Long-term effects of Pfizer-BioNTech COVID-19 vaccinations on platelets.

There is a global concern about the safety of COVID-19 vaccines associated with platelet function. However, their long-term effects on overall platelet activity remain poorly understood. Here we address this problem by image-based single-cell profiling and temporal monitoring of circulating platelet aggregates in the blood of healthy human subjects, before and after they received multiple Pfizer-BioNTech (BNT162b2) vaccine doses over a time span of nearly 1 year. Results show no significant or persisting platelet aggregation trends following the vaccine doses, indicating that any effects of vaccinations on platelet turnover, platelet activation, platelet aggregation, and platelet-leukocyte interaction was insignificant.

Using the internet search data to investigate symptom characteristics of COVID-19: A big data study.

OBJECTIVE: Analyzing the symptom characteristics of Coronavirus Disease 2019(COVID-19) to improve control and prevention. METHODS: Using the Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources to obtain the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020 and the epidemic data in Hubei province and the other top 9 impacted provinces in China. Data of 2020 were compared with those of the previous three years. Data of Hubei province were compared with those of the other 9 provinces. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlations between the SV of COVID-19 and the number of newly confirmed/suspected cases were analyzed. The lag effects were discussed. RESULTS: Comparing the SV from January 1, 2020 to February 20, 2020 with those for the same period of the previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea, and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P＜0.001). The SV of COVID-19 in Hubei province was significantly correlated with the number of newly confirmed/suspected cases (r confirmed = 0.723, r suspected = 0.863, both p < 0.001). The results of the distributed lag model suggested that the patients who searched relevant symptoms on the Internet may begin to see doctors in 2-3 days later and be confirmed in 3-4 days later. CONCLUSION: The total SV of lower respiratory symptoms was higher than that of upper respiratory symptoms, and the SV of diarrhea also increased significantly. It warned us to pay attention to not only the symptoms of the lower respiratory tract but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. Internet search behavior had a positive correlation with the number of newly confirmed/suspected cases, suggesting that big data has an important role in the early warning of infectious diseases.

Clinical characteristics of 95 patients with severe coronavirus disease 2019

Objective: To retrospectively analyze the clinical characteristics of 95 patients with severe coronavirus disease 2019 (COVID-19) admitted to Hankou Hospital of Wuhan, and provide evidence for clinical diagnosis and treatment of severe cases. Methods: From January to March 2020, 95 patients with severe COVID-19 were admitted to a designated Hankou Hospital of Wuhan. The clinical manifestations, laboratory examinations, chest CT, respiratory support, drug treatment, and outcomes were collected and analyzed. Results: Among the 95 patients, there were 76(80.0%) severe cases (severe group) and 19 (20.0%) critically ill cases (critically ill group);the average ages of the two groups were (56.9 .. 14.0) and (66.2 .. 14.1) years old, respectively. The main symptoms included fever [85 (89.5%)], cough [73 (76.8%)] dyspnea [57 (60.0%)], sputum expectoration [32 (33.7%)], diarrhea [20 (21.1%)], etc. The initial symptom was fever [64 (67.4%)], followed by cough [17 (17.9%)]. The main comorbidities were hypertension [29 (30.5%)], diabetes [18 (18.9%), coronary heart disease [12 (12.6%)], etc. Liver injury was the most frequently seen complication which occurred in 35 patients (36.8%), while myocardial damage in 20 patients (21.1%), heart failure in 10 patients (10.5%), and renal damage in 8 patients (8.4%). The level of urea nitrogen [7.5 (3.1-36.6) mmol/L], creatinine [88.0 (46.0-681.0) mol/L], aspartate aminotransferase (AST) [49.0 (8.0-2 290.0) U/L], total bilirubin [12.4 (6.8-112.4) mol/L], white blood cells [8.7 (2.7-16.3) .. 109], neutrophil count [7.9 (1.0-14.6) .. 109/L], high-sensitivity C-reactive protein (hsCRP) [35.6 (0.1-37.9) mg/L] and procalcitonin (PCT) [0.3 (0.1-9.6) ng/mL] in the critically ill group were higher than the severe group [4.5 (1.5-14.6) mmol/L, 70.0 (34.0-149.0) mol/L, 30.5 (10.0-184.0) U/L, 7.8 (1.4-24.5) mol/L, 4.5 (1.7- 10.7) .. 109/L 3.1 (0.6-9.1) .. 109/L, 31.8 (0.1- 40.4) mg/L, 0.1 (0.0- 1.2) ng/mL], and the difference were statistically significant (P all < 0.05);the albumin level reflecting nutritional status [30.2 (24.6-36.4) g/L] was lower than the severe group [35.2(23.5-44.5)g/L], and the difference was statistically significant (P < 0.001). Chest computed tomographic scans showed bilateral ground glass opacity or patchy shadows in the lungs of all patients. A total of 77 patients (82.1%) were discharged, and 13 patients (13.7%) died;of which, the mortality of the critically ill group was 68.4% (13 out of 19). Conclusions: The majority of patients with severe COVID- 19 were elderly. The main clinical manifestations were fever, cough, and dyspnea. Most patients had underlying diseases such as hypertension, diabetes and coronary heart disease. The occurrence of organ dysfunctions such as liver injury, cardiac damage, heart failure and kidney injury might be an important cause of death. The mortality of severe patients with COVID-19 was high, and treatment was even tough.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates.

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.

Dynamic changes of pulmonary diffusion capacity in survivors of non-critical COVID-19 during the first six months.

BACKGROUND: The dynamic trends of pulmonary function in coronavirus disease 2019 (COVID-19) survivors since discharge have been rarely described. We aimed to describe the changes of lung function and identify risk factors for impaired diffusion capacity. METHODS: Non-critical COVID-19 patients admitted to the Guangzhou Eighth People's Hospital, China, were enrolled from March to June 2020. Subjects were prospectively followed up with pulmonary function tests at discharge, three and six months after discharge. FINDINGS: Eighty-six patients completed diffusion capacity tests at three timepoints. The mean diffusion capacity for carbon monoxide (DLCO)% pred was 79.8% at discharge and significantly improved to 84.9% at Month-3. The transfer coefficient of the lung for carbon monoxide (KCO)% pred significantly increased from 91.7% at discharge to 95.7% at Month-3. Both of them showed no further improvement at Month-6. The change rates of DLCO% pred and KCO% pred were significantly higher in 0-3 months than in 3-6 months. The alveolar ventilation (VA) improved continuously during the follow-ups. At Month-6, impaired DLCO% pred was associated with being female (OR 5.2 [1.7-15.8]; p = 0.004) and peak total lesion score (TLS) of chest CT > 8.5 (OR 6.6 [1.7-26.5]; p = 0.007). DLCO% pred and KCO% pred were worse in females at discharge. And in patients with impaired diffusion capacity, females' DLCO% pred recovered slower than males. INTERPRETATION: The first three months is the critical recovery period for diffusion capacity. The impaired diffusion capacity was more severe and recovered slower in females than in males. Early pulmonary rehabilitation and individualized interventions for recovery are worthy of further investigations.

Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19.

A characteristic clinical feature of COVID-19 is the frequent incidence of microvascular thrombosis. In fact, COVID-19 autopsy reports have shown widespread thrombotic microangiopathy characterized by extensive diffuse microthrombi within peripheral capillaries and arterioles in lungs, hearts, and other organs, resulting in multiorgan failure. However, the underlying process of COVID-19-associated microvascular thrombosis remains elusive due to the lack of tools to statistically examine platelet aggregation (i.e., the initiation of microthrombus formation) in detail. Here we report the landscape of circulating platelet aggregates in COVID-19 obtained by massive single-cell image-based profiling and temporal monitoring of the blood of COVID-19 patients (n = 110). Surprisingly, our analysis of the big image data shows the anomalous presence of excessive platelet aggregates in nearly 90% of all COVID-19 patients. Furthermore, results indicate strong links between the concentration of platelet aggregates and the severity, mortality, respiratory condition, and vascular endothelial dysfunction level of COVID-19 patients.

Synthetic hydrogel nanoparticles for sepsis therapy.

