Aging and many age-related health conditions are associated with skeletal muscle loss. Furthermore, older adults are more susceptible to severe respiratory infections, which can in turn lead to muscle wasting. The mechanisms by which respiratory viral infection can impact skeletal muscle in older adults are not well understood. We determined the effects of acute infection with respiratory syncytial virus (RSV) on the lung and skeletal muscle of aged mice. RSV infection caused more severe disease in aged mice with enhanced weight loss, reduced feeding, higher viral load, and greater airway inflammation. Aged but not young mice showed decreased leg muscle weight at the peak of illness and decreased size of leg muscle fibers. Aged mice increased muscle-specific expression of atrophy-promoting enzymes (Atrogin-1 and MuRF-1) and failed to increase the rate of muscle protein synthesis during RSV infection. In aged mice, the changes in Atrogin-1 and MuRF-1 gene expression in skeletal muscle correlated with IL-6 levels in the lungs. These findings indicate that RSV infection of aged mice provides a model for studying the diverse adverse systemic consequences of respiratory viral infections on health and wellbeing in older adults.

To determine the occurrence and clinicodemographic associations of hospital-based specialist palliative care (SPC) referral before and during the COVID-19 pandemic, timing of completed SPC consultation and comparative prevalence of 'no cardiopulmonary resuscitation (CPR)' orders, and end-of-life medication use, according to SPC involvement.

Cross-sectional secondary analysis of a retrospective cohort study with a pre-pandemic (November 2019 to February 2020) group (Pre-COVID, n=170) and two intra-pandemic (March to August 2020) groups, one without (COVID-ve, n=170) and one with COVID-19 infection (COVID+ve, n=85). In the cohort study, Pre-COVID and COVID-ve group decedents were matched 2:1 on age, sex and care service (internal medicine/intensive care unit (ICU)) at death with COVID+ve decedents. In our current secondary analysis, clinicodemographic variables associated with SPC referral were examined in multivariable logistic regression, reporting adjusted ORs (aORs) and 95% CIs.

One quaternary and two tertiary acute care hospitals.

Decedent cohort with a terminal hospital admission (N=425).

SPC referral (yes/no) and timing of completed SPC consultation before death. Additional outcomes included 'no CPR' status and end-of-life medication prescription and dosing.

SPC referral occurred in 70 (41.2%), 71 (41.8%) and 26 (30.6%) of the Pre-COVID, COVID-ve and COVID+ve groups, respectively (p=0.18). The aORs for SPC referral were lower for deaths in ICU (0.07; 95% CI 0.03 to 0.16) and admissions from nursing homes/long-term care (0.45; 95% CI 0.23 to 0.9), and higher for active cancer (2.5; 95% CI 1.39 to 4.39). Recipients of SPC consultation, compared with non-recipients, more frequently had a 'no CPR' order, had it placed earlier and were more frequently prescribed palliative end-of-life medications.

Hospital SPC consultation rates early in the COVID-19 pandemic were largely maintained at pre-pandemic levels. Having active cancer was positively associated with SPC referral, whereas both ICU death and having a nursing home/long-term care pre-admission source were negatively associated with referral.

Assessing risk factors and creating prediction models from real-world medical data is challenging, requiring numerous modelling decisions with clinical guidance. Logistic regression is a common model for such studies, for which we advocate the use of Bayesian methods that can jointly deliver probabilistic risk factor inference and prediction. As an exemplar, we compare Bayesian logistic regression with horseshoe priors and Projective Prediction variable selection with the established frequentist LASSO approach, to predict severe COVID-19 outcomes (death or ICU admittance) from demographic and laboratory biomarker data. Our study serves as guidance on data curation, variable selection, and performance assessment with cross-validation.

Our source data is based on a retrospective observational cohort design with records from three National Health Service (NHS) Trusts in southwest England, UK. Models were fit to predict severe outcomes within 28 days after admission to hospital (or a positive PCR result if already admitted) using demographic data and the first result from 30 biomarker tests collected within 3 days after admission (or testing positive if already admitted).

Patients included hospitalized adults positive for COVID-19 from March to October 2020, 756 total patients: Mean age 71, 45% female, 31% (n=234) had a severe outcome, of whom 88% (n=206) died. Patients were split into training (n=534) and external validation groups (n=222). Using our Bayesian pipeline, we show a reduced variable model using Age, Urea, Prothrombin time (PT) C-reactive protein (CRP), and Neutrophil-Lymphocyte ratio (NLR) has better predictive performance (median external AUC: 0.71, 95% Quantile [0.7, 0.72]) relative to a GLM using all variables (external AUC: 0.67 [0.63, 0.71]).

Urea, PT, CRP, and NLR have been highlighted by other studies, and respectively suggest that hypovolemia, derangement of circulation via clotting, and inflammation are strong predictive risk factors of severity. This study provides guidance on conventional and Bayesian regression and prediction modelling with complex clinical data.

SARS-CoV-2 disease (COVID-19) has had an enormous health and economic impact globally. Although primarily a respiratory illness, multi-organ involvement is common in COVID-19, with evidence of vascular-mediated damage in the heart, liver, kidneys and brain in a substantial proportion of patients following moderate-to-severe infection. The pathophysiology and long-term clinical implications of multi-organ injury remain to be fully elucidated. Age, gender, ethnicity, frailty and deprivation are key determinants of infection severity, and both morbidity and mortality appear higher in patients with underlying comorbidities such as ischaemic heart disease, hypertension and diabetes. Our aim is to gain mechanistic insights into the pathophysiology of multiorgan dysfunction in people with COVID-19 and maximise the impact of national COVID-19 studies with a comparison group of COVID-negative controls.

