Mechanistic Plausibility Research Articles

Analysis of implicit and explicit uncertainties in QSAR prediction of chemical toxicity: A case study of neurotoxicity

Although uncertainties expressed in texts within QSAR studies can guide quantitative uncertainty estimations, they are often overlooked during uncertainty analysis. Using neurotoxicity as an example, this study developed a method to support analysis of implicitly and explicitly expressed uncertainties in QSAR modeling studies. Text content analysis was employed to identify implicit and explicit uncertainty indicators, whereafter uncertainties within the indicator-containing sentences were identified and systematically categorized according to 20 uncertainty sources. Our results show that implicit uncertainty was more frequent within most uncertainty sources (13/20), while explicit uncertainty was more frequent in only three sources, indicating that uncertainty is predominantly expressed implicitly in the field. The most highly cited sources included Mechanistic plausibility, Model relevance and Model performance, suggesting they constitute sources of most concern. The fact that other sources like Data balance were not mentioned, although it is recognized in the broader QSAR literature as an area of concern, demonstrates that the output from the type of analysis conducted here must be interpreted in the context of the broader QSAR literature before conclusions are drawn. Overall, the method established here can be applied in other QSAR modeling contexts and ultimately guide efforts targeted towards addressing the identified uncertainty sources.

A rough set-based model for predicting soil greenhouse gases response to biochar

Biochar application to soil is a potential climate change mitigation strategy. In addition to long-term sequestration of the carbon content of the biochar itself, its application may reduce the emissions of other greenhouse gases (GHGs) from the soil. However, the reported effects of biochar application on soil GHG fluxes exhibit inconsistent results. Prediction of such effects is an important gap that needs to be addressed in biochar research. In this study, rule-based machine learning models were developed based on rough-set theory. Data from the literature were used to generate the rules for predicting the effects of biochar application on soil GHG (CO2, N2O, and CH4) fluxes. Four rule-based models for CO2 fluxes, two rule-based models for N2O fluxes, and three rule-based models for CH4 fluxes were developed. The validity of these models was assessed based on both statistical measures and mechanistic plausibility. The final rule-based models can guide the prediction of changes in soil GHG fluxes due to biochar application, and thus serve as a decision-support tool to maximize the benefits of biochar application as a negative emission technology (NET). In particular, mechanistically plausible rules were identified that predict triggers for GHG fluxes that can offset carbon sequestration gains. This knowledge allows biochar application to be calibrated to local conditions for maximum efficacy.Graphical

Bubbling beyond the barrier: exosomal RNA as a vehicle for soma-germline communication.

'Weismann's barrier' has restricted theories of heredity to the transmission of genomic variation for the better part of a century. However, the discovery and elucidation of epigenetic mechanisms of gene regulation such as DNA methylation and histone modifications has renewed interest in studies on the inheritance of acquired traits and given them mechanistic plausibility. Although it is now clear that these mechanisms allow many environmentally acquired traits to be transmitted to the offspring, how phenotypic information is communicated from the body to its gametes has remained a mystery. Here, we discuss recent evidence that such communication is mediated by somatic RNAs that travel inside extracellular vesicles to the gametes where they reprogram the offspring epigenome and phenotype. How gametes learn about bodily changes has implications not only for the clinic, but also for evolutionary theory by bringing together intra- and intergenerational mechanisms of phenotypic plasticity and adaptation.

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

ObjectivesMotor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here,...

Determining the Mechanistic Plausibility for Acetyl-L-Carnitine as a Prognostic Biomarker in Sepsis

Determining the Mechanistic Plausibility for Acetyl-L-Carnitine as a Prognostic Biomarker in Sepsis

Medication-Wide Association Study Plus (MWAS+): A Proof of Concept Study on Drug Repurposing.

The high cost and time for developing a new drug or repositioning a partially-developed drug has fueled interest in “repurposing” drugs. Drug repurposing is particularly of interest for Alzheimer’s disease (AD) or AD-related dementias (ADRD) because there are no unrestricted disease-modifying treatments for ADRD. We have designed and pilot tested a 3-Step Medication-Wide Association Study Plus (MWAS+) approach to rigorously accelerate the identification of drugs with a high potential to be repurposed for delaying and preventing AD/ADRD: Step 1 is a hypothesis-free exploration; Step 2 is mechanistic filtering; And Step 3 is hypothesis testing using observational data and prospective cohort design. Our results demonstrated the feasibility of the MWAS+ approach. The Step 1 analysis identified potential candidate drugs including atorvastatin and GLP1. The literature search in Step 2 found evidence supporting the mechanistic plausibility of the statin-ADRD association. Finally, Step 3 confirmed our hypothesis that statin may lower the risk of incident ADRD, which was statistically significant using a target trial design that emulated randomized controlled trials.

