Progression Of Coronavirus Disease 2019 Research Articles

Differential gene expression of immune response in COVID-19 and its relationship with progression of COVID-19

Human immune response evolved in virus clearance and gene expression levels of immune response may be associated with progression of Coronavirus disease 2019 (COVID-19). Our current study aims to investigate the relationship between differential gene expression of immune response and progression of COVID-19. A total of 50 participants of COVID-19 group were studied, compared with 39 participants of healthy control group. There were different gene expression profiles in pathways of activation of neutrophil, defense response and adaptive immune response for COVID-19 group before treatment compared to the healthy control group. Distinct gene expression profiles showed that pathways of chemotaxis, immune response and antibacterial humoral response involved in rehabilitation of severe COVID-19 group while pathways of immune system process, defense response to virus and negative regulation of viral genome replication involved in rehabilitation of moderate COVID-19 group. Both protein expression of ATP-binding cassette transporter A1 levels and protein expression of low affinity Fc-receptor FcγRIIIb levels were significantly and positively correlated with COVID-19-IgM levels and might be suitable as biomarkers for monitoring of COVID-19. This study showed that differential gene expression of immune response predicted onset and rehabilitation of COVID-19.

Clinical usefulness of the serum levels of neuroinflammatory and lung fibrosis biomarkers in the assessment of cognitive dysfunction in post-COVID19 patients

A growing body of evidence indicates there is an increasing incidence of cognitive dysfunction in patients after coronavirus disease 2019 (COVID-19) infection. However, still lack diagnostic tools, which allow us to predict prognosis in such cases and improve the stratification of the disease. This study aims to evaluate the usefulness of the biomarkers that could allow to predict the severity and progression of COVID-19 in patients with post-COVID syndrome and cognitive problems. Data regarding clinical history, pre-existing conditions, chest CT scan, and therapy (remdesivir, steroids) were acquired. A total of 44 patients with hospitalized COVID-19, and healthy controls were enrolled in the investigation, and serum blood was obtained. After 6 months of observations, patients with COVID-19 were divided into two groups: first - without post-COVID syndrome and memory complaints, and second - with post-COVID and cognitive problems. Measurements of YKL-40 and MR-pro-ADM were taken in the serum with enzyme immunoassay kits at the time of admission (visit 1) and 6 months after discharge from the hospital (visit 2). Significantly higher concentrations of YKL-40 were found in patients with COVID-19 as compared to healthy individuals (p = 0.016). Moreover, YKL-40 ratio allowed to differentiate patients with and without post-COVID syndrome (median: 0.94 vs. 1.55, p = 0.004). Additionally, COVID-19 patients with dyspnea presented significantly elevated levels of MR-pro-ADM as compared to the group of COVID-19 survivors without dyspnea (p = 0.015). In the group of patients without post-COVID syndrome, the concentrations of YKL-40 and MR-pro-ADM decreased after treatment as compared to levels before therapy (77 vs. 36 ng/ml and 607 vs. 456 pmol/L). However, in patients with post-COVID syndrome and cognitive problems, the levels of both markers did not alter 6 months after hospital discharge in comparison to basal levels. Furthermore, after dexamethasone treatment the YKL-40 concentrations declined significantly (p = 0.003) in patients with COVID-19. This study demonstrated the predictive usefulness of YKL-40 as an indicator of successful treatment in patients with COVID-19 infection allowing risk stratification of hospitalized patients. It seems that indicators of neuroinflammation might have the potential to track development of cognitive complaints, however, it requires further investigations.

Patient Characteristics and Clinical Outcomes During the 2020-2021 COVID-19 Wave: An Observational Study at a Tertiary Hospital in Saudi Arabia.

Background and objective The continued prevalence and threat of coronavirus disease 2019 (COVID-19) have been reported, and evidence suggests that several people still get infected with the virus. Gaining a thorough understanding of the patient demographic factors and laboratory findings could contributeto assessing the severity, mortality, and progression of COVID-19. In light of this, the current study aimed to evaluate the demographic characteristics, laboratory findings, and outcomes of confirmed COVID-19 patients at a tertiary hospital in the Kingdom of Saudi Arabia (KSA). Methodology We collected data spanning the period 2020-2021 from the electronic health records of Al-Noor Specialized Hospital, Maincluding demographics (age, gender, and nationality), severity (i.e., ICU admission), length of hospital stay, mortality, and laboratory parameters. Results We observed an overall mortality rate of10.2% (338 of 3,307 patients). The mortality rate was significantly higher in males (n=210; 62.1%) and patients aged more than 70 years (n=91; 26.9%). Patients with blood group O comprised 131 (29%) of the 338 non-survivors, followed by those with A (n=85; 25.1%) and Bgroups (n=79; 23.4%). The mortality rate among ICU patients was 63.3% (n=214). Furthermore, the following laboratory findings showed abnormal mean values in terms of severity and mortality in COVID-19 patients: hemoglobin (HB) concentration, white blood cell (WBC) count, lymphocyte count (LC), C-reactive protein (CRP), creatinine (CREA), and uric acid (UA) levels. Conclusions Old age, male gender, and certain laboratory findings have a critical role inthe severity and mortality risk in COVID-19 patients. There was no significant association between blood type and the severity and mortality of COVID-19. Continuous monitoring based on these findings may be essential to managing COVID-19 patients.

The surfactant protein B polymorphisms (rs7316 and rs1130866) and their correlation with disease progression of COVID-19

BackgroundIt is critical to examine the pathogenic pathways in coronavirus disease 2019 (COVID-19) that resulted in the development of severe lung injury. Surfactant protein B (SFTPB) is a vital component for sustaining life and serves pivotal functions in the host’s defensive mechanisms and alveolar surface tension reduction. Our study aimed to determine the effect of SFTPB rs7316 and rs1130866 variants on the course of disease in COVID-19 patients. MethodsThe study cohort comprised 3,184 individuals diagnosed with COVID-19. We employed the RFLP approach to determine the variations of the SFTPB genes. ResultsSFTPB rs7316 did not exhibit a statistically significant correlation with COVID-19 mortality across different inheritance models. But, after making more changes for SARS-CoV-2 variants, it was found that there was a strong link between the TT and TC genotypes of SFTPB rs7316 and death rates, especially for the Delta variant. Furthermore, our study’s findings indicate a significant association between the SFTPB rs1130866 G allele and an elevated risk of mortality in COVID-19 across all variants of SARS-CoV-2. ConclusionsThe use of the SFTPB rs1130866 marker has the potential to facilitate the prediction of COVID-19 severity. On the other hand, for SFTPB rs7316, this kind of prediction seems to depend on the particular SARS-CoV-2 variants.

Interferon gene expression declines over time post-COVID infection and in long COVID patients

Background Interferons (IFNs) represent a first-line defense against viruses and other pathogens. It has been shown that an impaired and uncontrolled release of these glycoproteins can result in tissue damage and explain severe progression of coronavirus disease 2019 (COVID-19). However, their potential role in Long-COVID syndrome (LC) remains debateable. Objectives The objective of the present study is to shed further light on the possible role of IFNs (and related genes) gene expression patterns in the progression of COVID-19 and LC patients. Methods We carried out a multi-cohort study by analyzing the IFN gene expression patterns (using different IFN gene signatures) in five cohorts of acute COVID-19 (n = 541 samples) and LC patients (n = 188), and compared them to patterns observed in three autoimmune diseases (systemic lupus erythematous [n = 242], systemic sclerosis [n = 91], and Sjögren’s syndrome [n = 282]). Results The data show that, while the interferon signatures are strongly upregulated in severe COVID-19 patients and autoimmune diseases, it decays with the time from symptoms onset and in LC patients. Differential pathway analysis of IFN-related terms indicates an over activation in autoimmune diseases (IFN-I/II) and severe COVID-19 (IFN-I/II/III), while these pathways are mostly inactivated or downregulated in LC (IFN-I/III). By analyzing six proteomic LC datasets, we did not find evidence of a role of IFNs in this condition. Conclusion Our findings suggest a potential role of cytokine exhaustion mediated by IFN gene expression inactivation as a possible driver of LC.

