Different Levels Of Disease Severity Research Articles

Patient With Parkinson Disease and Care Partner Perceptions of Key Domains Affecting Health-Related Quality of Life: Systematic Review

To effectively customize Parkinson disease (PD) programs, it is important to incorporate the "individual's voice" and have a thorough understanding of the symptom priorities of people with PD (PwP) and care partners (CP). In this convergent integrated mixed-method systematic review, we aimed to analyze qualitative and quantitative evidence of PD motor and nonmotor symptoms affecting health-related quality of life (HRQOL) in PwP and CP, comparing priorities across different levels of disease severity. We searched MEDLINE, PsycINFO, Web of Science, Embase, and Scopus; ProQuest Dissertations and Theses Global; and the Michael J. Fox Foundation Data Resources for studies published up to June 29, 2022. We included qualitative, quantitative, and mixed-method studies investigating PD symptom priorities among PwP and CP. We critically appraised eligible studies for methodological quality using the Mixed-Methods Appraisal Tool. Derived terms were mapped and coded according to thematic attribution. Independent syntheses of qualitative and quantitative evidence and transformation of quantitative data into qualitative formats were performed. Of the 7,716 identified studies, we included 70 that provided qualitative (n = 13), quantitative (n = 53), and mixed (n = 4) evidence. We included 604 mapped terms representing 11 PwP-identified and CP-identified motor and nonmotor symptom categories. Across all PD stages, both PwP and CP considered 5 domains more affecting their HRQOL, namely: "motor functionality," "mood," "cognition," "gait, balance, posture, and falls," and "nighttime sleep disorders." In early disease, PwP and CP considered "mood" the domain that most affected their HRQOL. In advanced PD, PwP considered "pain" the domain that most affects their HRQOL, while CP considered "psychiatric symptoms." The domain "gait, balance, posture, and falls" was equally considered by both PwP and CP as the second domain that most affects their HRQOL in the advanced stage of PD. The ranking of the priority of symptoms is largely shared by PwP and CP, and motor symptom priorities dominate the full disease spectrum. However, the nonmotor symptom priorities shift according to the disease severity stage. Tailored care and research require that providers consider these shifting priorities and incorporate the "individual's voice" into treatment decisions.

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.

Tracking antigen‐specific TCR clonotypes in SARS‐CoV‐2 infection reveals distinct severity trajectories

AbstractDespite the importance of antigen‐specific T cells in infectious disease, characterizing and tracking clonally amplified T cells during the progression of a patient's symptoms remain unclear. Here, we performed a longitudinal, in‐depth single‐cell multiomics analysis of samples from asymptomatic, mild, usual severe, and delayed severe patients of SARS‐CoV‐2 infection. Our in‐depth analysis revealed that hyperactive or improper T‐cell responses were more aggressive in delayed severe patients. Interestingly, tracking of antigen‐specific T‐cell receptor (TCR) clonotypes along the developmental trajectory indicated an attenuation in functional T cells upon severity. In addition, increased glycolysis and interleukin‐6 signaling in the cytotoxic T cells were markedly distinct in delayed severe patients compared to usual severe patients, particularly in the middle and late stages of infection. Tracking B‐cell receptor clonotypes also revealed distinct transitions and somatic hypermutations within B cells across different levels of disease severity. Our results suggest that single‐cell TCR clonotype tracking can distinguish the severity of patients through immunological hallmarks, leading to a better understanding of the severity differences in and improving the management of infectious diseases by analyzing the dynamics of immune responses over time.

