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.

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.

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.