Dairy consumption, bone turnover biomarkers, and osteo sono assessment index in Japanese adults: A cross-sectional analysis of data from the Iwaki Health Promotion Project.

Purpose: Dairy foods are nutritional sources of calcium, phosphorus, protein, and other nutrients that improve bone health. However, the effects of dairy consumption on bone biomarkers in the Japanese population remain unclear. This study explored the association between dairy consumption and bone biomarkers in Japanese adults. Methods: This cross-sectional study was conducted as part of the Iwaki Health Promotion Project in Aomori, Japan. In total, 1063 adults were included in the analysis. Bone turnover marker levels were measured in local citizens during their annual medical checkups. The calcaneus osteo sono assessment index (OSI) was calculated using a quantitative ultrasound technique. The dietary intake of foods and nutrients was estimated using a food frequency questionnaire. Linear regression models were established using dairy consumption and bone biomarkers with adjustments. Statistic significance was considered by P < 0.05. Results: In multivariate models, the tartrate-resistant acid phosphatase 5b and parathyroid hormone concentrations were inversely associated with dietary dairy consumption after adjusting for age and sex. The undercarboxylated osteocalcin, a procollagen type I N-terminal peptide to bone alkaline phosphatase ratio, and OSI were the directly associated with dairy consumption in multivariate models with adjustment. Conclusions: Dairy consumption is partially associated with bone turnover biomarkers and OSI in adult Japanese participants. Habitual consumption of dairy foods may contribute to the nutritional supplementation for maintaining bone health, including turnover and structure. Clinical trial registry number and website where it was obtained: The Japanese Clinical Trials Registry (UMIN000040459), https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000046175.

Association between Gut Microbiota and Muscle Strength in Japanese General Population of the Iwaki Health Promotion Project.

The association between the gut microbiota and muscle strength has garnered attention in the context of mitigating muscle decline. However, many study subjects have been individuals with existing illnesses or the elderly only. This study aims to elucidate the association between the gut microbiota and muscle strength indicators using grip strength/BMI in a large-scale study of community residents. The mean age of men (n = 442) and women (n = 588) was 50.5 (15.3) and 51.2 (15.9) years, respectively. The muscle strength indicator used was grip/BMI. The association between total read count and genus-level gut microbiota and muscle strength was analyzed. The mean grip/BMI was 1.8 (0.3) for men and 1.2 (0.2) for women. The genus of the gut microbiota that showed an association in both sexes was Eggerthella (men: ß = 0.18, CI: 0.04-0.31, p = 0.009; women: ß = 0.07, CI: 0.00-0.12, p = 0.028). Blautia, Eggerthella and Faecalibacterium were found to be significantly associated with grip/BMI in both the multiple regression analysis and Spearman's correlation analysis after the multiple comparison adjustment. These results suggest that an increase in Blautia and Eggerthella, coupled with a decrease in Faecalibacterium, may contribute to muscle strengthening or the suppression of muscle weakness.

Effects of rewarming therapies on outcomes in accidental hypothermia: A secondary analysis of a multicenter prospective study.

BACKGROUND: Rewarming therapies for accidental hypothermia (AH) include extracorporeal membrane oxygenation (ECMO) and non-ECMO related (conventional) therapies. However, there are limited data available to inform the selection of conventional rewarming therapy. The aim of the present study was to explore what patients' factors and which rewarming therapy predicted favorable prognosis. METHODS: This study is a secondary analysis of the Intensive Care with Extra Corporeal membrane oxygenation Rewarming in Accidentally Severe Hypothermia (ICE-CRASH) study, a multicenter prospective, observational study conducted in Japan. Enrolled in the ICE-CRASH study were patients aged ≥18 years with a core temperature of ≤32 °C who were transported to the emergency departments of 36 tertiary care hospitals in Japan between 1 December 2019 and 31 March 2022, among whom those who were rewarmed with conventional rewarming therapy were included in the present study. Logistic regression analysis was performed with 28-day survival as the objective variable; and seven factors including age, activities of daily living (ADL) independence, sequential organ failure assessment (SOFA) score, and each rewarming technique as explanatory variables. We performed linear regression analysis to identify whether each rewarming technique was associated with rewarming rate. RESULTS: Of the 499 patients enrolled in the ICE-CRASH study, 371 were eligible for this secondary analysis. The median age was 81 years, 50.9% were male, and the median initial body temperature was 28.8 °C. Age (odds ratio [OR]: 0.97, 95% confidence interval [CI]: 0.94-1.00) and SOFA score (OR: 0.73, 95% CI: 0.67-0.81) were associated with lower survival, whereas ADL independence (OR: 2.31, 95% CI: 1.15-4.63) was associated with higher survival. No conventional rewarming therapy was associated with 28-day survival. Hot bath was associated with a high rewarming rate (regression coefficient: 1.14, 95% CI: 0.75-1.53). CONCLUSION: No conventional rewarming therapy was associated with improved 28-day survival, which suggests that background factors such as age, ADL, and severity of condition contribute more to prognosis than does the selection of rewarming technique.

FLAIR Hyperintensities in the Anterior Part of the Callosal Splenium in the Elderly Population: A Large Cohort Study.

RATIONALE AND OBJECTIVES: Although hyperintensity in the anterior portion of the callosal splenium on FLAIR (aCS-hyperintensity) is a common finding in elderly adults, no previous studies have examined the clinical significance. In this large elderly population study, we aimed to investigate the associations of aCS-hyperintensity with vascular risk factors, cognitive decline, and other MRI measurements. MATERIALS AND METHODS: This cross-sectional study included 2110 participants (median age, 69 years; 61.1% females) who underwent 3 T MRI. The participants were grouped as 215 with mild cognitive impairment (MCI) and 1895 cognitively normal older adults (NOAs). Two neuroradiologists evaluated aCS-hyperintensity by using a four-point scale (none, mild, moderate, and severe). Periventricular hyperintensities (PVHs) were also rated on a four-point scale according to the Fazekas scale. The total intracranial volume (ICV), total brain volume, choroid plexus volume (CPV), and lateral ventricle volume (LVV) were calculated. RESULTS: Logistic regression analysis showed diabetes was the main predictor of aCS-hyperintensity after adjusting for potential confounders (age, sex, hypertension, and hyperlipidemia) (p < 0.01), whereas PVH was associated with hypertension (p < 0.01). aCS-hyperintensity rated as "severe" was associated with a presence of MCI (p < 0.01). For the imaging factors, LVV was an independent predictor of aCS-hyperintensity when brain volume and PVH grade were added to the analysis (p < 0.01). CONCLUSION: Cerebral small vessel disease due to diabetes is a major contributor to the development of aCS-hyperintensity. Cerebrospinal fluid clearance failure may also relate to aCS-hyperintensity, which may offer new insights into the pathologic processes underlying MCI.

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-ß signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

Choroid plexus enlargement in mild cognitive impairment on MRI: a large cohort study.

OBJECTIVES: Previous studies have shown possible choroid plexus (CP) dysfunction in Alzheimer's disease (AD) and highlighted CP enlargement on magnetic resonance imaging (MRI) as a predictive factor of AD. However, few studies have assessed the relationship between CP volume (CPV) and mild cognitive impairment (MCI). In this large elderly population study, we investigated the changes in CPV in patients with MCI using MRI above 65 years. METHODS: This cross-sectional study included 2144 participants (median age, 69 years; 60.9% females) who underwent 3T MRI; they were grouped as 218 MCI participants and 1904 cognitively healthy controls. The total intracranial volume (ICV), total brain volume (TBV), CPV, hippocampal volume (HV), and lateral ventricle volume (LVV) were calculated. RESULTS: CPV/ICV was a significant independent predictor of MCI (p < 0.01) after adjusting for potential confounders (age, sex, hypertension, hyperlipidemia, diabetes, and education level). The CPV/ICV ratio was also a significant independent predictor of MCI after adjusting for the TBV/ICV ratio (p = 0.022) or HV/ICV ratio (p = 0.017), in addition to potential confounders. The CPV was significantly correlated with the LVV (r = 0.97, p < 0.01). CONCLUSION: We identified a relationship between CPV and MCI, which could not be explained by the degree of brain atrophy. Our results support CP dysfunction in MCI. CLINICAL RELEVANCE STATEMENT: Choroid plexus volume measurement may serve as a valuable imaging biomarker for diagnosing and monitoring mild cognitive impairment. The enlargement of the choroid plexus, independent of brain atrophy, suggests its potential role in mild cognitive impairment pathology. KEY POINTS: • The study examines choroid plexus volume in relation to cognitive decline in elderly. • Enlarged choroid plexus volume independently indicates mild cognitive impairment presence. • Choroid plexus volume could be a specific biomarker for early mild cognitive impairment diagnosis.

