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Since the beginning of March 2022, the epidemic due to the Omicron variant has developed rapidly in Jilin Province. To figure out the key controlling factors and validate the model to show the success of the Zero-COVID policy in the province, we constructed a Recursive Zero-COVID Model quantifying the strength of the control measures, and defined the control reproduction number as an index for describing the intensity of interventions. Parameter estimation and sensitivity analysis were employed to estimate and validate the impact of changes in the strength of different measures on the intensity of public health preventions qualitatively and quantitatively. The recursive Zero-COVID model predicted that the dates of elimination of cases at the community level of Changchun and Jilin Cities to be on April 8 and April 17, respectively, which are consistent with the real situation. Our results showed that the strict implementation of control measures and adherence of the public are crucial for controlling the epidemic. It is also essential to strengthen the control intensity even at the final stage to avoid the rebound of the epidemic. In addition, the control reproduction number we defined in the paper is a novel index to measure the intensity of the prevention and control measures of public health.

Xin-Yue Yang,1,* Lina Geng,2,* Ronghui Li,2 Ji-Xian Song,1 Cui-Ling Jia,1 Ji-Ren An,1,3 Meng-Fan Sun,1 Shan Xu,1 Ya-Jing Guo,1 Yashuo Zhao,1 En-Sheng Ji1 1Hebei Technology Innovation Center of TCM Combined Hydrogen Medicine, Hebei University of Chinese Medicine, Shijiazhuang, People’s Republic of China; 2College of Chemistry and Material Science, Hebei Normal University, Shijiazhuang, People’s Republic of China; 3The First Clinical College, Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yashuo Zhao; En-Sheng Ji, No. 3, Luqian Xingyuan Road, Shijiazhuang, 050200, People’s Republic of China, Email zys870207@126.com; jesphy@126.comBackground: Chronic intermittent hypoxia (CIH) could cause neuronal damage, accelerating the progression of dementia. However, safe and effective therapeutic drugs and delivery are needed for successful CIH therapy.Purpose: To investigate the neuroprotective effect of Huperzine A (HuA) packaged with nanoliposomes (HuA-LIP) on neuronal damage induced by CIH.Methods: The stability and release of HuA-LIP in vitro were identified. Mice were randomly divided into the Control, CIH, HuA-LIP, and HuA groups. The mice in the HuA and HuA-LIP groups received HuA (0.1 mg/kg, i.p.), and HuA-LIP was administered during CIH exposure for 21 days. HuA-LIP contains the equivalent content of HuA.Results: We prepared a novel formulation of HuA-LIP that had good stability and controlled release. First, HuA-LIP significantly ameliorated cognitive dysfunction and neuronal damage in CIH mice. Second, HuA-LIP elevated T-SOD and GSH-Px abilities and decreased MDA content to resist oxidative stress damage induced by CIH. Furthermore, HuA-LIP reduced brain iron levels by downregulating TfR1, hepcidin, and FTL expression. In addition, HuA-LIP activated the PKAα/Erk/CREB/BDNF signaling pathway and elevated MAP2, PSD95, and synaptophysin to improve synaptic plasticity. Most importantly, compared with HuA, HuA-LIP showed a superior performance against neuronal damage induced by CIH.Conclusion: HuA-LIP has a good sustained-release effect and targeting ability and efficiently protects against neural injury caused by CIH.Keywords: huperzine A, nanoliposomes, neuronal damage, chronic intermittent hypoxia, Iron

Objective: Thrombectomy greatly improves the clinical prognosis of patients with acute ischemic stroke (AIS). The aim of this study is to develop a nomogram model that can predict the prognosis of patients with acute ischemic stroke undergoing thrombectomy. Methods: We retrospectively collected information of patients with acute ischemic stroke who were admitted to the stroke Green Channel of the First Affiliated Hospital of Soochow University from September 2018 to May 2022. The main outcome was defined as a three-month unfavorable outcome (modified Rankin Scale 3-6). Based on the results of multivariate regression analysis, a nomogram was established. We tested the accuracy and discrimination of our nomogram by calculating the consistency index (C-index) and plotting the calibration curve. Results: National Institutes of Health Stroke Scale (NIHSS) score (OR, 1.418; 95% CI, 1.177-1.707; P＜0.001), low density lipoprotein cholesterol (LDL-C) (OR, 2.705; 95% CI, 1.203-6.080; P = 0.016), Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (OR, 0.633; 95% CI, 0.421-0.952; P = 0.028), infarct core volume (OR, 1.115; 95% CI, 1.043-1.192; P = 0.001) and ischemic penumbra volume (OR, 1.028; 95% CI, 1.006-1.050; P = 0.012) were independent risk factors for poor clinical prognosis of AIS patients treated with thrombectomy. The C-index of our nomogram was 0.967 and the calibration plot revealed a generally fit in predicting three-month unfavorable outcomes. Based on this nomogram, we stratified the risk of thrombectomy population. We found that low-risk population is less than or equal to 65 points, and patients of more than 65 points tend to have a poor clinical prognosis. Conclusion: The nomogram, composed of NIHSS, LDL-C, ASPECTS, infarct core volume and ischemic penumbra volume, may predict the clinical prognosis of cerebral infarction patients treated with thrombectomy.

