Search Results (238)

Background/Objectives: To investigate the incidence of new-onset diabetic retinopathy (DR) in individuals with pre-existing type 2 diabetes (T2D) up to 3 years post SARS-CoV-2 infection. Methods: This retrospective study consisted of 5151 COVID-19 and 5151 propensity-matched non-COVID-19 patients with T2D in the Montefiore Health System between 1 March 2020 and 17 January 2023. The primary outcome was new-onset DR at least 2 months after the index date up to 17 January 2023. Matching for index date between groups was also used to ensure the same follow-up duration. Hazard ratios (HRs) were computed, adjusted for competing risks. Results: T2D patients with COVID-19 had a higher cumulative incidence of DR than T2D patients. The unadjusted HR for COVID-19 status for developing new DR was 2.44 [1.60, 3.73], p < 0.001. The adjusted HR was 1.70 [1.08, 2.70], p < 0.05, and the adjusted HR for prior insulin use was 3.28 [2.10, 5.12], p < 0.001. Sex, ethnicity, and major comorbidities had no significant association with outcome. Conclusions: T2D patients who contracted COVID-19 exhibited a significantly higher risk of developing DR within three years post infection compared to propensity-matched controls. The increased incidence was primarily driven by greater pre-existing insulin usage and SARS-CoV-2 infection in the COVID-19 positive cohort. Full article

Sickle cell disease (SCD) is a prevalent genetic disorder caused by a mutation in the beta-globin gene. Hyperhemolysis (HS) is a severe complication involving the rapid destruction of both transfused and endogenous red blood cells, commonly found in SCD. This literature review explores the clinical presentation, diagnosis, pathogenesis, and management of HS in SCD. HS can manifest acutely or in a delayed manner, complicating diagnosis due to overlapping symptoms and varying reticulocyte responses. Immunohematological assessments often reveal delayed positivity in direct antiglobulin tests and antibody screens. HS typically presents severe anemia, jaundice, hemoglobinuria, and hemodynamic instability. Diagnostic markers include elevated bilirubin and lactate dehydrogenase levels alongside a reduced reticulocyte count. The management of HS is primarily empirical, with no clinical trials to support standardized treatment protocols. First-line treatments involve steroids and intravenous immunoglobulins (IVIG), which modulate immune responses and mitigate hemolysis. Refractory cases may require additional agents such as rituximab, eculizumab, tocilizumab, and, in some instances, plasma exchange or erythropoietin-stimulating agents. Novel therapeutic approaches, including bortezomib and Hemopure, have shown promise but require further investigation. Current management strategies are empirical, underscoring the need for robust clinical trials to establish effective treatment protocols that ultimately improve outcomes for SCD patients experiencing HS. Full article

Endothelial dysfunction appears early in type 1 diabetes (T1D). The detection of the first vascular disturbances in T1D patients is crucial, and the introduction of novel techniques, such as flow-mediated skin fluorescence (FMSF) and adaptive optics retinal camera (Rtx) imaging, gives hope for better detection and prevention of angiopathies in the future. In this study, we aimed to investigate microcirculation disturbances in pediatric patients with T1D with the use of FMSF and Rtx imaging. This research focused especially on the relationship between microvascular parameters obtained in FMSF and Rtx measurements, and the glycemic control evaluated in continuous glucose monitoring (CGM) reports. We observed significantly increased wall thickness (WT) and wall-to-lumen ratio (WLR) values in T1D patients in comparison to the control group. Although we did not observe significant differences between the T1D and control groups in the FMSF results, a trend toward significance between the time in range (TIR) and hyperemic response (HR_max) and an interesting correlation between the carotid intima-media thickness (cIMT_max) and HR_max. were observed. In conclusion, FMSF and Rtx measurments are innovative techniques enabling the detection of early microvascular disturbances. Full article

Background: Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy with a high mortality and poor prognosis. To elucidate the genetic underpinnings of ACCs, we have analyzed the transcriptome profiles of ACC tumor samples from patients enrolled in the TCGA and NCI cohorts. Methods: We developed a bimodal approach using Gaussian Mixture Models to identify genes with bimodal distribution in ACC samples. Among the 72 bimodally expressed genes that are used to stratify patients into prognostic groups, we focused on SEMA7A, as it encodes a glycosylphosphatidylinositol-anchored membrane glycoprotein (Semaphorin 7a) regulating integrin-mediated signaling, cell migration and immune responses. Results: Our findings reveal that high expression levels of SEMA7A gene are associated with poor prognosis (hazard ratio = 4.27; p-value < 0.001). In hormone-producing ACCs, SEMA7A expression is elevated and positively correlated with genes driving steroidogenesis, aldosterone and cortisol synthesis, including CYP17A1, CYP11A1, INHA, DLK1, NR5A1 and MC2R. Correlation analyses show that SEMA7A is co-expressed with the integrin-β1, FAK (focal adhesion kinase) and MAPK/ERK (mitogen-activated protein kinase/extracellular signal regulated kinases) signaling pathways. Immunohistochemistry (IHC) staining demonstrates the feasibility of evaluating SEMA7A in ACC tissues and shows a significant correlation between gene expression (RNA-Seq) and protein expression (IHC). Conclusions: These findings suggest SEMA7A as a candidate for further research in ACC biology and a candidate for cancer therapy, as well as a potential prognosis biomarker for ACC patients. Full article

Background/Objectives: Pathological factors are integral in the risk stratification and management of localized prostate cancer. In recent years, there has been an upsurge of studies that uncovered novel approaches and have refined prognostic factors for prostate cancer in needle biopsy and radical prostatectomy (RP) specimens. Methods: We conducted a review of literature and summarized the significant recent updates on pathological factors for localized prostate cancer. Results: Innovative factors derived from the traditional Gleason grading, such as the extent of Gleason pattern 4 and presence of cribriform pattern are now recognized to significantly improve discrimination of outcome. The components and rules of Gleason grading themselves underwent modifications, and the subsequent prognostic grouping of the different grades (Grade group) have resulted in enhanced stratification of behavior more meaningful in management decision. The approaches for grade reporting in systematic or targeted needle biopsies and in RP with multifocal cancers are also being optimized. Newer tumor growth pattern-based factors such as intraductal carcinoma and atypical intraductal proliferation can have ramifications in management, especially in the background of low to intermediate risk prostate cancers. Gleason grade considerations in the different post-treatment settings and for de novo and residual prostate cancers with varying treatment effects have also been explicated. Likewise, the application of more traditional factors in tumor extent and perineural invasion in biopsy, or positive surgical margin in RP, have also evolved. Conclusions: Some of these newer pathological factors are now officially recommended in standardized pathology reporting protocols and are applied in the management decision for localized prostate cancer. Full article

Calpains, calcium-dependent cytosolic cysteine proteases, perform controlled proteolysis of their substrates for various cellular and physiological activities. In different cancers, missense mutations accumulate in the genes coding for the calpain cleavage sites in various calpain substrates termed as the calpain cleavage-related mutations (CCRMs). However, the impact of such CCRMs on the calpain–substrate interaction is yet to be explored. This study focuses on the interaction of wild-type and mutant β-integrins with calpain-1 and 2 in uterine corpus endometrial carcinoma (UCEC). A total of 48 calpain substrates with 176 CCRMs were retrieved from different datasets and shortlisted on the basis of their involvement in cancer pathways. Finally, three calpain substrates, ITGB1, ITGB3, and ITGB7, were selected to assess the structural changes due to CCRMs. These CCRMs were observed towards the C-terminal of the cytoplasmic domain within the calpain cleavage site. The wild-type and mutant proteins were docked with calpain-1 and 2, followed by molecular simulation. The interaction between mutant substrates and calpains showcased variations compared to their respective wild-type counterparts. This may be attributed to mutations in the calpain cleavage sites, highlighting the importance of the cytoplasmic domain of β-integrins in the interactions with calpains and subsequent cellular signaling. Highlights: 1. Calpain cleavage-related mutations (CCRMs) can alter cellular signaling. 2. CCRMs impact the structure of C-domains of human integrin-β subunits. 3. Altered structure influences the cleavability of human integrin-β subunits by human calpains. 4. Altered cleavability impacts the cell signaling mediated through calpain–integrin-β axis. 5. Presence of CCRMS may influence the progression of uterine corpus endometrial carcinoma (UCEC). Full article

The term “big data analytics (BDA)” defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems. Full article

Background/Objectives: The COVID-19 pandemic overwhelmed many health care facilities with patients, leading to an increased risk of potential transmission. Though the disease process was identical, the triaging system was unique at different sites, without a unified system for emergency department triaging globally. Proper implementation of pre-screening and triaging is of paramount importance in tertiary care settings to prevent nosocomial spread of infection. Methods: Each country has its own triage guidelines and Infection, Prevention, and Control policies developed by its health ministry and may face significant challenges in implementing them. Triage guidelines followed by two tertiary care hospitals in Detroit, United States of America and Manipal, India are compared during the early phases of the COVID-19 pandemic. Results: This paper offers a unique perspective of the challenges experienced with the hospital triage practices and provides solutions to address them. The future trajectory of COVID-19 epidemiology in both countries will be determined by the adherence to best practices in Infection Prevention and Control and triage protocols. The healthcare facility triage algorithm is constantly evolving in both settings as new evidence is being added to hospital epidemiology and infection prevention practices. Conclusions: Training healthcare workers on new triage protocols is required. It is critical for infectious disease doctors, clinical microbiologists, hospital epidemiologists, and Infection Prevention and Control (IPC) staff to collaborate with clinicians, nurses, and other ancillary staff in order to successfully implement the triage protocols. Developing and modifying guidelines for cleaning hospital triage areas and providing high throughput for patient care are also important lessons learned. Usage of face shields and the quality of Personal Protective Equipment (PPE) should be ensured for all healthcare workers (HCWs). Resilient staff and resilient hospital infrastructure are crucial for a sustainable response to future pandemics. Full article

There is now a vast body of literature on ARMA and VARMA models with time-dependent or time-varying coefficients. A large part of it is based on local stationary processes using time rescaling and assumptions of regularity with respect to time. A recent paper has presented an alternative asymptotic theory for the parameter estimators based on several distinct assumptions that seem difficult to verify at first look, especially for time-dependent VARMA or tdVARMA models. The purpose of the present paper is to detail several examples that illustrate the verification of the assumptions in that theory. These assumptions bear on the moments of the errors, the existence of the information matrix, but also how the coefficients of the pure moving average representation of the derivatives of the residuals (with respect to the parameters and evaluated at their true value) behave. We will do that analytically for two bivariate first-order models, an autoregressive model, and a moving average model, before sketching a generalization to higher-order models. We also show simulation results for these two models illustrating the analytical results. As a consequence, not only the assumptions can be checked but the simulations show how well the small sample behavior of the estimators agrees with the theory. Full article

This study investigates magnetohydrodynamic (MHD) heat and mass transport in a water-based ternary hybrid nanofluid flowing past an exponentially accelerated vertical porous plate. Two critical scenarios are analyzed: (i) uniform heat flux with variable mass diffusion and (ii) varying heat source with constant species diffusion. The model integrates thermal radiation, heat sink/source, thermal diffusion, and chemical reaction effects to assess flow stability and thermal performance. Governing equations are non-dimensionalized and solved analytically using the Laplace transform method, with results validated against published data and finite difference method outcomes. Ternary hybrid nanofluids exhibit a significantly higher Nusselt number compared to hybrid and conventional nanofluids, demonstrating superior heat transfer capabilities. Magnetic field intensity reduces fluid velocity, while porosity enhances momentum transfer. Thermal radiation amplifies temperature profiles, critical for energy systems. Concentration boundary layer thickness decreases with higher chemical reaction rates, optimizing species diffusion. These findings contribute to the development of advanced thermal management systems, such as solar energy collectors and nuclear reactors, enhance energy-efficient industrial processes, and support biomedical technologies that require precise heat and mass control. This study positions ternary hybrid nanofluids as a transformative solution for optimizing high-performance thermal systems. Full article

Conventional Applicant Tracking Systems (ATSs) encounter considerable constraints in accurately aligning resumes with job descriptions (JD), especially in handling unstructured data and intricate qualifications. We provide Resume2Vec, an innovative method that utilizes transformer-based deep learning models, including encoders (BERT, RoBERTa, and DistilBERT) and decoders (GPT, Gemini, and Llama), to create embeddings for resumes and job descriptions, employing cosine similarity for evaluation. Our methodology integrates quantitative analysis via embedding-based evaluation with qualitative human assessment across several professional fields. Experimental findings indicate that Resume2Vec outperformed conventional ATS systems, achieving enhancements of up to 15.85% in Normalized Discounted Cumulative Gain (nDCG) and 15.94% in Ranked Biased Overlap (RBO) scores, especially within the mechanical engineering and health and fitness domains. Although conventional the ATS exhibited slightly superior nDCG scores in operations management and software testing, Resume2Vec consistently displayed a more robust alignment with human preferences across the majority of domains, as indicated by the RBO metrics. This research demonstrates that Resume2Vec is a powerful and scalable method for matching resumes to job descriptions, effectively overcoming the shortcomings of traditional systems, while preserving a high alignment with human evaluation criteria. The results indicate considerable promise for transformer-based methodologies in enhancing recruiting technology, facilitating more efficient and precise candidate selection procedures. Full article