Relaxing Mask Mandates in New Jersey

Relaxing Mask Mandates in New Jersey

Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature

ObjectiveDevelop a novel methodology to create a comprehensive knowledge graph (SuppKG) to represent a domain with limited coverage in the Unified Medical Language System (UMLS), specifically dietary supplement (DS) information for discovering drug-supplement interactions (DSI), by leveraging biomedical natural language processing (NLP) technologies and a DS domain terminology. Materials and MethodsWe created SemRepDS (an extension of an NLP tool, SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28,884 DS terms not found in the UMLS. PubMed abstracts were processed using SemRepDS to generate semantic relations, which were then filtered using a PubMedBERT model to remove incorrect relations before generating SuppKG. Two discovery pathways were applied to SuppKG to identify potential DSIs, which are then compared with an existing DSI database and also evaluated by medical professionals for mechanistic plausibility. ResultsSemRepDS returned 158.5% more DS entities and 206.9% more DS relations than SemRep. The fine-tuned PubMedBERT model (significantly outperformed other machine learning and BERT models) obtained an F1 score of 0.8605 and removed 43.86% of semantic relations, improving the precision of the relations by 26.4% over pre-filtering. SuppKG consists of 56,635 nodes and 595,222 directed edges with 2,928 DS-specific nodes and 164,738 edges. Manual review of findings identified 182 of 250 (72.8%) proposed DS-Gene-Drug and 77 of 100 (77%) proposed DS-Gene1-Function-Gene2-Drug pathways to be mechanistically plausible. DiscussionWith added DS terminology to the UMLS, SemRepDS has the capability to find more DS-specific semantic relationships from PubMed than SemRep. The utility of the resulting SuppKG was demonstrated using discovery patterns to find novel DSIs. ConclusionFor the domain with limited coverage in the traditional terminology (e.g., UMLS), we demonstrated an approach to leverage domain terminology and improve existing NLP tools to generate a more comprehensive knowledge graph for the downstream task. Even this study focuses on DSI, the method may be adapted to other domains.

Estimation of fast pyrolysis bio‐oil properties from feedstock characteristics using rough‐set ‐based machine learning

In this work, a data-driven rough-set-based machine learning model has been proposed as a pre-processing and predictive modelling tool to predict the pyrolysis bio-oil properties based on pyrolysis temperature and feedstock characteristics. A database consisting of feedstock proximate and ultimate analyses, pyrolysis temperature, bio-oil's pH value, and bio-oil's higher heating value was compiled and used to train the rough-set-based machine learning model. The resulting rule-based rough-set-based machine learning model demonstrated promising strength, certainty, and coverage factor. Furthermore, the emergent patterns and mechanistic plausibility of the rough-set-based machine learning models were analysed. The generated rules illustrated reasonable predictive capability in estimating the higher heating value and pH value of bio-oil based on the feedstock characterisation and pyrolysis temperature. Rough-set-based machine learning model is thus demonstrated to be a simple and straightforward approach for feedstock composition and pyrolysis temperature selection in pyrolysis/co-pyrolysis bio-oil production.

Daily intake of Panax ginseng extract (G115®) supplemented with vitamins and minerals reduces self-perception of mental and physical fatigue

Fatigue negatively impacts quality of life even in otherwise healthy individuals. Recent systematic reviews provide mechanistic plausibility for these effects. In addition, a recent study demonstrates that a combination of vitamins, minerals and Panax ginseng root dry extract (G115®) improves various domains of fatigue in real life.

International Consortium to Advance Cross-Species Extrapolation of the Effects of Chemicals in Regulatory Toxicology.

International Consortium to Advance Cross-Species Extrapolation of the Effects of Chemicals in Regulatory Toxicology.

The association between fluoroquinolones and aortic dissection and aortic aneurysms: a systematic review and meta-analysis

Previous studies have drawn causal associations between fluoroquinolone use and collagen pathologies including tendon rupture and retinopathy. This meta-analysisattempted to assess the association between fluoroquinolone use and the risk of aortic dissection or aortic aneurysm. A systematic search was performed on Medline, EMBASE, and the Cochrane library. 9 studies were included in final analysis. Primary random-effects meta-analysis of 7 studies, excluding 2 pharmacovigilance studies demonstrated statistically increased odds of aortic dissection (OR, 2.38; 95% CI, 1.71–3.32) aortic aneurysm (OR, 1.98; 95% CI, 1.59–2.48), and aortic aneurysm or dissection (OR, 1.47; 95% CI, 1.13–1.89; I2 = 72%) with current use of fluoroquinolones compared to their nonuser counterparts. Based on the “number needed-to-harm” analysis, 7246 (95% CI: 4329 to 14,085) patients would need to be treated with fluoroquinolones for a duration of at least three days in order for one additional patient to be harmed, assuming a population baseline incidence of aortic dissection and aneurysm rupture to be 10 per 100,000 patient-years. With strong statistical association, these findings suggest a causal relationship, warranting future research to elucidate the pathophysiological and mechanistic plausibility of this association. These findings however, should not cease prescription of fluoroquinolones, especially when clinically indicated.

Urgent needs to accelerate the race for COVID-19 therapeutics.

Urgent needs to accelerate the race for COVID-19 therapeutics.

Guideline-Directed Heart Failure Therapy in Patients after Left Ventricular Assist Device Implantation

Background: Left ventricular assist devices (LVADs) are used as an advanced therapy option for patients with stage D heart failure. These devices provide mechanical unloading of the heart as either a bridge to transplant or recovery, or as destination therapy. In patients with LVADs, there are emerging data on the use of heart failure guideline-directed medical therapy (GDMT) to improve outcomes. This review describes the current evidence available for the use of neurohormonal blocking agents in patients with LVADs. Methods: Articles were found using PubMed and web searches for heart failure therapies/neurohormonal blockade and LVADs. Studies were included if they evaluated the use of the heart failure therapies, either retrospectively or prospectively. Results: A total of 17 articles and 9 abstracts were reviewed. The totality of data surrounding the use of GDMT neurohormonal blockade in patients with LVADs are limited in nature. Much of the data are limited either by use of surrogate outcomes, single center evaluations, and/or small sample sizes. However, the evidence does support clinical benefit with associated mechanistic plausibility. More research is needed to understand appropriate patient selection and means to optimize these therapies in the LVAD population. Conclusion: Combined neurohormonal blockade appears to reduce morbidity and mortality in patients with LVAD implants.

Sclerostin Downregulation Globally by Naturally Occurring Genetic Variants, or Locally in Atherosclerotic Plaques, Does Not Associate With Cardiovascular Events in Humans.

ABSTRACTInhibition of sclerostin increases bone formation and decreases bone resorption, leading to increased bone mass, bone mineral density, and bone strength and reduced fracture risk. In a clinical study of the sclerostin antibody romosozumab versus alendronate in postmenopausal women (ARCH), an imbalance in adjudicated serious cardiovascular (CV) adverse events driven by an increase in myocardial infarction (MI) and stroke was observed. To explore whether there was a potential mechanistic plausibility that sclerostin expression, or its inhibition, in atherosclerotic (AS) plaques may have contributed to this imbalance, sclerostin was immunostained in human plaques to determine whether it was detected in regions relevant to plaque stability in 94 carotid and 50 femoral AS plaques surgically collected from older female patients (mean age 69.6 ± 10.4 years). Sclerostin staining was absent in most plaques (67%), and when detected, it was of reduced intensity compared with normal aorta and was located in deeper regions of the plaque/wall but was not observed in areas considered relevant to plaque stability (fibrous cap and endothelium). Additionally, genetic variants associated with lifelong reduced sclerostin expression were explored for associations with phenotypes including those related to bone physiology and CV risk factors/events in a population‐based phenomewide association study (PheWAS). Natural genetic modulation of sclerostin by variants with a significant positive effect on bone physiology showed no association with lifetime risk of MI or stroke. These data do not support a causal association between the presence of sclerostin, or its inhibition, in the vasculature and increased risk of serious cardiovascular events. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

33-Year-Old Woman With Postpartum Acute Shortness of Breath

33-Year-Old Woman With Postpartum Acute Shortness of Breath

Impact of Hearing Loss and Universal Face Masking in the COVID-19 Era

Impact of Hearing Loss and Universal Face Masking in the COVID-19 Era

An adverse outcome pathway for immune-mediated and allergic hepatitis: a case study with the NSAID diclofenac

Many drugs have the potential to cause drug-induced liver injury (DILI); however, underlying mechanisms are diverse. The concept of adverse outcome pathways (AOPs) has become instrumental for risk assessment of drug class effects. We report AOPs specific for immune-mediated and drug hypersensitivity/allergic hepatitis by considering genomic, histo- and clinical pathology data of mice and dogs treated with diclofenac. The findings are relevant for other NSAIDs and drugs undergoing iminoquinone and quinone reactive metabolite formation. We define reactive metabolites catalyzed by CYP monooxygenase and myeloperoxidases of neutrophils and Kupffer cells as well as acyl glucuronides produced by uridine diphosphoglucuronosyl transferase as molecular initiating events (MIE). The reactive metabolites bind to proteins and act as neo-antigen and involve antigen-presenting cells to elicit B- and T-cell responses. Given the diverse immune systems between mice and dogs, six different key events (KEs) at the cellular and up to four KEs at the organ level are defined with mechanistic plausibility for the onset and progression of liver inflammation. With mice, cellular stress response, interferon gamma-, adipocytokine- and chemokine signaling provided a rationale for the AOP of immune-mediated hepatitis. With dogs, an erroneous programming of the innate and adaptive immune response resulted in mast cell activation; their infiltration into liver parenchyma and the shift to M2-polarized Kupffer cells signify allergic hepatitis and the occurrence of granulomas of the liver. Taken together, diclofenac induces divergent immune responses among two important preclinical animal species, and the injury pattern seen among clinical cases confirms the relevance of the developed AOP for immune-mediated hepatitis.

Biomass Smoke Exposure Is Associated With Gastric Cancer and Probably Mediated Via Oxidative Stress and DNA Damage: A Case-Control Study.

PURPOSEWe investigated the association between gastric cancer and environmental and dietary exposures. In addition, we explored probable mechanistic pathways for the influence of biomass smoke on gastric carcinogenesis.PATIENTS AND METHODSThe study was conducted in Lusaka, Zambia. Questionnaires were used to collect data on risk factors, whereas enzyme-linked immunosorbent assays and high-performance liquid chromatography were used to measure biologic exposures. Study data were analyzed using contingency tables and logistic regression.RESULTSWe enrolled 72 patients with gastric adenocarcinoma and 244 controls. Gastric cancer was positively associated with rural residence (odds ratio [OR], 2.9; 95% CI, 1.5 to 5.3), poverty (OR, 4.2; 95% CI, 1.9 to 9.1), and daily consumption of processed meat (OR, 6.4; 95% CI, 1.3 to 32) and negatively associated with consumption of green vegetables (OR, 0.2; 95% CI, 0.1 to 0.5). Gastric cancer was also associated with biomass smoke exposure (OR, 3.5; 95% CI, 1.9 to 6.2; P < .0001), an association that was stronger for intestinal-type cancers (OR, 3.6; 95% CI, 1.5 to 9.1; P = .003). Exposure to biomass smoke in controls was associated with higher urinary levels of 8-isoprostane (P < .0001), 8-hydroxydeoxyguanosine (P = .029), and 1-hydroxypyrene (P = .041). Gastric cancer was not associated with biochemical measures of current exposure to aflatoxins or ochratoxins.CONCLUSIONIn Zambia, exposure to biomass smoke, daily consumption of processed meat, and poverty are risk factors for gastric cancer, whereas daily consumption of green vegetables is protective against gastric cancer. Exposure to biomass smoke was associated with evidence of oxidative stress and DNA damage, suggesting mechanistic plausibility for the observed association, and the association was restricted to intestinal-type gastric cancer.

Multi-omics data analysis implicating epigenetic inheritance in evolution and disease

Abstract Recent evidence surprisingly suggests existence of germline mediated epigenetic inheritance in diverse species including mammals. The evolutionary and health implications as well as the mechanistic plausibility of epigenetic inheritance are subjects of immense current interest and controversy, with integrative analysis expected to provide valuable insights. Here, an unbiased gene set enrichment analysis of existing multi-omics data is presented that readily supports a role of sperm DNA methylome in evolution and disease, as also in developmental mechanisms. In mice, differentially methylated sperm genes in cold induced inheritance specifically overrepresent genes associated with cold adaptation. Similarly, in humans, differentially methylated sperm genes associate with disease and adaptation in general, with specific disease association supported by prior evidence. Further, the sperm genes, like disease and adaptation genes, overrepresent genes known to exhibit higher mutability, loss-of-function intolerance, and haploinsufficiency. Finally, both mouse and human sperm genes show enrichment for genes that retain sperm methylation during development and are developmentally expressed. Together, the present analysis provides one-stop evidence to suggest that sperm DNA methylome acts as a melting pot of gene-environment interaction, inheritance, evolution, and health and disease.