Association of Obstructive Sleep Apnea With Cardiovascular Outcomes in Patients With Acute Coronary Syndrome.

Background The prognostic significance of obstructive sleep apnea ( OSA ) in patients with acute coronary syndrome ( ACS ) in the contemporary era is unclear. We performed a large, prospective cohort study and did a landmark analysis to delineate the association of OSA with subsequent cardiovascular events after ACS onset. Methods and Results Between June 2015 and May 2017, consecutive eligible patients admitted for ACS underwent cardiorespiratory polygraphy during hospitalization. OSA was defined as an apnea-hypopnea index ≥15 events·h-1. The primary end point was major adverse cardiovascular and cerebrovascular event ( MACCE ), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. OSA was present in 403 of 804 (50.1%) patients. During median follow-up of 1 year, cumulative incidence of MACCE was significantly higher in the OSA group than in the non- OSA group (log-rank, P=0.041). Multivariate analysis showed that OSA was nominally associated with incidence of MACCE (adjusted hazard ratio, 1.55; 95% CI, 0.94-2.57; P=0.085). In the landmark analysis, patients with OSA had 3.9 times the risk of incurring a MACCE after 1 year (adjusted hazard ratio, 3.87; 95% CI, 1.20-12.46; P=0.023), but no increased risk was found within 1-year follow-up (adjusted hazard ratio, 1.18; 95% CI, 0.67-2.09; P=0.575). No significant differences were found in the incidence of cardiovascular death, myocardial infarction, and ischemia-driven revascularization, except for a higher rate of hospitalization for unstable angina in the OSA group than in the non- OSA group (adjusted hazard ratio, 2.10; 95% CI, 1.09-4.05; P=0.027). Conclusions There was no independent correlation between OSA and 1-year MACCE after ACS . The increased risk associated with OSA was only observed after 1-year follow-up. Efficacy of OSA treatment as secondary prevention after ACS requires further investigation

Xenotransplantation for Islets from Clinical Side

Islet transplantation can eliminate severe hypoglycemia symptoms caused by conventional treatment, and has the advantages of less trauma and complications, which is considered as the most promising treatment for type 1 diabetes mellitus (T1DM). Regulatory guidance is needed for a standard pig source. In section 1, the regulation of medical grade designed pathogen free (DPF) donor pig for clinical xenotransplantation consists of five parts: genetic quality control, microbiological surveillance, formula feeds, specification of pathological diagnosis, and requirements of environment and housing facilities. In section 2, we present the current approach and progress in pig donor selecting, pancreatic digestion, isolation and preparation of porcine islet grafts, identification and quality assessment of final islet product in clinical trials. The liver is currently the most preferred site for islet transplantation, even though it is far from ideal. A large number of alternative sites have been used for islet transplantation in experimental animal models to provide improved engraftment and long-term survival. In Section 3, we introduce some commonly used sites in xenotransplantation. The benefits and drawbacks of each parameter above are discussed in an attempt to decide which is the most suitable for clinical use and to direct future research

Biosafety Barrier to Xenotransplantation

Biosafety barrier is most important for xenotransplantation clinical trial. Source animals used in xenotransplantation should be bred in a closed herd and raised in a well-controlled, pathogen-free environment with high standards of animal welfare. To ensure the source animals’ freedom from known pathogens under adequate biosecurity and surveillance, extensive tests must be done. Biosafety of DPF source pig should be proved by animal model before clinical trial. In addition, inclusion criteria for transplant recipients and clinical safe transplantation protocol should be established. Comprehensive anti-immune rejection treatment based on immune tolerance program can significantly prolong the xenograft survival and reduce the adverse impact on the immune system, which is suitable for clinical application. According to the clinical follow-up plan of the xenograft recipients, the patients should come back to the hospital for a check at regular intervals after the transplantation. The database of clinical trials for xenotransplantation should be established, including specimens, paper documents, and electronic documents. The information and samples of xenotransplantation donors and recipients should be preserved for long time

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment

Clinical significance of obstructive sleep apnea in patients with acute coronary syndrome in relation to diabetes status.

Objective: The prognostic significance of obstructive sleep apnea (OSA) in patients with acute coronary syndrome (ACS) according to diabetes mellitus (DM) status remains unclear. We aimed to elucidate the association of OSA with subsequent cardiovascular events in patients with ACS with or without DM. Research design and methods: In this prospective cohort study, consecutive eligible patients with ACS underwent cardiorespiratory polygraphy between June 2015 and May 2017. OSA was defined as an Apnea Hypopnea Index ≥15 events/hour. The primary end point was major adverse cardiovascular and cerebrovascular events (MACCEs), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. Results: Among 804 patients, 248 (30.8%) had DM and 403 (50.1%) had OSA. OSA was associated with 2.5 times the risk of 1 year MACCE in patients with DM (22.3% vs 7.1% in the non-OSA group; adjusted HR (HR)=2.49, 95% CI 1.16 to 5.35, p=0.019), but not in patients without DM (8.5% vs 7.7% in the non-OSA group, adjusted HR=0.94, 95% CI 0.51 to 1.75, p=0.85). Patients with DM without OSA had a similar 1 year MACCE rate as patients without DM. The increased risk of events was predominately isolated to patients with OSA with baseline glucose or hemoglobin A1c levels above the median. Combined OSA and longer hypoxia duration (time with arterial oxygen saturation22 min) further increased the MACCE rate to 31.0% in patients with DM. Conclusions: OSA was associated with increased risk of 1 year MACCE following ACS in patients with DM, but not in non-DM patients. Further trials exploring the efficacy of OSA treatment in high-risk patients with ACS and DM are warranted

Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition

The development of foundation vision models has pushed the general visual recognition to a high level, but cannot well address the fine-grained recognition in specialized domain such as invasive species classification. Identifying and managing invasive species has strong social and ecological value. Currently, most invasive species datasets are limited in scale and cover a narrow range of species, which restricts the development of deep-learning based invasion biometrics systems. To fill the gap of this area, we introduced Species196, a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U. The dataset provides four experimental settings for benchmarking the existing models and algorithms, namely, supervised learning, semi-supervised learning, self-supervised pretraining and zero-shot inference ability of large multi-modal models. To facilitate future research on these four learning paradigms, we conduct an empirical study of the representative methods on the introduced dataset. The dataset is publicly available at https://species-dataset.github.io/.Comment: Accepted by NeurIPS 2023 Track Datasets and Benchmark