Search Results (66)

Background/Objectives: Aneurysmal subarachnoid hemorrhage (aSAH) is a medical emergency with a high mortality rate requiring urgent treatment. This study aimed to identify clinical predictors of inpatient mortality and poor postoperative course after aSAH surgical clipping. Methods: We performed a retrospective review of medical records for 210 patients with aSAH treated via surgical clipping at our institution between 2010 and 2019. Baseline demographic data and clinical characteristics related to aSAH were collected. To identify factors associated with inpatient mortality and a poor postoperative course after aSAH microsurgical clipping, we conducted a univariate and bivariate analysis, as well as a multivariate analysis via the Poisson regression model. Results: The overall cumulative mortality over the 10-year study period was 11.43%. A severe WFNS scale score (aRR: 2.86; 95% CI: 1.28–6.39; p = 0.011) and having 1 (aRR: 5.76; 95% CI: 2.02–16.39, p = 0.001) or ≥2 (aRR: 18.86; 95% CI: 5.16–68.90, p < 0.001) postoperative neurosurgical complications were associated with an increased risk of inpatient mortality. A moderate (aRR: 3.71; 95% CI: 1.45–9.50; p = 0.006) or severe (aRR: 4.18; 95% CI: 1.12–15.60; p = 0.034) Glasgow scale score on admission, and presenting 1 (aRR: 2.31; 95% CI: 1.27–4.19; p = 0.006) or ≥2 postoperative clinical complications (aRR: 3.34; 95% CI: 1.83–6.10; p < 0.001) were associated with an increased risk of a poor postoperative course. Conclusions: While promising and widely supported by the published literature, these findings require further validation in a larger prospective and multi-centered study to adequately propose health policies on neurointensive care for the Peruvian population. Ultimately, developing socioeconomic setting-focused intervention algorithms and clinical practice guidelines could enhance the survival and postoperative course of patients presenting with aSAH. Full article

Aging is the primary risk factor for chronic diseases such as cardiovascular disease, cancer, and dementia. However, chronological age alone fails to capture individual variability in aging trajectories and disease susceptibility. Recent advances in epigenetic clocks—DNA methylation-based models that estimate biological age—have opened new possibilities for personalized and preventive medicine. This review explores the clinical potential of epigenetic clocks and EpiScores, composite biomarkers that predict health risks and physiological status. We present a comparative evaluation of widely used epigenetic clocks, including Horvath, GrimAge, PhenoAge, and DunedinPACE, and summarize their predictive performance for mortality, cognitive decline, and cardiovascular outcomes. EpiScores linked to inflammation, glycemic control, and immunosenescence are highlighted as tools for stratified risk assessment. When integrated with multi-omics data and electronic health records, these measures enhance the precision of population health management. Special emphasis is placed on applications in longevity clinics and anti-aging clinics, community-based care, and national health checkup systems. We also explore global standardization efforts and ethical considerations, as well as Japan’s unique initiatives—including the “Aging Measurement” project at the Osaka-Kansai Expo 2025. Furthermore, we propose the development of a Global Health and Aging Index that integrates the biological, functional, and subjective dimensions of aging, aligned with the WHO concept of Intrinsic Capacity. In conclusion, epigenetic clocks and EpiScores represent transformative tools for shifting from reactive treatment to proactive health optimization, and from chronological to biological metrics in aging science and public health policy. Full article

Introduction: Comorbidities affect diagnosis and treatments in cancer patients. This study explores the prevalence and patterns of comorbidities in non-small cell lung cancer (NSCLC) patients and their association with survival. Materials and Methods: This retrospective population-based cohort study included 1674 incident NSCLC patients. Comorbidities were classified based on the ICD-9-CM system, with 13 disease categories analyzed. Patients with more than two comorbidities were classified into three mutually exclusive and exhaustive latent classes (Latent Class Analysis [LCA]). The optimal number of latent classes was determined by applying the Akaike Information Criterion. Cox regression models were run to assess overall and cancer-specific mortality, adjusting for the comorbidity groups, sex, age, and stage at diagnosis. Results: In 1674 NSCLC patients, the most prevalent medical conditions were respiratory (35.8%) and cardiovascular (33.5%). The Cox regression showed that even one comorbidity is associated with an increased hazard of overall mortality (HR = 1.33, 95%CI: 1.11–1.59, p = 0.002). LCA-derived Class-1 (cardiovascular-respiratory and endocrine) reported HR = 1.74 (95%CI: 1.39–2.17, p < 0.001), Class-2 (multi-organ) HR = 1.44 (95%CI: 1.18–1.77, p < 0.001), and Class-3 (socio-multifactorial-neuro) HR = 1.62 (95%CI: 1.36–1.93, p < 0.001). Instead, in patients with one comorbidity, NSCLC-specific mortality showed no significant trend towards increased risk (HR = 1.17, 95%CI: 1.00–1.43, p = 0.114). Significant associations emerged between NSCLC-specific mortality and LCA-classes: Class-1: HR = 1.49 (95%CI: 1.20–1.91, p = 0.001); Class-2 HR = 1.25 (95%CI: 1.0–1.57 p = 0.048); and Class-3: HR = 1.23 (95%CI: 1.00–1.48, p = 0.035). Conclusions: The adverse impact of comorbidities on NSCLC-specific mortality requires their inclusion as risk factors in cancer treatment and prognosis. Full article

