Differences of TF-CBT treatment effects using various outcome measures: a meta-analysis.

Background: Diagnostic criteria of posttraumatic stress disorder in children and adolescents and corresponding instruments have undergone significant changes over time. However, the impact of different outcome measures on treatment effects in the context of posttraumatic stress symptoms (PTSS) has not yet been explored.Objective: TF-CBT is a well-researched first-line treatment for PTSS among children and adolescents and thus, an ideal candidate to examine the potential influence of different outcome measures by meta-analysis.Method: A comprehensive literature search was conducted in December 2023 using seven databases. Studies included RCTs as well as non-controlled studies examining the effects of TF-CBT on pediatric PTSS. We extracted treatment effects and investigated whether there were systematic differences in the effects based on the outcome measures and their underlying DSM version.Results: In total, 76 studies (35 RCTS) met the eligibility criteria. Hedges g effect sizes with 95% confidence intervals (CI) were computed and high-risk of bias studies were excluded. No significant difference was observed between DSM-IV and DSM-5 based instruments. Individual outcome measures were found to be comparable overall, with some appearing somewhat more sensitive to change. Although a small but significant difference in true effect sizes for individual outcome measures was found, this only concerned the UCLA PTSD (g = 1.06) and the CPSS (g = 1.61) with the effect most likely being due to chance or confounding variables. TF-CBT showed large effect sizes on PTSS in within-study comparison (g = 1.32) and medium between-studies effect sizes (g = .57).Conclusions: While we could not establish equivalence, there seems to be no difference regarding the measurement of treatment effects based on outcome measure and underlying DSM version. The updated TF-CBT effect size confirmed it as an effective treatment for PTSS and secondary outcomes in children and adolescents.

No difference between outcome measures for posttraumatic stress symptoms in children and adolescents and their underlying DSM-criteria could be established.TF-CBT has again been confirmed TF-CBT as a treatment of first choice for PTSS in children and adolescents.

Effects of Therapy at a Community Based Trauma Therapy Service Treating Child Abuse and Neglect: A Pre-Post Study Using Administrative Data.

This repeated-measures study examined the effects of a hybrid of Trauma-Focused Cognitive Behavioural Therapy (TF-CBT) with other therapeutic approaches at a community-based clinic in Perth Western Australia among a sample of children and young people overwhelmingly experiencing multiple forms of maltreatment and with complex family situations (i.e., family and domestic violence, parental mental health, parental substance abuse). Drawing on 1713 individual client records from between 2017 and 2020, the researchers identified 113 children and young people with viable pre-post treatment assessments including 78 on the TSCC, 36 on the TSCYC, and 12 on the CBCL. Significant improvements on most clinical scales were identified on the TSCC and TSCYC. Sub-analysis of the TSCC results found no differences across gender, age, care status, therapy funding source, and the presence of sexual abuse in the rate of improvement on trauma symptoms. Overall, the study highlights that integrating different therapy approaches for populations with multiple and complex trauma symptoms accessing community-based services can be useful for supporting the delivery of TF-CBT for difficult to treat populations.

Impact of Gender-Affirming Hormonal Therapy on Cardiovascular Risk Factors in Transgender Health: An Updated Meta-Analysis.

Background: Gender-affirming hormone therapy (GAHT) is common among transgender individuals, but its impact on lipid profile and cardiovascular health is not well studied. Objectives: The authors performed a systematic review and meta-analysis of existing literature to assess the impact of GAHT on lipid profiles and metabolic cardiovascular risk factors in transgender individuals. Methods: Online databases including MEDLINE/PubMed, Embase, and Cochrane Central registry were searched to find studies on lipid profile changes in women who are transgender, also referred to as transfeminine (TF), and men who are transgender, also referred to as transmasculine (TM) before and after GAHT. Baseline comorbidities were analyzed using descriptive statistics, and R-statistical software was used to analyze the mean difference in lipid profile change between the two cohorts (pre- and post-GAHT therapy) including transgender patients. Results: Overall, 1,241 TM and 992 TF patients were included from 12 observational studies and 12 randomized controlled trials. The mean age among TM and TF was 28 years and 30 years, respectively. The mean follow-up duration (including pre- and post-GAHT therapy) was 28 months in TM patients and 39 months in TF patients. When compared to baseline measures, TM patients had a significant increase in low-density lipoprotein, triglyceride levels, and total cholesterol while high-density lipoprotein levels decreased. In TF patients, there was a significant increase in triglyceride levels. Conclusions: GAHT affects lipid profiles in transgender patients; however, additional studies are needed to determine how these changes impact clinical outcomes.

Novel Galectins Purified from the Sponge Chondrilla australiensis: Unique Structural Features and Cytotoxic Effects on Colorectal Cancer Cells Mediated by TF-Antigen Binding.

We here report the purification of a novel member of the galectin family, the ß-galactoside-binding lectin hRTL, from the marine sponge Chondrilla australiensis. The hRTL lectin is a tetrameric proto-type galectin with a subunit molecular weight of 15.5 kDa, consisting of 141 amino acids and sharing 92% primary sequence identity with the galectin CCL from the congeneric species C. caribensis. Transcriptome analysis allowed for the identification of additional sequences belonging to the same family, bringing the total number of hRTLs to six. Unlike most other galectins, hRTLs display a 23 amino acid-long signal peptide that, according to Erdman degradation, is post-translationally cleaved, leaving an N-terminal end devoid of acetylated modifications, unlike most other galectins. Moreover, two hRTLs display an internal insertion, which determines the presence of an unusual loop region that may have important functional implications. The characterization of the glycan-binding properties of hRTL revealed that it had high affinity towards TF-antigen, sialyl TF, and type-1 N-acetyl lactosamine with a Galß1-3 structure. When administered to DLD-1 cells, a colorectal carcinoma cell line expressing mucin-associated TF-antigen, hRTL could induce glycan-dependent cytotoxicity.

The MYB transcription factor PpMYB5 regulates Pp4CL1/Pp4CL2 expression to promote lignin biosynthesis of fruit russeting in the flat nectarine.

KEY MESSAGE: Transcription factor PpMYB5 promotes lignin synthesis by directly binding to the Pp4CL1/Pp4CL2 promoter and affecting their expression, which may be related to nectarine russeting formation. Nectarine russeting is usually considered to be a non-invasive physiological disease that usually occurs on late-maturing cultivars and seriously affects their appearance quality and commercial value. The cause of nectarine fruit rust is currently unknown. In this study, we compared two flat nectarine cultivars, 'zhongyoupanweidi' (HD; russeting-free cultivar) and 'zhongyoupanweihou' (TH; russeting-prone cultivar), with respect to nectarine russeting by means of microscopy, transcriptomics, and hormone analysis. Compared to HD fruits, TH fruits had a broken cuticle, missing wax layer, and heavy lignin deposition. RNA sequencing (RNA-seq) revealed significant alternations in the expression of genes related to lignin synthesis. Moreover, structure genes Pp4CL1 and Pp4CL2, MYB transcription factor (TF) gene PpMYB5 were identified through weighted gene co-expression network analysis (WGCNA). Molecular experiments and transgenic evidence suggested that PpMYB5 regulates Pp4CL1/Pp4CL2 expression to promote lignin synthesis. Overall, in addition to providing new insights into the formation of mechanisms for nectarine russeting, our study also establishes a foundation for nectarine russeting prevention.

Dual Chemotherapeutic Loading in Oxalate Transferrin-Conjugated Polymersomes Incorporated into Chitosan Hydrogels for Site-Specific Targeting of Melanoma Cells.

In this work, we developed a smart drug delivery system composed of poly (ethylene glycol)-block-poly (ε-caprolactone) (PEG-PCL)-based polymersomes (Ps) loaded with doxorubicin (DOX) and vemurafenib (VEM). To enhance targeted delivery to malignant melanoma cells, these drug-loaded nanovesicles were conjugated to the oxalate transferrin variant (oxalate Tf) and incorporated into three-dimensional chitosan hydrogels. This innovative approach represents the first application of oxalate Tf for the precision delivery of drug-loaded polymersomes within a semi-solid dosage form based on chitosan hydrogels. These resulting semi-solids exhibited a sustained release profile for both encapsulated drugs. To evaluate their potency, we compared the cytotoxicity of native Tf-Ps with oxalate Tf-Ps. Notably, the oxalate Tf-Ps demonstrated a 3-fold decrease in cell viability against melanoma cells compared to normal cells and were 1.6-fold more potent than native Tf-Ps, indicating the greater potency of this nanoformulation. These findings suggest that dual-drug delivery using an oxalate-Tf-targeting ligand significantly enhances the drug delivery efficiency of Tf-conjugated nanovesicles and offers a promising strategy to overcome the challenge of multidrug resistance in melanoma therapy.

Development of an in vitro compound screening system that replicate the in vivo spine phenotype of idiopathic ASD model mice.

Autism Spectrum Disorder (ASD) is a developmental condition characterized by core symptoms including social difficulties, repetitive behaviors, and sensory abnormalities. Aberrant morphology of dendritic spines within the cortex has been documented in genetic disorders associated with ASD and ASD-like traits. We hypothesized that compounds that ameliorate abnormalities in spine dynamics might have the potential to ameliorate core symptoms of ASD. Because the morphology of the spine is influenced by signal inputs from other neurons and various molecular interactions, conventional single-molecule targeted drug discovery methods may not suffice in identifying compounds capable of ameliorating spine morphology abnormalities. In this study, we focused on spine phenotypes in the cortex using BTBR T + Itpr3 tf /J (BTBR) mice, which have been used as a model for idiopathic ASD in various studies. We established an in vitro compound screening system using primary cultured neurons from BTBR mice to faithfully represent the spine phenotype. The compound library mainly comprised substances with known target molecules and established safety profiles, including those approved or validated through human safety studies. Following screening of this specialized library containing 181 compounds, we identified 15 confirmed hit compounds. The molecular targets of these hit compounds were largely focused on the 5-hydroxytryptamine receptor (5-HTR). Furthermore, both 5-HT1AR agonist and 5-HT3R antagonist were common functional profiles in hit compounds. Vortioxetine, possessing dual attributes as a 5-HT1AR agonist and 5-HT3R antagonist, was administered to BTBR mice once daily for a period of 7 days. This intervention not only ameliorated their spine phenotype but also alleviated their social behavior abnormality. These results of vortioxetine supports the usefulness of a spine phenotype-based assay system as a potent drug discovery platform targeting ASD core symptoms.

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.

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.

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.

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.

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.

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.

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.

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.].

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.

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.

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.

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.