Database Tools Research Articles

P-478. Assessing HIV Transmitted Drug Resistance and cluster detection among Antiretroviral-Naïve individuals in Hispaniola

Abstract Background The use of antiretroviral therapy (ART) has surged globally, heralding an era of life-saving treatment for millions. However, with this widespread adoption of ART, a concerning trend has emerged: the escalation of antiretroviral resistance. Within the confines of La Hispaniola, encompassing the Dominican Republic (DO) and Haiti(HT), the prevalence of HIV stands among the highest in the Caribbean region. Our main objective is to identify and characterize the extent of transmitted drug resistance (TDR) of HIV among treatment naïve participants, its demographic and the clinical associations in the DO and the transmission networks. Phylogenetic relationships between DRII PRRT sequences and NNRTI SDRMs Methods During May 2021-June 2022, we enrolled 148 newly HIV-diagnosed ART naïve adults at Centro de Orientacion e Investigación (COIN) in DO. Demographic and clinical were collected, and blood samples obtained for partial pol genotyping using NGS. Subtyping and resistance interpretation were performed with Stanford Database tools. Maximum likelihood phylogenies were inferred by RAxML and clusters were defined as clades with bootstrap support of ≥0.8. Extended phylogenetic analyses included ∼1,500 publicly available Caribbean sequences including prior data set from our group. Fisher Exact tests and logistic regression were used to test associations of those in clusters. Phylogenetic relationships of DRI and DRII PRRT sequences Results Among participants the mean age was 30 years, 52% male, 48% were Dominican and 52% Haitian, 74% heterosexual. Among those with genotype, 8% (10 out of 127) had NNRTI SDRMs (Figure 1). A minority SDRMs, range of 1% to 20%, were found in the PI, NRTI and NNRTI. At the relaxed criteria, 17% of participants were in 8 clusters, ranging from 2 to 5 members. Those within clusters were less likely to be older, p=0.026 and more likely to be male, p=0029. Combination of current and prior dataset from the DO revealed 21 clusters, ranging from 2 to 6 members per cluster (Figure 2). Conclusion In the DO, NNRTI-associated TDR is increasing, aligning with the continued use of Efavirenz as first-line ART highlighting the need for an update in the national guidelines. Identification of minority mutations suggests need for TDR surveillance. Detection and response to clusters is a novel, effective modality to understand HIV transmission networks and address care gaps at individual and systems level. Disclosures All Authors: No reported disclosures

Integrated bioinformatics analysis to explore potential therapeutic targets and drugs for small cell carcinoma of the esophagus

BackgroundSmall cell carcinoma of the esophagus (SCCE) is a rare form of esophageal cancer, which also belongs to the category of neuroendocrine malignant tumors, with a low incidence but high aggressiveness, and a very poor prognosis for patients. Currently, there is a lack of unique staging and treatment guidelines for SCCE. Therefore, a deeper understanding of the therapeutic targets and the mechanisms underlying its occurrence and development is of great importance for early diagnosis, identification of potential therapeutic agents and improvement of the prognosis for patients.MethodsFirstly, the dataset of SCCE was downloaded from the GEO database and GEO2R tool was employed for the analysis of differentially expressed genes (DEGs). GO and KEGG analysis of DEGs were carried out by using the Bioinformatics and OmicStudio tools. Then, up- and down-regulated genes were intersected with the oncogenes and the tumor suppressor genes respectively, to obtain the differentially expressed onco/tumor suppressor genes in SCCE. The STRING database was employed to conduct protein-protein interaction (PPI) analysis of differentially expressed onco/tumor suppressor genes, the network was further constructed in Cytoscape, and hub genes of the network were obtained through the Cytohubba plugin. In addition, miRDB, miRwalk, Targetscan, OncomiR, starbase, Lncbase were used to predict miRNAs and lncRNAs that regulate hub genes, the ceRNA network was built based on this. Transcription factor-miRNA co-regulatory network was analyzed in the NetworkAnalyst database and embellished in Cytoscape. Finally, drugs that may target hub genes were searched through the DGIdb and ConnectivityMAP, and docking verification was performed using Schrodinger.ResultsA total of 820 genes were upregulated and 716 were downregulated, of these, 54 were identified as oncogenes and 85 as tumor suppressor genes. Seven hub genes were identified from the PPI network, which were AURKA, BIRC5, CDK1, EZH2, FOXM1, KLF4 and UBE2C. Furthermore, a total of 38 drugs were searched and filtered in DGIdb and ConnectivityMAP, in the molecular docking results of drugs with hub genes, the docking score of AURKA, CDK1, and EZH2 with multiple drugs were low (<6). In addition, crizotinib with AURKA, lapatinib with CDK1, rucaparib with EZH2, rucaparib with UBE2C were the lowest energy of all molecular docking results.ConclusionAURKA, BIRC5, CDK1, EZH2, FOXM1, KLF4 and UBE2C are the hub genes of SCCE, among them, AURKA, CDK1 and EZH2 may be used as targets of multiple drugs. Crizotinib, lapatinib, and rucaparib can act on the above targets to inhibit the progression of SCCE and play a therapeutic role.

BCL-2 overexpression exosomes promote the proliferation and migration of mesenchymal stem cells in hypoxic environment for skin injury in rats

ObjectiveThe direction of this study was to detect and analyze the specific mechanism of anti-apoptosis in mesenchymal stem cells (MSCs) cells caused by high expression of BCL2.MethodsBioinformatics was completed in Link omics. GO analysis and KEGG analysis were carried out, and the grope tool of Link omics database was used to evaluate PPI information and other core path analysis information. The cultured cells were divided into MSC + normoxic group (MSCs were cultured in conventional medium, including 10% depleted serum of fetal bovine exosomes, 37 °C, 5% CO2 and 95% air) and Exo-BCL-2 + MSC + normoxic group (a certain concentration of purified BCL-2 exosomes was co-cultured with MSC in conventional medium, 37 °C, 5% CO2 and 95% air), Exo-BCL-2 + MSC + hypoxia group (a certain concentration of purified BCL-2 exosomes and MSC were co-cultured in hypoxia medium at 37 °C, 80% CO2 and 20% air), MSC + hypoxia group (MSCs were cultured in hypoxia medium with 10% depleted serum of fetal bovine exosomes, 37 °C, 80% CO2 and 20% air), exo WT + MSC + normoxic group (co-cultured with MSC in conventional medium at 37 °C, 5% CO2 and 95% air) and exoWT + MSC + hypoxic group (co-cultured with MSC in hypoxic medium at 37 °C, 80% CO2 and 20%). Cell proliferation ability was monitored by cell proliferation test. Cell migration test was used to check the migration capacity of MSCs. The expressions of apoptosis-related proteins BCL-2, caspase3 and caspase9, Runx2, ALP and PPAR‐γ were analyzed by western blot. Tissue damage was scored by H&E and Ma Song trichrome staining. Masson staining was used to evaluate the collagen volume fraction of the wound. The expressions of KRT14, α‐SMA, CD31 and PCNA in rat trauma tissues were analyzed by immunofluorescence staining. The horizontal of apoptosis-related proteins in skin lesions was checked by Western blot. The horizontal of inflammatory factors TNF-α and IL-6 in traumatic tissue of rats were detected by ELISA.ResultsFrom KEGG's results, we can see that BCL2-2 was closely related to base excision and repair, cell cycle, steroid biosynthesis and other pathways. When cultured for 48h and 72h, the proliferation ability and migration number of MSCs in MSC + hypoxia group were lower than MSC + normoxic group, but the expressions of caspase3 and caspase9 were higher. The proliferation ability and migration number of MSCs in Exo-BCL-2 + MSC + hypoxia group and MSC + hypoxia group were lower than those in Exo-BCL-2 + MSC + normoxic group and MSC + normoxic group, and the horizontal of caspase3 and caspase9 were lower. Exo-BCL-2 + MSC + normoxic group increased the proliferation capacity and migration number of MSCs, but decreased the expression of caspase3 and caspase9. Compared with Exo-BCL-2 + MSC + normoxia and Exo-BCL-2 + MSC + normoxia, the proliferation ability and migration quantity of MSCs in exo WT + MSC + normoxia and exo WT + MSC + hypoxia groups were lower, and the horizontal of caspase3 and caspase9 proteins was higher.ConclusionBioinformatics analysis shows that BCL2-2 plays a worthwhile role in the process of cell apoptosis and proliferation. Exosomes with high expression of BCL-2 can encourage the proliferation of MSC in hypoxic environment. The wound treated with MSCs‐BCL-2 promotes the compose of new blood vessels and granulation tissue in the wound, the redifferentiation of epithelial cells and the remodeling of collagen, which has a high therapeutic prospect for chronic wounds and skin regeneration.

