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Radiology Radiology

Brain Injury and Dementia in Pakistan: Current Perspectives.

In Frontiers in neurology

Alzheimer's disease (AD) is the most common form of dementia, accounting for 50-75% of all cases, with a greater proportion of individuals affected at older age range. A single moderate or severe traumatic brain injury (TBI) is associated with accelerated aging and increased risk for dementia. The fastest growth in the elderly population is taking place in China, Pakistan, and their south Asian neighbors. Current clinical assessments are based on data collected from Caucasian populations from wealthy backgrounds giving rise to a "diversity" crisis in brain research. Pakistan is a lower-middle income country (LMIC) with an estimated one million people living with dementia. Pakistan also has an amalgamation of risk factors that lead to brain injuries such as lack of road legislations, terrorism, political instability, and domestic and sexual violence. Here, we provide an initial and current assessment of the incidence and management of dementia and TBI in Pakistan. Our review demonstrates the lack of resources in terms of speciality trained clinician staff, medical equipment, research capabilities, educational endeavors, and general awareness in the fields of dementia and TBI. Pakistan also lacks state-of-the-art assessment of dementia and its risk factors, such as neuroimaging of brain injury and aging. We provide recommendations for improvement in this arena that include the recent creation of Pakistan Brain Injury Consortium (PBIC). This consortium will enhance international collaborative efforts leading to capacity building for innovative research, clinician and research training and developing databases to bring Pakistan into the international platform for dementia and TBI research.

Adamson Maheen M, Shakil Sadia, Sultana Tajwar, Hasan Muhammad Abul, Mubarak Fatima, Enam Syed Ather, Parvaz Muhammad A, Razi Adeel


“Alzheimers disease”, Pakistan, TBI, aging, dementia, road traffic accidents, violence

General General

Sensor Measures of Affective Leaning.

In Frontiers in psychology ; h5-index 92.0

The aim of this study was to predict self-report data for self-regulated learning with sensor data. In a longitudinal study multichannel data were collected: self-report data with questionnaires and embedded experience samples as well as sensor data like electrodermal activity (EDA) and electroencephalography (EEG). 100 students from a private university in Germany performed a learning experiment followed by final measures of intrinsic motivation, self-efficacy and gained knowledge. During the learning experiment psychophysiological data like EEG were combined with embedded experience sampling measuring motivational states like affect and interest every 270 s. Results of machine learning models show that consumer grade wearables for EEG and EDA failed to predict embedded experience sampling. EDA failed to predict outcome measures as well. This gap can be explained by some major technical difficulties, especially by lower quality of the electrodes. Nevertheless, an average activation of all EEG bands at T7 (left-hemispheric, lateral) can predict lower intrinsic motivation as outcome measure. This is in line with the personality system interactions (PSI) theory of Julius Kuhl. With more advanced sensor measures it might be possible to track affective learning in an unobtrusive way and support micro-adaptation in a digital learning environment.

Martens Thomas, Niemann Moritz, Dick Uwe


EEG, affect, affective learning, emotion, motivation, process measures, self-regulated learning, sensor measures

General General

An Experimental Study of Step Test Index Combined With Heart Rate Variability in Estimating Maximum Oxygen Uptake in Women With Drug Use Disorder.

In Frontiers in physiology

Background : Maximal oxygen uptake (VO2max), a vital physiological indicator, has been widely used in many fields. In recent years, the measurement method of VO2max has been widely explored in various populations, but few studies have been conducted for women drug abusers. For the importance of VO2max in the formulation of aerobic exercise intensity for drug users, the present study estimated VO2max using the step test index combined with heart rate variability in women with drug use disorder.

Methods : Forty women methamphetamine (MA) users without cardiovascular disease and dyskinesia participated in a cardiopulmonary exercise test (CPX) and a 3-minute step test. Each of them performed a heart rate variability (HRV) monitoring test after the step test, and VO2max was estimated by step test index and HRV.

Results : (1) The step test index had a significant positive correlation with VO2max. The standard deviation of normal-to-normal interval (SDNN) had a significant positive correlation with VO2max and a significant positive correlation with the step test index; (2) the R-square values of the estimated VO2max by step test index and post-SDNN for overall MA users were 0.29 and 0.22, with an accuracy of 93.19 and 92.85%, respectively; (3) the R-square values of the estimated VO2max by step test index and post-SDNN in group I were 0.27 and 0.36, respectively, with an accuracy of 94.04 and 93.99%. The R-square value of the estimated VO2max by step test index in group II was 0.44, with an accuracy of 92.65%, however, post-SDNN cannot adequately estimate the VO2max in group II; and (4) there was no significant difference in VO2max obtained by CPX, step test index, or post-SDNN, regardless of overall or grouping variable analysis.

Conclusion : The 3-minute step test combined with HRV can estimate the VO2max of women MA users to a certain extent, but the size and the coverage of the sample size should be further considered. In the future, more methods, such as machine learning or artificial neural networks, should be used.

Wang Kun, Zhang Tingran, Ouyang Yiyi, Jiang Haonan, Qu Meichen, Peng Li, Luo Jiong


VO2max, cardiopulmonary exercise test, heart rate variability, methamphetamine users, step test, women

General General

Corrigendum: Artificial intelligence in dentistry: current applications and future perspectives.

In Quintessence international (Berlin, Germany : 1985)

The following amendments are made to the published article: Quintessence Int 2020;51(3):248-257; First published 4 February 2020; doi:10.3290/j.qi.a43952 Artificial intelligence in dentistry: current applications and future perspectives Yo-wei Chen, DDS, MSc/Kyle Stanley, DDS/Wael Att, DDS, Dr Med Dent, PhD.

Chen Yo-Wei, Stanley Kyle, Att Wael


Cardiology Cardiology

Cardiovascular implications of the COVID-19 pandemic: a global perspective.

In The Canadian journal of cardiology

The Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), represents the pandemic of the century, with approximately 3.5 million cases and 250,000 deaths worldwide as of May 2020. Although respiratory symptoms usually dominate the clinical presentation, COVID-19 is now known to also have potentially serious cardiovascular consequences, including myocardial injury, myocarditis, acute coronary syndromes, pulmonary embolism, stroke, arrhythmias, heart failure, and cardiogenic shock. The cardiac manifestations of COVID-19 might be related to the adrenergic drive, systemic inflammatory milieu and cytokine-release syndrome caused by SARS-CoV-2, direct viral infection of myocardial and endothelial cells, hypoxia due to respiratory failure, electrolytic imbalances, fluid overload, and side effects of certain COVID-19 medications. COVID-19 has profoundly reshaped usual care of both ambulatory and acute cardiac patients, by leading to the cancellation of elective procedures and by reducing the efficiency of existing pathways of urgent care, respectively. Decreased utilization of healthcare services for acute conditions by non-COVID-19 patients has also been reported and attributed to concerns about acquiring in-hospital infection. Innovative approaches that leverage modern technologies to tackle the COVID-19 pandemic have been introduced, which include telemedicine, dissemination of educational material over social media, smartphone apps for case tracking, and artificial intelligence for pandemic modelling, among others. This article provides a comprehensive overview of the pathophysiology and cardiovascular implications of COVID-19, its impact on existing pathways of care, the role of modern technologies to tackle the pandemic, and a proposal of novel management algorithms for the most common acute cardiac conditions.

Boukhris Marouane, Hillani Ali, Moroni Francesco, Annabi Mohamed Salah, Addad Faouzi, Ribeiro Marcelo Harada, Mansour Samer, Zhao Xiaohui, Ybarra Luiz Fernando, Abbate Antonio, Vilca Luz Maria, Azzalini Lorenzo


General General

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.

In Frontiers in pharmacology

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine-Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by "druggability" prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

Yan Wenying, Liu Xingyi, Wang Yibo, Han Shuqing, Wang Fan, Liu Xin, Xiao Fei, Hu Guang


drug targets, integrins, pancreatic ductal adenocarcinoma, protein-protein interactions, structural dynamics, support vector machine–recursive feature elimination