bluuebunny/crossref_metadata_2025_split huggingface dataset:
The relationship between low levels of albuminuria and cardiovascular mortality among apparently healthy adults
Authors: Claudel Sophie E , Waikar Sushrut S , Schmidt Insa M. , Vasan Ramachandran S. , Verma Ashish 
Published: December 2023

AbstractBackgroundAlbuminuria is associated with cardiovascular events among adults with underlying cardiovascular disease and diabetes, even at low levels of urinary albumin excretion. We hypothesized that low levels of albuminuria in the ‘normal’ range (urinary albumin-to-creatine ratio (UACR) <30 mg/g) are associated with cardiovascular death among apparently healthy adults.MethodsWe studied adults who participated in the 1999-2014 National Health and Nutrition Examination Survey. We excluded participants with baseline cardiovascular disease, hypertension, diabetes, estimated glomerular filtration rate (eGFR) <60ml/min/1.73m2, those who were currently pregnant, and those who had received dialysis in the last year. After excluding these conditions, only 5.0% of the remaining population had UACR ≥30 mg/g (N=873) and were excluded. The final sample size was 16,247. We assessed the relationship between UACR and cardiovascular and all-cause mortality using multivariable-adjusted Cox proportional hazards models. Models were adjusted for age, sex, race or ethnicity, smoking status, systolic blood pressure, hemoglobin A1c, total cholesterol, health insurance, food insecurity, serum albumin, body mass index, use of statins, and eGFR.ResultsMean age was 38.9 years (SD 13.6) and 53.7% were women. The median length of follow-up was 12.2 years. In multivariable-adjusted models, each doubling of UACR (within the <30 mg/g range) was associated with a 36% higher risk of cardiovascular death [HR 1.36 (95% confidence interval (CI) 1.11-1.65)] and a 28% higher risk of all-cause mortality [HR 1.28 (95%CI 1.17-1.41)]. The highest tertile of UACR (7.1-29.9 mg/g) was associated with an 87% higher risk of cardiovascular death [HR 1.87 (95%CI 1.20-2.92)] and 59% higher risk of all-cause mortality [HR 1.59 (95%CI 1.28-1.96)], compared with the lowest tertile (< 4.3 mg/g).ConclusionsIn a nationally representative sample of relatively healthy community-dwelling adults, higher levels of albuminuria in the conventionally “normal” range <30 mg/g in healthy individuals are associated with greater mortality. Overall, our findings contribute to the growing body of evidence on the existence of a risk gradient across all levels of albuminuria, even in the so-called normal range.
Detecting QT prolongation From a Single-lead ECG With Deep Learning
Authors: Alam Ridwan , Aguirre Aaron D. , Stultz Collin M. 
Published: December 2023

AbstractBackground and AimsFor a number of antiarrhythmics, drug loading requires a 3-day hospitalization with monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We aim to develop a deep learning model that infers QT intervals from ECG lead-I – the lead most often acquired from ambulatory ECG monitors – and we use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading.MethodsUsing 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs.ResultsQTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%.ConclusionsDrug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning. This research leads the path toward out-of-hospital care using wearable ECG devices for antiarrhythmic therapies.What’s New?Using only Lead-I ECG, a novel deep neural network, QTNet, can estimate the QT intervals that are similar to those generated from the 12-lead ECG by the clinical ECG machines, with a mean absolute error of 12ms and a Pearson correlation coefficient of 0.91.The same QTNet, when applied without any fine-tuning on an external population undergoing Dofetilide loading, can identify whether and when clinically critical QT prolongation occurs after the drug loading. In comparison to manual annotations of QT intervals by clinical experts, QTNet achieves 87% sensitivity and 77% specificity.QTNet is a novel regression model that can be used on Lead-I ECG streams, potentially from wearable devices at out-of-hospital settings, for health critical applications such as drug-induced QT prolongation tracking.
Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Healthcare
Authors: Lemyre Félix Camirand , Lévesque Simon , Domingue Marie-Pier , Herrmann Klaus , Ethier Jean-François 
Published: December 2023