BPRS domains, items and subgroups analyses, and CGI-I ratings in pooled data from non-interventional studies of aripiprazole once-monthly in schizophrenia (REACT study)

BackgroundPatients with schizophrenia may benefit from treatment with long-acting injectable (LAI) formulations of antipsychotics. Aripiprazole once-monthly (AOM) is an LAI that was tested in two non-interventional studies in Germany and Canada.MethodsHere, we report on analyses of pooled data from the two non-interventional studies. Patients were treated with AOM under real-life conditions. Data were analyzed for a timeframe of 6 months. We analyzed data on Brief Psychiatric Rating Scale (BPRS) domains and items, BPRS total scores in various patient subgroups (male vs. female patients, patients with disease duration ≤ 5 years and > 5 years, patients with different levels of disease severity at baseline), Clinical Global Impression – Improvement (CGI-I) ratings for the total population and subgroups, and comorbidities for the total population.ResultsData from 409 patients were included. 65.5% of the patients had comorbidities. Improvements were found in all BPRS domains and items. Furthermore, improvements were similar for male and female patients, patients with disease duration ≤ 5 years and > 5 years, and across different levels of disease severity at baseline. Numerically, more favorable results were found for younger patients, female patients, and those with shorter disease duration.ConclusionsAOM can be an effective treatment in the broad range of patients, across sexes, regardless of patient age and duration of disease, independently of disease severity, and across symptoms.Trial registrationNCT02131415 (May 6, 2014), vfa non-interventional studies registry 15960N.

Distribution of virulence genes and biofilm characterization of human isolates of Streptococcus agalactiae: A pilot study

This study included 21 newly isolated clinical samples of Streptococcus agalactiae (Group B Streptococcus) screened in patients (six male, fifteen female) from various states of India with different infections (urinary tract infections, blood, pus and eye infections). All isolates were identified as Group B Streptococcus (GBS) using hemolytic properties, serogrouping and MALDI-TOF-MS analysis. Six virulence genes, cfb (100%), cylE (90.4%), lmp (85.7%), bca (71.4%), rib (38%) and bac (4.7%) were detected via polymerase chain reaction (PCR). Distribution studies of these six genes revealed five isolates containing five virulence genes (23.8%), followed by ten isolates containing four virulence genes (47.6%). The twenty GBS isolates selected on the glass surface included non-biofilm producers (n = 6, 30%), weak (n = 11, 55%) and moderate biofilm producers (n = 3, 15%). On the polystyrene surface, weak (n = 4, 20%), moderate (n = 2, 10%) and strong (n = 14, 70%) biofilm producers were detected. Live-dead cell staining revealed that more viable cells accumulated in the S. ag 7420 isolate than in the AH1 isolate. Scanning electron microscope (SEM) biofilm analysis showed S. ag AH1 cells appeared as chain-like structures, whereas the S. ag 7420 isolate biofilm cells appeared as fork-like structures on the glass surface. Biofilm elements were analyzed using Energy Dispersive X-Ray Analysis (EDAX) for both isolates and 13 elements with different orders of composition were found. Thus, virulence gene detection, distribution and biofilm formation by these new clinical isolates suggested the virulent nature of these pathogens, which might cause different levels of disease severity in humans.

PI3K polymorphism in patients with sporadic Parkinson's disease.

Parkinson's disease (PD) is a common irreversible neurodegenerative disease associated with cognitive impairment. To investigate the serum level of phosphatidylinositol-3-kinase (PI3K) and the distribution of the genotypes and alleles of 3 PI3K single-nucleotide polymorphisms (RS37,30,087, RS37,30,088, and RS37,30,089) in PD patients with different clinical characteristics. A total of 54 PD patients and 50 healthy individuals were recruited. The serum PI3K level was measured using the enzyme-linked immunosorbent assay. The severity of PD was assessed using the modified Hoehn-Yahr scale. The cognitive function of PD patients was evaluated using the Mini-Mental State Examination scale and the Montreal Cognitive Assessment. The distribution of the alleles and genotypes of PI3K single-nucleotide polymorphisms (SNPs) was calculated using the Hardy-Weinberg equilibrium. PD patients showed a significantly higher serum level of PI3K compared to healthy individuals. Increased serum PI3K level was observed in PD patients with more severe disease, longer disease duration, and impaired cognitive function. Additionally, no significant differences were observed in the distributions of the genotypes and alleles of 3 PI3K SNPs between PD patients with normal cognitive function and those with cognitive impairment. PD patients with different levels of disease severity, disease duration, and cognitive function had significantly different serum levels of PI3K. However, the PI3K SNPs in patients with normal cognitive function were not significantly different from those in patients with cognitive impairment. These findings contribute to a better understanding of the roles of PI3K and SNPs of the PI3K gene in PD.

Comparative genomics and genomic diversity of Pseudomonas syringae clade 2b-a in Australia

BackgroundA zucchini disease outbreak with unusual symptoms associated with Pseudomonas syringae clade 2b was identified in Bundaberg, Australia during autumn 2016. To investigate the genetic diversity of the 11 Australian isolates obtained from the outbreak, the genomes were compared to the publicly available P. syringae strains in phylogroup 2.ResultsAverage nucleotide identity refined the P. syringae clade 2b-a into four clusters (Cluster A, B, C1 and C2), an expansion from the previously identified A, B and C. Australian isolates were in Cluster A, C1 and C2. Genomic analyses highlighted several key factors that may contribute to the virulence of these isolates. Six orthologous groups, including three virulence factors, were associated with P. syringae phylogroup 2 cucurbit-infecting strains. A region of genome plasticity analysis identified a type VI secretion system pathway in clade 2b-a strains which could also contribute to virulence. Pathogenicity assays on isolates KL004-k1, KFR003-1 and 77-4C, as representative isolates of Cluster A, C1 and C2, respectively, determined that all three isolates can infect pumpkin, squash, watermelon and zucchini var. Eva with different levels of disease severity. Subsequently, type III effectors were investigated and four type III effectors (avrRpt2, hopZ5, hopC1 and hopH1) were associated with host range. The hopZ effector family was also predicted to be associated with disease severity.ConclusionsThis study refined the taxonomy of the P. syringae clade 2b-a, supported the association between effector profile and pathogenicity in cucurbits established in a previous study and provides new insight into important genomic features of these strains. This study also provided a detailed and comprehensive resource for future genomic and functional studies of these strains.

Characteristics of lymphocyte subset alterations in COVID-19 patients with different levels of disease severity

BackgroundCoronavirus disease 2019 (COVID-19) is a respiratory disorder caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which had rapidly spread all over the world and caused public health emergencies in the past two years. Although the diagnosis and treatment for COVID-19 have been well defined, the immune cell characteristics and the key lymphocytes subset alterations in COVID-19 patients have not been thoroughly investigated.MethodsThe levels of immune cells including T cells, B cells, and natural killer (NK) cells in 548 hospitalized COVID-19 patients, and 30 types of lymphocyte subsets in 125 hospitalized COVID-19 patients admitted to Wuhan Huoshenshan Hospital of China were measured using flow cytometry. The relationship between lymphocytes subsets with the cytokine interleukin-6 (IL-6) and the characteristics of lymphocyte subsets in single-cell RNA sequencing (scRNA-seq) data obtained from peripheral blood mononuclear cells (PBMCs) were also analysed in COVID-19 patients.ResultsIn this study, we found that patients with critical COVID-19 infection exhibited an overall decline in lymphocytes including CD4+ T cells, CD8+ T cells, total T cells, B cells, and NK cells compared to mild and severe patients. However, the number of lymphocyte subsets, such as CD21low CD38low B cells, effector T4 cells, and PD1+ depleted T8 cells, was moderately increased in critical COVID-19 patients compared to mild cases. Notably, except for effector memory T4 cells, plasma blasts and Tregs, the number of all lymphocyte subsets was markedly decreased in COVID-19 patients with IL-6 levels over 30-fold higher than those in healthy cases. Moreover, scRNA-seq data showed obvious differences in the distribution and numbers of lymphocyte subsets between COVID-19 patients and healthy persons, and subsets-specific marker genes of lymphocyte subsets including CD4, CD19, CCR7, and IL7R, were markedly decreased in COVID-19 patients compared with those in healthy cases.ConclusionA comprehensive decrease in immune cell and lymphocyte subsets in critical COVID-19 patients, and peripheral lymphocyte subset alterations showed a clear association with clinical characteristics.