Background/Objectives: Hypertensive disorders of pregnancy are a leading cause of pregnancy-related deaths in the United States, accounting for 7% of maternal mortality. Labetalol and nifedipine are the first-line drugs for the management of hypertension in pregnancy, but there are little data guiding the choice of one drug over the other. The current pilot longitudinal study aims to characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of labetalol stereoisomers throughout pregnancy and postpartum. Methods: This is a single-center clinical study recruiting up to 40 pregnant individuals ≥ 18 years of age at the time of enrollment, taking labetalol as per the standard of care. The exclusion criteria include any pathophysiology impacting the PK of labetalol, e.g., liver failure. Maternal plasma, urine, amniotic fluid, cord blood, and breast milk will be collected, and labetalol stereoisomers will be measured using a validated LC-MS/MS assay. Heart rate and blood pressure will be measured as the PD endpoints. These may be assessed throughout a participant’s dosing interval at scheduled PK study visits, which will occur every 6–10 weeks during pregnancy, at delivery, during the 1st week postpartum, and up to 20 weeks postpartum. The primary aim is to characterize the PK and PD of labetalol during pregnancy and in the postpartum period. The secondary aim is to determine the extent of breast milk excretion of and infant exposure to labetalol from breast milk. The data will be analyzed using population PK modeling to evaluate the PK/PD relationship and ultimately develop trimester-specific dosing recommendations. Results/Conclusions: To our knowledge, this is the first study aiming to characterize the PK of labetalol stereoisomers across pregnancy and postpartum, utilizing individual stereoisomer data to evaluate the PK/PD relationship, and collecting postpartum samples, including breast milk, to model infant exposure to labetalol through breast milk. This study will provide important PK/PD data and knowledge which will be combined with large multi-center clinical trial data to develop trimester-specific dosing regimens for anti-hypertensive agents. Full article

Prostate cancer is a leading cause of cancer-related morbidity and mortality worldwide, with diagnostic challenges magnified in underrepresented regions like sub-Saharan Africa. This study introduces a novel application of ConvNeXt, an advanced convolutional neural network architecture, for multi-class classification of prostate histopathological images into normal, benign, and malignant categories. The dataset, sourced from a tertiary healthcare institution in Nigeria, represents a typically underserved African population, addressing critical disparities in global diagnostic research. We also used the ProstateX dataset (2017) from The Cancer Imaging Archive (TCIA) to validate our result. A comprehensive pipeline was developed, leveraging advanced data augmentation, Grad-CAM for interpretability, and an ablation study to enhance model optimization and robustness. The ConvNeXt model achieved an accuracy of 98%, surpassing the performance of traditional CNNs (ResNet50, 93%; EfficientNet, 94%; DenseNet, 92%) and transformer-based models (ViT, 88%; CaiT, 86%; Swin Transformer, 95%; RegNet, 94%). Also, using the ProstateX dataset, the ConvNeXt model recorded 87.2%, 85.7%, 86.4%, and 0.92 as accuracy, recall, F1 score, and AUC, respectively, as validation results. Its hybrid architecture combines the strengths of CNNs and transformers, enabling superior feature extraction. Grad-CAM visualizations further enhance explainability, bridging the gap between computational predictions and clinical trust. Ablation studies demonstrated the contributions of data augmentation, optimizer selection, and learning rate tuning to model performance, highlighting its robustness and adaptability for deployment in low-resource settings. This study advances equitable health care by addressing the lack of regional representation in diagnostic datasets and employing a clinically aligned three-class classification approach. Combining high performance, interpretability, and scalability, this work establishes a foundation for future research on diverse and underrepresented populations, fostering global inclusivity in cancer diagnostics. Full article

