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Background: The benefits of hyperbaric oxygen (HBO) in treating animals with heat stroke (HS) have been established. This study aims to retrospectively analyze the effect of HBO on multiple organ dysfunction following HS in humans. Methods: Retrospective data were collected from patients with HS admitted to our hospital in the past 7 years. Patients were categorized into groups based on whether they received HBO therapy. The study compared various factors, including sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation-Ⅱ (APACHE-Ⅱ) scores, mortality rates, neurological function scores, serum myocardial enzyme levels, liver, kidney, and coagulation function indicators, blood routine results, electrolyte levels, and modified Barthel index (MBI) score for standard daily living ability before treatment and after 2 and 4 weeks of treatment. Results: The mortality rates in the HBO and control group were 0% and 8.49%, respectively. Upon admission, the HBO group had higher SOFA and APACHE-Ⅱ scores and lower neurological, coagulation, and liver functions than those of the control group. HBO treatment significantly improved SOFA, APACHE-Ⅱ, and neurological scores while relieving levels of alanine aminotransferase, aspartate aminotransferase, creatinine, and myocardial enzymes. Additionally, it mitigating lymphocyte and platelet count decline caused by HS. The MBI score was significantly enhanced after treatment in the HBO group. Conclusions: Clinical practice advocates administering HBO therapy to patients with severe illness, organ damage, and nerve impairment. Compared with conventional treatment, combined HBO therapy demonstrated superior efficacy in alleviating multiple organ dysfunction and improving daily living ability in patients with HS.

Abstract Research into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive. To tackle these two issues, this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation (SAFE‐PSO) approach. Firstly, the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN. Secondly, the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates. Thirdly, a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost. The SAFE‐PSO algorithm is tested on two widely used datasets: The 10‐category (i.e., CIFAR10) and the 100−category (i.e., CIFAR100). The experimental results show that SAFE‐PSO is very effective and efficient, which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

BackgroundHeat stroke is a potentially fatal condition that is caused by elevated core temperature. Guillain–Barré syndrome (GBS) induced by heat stroke is extremely rare and has only been reported in few case reports. The purpose of this case study was to evaluate the clinical symptoms, neuroelectrophysiological and imageological features of GBS after heat stroke.MethodsWe reviewed our hospital records and previously published reports to find the cases of GBS after heat stroke. The clinical, imageological, and electrophysiological profiles, treatment and prognosis were presented and analyzed.ResultsWe retrieved three cases of GBS induced by heat stroke from our hospital, which presented as lesions on multiple cranial and peripheral nerves and albuminocytologic dissociation in the cerebrospinal fluid. All of these patients had disorders of consciousness at the early stage of heat stroke and a “pseudo-recovery period” after they recovered from coma after heat stroke. After immunoglobulin administration and immunoregulation therapy, these patients' neurological deficiencies were relieved significantly. But there are still disabilities and almost totally reliant on others.ConclusionsThe number of the cases of GBS induced by HS reported in this study has been the most in the recent 5 years. Clinicians should pay attention to patients with heat stroke with sustained coma and the sudden quadriplegia. Early, exact and timely diagnosis and treatment of GBS need to be performed, to accelerate recovery and improve prognosis.

Unconventional reservoirs usually contain many weak surfaces such as faults, laminae and natural fractures, and effective activation and utilization of these weak surfaces in reservoirs can significantly improve the extraction effect. In hydraulic fracturing, when the artificial fracture approaches the natural fracture, the natural fracture would be influenced by both the original in-situ stress field and the hydraulic fracturing-induced stress field. In this paper, the hydraulic fracturing-induced stress field is calculated based on the relative position of hydraulic fracture and natural fracture, the original in-situ stress, the net pressure inside the hydraulic fracture and the pore pressure of the formation. Furthermore, the stability model of the natural fracture is established by combining the Mohr-Coulomb rupture criterion, and extensive parametric studies are conducted to explore the impact of each parameter on the stability of the natural fracture. The validity of the proposed model is verified by comparing with the reservoir characteristics and fracturing process of the X-well 150e155 formation in the Songliao Basin. It is found that the stress field induced by the hydraulic fracture inhibits the activation of the natural fracture after the artificial fracture crossed the natural fracture. Therefore, for similar reservoirs as X-well 150e155, it is suggested to connect natural fractures with hydraulic fractures first and then activate natural fractures which can effectively utilize the natural fractures and form a complex fracture network. [ABSTRACT FROM AUTHOR]

This article discusses a case of oculogyric crisis (OGC) in a patient with Wilson's disease (WD). The patient, a 30-year-old Chinese female, presented with walking difficulties, dysarthria, and sustained upward gaze of both eyes. The article provides a detailed description of the patient's symptoms and clinical findings, including video footage of the OGC attacks. The patient was diagnosed with WD based on copper biochemical examination, brain magnetic resonance imaging (MRI), and genetic testing. The article also discusses the etiology and mechanism of OGC in WD, as well as its occurrence in other inherited metabolic and neurodegenerative diseases. [Extracted from the article]

Research into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive. To tackle these two issues, this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation (SAFE‐PSO) approach. Firstly, the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN. Secondly, the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates. Thirdly, a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost. The SAFE‐PSO algorithm is tested on two widely used datasets: The 10‐category (i.e., CIFAR10) and the 100−category (i.e., CIFAR100). The experimental results show that SAFE‐PSO is very effective and efficient, which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost. [ABSTRACT FROM AUTHOR]