An outbreak of school influenza complicated with Mycoplasma pneumoniae infection

Objective: To describe the clinical features, causal agent and transmission mode of a fever outbreak in a school in Shanghai. Methods: Field epidemiological approaches including case definition development, searching for contacts, distribution of diseases description, environmental sampling and laboratory testing. Results: A total of 16 influenza-like cases were included, all concentrated in the one class of grade two, including 15 students and 1 teacher. Among student cases, the incidence rate was 36.59%(15/41), the average age was 7.4 years, the incidence rate was 36.84%(7/19) for boys, 36.36%(8/22) for girls. The clinical course was 5-15 days, with the median of 9 days, and 18.75%(3/16) of the cases stayed studying while sick. The nasopharyngeal swab specimens in 16 cases all tested positive for influenza B, of which 11 tested positive for mycoplasma pneumoniae and 1 case also tested positive for coronavirus OC43. Body temperature, number of mononuclear cells, and treatment time of patients infected with Influenza B and mycoplasma pneumoniae were higher than those of patients infected with influenza B alone(P < 0.05). The outbreak lasted for 12 days, all sick students were treated and discharged from hospital, with no severe cases or death, and the outbreak was effectively controlled. Conclusion: This campus cluster outbreak caused by influenza B and mycoplasma pneumoniae. Patients with influenza B with mycoplasma pneumoniae have severe symptoms and a long course of illness, suggesting the importance of early management of the epidemic.

The impact of pandemic mental cognition on cultural values: an empirical study based on social media.

BACKGROUND: COVID-19 has triggered a global public health crisis, and had an impact on economies, societies, and politics around the world. Based on the pathogen prevalence hypothesis suggested that residents of areas with higher infection rates are more likely to be collectivists as compared with those of areas with lower infection rates. Many researchers had studied the direct link between infectious diseases and individualism/collectivism (infectious diseases→ cultural values), but no one has focused on the specific psychological factors between them: (infectious diseases→ cognition of the pandemic→ cultural values). To test and develop the pathogen prevalence hypothesis, we introduced pandemic mental cognition and conducted an empirical study on social media (Chinese Sina Weibo), hoping to explore the psychological reasons behind in cultural value changes in the context of a pandemic. METHODS: We downloaded all posts from active Sina Weibo users in Dalian during the pandemic period (January 2020 to May 2022) and used dictionary-based approaches to calculate frequency of words from two domains (pandemic mental cognition and collectivism/individualism), respectively. Then we used the multiple log-linear regression analysis method to establish the relationship between pandemic mental cognition and collectivism/individualism. RESULTS: Among three dimensions of pandemic mental cognition, only the sense of uncertainty had a significant positive relationship with collectivism, and also had a marginal significant positive relationship with individualism. There was a significant positive correlation between the first-order lag term AR(1) and individualism, which means the individualism tendency was mainly affected by its previous level. CONCLUSIONS: The study found that more collectivist regions are associated with a higher pathogen burden, and recognized the sense of uncertainty as its underlying cause. Results of this study validated and further developed the pathogen stress hypothesis in the context of the COVID-19 pandemic.

Nonlinear effects of pandemic uncertainty on depression, pandemic preventive behavior intentions, and positive life attitudes: Moderating effects of high and low uncertainty grouping

Backgrounds COVID-19 is difficult to end in a short time and people are still facing huge uncertainties. Since people's lives are gradually returning to normal, the sense of control and intolerance of uncertainty, which were mainly focused by past studies, are not specific to COVID-19 and will be more influenced by some factors unrelated to the pandemic. Therefore, they may be difficult to accurately reflect the individuals' perceptions of uncertainty. Besides, past research just after the outbreak mainly investigated people in high levels of uncertainty, we don't know the impact of uncertainties on individuals' psychological states when people gradually recovered their sense of control. To solve these problems, we proposed the concept of "pandemic uncertainty” and investigated its impact on people's daily lives. Methods During October 20, 2021 to October 22, 2021, this study obtained data about uncertainty, depression, positive attitude, pandemic preventive behavior intentions, personality, and social support from 530 subjects using convenient sampling. The subjects were all college students from the Dalian University of Technology and Dalian Vocational and Technical College. According to the distribution of uncertainty, we divided the dataset into high and low groups. Subsequently, by using uncertainty as the independent variable, the grouping variable as the moderating variable, and other variables as the control variables, the moderating effects were analyzed for depression, positive attitude, and pandemic preventive behavior intentions, respectively. Results The results showed that the grouping variable significantly moderate the influence of uncertainty on positive attitude and pandemic preventive behavior intentions but had no significant effect on depression. Simple slope analysis revealed that high grouping uncertainty significantly and positively predicted positive attitude and pandemic preventive behavior intentions, while low grouping effects were not significant. Conclusion These results reveal a nonlinear effect of pandemic uncertainty on the pandemic preventive behavior intentions and positive life attitudes and enlighten us about the nonlinear relationship of psychological characteristics during a pandemic.

Nonlinear effects of pandemic uncertainty on depression, pandemic preventive behavior intentions, and positive life attitudes: Moderating effects of high and low uncertainty grouping.

Backgrounds: COVID-19 is difficult to end in a short time and people are still facing huge uncertainties. Since people's lives are gradually returning to normal, the sense of control and intolerance of uncertainty, which were mainly focused by past studies, are not specific to COVID-19 and will be more influenced by some factors unrelated to the pandemic. Therefore, they may be difficult to accurately reflect the individuals' perceptions of uncertainty. Besides, past research just after the outbreak mainly investigated people in high levels of uncertainty, we don't know the impact of uncertainties on individuals' psychological states when people gradually recovered their sense of control. To solve these problems, we proposed the concept of "pandemic uncertainty" and investigated its impact on people's daily lives. Methods: During October 20, 2021 to October 22, 2021, this study obtained data about uncertainty, depression, positive attitude, pandemic preventive behavior intentions, personality, and social support from 530 subjects using convenient sampling. The subjects were all college students from the Dalian University of Technology and Dalian Vocational and Technical College. According to the distribution of uncertainty, we divided the dataset into high and low groups. Subsequently, by using uncertainty as the independent variable, the grouping variable as the moderating variable, and other variables as the control variables, the moderating effects were analyzed for depression, positive attitude, and pandemic preventive behavior intentions, respectively. Results: The results showed that the grouping variable significantly moderate the influence of uncertainty on positive attitude and pandemic preventive behavior intentions but had no significant effect on depression. Simple slope analysis revealed that high grouping uncertainty significantly and positively predicted positive attitude and pandemic preventive behavior intentions, while low grouping effects were not significant. Conclusion: These results reveal a nonlinear effect of pandemic uncertainty on the pandemic preventive behavior intentions and positive life attitudes and enlighten us about the nonlinear relationship of psychological characteristics during a pandemic.

A blessing in disguise-The effect of China's Covid-19 health code system on older people's mobile payment usage.

In early 2020, China launched a health code system to combat the spread of Covid-19. The required health code led to a drastic uptake of smartphone usage among older residents. This paper uses rich commercial data from the Zhejiang Province of China to track the change in consumption among the older population. The paper finds that older people significantly increased spending after switching to mobile payment. The paper contributes to the literature by identifying the unexpected windfall from the effect of the health code mandate on the older population and demonstrating that digital payment works well in the older population.

Effect of Direct Bilirubin Level on Clinical Outcome and Prognoses in Severely/Critically Ill Patients With COVID-19.

