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Abstract Background Renal impairment is a predictor of coronavirus disease (COVID-19) severity. No studies have compared COVID-19 outcomes in patients with chronic kidney disease (CKD) and patients with impaired renal function without a prior diagnosis of CKD. This study aimed to identify the impact of pre-existing impaired renal function without CKD on COVID-19 outcomes. Methods This retrospective study included 3,637 patients with COVID-19 classified into three groups by CKD history and estimated glomerular filtration rate (eGFR) on referral: Group 1 (n = 2,460), normal renal function without a CKD history; Group 2 (n = 905), impaired renal function without a CKD history; and Group 3 (n = 272), history of CKD. We compared the clinical characteristics of these groups and assessed the effect of CKD and impaired renal function on critical outcomes (requirement for respiratory support with high-flow oxygen devices, invasive mechanical ventilation, or extracorporeal membrane oxygen, and death during hospitalization) using multivariable logistic regression. Results The prevalence of comorbidities (hypertension, diabetes, and cardiovascular disease) and incidence of inflammatory responses (white blood counts, and C-reactive protein, procalcitonin, and D-dimer levels) and complications (bacterial infection and heart failure) were higher in Groups 2 and 3 than that in Group 1. The incidence of critical outcomes was 10.8%, 17.7%, and 26.8% in Groups 1, 2, and 3, respectively. The mortality rate and the rate of requiring IMV support was lowest in Group 1 and highest in Group 3. Compared with Group 1, the risk of critical outcomes was higher in Group 2 (adjusted odds ratio [aOR]: 1.32, 95% confidence interval [CI]: 1.03–1.70, P = 0.030) and Group 3 (aOR: 1.94, 95% CI: 1.36–2.78, P

The Tokyo 2020 Olympic and Paralympic Games was one of the largest international mass-gathering events held after the beginning of coronavirus disease 2019 (COVID-19) pandemic. In this scoping review, we extracted papers discussing COVID-19 risk assessment or management at the Tokyo 2020 Games to determine the nature of studies that were conducted. Among the 75 papers obtained from two search engines (PubMed and ScienceDirect) and four papers collected from hand-searches, 30 papers were extracted. Only eight papers performed both COVID-19 prior risk assessment and quantitative evaluation of effectiveness measures, highlighting the importance of rapid, solution-focused risk assessment. Furthermore, this review revealed that the findings regarding the spread of COVID-19 infection to citizens in the host country were inconsistent depending on the assessment methods and that assessments of the spread of infection outside the host country were lacking.

Abstract Single-cell RNA-sequencing (scRNA-seq) is a powerful technique that provides high-resolution expression profiling of individual cells. It significantly advances our understanding of cellular diversity and function. Despite its potential, the analysis of scRNA-seq data poses considerable challenges related to multicollinearity, data imbalance, and batch effect. One of the pivotal tasks in single-cell data analysis is cell type annotation, which classifies cells into discrete types based on their gene expression profiles. In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the contrastive learning for instance-type pairs instead of instance-instance pairs. More specifically, in the cell type annotation task, the contrastive learning is applied to learn cell and cell type representation that render cells of the same type to be clustered in the new embedding space. Through this approach, the knowledge derived from annotated cells is transferred to the feature representation for scRNA-seq data. The whole training process becomes more efficient when conducting contrastive learning for cell and their types. Our experiment results demonstrate that the proposed SCLSC method consistently achieves superior accuracy in predicting cell types compared to five state-of-the-art methods. SCLSC also performs well in identifying cell types in different batch groups. The simplicity of our method allows for scalability, making it suitable for analyzing datasets with a large number of cells. In a real-world application of SCLSC to monitor the dynamics of immune cell subpopulations over time, SCLSC demonstrates a capability to discriminate cell subtypes of CD19+ B cells that were not present in the training dataset.

Objectives: Adipose tissue is an endocrine and energy storage organ composed of several different cell types, including mature adipocytes, stromal cells, endothelial cells, and a variety of immune cells. Adipose tissue aging contributes to the pathogenesis of metabolic dysfunction and is likely induced by crosstalk between adipose progenitor cells (APCs) and immune cells, but the underlying molecular mechanisms remain largely unknown. In this study, we revealed the biological role of p16high senescent APCs, and investigated the crosstalk between each cell type in the aged white adipose tissue. Methods: We performed the single-cell RNA sequencing (scRNA-seq) analysis on the p16high adipose cells sorted from aged p16-CreERT2/Rosa26-LSL-tdTomato mice. We also performed the time serial analysis on the age-dependent bulk RNA-seq datasets of human and mouse white adipose tissues to infer the transcriptome alteration of adipogenic potential within aging. Results: We show that M2 macrophage-derived TGF-β induces APCs senescence which impairs adipogenesis in vivo. p16high senescent APCs increase with age and show loss of adipogenic potential. The ligand–receptor interaction analysis reveals that M2 macrophages are the donors for TGF-β and the senescent APCs are the recipients. Indeed, treatment of APCs with TGF-β1 induces senescent phenotypes through mitochondrial ROS-mediated DNA damage in vitro. TGF-β1 injection into gonadal white adipose tissue (gWAT) suppresses adipogenic potential and induces fibrotic genes as well as p16 in APCs. A gWAT atrophy is observed in cancer cachexia by APCs senescence, whose induction appeared to be independent of TGF-β induction. Conclusions: Our results suggest that M2 macrophage-derived TGF-β induces age-related lipodystrophy by APCs senescence. The TGF-β treatment induced DNA damage, mitochondrial ROS, and finally cellular senescence in APCs.

Objective This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.Methods This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months.Results The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p

Abstract The severity of chest X-ray (CXR) findings is a prognostic factor in patients with coronavirus disease 2019 (COVID-19). We investigated the clinical and genetic characteristics and prognosis of patients with worsening CXR findings during early hospitalization. We retrospectively included 1656 consecutive Japanese patients with COVID-19 recruited through the Japan COVID-19 Task Force. Rapid deterioration of CXR findings was defined as increased pulmonary infiltrates in ≥ 50% of the lung fields within 48 h of admission. Rapid deterioration of CXR findings was an independent risk factor for death, most severe illness, tracheal intubation, and intensive care unit admission. The presence of consolidation on CXR, comorbid cardiovascular and chronic obstructive pulmonary diseases, high body temperature, and increased serum aspartate aminotransferase, potassium, and C-reactive protein levels were independent risk factors for rapid deterioration of CXR findings. Risk variant at the ABO locus (rs529565-C) was associated with rapid deterioration of CXR findings in all patients. This study revealed the clinical features, genetic features, and risk factors associated with rapid deterioration of CXR findings, a poor prognostic factor in patients with COVID-19.

Abstract The epithelial-mesenchymal transition (EMT) process is tightly connected to tumors’ immune microenvironment. In colon adenocarcinoma (COAD), both the EMT and immune cell infiltration contribute to tumor progression; however, several questions regarding the mechanisms governing the interaction between EMT and the immune response remain unanswered. Our study aims to investigate the cross-talk between these two processes in cases of COAD and identify the key regulators involved. We utilized the EMT and immune signatures of samples from the COAD-TCGA database to identify three subtypes of COAD: high mesenchymal, medium mesenchymal, and low mesenchymal. We observed that EMT was associated with increased tumor immune response and infiltration mediated by pro-inflammatory cytokines. However, EMT was also linked to immunosuppressive activity that involved regulatory T cells, dendritic cells, and the upregulated expression of multiple immune checkpoints, such as PD-1, PDL-1, CTLA-4, and others. Finally, we employed the multivariate random forest feature importance method to identify key genes, such as DOK2 and MSRB3, that may play crucial roles in both EMT and the intratumoral immune response.

Abstract Background Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions. Methods This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions. Results The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59–19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60–8.76), IMV requirement (aOR 7.73, 95% CI 2.52–23.7), and mortality rate (aOR 6.46, 95% CI 1.87–22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36–9.52), older age (aOR 2.53, 95% CI 1.16–5.51), female sex (aOR 2.41, 95% CI 1.13–5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09–4.50) independently predicted persistent residual lung lesions. Conclusions AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

Objectives: Although SARS-CoV-2 RNAemia has been reported to strongly impact patients with severe COVID-19, the clinical characteristics of patients with COVID-19 harboring detectable intracellular SARS-CoV-2 RNA remain unknown. Methods: We included adult patients who had developed COVID-19 between February and September 2020. Total white blood cells derived from the buffy coat of peripheral whole blood were used to detect SARS-CoV-2 RNA using the Illumina COVIDSeq test. We compared the clinical characteristics between patients with and without detected viral RNA (detected and undetected groups). Results: Among the 390 patients included, 17 harbored SARS-CoV-2 RNA in peripheral white blood cells. All 17 patients required oxygen support during the disease course and had higher intensive care unit admission (52.9% vs 28.9%, P = 0.035), mortality (17.7% vs 3.5%, P = 0.004), kidney dysfunction (severe, 23.5% vs 6.4%, P = 0.029), and corticosteroid treatment rates (76.5% vs 46.5%, P = 0.016) than those of patients in the undetected group. Conclusion: We propose that patients with circulating intracellular SARS-CoV-2 RNA in the peripheral blood exhibited the most severe disease course.

Abstract Isolation of close contact people and negative test certification are used to manage the spread of new coronavirus infections worldwide. These effectively prevent the spread of infection in advance, but they can lead to a decline in socio-economic activity. Thus, the present study quantified the extent to which isolation and negative test certification respectively reduce the risk of infection. To this end, a discrete-time SEIR model was used as the infectious disease model, and equations for calculating the conditional probability of non-infection status given negative test results on two different days were derived. Then the respective non-infection probabilities with two negative PCR test results, and with one negative PCR test result and one antigen test result, were quantified. By substituting initial parameters of the SEIR model into these probabilities, the present study revealed the following: (1) isolating close contact individuals can reduce by $$80\%$$ 80 % the risk of infection during the first 5 days, but five more days are needed to reduce the risk $$10\%$$ 10 % more, and seven more days to reduce the risk $$20\%$$ 20 % more; and (2) if an individual with a negative PCR test result has a negative antigen test result the next day, then his or her infection probability is between 0.6 and $$0.7\%$$ 0.7 % . Our results show that 5-day isolation has a proportionally greater effect on risk reduction, compared to longer isolation; and thus, if an isolation period of longer than 5 days is contemplated, both the risk reduction and the negative effects from such increased isolation should be considered. Regarding negative test certification, our results provide those in managerial positions, who must decide whether to accept the risk and hold mass-gathering events, with quantitative information that may be useful in their decision-making.

Objectives: COVID-19 was severe in the Delta variant-dominated epidemic wave (fifth wave) in Japan. The clinical characteristics and effectiveness of COVID-19 vaccination are not fully understood in the Omicron variant-dominated wave (sixth and seventh waves), especially in hospitalized patients. We investigated the relationship between vaccination and disease severity in the Omicron-dominated wave and compared these variant-dominated waves. Methods: The nationwide COVID-19 database (Japan COVID-19 Task Force) was used to compare clinical characteristics and critical outcomes in patients hospitalized with Delta (fifth, N = 735) vs Omicron-dominated waves (sixth, N = 495; seventh, N = 128). Results: Patients in the sixth and seventh waves had a lower incidence of critical outcomes and respiratory outcomes, and a higher incidence of bacterial infection, although the mortality rate did not differ significantly between waves. In the sixth and seventh waves, 138 (27.9%) and 29 (22.7%) patients with COVID-19 were unvaccinated, respectively. Multivariable analysis adjusted with previously reported factors revealed that the proportion of (1) critical outcomes and (2) respiratory outcomes decreased in a frequency-dependent manner. Thus, (1) (the number of vaccinations): 1-2 times: adjusted odds ratio (aOR) 0.37 (95% confidence interval [CI]; 0.20-0.69); 3-4 times: aOR 0.25 (95% CI; 0.11-0.58); and (2) 1-2 times: aOR 0.43 (95% CI; 0.27-0.66); 3-4 times: aOR 0.36 (95% CI; 0.21-0.60). Conclusions: Patients hospitalized with COVID-19 with Omicron infections showed a lower incidence of critical outcomes than those with Delta infections, and COVID-19 vaccination may contribute to preventing respiratory failure.

When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI.

