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Since the outbreak of coronavirus disease 2019 (COVID-19), clinicians have tried every effort to understand the disease, and a brief portrait of its clinical features have been identified. In clinical practice, we noticed that many severe or critically ill COVID-19 patients developed typical clinical manifestations of shock, including cold extremities and weak peripheral pulses, even in the absence of overt hypotension. Understanding the mechanism of viral sepsis in COVID-19 is warranted for exploring better clinical care for these patients. With evidence collected from autopsy studies on COVID-19 and basic science research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV, we have put forward several hypotheses about SARS-CoV-2 pathogenesis after multiple rounds of discussion among basic science researchers, pathologists, and clinicians working on COVID-19. We hypothesise that a process called viral sepsis is crucial to the disease mechanism of COVID-19. Although these ideas might be proven imperfect or even wrong later, we believe they can provide inputs and guide directions for basic research at this moment.

The authors report the emergence and isolation of a previously unknown betacoronavirus, the seventh member of the family of coronaviruses that infect humans, in Wuhan, China. Findings in three patients are described.

Few data are available on the incidence of deep vein thrombosis (DVT) in critically ill COVID-19 with thrombosis prophylaxis. This study retrospectively included 88 patients in the ICU with critically ill COVID-19 at Jinyintan Hospital in Wuhan, China. All patients underwent compression ultrasonography for identifying DVT. Firth logistic regression was used to examine the association of DVT with sex, age, hypoalbuminemia, D-dimer, and SOFA score. The median (interquartile range [IQR]) age and SOFA score of 88 patients were 63 (55–71) years old and 5 (4–6), respectively. Despite all patients receiving guideline-recommended low-molecular-weight heparin (LMWH) thromboprophylaxis, the incidence of DVT was 46% (95% CI 35–56%). Proximal DVT was recognized in 9% (95% CI 3–15%) of the patients, while 46% (95% CI 35–56%) of patients had distal DVT. All of the proximal DVT combined with distal DVT. Risk factors of DVT extension occurred in all distal DVT patients. As Padua score ≥ 4 or IMPROVE score ≥ 2, 53% and 46% of patients had DVT, respectively. Mortality was higher in patients with acute DVT (30%) compared with non-DVT (17%), but did not reach statistical significance. Hypoalbuminemia (odds ratio [OR], 0.17; 95% CI 0.06–0.05, P = 0.001), higher SOFA score (OR per IQR, 2.07; 95% CI 1.38–3.39, P = 0.001), and elevated D-dimer (OR per IQR, 1.04; 95% CI 1.03–1.84, P = 0.029) were significant DVT risk factors in multivariable analyses. High incidence of DVT was identified in patients with critically ill COVID-19, despite the use of guideline-recommended pharmacologic thromboprophylaxis. The presence of hypoalbuminemia, higher SOFA score, and elevated D-dimer were significantly independent risk factors of DVT. More effective VTE prevention and management strategies may need to be addressed.

Background Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool to analyze large amounts of data and identify perioperative complication factors. This study aims to identify risk factors for PIH and develop predictive models to support anesthesia management. Methods A dataset of 5406 patients was analyzed using machine learning methods. Logistic regression, random forest, XGBoost, and neural network models were compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis (DCA). Results The logistic regression model achieved an AUROC of 0.74 (95% CI: 0.71–0.77), outperforming the random forest (AUROC: 0.71), XGBoost (AUROC: 0.72), and neural network (AUROC: 0.72) models. In terms of calibration, logistic regression demonstrated superior performance, as reflected by Brier Scores and calibration curves, followed by XGBoost, random forest, and neural network. Decision curve analysis indicated that the logistic regression model provided the greatest clinical utility among all models. Baseline blood pressure, age, sex, type of surgery, platelet count, and certain anesthesia-inducing drugs were identified as important features. Conclusions This study provides a valuable tool for personalized preoperative risk assessment and customized anesthesia management, allowing for early intervention and improved patient outcomes. Integration of machine learning models into electronic medical record systems can facilitate real-time risk assessment and prediction.

Heat stroke (HS) is a life-threatening systemic disease characterized by an elevated core body temperature of more than 40 ℃ and subsequent multiple organ dysfunction syndrome. With the growing frequency of global heatwaves, the incidence rate of HS has increased significantly, which has caused a huge burden on people's lives and health. Liver injury is a well-documented complication of HS and usually constitutes the direct cause of patient death. In recent years, a lot of research has been carried out on the pathogenesis and treatment strategies of HS-induced liver injury. In this review, we summarized the important pathogenesis of HS-induced liver injury that has been confirmed so far. In addition to the comprehensive effect of systemic factors such as heat cytotoxicity, coagulopathy, and systemic inflammatory response syndrome, excessive hepatocyte cell pyroptosis, dysfunction of Kupffer cells, abnormal expression of heat shock protein expression, and other factors are also involved in the pathogenesis of HS-induced liver injury. Furthermore, we have also established the current therapeutic strategies for HS-induced liver injury. Our study is of great significance in promoting the understanding of the pathogenesis and treatment of HS-induced liver injury.

