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How behavior arises from brain physiology has been one central topic of investigation in neuroscience. Considering the recent interest in predicting behavior from brain imaging using open datasets, there is the need for a principled approach to the categorization of behavioral variables. However, this is not trivial, as the definitions of psychological constructs and their relationships—their ontology—are not always clear. Here, we propose to use exploratory factor analysis (EFA) as a data-driven approach to find robust and interpretable domains of behavior in the Human Connectome Project (HCP) dataset. Additionally, we explore the clustering of behavioral variables using consensus clustering. We find that four and five factors offer the best description of the data, a result corroborated by the consensus clustering. In the four-factor solution, factors for Mental Health, Cognition, Processing Speed, and Substance Use arise. With five factors, Mental Health splits into Well-Being and Internalizing. Clustering results show a similar pattern, with clusters for Cognition, Processing Speed, Positive Affect, Negative Affect, and Substance Use. The factor structure is replicated in an independent dataset using confirmatory factor analysis (CFA). We discuss how the content of the factors fits with previous conceptualizations of general behavioral domains.

Background: Long-term hypertension is a substantial risk factor for coronary artery disease, stroke, heart failure, peripheral vascular disease, vision loss, and chronic kidney disease. Not only is echocardiography the most adaptable imaging technique for the cardiovascular system but it is also the most comprehensive and dependable hemodynamic tool. Aim and Objective: The purpose of this study was to analyze echocardiographic results in hypertensive patients. Material and Methods: It was a cross-sectional study of hypertension patients admitted to the medicine wards of the civil hospital connected to the P.D.U. Government Medical College in Rajkot, Gujarat, India. After considering inclusion and exclusion criteria, hypertensive patients were chosen at random for enrollment in the study. A total of 50 patients with hypertension were investigated. A skilled cardiologist performed the echocardiographic examinations. To simplify the observations made in the study, patients were separated into four groups based on their wall motion score. Results: The number of patients with systolic blood pressure of 169–179 mmHg was 32% (16 patients), while the number of patients with 140 mmHg was 18% (9 patients). Systolic blood pressure of 140–159 mmHg has 28% (14 patients) and more than 180 mmHg has 22% (11 patients). The most prevalent diastolic hypertension group had 22 patients (44%), and it was also the most common group among both sexes. Males had 12 patients with 90–90 mmHg and females had 10 individuals with 90–99 mmHg. Out of 50 hypertension patients, 21 (42%) had aberrant regional wall motion, while the remaining patients had no abnormal regional wall motion. In terms of diastolic function, 42 of 50 patients (84%) had diastolic dysfunction. Conclusion: Echocardiography is a valuable tool for assessing the consequences of hypertension in patients. It is also a widely and most regularly utilized imaging modality in cardiology practice due to its non-invasiveness and ease of access.

Background: The predictions of the silent ischemia in the asymptomatic patients were done with the Electrocardiographic (ECG) abnormalities findings. The high risk of the cardiac morbidity and mortality was statistically associated with abnormal ECG findings. Aims and Objectives: To reduce patient mortality and morbidity, the current analysis was performed on diabetes patients to look for ECG alterations. Materials and Methods: A total of 100 people were used, 50 of whom had type-2 diabetes mellitus and 50 of whom were healthy, non-diabetic volunteers whose ages and sexes matched the patients with type-2 diabetes. The 12-lead ECG instrument was used to record the electrographic markings. For fasting blood sugar blood was collected after a minimum 8 h overnight fasting and for PP2BS blood was collected 2 h after lunch. Results: In people with type II diabetes mellitus, the P wave’s amplitude significantly decreased. When compared to controls, type II diabetics’ QRS complex duration was noticeably longer. When compared to controls, (type II DM) Type 2 diabetes mellitus sufferers had a significantly longer QT and QTC interval. When type II diabetics were compared to controls, the ST segment alterations were significantly different. When compared to controls, type II diabetes showed a considerable T-wave alteration. Conclusion: The ST segment deviation from the isoelectric line is a predictor of coronary events in an asymptomatic diabetic population. The incidence of sudden mortality due to cardiovascular disease in diabetics is reduced as a result of early detection of QT and QTC alterations, which are good indicators of cardiovascular disease risk in diabetics.

MYC is overexpressed by many human tumour types and has been shown to regulate cell functions that are required for tumorigenesis. It is not clear, however, which of its target genes mediate these effects. A series of recent studies have indicated that this could be a result of the fact that MYC binds and regulates up to 15% of all genes. Does MYC function as a widespread regulator of transcription or as a classical transcription factor that regulates a limited number of downstream targets?

In recent times, the use of herbal products has increased tremendously in the western world as well as in developed countries. Lately, one of the outstandingly important medicinal plants, widely used therapeutically in the orient and becoming increasingly popular in the west is Bacopa monniera, a well-known nootropic. The present review summarizes our current knowledge of pharmacological actions, preclinical and clinical studies, major bioactives, reported mechanisms of actions, clinical efficacy, safety and the possibility of interactions of the herb with the conventional drugs. Simultaneously, research updates as well as avenues for further research are also mentioned concerning the plant.

Traditional classification systems based on broad nosological categories do not adequately capture the high heterogeneity of mental illness. One possible solution to this is to move to a multi-dimensional model of mental illness, as has been proposed by the Research Domain Criteria and Hierarchical Taxonomy of Psychopathology frameworks. In this study, we explored the dimensional structure of psychotic disorders. We focused on the question whether combining measures of psychosis with cognitive and depression-related measures results in meaningful, clinically relevant, and valid latent dimensions in a sample of early psychosis (n = 113) and chronic schizophrenia patients (n = 43, total n = 156). We used exploratory factor analysis to identify the symptom dimensions in the Lausanne Psychosis data, a multi-modal prospective data set that includes a broad behavioral assessment of patients diagnosed with psychotic disorders. We evaluated the validity of these dimensions by regressing them to several functioning measures. Our analysis revealed three dimensions: Cognition, Depression/Negative, and Thought Disorder, explaining 49.2% of the variance. They were related to measures of functioning, the R² ranging between 0.38 and 0.42. This study advances the development of a multi-dimensional characterization of psychotic disorders by identifying three symptom dimensions with predictive validity in people with psychosis.

One way to increase the statistical power and generalizability of neuroimaging studies is to collect data at multiple sites or merge multiple cohorts. However, this usually comes with site-related biases due to the heterogeneity of scanners and acquisition parameters, negatively impacting sensitivity. Brain structural connectomes are not an exception: Being derived from T1-weighted and diffusion-weighted magnetic resonance images, structural connectivity is impacted by differences in imaging protocol. Beyond minimizing acquisition parameter differences, removing bias with postprocessing is essential. In this work we create, from the exhaustive Human Connectome Project Young Adult dataset, a resampled dataset of different -values and spatial resolutions, modeling a cohort scanned across multiple sites. After demonstrating the statistical impact of acquisition parameters on connectivity, we propose a linear regression with explicit modeling of -value and spatial resolution, and validate its performance on separate datasets. We show that -value and spatial resolution affect connectivity in different ways and that acquisition bias can be reduced using a linear regression informed by the acquisition parameters while retaining interindividual differences and hence boosting fingerprinting performance. We also demonstrate the generative potential of our model, and its generalization capability in an independent dataset reflective of typical acquisition practices in clinical settings. One of the main roadblocks to using multisite neuroimaging data is the effect of acquisition bias due to the heterogeneity of acquisition parameters associated with various sites. This can negatively impact the sensitivity of machine learning models employed in neuroscience. Thus, it is extremely important to model the effect of this bias. In this work, we address this issue at the level of brain structural connectivity, an important biomarker for various brain disorders. We propose a simple linear regression model to minimize this effect using high-quality data from the Human Connectome Project, and show its generalizability to a clinical dataset.

The present study was undertaken to assess the antibiotic susceptibility patterns of Pseudomonas aeruginosa at a tertiary care hospital in Gujarat, India. Due to significant changes in microbial genetic ecology, as a result of indiscriminate use of anti-microbials, the spread of anti-microbial resistance is now a global problem. Out of 276 culture positive samples, 56 samples of Pseudomonas aeruginosa were examined and 10 different types of specimen were collected. Microbial sensitivity testing was done using disk diffusion test with Pseudomonas species NCTC 10662, as per CLSI guidelines. The highest number of Pseudomonas infections was found in urine, followed by pus and sputum. Pseudomonas species demonstrated marked resistance against monotherapy of penicillins, cephalosporins, fluoroquinolones, tetracyclines and macrolides. Only combination drugs like Ticarcillin + Clavulanic acid, Piperacillin + Tazobactum, Cefoperazone + Sulbactum, Cefotaxime + Sulbactum, Ceftriaxome + Sulbactum and monotherapy of amikacin showed higher sensitivity to Pseudomonas infections; however, the maximum sensitivity was shown by the Carbapenems. From the present study, we conclude that urinary tract infection was the most common hospital acquired infection. Also, co-administration of beta -lactamase inhibitors markedly expanded the anti-microbial sensitivity of semi-synthetic penicillins and cephalosporins. The aminoglycoside group of antibiotics - amikacin - demonstrated maximum sensitivity against pseudomonas species. Therefore, use of amikacin should be restricted to severe nosocomial infections, in order to avoid rapid emergence of resistant strains. Periodic susceptibility testing should be carried out over a period of two to three years, to detect the resistance trends. Also, a rational strategy on the limited and prudent use of anti-Pseudomonal agents is urgently required.

With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process. In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications. To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.

The c-myc oncogene is among the most commonly overexpressed genes in human cancer. c-myc encodes a basic helix-loop-helix/leucine zipper (bHLH/LZ) transcription factor (c-MYC) that activates a cascade of downstream targets that ultimately mediate cellular transformation. Although a large number of genes are regulated by c-MYC, only a few have been functionally linked to c-MYC-mediated transformation. By expression profiling, the metastasis-associated protein 1 (MTA1) gene was identified here as a target of the c-MYC oncoprotein in primary human cells, a result confirmed in human cancer cells. MTA1 itself has been previously implicated in cellular transformation, in part through its ability to regulate the epithelial-to-mesenchymal transition and metastasis. MTA1 is a component of the Mi-2/nucleosome remodeling and deacetylating (NURD) complex that contains both histone deacetylase and nucleosome remodeling activity. The data reported here demonstrate that endogenous c-MYC binds to the genomic MTA1 locus and recruits transcriptional coactivators. Most importantly, short hairpin RNA (shRNA)-mediated knockdown of MTA1 blocks the ability of c-MYC to transform mammalian cells. These data implicate MTA1 and the Mi-2/NURD complex as one of the first downstream targets of c-MYC function that are essential for the transformation potential of c-MYC.