COmorbidities and Sociodemographic factors on Multiorgan Injury following COVID-19 (COSMIC) is a prospective, multicentre UK study which will recruit 200 subjects without clinical evidence of prior COVID-19 and perform extensive phenotyping with multiorgan imaging, biobank serum storage, functional assessment and patient reported outcome measures, providing a robust control population to facilitate current work and serve as an invaluable bioresource for future observational studies.

Approved by the National Research Ethics Service Committee East Midlands (REC reference 19/EM/0295). Results will be disseminated via peer-reviewed journals and scientific meetings.

COSMIC is registered as an extension of C-MORE (Capturing Multi-ORgan Effects of COVID-19) on ClinicalTrials.gov (NCT04510025).

The COVID-19 pandemic has underscored the critical need for effective public health strategies to combat infectious diseases. This study examines the epidemiological characteristics and spatial distribution of COVID-19 incidence and mortality in Zanjan Province, northwest Iran, to inform future epidemic preparedness. Using data from 39,739 hospitalized COVID-19 cases recorded between February 2020 and September 2021, sourced from the Medical Care Monitoring Center, we conducted descriptive and geospatial analyses. Demographic, clinical, and spatial variables were analyzed using logistic regression and advanced spatial techniques, including Kernel Density Estimation and Local Moran's I, to identify risk factors and disease hotspots. Results revealed that women accounted for 52% of cases, with higher incidence rates, while men exhibited higher mortality rates (7.86% vs. 7.80%). Urban areas, particularly the provincial capital, were identified as hotspots, with the highest patient density (20,384 cases per 10 km²). Comorbidities such as HIV/AIDS (OR: 4.85), chronic liver disease (OR: 3.6), chronic blood diseases (OR: 2.8), and cancer (OR: 2.5) significantly increased mortality risk, with ventilator use showing the highest odds ratio for death (OR = 91). Vaccination significantly reduced mortality, with fully vaccinated individuals experiencing a 6.3% mortality rate compared to 8.1% in unvaccinated individuals. Spatial analysis highlighted population density and mobility as key drivers of disease spread. These findings emphasize the importance of integrating spatial and epidemiological data to enhance pandemic preparedness. Targeted interventions in urban hotspots, early detection systems, and prioritizing vaccination for high-risk populations are critical for mitigating future outbreaks. This study provides a foundation for evidence-based public health strategies to strengthen global epidemic response and improve preparedness for future health crises.

To develop and validate a clinical prediction model aimed at improving resource management and determining the prognosis of patients hospitalized with COVID-19.

A retrospective, single-center cohort study conducted at the University Hospital of Guadalajara, including 1,043 patients hospitalized with COVID-19 between March and May 2020. Data were extracted from hospital records and anonymized. Demographic, clinical, laboratory, radiological, and therapeutic variables were collected, and statistical analysis was performed to identify factors associated with mortality. Logistic regression and Cox models were employed to evaluate mortality predictors. Validation was conducted by comparing ROC curves.

The median age of the patients was 70.4years (P25-P75: 59-84), with 59.2% being male, and a mortality rate of 23.2%. The most common comorbidities were hypertension (54.8%), dyslipidemia (36.3%), and diabetes (27.1%). Independent predictors of mortality included age over 80years (OR: 6.18), chronic obstructive pulmonary disease (OR: 2.35), oxygen saturation <90% (OR: 1.7), multilobar pneumonia (OR: 2.4), and elevated LDH levels (OR: 1.2). The area under the curve (AUC) for the derivation model was 0.805 (P<.001), and for the validation model, the AUC was 0.78 (P<.001).

Advanced age, chronic obstructive pulmonary disease, low oxygen saturation, multilobar pneumonia, and elevated LDH levels are significantly associated with increased mortality risk. The validated predictive model enables classification of patients into high- or low-risk groups, thereby facilitating improved clinical decision-making and resource management.

The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted global health, exacerbated metabolic health issues, and altered lifestyle behaviors. This study examined the sex-specific impact of the COVID-19 outbreak on the incidence of metabolic syndrome using data from the Korea National Health and Nutrition Examination Survey (KNHANES).

Data from the KNHANES VII (2018) and VIII (2019-2021), including 15,499 participants, were analyzed. The study population was stratified by sex, and further subdivisions were conducted based on the timeframe relative to the COVID-19 outbreak. Variables such as age, education level, household income, smoking status, and high-risk drinking were analyzed to assess their influence on the prevalence of metabolic syndrome.

The overall prevalence of metabolic syndrome significantly increased from 28.11% before the outbreak to 29.69% after the outbreak. Both males and females reported significant increases in waist circumference and fasting glucose levels. Age and education level differentially influenced the prevalence of metabolic syndrome between the sex. Smoking was significantly associated with increased prevalence in males, whereas high-risk drinking was associated with increased prevalence in males and decreased prevalence in females.

The COVID-19 pandemic has significantly increased the prevalence of metabolic syndrome with notable sex-specific differences. These findings highlight the need for sex-specific public health interventions to mitigate the impact of the pandemic on metabolic health.

The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has profoundly affected global health systems. Healthcare systems across the globe have been pushed to their limits, with intensive care units (ICUs) witnessing a sharp rise in admissions, putting a strain on resources and personnel.

Examine ICU health outcomes, including mortality, length of stay (LOS), and discharge rates, among COVID-19 and non-COVID-19 patients.

Retrospective, cross-sectional study.

A national cross-sectional dataset provided by the Ministry of Health in Saudi Arabia.

All patients admitted to ICUs across Saudi Arabia between January 1, 2022, and December 31, 2022. Patients were classified as confirmed COVID-19 cases and non-COVID-19 cases. To evaluate the ICU outcomes, the study used multivariate regression models, adjusting for covariates including age, gender, region, citizenship, and comorbidity score.

ICU outcomes including mortality, LOS and discharge rate.

114 854 ICU patients.

The study population consisted of 114 854 ICU patients across various demographic and clinical categories. Mortality was found to be higher in COVID-19 patients than non-COVID-19 patients, with COVID-19 patients showing a 7% increase in mortality (OR=1.07, 95% CI: 1.02-1.12). Also, COVID-19 patients had 78% higher odds of being discharged home than the non-COVID-19 group (OR=1.78, 95% CI: 1.71-1.84). Moreover, the average LOS in the ICU was significantly shorter for COVID-19 patients than non-COVID-19 patients by 6% on average (Coefficient=-0.06, 95% CI: -0.07 to -0.03).

Significant differences were seen in ICU outcomes between patients with and without COVID-19, including mortality rates, discharge rates, and LOS. COVID-19 patients exhibited higher mortality rate and discharge rate, and shorter ICU LOS than those without COVID-19.

The data used in this study has missing critical information such as laboratory results, socioeconomic variables, and hospitalization characteristics.

Identifying individuals hospitalized for SARS-CoV-2 infection at increased risk of death is crucial for clinical decision making. Analyses must consider simultaneously the multitude of biomarkers across several domains and how these biomarker profiles change over time.

This electronic health records-based study included individuals hospitalized at a Massachusetts General Brigham hospital for at least 24 h within 5 days prior and 30 days after diagnosis of COVID-19. K-means clustering was used to identify profiles among 20 eligible biomarkers and proportional hazards models were used to model 30-day mortality at hospitalization and 7 days after hospitalization (i.e., landmark models).

Twelve thousand, nine hundred forty-two individuals were included, among whom 1,198 died within 30 days. Six states were identified, characterized by the following abnormalities: (1) normal/reference, (2) hematologic, (3) inflammatory and hematological, (4) metabolic, (5) kidney, hematologic, and metabolic, and (6) cardio-thrombotic, liver, and metabolic. Risk of death within 30 days was higher in States 3, 4, 5, and 6 (adjusted hazard ratios ranging from 3.6 to 7.8) compared to individuals in State 1 at hospitalization. Landmark model findings were similar.

Distinct sub-phenotypes based on biomarker profiles were identified among patients hospitalized with SARS-CoV-2 infection, and certain phenotypes are associated with greater risk of 30-day mortality.

Nirmatrelvir/ritonavir (NMV/r) is an oral antiviral treatment for mild to moderate coronavirus disease 2019 (COVID-19).

This phase 2/3, double-blind, randomized (1:1) study assessed oral NMV/r 300 mg/100 mg versus placebo every 12 hours for 5 days in high-risk, unvaccinated, nonhospitalized, symptomatic adults with COVID-19 from 343 sites across 21 countries. In testing the primary endpoint of COVID-19‒related hospitalization and all-cause deaths and key secondary endpoints, including symptom duration and COVID-19‒related medical visits, type I error was controlled with prespecified sequential testing and the Hochberg procedure.

Among 2113 randomized patients enrolled from July 2021 through December 2021, 1966 (NMV/r, n = 977; placebo, n = 989) were included in the prespecified analysis population (symptom onset ≤5 days, did not receive monoclonal antibodies). NMV/r significantly reduced times to sustained alleviation (median, 13 vs 15 days; hazard ratio = 1.27, P < .0001) and resolution (16 vs 19 days; hazard ratio = 1.20, P = .0022) through day 28 and significantly reduced the number of COVID-19‒related medical visits and the proportion of patients with such visits. Hospitalized patients treated with NMV/r had shorter stays, none required intensive care unit admission or mechanical ventilation, and all were discharged to home/self-care. Fewer NMV/r-treated patients required additional treatment for COVID-19. No NMV/r-treated patients died through week 24 compared with 15 placebo-treated patients.

In addition to reducing COVID-19‒related hospitalization or death from any cause through day 28, NMV/r was found to also reduce duration of COVID-19 symptoms and utilization of healthcare resources versus placebo in patients at high risk of progressing to severe disease.

ClinicalTrials.gov, NCT04960202, https://clinicaltrials.gov/study/NCT04960202.

Azvudine and nirmatrelvir-ritonavir are prioritized treatments for SARS-CoV-2 infection in China, but their effectiveness and safety in hospitalized patients with COVID-19 and pre-existing diabetes remains unknown. In this retrospective cohort study, we collected 32,864 hospitalized COVID-19 patients from nine hospitals, among which 636 azvudine recipients and 318 nirmatrelvir-ritonavir recipients were enrolled for final analysis after exclusion and propensity score matching. Kaplan-Meier and multivariate Cox regression analysis results showed that azvudine had a lower risk of all-cause death compared with nirmatrelvir-ritonavir for the treatment of patients with COVID-19 and pre-existing diabetes (log rank: p = 0.044; HR: 0.63; 95% CI: 0.431-0.934). No significant difference was found in composite disease progression between the two groups. Five sensitivity analyses verified the robustness of the results. Overall, azvudine may be comparable to nirmatrelvir-ritonavir in terms of both all-cause death and composite disease progression among hospitalized patients with COVID-19 and pre-existing diabetes, with acceptable adverse events.

To develop the first core Critical Care Data Dictionary (C2D2) with common data elements (CDEs) to characterize critical illness and injuries.

Group consensus process using modified Delphi approach.

Electronic surveys and in-person meetings.

A multidisciplinary workgroup of clinicians and researchers with expertise in the care of the critically ill and injured.

The Delphi process was divided into domain and CDE portions with each composed of two item generation rounds and one item reduction/refinement rounds. Two in-person meetings augmented this process to facilitate review and consideration of the domains and by panel members. The final set of domains and CDEs was then reviewed by the group to meet the competing criteria of utility and feasibility, resulting in the core dataset.

The 23-member Delphi panel was provided 1833 candidate variables for potential dataset inclusion. The final dataset includes 226 patient-level CDCs in nine domains, which include anthropometrics and demographics (8), chronic comorbid illnesses (18), advanced directives (1), ICU diagnoses (61), diagnostic tests (42), interventions (27), medications (38), objective assessments (26), and hospital course and outcomes (5). Upon final review, 91% of the panel endorsed the CDCs as meeting criteria for a minimum viable data dictionary. Data elements cross the lifespan of neonate through adult patients.

The resulting C2D2 provides a foundation to facilitate rapid collection, analyses, and dissemination of information necessary for research, quality improvement, and clinical practice to optimize critical care outcomes. Further work is needed to validate the effectiveness of the dataset in a variety of critical care settings.

Multimorbidity and physical function in older adults have been identified as associated with coronavirus disease 2019 (COVID-19) outcomes. This study aimed to investigate whether multimorbidity affects the association of impaired functional independence (FI) with critical COVID-19 among older inpatients during the peak of Omicron infection in China.

This is a multicentre, retrospective cohort study in northeastern China. Patients aged ≥ 60 years, who were diagnosed with COVID-19 at the time of admission or during hospitalisation. The Barthel index was used to assess FI. Patients were classified into independent, mildly dependent, moderately dependent, and severely dependent groups. Disease severity was classified as critical, severe, and non-severe and combined into severe or critical and non-severe. Binary logistic regression analysis was used to investigate any correlation between FI and disease severity. Patients were further stratified by presence or absence of multimorbidity.

In this study, of 1598 patients, 530 (33.17%) developed severe or critical infections during the entire hospital stay. Patients with severe dependency had 7.39 times (95% CI: [4.60, 12.15]) higher risk of serious or critical infections than those without dependency. An interaction was noted between reduced FI and multimorbidity (p for interaction < 0.001). Compared to non-multimorbid patients (OR = 3.71, 95% CI: [1.58, 9.16]), multimorbid patients (OR = 10.04, 95% CI: [5.63, 18.57]) had a more pronounced risk of severe or critical infection.

Our results provide further scientific evidence on the association between FI, multimorbidity, and disease severity in older COVID-19 patients, contributing to future health decision-making for COVID-19 and other infectious diseases.

This study evaluates the feasibility and accuracy of a pragmatic approach to predicting hospital bed occupancy for COVID-19-positive patients, using only simple methods accessible to typical health system teams.

We used an observational forecasting design for the study period 1st June 2021 to -21st January 2022. Evaluation data covered individuals registered with a general practitioner in North West London, through the Whole Systems Integrated Care deidentified dataset. We extracted data on COVID-19-positive tests, vaccination records and admissions to hospitals with confirmed COVID-19 within the study period. We used linear regression models to predict bed occupancy, using lagged, smoothed numbers of COVID-19 cases among unvaccinated individuals in the community as the predictor. We used mean absolute percentage error (MAPE) to assess model accuracy.

Model accuracy varied throughout the study period, with a MAPE of 10.8% from 12 July 2021 to 18 October 2021, rising to 20.0% over the subsequent period to 15 December 2021. After that, model accuracy deteriorated considerably, with MAPE 110.4% from December 2021 to 21 January 2022. Model outputs were used by senior healthcare system leaders to aid the planning, organisation and provision of healthcare services to meet demand for hospital beds.

The model produced useful predictions of COVID-19-positive bed occupancy prior to the emergence of the Omicron variant, but accuracy deteriorated after this. In practice, the model offers a pragmatic approach to predicting bed occupancy within a pandemic wave. However, this approach requires continual monitoring of errors to ensure that the periods of poor performance are identified quickly.

The COVID-19 pandemic has disproportionately affected individuals with diabetes mellitus (DM), significantly increasing their risk of adverse outcomes. This retrospective study aimed to explore the underlying factors contributing to the heightened vulnerability of individuals with DM to severe COVID-19. We reviewed medical records of patients diagnosed with DM from August 2020 to August 2022 and identified 60 equally divided into two groups. Group A (n = 30) included those diagnosed with an associated COVID-19 infection, while Group B (n = 30) served as the control group without a COVID-19 infection. Inflammatory biomarkers, venous blood glucose levels, and other parameters were assessed at hospital admission and again after a 14-day treatment period. Statistical analysis confirmed a strong association between diabetes and COVID-19 infection. In COVID-19 patients treated with Empagliflozin, correlations were observed between IL-1, TNF-alpha, IL-6, and blood glucose levels. Patients in Group B did not show significant improvements in inflammatory markers or blood glucose control. In contrast, in the first group, better correlations between interleukin levels and blood glucose were demonstrated, suggesting a higher success rate for that treatment. Our findings indicate that while Empagliflozin had limited efficacy in managing long-term diabetes-related complications, it might offer significant benefits in the acute phase of illness.