Objectives: We aimed to investigate how changes in direct bilirubin (DBiL) levels in severely/critically ill the coronavirus disease (COVID-19) patients during their first week of hospital admission affect their subsequent prognoses and mortality. Methods: We retrospectively enrolled 337 severely/critically ill COVID-19 patients with two consecutive blood tests at hospital admission and about 7 days after. Based on the trend of the two consecutive tests, we categorized patients into the normal direct bilirubin (DBiL) group (224), declined DBiL group (44) and elevated DBiL group (79). Results: The elevated DBiL group had a significantly larger proportion of critically ill patients (χ2-test, p < 0.001), a higher risk of ICU admission, respiratory failure, and shock at hospital admission (χ2-test, all p < 0.001). During hospitalization, the elevated DBiL group had significantly higher risks of shock, acute respiratory distress syndrome (ARDS), and respiratory failure (χ2-test, all p < 0.001). The same findings were observed for heart damage (χ2-test, p = 0.002) and acute renal injury (χ2-test, p = 0.009). Cox regression analysis showed the risk of mortality in the elevated DBiL group was 2.27 (95% CI: 1.50-3.43, p < 0.001) times higher than that in the normal DBiL group after adjusted age, initial symptom, and laboratory markers. The Receiver Operating Characteristic curve (ROC) analysis demonstrated that the second test of DBiL was consistently a better indicator of the occurrence of complications (except shock) and mortality than the first test in severely/critically ill COVID-19 patients. The area under the ROC curve (AUC) combined with two consecutive DBiL levels for respiratory failure and death was the largest. Conclusion: Elevated DBiL levels are an independent indicator for complication and mortality in COVID-19 patients. Compared with the DBiL levels at admission, DBiL levels on days 7 days of hospitalization are more advantageous in predicting the prognoses of COVID-19 in severely/critically ill patients.

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation.

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation is a hot topic, traditional deep learning methods are usually data-hungry with millions of parameters, easy to overfit under limited available COVID-19 training data. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional methods are usually computationally intensive. To address the above two problems, we propose MiniSeg, a lightweight model for efficient COVID-19 segmentation from CT images. Our efforts start with the design of an attentive hierarchical spatial pyramid (AHSP) module for lightweight, efficient, effective multiscale learning that is essential for image segmentation. Then, we build a two-path (TP) encoder for deep feature extraction, where one path uses AHSP modules for learning multiscale contextual features and the other is a shallow convolutional path for capturing fine details. The two paths interact with each other for learning effective representations. Based on the extracted features, a simple decoder is added for COVID-19 segmentation. For comparing MiniSeg to previous methods, we build a comprehensive COVID-19 segmentation benchmark. Extensive experiments demonstrate that the proposed MiniSeg achieves better accuracy because its only 83k parameters make it less prone to overfitting. Its high efficiency also makes it easy to deploy and develop. The code has been released at https://github.com/yun-liu/MiniSeg.

Does Using Corsi-Rosenthal Boxes to Mitigate COVID-19 Transmission Also Reduce Indoor Air Concentrations of PFAS and Phthalates?

The COVID-19 pandemic brought new emphasis on indoor air quality. However, few studies have investigated the impact of air filtration, a COVID-mitigation approach, on indoor air concentrations of semivolatile organic compounds (SVOCs). Using a quasi-experimental design, we quantified the impact of a relatively low-cost "do-it-yourself" air filter (Corsi-Rosenthal Box; CR Box) on indoor air concentrations of 42 PFAS and 24 other SVOCs. We sampled air before (October-November 2021) and during (February-March 2022) deployment of CR Boxes in 17 rooms located in an occupied Providence, Rhode Island office building. We measured sound levels in rooms with CR Boxes operating and not operating. While CR Boxes were deployed, concentrations of seven PFAS (N-EtFOSE, N-EtFOSA, FBSA, PFBS, PFHxS, PFOS, PFNA) were 28-61% lower and concentrations of five phthalates (DMP, DEP, DiBP, BBzP, DCHP) were 29-62% lower. Concentrations of five PFAS and one phthalate increased 23-44% during the intervention period, but the 95% CI of most of these estimates included the null. Daytime sound levels increased 5.0 dB when CR Boxes were operating. These results indicate that CR Boxes reduced exposure to several lower-volatility phthalates and sulfonated PFAS previously reported to be found in office building materials and products, with potentially distracting increases in sound levels.

Differential clinical characteristics and performance of home antigen tests between parents and children after household transmission of SARS-CoV-2 during the Omicron variant pandemic.

