Evaluation of the Genetic Association Between Adult Obesity and Neuropsychiatric Disease

Extreme obesity (EO, BMI>50) is frequently associated with neuropsychiatric disease (NPD). As both EO and NPD are heritable central nervous system disorders, we assessed the prevalence of protein truncating (PTV) and copy number variants (CNV) in genes/regions previously implicated in NPD, in adults with EO (n=149) referred for weight loss/bariatric surgery. We also assessed the prevalence of CNVs in patients referred to University College London Hospital (UCLH) with EO (n=218) and obesity (O, BMI 35-50, n=374) and a Swedish cohort of participants from the community with predominantly O (n=161). The prevalence of variants was compared to controls in ExAC/gnomAd database. In the discovery cohort (high NPD prevalence: 77%), the cumulative PTV/CNV allele frequency (AF) was 7.7 % vs 2.6% in controls (Odds Ratio (OR) 3.1, (95% CI 2-4.1, p<0.0001). In the UCLH EO cohort (intermediate NPD prevalence: 47%), CNV AF (1.8% vs 0.9% in controls, OR 1.95, 95% CI 0.96-3.93, p=0.06) was lower than the discovery cohort. CNV AF was not increased in the UCLH O cohort (0.8%). No CNVs were identified in the Swedish cohort with no NPD. These findings suggest PTV/CNVs, in genes/regions previously associated with NPD, may contribute to NPD in patients with EO

Oxaliplatin induces drug resistance more rapidly than cisplatin in H69 small cell lung cancer cells

Cisplatin produces good responses in solid tumours including small cell lung cancer (SCLC) but this is limited by the development of resistance. Oxaliplatin is reported to show activity against some cisplatin-resistant cancers but there is little known about oxaliplatin in SCLC and there are no reports of oxaliplatin resistant SCLC cell lines. Studies of drug resistance mainly focus on the cellular resistance mechanisms rather than how the cells develop resistance. This study examines the development of cisplatin and oxaliplatin resistance in H69 human SCLC cells in response to repeated treatment with clinically relevant doses of cisplatin or oxaliplatin for either 4 days or 2h. Treatments with 200ng/ml cisplatin or 400ng/ml oxaliplatin for 4 days produced sublines (H69CIS200 and H69OX400 respectively) that showed low level (approximately 2-fold) resistance after 8 treatments. Treatments with 1000ng/ml cisplatin or 2000ng/ml oxaliplatin for 2h also produced sublines, however these were not stably resistant suggesting shorter treatment pulses of drug may be more effective. Cells survived the first five treatments without any increase in resistance, by arresting their growth for a period and then regrowing. The period of growth arrest was reduced after the sixth treatment and the H69CIS200 and H69OX400 sublines showed a reduced growth arrest in response to cisplatin and oxaliplatin treatment suggesting that "regrowth resistance" initially protected against drug treatment and this was further upregulated and became part of the resistance phenotype of these sublines. Oxaliplatin dose escalation produced more surviving sublines than cisplatin dose escalation but neither set of sublines were associated with increased resistance as determined by 5-day cytotoxicity assays, also suggesting the involvement of regrowth resistance. The resistant sublines showed no change in platinum accumulation or glutathione levels even though the H69OX400 subline was more sensitive to buthionine sulfoximine treatment. The H69CIS200 cells were cross-resistant to oxaliplatin demonstrating that oxaliplatin does not have activity against low level cisplatin resistance. Relative to the H69 cells, the H69CIS200 and H69OX400 sublines were more sensitive to paclitaxel and taxotere suggests the taxanes may be useful in the treatment of platinum resistant SCLC. These novel cellular models of cisplatin and oxaliplatin resistant SCLC will be useful in developing strategies to treat platinum-resistant SCLC

Frequency and predictors of the lupus low disease activity state in a multi-national and multi-ethnic cohort

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Student and tutor perceptions on attributes of effective problems in problem-based learning

This study aimed to identify the attributes that students and tutors associated with effective PBL problems, and assess the extent to which these attributes related to the actual effectiveness of problems. To this end, students and tutors in focus groups were asked to discuss about possible attributes of effective problems. The same participants were then asked to individually and independently judge eight sample problems they had worked with. Text analysis of the focus group discussion transcripts identified eleven problem attributes. Participants' judgments of the sample problems were then frequency-scored on the eleven problem attributes. Relating the participants' judgments with the entire student cohort's grades yielded high and significant correlations, suggesting that the eleven problem attributes reflect aspects of problem effectiveness

Does voice amplification increase intelligibility in people with Parkinson's disease

Background/Aims: Patients with speech intelligibility difficulties associated with a quiet voice are often prescribed a voice amplifier. This study examined whether artificial voice amplification improved intelligibility in people with Parkinson's disease and whether there was an optimum increase that brought about best improvement. Methods: Twelve people diagnosed with Parkinson's disease (mild=4, moderate intelligibility difficulties=8) and five age-matched controls read low predictability sentences in their habitual voice. Audio recordings were digitally manipulated to create samples at +2.3 dB, +5 dB and +10 dB amplification. Listeners transcribed the recorded sentences. The percentage of words correctly identified was compared across levels of amplification and groups. Results: Participants with moderate Parkinson's disease were significantly less intelligible than controls in all conditions. Moderately, but not mildly affected participants with Parkinson's disease showed higher intelligibility in the amplified conditions, though statistically significantly only at +2.3 dB. No other significant effects of intensity or interactions with groups were found. At an individual level, some participants showed clear advantages of amplification. Conclusion: Based on results from the current participants, potential benefits of amplification cannot be promised to all people with Parkinson's disease. Nevertheless, several provisos regarding methods employed suggest the question can gainfully be pursued using broader measures to assess effects of amplification with more varied groups of people with Parkinson's disease and with other aetiologies where voice production can be an issue

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

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