Prostate cancer (PCa) is currently the second most prevalent cancer among men. Accurate diagnosis of PCa can provide effective treatment for patients and reduce mortality. Previous works have merely focused on either lesion detection or lesion classification of PCa from magnetic resonance imaging (MRI). In this work we focus on a critical, yet underexplored task of the PCa clinical workflow: distinguishing cases with cancer presence (pathologically confirmed PCa patients) from conditions with no suspicious PCa findings (no cancer presence). To this end, we conduct large-scale experiments for this task for the first time by adopting and processing the multi-centric ProstateNET Imaging Archive which contains more than 6 million image representations of PCa from more than 11,000 PCa cases, representing the largest collection of PCa MR images. Bi-parametric MR (bpMRI) images of 4504 patients alongside their clinical variables are used for training, while the architectures are evaluated on two hold-out test sets of 975 retrospective and 435 prospective patients. Our proposed multi-encoder-cross-attention-fusion architecture achieved a promising area under the receiver operating characteristic curve (AUC) of 0.91. This demonstrates our method’s capability of fusing complex bi-parametric imaging modalities and enhancing model robustness, paving the way towards the clinical adoption of deep learning models for accurately determining the presence of PCa across patient populations. Full article

Background: Prader–Willi Syndrome (PWS) is a rare, genetic, multi-systemic disorder. Its main characteristics are muscular hypotonia, behavioral problems, intellectual disability, endocrine deficiencies, hyperphagia, and a high risk of morbid obesity and related comorbidities. This study aimed to investigate the rate of comorbidity, prescription of endocrine medications, and mortality in individuals with PWS compared to the general population. Methods: The association between PWS and outcomes were investigated in a matched cohort study of individuals born in the period of 1930–2018 with data from Swedish national health and welfare registers. Each individual was matched with 50 non-PWS comparisons. The associations between PWS, outcomes and prescribed endocrine medications were estimated through Cox proportional hazard models, presented as Hazard Ratios (HR) with 95% Confidence Intervals (CIs). Results: Among 360 individuals (53% men) with PWS, 16% had diabetes mellitus, 6% heart failure, 4% vein thrombosis, 2% atrial fibrillation, 2% coronary heart disease, and 1% pulmonary embolism. Individuals with PWS had an increased rate of heart failure (HR: 23.85; 95% CI: 14.09–40.38), diabetes mellitus (HR: 17.49; 95% CI: 12.87–23.74), vein thrombosis (HR: 10.44; 95% CI: 5.69–19.13), pulmonary embolism (HR: 5.77; 95% CI: 2.27–14.67), atrial fibrillation (HR: 5.19; 95% CI: 2.48–10.86), and coronary heart disease (HR: 3.46; 95% CI: 1.50–7.97) compared to non-PWS individuals. Somatotropin was prescribed in 63%, antidiabetics in 18%, and thyroid hormones in 16% of the PWS individuals (<1%, 2%, and 3%, respectively, in non-PWS individuals). The rate of mortality was fifteen times higher in PWS than in non-PWS, with a mean age at death of 42 years. Conclusions: The rates of diabetes mellitus and cardiovascular comorbidities were higher in individuals with PWS. As expected, the prescription of somatotropin was high, but the endocrine prescription pattern also reflected the high prevalence of diabetes mellitus and thyroid illness. Although the mean age at death was older than previously reported, a higher awareness and intensified efforts to avoid obesity, as well as the prevention and early treatment of cardiovascular and endocrine comorbidity, are crucial aims in the care of people with PWS. Full article

