Comprehensive analysis of lncRNA-associated ceRNA network reveals novel potential prognostic regulatory axes in glioblastoma multiforme.

Deciphering the lncRNA-associated competitive endogenous RNA (ceRNA) network is essential in decoding glioblastoma multiforme (GBM) pathogenesis by regulating miRNA availability and controlling mRNA stability. This study aimed to explore novel biomarkers for GBM by constructing a lncRNA-miRNA-mRNA network. A ceRNA network in GBM was constructed using lncRNA, mRNA and miRNA expression profiles from the TCGA and GEO datasets. Seed nodes were identified by protein-protein interaction (PPI) network analysis of deregulated-mRNAs (DEmRNAs) in the ceRNA network. A lncRNA-miRNA-seed network was constructed by mapping the seed nodes into the preliminary ceRNA network. The impact of the seed nodes on the overall survival (OS) of patients was assessed by the GSCA database. Functional enrichment analysis of the deregulated-lncRNAs (DElncRNA) in the ceRNA network and genes interacting with OS-related genes in the PPI network were performed. Finally, the positive correlation between seed nodes and their associated lncRNAs and the expression level of these molecules in GBM tissue compared with normal samples was validated using the GEPIA database. Our analyzes revealed that three novel regulatory axes AL161785.1/miR-139-5p/MS4A6A, LINC02611/miR-139-5p/MS4A6A and PCED1B-AS1/miR-433-3p/MS4A6A may play essential roles in GBM pathogenesis. MS4A6A is upregulated in GBM and closely associated with shorter survival time of patients. We also identified that MS4A6A expression positively correlates with genes related to tumour-associated macrophages, which induce macrophage infiltration and immune suppression. The functional enrichment analysis demonstrated that DElncRNAs are mainly involved in neuroactive ligand-receptor interaction, calcium/MAPK signalling pathway, ribosome, GABAergic/Serotonergic/Glutamatergic synapse and immune system process. In addition, genes related to MS4A6A contribute to immune and inflammatory-related biological processes. Our findings provide novel insights to understand the ceRNA regulation in GBM and identify novel prognostic biomarkers or therapeutic targets.

Role of circular RNA/miRNA axes in the pathophysiology of polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a disorder resulted from interactions between genetic and environmental factors. Based on the importance of epigenetic factors in the pathoetiology of PCOS, the current review focused on identification of circular RNAs (circRNAs) that are involved in PCOS through acting as molecular sponges for microRNAs (miRNAs). The literature search led to identification of circ_0043533/miR-1179, circ_0030018/miR-136, circ_FURIN/miR-423-5p, circ-FURIN/miR-195-5p, circ_0043532/miR-182, circ_RANBP9/miR-136-5p, circRHBG/miR-515-5p, circMTO1/miR-320b, circASPH/miR-375, circPSMC3/miR-296-3p, circLDLR/miR-1294, circPUM1/miR-760, and hsa_circ_0118530/miR-136 as molecular axes contributing to the pathogenesis of PCOS. To set the stage for future research on the role of the ceRNA network in PCOS, in-silico analyses were performed using miRWalk, miRNet, and miRDIP databases. miRWalk identified 80 genes regulated by 5 miRNAs, miRNet revealed 6449 circRNAs potentially controlling 11 miRNAs, and miRDIP identified 11 miRNAs associated with 35 human pathways. These targets can be used in the treatment options, design of personalized medicine and prediction of prognosis of PCOS.

The optimal fluoroscopic views to rule out intra-articular screw penetration during acetabular fracture fixation.

PURPOSE: To determine the ideal view(s) and the minimum number of intraoperative fluoroscopic views required to rule out any intra-articular screw violation in acetabular fractures fixation. METHODS: This study was conducted using a series of fluoroscopic examinations of pelvic synthetic models with screws positioned in different planes around the acetabulum. Ten screws were placed in the synthetic pelvis models in different planes of the acetabulum. Seven views were taken for each screw. Radiographic images were evaluated by 14 orthopaedic surgeons who were asked to assess joint violation and the view(s) required for assessment. RESULTS: The observers' accuracy rate in identifying joint violation was 82.1% for the anterior part of the anterior column and the superior part of the posterior column, 89.3% for the posterior part of the anterior column and the inferior part of the posterior column, and 92.9% for the quadrilateral plate. The sensitivity was 100% for the anterior and posterior parts of the anterior column and the inferior part of the posterior column, 87.5% for the superior part of the posterior column, and 85.7% for the quadrilateral plate. The specificity was 100% for the quadrilateral plate, 80% for the superior part of the posterior column and the posterior part of the anterior column, 78.6% for the inferior part of the posterior column, and 66.7% for the anterior part of the anterior column. There was a strong overall interobserver and intra-observer agreement with intraclass correlation coefficient (ICC) of 0.709 and 0.86, respectively. CONCLUSIONS: This study confirms the hypothesis that in a concave surface/joint fixation, such as the acetabulum, the probability of joint violation is unlikely if there is no evidence of it within a single fluoroscopic view. In acetabulum fracture fixation with a screw violating the joint, the screw's presence was evident within the joint space in all fluoroscopic views. However, the absence of joint violation in one fluoroscopic view was adequate to rule out joint penetration.

Phytochemicals, Essential Oils Composition and Antioxidant Activity of Astragalus spp., Phlomis olivieri and Daphne mucronata in Habitats of Central Iran.

This study evaluated several secondary metabolites, essential oils (EOs) compositions, and antioxidant activity in four medicinal plants that originated in Isfahan rangelands. The species were Astragalus verus, Astragalus adscendens, Daphne mucronata, and Phlomis olivieri. Thirty-two genotypes of these species were evaluated for different biochemical traits. Based on the evaluation of EOs compounds, GC/MS analysis revealed the total number of identified compounds. These compounds were 25, 22, 12, and 22 for A. adscendens, A. verus, D. mucronata, and P. olivieri, respectively. The dominant compounds were phthalate (59.88 %) in A. adscendens, phytol (38.02 %) in A. verus, hexanoic acid (32.05 %) in D. mucronata and ß-cubebene (30.94 %) in P. olivieri. Phytochemical analysis showed that D. mucronata, A. adscendens, and P. olivieri had the highest total phenolics content (TPC) (18.24 mg gallic acid equivalent/g dry weight), total flavonoids content (5.57 mg QE/g DW), and total anthocyanins content (0.23 mg/g DW), respectively. The highest total chlorophyll (0.27 mg/g DW), total carotenoids (0.03 mg/g DW), and antioxidant activity (71.36 %) were observed in A. adscendens, A. adscendens and A. verus, respectively. Among all genotypes, the highest TPC (20.1 mg GAE/g DW) was observed in genotype 5 of D. mucronata. This study provided new information on the chemical compounds within the distribution range of these ecologically dominant rangeland species in Isfahan province, Iran. The data revealed that superior genotypes from these species are rich in natural antioxidants and bioactive compounds. Thus, they can be used in ethno pharmacological fields, food, and industrial applications.

Detection of brain lesion location in MRI images using convolutional neural network and robust PCA.

Purpose and aim: Detection of brain tumors plays a critical role in the treatment of patients. Before any treatment, tumor segmentation is crucial to protect healthy tissues during treatment and to destroy tumor cells. Tumor segmentation involves the detection, precise identification, and separation of tumor tissues. In this paper, we provide a deep learning method for the segmentation of brain tumors. Material and methods: In this article, we used a convolutional neural network (CNN) to segment tumors in seven types of brain disease consisting of Glioma, Meningioma, Alzheimer's, Alzheimer's plus, Pick, Sarcoma, and Huntington. First, we used the feature-reduction-based method robust principal component analysis to find tumor location and spot in a dataset of Harvard Medical School. Then we present an architecture of the CNN method to detect brain tumors. Results: Results are depicted based on the probability of tumor location in magnetic resonance images. Results show that the presented method provides high accuracy (96%), sensitivity (99.9%), and dice index (91%) regarding other investigations. Conclusion: The provided unsupervised method for tumor clustering and proposed supervised architecture can be potential methods for medical uses.

Construction of circRNA-miRNA-mRNA network in the pathogenesis of recurrent implantation failure using integrated bioinformatics study.

This research attempted to elucidate the molecular components are involved in the pathogenesis of recurrent implantation failure (RIF). We initially identified that 386 mRNAs, 144 miRNAs and 2548 circRNAs were differentially expressed (DE) in RIF and then investigated the genetic cause of the observed abnormal expression by constructing a circRNA-miRNA-mRNA network considering the competing endogenous RNA theory. We further analysed the upstream transcription factors and related kinases of DEmRNAs (DEMs) and demonstrated that SUZ12, AR, TP63, NANOG, and TCF3 were the top five TFs binding to these DEMs. Besides, protein-protein interaction analysis disclosed that ACTB, CXCL10, PTGS2, CXCL12, GNG4, AGT, CXCL11, SST, PENK, and FOXM1 were the top 10 hub genes in the acquired network. Finally, we performed the functional enrichment analysis and found that arrhythmogenic right ventricular cardiomyopathy (ARVC), hypertrophic cardiomyopathy (HCM), pathways in cancer, TNF signalling pathway and steroid hormone biosynthesis were the potentially disrupted pathways in RIF patients. Optimistically, our findings may deepen our apprehensions about the underlying molecular and biological causes of RIF and provide vital clues for future laboratory and clinical experiments that will ultimately bring a better outcome for patients with RIF.

Terahertz radiation detection with a cantilever-based photoacoustic sensor.

We report the photoacoustic (PA) response in the terahertz (THz) range by employing a detection process actuated with a silicon cantilever pressure sensor and a carbon-based radiation absorber. The detection relies on the mechanical response of the cantilever, when the volume of the carrier gas inside the PA cell expands with the heat produced by the radiation absorber. The detector interferometrically monitors the movement of the cantilever sensor to generate the PA signal. We selected the absorber material with the highest THz responsivity for detailed studies at 1.4 THz (214 µm wavelength). The observed responsivities of two different radiation absorbers are nearly the same at 1.4 THz and agree within 10% with responsivity values at 0.633 µm wavelength. The results demonstrate the potential of covering with a single PA detector a broad spectral range with approximately constant responsivity, large dynamic range, and high damage threshold.

Flucytosine-based prodrug activation by cold physical plasma.

Reactive oxygen species (ROS) are known to trigger drug release from arylboronate-containing ROS-responsive prodrugs. In cancer cells, elevated levels of ROS can be exploited for the selective activation of prodrugs via Baeyer-Villiger type oxidation rearrangement sequences. Here, we report a proof of concept to demonstrate that these cascades can as well be initiated by cold physical plasma (CPP). An analog of a recently reported fluorouracil prodrug based on the less toxic drug 5-fluorocytosine (5-FC) was synthesized with a view to laboratory safety reasons and used as a model compound to prove our hypothesis that CPP is suitable as a trigger for the prodrug activation. Although the envisioned oxidation and rearrangement with successive loss of boronic acid species could be achieved by plasma treatment, the anticipated spontaneous liberation of 5-FC was inefficient in the model case. However, the obtained results suggest that custom-tailored CPP-responsive prodrugs might become an evolving research field.

Insight into the Impact of Oxidative Stress on the Barrier Properties of Lipid Bilayer Models.

As a new field of oxidative stress-based therapy, cold physical plasma is a promising tool for several biomedical applications due to its potential to create a broad diversity of reactive oxygen and nitrogen species (RONS). Although proposed, the impact of plasma-derived RONS on the cell membrane lipids and properties is not fully understood. For this purpose, the changes in the lipid bilayer functionality under oxidative stress generated by an argon plasma jet (kINPen) were investigated by electrochemical techniques. In addition, liquid chromatography-tandem mass spectrometry was employed to analyze the plasma-induced modifications on the model lipids. Various asymmetric bilayers mimicking the structure and properties of the erythrocyte cell membrane were transferred onto a gold electrode surface by Langmuir-Blodgett/Langmuir-Schaefer deposition techniques. A strong impact of cholesterol on membrane permeabilization by plasma-derived species was revealed. Moreover, the maintenance of the barrier properties is influenced by the chemical composition of the head group. Mainly the head group size and its hydrogen bonding capacities are relevant, and phosphatidylcholines are significantly more susceptible than phosphatidylserines and other lipid classes, underlining the high relevance of this lipid class in membrane dynamics and cell physiology.

HSSAGA: Designation and scheduling of nurses for taking care of COVID-19 patients using novel method of Hybrid Salp Swarm Algorithm and Genetic Algorithm.

The COVID-19 pandemic is viewed as the most basic worldwide disaster that humankind has observed since the second World War. There is no report of any clinically endorsed antiviral medications or antibodies that are successful against COVID-19. It has quickly spread everywhere, presenting tremendous well-being, financial, ecological, and social difficulties to the whole human populace. The COVID flare-up is seriously disturbing the worldwide economy. Practically all the countries are battling to hinder the transmission of the malady by testing and treating patients, isolating speculated people through contact following, confining huge social affairs, keeping up total or incomplete lockdown, etc. Proper scheduling of nursing workers and optimal designation of nurses may significantly affect the quality of clinical facilities. It is delivered by eliminating unbalanced workloads or undue stress, which could lead to decreased nurse performance and potential human errors., Nurses are frequently asked to leave while caring for all sick patients. However, regular scheduling formulas are not thought to consider this possibility because they are out of scheduling control in typical scenarios. In this paper, a novel model of the Hybrid Salp Swarm Algorithm and Genetic Algorithm (HSSAGA) is proposed to solve nurses' scheduling and designation. The findings of the suggested test function algorithm demonstrate that this algorithm has outperformed state-of-the-art approaches.

Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches.

COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.

Evolutionary applications of phylogenetically-informed ecological niche modelling (ENM) to explore cryptic diversification over cryptic refugia.

We used the fat dormouse (Glis glis), a species from a monotypic genus of family Gliridae, as a model to promote the understanding of patterns of cryptic diversification along the ancient Hyrcanian Forests, one of the old-growth relicts of the temperate deciduous forests worldwide. Mitochondrial Cytb data was used to investigate the phylogenetic status of two geographically-different populations of G. glis along the Hyrcanian Forests among all the worldwide known lineages of the species. Regarding phylogenetically informed partitioning of occurrence data, we then used two analytically different ENMs (i.e. environmental-space and geographic-space) to address whether niche divergence conforms G. glis diversification over the study area. Phylogenetic reconstruction showed significant heterogeneity between other fat dormouse lineages and those belonging to the Hyrcanian Forests as well as within the two hypothesized cryptic groups in the study area. Quantifying niche differences using the two ENM frameworks additionally confirmed divergence between the two cryptic lineages by indicating niche conservatism. The integration of phylogeny and ENM in this study confirms the development of distinct cryptic species and suggests that the Hyrcanian Forests, a well-known Pleistocene refugium, might contain multiple cryptic refugia for small forest-dwelling species during paleontological oscillations.

Conservation Below the Species Level: Suitable Evolutionarily Significant Units among Mountain Vipers (the Montivipera raddei complex) in Iran.

Northern and western mountains of Iran are among the most important biodiversity and endemism hot spots for reptiles in the Middle East. Among herpetofauna, the montivipers represent an emblematic and fragmented endemic group for which estimating their level of genetic differentiation and defining conservation priorities is urgently needed. Here, we present the most comprehensive phylogenetic study on the Montivipera raddei species group comprising all 5 known taxa, among which 3 are endemic to Iran. Based on 2 mitochondrial genes, phylogenetic and phylogeographic analyses revealed 3 major lineages each presenting very contrasting distribution areas. The Iranian montivipers are highly structured in clades showing low genetic diversity and corresponding to high altitude summits. Molecular dating revealed the role of Quaternary paleo-climatic oscillations and altitudinal movements of montivipers in shaping genetic diversity and differentiation of these sky-island taxa. In addition, the best scenario of historical biogeography allowed identifying 3 possible refugial areas in Iran most likely arising by vicariance. Based on our mitochondrial results and pending additional data, we recognize 3 candidate species among the M. raddei complex: M. raddei, Montivipera latifii, and Montivipera kuhrangica that are coherent with their geographical distribution. We propose that the most appropriate evolutionary significant units for conservation of the montivipers are represented by 13 units among which 6 are recognized as high priority. Finally, we suggest some recommendations to the IUCN as well as to the Iranian conservation policies with respect to conservation prioritization.

Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals.

We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets.

The use of pulse oximetry in evaluation of pulp vitality in immature permanent teeth.

BACKGROUND AND AIM: The current methods of pulp vitality assessment, either electric or thermal, are of limited use in children. Recently, traumatized and immature teeth may not respond to such methods and because such methods require subjective responses, it may not provide accurate results particularly in children. Pulse oximetry, an atraumatic approach, is used to measure oxygen saturation in vascular system. The aim of this study was to investigate the use of pulse oximetry to evaluate pulp vitality status in immature permanent teeth. METHODS AND MATERIALS: The study was conducted on 329 maxillary central and lateral incisors in children. The negative control group consisted of 10 root filled teeth. Systemic oxygen saturation was first measured on the thumb of the individual using a custom-made sensor. Oxygen saturation values of the teeth were then evaluated. The correlation between oxygen saturation measurement obtained from finger and tooth, and the correlation between oxygen saturation values and stage of root development were analyzed. A further comparison was made between the teeth with open and closed apex. RESULTS: Mean oxygen values recorded in the patient's finger were 97.17%, and mean oxygen values in the maxillary central and lateral incisors were 86.77% and 83/92%, respectively. There was no significant correlation between blood oxygen levels in the finger and in the teeth. (P > 0.05) There was a significant negative correlation between the stage of root development and the blood oxygen levels in the patients' teeth. (P < 0.05) Mean oxygen values in the teeth with open apex were significantly higher than the teeth with closed apex. (P < 0.001). CONCLUSION: Vital teeth provided consistent oxygen saturation readings, and non-vital teeth recorded no oxygen saturation values. During tooth development, the oxygen saturation values decreased. These findings confirm that the pulse oximetry is capable of detecting the pulpal blood flow and oxygen saturation.

Package delivered: folate receptor-mediated transporters in cancer therapy and diagnosis.

Neoplasias pose a significant threat to aging society, underscoring the urgent need to overcome the limitations of traditional chemotherapy through pioneering strategies. Targeted drug delivery is an evolving frontier in cancer therapy, aiming to enhance treatment efficacy while mitigating undesirable side effects. One promising avenue utilizes cell membrane receptors like the folate receptor to guide drug transporters precisely to malignant cells. Based on the cellular folate receptor as a cancer cell hallmark, targeted nanocarriers and small molecule-drug conjugates have been developed that comprise different (bio) chemistries and/or mechanical properties with individual advantages and challenges. Such modern folic acid-conjugated stimuli-responsive drug transporters provide systemic drug delivery and controlled release, enabling reduced dosages, circumvention of drug resistance, and diminished adverse effects. Since the drug transporters' structure-based de novo design is increasingly relevant for precision cancer remediation and diagnosis, this review seeks to collect and debate the recent approaches to deliver therapeutics or diagnostics based on folic acid conjugated Trojan Horses and to facilitate the understanding of the relevant chemistry and biochemical pathways. Focusing exemplarily on brain and breast cancer, recent advances spanning 2017 to 2023 in conjugated nanocarriers and small molecule drug conjugates were considered, evaluating the chemical and biological aspects in order to improve accessibility to the field and to bridge chemical and biomedical points of view ultimately guiding future research in FR-targeted cancer therapy and diagnosis.

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction.

Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

Carcinogenic roles of MAFG-AS1 in human cancers.

The MAF bZIP transcription factor G-antisense RNA 1 (MAFG-AS1) is located on chromosome 17. MAFG-AS1 was upregulated in 15 human cancers. MAFG-AS1 not only suppresses 16 miRNAs but also directly impacts 22 protein-coding genes' expression. Notably, abnormal MAFG-AS1 expression is connected to clinicopathological characteristics and a worse prognosis in a variety of cancers. Moreover, MAFG-AS1 takes its part in the tumorigenesis and progression of various human malignancies by suppressing apoptosis and promoting proliferation, migration, invasion, aerobic glycolysis, ferroptosis, angiogenesis, EMT, and metastasis. Besides, it can predict treatment effectiveness in ER + breast cancer, urothelial bladder carcinoma, and liver cancer by functioning as a trigger of resistance to tamoxifen, sorafenib, and cisplatin. This study systematically presents the functions of MAFG-AS1 in various cancers, as well as the findings of bioinformatics analyses of the MAFG-AS1, which should give clear advice for future research.

An Update on the Application of CRISPR Technology in Clinical Practice.

The CRISPR/Cas system, an innovative gene-editing tool, is emerging as a promising technique for genome modifications. This straightforward technique was created based on the prokaryotic adaptive immune defense mechanism and employed in the studies on human diseases that proved enormous therapeutic potential. A genetically unique patient mutation in the process of gene therapy can be corrected by the CRISPR method to treat diseases that traditional methods were unable to cure. However, introduction of CRISPR/Cas9 into the clinic will be challenging because we still need to improve the technology's effectiveness, precision, and applications. In this review, we first describe the function and applications of the CRISPR-Cas9 system. We next delineate how this technology could be utilized for gene therapy of various human disorders, including cancer and infectious diseases and highlight the promising examples in the field. Finally, we document current challenges and the potential solutions to overcome these obstacles for the effective use of CRISPR-Cas9 in clinical practice.

"Hyperglycemic Memory": Observational Evidence to Experimental Inference.

Several epidemiological studies have appreciated the impact of "duration" and "level" of hyperglycemia on the initiation and development of chronic complications of diabetes. However, glycemic profiles could not fully explain the presence/absence and severity of diabetic complications. Genetic issues and concepts of "hyperglycemic memory" have been introduced as additional influential factors involved in the pathobiology of late complications of diabetes. In the extended phase of significant diabetes randomized, controlled clinical trials, including DCCT/EDIC and UKPDS, studies have concluded that the quality of glycemic or metabolic control at the early time around the diabetes onset could maintain its protective or detrimental impact throughout the following diabetes course. There is no reliable indication of the mechanism by which the transient exposure to a given glucose concentration level could evoke a consistent cellular response at target tissues at the molecular levels. Some biological phenomena, such as the production and the concentration of advanced glycation end products (AGEs), reactive oxygen species (ROS) and protein kinase C (PKC) pathway activations, epigenetic changes, and finally, the miRNAs-mediated pathways, may be accountable for the development of hyperglycemic memory. This work summarizes evidence from previous experiments that may substantiate the hyperglycemic memory soundness by its justification in molecular terms.