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Objective: Cardiovascular complications associated with COVID‐19 are being increasingly recognized. They include potential long‐term effects on the heart, although how these complications manifest in patients with stroke who are already vulnerable to cardiovascular issues is lacking. This study addressed this gap by investigating the influence of SARS‐CoV‐2 infection on cardiac hemodynamic changes in inpatients with stroke, thereby providing valuable insights into the management of cardiac complications in this population. Methods: This retrospective cohort study enrolled inpatients with stroke who did or did not experience SARS‐CoV‐2 infection in Shenzhen Second People's Hospital. Clinical information, hemodynamics data, serum myocardial enzyme levels, functional levels, including Barthel index, and Longshi scale assessment (bedridden, domestic, and community) were extracted from clinical records. An independent sample t‐test and the Mann–Whitney U test were used for comparison between groups. Multiple logistic regression analysis was used to identify indicators associated with decline in cardiac function in patients with stroke post‐COVID‐19. Results: Eighty‐three patients with stroke (average age of 66.0 ± 15.5 years) were recruited (42 patients in the study group and 41 in the control group). No significant difference in general characteristics was observed between the groups. The ejection fraction (EF, 56.45 ± 12.46 vs. 47.64 ± 14.76, p = 0.04) was decreased, and the end‐diastolic volume (EDV, 142.74 ± 80.00 vs. 193.34 ± 140.17, p = 0.001) was increased significantly in patients infected with SARS‐CoV‐2 compared with the noninfected participants. SARS‐CoV‐2 infection was an independent risk factor for EF (−10%) decrease (OR: 5.205, 95% CI: 1.621–16.720, p = 0.006). The risk of cardiac function decline among bedridden infected patients was 15.43 times higher than that of uninfected nonbedridden patients (OR: 15.430, 95% CI: 1.402–169.805, p = 0.025). Conclusion: Patients who are bedridden after stroke face a higher potential risk of cardiac hemodynamic impairment following SARS‐CoV‐2 infection, emphasizing the need for meticulous hemodynamic monitoring during treatment and rehabilitation of these patients to prevent any potential cardiovascular complications. Trial Registration: ClinicalTrials.gov identifier: ChiCTR2300071376. [ABSTRACT FROM AUTHOR]

Summary: Accurate and reliable model parameters are not only a prerequisite for model‐based estimation but also a significant part of battery operating characteristics. However, the measurement signal inevitably contains noise, which brings great challenges to model identification. This paper focuses on the noise immunity performance of model identification based on two‐swarm cooperative particle swarm optimization. An adaptive dynamic sliding window based on the current rate criterion and the identification results feedback is designed to avoid data redundancy and improve the robustness of model identification. The model parameters are obtained using two‐swarm cooperative particle swarm optimization based on the adaptive dynamic sliding window. The proposed method effectively improves the accuracy and speed of parameter identification through optimization of data fragments and particle update rules. Compared with two existing parameter identification methods, simulation studies illustrate that the average mean square deviation of the proposed method is reduced by at least 35 dB. The proposed method is superior to existing parameter identification methods in noise immunity performance, parameter identification reliability, and state‐of‐charge estimation accuracy. By employing the proposed method, the maximum errors of state‐of‐charge estimation are limited within 1% under experimental verification. The experiment results verify that the proposed method has the potential to extract reliable model features online. [ABSTRACT FROM AUTHOR]

Summary: Model‐based state of charge (SOC) estimation method depends on the accuracy of the online identified battery model. However, when battery model parameters are identified by conventional recursive least squares (RLS), voltage and current noise will lead to the deviation of parameters and further affect the accuracy of SOC estimation. To analyze the difference influence of voltage and current noise, a simulation is carried out under the same noise variance of both voltage and current. Results indicate that voltage noise is the main cause of parameter deviation. Based on these results, a novel method, the auxiliary model recursive least square (AM‐RLS) which enjoys low computational complexity is proposed to compensate the parameter deviation caused by voltage noise. The AM‐RLS method is further combined with an extended Kalman filter (EKF) to estimate SOC in real time. Both simulation and experimental results show that AM‐RLS can effectively attenuate the parameter deviation and SOC estimation error under noise interference. Compared with RLS, the maximum SOC errors of the proposed method are reduced by 2.6% and 5.86% respectively under two current profiles. [ABSTRACT FROM AUTHOR]

Abstract: Acute liver failure (ALF) is associated with high mortality, and a poor understanding of the underlying pathophysiology has resulted in a lack of effective treatments so far. Here, using an amatoxin‐induced rhesus monkey model of ALF, we panoramically revealed the cellular and molecular events that lead to the development of ALF. The challenged monkeys with toxins underwent a typical course of ALF including severe hepatic injury, systemic inflammation and eventual death. Adaptive immune was not noticeably disturbed throughout the progress of ALF. A systematic examination of serum factors and cytokines revealed that IL‐6 increase was the most rapid and drastic. Interestingly, we found that IL‐6 was mainly produced by circulating monocytes. Furthermore, ablation of monocyte‐derived IL‐6 in mice decreased liver injury and systemic inflammation following chemical injection. Our findings reveal a critical role of circulating monocytes in initiating and accelerating ALF, indicating a potential therapeutic target in clinical treatment for ALF. [ABSTRACT FROM AUTHOR]

Viral infection triggers the formation of mitochondrial antiviral signaling protein (MAVS) aggregates, which potently promote immune signaling. Autophagy plays an important role in controlling MAVS‐mediated antiviral signaling; however, the exact molecular mechanism underlying the targeted autophagic degradation of MAVS remains unclear. Here, we investigated the mechanism by which RNF34 regulates immunity and mitophagy by targeting MAVS. RNF34 binds to MAVS in the mitochondrial compartment after viral infection and negatively regulates RIG‐I‐like receptor (RLR)‐mediated antiviral immunity. Moreover, RNF34 catalyzes the K27‐/K29‐linked ubiquitination of MAVS at Lys 297, 311, 348, and 362 Arg, which serves as a recognition signal for NDP52‐dependent autophagic degradation. Specifically, RNF34 initiates the K63‐ to K27‐linked ubiquitination transition on MAVS primarily at Lys 311, which facilitates the autophagic degradation of MAVS upon RIG‐I stimulation. Notably, RNF34 is required for the clearance of damaged mitochondria upon viral infection. Thus, we elucidated the mechanism by which RNF34‐mediated autophagic degradation of MAVS regulates the innate immune response, mitochondrial homeostasis, and infection. Synopsis: Viral infection triggers the formation of mitochondrial antiviral signaling protein (MAVS) aggregates, which potently promote immune signaling. However, the exact molecular mechanism underlying the targeted autophagic degradation of MAVS remains unclear. Here, we investigated the mechanism by which RNF34 regulates immunity and mitophagy by targeting MAVS. RNF34 negatively regulates RIG‐I like receptor (RLR)‐mediated antiviral immunity.RNF34 catalyzes the K27/K29‐linked ubiquitination of MAVS at Lys 297, 311, 348, and 362.RNF34 initiates the K63‐ to K27‐linked ubiquitination transition on MAVS primarily at Lys 311.RNF34 facilitates the autophagic degradation of MAVS.RNF34 promotes NDP52‐dependent selective mitophagy. [ABSTRACT FROM AUTHOR]