Background/Objectives: Respiratory illnesses frequently lead to hospitalization in adults aged 60 and older, especially due to respiratory viral infectious (RVI). This study investigates hospitalization patterns and characteristics of RVI at HM Hospitals from October 2023 to March 2024; Methods: We retrospectively explored hospitalizations of patients aged 60 years and older with RVIs, gathering data on demographics, clinical profiles, comorbidities, and treatments. Outcomes included hospitalization, ICU admissions, and mortality, and independent factors associated with outcomes were identified using a multi-state model; Results: From October 2023 to March 2024, from a total of 3258 hospitalizations, 1933 (59.3%) were identified as positive for RVIs. Overall, SARS-CoV-2 was the most prevalent (52.6%), followed by influenza (32.7%), and RSV (11.8%). Most RVI involved single infections (88.2%). Hospitalization rates increased with age for SARS-CoV-2 (333.4 [95% CI: 295.0–375.2] to 651.6 [95% CI: 532.1–788.4]), influenza (169.8 [95% CI: 142.6–200.7] to 518.6 [95% CI: 412.1–643.1]), and RSV (69.2 [95% CI: 52.2–90.0] to 246.0 [95% CI: 173.8–337.5]), with SARS-CoV-2 showing the highest rate, followed by influenza and RSV. In the multi-state model, RSV infection significantly increased ICU admission risk (HR: 2.1, 95%, p = 0.037). Age on admission (HR: 1.1, 95%, p < 0.001) and Charlson score (HR: 1.4, 95%, p = 0.001) were associated with transitioning from admission to death. ICU to death risks included age at admission (HR: 1.7, 95%, p < 0.001); Conclusions: RVI in adults 60 years and older are associated with high hospitalization and mortality rates, primarily driven by influenza and SARS-CoV-2, followed by RSV. Age and comorbidities significantly impact disease severity, emphasizing the need for targeted prevention and management strategies for RSV in this vulnerable population. Full article

The objective of this research is to evaluate four distinct models for multi-population mortality projection in order to ascertain the most effective approach for forecasting the impact of the COVID-19 pandemic on mortality. Utilizing data from the Human Mortality Database for five countries—Finland, Germany, Italy, the Netherlands, and the United States—the study identifies the generalized additive model (GAM) within the age–period–cohort (APC) analytical framework as the most promising for precise mortality forecasts. Consequently, this model serves as the basis for projecting the impact of the COVID-19 pandemic on future mortality rates. By examining various pandemic scenarios, ranging from mild to severe, the study concludes that projections assuming a diminishing impact of the pandemic over time are most consistent, especially for middle-aged and elderly populations. Projections derived from the superior GAM-APC model offer guidance for strategic planning and decision-making within sectors facing the challenges posed by extreme historical mortality events and uncertain future mortality trajectories. Full article

Background/Objectives: Multi-organ failure (MOF) often complicates advanced heart failure (HF), contributing to a poor prognosis. The Model of End-Stage Liver Disease 3.0 (MELD-3.0) scale incorporates liver and kidney function parameters. This study aims to evaluate the prognostic significance of the MELD-3.0 score in patients with advanced HF who have undergone heart transplantation (HTx). Methods: The MELD-3.0 score was computed using the average values of the international normalized ratio and bilirubin, creatinine, sodium, and albumin levels during a hospital stay following HTx. The average MELD-3.0 scores from the period of 1 month preceding HTx and 1 week after HTx were analyzed. The primary endpoint of the study was the 6-month total mortality, and the secondary endpoint was ICU hospitalization time after HTx. Results: The analysis included 106 patients undergoing HTx, with a median age of 53 years (44–63), 81% of whom were male. Within 6 months post-HTx, 17 patients (16%) died; those patients had a higher 1-week post-HTx MELD-3.0 score of 18.3 (14.5–22.7) in comparison to survivors, whose average score was 13.9 (9.5–16.4), p < 0.01. There was no difference in MELD 3.0 score in the pre-HTx period: 16.6 (11.4–17.8) vs. 12.3 (8.6–17.1), p = 0.36. The post-HTx MELD-3.0 score independently predicted death: RR 1.17 (95% CI 1.05–1.30), p < 0.01. A Receiver Operating Characteristic (ROC) determined the cut-off value of the MELD-3.0 score as 17.3 (AUC = 0.83; sensitivity—67%; specificity—86%). Survivors with scores above this value had a longer ICU hospitalization time: 7 (5.0–11.0) vs. 12 (8–20) days (p = 0.01). Conclusions: The post-HTx MELD-3.0 score serves as an independent predictor of an unfavorable prognosis in patients with advanced HF undergoing HTx. The evaluation of MELD-3.0 scores provides additional prognostic information in this population. Full article

