Use of Dantrolene and Ativan to Treat Overlapping Diagnosis NMS Versus Malignant Catatonia Case Report

We present a case report of a patient who developed symptoms resembling malignant catatonia and neuroleptic malignant syndrome. Suspicion of neuroleptic malignant syndrome arose after treatment over his course of hospital stay with three different second-generation antipsychotics for a first-time bipolar type I manic episode. After a hospital stay of 5 days, the patient developed symptoms that could be interpreted as malignant catatonia or neuroleptic malignant syndrome. Administration of antipsychotics was immediately ceased, and the patient was transferred to the ICU where he was treated with dantrolene and higher dosages of Ativan. The patient improved after simultaneous intervention for both possible diagnoses. After approximately one month, quetiapine, one of the second generation antipsychotics previously prescribed, was restarted with good results and no reoccurrence of NMS or malignant catatonia. This case illustrates the potential dilemma faced when differentiation between the two obscure diagnoses is necessary. Diagnosis is typically established through clinical observation and monitoring of symptom evolution after the administration of neuroleptics. The treatment algorithms for each diagnosis vary as can the respective outcomes. Our case also highlights the dearth of research available on distinguishing neuropathologic psychiatric disorders from pathophysiologic psychomotor syndromes. It also focuses on the need for sound diagnostic scoring scales that will clarify the diagnostic picture as well as treatment guidelines to ensure best outcomes.

Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision

Background Observational studies are increasingly being used to provide supplementary evidence in addition to Randomized Control Trials (RCTs) because they provide a scale and diversity of participants and outcomes that would be infeasible in an RCT. Additionally, they more closely reflect the settings in which the studied interventions will be applied in the future. Well-established propensity-score-based methods exist to overcome the challenges of working with observational data to estimate causal effects. These methods also provide quality assurance diagnostics to evaluate the degree to which bias has been removed and the estimates can be trusted. In large medical datasets it is common to find the same underlying health condition being treated with a variety of distinct drugs or drug combinations. Conventional methods require a manual iterative workflow, making them scale poorly to studies with many intervention arms. In such situations, automated causal inference methods that are compatible with traditional propensity-score-based workflows are highly desirable. Methods We introduce an automated causal inference method BCAUS, that features a deep-neural-network-based propensity model that is trained with a loss which penalizes both the incorrect prediction of the assigned treatment as well as the degree of imbalance between the inverse probability weighted covariates. The network is trained end-to-end by dynamically adjusting the loss term for each training batch such that the relative contributions from the two loss components are held fixed. Trained BCAUS models can be used in conjunction with traditional propensity-score-based methods to estimate causal treatment effects. Results We tested BCAUS on the semi-synthetic Infant Health & Development Program dataset with a single intervention arm, and a real-world observational study of diabetes interventions with over 100,000 individuals spread across more than a hundred intervention arms. When compared against other recently proposed automated causal inference methods, BCAUS had competitive accuracy for estimating synthetic treatment effects and provided highly concordant estimates on the real-world dataset but was an order-of-magnitude faster. Conclusions BCAUS is directly compatible with trusted protocols to estimate treatment effects and diagnose the quality of those estimates, while making the established approaches automatically scalable to an arbitrary number of simultaneous intervention arms without any need for manual iteration.

Hotspot and Frontier Analysis of Exercise Training Therapy for Heart Failure Complicated With Depression Based on Web of Science Database and Big Data Analysis

