Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems.

Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.

Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.

This paper discusses the current literature surrounding the potential use of artificial intelligence and machine learning models in the diagnosis of acute obvious and occult scaphoid fractures. Current studies have notable methodological flaws and are at high risk of bias, precluding meaningful comparisons with clinician performance (the current reference standard). Specific areas should be addressed in future studies to help advance the meaningful and clinical use of artificial intelligence for radiograph interpretation.

Differential Risk Profiles of Incident Abdominal Aortic Aneurysms in Specific Subgroups: A Large Cohort-based Study.

Objective: The aim of this study is to explore the risk profiles associated with Abdominal aortic aneurysm (AAA) incidence in both the general population and diverse subpopulations.

Summary Background Data: AAA is a life-threatening arterial disease, and there is limited understanding of its etiological spectrum across the age, sex, and genetic risk subgroups, making early prevention efforts more complicated.

Methods: This study encompassed a sample size of 364399 participants from the UK.

A global review of national guidelines of post-exposure prophylaxis for the prevention of HIV.

Introduction: The World Health Organization (WHO) recommends the use of antiretroviral drugs as post-exposure prophylaxis (PEP) for preventing HIV acquisition for occupational and non-occupational exposures. To inform the development of global WHO recommendations on PEP, we reviewed national guidelines of PEP for their recommendations.

Methods: Policies addressing PEP from 38 WHO HIV priority countries were obtained by searching governmental and non-governmental websites and consulting country and regional experts; these countries were selected based on HIV burden, new HIV acquisitions and the number of HIV-associated deaths.

Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.

Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.

Determinants of physical function, as measured using PROMIS PF-10a, in patients with rheumatoid arthritis: results from the international COVID-19 Vaccination in Autoimmune Diseases (COVAD) study.

Objectives: Physical function in RA is largely influenced by multiple clinical factors, however, there is a growing body of evidence that psychological state and other comorbidities also play an essential role. Using data obtained in the COVID-19 Vaccination in Autoimmune Diseases study, an international self-reported e-survey, we aimed to explore the predictive ability of sociodemographic and clinical variables on Patient-Reported Outcomes Measurement Information System Physical Function Short Form 10a (PROMIS PF-10a) in RA and to investigate variation in disease activity and functional outcomes based on country-level socio-economic parameters.

Methods: Patient demographics, disease characteristics including current symptom status, functional status and treatment variables, as well as income level of the country of residence, were extracted from survey responses.

Assessing time series correlation significance: A parametric approach with application to physiological signals.

Correlation coefficients play a pivotal role in quantifying linear relationships between random variables. Yet, their application to time series data is very challenging due to temporal dependencies. This paper introduces a novel approach to estimate the statistical significance of correlation coefficients in time series data, addressing the limitations of traditional methods based on the concept of effective degrees of freedom (or effective sample size, ESS).

Spirometric pattern and cardiovascular risk: a prospective study of 0.3 million Chinese never-smokers.

Background: Existing studies have not provided robust evidence about the CVD risk of non-smoking patients with restrictive spirometric pattern (RSP) or airflow obstruction (AFO), and how the risk is modified by body shape. We aimed to bridge the gap.

Methods: We used never-smokers' data from the China Kadoorie Biobank (CKB) and performed Cox models by sex (278,953 females and 50,845 males).

Therapeutic potential of short-chain fatty acids for acute lung injury: a systematic review and meta-analysis of preclinical animal studies.

Background: Short-chain fatty acids (SCFAs), derived from the fermentation of dietary fiber by intestinal commensal bacteria, have demonstrated protective effects against acute lung injury (ALI) in animal models. However, the findings have shown variability across different studies. It is necessary to conduct a comprehensive evaluation of the efficacy of these treatments and their consistency.

Dietary patterns related to triglyceride glucose index and risk of type 2 diabetes: a large-scale cohort study.

Background: Triglyceride glucose (TyG) index has been proposed as a credible and simple surrogate indicator for insulin resistance. The primary aim of this study was to novelly examine the associations between dietary patterns reflecting variations in circulating TyG index and the risk of type 2 diabetes mellitus (T2DM).

Methods: This study included 120,988 participants from the UK Biobank, all of whom completed multiple 24-h dietary assessments.

Circulating visfatin concentrations in patients with chronic obstructive pulmonary disease: systematic review and meta-analysis.

Objective: The current study was designed with the aim of conducting a systematic review and meta-analysis to determine the circulating levels of visfatin in patients with chronic obstructive pulmonary disease (COPD) compared to healthy individuals.

Methods: Until March 2024, we searched the Web of Science, PubMed/Medline, and Scopus databases. The analysis included case-control studies assessing the association between circulating visfatin and COPD.

The efficacy and safety of qiwei baizhu san in the treatment of type 2 diabetes mellitus: a systematic review and meta-analysis.

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia, mostly resulting from impaired insulin production and diminished glucose metabolism regulation. Qiwei Baizhu San (QWBZS) is a classic formula used in traditional Chinese medicine for the treatment of T2DM. A comprehensive analysis of the efficacy and safety of QWBZS in the treatment of T2DM is essential.

Efficacy and safety of anticoagulants on venous thromboembolism: a systematic review and network meta-analysis of randomized controlled trials.

Background: Anticoagulants are the primary means for the treatment and prevention of venous thromboembolism (VTE), but their clinical standardized application still remains controversial. The present study intends to comprehensively compare the efficacy and safety of various anticoagulants in VTE.

Methods: Medline, Embase, and Cochrane Library from their inception up to August 2023 were searched to compare the efficacy and safety of various anticoagulants in VTE.

Investigation of commercial cut-resistant gloves claiming graphene additive content.

Five commercially available cut-resistant gloves were sourced from four different worldwide manufacturers which were advertised to contain graphene. A method was developed to assess the fibers composing each glove, including dissolution of the constituent fibers using sulfuric acid or liquid paraffin at elevated temperature, to extract and analyze particle additives. Scanning electron microscopy with energy-dispersive X-ray spectroscopy was applied to fibers and extracted particles for morphological and elemental analysis; Raman spectroscopy was applied to discern the composition of carbonaceous materials for the ultimate purpose of identifying any graphenic additives.

Development of a Novel Clinical Classification for Radiation-induced Cystitis: The Portuguese Navy Radiation-induced Cystitis (PNRC) Scale.

Background And Objective: Radiation-induced cystitis (RIC) is an important consequence of pelvic radiotherapy that can cause high morbidity and, in extreme cases, mortality. The lack of a widely accepted classification system makes it difficult to compare treatment regimens. Our aim was to develop a new classification system covering the RIC spectrum to improve treatment comparisons and accurate incidence estimates for systematic use in clinical and research settings.

A Cross-sectional Survey of Physicians to Understand Biomarker Testing and Treatment Patterns in Patients with Prostate Cancer in the USA, EU5, Japan, and China.

Background And Objective: Treatment landscape in advanced prostate cancer (PC) is evolving. There is limited understanding of the factors influencing decision-making for genetic/genomic testing and the barriers to recommending testing and treatment in international real-world clinical practice following the approval of poly-adenosine diphosphate-ribose polymerase inhibitors (PARPi) for metastatic castration-resistant PC (mCRPC). This work aims to assess genetic/genomic testing patterns and methods, including for homologous recombination repair mutation (HRRm), and treatment decisions among physicians caring for patients with PC across the USA, Europe, and Asia.

Identify biological Alzheimer's disease using a novel nucleic acid-linked protein immunoassay.

Blood-based biomarkers have been revolutionizing the detection, diagnosis and screening of Alzheimer's disease. Specifically, phosphorylated-tau variants (p-tau, p-tau and p-tau) are promising biomarkers for identifying Alzheimer's disease pathology. Antibody-based assays such as single molecule arrays immunoassays are powerful tools to investigate pathological changes indicated by blood-based biomarkers and have been studied extensively in the Alzheimer's disease research field.

Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.

Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS.

A new conceptual model for seed germination and seedling tillering of winter wheat in the field.

Seed germination is a crucial stage in plant development, intricately regulated by various environmental stimuli. Understanding these interactions is essential for optimizing planting and seedling management but remains challenging due to the trade-off effects of environmental factors on the germination process. We proposed a new conceptual model by viewing seed germination as a dynamic process in a physiological dimension, with the influence of environmental factors and seed heterogeneity characterized by a germination speed and a dispersion coefficient.

Stokes flows in a two-dimensional bifurcation.

The flow network model is an established approach to approximate pressure-flow relationships in a bifurcating network, and has been widely used in many contexts. Existing models typically assume unidirectional flow and exploit Poiseuille's law, and thus neglect the impact of bifurcation geometry and finite-sized objects on the flow. We determine the impact of bifurcation geometry and objects by computing Stokes flows in a two-dimensional (2D) bifurcation using the Lightning-AAA Rational Stokes algorithm, a novel mesh-free algorithm for solving 2D Stokes flow problems utilizing an applied complex analysis approach based on rational approximation of the Goursat functions.

Total cell N-glycosylation is altered during differentiation of induced pluripotent stem cells to neural stem cells and is disturbed by trisomy 21.

Down syndrome (DS), a genetic condition caused by trisomy 21 (T21), manifests various neurological symptoms, including intellectual disability, early neurodegeneration, and early-onset dementia. N-glycosylation is a protein modification that plays a critical role in numerous neurobiological processes and whose dysregulation is associated with a range of neurological disorders. However, whether N-glycosylation of neural glycoproteins is affected in DS has not been studied.

Resistify: A Novel NLR Classifier That Reveals Helitron-Associated NLR Expansion in Solanaceae.

Nucleotide-binding domain leucine-rich repeat (NLR) proteins are a key component of the plant innate immune system. In plant genomes, NLRs exhibit considerable presence/absence variation and sequence diversity. Recent advances in sequencing technologies have made the generation of high-quality novel plant genome assemblies considerably more straightforward.