Cell cycle regulation of proliferation versus differentiation in the central nervous system

Formation of the central nervous system requires a period of extensive progenitor cell proliferation, accompanied or closely followed by differentiation; the balance between these two processes in various regions of the central nervous system gives rise to differential growth and cellular diversity. The correlation between cell cycle lengthening and differentiation has been reported across several types of cell lineage and from diverse model organisms, both in vivo and in vitro. Furthermore, different cell fates might be determined during different phases of the preceding cell cycle, indicating direct cell cycle influences on both early lineage commitment and terminal cell fate decisions. Significant advances have been made in the last decade and have revealed multi-directional interactions between the molecular machinery regulating the processes of cell proliferation and neuronal differentiation. Here, we first introduce the modes of proliferation in neural progenitor cells and summarise evidence linking cell cycle length and neuronal differentiation. Second, we describe the manner in which components of the cell cycle machinery can have additional and, sometimes, cell-cycle-independent roles in directly regulating neurogenesis. Finally, we discuss the way that differentiation factors, such as proneural bHLH proteins, can promote either progenitor maintenance or differentiation according to the cellular environment. These intricate connections contribute to precise coordination and the ultimate division versus differentiation decision

Evaluation on the SPT Based Design Approach for Shallow Foundations

This research evaluated four SPT-based design equations used to estimate the carrying capacity of shallow footing. Using different methods, two plate load tests performed on silty clay and clay soil in Nasiriyah have been used to obtain the ultimate load-carrying capacity. Further, this study aims to utilize the finite element method based on Plaxis 3D foundation software to simulate the behavior of load settlement of the foundation with actual dimensions. It was concluded that the numerical analysis methods showed a good convergence to the actual test results, where the numerical results were 67 and 65 ton/m2 for two projects, respectively. The field values were 70 ton/m2 for projects with a number of possibilities in determining the failure areas of the soil to give a sufficient picture of the load expectations. The boundary of the influence zone obtained by the finite element method has functioned as an influence zone proposed for a new proposed equation which gave a good convergence with the measured bearing capacity values

Adeno-Associated Viral Transfer of Glyoxalase-1 Blunts Carbonyl and Oxidative Stresses in Hearts of Type 1 Diabetic Rats

Accumulation of methylglyoxal (MG) arising from downregulation of its primary degrading enzyme glyoxalase-1 (Glo1) is an underlying cause of diabetic cardiomyopathy (DC). This study investigated if expressing Glo1 in rat hearts shortly after the onset of Type 1 diabetes mellitus (T1DM) would blunt the development of DC employing the streptozotocin-induced T1DM rat model, an adeno-associated virus containing Glo1 driven by the endothelin-1 promoter (AAV2/9-Endo-Glo1), echocardiography, video edge, confocal imaging, and biochemical/histopathological assays. After eight weeks of T1DM, rats developed DC characterized by decreased E:A ratio, fractional shortening, and ejection fraction, and increased isovolumetric relaxation time, E: e’ ratio, and circumferential and longitudinal strains. Evoked Ca2+ transients and contractile kinetics were also impaired in ventricular myocytes. Hearts from eight weeks T1DM rats had lower Glo1 and GSH levels, elevated carbonyl/oxidative stress, microvascular leakage, inflammation, and fibrosis. A single injection of AAV2/9 Endo-Glo1 (1.7×1012 viron particles/kg) one week after onset of T1DM, potentiated GSH, and blunted MG accumulation, carbonyl/oxidative stress, microvascular leakage, inflammation, fibrosis and impairments in cardiac and myocyte functions that develop after eight weeks of T1DM. These new data indicate that preventing Glo1 downregulation by administering AAV2/9-Endo-Glo1 to rats one week after the onset of T1DM, blunted the DC that develops after eight weeks of diabetes by attenuating carbonyl/oxidative stresses, microvascular leakage, inflammation, and fibrosis

Evaluating Urban Streets and Public Transportation in Karbala City Using GIS

Because of the privilege of the city of Karbala with its religious character, this city has become a destination for all visitors from all cities of the world; as a consequence, there are now more people living in Karbala, which has increased the number of vehicles on the road and, consequently, the amount of traffic congestion. In this study, the degrees of public transportation service in the city of Karbala as well as the urban road network, were assessed using GIS. The collected data include the number of nodes, links, and the total length of Karbala's urban road network using a GIS program and traffic composition (bus and minibus). Then, a set of coefficients for evaluating the urban road network was calculated) β- Index, α- Index, γ- Index, η- Index and GTP- Index), After determining the percentage of buses and minibusses, it became clear that public transportation in Karbala is very weak and needs development, as the percentage of buses and minibusses did not exceed 30% of the total traffic volume

A comprehensive multimodal humanoid system for personality assessment based on the Big Five model

Personality analysis allows the experts to get insights into an individual's conduct, vulnerabilities, and prospective capabilities. Some common methods employed for personality prediction include text analysis, social media data, facial expressions, and emotional speech extraction. Recently, some studies have utilized the big five model to predict personality traits using non-verbal cues (gaze score, body motion, head motion). However, these studies mostly target only three aspects of the big five mode. None of the studies so far have used non-verbal cues to target all five traits (extraversion, openness, neuroticism, agreeableness, and conscientiousness) of the Big Five model. In this paper, we propose a multi-modal system that predicts all five personality traits of the Big Five model using non-verbal cues (facial expressions, head poses, body poses), 44-item Big Five Inventory (BFI) questionnaire, and expert analysis. The facial expression module utilizes the Face Emotion Recognition Plus (FER+) dataset trained with Convolution Neural Network (CNN) model achieving 95.14% accuracy. Evaluating 16 subjects in verbal interaction with humanoid robot NAO, we combined questionnaire feedback, human-robot interaction data, and expert perspectives to deduce their Big Five traits. Findings reveal 100% accuracy in personality prediction via expert insights and the system, and 75% for the questionnaire-based approach

Dephosphorylation of the Proneural Transcription Factor ASCL1 Re-Engages a Latent Post-Mitotic Differentiation Program in Neuroblastoma.

Pediatric cancers often resemble trapped developmental intermediate states that fail to engage the normal differentiation program, typified by high-risk neuroblastoma arising from the developing sympathetic nervous system. Neuroblastoma cells resemble arrested neuroblasts trapped by a stable but aberrant epigenetic program controlled by sustained expression of a core transcriptional circuit of developmental regulators in conjunction with elevated MYCN or MYC (MYC). The transcription factor ASCL1 is a key master regulator in neuroblastoma and has oncogenic and tumor-suppressive activities in several other tumor types. Using functional mutational approaches, we find that preventing CDK-dependent phosphorylation of ASCL1 in neuroblastoma cells drives coordinated suppression of the MYC-driven core circuit supporting neuroblast identity and proliferation, while simultaneously activating an enduring gene program driving mitotic exit and neuronal differentiation. IMPLICATIONS: These findings indicate that targeting phosphorylation of ASCL1 may offer a new approach to development of differentiation therapies in neuroblastoma. VISUAL OVERVIEW: http://mcr.aacrjournals.org/content/molcanres/18/12/1759/F1.large.jpg.Work was supported by Cancer Research UK Programme Grant RG91505 (AP), Wellcome Trust Investigator Award 212253/Z/18/Z (AP), MRC Research Grant MR/L021129/1 (F.A, A.P); Neuroblastoma UK (D.M, T.P, A.P), CRUK Cambridge Centre Paediatric Programme (L.P), The Terry Fox Foundation (FA), MBRU College of Medicine Internal grant award MBRU-CM-RG2019-14 (FA), MBRU-ALMAHMEED Collaborative Research Award ALM1909 (FA) and core support from the Wellcome Trust and the MRC Cambridge Stem Cell Institute (F.A, D.M, J.D., A.P.) and Cancer Research UK Cambridge Insititute (I.C, J.C)

Obsessive-Compulsive Disorder in Primary Care: Overview on Diagnosis and Management

Background: Obsessive-Compulsive Disorder (OCD) is a debilitating condition marked by the presence of intrusive obsessions and repetitive compulsions. The primary care setting often serves as the first line of contact for individuals grappling with mental health issues, making it a crucial frontier in the early detection and management of OCD. Therefore, the accurate diagnosis of OCD in such settings is essential for effective management. Objective: This review article aims to provide a comprehensive overview of the diagnostic process for OCD, emphasizing the clinical presentation, differential diagnosis, and various diagnostic tools available. Additionally, it explores current strategies for managing OCD, including pharmacological and psychotherapeutic interventions. Methodology: For this review, a comprehensive literature search was conducted using Google Scholar and PubMed databases. Keywords such as "Diagnosis," "obsessive compulsive disorder," and "management" were employed to narrow down relevant studies. Both qualitative and quantitative research papers were included, while non-English publications and those lacking peer-review were excluded. Results: Core symptoms of OCD include obsessions and compulsions, with the Y-BOCS being a standard measure for diagnosis. Differential diagnosis is essential to distinguish OCD from other conditions. SSRIs have been recognized as first-line pharmacological treatments. CBT, particularly Exposure and Response Prevention, remains a potent psychotherapeutic intervention. Emerging treatments like DBS and TMS offer hope for those unresponsive to conventional treatments. Combination therapies have shown enhanced efficacy in certain cases. Conclusion: The meticulous diagnosis of OCD requires recognizing its core symptoms, ruling out other conditions, and leveraging validated clinical tools. A multi-faceted management approach combining pharmacological and psychological treatments ensures optimal patient outcomes, with ongoing research introducing promising new interventions

Fractal feature selection model for enhancing high-dimensional biological problems

The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%

Perceived Risk of falls among Acute Care Patients

Purpose: In an effort to lower the number of falls that occur among hospitalized patients, several facilities have begun introducing various fall prevention programs. However, the efficacy of fall prevention programs is diminished if patients do not consider themselves to be at risk for falls and do not follow recommended procedures. The goal of this study was to characterize how patients in four different acute care specialist services felt about their risk of falling while in the hospital. Methods: One hundred patients admitted to the study hospital with a Morse Fall Scale score of 45 or higher were given the Patient Perception Questionnaire, a tool designed to assess a patient's perception of their own fall risk, fear of falling, and motivation to take part in fall prevention efforts. Scores on the Morse Fall Scale were gathered through a historical assessment of medical records. Descriptive statistics, Pearson's correlation coefficients, and independent sample t tests were used to examine the data. Results: The average age was 65, and around half (52%) were men and half (48%) were women. Based on their ratings on the Morse Fall Scale, all 100 participants were classified as being at high risk for falls. However, only 55.5% of the individuals agreed with this assessment. The likelihood that a patient would seek assistance and the degree to which they feared falling both declined as their faith in their mobility improved. Patients hospitalized after a fall exhibited considerably lower confidence scores and greater fear scores than patients who had not been injured in a fall. Conclusions: Patients who have a high fall risk assessment score may not believe they are at risk for falls and may not take any steps to reduce their risk. The prevalence of falls in hospitals might be mitigated by the creation of a fall risk assessment technique that takes into account both objective and subjective factors

The Association of Toll-Like Receptor 4 Polymorphism with Hepatitis C Virus Infection in Saudi Arabian Patients

Hepatitis C virus (HCV) is a single stranded RNA virus. It affects millions of people worldwide and is considered as a leading cause of liver diseases including cirrhosis and hepatocellular carcinoma. A recent study reported that TLR4 gene polymorphisms are good prognostic predictors and are associated with protection from liver fibrosis among Caucasians. This study aims to investigate the implication of genetic polymorphisms of TLR4 gene on the HCV infection in Saudi Arabian patients. Two SNPs in the TLR4 gene, rs4986790 (A/G) and rs4986791 (C/T), were genotyped in 450 HCV patients and 600 uninfected controls. The association analysis confirmed that both SNPs showed a significant difference in their distribution between HCV-infected patients and uninfected control subjects ( < 0.0001; OR = 0.404, 95% CI = 0.281-0.581) and ( < 0.0001; OR = 0.298, 95% CI = 0.201-0.443), respectively. More importantly, haplotype analysis revealed that four haplotypes, AC, GT, GC, and AT (rs4986790, rs4986791), were significantly associated with HCV infection when compared with control subjects. One haplotype AC was more prominently found when chronic HCV-infected patients were compared with cirrhosis/HCC patients (frequency = 94.7% and = 0.04). Both TLR4 SNPs under investigation were found to be significantly implicated with HCV-infection among Saudi Arabian population