Sepsis is a life-threatening condition caused by the extreme release of inflammatory mediators into the blood in response to infection (e.g., bacterial infection, COVID-19), resulting in the dysfunction of multiple organs. Currently, there is no direct treatment for sepsis. Here we report an abiotic hydrogel nanoparticle (HNP) as a potential therapeutic agent for late-stage sepsis. The HNP captures and neutralizes all variants of histones, a major inflammatory mediator released during sepsis. The highly optimized HNP has high capacity and long-term circulation capability for the selective sequestration and neutralization of histones. Intravenous injection of the HNP protects mice against a lethal dose of histones through the inhibition of platelet aggregation and migration into the lungs. In vivo administration in murine sepsis model mice results in near complete survival. These results establish the potential for synthetic, nonbiological polymer hydrogel sequestrants as a new intervention strategy for sepsis therapy and adds to our understanding of the importance of histones to this condition.

Use of personal protective equipment against coronavirus disease 2019 by healthcare professionals in Wuhan, China: cross sectional study.

OBJECTIVE: To examine the protective effects of appropriate personal protective equipment for frontline healthcare professionals who provided care for patients with coronavirus disease 2019 (covid-19). DESIGN: Cross sectional study. SETTING: Four hospitals in Wuhan, China. PARTICIPANTS: 420 healthcare professionals (116 doctors and 304 nurses) who were deployed to Wuhan by two affiliated hospitals of Sun Yat-sen University and Nanfang Hospital of Southern Medical University for 6-8 weeks from 24 January to 7 April 2020. These study participants were provided with appropriate personal protective equipment to deliver healthcare to patients admitted to hospital with covid-19 and were involved in aerosol generating procedures. 77 healthcare professionals with no exposure history to covid-19 and 80 patients who had recovered from covid-19 were recruited to verify the accuracy of antibody testing. MAIN OUTCOME MEASURES: Covid-19 related symptoms (fever, cough, and dyspnoea) and evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, defined as a positive test for virus specific nucleic acids in nasopharyngeal swabs, or a positive test for IgM or IgG antibodies in the serum samples. RESULTS: The average age of study participants was 35.8 years and 68.1% (286/420) were women. These study participants worked 4-6 hour shifts for an average of 5.4 days a week; they worked an average of 16.2 hours each week in intensive care units. All 420 study participants had direct contact with patients with covid-19 and performed at least one aerosol generating procedure. During the deployment period in Wuhan, none of the study participants reported covid-19 related symptoms. When the participants returned home, they all tested negative for SARS-CoV-2 specific nucleic acids and IgM or IgG antibodies (95% confidence interval 0.0 to 0.7%). CONCLUSION: Before a safe and effective vaccine becomes available, healthcare professionals remain susceptible to covid-19. Despite being at high risk of exposure, study participants were appropriately protected and did not contract infection or develop protective immunity against SARS-CoV-2. Healthcare systems must give priority to the procurement and distribution of personal protective equipment, and provide adequate training to healthcare professionals in its use.

Chest CT Features from 58 Patients with COVID-19 Pneumonia from the Perspective of ACE2.

BACKGROUND: Chest CT is important for the diagnosis of Corona Virus Disease 2019, which is caused by SARS-CoV-2 via the receptor angiotensin-converting enzyme 2. This study aimed to present special chest CT changes in the detection and management of COVID-19. METHODS: From February 20 to March 6, 2020, clinical data and chest CT of patients with COVID-19 being treated by the Hubei Medical Team were retrospectively analyzed with a time-interval of 2 weeks. In addition, the expressions of ACE2 in different parts of the respiratory system were detected by immunohistochemical staining to explain the special chest CT features of COVID-19 by ACE2 expression. RESULTS: Of 58 patients, the main respiratory manifestations were fever and cough. Spherical or patchy GGO was the initial CT manifestation of COVID-19 pneumonia. CT findings manifested as rapid evolution from focal unilateral to diffuse bilateral ground-glass opacities (GGO) that progressed to or co-existed with consolidations in chest CT scans. Lung consolidation increased as the disease progressed, accounting for 63.2%, 76.3%, and 87.5% in group 1 (disease course with 0 - 2 weeks), group 2 (2 - 4 weeks), and group 3 (> 4 weeks). Fibrous lesions (72.3%), high density vascular shadow (69.2%), reticular pattern (63.1%), and subpleural parallel sign (61.5%) were common signs of chest CT of COVID-19. IHC results showed that ACE2-expression in the pulmonary alveoli was significantly higher than that in the bronchial mucosa and pleura (p < 0.001). CONCLUSIONS: The special change of CT features in the lung of COVID-19 pneumonia patients have a connection with ACE2 expression patterns in the respiratory system.

Effects of Smoking on ACE2 Expression Pattern: Risk and Severity of SARS-CoV-2 Infection.

BACKGROUND: Respiratory epithelium expressing angiotensin-converting enzyme 2 (ACE2) is the entry for novel coronavirus (SARS-CoV-2), pathogen of the COVID-19 pneumonia outbreak, although a few recent studies have found different ACE2 expression in lung tissue of smokers. The effect of smoking on ACE2 expression and COVID-19 is still not clear. So, we did this research to determine the effect of smoking on ACE2 expression pattern and its relationship with the risk and severity of COVID-19. METHODS: The clinical data of COVID-19 patients with smoking and non-smoking were analyzed, and ACE2 expression of respiratory and digestive mucosa epithelia from smoker and non-smoker patients or healthy subjects were detected by immunohistochemical (IHC) staining. RESULTS: Of all 295 laboratory-confirmed COVID-19 patients, only 24 (8.1%) were current smokers with moderate smoking or above, which accounted for 54.2% of severe cases with higher mortality than non-smokers (8.3% vs. 0.4%, p = 0.018). Data analysis showed the proportion of smokers in COVID-19 patients was lower than that in general population of China (Z = 11.65, P < 0.001). IHC staining showed ACE2 expression in respiratory and digestive epithelia of smokers were generally downregulated. CONCLUSIONS: The proportion of smokers in COVID-19 patients was lower, which may be explained by ACE2 downregulation in respiratory mucosa epithelia. However, smoking COVID-19 patients accounted for a higher proportion in severe cases and higher mortality than for non-smoking COVID-19 patients, which needs to be noted.

Using the Internet Big Data to Investigate the Epidemiological Characteristics of Allergic Rhinitis and Allergic Conjunctivitis.

BACKGROUND: To explore the epidemiological characteristics of allergic rhinitis (AR) and allergic conjunctivitis (AC) based on the Internet big data. METHODS: The Baidu index (BDI) of keywords "allergic rhinitis" and "allergic conjunctivitis" in Mandarin, the daily pollen concentration (PC) released by the Beijing Meteorological Bureau and the volumes of outpatient visits (OV) of the Beijing Tongren Hospital (Beijing) and the Third Affiliated Hospital of Sun Yat-sen University (Guangzhou) from 2017 to 2020 were obtained. The temporal and spatial changes of AR and AC were discussed. The correlations between BDI and PC/OV were analyzed by Spearman correlation analysis. RESULTS: The trends of BDI of "AR"/"AC" in Beijing showed obvious seasonal variations, but not in Guangzhou. The BDI of "AR" and "AC" was consistent with the OV in both cities (r1AR-BJ=0.580, P<0.001; r1AR-GZ=0.360, P=0.031; r1AC-BJ=0.885, P<0.001; r1AC-GZ=0.694, P<0.001). The BDI of "AR" and "AC" was highly consistent with the change of the PC in Beijing (r AR-Pollen=0.826, P<0.001; r AC-Pollen=0.564, P<0.001). The OV of AR in Beijing and Guangzhou decreased significantly in the first half of 2020, but there was no significant change in AC. In the first half of 2020, the OV of AC in Beijing was significantly higher than that of AR, while that of AC in Guangzhou was slightly higher than that of AR. CONCLUSION: The BDI could reflect the real-world situation to some extent and has the potential to predict the epidemiological characteristics of AR and AC. The BDI and OV of AR decreased significantly, but those of AC were still at a high level, during the COVID-19 pandemic, in the environment where most people in Beijing and Guangzhou wore masks without eye protection.

Intelligent classification of platelet aggregates by agonist type.

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the "intelligent platelet aggregate classifier" or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and "learned" to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method's accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient's heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.