Effect of low molecular weight heparin and ulinastatin as a combined therapy on soluble myeloid cell expression and intestinal mucosal function in patients with severe pancreatitis

Purpose: To investigate the effect of low molecular weight heparins (LMWHs) and ulinastatin on soluble myeloid cells and intestinal mucosal function (IMF) in patients with severe pancreatitis. Methods: A total of 107 patients with severe pancreatitis were divided into two groups: control group (CG, n = 53) and study group (SG, n = 54). The CG was treated with LMWH while SG was similarly treated but in addition received ulinastatin simultaneously. The following parameters were evaluated in the two groups: treatment effects, IMF, time for various indicators to normalize, vascular endothelial function, complication symptoms, T lymphoid subgroup indicators, inflammatory factors, anti-inflammatory factors, soluble B7-H2, and soluble myeloid cell receptor-1 level changes. Results: After treatment, SG showed lower levels of L/M value, DAO and D-lactic acid than in CG (p < 0.05). Gastrointestinal function, leukocytes, amylase, and body temperature in SG had a shorter time to return to normal than in CG (p < 0.05). The levels of IL-10 in SG were higher than in CG, while sB7-H2, TNF-α, sTREM-1 and IL-1 levels were lower than those in the CG (p < 0.05). After treatment, NO levels in SG were higher, but TXB2, vWF and ET levels were lower than in CG (p < 0.05). In addition, CD4+, CD4+/CD8+ indicators were higher and CD8+ lower in SG than in CG (p < 0.05). Conclusion: Ulinastatin + LMWHs improves IMF in patients suffering from severe pancreatitis, shortens the time for various indicators to normalize, and reduces incidence of complications. However, further clinical trials are required to ascertain this therapeutic strategy for the management of severe pancreatitis. Keywords: Low molecular weight heparin; Ulinastatin; Severe pancreatitis; Soluble myeloid cell expression; Intestinal mucosal function; Treatment effec

Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus, they are useful to enable energy-efficient hardware inference. However, exploiting spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. In this work, we propose an FPGA-based convolutional SNN accelerator called Skydiver that exploits spatio-temporal workload balance. We propose the Approximate Proportional Relation Construction (APRC) method that can predict the relative workload channel-wisely and a Channel-Balanced Workload Schedule (CBWS) method to increase the hardware workload balance ratio to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on image segmentation and MNIST classification tasks. Results show improved throughput by 1.4X and 1.2X for the two tasks. Skydiver achieved 22.6 KFPS throughput, and 42.4 uJ/Image prediction energy on the classification task with 98.5% accuracy.Comment: Accepted to be published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 202

FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation

Fast video recognition is essential for real-time scenarios, e.g., autonomous driving. However, applying existing Deep Neural Networks (DNNs) to individual high-resolution images is expensive due to large model sizes. Spiking Neural Networks (SNNs) are developed as a promising alternative to DNNs due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus they are useful to enable energy-efficient computation. However, exploiting the spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading energy efficiency. In this work, we, therefore, propose an SNN accelerator called FrameFire for efficient video processing. We introduce a Keyframe-dominated Workload Balance Schedule (KWBS) method. It accelerates the image recognition network with sparse keyframes, then records and analyzes the current workload distribution on hardware to facilitate scheduling workloads in subsequent frames. FrameFire is implemented on a Xilinx XC7Z035 FPGA and verified by video segmentation tasks. The results show that the throughput is improved by 1.7× with the KWBS method. FrameFire achieved 1.04 KFPS throughput and 1.15 mJ/frame recognition energy

Impact of coronary collateralization on major adverse cardiovascular and cerebrovascular events after successful recanalization of chronic total occlusion

AimsThis study aims to investigate the effects of coronary collateral circulation (CCC) on the prognosis of chronic total occlusion (CTO) patients with or without metabolic syndrome (MetS).MethodsThe study included 342 CTO patients who underwent successful percutaneous coronary intervention at the People's Hospital of Liaoning Province between 1 February 2021 and 30 September 2023. The Rentrop score was used to assess the status of CCC. The outcome was major adverse cardiovascular and cerebrovascular events (MACCEs), defined as a composite of all-cause mortality, cardiac death, non-fatal myocardial infarction (MI), target vessel revascularization (TVR), and non-fatal stroke. Univariate and multivariate logistic analyses were used to investigate the association of CCC, MetS, and MACCEs with odds ratios (ORs) and 95% confidence intervals (CIs). The effect of CCC was further investigated in different MetS, diabetes mellitus (DM), and Syntax score groups.ResultsMACCEs were more common in patients with poor CCC compared to those with good CCC (38.74% vs. 16.56%). Statistical differences were found in MACCEs (OR = 3.33, 95% CI: 1.93–5.72), MI (OR = 3.11, 95% CI: 1.73–5.58), TVR (OR = 3.06, 95% CI: 1.70–5.53), and stent thrombosis (OR = 6.14, 95% CI: 2.76–13.65) between the good and poor CCC groups. Poor CCC patients with MetS had a higher incidence of MACCEs (OR = 4.21, 95% CI: 2.05–8.65), non-fatal MI (OR = 4.44, 95% CI: 2.01–9.83), TVR (OR = 3.28, 95% CI: 1.51–7.11), and stent thrombosis (OR = 10.80, 95% CI: 3.11–37.54). Similar findings were also observed in CTO patients with DM and a Syntax score ≥23.ConclusionPoor CCC could increase the risk of MACCEs in CTO patients, particularly those with MetS, DM, and a Syntax score ≥23. Further prospective, multicenter studies are needed to validate our findings and to explore potential therapeutic interventions

Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares. However, the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited. Herein, we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal (LC) holograms. The LC holograms are used as spatially separated shares to carry secret images. The polarization of the incident light and the distance between different shares are served as secret keys, which can significantly improve the information security and capacity. Besides, the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency, which further increases the information security. In implementation, an artificial neural network (ANN) model is developed to carefully design the phase distribution of each LC hologram. With the advantage of high security, high capacity and simple configuration, our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display

Methylprednisolone as Adjunct to Endovascular Thrombectomy for Large-Vessel Occlusion Stroke

Importance It is uncertain whether intravenous methylprednisolone improves outcomes for patients with acute ischemic stroke due to large-vessel occlusion (LVO) undergoing endovascular thrombectomy. Objective To assess the efficacy and adverse events of adjunctive intravenous low-dose methylprednisolone to endovascular thrombectomy for acute ischemic stroke secondary to LVO. Design, Setting, and Participants This investigator-initiated, randomized, double-blind, placebo-controlled trial was implemented at 82 hospitals in China, enrolling 1680 patients with stroke and proximal intracranial LVO presenting within 24 hours of time last known to be well. Recruitment took place between February 9, 2022, and June 30, 2023, with a final follow-up on September 30, 2023.InterventionsEligible patients were randomly assigned to intravenous methylprednisolone (n = 839) at 2 mg/kg/d or placebo (n = 841) for 3 days adjunctive to endovascular thrombectomy. Main Outcomes and Measures The primary efficacy outcome was disability level at 90 days as measured by the overall distribution of the modified Rankin Scale scores (range, 0 [no symptoms] to 6 [death]). The primary safety outcomes included mortality at 90 days and the incidence of symptomatic intracranial hemorrhage within 48 hours. Results Among 1680 patients randomized (median age, 69 years; 727 female [43.3%]), 1673 (99.6%) completed the trial. The median 90-day modified Rankin Scale score was 3 (IQR, 1-5) in the methylprednisolone group vs 3 (IQR, 1-6) in the placebo group (adjusted generalized odds ratio for a lower level of disability, 1.10 [95% CI, 0.96-1.25]; P = .17). In the methylprednisolone group, there was a lower mortality rate (23.2% vs 28.5%; adjusted risk ratio, 0.84 [95% CI, 0.71-0.98]; P = .03) and a lower rate of symptomatic intracranial hemorrhage (8.6% vs 11.7%; adjusted risk ratio, 0.74 [95% CI, 0.55-0.99]; P = .04) compared with placebo. Conclusions and Relevance Among patients with acute ischemic stroke due to LVO undergoing endovascular thrombectomy, adjunctive methylprednisolone added to endovascular thrombectomy did not significantly improve the degree of overall disability.Trial RegistrationChiCTR.org.cn Identifier: ChiCTR210005172

Creation of synthetic samples for physical modelling of natural shale

Natural shale samples, particularly well-preserved, drilled core samples, are extremely difficult to obtain for laboratory research. Multiple tests must be carried out on one sample, and some samples are disposed after destructive tests. Therefore, rarity and non-reusability of samples strongly restrict shale studies. In this study, based on statistical data from the world's major shale block, a new type of synthetic shale was physically constructed via a process of interfusion, stuffing, and compaction using quartz, clay, carbonate, and kerogen as the primary materials, according to statistical data from the world's major shale blocks. Further evaluation of the synthetic shale involved the use of scanning electron microscopy imagery and analysis of its anisotropic characteristics in comparison with natural shale. The synthetic shale had a laminated microstructure similar to natural shale, and its velocity anisotropy corresponded to Thomsen's anisotropy of a transversely isotropic medium. The results of tests for homogeneity and repeatability indicated that the construction process was stable and that several identical synthetic samples, which were satisfactorily similar to natural shale, could be produced for both iterative and destructive tests. The composition of each mineral, as well as the density, porosity, permeability, and anisotropy of the samples, were all variable. Therefore, a series of synthetic samples could be obtained with properties set to meet the requirements of petrophysical experimentation. Moreover, gas or oil saturation was also considered in the construction of the synthetic shale, meaning that the characteristics of gas or oil saturation (or the complete range of data from dry to saturated samples) could be tested using the synthetic shal

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Abstract Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction, and deep-learning diffractive layers as a classifier. The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding, leading to an accuracy as high as 97.2% for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes, as well as a resistance to eavesdropping in point-to-point free-space transmission. Moreover, through extending the target encoded modes into multiplexed OAM states, we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%. Our work provides a deep insight to the mechanism of machine learning with spatial modes basis, which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder