Heterogeneous hybrid immunity against Omicron variant JN.1 at 11 months following breakthrough infection (preprint)

A highly transmissible SARS-CoV-2 variant JN.1 is rapidly spreading throughout the nation, becoming the predominant strain in China and worldwide. However, the current immunity against the circulating JN.1 at population level has yet to be fully evaluated. We recruited representative cohorts with stratified age groups and diverse combinations of vaccination and/or infection in recent months, and promptly assessed humoral immunity for these subjects predominantly exhibiting hybrid immunity. We report that at 11 months following BA.5-wave breakthrough infection (BTI), these vaccinated individuals generally showed above-the-threshold yet low level of neutralizing activity against JN.1, with slightly greater potency observed in children and adolescents compared to adults and seniors. Meanwhile, XBB/EG.5-wave reinfection post-BTI significantly boosted the neutralizing antibodies against Omicron variants, including JN.1 in both adults (13.4- fold increase) and seniors (24.9-fold increase). To better understand respiratory mucosal protection against JN.1 over an extended period of months post-BTI, we profiled the humoral immunity in bronchoalveolar lavage samples obtained from vaccinated subjects with or without BTI, and revealed increased potency of neutralizing activity against the BA.5 and JN.1 variants in the respiratory mucosa through natural infection. Notably, at 11 months post-BTI, memory B cell responses against prototype and JN.1 were detectable in both blood and respiratory mucosa, displaying distinct memory features in the circulation and airway compartments. XBB/EG.5-wave reinfection drove the expansion of JN.1-specific B cells, along with the back-boosting of B cells responding to the ancestral viral strain, suggesting the involvement of immune imprinting. Together, this study indicates heterogeneous hybrid immunity over 11 months post-BTI, and underscores the vulnerability of individuals, particularly high-risk seniors, to JN.1 breakthrough infection. An additional booster with XBB-containing vaccine may greatly alleviate the onward transmission of immune-evasive SARS-CoV-2 variants.

A Nomogram for Predicting Delayed Viral Shedding in Non-Severe SARS-CoV-2 Omicron Infection.

Purpose: The Omicron variant of SARS-CoV-2 has emerged as a significant global concern, characterized by its rapid transmission and resistance to existing treatments and vaccines. However, the specific hematological and biochemical factors that may impact the clearance of Omicron variant infection remain unclear. The present study aimed to identify easily accessible laboratory markers that are associated with prolonged virus shedding in non-severe patients with COVID-19 caused by the Omicron variant. Patients and Methods: A retrospective cohort study was conducted on 882 non-severe COVID-19 patients who were diagnosed with the Omicron variant in Shanghai between March and June 2022. The least absolute shrinkage and selection operator regression model was used for feature selection and dimensional reduction, and multivariate logistic regression analysis was performed to construct a nomogram for predicting the risk of prolonged SARS-CoV-2 RNA positivity lasting for more than 7 days. The receiver operating characteristic (ROC) curve and calibration curves were used to assess predictive discrimination and accuracy, with bootstrap validation. Results: Patients were randomly divided into derivation (70%, n = 618) and validation (30%, n = 264) cohorts. Optimal independent markers for prolonged viral shedding time (VST) over 7 days were identified as Age, C-reactive protein (CRP), platelet count, leukocyte count, lymphocyte count, and eosinophil count. These factors were subsequently incorporated into the nomogram utilizing bootstrap validation. The area under the curve (AUC) in the derivation (0.761) and validation (0.756) cohorts indicated good discriminative ability. The calibration curve showed good agreement between the nomogram-predicted and actual patients with VST over 7 days. Conclusion: Our study confirmed six factors associated with delayed VST in non-severe SARS-CoV-2 Omicron infection and constructed a Nomogram which may assist non-severely affected patients to better estimate the appropriate length of self-isolation and optimize their self-management strategies.

Effects of EEG burst suppression on cerebral oxygen metabolism and postoperative cognitive function in elderly surgical patients: A randomized clinical trial.

BACKGROUND: This randomized clinical trial determined the effects of electroencephalographic burst suppression on cerebral oxygen metabolism and postoperative cognitive function in elderly surgical patients. METHODS: The patients were placed into burst suppression (BS) and non-burst suppression (NBS) groups. All patients were under bispectral index monitoring of an etomidate target-controlled infusion for anesthesia induction and intraoperative combination sevoflurane and remifentanil for anesthesia maintenance. The cerebral oxygen extraction ratio (CERO2), jugular bulb venous saturation (SjvO2), and difference in arteriovenous oxygen (Da-jvO2) were measured at T0, T1, and T2. One day before surgery, and 1, 3, and 7 days after surgery, postoperative cognitive dysfunction was assessed using the mini-mental state examination (MMSE). RESULTS: Compared with T0, the Da-jvO2 and CERO2 values were decreased, and SjvO2 was increased in the 2 groups at T1 and T2 (P < .05). There was no statistical difference in the SjvO2, Da-jvO2, and CERO2 values between T1 and T2. Compared with the NBS group, the SjvO2 value increased, and the Da-jvO2 and CERO2 values decreased at T1 and T2 in the BS group (P < .05). The MMSE scores on the 1st and 3rd days postoperatively were significantly lower in the 2 groups compared to the preoperative MMSE scores (P < .05). The MMSE scores of the NBS group were higher than the BS group on the 1st and 3rd days postoperatively (P < .05). CONCLUSION: In elderly patients undergoing surgery, intraoperative BS significantly reduced cerebral oxygen metabolism, which temporarily affected postoperative neurocognitive function.

The salvage therapy utilizing human umbilical cord-derived mesenchymal stem cells to treat severe COVID-19 patients: case series (preprint)

Background: COVID-19 has become one of the biggest challenges globally, yet no specifically effective medication has been developed. Mesenchymal stem cells (MSCs) exhibit properties of immune regulation and regeneration, which may suppress the over-inflammatory response and promote recovery of lung damage caused by COVID-19 and offer the potential as a therapeutic option. Methods: At the very beginning of COVID-19 epidemic in 2020, we investigated the use of human umbilical cord-derived MSCs (hUC-MSCs) as a salvage therapy in five severe COVID-19 patients, with each patient receiving intravenous infusion of hUC-MSCs three times. Results: All patients showed significant improvement in clinical manifestations, including laboratory biomarkers and findings of lung computed tomography images, after at least one dose of MSC therapy. Conclusion: These results suggest that MSC therapy is safe and can reduce the inflammatory response and alleviate the clinical symptoms of critically ill COVID-19 patients.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Lymphocyte-C-reactive protein ratio can differentiate disease severity of COVID-19 patients and serve as an assistant screening tool for hospital and ICU admission.

In this study, we aimed to explore whether lymphocyte-C-reactive protein ratio (LCR) can differentiate disease severity of coronavirus disease 2019 (COVID-19) patients and its value as an assistant screening tool for admission to hospital and intensive care unit (ICU). A total of 184 adult COVID-19 patients from the COVID-19 Treatment Center in Heilongjiang Province at the First Affiliated Hospital of Harbin Medical University between January 2020 and March 2021 were included in this study. Patients were divided into asymptomatic infection group, mild group, moderate group, severe group, and critical group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition). Demographic and clinical data including gender, age, comorbidities, severity of COVID-19, white blood cell count (WBC), neutrophil proportion (NEUT%), lymphocyte count (LYMPH), lymphocyte percentage (LYM%), red blood cell distribution width (RDW), platelet (PLT), C-reactive protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (SCr), albumin (ALB), total bilirubin (TB), direct bilirubin (DBIL), indirect bilirubin (IBIL), and D-dimer were obtained and collated from medical records at admission, from which sequential organ failure assessment (SOFA) score and LCR were calculated, and all the above indicators were compared among the groups. Multiple clinical parameters, including LYMPH, CRP, and LCR, showed significant differences among the groups. The related factors to classify COVID-19 patients into moderate, severe, and critical groups included age, number of comorbidities, WBC, LCR, and AST. Among these factors, the number of comorbidities showed the greatest effect, and only WBC and LCR were protective factors. The area under the receiver operating characteristic (ROC) curve of LCR to classify COVID-19 patients into moderate, severe, and critical groups was 0.176. The cutoff value of LCR and the sensitivity and specificity of the ROC curve were 1,780.7050 and 84.6% and 66.2%, respectively. The related factors to classify COVID-19 patients into severe and critical groups included the number of comorbidities, PLT, LCR, and SOFA score. Among these factors, SOFA score showed the greatest effect, and LCR was the only protective factor. The area under the ROC curve of LCR to classify COVID-19 patients into severe and critical groups was 0.106. The cutoff value of LCR and the sensitivity and specificity of the ROC curve were 571.2200 and 81.3% and 90.0%, respectively. In summary, LCR can differentiate disease severity of COVID-19 patients and serve as a simple and objective assistant screening tool for hospital and ICU admission.

Lymphocyte–C-reactive protein ratio can differentiate disease severity of COVID-19 patients and serve as an assistant screening tool for hospital and ICU admission

In this study, we aimed to explore whether lymphocyte–C-reactive protein ratio (LCR) can differentiate disease severity of coronavirus disease 2019 (COVID-19) patients and its value as an assistant screening tool for admission to hospital and intensive care unit (ICU). A total of 184 adult COVID-19 patients from the COVID-19 Treatment Center in Heilongjiang Province at the First Affiliated Hospital of Harbin Medical University between January 2020 and March 2021 were included in this study. Patients were divided into asymptomatic infection group, mild group, moderate group, severe group, and critical group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition). Demographic and clinical data including gender, age, comorbidities, severity of COVID-19, white blood cell count (WBC), neutrophil proportion (NEUT%), lymphocyte count (LYMPH), lymphocyte percentage (LYM%), red blood cell distribution width (RDW), platelet (PLT), C-reactive protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (SCr), albumin (ALB), total bilirubin (TB), direct bilirubin (DBIL), indirect bilirubin (IBIL), and D-dimer were obtained and collated from medical records at admission, from which sequential organ failure assessment (SOFA) score and LCR were calculated, and all the above indicators were compared among the groups. Multiple clinical parameters, including LYMPH, CRP, and LCR, showed significant differences among the groups. The related factors to classify COVID-19 patients into moderate, severe, and critical groups included age, number of comorbidities, WBC, LCR, and AST. Among these factors, the number of comorbidities showed the greatest effect, and only WBC and LCR were protective factors. The area under the receiver operating characteristic (ROC) curve of LCR to classify COVID-19 patients into moderate, severe, and critical groups was 0.176. The cutoff value of LCR and the sensitivity and specificity of the ROC curve were 1,780.7050 and 84.6% and 66.2%, respectively. The related factors to classify COVID-19 patients into severe and critical groups included the number of comorbidities, PLT, LCR, and SOFA score. Among these factors, SOFA score showed the greatest effect, and LCR was the only protective factor. The area under the ROC curve of LCR to classify COVID-19 patients into severe and critical groups was 0.106. The cutoff value of LCR and the sensitivity and specificity of the ROC curve were 571.2200 and 81.3% and 90.0%, respectively. In summary, LCR can differentiate disease severity of COVID-19 patients and serve as a simple and objective assistant screening tool for hospital and ICU admission.

Association between diagnostic delay and prognosis of pulmonary tuberculosis in Shandong, China: a retrospective study.

BACKGROUND: Tuberculosis (TB) is one of the main infectious diseases that seriously threatens global health, while diagnostic delay (DD) and treatment dramatically threaten TB control. METHODS: Between 2005 and 2017 in Shandong, China, we enrolled pulmonary tuberculosis (PTB) patients with DD. DD trends were evaluated by Joinpoint regression, and associations between PTB patient characteristics and DD were estimated by univariate and multivariate logistic regression. The influence of DD duration on prognosis and sputum smear results were assessed by Spearman correlation coefficients. RESULTS: We identified 208,822 PTB cases with a median DD of 33 days (interquartile range (IQR) 18-63). The trend of PTB with DD declined significantly between 2009 and 2017 (annual percent change (APC): - 4.0%, P = 0.047, 2009-2013; APC: - 6.6%, P = 0.001, 2013-2017). Patients aged > 45 years old (adjusted odds ratio (aOR): 1.223, 95% confidence interval (CI) 1.189-1.257, 46-65 years; aOR: 1.306, 95% CI 1.267-1.346, > 65 years), farmers (aOR: 1.520, 95% CI 1.447-1.596), and those with a previous treatment history (aOR: 1.759, 95% CI 1.699-1.821) were prone to developing long DD (> 30 days, P < 0.05). An unfavorable outcome was negatively associated with a short DD (OR: 0.876, 95% CI 0.843-0.910, P < 0.001). Sputum smear positive rate and unfavorable outcomes were positively correlated with DD duration (Spearman correlation coefficients (rs) = 1, P < 0.001). CONCLUSIONS: The DD situation remains serious; more efficient and comprehensive strategies are urgently required to minimize DD, especially for high-risk patients.

Comparison of demographic features and laboratory parameters between COVID-19 deceased patients and surviving severe and critically ill cases.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been far more devastating than expected, showing no signs of slowing down at present. Heilongjiang Province is the most northeastern province of China, and has cold weather for nearly half a year and an annual temperature difference of more than 60ºC, which increases the underlying morbidity associated with pulmonary diseases, and thus leads to lung dysfunction. The demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province, China with such climatic characteristics are still not clearly illustrated. AIM: To illustrate the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province by comparing with those of surviving severe and critically ill cases. METHODS: COVID-19 deceased patients from different hospitals in Heilongjiang Province were included in this retrospective study and compared their characteristics with those of surviving severe and critically ill cases in the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were divided into severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (the seventh edition). Demographic data were collected and recorded upon admission. Laboratory parameters were obtained from the medical records, and then compared among the groups. RESULTS: Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. No differences in age, gender, and number of comorbidities between groups were found. Neutrophil percentage (NEUT%), platelet (PLT), C-reactive protein (CRP), creatine kinase isoenzyme (CK-MB), serum troponin I (TNI) and brain natriuretic peptides (BNP) showed significant differences among the groups (P = 0.020, P = 0.001, P < 0.001, P = 0.001, P < 0.001, P < 0.001, respectively). The increase of CRP, D-dimer and NEUT% levels, as well as the decrease of lymphocyte count (LYMPH) and PLT counts, showed significant correlation with death of COVID-19 patients (P = 0.023, P = 0.008, P = 0.045, P = 0.020, P = 0.015, respectively). CONCLUSION: Compared with surviving severe and critically ill cases, no special demographic features of COVID-19 deceased patients were observed, while some laboratory parameters including NEUT%, PLT, CRP, CK-MB, TNI and BNP showed significant differences. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Lymphocyte-C-reactive protein ratio can differentiate disease severity of COVID-19 patients and serve as an assistant screening tool for hospital and ICU admission (preprint)

In this study, we aimed to explore whether Lymphocyte-C-reactive protein ratio (LCR) can differentiate disease severity of Coronavirus disease 2019 (COVID-19) patients and its value as an assistant screening tool for admission to the hospital and the intensive care unit (ICU). A total of 184 adult COVID-19 patients from the COVID-19 Treatment Center in Heilongjiang Province at the First Affiliated Hospital of Harbin Medical University between January 2020 and March 2021 were included in this study. Patients were divided into asymptomatic infection group, mild group, moderate group, severe group, and critical group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (9th edition). Demographic and clinical data including gender, age, comorbidities, severity of COVID-19, white blood cell count (WBC), neutrophil proportion (NEUT%), lymphocyte count (LYMPH), lymphocyte percentage (LYM%), red blood cell distribution width (RDW), platelet (PLT), C-reaction protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (SCr), albumin (ALB), total bilirubin (TB), direct bilirubin (DBIL), indirect bilirubin (IBIL), and D-Dimer were obtained and collated from medical records at admission, from which sequential organ failure assessment (SOFA) score and LCR were calculated, and all above indicators were compared among groups. Multiple clinical parameters, including LYMPH, CRP and LCR, showed significant differences among groups. The related factors to classify COVID-19 patients into moderate, severe and critical groups included age, number of comorbidities, WBC, LCR, and AST. Among these factors, number of comorbidities showed the greatest effect, and only WBC and LCR were protective factors. The area under the receiver operating characteristic (ROC) curve of LCR to classify COVID-19 patients into moderate, severe and critical groups was 0.176. The cut-off value of LCR, and the sensitivity and specificity of ROC curve were 1780.7050, 84.6% and 66.2%, respectively. The related factors to classify COVID-19 patients into severe and critical groups included number of comorbidities, PLT, LCR, and SOFA score. Among these factors, SOFA score showed the greatest effect, and LCR was the only protective factor. The area under ROC curve of LCR to classify COVID-19 patients into severe and critical groups was 0.106. The cut-off value of LCR and the sensitivity and specificity of ROC curve were 571.2200, 81.3% and 90.0%, respectively. In summary, LCR can differentiate disease severity of COVID-19 patients and serve as a simple and objective assistant screening tool for hospital and ICU admission.

Advanced graph and sequence neural networks for molecular property prediction and drug discovery.

MOTIVATION: Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep-learning methods, computational approaches for predicting molecular properties are gaining increasing momentum. However, there lacks customized and advanced methods and comprehensive tools for this task currently. RESULTS: Here, we develop a suite of comprehensive machine-learning methods and tools spanning different computational models, molecular representations and loss functions for molecular property prediction and drug discovery. Specifically, we represent molecules as both graphs and sequences. Built on these representations, we develop novel deep models for learning from molecular graphs and sequences. In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves (PRCs) and receiver operating characteristic (ROC) curves. Altogether, our work not only serves as a comprehensive tool, but also contributes toward developing novel and advanced graph and sequence-learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that our methods achieve consistent improvements over prior methods. In particular, our methods achieve #1 ranking in terms of both ROC-AUC (area under curve) and PRC-AUC on the AI Cures open challenge for drug discovery related to COVID-19. AVAILABILITY AND IMPLEMENTATION: Our source code is released as part of the MoleculeX library (https://github.com/divelab/MoleculeX) under AdvProp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of Improved Synthetic Routes to Pixatimod (PG545), a Sulfated Oligosaccharide-Steroid Conjugate.

The heparan sulfate (HS) mimetic pixatimod (PG545) is a highly potent inhibitor of angiogenesis, tumor growth, and metastasis currently in clinical trials for cancer. PG545 has also demonstrated potent antiviral activity against numerous HS-dependent viruses, including SARS-CoV-2, and shows promise as an antiviral drug for the treatment of COVID-19. Structurally, PG545 consists of a fully sulfated tetrasaccharide conjugated to the steroid 5α-cholestan-3ß-ol. The reported synthesis of PG545 suffers from a low yield and poor selectivity in the critical glycosylation step. Given its clinical importance, new efficient routes for the synthesis of PG545 and analogues were developed. Particular attention was given to improving the key glycosylation step by using more stable protecting groups and optimized glycosyl donors.

One-Year Recording of Cardiac Arrhythmias in a Non-Infected Population with Cardiac Implantable Devices During the COVID-19 Pandemic.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) was associated with a higher risk of arrhythmia in infected patients. However, there are no reports about the effect of the ongoing pandemic on arrhythmias in the non-infected population. We measured the arrhythmia burden in a non-infected population with cardiac implantable devices. METHODS: The arrhythmia burden during the COVID-19 pandemic was compared to a 6-month interval in the pre-COVID-19 period. The COVID-19 pandemic was divided into high-risk (17 January 2020 to 16 March 2020) and low-risk periods (17 March 2020 to 17 July 2020) according to whether there were locally infected patients. Arrhythmia burdens were compared among the pre-COVID-19, high-risk, and low-risk periods. RESULTS: A total of 219 patients with 1859 episodes were included. We observed a larger proportion of patients with atrial fibrillation (AF) during the COVID-19 pandemic (38.36% vs 26.03%, p = 0.006). There was not significantly more ventricular arrhythmia during the COVID period than the pre-COVID-19 period (p > 0.05). During the high-risk period, daily frequency of non-sustained ventricular tachycardia (NSVT) (0.0172, 0.0475 vs 0.0109, 0.0164, p < 0.05), atrial tachycardia (AT) (0.0345, 0.0518 vs 0.0164, 0.0219 p < 0.05) and AF (0.0345, 0.0432 vs 0.0164, 0.0186, p < 0.05) and daily duration of NSVT (0.1982, 0.2845 vs 0.0538, 0.1640 p < 0.05) were higher and longer than those in the pre-COVID-19 period. Regression modeling showed that the impact of COVID-19 pandemic lead to an increased onset of AF (odds ratio 2.465; p < 0.01). Patients with paroxysmal AF who had undergone a previous radiofrequency ablation had a lower burden of AF (incidence 21.43% vs 55.00%, P = 0.049, daily frequency 0.0000, 0.0027 vs 0.0000, 241.7978, P = 0.020) during the pandemic. CONCLUSION: The COVID-19 pandemic contributed to a higher burden of arrhythmias in non-infected patients. Patients would experience a lower burden of AF following radiofrequency ablation treatment, and this effect persisted during the pandemic.

COVID-19 and Tuberculosis Coinfection: An Overview of Case Reports/Case Series and Meta-Analysis.

Background: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are two major infectious diseases posing significant public health threats, and their coinfection (aptly abbreviated COVID-TB) makes the situation worse. This study aimed to investigate the clinical features and prognosis of COVID-TB cases. Methods: The PubMed, Embase, Cochrane, CNKI, and Wanfang databases were searched for relevant studies published through December 18, 2020. An overview of COVID-TB case reports/case series was prepared that described their clinical characteristics and differences between survivors and deceased patients. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for death or severe COVID-19 were calculated. The quality of outcomes was assessed using GRADEpro. Results: Thirty-six studies were included. Of 89 COVID-TB patients, 19 (23.46%) died, and 72 (80.90%) were male. The median age of non-survivors (53.95 ± 19.78 years) was greater than that of survivors (37.76 ± 15.54 years) (p < 0.001). Non-survivors were more likely to have hypertension (47.06 vs. 17.95%) or symptoms of dyspnea (72.73% vs. 30%) or bilateral lesions (73.68 vs. 47.14%), infiltrates (57.89 vs. 24.29%), tree in bud (10.53% vs. 0%), or a higher leucocyte count (12.9 [10.5-16.73] vs. 8.015 [4.8-8.97] × 109/L) than survivors (p < 0.05). In terms of treatment, 88.52% received anti-TB therapy, 50.82% received antibiotics, 22.95% received antiviral therapy, 26.23% received hydroxychloroquine, and 11.48% received corticosteroids. The pooled ORs of death or severe disease in the COVID-TB group and the non-TB group were 2.21 (95% CI: 1.80, 2.70) and 2.77 (95% CI: 1.33, 5.74) (P < 0.01), respectively. Conclusion: In summary, there appear to be some predictors of worse prognosis among COVID-TB cases. A moderate level of evidence suggests that COVID-TB patients are more likely to suffer severe disease or death than COVID-19 patients. Finally, routine screening for TB may be recommended among suspected or confirmed cases of COVID-19 in countries with high TB burden.

COVID-19 pandemic related long-term chronic stress on the prevalence of depression and anxiety in the general population.

BACKGROUND: The COVID-19 pandemic has lasted for more than 1 year, causing far-reaching and unprecedented changes in almost all aspects of society. This study aimed to evaluate the long-term consequences of the COVID-19 pandemic on depression and anxiety, and explore the factors associated with it. METHODS: A cross-sectional study using an online survey was conducted to assess mental health problems from February 2 to February 9, 2021 by using patient health questionnaire-9 (PHQ-9) and generalized anxiety disorder-7 (GAD-7). The insomnia severity index (ISI), demographic data and COVID-19 related variables were measured by a self-designed questionnaire. The factors associated with depressive and anxiety symptoms were identified by Pearson chi-square test and binary logistic regression analysis. RESULTS: In the study that 1171 participants enrolled, the overall prevalence of depressive and anxiety symptoms among general people was 22.6 and 21.4% respectively in the present study. Living alone was a potential risk factor for depressive symptoms, while regular exercises was a potential protective factor. The prevalence of depressive and anxiety symptoms was significantly associated with the severity of insomnia symptoms and the negative feelings about pandemic. CONCLUSION: COVID-19 pandemic- related chronic stress has brought about profound impacts on long-term mental health in the general population. The level of insomnia and a negative attitude towards the pandemic are significantly correlated with unfavorable mental health. However, we failed to found a significant association of age and gender with the mental health symptoms, although they were recognized as well-established risk factors during the outbreak by some other studies. This discrepancy may be because the acute and chronic effects of the pandemic are influenced by different factors, which reminds that more attention should be paid to the intrinsic psychological factors and physical reactions towards COVID-19.

Particulate matter exposure exacerbates susceptibility to SARS-CoV-2 infection in humanized ACE2 mice.

The global outbreak of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as of 8 May 2021, has surpassed 150 700 000 infections and 3 279 000 deaths worldwide. Evidence indicates that SARS-CoV-2 RNA can be detected on particulate matter (PM), and COVID-19 cases are correlated with levels of air pollutants. However, the mechanisms of PM involvement in the spread of SARS-CoV-2 remain poorly understood. Here, we found that PM exposure increased the expression level of angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) in several epithelial cells and increased the adsorption of the SARS-CoV-2 spike protein. Instillation of PM in a hACE2 mouse model significantly increased the expression of ACE2 and Tmprss2 and viral replication in the lungs. Furthermore, PM exacerbated the pulmonary lesions caused by SARS-CoV-2 infection in the hACE2 mice. In conclusion, our study demonstrated that PM is an epidemiological factor of COVID-19, emphasizing the necessity of wearing anti-PM masks to cope with this global pandemic.

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery (preprint)

Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predicting molecular properties are gaining increasing momentum. However, there lacks customized and advanced methods and comprehensive tools for this task currently. Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery. Specifically, we represent molecules as both graphs and sequences. Built on these representations, we develop novel deep models for learning from molecular graphs and sequences. In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves. Altogether, our work not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that our methods achieve consistent improvements over prior methods. In particular, our methods achieve #1 ranking in terms of both ROC-AUC and PRC-AUC on the AI Cures Open Challenge for drug discovery related to COVID-19. Our software is released as part of the MoleculeX library under AdvProp.