There had been concerns about the acute complications during or shortly after coronavirus disease 2019 (COVID-19) treatment with nirmatrelvir/ritonavir (NMVr) and molnupiravir (MOL). This study aimed to compare the risks of selected acute safety events in patients treated with or without NMVr or MOL using the COVID-19 oral treatment safety assessment data, constructed through the linkage of nationwide databases: National COVID-19 registry, Real-time Prescription Surveillance, and National Health Insurance data. We identified all adults diagnosed with COVID-19 between January and November 2022, and then constructed two cohorts by matching up to four patients without antiviral treatment records to NMVr or MOL users using propensity score matching. Outcomes of interest were incident-selected cardiac (i.e., atrial fibrillation, other arrhythmia, bradycardia), neurological (i.e., seizure, neuropathy, encephalomyelitis), and miscellaneous (i.e., acute pancreatitis, acute liver injury, dysgeusia) events. A total of 739,935 NMVr users were matched with 2,951,690 comparators and 150,431 MOL users with 759,521 comparators. NMVr users were at lower risk for developing selected cardiac events (hazard ratio 0.74 [95% CI 0.65-0.87] for atrial fibrillation, 0.81 [0.65-0.99] for other arrhythmia, and 0.82 [0.70-0.96] for bradycardia) and dysgeusia (0.58 [0.45-0.74]). For MOL users, the risk was lower for atrial fibrillation (0.72 [0.53-0.96]) and dysgeusia (0.34 [0.18-0.65]). Overall, there were no increased risks of acute complications during and shortly after treatment with oral COVID-19 antivirals. Rather, the findings underscore their effectiveness in attenuating the risk of potential acute sequelae of COVID-19.

COVID-19 was a nightmare in humankind's history that challenged our advanced medical technology. All credit goes to the researchers who played a crucial role in curbing COVID-19 and proved our medical technology supremacy. However, COVID-19 has left some mysterious scars on human well-being. It is believed that COVID-19 has a significant negative impact on various cardiovascular (CVS) and central nervous system (CNS) diseases, especially in the case of CNS diseases like Alzheimer's. Surprisingly, COVID-19 affects the respiratory system, whereas Alzheimer's disease (AD) alters brain function. To explain this phenomenon, several hypotheses were proposed, but the mechanism needs to be clearly understood. Another critical thing to be concerned about is that COVID-19 will worsen pre-existing conditions and lead to the onset of AD. In the race to curb COVID-19, the invention of vaccines was speeded up, and it is necessary to fight against COVID-19. However, postvaccination follow-up is mandatory when an individual is a victim of AD. In this review article, we compiled the various dreadful effects of the COVID-19 virus on AD, the Post effects of the virus on AD, and the effect of the COVID-19 vaccination on AD. This article provides a new direction for research concerning COVID-19 and AD.

Obesity is a complex, multifactorial, chronic disease that acts as a gateway to a range of other diseases. Evidence from recent studies suggests that changes in oxygen availability in the microenvironment of metabolic organs may exert an important role in the development of obesity-related cardiometabolic complications. In this review, we will first discuss results from observational and controlled laboratory studies that examined the relationship between reduced oxygen availability and obesity-related metabolic derangements. Next, the effects of alterations in oxygen partial pressure (pO2) in the adipose tissue, skeletal muscle and the liver microenvironment on physiological processes in these key metabolic organs will be addressed, and how this might relate to cardiometabolic complications. Since many obesity-related chronic diseases, including type 2 diabetes mellitus, cardiovascular diseases, chronic kidney disease, chronic obstructive pulmonary disease and obstructive sleep apnea, are characterized by changes in pO2 in the tissue microenvironment, a better understanding of the metabolic impact of altered tissue oxygenation can provide valuable insights into the complex interplay between environmental and biological factors involved in the pathophysiology of metabolic impairments. This may ultimately contribute to the development of novel strategies to prevent and treat obesity-related cardiometabolic diseases.

Cepharanthine (CEP) is a natural remedy that potently inhibits SARS-CoV-2 activity both in vitro and in vivo. To evaluate the efficacy and safety of CEP compared with placebo in adults with asymptomatic or mild coronavirus disease 2019 (COVID-19), we conducted a proof-of-concept, double-blind, randomized, placebo-controlled trial. Patients were randomized to receive 120 mg/day of CEP, 60 mg/day CEP or placebo for 5 days. Main outcome was the time from randomization to negative nasopharyngeal swab and safety. Among 262 randomized participants, 188 completed the trial among group of 120 mg/day CEP (n = 65), 60 mg/day CEP (n = 68) and placebo (n = 55). Neither 120 mg/day or 60 mg/day CEP shortened the time to negative significantly compared with placebo. However, 60 mg/day CEP showed a slight trend (difference=-0.77 days, hazard ratio (HR) = 1.40, 95% CI 0.97-2.01, p = 0.072). In analysis of participants with good medication compliance, 60 mg/day CEP significantly shortened the time to negative (difference=-0.87 days, HR = 1.56, 95% CI 1.03-2.37, p = 0.035). Adverse events were not different among the three groups, and no serious adverse events occurred. In conclusion, treatment of asymptomatic or mild Covid-19 with 120 mg/day or 60 mg/day did not shorten the time to negative significantly. However, 60 mg/day CEP showed a slight trend which needs future confirmatory trials to validate. (NCT05398705).

The widespread impact of the coronavirus disease 2019 (COVID-19) epidemic on human health causes sudden lifestyle changes, through social distancing and isolation at home, with social and economic consequences. This qualitative study aimed to identify the lived experiences of recovered adult patients from COVID-19 in Iran.

This qualitative research was conducted using a national phenomenological approach. The participants were patients who recovered from COVID-19 through their treatment period in the hospital or at home. Semi-structured in-depth interviews were applied for 45 participants who were invited using purposeful sampling and continued to achieve data saturation. The five-stage inductive process to analyze the structure of lived experience (IPSE) approach was used to analyze the data using MAXQDA 2020 software.

According to the statements of the participants in the study, five types of experiences extracted during the period of suffering from the disease: nutritional problems, physical problems, the psychological burden caused by the disease, the supporting role of others in the disease tolerance, and the unpleasant and better experiences of the disease.

Patients with COVID-19 disease experience many physical and psychosocial consequences which affect their quality of life. Therefore, sociopsychological support provided by psychologists and family members can have ameliorating effects in reducing disease consequences. Further interventional studies were needed to capture these aspects of diseases.

The post-acute impact of SARS-CoV-2 infections on chronic conditions remains poorly understood, particularly in general populations.

Our primary aim was to assess the association between SARS-CoV-2 infections and new diagnoses of chronic conditions. Our two secondary aims were to explore geographic variations in this association and to assess the association between SARS-CoV-2 infections and the exacerbation of pre-existing conditions.

This longitudinal study used data from 8086 participants of the Specchio-COVID-19 cohort in the canton of Geneva, Switzerland (2021-2023). Mixed-effects logistic regressions and geographically weighted regressions adjusted for sociodemographic, socioeconomic, and healthcare access covariates were used to analyze self-reported SARS-CoV-2 infections, new diagnoses of chronic conditions, and the exacerbation of pre-existing ones.

Participants reporting a SARS-CoV-2 infection were more likely to be diagnosed with a new chronic condition compared to those who did not report an infection (adjusted odds ratio [aOR] = 2.15, 95% CI 1.43-3.23, adjusted p-value = 0.002). Notable geographic variations were identified in the association between SARS-CoV-2 infections and new diagnoses. While a positive association was initially observed between SARS-CoV-2 infections and exacerbation of pre-existing chronic conditions, this association did not remain significant after adjusting p-values for multiple comparisons.

These findings contribute to understanding COVID-19's post-acute impact on chronic conditions, highlighting the need for targeted health management approaches and calling for tailored public health strategies to address the pandemic's long-term effects.

As severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) variants continue to emerge, there is an urgent need to develop more effective antiviral drugs capable of combating the COVID-19 pandemic. The main protease (Mpro) of SARS-CoV-2 is an evolutionarily conserved drug discovery target. The present study mainly focused on chemoinformatics computational methods to investigate the efficacy of our newly designed trifluoromethyl-1,3,4-oxadiazole amide derivatives as SARS-CoV-2 Mpro inhibitors. Drug-likeness absorption, distribution, metabolism, excretion, and toxicity analysis, molecular docking simulation, density functional theory (DFT), and molecular dynamics simulation methods were included. A comprehensive drug-likeness analysis was performed on the 14 newly designed compounds (1a-1n), and this series of small molecule inhibitors showed potential anti-SARS-CoV-2 activity. In order to reveal the mechanism of drug interaction, these novel compounds were classified by structure, and molecular docking simulations were performed. The results showed good interactions and identified the key amino acid residue GLY-143. Further DFT analysis using B3LYP-D3BJ functional and 6-311 + + G (d, p) basis set was performed to optimize the optimal configuration of the Mpro inhibitors, and the infrared spectrum of the vibration frequency was analyzed to clearly understand the structure and stability of the drug. The electrostatic potential map was analyzed to predict the reactivity of functional groups and protein-substrate interactions. The frontier molecular orbital analysis and density of states map showed the reactivity level and stability of the drug itself, among which 1i had the smallest energy gap difference (ΔEgap = 3.64 ev), showing good reactivity. The analysis of global reactivity descriptors such as electrophilic index (ω) and chemical potential (μ) also showed that our newly designed Mpro inhibitors had stronger interactions. Molecular dynamics simulation further revealed the stable binding of the Mpro inhibitors in a solvent environment. The binding free energy results calculated by Molecular Mechanics / Poisson Boltzmann Surface Area (MM/PBSA) all exceeded the Food and Drug Administration-approved standard reference drug (Nirmatrelvir), and the free energy landscape and principal component analysis also further described the energy sites formed during the binding process between the drug molecule and the ligand-protein and the changes in conformation. These new series of small molecule inhibitors studied in this work will provide the necessary theoretical basis for the synthesis and activity evaluation of novel SARS-CoV-2 Mpro inhibitors.

To investigate the outcomes of geriatric COVID-19 patients in a German academic setting during the pandemic. This study included 468 consecutive geriatric patients (≥ 70 years) who tested positive for SARS-CoV-2 and were treated at the University of Duisburg-Essen from 2/2020 to 3/2021. 74 patients were transferred to a geriatric hospital and a 12-month follow-up (prospective study) was performed in 51 patients. Clinical assessments evaluated depression (GDS), apathy (AES), cognitive status (MMST), mobility (TUG), health status (EQ-5D-5 L), and daily living activities (Barthel Index). Demographic and clinical data were also analyzed. Results showed that the mortality in this vulnerable group was 52% (n = 209). Long-term survival was higher in patients who received comprehensive geriatric treatment (74.3% vs. 51.8%). The duration of inpatient stay at the primary hospital was 13.3 ± 3.6 days, with 28.8% (n = 135) requiring intensive care. At the 12-month mark more patients with geriatric treatment lived in nursing homes. Barthel-Index/Timed-Up-and-Go-Test/MMST/AES/GDS, and EQ-5D-5 L indicated worse outcomes in the group who received geriatric treatment. Specialized geriatric care may improve survival in geriatric COVID-19 patients despite decreased long-term outcomes. Further research, including international studies like NAPKON, are encouraged to confirm these findings and explore potential interventions for improved outcomes in this vulnerable population.

Routine use of genetic data in healthcare is much-discussed, yet little is known about its performance in epidemiological models including traditional risk factors. Using severe COVID-19 as an exemplar, we explore the integration of polygenic risk scores (PRS) into disease models alongside sociodemographic and clinical variables. PRS were optimized for 23 clinical variables and related traits previously-associated with severe COVID-19 in up to 450,449 UK Biobank participants, and tested in 9,560 individuals diagnosed in the pre-vaccination era. Associations were further adjusted for (i) sociodemographic and (ii) clinical variables. Pathway analyses of PRS were performed to improve biological understanding of disease. In univariate analyses, 17 PRS were associated with increased risk of severe COVID-19 and, of these, four remained associated with COVID-19 outcomes following adjustment for sociodemographic/clinical variables: hypertension PRS (OR = 1.1, 95%CI 1.03-1.18), atrial fibrillation PRS (OR = 1.12, 95%CI 1.03-1.22), peripheral vascular disease PRS (OR = 0.9, 95%CI 0.82-0.99), and Alzheimer's disease PRS (OR = 1.14, 95%CI 1.05-1.25). Pathway analyses revealed enrichment of genetic variants in pathways for cardiac muscle contraction (genes N = 5; beta[SE] = 3.48[0.60]; adjusted-P = 1.86 × 10-5). These findings underscore the potential for integrating genetic data into epidemiological models and highlight the advantages of utilizing multiple trait PRS rather than a single PRS for a specific outcome of interest.

There is increasing evidence that chronic obstructive pulmonary disease (COPD) is associated with coronary heart disease (CHD). In this study, we provide valuable insights in the field by examining the evolution of the relationship between COPD and CHD over the past 20 years.

A comprehensive computer search was conducted in the Web of Science (WOS) core dataset, covering literature on COPD combined with CHD from January 1, 2005, to August 20, 2024. Visual analyses were performed using VOSviewer, CiteSpace, and Bibliometrix to assess countries, institutions, the centrality of institutional intermediaries, authorship patterns, including co-cited authors and references, and keywords; Excel (version 2021) software was utilized for generating relevant descriptive analysis tables.

A total of 2420 publications sourced from WOS were included in this study. Since 2005, there has been a continuous increase in the literature about COPD combined with CHD; polynomial fitting yielded an R² value of 0.7758. The volume of literature in this domain is projected to continue growing steadily. The United States emerged as the leading country by publication count; Lin Cheng-li ranked first among authors, while China Medical University topped institutional contributions. Notably, Sin dd, Mannino dm, and Helvaci Mr were identified as the top three authors based on citation frequency. The Journal of Vascular Surgery recorded the highest number of publications, whereas The Lancet was recognized as the most influential among the top ten co-cited journals. The most frequently cited reference pertains to systemic inflammation's role in increasing cardiovascular risk among patients with COPD. Through keyword clustering analysis, we categorized all keywords into three distinct groups: management strategies for COPD and CHD; diseases associated with both conditions; and epidemiological characteristics concerning their burden-current hotspots include multimorbidity factors such as hypertension and obesity alongside outcomes like diagnosis during COVID-19 pandemic implications within societal contexts are highlighted here too.

Presently focused research on COPD coupled with CHD primarily revolves around five key areas: pathogenesis exploration, early diagnostic techniques, COVID-19 infection, dynamics intervention, methodologies, and treatment protocol development efforts. To improve the early detection rate of COPD complicated with CHD, the main development direction in the future is to extract computed tomography (CT) features using imaging omics and establish an early prediction model. The results of this study will provide new ideas and directions for subsequent related research.

To describe the demographic/clinical characteristics, treatment patterns, and mortality among patients hospitalized with COVID-19 during Omicron predominance by immunocompromised and high-risk status.

Retrospective observational study of patients hospitalized with COVID-19 between January 1, 2022 and November 30, 2022, using data from the Optum de-identified Clinformatics Data Mart Database. Patient demographic/clinical characteristics, treatments, mortality and costs, were assessed, during the emergence of BA.1 BA.4, BA.5, BA.2.12.1, BA.2.75, BQ.1, XBB Omicron viral subvariants.

Overall, 43,123 patients were included, with a mean (standard deviation [SD]) age of 75.5 (12.4) years, 51.8% were female. Immunocompromised patients accounted for 36% of hospitalized patients while only 5.8% received any outpatient COVID-19 treatment within 30 days of hospital admission. The mean (SD) hospital length of stay was 7.9 (7.5) days with 15.5% mortality within 30 days of admission. Mean (SD) hospital costs were $33,975 ($26,392), and 30-day all-cause readmission was 15.1%. Patients with immunocompromised status and those with a higher number of high-risk conditions proceeded to have an elevated proportion of hospital readmissions and mortality within 30 days. Moreover, a higher proportion of mortality was observed during the BA.1 period (20.1%) relative to other variant periods (11.0%).

COVID-19 imposed a large healthcare burden, particularly among immunocompromised patients and those with underlying high-risk conditions during Omicron period. Low utilization of outpatient COVID-19 treatments was observed in these high-risk populations eligible for treatment. Continued surveillance and research regarding COVID-19 variants and the impact of outpatient treatment options on high-risk patients is crucial to inform and guide public health action.

People with diabetes are at increased risk of hospitalisation, morbidity, and mortality following SARS-CoV-2 infection. Long-term outcomes for people with diabetes previously hospitalised with COVID-19 are, however, unknown. This study aimed to determine the longer-term physical and mental health effects of COVID-19 in people with and without diabetes.

The PHOSP-COVID study is a multicentre, long-term follow-up study of adults discharged from hospital between 1 February 2020 and 31 March 2021 in the UK following COVID-19, involving detailed assessment at 5 and 12 months after discharge. The association between diabetes status and outcomes were explored using multivariable linear and logistic regressions.

People with diabetes who survived hospital admission with COVID-19 display worse physical outcomes compared to those without diabetes at 5- and 12-month follow-up. People with diabetes displayed higher fatigue (only at 5 months), frailty, lower physical performance, and health-related quality of life and poorer cognitive function. Differences in outcomes between diabetes status groups were largely consistent from 5 to 12-months. In regression models, differences at 5 and 12 months were attenuated after adjustment for BMI and presence of other long-term conditions.

People with diabetes reported worse physical outcomes up to 12 months after hospital discharge with COVID-19 compared to those without diabetes. These data support the need to reduce inequalities in long-term physical and mental health effects of SARS-CoV-2 infection in people with diabetes.

UK Research and Innovation and National Institute for Health Research. The study was approved by the Leeds West Research Ethics Committee (20/YH/0225) and is registered on the ISRCTN Registry (ISRCTN10980107).

Background COVID-19 has been a fatal pandemic in modern history, with diabetes mellitus (DM) as a common comorbidity. However, data on the effect of DM on Indian COVID-19 patients are still scarce. We aimed to evaluate the prevalence and prognostic impact of DM in COVID-19 patients hospitalized at Index Medical College Hospital and Research Center (IMCHRC), Indore, Madhya Pradesh. Methods This is a retrospective monocentric observational study including all consecutive adult COVID-19 patients admitted to our center from July 2021 to March 2022. Data including demographics, clinical features, DM history, laboratory investigations, comorbidities, disease severity, intensive care unit (ICU) admission, invasive fungal infections, multi-organ dysfunction, and death were retrieved from the medical records of the patients and analyzed. Results A total of 357 COVID-19 patients (236 males) with a mean age of 52+9.7 years were evaluated. The overall prevalence of DM in the patients was 32% (115/357), comprising of 23% (83/357) cases of pre-existing DM and 9% (32/357) cases of new onset of DM. Vascular comorbidities consisting of hypertension (62% vs 27%; p=0.002), cardiovascular disease (39% vs 21%; p=0.016), and kidney disease (42% vs 27%; p=0.019) were significantly associated with DM. Diabetic versus non-diabetic COVID-19 patients had a higher rate of ICU admission (23% vs 10%; p=0.042), severe disease (43% [49/115] vs 5% [13/242]; p=0.002), acute respiratory distress syndrome (25% vs 7%), secondary infections (30% vs 11%; p=0.033), multi-organ dysfunction (16% vs 9%; p=0.045), and in-hospital mortality (21% [24/115] vs 6% [15/242]; p=0.011). Conclusions Our study shows a high prevalence of DM in COVID-19 patients and an adverse effect of DM on the prognosis of COVID-19 patients including increased disease severity and in-hospital mortality.

Older age and associated comorbid conditions increase the risk of severe form of COVID-19 and death. The SARS-CoV-2 vaccination campaign began in France in December 2020 targeting institutionalized older population before having been evaluated in this population. The objective of our study was to assess the tolerability of vaccination 21 days (D21) and 90 days after the first vaccination (D90) in institutionalized old people. Secondary objective was to assess its effectiveness (mortality, hospitalization and occurrence of COVID) at D21 and D90.

People living in nursing homes or in long-term hospitalization facilities in France were included 12-2020-06-2021. They were divided into SARS-CoV-2 vaccinated and unvaccinated groups. Vaccine tolerability was prospectively assessed by the occurrence of health events at D21 and D90 (local and systemic side effects, geriatric syndromes, cardiovascular events). Vaccine efficacy was assessed by the occurrence of COVID-19 and serious adverse events (unscheduled hospitalization and all-cause mortality).

The mean age of the 2595 participants was 86 years, 83% received COVID-19 vaccine. There were no significant difference between the vaccinated and unvaccinated for systemic or local adverse events at D21 and D90. At D90, vaccinated participants had significantly fewer SARS-CoV-2 infections (odds ratio (95% confidence interval) = 0.35 (0.22-0.58)), fewer deaths or hospitalizations (0.50 (0.31-0.81)), fewer cardiovascular events (0.28 (0.12-0.64)) and fewer pressure ulcers (0.38 (0.17-0.88)).

In this prospective cohort study, COVID-19 vaccine in a very old institutionalized geriatric population had a reassuring safety profile and a protective effect on COVID-19, hospitalizations and deaths, cardiovascular events and pressure ulcers.

Traditional Chinese medicine (TCM) has developed a rich theoretical system and practical experience in fighting to infectious diseases over the past thousands of years, and has played an important role in controlling the spread owing to its unique advantages. In particular, its significant contribution to the prevention and control of Corona Virus Disease 2019 (COVID-19) is widely recognized. COVID-19 infection is mainly non-severe with a favorable overall outcome, but patients with comorbidities tend to have a poor prognosis. However, a comprehensive review of TCM for preventing and treating COVID-19 with comorbidities across various systems is still lacking. Hence, this scoping review aims to conduct a comprehensive investigation on treatment outcome of TCM for treating COVID-19 with comorbidities across various systems.

The scoping review was conducted by searching English databases including PubMed and Web of Science, and Chinese databases including China National Knowledge Infrastructure and Wanfang between January 2020 and January 2024. We followed the inclusion and exclusion criteria to identify relevant literature. Information for inclusion in the literature were subsequently extracted and consolidated.

We enrolled 13 literature that met the inclusion criteria in the review finally. Our analysis revealed that research on COVID-19 with comorbidities was mostly focused on circulatory diseases, including hypertension, heart failure, and cerebrovascular diseases, most common comorbidities were hypertension. Followed by endocrine and metabolic diseases such as diabetes, respiratory diseases including pulmonary tuberculosis and chronic obstructive pulmonary disease have been also addressed. However, there were few studies on co-infectious urogenital system disease, and no studies on the rheumatic, immune, hematological, nervous, reproductive, and skin systems diseases. Based on existing studies, TCM has significantly improved the clinical symptoms of COVID-19 with comorbidities such as fever, fatigue, dry cough, anorexia and asthma, the absorption of lung lesions, shortened the duration of viral shedding and the course of disease.

TCM has great application prospects in treating COVID-19 with comorbidities. These findings could provide important evidence for clinicians to treat COVID-19 with comorbidities. Multi-center studies are required to confirm our results in the future.