AbstractData from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for healthcare research frameworks. This paper aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data; (2) describing the methods applicable to generalized linear models (GLM) and assessing their underlying distributional assumptions; (3) adapting existing methods to make them fully usable in healthcare research. A scoping review methodology was employed for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in healthcare research. From the review, 41 articles were selected, and six approaches were extracted for conducting standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information-sharing requirements and operational complexity.
A triple bottom line approach to antimicrobial stewardship: the effects of simplifying treatment
Authors: Balm Michelle , Bupha-Intr Olivia , Sinha Tanya , Kelly Matthew , Stewart Lucy , Stephen Ruth , Blackmore Tim , Bloomfield Max 
Published: December 2023

AbstractAimOur antimicrobial guidelines (AGs) were changed in 2021 to recommend once-daily ceftriaxone in place of three-times-daily cefuroxime as preferred cephalosporin. This analysis sought to assess the effects of this on incidence ofClostridioides difficileinfection (CDI), third-generation cephalosporin resistant Enterobacterales (3GCR-E), and resource utilisation.MethodBefore and after analysis of 30-day CDI and 3GCR-E incidence following receipt of cefuroxime/ceftriaxone pre- and post-AG change. Total nursing time and waste production relating to cefuroxime/ceftriaxone delivery were calculated pre- and post-change.ResultsCDI incidence was 0.6% pre- and 1.0% post-change (adjusted odds ratio [aOR] 1.44, p=0.07) and 3GCR-E incidence 3.5% and 3.1% (aOR 0.90, p=0.33). Mean per-quarter estimated nursing administration time decreased from 2065 to 1163 hours (902 nurse-hour reduction) and antibiotic-related waste generation from 1131kg to 748kg (383kg reduction). Overall days of therapy per-quarter of cefuroxime/ceftriaxone were unchanged between periods.ConclusionThis simplification of our AG from a three-times-daily to a once-daily antibiotic resulted in considerable savings for our hospital (roughly 1.7 full-time equivalent nurses and over a tonne of waste yearly), with no significant increases in CDI or 3GCR-E. The impact of dosing schedules on non-antibiotic-spectrum factors, such nursing time and resource usage, is worthy of consideration when designing AGs.
A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems
Authors: Jordan Daniel M. , Vy Ha My T. , Do Ron 
Published: December 2023

AbstractIt is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.
Challenges faced by Human Resources for Health in Morocco: a scoping review
Authors: Al Hassani Wafaa , Achhab El Youness , Nejjari Chakib 
Published: December 2023

AbstractBackgroundHuman resources for health (HRH) play a pivotal role in effective health system operation, yet various impediments challenge sustainable development. This scoping review aimed to explore these challenges and potential solutions in aligning the health workforce to meet the evolving healthcare needs of the Moroccan population.MethodsWe conducted a scoping review searching PubMed, Science Direct, Cairn and Google Scholar for relevant articles published between 2014 and 2023. Additionally, non-peer-reviewed literature sourced from Ministry of Health consultations and allied websites was included.ResultsAmong the nineteen studies meeting our inclusion criteria, the majority were cross-sectional and predominantly focused on challenges faced by nurses. While some papers delineated multiple HRH challenges (5/19), the rest addressed specific challenges. The identified challenges span organizational and personal levels. Organizationally, the focus was on training, lifelong learning, continuing education, health coverage and shortages, and job satisfaction. At a personal level, HRH in the public health sector encountered challenges such as burnout, stress, and broader occupational health concerns.ConclusionsThe reviewed publications underscored a spectrum of challenges necessitating robust policy interventions. Despite promising developments in the Moroccan healthcare system, addressing the unequal urban-rural HRH distribution, augmenting funding, and enhancing HRH quality of life stand as pivotal imperatives.
Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data
Authors: Li Yanming , Sardiu Mihaela , Koestler Devin C. , Yang Fengwei , Islam Md Tamzid , Komladzei Stephan , Akhter Murshalina 
Published: December 2023