The population of the Venafro Valley (Southern Italy) faces various type of air pollution problems (industrial facilities, traffic, and biomass combustion). To estimate exposure to various pollution sources, a multi-stage random forest model was used, integrating particulate matter (PM) data with satellite observations, land-use patterns, and meteorological information generating maps of PM_2.5 concentration. Four distinct PM_2.5 exposure categories were established using the quartile method. To assess the association between PM_2.5 and cause-specific mortality and morbidity, a time-dependent and sex-specific Cox multiple regression analysis was conducted, adjusting for age classes. In addition, the hazard ratios were accompanied by a probability measure of the strength of the evidence toward a hypothesis of health risk associated with the exposure under study (1−p value). The whole cohort was exposed to PM_2.5 annual levels exceeding the 5 µg/m³ limit recommended by the World Health Organization. Mortality excesses were observed in class 3 for both sexes for cardiac heart diseases. Excesses of cardiovascular diseases were observed for both sexes in class 3 and 4. The study highlights significant signals warranting mitigation actions, which regional authorities are currently considering. Full article

Preeclampsia (PE) is a major cause of maternal and neonatal morbidity and mortality worldwide, with the placenta playing a central role in disease pathophysiology. This review synthesizes recent advancements in understanding the molecular mechanisms underlying PE, focusing on placental genes, proteins, and genetic variants identified through multi-omic approaches. Transcriptomic studies in bulk placental tissue have identified many dysregulated genes in the PE placenta, including the PE signature gene, Fms-like tyrosine kinase 1 (FLT1). Emerging single-cell level transcriptomic data have revealed key cell types and molecular signatures implicated in placental dysfunction and PE. However, the considerable variability among studies underscores the need for standardized methodologies and larger sample sizes to enhance the reproducibility of results. Proteomic profiling of PE placentas has identified numerous PE-associated proteins, offering insights into potential biomarkers and pathways implicated in PE pathogenesis. Despite significant progress, challenges such as inconsistencies in study findings and lack of validation persist. Recent fetal genome-wide association studies have identified multiple genetic loci associated with PE, with ongoing efforts to elucidate their impact on placental gene expression and function. Future directions include the integration of multi-omic data, validation of findings in diverse PE populations and clinical subtypes, and the development of analytical approaches and experimental models to study the complex interplay of placental and maternal factors in PE etiology. These insights hold promise for improving risk prediction, diagnosis, and management of PE, ultimately reducing its burden on maternal and neonatal health. Full article

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution’s robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings. Full article

Liver disease-related mortality is a major cause of death worldwide. Hepatic innate and adaptive immune cells play diverse roles in liver homeostasis and disease. Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells. MDSCs can be broadly divided into monocytic MDSCs and polymorphonuclear or granulocytic MDSCs, and they functionally interact with both liver parenchymal and nonparenchymal cells, such as hepatocytes and regulatory T cells, to impact liver disease progression. The infiltration and activation of MDSCs in liver disease can be regulated by inflammatory chemokines and cytokines, tumor-associated fibroblasts, epigenetic regulation factors, and gut microbiota during liver injury and cancer. Given the pivotal roles of MDSCs in advanced liver diseases, they can be targeted to treat primary and metastatic liver cancer, liver generation, alcoholic and nonalcoholic liver disease, and autoimmune hepatitis. Currently, several treatments such as the antioxidant and anti-inflammatory agent berberine are under preclinical and clinical investigation to evaluate their therapeutic efficacy on liver disease and their effect on MDSC infiltration and function. Phenotypic alteration of MDSCs in different liver diseases that are in a model-dependent manner and lack special markers for distinct MDSCs are challenges for targeting MDSCs to treat liver disease. Multi-omics study is an option to uncover the features of disease-specific MDSCs and potential gene or protein targets for liver disease treatment. In summary, MDSCs play important roles in the pathogenesis and progression of liver disease by regulating both intrahepatic innate and adaptive immune responses. Full article

In recent multi-population stochastic mortality models, one critical scientific issue is the vague distinction between trend risk and population basis risk. In particular, the cross- and auto-correlations between the innovations of the latent factors representing the common trend and the population-specific trends are often assumed to be non-existent, although they are possibly statistically significant. While it is theoretically possible to capture such correlations by treating the latent factors as a vector time series, the resulting model would contain a large number of parameters, which may in turn lead to robustness problems. In this paper, we address these issues by the use of the product–ratio model. Contrary to the prevalent assumption of non-existent correlations, the latent factors under the product–ratio model are approximately uncorrelated. This permits us to disentangle trend risk and population basis risk, thereby sparing us from the need to use a heavily parameterized vector time-series process. Compared to the augmented common factor model, our approach demonstrates improved robustness in terms of correlation structures and hedging performance, offering a new perspective on treating cross- and auto-correlations between latent factors in mortality modeling. Full article