Increased Circulating Th17 Cells, Serum IL-17A, and IL-23 in Takayasu Arteritis

Introduction. Th17, γδT, NK, and NKT cells in peripheral blood and serum IL-17 and IL-23 in Takayasu arteritis (TA) were measured and correlated with disease activity. Methods. Th17 (anti-CD3APC, CD4PECy7, and IL-17PE), NKT, NK (anti-CD3APC, CD56FITC), and γδT (anti-CD3FITC and γδTCRAPC) cells were enumerated by flow cytometry in peripheral blood of 30 patients with TA (ACR1990 criteria) and 20 healthy controls, serum IL-17 and IL-23 measured by ELISA. Relation with disease activity (NIH criteria, ITAS2010) was analyzed (using nonparametric tests, median with interquartile range). Results. Mean age of patients was 33.47±11.78 years (25 females); mean symptom duration was 7.1±5.3 years. 13 were not on immunosuppressants; 12 were active (ITAS2010 ≥ 4). The percentage of Th17 cells was significantly expanded in TA (patients 2.1 (1.5–3.2) versus controls 0.75 (0.32–1.2); p<0.0001) with no differences in other cell populations. Serum IL-17 and IL-23 (pg/mL) in patients (6.2 (4.6–8.5) and 15 (14.9–26.5), resp.) were significantly higher (p<0.001) than controls (3.9 (3.9–7.3) and undetectable median value, resp.). Subgroup analysis revealed no correlation of Th17 cells, serum IL-17, and IL-23 with disease activity or medications, nor any significant difference before and after medication. Conclusions. There is significant expansion of Th17 cells and elevated serum IL-17 and IL-23 levels in TA patients compared to healthy controls

Multi-scale approach for modeling stability, aggregation, and network formation of nanoparticles suspended in aqueous solutions

Multi-scale computational framework to investigate interactions between bare and surfactant-coated nanoparticles in aqueous solutions beyond classical DLVO and aggregation theories

Plagiarism in non-anglophone countries: A cross-sectional survey of researchers and journal editors

Background: Plagiarism is one of the most common violation of publication ethics, and it still remains an area with several misconceptions and uncertainties.Methods: This online cross-sectional survey was conducted to analyze plagiarism perceptions among researchers and journal editors, particularly from non-Anglophone countries.Results: Among 211 respondents (mean age 40 years; M:F, 0.85:1), 26 were scholarly journal editors and 70 were reviewers with a large representation from India (50, 24%), Turkey (28, 13%), Kazakhstan (25, 12%) and Ukraine (24, 11%). Rigid and outdated pre- and post-graduate education was considered as the origin of plagiarism by 63% of respondents. Paraphragiarism was the most commonly encountered type of plagiarism (145, 69%). Students (150, 71%), non-Anglophone researchers with poor English writing skills (117, 55%), and agents of commercial editing agencies (126, 60%) were thought to be prone to plagiarize. There was a significant disagreement on the legitimacy of text copying in scholarly articles, permitted plagiarism limit, and plagiarized text in methods section. More than half (165, 78%) recommended specifically designed courses for plagiarism detection and prevention, and 94.7% (200) thought that social media platforms may be deployed to educate and notify about plagiarism.Conclusion: Great variation exists in the understanding of plagiarism, potentially contributing to unethical publications and even retractions. Bridging the knowledge gap by arranging topical education and widely employing advanced anti-plagiarism software address this unmet need

Reciprocal Relationship Between HDAC2 and P-Glycoprotein/MRP-1 and Their Role in Steroid Resistance in Childhood Nephrotic Syndrome

Background: Reduced HDACs levels have been reported in steroid resistant chronic obstructive pulmonary disease and bronchial asthma patients. P-glycoprotein (P-gp) over expression in peripheral blood mononuclear cells (PBMCs) has been reported in patients with steroid resistant nephrotic syndrome (NS). Whether and how HDACs and P-gp are linked with each other is not clear, especially in NS patients.Aim: To evaluate mRNA expression of P-gp/MRP-1 and HDAC2 in PBMCs of steroid sensitive (SSNS) and steroid resistant nephrotic syndrome (SRNS) patients, and determine the relationship between expression of HDAC2 and P-gp/ MRP-1in NS patients.Methods: Twenty subjects (10 in each group), SSNS (mean age 7.54 ± 3.5 years), and SRNS (mean age 8.43 ± 3.8 years) were recruited. mRNA expression of HDAC2 and P-gp/MRP-1 was studied by quantitative real time PCR. PBMCs were treated with Theophylline, 1 μM, and Trichostatin A, 0.8 μM, for 48 h for induction and suppression of HDAC2, respectively.Results: At baseline, expression of P-gp (4.79 ± 0.10 vs. 2.13 ± 0.12, p &lt; 0.0001) and MRP-1 (3.99 ± 0.08 vs. 1.99 ±0.11, p &lt; 0.0001) on PBMCs were increased whereas, HDAC2 mRNA levels (2.97 ± 0.15 vs. 6.02 ± 0.13, p &lt; 0.0001) were significantly decreased in SRNS as compared to that of SSNS patients. Compared to baseline, theophylline reduced mRNA expression of P-gp and MRP-1 (fold change 2.65 and 2.21, *p &lt; 0.0001 in SRNS) (fold change 1.25, 1.24, *p &lt; 0.0001 in SSNS), respectively. However, it increased the expression of HDAC2 (fold change 5.67, *p &lt; 0.0001 in SRNS) (fold change 6.93, *p &lt; 0.0001 in SSNS). Compared to baseline, TSA treatment increased mRNA levels of P-gp and MRP-1 (fold change 7.51, 7.31, *p &lt; 0.0001 in SRNS) and (fold change 3.49, 3.35, *p &lt; 0.0001 in SSNS), respectively. It significantly decreased the level of HDAC2 (fold change 1.50, *p &lt; 0.0001 in SRNS) (fold change 2.53, *p &lt; 0.0001 in SSNS) patients.Conclusion: Reduced HDAC2 and increased P-gp/MRP-1 activity may play a role in response to steroids in childhood NS. HDAC2 and P-gp/MRP-1 are in reciprocal relationship with each other

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Cerebral microbleeds and intracranial haemorrhage risk in patients anticoagulated for atrial fibrillation after acute ischaemic stroke or transient ischaemic attack (CROMIS-2):a multicentre observational cohort study

Background: Cerebral microbleeds are a potential neuroimaging biomarker of cerebral small vessel diseases that are prone to intracranial bleeding. We aimed to determine whether presence of cerebral microbleeds can identify patients at high risk of symptomatic intracranial haemorrhage when anticoagulated for atrial fibrillation after recent ischaemic stroke or transient ischaemic attack. Methods: Our observational, multicentre, prospective inception cohort study recruited adults aged 18 years or older from 79 hospitals in the UK and one in the Netherlands with atrial fibrillation and recent acute ischaemic stroke or transient ischaemic attack, treated with a vitamin K antagonist or direct oral anticoagulant, and followed up for 24 months using general practitioner and patient postal questionnaires, telephone interviews, hospital visits, and National Health Service digital data on hospital admissions or death. We excluded patients if they could not undergo MRI, had a definite contraindication to anticoagulation, or had previously received therapeutic anticoagulation. The primary outcome was symptomatic intracranial haemorrhage occurring at any time before the final follow-up at 24 months. The log-rank test was used to compare rates of intracranial haemorrhage between those with and without cerebral microbleeds. We developed two prediction models using Cox regression: first, including all predictors associated with intracranial haemorrhage at the 20% level in univariable analysis; and second, including cerebral microbleed presence and HAS-BLED score. We then compared these with the HAS-BLED score alone. This study is registered with ClinicalTrials.gov, number NCT02513316. Findings: Between Aug 4, 2011, and July 31, 2015, we recruited 1490 participants of whom follow-up data were available for 1447 (97%), over a mean period of 850 days (SD 373; 3366 patient-years). The symptomatic intracranial haemorrhage rate in patients with cerebral microbleeds was 9·8 per 1000 patient-years (95% CI 4·0–20·3) compared with 2·6 per 1000 patient-years (95% CI 1·1–5·4) in those without cerebral microbleeds (adjusted hazard ratio 3·67, 95% CI 1·27–10·60). Compared with the HAS-BLED score alone (C-index 0·41, 95% CI 0·29–0·53), models including cerebral microbleeds and HAS-BLED (0·66, 0·53–0·80) and cerebral microbleeds, diabetes, anticoagulant type, and HAS-BLED (0·74, 0·60–0·88) predicted symptomatic intracranial haemorrhage significantly better (difference in C-index 0·25, 95% CI 0·07–0·43, p=0·0065; and 0·33, 0·14–0·51, p=0·00059, respectively). Interpretation: In patients with atrial fibrillation anticoagulated after recent ischaemic stroke or transient ischaemic attack, cerebral microbleed presence is independently associated with symptomatic intracranial haemorrhage risk and could be used to inform anticoagulation decisions. Large-scale collaborative observational cohort analyses are needed to refine and validate intracranial haemorrhage risk scores incorporating cerebral microbleeds to identify patients at risk of net harm from oral anticoagulation. Funding: The Stroke Association and the British Heart Foundation