Deciphering craniopharyngioma subtypes: Single-cell analysis of tumor microenvironment and immune networks.

Craniopharyngiomas, including adamantinomatous (ACP) and squamous papillary (PCP) types, are challenging to treat because of their proximity to crucial pituitary structures. This study aimed to characterize the cellular composition, tumor tissue diversity, and cell-cell interactions in ACPs and PCPs using single-cell RNA sequencing. Single-cell clustering revealed diverse cell types, further classified into developing epithelial, calcification, and immune response for ACP and developing epithelial, cell cycle, and immune response for PCP, based on gene expression patterns. Subclustering revealed the enrichment of classical M1 and M2 macrophages in ACP and PCP, respectively, with high expression of pro-inflammatory markers in classical M1 macrophages. The classical M1 and M2 macrophage ratio significantly correlated with the occurrence of diabetes insipidus and panhypopituitarism. Cell-cell interactions, particularly involving CD44-SPP, were identified between tumor cells. Thus, we developed a comprehensive cell atlas that elucidated the molecular characteristics and immune cell inter-networking in ACP and PCP tumor microenvironments.

Comparative analysis of aldosterone and renin assays for primary aldosteronism screening.

The transition from radioimmunoassay (RIA) to chemiluminescent enzyme immunoassay (CLEIA) for plasma aldosterone concentration (PAC) assays has raised concerns over its impact on primary aldosteronism (PA) diagnosis. This study investigated the correlation between PAC and renin values using RIA, CLEIA, and liquid chromatography/mass spectrometry/mass spectrometry (LC-MS/MS), established cutoff values for PA diagnosis using the aldosterone-to-renin ratio (ARR) with PAC_CLEIA, and assessed the differences in PAC values by measuring weak mineralocorticoids (WMs). This retrospective study evaluated 312 serum PAC samples using RIA, CLEIA, and LC-MS/MS, and analyzed 315 plasma renin samples. Method correlations were assessed through Passing-Bablok regression. Receiver operating characteristic curves determined ARR cutoffs for PA diagnosis. WMs were quantified to evaluate their impact on ΔPAC (RIA-LC-MS/MS) through multiple regression analysis. PAC_CLEIA and PAC_LC-MS/MS values were highly correlated. ARRs derived from PAC_RIAs demonstrated more false positives and lower specificity than ARRs using PAC_CLEIA or PAC_LC-MS/MS. WMs significantly influenced ΔPAC in both the PA and non-PA groups. ARRs using PAC_CLEIA are valuable for determining PA cutoffs in clinical practice. The transition to PAC using CLEIA may enhance PA detection rates. WMs were found to interfere with PAC measurements in the RIA method, affecting outcomes.

RelB and C/EBPα critically regulate the development of Peyer's patch mononuclear phagocytes.

To establish protection against harmful foreign antigens, the small intestine harbors guardian sites called Peyer's patches (PPs). PPs take up antigens through microfold (M) cells and transfer them to the sub-epithelial dome (SED), which contains a high density of mononuclear phagocytes (MPs), for T cell-priming. Accumulating evidence indicates that SED-MPs have unique functions other than T cell-priming to facilitate mucosal immune responses; however, the crucial factors regulating the functions of SED-MPs have not been determined. Here we performed transcriptome analysis, and identified the gene signatures of SED-MPs. Further data interpretation with transcription factor (TF) enrichment analysis estimated TFs responsible for the functions of SED-MPs. Among them, we found that RelB and C/EBPα were preferentially activated in SED-MPs. RelB-deficiency silenced the expression of IL-22BP and S100A4 by SED-MPs. On the other hand, C/EBPα-deficiency decreased the expression of lysozyme by SED-MPs, resulting the increased invasion of orally administered pathogenic bacteria into PPs and mesenteric lymph nodes. Our findings thus demonstrate that RelB and C/EBPα are essential to regulate the functions of SED-MPs.

Machine learning-based reproducible prediction of type 2 diabetes subtypes.

AIMS/HYPOTHESIS: Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist's subtypes consistently over time. METHODS: Cohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2Dkmeans) by unsupervised k-means clustering based on Ahlqvist's variables (age at diagnosis, BMI, HbA1c, HOMA2-B and HOMA2-IR) to four subtypes: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). We adopted 15 variables for a multiclass classification random forest (RF) algorithm to predict type 2 diabetes subtypes (T2DRF15). The proximity matrix computed by RF was visualised using a uniform manifold approximation and projection. Finally, we used a putative subset with missing insulin-related variables to test the predictive performance of the validation cohort, consistency of subtypes over time and prediction ability of diabetes complications. RESULTS: T2DRF15 demonstrated a 94% accuracy for predicting T2Dkmeans type 2 diabetes subtypes (AUCs ≥0.99 and F1 score [an indicator calculated by harmonic mean from precision and recall] ≥0.9) and retained the predictive performance in the external validation cohort (86.3%). T2DRF15 showed an accuracy of 82.9% for detecting T2Dkmeans, also in a putative subset with missing insulin-related variables, when used with an imputation algorithm. In Kaplan-Meier analysis, the diabetes clusters of T2DRF15 demonstrated distinct accumulation risks of diabetic retinopathy in SIDD and that of chronic kidney disease in SIRD during a median observation period of 11.6 (4.5-18.3) years, similarly to the subtypes using T2Dkmeans. The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an 'undecidable' cluster. T2DRF15, after excluding undecidable individuals, showed higher consistency (100% for SIDD, 68.6% for SIRD, 94.4% for MOD and 97.9% for MARD) than T2Dkmeans. CONCLUSIONS/INTERPRETATION: The new ML model for predicting Ahlqvist's subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations.

Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study.

It is expected but unknown whether machine-learning models can outperform regression models, such as a logistic regression (LR) model, especially when the number and types of predictor variables increase in electronic health records (EHRs). We aimed to compare the predictive performance of gradient-boosted decision tree (GBDT), random forest (RF), deep neural network (DNN), and LR with the least absolute shrinkage and selection operator (LR-LASSO) for unplanned readmission. We used EHRs of patients discharged alive from 38 hospitals in 2015-2017 for derivation and in 2018 for validation, including basic characteristics, diagnosis, surgery, procedure, and drug codes, and blood-test results. The outcome was 30-day unplanned readmission. We created six patterns of data tables having different numbers of binary variables (that ≥5% or ≥1% of patients or ≥10 patients had) with and without blood-test results. For each pattern of data tables, we used the derivation data to establish the machine-learning and LR models, and used the validation data to evaluate the performance of each model. The incidence of outcome was 6.8% (23,108/339,513 discharges) and 6.4% (7,507/118,074 discharges) in the derivation and validation datasets, respectively. For the first data table with the smallest number of variables (102 variables that ≥5% of patients had, without blood-test results), the c-statistic was highest for GBDT (0.740), followed by RF (0.734), LR-LASSO (0.720), and DNN (0.664). For the last data table with the largest number of variables (1543 variables that ≥10 patients had, including blood-test results), the c-statistic was highest for GBDT (0.764), followed by LR-LASSO (0.755), RF (0.751), and DNN (0.720), suggesting that the difference between GBDT and LR-LASSO was small and their 95% confidence intervals overlapped. In conclusion, GBDT generally outperformed LR-LASSO to predict unplanned readmission, but the difference of c-statistic became smaller as the number of variables was increased and blood-test results were used.

The muscle tissue transcriptome of idiopathic inflammatory myopathy reflects the muscle damage process by monocytes and presence of skin lesions.

OBJECTIVE: To investigate transcriptomic and immunophenotypic features of muscle specimens from patients with idiopathic inflammatory myopathy (IIM). METHODS: Bulk RNA-sequencing was performed on muscle biopsy samples from 16 patients with dermatomyositis (DM) and 9 patients with polymyositis (PM). Seven tested positive for anti-aminoacyl t-RNA synthetase antibodies in the DM patients (ARS-DM). We conducted weighted gene coexpression network analysis (WGCNA), differentially expressed gene (DEG) analysis, and gene set variation analysis (GSVA) to assess contributions of specific pathways. Cell proportions in muscle specimens were estimated using a deconvolution approach. RESULTS: WGCNA revealed significant positive correlations between serum creatine kinase (CK) levels and gene modules involved in cellular respiration, phagocytosis, and oxidative phosphorylation (OXPHOS). Significant positive correlations were also observed between CK levels and proportions of CD16-positive and -negative monocytes and myeloid dendritic cells. Notably, DM patients demonstrated enrichment of complement and interferon-α and -γ pathway genes compared to those with PM. Furthermore, ARS-DM demonstrated a higher proportion of Th1 cells and DEGs related to OXPHOS. Additionally, serum Krebs von den Lungen-6 levels correlated with gene modules associated with extracellular matrix and transforming growth factor-ß signaling pathway. CONCLUSION: Our study highlights a significant involvement of monocytes in muscle damage and delineates pathological differences among IIM subtypes. DM was characterized by complement, interferon-α and -γ signaling, whilst ARS-DM was associated with OXPHOS. Distinctive gene expression variations in muscle specimens suggest that different pathologic mechanisms underlie muscle damage in each IIM phenotype.

Host-derived protein profiles of human neonatal meconium across gestational ages.

Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted an extensive proteomic analysis of first meconium from 259 newborns across varied gestational ages to delineate protein composition and elucidate its relevance to neonatal diseases. The first meconium samples were collected, with the majority obtained before feeding, and the mean time for the first meconium passage from the anus was 11.9 ± 9.47 h. Our analysis revealed 5370 host-derived meconium proteins, which varied depending on sex and gestational age. Specifically, meconium from preterm infants exhibited elevated concentrations of proteins associated with the extracellular matrix. Additionally, the protein profiles of meconium also exhibited unique variations depending on both specific diseases, including gastrointestinal diseases, congenital heart diseases, and maternal conditions. Furthermore, we developed a machine learning model to predict gestational ages using meconium proteins. Our model suggests that newborns with gastrointestinal diseases and congenital heart diseases may have immature gastrointestinal systems. These findings highlight the intricate relationship between clinical parameters and meconium protein composition, offering potential for a novel approach to assess neonatal gastrointestinal health.

PSA doubling time 4.65 months as an optimal cut-off of Japanese nonmetastatic castration-resistant prostate cancer.

A multicenter study of nonmetastatic castration-resistant prostate cancer (nmCRPC) was conducted to identify the optimal cut-off value of prostate-specific antigen (PSA) doubling time (PSADT) that correlated with the prognosis in Japanese nmCRPC. Of the 515 patients diagnosed and treated for nmCRPC at 25 participating Japanese Urological Oncology Group centers, 450 patients with complete clinical information were included. The prognostic values of clinical factors were evaluated with respect to prostate specific antigen progression-free (PFS), cancer-specific survival (CSS), and overall survival (OS). The optimal cutoff value of PSADT was identified using survival tree analysis by Python. The Median PSA and PSADT at diagnosis of nmCRPC were 3.3 ng/ml, and 5.2 months, respectively. Patients treated with novel hormonal therapy (NHT) showed significantly longer PFS (HR: hazard ratio 0.38, p < 0.0001) and PFS2 (HR 0.45, p < 0.0001) than those treated with vintage nonsteroidal antiandrogen agent (Vintage). The survival tree identified 4.65 months as the most prognostic PSADT cutoff point. Among the clinical and pathological factors PSADT of < 4.65 months remained an independent prognostic factor for OS (HR 2.96, p = 0.0003) and CSS (HR 3.66, p < 0.0001). Current data represented optimal cut-off of PSADT 4.65 months for a Japanese nmCRPC.

Sodium-glucose cotransporter 2 inhibitor therapy reduces the administration frequency of steroid injection in patients with diabetic macular edema: A cohort study using the Japanese health insurance claims database.

AIMS/INTRODUCTION: Severe diabetic macular edema (DME) is often resistant to anti-vascular endothelial growth factor therapy. Steroids are particularly effective at reducing edema by suppressing inflammation; they are also used as an alternative to expensive anti-vascular endothelial growth factor therapy in some patients. Therefore, the use of steroids in DME reflects an unmet need for anti-vascular endothelial growth factor therapy. Notably, triamcinolone acetonide (TA) injections are widely used in Japan. Here, we evaluated the frequency of TA as an indicator of the efficacy of sodium-glucose cotransporter 2 inhibitors (SGLT2is) in DME treatment using a health insurance claims database. MATERIALS AND METHODS: In this cohort study, we retrospectively analyzed the health insurance claims data of 11 million Japanese individuals from 2005 to 2019. The frequency and duration of TA injection after the initiation of SGLT2is or other antidiabetic drugs were analyzed. RESULTS: Among the 2,412 matched patients with DME, the incidence rate of TA injection was 63.8 times per 1,000 person-years in SGLT2i users and 94.9 times per 1,000 person-years in non-users. SGLT2is reduced the risk for the first (P = 0.0024, hazard ratio 0.66, 95% confidence interval 0.50-0.87), second (P = 0.0019, hazard ratio 0.53, 95% confidence interval 0.35-0.80) and third TA (P = 0.0053, hazard ratio 0.44, 95% confidence interval 0.25-0.80) injections. A subanalysis of each baseline characteristic of the patients showed that SGLT2is were effective regardless of the background factors. CONCLUSIONS: The use of SGLT2is reduced the frequency of TA injection in patients with DME. Therefore, SGLT2i therapy might be a novel, noninvasive and low-cost adjunctive therapy for DME.

Memory CD8 T cells are vulnerable to chronic IFN-γ signals but not to CD4 T cell deficiency in MHCII-deficient mice.

The mechanisms by which the number of memory CD8 T cells is stably maintained remains incompletely understood. It has been postulated that maintaining them requires help from CD4 T cells, because adoptively transferred memory CD8 T cells persist poorly in MHC class II (MHCII)-deficient mice. Here we show that chronic interferon-γ signals, not CD4 T cell-deficiency, are responsible for their attrition in MHCII-deficient environments. Excess IFN-γ is produced primarily by endogenous colonic CD8 T cells in MHCII-deficient mice. IFN-γ neutralization restores the number of memory CD8 T cells in MHCII-deficient mice, whereas repeated IFN-γ administration or transduction of a gain-of-function STAT1 mutant reduces their number in wild-type mice. CD127high memory cells proliferate actively in response to IFN-γ signals, but are more susceptible to attrition than CD127low terminally differentiated effector memory cells. Furthermore, single-cell RNA-sequencing of memory CD8 T cells reveals proliferating cells that resemble short-lived, terminal effector cells and documents global downregulation of gene signatures of long-lived memory cells in MHCII-deficient environments. We propose that chronic IFN-γ signals deplete memory CD8 T cells by compromising their long-term survival and by diverting self-renewing CD127high cells toward terminal differentiation.

ARID5B is a negative modulator of IL-6 production in rheumatoid arthritis synovial fibroblasts.

Recent single-cell RNA-sequencing analysis of rheumatoid arthritis (RA) synovial tissues revealed the heterogeneity of RA synovial fibroblasts (SFs) with distinct functions such as high IL-6 production. The molecular mechanisms responsible for high IL-6 production will become a promising drug target of RASFs to treat RA. In this study, we performed siRNA screening of 65 transcription factors (TFs) differentially expressed among RASF subsets to identify TFs involved in IL-6 production. The siRNA screening identified 7 TFs including ARID5B, a RA risk gene, that affected IL-6 production. Both long and short isoforms of ARID5B were expressed and negatively regulated by TNF-α in RASFs. The siRNA knockdown and lentiviral overexpression of long and short isoforms of ARID5B revealed that the long isoform suppressed IL-6 production stimulated with TNF-α. eQTL analysis using 58 SFs demonstrated that RA risk allele, rs10821944, in intron 4 of the ARID5B gene had a trend of eQTL effects to the expression of long isoform of ARID5B in SFs treated with TNF-α. ARID5B was found to be a negative modulator of IL-6 production in RASFs. The RA risk allele of ARID5B intron may cause high IL-6 production, suggesting that ARID5B will become a promising drug target to treat RA.

Generative artificial intelligence and non-pharmacological bias: an experimental study on cancer patient sexual health communications.

Objectives The objective of this study was to explore the feature of generative artificial intelligence (AI) in asking sexual health among cancer survivors, which are often challenging for patients to discuss.Methods We employed the Generative Pre-trained Transformer-3.5 (GPT) as the generative AI platform and used DocsBot for citation retrieval (June 2023). A structured prompt was devised to generate 100 questions from the AI, based on epidemiological survey data regarding sexual difficulties among cancer survivors. These questions were submitted to Bot1 (standard GPT) and Bot2 (sourced from two clinical guidelines).Results No censorship of sexual expressions or medical terms occurred. Despite the lack of reflection on guideline recommendations, 'consultation' was significantly more prevalent in both bots' responses compared with pharmacological interventions, with ORs of 47.3 (p<0.001) in Bot1 and 97.2 (p<0.001) in Bot2.Discussion Generative AI can serve to provide health information on sensitive topics such as sexual health, despite the potential for policy-restricted content. Responses were biased towards non-pharmacological interventions, which is probably due to a GPT model designed with the 's prohibition policy on replying to medical topics. This shift warrants attention as it could potentially trigger patients' expectations for non-pharmacological interventions.

Dimension reduction of microbiome data linked Bifidobacterium and Prevotella to allergic rhinitis.

Dimension reduction has been used to visualise the distribution of multidimensional microbiome data, but the composite variables calculated by the dimension reduction methods have not been widely used to investigate the relationship of the human gut microbiome with lifestyle and disease. In the present study, we applied several dimension reduction methods, including principal component analysis, principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and non-negative matrix factorization, to a microbiome dataset from 186 subjects with symptoms of  allergic rhinitis (AR) and 106 controls. All the dimension reduction methods supported that the distribution of microbial data points appeared to be continuous rather than discrete. Comparison of the composite variables calculated from the different dimension reduction methods showed that the characteristics of the composite variables differed depending on the distance matrices and the dimension reduction methods. The first composite variables calculated from PCoA and NMDS with the UniFrac distance were strongly associated with AR (FDR adjusted P = 2.4 × 10-4 for PCoA and P = 2.8 × 10-4 for NMDS), and also with the relative abundance of Bifidobacterium and Prevotella. The abundance of Bifidobacterium was also linked to intake of several nutrients, including carbohydrate, saturated fat, and alcohol via composite variables. Notably, the association between the composite variables and AR was much stronger than the association between the relative abundance of individual genera and AR. Our results highlight the usefulness of the dimension reduction methods for investigating the association of microbial composition with lifestyle and disease in clinical research.

Preoperative prediction for periprosthetic bone loss and individual evaluation of bisphosphonate effect after total hip arthroplasty using artificial intelligence.

Aims: This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. Methods: The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate. Results: Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate. Conclusion: Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss.

Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based Cross-Sectional Questionnaire Study.

BACKGROUND: One life event that requires extensive resilience and adaptation is parenting. However, resilience and perceived support in child-rearing vary, making the real-world situation unclear, even with postpartum checkups. OBJECTIVE: This study aimed to explore the psychosocial status of mothers during the child-rearing period from newborn to toddler, with a classifier based on data on the resilience and adaptation characteristics of mothers with newborns. METHODS: A web-based cross-sectional survey was conducted. Mothers with newborns aged approximately 1 month (newborn cohort) were analyzed to construct an explainable machine learning classifier to stratify parenting-related resilience and adaptation characteristics and identify vulnerable populations. Explainable k-means clustering was used because of its high explanatory power and applicability. The classifier was applied to mothers with infants aged 2 months to 1 year (infant cohort) and mothers with toddlers aged >1 year to 2 years (toddler cohort). Psychosocial status, including depressed mood assessed by the Edinburgh Postnatal Depression Scale (EPDS), bonding assessed by the Postpartum Bonding Questionnaire (PBQ), and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) between the classified groups, was compared. RESULTS: A total of 1559 participants completed the survey. They were split into 3 cohorts, comprising populations of various characteristics, including parenting difficulties and psychosocial measures. The classifier, which stratified participants into 5 groups, was generated from the self-reported scores of resilience and adaptation in the newborn cohort (n=310). The classifier identified that the group with the greatest difficulties in resilience and adaptation to a child's temperament and perceived support had higher incidences of problems with depressed mood (relative prevalence [RP] 5.87, 95% CI 2.77-12.45), bonding (RP 5.38, 95% CI 2.53-11.45), and sleep quality (RP 1.70, 95% CI 1.20-2.40) compared to the group with no difficulties in perceived support. In the infant cohort (n=619) and toddler cohort (n=461), the stratified group with the greatest difficulties had higher incidences of problems with depressed mood (RP 9.05, 95% CI 4.36-18.80 and RP 4.63, 95% CI 2.38-9.02, respectively), bonding (RP 1.63, 95% CI 1.29-2.06 and RP 3.19, 95% CI 2.03-5.01, respectively), and sleep quality (RP 8.09, 95% CI 4.62-16.37 and RP 1.72, 95% CI 1.23-2.42, respectively) compared to the group with no difficulties. CONCLUSIONS: The classifier, based on a combination of resilience and adaptation to the child's temperament and perceived support, was able identify psychosocial vulnerable groups in the newborn cohort, the start-up stage of childcare. Psychosocially vulnerable groups were also identified in qualitatively different infant and toddler cohorts, depending on their classifier. The vulnerable group identified in the infant cohort showed particularly high RP for depressed mood and poor sleep quality.

Sodium-glucose co-transporter 2 inhibitor therapy reduces the administration frequency of anti-vascular endothelial growth factor agents in patients with diabetic macular oedema with a history of anti-vascular endothelial growth factor agent use: A cohort study using the Japanese health insurance claims database.

AIM: We assessed the effectiveness of sodium-glucose co-transporter 2 inhibitors (SGLT2is) in reducing the administration frequency of anti-vascular endothelial growth factor (VEGF) agents in patients with diabetic macular oedema (DMO) using a health insurance claims database. MATERIALS AND METHODS: This retrospective cohort study analysed health insurance claims data covering 11 million Japanese patients between 2005 and 2019. We analysed the frequency and duration of intravitreal injection of anti-VEGF agents after initiating SGLT2is or other antidiabetic drugs. RESULTS: Among 2412 matched patients with DMO, the incidence rates of anti-VEGF agent injections were 230.1 per 1000 person-year in SGLT2i users and 228.4 times per 1000 person-year in non-users, respectively, and the risk ratio for events was unchanged in both groups. Sub-analysis of each baseline characteristic of the patients showed that SGLT2is were particularly effective in patients with a history of anti-VEGF agent use [p = .027, hazard ratio (HR): 0.44, 95% confidence interval (CI): 0.22-0.91]. SGLT2is reduced the risk for the first (p = .023, HR: 0.45, 95% CI: 0.22-0.91) and second (p = .021, HR: 0.39, 95% CI: 0.17-0.89) anti-VEGF agent injections. CONCLUSIONS: There was no difference in the risk ratio for the addition of anti-VEGF therapy between the two treatment groups. However, the use of SGLT2is reduced the frequency of anti-VEGF agent administration in patients with DMO requiring anti-VEGF therapy. Therefore, SGLT2i therapy may be a novel, non-invasive, low-cost adjunctive therapy for DMO requiring anti-VEGF therapy.

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case.

BACKGROUND: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. METHODS: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. RESULTS: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. CONCLUSIONS: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.

Developing Medical Education Curriculum Reform Strategies to Address the Impact of Generative AI: Qualitative Study.

BACKGROUND: Generative artificial intelligence (GAI), represented by large language models, have the potential to transform health care and medical education. In particular, GAI's impact on higher education has the potential to change students' learning experience as well as faculty's teaching. However, concerns have been raised about ethical consideration and decreased reliability of the existing examinations. Furthermore, in medical education, curriculum reform is required to adapt to the revolutionary changes brought about by the integration of GAI into medical practice and research. OBJECTIVE: This study analyzes the impact of GAI on medical education curricula and explores strategies for adaptation. METHODS: The study was conducted in the context of faculty development at a medical school in Japan. A workshop involving faculty and students was organized, and participants were divided into groups to address two research questions: (1) How does GAI affect undergraduate medical education curricula? and (2) How should medical school curricula be reformed to address the impact of GAI? The strength, weakness, opportunity, and threat (SWOT) framework was used, and cross-SWOT matrix analysis was used to devise strategies. Further, 4 researchers conducted content analysis on the data generated during the workshop discussions. RESULTS: The data were collected from 8 groups comprising 55 participants. Further, 5 themes about the impact of GAI on medical education curricula emerged: improvement of teaching and learning, improved access to information, inhibition of existing learning processes, problems in GAI, and changes in physicians' professionality. Positive impacts included enhanced teaching and learning efficiency and improved access to information, whereas negative impacts included concerns about reduced independent thinking and the adaptability of existing assessment methods. Further, GAI was perceived to change the nature of physicians' expertise. Three themes emerged from the cross-SWOT analysis for curriculum reform: (1) learning about GAI, (2) learning with GAI, and (3) learning aside from GAI. Participants recommended incorporating GAI literacy, ethical considerations, and compliance into the curriculum. Learning with GAI involved improving learning efficiency, supporting information gathering and dissemination, and facilitating patient involvement. Learning aside from GAI emphasized maintaining GAI-free learning processes, fostering higher cognitive domains of learning, and introducing more communication exercises. CONCLUSIONS: This study highlights the profound impact of GAI on medical education curricula and provides insights into curriculum reform strategies. Participants recognized the need for GAI literacy, ethical education, and adaptive learning. Further, GAI was recognized as a tool that can enhance efficiency and involve patients in education. The study also suggests that medical education should focus on competencies that GAI hardly replaces, such as clinical experience and communication. Notably, involving both faculty and students in curriculum reform discussions fosters a sense of ownership and ensures broader perspectives are encompassed.

Multifaceted analysis of cross-tissue transcriptomes reveals phenotype-endotype associations in atopic dermatitis.

Atopic dermatitis (AD) is a skin disease that is heterogeneous both in terms of clinical manifestations and molecular profiles. It is increasingly recognized that AD is a systemic rather than a local disease and should be assessed in the context of whole-body pathophysiology. Here we show, via integrated RNA-sequencing of skin tissue and peripheral blood mononuclear cell (PBMC) samples along with clinical data from 115 AD patients and 14 matched healthy controls, that specific clinical presentations associate with matching differential molecular signatures. We establish a regression model based on transcriptome modules identified in weighted gene co-expression network analysis to extract molecular features associated with detailed clinical phenotypes of AD. The two main, qualitatively differential skin manifestations of AD, erythema and papulation are distinguished by differential immunological signatures. We further apply the regression model to a longitudinal dataset of 30 AD patients for personalized monitoring, highlighting patient heterogeneity in disease trajectories. The longitudinal features of blood tests and PBMC transcriptome modules identify three patient clusters which are aligned with clinical severity and reflect treatment history. Our approach thus serves as a framework for effective clinical investigation to gain a holistic view on the pathophysiology of complex human diseases.