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Furthermore, a kinetic model based on elementary oxidative reactions was constructed to help optimize the reoxidation conditions and to predict product purity. Together, the deep understanding of interchain disulfide bond reoxidation, combined with the predictive kinetic model, provided a good foundation to implement a rescue strategy to generate high-purity antibodies with substantial cost savings in manufacturing processes.The gut-brain axis (GBA) is a biochemical link that connects the central nervous system (CNS) and enteric nervous system (ENS). Clinical and experimental evidence suggests gut microbiota as a key regulator of the GBA. Microbes living in the gut not only interact locally with intestinal cells and the ENS but have also been found to modulate the CNS through neuroendocrine and metabolic pathways. Studies have also explored the involvement of gut microbiota dysbiosis in depression, anxiety, autism, stroke, and pathophysiology of other neurodegenerative diseases. Recent reports suggest that microbe-derived metabolites can influence host metabolism by acting as epigenetic regulators. Butyrate, an intestinal bacterial metabolite, is a known histone deacetylase inhibitor that has shown to improve learning and memory in animal models. Due to high disease variability amongst the population, a multi-omics approach that utilizes artificial intelligence and machine learning to analyze and integrate omics data is necessary to better understand the role of the GBA in pathogenesis of neurological disorders, to generate predictive models, and to develop precise and personalized therapeutics. This review examines our current understanding of epigenetic regulation of the GBA and proposes a framework to integrate multi-omics data for prediction, prevention, and development of precision health approaches to treat brain disorders.

Uncontrolled epilepsy has persisted despite development of numerous antiseizure medications (ASMs) over the past 25 years, and more effective treatments are needed. BMS-1166 price Cenobamate is a new ASM approved in the US for treatment of adults with focal onset seizures.

This review outlines cenobamate study results from preclinical animal models through phase 2 and 3 clinical studies. Topics include mechanisms of action, pharmacokinetics, efficacy, and safety of cenobamate. Information on dosing, tolerability, and special populations are included to help healthcare providers understand this new ASM.

Adjunctive cenobamate shows a high level of efficacy in patients with refractory focal epilepsy compared to that reported for other ASMs. Most notable are reductions in monthly seizure frequency (up to 55%) and unprecedented seizure-free rates (up to 28%) with cenobamate in patients with refractory epilepsy despite the concomitant use of 1-3 ASMs. Cenobamate was generally safe and well-tolerated, with a safety profile s option.

Myocardial fibrosis is key for atrial fibrillation maintenance. We aimed to test the efficacy of ablating cardiac magnetic resonance (CMR)-detected atrial fibrosis plus pulmonary vein isolation (PVI).

This was an open-label, parallel-group, randomized, controlled trial. Patients with symptomatic drug-refractory atrial fibrillation (paroxysmal and persistent) undergoing first or repeat ablation were randomized in a 11 basis to receive PVI plus CMR-guided fibrosis ablation (CMR group) or PVI alone (PVI-alone group). The primary end point was the rate of recurrence (>30 seconds) at 12 months of follow-up using a 12-lead ECG and Holter monitoring at 3, 6, and 12 months. The analysis was conducted by intention-to-treat.

In total, 155 patients (71% male, age 59±10, CHA

DS

-VASc 1.3±1.1, 54% paroxysmal atrial fibrillation) were allocated to the PVI-alone group (N=76) or CMR group (N=79). First ablation was performed in 80% and 71% of patients in the PVI-alone and CMR groups, respectively. The mean atrial /www.clinicaltrials.gov. Unique identifier NCT02698631.

Cause-of-death information, reported by frontline clinicians after a patient’s death, is an irreplaceable source of public health data. However, systematic bias in cause-of-death reporting can lead to over- or underestimation of deaths attributable to different causes. New York City consistently reports higher rates of deaths attributable to pneumonia and influenza than many other US cities and the country. We investigated systematic erroneous reporting as a possible explanation for this phenomenon.

We reviewed all deaths from 2 New York City hospitals during 2013-2014 in which pneumonia or influenza was reported as the underlying cause of death (n = 188), and we examined the association between erroneous reporting and multiple extrinsic factors that may influence cause-of-death reporting (patient demographic characteristics and medical comorbidities, time and hospital location of death, type of medical provider reporting the death, and availability of certain diagnostic information).

Pneumonia was erroneously reported as the underlying cause of death in 163 (86.7%) reports. We identified heart disease and dementia as the more likely underlying cause of death in 21% and 17% of erroneously reported deaths attributable to pneumonia, respectively. We found no significant association between erroneous reporting and the multiple extrinsic factors examined.

Our results underscore how erroneous reporting of 1 condition can lead to underreporting of other causes of death. Misapplication or misunderstanding of procedures by medical providers, rather than extrinsic factors influencing the reporting process, are key drivers of erroneous cause-of-death reporting.

Our results underscore how erroneous reporting of 1 condition can lead to underreporting of other causes of death. Misapplication or misunderstanding of procedures by medical providers, rather than extrinsic factors influencing the reporting process, are key drivers of erroneous cause-of-death reporting.

We quantify the effect of a set of interventions including asthma self-management education, influenza vaccination, spacers, and nebulizers on healthcare utilization and expenditures for Medicaid-enrolled children with asthma in New York and Michigan.

We obtained patients’ data from Medicaid Analytic eXtract files and evaluated patients with persistent asthma in 2010 and 2011. We used difference-in-difference regression to quantify the effect of the intervention on the probability of asthma-related healthcare utilization, asthma medication, and utilization costs. We estimated the average change in outcome measures from pre-intervention/intervention (2010) to post-intervention (2011) periods for the intervention group by comparing this with the average change in the control group over the same time horizon.

All of the interventions reduced both utilization and asthma medication costs. Asthma self-management education, nebulizer, and spacer interventions reduced the probability of emergency department (20.