AbstractBackgroundOvarian cancer (OC) is a significant gynecological malignancy characterized by its high mortality rate, poor long-term survival rate, and late-stage diagnosis. OC is the 5th leading cause of cancer death among woman and counts 2.1% of all cancer death. OC survival rates are much lower than other cancers that affect woman. Its 5-year survival rate is less than 50%. Only ∼17% of OC patients are diagnosed within the early stage. The majority are diagnosed at an advanced stage, making early detection and effective treatment critical challenges. Currently, the identified OC predictive genes are still very sparse, resulting in pool prognostic performance. There exists unmet needs to identify novel prognostic gene biomarkers for OC occurrence, survival, and clinical stages to promote the likelihood of survival and to perform optimal treatments or therapeutic strategies at the earliest stage possible.MethodsPrevious RNAseq analysis on OC focused on detecting differentially expressed (DE) genes only. Many genes, although having weak marginal differential effects, may still exude strong predictive effects on disease outcomes though regulating other DE genes. In this work, we employed a new machine learning method, netLDA, to detect such predictive coregulating genes with weak marginal DE effects for predicting OC occurrence, 5-year survival, and clinical stage. The netLDA detects predictive gene networks (PGN) containing strong DE genes as hub genes and detects coregulating weak genes within the PGNs. The network structures of the detected PGNs along with the strong and weak genes therein are then used in outcome prediction on test datasets.ResultsWe identified different sets of signature genes for OC occurrence, survival, and clinical stage. Previously identified prognostic genes, such asEPCAM, UBE2C, CHD1L, TP53,CD24,WFDC2, andFANCI,were confirmed. We also identified novel predictive coregulating weak genes includingGIGYF2, GNPAT, RAD54L, andELL.Many of the detected predictive gene networks and coregulating weak genes therein overlapped with OC-related biological pathways such as KEGGtight junction, ribosome, andcell cyclepathways. The detection and incorporation of the gene networks and weak genes significantly improved the prediction performance. Cellular mapping of selected feature genes using single-cell RNAseq data further revealed the heterogeneous expression distributions of the signature genes on different cell types.ConclusionsWe established a transcriptomic gene network profile for OC prediction. The novel genes detected provide new targets for early diagnostics and new drug development for OC.
In-hospital Outcomes of Acute Ischemic Stroke in Atrial Fibrillation Patients on Anticoagulation: A Retrospective Analysis
Authors: Vasquez Moises A , Lambrakos Litsa K. , Velasquez Alex , Goldberger Jeffrey J. , Mitrani Raul D. 
Published: December 2023

ABSTRACTBackgroundLong-term anticoagulation (AC) therapy reduces the risk of stroke in patients with Atrial Fibrillation (AF). Data on the impact of AC on in-hospital stroke outcomes is lacking.MethodsThe National Inpatient Sample was used to identify adult inpatients with AF and a primary diagnosis of ischemic stroke between 2016 and 2020. Data was stratified between patients on AC users and nonusers. A multivariate regression model was used to describe the in-hospital outcomes, adjusting for significant comorbidities.ResultsA total of 655,540 patients with AF and a diagnosis of ischemic stroke were included, of which 194,560 (29.7%) were on long-term AC. Patients on AC tended to be younger (mean age, 77 vs 78), had a higher average CHA2DS2VASc score (4.48 vs 4.20), higher rates of hypertension (91% vs 88%), hyperlipidemia (64% vs 59%), and heart failure (34% vs 30%) compared to patients not on long-term AC. Use of AC was associated with decreased in-hospital mortality (aOR [95% CI]: 0.62 [0.60 - 0.63]), decreased stroke severity (mean NIHSS, 8 vs 10), decreased use of tPA (aOR 0.42 [0.41 - 0.43]), mechanical thrombectomy (aOR 0.85 [0.83 - 0.87]), hemorrhagic conversion (aOR 0.69 [0.67 - 0.70]), gastrointestinal bleeding (aOR 0.74 [0.70 - 0.77]), and discharge to skilled nursing facilities (aOR 0.90 [0.89 - 0.91]), compared to patients not on AC (P<0.001 for all comparisons).ConclusionAmong patients with AF admitted with acute ischemic stroke, AC use prior to stroke was associated with decreased in-hospital mortality, decreased stroke severity, decreased discharge to SNF, and fewer stroke-related and bleeding complications.
Biomimetic Lipid-polymer composite membrane model to study Permeability through Cornea
Authors: Chakraborty Ruchira , Avinash Chavan Tejas , Misra Manju , Kumar Prasoon 
Published: December 2023

