Orientações da COREMU SMS SP como estratégia de comunicação efetiva nos programas de Residência em Saúde

Orientações da COREMU SMS SP como estratégia de comunicação efetiva nos programas de Residência em Saúde. 2º Congresso Mineiro de Enfermagem ( 2ª CMENF).

Prognostic significance of the chemerin level in coronavirus disease 2019 patients.

Increased serum chemerin levels have been reported in several inflammatory diseases. Few studies have investigated the relationship between chemerin and clinical features of COVID-19. Thus, chemerin may modulate the development and progression of COVID-19. We compared the serum chemerin concentration between patients with and without SARS-CoV-2 infection and its association with the severity and prognosis of COVID-19 pneumonia. This is a prospective, single-center, cross-sectional study. We enrolled COVID-19 patients who presented to our tertiary hospital and healthy controls. The COVID-19 patients were conducted and the dates of symptom onset were recorded. After admission to the hospital and stabilization, blood samples were obtained for routine hemogram, biochemistry, and chemerin. The chemerin level was 37.93 ±â€…17.3 ng/mL in patients followed in the ICU, 29.41 ±â€…12.79 ng/mL in inpatients, 30.48 ±â€…10.86 ng/mL in outpatients, and 25.12 ±â€…9.82 ng/mL in healthy controls. The difference between patients treated in the ICU and healthy controls was significant (P < .001). The high-sensitivity C-reactive protein (hs-CRP), ferritin, procalcitonin (PCT), and D-dimer levels were significantly higher in the intensive care unit (ICU) group (P < .001). Moreover, the chemerin level of patients who died was significantly higher than that of those who survived (P < .001). The chemerin level was increased in COVID-19 patients and also increased with increasing disease severity. The chemerin level was higher in the COVID-19 patients than healthy controls and was significantly higher in patients who died compared to those who did not.

COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study.

BACKGROUND: Air pollution and a number of metabolic disorders have been reported to increase the risk of severe COVID-19 outcomes. This study explored the association between severe COVID-19 outcomes, metabolic disorders and environmental air pollutants, at regional level, across 38 countries. METHODS: We conducted an ecological study using COVID-19 data related to countries of the Organization for Economic Cooperation and Development (OECD), with an estimated population of 1.4 billion. They were divided into 3 regions: 1. Europe & Middle east; 2. Americas (north, central & south America); 3. East-Asia & West Pacific. The outcome variables were: COVID-19 case-fatality rate (CFR) and disability-adjusted life years (DALYs) at regional level. Freely accessible datasets related to regional DALYs, demographics and other environmental pollutants were obtained from OECD, WHO and the World in Data websites. Generalized linear model (GLM) was performed to determine the regional determinants of COVID-19 CFR and DALYs using the aggregate epidemiologic data (Dec. 2019-Dec. 2021). RESULTS: Overall cumulative deaths were 65,000 per million, for mean CFR and DALYs of 1.31 (1.2)% and 17.35 (2.3) years, respectively. Globally, GLM analysis with adjustment for elderly population rate, showed that COVID-19 CFR was positively associated with atmospheric PM2.5 level (beta = 0.64(0.0), 95%CI: 0.06-1.35; p < 0.05), diabetes prevalence (beta = 0.26(0.1), 95%CI: 0.12-0.41; p < 0.001). For COVID-19 DALYs, positive associations were observed with atmospheric NOx level (beta = 0.06(0.0), 95%CI: 0.02-0.82; p < 0.05) and diabetes prevalence (beta = 0.32(0.2), 95%CI: 0.04-0.69; p < 0.05). At regional level, adjusted GLM analysis showed that COVID-19 CFR was associated with atmospheric PM2.5 level in the Americas and East-Asia & Western Pacific region; it was associated with diabetes prevalence for countries of Europe & Middle east and East-Asia & Western Pacific region. Furthermore, COVID-19 DALYs were positively associated with atmospheric PM2.5 and diabetes prevalence for countries of the Americas only. CONCLUSION: These findings confirm that diabetes and air pollution increase the risk of disability and fatality due to COVID-19, with disparities in terms of their impact. They suggest that efficient preventive and management programs for diabetes and air pollution countermeasures would have curtailed severe COVID-19 outcome rates.

On-site detection of infectious disease based on CaCO3-based magnetic micromotor integrated with graphene field effect transistor.

A new detection platform based on CaCO3-based magnetic micromotor (CaCO3@Fe3O4) integrated with graphene field effect transistor (GFET) was construct and used for on-site SARS-CoV-2 coronavirus pathogen detection. The CaCO3@Fe3O4 micromotor, which was modified with anti-SARS-CoV-2 (labelled antibody, AntiE1), can self-moved in the solution containing hydrochloric acid (HCl) and effective to capture the SARS-CoV-2 coronavirus pathogens. After magnetic field separation, the capture micromotor was detected by GFET, exhibiting a good linear relationship within the range of 1 ag/mL to 100 ng/mL and low detection limit (0.39 ag/mL). Furthermore, the detection platform was also successfully applied to detection of SARS-CoV-2 coronavirus pathogens in soil solution, indicating the potential use in on-site application.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians. Methods: Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity. Results: Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively. Conclusion: Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression.

Background: Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses. Methods: Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes. Results: The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified. Conclusion: This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.

Background: Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. Methods: We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD. Results: Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion. Conclusion: Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

How inclusive were UK-based randomised controlled trials of COVID-19 vaccines? A systematic review investigating enrolment of Black adults and adult ethnic minorities.

OBJECTIVES: To establish if Black adults and adult ethnic minorities, defined as any group except White British, were represented in UK-based COVID-19 vaccination randomised controlled trials (RCTs) when compared to corresponding UK population proportions, based on 2011 census data. DESIGN: Systematic review of COVID-19 Randomised Controlled Vaccine Trials SETTING: United Kingdom PARTICIPANTS: Randomised Controlled Trials of COVID-19 vaccines conducted in the UK were systematically reviewed following PRISMA guidelines. MeSH terms included "Covid-19 vaccine", "Ad26COVS1", and "BNT162 Vaccine" with keywords such as [covishield OR coronavac OR Vaxzevria OR NVX-CoV2373] also used. Studies that provided (A) participant demographics and (B) full eligibility criteria were included. The following key data was extracted for analysis: number of participants analysed, number of Black adults and number of adult minority ethnicity participants. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is the mean percentage of Black adults randomised to COVID-19 vaccine trials deemed eligible within this review. The secondary outcome is the mean percentage of adult ethnic minorities randomised. RESULTS: The final review included 7 papers and a total of 87 sets of data collated from trial sites across the UK. The standard mean percentage of Black adults included in the trials (0.59%, 95% CI: 0.13% - 1.05%) was significantly lower compared to the recorded Black adult population (2.67%) indicating that they were under-served in UK based COVID-19 vaccine RCTs (p < 0.001). Adult ethnic minority presence (8.94%, 95% CI: 2.07% - 15.80%) was also lower than census data (16.30%), indicating they were also under-served (p = 0.039). CONCLUSION: The findings show that COVID-19 vaccine trials failed to adequately randomise proportionate numbers of Black adults and adult minority ethnicities. More inclusive practices must be developed and implemented in the recruitment of underserved groups to understand the true impact of COVID-19.

The impact of the COVID-19 pandemic on renal cancer care.

PURPOSE: To evaluate the impact of the COVID-19 pandemic on renal cell carcinoma (RCC) care in the Netherlands. METHODS: Newly diagnosed RCCs between 2018 and 2021 were selected from the Netherlands Cancer Registry; 2020-2021 was defined as COVID period and 2018-2019 as reference period. Numbers of RCCs were evaluated using 3-week-moving averages, overall and by disease stage and age. Changes in treatment were evaluated with logistic regression analyses. To evaluate possible delays in care, time to start of treatment was assessed. The cumulative number of metastatic RCC (mRCC) over time was assessed to evaluate stage shift. RESULTS: During the 1st COVID wave (weeks 9-22, 2020), the number of new RCC diagnoses decreased with 15%. Numbers restored partially in 2020, but remained 10% lower compared to 2018/2019. The decline was mostly due to a drop in T1a/T1b RCCs and in age > 70 years. 2021 showed similar numbers of new RCC diagnoses compared to 2018/2019 without an increase due to previously missed RCCs. Treatment-related changes during the 1st COVID wave were limited and temporarily; less surgery in T1a RCCs in favor of more active surveillance, and in mRCC targeted therapy was preferred over immunotherapy. Time to start of firstline treatment was not prolonged during the 1st COVID wave. No increase in mRCC was found until the end of 2021. CONCLUSIONS: The COVID-19 pandemic resulted in fewer RCC diagnoses, especially T1a/T1b tumors. Treatment-related changes appeared to be limited, temporarily and in accordance with the adapted guidelines. The diagnostic delay could lead to more advanced RCCs in later years but there are no indications for this yet.

Community health worker outreach to farmworkers in rural North Carolina: Learning from adaptations to the SARS-CoV-2 pandemic.

BACKGROUND: Community health workers represent a critical part of the health outreach and services for migrant and seasonal farmworkers ('farmworkers') in rural areas of the United States. PURPOSE: We sought to identify adaptations to farmworker patient engagement and health outreach made by community health workers during the first 18 months of the COVID-19 pandemic. METHODS: In this qualitative study, we used semi-structured interviews with community health workers from August 2020 to February 2022 (n = 21). Two coders used thematic analysis to identify three themes related to the experiences of community health workers in conducting health education and outreach to farmworkers prior to and following the onset of the pandemic. FINDINGS: We found themes related to pre-pandemic outreach efforts to provide health education resource sharing with farmworkers and pandemic-related outreach efforts that included adoption of porch drops and distanced delivery of health education, adaptation of modes of health education and communication through technology and the internet, and taking on new roles related to COVID-19. Finally, we identified changes that reverted after the pandemic or will continue as adaptations. CONCLUSIONS: Community health workers created practice-based innovations in outreach in response to the COVID-19 pandemic. These innovations included new COVID-19 related roles and new modes of health education and outreach, including the use of digital resources. The changes developed for emergency use in COVID-19, particularly related to internet and technology, have likely altered how community health workers conduct outreach in North Carolina going forward. Funders, community health worker training programs, and researchers should take note of these innovations. PATIENT OR PUBLIC CONTRIBUTION: Community health workers who typically come from patient populations and provide critical navigation and connection with the health care system advised on the design and creation of this research project, including serving on an advisory board. Two authors have experience working as community health workers.

Trust-building in temporary public health partnerships: a qualitative study of the partnership formation process of a Covid-19 test, trace and protect service.

BACKGROUND: Public health initiatives require coordinated efforts from healthcare, social services and other service providers. Organisational theory tells us that trust is essential for reaching collaborative effectiveness. This paper explores the drivers for initiating and sustaining trust in a temporary public health partnership, in response to a sudden health threat. METHODS: This qualitative study analysed the formation process of a multisector partnership for a Covid-19 contact tracing service. Data was collected through 12 interviews, two focus groups, one feedback workshop, and an online survey with workforce members from all seven partner organisations. Purposive maximum variation sampling was used to capture the reflections and experiences of workforce members from all seven partner organisations. A deductive code scheme was used to identify drivers for building and sustaining trust in inter-organisational collaboration. RESULTS: Relational mechanisms emanating from the commitment to the common aim, shared norms and values, and partnership structures affected trust-building. Shared values and the commitment to the common aim appeared to channel partners' behaviour when interacting, resulting in being perceived as a fair, reliable and supportive partner. Shared values were congruent with the design of the partnership in terms of governance structure and communication lines reflecting flat hierarchies and shared decision-making power. Tensions between partner organisations arose when shared values were infringed. CONCLUSIONS: When managing trust in a collaboration, partners should consider structural components like governance structure, organisational hierarchy, and communication channels to ensure equal power distribution. Job rotation, recruitment of candidates with the desired personality traits and attitudes, as well as training and development, encourage inter-organisational networking among employees, which is essential for building and strengthening relationships with partner organisations. Partners should also be aware of managing relational dynamics, channelling behaviours through shared values, objectives and priorities and fostering mutual support and equality among partner organisations.

Changes in long-term care insurance revenue among service providers during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has impacted peoples' health-related behaviors, especially those of older adults, who have restricted their activities in order to avoid contact with others. Moreover, the pandemic has caused concerns in long-term care insurance (LTCI) providers regarding management and financial issues. This study aimed to examine the changes in revenues among LTCI service providers in Japan during the pandemic and analyze its impact on different types of services. METHODS: In this study, we used anonymized data from "Kaipoke," a management support platform for older adult care operators provided by SMS Co., Ltd. Kaipoke provides management support services to more than 27,400 care service offices nationwide and has been introduced in many home-care support offices. The data used in this study were extracted from care plans created by care managers on the Kaipoke platform. To examine the impact of the pandemic, an interrupted time-series analysis was conducted in which the date of the beginning of the pandemic was set as the prior independent variable. RESULTS: The participating providers were care management providers (n = 5,767), home-visit care providers (n = 3,506), home-visit nursing providers (n = 971), and adult day care providers (n = 4,650). The results revealed that LTCI revenues decreased significantly for care management providers, home-visit nursing providers, and adult day care providers after the COVID-19 pandemic began. The largest decrease was an average base of USD - 1668.8 in adult day care. CONCLUSION: The decrease in revenue among adult day care providers was particularly concerning in terms of the sustainability of their business. This decrease in revenue may have made it difficult to retain personnel, and staff may have needed to be laid off as a result. Although this study has limitations, it may provide useful suggestions for countermeasures in such scenarios, in addition to support conducted measures.

Trajectory of cardiac troponin T following moderate-to-severe COVID-19 and the association with cardiac abnormalities.

BACKGROUND: COVID-19 has been associated with cardiac troponin T (cTnT) elevations and changes in cardiac structure and function, but the link between cardiac dysfunction and high-sensitive cardiac troponin T (hs-cTnT) in the acute and convalescent phase is unclear. OBJECTIVE: To assess whether hs-cTnT concentrations are associated with cardiac dysfunction and structural abnormalities after hospitalization for COVID-19, and to evaluate the performance of hs-cTnT to rule out cardiac pathology. METHODS: Patients hospitalized with COVID-19 had hs-cTnT measured during the index hospitalization and after 3-and 12 months, when they also underwent an echocardiographic study. A subset also underwent cardiovascular magnetic resonance imaging (CMR) after 6 months. Cardiac abnormalities were defined as left ventricular hypertrophy or dysfunction, right ventricular dysfunction, or CMR late gadolinium. RESULTS: We included 189 patients with hs-cTnT concentrations measured during hospitalization for COVID-19, and after 3-and 12 months: Geometric mean (95%CI) 13 (11-15) ng/L, 7 (6-8) ng/L and 7 (6-8) ng/L, respectively. Cardiac abnormalities after 3 months were present in 45 (30%) and 3 (8%) of patients with hs-cTnT ≥ and < 5 ng/L at 3 months, respectively (negative predictive value 92.3% [95%CI 88.5-96.1%]). The performance was similar in patients with and without dyspnea. Hs-cTnT decreased from hospitalization to 3 months (more pronounced in intensive care unit-treated patients) and remained unchanged from 3 to 12 months, regardless of the presence of cardiac abnormalities. CONCLUSION: Higher hs-cTnT concentrations in the convalescent phase of COVID-19 are associated with the presence of cardiac pathology and low concentrations (< 5 ng/L) may support in ruling out cardiac pathology following the infection.

Burdens and resources of Austrian clinical psychologists: results of a qualitative study two years into the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic increased the mental health burden in the general population, enhancing the demands placed on mental healthcare professionals. METHODS: This study aimed to assess the burdens and resources of clinical psychologists that emerged since the beginning of the pandemic. N = 172 Austrian clinical psychologists participated in a cross-sectional online survey between April and May 2022. The burdens and the sources of support that emerged during the pandemic were analyzed using qualitative content analysis. RESULTS: Mental health-related issues were identified as the greatest burden, followed by work-related themes and restrictions imposed by the government to combat the spreading of the virus. The most important resources mentioned by the clinical psychologists were social contacts and recreational activities. Practising mindfulness and focusing on inner processes and work-related aspects were further important resources mentioned. CONCLUSION: Overall, it seems that clinical psychologists have a high awareness of mental health-related problems related to the pandemic and use adaptive coping strategies to deal with them.

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and ß chains. Here, we collect and curate a dataset of 17,715 αßTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques.

The COVID-19 pandemic has profoundly reshaped human life. The development of COVID-19 vaccines has offered a semblance of normalcy. However, obstacles to vaccination have led to substantial loss of life and economic burdens. In this study, we analyze data from a prominent health insurance provider in the United States to uncover the underlying reasons behind the inability, refusal, or hesitancy to receive vaccinations. Our research proposes a methodology for pinpointing affected population groups and suggests strategies to mitigate vaccination barriers and hesitations. Furthermore, we estimate potential cost savings resulting from the implementation of these strategies. To achieve our objectives, we employed Bayesian data mining methods to streamline data dimensions and identify significant variables (features) influencing vaccination decisions. Comparative analysis reveals that the Bayesian method outperforms cutting-edge alternatives, demonstrating superior performance.

Intra and inter-rater remote assessment of bradykinesia in Parkinson's disease.

INTRODUCTION: Reliable assessment of individuals with Parkinson's disease (PD) is essential for providing adequate treatment. Clinical assessment is a complex and time-consuming task, especially for bradykinesia, since its evaluation can be influenced by the degree of experience of the examiner, patient collaboration and individual bias. Improvement of the clinical evaluation can be obtained by considering assessments from several professionals. However, this is only true when inter and intra-rater agreement are high. Recently, the Movement Disorder Society highlighted, during the COVID-19 pandemic, the need to develop and validate technologies for remote assessment of the motor status of people with PD. Thus, this study introduces an objective strategy for the remote evaluation of bradykinesia using multi-specialist analysis. METHODS: Twelve volunteers with PD participated and these were asked to execute finger tapping, hand opening/closing and pronation/supination movements. Each task was recorded and rated by fourteen PD health experts for each patient. The scores were assessed on an individual basis. Intra and inter-rater agreement and correlation were estimated. RESULTS: The results showed that agreements and correlations between experienced examiners were high with low variability. In addition, group analysis was noted as possessing the potential to solve individual inconsistency bias. CONCLUSION: Furthermore, this study demonstrated the need for a group with prior training and experience, along with indicating the importance for the development of a clinical protocol that can use telemedicine for the evaluation of individuals with PD, as well as the inclusion of a specialized mediating group. In Addition, this research helps to the development of a valid remote assessment of bradykinesia.

COVID-19 inequalities in England: a mathematical modelling study of transmission risk and clinical vulnerability by socioeconomic status.

BACKGROUND: The COVID-19 pandemic resulted in major inequalities in infection and disease burden between areas of varying socioeconomic deprivation in many countries, including England. Areas of higher deprivation tend to have a different population structure-generally younger-which can increase viral transmission due to higher contact rates in school-going children and working-age adults. Higher deprivation is also associated with a higher presence of chronic comorbidities, which were convincingly demonstrated to be risk factors for severe COVID-19 disease. These two major factors need to be combined to better understand and quantify their relative importance in the observed COVID-19 inequalities. METHODS: We used UK Census data on health status and demography stratified by decile of the Index of Multiple Deprivation (IMD), which is a measure of socioeconomic deprivation. We calculated epidemiological impact using an age-stratified COVID-19 transmission model, which incorporated different contact patterns and clinical health profiles by decile. To separate the contribution of each factor, we considered a scenario where the clinical health profile of all deciles was at the level of the least deprived. We also considered the effectiveness of school closures and vaccination of over 65-year-olds in each decile. RESULTS: In the modelled epidemics in urban areas, the most deprived decile experienced 9% more infections, 13% more clinical cases, and a 97% larger peak clinical size than the least deprived; we found similar inequalities in rural areas. Twenty-one per cent of clinical cases and 16% of deaths in England observed under the model assumptions would not occur if all deciles experienced the clinical health profile of the least deprived decile. We found that more deaths were prevented in more affluent areas during school closures and vaccination rollouts. CONCLUSIONS: This study demonstrates that both clinical and demographic factors synergise to generate health inequalities in COVID-19, that improving the clinical health profile of populations would increase health equity, and that some interventions can increase health inequalities.

[Singularities of COVID-19 in immunosuppressed persons]. / Singularités du Covid-19 chez les personnes immunosupprimées.

Immunosuppressed persons are a heterogeneous population that represents approximately 3 % of the adult population. They are more vulnerable to infectious agents, such as SARS-CoV-2. This is reflected by a reduced response to vaccination, a higher rate of progression towards a severe form of the disease, and recurrent or persistent infections associated with intra-host viral evolution. This review summarizes the evidence regarding vaccine efficacy, clinical and virological singularities, and the management in immunosuppressed patients.

Les personnes immunosupprimées (PI) constituent une population hétérogène représentant environ 3 % de la population adulte et sont plus vulnérables aux infections, telles que le Covid-19, face auquel elles présentent une réponse vaccinale diminuée, un taux plus élevé d'évolution vers une forme sévère de la maladie, et des infections persistantes associées à une excrétion virale prolongée et à une possible évolution virale intrahôte. Cet article résume l'évidence concernant l'efficacité vaccinale, les particularités clinico-virologiques et la prise en charge spécifique, dans la population des PI.

Respiratory pathogenic microbial infections: a narrative review.

Respiratory infectious diseases have long been recognised as a substantial global healthcare burden and are one of the leading causes of death worldwide, particularly in vulnerable individuals. In the post COVID-19 era, there has been a surge in the prevalence of influenza virus A and other multiple known viruses causing cold compared with during the same period in the previous three years, which coincided with countries easing COVID-19 restrictions worldwide. This article aims to review community-acquired respiratory illnesses covering a broad spectrum of viruses, bacteria, and atypical microorganisms and focuses on the cluster prevalence of multiple known respiratory pathogens in China, thereby providing effective prevention and control measures.