The sex-dependent impact of adipose tissue and inflammation on chronic pain - A cross-sectional study from the all of us research program.

Emerging evidence suggests an association between chronic pain and elevated body fat. We sought to determine if individuals with higher body fat, measured by hip circumference (HC) and waist circumference (WC), are at risk for chronic pain when they demonstrate higher expression of inflammatory markers. We investigated the incidence and severity of pain in patients with varying WC/HC and inflammatory markers (C-Reactive Protein, IL-6, leptin) using the NIH-sponsored All of Us Database. For each inflammatory marker and sex, participants were divided into four groups based on combinations of normal/high marker levels and small/large WC/HC. We used statistical analysis to compare WC/HC and pain severity (mean NRS pain score) between groups of the same sex. In females, but not males, combinations of elevated CRP with large WC/HC exerted additive effects on the incidence of chronic pain (p < 0.01) and severe pain (p < 0.001), as well as on the severity of pain evaluated by the mean NRS pain score (p < 0.01). This relationship held true for females with high IL-6 or leptin and large WC or HC (p < 0.001 for chronic pain and severe pain incidence, and p < 0.05 for pain severity). Neither IL-6 nor leptin showed any significant impact on pain in males. Obesity status and CRP exert additive prognostic effects for chronic pain in females, but not in males. The concomitant evaluation of other inflammatory factors, such as IL-6 or leptin in females, may further augment the prediction of chronic pain. PERSPECTIVE: This article investigates the relationship between chronic pain, obesity, and inflammatory markers. It could help elucidating sex difference in pain mechanisms, as well as the risk factors for chronic pain, potentially improving patient diagnosis, follow-up and treatment.

Machine Learning in Spinal Cord Stimulation for Chronic Pain.

Spinal cord stimulation (SCS) is an effective treatment for chronic neuropathic pain. The success of SCS is dependent on candidate selection, response to trialing, and programming optimization. Owing to the subjective nature of these variables, machine learning (ML) offers a powerful tool to augment these processes. Here we explore what work has been done using data analytics and applications of ML in SCS. In addition, we discuss aspects of SCS which have narrowly been influenced by ML and propose the need for further exploration. ML has demonstrated a potential to complement SCS to an extent ranging from assistance with candidate selection to replacing invasive and costly aspects of the surgery. The clinical application of ML in SCS shows promise for improving patient outcomes, reducing costs of treatment, limiting invasiveness, and resulting in a better quality of life for the patient.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions.

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Aconite poisoning with arrhythmia and shock.

A 55-year-old male presented with history of nausea, vomiting, palpitation paresthesis and profuse sweating in emergency department 2h after ingestion of "Bachnaag" (Aconite) root. Examination revealed shock with irregular pulses. Initial ECG showed frequent multifocal ventricular ectopics (VE), which later turned to short runs of ventricular tachycardia (VT). Immediate gastric lavage was done and activated charcoal given. Patient was treated with fluid resuscitation without any improvement in blood pressure. Patient was started on nor-adrenaline infusion with gradual recovery from hypotension over a period of 6h, but support was continued for 48h. Amiodarone was started to control ventricular excitability, which persisted over 72h with gradual decrease in frequency of VT and VE. Patient was discharged with normal sinus rhythm on oral amiodarone on 5th day of hospitalization. On follow-up after 2 weeks patient was totally asymptomatic and amiodarone was stopped.

Role of non-high-density lipoprotein cholesterol in predicting cerebrovascular events in patients following myocardial infarction.

Although there appears to be a role for statins in reducing cerebrovascular events, the exact role of different lipid fractions in the etiopathogenesis of cerebrovascular disease (CVD) is not well understood. A secondary analysis of data collected for the placebo arm (n = 2,078) of the Cholesterol and Recurrent Events (CARE) trial was performed. The CARE trial was a placebo-controlled trial aimed at testing the effect of pravastatin on patients after myocardial infarction. Patients with histories of CVD were excluded from the study. A Cox proportional-hazards model was used to evaluate the association between plausible risk factors (including lipid fractions) and risk for first incident CVD in patients after myocardial infarction. At the end of 5 years, 123 patients (6%) had incident CVD after myocardial infarction (76 with stroke and 47 with transient ischemic attack). Baseline non-high-density lipoprotein (HDL) cholesterol level emerged as the only significant lipid risk factor that predicted CVD; low-density lipoprotein cholesterol and HDL cholesterol were not significant. The adjusted hazard ratios (adjusted for age, gender, hypertension, diabetes mellitus, and smoking) for CVD were 1.28 (95% confidence interval [CI] 1.06 to 1.53) for non-HDL cholesterol, 1.14 (95% CI 0.96 to 1.37) for low-density lipoprotein cholesterol, and 0.90 (95% CI 0.75 to 1.09) for HDL cholesterol (per unit SD change of lipid fractions). This relation held true regardless of the level of triglycerides. After adjustment for age and gender, the hazard ratio for the highest natural quartile of non-HDL was 1.76 (95% CI 1.05 to 2.54), compared to 1.36 (95% CI 0.89 to 1.90) for low-density lipoprotein cholesterol. In conclusion, non-HDL cholesterol is the strongest predictor among the lipid risk factors of incident CVD in patients with established coronary heart disease.

Calcific aortic valve and spontaneous embolic stroke: a review of literature.

Aortic valve calcification is common in the elderly and in patients with congenital bicuspid aortic valve but unlike calcific mitral valve disease it is not a well recognized risk factor for stroke. Although autopsy studies have revealed evidence of systemic embolism in one-third of cases with calcific aortic valves, there is conflicting data from larger clinical studies examining the association between calcific aortic valve and stroke. There are only 8 reported cases of symptomatic stroke from spontaneous cerebral thromboembolism associated with calcific aortic valve in the literature. Computerized tomography (CT) angiography and CT without contrast are modalities of choice to diagnose calcific embolism, while MRI may be useful in delineating the extent of ischemia. Ideal management strategy, the role of antiplatelet therapy, anticoagulation or recommendations for valve replacements are poorly defined. We present a focused literature review on this topic.