Investigating response behavior through TF-IDF and Word2vec text analysis: A case study of PISA 2012 problem-solving process data.

The process data in computer-based problem-solving evaluation is rich in valuable implicit information. However, its diverse and irregular structure poses challenges for effective feature extraction, leading to varying degrees of information loss in existing methods. Process-response behavior exhibits similarities to textual data in terms of the key units and contextual relationships. Despite the scarcity of relevant research, exploring text analysis methods for feature recognition in process data is significant. This study investigated the efficacy of Term Frequency-Inverse Document Frequency (TF-IDF) and Word to Vector (Word2vec) in extracting response behavior features and compared the predictive, analytical, and clustering effects of classical machine learning methods (supervised and unsupervised) on response behavior. An analysis of the PISA 2012 computer-based problem-solving dataset revealed that TF-IDF effectively extracted key response behaviors, whereas Word2vec captured effective features from sequenced response behaviors. In addition, in supervised machine learning using both methods, the random forest model based on TF-IDF performed the best, followed by the SVM model based on Word2vec. Word2vec-based models outperformed TF-IDF-based ones in the F1-score, accuracy, and recall (except for precision) across the logistic regression, k-nearest neighbor, and support vector machine algorithms. In unsupervised machine learning, the k-means algorithm effectively clustered different response behavior patterns extracted by these methods. The findings underscore the theoretical and methodological transferability of these text analysis methods in educational and psychological assessment contexts. This study offers valuable insights for research and practice in similar domains by yielding rich feature representations, supplementing fine-grained assessment evidence, fostering personalized learning, and introducing novel insights for educational assessment.

Characterization of Expression-Based Gene Clusters Gives Insights into Variation in Patient Response to Cancer Therapies.

Background: Transcriptomics can reveal much about cellular activity, and cancer transcriptomics have been useful in investigating tumor cell behaviors. Patterns in transcriptome-wide gene expression can be used to investigate biological mechanisms and pathways that can explain the variability in patient response to cancer therapies. Methods: We identified gene expression patterns related to patient drug response by clustering tumor gene expression data and selecting from the resulting gene clusters those where expression of cluster genes was related to patient survival on specific drugs. We then investigated these gene clusters for biological meaning using several approaches, including identifying common genomic locations and transcription factors whose targets were enriched in these clusters and performing survival analyses to support these candidate transcription factor-drug relationships. Results: We identified gene clusters related to drug-specific survival, and through these, we were able to associate observed variations in patient drug response to specific known biological phenomena. Specifically, our analysis implicated 2 stem cell-related transcription factors, HOXB4 and SALL4, in poor response to temozolomide in brain cancers. In addition, expression of SNRNP70 and its targets were implicated in cetuximab response by 3 different analyses, although the mechanism remains unclear. We also found evidence that 2 cancer-related chromosomal structural changes may impact drug efficacy. Conclusion: In this study, we present the gene clusters identified and the results of our systematic analysis linking drug efficacy to specific transcription factors, which are rich sources of potential mechanistic relationships impacting patient outcomes. We also highlight the most promising of these results, which were supported by multiple analyses and by previous research. We report these findings as promising avenues for independent validation and further research into cancer treatments and patient response.

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes: a bioinformatics analysis.

AIM: To prevent neovascularization in diabetic retinopathy (DR) patients and partially control disease progression. METHODS: Hypoxia-related differentially expressed genes (DEGs) were identified from the GSE60436 and GSE102485 datasets, followed by gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Potential candidate drugs were screened using the CMap database. Subsequently, a protein-protein interaction (PPI) network was constructed to identify hypoxia-related hub genes. A nomogram was generated using the rms R package, and the correlation of hub genes was analyzed using the Hmisc R package. The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve (ROC) curves. Finally, a hypoxia-related miRNA-transcription factor (TF)-Hub gene network was constructed using the NetworkAnalyst online tool. RESULTS: Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified, such as ruxolitinib, meprylcaine, and deferiprone. In addition, 8 hub genes were also identified: glycogen phosphorylase muscle associated (PYGM), glyceraldehyde-3-phosphate dehydrogenase spermatogenic (GAPDHS), enolase 3 (ENO3), aldolase fructose-bisphosphate C (ALDOC), phosphoglucomutase 2 (PGM2), enolase 2 (ENO2), phosphoglycerate mutase 2 (PGAM2), and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3). Based on hub gene predictions, the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs, 77 TFs, and hub genes. The results of ROC showed that the except for GAPDHS, the area under curve (AUC) values of the other 7 hub genes were greater than 0.758, indicating their favorable diagnostic performance. CONCLUSION: PYGM, GAPDHS, ENO3, ALDOC, PGM2, ENO2, PGAM2, and PFKFB3 are hub genes in DR, and hypoxia-related hub genes exhibited favorable diagnostic performance.