Altered structural hippocampal intra-networks in a general elderly Japanese population with mild cognitive impairment.

Although altered networks inside the hippocampus (hippocampal intra-networks) have been observed in dementia, the evaluation of hippocampal intra-networks using magnetic resonance imaging (MRI) is challenging. We employed conventional structural imaging and incident component analysis (ICA) to investigate the structural covariance of the hippocampal intra-networks. We aimed to assess altered hippocampal intra-networks in patients with mild cognitive impairment (MCI). A cross-sectional study of 2122 participants with 3T MRI (median age 69 years, 60.9% female) were divided into 218 patients with MCI and 1904 cognitively normal older adults (CNOA). By employing 3D T1-weighted imaging, voxels within the hippocampus were entered into the ICA analysis to extract the structural covariance intra-networks within the hippocampus. The ICA extracted 16 intra-networks from the hippocampal structural images, which were divided into two bilateral networks and 14 ipsilateral networks. Of the 16 intra-networks, two (one bilateral network and one ipsilateral networks) were significant predictors of MCI from the CNOA after adjusting for age, sex, education, disease history, and hippocampal volume/total intracranial volume ratio. In conclusion, we found that the relationship between hippocampal intra-networks and MCI was independent from the hippocampal volume. Our results suggest that altered hippocampal intra-networks may reflect a different pathology in MCI from that of brain atrophy.

Individual health-disease phase diagrams for disease prevention based on machine learning.

Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in chronic disease development. Machine-learning techniques have enabled precise personal-level disease prediction by capturing individual differences in multivariate data. However, it is challenging to identify what aspects should be improved for disease prevention based on future disease-onset prediction because of the complex relationships among multiple biomarkers. Here, we present a health-disease phase diagram (HDPD) that represents an individual's health state by visualizing the future-onset boundary values of multiple biomarkers that fluctuate early in the disease progression process. In HDPDs, future-onset predictions are represented by perturbing multiple biomarker values while accounting for dependencies among variables. We constructed HDPDs for 11 diseases using longitudinal health checkup cohort data of 3,238 individuals, comprising 3,215 measurement items and genetic data. The improvement of biomarker values to the non-onset region in HDPD remarkably prevented future disease onset in 7 out of 11 diseases. HDPDs can represent individual physiological states in the onset process and be used as intervention goals for disease prevention.

Definition of a Dietary Pattern Expressing the Intake of Vegetables and Fruits and Its Association with Intestinal Microbiota.

Daily dietary habits directly or indirectly influence the intestinal microbiota, and the resulting changes in its composition and metabolic activity alter the health conditions of the host. Although many studies have analyzed the association between individual nutrients/food items and intestinal microbiota, the assessment of the diet and intestinal microbiota from a macroscopic perspective has not yet been performed in Japan. Therefore, we focused on vegetables and fruits and aimed to identify dietary patterns of high intake of these foods and to examine their relationship with the intestinal microbiota. This cross-sectional study included 1019 healthy individuals aged ≥20 years in a rural area in northern Japan. Six dietary patterns were detected by factor analysis using the brief-type self-administered diet history questionnaire (BDHQ) data to identify the "vege pattern", which was the dietary pattern rich in vegetables and fruits. Permutational multivariate analysis of variance revealed changes in ß-diversity according to dietary patterns. In multivariable-adjusted models, the adherence to the vege pattern was positively correlated with α-diversity. This is the first study to reveal a correlation between intestinal microbiota and dietary habits rich in vegetables and fruits in a rural area of Japan.

Inhibition of the bone morphogenetic protein pathway suppresses tumor growth through downregulation of epidermal growth factor receptor in MEK/ERK-dependent colorectal cancer.

The bone morphogenetic protein (BMP) pathway promotes differentiation and induces apoptosis in normal colorectal epithelial cells. However, its role in colorectal cancer (CRC) is controversial, where it can act as context-dependent tumor promoter or tumor suppressor. Here we have found that CRC cells reside in a BMP-rich environment based on curation of two publicly available RNA-sequencing databases. Suppression of BMP using a specific BMP inhibitor, LDN193189, suppresses the growth of select CRC organoids. Colorectal cancer organoids treated with LDN193189 showed a decrease in epidermal growth factor receptor, which was mediated by protein degradation induced by leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) expression. Among 18 molecularly characterized CRC organoids, suppression of growth by BMP inhibition correlated with induction of LRIG1 gene expression. Notably, knockdown of LRIG1 in organoids diminished the growth-suppressive effect of LDN193189. Furthermore, in CRC organoids, which are susceptible to growth suppression by LDN193189, simultaneous treatment with LDN193189 and trametinib, an FDA-approved MEK inhibitor, resulted in cooperative growth inhibition both in vitro and in vivo. Taken together, the simultaneous inhibition of BMP and MEK could be a novel treatment option in CRC cases, and evaluating in vitro growth suppression and LRIG1 induction by BMP inhibition using patient-derived organoids could offer functional biomarkers for predicting potential responders to this regimen.

Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment.

BACKGROUND: Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed. METHODS: Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most important features for predicting aMCI. FINDINGS: We found that hsa-miR-4455 is the best biomarker in all omics analyses. The diagnostic index taking a ratio of hsa-miR-4455 to hsa-let-7b-3p predicted aMCI patients against healthy subjects with 97% overall accuracy. An integrated review of multiomics data suggested that a subset of T cells and the GCN (general control nonderepressible) pathway are associated with aMCI. INTERPRETATION: The multiomics approach has enabled aMCI biomarkers with high specificity and illuminated the accompanying changes in peripheral blood. Future large-scale studies are necessary to validate candidate biomarkers for clinical use.

Effect of gut microbial composition and diversity on major inhaled allergen sensitization and onset of allergic rhinitis.

BACKGROUND: Decreased gut microbiota diversity is associated with gut dysbiosis and causes various diseases, including allergic diseases. We investigated the relationship between gut microbial diversity and sensitization to major inhaled allergens. Furthermore, the relationship of allergic symptom onset with bacterial composition in sensitized individuals was investigated. METHODS: This study included 1092 local residents who had participated in the Iwaki Health Promotion Project in 2016. Blood samples were analyzed to ascertain specific IgE levels against major inhaled allergens (JCP, HD1, Grass-mix, Weed-mix). Nasal symptoms were estimated by questionnaires. Fecal samples were analyzed for bacterial 16S rRNA using next generation sequencing. The diversity index (α-diversity, ß-diversity) and the composition of gut microbes in phylum/order levels were compared between patients sensitized or unsensitized to allergen, and symptomatic and asymptomatic groups. RESULTS: Some α-diversity metrics were significantly decreased in patients who were sensitized to any/all four allergens compared with the unsensitized group. ß-diversity differed significantly between those unsensitized and sensitized to all allergens (aged 20-49 years), and between those unsensitized and sensitized to any/all four allergens (aged ≥50 years). The relative abundance of Bacteroidales was significantly lower in the unsensitized than in the sensitized group. The composition and diversity of gut microbiota were similar between the symptomatic and asymptomatic groups. CONCLUSIONS: Our results suggest that lack of diversity in gut microbiota has an effect on sensitization to allergens. Bacteroidales in order level may affect sensitization; however, the onset of allergy symptoms was not significantly associated with bacterial composition and diversity.

Association of Gut Microbial Genera with Heart Rate Variability in the General Japanese Population: The Iwaki Cross-Sectional Research Study.

The gut microbiota has become a significant factor associated with health and disease. Although many studies have reported the implications of changes in the gut microbiota on cardiovascular diseases, there are no reports on the relationship between heart rate variability (HRV) and the gut microbiota. Therefore, we investigated the association between gut microbiota abundance and HRV parameters in this cross-sectional study of the general Japanese population. This study included 950 participants of the Iwaki Health Promotion Project who underwent a medical examination in 2019 that included HRV and gut microbiota measurements. At the genus level, multivariate regression analysis showed that higher gut microbial diversity was associated with a higher standard deviation of RR intervals (SDNN). Moreover, a higher SDNN was associated with a higher relative count of Lachnospiraceae incertae sedis. L. incertae sedis abundance was associated with higher HRV parameters such as SDNN, coefficient of variation of RR intervals, low-frequency component power (LF)/high-frequency component power, and LF. In the general Japanese population, higher gut microbial diversity and L. incertae sedis abundance were associated with higher HRV parameters.

Correlation between Glycation-Related Biomarkers and Quality of Life in the General Japanese Population: The Iwaki Cross-Sectional Research Study.

