Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.

Persistent destruction of pancreatic ß-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic understanding of biological processes and T1D health outcomes. The main challenge is to design such a reliable framework to analyze the highly orchestrated biology of T1D based on the knowledge of cellular networks and biological parameters. We constructed a novel hybrid in-silico computational model to unravel T1D onset, progression, and prevention in a non-obese-diabetic mouse model. The computational approach that integrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks of cell behaviors. By integrating interactions between multiple cell types, model results captured the individual-specific dynamics of T1D progression and were validated against experimental data for the number of infiltrating CD8+T-cells. Our simulation results uncovered the correlation between five auto-destructive mechanisms identifying a combination of potential therapeutic strategies: the average lifespan of cytotoxic CD8+T-cells in islets; the initial number of apoptotic ß-cells; recruitment rate of dendritic-cells (DCs); binding sites on DCs for naïve CD8+T-cells; and time required for DCs movement. Results from therapy-directed simulations further suggest the efficacy of proposed therapeutic strategies depends upon the type and time of administering therapy interventions and the administered amount of therapeutic dose. Our findings show modeling immunogenicity that underlies autoimmune T1D and identifying autoantigens that serve as potential biomarkers are two pressing parameters to predict disease onset and progression.

Predicting the Prognostic Value of POLI Expression in Different Cancers via a Machine Learning Approach.

Translesion synthesis (TLS) is a cell signaling pathway that facilitates the tolerance of replication stress. Increased TLS activity, the particularly elevated expression of TLS polymerases, has been linked to resistance to cancer chemotherapeutics and significantly altered patient outcomes. Building upon current knowledge, we found that the expression of one of these TLS polymerases (POLI) is associated with significant differences in cervical and pancreatic cancer survival. These data led us to hypothesize that POLI expression is associated with cancer survival more broadly. However, when cancers were grouped cancer type, POLI expression did not have a significant prognostic value. We presented a binary cancer random forest classifier using 396 genes that influence the prognostic characteristics of POLI in cervical and pancreatic cancer selected via graphical least absolute shrinkage and selection operator. The classifier was then used to cluster patients with bladder, breast, colorectal, head and neck, liver, lung, ovary, melanoma, stomach, and uterus cancer when high POLI expression was associated with worsened survival (Group I) or with improved survival (Group II). This approach allowed us to identify cancers where POLI expression is a significant prognostic factor for survival (p = 0.028 in Group I and p = 0.0059 in Group II). Multiple independent validation approaches, including the gene ontology enrichment analysis and visualization tool and network visualization support the classification scheme. The functions of the selected genes involving mitochondrial translational elongation, Wnt signaling pathway, and tumor necrosis factor-mediated signaling pathway support their association with TLS and replication stress. Our multidisciplinary approach provides a novel way of identifying tumors where increased TLS polymerase expression is associated with significant differences in cancer survival.

Pharmacovigilance in patients with diabetes: A data-driven analysis identifying specific RAS antagonists with adverse pulmonary safety profiles that have implications for COVID-19 morbidity and mortality.

OBJECTIVES: The current demographic information from China reports that 10%-19% of patients hospitalized with coronavirus disease (COVID-19) were diabetic. Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are considered first-line agents in patients with diabetes because of their nephroprotective effects, but administration of these drugs leads to upregulation of angiotensin-converting enzyme 2 (ACE2), which is responsible for the viral entry of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2). Data are lacking to determine what pulmonary effects ACEIs or ARBs may have in patients with diabetes, which could be relevant in the management of patients infected with SARS-CoV-2. This study aims to assess the prevalence of pulmonary adverse drug effects (ADEs) in patients with diabetes who were taking ACEI or ARBs to provide guidance as to how these medications could affect outcomes in acute respiratory illnesses such as SARS-CoV-2 infection. METHODS: 1DATA, a unique data platform resulting from collaboration across veterinary and human health care, used an intelligent medicine recommender system (1DrugAssist) developed using several national and international databases to evaluate all ADEs reported to the Food and Drug Administration for patients with diabetes taking ACEIs or ARBs. RESULTS: Mining of this data elucidated the proportion of a cluster of pulmonary ADEs associated with specific medications in these classes, which may aid health care professionals in understanding how these medications could worsen or predispose patients with diabetes to infections affecting the respiratory system, specifically COVID-19. Based on this data mining process, captopril was found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs (P = 0.005) as well as ARBs (P = 0.012), though other specific drugs also had important pulmonary ADEs associated with their use. CONCLUSION: These analyses suggest that pharmacists and clinicians will need to consider the specific medication's adverse event profile, particularly captopril, on how it may affect infections and other acute disease states that alter pulmonary function, such as COVID-19.

