Epidemiological characteristics of infected patients aged 60 years and older during the outbreak of SARS-CoV-2 Omicron variant in Sanya, Hainan in 2022

Objective: To analyze the epidemic characteristics of coronavirus disease 2019 (COVID-19) cases aged 60 years or older during the outbreak epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in Sanya City, Hainan Province, August-September 2022, and to provide scientific and effective basis for formulating strategies for the prevention and treatment of COVID-19 in the elderly in the future. Methods: The data of 1 785 COVID-19 cases aged 60 years reported from August 1 to September 8, 2022 in Sanya City, and 537 local COVID-19 inpatients aged 60 years treated in Sanya Central Hospital were collected and analyzed using WPS Office 2018 software for data cleaning, database assistance, and drawing, and SPSS 22.0 software was used to statistically describe and analyze the counting data. Results: A total of 1 785 local COVID-19 cases aged 60 years were reported in China's Disease Prevention and Control Information System, including 1 509 confirmed cases (84.54%) and 276 asymptomatic cases (15.46%). Of the confirmed cases, 1 448 were mild cases (95.96%), 40 were common cases (2.65%), 10 were severe cases (0.66%), and 11 were critical cases (0.73%), and no deaths were reported. The ratio of male to female was 0.88:1. The youngest was 60 years old, the oldest age was 107 years old, with a median age of 67 years old. Most of them (1 559 cases, 87.34%) were non-social employed personnel. Among them, 952 (53.33%) were from Tianya District and 733 (41.06%) from Jiyang District. Among the 537 COVID-19 elderly infected patients hospitalized in Sanya Central Hospital, infected patients with underlying diseases accounted for 287 (53.45%) cases of hypertension, diabetes and other types of basic diseases (chronic kidney disease, hyperlipidemia, chronic gastritis, gastric ulcer, etc.), and there were serious infections. Conclusions: Most of the elderly infected with Omicron variants were mild cases with few symptoms, but the elderly infected with Omicron variants were difficult to recover or even worsen the condition due to old age and underlying diseases. Therefore, by analyzing the various aspects of elderly infected cases, scientific and effective basis can be provided to prevent and control the epidemic situation in the future, and reduce the occurrence of severe, critical and death cases of COVID-19 in the elderly.

Long-lasting brain fog is related with severity clusters of symptoms in COVID-19 patients. Background: COVID-19 patients may experience lon-lasting symptoms from weeks to even months. Aim: to evaluate long-term cognitive impairment based on the severity

Background: COVID-19 patients may experience lon-lasting symptoms from weeks to even months. Aim: To evaluate long-term cognitive impairment based on the severity of symptoms of COVID-19 infection in a primary health system setting. Material and methods: From a database of 363 patients, 83 cases aged 47 +or- 15 years, (58% females) were selected from June to August 2020. In patients who survived the virus, 24 infection-related symptoms were collected to create three severity clusters (mild, moderate, and severe). The follow-up time was at least seven months. Comparing the first two clusters with the severe cluster, the existence of brain fog and risk factors (obesity, hypertension, diabetes, chronic lung disease, and hypothyroidism) were analyzed. Results: Thirty-one patients (37%) had persistent symptoms lasting up to 240 days. Fifty-one patients (61%) experienced brain fog. Concentration was affected by symptom severity (odds ratio [OR] 3.63, 95% confidence interval [CI] 1.26-10.46, p = 0.02). Short- or long-term memory loss was not affected. Moreover, symptom severity was related to brain fog (OR 3.16, 95% CI 1.05-9.51, p = 0.04). Patients with persistent symptoms had a concentration impairment associated with severity patterns (OR 24.3, 95% CI 1.73-340.11, p = 0.03). Conclusions: Brain fog is associated with symptom severity in COVID-19 survivors and lasts for more than eight months.

Comparative study of different machine learning algorithms for chronic kidney disease prediction

Chronic kidney disease (CKD) is a huge problem on the health-care system due to its growing occurrence, high risk of process to final phase kidney disease, and increased death rate. It is gradually increasing into a major public health problem. An early diagnostic method to measure renal function is necessary and highly needed in many respects. Machine Learning technique uses understanding of various key medical problems, including diabetes projection, heart observation, and coronavirus detection. Machine learning may be a better solution to achieve higher performance in the prediction of CKD and may be effective for other diseases as well. The main purpose of this study is to determine the optimal and appropriate machine learning techniques which may effectively identify and predict the CKD status. The dataset from Kaggle has been used for this study. Machine learning models such as Random Forest, Decision Tree and SVM have been used in this research to predict chronic kidney disease. Therefore, the results showed that Random Forest provides the highest accuracy in identifying chronic kidney disease. The overall accuracy of an algorithms used in this research is basically greater than that of previous research, intimating that this research is also more reliable than previous model.

Marked changes in NAMPT expression, CRP and D-dimer levels were associated with type 2 diabetes and those WHO were previously infected with COVID-19

As our understanding grows, it has been demonstrated that the metabolism of many biomolecules and immune responses can be clearly studied. via delving deeply into the relationship between intracellular NAD levels and immune responses. NAD has been shown to play an important role as a substrate for several NAD-dependent signaling enzymes (both degrading and synthesis enzymes) and has been linked to a variety of diseases, including diabetes and COVID 19. In this study, we looked at the expression of the main NAD-synthesizing enzyme (NAMPT), as well as D-Dimer and C-reactive protein levels in serum. This study included one hundred participants: twenty-five patients with type 2 diabetes who recovered from COVID 19, twenty-five type 2 diabetic patients who were not infected with COVID-19, and twenty-five people who recovered from COVID 19 but were not diabetic compared to twenty-five controls. NAMPT levels were determined using quantitative PCR;C-reactive protein levels as well as D-Dimer were determined using competitive fluorescent immunoassay technology. The results revealed a decrease in the expression levels of NAD-homeostasis enzyme (NAMPT), whileC-reactive protein and D-Dimer levels increased, in addition to the change in malondialdehyde (MDA) levels and catalase activity. These findings suggest that low NAD+ levels during infection may be associated with an inflammatory response as well as During infection with COVID 19, diabetes patients had elevated C-reactive protein and D-dimer levels.

Deal struck to help protect world from future pandemics

The World Health Assembly (WHA) adopted a resolution on May 27, 2022, with the goal of assisting developing nations in building up their capacity to conduct their own clinical trials. This will allow them to test new medications, vaccines, and other health interventions for the benefit of their populations while cooperating with other nations to respond to pandemics more quickly. The resolution aims to assist nations in addressing current health issues like tuberculosis, diabetes, and mental health issues in addition to assisting nations in responding more quickly to public health emergencies. Additionally, there will be greater global collaboration among clinical trial researchers, which will cut down on research waste, and greater transparency will make it easier for nations to share trial results. The resolution aspires to strengthen clinical trials for all types of healthcare interventions, including novel diagnostics, surgical techniques, and behavioral interventions, in addition to trials for medications and vaccines. The resolution also calls for the creation of fresh diagnostics, treatments, and vaccines within 100 days of the discovery of a fresh pandemic threat. The resolution calls on research funders to allocate funds to developing nations and conduct clinical trials to address health issues that disproportionately affect their populations. It also calls on nations to increase international coordination to quickly assess medications and vaccines in the event of a pandemic in the future.

Circulatory cytokine levels as a predictor of disease severity in COVID-19: a study from Western India

Background: Inflammatory response in COVID-19 responsible for acute respiratory distress syndrome (ARDS) and multiorgan failure and play a major role in morbidity and mortality of patients. The present study was undertaken to assess serum level of cytokines and its association with other inflammatory markers and disease severity in COVID-19 and hence their prognostic significance.

Influencing factors of proteinuria in patients with hypertension in Qinghai-Tibet Plateau

Objective: To investigate the risk factors of proteinuria in patients with hypertension in Qinghai-Tibet Plateau.

Investigating the prevalence of COVID-19 in a deprived area using diagnostic tests: a cross-sectional study reflecting capability of Jihadi and military forces

Background and Aim: Corona antibody testing is one of the common methods used for diagnosing coronavirus infection due to its low cost, easy access, short testing time, and convenient sampling. Iran, with its valuable Jihadi and military resources, has been able to take some effective steps in controlling the crisis caused by this disease. This is especially important in disadvantaged and underprivileged areas. The present study aimed at investigating the prevalence of Covid-19 among people living around Hamun Lake, Sistan and Baluchestan, Iran, using serological tests while considering the access to medical and healthcare facilities.

Clinical value of admission blood glucose level on prognosis of COVID-19 patients

Objective: To explore the clinical value of admission blood glucose level on prognosis of COVID- 19 patients. Methods A total of 420 novel coronavirus pneumonia (COVID-19) patients admitted to Tongji Hospital of Tongji MedicalCollege from January 18, 2020 to February 26, 2020 were selected as the subjects of study. The data of diabetes or not, admissionblood glucose level(GLU), clinical severity grade were collected through the electronic medical record system, and the outcome, which defined as in-hospital motality, was also monitored. The patients were divided into diabetes group and non-diabetes groupin terms of the complication of diabetes, and then, firstly, stratified these two groups into survival subgroup and non-survivalsubgroup in according to the event of in-hospital motality, GLU between these two subgroups were compared. Secondly, according to the clinical severity grade, these two groups were stratified into moderate subgroup, severe subgroup and criticalsubgroup, and GLU among these subgroups were also compared. Thirdly, according to the admission blood glucose level, stratified these two groups into GLU 3.9~7.8 mmol /L subgroup, GLU 7.8~10.0 mmol/L subgroup and GLU>10.0 mmol/Lsubgroup, the in-hospital motality rates among these subgroups were compared. Finally, the multivariate logistic regression wasused to explore whether increased GLU were independent risk factor for in-hospital motality in diabetes group and non-diabetesgroup when adjusted for sex, age and underlying disease. Results In non-diabetes group, compared with Survival subgroup, GLUwas significantly elevated in non-Survival subgroup[6.96(5.95, 8.23)mmol/L vs 5.96 (5.32, 6.92) mmol/L], the difference wasstatistically significant(U=6047.0, P < 0.001), but in diabetes group, there was no significant difference between non-survivalsubgroup and Survival subgroup [12.42(8.41, 18.17) mmol/L vs 9.88 (7.79, 14.02) mmol/L], the difference was statisticallysignificant(U=1 200.5, P=0.059).In Non-diabetes group, GLU elevated remarkably along with the clinical severity gradeincreased, moderate subgroup, severe subgroup, critical subgroup GLU were 5.87(5.24, 6.69) mmol/L, 6.94(5.95, 7.90) mmol/L,9.73 (6.22, 11.64) mmol/L, the difference were statistically significant, respectively(U=723.0~4978.0, all P < 0.01). However indiabetes group, there was no significant difference on GLU when the clinical severity grade increased, moderate subgroup, severesubgroup, critical subgroup GLU were 9.88(7.81, 11.93)mmol/L, 12.42(8.43, 16.94)mmol/L, 11.43(7.89, 18.76)mmol/L, the difference were statistically significant, respectively (U=262.0~946.5, all P>0.05).In non-diabetes group, GLU> 10.0 mmol/L subgroup had the hightest in-hospital motality rate (72.0%) among all three subgroups, the differences were statisticallysignificant(X2=24.607, 9.625, all P < 0.01), when compared between GLU 3.9~7.8 mmol/L subgroup (in-hospital motality rate24.8%) and GLU 7.8~10.0 mmol/L subgroup (in-hospital motality rate 30.0%), there was no significant difference on in-hospitalmotality rate (X2=0.383, P > 0.05). However, in diabetes group, along with GLU increased, it had no significant difference on inhospitalmotality rate, GLU 3.9~7.8 mmol/L subgroup, GLU 7.8~10.0 mmol/L subgroup, GLU> 10.0 mmol/L subgroup, the inhospitalmotality rate were 34.8%, 41.4%, 49.2%, respectively(X2=0.236~1.380, all P> 0.05). Multivariate logistic regressionshowed, in non-diabets group, GLU>10.0 mmol/L was the independent risk factor when adjusted for sex, age and underlyingdisease, odds ratio was 7.969, and 95% confidence interval was 3.022~21.013, but in diabets group.It seemed that GLU>10.0 mmol/L was not the independent risk factor. Conclusion Admission blood glucose is a good predictor for disease severity andoutcome in non-diabetes patients with COVID-19. When admission hyperglycemia occurs, it tends to predict a poor prognosis.

Special Issue: Chronic health consequences of COVID-19: mechanisms, diagnosis, and management. (Special Issue: Chronic health consequences of COVID-19: mechanisms, diagnosis, and management.)

This special issue consists of the following papers: (1) Long-term immunologic effects of SARS-CoV-2 infection: leveraging translational research methodology to address emerging questions;(2) From ARDS to pulmonary fibrosis: the next phase of the COVID-19 pandemic?;(3) COVID-lateral damage: cardiovascular manifestations of SARS-CoV-2 infection;(4) Central nervous system outcomes of COVID-19;(5) Impaired glucose regulation, SARS-CoV-2 infections and adverse COVID-19 outcomes;(6) COVID-19 Survival and its impact on chronic kidney disease;(7) Immune mechanisms in cancer patients that lead to poor outcomes of SARS-CoV-2 infection;and (8) Immune mechanisms in cancer patients that lead to poor outcomes of SARS-CoV-2 infection.