Notes

Prudential Use of the Fairly Questionable COVID-19 Vaccinations.
Though common models suggest that affect intensity can be thought of as orthogonal to arousal, examples of intensely pleasant low arousal stimuli remain rare. To support this orthogonal model, we examined whether a specific meditative sexual practice, Orgasmic Meditation (OM), induces such a state. Thus, this study measured changes in subjective affect as well as skin conductance responses (SCR), as a proxy for physiological arousal associated with sympathetic nervous system activity, during a single 15-minute partnered sexual meditative practice (Orgasmic Meditation; OM) in 93 participants. Almost all participants experienced sustained positive affect during the task. Whereas seconds after OM start approximately half the participants experienced sustained increased SCR, the other half experienced sustained decreased SCR. This observation suggests that the experience of sustained positive affect in intimate interactions may be associated with multiple mechanistic profiles including both decreased and increased arousal.Ex-vivo simple models are powered tools to study cardiac hypertrophy. It is possible to control the activation of critical genes and thus test the effects of drug therapies before the in vivo tests. A zebrafish cardiac hypertrophy developed by 500 μM phenylephrine (PE) treatment in ex vivo culture has been demonstrated to activate the essential expression of the embryonal genes. These genes are the same as those described in several previous pieces of research on hypertrophic pathology in humans. The efficacy of the chemical drug Blebbistatin (BL) on hypertrophy induced ex vivo cultured hearts is studied in this research. BL can inhibit the myosins and the calcium wave in counteracting the hypertrophy status caused by PE. Samples treated with PE, BL and PE simultaneously, or pre/post-treatment with BL, have been analysed for the embryonal gene activation concerning the hypertrophy status. The qRTPCR has shown an inhibitory effect of BL treatments on the microRNAs downregulation with the consequent low expression of essential embryonal genes. In particular, BL seems to be effective in blocking the hyperplasia of the epicardium but less effective in myocardium hypertrophy. The model can make it possible to obtain knowledge on the transduction pathways activated by BL and investigate the potential use of this drug in treating cardiac hypertrophy in humans.Identification of molecular targets in any cellular phenomena is a challenge and a path that one endeavors upon independently. We have identified a phosphatase SHP-1 as a point of intervention of IL-10 and IL-12 reciprocity in leishmaniasis. The therapeutic model that we have developed uniquely targets this protein but the pipeline in general can be used by the researchers for their unique targets. Naturally occurring peptides are well known for their biochemical participation in cellular functions hence we were motivated to use this uniqueness of physico-chemical properties of peptides conferred by amino acids through machine learning to channelize a mode of therapeutic exploration in infectious disease. Using computational approaches, we identified high order sequence conservation and similarity in SHP-1 sequence which was also evolutionarily conserved, complete structure of Mouse SHP-1 was predicted and validated, a unique motif of the same was identified against which library of synthetic peptides were designed and validated followed by screening the library by docking them with MuSHP-1 protein structure. Our findings showed 3 peptides had high binding affinity and in future can be validated using cell based and in vivo assays.
We compared the characteristics and outcomes of vaccinated and nonvaccinated patients hospitalized with COVID-19.

We analyzed patients hospitalized in a COVID hub during three one-month periods (i) October 15, 2020-November 15, 2020 (prevaccination peak); (ii) October 15, 2021-November 15, 2021 (Delta wave); (iii) December 15, 2021-January 15, 2022 (Omicron wave). To define the epidemiologic context, SARS-CoV-2 infection in healthcare workers was analyzed.

SARS-CoV-2 infection incidence in healthcare workers was 146 cases per 1000 persons in 2020 (prevaccination) and 67 in 2021 (postvaccination, when the Omicron variant caused most infections). There were 420 hospitalized patients in the prevaccination period, 51 during the Delta wave (52.1% vaccinated) and 165 during the Omicron wave (52.9% vaccinated). During the Delta wave, a significantly higher number of nonvaccinated (29.2%) than vaccinated patients (3.7%) were admitted to the intensive care unit (ICU) (p=0.019). Nonvaccinated patients were younger and had a lower rate of concomitant medical conditions (53.2% vs 83.7%; p < 0.001) during the Omicron wave when 80% of patients admitted to ICU and all those who died were still infected by the Delta variant.

