLA between January 2017 and March 2023 were analyzed.

expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer.

Background: Homologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data.

Heterogeneous osteoimmune profiles via single-cell transcriptomics in osteoporotic patients who fail bisphosphonate treatment.

Postmenopausal osteoporosis arises from imbalanced osteoclast and osteoblast activity, and mounting evidence suggests a role for the osteoimmune system in bone homeostasis. Bisphosphonate (BP) is an antiresorptive agent, but its treatment failure rate can be as high as 40%. Here, we performed single-cell RNA sequencing on peripheral immune cells from carefully selected postmenopausal women: non-osteoporotic, osteoporosis improved after BP treatment, and BP-failed cases.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome.

OASIS portable: User-friendly offline suite for secure survival analysis.

Online application for survival analysis (OASIS) and its update, OASIS 2, have been widely used for survival analysis in biological and medical sciences. Here, we provide a portable version of OASIS, an all-in-one offline suite, to facilitate secure survival analysis without uploading the data to online servers. OASIS portable provides a virtualized and isolated instance of the OASIS 2 webserver, operating on the users' personal computers, and enables user-friendly survival analysis without internet connection and security issues.

Identification of novel candidate genes associated with non-syndromic tooth agenesis in Mongolian families.

Objectives: This study aimed to identify genetic variants associated with non-syndromic tooth agenesis (TA) in nine families from Mongolia using whole-exome sequencing (WES) and bioinformatics analysis.

Material And Methods: The study enrolled 41 participants, including three inherited and six non-inherited families. WES analysis was performed on 14 saliva samples from individuals with non-syndromic TA.

Identification of potential key variants in mandibular premolar hypodontia through whole-exome sequencing.

Determining genotype-phenotype correlations in patients with hypodontia is important for understanding disease pathogenesis, although only a few studies have elucidated it. We aimed to identify genetic variants linked to non-syndromic bilateral mandibular second premolar hypodontia in a Korean population for the first time by specifying the phenotype of hypodontia. Twenty unrelated individuals with non-syndromic bilateral mandibular second premolar hypodontia were enrolled for whole-exome sequencing.

Comparison of treatment outcomes and complications of coil embolization in elderly and very elderly patients with cerebral aneurysms: a propensity score matching analysis.

Purpose: This single center study aims to compare the treatment outcomes and procedure-related complications of coil embolization in elderly patients (60-79 years) and very elderly patients (aged 80 years or older) with cerebral aneurysms.

Methods: Data was collected from 504 elderly patients aged 60 years or older who underwent coil embolization for intracranial aneurysms from 2018 to 2021. The study evaluated patient-related and anatomical factors and assessed various outcomes, comparing results between groups using statistical analysis and propensity score matching.

Analysis of Factors Affecting Good Neovascularization After Indirect Bypass Surgery: A Two-center Retrospective Study.

Objective: The purpose of this study was to analyze factors affecting good neovascularization after indirect bypass surgery.

Methods: From August 2000 to July 2020, postoperative image results and medical records of 132 patients (159 hemispheres) who underwent EDAS of indirect bypass surgery at two institutions were reviewed retrospectively. Based on DSA results, angiogenesis after indirect bypass was divided into "good" or "poor" according to the Matsushima criteria.

Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans.

Background: Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials.

Methods: Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects.

An evolution-based machine learning to identify cancer type-specific driver mutations.

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance.

Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence.

Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or molecular factors, including tumor stage, metastasis, and microsatellite instability status, have been used to assess the risk of recurrence, although their predictive ability is limited. Here, we predicted colorectal cancer recurrence based on cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated (tumor-infiltrating immune cell-like) and noncancer-associated (peripheral blood mononuclear cell-like).

Development of bioinformatics and multi-omics analyses in organoids.

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results.

Enzyme activity engineering based on sequence co-evolution analysis.

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection.

Network-based machine learning approach to predict immunotherapy response in cancer patients.

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions.

The implication of holocytochrome c synthase mutation in Korean familial hypoplastic amelogenesis imperfecta.

Objectives: This study aimed to comprehensively characterise genetic variants of amelogenesis imperfecta in a single Korean family through whole-exome sequencing and bioinformatics analysis.

Material And Methods: Thirty-one individuals of a Korean family, 9 of whom were affected and 22 unaffected by amelogenesis imperfecta, were enrolled. Whole-exome sequencing was performed on 12 saliva samples, including samples from 8 affected and 4 unaffected individuals.

Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes.

Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor.

MON-2, a Golgi protein, mediates autophagy-dependent longevity in .

The Golgi apparatus plays a central role in trafficking cargoes such as proteins and lipids. Defects in the Golgi apparatus lead to various diseases, but its role in organismal longevity is largely unknown. Using a quantitative proteomic approach, we found that a Golgi protein, MON-2, was up-regulated in long-lived mutants with mitochondrial respiration defects and was required for their longevity.

Is a small coil more effective as a finishing coil for the embolization of intracranial aneurysms? Outcomes using GALAXY G3™ MINI micro-coils as finishing coils in a single center.

Objective: The purpose of this study was to compare the outcomes of coil embolization using a 0.009 inches primary outer diameter coil as finishing coil (FC) to that of 0.01 inches.

Creation of bladder assembloids mimicking tissue regeneration and cancer.

Current organoid models are limited by their inability to mimic mature organ architecture and associated tissue microenvironments. Here we create multilayer bladder 'assembloids' by reconstituting tissue stem cells with stromal components to represent an organized architecture with an epithelium surrounding stroma and an outer muscle layer. These assembloids exhibit characteristics of mature adult bladders in cell composition and gene expression at the single-cell transcriptome level, and recapitulate in vivo tissue dynamics of regenerative responses to injury.

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models.

Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells.

Invariant natural killer T (iNKT), mucosal-associated invariant T (MAIT), and γδ T cells are innate T cells that acquire memory phenotype in the thymus and share similar biological characteristics. However, how their effector differentiation is developmentally regulated is still unclear. Here, we identify analogous effector subsets of these three innate T cell types in the thymus that share transcriptional profiles.