Risk of influenza infection with low vaccine effectiveness: the role of avoidance behaviour.

Low vaccine-effectiveness has been recognised as a key factor undermining efforts to improve strategies and uptake of seasonal influenza vaccination. Aiming to prevent disease transmission, vaccination may influence the perceived risk-of-infection and, therefore, alter the individual-level behavioural responses, such as the avoidance of contact with infectious cases. We asked how the avoidance behaviour of vaccinated individuals changes disease dynamics, and specifically the epidemic size, in the context of imperfect vaccination. For this purpose, we developed an agent-based simulation model, and parameterised it with published estimates and relevant databases for population demographics and agent characteristics. Encapsulating an age-stratified structure, we evaluated the per-contact risk-of-infection and estimated the epidemic size. Our results show that vaccination could lead to a larger epidemic size if the level of avoidance behaviour in vaccinated individuals reduces below that of susceptible individuals. Furthermore, the risk-of-infection in vaccinated individuals, which follows the pattern of age-dependent frailty index of the population, increases for older age groups, and may reach, or even exceed, the risk-of-infection in susceptible individuals. Our findings indicate that low engagement in avoidance behaviour can potentially offset the benefits of vaccination even for vaccines with high effectiveness. While highlighting the protective effects of vaccination, seasonal influenza immunisation programmes should enhance strategies to promote avoidance behaviour despite being vaccinated.

Establishing Statistical Equivalence of Data from Different Sampling Approaches for Assessment of Bacterial Phenotypic Antimicrobial Resistance.

To assess phenotypic bacterial antimicrobial resistance (AMR) in different strata (e.g., host populations, environmental areas, manure, or sewage effluents) for epidemiological purposes, isolates of target bacteria can be obtained from a stratum using various sample types. Also, different sample processing methods can be applied. The MIC of each target antimicrobial drug for each isolate is measured. Statistical equivalence testing of the MIC data for the isolates allows evaluation of whether different sample types or sample processing methods yield equivalent estimates of the bacterial antimicrobial susceptibility in the stratum. We demonstrate this approach on the antimicrobial susceptibility estimates for (i) nontyphoidal Salmonella spp. from ground or trimmed meat versus cecal content samples of cattle in processing plants in 2013-2014 and (ii) nontyphoidal Salmonella spp. from urine, fecal, and blood human samples in 2015 (U.S. National Antimicrobial Resistance Monitoring System data). We found that the sample types for cattle yielded nonequivalent susceptibility estimates for several antimicrobial drug classes and thus may gauge distinct subpopulations of salmonellae. The quinolone and fluoroquinolone susceptibility estimates for nontyphoidal salmonellae from human blood are nonequivalent to those from urine or feces, conjecturally due to the fluoroquinolone (ciprofloxacin) use to treat infections caused by nontyphoidal salmonellae. We also demonstrate statistical equivalence testing for comparing sample processing methods for fecal samples (culturing one versus multiple aliquots per sample) to assess AMR in fecal Escherichia coli These methods yield equivalent results, except for tetracyclines. Importantly, statistical equivalence testing provides the MIC difference at which the data from two sample types or sample processing methods differ statistically. Data users (e.g., microbiologists and epidemiologists) may then interpret practical relevance of the difference.IMPORTANCE Bacterial antimicrobial resistance (AMR) needs to be assessed in different populations or strata for the purposes of surveillance and determination of the efficacy of interventions to halt AMR dissemination. To assess phenotypic antimicrobial susceptibility, isolates of target bacteria can be obtained from a stratum using different sample types or employing different sample processing methods in the laboratory. The MIC of each target antimicrobial drug for each of the isolates is measured, yielding the MIC distribution across the isolates from each sample type or sample processing method. We describe statistical equivalence testing for the MIC data for evaluating whether two sample types or sample processing methods yield equivalent estimates of the bacterial phenotypic antimicrobial susceptibility in the stratum. This includes estimating the MIC difference at which the data from the two approaches differ statistically. Data users (e.g., microbiologists, epidemiologists, and public health professionals) can then interpret whether that present difference is practically relevant.