Vaccine effectiveness in fragile individuals appears to be lower because of a faster immunity decline. However, the Omicron variant seems to cause less severe COVID-19.
Vaccine effectiveness in fragile individuals appears to be lower because of a faster immunity decline. However, the Omicron variant seems to cause less severe COVID-19.Vaccines against SARS-CoV-2 ameliorate infection and adverse outcomes from SARS-CoV-2. Elicitation of high affinity and durable protective antibody responses is a hallmark of a successful humoral immune response to vaccination. To assess the relevance of serum levels of SARS-CoV-2 specific antibodies and to further characterize the immune response to SARS-CoV-2 vaccines, we report i) the levels of spike-binding and neutralizing antibodies to SARS-COV-2 in the sera of 30 healthy volunteers at nine months after the second vaccination dose of mRNA vaccine and one month after the booster dose; ii) the levels of IFN-γ production by blood T cells exposed to SARS-CoV-2 spike antigen (Wuhan, Alpha B.1.1.7, Delta B.1.617.2, and Omicron B1.1.529 variants); and iii) the specific phenotype of T cells related with exposure to SARS-CoV-2 spike antigen. We observed that the booster dose induced increased humoral and adaptive immune responses and led to early activation of the memory CD8+ T subset.Unnecessary/unsafe opioid prescribing has become a major public health concern in the U.S. Statewide prescription drug monitoring programs (PDMPs) with varying characteristics have been implemented to improve safe prescribing practice. Yet, no studies have comprehensively evaluated the effectiveness of PDMP characteristics in reducing opioid-related potentially inappropriate prescribing (PIP) practices. The objective of the study is to apply machine learning methods to evaluate PDMP effectiveness by examining how different PDMP characteristics are associated with opioid-related PIPs for non-cancer chronic pain (NCCP) treatment. This was a retrospective observational study that included 802,926 adult patients who were diagnosed NCCP, obtained opioid prescriptions, and were continuously enrolled in plans of a major U.S. insurer for over a year. Four outcomes of opioid-related PIP practices, including dosage ≥50 MME/day and ≥90 MME/day, days supply ≥7 days, and benzodiazepine-opioid co-prescription were examined. Machine learning models were applied, including logistic regression, least absolute shrinkage and selection operation regression, classification and regression trees, random forests, and gradient boost modeling (GBM). The SHapley Additive exPlanations (SHAP) method was applied to interpret model results. The results show that among 1,886,146 NCCP opioid-related claims, 22.8% had an opioid dosage ≥50 MME/day and 8.9% ≥90 MME/day, 70.3% had days supply ≥7 days, and 10.3% were when benzodiazepine was filled ≤7 days ago. GBM had superior model performance. We identified the most salient PDMP characteristics that predict opioid-related PIPs (e.g., broader access to patient prescription history, monitoring Schedule IV controlled substances), which could be informative to the states considering the redesign of PDMPs.Although the positive relationship between arts engagement and mental health is well documented, arts participation may be an emergent factor in the ecology of childhood obesity. Prior research hypothesized several potential health benefits of arts participation including healthy diet and lifestyles, but the available evidence is mainly limited to cross-sectional covariate-adjustment models for the adult population. We employed a newly released panel of the Early Childhood Longitudinal Study kindergarten cohort (ECLS-K 2011), which is a nationally representative sample of American children who entered kindergarten in 2010-2011 (n = 15,820). We applied both dynamic panel models with Maximum Likelihood estimation as well as difference-in-differences models to address unobserved heterogeneity. Our results showed that childhood arts activity is significantly associated with reduced weight status in elementary schooling. In particular, arts participation in elementary schooling reduced the risk of being overweight on a year-to-year basis; the effect size was between 12% and 23% of a SD of BMI for all children. Arts participation at kindergarten also had a significant relationship with cumulative changes in BMI over the course of elementary schooling, especially for female and White female children (about 22% and 32% of a SD of BMI). There are considerable arts participation gaps between families and regions, and these early artistic experiences appear to affect the risk of being overweight. This suggests the possibility of a larger social reproduction process via an ecological pathway that might be easily overlooked-the accumulation of arts experience and concurrent health inequalities in childhood.Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed evaluation of the head shape. Artificial intelligence (AI) methods, namely deep learning (DL), can be explored to develop fast and automatic approaches for shape evaluation. However, due to the clinical variability of patients' head anatomy, generalization of AI networks to the clinical needs is paramount and extremely challenging. In this work, a new framework is proposed to augment the 3D data used for training DL networks for shape evaluation. The proposed augmentation strategy deforms head surfaces towards different deformities. For that, a point-based 3D morphable model (p3DMM) is developed to generate a statistical model representative of head shapes of different cranial deformities. Afterward, a constrained transformation approach (3DHT) is applied to warp a head surface towards a target deformity by estimating a dense motion field from a sparse one resulted from the p3DMM. Qualitative evaluation showed that the proposed method generates realistic head shapes indistinguishable from the real ones. Moreover, quantitative experiments demonstrated that DL networks training with the proposed augmented surfaces improves their performance in terms of head shape analysis. Overall, the introduced augmentation allows to effectively transform a given head surface towards different deformity shapes, potentiating the development of DL approaches for head shape analysis.
Here's my website: