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Most drug repurposing studies using real-world data focused on validating, instead of generating, hypotheses. We used tree-based scan statistics to generate repurposing hypotheses for sodium-glucose cotransporter-2 inhibitors (SGLT2i). We used an active-comparator, new-user design to create a 1:1 propensity-score matched cohort of SGLT2i and dipeptidyl peptidase-4 inhibitors (DPP4i) initiators in the MerativeTM MarketScan® Research Databases. Tree-based scan statistics were estimated across an ICD-10-CM-based hierarchical outcome tree using incident outcomes identified from hospital and outpatient diagnoses. We used an adjusted P≤0.01 as the threshold for statistical alert to prioritize associations for evaluation as repurposing signals. We varied the analyses by tree size, scanning level, and clinical settings for outcomes. There were 80,510 matched SGLT2i-DPP4i initiator pairs with 215,333 outcomes among SGLT2i initiators and 223,428 outcomes among DPP4i initiators. There were 18 prioritized associations, which included chronic kidney disease (P=0.0001), an expected signal, and anemia (P=0.0001). Heart failure (P=0.0167), another expected signal, was identified slightly beyond the statistical alert threshold. Narrowing the outcome tree, scanning at different tree levels, and including outcomes from different clinical settings influenced the scan statistics. We identified signals aligning with recently approved indications of SGLT2i, plus potential repurposing signals supported by existing evidence but requiring future validation.

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Continuous fiber-reinforced composites are increasingly used in industry for their superior specific modulus and strength. The curing process-induced deformation (PID) has been a critical problem during manufacturing, which always exhibits dispersed values even if the curing process curve and structural parameters remain consistent. This work conducted probability prediction of PID for V-shape composite structures based on the FEM method and data mining. A sequential coupling thermal-chemical-mechanical coupling FE model is established in ABAQUS. The prediction accuracy of the included angle between two sides is verified by the experimental results. Material parameter uncertainties are considered for V-shape structures with different radii and thicknesses. Based on the dataset from the FE model, a decision tree is established and trained to analyze the sensitivity and to predict the probability distribution of PID. The results show that PID increases with the coefficients of thermal expansion in the in-plane perpendicular fiber direction and out-of-plane normal direction. The data-mining method is accurate enough for the PID prediction, and its efficiency provides an additional calculation option in engineering applications.

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The present research aims to describe how turkey meat and carcass quality traits define the interest of the scientific community through the quality standards of journals in which studies are published. To this end, an analysis of 92 research documents addressing the study of turkey carcass and meat quality over the last 57 years was performed. Meat and carcass quality attributes were dependent variables and included traits related to carcass dressing, muscle fiber, pH, colorimetry, water-holding capacity, texture, and chemical composition. The independent variables comprised publication quality traits, including journal indexation, database, journal impact factor (JIF), quartile, publication area, and JIF percentage. For each dependent variable, a data-mining chi-squared automatic interaction detection (CHAID) decision tree was developed. Carcass or piece yield was the only variable that did not show an impact on the publication quality. Moreover, color and pH measurements taken at 72 h postmortem showed a negative impact on publication interest. On the other hand, variables including water-retaining attributes, colorimetry, pH, chemical composition, and shear force traits stood out among the quality-enhancing variables due to their low inclusion in papers, while high standards improved power.

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BACKGROUND: Establishing adequate reference intervals (RIs) for vitamins A and E is essential for diagnosing and preventing deficiencies. Due to the current boom in data mining and its easy applicability, more laboratories are establishing RIs using indirect methods. Our study aims to obtain RIs using four indirect data-mining procedures (Bhattacharya, Hoffmann, Kosmic, and RefineR) for vitamins A and E. MATERIAL AND METHODS: 8943 individuals were collected to establish the RIs. After using different data cleaning steps and checking whether these data should be divided according to age and gender based on multiple linear regression and variance component analyses, indirect RIs were calculated using specific Excel spreadsheets or R-packages software. RESULTS: A total of 2004 records were eligible. For vitamin A, the RIs obtained were (1.11 - 2.68) µmol/L, (1.13 - 2.70) µmol/L, (1.13 - 2.71) µmol/L, and (1.17 - 2.66) µmol/L using the Bhattacharya, Hoffmann, Kosmic and RefineR approaches, respectively. For vitamin E, these intervals were (17.3 - 49.9) µmol/L (Bhattacharya), (17.3 - 48.9) µmol/L (Hoffmann), (19.6 - 50.3) µmol/L (Kosmic), and (19.4 - 50.9) µmol/L (RefineR). In all cases, the RIs were comparable. CONCLUSIONS: Suitable RIs for vitamins A and E were calculated using four indirect methods that are suitable and adapted to our population's demographic characteristics.

