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.

FBLN2 is associated with basal cell markers Krt14 and ITGB1 in mouse mammary epithelial cells and has a preferential expression in molecular subtypes of human breast cancer.

BACKGROUND: Fibulin-2 (FBLN2) is a secreted extracellular matrix (ECM) glycoprotein and has been identified in the mouse mammary gland, in cap cells of terminal end buds (TEBs) during puberty, and around myoepithelial cells during early pregnancy. It is required for basement membrane (BM) integrity in mammary epithelium, and its loss has been associated with human breast cancer invasion. Herein, we attempted to confirm the relevance of FBLN2 to myoepithelial phenotype in mammary epithelium and to assess its expression in molecular subtypes of human breast cancer. METHODS: The relationship between FBLN2 expression and epithelial markers was investigated in pubertal mouse mammary glands and the EpH4 mouse mammary epithelial cell line using immunohistochemistry, immunocytochemistry, and immunoblotting. Human breast cancer mRNA data from the METABRIC and TCGA datasets from Bioportal were analyzed to assess the association of Fbln2 expression with epithelial markers, and with molecular subtypes. Survival curves were generated using data from the METABRIC dataset and the KM databases. RESULTS: FBLN2 knockdown in mouse mammary epithelial cells was associated with a reduction in KRT14 and an increase in KRT18. Further, TGFß3 treatment resulted in the upregulation of FBLN2 in vitro. Meta-analyses of human breast cancer datasets from Bioportal showed a higher expression of Fbln2 mRNA in claudin-low, LumA, and normal-like breast cancers compared to LumB, Her2 +, and Basal-like subgroups. Fbln2 mRNA levels were positively associated with mesenchymal markers, myoepithelial markers, and markers of epithelial-mesenchymal transition. Higher expression of Fbln2 mRNA was associated with better prognosis in less advanced breast cancer and this pattern was reversed in more advanced lesions. CONCLUSION: With further validation, these observations may offer a molecular prognostic tool for human breast cancer for more personalized therapeutic approaches.

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.

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.

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.

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.

Small cell lung cancer: emerging subtypes, signaling pathways, and therapeutic vulnerabilities.

Small cell lung cancer (SCLC) is a recalcitrant cancer characterized by early metastasis, rapid tumor growth and poor prognosis. In recent decades, the epidemiology, initiation and mutation characteristics of SCLC, as well as abnormal signaling pathways contributing to its progression, have been widely studied. Despite extensive investigation, fewer drugs have been approved for SCLC. Recent advancements in multi-omics studies have revealed diverse classifications of SCLC that are featured by distinct characteristics and therapeutic vulnerabilities. With the accumulation of SCLC samples, different subtypes of SCLC and specific treatments for these subtypes were further explored. The identification of different molecular subtypes has opened up novel avenues for the treatment of SCLC; however, the inconsistent and uncertain classification of SCLC has hindered the translation from basic research to clinical applications. Therefore, a comprehensives review is essential to conclude these emerging subtypes and related drugs targeting specific therapeutic vulnerabilities within abnormal signaling pathways. In this current review, we summarized the epidemiology, risk factors, mutation characteristics of and classification, related molecular pathways and treatments for SCLC. We hope that this review will facilitate the translation of molecular subtyping of SCLC from theory to clinical application.

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.

Lung Cancer Subtyping: A Short Review.

As of 2022, lung cancer is the most commonly diagnosed cancer worldwide, with the highest mortality rate. There are three main histological types of lung cancer, and it is more important than ever to accurately identify the subtypes since the development of personalized, type-specific targeted therapies that have improved mortality rates. Traditionally, the gold standard for the confirmation of histological subtyping is tissue biopsy and histopathology. This, however, comes with its own challenges, which call for newer sampling techniques and adjunctive tools to assist in and improve upon the existing diagnostic workflow. This review aims to list and describe studies from the last decade (n = 47) that investigate three such potential omics techniques-namely (1) transcriptomics, (2) proteomics, and (3) metabolomics, as well as immunohistochemistry, a tool that has already been adopted as a diagnostic adjunct. The novelty of this review compared to similar comprehensive studies lies with its detailed description of each adjunctive technique exclusively in the context of lung cancer subtyping. Similarities between studies evaluating individual techniques and markers are drawn, and any discrepancies are addressed. The findings of this study indicate that there is promising evidence that supports the successful use of omics methods as adjuncts to the subtyping of lung cancer, thereby directing clinician practice in an economical and less invasive manner.

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.

Correlations of Imaging and Therapy in Breast Cancer Based on Molecular Patterns: An Important Issue in the Diagnosis of Breast Cancer.

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer.

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.

Update of newly-recognized salivary gland neoplasms: molecular and immunohistochemical findings and clinical importance.

With the advancement of molecular testing and the routine use of immunohistochemical stains, salivary gland tumours previously categorized as adenoma or adenocarcinoma, not otherwise specified, are being reclassified with distinct diagnoses. Newly recognized benign entities include: sclerosing polycystic adenoma, keratocystoma, intercalated duct hyperplasia and adenoma, and striated duct adenoma. Newly recognized malignant salivary gland tumours include: microsecretory adenocarcinoma, sclerosing microcytic adenocarcinoma, and mucinous adenocarcinoma. Additionally, rare subtypes of mucoepidermoid carcinoma have been described, including Warthin-like and oncocytic. Understanding of intraductal carcinoma continues to evolve. Correctly distinguishing these lesions from mimickers can be crucial for appropriate patient care and prognostication, as well as future conceptualization of salivary disease.

Dual and triple gene combinations of KRT5, KRT17, and S100A2 identify basal-like subtype of pancreatic ductal adenocarcinoma and correlate with survival outcome.