Abstract Background Although cases of respiratory bacterial infections associated with coronavirus disease 2019 (COVID-19) have often been reported, their impact on the clinical course remains unclear. Herein, we evaluated and analyzed the complication rates of bacterial infections, causative organisms, patient backgrounds, and clinical outcome in Japanese patients with COVID-19. Methods We performed a retrospective cohort study that included inpatients with COVID-19 from multiple centers participating in the Japan COVID-19 Taskforce (April 2020 to May 2021) and obtained demographic, epidemiological, and microbiological results and the clinical course and analyzed the cases of COVID-19 complicated by respiratory bacterial infections. Results Of the 1,863 patients with COVID-19 included in the analysis, 140 (7.5%) had respiratory bacterial infections. Community-acquired co-infection at COVID-19 diagnosis was uncommon (55/1,863, 3.0%) and was mainly caused by Staphylococcus aureus, Klebsiella pneumoniae and Streptococcus pneumoniae. Hospital-acquired bacterial secondary infections, mostly caused by Staphylococcus aureus, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia, were diagnosed in 86 patients (4.6%). Severity-associated comorbidities were frequently observed in hospital-acquired secondary infection cases, including hypertension, diabetes, and chronic kidney disease. The study results suggest that the neutrophil–lymphocyte ratio (> 5.28) may be useful in diagnosing complications of respiratory bacterial infections. COVID-19 patients with community-acquired or hospital-acquired secondary infections had significantly increased mortality. Conclusions Respiratory bacterial co-infections and secondary infections are uncommon in patients with COVID-19 but may worsen outcomes. Assessment of bacterial complications is important in hospitalized patients with COVID-19, and the study findings are meaningful for the appropriate use of antimicrobial agents and management strategies.

Abstract Myxofibrosarcoma (MFS) and undifferentiated sarcoma (US) have been considered as tumors of the same lineage based on genetic/epigenetic profiling. Although MFS shows a notably better prognosis than US, there are no clear criteria for distinguishing between them. Here, we examined 85 patients with MFS/US and found that tumors with infiltrative growth patterns tended to have more myxoid areas and higher local recurrence rates but fewer distant metastases and better overall survival. Morphologically characteristic sickle-shaped blood vessels, which tended to have fewer αSMA-positive cells, were also observed in these tumors, compared with normal vessels. Based on the incidence of these sickle-shaped blood vessels, we subdivided conventionally diagnosed US into two groups. This stratification was significantly correlated with metastasis and prognosis. RNA sequencing of 24 tumors (9 MFS and 15 US tumors) demonstrated that the proteasome, NF-kB, and VEGF pathways were differentially regulated among these tumors. Expression levels of KDR and NFATC4, which encode a transcription factor responsible for the neuritin-insulin receptor angiogenic signaling, were elevated in the sickle-shaped blood vessel-rich US tumors. These findings indicate that further analyses may help elucidate the malignant potential of MFS/US tumors as well as the development of therapeutic strategies for such tumors.

Background Krebs von den Lungen-6 (KL-6) is a known biomarker for diagnosis and monitoring of interstitial lung diseases. However, the role of serum KL-6 and the mucin 1 (MUC1) variant (rs4072037) in COVID-19 outcomes remains to be elucidated. We aimed to evaluate the relationships among serum KL-6 levels, critical outcomes and the MUC1 variant in Japanese patients with COVID-19.Methods This is a secondary analysis of a multicentre retrospective study using data from the Japan COVID-19 Task Force collected from February 2020 to November 2021, including 2226 patients with COVID-19 whose serum KL-6 levels were measured. An optimal serum KL-6 level cut-off to predict critical outcomes was determined and used for multivariable logistic regression analysis. Furthermore, the relationship among the allele dosage of the MUC1 variant, calculated from single nucleotide polymorphism typing data of genome-wide association studies using the imputation method, serum KL-6 levels and COVID-19 critical outcomes was evaluated.Results Serum KL-6 levels were significantly higher in patients with COVID-19 with critical outcomes (511±442 U/mL) than those without (279±204 U/mL) (p

Objectives: Smoking and chronic obstructive pulmonary disease (COPD) are risk factors for severe COVID-19. However, limited literature exists on the effect of COPD and smoking on COVID-19 outcomes. This study examined the impact of smoking exposure in pack-years (PY) and COPD on COVID-19 outcomes among smokers in Japan. Methods: The study included 1266 smokers enrolled by the Japan COVID-19 task force between February 2020 and December 2021. PY and COPD status was self-reported by patients. Patients were classified into the non-COPD (n = 1151) and COPD (n = 115) groups; the non-COPD group was further classified into 30 PY (n = 361). The study outcome was the need for invasive mechanical ventilation (IMV). Results: The incidence of IMV increased with increasing PY and was highest in the COPD group (30 PY = 15.2%, COPD = 26.1%; P 30 PY and COPD groups through univariate (odds ratio [OR]: >30 PY = 2.11, COPD = 4.14) and multivariate (OR: >30 PY = 2.38; COPD = 7.94) analyses. Increasing PY number was also associated with increased IMV requirement in patients aged

Abstract Background We aimed to elucidate differences in the characteristics of patients with coronavirus disease 2019 (COVID-19) requiring hospitalization in Japan, by COVID-19 waves, from conventional strains to the Delta variant. Methods We used secondary data from a database and performed a retrospective cohort study that included 3261 patients aged ≥ 18 years enrolled from 78 hospitals that participated in the Japan COVID-19 Task Force between February 2020 and September 2021. Results Patients hospitalized during the second (mean age, 53.2 years [standard deviation {SD}, ± 18.9]) and fifth (mean age, 50.7 years [SD ± 13.9]) COVID-19 waves had a lower mean age than those hospitalized during the other COVID-19 waves. Patients hospitalized during the first COVID-19 wave had a longer hospital stay (mean, 30.3 days [SD ± 21.5], p

