Delayed feedback as a means of control of noise-induced motion

Time--delayed feedback is exploited for controlling noise--induced motion in coherence resonance oscillators. Namely, under the proper choice of time delay, one can either increase or decrease the regularity of motion. It is shown that in an excitable system, delayed feedback can stabilize the frequency of oscillations against variation of noise strength. Also, for fixed noise intensity, the phenomenon of entrainment of the basic oscillation period by the delayed feedback occurs. This allows one to steer the timescales of noise-induced motion by changing the time delay.Comment: 4 pages, 4 figures. In the replacement file Fig. 2 and Fig. 4(b),(d) were amended. The reason is numerical error found, that affected the quantitative estimates of correlation time, but did not affect the main messag

Minimally Invasive Revisional Bariatric Surgery in a MBSAQIP Accredited High-Volume Center

Background: With the rising number of bariatric surgeries performed annually, there has also been an increase in revisional bariatric surgeries (RBS). The aim of this study is to evaluate the safety and postoperative outcomes of RBS performed with a minimally invasive approach. Methods: Retrospective analysis on a prospectively collected database of patients who underwent minimally invasive RBS between 2012 and 2019. Primary endpoints were conversion rate, major morbidity, mortality, and 30-day reoperation rate. Comparative analysis of laparoscopic adjustable gastric banding (LAGB) conversion to sleeve gastrectomy (SG) vs. conversion to Roux-en-Y gastric bypass (RYGB) was performed. Results: A total of 221 patients underwent minimally invasive RBS, 137 (62%) laparoscopically and 84 (38%) robotically. The most common RBS were LAGB to SG (59.3%) and LAGB to RYGB conversions (16.7%). The main indication was weight loss failure (88.7%). Conversion rate, major morbidity, and mortality were 0.9, 3.2, and 0.4%, respectively. Urgent reoperation was required in 3.2% of cases. Total weight loss at 1 and 2-years follow- were 14.3 and 17.3%, respectively. Comparative analysis of LAGB conversion to SG vs. RYGB showed similar major morbidity (SG: 2.3% vs. RYGB 0%, p = 1). Greater total weight loss was achieved in LAGB to RYGB conversions at 1-year (SG: 14.8% vs. RYGB 25.3%, p < 0.001). Conclusions: Minimally invasive RBS can be performed safely in a broad patient population with low conversion and complication rates, and improved weight loss outcomes. LAGB to RYGB conversions are associated with greater weight loss. Further randomized trials are needed to draw more conclusive recommendations

Current Status of Robot-Assisted Revisional Bariatric Surgery

Bariatric surgery has been demonstrated to be effective in achieving significant weight loss and remission of obesity-related comorbidities. However, a percentage of patients fail to lose enough weight, regain weight, or experience postoperative complications, requiring additional interventions. Revisional bariatric surgeries (RBS) involve the wide spectrum of procedures that aim to treat complications of the index operation or achieve further weight loss. These are technically challenging procedures due to adhesions of the internal organs, reduced working space, and a distorted anatomy. Indications, timing, and type of operation for RBS are not standardized, and there is no consensus on the best surgical approach. Some authors claim a robotic platform could be advantageous in these types of procedures that are performed in reduced, deep operating fields, or those requiring precision and accuracy. This review examines the most current and representative literature on the outcomes of robot-assisted RBS. Included studies demonstrate the safety and feasibility of the robotic approach for RBS. However, long operative times and high costs remain major drawbacks of the device. Finally, if we consider that many centers have not yet completed the learning curve for robot-assisted RBS, the potential for improved outcomes seems promising

Valproic acid and fatalities in children: a review of individual case safety reports in VigiBase

Introduction Valproic acid is an effective first line drug for the treatment of epilepsy. Hepatotoxicity is a rare and potentially fatal adverse reaction for this medicine. Objective Firstly to characterise valproic acid reports on children with fatal outcome and secondly to determine reporting over time of hepatotoxicity with fatal outcome. Methods Individual case safety reports (ICSRs) for children ≤17 years with valproic acid and fatal outcome were retrieved from the WHO Global ICSR database, VigiBase, in June 2013. Reports were classified into hepatotoxic reactions or other reactions. Shrinkage observed-to-expected ratios were used to explore the relative reporting trend over time and for patient age. The frequency of polytherapy, i.e. reports with more than one antiepileptic medicine, was investigated. Results There have been 268 ICSRs with valproic acid and fatal outcome in children, reported from 25 countries since 1977. A total of 156 fatalities were reported with hepatotoxicity, which has been continuously and disproportionally reported over time. There were 31 fatalities with pancreatitis. Other frequently reported events were coma/encephalopathy, seizures, respiratory disorders and coagulopathy. Hepatotoxicity was disproportionally and most commonly reported in children aged 6 years and under (104/156 reports) but affected children of all ages. Polytherapy was significantly more frequently reported for valproic acid with fatal outcome (58%) compared with non-fatal outcome (34%). Conclusion Hepatotoxicity remains a considerable problem. The risk appears to be greatest in young children (6 years and below) but can occur at any age. Polytherapy is commonly reported and seems to be a risk factor for hepatotoxicity, pancreatitis and other serious adverse drug reactions with valproic acid

Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study