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As a respiratory tract‐transmitted disease, coronavirus disease 2019 (COVID‐19) exerts a profound immune injury effect, leading not only to pulmonary impairment but also to cardiac complications. We present a case of a 79‐year‐old woman, who had previously contracted COVID‐19 and subsequently developed sinus arrest (SA) following her second infection. The longest asystole time detected by Holter monitoring was 7.2 seconds. Although the patient met criteria for permanent pacemaker implantation, her family declined this intervention and conservative management was pursued instead. However, after a period of observation, the patient's SA resolved. The present case study describes a patient who experienced SA upon reinfection with COVID‐19, which was not present during the initial infection. It emphasizes the impact of COVID‐19 on cardiac health, particularly its potential to induce arrhythmias. In addition, it is worth noting that the arrhythmia induced by a COVID‐19 infection may show reversibility, suggesting that a permanent pacemaker might not be the priority option if further pacing therapy is being considered. [ABSTRACT FROM AUTHOR]

Radiation pneumonitis (RP) is a prevalent and fatal complication of thoracic radiotherapy due to the lack of effective treatment options. RP primarily arises from mitochondrial injury in lung epithelial cells. The mitochondrial-derived peptide MOTS-c has demonstrated protective effects against various diseases by mitigating mitochondrial injury. C57BL/6 mice were exposed to 20 Gy of lung irradiation (IR) and received daily intraperitoneal injections of MOTS-c for 2 weeks. MOTS-c significantly ameliorated lung tissue damage, inflammation, and oxidative stress caused by radiation. Meanwhile, MOTS-c reversed the apoptosis and mitochondrial damage of alveolar epithelial cells in RP mice. Furthermore, MOTS-c significantly inhibited oxidative stress and mitochondrial damage in MLE-12 cells and primary mouse lung epithelial cells. Mechanistically, MOTS-c increased the nuclear factor erythroid 2-related factor (Nrf2) level and promoted its nuclear translocation. Notably, Nrf2 deficiency abolished the protective function of MOTS-c in mice with RP. In conclusion, MOTS-c alleviates RP by protecting mitochondrial function through an Nrf2-dependent mechanism, indicating that MOTS-c may be a novel potential protective agent against RP. [ABSTRACT FROM AUTHOR]

Currently, data-driven deep learning models are widely applied in the field of wind power prediction. However, when historical data are insufficient, deep learning models struggle to exhibit satisfactory predictive performance. In order to overcome the issue of limited training data for new wind farms, this study proposes a novel transfer learning strategy to address the challenge of less-sample learning in short-term wind power prediction. The research is conducted in two stages. In the pre-training stage, the TimesNet-GRU prediction model is established using data from a source wind farm. Parallel TimesNet modules are employed to extract multi-period features from various input feature sequences, followed by the extraction of long- and short-term features from the time series through gate recurrent unit (GRU). In the transfer learning stage, an effective transfer strategy is designed to freeze and retrain certain parameters of the TimesNet-GRU, thereby constructing a prediction model for the target wind farm. To validate the effectiveness of this approach, the results from testing with actual data from five wind farms in northwest China demonstrate that the proposed method exhibits significant advantages over models without transfer learning as explored in this study. [ABSTRACT FROM AUTHOR]

Uncovering the immune response to an inactivated SARS‐CoV‐2 vaccine (In‐Vac) and natural infection is crucial for comprehending COVID‐19 immunology. Here we conducted an integrated analysis of single‐cell RNA sequencing (scRNA‐seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In‐Vac compared with those from COVID‐19 patients. Our study reveals that In‐Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In‐Vac modestly upregulates IFN‐α but downregulates NF‐κB pathways, while natural infection triggers hyperactive IFN‐α and NF‐κB pathways. Both In‐Vac and natural infection alter T/B cell receptor repertoires, but COVID‐19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL‐15RA/IL‐15 receptor pathway after In‐Vac boost, suggesting its potential role in enhancing In‐Vac‐induced immunity. Collectively, our study illuminates multifaceted immune responses to In‐Vac and natural infection, providing insights for optimizing SARS‐CoV‐2 vaccine efficacy. [ABSTRACT FROM AUTHOR]

In response to the urgent need for efficient pneumonia diagnosis—a significant health challenge that has been intensified during the COVID-19 era—this study introduces the RCGAN-CTL model. This innovative approach combines a coupled generative adversarial network (GAN) with relativistic and conditional discriminators to optimize performance in contexts with limited data resources. It significantly enhances the efficacy of small or incomplete datasets through the integration of synthetic images generated by an advanced RCGAN. Rigorous evaluations using a wide range of lung X-ray images validate the model's effectiveness. In binary classification tasks that differentiate between normal and pneumonia cases, RCGAN-CTL demonstrates exceptional accuracy, exceeding 99%, with an area under the curve (AUC) of around 95%. Its capabilities extend to a complex triple classification task, accurately distinguishing between normal, viral pneumonia, and bacterial pneumonia, with precision scores of 89.9%, 95.5%, and 90.5%, respectively. A notable improvement in sensitivity further evidences the model's robustness. Comprehensive validation underscores RCGAN-CTL's superior accuracy and reliability in both binary and triple classification scenarios. This advancement is pivotal for enhancing deep learning applications in medical diagnostics, presenting a significant tool in addressing the challenges of pneumonia diagnosis, a key concern in contemporary healthcare. [ABSTRACT FROM AUTHOR]

The effect of sodium tripolyphosphate, glycerol, vitamin B2, mannitol and solid malt extract on the freeze-dried powder survival rate and the viable cells of the B. bifidum BB01 were studied by single-factor test in this study. The optimal concentration of protectant for B. bifidum during freeze-drying were 1.5% (W/V) sodium tripolyphosphate, 12% (W/V) glycerol, 6% (W/V) vitamin B2, 6% (W/V) mannitol and 6% (W/V) solid malt extract, and the survival rate of bacteria was 18.63%, 22.98%, 24.13%, 24.19% and 39.77%, respectively.

A precision adjusting mechanism of six-degree-of-freedom（SDOF）based on Stewart structure with high accuracy and high stiffness is designed in this paper. Firstly, the configuration of adjusting mechanism was designed and the kinematics analysis was conducted; Then, the error model was established and the precision of the configuration of parallel platform was analyzed. After that, the structure of adjusting mechanism was designed according to the theoretical analysis results, including the single displacement actuator and the offset hinge; Finally, the stiffness of the adjusting mechanism was analyzed. This article obtaining the analysis results that the repeatability of the SDOF adjusting mechanism were ±2 m in X and Y direction,±0.54 m in Z direction, ±6.13 rad in Rx, Ry, Rz direction, and the fundamental frequency of the transverse vibration was 104.3 Hz and the axial stiffness was 61.3 N/μm. The analysis results showed that the SDOF precision adjusting mechanism fulfilled the design requirements, and the rationality for structure design of adjustment mechanism was verified.