Monoclonal antibodies against SARS-CoV-2 to prevent COVID-19 worsening in a large multicenter cohort

ObjectiveMonoclonal antibodies (mAbs) against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) reduced Coronavirus Disease 2019 (COVID-19) hospitalizations in people at risk of clinical worsening. Real-world descriptions are limited. MethodsCONDIVIDIAMO, a two-year multicenter observational study, consecutively enrolled SARS-CoV-2 outpatients with ≥1 risk factor for COVID-19 progression receiving mAbs. Demographic data, underlying medical condition, type of mAbs combination received, duration of symptoms before mAbs administration, COVID-19 vaccination history, were collected upon enrolment and centrally recorded. Data on outcomes (hospitalizations, reasons of hospitalization, deaths) were prospectively collected. The primary endpoint was the rate of hospitalization or death in a 28-day follow-up, whichever occurred first; subjects were censored at the day of last follow-up or up to 28 days. The Kaplan-Meier method was used to estimate the incidence rate curve in time. The Cox regression model was used to assess potential risk factors for unfavorable outcome. Results were shown as hazard ratio (HR) along with the corresponding 95 % Confidence Interval (95%CI). ResultsAmong 1534 subjects (median [interquartile range, IQR] age 66.5 [52.4–74.9] years, 693 [45.2 %] women), 632 (41.2 %) received bamlanivimab ± etesevimab, 209 (13.6 %) casirivimab/imdevimab, 586 (38.2 %) sotrovimab, 107 (7.0 %) tixagevimab/cilgavimab. After 28-day follow-up, 87/1534 (5.6 %, 95%CI: 4.4%–6.8 %) met the primary outcome (85 hospitalizations, 2 deaths). Hospitalizations for COVID-19 (52, 3.4 %) occurred earlier than for other reasons (33, 2.1 %), after a median (IQR) of 3.5 (1–7) versus 8 (3–15) days (p = 0.006) from mAbs administration.In a multivariable Cox regression model, factors independently associated with increased hospitalization risk were age (hazard ratio [HR] 1.02, 95%CI 1.00–1.03, p = 0.021), immunodeficiency (HR 1.78, 95%CI 1.11–2.85, p = 0.017), pre-Omicron calendar period (HR 1.66, 95%CI 1.02–2.69, p = 0.041). ConclusionsMAbs real-world data over a 2-year changing pandemic landscape showed the feasibility of the intervention, although the hospitalization rate was not negligible. Immunosuppressed subjects remain more at risk of clinical worsening.

Real-world effectiveness of sotrovimab in preventing hospitalization and mortality in high-risk patients with COVID-19 in the United States: A cohort study from the Mayo Clinic electronic health records.

To describe outcomes of high-risk patients with coronavirus disease 2019 (COVID-19) treated with sotrovimab, other monoclonal antibodies (mAbs), or antivirals, and patients who did not receive early COVID-19 treatment. We also evaluate the comparative effectiveness of sotrovimab versus no treatment in preventing severe clinical outcomes. This observational retrospective cohort study analyzed Mayo Clinic electronic health records. Non-hospitalized adult patients diagnosed with COVID-19 from May 26, 2021 and April 23, 2022 and at high risk of COVID-19 progression were eligible. The primary outcome was 29-day all-cause hospitalization and/or death. Outcomes were described for patients treated with sotrovimab, other mAbs, or antivirals, and eligible but untreated patients, and compared between sotrovimab-treated and propensity score (PS)-matched untreated cohorts. We included 35,485 patients (sotrovimab, 1369; other mAbs, 6488; antivirals, 133; high-risk untreated, 27,495). A low proportion of patients treated with sotrovimab (n = 33/1369, 2.4%), other mAbs (n = 147/6488, 2.3%), or antivirals (n = 2/133, 1.5%) experienced all-cause hospitalization or death. Among high-risk untreated patients, the percentage of all-cause hospitalization or death was 3.3% (n = 910/27,495). In the PS-matched analysis, 2.5% (n = 21/854) of sotrovimab-treated patients experienced all-cause hospitalization and/or death versus 2.8% (n = 48/1708) of untreated patients (difference, -0.4%; p = 0.66). Significantly fewer sotrovimab-treated patients required intensive care unit admission (0.5% vs 1.8%; difference, -1.3%; p = 0.002) or respiratory support (3.5% vs 8.7%; difference, -5.2%; p < 0.001). There was no significant difference in the proportion of sotrovimab-treated and PS-matched untreated patients experiencing 29-day all-cause hospitalization or mortality, although significantly fewer sotrovimab-treated patients required intensive care unit admission or respiratory support.

Prediction equations for detecting COVID-19 infection using basic laboratory parameters.

Coronavirus disease 2019 (COVID-19) emerged as a global pandemic during 2019 to 2022. The gold standard method of detecting this disease is reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR has a number of shortcomings. Hence, the objective is to propose a cheap and effective method of detecting COVID-19 infection by using machine learning (ML) techniques, which encompasses five basic parameters as an alternative to the costly RT-PCR. Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively. The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily. It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.

The Protective Role of IL-17 and IL-22 in COVID-19 Infection.

The development of a cytokine storm in Coronavirus Disease 2019 (COVID-19) infection can make the disease fatal. We hypothesize that this excessive cytokine production impairs mucosal healing. IL-17 and IL-22 are cytokines that play a key role in protecting and regenerating mucosal tissues.IL-17 and IL-22 support each other and the imbalance between them plays a role in the pathogenesis of many rheumatologic diseases. To investigate whether COVID-19 severity is related to IL17, IL-22, and the IL-17/IL-22 ratio. The study was planned prospectively and included 69 patients with active COVID-19 infection.Three groups were created: patients with upper respiratory tract infection, pneumonia, and cytokine storm. Blood samples were taken from the patients upon their first admission and serum levels of IL-17 and IL-22 were measured using the enzyme-linked immunosorbent assay (ELISA). We assessed the relationship between IL17, IL22, IL17/IL22 ratio, clinical and lung involvement by comparing them with the healthy group. The levels of IL-17 were significantly higher in COVID-19 patients with upper respiratory tract infection compared to the control group (p=0.027). IL17/IL-22 ratio significantly increased in patients with cytokine storm compared to the healthy controls (p=0.027). Serum levels of IL-22 were negatively correlated with the CO-RADS score (r=-0.31, p=0.004), while IL-17/IL-22 ratio was positively correlated with the CO-RADS score (r= 0.29, p=0.008). Levels of IL-17, IL-22 and IL-17/IL-22 may provide valuable insights into the progression of COVID-19.

Bioinformatics analysis of potential common pathogenic mechanisms for COVID-19 and venous thromboembolism

BackgroundA growing body of research has shown that patients with coronavirus disease 2019 (COVID-19) have significantly higher rates of venous thromboembolism (VTE) than healthy. However, the mechanism remains incompletely elucidated. This study aimed to further investigate the molecular mechanisms underlying the development of this complication. MethodsThe gene expression profiles of COVID-19 and VTE were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for COVID-19 and VTE, functional annotation, a protein–protein interactions (PPI) network, module construction, and hub gene identification were performed. Finally, we constructed a transcription factor (TF)-gene regulatory network and a TF-miRNA regulatory network for hub genes. ResultsA total of 42 common DEGs were selected for subsequent analyses. Functional analyses showed that biological function and signaling pathways collectively participated in the development and progression of VTE and COVID-19. Finally, 8 significant hub genes were identified using the cytoHubba plugin, including RSL24D1, RPS17, RPS27, HINT1, COX7C, RPL35, RPL34, and NDUFA4, which had preferable values as diagnostic markers for COVID-19 and VTE. ConclusionsOur study revealed the common pathogenesis of COVID-19 and VTE. These common pathways and pivotal genes may provide new ideas for further mechanistic studies.

COVID-19 influenced gut dysbiosis, post-acute sequelae, immune regulation, and therapeutic regimens.

The novel coronavirus disease 2019 (COVID-19) pandemic outbreak caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has garnered unprecedented global attention. It caused over 2.47 million deaths through various syndromes such as acute respiratory distress, hypercoagulability, and multiple organ failure. The viral invasion proceeds through the ACE2 receptor, expressed in multiple cell types, and in some patients caused serious damage to tissues, organs, immune cells, and the microbes that colonize the gastrointestinal tract (GIT). Some patients who survived the SARS-CoV-2 infection have developed months of persistent long-COVID-19 symptoms or post-acute sequelae of COVID-19 (PASC). Diagnosis of these patients has revealed multiple biological effects, none of which are mutually exclusive. However, the severity of COVID-19 also depends on numerous comorbidities such as obesity, age, diabetes, and hypertension and care must be taken with respect to other multiple morbidities, such as host immunity. Gut microbiota in relation to SARS-CoV-2 immunopathology is considered to evolve COVID-19 progression via mechanisms of biochemical metabolism, exacerbation of inflammation, intestinal mucosal secretion, cytokine storm, and immunity regulation. Therefore, modulation of gut microbiome equilibrium through food supplements and probiotics remains a hot topic of current research and debate. In this review, we discuss the biological complications of the physio-pathological effects of COVID-19 infection, GIT immune response, and therapeutic pharmacological strategies. We also summarize the therapeutic targets of probiotics, their limitations, and the efficacy of preclinical and clinical drugs to effectively inhibit the spread of SARS-CoV-2.

Lipidome is a valuable tool for the severity prediction of coronavirus disease 2019.

To describe the lipid metabolic profile of different patients with coronavirus disease 2019 (COVID-19) and contribute new evidence on the progression and severity prediction of COVID-19. This case-control study was conducted in Peking University Third Hospital, China. The laboratory-confirmed COVID-19 patients aged ≥18 years old and diagnosed as pneumonia from December 2022 to January 2023 were included. Serum lipids were detected. The discrimination ability was calculated with the area under the curve (AUC). A random forest (RF) model was conducted to determine the significance of different lipids. Totally, 44 COVID-19 patients were enrolled with 16 mild and 28 severe patients. The top 5 super classes were triacylglycerols (TAG, 55.9%), phosphatidylethanolamines (PE, 10.9%), phosphatidylcholines (PC, 6.8%), diacylglycerols (DAG, 5.9%) and free fatty acids (FFA, 3.6%) among the 778 detected lipids from the serum of COVID-19 patients. Certain lipids, especially lysophosphatidylcholines (LPCs), turned to have significant correlations with certain immune/cytokine indexes. Reduced level of LPC 20:0 was observed in severe patients particularly in acute stage. The AUC of LPC 20:0 reached 0.940 in discriminating mild and severe patients and 0.807 in discriminating acute and recovery stages in the severe patients. The results of RF models also suggested the significance of LPCs in predicting the severity and progression of COVID-19. Lipids probably have the potential to differentiate and forecast the severity, progression, and clinical outcomes of COVID-19 patients, with implications for immune/inflammatory responses. LPC 20:0 might be a potential target in predicting the progression and outcome and the treatment of COVID-19.

Is it possible to identify COVID-19 infection-related biomarkers during pregnancy?: A prospective study in Turkish population.

The coronavirus disease 2019 (COVID-19) has raised concerns about the potential complications it may cause in pregnant women. Therefore, biomarkers that can predict the course of COVID-19 in pregnant women may be of great benefit as they would provide valuable insights into the prognosis and, thus, the management of the disease. In this context, the objective of this study is to identify the biomarkers that can predict COVID-19 progression in pregnant women, focusing on composite hemogram parameters and systemic inflammatory and spike markers. The population of this single-center prospective case-control study consisted of all consecutive pregnant women with single healthy fetuses who tested positive for COVID-19 and who were admitted to Bakirköy Dr Sadi Konuk Training and Research Hospital in Istanbul, Turkey, a COVID-19 referral hospital, between April 2020 and March 2021, with an obstetric indication, during their second or third trimester. The control group consisted of consecutive pregnant women with a single healthy fetus who were admitted to the same hospital within the same date range, had demographic and obstetric characteristics matching the patient group, but tested negative for COVID-19. The patient and control groups were compared in terms of platelet-to-lymphocyte ratio (PLR), platelet-to-neutrophil ratio (PNR), and neutrophil-to-lymphocyte ratio (NLR), and systemic inflammatory and spike markers, including C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-10 (IL-10), cluster of differentiation 26 (CD26), and B7 homolog 4 (B7H4). There were 45 (51.1%) and 43 (48.8%) pregnant women in the patient and control groups, respectively. There was no significant difference between the groups in demographic and obstetric characteristics (P > .05). The PNR, PLR, and CRP values were significantly higher in the patient group than in the control group (P < .05). On the other hand, there was no significant difference between the groups in IL-6, IL-10, CD26, and B7H4 levels (P > .05). The findings of our study showed that specific inflammatory markers, such as CRP, PLR, and PNR, can potentially predict the course of COVID-19 in pregnant women. However, more comprehensive, well-controlled studies are needed to corroborate our study's findings and investigate other potential inflammatory markers.

Risk Score Model for Predicting COVID-19 Progression in Iranian Patients: Development and Validation Study

Background: The recent novel coronavirus disease 2019 (COVID-19) pandemic has underlined the importance of risk score models in public health emergencies. This study aimed to develop a risk prediction score to identify high-risk hospitalized patients for disease progression on admission. Methods: This prospective cohort study included 171 COVID-19 patients, identified through the reverse transcription polymerase chain reaction test, admitted to Bohlool Hospital in Gonabad City, Iran, between April 4 and June 5, 2021. The patients’ demographic, clinical, and laboratory data were collected upon admission, and clinical outcomes were monitored until the end of the study. The discovery dataset (80% of the data) was used to develop the risk score model based on clinical and laboratory features and patient characteristics to predict COVID-19 progression. An additive risk score model was developed based on the regression coefficients of the significant variables in a multiple logistic regression model. The performance of the risk score model was evaluated on the validation dataset (20% of the data) using the receiver operating characteristic (ROC) curve. Statistical analyses were performed with SPSS software, version 21. Results: The Mean±SD for age of participants was 59.54±20.52 years, and 48.6% were male. Most patients (82.5%) fully recovered or showed improvement, while 5.2% experienced disease progression and 12.3% died. Three variables, interleukin-6, neutrophil-to-lymphocyte ratio, and lung involvement, were found to be significant in predicting risk, with a good discriminatory ability, having an area under the ROC curve of 0.970 (95% CI, 0.935%, 1.00%) in the discovery set and 0.973 (95% CI, 0.923%, 1.00%) in the validation set. Conclusion: The developed risk score model in this study can be used as a clinical diagnostic tool to identify COVID-19 patients at higher risk of disease progression and aid in informed decision-making and resource utilization in similar situations, such as respiratory disease outbreaks in the post-corona era.

The correlation between neutrophil lymphocyte ratio in pregnant women with COVID-19 and neonatal outcome

Background and objectives. Coronavirus Disease 2019 (COVID-19) in pregnancy periods can adversely affect the neonatal outcomes. Neutrophil Lymphocyte Ratio (NLR) were reported as sensitive markers and good predictor of COVID-19 progression in symptomatic pregnant women. The aim of this study to find correlation on NLR value of the pregnant women with COVID-19 to the neonatal outcome. Materials and methods. We retrospectively included neonates who was delivered from pregnant women with COVID-19 at Dr. Soetomo Hospital from June 2021 to July 2022. NLR value of the pregnant women were calculated. Demographic data and clinical characteristic were taken from medical records. The diagnosis of COVID-19 in pregnant women is confirmed by PCR nasopharyngeal swab, as well as in the neonates who undergo nasopharyngeal swab a maximum of 24 hours after birth. Results. Total of 1058 births delivered between June 2021- July 2022, 224 neonates (21.17%) met inclusion criteria. We analyzed the severity of maternal factors described by NLR and correlated with neonate outcomes, such as mortality, Apgar Score (AS), birth weight, gestational age, type of delivery and Polymerase Chain Reaction (PCR) swab of the neonates. the results of our analysis, the maternal NLR was correlated with the birth weight (p=0.005). However, NLR of the pregnant women are not correlated with mortality of the neonates (p=0.681), type of delivery (p= 1), AS at 1 minute (p= 0.158), AS at 5 minutes (p= 0.158), gestational age (p= 0.110) length of stay (LOS) (p=0,258) and PCR swab of the neonates (p= 1). Conclusions. NLR value in the majority of pregnant women with COVID-19 was found to be increasing, but these results are not related for the neonatal outcome except the birth weight.

Retrospective study of the impact of diabetes on the severity and prognosis of COVID‑19.

Patients with diabetes coexisting with viral infection tend to have poor outcomes, but the association between diabetes and coronavirus disease 2019 (COVID-19) prognosis is controversial at present. The present study reviewed and analyzed the data of 1,892 patients with COVID-19 admitted to Shaanxi Provincial People's Hospital (Xi'an, China). Demographic, clinical, laboratory and treatment data as well as clinical outcomes were extracted from the electronic medical records and compared between patients with and without diabetes. Multivariate logistic regression analysis was used to determine the risk factors affecting the prognosis of COVID-19. Compared with patients without diabetes, the levels of glucose, C-reactive protein, procalcitonin, creatinine, total bilirubin and plasma D-dimer were significantly increased in patients with diabetes, while the levels of lymphocytes and albumin were significantly decreased (P<0.05). Multivariate logistic regression analysis revealed that platelet count, albumin, total bilirubin and lymphocytes were significantly correlated with the severity of COVID-19. Diabetes mellitus was an independent prognostic factor that affected the mortality outcome of patients with COVID-19. Additionally, an age of ≥80 years, male sex, cerebral infarction complications and a critical diagnosis of COVID-19 at admission were risk factors for critical illness during hospitalization. The results of the present study suggest that diabetes may be a risk factor for the rapid progression and poor prognosis of COVID-19. Therefore, further attention should be paid to individuals with diabetes in order to prevent rapid deterioration.

High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R2 of 0.125 (original ConvLSTM: RMSE = 0.702, R2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

Zinc deficiency is a potential risk factor for COVID-19 progression to pneumonia requiring oxygen therapy

IntroductionVarious risk factors for developing severe coronavirus disease 2019 (COVID-19) have been reported. However, studies on the nutritional-related risk factors are limited. In this study, we investigated the effects of serum zinc deficiency on the severity of COVID-19. MethodsThe study included a total of 60 COVID-19 patients who were admitted to Tsuyama Chuo Hospital between March 2020 and April 2021. We divided the patients into two categories based on serum levels of zinc (normal and latent zinc deficiency vs. zinc deficiency [<60 μg/dL]) at the time of diagnosis. Severity of COVID-19 was defined as the most exaggerated disease status during admission. The associations between serum zinc deficiency and the severity of COVID-19 were examined using a logistic regression model adjusted for potential confounders. ResultsPatients who required oxygen therapy had a higher prevalence of comorbidities and poorer nutritional status, including zinc deficiency, than those who did not require oxygen therapy. Zinc deficiency was associated with an increased risk of COVID-19 severity, with an adjusted odds ratio of 7.29 (95% confidence interval: 1.70–31.18). This result remained significant in the sensitivity analyses conducted after adjusting for patient background factors. ConclusionsZinc deficiency at the time of COVID-19 diagnosis is an independent risk factor for severe disease. Our findings need to be validated in external studies.

Stimulation of PSTPIP1 to trigger proinflammatory responses in asymptomatic SARS-CoV-2 infections

BackgroundA hyperinflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection gravely worsens the clinical progression of coronavirus disease 2019 (COVID-19). Although the undesirable effects of inflammasome activation have been correlated to the severity of COVID-19, the mechanisms of this process in the asymptomatic infection and disease progression have not yet been clearly elucidated. MethodsWe performed strand-specific RNA sequencing in 39 peripheral blood mononuclear cell (PBMC) samples from asymptomatic individuals（n = 10）, symptomatic patients（n = 16） and healthy donors（n = 13）. ResultsDysregulation of pyrin inflammasomes along with the proline-serine-threonine phosphatase-interacting protein 1 (PSTPIP1) gene was identified in SARS-COV-2 infection. Notably, the PSTPIP1 expression level showed a significant negative correlation with an adjacent long-noncoding RNA (lncRNA) RP11-797A18.6 in the asymptomatic individuals compared with the healthy controls. In addition, a decline in the nuclear factor kappa B subunit 1 (NFKB1) gene expression was observed in asymptomatic infection, followed by a rise in the mild and moderate disease stages, suggesting that altered NFKB1 expression and associated proinflammatory signals may trigger a disease progression. ConclusionsOverall, our results indicate that PSTPIP1-dependent pyrin inflammasomes-mediated pyroptosis and NF-κB activation might be potential preventive targets for COVID-19 disease development and progression.

Elevated hyaluronic acid levels in severe SARS-CoV-2 infection in the post-COVID-19 era.

Human identical sequences of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) promoted the coronavirus disease 2019 (COVID-19) progression by upregulating hyaluronic acid (HA) via NamiRNA-enhancer network, based on previous experimental research. This study aimed to investigate the predictive value of HA for the severity of SARS-CoV-2 infection in the post-COVID-19 era. A total of 217 consecutive patients with COVID-19 were enrolled at Beijing Ditan Hospital between July 2023 and October 2023. HA levels were analyzed using biochemical detector. Logistic regression analysis was used to screen independent factors for severe COVID-19. The predictive performance of HA for severe infection was assessed by ROC curve. Furthermore, the relationship between HA levels and COVID-19 severity was investigated using multivariate logistic regression models after adjustment for potential confounders. According to the cut-off value of HA, COVID-19 patients were divided into HA < 90 ng/mL group (80 cases) and HA ≥ 90 ng/mL group (137 cases). High HA levels were positively associated with the severe SARS-CoV-2 infection, including elevated inflammatory indicators, severe lung involvement, prolonged clinical course, and higher incidence of respiratory failure and death (P < 0.05). Logistic regression analysis suggested that HA was an independent predictor of severe COVID-19 (OR = 4.540, 95% CI = 2.105-9.790, P < 0.001). ROC curve analysis showed that the AUC of HA for severe infection was 0.724. HA levels were significantly higher in COVID-19 cases compared to the healthy population (123.9 (82.6, 174.1) vs. 50.5 (37.8, 66.8), P < 0.001), but similar to those with non-SARS-CoV-2 lung infection (121.6 (78.5, 175.6) vs. 106.0 (66.5, 149.7), P = 0.244). We also found that the first COVID-19 infections had higher HA levels (118.8 (79.5, 174.3) vs. 85.0 (61.1, 128.8), P < 0.001) and a higher proportion of severe infection (37.1% vs. 21.3%, P = 0.043) than re-infections. However, HA expression failed to fully return to normal levels with infection recovery (204.7 (152.9, 242.2) vs. 97.0 (69.3, 137.3), P < 0.001). HA was associated with severe SARS-CoV-2 infection and could be used as a novel serum biomarker to predict the risk of COVID-19 progression in the post-COVID-19 era.