Vegetable Fibers in Cement Composites: A Bibliometric Analysis, Current Status, and Future Outlooks.

The use of vegetable fibers (VFs) in cement-based composites has increased in recent years owing to their minimal environmental impact and notable particular properties. VFs have aroused interest within the scientific community because of their potential as a sustainable alternative for construction. This study presents a comprehensive bibliometric analysis of VFs in cement composites using data from the Scopus database and scientometric tools to explore publication trends, influential sources, and research directions. Key findings reveal a steady increase in publications, with Construction and Building Materials identified as a leading journal in the field and China and Brazil as prominent contributors in terms of publications and citations. The analysis highlights a strong focus on mechanical properties and durability, reflecting the interest of the scientific community in optimizing VF composites for construction. Furthermore, this study includes a revision of the most influential studies addressing VF classification, durability improvements, and advanced applications of VFs in building applications. Finally, future research opportunities are outlined, emphasizing Life Cycle Assessment (LCA), industry integration, CO2 absorption, and the application of machine learning techniques to advance the development of VF composites. This work provides a comprehensive overview of the field, suggesting future guidelines and promoting collaborative research.

Study on mechanism of Spatholobi Caulis in the treatment of the hand-foot skin reaction induced by targeted drug therapy based on network pharmacology and molecular docking: An observational study.

Based on network pharmacology and molecular docking methods, this study explored its active compounds and confirmed its potential mechanism of action against Hand-foot skin reaction induced by tumor-targeted drugs. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and UniProt Database were used to obtain the active ingredients and target proteins of Spatholobi Caulis. All hand-foot skin reaction (HFSR)-related targets were obtained with the help of the Human Gene Database, Online Mendelian Inheritance in Humans (OMIM), DisGeNET and DrugBank databases. Cytoscape 3.7.1 software was used to construct the active ingredient-target visualization network of Spatholobi Caulis, and the common target network of Spatholobi Caulis and HFSR. And through the BisoGenet plug-in in Cytoscape, PPI network topology analysis was performed. The Metescape database and online mapping tool platform were used for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment analysis to identify the key pathways of Spatholobi Caulis. Finally, Autodock Vina software and PyMol 2.5 software were used for molecular docking verification. There were 24 active components of Spatholobi Caulis and 244 target genes, 1635 disease-related target genes, 51 common target genes of Spatholobi Caulis and Hand-foot skin reaction, and key targets included NTRK1, epidermal growth factor receptor (EGFR), APP, TP53, HSP90AB1, HSP90AA, CUL3, etc. GO functional analysis involved a total of 66 molecular functions, 39 cellular components, and 817 biological processes. KEGG pathway analysis found 154 related signaling pathways, mainly enriched in Pathways in cancer, Human cytomegalovirus infection, Kaposi arcoma-associated herpesvirus infection, panic cancer, EGFR tyrosine kinase inhibitor resistance, P13K-Akt signaling pathway, Proteoglycans in cancer, HIF-1 signaling pathway and Ras signaling pathway, etc. Molecular docking results showed that luteolin, the active component of Spatholobi Caulis, had a high affinity with EGFR. Medicagol, the active components of Spatholobi Caulis, is proved in the Hand-foot skin reaction induced by lung cancer targeted therapy by regulating multiple signaling pathways through EGFR. It is confirmed that the treatment of Hand-foot skin reaction has the characteristics of multi-component, multi-target and multi-pathway regulation.

Computational Insights into the Functional Impact of Missense Mutations in RFX1

Abstract: Genetic variations, particularly non-synonymous SNPs (nsSNPs), can profoundly influence protein function by altering stability, molecular interactions, and regulatory pathways, thereby contributing to disease mechanisms, including cancer. Despite advancements in high-throughput sequencing, the effects of nsSNPs on structure and function, especially in transcriptional regulators like RFX1, remain poorly understood. Addressing these challenges is crucial for uncovering potential biomarkers and therapeutic targets in cancer biology. This study focused on assessing the impact of deleterious nsSNPs in RFX1 on the stability, function, and structure of proteins, with a particular emphasis on its interaction with HDAC1. Using data from the COSMIC database and computational tools such as I-Mutant, MutPred, and Arpeggio, we identified 19 missense mutations predicted to be deleterious, significantly impacting RFX1 function. The most detrimental variants included R495L, V786G, R494C, A715P, Y460C, L812R, R311H, and R311C. Further analysis using STRING highlighted RFX1's involvement in gene transcription and chromatin remodeling pathways, mediated through interactions with key regulatory proteins such as CREB1, CIITA, and HDAC1. RFX1 expression analysis revealed its potential as a prognostic biomarker, with statistically significant hazard ratios (HR < 1) observed in breast, ovarian, and pancreatic cancers, suggesting its role as a protective factor in these malignancies. Prognostic evaluations further underscored RFX1's potential as a key biomarker in cancer. Future research will focus on experimental validation of these deleterious mutations and expression-based findings to further elucidate their diagnostic and therapeutic relevance.

NRF3 suppresses the malignant progression of TNBC by promoting M1 polarization of macrophages via ROS/HMGB1 axis

ABSTRACT Background Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer. Due to its lack of targeted therapy options, TNBC remains a significant clinical challenge. In this study, we investigated the role of nuclear respiratory factor 3 (NRF3) and high-mobility group box 1 (HMGB1) in the progression of TNBC. Methods The study analyzed NRF3’s clinical expression, differentially expressed genes (DEGs), and immune infiltration in TNBC using the TCGA database and bioinformatics tools. Cellular functions of MDA-MB-468 and Hs578t cells were evaluated through MTT, colony formation, transwell, flow cytometry, and western blotting. The regulatory function of NRF3 in TNBC cell lines was assessed using Immunofluorescence, Immunohistochemistry, qRT-PCR, CHIP, luciferase assay, and ELISA. Moreover, a xenograft model was established to investigate the role of NRF3 in TNBC in vivo. Results Low expression of NRF3 in TNBC tumors was associated with unfavorable prognosis and transcripts from tumors with higher NRF3 levels were enriched in oxidative stress and immune-related pathways. The subsequent gain- and loss-functional experiments indicated that NRF3 overexpression significantly suppressed malignant phenotypes, MAPK/ERK signaling pathways, and epithelial-mesenchymal transition (EMT), whereas it promoted reactive oxygen species (ROS) levels in TNBC. Further mechanistic exploration showed that NRF3 inhibited TNBC cell function by regulating oxidative stress-related genes to inhibit the MAPK/ERK signaling pathway by promoting the release of HMGB1 via ROS, thereby promoting M1 macrophage polarization. Conclusion NRF3 promotes M1 macrophage polarization through the ROS/HMGB1 axis, thereby inhibiting the malignant progression of TNBC. It is expected to become a therapeutic biomarker for TNBC.

Local Climate Zones Classification Applied to a Brazilian Amazon City

Urban elements influence atmospheric flow, turbulence, and the local microclimate, modifying the transport and composition of pollutants. However, although this focus on cities is crucial for managing climate change, our knowledge of most cities in the world is still quite limited. Thus, the classification of Local Climate Zones (LCZs) aims to increase the accuracy of urban studies and has already been applied in various regions of the world, including, more recently, in Brazil. This article aimed to apply the LCZ to Santarém, a city in the Brazilian Amazon. The methodological procedures included the digital mapping protocol of the World Urban Database and Access Portal Tools (WUDAPT-Level 0) and the supervised classification tool of the LCZ Generator application, resulting in 78 polygons representing 10.02% of the training area and 13.94% of the study area (urban zone). The research identified 7 of the 17 main LCZ classes in Santarém. The use of the NDVI was essential for assessing the vegetation in each class, highlighting variations in green areas and emphasizing that vegetation is reduced in built environments. This approach enhances the understanding of urban morphology and enables future research into urbanization and the climate in the Amazon.

Establishing a comprehensive web-based analysis platform for Nicotiana benthamiana genome and transcriptome.

Nicotiana benthamiana has long served as a crucial plant material extensively used in plant physiology research, particularly in the field of plant pathology, because of its high susceptibility to plant viruses. Additionally, it serves as a production platform to test vaccines and other valuable substances. Among its approximately 3.1 Gb genome, 57 583 genes have been annotated within a 61 Mb region. We created a comprehensive and easy-to-use platform to use transcriptomes for modern annotation. These tools allow to visualize gene expression profiles, draw molecular evolutionary phylogenetic trees of gene families, perform functional enrichment analyses, and facilitate output downloads. To demonstrate their utility, we analyzed the gene expression profiles of enzymes within the nicotine biosynthesis pathway, a secondary metabolic pathway characteristic of the Nicotiana genus. Using the developed tool, expression profiles of the nicotine biosynthesis pathway genes were generated. The expression patterns of eight gene groups in the pathway were strongly expressed in the roots and weakly expressed in leaves and flowers of N. benthamiana. The results were consistent with the established gene expression profiles in Nicotiana tabacum and provided insights into gene family composition and expression trends. The compilation of this database tool can facilitate genetic analysis of N. benthamiana in the future.

African bat database: curated data of occurrences, distributions and conservation metrics for sub-Saharan bats

Accurate knowledge of species distributions is foundational for effective conservation efforts. Bats are a diverse group of mammals, with important roles in ecosystem functioning. However, our understanding of bats and their ecological importance is hindered by poorly defined ranges, mostly as a result of under-recording. This issue is exacerbated in Africa by the ongoing rapid discovery of new species, both de novo and splits of existing species, and by inaccessibility to museum specimens that are mostly hosted outside of the continent. Here we present the African bat database – a curated set of 17,285 unique locality records of all 266 species of bats from sub-Saharan Africa, vouched for by specimens and/or genetic sequencing, and aligned with current taxonomy. Based on these records, we also present Maxent-based distribution models and calculate the IUCN Red List metrics for Extent of Occurrence and Area of Occupancy. This database and online visualization tool provide an important open-source resource and is expected to significantly advance studies in ecology, and aid in bat conservation.

Deep Learning-Assisted Design of de novo Protein Binders Targeting Hepatitis C Virus E2 Protein

Hepatitis C virus is a grievous disease with an increased mortality rate worldwide. Chemical-based medications possess deleterious side effects and are considered inefficient in combating viral infections. Advanced therapeutic strategies are being examined with increased specificity against viral proteins such as designing highly regular proteins facilitating the development of highly effective inhibitors. Here, we present for the first time, the use of deep learning-based large language protein model ProtGPT2 with a unique strategy to design novel therapeutic binders that have the potential to mimic host receptors and inhibit the viral protein, especially the HCV envelope glycoprotein E2 for clinically relevant genotype 1a and 1b. We generated five de novo proteins for each host receptor that mimic the human receptors, based on the interacting residues which were identified by the tools of the PDBSum database in the docked host-E2 complexes generated with the ClusPro web server. The Root Mean Square Deviation score revealed that each de novo designed binder exhibited high similarity with the human receptors indicating a successful generation. Furthermore, multiple interactions were observed between these de novo designed proteins and E2 protein, emphasizing the potential of these de novo designed proteins as significant inhibitors. A comparative analysis of molecular docking between human interacting partners and de novo designed proteins revealed that de novo proteins, such as CD81-D1 and CLDN-D4, are the most effective inhibitors having the lowest binding energy when interacting with the most conserved regions of the E2 protein. These generated proteins may inhibit the interaction of E2 with CD81 and CLDN host receptors.

The effect of physical activity on sleep quality in people with diabetes: systematic review and meta-analysis.

To revise and critically summarize the available scientific evidence regarding the effect of exercise on sleep quality in people with diabetes. Three electronic databases (MEDLINE/PubMed, PEDro Database and Scopus) were searched systematically from their inception until February 2024. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database and Quality Assessment Tool for Before-After Studies with No Control Group scales. A total of 7 randomized controlled trials and 3 single-arm studies were included. Most of the studies included patients with type 2 diabetes (n = 8). Self-reported sleep quality (n = 9) and objective sleep status (n = 1) were the main outcomes analysed. A variety of training programs were assessed over durations ranging from 4 to 16 weeks in the studies included. Data from eleven interventions demonstrated a significant improvement in self-reported sleep quality among patients with type 2 diabetes (Hedges' g -1.45; 95% CI -2.6; -0.29, p = 0.005). However, data synthesis indicated that participants who exercised did not obtain significant improvements on their self-reported sleep quality compared to those in the control groups (Hedges' g 1.40; 95% CI -1.36; 4.18, p = 0.111). Preliminary evidence suggests that exercise can be prescribed to manage self-reported sleep quality in this population, although its effects may not surpass those of usual care.

Using Interactive Whiteboards (IWB) in the English Classroom as Supporting Technology in the Teaching and Learning Process: Opportunities and Challenges

This research describes the teachers' and students' perception of using Interactive Whiteboards (IWB) and explain the opportunities and challenges of using IWB in English classrooms. This research uses library research. IWBs are large interactive displays used as input devices for computers. They can be used in educational institutions combining traditional presentation benefits with cutting-edge technology. IWB technology consists of a digital board, computer, and projector, which can be linked to a personal computer for easy use. It offers various tools including graphics, word processing, database, and multimedia tools. Using IWB as an educational tool increase new knowledge, provide innovations to students, improve the quality of students’ learning, facilitate interaction between teaching teams and students, improve teaching efficiency, and reduce paper usage. IWB has been found to significantly improve students' English skills, vocabulary proficiency, engagement, and attitude. However, implementing IWBs in schools presents challenges including large costs, insufficient technical support, technical issues, lack of adequate e-materials, limited time for lesson preparation, negative attitudes, lack of consensus among decision-makers, inadequate proficiency, and inadequate teacher training. Teachers need to master this technology so they can use it effectively in teaching. It is proposed that teachers receive training on utilizing IWBs more efficiently in various classroom settings. Teaching using proper operation of IWBs is a major factor in the teachers' ability to implement it successfully in the classroom. The trained teachers possessed a comprehension of the most efficient methods for utilizing the IWBs.

Cannabis Expression Atlas: a comprehensive resource for integrative analysis of Cannabis sativa L. gene expression.

Cannabis sativa L., a plant originating from Central Asia, is a versatile crop with applications spanning textiles, construction, pharmaceuticals, and food products. This study aimed to compile and analyze publicly available Cannabis RNA-Seq data and develop an integrated database tool to help advance Cannabis research in various topics such as fiber production, cannabinoid biosynthesis, sex determination, and plant development. We identified 515 publicly available RNA-Seq samples that, after stringent quality control, resulted in a high-quality dataset of 394 samples. Utilizing the Jamaican Lion genome as reference, we constructed a comprehensive database and developed the Cannabis Expression Atlas (https://cannatlas.venanciogroup.uenf.br/), a web application for visualization of gene expression, annotation, and functional classification. Key findings include the quantification of 27,640 Cannabis genes and their classification into seven expression categories: not-expressed, low-expressed, housekeeping, tissue-specific, group-enriched, mixed, and expressed-in-all tissues. The study revealed substantial variability and coherence in gene expression across different tissues and chemotypes. We found 2,382 tissue-specific genes, including 177 transcription factors. The Cannabis Expression Atlas constitutes a valuable tool for exploring gene expression patterns and offers insights into Cannabis biology, supporting research in plant breeding, genetic engineering, biochemistry, and functional genomics.

AI- and LLM-driven search tools: A paradigm shift in information access for education and research

The article reports on an exploratory study that assesses the results produced by emerging artificial intelligence (AI)- and large language model-driven search tools in response to a series of queries and prompts based on four scenarios of information-intensive tasks of university students and researchers. Sixteen questions and prompts were created based on four scenarios of information-intensive tasks of university students. Each of these questions and prompts was presented to six AI-driven search tools, and the results were manually checked to assess their suitability for specific user needs and contexts. Based on the findings, it was argued that while the AI-driven tools bring a paradigm shift in information access for education and research, outputs generated by these tools vary quite significantly. Choice of the right tool, framing the question and further prompting play a key role. Also, users need to scrutinise each output to check their quality and reliability in the context of the specific search tasks. It was concluded that further research is needed involving different user groups, scenarios and search tasks and different AI-driven search tools. Implications of the use of AI-driven search tools for libraries and scholarly databases, as well as for research and scholarship in different areas of information science, are discussed.

The immunomodulatory role of the MAFB gene in hepatocellular carcinoma and its impact on biological activities

ObjectiveThe transcription factor MAFB is part of the MAF family and is known to promote hepatocellular carcinoma (HCC) by upregulating cyclin D1. However, its role in HCC immunity and prognosis remains unclear. This study explores the biological function, prognostic significance, and immune impact of MAFB in HCC. MethodsImmunohistochemistry was used to analyze MAFB expression in HCC and adjacent non-tumor tissues. RT-qPCR and Western blotting measured MAFB levels in HCC cell lines. Specific siRNA was used to knockdown MAFB in HCCLM3 and MHCC97H cells, followed by assays to evaluate cell proliferation, migration, and colony formation. Data from the TCGA database and online tools TIMER and TISDB were used to assess the relationship between MAFB and immune responses. A prognostic model based on MAFB-related immune genes was established, and drug sensitivity analysis was performed. ResultsMAFB was significantly overexpressed in HCC tissues. Knockdown of MAFB in HCC cell lines reduced their proliferation and migration abilities. The risk model based on MAFB-related immune genes effectively predicted patient prognosis, supported by ROC curves. Gene set enrichment analysis indicated that MAFB is involved in immune-related pathways. Several drugs were identified as potentially sensitive to MAFB expression levels. ConclusionMAFB plays a significant role in HCC development and immune regulation. The prognostic model combining MAFB-related immune genes provides valuable insights for predicting patient outcomes and identifying potential therapeutic targets.

Carry-forward effect: providing proactive scaffolding to learning processes

ABSTRACT Multimodal data enables us to capture the cognitive and affective states of students to provide a holistic understanding of learning processes in a wide variety of contexts. With the use of sensing technology, we can capture learners' states in near real-time and support learning. Moreover, multimodal data allows us to obtain early predictions of learning performance, and support learning in a timely manner. In this contribution, we utilise the notion of ‘carry forward effect’, an inferential and predictive modelling approach that utilises multimodal data measurements detrimental to learning performance to provide timely feedback suggestions. Carry forward effect can provide a way to prioritise conflicting feedback suggestions in a multimodal data-based scaffolding tool. We showcase the empirical proof of the carry forward effect with the use of three different learning scenarios: game-based learning, individual debugging, and collaborative debugging.

Development Of A Web-Based E-Presence Application For Tracking Maps And Selfies Using Laravel

Attendance is an activity aimed at assessing the level of employee presence and discipline within an organization or company. The E-Presence application is developed as a tool to enhance efficiency, accountability, and transparency in employee attendance, as well as to facilitate the processing of attendance data The E-Presence application aims to reduce errors and improve the accuracy of employee attendance records, which are crucial for the decision-making process. The development of the E-Presence application uses the Laravel framework, MySQL database, and UML (Unified Modeling Language) tools to design Use Case Diagrams, Activity Diagrams, and Class Diagrams. The system development method follows the waterfall model, and Black Box Testing is employed as the testing method. The urgency of this research lies in the system's ability to perform real-time attendance tracking that can be conducted outside the office within a maximum distance of 20 meters. With the implementation of tracking maps, employees do not need to queue, thereby increasing work efficiency. Attendance recording can be done in real time, is flexible, easily accessible, improves efficiency in attendance recording, reduces the potential for errors, enhances human resource management, and provides attendance reports in Excel format. The novelty of this research is the development of a web-based application that incorporates Tracking Maps and Selfie features, ensuring that employees must be in a designated area to check in. Thus, the company/organization can easily analyze and evaluate employee discipline. The testing results indicate that the application functions well and meets user needs.

Potential molecular mechanisms of Danggui-Shaoyao-San in the treatment of melasma based on network pharmacology with molecular docking

Although Danggui-Shaoyao-San (DSS) is frequently used in China to treat melasma, its underlying mechanism still needs to be better understood. Our aim is to determine the mechanism behind DSS in treating melasma through network pharmacology (NP). The DSS active compounds alongside corresponding target genes are identified by accessing the TCMSP database and SwissTargetPrediction. Melasma-associated targets are retrieved from the GeneCards, DisGeNet, Durgbanks, OMIM, and TTD databases. Next, we build a component-target association network via Cytoscape software while generating a protein-protein interaction network utilizing the STRING database. Subsequently, both core target genes and active compounds are determined. Through the DAVID database and Bioinformatics tools, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses are conducted. Lastly, the CB-Dock2 server is deployed to conduct molecular docking (MOD). The results manifest that alisol B, cerevisterol, and Wallichilide, among others, are active compounds in DSS, while PTGS2, ESR1, and ESR2 are core target genes. Both GO and KEGG analyses showcase that the potential core drug components modulate pathways in cancer, chemical carcinogenesis-receptor activation, calcium, relaxin, and estrogen signaling by exerting their effects on biological processes. These processes include negative gene expression regulation as well as positive regulation of both cytosolic calcium ion concentration and transcription from the RNA polymerase II promoter, thereby playing an anti-melasma pharmacological role. The MOD displays that core target genes have good binding activity with the active compounds. To conclude, NP demonstrates that DSS can serve as an innovative medication for treating melasma.

Design of Tools for Visualizing Thermodynamic Concepts in Steam Power Plant Trainer Processes with Web-Based Exploratory Data Analysis (EDA)

Thermodynamics is considered one of the most complex and challenging subjects for many students. This is primarily due to comprehending abstract concepts such as entropy, enthalpy, and energy flow, which involve complex mathematical equations and are rarely accompanied by tangible visualizations. This research aims to design, develop, and test a data-based visualization tool for thermodynamics testing results. This study collected and processed data from thermodynamics testing and simulations, such as the mini-steam power plant trainer used as a teaching aid in thermodynamics education, as the foundation for designing a data-based visualization tool for thermodynamics concepts. The visualization tool was created using the Python programming language integrated with the web-based Streamlit framework. The designed visualization tool encompasses various features, including automated data reporting, visualization of variable correlations using correlation heatmaps, Sankey diagrams for visualizing energy flow, and the capability to predict electrical output using machine learning integrated with three different machine learning algorithms. The visualization tool was evaluated by thermodynamics experts using a Likert scale. Based on the results obtained, the experts gave an average score of 4 in the information accuracy aspect in the good category. This shows that the information displayed in this visualization tool is by thermodynamics learning at Padang State University. In the visualization aspect, experts gave an average score of 4.25, which is in the Good and Very Good range. In alignment with the education aspect, experts gave an average score of 3.75, which is close to the good category. This shows that this aspect is considered suitable for studying thermodynamics, although shortcomings still need to be corrected. Experts gave a relatively high assessment of the Ease-of-Use aspect, with an average score of 4.5, with a range of Good and Very Good. This enables students to better understand complex patterns, cause-and-effect relationships, and parameter changes within thermodynamics concepts.