Autoimmune gene expression profiling of fingerstick whole blood in Chronic Fatigue Syndrome

BackgroundMyalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating condition that can lead to severe impairment of physical, psychological, cognitive, social, and occupational functions. The cause of ME/CFS remains incompletely understood. There is no clinical diagnostic test for ME/CFS. Although many therapies have been used off-label to manage symptoms of ME/CFS, there are limited, if any, specific therapies or cure for ME/CFS. In this study, we investigated the expression of genes specific to key immune functions, and viral infection status in ME/CFS patients with an aim of identifying biomarkers for characterization and/or treatment of the disease.MethodsIn 2021, one-hundred and sixty-six (166) patients diagnosed with ME/CFS and 83 healthy controls in the US participated in this study via a social media-based application (app). The patients and heathy volunteers consented to the study and provided self-collected finger-stick blood and first morning void urine samples from home. RNA from the fingerstick blood was tested using DxTerity’s 51-gene autoimmune RNA expression panel (AIP). In addition, DNA from the same fingerstick blood sample was extracted to detect viral load of 4 known ME/CFS associated viruses (HHV6, HHV7, CMV and EBV) using a real-time PCR method.ResultsAmong the 166 ME/CFS participants in the study, approximately half (49%) of the ME/CFS patients reported being house-bound or bedridden due to severe symptoms of the disease. From the AIP testing, ME/CFS patients with severe, bedridden conditions displayed significant increases in gene expression of IKZF2, IKZF3, HSPA8, BACH2, ABCE1 and CD3D, as compared to patients with mild to moderate disease conditions. These six aforementioned genes were further upregulated in the 22 bedridden participants who suffer not only from ME/CFS but also from other autoimmune diseases. These genes are involved in T cell, B cell and autoimmunity functions. Furthermore, IKZF3 (Aiolos) and IKZF2 (Helios), and BACH2 have been implicated in other autoimmune diseases such as systemic lupus erythematosus (SLE) and Rheumatoid Arthritis (RA). Among the 240 participants tested with the viral assays, 9 samples showed positive results (including 1 EBV positive and 8 HHV6 positives).ConclusionsOur study indicates that gene expression biomarkers may be used in identifying or differentiating subsets of ME/CFS patients having different levels of disease severity. These gene targets may also represent opportunities for new therapeutic modalities for the treatment of ME/CFS. The use of social media engaged patient recruitment and at-home sample collection represents a novel approach for conducting clinical research which saves cost, time and eliminates travel for office visits.

Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach.

COVID-19 has been widely explored in relation to its symptoms, outcomes, and risk profiles for the severe form of the disease. Our aim was to identify clusters of pregnant and postpartum women with severe acute respiratory syndrome (SARS) due to COVID-19 by analyzing data available in the Influenza Epidemiological Surveillance Information System of Brazil (SIVEP-Gripe) between March 2020 and August 2021. The study’s population comprised 16,409 women aged between 10 and 49 years old. Multiple correspondence analyses were performed to summarize information from 28 variables related to symptoms, comorbidities, and hospital characteristics into a set of continuous principal components (PCs). The population was segmented into three clusters based on an agglomerative hierarchical cluster analysis applied to the first 10 PCs. Cluster 1 had a higher frequency of younger women without comorbidities and with flu-like symptoms; cluster 2 was represented by women who reported mainly ageusia and anosmia; cluster 3 grouped older women with the highest frequencies of comorbidities and poor outcomes. The defined clusters revealed different levels of disease severity, which can contribute to the initial risk assessment of the patient, assisting the referral of these women to health services with an appropriate level of complexity.