Myelodysplastic syndromes (MDSs), either primary or secondary, are a heterogeneous group of clonal hematological neoplasms characterized by bone marrow dyshematopoiesis, peripheral blood cytopenia, and the potential risk of acute myeloid leukemia (AML) transformation. The clinical heterogeneity in MDS is a reflection of the underlying multitude of genetic defects playing a role in the pathogenesis. Recent advances in the clinicopathological, immunophenotypic, and molecular landscape in understanding the pathophysiology of MDS lead to evolving and refined classification systems with newer entities. Evolving MDS therapies will target the disease’s core mechanisms, allowing for personalized treatment based on individual patient’s genes and leading to better outcomes. This review provides an overview of MDS pathogenesis to enhance comprehension of its various subgroups. Additionally, we examine the updated classification systems of the World Health Organization (WHO) and the International Consensus Classification (ICC) pertaining to MDS, along with relevant therapeutic approaches. Full article

Several authors have attempted to compute the asymptotic Fisher information matrix for a univariate or multivariate time-series model to check for its identifiability. This has the form of a contour integral of a matrix of rational functions. A recent paper has proposed a short Wolfram Mathematica notebook for VARMAX models that makes use of symbolic integration. It cannot be used in open-source symbolic computation software like GNU Octave and GNU Maxima. It was based on symbolic integration but the integrand lacked symmetry characteristics in the appearance of polynomial roots smaller or greater than 1 in modulus. A more symmetric form of the integrand is proposed for VARMA models that first allows a simpler approach to symbolic integration. Second, the computation of the integral through Cauchy residues is also possible. Third, an old numerical algorithm by Söderström is used symbolically. These three approaches are investigated and compared on a pair of examples, not only for the Wolfram Language in Mathematica but also for GNU Octave and GNU Maxima. As a consequence, there are now sufficient conditions for exact model identifiability with fast procedures. Full article

The global outbreak of coronavirus disease 2019 (COVID-19) has posed a severe threat to public health and caused widespread socioeconomic disruptions in the past several years. While the pandemic has subsided, it is essential to explore effective disease surveillance tools to aid in controlling future pandemics. Several studies have proposed methods to capture the epidemic trend and forecast new daily confirmed cases. In this study, we propose the use of exponentially weighted moving average (EWMA) control charts integrated with time series models to monitor the number of daily new confirmed cases of COVID-19. The conventional EWMA control chart directly monitors the number of daily new confirmed cases. The proposed methods, however, monitor the residuals of time series models fitted to these data. In this study, two time series models—the auto-regressive integrated moving average (ARIMA) model and the vector auto-regressive moving average (VARMA) model—are considered. The results are compared with those of the conventional EWMA control chart using three datasets from India, Malaysia, and Thailand. The findings demonstrate that the proposed method can detect disease outbreak signals earlier than conventional control charts. Full article

Our understanding of stellar evolution and nucleosynthesis is limited by the uncertainties coming from the complex multi-dimensional processes in stellar interiors, such as convection and nuclear burning. Three-dimensional stellar models can improve this knowledge by studying multi-D processes, but only for a short time range (minutes or hours). Recent advances in computing resources have enabled 3D stellar models to reproduce longer time scales and include nuclear reactions, making the simulations more accurate and allowing to study explicit nucleosynthesis. Here, we present results from 3D stellar simulations of a convective neon-burning shell from a 20 M_⊙ star, run with an explicit nuclear network from its early development to complete fuel exhaustion. We show that convection halts when fuel is exhausted, stopping its further growth after the entrainment of fresh material. These results, which highlight the differences and similarities between 1D and multi-D stellar models, have important implications for the evolution of convective regions in stars and their nucleosynthesis. Full article