Identification of novel hub genes and immune infiltration in atopic dermatitis using integrated bioinformatics analysis.

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein-protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine-cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD.

Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer.

Copper (Cu) is used as a cofactor in all organisms, and yet it can be toxic at high intracellular concentrations, causing cell death. Diethyldithiocarbamate (DDC) is a Cu ionophore that can transport Cu effectively into the cell. Copper-diethyldithiocarbamate (Cu-DDC) can treat prostate cancer (PCa) and may correlate with the cell death process. However, the specific Cu-DDC-related cell death genes in PCa are still unknown. Information about the Cu-DDC-related cell death genes was obtained from a previous study. Concurrently, the RNA expression profiles and clinical data were downloaded from public databases such as GEO, TCGA, and CPGEA. Using data from TCGA database, the logistic and lasso regression models were generated using R software. The influence of these genes in affecting PCa progression and prognosis was analyzed. Finally, the expression of these genes was verified in clinical samples. We found five Cu-DDC-related cell death genes associated with the occurrence of PCa from GSE35988, a gene dataset, namely, CDKN2A, PRC1, CDK1, SOX2, and ZNF365. CDKN2A, PRC1, and CDK1 are known to influence PCa patients' disease-free survival (DFS) status and were overexpressed, whereas SOX2 and ZNF365 were under-expressed in PCa in the different databases. Some of these genes can affect PCa progression. Consistent with the database results, the mRNA and protein expression of CDKN2A, PRC1, and CDK1 was also higher in clinical samples. In conclusion, we identified five hub genes which are important for Cu-DDC-related cell death process that can predict the development of PCa.

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells.

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Strengthening cardiac services in Faridabad District: A facility mapping exercise to explore implementation of a hub-and-spoke model.

Background & objectives Improving access to acute cardiac care requires remodelling of existing health systems into a service delivery network with an anchor establishment (Hub) offering a full array of services, complemented by spoke establishments that offer limited services. We assessed the availability of cardiac services in the district of Faridabad in the northern State of Haryana, India and explored the feasibility and challenges of implementing a hub-and-spoke model. Methods In 2019-2020, we listed all the facilities in private and public sectors in the study-district and mapped their geocoordinates with the help of QGIS (Quantum Geographic Information System) software version 3.20. After consent, we assessed the availability of specific cardiac care-related inputs (medicines, technologies and staff) using a checklist by enquiring from the hospital staff. Each facility was classified as L1 (No ECG) to L5 (cardiac catheterization) as per the national guidelines for the management of ST-elevation myocardial infarction (STEMI). Results There were 109 health facilities (66% private) in the district, 1.6 cardiologists and 5.4 coronary care unit beds per 100,000 population (94% private). Only one district hospital running in a public-private partnership mode at the L5 level provided any cardiac services. Private facilities were providing a range of services with a considerable number of them functional at L5. The higher-level facilities were concentrated in the central and urban parts of the district. Only 46 per cent of the ambulances had oxygen cylinders and 14.7 per cent had defibrillators. Interpretation & conclusions Implementation of a hub-and-spoke model for cardiac care in Faridabad district will require significant strengthening of public health services, development of a private-sector participation model, and strengthening of ambulance services.

Screening and identification of the hub genes in severe acute pancreatitis and sepsis.

Background: Severe acute pancreatitis (SAP) is accompanied with acute onset, rapid progression, and complicated condition. Sepsis is a common complication of SAP with a high mortality rate. This research aimed to identify the shared hub genes and key pathways of SAP and sepsis, and to explore their functions, molecular mechanism, and clinical value. Methods: We obtained SAP and sepsis datasets from the Gene Expression Omnibus (GEO) database and employed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify the shared differentially expressed genes (DEGs). Functional enrichment analysis and protein-protein interaction (PPI) was used on shared DEGs to reveal underlying mechanisms in SAP-associated sepsis. Machine learning methods including random forest (RF), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were adopted for screening hub genes. Then, receiver operating characteristic (ROC) curve and nomogram were applied to evaluate the diagnostic performance. Finally, immune cell infiltration analysis was conducted to go deeply into the immunological landscape of sepsis. Result: We obtained a total of 123 DEGs through cross analysis between Differential expression analysis and WGCNA important module. The Gene Ontology (GO) analysis uncovered the shared genes exhibited a significant enrichment in regulation of inflammatory response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the shared genes were primarily involved in immunoregulation by conducting NOD-like receptor (NLR) signaling pathway. Three machine learning results revealed that two overlapping genes (ARG1, HP) were identified as shared hub genes for SAP and sepsis. The immune infiltration results showed that immune cells played crucial part in the pathogenesis of sepsis and the two hub genes were substantially associated with immune cells, which may be a therapy target. Conclusion: ARG1 and HP may affect SAP and sepsis by regulating inflammation and immune responses, shedding light on potential future diagnostic and therapeutic approaches for SAP-associated sepsis.