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Tafamidis is the world's first and only oral drug approved to treat the rare disease transthyretin amyloid cardiomyopathy (ATTR-CM). Medicines are known to have different adverse reactions during the course of treatment. However, the current limited clinical studies did not identify significant adverse drug reactions to tafamidis. Tafamidis has been on the market for 5 years now, a large number of adverse drug event (ADE) reports with tafamidis as the primary suspected drug have been reported in the United Food and Drug Administration's adverse event reporting system (FAERS). We retrieved 8170 adverse event reports in FAERS with tafamidis as the first suspected drug, and mined these reports for positive signals to perform risk warnings for potentially possible adverse events with tafamidis. We found that a large number of adverse events associated with the primary disease were reported due to insufficient awareness of ATTR among the reporters, leading to a large number of positive signals reported in the cardiac disorders system. We also found that tafamidis has the potential to cause an adverse event risks of ear and labyrinth disorders system and urinary tract infection bacterial, which deserve continued clinical attention.

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The pathogeny of polycystic ovary syndrome (PCOS) is intricate, with endocrine disruptors (EDCs) being acknowledged as significant environmental factors. Research has shown a link between exposure to per- and polyfluoroalkyl substances (PFAS) and the development and progression of PCOS, although the precise mechanism is not fully understood. This study utilized toxicogenomics and comparative toxicogenomics databases to analyze data and investigate how PFAS mixtures may contribute to the development of PCOS. The results indicated that 74 genes are associated with both PFAS exposure and PCOS progression. Enrichment analysis suggested that cell cycle regulation and steroid hormone synthesis may be crucial pathways through which PFAS mixtures participate in the development of PCOS, involving important genes such as CCNB1 and SRD5A1. Furthermore, the study identified transcription factors (TFs) and miRNAs that may be involved in the onset and progression of PCOS, constructing regulatory networks encompassing TFs-mRNA interactions and miRNA-mRNA relationships to elucidate their regulatory roles in gene expression. By utilizing data mining techniques based on toxicogenomic databases, this study provides relatively comprehensive insights into the association between exposure factors and diseases compared to traditional toxicology studies. These findings offer new perspectives for further in vivo or in vitro investigations and contribute to understanding the pathogenesis of PCOS, thereby providing valuable references for identifying clinical treatment targets.

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Underground mining is currently one of the Spanish economic sectors with the worst accident rates. Besides, the most frequent type of accident, and with the most serious consequences, is the one in which the injured worker is hit by a moving object. For this reason, this study focuses on the analysis of this type of accident, divided into 3 subgroups to better understand the behavioural patterns. Data mining techniques were applied using the Apriori algorithm to extract as much information as possible about the genesis of these accidents. Similarly, each subset of accidents was processed in two different ways to improve the data analysis, depending on the causal variables used in each case, so that a study of six different scenarios was carried out. The five best association rules or behaviour patterns for each of the six scenarios are shown as a function of their frequency for each rule with 1-4 causal variables.

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This poster describes the findings from a study that attempts to characterize an underserved Black stroke cohort who are at risk for discontinuities of care using electronic health record (EHR). Preliminary analysis revealed that 90.1% of the patients were Black. After being diagnosed with a stroke, 57% of the population did not return for follow-up. The objective is to understand the factors contributing to discontinuity of care in Black stroke patients.

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The constant exposure of humans to a mixture of low doses of toxic substances, emerging from the daily emission of toxic dust containing various metals and organic compounds in electrical and electronic waste (e-waste) recycling areas, poses potential harmful effects on health and the environment. While individually recognized as endocrine disruptors affecting hormonal balance, the combined impact of these toxic substances in a mixture remains insufficiently explored, particularly in relation to reproductive health. Thus, the aim of this in silico analysis was to: (i) assess the relationship between the exposure to a mixture of DBDE, DBDPE, TBBPA, Pb, Cd and Ni and development of male and female reproductive system disorders; and (ii) demonstrate the ability of in silico toxicogenomic tools in revealing the potential molecular mechanisms involved in the mixture toxicity. As the main data-mining tool, Comparative Toxicogenomics Database (CTD) was used, along with the ToppGene Suite portal and GeneMANIA online server. Our analysis identified 5 genes common to all the investigated substances and linked to reproductive system disorders. Notably, the most prominent interactions among these genes were physical interactions (77.64 %). Pathway enrichment analysis identified oxidative stress response as the central disrupted molecular pathway linked to reproductive pathology in the investigated mixture, while our chemical-phenotype CTD analysis uncovered additional affected pathways - apoptosis, hormonal regulation, and developmental functions. These findings highlight an increased risk of reproductive system disorders associated with the exposure to the investigated mixture of toxic substances in electronic waste recycling areas, emphasizing the urgent need for attention to address this environmental health concern. Hence, future laboratory studies should prioritize investigating the specific genes and common mechanisms identified in this study.