Abstract Background Respiratory symptoms are associated with coronavirus disease 2019 (COVID-19) outcomes. However, the impacts of upper and lower respiratory symptoms on COVID-19 outcomes in the same population have not been compared. The objective of this study was to characterize upper and lower respiratory symptoms and compare their impacts on outcomes of hospitalized COVID-19 patients. Methods This was a multicenter, retrospective cohort study; the database from the Japan COVID-19 Task Force was used. A total of 3314 COVID-19 patients were included in the study, and the data on respiratory symptoms were collected. The participants were classified according to their respiratory symptoms (Group 1: no respiratory symptoms, Group 2: only upper respiratory symptoms, Group 3: only lower respiratory symptoms, and Group 4: both upper and lower respiratory symptoms). The impacts of upper and lower respiratory symptoms on the clinical outcomes were compared. The primary outcome was the percentage of patients with poor clinical outcomes, including the need for oxygen supplementation via high-flow oxygen therapy, mechanical ventilation, and extracorporeal membrane oxygenation or death. Results Of the 3314 COVID-19 patients, 605, 1331, 1229, and 1149 were classified as Group 1, Group 2, Group 3, and Group 4, respectively. In univariate analysis, patients in Group 2 had the best clinical outcomes among all groups (odds ratio [OR]: 0.21, 95% confidence interval [CI]: 0.11–0.39), while patients in Group 3 had the worst outcomes (OR: 3.27, 95% CI: 2.43–4.40). Group 3 patients had the highest incidence of pneumonia, other complications due to secondary infections, and thrombosis during the clinical course. Conclusions Upper and lower respiratory tract symptoms had vastly different impacts on the clinical outcomes of COVID-19.

Abstract Background The clinical course of coronavirus disease (COVID-19) is diverse, and the usefulness of phenotyping in predicting the severity or prognosis of the disease has been demonstrated overseas. This study aimed to investigate clinically meaningful phenotypes in Japanese COVID-19 patients using cluster analysis. Methods From April 2020 to May 2021, data from inpatients aged ≥ 18 years diagnosed with COVID-19 and who agreed to participate in the study were collected. A total of 1322 Japanese patients were included. Hierarchical cluster analysis was performed using variables reported to be associated with COVID-19 severity or prognosis, namely, age, sex, obesity, smoking history, hypertension, diabetes mellitus, malignancy, chronic obstructive pulmonary disease, hyperuricemia, cardiovascular disease, chronic liver disease, and chronic kidney disease. Results Participants were divided into four clusters: Cluster 1, young healthy (n = 266, 20.1%); Cluster 2, middle-aged (n = 245, 18.5%); Cluster 3, middle-aged obese (n = 435, 32.9%); and Cluster 4, elderly (n = 376, 28.4%). In Clusters 3 and 4, sore throat, dysosmia, and dysgeusia tended to be less frequent, while shortness of breath was more frequent. Serum lactate dehydrogenase, ferritin, KL-6, d-dimer, and C-reactive protein levels tended to be higher in Clusters 3 and 4. Although Cluster 3 had a similar age as Cluster 2, it tended to have poorer outcomes. Both Clusters 3 and 4 tended to exhibit higher rates of oxygen supplementation, intensive care unit admission, and mechanical ventilation, but the mortality rate tended to be lower in Cluster 3. Conclusions We have successfully performed the first phenotyping of COVID-19 patients in Japan, which is clinically useful in predicting important outcomes, despite the simplicity of the cluster analysis method that does not use complex variables.

Abstract Gene expression, DNA methylation, and their organizational relationships are commonly altered in lung squamous cell carcinoma (LUSC). To elucidate these complex interactions, we reconstructed a differentially expressed gene network and a differentially methylated cytosine (DMC) network by partial information decomposition and an inverse correlation algorithm, respectively. Then, we performed graph union to integrate the networks. Community detection and enrichment analysis of the integrated network revealed close interactions between the cell cycle, keratinization, immune system, and xenobiotic metabolism gene sets in LUSC. DMC analysis showed that hypomethylation targeted the gene sets responsible for cell cycle, keratinization, and NRF2 pathways. On the other hand, hypermethylated genes affected circulatory system development, the immune system, extracellular matrix organization, and cilium organization. By centrality measurement, we identified NCAPG2, PSMG3, and FADD as hub genes that were highly connected to other nodes and might play important roles in LUSC gene dysregulation. We also found that the genes with high betweenness centrality are more likely to affect patients’ survival than those with low betweenness centrality. These results showed that the integrated network analysis enabled us to obtain a global view of the interactions and regulations in LUSC.

Objectives: This study aimed to identify the relationship between abnormal serum uric acid levels or a history of hyperuricemia and COVID-19 severity in the Japanese population. Methods: We included 1523 patients enrolled in the Japan COVID-19 Task Force cohort between February 2020 and May 2021. We compared the clinical characteristics, including co-morbidities, laboratory findings, and outcomes, particularly invasive mechanical ventilation (IMV), among patients with and without abnormal uric acid levels or a history of hyperuricemia. Results: Patients with high serum uric acid levels were older and had higher body weight and body mass index than those without. In addition, the multiple logistic regression analysis revealed a significant association between high serum uric acid levels or a history of hyperuricemia and an increased risk of IMV (odds ratio [OR] = 1.77; P = 0.03/OR = 1.56; P = 0.04). Moreover, patients with low uric acid levels on admission were also associated significantly with the requirement of IMV (OR = 5.09; P

Abstract Background Gene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology. Results To effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks. Conclusion We identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.

Abstract Background Obesity is reported to be a risk factor for severe disease in patients with coronavirus disease 2019 (COVID-19). However, there are no specific reports on the risk of severe disease according to body mass index (BMI) in Japan. Thus, this study aimed to investigate the effect of obesity stratified by BMI on the severity of COVID-19 in the general Japanese population. Methods From February 2020 to May 2021, 1 837 patients aged ≥18 years were enrolled in the Japan COVID-19 Task Force. Patients with known BMI and disease severity were analyzed. Severity was defined as critical if the patient was treated in the intensive care unit, required invasive mechanical ventilation, or died. Results Class 1 obesity (25.0 ≤ BMI

Genetic mechanisms influencing COVID-19 susceptibility are not well understood. Here, the authors analyzed whole blood RNA-seq data of 465 Japanese individuals with COVID-19, highlighting thousands of fine-mapped variants affecting expression and splicing of genes, as well as the presence of COVID-19 severity-interaction eQTLs.