Identification and validation of hub differential genes in pulmonary sarcoidosis.

A total of 138 cDEGs were screened from mediastinal lymph nodes and peripheral whole blood. Among them, 6 hub cDEGs including CTSS, CYBB, FPR2, MNDA, TLR1 and TLR8 with elevated degree and betweenness levels were illustrated in protein-protein interaction network. In comparison to healthy controls, CTSS (1.61 vs. 1.05), CYBB (1.68 vs. 1.07), FPR2 (2.77 vs. 0.96), MNDA (2.14 vs. 1.23), TLR1 (1.56 vs. 1.09), and TLR8 (2.14 vs. 0.98) displayed notably elevated expression levels within pulmonary sarcoidosis PBMC samples (P < 0.0001 for FPR2 and P < 0.05 for others), echoing with prior mRNA microarray findings. The most significant functional pathways were immune response, inflammatory response, plasma membrane and extracellular exosome, with 6 hub cDEGs distributing along these pathways. CTSS, CYBB, FPR2, MNDA, TLR1, and TLR8 could be conducive to improving the diagnostic process and understanding the underlying mechanisms of pulmonary sarcoidosis.

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology.

Background: Coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to people's health, which is a high-risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. Methods: We employed bioinformatics and systems biology to explore these links between GC and COVID-19. Gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were obtained from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. Results: We identified 209 shared DEGs between COVID-19 and GC. Functional analyses highlighted immune-related pathways as key players in both diseases. Ten hub genes (CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, IFIT2) were identified. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. Conclusion: This research offer valuable insights into the molecular interplay between COVID-19 and GC, potentially guiding future therapeutic strategies.

The Effect of Vitamin D Deficiency on Immune-Related Hub Genes: A Network Analysis Associated With Type 1 Diabetes.

Background Type 1 diabetes (T1D) is an autoimmune disorder that results in the destruction of pancreatic beta cells, causing a shortage of insulin secretion. The development of T1D is influenced by both genetic predisposition and environmental factors, such as vitamin D. This vitamin is known for its ability to regulate the immune system and has been associated with a decreased risk of T1D. However, the specific ways in which vitamin D affects immune regulation and the preservation of beta cells in T1D are not yet fully understood. Gaining a better understanding of these interactions is essential for identifying potential targets for preventing and treating T1D. Methods The analysis focused on two Gene Expression Omnibus (GEO) datasets, namely, GSE55098 and GSE50012, to detect differentially expressed genes (DEGs). Enrichr (Ma'ayan Laboratory, New York, NY) was used to perform enrichment analysis for the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The Search Tool for the Retrieval of Interacting Genes 12.0 (STRING) database was used to generate a protein-protein interaction (PPI) network. The Cytoscape 3.10.1 (Cytoscape Team, San Diego, CA) was used to analyze the PPI network and discover the hub genes. Results The DEGs in both datasets were identified using the GEO2R tool, with a particular focus on genes exhibiting contrasting regulations. Enrichment analysis unveiled the participation of these oppositely regulated DEGs in processes relevant to the immune system. Cytoscape analysis of the PPI network revealed five hub genes, MNDA, LILRB2, FPR2, HCK, and FCGR2A, suggesting their potential role in the pathogenesis of T1D and the response to vitamin D. Conclusion The study elucidates the complex interaction between vitamin D metabolism and immune regulation in T1D. The identified hub genes provide important knowledge on the molecular pathways that underlie T1D and have the potential to be targeted for therapeutic intervention. This research underscores the importance of vitamin D in the immune system's modulation and its impact on T1D development.

Exploring the hub Genes and Potential Mechanisms of Complement system-related Genes in Parkinson Disease: Based on Transcriptome Sequencing and Mendelian Randomization.

An accurate diagnosis of Parkinson's disease (PD) remains challenging and the exact cause of the disease is unclean. The aims are to identify hub genes associated with the complement system in PD and to explore their underlying molecular mechanisms. Initially, differentially expressed genes (DEGs) and key module genes related to PD were mined through differential expression analysis and WGCNA. Then, differentially expressed CSRGs (DE-CSRGs) were obtained by intersecting the DEGs, key module genes and CSRGs. Subsequently, MR analysis was executed to identify genes causally associated with PD. Based on genes with significant MR results, the expression level and diagnostic performance verification were achieved to yield hub genes. Functional enrichment and immune infiltration analyses were accomplished to insight into the pathogenesis of PD. qRT-PCR was employed to evaluate the expression levels of hub genes. After MR analysis and related verification, CD93, CTSS, PRKCD and TLR2 were finally identified as hub genes. Enrichment analysis indicated that the main enriched pathways for hub genes. Immune infiltration analysis found that the hub genes showed significant correlation with a variety of immune cells (such as myeloid-derived suppressor cell and macrophage). In the qRT-PCR results, the expression levels of CTSS, PRKCD and TLR2 were consistent with those we obtained from public databases. Hence, we mined four hub genes associated with complement system in PD which provided novel perspectives for the diagnosis and treatment of PD.

Microbial hub signaling compounds: natural products disproportionally shape microbiome composition and structure.

Microbiomes are shaped by abiotic factors like nutrients, oxygen availability, pH, temperature, and so on, but also by biotic factors including low molecular weight organic compounds referred to as natural products (NPs). Based on genome analyses, millions of these compounds are predicted to exist in nature, some of them have found important applications e.g. as antibiotics. Based on recent data I propose a model that some of these compounds function as microbial hub signaling compounds, i.e. they have a higher hierarchical influence on microbiomes. These compounds have direct effects e.g. by inhibiting microorganisms and thereby exclude them from a microbiome (excluded). Some microorganisms do not respond at all (nonresponder), others respond by producing themselves NPs like a second wave of information molecules (message responder) influencing other microorganisms, but conceivably a more limited spectrum. Some microorganisms may respond to the hub compounds with their chemical modification (message modifiers). This way, the modified NPs may have themselves signaling function for a subset of microorganisms. Finally, it is also likely that NPs act as food source (C- and/or N-source) for microorganisms specialized on their degradation. As a consequence, such specialized microorganisms are selectively recruited to the microbiota.

Exogenous interactome analysis of bovine viral diarrhea virus-host using network based-approach and identification of hub genes and important pathways involved in virus pathogenesis.

Bovine viral diarrhea (BVD) is one of the most important diseases in livestock, caused by BVD virus (BVDV). During the pathogenesis of the virus, many interactions occur between host and viral proteins. Studying these interactions can help better understand the pathogenesis of the virus, identify putative functional proteins, and find new treatment and prevention strategies. To this aim, a BVDV-host protein-protein interaction (PPI) network map was constructed using Cytoscape and analyzed with cytoHubba, Kyoto Encyclopedia of Genes and Genomics (KEGG), Gene Ontology (GO), and Protein Analysis Through Evolutionary Relationships (PANTHER). Npro with 125 connections had the greatest number of interactions with host proteins. CD46, EEF-2, and TXN genes were detected as hub genes using different ranking algorithms in cytoHubba. BVDV interactions with its host mainly focus on targeting translation, protein synthesis, and cellular metabolism pathways. Different classes of proteins including translational proteins, nucleic acid metabolism proteins, metabolite interconversion enzymes, and protein-modifying enzymes are affected by BVDV. These findings improve our understanding of the effects of the virus on the cell. Hub genes and key pathways identified in the present study can serve as targets for novel BVDV prevention or treatment strategies.

Enhancing energy hub efficiency through advanced modelling and optimization techniques: A case study on micro-refinery output products and parking lot integration.

A new model of energy carriers (micro-refinery output products) in the concept of an energy hub is presented. In addition, in the presented model, the effect of different models of parking lot in an energy hub is analyzed. In this study, the uncertainty of the number of electric vehicles was modeled using the Monte Carlo method, and then considering the same conditions, the uncertainty of the number of electric vehicles was calculated using the Probability-Possibility hybrid method. In addition, the study uses a scenario-based approach to address uncertainties related to multi-carrier energy demand and multi-carrier energy prices. In the present paper, ensuring water demand is of great importance, which is why the proposed energy hub structure of a sea desalination unit and sour water treatment that is extracted from the micro refinery output was analyzed. The optimization model presented in this paper for the energy hub is a complex integer linear programming (MILP) that is solved using a CPLEX solver in the GAMS environment. The results show that the use of the Probability-Possibility method resulted in an 8 % reduction in the final energy supply cost in the energy hub compared to the Monte Carlo method.

Cross-species striatal hubs: Linking anatomy to resting-state connectivity.

Corticostriatal connections are essential for motivation, cognition, and behavioral flexibility. There is broad interest in using resting-state functional magnetic resonance imaging (rs-fMRI) to link circuit dysfunction in these connections with neuropsychiatric disorders. In this paper, we used tract-tracing data from non-human primates (NHPs) to assess the likelihood of monosynaptic connections being represented in rs-fMRI data of NHPs and humans. We also demonstrated that existing hub locations in the anatomical data can be identified in the rs-fMRI data from both species. To characterize this in detail, we mapped the complete striatal projection zones from 27 tract-tracer injections located in the orbitofrontal cortex (OFC), dorsal anterior cingulate cortex (dACC), ventromedial prefrontal cortex (vmPFC), ventrolateral PFC (vlPFC), and dorsal PFC (dPFC) of macaque monkeys. Rs-fMRI seeds at the same regions of NHP and homologous regions of human brains showed connectivity maps in the striatum mostly consistent with those observed in the tracer data. We then examined the location of overlap in striatal projection zones. The medial rostral dorsal caudate connected with all five frontocortical regions evaluated in this study in both modalities (tract-tracing and rs-fMRI) and species (NHP and human). Other locations in the caudate also presented an overlap of four frontocortical regions, suggesting the existence of different locations with lower levels of input diversity. Small retrograde tracer injections and rs-fMRI seeds in the striatum confirmed these cortical input patterns. This study sets the ground for future studies evaluating rs-fMRI in clinical samples to measure anatomical corticostriatal circuit dysfunction and identify connectional hubs to provide more specific treatment targets for neurological and psychiatric disorders.

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis.

BACKGROUND: The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways. METHODS: In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA). RESULTS: A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients. CONCLUSIONS: In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.

Integration of bioinformatics and multi-layered experimental validation reveals novel functions of acetylation-related genes in intervertebral disc degeneration.

BACKGROUND: The molecular mechanisms underlying intervertebral disc degeneration (IDD) remain poorly understood. The purpose of this work is to elucidate key molecules and investigate the roles of acetylation-related RNAs and their associated pathways in IDD. METHOD: Datasets GSE70362 and GSE124272 were obtained from the Gene Expression Omnibus (GEO) and combined to investigate differentially expressed genes (DEGs) associated with acetylation in IDD patients compared to healthy controls. Critical genes were pinpointed by integrating GO, KEGG and PPI networks. Furthermore, CIBERSORTx analysis was used to investigate the differences in immune cell infiltration between different groups and the biological processes (BP), cellular components (CC) and molecular functions (MF) were calculated by GSEA and GSVA. In addition, The single-cell database GSE165722 was incorporated to validate the specific expression patterns of hub genes in cells and identify distinct cell subtypes. This provides a theoretical basis for a more in-depth understanding of the roles played by critical cell subtypes in the process of IDD. Subsequently, tissues from IVD with varying degrees of degeneration were collected to corroborate the key DEGs using western blot, RT-qPCR, and immunofluorescence staining. RESULTS: By integrating various datasets and references, we identified a total of 1620 acetylation-related genes. These genes were subjected to a combined analysis with the DEGs from the databases included in this study, resulting in the discovery of 358 acetylation-related differentially expressed genes (ARDEGs). A comparative analysis with differentially expressed genes obtained from three databases yielded 19 ARDEGs. The PPI network highlighted the top 10 genes (IL1B, LAMP1, PPIA, SOD2, LAMP2, FBL, MBP, SELL, IRF1 and KHDRBS1) based on their protein interaction relationships. CIBERSORTx immune infiltration analysis revealed a moderate positive correlation between the gene IL1ß and Mast.cells.activated, as well as a similar correlation between the gene IRF1 and Mast.cells.activated. Single-cell dataset was used to identify cell types and illustrate the distribution of hub genes in different cell types. The two cell types with the highest AUCell scores (Neutrophils and Monocytes) were further explored, leading to the subdivision of Neutrophils into two new cell subtypes: S100A9-type Neutrophils and MARCKS-type Neutrophils. Monocytes were labeled as HLA-DRA9-type Monocytes and IGHG3-type Monocytes. Finally, molecular biology techniques were employed to validate the expression of the top 10 hub genes. Among them, four genes (IL1ß, SOD2, LAMP2, and IRF1) were confirmed at the gene level, while two (IL1ß and SOD2) were validated at the protein level. CONCLUSION: In this study, we carried out a thorough analysis across three databases to identify and compare ARDEGs between IDD patients and healthy individuals. Furthermore, we validated a subset of these genes using molecular biology techniques on clinical samples. The identification of these differently expressed genes has the potential to offer new insights for diagnosing and treating IDD.

Evaluating the Barriers and Facilitators to Implementing a Novel Referral System for Outpatient Geriatric Services: The Geri-Hub Quality Improvement Initiative.

Background: In healthcare systems prioritizing care of older adults, resource limitations and escalating demand often impede access to outpatient specialized geriatric services. Objectives: This study, theoretically guided by the Consolidated Framework for Implementation Research (CFIR), aimed to explore barriers and facilitators in implementing a centralized "Geri-Hub." The Geri-Hub is a centralized intake system established within 2 hospital systems to coordinate outpatient and community-based services for older adults, aiming to connect them with the most appropriate care in a timely manner. Methods: Qualitative insights were gathered from healthcare professionals at 2 academic institutions in the process of consolidating services. Through open-ended surveys and semi-structured interviews, we solicited feedback on referral management, waiting times, and overall work experiences. Results: Thirteen frequently referring providers and a cohort of 9 geriatricians, along with 4 administrators, contributed to the study. Geriatricians emphasized streamlined referrals, flexible scheduling for urgent cases, and a target wait time of 3 months. Administrators stressed standardized referral procedures, defined roles, and accessible referral information. Discussion: The findings underscored the need for straightforward referral processes, enhanced communication on referral statuses, and reduced wait times. Optimizing these processes could potentially mitigate resource utilization issues and improve patient outcomes in healthcare systems. This research highlights the critical role of timely access to geriatric services during transformative phases in healthcare delivery.

Investigation of Common Pathways and Putative Biomarker Candidates of Colorectal Cancer and Insomnia by Using Integrative In-Silico Approaches.

Background: Colorectal cancer (CRC) is one of the leading causes of cancer-related mortalities across the globe. Accumulating evidence shows that individuals having sleep disorders such as insomnia are at high risk of developing CRC, yet the association of sleep disorders with CRC risk is still unclear. Here, we investigated the potential molecular connections between CRC and insomnia using integrative in silico approaches. Objective: This study aims to explore the potential molecular connections between CRC and insomnia utilizing integrative in-silico methodologies. Methods and Methods: Gene expression microarray datasets for CRC and insomnia samples were retrieved from the NCBI-GEO database and analyzed using R. Functional enrichment analysis of common differentially expressed genes (DEGs) was performed by the g: Profiler tool. Cytoscape software was used to construct a protein-protein interaction network and hub gene identification. Expression profiles of hub genes in TCGA datasets were also determined, and predicted miRNAs targeting hub genes were analyzed by miRNA target prediction tools. Results: Our results revealed a total of 113 shared DEGs between the CRC and insomnia datasets. Six genes (HSP8A, GAPDH, HSP90AA1, EEF1G, RPS6, and RPLP0), which were also differently expressed in TCGA datasets, were prioritized as hub genes and were found to be enriched in pathways related to protein synthesis. hsa-miR-324-3p, hsa-miR-769-3p, and hsa-miR-16-5p were identified as promising miRNA biomarkers for two diseases. Conclusions: Our in-silico analysis provides promising evidence of the molecular link between CRC and insomnia and highlights multiple potential molecular biomarkers and pathways. Validation of the results by wet lab work can be utilized for novel translational and precision medicine applications to alleviate the public health burden of CRC.

Optimal control strategies for energy storage systems for HUB substation considering multiple distribution networks.

With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.

Bioinformatics analysis-based mining of potential markers for inflammatory bowel disease and their immune relevance.

Background: The incidence of inflammatory bowel disease (IBD) is increasing every year and is characterized by a prolonged course, frequent relapses, difficulty in curing, and a lack of more efficacious therapeutic biomarkers. The aim of this study was to find key core genes as therapeutic biomarkers for IBD. Methods: GSE75214 in Gene Expression Omnibus (GEO) was used as the experimental set. The genes in the top 25% of standard deviation of all samples in the experimental set were subjected to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, least absolute shrinkage and selection operator (LASSO) logistic regression was used to further screen the central genes. Finally, the validity of hub genes was verified on GEO dataset GSE179285 using "BiocManager" R package. Results: Twelve well-preserved modules were identified in the experimental set using the WGCNA method. Among them, five modules significantly associated with IBD were screened as clinically significant modules, and four candidate genes were screened from these five modules. Then TIMP1, GUCA2B, and HIF1A were screened as hub genes. These hub genes successfully distinguished tumor samples from healthy tissues by artificial neural network algorithm in an independent test set with an area under the working characteristic curve of 0.946 for the subjects. Conclusions: IBD differentially expressed gene (DEGs) are involved in immunoregulatory processes. TIMP1, GUCA2B, and HIF1A, as core genes of IBD, have the potential to be therapeutic targets for patients with IBD, and our findings may provide a new outlook on the future treatment of IBD.

Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning.

Background: Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF. Methods: We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS. Results: Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4. Conclusion: In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.