AbstractThe excised animal cornea is the gold standard for testing and evaluating drug permeability through a cornea. However, it has a concise shelf life and encounters ethical concerns. Also, ex-vivo models, which are incomplete replicas of the human cornea, may provide faulty results in the pre-clinical studies. To circumvent these problems, we have proposed an in-vitro biomimetic lipid-polymer composite membrane (BLCM) model as an artificial cornea to study drug permeability. We designed and fabricated a free-standing, electro-spun polystyrene (PS) nanofibrous membrane and impregnated its pores with phosphatidylcholine (PC). SEM, FTIR, and goniometer characterized the BLCM. Permeation data of the drug ganciclovir through the BLCM model in a Franz-diffusion cell corroborates with the excised goat corneal system. Also, owing to the simple and scalable fabrication method, BLCM can be used as an alternative to animal models for initial drug permeability screening and studies and accelerate drug development.
Growing out of the fins: implications of isometric and allometric scaling of morphology relative to increasing mass in blue sharks (Prionace glauca)
Authors: Seamone Scott G. , Sternes Phillip C. , McCaffrey Theresa M. , Tsao Natalie K. , Syme Douglas A. 
Published: December 2023

ABSTRACTDisproportional changes (i.e. allometry) in shark morphology have been attributed to shifts in function associated with niche shifts in life history, such as in habitat and diet. Photographs of blue sharks (Prionace glauca,26-145 kg) were used to analyze changes in parameters of body and fin morphology with increasing mass that are fundamental to swimming and feeding. We hypothesized that blue sharks would demonstrate proportional changes (i.e. isometry) in morphology with increasing mass because they do not undergo profound changes in prey and habitat type, but as a result, we predicted that blue sharks would grow into bodies with greater turning inertias and smaller frontal and surface areas, in addition to smaller spans and areas of the fins relative to mass. Many aspects of morphology increased with isometry. However, blue sharks demonstrated negative allometry in body density, whereas surface area, volume and roll inertia of the body, area, span and aspect ratio of both dorsal fins, span and aspect ratio of the ventral caudal fin, and span, length and area of the mouth increased with positive allometry. The dataset was divided in half based on mass to form two groups: smaller and larger sharks. Besides area of both dorsal fins, relative to mass, larger sharks had bodies with significantly greater turning inertia and smaller frontal and surface areas, in addition to fins with smaller spans and areas, compared to smaller sharks. Hence, isometric scaling does not necessarily imply functional similarity, and allometric scaling may sometimes be critical in maintaining, rather than shifting, function relative to mass. Both allometric and isometric changes in blue sharks are predicted to promote reduced costs of transport in migration, but conversely, decreased unsteady performance, such as in escape responses. These changes are likely beneficial for larger sharks that probably experience reductions in predation pressure.
Bio-contaminated Plastic Micropipette Tip Sterilization Stations: Environmentally, Economically, and Energetically Viable Solution
Authors: Veyssi Arian , Samineni Laxmicharan , Mohanty Rashmi P 
Published: December 2023

ABSTRACTBioscientific research laboratories significantly contribute to global plastic waste production through their widespread use of plastic products, such as single-use micropipette tips. However, biologically contaminated pipette tips must undergo several washing and sterilization steps before being reused or recycled. Grenova Solutions provides such a decontamination station called TipNovus, which has been implemented by academic and government research labs to reuse pipette tips in sensitive biological assays. Despite this success, the high initial purchasing cost of these washing stations deter many laboratories from incorporating it into their workflow. Additionally, researchers are reluctant to reuse pipette tips due to concerns that the washing process may not thoroughly remove all contaminants. To mitigate these concerns, considering the University of Texas at Austin as an example, we performed a cost-benefit analysis of employing a university-wide washing station. We estimated that only single-time reuse of the pipette tips could result in a 100% return on investment from the equipment purchase cost within 3 months. Then, with our pilot experiments, we confirmed the TipNovus washing steps to be 100% efficient in sterilizing pipette tips contaminated with T7 bacteriophage, enabling their reuse in bacteriophage functionality assays. Finally, we proposed an alternative and more convenient autoclave-based sterilization method to decontaminate pipette tips.Abstract FigureSYNOPSISSingle-use plastic micropipette tips discarded by bioresearch labs generate substantial waste. This study reports adopting standardized tip-washing and reuse systems in labs greatly reduces plastic waste and research expenditures.