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BACKGROUND: In 2011, Korean Neuropsychiatric Association renamed schizophrenia from 'mind split disorder' ('Jungshinbunyeolbyung' in Korean) to 'attunement disorder' ('Johyeonbyung' in Korean), in a strategic way to reduce social stigma toward people with schizophrenia. However, there remains an elusive consensus that how the renaming effort has contributed to changes in the social perception of schizophrenia in Korea. METHODS: With this regard, we explored whether media frames alter the social perception, in ways of respecting or disrespecting schizophrenia patients before and after the renaming. This study extensively investigated media keywords related to schizophrenia across the time by applying both language and epidemiologic analyses. RESULTS: In results, the media keywords have been negatively described for schizophrenia patients both before and after the renaming. Further, from an analysis using the regression model, a significant correlation was observed between the frequency of negative keywords and the hospitalization frequency of schizophrenia patients. CONCLUSIONS: These findings suggest that the social perception of schizophrenia has been scarcely changed, but rather remained negatively biased against schizophrenia patients, in spite of the renaming effort. Notably, the biased media frames have been demonstrated to negatively impact on the social perception, and even on the medical use patterns of general schizophrenia patients. In conclusion, we suggest that the unbiased media frames along with the renaming effort may collectively help reduce the negative social perception of schizophrenia. TRIAL REGISTRATION: This study was approved from the Institute of Review Board (IRB) of the Yoing-In Mental Hospital (IRB No. YIMH-IRB-2019-02).

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Lichens are unique organisms that exhibit a permanent symbiosis between fungi and algae or fungi and photosynthetic bacteria. Lichens have been found to produce biotechnologically valuable secondary metabolites. A handful of studies showed that tailoring enzymes such as cytochrome P450 monooxygenases (CYPs/P450s) play a key role in synthesizing these metabolites. Despite the critical role of P450s in the biosynthesis of secondary metabolites, the systematic analysis of P450s in lichens has yet to be reported. This study is aimed to address this research gap. A genome-wide analysis of P450s in five lichens from the fungal class Lecanoromycetes revealed the presence of 434 P450s that are grouped into 178 P450 families and 345 P450 subfamilies. The study indicated that none of the P450 families bloomed, and 15 P450 families were conserved in all five Lecanoromycetes. Lecanoromycetes have more P450s and higher P450 family diversity compared to Pezizomycetes. A total of 73 P450s were found to be part of secondary metabolite gene clusters, indicating their potential involvement in the biosynthesis of secondary metabolites. Annotation of P450s revealed that CYP682BG1 and CYP682BG2 from Cladonia grayi and Pseudevernia furfuracea (physodic acid chemotype) are involved in the synthesis of grayanic acid and physodic acid, CYP65FQ2 from Stereocaulon alpinum is involved in the synthesis of atranorin, and CYP6309A2 from Cladonia uncialis is involved in the synthesis of usnic acid. This study serves as a reference for future annotation of P450s in lichens.

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Genipin, an aglycone of geniposide, is a rich iridoid component in the fruit of Gardenia jasminoides Ellis and has numerous biological activities. However, its metabolic profiles in vivo and vitro remain unclear. In this study, an effective analytical strategy based on ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) in positive and negative ion modes was developed to analyze and identify genipin metabolites in rat urine, blood, feces, and fecal fermentation in combination with many methods including post-collection data mining methods, high-resolution extracted ion chromatography (HREIC), and multiple mass defect filtering (MMDF). Simultaneously, the metabolites of genipin in vivo were verified by fecal fermentation of SD rats at different times. Finally, based on information such as reference substances, chromatographic retention behavior, and accurate mass determination, a total of 50 metabolites (including prototypes) were identified in vivo. Among them, 7, 31 and 28 metabolites in vivo were identified in blood, urine, and feces, respectively. Our results showed that genipin could generate different metabolites that underwent multiple metabolic reactions in vivo including methylation, hydroxylation, dehydroxylation, hydrogenation, sulfonation, glucuronidation, demethylation, and their superimposed reactions. Forty-six metabolites were verified in vitro. Meanwhile, 2 and 19 metabolites identified in blood and urine were also verified in fecal fermentation at different times. These results demonstrated that metabolites were produced in feces and reabsorbed into the body. In conclusion, the newly discovered metabolites of genipin can provide a new perspective for understanding its pharmacological effects and build the foundation for thee toxicity and safety evaluations of genipin.

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BACKGROUND: Traditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination. METHODS: Vaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code "tree" and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance. RESULTS: Analysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1-3, p = 0.0001-0.0007), influenza (Days 35-56, p = 0.0001), cough (Days 44-55, p = 0.0002), and COVID-19 (Days 52-56, p = 0.0004). CONCLUSIONS: For Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the "tree."

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PURPOSE: Although spirituality seems to protect against suicidality and substance misuse, in 2022 81% of United States residents believe in God and yet prevalence of these global health issues are increasing. 12-Step programs are inherently spiritual in their approach to recovery. METHOD: We used a clinically mined dataset built from everyday clinical data that a substance use day treatment program in a midwestern state of the USA originally collected for their treatment purposes. Data included information from 444 client files from three day treatment sites within the same agency. Using logistic regression, we analyzed the relationships between suicidality, spirituality, and treatment discharge. RESULTS: Suicidality and spirituality, including 12-Step involvement prior to treatment, did not have a statistically significant effect on substance use day treatment discharge. However, treatment length and age were associated with completed treatment. DISCUSSION AND CONCLUSION: While spirituality and suicidality are important concepts with relevance to recovery, they did not affect whether clients completed substance use day treatment. However, because recovery is not just about abstinence or risk reduction, suicidality and spiritually likely have relevance for the overall recovery process.

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Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.

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The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB's role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region's propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of "high-confidence" hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

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The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.

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The current study aimed to assess the connection between the mixture of lead (Pb), cadmium (Cd), arsenic (As), methylmercury (MeHg) and decabrominated diphenyl ether (decaBDE) and thyroid function, by using in silico toxicogenomic data-mining approach. To obtain the linkage between investigated toxic mixture and thyroid diseases (TDs), the Comparative Toxicogenomics Database (CTD) was used, while gene ontology (GO) enrichment analysis was performed by ToppGeneSuite portal. The analysis has shown 10 genes connected to all chemicals present in the mixture and TDs (CAT, GSR, IFNG, IL1B, IL4, IL6, MAPK1, SOD2, TGFB1, TNF), most of which were in co-expression (45.68%), or belonged to the same pathway (30.47%). Top 5 biological processes and molecular functions affected by the investigated mixture emphasized the role of two common mechanisms - oxidative stress and inflammation. Cytokines and inflammatory response was listed as the main molecular pathway that may be triggered by simultaneous exposure to toxic metal(oid)s and decaBDE and connected to TDs. The direct relations between Pb/decaBDE and redox status impairment in thyroid tissue was confirmed by our chemical-phenotype interaction analysis, while the strongest linkage between Pb, As and decaBDE and thyroid disorders was found. The obtained results provide better understanding of molecular mechanisms involved in the thyrotoxicity of the investigated mixture, and can be used to direct further research.

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BACKGROUND: Cycloastragenol (CAG) is a sapogenin derived from the main bioactive constituents of Astragali Radix (AR). However, the current research on CAG metabolism in vivo and in vitro is still inadequate, and the metabolite cluster is incomplete due to incomplete analysis strategy. OBJECTIVE: The objective of this study was to screen and identify the metabolic behavior of CAG in vivo and in vitro. METHODS: A simple and rapid analysis strategy based on UHPLC-Q-Exactive Orbitrap mass spectrometry combined with data-mining processing technology was developed and used to screen and identify CAG metabolites in rat body fluids and tissues after oral administration. RESULTS: As a result, a total of 82 metabolites were fully or partially characterized based on their accurate mass, characteristic fragment ions, retention times, corresponding Clog P values, and so on. Among the metabolites, 61 were not been reported in previous reports. These metabolites (6 metabolites in vitro and 91 in vivo) were generated through reactions of hydroxylation, glucuronidation, sulfation, hydrogenation, hydroxylation, demethylation, deisopropylation, dehydroxylation, ring cleavage, and carboxyl substitution and their composite reactions, and the hydroxylation might be the main metabolic reaction of CAG. In addition, the characteristic fragmentation pathways of CAG were summarized for the subsequent metabolite identification. CONCLUSION: The current study not only clarifies the metabolite cluster-based and metabolic regularity of CAG in vivo and in vitro, but also provides ideas for metabolism of other saponin compounds.

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BACKGROUND: The Centers for Disease Control and Prevention's Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study's objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. METHODS: VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. RESULTS: More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10-15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1-10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. CONCLUSIONS: In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days.

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