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Faith-based organizations are a powerful resource for addressing health issues within the African American community. In this paper, we describe two projects led by volunteer faith-based leaders and community residents who collaborated with public agencies to design programs to combat obesity. The first project, the Southeast Raleigh Community Garden Project, involved the construction of a community garden and the delivery of educational programs on healthy eating among youth. The second one, Project FACT (Faith-based groups Addressing health issues through Community outreach Together in the community), implemented church-based nutrition education programs and community walking programs led by church volunteers. The purpose of the two faith-based health promotion projects was to increase opportunities for physical activity and to encourage consumption of more fruits and vegetables. These projects resulted in the construction of a walking trail with the community garden as its destination, implementation of education programs to increase nutritional awareness, the initiation of multiple walking programs, and policy changes within the church to encourage a healthier lifestyle. The projects demonstrated the feasibility of collaboration among faith-based organizations, community residents and government agencies to promote physical activity and healthful eating among families by creating physical changes in the environment and implementing educational and walking programs in low income communities. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The objective of this study was to determine the prevalence, incidence, and risk factors for occupational infection with tuberculosis among healthcare workers employed in correctional facilities. METHODS: The authors conducted a self-administered survey, clinical interview, and tuberculin skin testing. RESULTS: The overall tuberculin skin test point prevalence rate was 17.7%, the reactivity rate was 2.2%, and the annual incidence was 1.3%. At the multivariate level, after controlling for bacille Calmette-Guérin vaccination, only origin of birth remained significantly associated with prevalence of tuberculosis infection. CONCLUSIONS: Although the prevalence of tuberculin reactivity was high in this population, the risk factors were predominantly demographic rather than occupational. Nevertheless, continued vigilance to control occupational exposure to this and other respiratory pathogens is warranted, given the potential for future outbreaks of tuberculosis, as well as other known and emerging airborne pathogens. [ABSTRACT FROM AUTHOR]

The overlapping molecular pathophysiology of Alzheimer's Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal Dementia (FTD) was analyzed using relationships from a knowledge graph of 33+ million biomedical journal articles. The unsupervised learning rank aggregation algorithm from SemNet 2.0 compared the most important amino acid, peptide, and protein (AAPP) nodes connected to AD, ALS, or FTD. FTD shared 99.9% of its nodes with ALS and AD; AD shared 64.2% of its nodes with FTD and ALS; and ALS shared 68.3% of its nodes with AD and FTD. The results were validated and mapped to functional biological processes using supervised human supervision and an external large language model. The overall percentages of mapped intersecting biological processes were as follows: inflammation and immune response, 19%; synapse and neurotransmission, 19%; cell cycle, 15%; protein aggregation, 12%; membrane regulation, 11%; stress response and regulation, 9%; and gene regulation, 4%. Once normalized for node count, biological mappings for cell cycle regulation and stress response were more prominent in the intersection of AD and FTD. Protein aggregation, gene regulation, and energetics were more prominent in the intersection of ALS and FTD. Synapse and neurotransmission, membrane regulation, and inflammation and immune response were greater at the intersection of AD and ALS. Given the extensive molecular pathophysiology overlap, small differences in regulation, genetic, or environmental factors likely shape the underlying expressed disease phenotype. The results help prioritize testable hypotheses for future clinical or experimental research.

This paper describes the work-related information collected in several important U.S. national health and behavior surveys, to highlight data gaps that prevent identifying responses by vulnerable workers in the gig economy, with emphasis on the growing digital platform sector of the work force. The national information systems used to understand health status and health behaviors, including drug use, rely on outdated census categories for self-employed workers. This paper describes the importance of understanding the needs of this growing part of the labor sector and describes how some of the most well-known and utilized national surveys fail to meet this need. For the agencies conducting national health and behavior surveys, we propose revisions to the categories used to classify type of worker and recommend adoption of a new Worker-Employer Relationship Classification model., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: We aimed to describe the occupational pattern of opioid overdose deaths in Maryland between 2018 and 2022 and determine the occupations at higher risk of opioid overdose death., Methods: The sample included undetermined or unintentional opioid overdose deaths among those aged 16 years or older in Maryland, drawn from the State Unintentional Drug Overdose Reporting System. We calculated population-based incidence overdose rates by occupation, stratified by sex and race. We further calculated the incidence rate ratios (IRRs) comparing each occupation with all other groups combined and estimated the IRRs among males versus females and non-Hispanic whites versus other racial/ethnic groups., Results: The pooled sample included 11 455 opioid overdose decedents (72% male and 55% non-Hispanic whites) of whom 80% were employed. The three occupation groups with the highest incidence rates were 'construction and extraction', 'transportation and material moving' and 'installation/maintenance and repair' with 291, 137 and 133 deaths per 100 000 workers in these respective occupational groups. Incidence rates were significantly higher in males than females in all categories except those 'Not in Labour Force' (IRR=0.51, p<0.001). Non-Hispanic whites relative to other racial/ethnic groups had a lower incidence of opioid overdose death in 'Military-Specific' occupations (IRR=0.53, p=0.031)., Conclusion: Opioid overdose deaths vary by type of occupation and certain occupations are at higher risk of overdose death. The findings highlight the need for priority setting in the implementation and expansion of existing strategies to target the workers most impacted by opioid overdose., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Neuropeptide Y (NPY) plays a crucial role in controlling energy homeostasis and feeding behaviour. The role of NPY neurons located in the arcuate nucleus of the hypothalamus (Arc) in responding to homeostatic signals has been the focus of much investigation, but most studies have used AgRP promoter-driven models, which do not fully encompass Arc NPY neurons. To directly investigate NPY-expressing versus AgRP-expressing Arc neurons function, we utilised chemogenetic techniques in NPY-Cre and AgRP-Cre animals to activate Arc NPY or AgRP neurons in the presence of food and food-related stimuli. Our findings suggest that chemogenetic activation of the broader population of Arc NPY neurons, including AgRP-positive and AgRP-negative NPY neurons, has equivalent effects on feeding behaviour as activation of Arc AgRP neurons. Our results demonstrate that these Arc NPY neurons respond specifically to caloric signals and do not respond to non-caloric signals, in line with what has been observed in AgRP neurons. Activating Arc NPY neurons significantly increases food consumption and influences macronutrient selection to prefer fat intake., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Insulin is transported across the blood-brain barrier (BBB) endothelium to regulate aspects of metabolism and cognition. Brain insulin resistance often results from high-fat diet (HFD) consumption and is thought to contribute to spatial cognition deficits. To target BBB insulin function, we used Cre-LoxP genetic excision of the insulin receptor (InsR) from endothelial cells in adult male mice. We hypothesized that this excision would impair spatial cognition, and that high-fat diet consumption would exacerbate these effects. Excision of the endothelial InsR did not impair performance in two spatial cognition tasks, the Y-Maze and Morris Water Maze, in tests held both before and after 14 weeks of access to high-fat (or chow control) diet. The HFD increased body weight gain and induced glucose intolerance but did not impair spatial cognition. Endothelial InsR excision tended to increase body weight and reduce sensitivity to peripheral insulin, but these metabolic effects were not associated with impairments to spatial cognition and did not interact with HFD exposure. Instead, all mice showed intact spatial cognitive performance regardless of whether they had been fed chow or a HFD, and whether the InsR had been excised or not. Overall, the results indicate that loss of the endothelial InsR does not impact spatial cognition, which is in line with pharmacological evidence that other mechanisms at the BBB facilitate insulin transport and allow it to exert its pro-cognitive effects., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: Opioid-related overdose deaths (OROD) increase annually, yet little is known about workplace risk factors. This study assessed differences in OROD rates across industry and occupation in Maryland, in addition to demographic differences within industry and occupation., Methods: The 2018 State Unintentional Drug Overdose Reporting System was used to compare OROD between industries and occupations., Results: The leading industries in OROD included the following: construction, manufacturing, and transportation and warehousing. Occupational groups were similar: construction and extraction, production, and transportation and material moving. There were also differences by sex (greater rates in men), age (greater rates in older workers), and race/ethnicity (varied patterns in rates)., Conclusions: Employers and state leaders should work collaboratively to target prevention and intervention for workplaces at highest risk for OROD. Construction was highest and needs supports that respond to the workplace culture., Competing Interests: Conflict of interest: None declared., (Copyright © 2024 American College of Occupational and Environmental Medicine.)

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study's goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains-DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NFκB factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/β-catenin pathway, the renin-angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein-protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments.

Environmental health (EH) services in the United States lag behind other areas of public health and health care with respect to information system interoperability and data sharing. This is partly due to an absence of well-defined use cases, the lack of direct economic drivers and resources to improve, the multiple jurisdictional elements that govern EH services across the United States, and no central organization to drive modernization of EH data. We summarize the status of EH information systems; argue for greater interoperability, including use cases for a messaging standard for environmental inspections; and present recommendations to better align EH services and data modernization efforts currently underway in other areas of public health., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

Background : Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods : The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results : An interpretable decision tree classified the risk of infection as either "high risk" or "low risk" in pediatric ALL ( n = 580) and AML ( n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions : The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

Background: Amyloid-β plaques (Aβ) are associated with Alzheimer's disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research., Objective: The mean threshold of Aβ reduction necessary to achieve cognitive improvement was measured via pooled assessment ( n  = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aβ via reduction of beta-secretase cleaving enzyme (BACE)., Methods: Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies., Results: K-means clustering identified 4 clusters that primarily corresponded with level of Aβ: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% "medium reduction" of Aβ or 50% "high reduction" of Aβ compared to untreated control. A 25% Aβ reduction achieved a 28% cognitive improvement, and a 50% Aβ reduction resulted in a significant 32% improvement compared to untreated transgenic mice ( p  < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice ( p  < 0.05). BACE reduction had a lesser impact on the ratio of Aβ 42 to Aβ 40 . Supervised learning with an 80% -20% train-test split confirmed Aβ reduction was a key feature for predicting MWM escape latency (R 2  = 0.8 to 0.95)., Conclusions: Results suggest a 25% reduction in Aβ as a meaningful treatment threshold for improving transgenic AD mouse cognition., Competing Interests: The authors have no conflict of interest to report., (© 2024 – The authors. Published by IOS Press.)

Impaired motivational drive is a key feature of depression. Chronic stress is a known antecedent to the development of depression in humans and depressive-like states in animals. Whilst there is a clear relationship between stress and motivational drive, the mechanisms underpinning this association remain unclear. One hypothesis is that the endocrine system, via corticotropin-releasing hormone (CRH) in the paraventricular nucleus of the hypothalamus (PVN; PVN CRH ), initiates a hormonal cascade resulting in glucocorticoid release, and that excessive glucocorticoids change brain circuit function to produce depression-related symptoms. Another mostly unexplored hypothesis is that the direct activity of PVN CRH neurons and their input to other stress- and reward-related brain regions drives these behaviors. To further understand the direct involvement of PVN CRH neurons in motivation, we used optogenetic stimulation to activate these neurons 1 h/day for 5 consecutive days and showed increased acute stress-related behaviors and long-lasting deficits in the motivational drive for sucrose. This was associated with increased Fos-protein expression in the lateral hypothalamus (LH). Direct stimulation of the PVN CRH inputs in the LH produced a similar pattern of effects on sucrose motivation. Together, these data suggest that PVN CRH neuronal activity may be directly responsible for changes in motivational drive and that these behavioral changes may, in part, be driven by PVN CRH synaptic projections to the LH., (© 2024. The Author(s).)

The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework to conduct data augmentation in a cross-modal dataset by translating between modalities and oversampling imputations of missing data. DACMVA was inspired by previous work on the alignment of latent spaces in Autoencoders. DACMVA is a DL data augmentation pipeline that improves the performance in a downstream prediction task. The unique DACMVA framework leverages a cross-modal loss to improve the imputation quality and employs training strategies to enable regularized latent spaces. Oversampling of augmented data is integrated into the prediction training. It is empirically demonstrated that the new DACMVA framework is effective in the often-neglected scenario of DL training on tabular data with continuous labels. Specifically, DACMVA is applied towards cancer survival prediction on tabular gene expression data where there is a portion of missing data in a given modality. DACMVA significantly ( p << 0.001, one-sided Wilcoxon signed-rank test) outperformed the non-augmented baseline and competing augmentation methods with varying percentages of missing data (4%, 90%, 95% missing). As such, DACMVA provides significant performance improvements, even in very-low-data regimes, over existing state-of-the-art methods, including TDImpute and oversampling alone., Competing Interests: Conflicts of Interest: The authors declare no conflicts of interest.

Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking.

Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin-angiotensin-aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.

Chronic myeloid leukemia (CML) is treated with tyrosine kinase inhibitors (TKI) that target the pathological BCR-ABL1 fusion oncogene. The objective of this statistical meta-analysis was to assess the prevalence of other hematological adverse events (AEs) that occur during or after predominantly first-line treatment with TKIs. Data from seventy peer-reviewed, published studies were included in the analysis. Hematological AEs were assessed as a function of TKI drug type (dasatinib, imatinib, bosutinib, nilotinib) and CML phase (chronic, accelerated, blast). AE prevalence aggregated across all severities and phases was significantly different between each TKI ( p < 0.05) for anemia-dasatinib (54.5%), bosutinib (44.0%), imatinib (32.8%), nilotinib (11.2%); neutropenia-dasatinib (51.2%), imatinib (29.8%), bosutinib (14.1%), nilotinib (14.1%); thrombocytopenia-dasatinib (62.2%), imatinib (30.4%), bosutinib (35.3%), nilotinib (22.3%). AE prevalence aggregated across all severities and TKIs was significantly ( p < 0.05) different between CML phases for anemia-chronic (28.4%), accelerated (66.9%), blast (55.8%); neutropenia-chronic (26.7%), accelerated (63.8%), blast (36.4%); thrombocytopenia-chronic (33.3%), accelerated (65.6%), blast (37.9%). An odds ratio (OR) with 95% confidence interval was used to compare hematological AE prevalence of each TKI compared to the most common first-line TKI therapy, imatinib. For anemia, dasatinib OR = 1.65, [1.51, 1.83]; bosutinib OR = 1.34, [1.16, 1.54]; nilotinib OR = 0.34, [0.30, 0.39]. For neutropenia, dasatinib OR = 1.72, [1.53, 1.92]; bosutinib OR = 0.47, [0.38, 0.58]; nilotinib OR = 0.47, [0.42, 0.54]. For thrombocytopenia, dasatinib OR = 2.04, [1.82, 2.30]; bosutinib OR = 1.16, [0.97, 1.39]; nilotinib OR = 0.73, [0.65, 0.82]. Nilotinib had the greatest fraction of severe (grade 3/4) hematological AEs (30%). In conclusion, the overall prevalence of hematological AEs by TKI type was: dasatinib > bosutinib > imatinib > nilotinib. Study limitations include inability to normalize for dosage and treatment duration.

This work presents SeizFt-a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

Parkinson's disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa-carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease.

Seizure detection using machine learning is a critical problem for the timely intervention and management of epilepsy. We propose SeizFt, a robust seizure detection framework using EEG from a wearable device. It uses features paired with an ensemble of trees, thus enabling further interpretation of the model's results. The efficacy of the underlying augmentation and class-balancing strategy is also demonstrated. This study was performed for the Seizure Detection Challenge 2023, an ICASSP Grand Challenge.

The Event Based Model (EBM) is a probabilistic generative model to explore biomarker changes occurring as a disease progresses. Disease progression is hypothesized to occur through a sequence of biomarker dysregulation "events". The EBM estimates the biomarker dysregulation event sequence. It computes the data likelihood for a given dysregulation sequence, and subsequently evaluates the posterior distribution on the dysregulation sequence. Since the posterior distribution is intractable, Markov Chain Monte-Carlo is employed to generate samples under the posterior distribution. However, the set of possible sequences increases as N ! where N is the number of biomarkers (data dimension) and quickly becomes prohibitively large for effective sampling via MCMC. This work proposes the "scaled EBM" (sEBM) to enable event based modeling on large biomarker sets (e.g. high-dimensional data). First, sEBM implicitly selects a subset of biomarkers useful for modeling disease progression and infers the event sequence only for that subset. Second, sEBM clusters biomarkers with similar positions in the event sequence and only orders the "clusters", with each successive cluster corresponding to the next stage in disease progression. These two modifications used to construct the sEBM method provably reduces the possible space of event sequences by multiple orders of magnitude. The novel modifications are supported by theory and experiments on synthetic and real clinical data provides validation for sEBM to work in higher dimensional settings. Results on synthetic data with known ground truth shows that sEBM outperforms previous EBM variants as data dimensions increase. sEBM was successfully implemented with up to 300 biomarkers, which is a 6-fold increase over previous EBM applications. A real-world clinical application of sEBM is performed using 119 neuroimaging markers from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data to stratify subjects into 6 stages of disease progression. Subjects included cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD). sEBM stage is differentiated for the 3 groups ( χ 2 p - v a l u e < 4.6 e - 32 ) . Increased sEBM stage is a strong predictor of conversion risk to AD ( p - v a l u e < 2.3 e - 14 ) for MCI subjects, as verified with a Cox proportional-hazards model adjusted for age, sex, education and APOE4 status. Like EBM, sEBM does not rely on apriori defined diagnostic labels and only uses cross-sectional data.

Alzheimer's disease (AD) progresses through a lengthy asymptomatic period during which pathological changes accumulate prior to development of clinical symptoms. As disease-modifying treatments are developed, tools to stratify risk of clinical disease will be required to guide their use. In this study, we examine the relationship of AD biomarkers in healthy middle-aged individuals to health history, family history, and neuropsychological measures and identify cerebrospinal fluid (CSF) biomarkers to stratify risk of progression from asymptomatic to symptomatic AD. CSF from cognitively normal (CN) individuals (N=1149) in the Emory Healthy Brain Study were assayed for Aβ 42 , total Tau (tTau), and phospho181-Tau (pTau), and a subset of 134 cognitively normal, but biomarker-positive, individuals were identified with asymptomatic AD (AsymAD) based on a locally-determined cutoff value for ratio of tTau to Aβ 42 . These AsymAD cases were matched for demographic features with 134 biomarker-negative controls (CN/BM-) and compared for differences in medical comorbidities and family history. Dyslipidemia emerged as a distinguishing feature between AsymAD and CN/BM-groups with significant association with personal and family history of dyslipidemia. A weaker relationship was seen with diabetes, but there was no association with hypertension. Examination of the full cohort by median regression revealed a significant relationship of CSF Aβ 42 (but not tTau or pTau) with dyslipidemia and diabetes. On neuropsychological tests, CSF Aβ 42 was not correlated with performance on any measures, but tTau and pTau were strongly correlated with visuospatial perception and visual episodic memory. In addition to traditional CSF AD biomarkers, a panel of AD biomarker peptides derived from integrating brain and CSF proteomes were evaluated using machine learning strategies to identify a set of 8 peptides that accurately classified CN/BM- and symptomatic AD CSF samples with AUC of 0.982. Using these 8 peptides in a low dimensional t-distributed Stochastic Neighbor Embedding analysis and k-Nearest Neighbor (k=5) algorithm, AsymAD cases were stratified into "Control-like" and "AD-like" subgroups based on their proximity to CN/BM- or AD CSF profiles. Independent analysis of these cases using a Joint Mutual Information algorithm selected a set of 5 peptides with 81% accuracy in stratifying cases into AD-like and Control-like subgroups. Performance of both sets of peptides was evaluated and validated in an independent data set from the Alzheimer's Disease Neuroimaging Initiative. Based on our findings, we conclude that there is an important role of lipid metabolism in asymptomatic stages of AD. Visuospatial perception and visual episodic memory may be more sensitive than language-based abilities to earliest stages of cognitive decline in AD. Finally, candidate CSF peptides show promise as next generation biomarkers for predicting progression from asymptomatic to symptomatic stages of AD.

Introduction: Amyotrophic Lateral Sclerosis (ALS) is a paralyzing, multifactorial neurodegenerative disease with limited therapeutics and no known cure. The study goal was to determine which pathophysiological treatment targets appear most beneficial., Methods: A big data approach was used to analyze high copy SOD1 G93A experimental data. The secondary data set comprised 227 published studies and 4,296 data points. Treatments were classified by pathophysiological target: apoptosis, axonal transport, cellular chemistry, energetics, neuron excitability, inflammation, oxidative stress, proteomics, or systemic function. Outcome assessment modalities included onset delay, health status (rotarod performance, body weight, grip strength), and survival duration. Pairwise statistical analysis (two-tailed t -test with Bonferroni correction) of normalized fold change (treatment/control) assessed significant differences in treatment efficacy. Cohen's d quantified pathophysiological treatment category effect size compared to "all" (e.g., all pathophysiological treatment categories combined)., Results: Inflammation treatments were best at delaying onset ( d = 0.42, p > 0.05). Oxidative stress treatments were significantly better for prolonging survival duration ( d = 0.18, p < 0.05). Excitability treatments were significantly better for prolonging overall health status ( d = 0.22, p < 0.05). However, the absolute best pathophysiological treatment category for prolonging health status varied with disease progression: oxidative stress was best for pre-onset health ( d = 0.18, p > 0.05); excitability was best for prolonging function near onset ( d = 0.34, p < 0.05); inflammation was best for prolonging post-onset function ( d = 0.24, p > 0.05); and apoptosis was best for prolonging end-stage function ( d = 0.49, p > 0.05). Finally, combination treatments simultaneously targeting multiple pathophysiological categories (e.g., polytherapy) performed significantly ( p < 0.05) better than monotherapies at end-stage., Discussion: In summary, the most effective pathophysiological treatments change as function of assessment modality and disease progression. Shifting pathophysiological treatment category efficacy with disease progression supports the homeostatic instability theory of ALS disease progression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lee, Kittel, Kim, Bach, Zhang and Mitchell.)

Background: The complex and not yet fully understood etiology of Alzheimer's disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms., Objective: The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer's disease (AsymAD), and AD., Methods: Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning., Results: A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.92) or Control (AUC = 0.92) from AD (AUC = 0.98). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PRKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, RABEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, ENPP6, HAPLN2, PSMD4, and CMAS., Conclusion: Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

Tyrosine kinase inhibitors (TKIs) are prescribed for chronic myeloid leukemia (CML) and some other cancers. The objective was to predict and rank TKI-related adverse events (AEs), including under-reported or preclinical AEs, using novel text mining. First, k-means clustering of 2575 clinical CML TKI abstracts separated TKIs by significant (p < 0.05) AE type: gastrointestinal (bosutinib); edema (imatinib); pulmonary (dasatinib); diabetes (nilotinib); cardiovascular (ponatinib). Next, we propose a novel cross-domain text mining method utilizing a knowledge graph, link prediction, and hub node network analysis to predict new relationships. Cross-domain text mining of 30+ million articles via SemNet predicted and ranked known and novel TKI AEs. Three physiology-based tiers were formed using unsupervised rank aggregation feature importance. Tier 1 ranked in the top 1%: hematology (anemia, neutropenia, thrombocytopenia, hypocellular marrow); glucose (diabetes, insulin resistance, metabolic syndrome); iron (deficiency, overload, metabolism), cardiovascular (hypertension, heart failure, vascular dilation); thyroid (hypothyroidism, hyperthyroidism, parathyroid). Tier 2 ranked in the top 5%: inflammation (chronic inflammatory disorder, autoimmune, periodontitis); kidney (glomerulonephritis, glomerulopathy, toxic nephropathy). Tier 3 ranked in the top 10%: gastrointestinal (bowel regulation, hepatitis, pancreatitis); neuromuscular (autonomia, neuropathy, muscle pain); others (secondary cancers, vitamin deficiency, edema). Results suggest proactive TKI patient AE surveillance levels: regular surveillance for tier 1, infrequent surveillance for tier 2, and symptom-based surveillance for tier 3.

Background: Prescription Drug Monitoring Programs (PDMPs) are electronic databases that track controlled substance prescriptions in a state. They are underused tools in preventing opioid abuse. Most PDMP education research measures changes in knowledge or confidence rather than behavior., Objective: To evaluate the impact of online case-based training on healthcare provider use of the Maryland (USA) PDMP., Methods: We used e-mail distribution lists to recruit providers to complete a brief educational module. Using a pre-training and post-training survey in the module, we measured self-reported PDMP use patterns and perceived PDMP value in specific clinical situations and compared pre- and post-training responses. Within the module, we presented three fictional pain cases and asked participants how they would manage each, both before, and then after presenting prescription drug history simulating a PDMP report. We measured changes in the fictional case treatment plans before and after seeing prescription history. Finally, we measured and compared how often each participant accessed the Maryland PDMP database before and after completing the educational module. We used multivariate logistic regression to measure the effect of the intervention on actual PDMP use frequency., Results: One hundred and fifty participants enrolled and completed the training module, and we successfully retrieved real-world PDMP use data of 137 of them. Participants' decisions to prescribe opioids changed significantly after reviewing PDMP data in each of the fictional cases provided in the module. In the months following the training, the rate of PDMP use increased by a median of four use-cases per month among providers in practice for less than 20 years (p = 0.039) and two use-cases per month among infrequent opioid prescribers (p = 0.014)., Conclusion: A brief online case-based educational intervention was associated with a significant increase in the rate of PDMP use among infrequent opioid prescribers and those in practice less than 20 years., Competing Interests: The authors have declared that no competing interests exist.

Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Obesogenic diets can produce hippocampal insulin resistance and impairments to hippocampal-dependent cognition. This study investigated the effect of disrupted insulin signaling in Neuropeptide Y (NPY) neurons on diet-induced deficits in hippocampal-dependent memory. Wild-type mice and mice that had a targeted knockout of insulin receptors on NPY cells (IR lox/lox ;NPY Cre/+ ) were given ad libitum access to a high-fat diet (high fat; HF), 10% sucrose solution (high sugar; HS), both high-fat diet and sucrose solution (high fat, high sugar; HFHS), or a normal fat control chow for 12 weeks. Mice were tested in the Morris Water Maze (MWM), a hippocampal-dependent spatial memory task. Glucose homeostasis was assessed via a glucose tolerance test. Independent of genotype, consumption of HF, but not HS, diet increased energy intake, body weight, and plasma leptin, and impaired glucose tolerance. Disrupted insulin signaling in NPY cells and dietary interventions did not significantly affect the ability of mice to learn the location of the platform in the MWM. However, for IR lox/lox control mice, consumption of HF, but not HS, diet resulted in reduced time spent in the target quadrant during the probe trial, suggesting a hippocampal-dependent memory deficit. IR lox/lox ;NPY Cre/+ mice had poor performance in the probe trial regardless of diet, suggesting a floor effect. This study did not find adverse effects of chronic sucrose intake on metabolic outcomes or hippocampal-dependent memory. These data also suggest that the effects of HF diet on hippocampal-dependent memory may be dependent on insulin signaling in hippocampal NPY cells., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Mitchell, Goodman, Tedesco, Nguyen, Zhang, Herzog and Begg.)