Potential Information Loss Due to Categorization of Minimum Inhibitory Concentration Frequency Distributions.

A bacterial isolate's susceptibility to antimicrobial is expressed as the lowest drug concentration inhibiting its visible growth, termed minimum inhibitory concentration (MIC). The susceptibilities of isolates from a host population at a particular time vary, with isolates with specific MICs present at different frequencies. Currently, for either clinical or monitoring purposes, an isolate is most often categorized as Susceptible, Intermediate, or Resistant to the antimicrobial by comparing its MIC to a breakpoint value. Such data categorizations are known in statistics to cause information loss compared to analyzing the underlying frequency distributions. The U.S. National Antimicrobial Resistance Monitoring System (NARMS) includes foodborne bacteria at the food animal processing and retail product points. The breakpoints used to interpret the MIC values for foodborne bacteria are those relevant to clinical treatments by the antimicrobials in humans in whom the isolates were to cause infection. However, conceptually different objectives arise when inference is sought concerning changes in susceptibility/resistance across isolates of a bacterial species in host populations among different sampling points or times. For the NARMS 1996-2013 data for animal processing and retail, we determined the fraction of comparisons of susceptibility/resistance to 44 antimicrobial drugs of twelve classes of a bacterial species in a given animal host or product population where there was a significant change in the MIC frequency distributions between consecutive years or the two sampling points, while the categorization-based analyses concluded no change. The categorization-based analyses missed significant changes in 54% of the year-to-year comparisons and in 71% of the slaughter-to-retail within-year comparisons. Hence, analyses using the breakpoint-based categorizations of the MIC data may miss significant developments in the resistance distributions between the sampling points or times. Methods considering the MIC frequency distributions in their entirety may be superior for epidemiological analyses of resistance dynamics in populations.

Modulation of chemical dermal absorption by 14 natural products: a quantitative structure permeation analysis of components often found in topical preparations.

A large number of cosmetics and topical pharmaceuticals contain compounds of natural origin. There is a rising concern if these compounds can interact with the activity of other topically applied components in these formulations. The current study demonstrates modulation of dermal absorption of model components often found in topical preparations (14C caffeine and 14C salicylic acid) by a set of 14 compounds of natural origin using a flow through in vitro porcine skin diffusion system. The parameters of flux and permeability were calculated and quantitative structure permeation relationship (QSPR) analysis conducted on different molecular descriptors of modulator compounds. Terpinyl acetate was the greatest permeability/flux enhancer for caffeine followed by s-perillyl acetate and limonene 1,2-epoxide. The permeability/flux of salicylic acid was highest with hydroxycitronellal followed by limonene 1,2-epoxide and s-perillyl acetate. The optimum descriptors using stepwise regression analysis for predicting additive modulation on penetrant permeability/flux were polar surface area, log P for caffeine and Henry's Law constant, number of freely rotatable bonds, and water solubility for salicylic acid. In parallel with the experimental techniques, a novel mathematical model was developed to estimate the permeability coefficients and improve the stepwise regression analysis for assessing modulator effects. The r2 values significantly increased for multicomponent QSPR models. Notably, limonene 1,2-epoxide and s-perillyl acetate were excellent enhancers for both caffeine and salicylic acid. These results confirm that some natural products incorporated into topical formulations will enhance absorption of other components which could affect their safety and efficacy profiles.

Continuum model of T-cell avidity: Understanding autoreactive and regulatory T-cell responses in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease that results from the destruction of insulin-secreting pancreatic ß cells, leading to abolition of insulin secretion and onset of diabetes. Cytotoxic CD4(+) and CD8(+) T cells, activated by antigen presenting cells (APCs), are both implicated in disease onset and progression. Regulatory T cells (Tregs), on the other hand, play a leading role in regulating immunological tolerance and resistant homoeostasis in T1D by suppressing effector T cells (Teffs). Recent data indicates that after activation, conventional Teffs transiently produce interleukin IL-2, a cytokine that acts as a growth factor for both Teffs and Tregs. Tregs suppress Teffs through IL-2 deprivation, competition and Teff conversion into inducible Tregs (iTregs). To investigate the interactions of these components during T1D progression, a mathematical model of T-cell dynamics is developed as a predictor of ß-cell loss, with the underlying hypothesis that avidity of Teffs and Tregs, i.e., the binding affinity of T-cell receptors to peptide-major histocompatibility complexes on host cells, is continuum. The model is used to infer a set of criteria that determines susceptibility to T1D in high risk subjects. Our findings show that diabetes onset is guided by the absence of Treg-to-Teff dominance at specific high avidities, rather than over the whole range of avidity, and that the lack of overall dominance of Teffs-to-Tregs over time is the underlying cause of the "honeymoon period", the remission phase observed in some T1D patients. The model also suggests that competition between Teffs and Tregs is more effective than Teff-induction into iTregs in suppressing Teffs, and that a prolonged full width at half maximum of IL-2 release is a necessary condition for curbing disease onset. Finally, the model provides a rationale for observing rapid and slow progressors of T1D based on modest heterogeneity in the kinetic parameters.

