DOMINO trial post hoc analysis: evaluation of the diet effects on symptoms in IBS subtypes.

Background: Irritable bowel syndrome (IBS) is a disorder of gut-brain interaction characterized by recurrent abdominal pain related to defecation and/or associated to a change in bowel habits. According to the stool type, four different IBS subtypes can be recognized, constipation predominant (IBS-C), diarrhea predominant (IBS-D), mixed (IBS-M), and undefined (IBS-U). Patients report that their IBS symptoms are exacerbated by food. Thus, it is important to find a nutritional approach that could be effective in reducing IBS symptoms. Objective: The present work is a post hoc analysis of the previously published DOMINO trial. It aimed to evaluate the effects of a self-instructed FODMAP-lowering diet smartphone application on symptoms and psychosocial aspects in primary care IBS stratifying the results for each IBS subtypes. Design: Post hoc analysis. Methods: Two hundred twenty-two primary care IBS patients followed a FODMAP-lowering diet for 8 weeks with the support of a smartphone application. Two follow-up visits were scheduled after 16 and 24 weeks. IBS-Symptoms Severity Score (IBS-SSS), quality of life (QoL), and adherence and dietary satisfaction were evaluated. Results: After 8 weeks, IBS-SSS improved in all IBS subtypes (p < 0.0001). Physician Health Questiionnaire (PHQ-15) improved only in IBS-D (p = 0.0006), whereas QoL improved both in IBS-D (p = 0.01) and IBS-M (p = 0.005). Conclusion: This post hoc analysis showed that the app is useful in all IBS subtypes; thus, it could be used as an effective tool by both general practitioners and patients to manage symptoms in primary care. Trial registration: Ethical Commission University Hospital of Leuven reference number: S59482. Clinicaltrial.gov reference number: NCT04270487.

What is already known about this subject? The low FODMAP (fermentable oligo-, di-, and monosaccharides and polyols) diet has shown efficacy for controlling IBS (irritable bowel syndrome) symptoms in small controlled trials in tertiary care patients. As this approach requires several visits with an experienced dietitian, it seems less suitable for primary care. What are the new findings? The benefit of the FODMAP lowering app was already present at 4 weeks and persisted during follow-up until 24 weeks. How might it impact on clinical practice in the foreseeable future? Given its superiority to standard first-line pharmacotherapy, and its ease of use, a FODMAP lowering app has the potential to become the preferred first-line treatment for primary care IBS.

Genetic algorithms applied to translational strategy in metabolic-dysfunction associated steatohepatitis (MASH). Learning from mouse models.

BACKGROUND & AIMS: We previously identified subsets of patients with metabolic (dysfunction)-associated steatotic liver disease (MASLD) with different metabolic phenotypes. Here, we aimed to refine this classification based on genetic algorithms implemented in a Python package. The use of these genetic algorithms can help scientists to solve problems which cannot be solved with other methods. We present this package and its capabilities with specific problems. The name, PyGenMet, comes from its main goal, solving problems in Python with Genetic Algorithms and Metabolomics data. METHODS: We collected serum from methionine adenosyltransferase 1a knockout (Mat1a-KO) mice, which have chronically low level of hepatic S-adenosylmethionine (SAMe) and the metabolomes of all samples were determined. We also analyzed serum metabolomes of 541 patients with biopsy proven MASLD (182 with simple steatosis and 359 with metabolic (dysfunction)-associated steatohepatitis or MASH) and compared them with the serum metabolomes of this specific MASLD mouse model using Genetic Algorithms in order to select patients with a specific phenotype. RESULTS: By applying genetic algorithms, we have found a subgroup of patients with a lipid profile similar to that observed in the mouse model. When analyzing the two groups of patients, we have seen that patients with a lipid profile reflecting the mouse model characteristics show significant differences in lipoproteins, especially in LDL-4, LDL-5, and LDL-6 associated with atherogenic risk. CONCLUSION: The results show that the application of genetic algorithms to subclassify patients with MASLD (or other metabolic disease) give consistent results and are a good approximation for the treatment of large volumes of data such as those from omics sciences and patient classification.

The Role of Surgery for Stage IV Breast Cancer.

Surgery for the management metastatic breast cancer has traditionally been considered a palliative procedure. However, some retrospective publications indicated that there may be a survival benefit to surgery in the presence of metastatic disease. Recent randomized trials will be reviewed for both management of the intact primary tumor in de novo breast cancer and systemic secondary metastases.

COPD Subtypes Are Differentially Associated with Cardiovascular Events and COPD Exacerbations.

BACKGROUND: The coronary artery calcium score (CACS) and ratio of the pulmonary artery to aorta diameters (PA:A ratio) measured from chest CT scans have been established as predictors of cardiovascular events and chronic obstructive pulmonary disease (COPD) exacerbations, respectively. However, little is known about the reciprocal relationship between these predictors and outcomes. Furthermore, the prognostic implications of COPD subtypes on clinical outcomes remain insufficiently characterized. RESEARCH QUESTION: How can these two chest CT-derived parameters predict subsequent cardiovascular events and COPD exacerbations in different COPD subtypes? STUDY DESIGN AND METHODS: Using COPDGene study data, we assessed prospective cardiovascular disease (CVD) and COPD exacerbation risk in COPD subjects (Global Initiative for Chronic Obstructive Lung Disease spirometric grades 2-4), focusing on CACS and PA:A ratio at study enrollment, with logistic regression models. These outcomes were analyzed in three COPD subtypes: 1,042 Non-emphysema-predominant COPD (NEPD; low attenuation area at -950 Hounsfield units [LAA-950]<5%), 1,324 Emphysema-predominant COPD (EPD; LAA-950≥10%), and 465 Intermediate Emphysema COPD (IE; 5≤LAA-950<10%). RESULTS: Our study indicated significantly higher overall risk for cardiovascular events in subjects with higher CACS (≥median; Odds Ratio (OR): 1.61, 95% Confidence Interval (CI)=1.30-2.00) and increased COPD exacerbations in those with higher PA:A ratios (≥1; OR: 1.80, 95% CI=1.46-2.23). Notably, NEPD subjects showed a stronger association between these indicators and clinical events compared to EPD (with CACS/CVD, NEPD vs. EPD, OR 2.02 vs. 1.41; with PA:A ratio/COPD exacerbation, NEPD vs. EPD, OR 2.50 vs. 1.65); the difference in odds ratios between COPD subtypes was statistically significant for CACS/CVD. INTERPRETATION: Two chest CT parameters, CACS and PA:A ratio, hold distinct predictive values for cardiovascular events and COPD exacerbations that are influenced by specific COPD subtypes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00608764.

Histopathological, Molecular and Clinical Profiling of Lymphoepithelioma-Like Carcinoma of the Bladder (LELC-B).

Lymphoepithelioma-like urothelial carcinoma of the urinary bladder (LELC-B) is a rare histologic subtype characterized by strong immune cell infiltrates. A better prognosis and favorable response rates to immune-checkpoint inhibitors (ICI) have been described. We aimed to characterize the molecular profiles and immune cell infiltration of LELC-B for a better understanding and its therapeutic implications. We identified eleven muscle-invasive bladder cancer cases with pure and mixed LELC-B. PD-L1 expression and mismatch-repair (MMR) proteins were evaluated using immunohistochemistry. We calculated the tumor-mutational burden (TMB) and characterized mutational profiles using whole exome DNA-sequencing data. Transcriptomic signatures were detected using the NanoString nCounter PanCancer IO360 panel. Multiplex immunofluorescence of tumor microenvironment (PD-L1, PanCK, aSMA, Vimentin, CD45, Ki67) and T-cells (CD4, CD3, PD-1, CD163, CD8, FoxP3) was used to quantify cell populations. All LELC-B cases were highly positive for PD-L1 (median TPS/TC 70%; range 20-100; median CPS 100; range 50-100), MMR-proficient and negative for Epstein-Barr virus infection. Immune cell infiltrates were characterized by high CD8+ T-cell count and high PD-1/PD-L1 expression on immune and tumor cells. LELC-B showed upregulation of signaling pathways involved in immune cell response. Most common mutations were found in chromatin remodeling genes causing epigenetic dysregulation. All LELC-B cases showed high TMB of 39 Mut/Mb (IQR 29-66). In conclusion, LELC-B is a highly immunogenic tumor, showing strong upregulation of PD1/PD-L1 and making ICI a promising treatment option.

Development of a dual-component biosensor for rapid and sensitive detection of influenza H7 and H5 subtypes.

The outbreak of highly pathogenic influenza virus subtypes, such as H7 and H5, presents a significant global health challenge, necessitating the development of rapid and sensitive diagnostic methods. In this study, we have developed a novel dual-component biosensor assembly, each component of which incorporates an antibody fused with a nano-luciferase subunit. Our results demonstrate the effectiveness of this biosensor in enabling the rapid and sensitive detection of influenza H7 and other subtypes. Additionally, we successfully applied the biosensor in paper-based assay and lateral flow assay formats, expanding its versatility and potential for field-deployable applications. Notably, we achieved effective detection of the H7N9 virus using this biosensor. Furthermore, we designed and optimized a dedicated biosensor to the sensitive detection of the influenza H5 subtype. Collectively, our findings underscore the significant potential of this dual-component biosensor assembly as a valuable and versatile tool for accurate and timely diagnosis of influenza virus infections, promising to advance the field of influenza diagnostics and contribute to outbreak management and surveillance efforts.

Investigating the prognostic impact of NY-ESO-1 expression and HLA subtypes in metastatic synovial sarcoma.

BACKGROUND: To better understand the importance of New York esophageal squamous cell carcinoma 1 (NY-ESO-1) and human leukocyte antigen (HLA) subtype in treatment decision making, further investigation of their prevalence and prognostic impact among patients with metastatic synovial sarcoma (mSS) is needed. PATIENTS AND METHODS: This was a retrospective clinico-biological cohort study of adults with mSS. Patient data were collected from the French Sarcoma Group NetSARC database and supplemented by electronic medical records. Primary tumor samples were collected and analyzed for NY-ESO-1 expression by immunohistochemistry (IHC) and HLA-A∗02 status by RNA sequencing (RNA-seq). The primary cohort included patients with available primary tumor samples; the impact of a larger sample size was explored by including patients who had either a primary or metastatic sample (termed the exploratory cohort). P values are provided for descriptive purposes. RESULTS: In 92 patients with primary tumor samples, ∼25% (n = 23) were positive for NY-ESO-1 and HLA-A∗02 expression (dual positive). Among 106 patients with IHC data, 61% (n = 65) were NY-ESO-1 positive, and among 94 patients with RNA-seq data, 45% (n = 42) were HLA-A∗02 positive. The median overall survival (OS) for positive versus negative NY-ESO-1 status was 35.3 and 21.7 months, respectively (unadjusted P = 0.0428). We observed no difference in median OS for HLA-A∗02-positive versus -negative and dual-positive patients versus others (both unadjusted P > 0.05). Multivariate analyses of OS showed no prognostic impact for NY-ESO-1 among primary tumor samples and in the exploratory cohort. However, in the latter, we observed an association between NY-ESO-1 expression and OS in the first-line (P = 0.0041) but not in the second-line setting. CONCLUSIONS: The primary tumor cohort showed no association between NY-ESO-1 expression and OS (including stratification by HLA-A∗02 subtype and treatment line), when adjusting for important prognostic factors, possibly due to small sample sizes.

Radiologic-Pathologic Correlation: Is There an Association Between Contrast-Enhanced Mammography Imaging Features and Molecular Subtypes of Breast Cancer?

OBJECTIVE: This study aims to assess the correlation between imaging features of contrast-enhanced mammography (CEM) and molecular subtypes of breast cancer. METHODS: This is a retrospective single-institution study of patients who underwent CEM from December 2019 to August 2023. Each patient had at least one histologically proven invasive breast cancer with a core biopsy performed. Patients with a history of breast cancer treatment and lesions not entirely included in the CEM images were excluded. The images were interpreted using the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) lexicon for CEM, published in 2022. Different imaging features, including the presence of calcifications, architectural distortion, non-mass enhancement, mass morphology, internal enhancement pattern, the extent of enhancement, and lesion conspicuity, were analyzed. The molecular subtypes were studied as dichotomous variables, including luminal A, luminal B, HER2, and basal-like. The association between the imaging features and molecular subtypes was analyzed with a Fisher's exact test. Statistical significance was assumed when the p-value was <0.05. RESULTS: A total of 31 patients with 36 malignant lesions were included in this study. Sixteen lesions (44.4%) were luminal A, four lesions (11.1%) were luminal B, 10 lesions (27.8%) were HER2, and six (16.7%) were basal-like subtypes. The presence of calcifications was associated with the HER2 subtype (p=0.024). Rim-enhancement on recombined images was associated with a basal-like subtype (p=0.001). Heterogeneous enhancement on recombined images was associated with non-basal-like breast cancer (p=0.027). No statistically significant correlation was found between other analyzed CEM imaging features and molecular subtypes. CONCLUSION: CEM imaging features, including the presence of calcifications and certain internal enhancement patterns, were correlated with distinguishing breast cancer molecular subtypes and thus may further expand the role of CEM.

Prognostic factors for lacrimal gland adenoid cystic carcinoma: a retrospective study in Chinese patients.

AIM: To explore the prognostic factors for lacrimal gland adenoid cystic carcinoma (LGACC) in Chinese patients. METHODS: Clinical and histopathological data were reviewed in patients with pathologically confirmed LGACC. Local recurrence, metastasis, and disease-specific death were the main outcome measures. Univariate and multivariate analyses were performed by the Kaplan-Meier method and a Cox proportional hazard model. RESULTS: This retrospective cohort study included 45 patients with pathologically confirmed LGACC between January 2008 and June 2022. Tumor (T) classification (P=0.005), nodal metastasis (N) classification (P=0.018) and positive margin (P=0.008) were independent risk factors of recurrence; T (P=0.013) and N (P=0.003) classification and the basaloid tumor type (P=0.032) were independent risk factors for metastasis; T classification (P<0.001) was an independent factor of death of disease. In the further analysis, the durations from first surgery to radiotherapy is correlated with metastatic risk in LGACC patients with basaloid component (P=0.022). CONCLUSION: Histological subtype should be emphasized when evaluating prognosis and guiding treatment. Timely radiotherapy may reduce the risk of metastasis in patients with basaloid component.

A cross-sectional survey of Blastocystis sp. and Dientamoeba fragilis in non-human primates and their caregivers in Czech zoos.

Intestinal protists in the gut microbiome are increasingly studied, but their basic epidemiology is not well understood. We explored the prevalence, genetic diversity, and potential zoonotic transmission of two protists colonizing the large intestine - Blastocystis sp. and Dientamoeba fragilis - in 37 species of non-human primates (NHPs) and their caregivers in six zoos in the Czech Republic. We analyzed 179 fecal samples (159 from NHPs, 20 from humans) by qPCR. Blastocystis sp. was detected in 54.7% (98/179) of samples, in 24 NHP species and in 57.2% of NHP samples (prevalence ranged between 36 and 80%), and in 35% of human samples (prevalence ranged between 0 and 67%). Using next generation amplicon sequencing, nine Blastocystis subtypes (ST1-ST5, ST7, ST8, and two novel subtypes) were identified. The two new Blastocystis subtypes (named ST47 and ST48) were described using Nanopore sequencing to produce full-length reference sequences of the small subunit ribosomal RNA gene. Some subtypes were shared between NHPs and their caregivers, suggesting potential zoonotic transmission. Mixed subtype colonization was frequently observed, with 52% of sequenced samples containing two or more subtypes. Dientamoeba was found only in NHPs with a prevalence of 6%. This study emphasizes the critical role of molecular diagnostics in epidemiological and transmission studies of these protists and calls for further research to better understand their impact on public health.

DNA methylation-estimated phenotypes, telomere length and risk of ischemic stroke: epigenetic age acceleration of screening and a Mendelian randomization study.

BACKGROUND: Aging is a complex biological process that may be accelerated in certain pathological conditions. DNA methylation age (DNAmAge) has emerged as a biomarker for biological age, which can differ from chronological age. This research peels back the layers of the relationship between fast-forward aging and ischemic stroke, poking and prodding the potential two-way causal influences between stroke and biological aging indicators. METHODS: We analyzed a cohort of ischemic stroke patients, comparing DNAmAge with chronological age to measure age acceleration. We assessed variations in age acceleration among stroke subtypes and between sexes. Using Mendelian randomization, we examined the causal links between stroke, aging biomarkers like telomere length, and age acceleration's effect on stroke risk. RESULTS: Our investigation reveals a pronounced association between ischemic stroke and age acceleration, most notably in patients with cardioembolic strokes, who exhibited a striking median difference of 9 years between DNAmAge and chronological age. Furthermore, age acceleration differed significantly across stroke subtypes and was higher in women than in men. In terms of causality, MR analysis indicated a modest negative effect of stroke on telomere length, but no causal effect of age phenotypes on stroke or its subtypes. However, some indication of a potential causal effect of ischemic stroke on PhenoAge acceleration was observed. CONCLUSION: The study provides insight into the relationship between DNAmAge and ischemic stroke, particularly cardioembolic stroke, and suggests possible gender differences. These insights carry profound clinical significance and set stage for future investigations into the entwined pathways of stroke and accelerated aging.

The effect of central sleep apnea on sleep architecture in children with obstructive sleep apnea.

OBJECTIVE: This study aimed to investigate how central sleep apnea (CSA) impacts sleep patterns in children with obstructive sleep apnea (OSA). METHODS: Children undergoing polysomnography (PSG) were enrolled and sorted into two groups: those with OSA alone (Group A) and those with both OSA and CSA (CAI <1 nd: children with 10 % CSA or more and less than 50 %, Group B). Statistical analysis was conducted to compare sleep structure and clinical features between Group A and Group B. RESULTS: Group B exhibited significantly higher respiratory events, apnea hypoventilation index, apnea index and oxygen desaturation index (ODI) compared to Group A (p < 0.05). Group B also showed higher total sleep time and arousal index than Group A (P < 0.05). The proportion of time spent in stage N3 was lower in Group B than in Group A (P < 0.05). Moreover, mean heart rate and minimum heart rate were higher in Group B compared to Group A (P < 0.05).Minimum oxygenation levels (including non-rapid eye movement (NREM) stages) were lowe in Group B than in Group A (P < 0.05). Additionally, the prevalence of positional obstructive sleep apnea (P-OSA) was greater in Group B than in Group A (P < 0.05). CONCLUSION: In comparison to those with OSA alone, children with OSA and concurrent CSA exhibited distinct sleep patterns, including reduced N3uration, higher arousal index, longer respiratory events, higher ODI, and lower oxygen saturation, higher heart rate.

Integrated genomic/epigenomic analysis stratifies subtypes of clear cell ovarian carcinoma, highlighting their cellular origin.

The cellular origin of clear cell ovarian carcinoma (CCOC), a major histological subtype of ovarian carcinoma remains elusive. Here, we explored the candidate cellular origin and identify molecular subtypes using integrated genomic/epigenomic analysis. We performed whole exome-sequencing, microarray, and DNA methylation array in 78 CCOC samples according to the original diagnosis. The findings revealed that ARID1A and/or PIK3CA mutations were mutually exclusive with DNA repair related genes, including TP53, BRCA1, and ATM. Clustering of CCOC and other ovarian carcinomas (n = 270) with normal tissues from the fallopian tube, ovarian surface epithelium, endometrial epithelium, and pelvic peritoneum mesothelium (PPM) in a methylation array showed that major CCOC subtypes (with ARID1A and/or PIK3CA mutations) were associated with the PPM-lile cluster (n = 64). This cluster was sub-divided into three clusters: (1) mismatch repair (MMR) deficient with tumor mutational burden-high (n = 2), (2) alteration of ARID1A (n = 51), and (3) ARID1A wild-type (n = 11). The remaining samples (n = 14) were subdivided into (4) ovarian surface epithelium-like (n = 11) and (5) fallopian tube-like (considered as high-grade serous histotype; n = 3). Among these, subtypes (1-3) and others (4 and 5) were found to be associated with immunoreactive signatures and epithelial-mesenchymal transition, respectively. These results contribute to the stratification of CCOC into biological subtypes.

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and 18F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.

Introduction: Although positron emission tomography/computed tomography (PET/CT) is a common tool for measuring breast cancer (BC), subtypes are not automatically classified by it. Therefore, the purpose of this research is to use an artificial neural network (ANN) to evaluate the clinical subtypes of BC based on the value of the tumor marker. Materials and Methods: In our nuclear medical facility, 122 BC patients (training and testing) had 18F-fluoro-D-glucose (18F-FDG) PET/CT to identify the various subtypes of the disease. 18F-FDG-18 injections were administered to the patients before the scanning process. We carried out the scan according to protocol. Based on the tumor marker value, the ANN's output layer uses the Softmax function with cross-entropy loss to detect different subtypes of BC. Results: With an accuracy of 95.77%, the result illustrates the ANN model for K-fold cross-validation. The mean values of specificity and sensitivity were 0.955 and 0.958, respectively. The area under the curve on average was 0.985. Conclusion: Subtypes of BC may be categorized using the suggested approach. The PET/CT may be updated to diagnose BC subtypes using the appropriate tumor maker value when the suggested model is clinically implemented.

Genetic approaches to elucidating cortical and hippocampal GABAergic interneuron diversity.

GABAergic interneurons (INs) in the mammalian forebrain represent a diverse population of cells that provide specialized forms of local inhibition to regulate neural circuit activity. Over the last few decades, the development of a palette of genetic tools along with the generation of single-cell transcriptomic data has begun to reveal the molecular basis of IN diversity, thereby providing deep insights into how different IN subtypes function in the forebrain. In this review, we outline the emerging picture of cortical and hippocampal IN speciation as defined by transcriptomics and developmental origin and summarize the genetic strategies that have been utilized to target specific IN subtypes, along with the technical considerations inherent to each approach. Collectively, these methods have greatly facilitated our understanding of how IN subtypes regulate forebrain circuitry via cell type and compartment-specific inhibition and thus have illuminated a path toward potential therapeutic interventions for a variety of neurocognitive disorders.

Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization.

PROBLEMS: Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model. AIMS: Using Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information. METHODS: Optimizing network parameters with the help of sparrow search algorithm (SSA) for the multiple parameters in the CNN to improve the prediction performance of the model. To avoid the contingency of the results, multiple sets of data were generated through Monte Carlo sampling and used to train the model, thereby improving the credibility of the results. Based on the accurate prediction of the model, the spectral regions that contributed to the classification were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM), achieving the goal of visualizing characteristic peaks. RESULTS: Compared with other algorithms, optimized CNN could obtain the highest accuracy and lowest standard error. And there was no significant difference between using full spectra and fingerprint regions (within 2 %), indicating that the fingerprint region provided the most contribution in classifying sub-types. Based on the classification results from the fingerprint region, the model performances about various sub-types were as follows: CNN (95.34 %±2.18 %)>SVM(94.90 %±1.88 %)>PLS-DA(94.52 %±2.22 %)> KNN (80.00 %±5.27 %). The critical features visualized by Grad-CAM could match well with IHC information, allowing for a more distinct differentiation of sub-types in their spatial positions. CONCLUSION: Raman spectroscopy combined with CNN could achieve accurate and rapid identification of breast cancer molecular sub-types. Proposed visualization strategy could be proved from biochemistry information and spatial location, demonstrated that the strategy might be used for the mining of biomarkers in future.

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.

Small cell lung cancer profiling: an updated synthesis of subtypes, vulnerabilities, and plasticity.

Small cell lung cancer (SCLC) is a devastating disease with high proliferative and metastatic capacity. SCLC has been classified into molecular subtypes based on differential expression of lineage-defining transcription factors. Recent studies have proposed new subtypes that are based on both tumor-intrinsic and -extrinsic factors. SCLC demonstrates substantial intratumoral subtype heterogeneity characterized by highly plastic transcriptional states, indicating that the initially dominant subtype can shift during disease progression and in association with resistance to therapy. Strategies to promote or constrain plasticity and cell fate transitions have nominated novel targets that could prompt the development of more durably effective therapies for patients with SCLC. In this review, we describe the latest advances in SCLC subtype classification and their biological and clinical implications.

Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant.

Objectives: To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset. Materials and Methods: Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns. Results: The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification. Conclusion: Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions. Discussion: This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification.

Advancing bladder cancer management: development of a prognostic model and personalized therapy.

Background: Bladder cancer (BLCA) was recognized as a significant public health challenge due to its high incidence and mortality rates. The influence of molecular subtypes on treatment outcomes was well-acknowledged, necessitating further exploration of their characterization and application. This study was aimed at enhancing the understanding of BLCA by mapping its molecular heterogeneity and developing a robust prognostic model using single-cell and bulk RNA sequencing data. Additionally, immunological characteristics and personalized treatment strategies were investigated through the risk score. Methods: Single-cell RNA sequencing (scRNA-seq) data from GSE135337 and bulk RNA-seq data from several sources, including GSE13507, GSE31684, GSE32894, GSE69795, and TCGA-BLCA, were utilized. Molecular subtypes, particularly the basal-squamous (Ba/Sq) subtype associated with poor prognosis, were identified. A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype. this model was validated across internal and external datasets to ensure predictive accuracy. High- and low-risk groups based on the risk score derived from TCGA-BLCA data were analyzed to examine their immune-related molecular profiles and treatment responses. Results: Six molecular subtypes were identified, with the Ba/Sq subtype being consistently associated with poor prognosis. The prognostic model, based on basal-squamous subtype-related genes (BSSRGs), was shown to have strong predictive performance across diverse clinical settings with AUC values at 1, 3, and 5 years indicating robust predictability in training, testing, and entire datasets. Analysis of the different risk groups revealed distinct immune infiltration and microenvironments. Generally higher tumor mutation burden (TMB) scores and lower tumor immune dysfunction and exclusion (TIDE) scores were exhibited by the low-risk group, suggesting varied potentials for systemic drug response between the groups. Finally, significant differences in potential systemic drug response rates were also observed between risk groups. Conclusions: The study introduced and validated a new prognostic model for BLCA based on BSSRGs, which was proven effective in prognosis prediction. The potential for personalized therapy, optimized by patient stratification and immune profiling, was highlighted by our risk score, aiming to improve treatment efficacy. This approach was promised to offer significant advancements in managing BLCA, tailoring treatments based on detailed molecular and immunological insights.