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.

Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction.

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information.

The dissemination and implementation of trauma-focused cognitive behavioural therapy for children and adolescents in seven European countries.

BACKGROUND: There is broad scientific evidence for the effectiveness of individual trauma-focused evidence-based treatments (EBTs) such as "trauma-focused cognitive behavioural therapy" (TF-CBT) for children and adolescents with posttraumatic stress symptoms. However, there is a significant research-to-practice gap resulting in traumatized children in high-income countries in Europe having only very limited access to these treatments. The aim of this study was, therefore, to identify common barriers and successful dissemination and implementation (D&I) strategies of evidence-based trauma-focused treatments (in particular TF-CBT) in seven European countries. METHODS: For this study, we chose a mixed-method approach: an online survey among certified European TF-CBT trainers (N = 22) and the collection of country-based narratives from TF-CBT experts in different European countries (Finland, Germany, Italy, Netherlands, Norway, Sweden). RESULTS: Common modifiable barriers to the implementation of TF-CBT were identified on different levels (e.g. government or treatment level), and successful D&I strategies were highlighted across all countries, such as translations of materials. Additionally, the experts from the country narratives put together a broad overview of TF-CBT research in Europe. CONCLUSIONS: The results of this study revealed that especially learning collaborations and the development of joint European efforts in funding and researching D&I strategies are crucial for future implementation of trauma-focused EBTs in Europe.

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.

BACKGROUND: Recently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports' classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results. The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification. RESULTS: The final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607). CONCLUSION: In this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

Combinatorial transcription factor binding encodes cis-regulatory wiring of mouse forebrain GABAergic neurogenesis.

Transcription factors (TFs) bind combinatorially to cis-regulatory elements, orchestrating transcriptional programs. Although studies of chromatin state and chromosomal interactions have demonstrated dynamic neurodevelopmental cis-regulatory landscapes, parallel understanding of TF interactions lags. To elucidate combinatorial TF binding driving mouse basal ganglia development, we integrated chromatin immunoprecipitation sequencing (ChIP-seq) for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and gene expression data, and functional enhancer assays. We identified sets of putative regulatory elements with shared TF binding (TF-pRE modules) that orchestrate distinct processes of GABAergic neurogenesis and suppress other cell fates. The majority of pREs were bound by one or two TFs; however, a small proportion were extensively bound. These sequences had exceptional evolutionary conservation and motif density, complex chromosomal interactions, and activity as in vivo enhancers. Our results provide insights into the combinatorial TF-pRE interactions that activate and repress expression programs during telencephalon neurogenesis and demonstrate the value of TF binding toward modeling developmental transcriptional wiring.

Effect of three group interventions on psychosocial functioning in adolescents exposed to interpersonal violence in Chile: A pilot clinical trial.

BACKGROUND: In Chile demand for specialist care following exposure to interpersonal violence (IPV) in youth far exceeds capacity. Group interventions may improve access to care for youth. OBJECTIVE: To evaluate the effectiveness and acceptability of two low-intensity group interventions: Trama Focused Cognitive Behavioral Therapy (TF-CBT); Interpersonal Psychotherapy (IPT); and treatment as usual, Art therapy-based support (ATBS). Outcomes measured were post-traumatic stress symptoms, depression, interpersonal functioning and affect regulation. PARTICIPANTS AND SETTING: Participants were 67 Chilean youth aged 13-17 years, victims of IPV on a waiting list to receive specialist individual intervention. METHODS: Using a randomised controlled trial design, participants were randomly assigned to one of the interventions. Self-report measures were completed at 5 timepoints between baseline and follow up eight weeks after intervention ended. Dropout rates and attendance were also analysed. RESULTS: TF-CBT showed significant decreases for PTSD (d = 0.91) and depression (d = 0.77) symptoms, sustained at follow-up with affect regulation problems also showing significant decrease from baseline (d = 0.43). IPT showed significant decreases in PTSD symptoms (d = 0.64) and affect regulation problems (d = 0.66), both sustained at follow-up. ATBS showed statistically significant decrease for PTSD (d = 0.79) and interpersonal problems (d = 0.65) but only change in PTSD was sustained at follow-up. There were no significant differences in dropout or attendance between the interventions. CONCLUSION: Group interventions provide a viable and effective first-phase option for reducing psychological distress in IPV-exposed youth in high-demand contexts. Effectiveness may be further improved through the more active involvement of parents and carers.

Chiron approach toward the synthesis of the fused tricyclic core of epi-parvistemonine A.

A stereoselective synthesis of fused tricyclic framework of epi-parvistemonine A from D-glucono-δ-lactone is described. The synthetic strategic is based on the stereoselective construction of the 7-membered cyclic skeleton via a cross-metathesis reaction followed by a Michael type cyclization promoted by Tf2O.

MYB transcription factors in plants: A comprehensive review of their discovery, structure, classification, functional diversity and regulatory mechanism.

The MYB transcription factor (TF) family is one of the largest families in plants and performs highly diverse regulatory functions, particularly in relation to pathogen/pest resistance, nutrient/noxious substance absorption, drought/salt resistance, trichome growth, stamen development, leaf senescence, and flavonoid/terpenoid biosynthesis. Owing to their vital role in various biological regulatory processes, the mechanisms of MYB TFs have been extensively studied. Notably, MYB TFs not only directly regulate targets, such as phytohormones, reactive oxygen species signaling and secondary cell wall formation, but also serve as crucial points of crosstalk between these signaling networks. Here, we have comprehensively described the structures, classifications, and biological functions of MYB TFs, with a specific focus on their roles and mechanisms in the response to biotic and abiotic stresses, plant morphogenesis, and secondary metabolite biosynthesis. Different from other reported reviews, this review provides comprehensive knowledge on plant MYB TFs and will provide valuable insights in understanding regulatory networks and associated functions of plant MYB TFs to apply in resistance breeding and crop improvement.

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.

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.

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.

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.

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.

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.

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.