Acupuncture for swallowing disorder after recovery from COVID-19: A protocol for systematic review and meta analysis.

BACKGROUND: Swallowing disorder is a common sequela after recovery from COVID-19. Acupuncture is an important traditional therapy for treating swallowing disorder. However, the efficacy of acupuncture for swallowing disorder after recovery from COVID-19 lacks evidence-based medicine. METHODS: All randomized controlled trials of acupuncture for swallowing disorder after recovery from COVID-19 will be retrieved and collected from December 2019 to November 2022 with no language restrictions. PubMed, EMBASE, Cochrane Library, Web of Science, China National Knowledge Infrastructure Database, Chinese Biomedical Database, Chinese Science and Technology Journal Database (VIP), and the Wanfang Database will be searched. Two researchers will independently select studies, extract data, and evaluate study quality. The Cochrane risk of bias tool for randomized trials will be used to assess the risk of bias in the included studies. Statistical analyses will be performed using Review Manager version 5.3. RESULTS: This study will provide a high-quality and convincing assessment of the efficacy and safety of acupuncture for swallowing disorder after recovery from COVID-19 and will be published in peer-reviewed journals. CONCLUSION: Our findings will provide a reference for future clinical decisions and guidance development.

Mediator role of presence of meaning and self-esteem in the relationship of social support and death anxiety.

Introduction: Death anxiety has increased following the COVID-19 pandemic. Although terror management theory has suggested social support, presence of meaning and self-esteem functioned as death anxiety buffers, few existing works have explored the mechanism of how social support, presence of meaning, and self-esteem buffer death anxiety. To identify these mechanisms is the aim of this study. Methods: Our cross-sectional study was conducted with 1167 people in China from 19 May 2020 to 1 June 2020 during the COVID-19 outbreak. The average age of participants was 26 years. Data were by questionnaire, including demographic information, the Templer's Death anxiety scale, the multidimensional scale of perceived social support, the presence of meaning scale, and the Rosenberg self-esteem scale. Results: Results using structural equation modeling showed presence of meaning and self-esteem fully mediated the relationship between social support and death anxiety, respectively and sequentially. The proposed model showed good fit of indices: χ2 = 243.384, df = 58, p < 0.001; CFI = 0.968, TLI = 0.954, RMSEA = 0.052, SRMR = 0.044. Discussion: This study demonstrates significant mediator roles of presence of meaning and self-esteem in the relationship of social support and death anxiety. Multi-component interventions are needed to manage death anxiety by targeting increasing social support, presence of meaning and self-esteem and increasing presence of meaning and self-esteem when social support is diminished in the pandemic.

Efficacy and safety of acupuncture in the treatment of the sequela of olfactory disorders after infection with COVID-19: A protocol for systematic review and meta analysis.

BACKGROUND: From the end of 2019 to now, coronavirus disease 2019 (COVID-19) has put enormous strain on the world's health systems. As a characteristic sign of COVID-19 patient, olfactory dysfunction (OD) poses considerable problems for patients. In China, acupuncture has been widely used to treat OD caused by COVID-19, but there is still a lack of evidence-based medical evaluation. This study was designed to evaluate the effectiveness and safety of acupuncture for the treatment of COVID-19 OD. METHODS: According to the retrieval strategies, randomized controlled trials on the acupuncture for COVID-19 OD were obtained from Cochrane Central Register of Controlled Trials, Embase, PubMed, Web of Science, the Chinese National Knowledge Infrastructure, the Chinese Biomedical Literature Database, the Chinese Scientific Journal Database and the Wanfang Database, regardless of publication date, or language. Studies were screened based on inclusion and exclusion criteria, and the Cochrane risk bias assessment tool was used to evaluate the quality of the studies. The meta-analysis was performed using Review Manager (RevMan 5.3) and STATA 14.2 software. Ultimately, the evidentiary grade for the results will be evaluated. RESULTS: The results of this meta-analysis will be submitted to a peer-reviewed journal for publication. CONCLUSION: This study will provide up-to-date summary proof for evaluating the effectiveness and safety of acupuncture for COVID-19 OD.

Traditional Chinese medicine combined with Moxibustion in the treatment of "long-COVID": A protocol for systematic review and meta-analysis.

BACKGROUND: From the end of 2019 to now, COVID-19 is still prevalent, which poses a great threat to international public health. With the increasing number of people infected, the number of patients with COVID-19 sequelae is also increasing, but there is no specific drug for COVID-19 sequelae. In China, traditional Chinese medicine combined with acupuncture has been widely used in COVID-19 sequelae, but there is still a lack of evidence-based medicine evaluation. The purpose of this study was to evaluate the efficacy and safety of traditional Chinese medicine combined with moxibustion in the treatment of COVID-19 sequelae. METHODS: According to the retrieval strategy, the "long COVID" randomized controlled trial of traditional Chinese medicine combined with moxibustion will be search in eight databases composed of PubMed, Embase, Web of Science, China National knowledge Infrastructure Database, China Biomedical Database and China Science and Technology Journal Database, regardless of publication date or language. The study was screened according to the inclusion and exclusion criteria, and the Cochrane risk bias assessment tool was used to evaluate the quality of the study. Meta-analysis was carried out using RevMan5.3 and STATA12.0 software. Finally, the level of evidence of the results will be evaluated. RESULTS: This study will evaluate whether traditional Chinese medicine combined with moxibustion can effectively treat the symptoms of COVID-19 sequelae. CONCLUSION: This study will provide evidence whether there is benefit of traditional Chinese medicine combined with moxibustion in the treatment of COVID-19 sequelae. At the same time, our research results will provide a reference for clinical decision-making and guiding development in the future.

The United States COVID-19 Forecast Hub dataset.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Exploring New Characteristics: Using Deep Learning and 3D Reconstruction to Compare the Original COVID-19 and Its Delta Variant Based on Chest CT.

Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant. Methods: This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient's lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI). Results: The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0-3 days), progressive and peak stage (4-16 days), and absorption stage (17-42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17-30] vs. 39 [31-44], p < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, p = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time. Conclusion: Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.

Mechanism and practice of COVID-19 complicated with sepsis treated by reinforcing healthy qi to eliminate pathogenic factors and truncation and reversal

To discuss the scientific basis of "reinforcing healthy qi to eliminate pathogenic factors. truncation and reversal- in the treatment of coronavirus disease 2019 (COVID-19) complicated with sep-sis. An inflammatory response caused by novel coronavirus infection was the most important pathological basis in COVID-19 patients, especially in the advanced stage. the critical patients often died of sepsis. Chinese medicine had potential advantages in the treatment of sepsis, among which the basic treatment principle of COVID-19 was to reinforcing healthy qi to eliminate pathogenic factors. The truncation and re-versal therapy was proposed by famous traditional Chinese medicine professor JIANG Chun-hua, and used by Qihuang scholar professor FANG Bang-jiang in the treatment of sepsis. with good effort. There-fore, we suggested that patients with COVID-19 complicated with sepsis should be treated with the therapy of "reinforcing healthy qi to eliminate pathogenic factors, truncation and reversal- in the early stage, and large-scale clinical research should be carried out to furtherly verify its effect.

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.