Moxibustion for cognitive impairment: a systematic review and meta-analysis of animal studies.

Background: Cognitive impairment is an age-dependent chronic disorder that exponentially worsens with age; however, its treatment is mostly symptomatic. Moxibustion is widely accepted in East Asia as a treatment for cognitive impairment. This systematic review aimed to verify the efficacy and underlying mechanism of moxibustion in treating cognitive impairment.

Prediction of age and sex from paranasal sinus images using a deep learning network.

This study was conducted to develop a convolutional neural network (CNN)-based model to predict the sex and age of patients by identifying unique unknown features from paranasal sinus (PNS) X-ray images.We employed a retrospective study design and used anonymized patient imaging data. Two CNN models, adopting ResNet-152 and DenseNet-169 architectures, were trained to predict sex and age groups (20-39, 40-59, 60+ years).

In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses.

To exploit negatively interacting pairs of cancer somatic mutations in chemotherapy responses or synthetic cytotoxicity (SC), we systematically determined mutational pairs that had significantly lower paclitaxel half maximal inhibitory concentration (IC) values. We evaluated 407 cell lines with somatic mutation profiles and estimated their copy number and drug-inhibitory concentrations in Genomics of Drug Sensitivity in Cancer (GDSC) database. The SC effect of 142 mutated gene pairs on response to paclitaxel was successfully cross-validated using human cancer datasets for urogenital cancers available in The Cancer Genome Atlas (TCGA) database.

Technical feasibility of radiomics signature analyses for improving detection of occult tonsillar cancer.

Diagnosis of occult palatine tonsil squamous cell carcinoma (SCC) using conventional magnetic resonance imaging (MRI) is difficult in patients with cervical nodal metastasis from an unknown primary site at presentation. We aimed to establish a radiomics approach based on MRI features extracted from the volume of interest in these patients. An Elastic Net model was developed to differentiate between normal palatine tonsils and occult palatine tonsil SCC.

Effectiveness of antipsychotic drugs in schizophrenia: a 10-year retrospective study in a Korean tertiary hospital.

Extensive research has been carried out on the comparative effectiveness of antipsychotic medications. Most studies, however, have been performed in Western countries. The purpose of this study was to compare the effectiveness, indicated by time to any-cause discontinuation, of antipsychotic drugs in a large number of patients with schizophrenia in South Korea.

Genetic markers for depressive disorders with earlier age at onset.

Age at onset has been considered a potential indicator of underlying genetic risk in depression research. However, the variants associated with earlier age at onset of depressive disorder have not been elucidated. To evaluate the genetic architecture of depression onset, whole-exome sequencing of samples from 1000 patients with depressive disorder was performed.

Classification of cervical neoplasms on colposcopic photography using deep learning.

Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and validated deep learning models to automatically classify cervical neoplasms on colposcopic photographs.

Gene-wise variant burden and genomic characterization of nearly every gene.

Current gene-level prioritization methods aim to provide information for further prioritization of 'disease-causing' mutations. Since, they are inherently biased toward disease genes, methods specific to pharmacogenetic (PGx) genes are required. We proposed a gene-wise variant burden (GVB) method that integrates deleteriousness scores of the multitude of variants of a given gene at a personal-genome level.

Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants.

Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. Among 1000 patients, early improvement at 2 weeks (reduction in Hamilton Depression Rating Scale [HAM-D] score ≥ 20%) and remission at 12 weeks (HAM-D score ≤ 7) were evaluated.

Composite CDE: modeling composite relationships between common data elements for representing complex clinical data.

Background: Semantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability.

Homozygote CRIM1 variant is associated with thiopurine-induced neutropenia in leukemic patients with both wildtype NUDT15 and TPMT.

Background: NUDT15 and TPMT variants are strong genetic determinants of thiopurine-induced hematological toxicity that results in therapeutic failure in pediatric acute lymphoblastic leukemia (ALL). However, many patients with both wild-type (WT) NUDT15 and TPMT still suffer from thiopurine toxicity and therapeutic failure.

Methods: Whole-exome sequencing was done for discovery (N = 244) and replication (N = 76) cohorts.

Sex differences in the genetic architecture of depression.

The prevalence and clinical characteristics of depressive disorders differ between women and men; however, the genetic contribution to sex differences in depressive disorders has not been elucidated. To evaluate sex-specific differences in the genetic architecture of depression, whole exome sequencing of samples from 1000 patients (70.7% female) with depressive disorder was conducted.

Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning.

Endoscopic resection is recommended for gastric neoplasms confined to mucosa or superficial submucosa. The determination of invasion depth is based on gross morphology assessed in endoscopic images, or on endoscopic ultrasound. These methods have limited accuracy and pose an inter-observer variability.

Automated Classification of Colorectal Neoplasms in White-Light Colonoscopy Images via Deep Learning.

Classification of colorectal neoplasms during colonoscopic examination is important to avoid unnecessary endoscopic biopsy or resection. This study aimed to develop and validate deep learning models that automatically classify colorectal lesions histologically on white-light colonoscopy images. White-light colonoscopy images of colorectal lesions exhibiting pathological results were collected and classified into seven categories: stages T1-4 colorectal cancer (CRC), high-grade dysplasia (HGD), tubular adenoma (TA), and non-neoplasms.

PG-path: Modeling and personalizing pharmacogenomics-based pathways.

A pharmacogenomics-based pathway represents a series of reactions that occur between drugs and genes in the human body after drug administration. PG-path is a pharmacogenomics-based pathway that standardizes and visualizes the components (nodes) and actions (edges) involved in pharmacokinetic and pharmacodynamic processes. It provides an intuitive understanding of the drug response in the human body.

Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants.

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico.

Gene regulatory network analysis with drug sensitivity reveals synergistic effects of combinatory chemotherapy in gastric cancer.

The combination of docetaxel, cisplatin, and fluorouracil (DCF) is highly synergistic in advanced gastric cancer. We aimed to explain these synergistic effects at the molecular level. Thus, we constructed a weighted correlation network using the differentially expressed genes between Stage I and IV gastric cancer based on The Cancer Genome Atlas (TCGA), and three modules were derived.

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training.

Purpose: This study aimed to validate a deep learning model's diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model's clinical utility for resident training.

Methods: The performance of eight deep learning models was validated using 3838 axial CT images from 698 consecutive patients with thyroid cancer who underwent preoperative CT imaging between January and August 2018 (3606 and 232 images from benign and malignant lymph nodes, respectively). Six trainees viewed the same patient images (n = 242), and their diagnostic performance and confidence level (5-point scale) were assessed before and after computer-aided diagnosis (CAD) was included.

Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine.

In light of recent developments in genomic technology and the rapid accumulation of genomic information, a major transition toward precision medicine is anticipated. However, the clinical applications of genomic information remain limited. This lag can be attributed to several complex factors, including the knowledge gap between medical experts and bioinformaticians, the distance between bioinformatics workflows and clinical practice, and the unique characteristics of genomic data, which can make interpretation difficult.

Real-world treatment patterns of renal anemia in hemodialysis patients: A multicenter cohort study performed using DialysisNet (RRAHD study).

A multicenter cohort study.The DialysisNet was previously developed for the management of hemodialysis (HD) patients based on the American Society for Testing and Materials Continuity of Care Records by metadata transformation. DialysisNet is a dialysis patient management program created by using the personal health record care platform to overcome the problems of registry studies, in real-time.

The translational network for metabolic disease - from protein interaction to disease co-occurrence.

Background: The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases.

HLA Allele Distribution Associated with Adverse Drug Reactions in Organ Transplant Patients.

The results of Human Leukocyte Antigen (HLA) antigen testing in transplant patients are not generally used to predict future adverse events. In this study, free-text HLA screening results of transplant patients were analyzed and stored in a database, and the frequencies of patients with adverse events according to HLA allele were extracted. Approximately 25% of patients had HLA alleles associated with serious drug side effects.

Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics.

Background: The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents.

Methods: CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.

Star Allele-Based Haplotyping versus Gene-Wise Variant Burden Scoring for Predicting 6-Mercaptopurine Intolerance in Pediatric Acute Lymphoblastic Leukemia Patients.

and are strong genetic determinants of thiopurine toxicity in pediatric acute lymphoblastic leukemia (ALL) patients. Since patients with or deficiency suffer severe adverse drug reactions, star (*) allele-based haplotypes have been used to predict an optimal 6-mercaptopurine (6-MP) dosing. However, star allele haplotyping suffers from insufficient, inconsistent, and even conflicting designations with uncertain and/or unknown functional alleles.