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COVID-19 is associated with 5.1% mortality. Although the virological, epidemiological, clinical, and management outcome features of COVID-19 patients have been defined rapidly, the inflammatory and immune profiles require definition as they influence pathogenesis and clinical expression of COVID-19. Here we show lymphopenia, selective loss of CD4+ T cells, CD8+ T cells and NK cells, excessive T-cell activation and high expression of T-cell inhibitory molecules are more prominent in severe cases than in those with mild disease. CD8+ T cells in patients with severe disease express high levels of cytotoxic molecules. Histochemical studies of lung tissue from one fatality show sub-anatomical distributions of SARS-CoV-2 RNA and massive infiltration of T cells and macrophages. Thus, aberrant activation and dysregulation of CD8+ T cells occur in patients with severe COVID-19 disease, an effect that might be for pathogenesis of SARS-CoV-2 infection and indicate that immune-based targets for therapeutic interventions constitute a promising treatment for severe COVID-19 patients. Immunophenotyping of patients with COVID-19 is ongoing, but much remains to be learned. Here the authors analyze 41 hospitalized patients with COVID-19 and show a higher degree of lymphopenia in various immune cell subsets as well as cytotoxicity and T cell inhibitory marker expression in severe cases compared with mild.

Leakage interference between memory cells is the primary obstacle for enlarging X‐point memory arrays. Metal‐filament threshold switches, possessing excellent selectivity and low leakage current, are developed in series with memory cells to reduce sneak path current and lower power consumption. However, these selectors typically have limited on‐state currents (≤10 µA), which are insufficient for memory RESET operations. Here, a strategy is proposed to achieve sufficiently large RESET current (≈2.3 mA) by introducing highly ordered Ag nanodots to the threshold switch. Compared to the Ag thin film case, Ag nanodots as active electrode could avoid excessive Ag atoms migration into solid electrolyte during operations, which causes stable conductive filament growth. Furthermore, Ag nanodots with rapid thermal processing contribute to forming multiple weak Ag filaments at a lower voltage and then spontaneous rupture as the applied voltage reduced, according to quantized conductance and simulation analysis. Impressively, the Ag nanodots based threshold switch, which is bidirectional and truly electroforming‐free, demonstrates extremely high selectivity >109, ultralow leakage current <1 pA, very steep slope of 0.65 mV dec−1, and good thermal stability up to 200 °C, and further represents significant suppression of leakage currents and excellent performances for SET/RESET operations in the one‐selector‐one‐resistor configuration. A bidirectional threshold switching selector based on highly ordered Ag nanodots is achieved to provide sufficiently large RESET driving current of ≈2.3 mA and extremely high selectivity beyond 109. The improved selector performance may be attributed to limited quantity of Ag migration into electrolyte and multiple weak Ag filaments formation/rupture with rapid thermal processed Ag nanodots as electrochemically active electrode.

Opioid use disorder (OUD) is associated with a high risk of premature death. Medication-assisted treatment (MAT) is the primary treatment for opioid dependence. We comprehensively assessed the effects of different MAT-related characteristics on mortality among those with OUD by a systematic review and meta-analysis. The all-cause and overdose crude mortality rates (CMRs) and relative risks (RRs) by treatment status, different type, period, and dose of medication, and retention time were pooled using random effects, subgroup analysis, and meta-regression. Thirty cohort studies involving 370,611 participants (1,378,815 person-years) were eligible in the meta-analysis. From 21 studies, the pooled all-cause CMRs were 0.92 per 100 person-years (95% CI: 0.79–1.04) while receiving MAT, 1.69 (1.47–1.91) after cessation, and 4.89 (3.54–6.23) for untreated period. Based on 16 studies, the pooled overdose CMRs were 0.24 (0.20–0.28) while receiving MAT, 0.68 (0.55–0.80) after cessation of MAT, and 2.43 (1.72–3.15) for untreated period. Compared with patients receiving MAT, untreated participants had higher risk of all-cause mortality (RR 2.56 [95% CI: 1.72–3.80]) and overdose mortality (8.10 [4.48–14.66]), and discharged participants had higher risk of all-cause death (2.33 [2.02–2.67]) and overdose death (3.09 [2.37–4.01]). The all-cause CMRs during and after opioid substitution treatment with methadone or buprenorphine were 0.93 (0.76–1.10) and 1.79 (1.47–2.10), and corresponding estimate for antagonist naltrexone treatment were 0.26 (0–0.59) and 1.97 (0–5.18), respectively. Retention in MAT of over 1-year was associated with a lower mortality rate than that with retention ≤1 year (1.62, 1.31–1.93 vs. 5.31, −0.09–10.71). Improved coverage and adherence to MAT and post-treatment follow-up are crucial to reduce the mortality. Long-acting naltrexone showed positive advantage on prevention of premature death among persons with OUD.

Developing exports of technology-intensive products is a key focus for China’s high-quality foreign trade development. The rapid growth of global digital trade brings new opportunities and challenges for China’s exports of technology-intensive products. This paper utilizes panel data from 2005 to 2022. And we construct an extended gravity model to thoroughly investigate the impact of digital trade development in importing countries on China’s exports of technology-intensive products. The research findings indicate: (1) The development of digital trade in importing countries significantly promotes China’s exports of technology-intensive products. This effect is more pronounced beyond a certain threshold. (2) The reduction of trade costs and increased foreign direct investment play intermediary roles in facilitating the impact of digital trade in importing countries on China’s exports of technology-intensive products. However, institutional distance exerts a negative inhibitory effect on this process. (3) The impact of digital trade development in importing countries on China’s exports of technology products varies due to differences in product types, national income levels, regional characteristics, economic cooperation, and market potential. The conclusions of this paper provide theoretical and empirical evidence for the Chinese government to enhance the efficiency of exporting technology-intensive products.

Chemotherapeutic agents have been linked to immunogenic cell death (ICD) induction that is capable of augmenting anti-tumor immune surveillance. The cardiac glycoside oleandrin, which inhibits Na + /K + -ATPase pump (NKP), has been shown to suppress breast cancer growth via inducing apoptosis. In the present study, we showed that oleandrin treatment triggered breast cancer cell ICD by inducing calreticulin (CRT) exposure on cell surface and the release of high-mobility group protein B1 (HMGB1), heat shock protein 70/90 (HSP70/90), and adenosine triphosphate (ATP). The maturation and activation of dendritic cells (DCs) were increased by co-culturing with the oleandrin-treated cancer cells, which subsequently enhanced CD8 + T cell cytotoxicity. Murine breast cancer cell line EMT6 was engrafted into BALB/c mice, and tumor-bearing mice were administered with oleandrin intraperitoneally every day. Oleandrin inhibited tumor growth and increased tumor infiltrating lymphocytes including DCs and T cells. Furthermore, the differential mRNA expression incurred by oleandrin was investigated by mRNA sequencing and subsequently confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Mechanistically, oleandrin induced endoplasmic reticulum (ER) stress-associated, caspase-independent ICD mainly through PERK/elF2α/ATF4/CHOP pathway. Pharmacological and genetic inhibition of protein kinase R-like ER kinase (PERK) suppressed oleandrin-triggered ICD. Taken together, our findings showed that oleandrin triggered ER stress and induced ICD-mediated immune destruction of breast cancer cells. Oleandrin combined with immune checkpoint inhibitors might improve the efficacy of immunotherapy.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. Memristors hold promise for massively-parallel computing at low power. Aguirre et al. provide a comprehensive protocol of the materials and methods for designing memristive artificial neural networks with the detailed working principles of each building block and the tools for performance evaluation.

Solid-state photochemical reactions of olefinic compounds have been demonstrated to represent powerful access to organic cyclic molecules with specific configurations. However, the precise control of the stereochemistry in these reactions remains challenging owing to complex and fleeting configuration transformations. Herein, we report a unique approach to control the regiospecific configurations of C = C groups and the intermediates by varying temperatures in multiple-step thermal/photoinduced reactions, thus successfully realizing reversible ring closing/opening changes using a single-crystal coordination polymer platform. All stereochemical transitions are observed by in situ single-crystal X-ray diffraction, powder X-ray diffraction and infrared spectroscopy. Density functional theory calculations allow us to rationalize the mechanism of the synergistic thermal/photoinduced transformations. This approach can be generalized to the analysis of the possible configuration transformations of functional groups and intermediates and unravel the detailed mechanism for any inorganic, organic and macromolecular reactions susceptible to incorporation into single-crystal coordination polymer platforms. Solid-state photochemical reactions of olefinic compounds provide access to organic cyclic molecules with specific configurations but the precise control of the stereochemistry in these reactions remains challenging. Here, the authors demonstrate control of the regiospecific configurations of C=C groups and the intermediates by varying temperatures in multi-step thermal and photoinduced ring opening and closing reactions using a single-crystal coordination polymer platform.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Mining is a high-risk industry, and mine accidents occur frequently. To better understand the characteristics and trends of current coal mine accidents, 29 cases of significant accidents occurred in China in 2016 are introduced first in this manuscript; then, the accident types, occurrence time, occurrence locations, and direct causes were analyzed for these accidents. Finally, according to the analysis results, coal mine accident prevention and control suggestions are presented. This data analysis not only plays a positive role in the prevention and control of mine accidents in China but also has reference significance for the safe production of coal mines in other countries of the world.

Hepatitis B and C viruses (HBV and HCV) remain among the leading causes of liver disease worldwide. Current antiviral drugs, such as nucleotide analogues (NAs), can reduce the replication of new HBV and HCV infections but cannot completely eliminate chronic infections. This is primarily because a stable form of viral DNA, known as covalently closed circular DNA (cccDNA), persists in liver cells and continues to sustain the infection. In recent years, the CRISPR/Cas9 gene‐editing system has emerged as a powerful tool for precisely cutting and inactivating specific DNA sequences. Due to its efficiency and ease of use, researchers have applied CRISPR/Cas9 in numerous studies to directly target and disrupt the HBV genome, demonstrating promising antiviral effects in both cell cultures and animal models. Targeting multiple sites within the HBV genome has been shown to further enhance its effectiveness, paving the way for potential combination therapies aimed at disabling both cccDNA and HBV and HCV DNA integrated into the host genome. Despite its potential, CRISPR/Cas9 still faces significant challenges before clinical application, most notably the risk of off‐target effects—unintended cleavage of non‐target DNA sequences—and the difficulty of delivering the system efficiently into liver cells in vivo. Future progress will depend on improving the tool's precision, efficiency, flexibility and delivery methods. In this review, we explore recent advances in designing guide RNAs (gRNAs) for targeting HBV and HCV, as well as the delivery systems used to transport CRISPR/Cas9 into cells. We also discuss the remaining challenges and potential strategies for advancing CRISPR/Cas9 from the laboratory toward a viable clinical cure for HBV and HCV.