Optimized reporters for multiplexed detection of transcription factor activity.

In any given cell type, dozens of transcription factors (TFs) act in concert to control the activity of the genome by binding to specific DNA sequences in regulatory elements. Despite their considerable importance in determining cell identity and their pivotal role in numerous disorders, we currently lack simple tools to directly measure the activity of many TFs in parallel. Massively parallel reporter assays (MPRAs) allow the detection of TF activities in a multiplexed fashion; however, we lack basic understanding to rationally design sensitive reporters for many TFs. Here, we use an MPRA to systematically optimize transcriptional reporters for 86 TFs and evaluate the specificity of all reporters across a wide array of TF perturbation conditions. We thus identified critical TF reporter design features and obtained highly sensitive and specific reporters for 60 TFs, many of which outperform available reporters. The resulting collection of "prime" TF reporters can be used to uncover TF regulatory networks and to illuminate signaling pathways.

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) of Epigenetic Regulators.

Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) allows for the identification of genomic targeting of DNA-binding proteins. Cleavage Under Targets and Release Using Nuclease (CUT&RUN) modifies this process by including a nuclease to digest DNA around a protein of interest. The result is a higher signal-to-noise ratio and decreased required starting material. This allows for high-fidelity sequence identification from as few as 500 cells, enabling chromatin profiling of precious tissue samples or primary cell types, as well as less abundant chromatin-binding proteins: all at significantly increased throughput.

Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management.

BACKGROUND: Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying degrees of computational infrastructure available and budget constraints. OBJECTIVE: To this end, we compared the performance of the deep learning, Bidirectional Encoder Representations from Transformers (BERT)-based model, Bio-Clinical-BERT, with a bag-of-words (BOW) logistic regression (LR) model incorporating term frequency-inverse document frequency (TF-IDF). These choices represent different levels of computational requirements. METHODS: A retrospective analysis was conducted using data from 1,391,988 patients who visited emergency departments in the Mount Sinai Health System spanning from 2017 to 2022. The models were trained on 4 hospitals' data and externally validated on a fifth hospital's data. RESULTS: The Bio-Clinical-BERT model achieved higher areas under the receiver operating characteristic curve (0.82, 0.84, and 0.85) compared to the BOW-LR-TF-IDF model (0.81, 0.83, and 0.84) across training sets of 10,000; 100,000; and ~1,000,000 patients, respectively. Notably, both models proved effective at using triage notes for prediction, despite the modest performance gap. CONCLUSIONS: Our findings suggest that simpler machine learning models such as BOW-LR-TF-IDF could serve adequately in resource-limited settings. Given the potential implications for patient care and hospital resource management, further exploration of alternative models and techniques is warranted to enhance predictive performance in this critical domain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2023.08.07.23293699.

Authentication and validation of key genes in the treatment of atopic dermatitis with Runfuzhiyang powder: combined RNA-seq, bioinformatics analysis, and experimental research.

Background: Atopic dermatitis (AD) is inflammatory disease. So far, therapeutic mechanism of Runfuzhiyang powder on AD remains to be studied. This study aimed to mine key biomarkers to explore potential molecular mechanism for AD incidence and Runfuzhiyang powder treatment. Methods: The control group, AD group, treat group (AD mice treated with Runfuzhiyang powder were utilized for studying. Differentially expressed AD-related genes were acquired by intersecting of key module genes related to control group, AD group and treatment group which were screened by WGCNA and AD-related differentially expressed genes (DEGs). KEGG and GO analyses were further carried out. Next, LASSO regression analysis was utilized to screen feature genes. The ROC curves were applied to validate the diagnostic ability of feature genes to obtain AD-related biomarkers. Then protein-protein interaction (PPI) network, immune infiltration analysis and single-gene gene set enrichment analysis (GSEA) were presented. Finally, TF-mRNA-lncRNA and drug-gene networks of biomarkers were constructed. Results: 4 AD-related biomarkers (Ddit4, Sbf2, Senp8 and Zfp777) were identified in AD groups compared with control group and treat group by LASSO regression analysis. The ROC curves revealed that four biomarkers had good distinguishing ability between AD group and control group, as well as AD group and treatment group. Next, GSEA revealed that pathways of E2F targets, KRAS signaling up and inflammatory response were associated with 4 biomarkers. Then, we found that Ddit4, Sbf2 and Zfp777 were significantly positively correlated with M0 Macrophage, and were significantly negatively relevant to Resting NK. Senp8 was the opposite. Finally, a TF-mRNA-lncRNA network including 200 nodes and 592 edges was generated, and 20 drugs targeting SENP8 were predicted. Conclusion: 4 AD-related and Runfuzhiyang powder treatment-related biomarkers (Ddit4, Sbf2, Senp8 and Zfp777) were identified, which could provide a new idea for targeted treatment and diagnosis of AD.

Corrigendum: YKL-40 promotes chemokine expression following drug-induced liver injury via TF-PAR1 pathway in mice.

[This corrects the article DOI: 10.3389/fphar.2023.1205062.].

Activated Factor VII-Antithrombin Complex, a Biomarker of Tissue Factor-Related Pathways in Different Clinical Settings: A Narrative Review from Cardiovascular Diseases to Cancer.

Tissue factor (TF) is a transmembrane glycoprotein that represents the fundamental physiological initiator of the coagulation cascade through its interaction with factor VII. TF belongs to the cytokine receptor protein superfamily and contributes to the transduction of cellular signaling. Therefore, TF-related pathways are involved in multiple pathophysiological processes, not only in coagulation/thrombosis but in a wider mechanisms' panorama, ranging from infective to neoplastic diseases. Consistently, the measurement of TF activity could have a diagnostic and/or prognostic meaning in different clinical conditions. However, the transmembrane localization, the expression on different cellular types and circulating extracellular vesicles, and the different conformations (encrypted and decrypted) and variants (such as the soluble alternatively spliced TF) hamper TF assessment in clinical practice. The activated factor VII-antithrombin (FVIIa-AT) complex is proposed as an indirect biomarker of the TF-FVIIa interaction and, consequently, of the functionally active TF expression. In this narrative review, we evaluate the clinical studies investigating the role of plasma concentration of FVIIa-AT in health and disease. Although without conclusive data, high FVIIa-AT concentrations predict the worst clinical outcomes in different pathologic conditions, such as cardiovascular disease and cancer, thereby suggesting that overactivation of TF-related pathways may play an unfavorable role in various clinical settings.

TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

A community-based study of the impact of trauma exposure on school-aged children's self-concept and improvements following TF-CBT.

BACKGROUND: Experiencing trauma in childhood has been associated with more severe psychopathology and a greater risk of engaging in harmful behavior later in life. Traumatic exposure can also erode a child's self-concept. Negative self-concept has been associated with shame, self-doubt, and helplessness in the face of adverse experiences. Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) is an evidence-based model for children; however, research on its effectiveness in improving children's self-concept is limited. OBJECTIVE: To investigate the impact of trauma on school-aged children's self-concept and improvements following TF-CBT. PARTICIPANTS AND SETTING: A demographically diverse sample of trauma-exposed school-aged children referred to community-based agencies in Canada and a normative sample of school-aged children randomly selected from the general population in the United States. METHOD: A longitudinal design was used to assess trauma-exposed children's self-reported self-concept using the short-form Tennessee Self-Concept Scale - Second Edition (TSCS:2; Fitts & Warren, 1996) prior to and following TF-CBT. RESULTS: Trauma-exposed children had a significantly more negative mean self-concept compared to that of the normative sample. Improvements following TF-CBT - and not the passage of time alone - were found with gains maintained six months post-therapy. CONCLUSIONS: School-aged children awaiting treatment at community-based agencies are likely to hold clinically concerning negative views of themselves. TF-CBT was effective in significantly improving their self-concept with continued and lasting improvements observed after the therapy had been completed.

Genome-Wide Identification of the Alfin-like Gene Family in Cotton (Gossypium hirsutum) and the GhAL19 Gene Negatively Regulated Drought and Salt Tolerance.

Alfin-like (AL) is a small plant-specific gene family characterized by a PHD-finger-like structural domain at the C-terminus and a DUF3594 structural domain at the N-terminus, and these genes play prominent roles in plant development and abiotic stress response. In this study, we conducted genome-wide identification and analyzed the AL protein family in Gossypium hirsutum cv. NDM8 to assess their response to various abiotic stresses for the first time. A total of 26 AL genes were identified in NDM8 and classified into four groups based on a phylogenetic tree. Moreover, cis-acting element analysis revealed that multiple phytohormone response and abiotic stress response elements were highly prevalent in AL gene promoters. Further, we discovered that the GhAL19 gene could negatively regulate drought and salt stresses via physiological and biochemical changes, gene expression, and the VIGS assay. The study found there was a significant increase in POD and SOD activity, as well as a significant change in MDA in VIGS-NaCl and VIGS-PEG plants. Transcriptome analysis demonstrated that the expression levels of the ABA biosynthesis gene (GhNCED1), signaling genes (GhABI1, GhABI2, and GhABI5), responsive genes (GhCOR47, GhRD22, and GhERFs), and the stress-related marker gene GhLEA14 were regulated in VIGS lines under drought and NaCl treatment. In summary, GhAL19 as an AL TF may negatively regulate tolerance to drought and salt by regulating the antioxidant capacity and ABA-mediated pathway.

A novel optimization-assisted multi-scale and dilated adaptive hybrid deep learning network with feature fusion for event detection from social media.

Social media networks become an active communication medium for connecting people and delivering new messages. Social media can perform as the primary channel, where the globalized events or instances can be explored. Earlier models are facing the pitfall of noticing the temporal and spatial resolution for enhancing the efficacy. Therefore, in this proposed model, a new event detection approach from social media data is presented. Firstly, the essential data is collected and undergone for pre-processing stage. Further, the Bidirectional Encoder Representations from Transformers (BERT) and Term Frequency Inverse Document Frequency (TF-IDF) are employed for extracting features. Subsequently, the two resultant features are given to the multi-scale and dilated layer present in the detection network of GRU and Res-Bi-LSTM, named as Multi-scale and Dilated Adaptive Hybrid Deep Learning (MDA-HDL) for event detection. Moreover, the MDA-HDL network's parameters are tuned by Improved Gannet Optimization Algorithm (IGOA) to enhance the performance. Finally, the execution of the system is done over the Python platform, where the system is validated and compared with baseline methodologies. The accuracy findings of model acquire as 94.96 for dataset 1 and 96.42 for dataset 2. Hence, the recommended model outperforms with the superior results while detecting the social events.

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.

Motivation: The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking. Results: Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

Establishment of a Mitochondrial Metabolism-Related Diagnostic Model in Schizophrenia Based on LASSO Algorithm.

OBJECTIVE: Schizophrenia is a common mental disorder, and mitochondrial function represents a potential therapeutic target for psychiatric diseases. The role of mitochondrial metabolism-related genes (MRGs) in the diagnosis of schizophrenia remains unknown. This study aimed to identify candidate genes that may influence the diagnosis and treatment of schizophrenia based on MRGs. METHODS: Three schizophrenia datasets were obtained from the Gene Expression Omnibus database. MRGs were collected from relevant literature. The differentially expressed genes between normal samples and schizophrenia samples were screened using the limma package. Venn analysis was performed to identify differentially expressed MRGs (DEMRGs) in schizophrenia. Based on the STRING database, hub genes in DEMRGs were identified using the MCODE algorithm in Cytoscape. A diagnostic model containing hub genes was constructed using LASSO regression and logistic regression analysis. The relationship between hub genes and drug sensitivity was explored using the DSigDB database. An interaction network between miRNA-transcription factor (TF)-hub genes was created using the Network-Analyst website. RESULTS: A total of 1,234 MRGs, 172 DEMRGs, and 6 hub genes with good diagnostic performance were identified. Ten potential candidate drugs (rifampicin, fulvestrant, pentadecafluorooctanoic acid, etc.) were selected. Thirty-four miRNAs targeting genes in the diagnostic model (ANGPTL4, CPT2, GLUD1, MED1, and MED20), as well as 137 TFs, were identified. CONCLUSION: Six potential candidate genes showed promising diagnostic significance. rifampicin, fulvestrant, and pentadecafluorooctanoic acid were potential drugs for future research in the treatment of schizophrenia. These findings provided valuable evidence for the understanding of schizophrenia pathogenesis, diagnosis, and drug treatment.

2-Site versus 3-site models of ATP hydrolysis by F1-ATPase: definitive mathematical proof using combinatorics and conservation equations.

The F1-ATPase enzyme is the smallest-known molecular motor that rotates in 120° steps, driven by the hydrolysis of ATP. It is a multi-subunit enzyme that contains three catalytic sites. A central question is how the elementary chemical reactions that occur in the three sites are coupled to mechanical rotation. Various models and coupling schemes have been formulated in an attempt to answer this question. They can be classified as 2-site (bi-site) models, exemplified by Boyer's binding change mechanism first proposed 50 years ago, and 3-site (tri-site) models such as Nath's torsional mechanism, first postulated 25 years ago and embellished 1 year back. Experimental data collated using diverse approaches have conclusively shown that steady-state ATP hydrolysis by F1-ATPase occurs in tri-site mode. Hence older models have been continually modified to make them conform to the new facts. Here, we have developed a pure mathematical approach based on combinatorics and conservation laws to test if proposed models are 2-site or 3-site. Based on this novel combinatorial approach, we have proved that older and modified models are effectively bi‒site models in that catalysis and rotation in F1-ATPase occurs in these models with only two catalytic sites occupied by bound nucleotide. Hence these models contradict consensus experimental data. The recent 2023 model of ATP hydrolysis by F1-ATPase has been proved to be a true tri-site model based on our novel mathematical approach. Such pure mathematical proofs constitute an important step forward for ATP mechanism. However, in what must be considered an aspect with great scientific potential, the power of such mathematical proofs has not been fully exploited to solve molecular biological problems, in our opinion. We believe that the creative application of pure mathematical proofs (for another example see Nath in Theory Biosci 141:249-260, 2022) can help resolve with finality various longstanding molecular-level issues that arise as a matter of course in the analysis of fundamental biological problems. Such issues have proved extraordinarily difficult to resolve by standard experimental, theoretical, or computational approaches.

Psychological intervention in women victims of childhood sexual abuse: a randomized controlled clinical trial comparing EMDR psychotherapy and trauma-focused cognitive behavioral therapy.

Introduction: Childhood sexual abuse persists as a painful societal reality, necessitating responses from institutions and healthcare professionals to prevent and address its severe long-term consequences in victims. This study implements an intervention comprising two psychotherapeutic approaches recommended by the WHO and international clinical guidelines for addressing short-, medium-, and long-term posttraumatic symptomatology: Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) and Eye Movement Desensitization and Reprocessing (EMDR). Both approaches are adapted from group formats for implementation in small online groups via Zoom. Methods: The impact of both therapeutic approaches on trauma improvement was assessed in a sample of 19 women who were victims of childhood sexual abuse through a Randomized Clinical Trial comparing EMDR Psychotherapy and Trauma-Focused Cognitive Behavioral Therapy after a baseline period. Intra and inter comparison were made using statistics appropriate to the sample. Results: Both therapeutic approaches significantly reduced symptomatology across various evaluated variables, suggesting their efficacy in improving the quality of life for these individuals. Following CBT-FT treatment, patients exhibited enhanced emotional regulation, reduced reexperiencing, and avoidance. The EMDR group, utilizing the G-TEP group protocol, significantly improved dissociation, along with other crucial clinical variables and the perception of quality of life. Discussion: Although the limitations of this study must be taken into account due to the size of the sample and the lack of long-term follow-up, the results align with existing scientific literature, underscoring the benefits of trauma-focused psychological treatments. The online group format appears promising for enhancing the accessibility of psychological treatment for these women. Furthermore, the differential outcomes of each treatment support recent research advocating for the inclusion of both approaches for individuals with trauma-related symptomatology. Ethics and dissemination: The study has been approved by the Ethics Committee of the Valencian International University (VIU) (Valencia, Spain) (Ref. CEID2021_07). The results will be submitted for publication in peer-reviewed journals and disseminated to the scientific community. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT04813224, identifier NCT04813224.

Calpain Small Subunit Mediated Secretion of Galectin-3 Regulates Traction Stress.

The complex regulation of traction forces (TF) produced during cellular migration remains poorly understood. We have previously found that calpain 4 (Capn4), the small non-catalytic subunit of the calpain 1 and 2 proteases, regulates the production of TF independent of the proteolytic activity of the larger subunits. Capn4 was later found to facilitate tyrosine phosphorylation and secretion of the lectin-binding protein galectin-3 (Gal3). In this study, recombinant Gal3 (rGal3) was added to the media-enhanced TF generated by capn4-/- mouse embryonic fibroblasts (MEFs). Extracellular Gal3 also rescued defects in the distribution, morphology, and adhesive strength of focal adhesions present in capn4-/- MEF cells. Surprisingly, extracellular Gal3 does not influence mechanosensing. c-Abl kinase was found to affect Gal3 secretion and the production of TF through phosphorylation of Y107 on Gal3. Our study also suggests that Gal3-mediated regulation of TF occurs through signaling pathways triggered by ß1 integrin but not by focal adhesion kinase (FAK) Y397 autophosphorylation. Our findings provide insights into the signaling mechanism by which Capn4 and secreted Gal3 regulate cell migration through the modulation of TF distinctly independent from a mechanosensing mechanism.

Screening and Treatment of Posttraumatic Stress Disorder in Wildfire Evacuees: A Cost-Utility Analysis.

Background. Global climate change is resulting in dramatic increases in wildfires. Individuals exposed to wildfires experience a high burden of posttraumatic stress disorder (PTSD), and the cost-effectiveness of the treatment options to address PTSD from wildfires has not been studied. The objective of this study was to conduct a cost-utility analysis comparing screening followed by treatment with paroxetine or trauma-focused cognitive behavioral therapy (TF-CBT) versus no screening in Canadian adult wildfire evacuees. Methods. Using a Markov model, quality-adjusted life-years (QALYs) and costs were evaluated over a 5-y time horizon using health care and societal perspectives. All costs and utilities in the model were discounted at 1.5%. Probabilistic and deterministic sensitivity analyses examined the uncertainty in the incremental net monetary benefit (INMB) under a willingness-to-pay threshold of $50,000. Results. From a societal perspective, no screening (NMB = $177,641) was dominated by screening followed by treatment with paroxetine (NMB = $180,733) and TF-CBT (NMB = $181,787), with TF-CBT having the highest likelihood of being cost-effective at a willingness-to-pay threshold of $50,000 per QALY (probability = 0.649). The initial prevalence of PTSD, probability of acceptance of treatment, and costs of productivity had the largest impact on the INMB of both paroxetine or TF-CBT versus no screening. Neither intervention was cost-effective at a willingness-to-pay threshold of $50,000 per QALY from a health care perspective. Interpretation. Screening followed by treatment with paroxetine or TF-CBT compared with no screening was found to be cost-saving while providing additional QALYs in wildfire evacuees. Governments should consider funding screening programs for PTSD followed by treatment with TF-CBT for wildfire evacuees. Highlights: Two prior studies examined the cost-effectiveness of screening followed by treatment for PTSD among individuals exposed to other disaster-type events (i.e., terrorist attack and Hurricane Sandy) and found screening followed by treatment (i.e., cognitive behavioral therapy [CBT]) to be highly cost-effective.Among wildfire evacuees, screening followed by treatment with paroxetine or trauma-focused (TF)-CBT provides additional quality-adjusted life-years (QALYs) and is cost-saving from a societal perspective. TF-CBT was the treatment option found most likely to be cost-effective.Neither treatment option was cost-effective at a willingness-to-pay threshold of $50,000 per QALY from a health care perspective.Screening programs for PTSD should be considered for wildfire evacuees, and individuals diagnosed with PTSD could be prescribed either TF-CBT or paroxetine depending on their preference and resources availability.

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Introduction: Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. Methods: We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. Results: The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Discussion: Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.