Unraveling the contribution of pancreatic beta-cell suicide in autoimmune type 1 diabetes.

In type 1 diabetes, an autoimmune disease mediated by autoreactive T-cells that attack insulin-secreting pancreatic beta-cells, it has been suggested that disease progression may additionally require protective mechanisms in the target tissue to impede such auto-destructive mechanisms. We hypothesize that the autoimmune attack against beta-cells causes endoplasmic reticulum stress by forcing the remaining beta-cells to synthesize and secrete defective insulin. To rescue beta-cell from the endoplasmic reticulum stress, beta-cells activate the unfolded protein response to restore protein homeostasis and normal insulin synthesis. Here we investigate the compensatory role of unfolded protein response by developing a multi-state model of type 1 diabetes that takes into account beta-cell destruction caused by pathogenic autoreactive T-cells and apoptosis triggered by endoplasmic reticulum stress. We discuss the mechanism of unfolded protein response activation and how it counters beta-cell extinction caused by an autoimmune attack and/or irreversible damage by endoplasmic reticulum stress. Our results reveal important insights about the balance between beta-cell destruction by autoimmune attack (beta-cell homicide) and beta-cell apoptosis by endoplasmic reticulum stress (beta-cell suicide). It also provides an explanation as to why the unfolded protein response may not be a successful therapeutic target to treat type 1 diabetes.

Autoimmune responses in T1DM: quantitative methods to understand onset, progression, and prevention of disease.

Understanding the physiological processes that underlie autoimmune disorders and identifying biomarkers to predict their onset are two pressing issues that need to be thoroughly sorted out by careful thought when analyzing these diseases. Type 1 diabetes (T1D) is a typical example of such diseases. It is mediated by autoreactive cytotoxic CD4⁺ and CD8⁺ T-cells that infiltrate the pancreatic islets of Langerhans and destroy insulin-secreting ß-cells, leading to abnormal levels of glucose in affected individuals. The disease is also associated with a series of islet-specific autoantibodies that appear in high-risk subjects (HRS) several years prior to the onset of diabetes-related symptoms. It has been suggested that T1D is relapsing-remitting in nature and that islet-specific autoantibodies released by lymphocytic B-cells are detectable at different stages of the disease, depending on their binding affinity (the higher, the earlier they appear). The multifaceted nature of this disease and its intrinsic complexity make this disease very difficult to analyze experimentally as a whole. The use of quantitative methods, in the form of mathematical models and computational tools, to examine the disease has been a very powerful tool in providing predictions and insights about the underlying mechanism(s) regulating its onset and development. Furthermore, the models developed may have prognostic implications by aiding in the enrollment of HRS into trials for T1D prevention. In this review, we summarize recent advances made in determining T- and B-cell involvement in T1D using these quantitative approaches and delineate areas where mathematical modeling can make further contributions in unraveling certain aspect of this disease.

In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects.

INTRODUCTION: Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies. AREAS COVERED: Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023. EXPERT OPINION: Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

COVID-19 vaccine immune response in patients with plasma cell dyscrasia: a systematic review.

