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In this ecological study, we aim to establish the role vaccines play in bringing the pandemic under control, as well as the impact of pathogen variants, vaccine hesitancy, and medical resource availability during the process by utilizing publicly available data. The study spans a three-year data collection period for daily hospital admissions due to COVID-19 and the daily reported cases of COVID-19 across all 50 states in the USA. In doing so, we aim to demonstrate the difference in severity of the SARS-CoV-2 pathogen among vaccinated and unvaccinated populations in the USA. The study assesses the correlation of COVID-19 vaccines (e.g., Pfizer, Moderna, and Janssen) and disease outcomes (transmissibility, severity, and deaths) caused by different strains of SARS-CoV-2 and establishes a negative correlation between COVID-19 vaccine and disease outcomes. By considering potential confounders in vaccine hesitancy, medical resource availability and vaccine dosage, we demonstrate the aforementioned to be insubstantial in predicting disease outcomes while the latter displays a contrasting significance in terms of disease outcomes. Between all the major variants of concern, the Delta and Omicron variants in particular have been associated with higher virulence and transmissibility factors respectively. Hence, the CDC continues to encourage the US population to get vaccinated since vaccines are one of the most effective ways to protect the community from potential outbreaks and prevent severe disease manifestations.

Triple-negative breast cancer (TNBC), characterized by the absence or low expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2), is the most aggressive subtype of breast cancer. TNBC accounts for about 15% of breast cancer cases in the U.S., and is known for high relapse rates and poor overall survival (OS). Chemo-resistant TNBC is a genetically diverse, highly heterogeneous, and rapidly evolving disease that challenges our ability to individualize treatment for incomplete responders and relapsed patients. Currently, the frontline standard chemotherapy, composed of anthracyclines, alkylating agents, and taxanes, is commonly used to treat high-risk and locally advanced TNBC. Several FDA-approved drugs that target programmed cell death protein-1 (Keytruda) and programmed death ligand-1 (Tecentriq), poly ADP-ribose polymerase (PARP), and/or antibody drug conjugates (Trodelvy) have shown promise in improving clinical outcomes for a subset of TNBC. These inhibitors that target key genetic mutations and specific molecular signaling pathways that drive malignant tumor growth have been used as single agents and/or in combination with standard chemotherapy regimens. Here, we review the current TNBC treatment options, unmet clinical needs, and actionable drug targets, including epidermal growth factor (EGFR), vascular endothelial growth factor (VEGF), androgen receptor (AR), estrogen receptor beta (ERβ), phosphoinositide-3 kinase (PI3K), mammalian target of rapamycin (mTOR), and protein kinase B (PKB or AKT) activation in TNBC. Supported by strong evidence in developmental, evolutionary, and cancer biology, we propose that the K-RAS/SIAH pathway activation is a major tumor driver, and SIAH is a new drug target, a therapy-responsive prognostic biomarker, and a major tumor vulnerability in TNBC. Since persistent K-RAS/SIAH/EGFR pathway activation endows TNBC tumor cells with chemo-resistance, aggressive dissemination, and early relapse, we hope to design an anti-SIAH-centered anti-K-RAS/EGFR targeted therapy as a novel therapeutic strategy to control and eradicate incurable TNBC in the future.

Pancreatic cancer is the fourth most common cause of cancer-related deaths with a dismal average five-year survival rate of six percent. Substitutional progress has been made in understanding how pancreatic cancer develops and progresses. Evidence is mounting which demonstrates that diet and nutrition are key factors in carcinogenesis. In particular, diets low in folate and high in fruits, vegetables, red/processed meat, and saturated fat have been identified as pancreatic cancer risk factors with a proposed mechanism involving epigenetic modifications or gene regulation. We review the current literature assessing the correlation between diet, epigenetics, and pancreatic cancer.

This retrospective cohort study was conducted to determine the prevalence of HCV infections among individuals incarcerated in a state prison system and identify potential contributing factors to HCV infection. North Dakota Department of Corrections and Rehabilitation (NDDOCR) data from 2009 to 2018 was used and period prevalence was calculated for this 10-year time period. The period prevalence of HCV infection was (15.13% (95% CI 14.39–15.90) with a marginally significant (p-value: 0.0542) increasing linear trend in annual prevalence over this period. Multivariate logistic regression analysis was used to identify risk factors associated with HCV infection. The main significant independent risk factors for HCV infection in this incarcerated population were age >40 years [OR: 1.78 (1.37–2.32)]; sex [OR: 1.21 (1.03–1.43)]; race/ethnicity [OR: 1.97 (1.69–2.29)]; history of intravenous drug use (IVDU) [OR: 7.36 (6.41–8.44)]; history of needle or syringe sharing [OR: 7.57 (6.62–8.67)]; and alcohol use [OR: 0.87 (0.77–0.99)]. Study limitations include uncollected information on sexual history, frequency or duration of injection drug use and blood transfusion history of the incarcerated population. Considering the high prevalence of HCV infection and its associated risk factors, it is important to implement prevention programs such as syringe/needle exchanges and counsel with imprisoned IVD users.

The methylome is subject to genetic and environmental effects. Their impact may depend on sex and age, resulting in sex- and age-related physiological variation and disease susceptibility. Here we estimate the total heritability of DNA methylation levels in whole blood and estimate the variance explained by common single nucleotide polymorphisms at 411,169 sites in 2,603 individuals from twin families, to establish a catalogue of between-individual variation in DNA methylation. Heritability estimates vary across the genome (mean=19%) and interaction analyses reveal thousands of sites with sex-specific heritability as well as sites where the environmental variance increases with age. Integration with previously published data illustrates the impact of genome and environment across the lifespan at methylation sites associated with metabolic traits, smoking and ageing. These findings demonstrate that our catalogue holds valuable information on locations in the genome where methylation variation between people may reflect disease-relevant environmental exposures or genetic variation. Differential impact of genetic and environmental influences on DNA methylation may result in sex- and age-related physiological variation and disease susceptibility. By analysing DNA methylome of 2,603 individuals from twin families, here, the authors establish a catalogue of between-individual variation in DNA methylation.

DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feature engineering techniques have been utilized to reduce data volume and make deep learning scalable. A comparative analysis of the proposed approach on Illumina 27K and 450K methylation data reveals that deep learning methodologies for cancer prediction can be coupled with feature selection models to enhance prediction accuracy. Prediction using 450K methylation markers can be accomplished in less than 13 s with an accuracy of 98.75%. Of the list of 685 genes in the feature selected 27K dataset, 578 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in five biological processes and one molecular function. Of the list of 1572 genes in the feature selected 450K data set, 1290 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in 95 biological processes and 17 molecular functions. Seven oncogene/tumor suppressor genes were common between the 27K and 450K feature selected gene sets. These genes were RTN4IP1, MYO18B, ANP32A, BRF1, SETBP1, NTRK1, and IGF2R. Our bioinformatics deep learning workflow, incorporating imputation and data balancing methods, is able to identify important methylation markers related to functionally important genes in breast cancer with high accuracy compared to deep learning or statistical models alone.

Biological clocks have been developed at different molecular levels and were found to be more advanced in the presence of somatic illness and mental disorders. However, it is unclear whether different biological clocks reflect similar aging processes and determinants. In ~3000 subjects, we examined whether five biological clocks (telomere length, epigenetic, transcriptomic, proteomic, and metabolomic clocks) were interrelated and associated to somatic and mental health determinants. Correlations between biological aging indicators were small (all r < 0.2), indicating little overlap. The most consistent associations of advanced biological aging were found for male sex, higher body mass index (BMI), metabolic syndrome, smoking, and depression. As compared to the individual clocks, a composite index of all five clocks showed most pronounced associations with health determinants. The large effect sizes of the composite index and the low correlation between biological aging indicators suggest that one’s biological age is best reflected by combining aging measures from multiple cellular levels.

Abstract Background Contact days are increasingly used as a measure of time toxicity, but current measurement approaches (eg, claims data) face limitations. To determine whether participant recall of contact days could serve as a practical approach, we evaluated contact day recall accuracy among patients, care partners, and clinicians across multiple recall windows. Methods This single-center cross-sectional survey study enrolled adults with cancer, their informal care partner, and their primary oncologist. Participants were asked to recall the number of patient contact days and associated care burden over the prior 7, 14, 30, and 60-days. Electronic medical record (EMR) data provided gold standard contact day data. The primary outcome was recall accuracy (within ±1, ±2, ±4, and ±8 days of EMR counts across windows). Results We enrolled 42 patients, 29 care partners, and 8 clinicians. Median EMR-derived contact days were 2, 3, 7, and 11 across the 7-, 14-, 30-, and 60-day periods. Accuracy was high for all groups at 7 and 14 days (≥75%) and declined with longer windows. Participant-estimated contact days were moderately correlated with participant-assessed patient burden for patients and clinicians. We did not observe a meaningful correlation between EMR-derived contact days and patient-reported burden. Conclusion A single-item patient (or care partner or clinician)-reported outcome of recalling contact days may be a practical and accurate method of determining recent contact days, particularly over shorter recall periods. The subjective recall of contact days may be more associated with burden than the exact objective contact day number.

Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an “atypical/energy-related” symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An ‘AES profile’ score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (β = 0.13, p = 1.35*10-12), isoleucine (β = 0.13, p = 1.45*10-10), very-low-density lipoproteins cholesterol (β = 0.11, p = 6.19*10-9) and saturated fatty acid levels (β = 0.09, p = 3.68*10-10), and lower high-density lipoproteins cholesterol (β = −0.07, p = 1.14*10-4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were internally replicated using data from the same subjects (N = 2015) collected at 6-year follow-up. We identified a specific metabolomic signature—commonly linked to cardiometabolic disorders—associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile identifies a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and may represent a valuable target for interventions aiming at reducing depression’s detrimental impact on health.

Intensive efforts have been made to identify features that could serve as biomarkers of aging. Yet, drug-based interventions aimed at lessening the detrimental effects of getting older are lacking. This is largely attributable to tissue-specificity, sex-related differences, and to the difficulty of identifying actionable targets, which continues to pose a significant challenge. Here, we implement a bioinformatics approach revealing that aging-associated increase of the transmembrane Ectodysplasin-A2-Receptor is a prominent tissue-independent alteration occurring in humans and other species, and is particularly pronounced in models of accelerated aging. We show that strengthening of the Ectodysplasin-A2-Receptor signalling axis in myogenic precursors and differentiated myotubes suffices to trigger potent parainflammatory responses, mirroring aspects of aging-driven sarcopenia. Intriguingly, obesity, insulin-resistance, and aging-related comorbidities, such as type-2-diabetes, result in heightened levels of the Ectodysplasin-A2 ligand. Our findings suggest that targeting the Ectodysplasin-A2 surface receptor represents a promising pharmacological strategy to mitigate the development of aging-associated phenotypes. In a broad cross-tissue analysis, the authors show that a receptor called EDA2R steadily increases with age in both humans and animal models, and becomes even more active in conditions like obesity and diabetes, intensifying inflammation-like processes in muscle cells.