Recently, novel 2D InGeTe3 has been successfully synthesized and attracted attention due to its excellent properties.

The shortage of donor liver restricts liver transplantation (LT). Nowadays, donor liver with ABO blood group incompatibility between donor and recipient has become an option to expand the source of donor liver. Although it is now possible to perform ABO-incompatible (ABO-I) LT, antibody-mediated rejection (AMR) has been recognized as the primary cause of desperate outcomes after ABO-I LT. Anti-A/B antibody is the trigger of immune response to ABO-I LT graft injury. Therapeutic plasma exchange (TPE) can quickly reduce the titer of plasma antibodies and effectively inhibit humoral immunity.We searched PubMed and CNKI databases using search terms "therapeutic plasma exchange", "ABO-incompatible liver transplantation", "ABO-I LT", "liver transplantation", "LT", "antibody-mediated rejection", and "AMR". Additional publications were identified by a manual search of references from key articles. The relevant publications published before September 30, 2020 were included in this review.Different centers have made different attempts on whether to use TPE, when to use TPE and how often to use TPE. However, the control standard of lectin revision level is always controversial, the target titer varies significantly from center to center, and the standard target titer has not yet been established. TPE has several schemes to reduce antibody titers, but there is a lack of clinical trials that provide standardized procedures.TPE is essential for ABO-I LT. Hence, further research and clinical trials should be conducted to determine the best regimen for TPE to remove ABO antibodies and prevent AMR.

Cancer-derived exosomes are involved in the development of cancer cachexia. Carnosol, which exhibited ameliorating effects on cancer cachexia of C26 tumour-bearing mice in our previous study, alleviated atrophy of C2C12 myotubes induced by exosomes of C26 tumour cells in the present study. MiR-183-5p was found to be rich in C26 cells and C26 exosomes, and miR-183-5p mimic could directly induce atrophy of C2C12 myotubes. Carnosol at 5 to 20 μM could dose-dependently ameliorate the myotube atrophy induced by miR-183-5p. Four and a half LIM domain protein 1 (FHL1) was shown to be the direct target of miR-183-5p. Increase in myostatin, p-Smad3, MuRF-1, Atrogin-1, HIF-1α and p-STAT3 and decrease in mitochondrial respiration were also induced by miR-183-5p mimic in C2C12 myotubes. Carnosol could not affect the decrease in FHL-1 and the activation of STAT3 pathway but could significantly alleviate the increase in myostatin, p-Smad3, MuRF-1, Atrogin-1 and the decrease in mitochondrial respiration induced by miR-183-5p. The protective effects of carnosol on myotubes against atrophy of C2C12 myotubes induced by miR-183-5p, based on both its inhibiting effects on MuRF-1 and Atrogin-1-mediated protein degradation and its ability of keeping the mitochondrial respiration, might contribute to its ameliorating effects on cancer cachexia.

To shield increasingly severe radiation pollution, ultrathin MXene-based electromagnetic interference (EMI) shielding materials with excellent mechanical properties are urgently demanded in wearable electrical devices or aerospace fields. However, it is still a challenge to fabricate ultrastrong and stiff MXene-based nanocomposites with excellent EMI shielding capacity in a universal and scalable manner. Here, inspired by the natural nacre structure, we propose an efficient superspreading strategy to construct a highly oriented layered "brick-and-mortar" structure using shear-flow-induced alignment of MXene nanosheets at an immiscible hydrogel/oil interface. A continuous and large-area MXene nanocomposite film has been fabricated through a homemade industrial-scale continuous fabrication setup. The prepared MXene nanocomposite films exhibit a tensile strength of 647.6 ± 56 MPa and a Young's modulus of 59.8 ± 6.1 GPa, respectively. These outstanding mechanical properties are attributed to the continuous interphase layer that formed between the well-aligned MXene nanosheets. Moreover, the obtained MXene nanocomposites also show great EMI shielding effectiveness (51.6 dB). We consider that our MXene-based nanocomposite films may be potentially applied as electrical or aerospace devices with superior mechanical properties and high EMI shielding capacity.

Non-volatile computing-in-memory macros that are based on two-dimensional arrays of memristors are of use in the development of artificial intelligence edge devices. Scaling such systems to three-dimensional arrays could provide higher parallelism, capacity and density for the necessary vector–matrix multiplication operations. However, scaling to three dimensions is challenging due to manufacturing and device variability issues. Here we report a two-kilobit non-volatile computing-in-memory macro that is based on a three-dimensional vertical resistive random-access memory fabricated using a 55 nm complementary metal–oxide–semiconductor process. Our macro can perform 3D vector–matrix multiplication operations with an energy efficiency of 8.32 tera-operations per second per watt when the input, weight and output data are 8, 9 and 22 bits, respectively, and the bit density is 58.2 bit µm–2. We show that the macro offers more accurate brain MRI edge detection and improved inference accuracy on the CIFAR-10 dataset than conventional methods.

BackgroundHashimoto’s thyroiditis (HT) is a chronic autoimmune disease that poses a risk factor for papillary thyroid carcinoma (PTC). The present study aimed to identify the key genes shared by HT and PTC for advancing the current understanding of their shared pathogenesis and molecular mechanisms.MethodsHT- and PTC-related datasets (GSE138198 and GSE33630, respectively) were retrieved from the Gene Expression Omnibus (GEO) database. Genes significantly related to the PTC phenotype were identified using weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) were identified between PTC and healthy samples from GSE33630, and between HT and normal samples from GSE138198. Subsequently, functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Transcription factors and miRNAs regulating the common genes in PTC and HT were forecasted using the Harmonizome and miRWalk databases, respectively, and drugs targeting these genes were investigated using the Drug-Gene Interaction Database (DGIdb). The key genes in both GSE138198 and GSE33630 were further identified via Receiver Operating Characteristic (ROC) analysis. The expression of key genes was verified in external validation set and clinical samples using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).ResultsIn total, 690 and 1945 DEGs were associated with PTC and HT, respectively; of these, 56 were shared and exhibited excellent predictive accuracy in the GSE138198 and GSE33630 cohorts. Notably, four genes, Alcohol Dehydrogenase 1B (ADH1B), Active BCR-related (ABR), alpha-1 antitrypsin (SERPINA1), and lysophosphatidic acid receptor 5 (LPAR5) were recognized as key genes shared by HT and PTC. Subsequently, EGR1 was identified as a common transcription factor regulating ABR, SERPINA1, and LPAR5 expression. These findings were confirmed using qRT-PCR and immunohistochemical analysis.ConclusionFour (ADH1B, ABR, SERPINA1, and LPAR5) out of 56 common genes exhibited diagnostic potential in HT and PTC. Notably, this study, for the first time, defined the close relationship between ABR and HT/PTC progression. Overall, this study provides a basis for understanding the shared pathogenesis and underlying molecular mechanisms of HT and PTC, which might help improve patient diagnosis and prognosis.

Deforestation is a major contributor to biodiversity loss, yet the impact of forest loss on daily microclimate variability and its implications for species with different daily activity patterns remain poorly understood. Using a recently developed microclimate model, we investigated the effects of deforestation on the daily temperature range (DTR) in low-elevation tropical regions and high-elevation temperate regions. Our results show that deforestation substantially increases DTR in these areas, suggesting a potential impact on species interactions. To test this hypothesis, we studied the competitive interactions between nocturnal burying beetles and all-day-active blowfly maggots in forested and deforested habitats in Taiwan. We show that deforestation leads to increased DTR at higher elevations, which enhances the competitiveness of blowfly maggots during the day and leads to a higher failure rate of carcass burial by the beetles at night. Thus, deforestation-induced temperature variability not only modulates exploitative competition between species with different daily activity patterns, but also likely exacerbates the negative impacts of climate change on nocturnal organisms. In order to limit potential adverse effects on species interactions and their ecological functions, our study highlights the need to protect forests, especially in areas where deforestation can greatly alter temperature variability.

We have studied the decomposition of methanol-d4 on thin film Al2O3/NiAl(100) under near-ambient-pressure conditions, with varied surface-probe techniques and calculations based on density-functional theory. Methanol-d4 neither adsorbed nor reacted on Al2O3/NiAl(100) at 400 K under ultrahigh vacuum conditions, whereas they dehydrogenated, largely to methoxy-d3 (CD3O*, * denoting adsorbates) and formaldehyde-d2 (CD2O*), on the surface when the methanol-d4 partial pressure was increased to 10−3 mbar and above. The dehydrogenation was facilitated by hydroxyl (OH* or OD*) from the dissociation of little co-adsorbed water; a small fraction of CD2O* interacted further with OH* (OD*) to form, via intermediate CD2OOH* (CD2OOD*), formic acid (DCOOH* or DCOOD*). A few surface carbonates were also yielded, likely on the defect sites of Al2O3/NiAl(100). The results suggest that alumina not only supports metal clusters but also participates in reactions under realistic catalytic conditions. One may consider accordingly the multiple functions of alumina while designing ideal catalysts.

This file contains parameter setting methods of the model and two supplementary tables. Table S1 shows the model validation results and Table S2 shows the parameters most sensitive to the ICER of each screening scenario.

Although lung cancer screening with low-dose CT (LDCT) can reduce lung cancer mortality by 20%, without an appropriate eligibility criteria, it may result in a waste of medical resources and a degree of unnecessary damage to participants' health. This study aims to give the optimal screening strategy in China based on cost-effectiveness analysis on pros and cons of different situations. From the perspective of primary healthcare system, a Markov model was built to simulate LDCT screening of 100,000 heavy smokers (>30 pack years) aged 40 in different situations. Model parameters mainly came from screening programs conducted in China and other countries, official public data, and published literature. Two indicators of primary outcome, incremental cost-effectiveness ratio (ICER) and net health benefits (NHB), were compared with those of no screening. Sensitivity analysis was conducted to evaluate model uncertainties. We defined the optimal strategy as the one with both acceptable cost effectiveness and maximal NHB. Base-case analysis results showed that for all screening strategies, ICERs were less than three times of GDP per capita. As for NHB results, it showed that when the willingness to pay for screening was less than three times of GPD per capita, the largest NHB was obtained in the strategy which started screening at 50 years old and this strategy showed stable performance in univariate and probabilistic sensitivity as well.Prevention Relevance:LDCT screening is cost effective in heavy smokers in China, and the optimal age to start screening is suggested to be 50 years old.

Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. In addition, due to the hardware constraints, the way to deploy neural network models on memristor crossbar arrays affects the computation parallelism and communication overheads. To enable reliable and energy-efficient memristor-based accelerators, a simulation platform is needed to precisely analyze the impact of non-ideal circuit/device properties on the inference accuracy and the influence of different deployment strategies on performance and energy consumption. In this paper, we propose a flexible simulation framework, DL-RSIM, to tackle this challenge. A rich set of reliability impact factors and deployment strategies are explored by DL-RSIM, and it can be incorporated with any deep learning neural networks implemented by TensorFlow. Using several representative convolutional neural networks as case studies, we show that DL-RSIM can guide chip designers to choose a reliability-friendly design option and energy-efficient deployment strategies and develop optimization techniques accordingly.

Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computation cost of CNNs are problematic in hardware accelerators. Computing-in-memory (CIM) architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. However, the intensive multiply and accumulation (MAC) operations executed at the crossbar array and the limited capacity of CIM macros remain bottlenecks for further improvement of energy efficiency and throughput. To reduce computation costs, network pruning and quantization are two widely studied compression methods to shrink the model size. However, most of the model compression algorithms can only be implemented in digital-based CNN accelerators. For implementation in a static random access memory (SRAM) CIM-based accelerator, the model compression algorithm must consider the hardware limitations of CIM macros, such as the number of word lines and bit lines that can be turned on at the same time, as well as how to map the weight to the SRAM CIM macro. In this study, a software and hardware co-design approach is proposed to design an SRAM CIM-based CNN accelerator and an SRAM CIM-aware model compression algorithm. To lessen the high-precision MAC required by batch normalization (BN), a quantization algorithm that can fuse BN into the weights is proposed. Furthermore, to reduce the number of network parameters, a sparsity algorithm that considers a CIM architecture is proposed. Last, MARS, a CIM-based CNN accelerator that can utilize multiple SRAM CIM macros as processing units and support a sparsity neural network, is proposed., IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2021

Background: The impact of PICC-related thrombosis is worth paying attention to, and it is important to predict the risk factors for thrombosis in patients with PICC catheterization， accurate scientific assessment tools are critical forpredictingand preventing thrombosis in patients with PICCs.The main objective is to develop and validate a machine learning model for predicting the risk of peripherally inserted central catheter-related venous thrombosis. Methods: Overall, 626 patients undergoing peripherally inserted central catheter placement from January 2016 to October 2020 were enrolled. The variables included patient demographic characteristics, clinical condition, laboratory examinations, treatment, and catheter-related factors. Support vector machine and genetic algorithm were used to develop and optimize the model, respectively. SHapley Additive exPlanations was used to interpret the model. Results: The model obtained an average area under the receiver operating characteristic curve of 0.95. The SHapley Additive exPlanations summary plot was used to illustrate the effects of the top 20 features from support vector machine. This study provides a visual way to illustrate the impact of input features on the result prediction. Conclusions: The machine learning model developed based on genetic algorithm shows good predictive ability in patients with a high risk of thrombosis-related peripherally inserted central catheter. Trial registration:retrospectively registered.