Chemical burn wounds as a risk factor for gastric cancer: in-silico analyses-experimental research.

Introduction: The present study employs bioinformatics tools to identify shared upregulated genes between chemical burns and gastric cancer. Methods: Gene Expression Omnibus (GEO) retrieved gene sets for this investigation. GSEs with P value less than 0.05 and LOG fold change (FC) greater than 1 were valid and upregulated. Gastric cancer and chemical burn common elevated genes were found using Venn diagram online tools. In the second stage, the "string" visualized gastric cancer elevated genes network, and non-coding RNAs were deleted, and "interaction" greater than 1 was examined to choose important gene nodes. Next, they explored the String gene-interaction network for common genes. To determine the most interacting genes, Gephi (V 0.9.7) used "betweenness centrality" greater than "0" to evaluate the twenty-gene network. TISIDB and drug banks provide gene-related medications. Results: In the present study, two genes, including ALOX5AP and SERPINB2, were obtained, with the highest centrality among chemical burns and gastric cancer shared genes. Additionally, the current study presented five drugs, including Urokinase, Tenecteplase, DG031, AM103, and Fiboflapon, which can have predicted effects on gastric cancer following chemical burns. Conclusion: According to current in-silicon analyses, ALOX5AP and SERPINB2 are linked genetic keys between gastric chemical burn and cancer. Considering that burn is an environmental factor that leads to the upregulation of the two genes thus, the chemical burn can be related to the incidence of gastric cancer.

Esophageal chemical burns as a risk factor for esophageal malignancies: in-silico analyses - experimental research.

Introduction: Esophageal chemical burns often occur through accidental or intentional oral consumption of chemical agents and lead to severe complications such as esophageal stricture, acute perforation, and even death. Esophageal squamous cell carcinoma is a squamous epithelium tumor that lines the normal esophagus. Additionally, adenocarcinomas are tumors located at the interface between the distal esophagus and the proximal gastric and divided into esophageal adenocarcinoma and gastric-cardia adenocarcinoma. Various conditions, such as chemical burns, are considered risk factors in the disease's pathogenesis. In the in-silico study, the authors aim to present the relationship between chemical burns and esophageal cancer by analyzing bioinformatics genetic data. Methods: The proper gene set was extracted using the 'GEO' database. The string web tool was utilized to form the gene-interaction network. Gephi and Cytoscape software were applied to achieve network analysis. Results: According to in-silico data, 26 genes, including NCAPH, DLGAP5, CCNB1, KIF11, KIAA0101, CDCA5, BIRC5, NUF2, BUB1B, RRM2, TTK, CDC20, NUSAP1, CCNB2, CCNA2, MELK, TPX2, PRC1, KIF4A, CENPF, TOP2A, CDK1, ASPM, CEP55, BUB1, KIF20A were extracted that can be regarded as the most critical shared genes between chemical burns and esophageal cancer. Conclusion: In sum, esophageal chemical burns can be related to the occurrence of esophageal cancer. Moreover, esophageal chemical burn is an external factor that upregulates present genes and can be regarded as a worsening prognosis or risk factor for esophageal cancer.

Complement protein expression changes in various conditions of breast cancer: in-silico analyses-experimental research.

Introduction: Breast cancer is the most prevalent cancer diagnosed in females worldwide. The known biomarkers are insufficient to understand the actual prognosis of breast cancer, and identifying new biomarkers is desirable and valuable data to improve the patient's survival. Many inflammatory biomarkers, such as the complement system, can be regarded as prognostic values and as potent inflammatory mediators; complement proteins have a critical role in tumorigenesis. In the current study, the authors aim to investigate complement protein expression changes, particularly complement 3 (C3), complement 7 (C7), complement factor B (CFB), and complement factor D (CFD), in various conditions of breast cancer using in-silico tools. Methods: The intent data were extracted using webtools, including; Kaplan-Meier plotter, BcGenExMiner, UALCAN, cbioportal, GeneMania, and Enrichr. To select valid data, a P greater than 0.05 was considered. Results: The current study clarified that 21 complement genes correlated to survival conditions. Also, down or upregulation of extracted genes and breast cancer statuses were identified. Additionally, expression level difference of complement genes in various breast cancer four stages was detected. Ultimately, co-expression genes with complement genes were extracted and networked. Conclusion: Changes in the expression of complement proteins can strongly correlate to breast cancer's prognosis, status, and survival. Furthermore, considering the vital role of CFD and CFB complement proteins in the alternative pathway in different stages of breast cancer, CFD and CFB can be regarded as reliable prognostic values for diagnosis.

Prediction of immune molecules activity during burn wound healing among elderly patients: in-silico analyses: experimental research.

Introduction: Burn injuries lead to dysregulation of immune molecules, impacting cellular and humoral immune pathways. This study aims to determine the prediction of immune molecule activity during burn wound healing among elderly patients. Methods: The current study utilized the Gene Expression Omnibus (GEO) database to extract the proper gene set. Also, the literature review was conducted in the present study to find immune signatures. The study used the "enrich r" website to identify the biological functions of extracted genes. The critical gene modules related to mortality were identified using the weighted gene co-expression network analysis (WGCNA) R package. Results: The appreciated GSE was extracted. According to the data, the most upregulated signatures were related to natural killer (NK) cells, and the most downregulated signatures were associated with M1 macrophages. Also, the results of WGCNA have shown that the most related gene modules (P<107 and score 0.17) to mortality were investigated, and the modules 100 first genes were extracted. Additionally, the enrich r analysis has demonstrated related pathways, including the immune process, including regulation of histamine secreted from mast cell (P<0.05), T helper 17 cell differentiation (P<0.05), and autophagy (P<0.05) were obtained. Finally, by network analysis, the critical gene "B3GNT5" were obtained (degree>ten and "betweenness and centrality">30 were considered). Conclusion: The study identified significant changes in macrophage and NK cell expression patterns post-burn injury, linking them to potential improvements in clinical outcomes and wound healing. The gene B3GNT5, associated with mortality, was highlighted as a key marker for prognostic evaluation.

Identification of a valuable gene network for the diagnosis and treatment of non-obstructive azoospermia: in-silico analyses - experimental research.

Introduction: Non-obstructive azoospermia (NOA) is an etiology of infertility in men. NOA may have various classifications; however, hypogonadotropic hypogonadism can be regarded as a class of NOA associated with genetic factors. Former studies have shown that noncoding RNA (ncRNA) plays an essential role in NOA incidence, but few studies have been performed on the NOA-related ncRNA interaction network. In the current study, genes, NOA-related microRNA (miRNA), and circular RNA (circRNA) were found by bioinformatics methods to offer a new perspective on NOA treatment. Methods: The gonadotropin-releasing hormone receptor (GnRHR)-related protein-protein interaction (PPI) network was extracted by searching in 'string-database'. GO, KEGG, and Enrichr databases were used to identify pathways, molecular function, and biological processing. Four databases, including TargetScan, mirDIP, miRmap, and miRWalk, were used to extract miRNAs. At last, the circ2GO, circBase, and literature were used to identify circRNAs and their genes. Results: The current study identified the four proteins associated with the GnRHR signaling; eight shared miRNAs that affect the expression of found proteins and 25 circRNAs and their origin genes that regulate the miRNAs' function. Conclusion: The two miRNAs, hsa-miR-134-3p and hsa-miR-513C-3p, the three genes, VCAN, NFATC3, and PRDM5, and their associated circRNAs can perform as a valuable gene network in the diagnosis and treatment of NOA pathogenesis.

Lateral habenula deep brain stimulation alleviates depression-like behaviors and reverses the oscillatory pattern in the nucleus accumbens in an animal model of depression.

Depression is a series of symptoms that influence mood, thinking, and behavior and create unpleasant emotions like hopelessness and apathy. Treatment-resistant depression (TRD) affects 30 % of depression patients despite the availability of several non-invasive therapies. Deep brain stimulation (DBS) is a novel therapy for TRD. The aim of the current study was to evaluate the effect of LHb-DBS by recording local field potentials (LFP) and conducting behavioral experiments. Thirty-two mature male Wistar rats were randomly divided into four groups: control, chronic mild stress (CMS), CMS+DBS, and DBS. After surgery and electrode placement in the lateral habenula (LHb), nucleus accumbens (NAc), and prelimbic cortex (PrL), the CMS protocol was applied for 3 weeks to create depression-like models. The open field test (OFT), sucrose preference test (SPT), and forced swim test (FST) were also performed. In the DBS groups, the LHb area was stimulated for four consecutive days. Finally, on the 22nd day, LFP was recorded from the NAc and PrL and analyzed using MATLAB software. Analyzing the findings using ANOVA and P-values ≤ 0.05 was considered. LHb-DBS alleviated depression-like behaviors in chronic moderate stress model rats (P ≤ 0.05). Three weeks of CMS enhanced almost all band powers in the NAc, while LHb-DBS decreased the power of the theta, alpha, beta, and gamma bands in the NAc (P ≤ 0.05), and the low-gamma band in the PrL. CMS also boosted the NAc-PrL coherence in low-frequency bands, while LHb-DBS increased beta and low gamma band coherence (P ≤ 0.05). In sum, the results of the present study showed that depression enhances low-frequency coherence between NAc and PrL cortex. Depression also potentiates many brain oscillations in the NAc, which can be mainly reversed by LHb-DBS.

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation-maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.

Stem cell-conditioned medium is a promising treatment for Alzheimer's disease.

BACKGROUND AND AIM: Alzheimer's disease (AD), a prevalent progressive neurodegenerative disease, is mainly characterized by dementia, memory loss, and cognitive disorder. Rising research was performed to develop pharmacological or non-pharmacological approaches to treat or improve AD complications. Mesenchymal stem cells (MSCs) are stromal cells that can self-renew and exhibit multilineage differentiation. Recent evidence suggested that some of the therapeutic effects of MSCs are mediated by the secreted paracrine factors. These paracrine factors, called MSC- conditioned medium (MSC-CM), may stimulate endogenous repair, promote angio- and artery genesis, and reduce apoptosis through paracrine mechanisms. The current study aims to systematically review the advantages of MSC-CM to the development of research and therapeutic concepts for AD management. MATERIAL AND METHODS: The present systematic review was performed using PubMed, Web of Science, and Scopus from April 2020 to May 2022 following the "Preferred Reporting Items for Systematic Reviews" (PRISMA) guidelines. The keywords, including "Conditioned medium OR Conditioned media OR Stem cell therapy" AND "Alzheimer's," was searched, and finally, 13 papers were extracted. RESULTS: The obtained data revealed that MSC-CMs might positively affect neurodegenerative diseases prognosis, especially AD, through various mechanisms, including a decrease in neuro-inflammation, reduction of oxidative stress and Aß formation, modulation of Microglia function and count, reduction of apoptosis, induction of synaptogenesis and neurogenesis. Also, the results showed that MSC-CM administration could significantly improve cognitive and memory function, increase the expression of neurotrophic factors, decrease the production of pro-inflammatory cytokines, improve mitochondrial function, reduce cytotoxicity, and increase neurotransmitter levels. CONCLUSION: While inhibiting the induction of neuroinflammation could be considered the first therapeutic effect of CMs, the prevention of apoptosis could be regarded as the most crucial effect of CMs on AD improvement.