Background: Exercise training has been extensively studied in heart failure (HF) and psychological disorders, which has been shown to worsen each other. However, our understanding of how exercise simultaneously protect heart and brain of HF patients is still in its infancy. The purpose of this study was to take advantage of big data techniques to explore hotspots and frontiers of mechanisms that protect the heart and brain simultaneously through exercise training.Methods: We studied the scientific publications on related research between January 1, 2003 to December 31, 2020 from the WoS Core Collection. Research hotspots were assessed through open-source software, CiteSpace, Pajek, and VOSviewer. Big data analysis and visualization were carried out using R, Cytoscape and Origin.Results: From 2003 to 2020, the study on HF, depression, and exercise simultaneously was the lowest of all research sequences (two-way ANOVAs, p < 0.0001). Its linear regression coefficient r was 0.7641. The result of hotspot analysis of related keyword-driven research showed that inflammation and stress (including oxidative stress) were the common mechanisms. Through the further analyses, we noted that inflammation, stress, oxidative stress, apoptosis, reactive oxygen species, cell death, and the mechanisms related to mitochondrial biogenesis/homeostasis, could be regarded as the primary mechanism targets to study the simultaneous intervention of exercise on the heart and brain of HF patients with depression.Conclusions: Our findings demonstrate the potential mechanism targets by which exercise interferes with both the heart and brain for HF patients with depression. We hope that they can boost the attention of other researchers and clinicians, and open up new avenues for designing more novel potential drugs to block heart-brain axis vicious circle.

Different Effects of Leucine Supplementation and/or Exercise on Systemic Insulin Sensitivity in Mice

ObjectiveObesity-related diseases such as diabetes, hypertension, dyslipidemia, and cardiovascular diseases have increased due to the obesity epidemic. Early intervention for obesity through lifestyle and nutrition plays an important role in preventing obesity-related diseases. Therefore, the purpose of this study is to explore the role of leucine and exercise in adiposity, systemic insulin resistance, and inflammation to provide theoretical and guiding basis for the early prevention and treatment of obesity.MethodsC57BL/6J male mice were randomly divided into HFD or LFD-fed mice group. After 9 weeks, glucose tolerance test (GTT) was performed to detect their systemic insulin sensitivity. Starting from week 10, mice were divided into eight groups and treated with moderate exercise or/and 1.5% leucine. At week 13, systemic insulin sensitivity was detected by GTT. At week 14, mice were dissected to analyze adiposity and inflammation.ResultsIn LFD mice, exercise significantly increased systemic insulin sensitivity by increasing GLUT4 expression in the muscle and decreasing adiposity through increasing AMPK phosphorylation in adipose tissue. In HFD mice, the simultaneous intervention of exercise and leucine increases systemic insulin sensitivity by reducing liver and adipose tissue inflammation via decreasing NF-κB p65 phosphorylation, and increasing the expression of adiponectin in adipose tissue.ConclusionThere are different mechanisms underlying the effects of exercise and leucine on insulin resistance and inflammation in LFD-fed mice or HFD-fed mice.

Atrial Septal Defect with Pulmonary Stenosis: Staged or Simultaneous Intervention?

Atrial Septal Defect with Pulmonary Stenosis: Staged or Simultaneous Intervention?

Outcomes of a Combined Approach of Percutaneous Coronary Revascularization and Cardiac Valve Surgery

A subset of patients requiring coronary revascularization and valve surgery may benefit from a combined approach of percutaneous coronary intervention (PCI) and valve surgery, as opposed to the standard median sternotomy approach of combined coronary artery bypass and valve surgery. To evaluate its potential benefits and limitations, a literature search was performed using PubMed EMBASE, Ovid, and the Cochrane library, through March 2016 to identify all studies involving a combined approach of PCI and valve surgery in patients with coronary artery and valvular disease. There were five studies included in the study with a total of 324 patients, of which 75 (23.1%) had a history of previous cardiac surgery. The interval between PCI and surgery ranged from simultaneous intervention to a median of 38 days (interquartile range, 18–65 days). The surgical approach performed consisted of a minimally invasive one or median sternotomy. There were 275 single valve surgery (84.9%) and 49 double-valve surgery (15.1%) with a 30-day mortality ranging from 0% to 5.5%. The 1-year survival ranged from 78% to 96%, and the follow-up period ranged from 1.3 to 5 years. Herein, we present a review of the literature using this technique.