There is a significant difference in prognosis and response to chemotherapy between basal and classical subtypes of pancreatic ductal adenocarcinoma (PDAC). Further biomarkers are required to identify subtypes of PDAC. We selected candidate biomarkers via review articles. Correlations between these candidate markers and the PDAC molecular subtype gene sets were analyzed using bioinformatics, confirming the biomarkers for identifying classical and basal subtypes. Subsequently, 298 PDAC patients were included, and their tumor tissues were immunohistochemically stratified using these biomarkers. Survival data underwent analysis, including Cox proportional hazards modeling. Our results indicate that the pairwise and triple combinations of KRT5/KRT17/S100A2 exhibit a higher correlation coefficient with the basal-like subtype gene set, whereas the corresponding combinations of GATA6/HNF4A/TFF1 show a higher correlation with the classical subtype gene set. Whether analyzing unmatched or propensity-matched data, the overall survival time was significantly shorter for the basal subtype compared with the classical subtype (p < .001), with basal subtype patients also facing a higher risk of mortality (HR = 4.017, 95% CI 2.675-6.032, p < .001). In conclusion, the combined expression of KRT5, KRT17, and S100A2, in both pairwise and triple combinations, independently predicts shorter overall survival in PDAC patients and likely identifies the basal subtype. Similarly, the combined expression of GATA6, HNF4A, and TFF1, in the same manner, may indicate the classical subtype. In our study, the combined application of established biomarkers offers valuable insights for the prognostic evaluation of PDAC patients.

Role of miRNA in Highly Pathogenic H5 Avian Influenza Virus Infection: An Emphasis on Cellular and Chicken Models.

The avian influenza virus, particularly the H5N1 strain, poses a significant and ongoing threat to both human and animal health. Recent outbreaks have affected domestic and wild birds on a massive scale, raising concerns about the virus' spread to mammals. This review focuses on the critical role of microRNAs (miRNAs) in modulating pro-inflammatory signaling pathways during the pathogenesis of influenza A virus (IAV), with an emphasis on highly pathogenic avian influenza (HPAI) H5 viral infections. Current research indicates that miRNAs play a significant role in HPAI H5 infections, influencing various aspects of the disease process. This review aims to synthesize recent findings on the impact of different miRNAs on immune function, viral cytopathogenicity, and respiratory viral replication. Understanding these mechanisms is essential for developing new therapeutic strategies to combat avian influenza and mitigate its effects on both human and animal populations.

Patterns of Diversity and Humoral Immunogenicity for HIV-1 Antisense Protein (ASP).

HIV-1 has an antisense gene overlapping env that encodes the ASP protein. ASP functions are still unknown, but it has been associated with gp120 in the viral envelope and membrane of infected cells, making it a potential target for immune response. Despite this, immune response patterns against ASP are poorly described and can be influenced by the high genetic variability of the env gene. To explore this, we analyzed 100k HIV-1 ASP sequences from the Los Alamos HIV sequence database using phylogenetic, Shannon entropy (Hs), and logo tools to study ASP variability in worldwide and Brazilian sequences from the most prevalent HIV-1 subtypes in Brazil (B, C, and F1). Data obtained in silico guided the design and synthesis of 15-mer overlapping peptides through spot synthesis on cellulose membranes. Peptide arrays were screened to assess IgG and IgM responses in pooled plasma samples from HIV controllers and individuals with acute or recent HIV infection. Excluding regions with low alignment accuracy, several sites with higher variability (Hs > 1.5) were identified among the datasets (25 for worldwide sequences, 20 for Brazilian sequences). Among sites with Hs < 1.5, sequence logos allowed the identification of 23 other sites with subtype-specific signatures. Altogether, amino acid variations with frequencies > 20% in the 48 variable sites identified were included in 92 peptides, divided into 15 sets, representing near full-length ASP. During the immune screening, the strongest responses were observed in three sets, one in the middle and one at the C-terminus of the protein. While some sets presented variations potentially associated with epitope displacement between IgG and IgM targets and subtype-specific signatures appeared to impact the level of response for some peptides, signals of cross-reactivity were observed for some sets despite the presence of B/C/F1 signatures. Our data provides a map of ASP regions preferentially targeted by IgG and IgM responses. Despite B/C/F1 subtype signatures in ASP, the amino acid variation in some areas preferentially targeted by IgM and IgG did not negatively impact the response against regions with higher immunogenicity.

Effect of health lifestyle on the risk of stroke: A prospective cohort study from Chongqing, China.

OBJECTIVE: To prospectively investigate the associations of healthy lifestyle factors on the risk of stroke and stroke subtypes, as studies exploring this relationship are limited in China. METHODS: The 22,661 participants in the prospective cohort study in Chongqing, China, aged 30-79 years and stroke-free at baseline completed follow-up from 2018 to 2022. We included seven healthy lifestyle factors, including non-smoking, non-excessive drinking, sufficient physical activity, healthy diet, sleep duration of 7-9 h/d, and standard range of body mass index and waist-to-hip ratio. The healthy lifestyle score was calculated based on the number of healthy lifestyle factors. RESULTS: Compared with participants who had scores ≤2, participants with scores ≥6 had an HRs (95 % CIs) of 0.56 (0.34, 0.92) for total stroke and 0.53 (0.30, 0.93) for ischemic stroke. For every 1-point increase in healthy lifestyle scores, the HRs (95 % CIs) for total stroke and ischemic stroke was 0.86 (0.78, 0.95) and 0.86 (0.77, 0.96), respectively. CONCLUSIONS: Maintaining multiple healthy lifestyle factors can significantly reduce the risk of stroke. As the number of healthy lifestyle factors increased, the stroke risk gradually decreased. Our findings emphasize the significance of comprehensive lifestyle interventions.