A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this burden but require substantial human input to select a highly informative subset of instances or to curate labeling functions. REGAL (Rule-Enhanced Generative Active Learning) is an improved framework for weakly supervised text classification that performs active learning over labeling functions rather than individual instances. REGAL interactively creates high-quality labeling patterns from raw text, enabling a single annotator to accurately label an entire dataset after initialization with three keywords for each class. Experiments demonstrate that REGAL extracts up to 3 times as many high-accuracy labeling functions from text as current state-of-the-art methods for interactive weak supervision, enabling REGAL to dramatically reduce the annotation burden of writing labeling functions for weak supervision. Statistical analysis reveals REGAL performs equal or significantly better than interactive weak supervision for five of six commonly used natural language processing (NLP) baseline datasets., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or "knowledge graph" of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer's disease and metabolic co-morbidities.

Insulin is known to act in the central nervous system to regulate several physiological and behavioural outcomes, including energy balance, glucose homeostasis and cognitive functioning. However, the neuronal populations through which insulin enhances cognitive performance remain unidentified. Insulin receptors are found in neuropeptide-Y (NPY) expressing neurons, which are abundant in the hypothalamus and hippocampus; regions involved in feeding behaviour and spatial memory, respectively. Here we show that mice with a tissue specific knockout of insulin receptors in NPY expressing neurons (IRl⁢o⁢x/l⁢o⁢x; NPYC⁢r⁢e⁣/+) display an impaired performance in the probe trial of the Morris Water Maze compared with control mice at both the 6 and the 12, but not at the 24 months time point, consistent with a crucial role of insulin and NPY in cognitive functioning. By 24 months of age all groups demonstrated similar reductions in spatial memory performance. Together, these data suggest that the mechanisms through which insulin influences cognitive functioning are, at least in part, via insulin receptor signaling in NPY expressing neurons. These results also highlight that cognitive impairments observed in aging may be due to impaired insulin signaling., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s). Published by IMR Press.)

The dual challenges of COVID-19 and the opioid epidemic have heightened the need of Maryland workplaces for accessible resources and supports. This paper describes efforts of the Workplace PROSPER (Partnering to Reduce Opioid Stigma and Support Employment in Recovery) project team to explore opioid-related state employment needs from Key Stakeholder perspectives. Discussion revealed significant overlap between the needs identified by stakeholders and pre-existing recovery friendly initiatives in other states. However, this convening identified the need for increased training of medical professionals in communicating about work capacity and safety as well as for resources to support family members of individuals with Opioid Use Disorder and model programs for hiring individuals in recovery. Next steps include the creation and dissemination of a survey to obtain a broader base of feedback and the development of a robust set of online recovery resources for Maryland employers and employees.

The purpose of this manuscript was to design a better method for recovery from rhegmatogenous retinal detachment (RRD) surgery. We attempted to achieve this by designing a helmet that can manipulate intraocular magnetic nanoparticles (MNPs) and create a magnetic tamponade, eliminating the need for postoperative head positioning. A simulated analysis was developed to predict the pattern of magnetic force applied to the magnetic nanoparticles by external magnetic field. No participants were involved in this study. Instead, magnetic flux and force data for three different helmet designs were collected using virtual simulation tools. A prototype helmet was then constructed and magnetic flux and force data were recorded and compared to virtual data. For both virtual and physical scenarios, magnitude and direction of the resulting forces were compared to determine which design created the controlled direction and strongest forces into the back of the eye. Of the three virtual designs, both designs containing a visor had greater force magnitude than magnet alone. Between both designs with visors, the visor with bends resulted in forces more directed at the back of the eye. The physical prototype helmet shared similar measurements to virtual simulation with minimal percent error (Average = 5.47%, Standard deviation = 0.03). Of the three designs, the visor with bends generated stronger forces directed at the back of the eye, which is most appropriate for creating a tamponade on the retina. We believe that this design has shown promising capability for manipulating intraocular MNPs for the purpose of creating a tamponade for RRD.

Heterogeneity among Alzheimer's disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [-4.6, +3.8] and cluster-3 [+10.8, -4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [-18.4, -8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population "clusters" using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis.

Obesity is a growing health problem worldwide. The renin-angiotensin system (RAS) is present in adipose tissue, and evidence suggests that it is involved in both diet-induced obesity and the inflammation associated with obesity. The present experiments determined the effect of (1) different angiotensin-converting enzyme (ACE) inhibitors (captopril, perindopril, enalapril) and angiotensin receptor blockers (ARBs: telmisartan, losartan) on adiposity of mice fed a high-fat diet for 28 days (2); acute treatment with the ACE-inhibitor captopril on gene expression of inflammatory markers in mice fed a high-fat diet (HFD); and (3) short-term (2 days) and chronic (28 days) treatment of ACE-inhibition on energy expenditure (EE) and energy balance in mice fed HFD ad libitum (AL), as well as receiving HFD limited to the amount of calories eaten by controls (pair-fed (PF) group). Body weight, food intake, adiposity and plasma leptin were lower in ACE inhibitor or ARB-treated groups over 28 days compared with HFD untreated mice. Short-term treatment with captopril led to increased EE relative to the level in the PF group. After 28 days, EE was lower in both captopril-treated and PF mice compared with AL, but the effect was greater in the captopril-treated group. Adiponectin was elevated in captopril-treated mice, but not in PF mice, after both 2 and 28 days. Additionally, acute RAS blockade in HFD-fed mice reduced mRNA expression for MCP-1, IL-6, TLR4, and leptin in adipose tissue relative to values in untreated groups. These data demonstrate that ACE inhibition and angiotensin receptor blockade reduce food intake to produce weight loss and suggest that the anti-inflammatory effects of ACE inhibition may be independent of weight loss., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mitchell, Premaratna, Bennett, Lambrou, Stahl, Jois, Barber, Antoniadis, Woods, Cameron-Smith, Weisinger and Begg.)

Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases.

Food intake and energy expenditure are regulated by peripheral signals providing feedback on nutrient status and adiposity to the central nervous system. One of these signals is the pancreatic hormone, insulin. Unlike peripheral administration of insulin, which often causes weight gain, central administration of insulin leads to a reduction in food intake and body weight when administered long-term. This is a result of feedback processes in regions of the brain that regulate food intake. Within the hypothalamus, the arcuate nucleus (ARC) contains subpopulations of neurones that produce orexinergic neuropeptides agouti-related peptide (AgRP)/neuropeptide Y (NPY) and anorexigenic neuropeptides, pro-opiomelanocortin (POMC)/cocaine- and amphetamine-regulated transcript (CART). Intracerebroventricular infusion of insulin down-regulates the expression of AgRP/NPY at the same time as up-regulating expression of POMC/CART. Recent evidence suggests that insulin activity within the amygdala may play an important role in regulating energy balance. Insulin infusion into the central nucleus of the amygdala (CeA) can decrease food intake, possibly by modulating activity of NPY and other neurone subpopulations. Insulin signalling within the CeA can also influence stress-induced obesity. Overall, it is evident that the CeA is a critical target for insulin signalling and the regulation of energy balance., (© 2021 British Society for Neuroendocrinology.)

Tyrosine kinase inhibitors (TKIs) are the frontline therapy for BCR-ABL (Ph+) chronic myeloid leukemia (CML). A systematic meta-analysis of 43 peer-reviewed studies with 10,769 CML patients compared the incidence of gastrointestinal adverse events (GI AEs) in a large heterogeneous CML population as a function of TKI type. Incidence and severity of nausea, vomiting, and diarrhea were assessed for imatinib, dasatinib, bosutinib, and nilotinib. Examination of combined TKI average GI AE incidence found diarrhea most prevalent (22.5%), followed by nausea (20.6%), and vomiting (12.9%). Other TKI GI AEs included constipation (9.2%), abdominal pain (7.6%), gastrointestinal hemorrhage (3.5%), and pancreatitis (2.2%). Mean GI AE incidence was significantly different between TKIs ( p < 0.001): bosutinib (52.9%), imatinib (24.2%), dasatinib (20.4%), and nilotinib (9.1%). Diarrhea was the most prevalent GI AE with bosutinib (79.2%) and dasatinib (28.1%), whereas nausea was most prevalent with imatinib (33.0%) and nilotinib (13.2%). Incidence of grade 3 or 4 severe GI AEs was ≤3% except severe diarrhea with bosutinib (9.5%). Unsupervised clustering revealed treatment efficacy measured by the complete cytogenetic response, major molecular response, and overall survival is driven most by disease severity, not TKI type. For patients with chronic phase CML without resistance, optimal TKI selection should consider TKI AE profile, comorbidities, and lifestyle.

An edible cannabis product (ECP) manufactured with food ingredients is subject to the same types of contamination as any conventional food product. Physical, microbial, and chemical hazards are a potential threat to anyone consuming cannabinoid-containing products by mouth. Preventing the unintentional ingestion of ECPs is also a concern for public health professionals. An analysis of the regulatory landscape in the United States (US) was conducted to identify best practices specific to ECPs and to pinpoint preventative safety measures that had not been extensively implemented. Widespread adoption of some of the more useful precedents set by US jurisdictions, as examined in this work, could be of great value in protecting public health.

Background: Apolipoprotein E (APOE) genotypes typically increase risk of amyloid-β deposition and onset of clinical Alzheimer's disease (AD). However, cognitive assessments in APOE transgenic AD mice have resulted in discord., Objective: Analysis of 31 peer-reviewed AD APOE mouse publications (n = 3,045 mice) uncovered aggregate trends between age, APOE genotype, gender, modulatory treatments, and cognition., Methods: T-tests with Bonferroni correction (significance = p < 0.002) compared age-normalized Morris water maze (MWM) escape latencies in wild type (WT), APOE2 knock-in (KI2), APOE3 knock-in (KI3), APOE4 knock-in (KI4), and APOE knock-out (KO) mice. Positive treatments (t+) to favorably modulate APOE to improve cognition, negative treatments (t-) to perturb etiology and diminish cognition, and untreated (t0) mice were compared. Machine learning with random forest modeling predicted MWM escape latency performance based on 12 features: mouse genotype (WT, KI2, KI3, KI4, KO), modulatory treatment (t+, t-, t0), mouse age, and mouse gender (male = g&#95;m; female = g&#95;f, mixed gender = g&#95;mi)., Results: KI3 mice performed significantly better in MWM, but KI4 and KO performed significantly worse than WT. KI2 performed similarly to WT. KI4 performed significantly worse compared to every other genotype. Positive treatments significantly improved cognition in WT, KI4, and KO compared to untreated. Interestingly, negative treatments in KI4 also significantly improved mean MWM escape latency. Random forest modeling resulted in the following feature importance for predicting superior MWM performance: [KI3, age, g&#95;m, KI4, t0, t+, KO, WT, g&#95;mi, t-, g&#95;f, KI2] = [0.270, 0.094, 0.092, 0.088, 0.077, 0.074, 0.069, 0.061, 0.058, 0.054, 0.038, 0.023]., Conclusion: APOE3, age, and male gender was most important for predicting superior mouse cognitive performance.

Numerous recent assessments indicate that meat and poultry processing facility workers are at increased risk for infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1-4). Physical proximity to other workers and shared equipment can facilitate disease transmission in these settings (2-4). The disproportionate number of foreign-born workers employed in meat and poultry processing reflects structural, social, and economic inequities that likely contribute to an increased COVID-19 incidence in this population* (5). In May 2020, the Maryland Department of Health and CDC investigated factors that might affect person-to-person SARS-CoV-2 transmission among persons who worked at two poultry processing facilities. † A survey administered to 359 workers identified differences in risk factors for SARS-CoV-2 infection between workers born outside the United States and U.S.-born workers. Compared with U.S.-born workers, foreign-born workers had higher odds of working in fixed locations on the production floor (odds ratio [OR] for cutup and packaging jobs = 4.8), of having shared commutes (OR = 1.9), and of living with other poultry workers (OR = 6.0). They had lower odds of participating in social gatherings (OR for visits to family = 0.2; OR for visits to friends = 0.4), and they visited fewer businesses in the week before the survey than did their U.S.-born coworkers. Some workplace risk factors can be mitigated through engineering and administrative controls focused on the production floor, and this will be of particular benefit to the foreign-born workers concentrated in these areas. Employers and health departments can also partner with local organizations to disseminate culturally and linguistically tailored messages about risk reduction behaviors in community settings, including shared transportation § and household members dwelling in close quarters. ¶ ., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Associations of modulators of quality of life (QoL) and survival duration are assessed in the fatal motor neuron disease, Amyotrophic Lateral Sclerosis. Major categories include clinical impression of mood (CIM); physical health; patient social support; and usage of interventions, pharmaceuticals, and supplements. Associations were assessed at p < 0.05 and p < 0.001 significance thresholds using applicable methods (Chi-square, t- test, ANOVA, logistical regression, random forests, Fisher's exact test) within a retrospective cohort of 1585 patients. Factors significantly correlated with positive (happy or normal) mood included family support and usage of bi-level positive airway pressure (Bi-PAP) and/or cough assist. Decline in physical factors like presence of dysphagia, drooling, general pain, and decrease in ALSFRS-R total score or forced vital capacity (FVC) significantly correlated with negative (depressed or anxious) mood ( p < 0.05). Use of antidepressants or pain medications had no association with ALS patient mood ( p > 0.05), but were significantly associated with increased survival ( p < 0.05). Positive patient mood, Bi-PAP, cough assist, percutaneous endoscopic gastrostomy (PEG), and accompaniment to clinic visits associated with increased survival duration ( p < 0.001). Of the 47 most prevalent pharmaceutical and supplement categories, 17 associated with significant survival duration increases ranging +4.5 to +16.5 months. Tricyclic antidepressants, non-opioids, muscle relaxants, and vitamin E had the highest associative increases in survival duration ( p < 0.05). Random forests, which examined complex interactions, identified the following pharmaceuticals and supplements as most predictive to survival duration: Vitamin A, multivitamin, PEG supplements, alternative herbs, antihistamines, muscle relaxants, stimulant laxatives, and antispastics. Statins, metformin, and thiazide diuretics had insignificant associations with decreased survival., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

There is literature discord regarding the impact of percutaneous endoscopic gastrostomy (PEG), or "feeding tube", on amyotrophic lateral sclerosis (ALS) outcomes. We assess one of the largest retrospective ALS cohorts to date (278 PEG users, 679 non-users). Kruskal-Wallis and Kaplan-Meier analysis compared cohort medians and survival duration trends. A meta-analysis determined the aggregate associative effect of PEG on survival duration by combining primary results with 7 published studies. Primary results ( p < 0.001) and meta-analysis ( p < 0.05) showed PEG usage is associated with an overall significant increase in ALS survival duration, regardless of onset type. Percent predicted forced vital capacity (FVC %predict) ≥50 at PEG insertion significantly increases survival duration ( p < 0.001); FVC %predict ≥60 has the largest associative benefit (+6.7 months, p < 0.05). Time elapsed from ALS onset until PEG placement is not predictive ( p > 0.05). ALSFRS-R survey assessment illustrates PEG usage does not slow functional ALS pathology ( p > 0.05), but does stabilize weight and/or body mass index (BMI) ( p < 0.05). Observed clinical impression of mood (CIM), was not impacted by PEG usage ( p > 0.05). Overall results support PEG as a palliative intervention for ALS patients with ≥50 FVC %predict at PEG insertion.

A Pseudomonas aeruginosa outbreak was investigated in a neonatal intensive care unit that had experienced a prior similar outbreak. The 8 cases identified included 2 deaths. An investigation found the cause of the outbreak: tap water from contaminated hospital plumbing which was used for humidifier reservoirs, neonatal bathing, and nutritional preparation. Our findings reinforce a recent Centers for Medicare & Medicaid Services memo recommending increased attention to water management to improve awareness, identification, mitigation, and prevention of water-associated, health care-associated infections., (Published by Elsevier Inc.)