The correlation between diabetes-related biomarkers and quality of life (QOL) remains unclear. In this cross-sectional study, we investigated the correlation between diabetes-related biomarkers and QOL in a general Japanese population who underwent health checkups as a part of the Iwaki Health Promotion Project. Male and female participants aged ≥ 20 years from Iwaki District, Hirosaki City, Aomori Prefecture who participated in the 2019 medical evaluation were recruited. QOL was evaluated using the Short Form Health Survey 36 (SF-36). Fasting blood glucose, homeostatic model assessment-estimated insulin resistance (HOMA-IR), hemoglobin A1c (HbA1c), glycoalbumin, and plasma pentosidine were also evaluated as diabetes-related markers. Of the 1065 recruited participants, 1053 completed the clinical and QOL evaluations. Multivariate regression analysis revealed that upregulated diabetes-related markers levels were correlated with decreased SF-36 scores. Blood glucose, HOMA-IR, HbA1c, glycoalbumin, and plasma pentosidine levels were correlated with general health. Moreover, plasma pentosidine levels were correlated with role physical, social functioning, and role emotional in addition to general health. These results indicated that the levels of diabetes-related biomarkers, particularly the levels of plasma pentosidine, a glycation marker, were associated with QOL in our cohort.

CBNplot: Bayesian network plots for enrichment analysis.

SUMMARY: When investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot, which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot, including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets. AVAILABILITY AND IMPLEMENTATION: The library, documentation and web server are available at https://github.com/noriakis/CBNplot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Factors Associated with Reduced Heart Rate Variability in the General Japanese Population: The Iwaki Cross-Sectional Research Study.

Although many studies have reported factors associated with reduced heart rate variability (HRV) in Western populations, evidence is limited among Asian populations. Therefore, we investigated the factors associated with reduced HRV values in a general Japanese population by measuring HRV among the participants of the Iwaki Health Promotion Project who underwent medical examination in 2019. We performed 90-s HRV measurements in 1065 participants. Of these, we evaluated the coefficient of variation in R-R intervals (CVRR) and standard deviation in R-R intervals (SDNN). Blood was collected under a fasting condition, and investigations of glucose metabolism, lipid metabolism, renal function, liver function, advanced glycation end products, and blood pressure were performed. A multivariate regression analysis of the association between CVRR or SDNN and blood test parameters and blood pressure in 987 participants with adequately completed HRV assessments showed that reduced CVRR or SDNN was associated with higher levels of glycated hemoglobin (HbA1c), glycoalbumin, blood glucose, triglycerides, creatinine, plasma pentosidine, and diastolic blood pressure. In the general Japanese population, higher levels of HbA1c, glycoalbumin, blood glucose, triglycerides, creatinine, plasma pentosidine, and diastolic blood pressure are associated with reduced CVRR or SDNN, which are typical HRV parameters.

Two Blautia Species Associated with Visceral Fat Accumulation: A One-Year Longitudinal Study.

Intestinal microflora has been associated with obesity. While visceral fat is more strongly associated with cardiovascular disorder, a complication linked to obesity, than the body mass index (BMI), the association between intestinal microflora and obesity (as defined in terms of BMI) has been studied widely. However, the link between visceral fat area (VFA) and intestinal microflora has been little studied. In this study, we investigate the association between intestinal microflora and VFA and BMI using a longitudinal study on Japanese subjects with different VFA statuses (N = 767). Principal component analysis of the changes in intestinal microflora composition over the one-year study period revealed the different associations between intestinal microflora and VFA and BMI. As determined by 16S rRNA amplicon sequencing, changes in the abundance ratio of two microbial genera-Blautia and Flavonifractor-were significantly associated with VFA changes and changes in the abundance ratio of four different microbial genera were significantly associated with BMI changes, suggesting that the associated intestinal microbes are different. Furthermore, as determined by metagenomic shotgun sequences, changes in the abundance ratios of two Blautia species-Blautia hansenii and Blautia producta-were significantly and negatively associated with VFA changes. Our findings might be used to develop a new treatment for visceral fat.

Novel cancer subtyping method based on patient-specific gene regulatory network.

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection.

The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable to understand how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effects of SARS-CoV-2 infection on human host cells. Here, we present gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes in the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, thus revealing that interferon signaling gradually switched to the subsequent inflammatory cytokine signaling cascades. Furthermore, we succeeded in capturing a COVID-19 patient-specific network in which transduction of these signals was concurrently induced. This enabled us to explore the local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insights into SARS-CoV-2 research from the perspective of cellular systems.

Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach.

BACKGROUND: Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. METHODS: We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. RESULTS: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). CONCLUSION: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.