OBJECTIVES: The SARS-CoV-2 Omicron variant pandemic struck Taiwan in April 2022. Rapid antigen tests (RATs) play an important role in providing rapid results during a pandemic. However, self-collected samples by the children's caregivers without the supervision of medical personnel raise some concerns. METHODS: This study was performed to investigate household transmission, clinical characteristics, and antigen performance in a special COVID-19 family clinic in a children's hospital. The performance of at-home RATs was evaluated based on reverse transcription-polymerase chain reaction. RESULTS: We included 627 patients in our study between May 11 and June 10, 2022. The COVID-19 full vaccination rate was significantly higher in adults (98.5%) than in children (5.9%, P <0.001). The transmission rate was significantly higher in children (91.3%) than in adults (76.6%, P <0.001). Infected children had more incidents of fever (82.4% vs 22.4%, P <0.001) and a higher peak fever than adults. Based on the reverse transcription-polymerase chain reaction, the negative predictive rate of the home RAT was only 38.7% (95% confidence interval: 31.9-46.0%) in children. The cycle threshold value of those with false-negative antigen tests was significantly lower in children. CONCLUSION: Children had a higher transmission rate, more fever, and higher peak fever than adults. Home RAT has a suboptimal negative predictive rate in children.

Chinese Guideline on Allergen Immunotherapy for Allergic Rhinitis: The 2022 Update.

In the last few decades, there has been a progressive increase in the prevalence of allergic rhinitis (AR) in China, where it now affects approximately 250 million people. AR prevention and treatment include allergen avoidance, pharmacotherapy, allergen immunotherapy (AIT), and patient education, among which AIT is the only curative intervention. AIT targets the disease etiology and may potentially modify the immune system as well as induce allergen-specific immune tolerance in patients with AR. In 2017, a team of experts from the Chinese Society of Allergy (CSA) and the Chinese Allergic Rhinitis Collaborative Research Group (C2AR2G) produced the first English version of Chinese AIT guidelines for AR. Since then, there has been considerable progress in basic research of and clinical practice for AIT, especially regarding the role of follicular regulatory T (TFR) cells in the pathogenesis of AR and the use of allergen-specific immunoglobulin E (sIgE) in nasal secretions for the diagnosis of AR. Additionally, potential biomarkers, including TFR cells, sIgG4, and sIgE, have been used to monitor the incidence and progression of AR. Moreover, there has been a novel understanding of AIT during the coronavirus disease 2019 pandemic. Hence, there was an urgent need to update the AIT guideline for AR by a team of experts from CSA and C2AR2G. This document aims to serve as professional reference material on AIT for AR treatment in China, thus improving the development of AIT across the world.

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

Association between continuous renal replacement therapy and 28-day mortality of critically ill patients with COVID-19 receiving mechanical ventilation

Background: Continuous renal replacement therapy (CRRT) has become an important multiple organ support therapy and it is widely used in the intensive care unit (ICU). The aim of this study was to clarify the association between CRT and 28-day mortality in critically ill coronavirus disease 2019 (COVID-19) patients receiving mechanical ventilation. Materials and methods: 112 respiratory decompensated critically ill adult patients with COVID-19 admitted to a COVID-19-designated ICU were included in this retrospective cohort study. Data on demographic information, comorbidities, laboratory findings upon ICU admission, and clinical outcomes were collected. The Kaplan-Meier method and Cox proportional hazard model were applied to determine the potential risk factors associated with 28-day mortality.

Would you zoom with your doctor? A discrete choice experiment to identify patient preferences for video and in-clinic consultations in German primary care.

INTRODUCTION: The popularity of video consultations in healthcare has accelerated during the COVID-19 pandemic. Despite increased availability and obvious benefits, many patients remain hesitant to use video consultations. This study investigates the relative importance of the consultation mode compared to other attributes in patients' appointment choices in Germany. METHODS: A discrete choice experiment was conducted to examine the influence of appointment attributes on preferences for video over in-clinic consultations. A total of 350 participants were included in the analysis. RESULTS: The level of continuity of care (46%) and the waiting time until the next available appointment (22%) were shown to have higher relative importance than consultation mode (18%) and other attributes. Participants with fewer data privacy concerns, higher technology proficiency, and more fear of COVID-19 tended to prefer video over in-clinic consultations. The predicted choice probability of a video over a typical in-clinic consultation and opting out increased from <1% to 40% when the video consultation was improved from the worst-case to the best-case scenario. CONCLUSION: This study provides insight into the effect of the consultation mode on appointment choice at a time when telemedicine gains momentum. The results suggest that participants preferred in-clinic over video consultations. Policymakers and service providers should focus on increasing the level of continuity of care and decreasing the time until the next available appointment to prompt the adoption of video consultations. Although participants preferred to talk to their physician in person over consulting via video per se, the demand for video consultations can be increased significantly by improving the other appointment attributes of video consultations such as the level of continuity of care.

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and standard of care during the COVID-19 pandemic. A common partial mitigation is transfer learning by pretraining a "generic network" on a large nonmedical data set and then fine-tuning on a task-specific radiology data set. Purpose To reduce data set size requirements for chest radiography deep learning models by using an advanced machine learning approach (supervised contrastive [SupCon] learning) to generate chest radiography networks. Materials and Methods SupCon helped generate chest radiography networks from 821 544 chest radiographs from India and the United States. The chest radiography networks were used as a starting point for further machine learning model development for 10 prediction tasks (eg, airspace opacity, fracture, tuberculosis, and COVID-19 outcomes) by using five data sets comprising 684 955 chest radiographs from India, the United States, and China. Three model development setups were tested (linear classifier, nonlinear classifier, and fine-tuning the full network) with different data set sizes from eight to 85. Results Across a majority of tasks, compared with transfer learning from a nonmedical data set, SupCon reduced label requirements up to 688-fold and improved the area under the receiver operating characteristic curve (AUC) at matching data set sizes. At the extreme low-data regimen, training small nonlinear models by using only 45 chest radiographs yielded an AUC of 0.95 (noninferior to radiologist performance) in classifying microbiology-confirmed tuberculosis in external validation. At a more moderate data regimen, training small nonlinear models by using only 528 chest radiographs yielded an AUC of 0.75 in predicting severe COVID-19 outcomes. Conclusion Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations. © RSNA, 2022 Online supplemental material is available for this article.

Does Health Crises Effect Tourism: Role of Financial Inclusion for Green Financial Development.

Tourism is impacted by all types of crises, no matter how big or small. Even though many studies have examined tourism crises, most focus on the number of tourists arriving and departing. As a result of this lack of information, The adaptive differences in tourist behavior caused by various crises are not well understood. When it comes to inbound tourism, the financial and health-related crisis can significantly impact the tourist profile of the country and its visitors' spending habits. The findings show that the health crisis has a significant positive impact on tourism. Moreover, COVID_deaths and COVID_confirm_cases decrease the international tourism in developed and developing countries. According to the study's findings, tourists' sensitivity to crises varies between short- and long-haul markets. The evidence shows that financial inclusion has a significant positive impact on various aspects of tourism development in China. Hence, this article offers numerous policy and practical suggestions for sustainable tourism management.

Psychological responses of medical staff during COVID-19 and the adjustment effect of brief mindfulness meditation.

BACKGROUND: COVID-19 has posed an unprecedented threat to public health and remains a critical challenge for medical staff, especially those who have been fighting against the virus in Wuhan, China. Limited data have been reported regarding the psychological status of these medical staff members. Therefore, we conducted this study to explore the mental health status of medical staff and the efficacy of brief mindfulness meditation (BMM) in improving their mental health. METHODS: A survey was conducted between April 18 and May 3, 2020. Upon completing the pre-test, participants in the treatment group received a 15-min BMM intervention every day at 8 p.m. Post-test questionnaires were completed after 16 days of therapy. The questionnaire comprised demographic data and psychological measurement scales. The levels of pre and post-test depression, anxiety, stress, and insomnia were assessed using the 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, Perceived Stress Scale, and Athens Insomnia Scale, respectively. RESULTS: A total of 134 completed questionnaires were received. Of the medical staff, 6.7%, 1.5%, and 26.7% reported symptoms of depression, anxiety, and insomnia, respectively. Public officials from military hospitals reported experiencing greater pressure than private officials (t = 2.39, p = 0.018, d = 0.50). Additionally, BMM treatment appeared to effectively alleviate insomnia (t = 2.27, p = 0.027, d = 0.28). CONCLUSIONS: The medical staff suffered negative psychological effects during the COVID-19 pandemic. BMM interventions are advantageous in supporting the mental health of medical staff.

Effect of Direct Bilirubin Level on Clinical Outcome and Prognoses in Severely/Critically Ill Patients With COVID-19

Objectives We aimed to investigate how changes in direct bilirubin (DBiL) levels in severely/critically ill the coronavirus disease (COVID-19) patients during their first week of hospital admission affect their subsequent prognoses and mortality. Methods We retrospectively enrolled 337 severely/critically ill COVID-19 patients with two consecutive blood tests at hospital admission and about 7 days after. Based on the trend of the two consecutive tests, we categorized patients into the normal direct bilirubin (DBiL) group (224), declined DBiL group (44) and elevated DBiL group (79). Results The elevated DBiL group had a significantly larger proportion of critically ill patients (χ2-test, p < 0.001), a higher risk of ICU admission, respiratory failure, and shock at hospital admission (χ2-test, all p < 0.001). During hospitalization, the elevated DBiL group had significantly higher risks of shock, acute respiratory distress syndrome (ARDS), and respiratory failure (χ2-test, all p < 0.001). The same findings were observed for heart damage (χ2-test, p = 0.002) and acute renal injury (χ2-test, p = 0.009). Cox regression analysis showed the risk of mortality in the elevated DBiL group was 2.27 (95% CI: 1.50–3.43, p < 0.001) times higher than that in the normal DBiL group after adjusted age, initial symptom, and laboratory markers. The Receiver Operating Characteristic curve (ROC) analysis demonstrated that the second test of DBiL was consistently a better indicator of the occurrence of complications (except shock) and mortality than the first test in severely/critically ill COVID-19 patients. The area under the ROC curve (AUC) combined with two consecutive DBiL levels for respiratory failure and death was the largest. Conclusion Elevated DBiL levels are an independent indicator for complication and mortality in COVID-19 patients. Compared with the DBiL levels at admission, DBiL levels on days 7 days of hospitalization are more advantageous in predicting the prognoses of COVID-19 in severely/critically ill patients.

Incidence and outcomes of patients with COVID-19 associated pulmonary aspergillosis (CAPA) in intensive care units: a systematic review and meta-analysis of 31 cohort studies.

BACKGROUND: We aimed to identify studies systematically that describe the incidence and outcome of COVID-19-related pulmonary aspergillosis (CAPA). METHODS: We searched ScienceDirect, PubMed, CNKI, and MEDLINE (OVID) from December 31, 2019 to November 20, 2021 for all eligible studies. Random-model was used to reported the incidence, all-cause case fatality rate (CFR) and 95% confidence intervals (CIs). The meta-analysis was registered with PROSPERO (CRD42021242179). RESULTS: In all, thirty-one cohort studies were included in this study. A total of 3,441 patients with severe COVID-19 admitted to an intensive care unit (ICU) were investigated and 442 cases of CAPA were reported (30 studies). The pooled incidence rate of CAPA was 0.14 (95% CI: 0.11-0.17, I2=0.0%). Twenty-eight studies reported 287 deceased patients and 269 surviving patients. The pooled CFR of CAPA was 0.52 (95% CI: 0.47-0.56, I2=3.9%). Interestingly, patients with COVID19 would develop CAPA at 7.28 days after mechanical ventilation (range, 5.48-9.08 days). No significant publication bias was detected in this meta-analysis. DISCUSSION: Patients with COVID-19 admitted to an ICU might develop CAPA and have high all-cause CFR. We recommend conducting prospective screening for CAPA among patients with severe COVID-19, especially for those who receive mechanical ventilation over 7 days.

Are gastrointestinal symptoms associated with higher risk of Mortality in COVID-19 patients? A systematic review and meta-analysis.

BACKGROUND: Gastrointestinal symptoms have been reported in patients with COVID-19. Several clinical investigations suggested that gastrointestinal symptoms were associated with disease severity of COVID-19. However, the relevance of gastrointestinal symptoms and mortality of COVID-19 remains largely unknown. We aim to investigate the relationship between gastrointestinal symptoms and COVID-19 mortality. METHODS: We searched the PubMed, Embase, Web of science and Cochrane for studies published between Dec 1, 2019 and May 1, 2021, that had data on gastrointestinal symptoms in COVID-19 patients. Additional literatures were obtained by screening the citations of included studies and recent reviews. Only studies that reported the mortality of COVID-19 patients with/without gastrointestinal symptoms were included. Raw data were pooled to calculate OR (Odds Ratio). The mortality was compared between patients with and without gastrointestinal symptoms, as well as between patients with and without individual symptoms (diarrhea, nausea/vomiting, abdominal pain). RESULTS: Fifty-three literatures with 55,245 COVID-19 patients (4955 non-survivors and 50,290 survivors) were included. The presence of GI symptoms was not associated with the mortality of COVID-19 patients (OR=0.88; 95% CI 0.71-1.09; P=0.23). As for individual symptoms, diarrhea (OR=1.01; 95% CI 0.72-1.41; P=0.96), nausea/vomiting (OR=1.16; 95% CI 0.78-1.71; P=0.46) and abdominal pain (OR=1.55; 95% CI 0.68-3.54; P=0.3) also showed non-relevance with the death of COVID-19 patients. CONCLUSIONS: Gastrointestinal symptoms are not associated with higher mortality of COVID-19 patients. The prognostic value of gastrointestinal symptoms in COVID-19 requires further investigation.

Anti-Inflammatory Effects of Camellia fascicularis Polyphenols via Attenuation of NF-κB and MAPK Pathways in LPS-Induced THP-1 Macrophages

Purpose Plant polyphenols possess beneficial functions against various diseases. This study aimed to identify phenolic ingredients in Camellia fascicularis (C. fascicularis) and investigate its possible underlying anti-inflammatory mechanism in lipopolysaccharide (LPS)-induced human monocytes (THP-1) macrophages. Methods C. fascicularis polyphenols (CFP) were characterized by ultra-performance liquid chromatography (UPLC) combined with quadrupole-time-of-flight mass/mass spectrometry (Q-TOF-MS/MS). The THP-1 cells were differentiated into macrophages under the stimulation of phorbol 12-myristate 13-acetate (PMA) and then treated with LPS to build a cellular inflammation model. The cell viability was detected by CCK-8 assay. The levels of reactive oxygen species (ROS) were assessed by flow cytometry. The secretion and expression of inflammatory cytokines were tested by enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR). In addition, the nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways were analyzed by Western blotting. Results Twelve phenolic constituents including (–)-epicatechin, casuariin, agastachoside, etc. in CFP were identified. The CCK-8 assay showed that CFP exhibited no significant cytotoxicity between 100 and 300 μg/mL. After treated with CFP, the release of ROS was significantly suppressed. CFP inhibited inflammation in macrophages by attenuating the polarization of LPS-induced THP-1 macrophages, down-regulating the expression of the pro-inflammatory cytokines IL-6, IL-1β and TNF-α, and up-regulating the expression of the anti-inflammatory cytokine IL-10. Western blotting experiments manifested that CFP could markedly inhibit the phosphorylation of p65, ERK and JNK, thereby suppressing the activation of NF-κB and MAPK signaling pathways. Conclusion These findings indicated that CFP exerted anti-inflammatory activity by inhibiting the activation NF-κB and MAPK pathways which may induce the secretion of pro-inflammatory cytokines. This study offers a reference for C. fascicularis as the source of developing natural, safe anti-inflammatory agents in the future.