Abstract Physicians play an active role in public health. However, there is a limit to the knowledge and experience that can be gained through hospital work alone. This was a secondary data analysis from 100,000 doctors in Japan (15,677 respondents). The results of the analysis showed that 898 (8.4%) male and 190 (6.0%) female doctors worked 60 h or more in a week. The percentage of physicians whose spouse was a physician was found to be 31.4% (male) and 61.7% (female) (p

Summary: Background: Birt-Hogg-Dubé (BHD) syndrome, caused by germline alteration of folliculin (FLCN) gene, develops hybrid oncocytic/chromophobe tumour (HOCT) and chromophobe renal cell carcinoma (ChRCC), whereas sporadic ChRCC does not harbor FLCN alteration. To date, molecular characteristics of these similar histological types of tumours have been incompletely elucidated. Methods: To elucidate renal tumourigenesis of BHD-associated renal tumours and sporadic renal tumours, we conducted whole genome sequencing (WGS) and RNA-sequencing (RNA-seq) of sixteen BHD-associated renal tumours from nine unrelated BHD patients, twenty-one sporadic ChRCCs and seven sporadic oncocytomas. We then compared somatic mutation profiles with FLCN variants and RNA expression profiles between BHD-associated renal tumours and sporadic renal tumours. Findings: RNA-seq analysis revealed that BHD-associated renal tumours and sporadic renal tumours have totally different expression profiles. Sporadic ChRCCs were clustered into two distinct clusters characterized by L1CAM and FOXI1 expressions, molecular markers for renal tubule subclasses. Increased mitochondrial DNA (mtDNA) copy number with fewer variants was observed in BHD-associated renal tumours compared to sporadic ChRCCs. Cell-of-origin analysis using WGS data demonstrated that BHD-associated renal tumours and sporadic ChRCCs may arise from different cells of origin and second hit FLCN alterations may occur in early third decade of life in BHD patients. Interpretation: These data further our understanding of renal tumourigenesis of these two different types of renal tumours with similar histology. Funding: This study was supported by JSPS KAKENHI Grants, RIKEN internal grant, and the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), Center for Cancer Research.

Summary: Bromodomain-containing protein 8 (BRD8) is a subunit of the NuA4/TIP60-histone acetyltransferase complex. Although BRD8 has been considered to act as a co-activator of the complex, its biological role remains to be elucidated. Here, we uncovered that BRD8 accumulates in colorectal cancer cells through the inhibition of ubiquitin-dependent protein degradation by the interaction with MRG domain binding protein. Transcriptome analysis coupled with genome-wide mapping of BRD8-binding sites disclosed that BRD8 transactivates a set of genes independently of TIP60, and that BRD8 regulates the expression of multiple subunits of the pre-replicative complex in concert with the activator protein-1. Depletion of BRD8 induced cell-cycle arrest at the G1 phase and suppressed cell proliferation. We have also shown that the bromodomain of BRD8 is indispensable for not only the interaction with histone H4 or transcriptional regulation but also its own protein stability. These findings highlight the importance of bromodomain as a therapeutic target.

Although studies have demonstrated that infections with various viruses, bacteria, and parasites can modulate the immune system, no study has investigated changes in antibodies against microbial antigens after the COVID-19 mRNA vaccination. IgG antibodies against microbial antigens in the blood of vaccinees were comprehensively analyzed using microbial protein microarrays that carried approximately 5000 microbe-derived proteins. Changes in antibodies against microbial antigens were scrutinized in healthy participants enrolled in the Fukushima Vaccination Community Survey conducted in Fukushima Prefecture, Japan, after their second and third COVID-19 mRNA vaccinations. Antibody profiling of six groups stratified by antibody titer and the remaining neutralizing antibodies was also performed to study the dynamics of neutralizing antibodies against SARS-CoV-2 and the changes in antibodies against microbial antigens. The results showed that changes in antibodies against microbial antigens other than SARS-CoV-2 antigens were extremely limited after COVID-19 vaccination. In addition, antibodies against a staphylococcal complement inhibitor have been identified as microbial antigens that are associated with increased levels of neutralizing antibodies against SARS-CoV-2. These antibodies may be a predictor of the maintenance of neutralizing antibodies following the administration of a COVID-19 mRNA vaccine.

Trophoblast cell surface antigen 2 (Trop-2, encoded by TACSTD2) is the target protein of sacituzumab govitecan, a novel antibody-drug conjugate for locally advanced or metastatic urothelial carcinoma. However, the expression status of Trop-2 in upper tract urothelial carcinoma (UTUC) remains unclear. We performed immunohistochemical analysis of 99 UTUC samples to evaluate the expression status of Trop-2 in patients with UTUC and analyze its association with clinical outcomes. Trop-2 was positive in 94 of the 99 UTUC samples, and high Trop-2 expression was associated with favorable progression-free survival (PFS) and cancer-specific survival (p = 0.0011, 0.0046). Multivariate analysis identified high Trop-2 expression as an independent predictor of favorable PFS (all cases, p = 0.045; high-risk group (pT3≤ or presence of lymphovascular invasion or lymph node metastasis), p = 0.014). Gene expression analysis using RNA sequencing data from 72 UTUC samples demonstrated the association between high TACSTD2 expression and favorable PFS (all cases, p = 0.069; high-risk group, p = 0.029). In conclusion, we demonstrated that Trop-2 is widely expressed in UTUC. Although high Trop-2 expression was a favorable prognostic factor in UTUC, its widespread expression suggests that sacituzumab govitecan may be effective for a wide range of UTUC.

Abstract Amylase genes reside in a structurally complex locus, and their copy numbers vary greatly, and several studies have reported their association with obesity. The mechanism of this effect was partially explained by changes in the oral and gut microbiome compositions; however, a detailed mechanism has been unclarified. In this study, we showed their association with diabetes in addition to obesity, and further discovered a plausible mechanism of this association based on the function of commensal bacteria. First, we confirmed that the amylase copy number in the population tends to be larger than that reported in other studies and that there is a positive association between obesity and diabetes (p = 1.89E–2 and 8.63E–3). Second, we identified that relative abundance of some genus level microbiome, Capnocytophaga, Dialister, and previously reported bacteria, were significantly associated with amylase copy numbers. Finally, through functional gene-set analysis using shotgun sequencing, we observed that the abundance of genes in the Acarbose pathway in the gut microbiome was significantly decreased with an increase in the amylase copy number (p-value = 5.80E–4). Our findings can partly explain the mechanism underlying obesity and diabetes in populations with high amylase copy numbers.

The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates' important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. The discovered candidates' important microbiome genes and their functions provide novel insights into microbe functional contribution.

Biological condition-responsive gene network analysis has attracted considerable research attention because of its ability to identify pathways or gene modules involved in the underlying mechanisms of diseases. Although many condition-specific gene network identification methods have been developed, they are based on partial or incomplete gene regulatory network information, with most studies only considering the differential expression levels or correlations among genes. However, a single gene-based analysis cannot effectively identify the molecular interactions involved in the mechanisms underlying diseases, which reflect perturbations in specific molecular network functions rather than disorders of a single gene. To comprehensively identify differentially regulated gene networks, we propose a novel computational strategy called comprehensive analysis of differential gene regulatory networks (CIdrgn). Our strategy incorporates comprehensive information on the networks between genes, including the expression levels, edge structures and regulatory effects, to measure the dissimilarity among networks. We extended the proposed CIdrgn to cell line characteristic-specific gene network analysis. Monte Carlo simulations showed the effectiveness of CIdrgn for identifying differentially regulated gene networks with different network structures and scales. Moreover, condition-responsive network identification in cell line characteristic-specific gene network analyses was verified. We applied CIdrgn to identify gastric cancer and itsf chemotherapy (capecitabine and oxaliplatin) -responsive network based on the Cancer Dependency Map. The CXC family of chemokines and cadherin gene family networks were identified as gastric cancer-specific gene regulatory networks, which was verified through a literature survey. The networks of the olfactory receptor family with the ASCL1/FOS family were identified as capecitabine- and oxaliplatin sensitive -specific gene networks. We expect that the proposed CIdrgn method will be a useful tool for identifying crucial molecular interactions involved in the specific biological conditions of cancer cell lines, such as the cancer stage or acquired anticancer drug resistance.

Objectives Rapid antigen tests have been used to prevent the spread of the COVID-19; however, there have been concerns about their decreased sensitivity to the Omicron variant. In this study, we assessed the sensitivity and specificity of the rapid antigen test compared with the PCR test among the players and staff members of the Japan Professional Football League and clubs. Furthermore, we evaluated the relationship between the sensitivity and the duration from the onset of symptoms to testing or vaccine status.Design This was a retrospective observational study.Methods We used 656 results from both the rapid antigen and PCR tests for COVID-19 using samples collected on the same day from 12 January to 2 March 2022, during the Omicron variant outbreak in Japan.Results The sensitivity of the rapid antigen test compared with the PCR test was 0.63 (95% CI: 0.53 to 0.73) and the specificity was 0.998 (95% CI: 0.995 to 1.000). There were no significant associations between the sensitivity and the duration from the onset of symptoms to testing (including asymptomatic cases in the category) or vaccination status (p>0.05) with small effect sizes (Cramer’s V or φ: ≤0.22).Conclusions Even during the Omicron outbreak, the sensitivity of the rapid antigen tests did not depend on the duration from the onset of symptoms to testing.

Background and design: The coronavirus disease (COVID-19) pandemic is having a devastating effect worldwide. Host genome differences between populations may influence the severity of COVID-19.The Japan COVID-19 Task Force is conducting host genome analysis of hospitalized patients with COVID-19 from more than 70 institutions nationwide in Japan. This report describes the clinical characteristics of patients enrolled to date. Results: The median (interquartile range) age of the 1674 patients included in the analysis was 59 (45–71) years, and more than half of the patients (66.2%) were male. Less than half of the patients (41.2%) had severe disease. The case fatality rate was 3.2%. Conclusions: Since this is a hospital-based study, the number of severe cases was relatively high, but the case fatality rate was relatively low, when compared to that of other countries. In the future, we will continue to enroll patients and conduct genome analyses of patients with COVID-19.

Abstract The aim of this work was to investigate the effect of age on the association between daily gait speed (DGS) and abdominal obesity defined by visceral fat area (VFA). A cross-sectional study was performed using data from an annual community-based health check-up. A total of 699 participants aged 20–88 years were enrolled in this analysis. DGS was assessed using tri-axial accelerometers worn for ≥ 7 days with at least 10 measuring hours each day. VFA was measured using a visceral fat meter. Since DGS differed significantly with age, the participants were divided into two groups: younger adults (YA), aged 20–49 years, and older adults (OA), aged 50–88 years. The association between DGS and VFA differed significantly with age (r = 0.099 for YA and r = − 0.080 for OA; test for difference between correlation coefficients, P = 0.023). In OA, the adjusted odds ratio of abdominal obesity (VFA ≥ 100 cm2) was 0.40 (95% confidence interval 0.18, 0.88, P = 0.022) for the highest DGS quartile (DGS ≥ 1.37 m/s) compared to that for the lowest quartile (DGS

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Abstract Immune reactions in the tumor microenvironment are an important hallmark of cancer, and emerging immune therapies have been proven effective against several types of cancers. To investigate cancer genome-immune interactions and the role of immunoediting or immune escape mechanisms in cancer development, we analyzed 2834 whole genome and RNA sequencing datasets across 31 distinct tumor types with respect to key immunogenomic aspects and provided comprehensive immunogenomic profiles of pan-cancers. We found that selective copy number changes in immune-related genes may contribute to immune escape. Furthermore, we developed an index of the immunoediting history of each tumor sample based on the information of mutations in exonic regions and pseudogenes and evaluated the immunoediting history of each tumor. Our immuno-genomic analyses of pan-cancers have the potential to identify a subset of tumors with immunogenicity and diverse backgrounds or intrinsic pathways associated with their immune status and immunoediting history.

Langerhans cell histiocytosis (LCH) and acute myeloid leukemia (AML) are distinct entities of blood neoplasms, and the exact developmental origin of both neoplasms are considered be heterogenous among patients. However, reports of concurrent LCH and AML are rare. Herein we report a novel case of concurrent LCH and AML which shared same the driver mutations, strongly suggesting a common clonal origin.An 84-year-old female presented with cervical lymphadenopathy and pruritic skin rash on the face and scalp. Laboratory tests revealed pancytopenia with 13% of blasts, elevated LDH and liver enzymes, in addition to generalised lymphadenopathy and splenomegaly by computed tomography. Bone marrow specimens showed massive infiltration of MPO-positive myeloblasts, whereas S-100 and CD1a positive atypical dendritic cell-like cells accounted for 10% of the atypical cells on bone marrow pathology, suggesting a mixture of LCH and AML. A biopsy specimen from a cervical lymph node and the skin demonstrated the accumulation of atypical cells which were positive for S-100 and CD1a. LCH was found in lymph nodes, skin and bone marrow; AML was found in peripheral blood and bone marrow (AML was predominant compared with LCH in the bone marrow).Next generation sequencing revealed four somatic driver mutations (NRAS-G13D, IDH2-R140Q, and DNMT3A-F640fs/-I715fs), equally shared by both the lymph node and bone marrow, suggesting a common clonal origin for the concurrent LCH and AML. Prednisolone and vinblastine were initially given with partial response in LCH; peripheral blood blasts also disappeared for 3 months. Salvage chemotherapy with low dose cytarabine and aclarubicin were given for relapse, with partial response in both LCH and AML. She died from pneumonia and septicemia on day 384. Our case demonstrates a common cell of origin for LCH and AML with a common genetic mutation, providing evidence to support the proposal to classify histiocytosis, including LCH, as a myeloid/myeloproliferative malignancy.

Tokyo Olympic and Paralympic Games, postponed for the COVID-19 pandemic, were finally held in the summer of 2021. Just before the games, the Alpha variant was being replaced with the more contagious Delta variant. AY.4 substrain AY.29, which harbors two additional characteristic mutations of 5239C > T (NSP3 Y840Y) and 5514T > C (NSP3 V932A), emerged in Japan and became dominant in Tokyo by the time of the Olympic Games. Variants of SARS-CoV-2 genomes were performed to extract AY.29 Delta substrain samples with 5239C > T and 5514T > C. Phylogenetic analysis was performed to illustrate how AY.29 strains evolved and were introduced into countries abroad. Simultaneously, ancestral searches were performed for the overseas AY.29 samples to identify their origins in Japan using the maximum variant approach. As of January 10, 2022, 118 samples were identified in 20 countries. Phylogenetic analysis and ancestral searches identified 55 distinct introductions into those countries. The United States had 50 samples with 10 distinct introductions, and the United Kingdom had 13 distinct strains introduced in 18 samples. Other countries or regions with multiple introductions were Canada, Germany, South Korea, Hong Kong, Thailand, and the Philippines. Among the 20 countries, most European and North American countries have vaccination rates over 50% and sufficient genomic surveillances are conducted; transmissions seem contained. However, propagation to unvaccinated regions might have caused unfathomable damages. Since samples in those unvaccinated countries are also undersampled with a longer lead time for data sharing, it will take longer to grasp the whole picture. More rigorous departure screenings for the participants from the unvaccinated countries might have been necessary.

We developed an environmental exposure model to estimate the coronavirus disease 2019 (COVID-19) risk among participants at outdoor music festivals and validated the model using two real events—one in Japan (Event 1) and one in Spain (Event 2). Furthermore, we considered a hypothetical situation in which Event 1 was held but enhanced measures were implemented to evaluate the extent to which the risk could be reduced by additional infection control measures, such as negative antigen tests on the day of the event, wearing of masks, disinfection of environmental surfaces, and vaccination. Among 7,392 participants, the total number of already- and newly-infected individuals who participated in Event 1 according to the new model was 47.0 (95% uncertainty interval: 12.5–185.5), which is in good agreement with the reported value (45). The risk of infection at Event 2 (1.98 × 10−2; 95% uncertainty interval: 0.55 × 10−2–6.39 × 10−2), calculated by the model in this study, was also similar to the estimated value in the previous epidemiological study (1.25 × 10−2). These results for the two events in different countries highlighted the validity of the model. Among the additional control measures in the hypothetical Event 1, vaccination, mask-wearing, and disinfection of surfaces were determined to be effective. Based on the combination of all measures, a 94% risk reduction could be achieved. In addition to setting a benchmark for an acceptable number of newly-infected individuals at the time of an event, the application of this model will enable us to determine whether it is necessary to implement additional measures, limit the number of participants, or refrain from holding an event.

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 β-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.

Bronchoalveolar lavage is commonly performed to assess inflammation and identify responsible pathogens in lung diseases. Findings from bronchoalveolar lavage might be used to evaluate the immune profile of the lung tumor microenvironment (TME). To investigate whether bronchoalveolar lavage fluid (BALF) analysis can help identify patients with non–small cell lung cancer (NSCLC) who respond to immune checkpoint inhibitors (ICIs), BALF and blood were prospectively collected before initiating nivolumab. The secreted molecules, microbiome, and cellular profiles based on BALF and blood analysis of 12 patients were compared with regard to therapeutic effect. Compared with ICI nonresponders, responders showed significantly higher CXCL9 levels and a greater diversity of the lung microbiome profile in BALF, along with a greater frequency of the CD56+ subset in blood T cells, whereas no significant difference in PD-L1 expression was found in tumor cells. Antibiotic treatment in a preclinical lung cancer model significantly decreased CXCL9 in the lung TME, resulting in reduced sensitivity to anti–PD-1 antibody, which was reversed by CXCL9 induction in tumor cells. Thus, CXCL9 might be associated with the lung TME microbiome, and the balance of CXCL9 and lung TME microbiome could contribute to nivolumab sensitivity in patients with NSCLC. BALF analysis can help predict the efficacy of ICIs when performed along with currently approved examinations.

Keratinization is one of lung squamous cell cancer’s (LUSC) hallmark histopathology features. Epithelial cells produce keratin to protect their integrity from external harmful substances. In addition to their roles as cell protectors, recent studies have shown that keratins have important roles in regulating either normal cell or tumor cell functions. The objective of this study is to identify the genes and microRNAs (miRNAs) that act as key regulators of the keratinization process in LUSC. To address this goal, we classified LUSC samples from GDC-TCGA databases based on their keratinization molecular signatures. Then, we performed differential analyses of genes, methylation, and miRNA expression between high keratinization and low keratinization samples. By reconstruction and analysis of the differentially expressed genes (DEGs) network, we found that TP63 and SOX2 were the hub genes that were highly connected to other genes and displayed significant correlations with several keratin genes. Methylation analysis showed that the P63, P73, and P53 DNA-binding motif sites were significantly enriched for differentially methylated probes. We identified SNAI2, GRHL3, TP63, ZNF750, and FOXE1 as the top transcription factors associated with these binding sites. Finally, we identified 12 miRNAs that influence the keratinization process by using miRNA–mRNA correlation analysis.

Background: To treat diseases caused by genetic variants, it is necessary to identify disease-causing variants in patients. However, since there are a large number of disease-causing variants, the application of AI is required. We propose AI to solve this problem and report the results of its application in identifying disease-causing variants. Methods: To assist physicians in their task of identifying disease-causing variants, we propose an explainable AI (XAI) that combines high estimation accuracy with explainability using a knowledge graph. We integrated databases for genomic medicine and constructed a large knowledge graph that was used to achieve the XAI. Results: We compared our XAI with random forests and decision trees. Conclusion: We propose an XAI that uses knowledge graphs for explanation. The proposed method achieves high estimation performance and explainability. This will support the promotion of genomic medicine.

Lineage switches in acute leukemia occur rarely, and the underlying mechanisms are poorly understood. Herein, we report the case of an elderly patient with leukemia in which the leukemia started as B-cell acute lymphoblastic leukemia (B-ALL) and later changed to B- and T-cell mixed phenotype acute leukemia (MPAL) and acute myeloid leukemia (AML) during consecutive induction chemotherapy treatments. A 65-year-old woman was initially diagnosed with Philadelphia chromosome-negative B-ALL primarily expressing TdT/CD34/HLA-DR; more than 20% of the blasts were positive for CD19/CD20/cytoplasmic CD79a/cytoplasmic CD22/CD13/CD71.The blasts were negative for T-lineage markers and myeloperoxidase (MPO). Induction chemotherapy with the standard regimen for B-ALL resulted in primary induction failure. After the second induction chemotherapy regimen, the blasts were found to be B/T bi-phenotypic with additional expression of cytoplasmic CD3. A single course of clofarabine (the fourth induction chemotherapy regimen) dramatically reduced lymphoid marker levels. However, the myeloid markers (e.g., MPO) eventually showed positivity and the leukemia completely changed its lineage to AML. Despite subsequent intensive chemotherapy regimens designed for AML, the patient’s leukemia was uncontrollable and a new monoblastic population emerged. The patient died approximately 8 months after the initial diagnosis without experiencing stable remission. Several cytogenetic and genetic features were commonly identified in the initial diagnostic B-ALL and in the following AML, suggesting that this case should be classified as lineage switching leukemia rather than multiple simultaneous cancers (i.e., de novo B-ALL and de novo AML, or primary B-ALL and therapy-related myeloid neoplasm). A complex karyotype was persistently observed with a hemi-allelic loss of chromosome 17 (the location of the TP53 tumor suppressor gene). As the leukemia progressed, the karyotype became more complex, with the additional abnormalities. Sequential target sequencing revealed an increased variant allele frequency of TP53 mutation. Fluorescent in situ hybridization (FISH) revealed an increased number of mixed-lineage leukemia (MLL) genes, both before and after lineage conversion. In contrast, FISH revealed negativity for MLL rearrangements, which are well-known abnormalities associated with lineage switching leukemia and MPAL. To our best knowledge, this is the first reported case of acute leukemia presenting with lineage ambiguity and MLL gene amplification.

In this study, we quantitatively assessed the effectiveness of systems for COVID-19 testing in small groups of sport teams that are semi-isolated from the general population by countermeasures against infection. Two types of group were assumed, and the dynamics of infection within each group was modeled by using a compartment model of infectious disease. One group (Group A) comprised domestic professional sports teams that play many games over a season while remaining within a relatively small region. Polymerase chain reaction (PCR) tests were routinely conducted once every 2 weeks, and the number of infected individuals that could not be quarantined after identification by testing or checking for symptoms was defined as the risk. The other group (Group B) comprised teams that travel across borders for mass-gathering events like the Olympic and Paralympic Games. The teams were isolated for 2 weeks at their destination; frequent testing and checking for symptoms was conducted, and any infected individuals were quarantined. The number of infected individuals participating in games after the isolation period was defined as the risk. In Group A, the number of infected individuals detected by routinely conducted PCR testing was lower than the number of infected individuals detected by checking for symptoms, indicating that routine testing every 2 weeks was not very effective. In Group B, daily PCR testing was the most effective, followed by daily antigen testing. Dual testing, in which individuals with a positive antigen test were given an additional PCR test, was the least effective with an effect equal to PCR testing every other day. These results indicate that repeated testing does not necessarily increase the detection of infected individuals.

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

Chronic neutrophilic leukemia (CNL) is a rare myeloproliferative neoplasm (MPN) characterized by sustained mature neutrophilic leukocytosis. Recently, presence of colony-stimulating factor 3 receptor (CSF3R) mutations has been added to the diagnostic criteria for CNL. Anti-inflammatory effects of the JAK1/2 inhibitor ruxolitinib relieve constitutional symptoms associated with MPN, such as fatigue, night sweats, and fever. We present a case of CNL harboring CSF3R-T618I mutation exacerbated by concomitant bilateral renal abscesses, which was refractory to antibiotics, at the time of initial diagnosis. In this case, ruxolitinib rapidly improved not only CNL but the infection, due to its anti-inflammatory potency.

Abstract Background Nanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real-time. Through detecting the change of ion currency signals during a DNA/RNA fragment’s pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore basecalling has a higher error rate than the basecalling of short-read sequencing. Through utilizing deep neural networks, the-state-of-the art nanopore basecallers achieve basecalling accuracy in a range from 85% to 95%. Result In this work, we proposed a novel basecalling approach from a perspective of instance segmentation. Different from previous approaches of doing typical sequence labeling, we formulated the basecalling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed basecaller URnano achieves competitive results on the in-species data, compared to the recently proposed CTC-featured basecallers. Conclusion Our results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly.