Background & Objective: Parkinson’s disease (PD) is a chronic neurological disease, many a times presenting with non-motor symptoms. Pain is one of the most important non-motor symptom and there is no consensus regarding its exact mechanism and characterisation. This study was planned to evaluate the characteristics of pain and possible factors influencing it, in a cohort of patients with established Parkinson’s disease. Methods: 104 patients consenting to participate were included in the study. Data regarding age of onset, duration of disease, treatment, Hoehn-Yahr scale, phenotype of PD, UPDRS scores, pain type and distribution of pain were noted. Single and multiple logistical regression models with pain (1/0) as the outcome variable were used to check the association of pain with the above mentioned variables. Results: 54.8% of patients with PD experience pain. Presence of sensory symptoms was significantly associated with the pain group (42.1%) than the no pain group (21%). Pain was more pronounced on the side with predominant motor symptoms (72%) and in 68.4 % patients pain responded to dopaminergic treatment. Musculoskeletal pain (82.5%) was the commonest type and lower limbs were the commonest site of pain (43.2%). Conclusion: Pain in Parkinson’s disease has multiple dimensions and characteristics. Pain itself may be the reason for early diagnosis. Proper classification of pain will help in improved management of these patients.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established.It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction.Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

OBJECTIVE: To develop a qNMR method for quantitative analysis of triacylglycerols in fruit oil of Hippophae rhamnoides (seabuckthorn, SBT) and analyze commercial samples of SBT oils using GC-MS and FTIR. METHODS: SBT fruit oil (IPHRFH) was extracted with hexane and the triglyceride (TAG) was isolated by vacuum liquid chromatography. Six different branded SBT oils purchased from e-commerce suppliers (Amazon) and in-house prepared SBT oil was analyzed by qNMR and fatty acyl composition of TAGs determined by using NMR. In-house oil was also analysed by GC-MS and FTIR spectroscopy. RESULTS: The qNMR results showed that the oil contained 80.3% of triacylglycerol (TAG). The SBT oil TAGs comprised of linolenate 6.6%, palmitoleate/oleate 65.4%, and total saturated fatty acyl chain including palmitate 28% as determined by qNMR. GC-MS analysis revealed that the major acyl functionalities present in the TAG were palmitoleic acid 36.5%, oleic acid 12.9%, palmitic acid 21.2%, and linoleic acid 18%. Of the six commercial samples analyzed, samples from only one supplier (SW) were fruit oil; All others were the seed oils or mix of fruit oil and seed oil. The labels for samples except for the SW did not indicate whether it was fruit oil or seed oil. CONCLUSION The results suggest that SBT oil should be analyzed by combination of GC-MS, FTIR and qNMR for factual content of free fatty acid or TAGs, which are chemically different in nature and affect the quality of oil. GC-MS showed the content of omega free fatty acids after hydrolysis, while qNMR and FTIR showed the content of TAGs. The major acyl functionalities found in SBT fruit oil TAGs are palmitoleate/palmitate/oleate, while linoleate and linonelate make up a minor fraction. Furthermore, analysis of commercial samples showed discrepancies between label claims and actual content.