Acute lung injury(ALI)/acute respiratory distress syndrome(ARDS) is a form of acute-onset hypoxemic respiratory failure characterised by an acute, diffuse, inflammatory lung injury, and increased alveolar-capillary permeability, which is caused by a variety of pulmonary or nonpulmonary insults. Recently, aberrant mitochondria and mitochondrial DNA(mtDNA) level are associated with the development of ALI/ARDS, and plasma mtDNA level shows the potential to be a promising biomarker for clinical diagnosis and evaluation of lung injury severity. In mechanism, the mtDNA and its oxidised form, which are released from impaired mitochondria, play a crucial role in the inflammatory response and histopathological changes in the lung. In this review, we discuss mitochondrial outer membrane permeabilisation (MOMP), mitochondrial permeability transition pore(mPTP), extracellular vesicles (EVs), extracellular traps (ETs), and passive release as the principal mechanisms for the release of mitochondrial DNA into the cytoplasm and extracellular compartments respectively. Further, we explain how the released mtDNA and its oxidised form can induce inflammatory cytokine production and aggravate lung injury through the Toll-like receptor 9(TLR9) signalling, cytosolic cGAS-stimulator of interferon genes (STING) signalling (cGAS-STING) pathway, and inflammasomes activation. Additionally, we propose targeting mtDNA-mediated inflammatory pathways as a novel therapeutic approach for treating ALI/ARDS.

Flexible sensors, friendly interfaces, and intelligent recognition are important in the research of novel human-computer interaction and the development of smart devices. However, major challenges are still encountered in designing user-centered smart devices with natural, convenient, and efficient interfaces. Inspired by the characteristics of textile-based flexible electronic sensors, in this article, we report a braided electronic cord with a low-cost, and automated fabrication to realize imperceptible, designable, and scalable user interfaces. The braided electronic cord is in a miniaturized form, which is suitable for being integrated with various occasions in life. To achieve high-precision interaction, a multi-feature fusion algorithm is designed to recognize gestures of different positions, different contact areas, and different movements performed on a single braided electronic cord. The recognized action results are fed back to varieties of interactive terminals, which show the diversity of cord forms and applications. Our braided electronic cord with the features of user friendliness, excellent durability and rich interaction mode will greatly promote the development of human-machine integration in the future. Inspired by the characteristics of textile-based flexible electronic sensors, the authors report a braided electronic cord with a low-cost, and automated fabrication to realize imperceptible, designable, and scalable user interfaces with the features of user-friendliness, excellent durability and rich interaction mode.

Background The global numbers of confirmed cases and deceased critically ill patients with COVID-19 are increasing. However, the clinical course, and the 60-day mortality and its predictors in critically ill patients have not been fully elucidated. The aim of this study is to identify the clinical course, and 60-day mortality and its predictors in critically ill patients with COVID-19. Methods Critically ill adult patients admitted to intensive care units (ICUs) from 3 hospitals in Wuhan, China, were included. Data on demographic information, preexisting comorbidities, laboratory findings at ICU admission, treatments, clinical outcomes, and results of SARS-CoV-2 RNA tests and of serum SARS-CoV-2 IgM were collected including the duration between symptom onset and negative conversion of SARS-CoV-2 RNA. Results Of 1748 patients with COVID-19, 239 (13.7%) critically ill patients were included. Complications included acute respiratory distress syndrome (ARDS) in 164 (68.6%) patients, coagulopathy in 150 (62.7%) patients, acute cardiac injury in 103 (43.1%) patients, and acute kidney injury (AKI) in 119 (49.8%) patients, which occurred 15.5 days, 17 days, 18.5 days, and 19 days after the symptom onset, respectively. The median duration of the negative conversion of SARS-CoV-2 RNA was 30 (range 6–81) days in 49 critically ill survivors that were identified. A total of 147 (61.5%) patients deceased by 60 days after ICU admission. The median duration between ICU admission and decease was 12 (range 3–36). Cox proportional-hazards regression analysis revealed that age older than 65 years, thrombocytopenia at ICU admission, ARDS, and AKI independently predicted the 60-day mortality. Conclusions Severe complications are common and the 60-day mortality of critically ill patients with COVID-19 is considerably high. The duration of the negative conversion of SARS-CoV-2 RNA and its association with the severity of critically ill patients with COVID-19 should be seriously considered and further studied.

A complete diagnostic autopsy is the gold-standard to gain insight into Coronavirus disease 2019 (COVID-19) pathogenesis. To delineate the in situ immune responses to SARS-CoV-2 viral infection, here we perform comprehensive high-dimensional transcriptional and spatial immune profiling in 22 COVID-19 decedents from Wuhan, China. We find TIM-3-mediated and PD-1-mediated immunosuppression as a hallmark of severe COVID-19, particularly in men, with PD-1 + cells being proximal rather than distal to TIM-3 + cells. Concurrently, lymphocytes are distal, while activated myeloid cells are proximal, to SARS-CoV-2 viral antigens, consistent with prevalent SARS-CoV-2 infection of myeloid cells in multiple organs. Finally, viral load positively correlates with specific immunosuppression and dendritic cell markers. In summary, our data show that SARS-CoV-2 viral infection induces lymphocyte suppression yet myeloid activation in severe COVID-19, so these two cell types likely have distinct functions in severe COVID-19 disease progression, and should be targeted differently for therapy. Postmortem analyses provide useful information for COVID-19 etiology. Here the authors profile 22 deceased severe COVID-19 patients with transcriptomic and histological approaches to find correlations between the presence of viral antigens with lymphocyte suppression yet myeloid activation, hinting distinct functions of these cells during pathogenesis.