Background: Patients with plasma cell dyscrasia are at a higher risk of developing a severe Coronavirus-2019 (COVID-19) infection. Here we present a systematic review of clinical studies focusing on the immune response to the COVID-19 vaccination in patients with plasma cell dyscrasia. Objectives: This study aims to evaluate the immune response to COVID-19 vaccines in patients with plasma cell dyscrasia and to utilize the results to improve day-to-day practice. Design: Systematic Review. Methods: Online databases (PubMed, CINAHL, Ovid, and Cochrane) were searched following the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. Only articles published in the English language were included. Out of 59 studies, nine articles (seven prospective and two retrospective studies) were included in this systematic review. Abstracts, case reports, and case series were excluded. Results: In all nine studies (N = 1429), seroconversion post-vaccination was the primary endpoint. Patients with plasma cell disorders had a lower seroconversion rate compared to healthy vaccinated individuals and the overall percentage of seroconversion ranged between 23% and 95.5%. Among patients on active therapy, lower seroconversion rates were seen on an anti-CD38 agent, ranging from 6.5 up to 100%. In addition, a significantly lower percentage was recorded in older patients, especially in those aged equal to or greater than 65 years and those who have been treated with multiple therapies previously. Only one study reported a statistically significant better humoral response rate with the mRNA vaccine compared to ADZ1222/Ad26.Cov.S. Conclusion: Variable seropositive rates are seen in patients with plasma cell dyscrasia. Lower rates are reported in patients on active therapy, anti-CD38 therapy, and elderly patients. Hence, we propose patients with plasma cell dyscrasias should receive periodic boosters to maintain clinically significant levels of antibodies against COVID-19. Registration: PROSPERO ID: CRD42023404989.

COVID-19 vaccine immune response in patients with plasma cell dyscrasia- a systematic review Background: Patients with plasma cell disorders are at a higher risk of developing a severe coronavirus-19 infection. Here we present a systematic review of clinical studies focusing on the immune response to the coronavirus-19 vaccination in patients with plasma cell dyscrasia. Objectives: This study aims to evaluate the immune response to COVID-19 vaccines in patients with plasma cell dyscrasia and to transcribe the results to day-to-day practice. Design: Systematic Review Data sources: PubMed, CINAHL, Ovid, and Cochrane. Methods: Online databases were searched following the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. Only articles published in the English language were included. Abstracts, case reports, and case series were excluded. Out of 59 studies, nine articles were selected for a systematic review. Results: In all 9 studies (N = 1,429), seroconversion post-vaccination was the primary endpoint that our review assessed. Patients with plasma cell disorders had a lower seroconversion rate compared to healthy vaccinated individuals and the overall percentage of seroconversion ranged between 23 and 95.5%. Amongst patients on active therapy, lower seroconversion rates were seen in patients on an anti-CD38 agent, ranging from 6.5 up to 100%. In addition, a significantly lower percentage was recorded in older patients, especially those aged equal to or greater than 65 years and those who have been treated with multiple therapies previously. Only one study reported a statistically significant better humoral response rate with the mRNA vaccine compared to ADZ1222/Ad26.Cov.S. Conclusion: Variable seropositive rates are seen in patients with plasma cell dyscrasia. Lower rates are reported in patients on active therapy, anti-CD38 therapy, and elderly patients. Hence, we propose patients with plasma cell disorders should receive periodic boosters to maintain clinically significant levels of antibodies against COVID-19.

A retrospective study of adverse drug events in anticoagulant administration with relevance to COVID-19.

Initial studies in COVID-19 patients reported lower mortality rates associated with the use of the drug heparin, a widely used anticoagulant. The objective of this analysis was to determine whether there are adverse events associated with the administration of anticoagulants, and specifically how this might apply in patients known to have COVID-19. Data for this study were obtained from the Food and Drug Administration's Adverse Event Reporting System (FAERS) public database and from the NIH's clinical trials website. Proportional Reporting Ratios (PRR) with lower 95% confidence intervals (lower CI) and empirical Bayes geometric mean (EBGM) scores with lower 95% confidence limits were calculated for data from the FAERS database where the adverse events studied mimicked COVID-19 symptoms.

Development of machine learning algorithms to estimate maximum residue limits for veterinary medicines.

Establishing maximum-residue limits (MRLs) for veterinary medicine helps to protect the human food supply. Guidelines for establishing MRLs are outlined by regulatory authorities that drug sponsors follow in each country. During the drug approval process, residue limits are targeted for specific animal species and matrices. Therefore, MRLs are commonly not established for other species. This study demonstrates unestablished MRLs can be reliably predicted for under-represented food commodity groups using machine learning (ML). Classification methods with imbalanced data were used to analyze MRL data from multiple countries by implementing resampling techniques in different ML classifiers. Afterward, we developed and evaluated a data-mining method for predicting unestablished MRLs. Seven different ML classifiers such as support vector classifier, multi-layer perceptron (MLP), random forest, decision tree, k-neighbors, Gaussian NB, and AdaBoost have been selected in this baseline study. Among these, the neural network MLP classifier reliably scored the highest average-weighted F1 score (accuracy >99% with markers and ≈88% without markets) in predicting unestablished MRLs. This provides the first study to apply ML algorithms in regulatory food animal medicine. By predicting and estimating MRLs, we can potentially decrease the use and cost of live animals and the overall research burden of determining new MRLs.

An Automated Customizable Live Web Crawler for Curation of Comparative Pharmacokinetic Data: An Intelligent Compilation of Research-Based Comprehensive Article Repository.

Data curation has significant research implications irrespective of application areas. As most curated studies rely on databases for data extraction, the availability of data resources is extremely important. Taking a perspective from pharmacology, extracted data contribute to improved drug treatment outcomes and well-being but with some challenges. Considering available pharmacology literature, it is necessary to review articles and other scientific documents carefully. A typical method of accessing articles on journal websites is through long-established searches. In addition to being labor-intensive, this conventional approach often leads to incomplete-content downloads. This paper presents a new methodology with user-friendly models to accept search keywords according to the investigators' research fields for metadata and full-text articles. To accomplish this, scientifically published records on the pharmacokinetics of drugs were extracted from several sources using our navigating tool called the Web Crawler for Pharmacokinetics (WCPK). The results of metadata extraction provided 74,867 publications for four drug classes. Full-text extractions performed with WCPK revealed that the system is highly competent, extracting over 97% of records. This model helps establish keyword-based article repositories, contributing to comprehensive databases for article curation projects. This paper also explains the procedures adopted to build the proposed customizable-live WCPK, from system design and development to deployment phases.

The Potential for Cover Crops to Reduce the Load of Escherichia coli in Contaminated Agricultural Soil.

Cover crops are plants seeded before or after cash crops to improve soil health, reduce weed pressure, and prevent erosion. Cover crops also produce various antimicrobial secondary metabolites (i.e., glucosinolates, quercetin), yet the role of cover crops in moderating the population of human pathogens in the soil has rarely been investigated. This study aims to determine the antimicrobial capacity of three cover crop species to reduce the population of generic Escherichia coli (E. coli) in contaminated agricultural soil. Four-week-old mustard greens (Brassicajuncea), sunn hemp (Crotalaria juncea), and buckwheat (Fagopyrum esculentum) were mixed into autoclaved soil and inoculated with rifampicin-resistant generic E. coli to achieve a starting concentration of 5 log CFU/g. The surviving microbial populations on days 0, 4, 10, 15, 20, 30, and 40 were enumerated. All three cover crops significantly reduced the population of generic E. coli compared to the control (p < 0.0001), particularly between days 10 and to 30. Buckwheat resulted in the highest reduction (3.92 log CFU/g). An inhibitory effect (p < 0.0001) on microbial growth was also observed in soils containing mustard greens and sunn hemp. This study provides evidence for the bacteriostatic and bactericidal effect of particular cover crops. More research regarding the secondary metabolites produced by certain cover crops and their potential as a bio mitigation strategy to improve on-farm produce safety is warranted.

Evaluation of Precision Livestock Technology and Human Scoring of Nursery Pigs in a Controlled Immune Challenge Experiment.

The objectives were to determine the sensitivity, specificity, and cutoff values of a visual-based precision livestock technology (NUtrack), and determine the sensitivity and specificity of sickness score data collected with the live observation by trained human observers. At weaning, pigs (n = 192; gilts and barrows) were randomly assigned to one of twelve pens (16/pen) and treatments were randomly assigned to pens. Sham-pen pigs all received subcutaneous saline (3 mL). For LPS-pen pigs, all pigs received subcutaneous lipopolysaccharide (LPS; 300 µg/kg BW; E. coli O111:B4; in 3 mL of saline). For the last treatment, eight pigs were randomly assigned to receive LPS, and the other eight were sham (same methods as above; half-and-half pens). Human data from the day of the challenge presented high true positive and low false positive rates (88.5% sensitivity; 85.4% specificity; 0.871 Area Under Curve, AUC), however, these values declined when half-and-half pigs were scored (75% sensitivity; 65.5% specificity; 0.703 AUC). Precision technology measures had excellent AUC, sensitivity, and specificity for the first 72 h after treatment and AUC values were >0.970, regardless of pen treatment. These results indicate that precision technology has a greater potential for identifying pigs during a natural infectious disease event than trained professionals using timepoint sampling.

Adult Ossabaw Pigs Prefer Fermented Sorghum Tea over Isocaloric Sweetened Water.

Ossabaw pigs (n = 11; 5-gilts, 6-barrows; age 15.6 ± 0.62 SD months) were exposed to a three-choice preference maze to evaluate preference for fermented sorghum teas (FSTs). After conditioning, pigs were exposed, in four sessions, to choices of white FST, sumac FST, and roasted sumac-FST. Then, pigs were exposed, in three sessions, to choices of deionized H2O (-control; avoidance), isocaloric control (+control; deionized H2O and sucrose), and blended FST (3Tea) (equal portions: white, sumac, and roasted sumac). When tea type was evaluated, no clear preference behaviors for tea type were observed (p > 0.10). When the 3Tea and controls were evaluated, pigs consumed minimal control (p < 0.01;18.0 ± 2.21% SEM), and they consumed great but similar volumes of +control and 3Tea (96.6 and 99.0 ± 2.21% SEM, respectively). Likewise, head-in-bowl duration was the least for -control, but 3Tea was the greatest (p < 0.01; 5.6 and 31.9 ± 1.87% SEM, respectively). Head-in-bowl duration for +control was less than 3Tea (p < 0.01; 27.6 vs. 31.9 ± 1.87% SEM). Exploration duration was the greatest in the area with the -control (p < 0.01; 7.1 ± 1.45% SEM), but 3Tea and +control exploration were not different from each other (1.4 and 3.0 ± 1.45% SEM, respectively). Regardless of tea type, adult pigs show preference for FST, even over +control. Adult pigs likely prefer the complexity of flavors, rather than the sweetness alone.

Global Trends in Cancer Nanotechnology: A Qualitative Scientific Mapping Using Content-Based and Bibliometric Features for Machine Learning Text Classification.

This study presents a new way to investigate comprehensive trends in cancer nanotechnology research in different countries, institutions, and journals providing critical insights to prevention, diagnosis, and therapy. This paper applied the qualitative method of bibliometric analysis on cancer nanotechnology using the PubMed database during the years 2000-2021. Inspired by hybrid medical models and content-based and bibliometric features for machine learning models, our results show cancer nanotechnology studies have expanded exponentially since 2010. The highest production of articles in cancer nanotechnology is mainly from US institutions, with several countries, notably the USA, China, the UK, India, and Iran as concentrated focal points as centers of cancer nanotechnology research, especially in the last five years. The analysis shows the greatest overlap between nanotechnology and DNA, RNA, iron oxide or mesoporous silica, breast cancer, and cancer diagnosis and cancer treatment. Moreover, more than 50% of the information related to the keywords, authors, institutions, journals, and countries are considerably investigated in the form of publications from the top 100 journals. This study has the potential to provide past and current lines of research that can unmask comprehensive trends in cancer nanotechnology, key research topics, or the most productive countries and authors in the field.

Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity.

Hypertension is a recognized comorbidity for COVID-19. The association of antihypertensive medications with outcomes in patients with hypertension is not fully described. However, angiotensin-converting enzyme 2 (ACE2), responsible for host entry of the novel coronavirus (SARS-CoV-2) leading to COVID-19, is postulated to be upregulated in patients taking angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). Here, we evaluated the occurrence of pulmonary adverse drug events (ADEs) in patients with hypertension receiving ACEIs/ARBs to determine if disparities exist between individual drugs within the respective classes using data from the FDA Spontaneous Reporting Systems. For this purpose, we proposed the proportional reporting ratio to provide a statistical summary for the commonality of an ADE for a specific drug as compared to the entire database for drugs in the same or other classes. In addition, a statistical procedure, multiple logistic regression analysis, was employed to correct hidden confounders when causative covariates are underreported or untrusted to correct analyses of drug-ADE combinations. To date, analyses have been focused on drug classes rather than individual drugs which may have different ADE profiles depending on the underlying diseases present. A retrospective analysis of thirteen pulmonary ADEs showed significant differences associated with quinapril and trandolapril, compared to other ACEIs and ARBs. Specifically, quinapril and trandolapril were found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs as well as ARBs (P < 0.0001) for group comparison (i.e., ACEIs vs. ARBs vs. quinapril vs. trandolapril) and (P ≤ 0.0007) for pairwise comparison (i.e., ACEIs vs. quinapril, ACEIs vs. trandolapril, ARBs vs. quinapril, or ARBs vs. trandolapril). This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.