Timing and severity of COVID-19 during pregnancy and risk of preterm birth in the International Registry of Coronavirus Exposure in Pregnancy

BackgroundStudies of preterm delivery after COVID-19 are often subject to selection bias and do not distinguish between early vs. late infection in pregnancy, nor between spontaneous vs. medically indicated preterm delivery. This study aimed to estimate the risk of preterm birth (overall, spontaneous, and indicated) after COVID-19 during pregnancy, while considering different levels of disease severity and timing.MethodsPregnant and recently pregnant people who were tested for or clinically diagnosed with COVID-19 during pregnancy enrolled in an international internet-based cohort study between June 2020 and July 2021. We used several analytic approaches to minimize confounding and immortal time bias, including multivariable regression, time-to-delivery models, and a case-time-control design.ResultsAmong 14,264 eligible participants from 70 countries who did not report a pregnancy loss before 20 gestational weeks, 5893 had completed their pregnancies and reported delivery information; others were censored at time of their last follow-up. Participants with symptomatic COVID-19 before 20 weeks’ gestation had no increased risk of preterm delivery compared to those testing negative, with adjusted risks of 10.0% (95% CI 7.8, 12.0) vs. 9.8% (9.1, 10.5). Mild COVID-19 later in pregnancy was not clearly associated with preterm delivery. In contrast, severe COVID-19 after 20 weeks’ gestation led to an increase in preterm delivery compared to milder disease. For example, the risk ratio for preterm delivery comparing severe to mild/moderate COVID-19 at 35 weeks was 2.8 (2.0, 4.0); corresponding risk ratios for indicated and spontaneous preterm delivery were 3.7 (2.0, 7.0) and 2.3 (1.2, 3.9), respectively.ConclusionsSevere COVID-19 late in pregnancy sharply increased the risk of preterm delivery compared to no COVID-19. This elevated risk was primarily due to an increase in medically indicated preterm deliveries, included preterm cesarean sections, although an increase in spontaneous preterm delivery was also observed. In contrast, mild or moderate COVID-19 conferred minimal risk, as did severe disease early in pregnancy.

Comparison of 10-2 and 24-2 Perimetry to Diagnose Glaucoma Using OCT as an Independent Reference Standard

To compare the performance of the 10-2 test versus 24-2 standard automated perimetry (SAP) test for the diagnosis of glaucoma using OCT as an independent standard for glaucomatous damage. Cross-sectional study. A total of 1375 pairs of 10-2 and 24-2 SAP tests from 569 eyes of 339 subjects were used for the analysis. A total of 440 (77%) eyes had a diagnosis of glaucoma, and 129 (23%) eyes were normal. All participants underwent 10-2 and 24-2 SAP tests within 30 days. Glaucomatous severity was quantified based on OCT macula ganglion cell layer (mGCL) and circumpapillary retinal nerve fiber layer. The area under the receiver operating characteristic (ROC) curve (AUC) was used to compare 10-2 and 24-2 metrics for discriminating healthy eyes from those of glaucoma, at different levels of disease severity. Areas under the ROC curves and sensitivities at fixed specificities of 80% and 95%. The overall AUC for mean deviation (MD) for the 24-2 test (0.808) was significantly higher than that of the 10-2 test (0.742; P < 0.001). When compared at different stages of the disease, the 24-2 test performed generally better than the 10-2 test, notably in the earlier stages of the disease. For early damage (first quartile), the 24-2 MD had an AUC of 0.658 versus 0.590 for 10-2 MD (P= 0.018). For advanced damage (fourth quartile), corresponding values were 0.954 vs. 0.903 (P= 0.013). Similar trends were observed when glaucoma severity was defined based on structural macular damage with mGCL thickness. The 24-2 SAP test had better diagnostic accuracy compared with that of the 10-2 test for detecting equivalent levels of glaucomatous damage, as measured by quantitative assessment of retinal nerve fiber layer and macula by OCT. Proprietary or commercial disclosure may be found after the references.

Effect of Chemokine Gene Variants on Covid-19 Disease Severity

ABSTRACT Patients immune phenotype/genotype data may be useful to understand the molecular mechanisms involved in SARS-CoV-2 infection and can contribute to the identify the different levels of disease severity. The roles of chemokines have been reported in the coronavirus-related diseases SARS and MERS and they may likewise play a critical role in the development of the symptoms of COVID-19 disease. We analyzed the association of the MCP-1-A2518 G, SDF-1-3’A, CCR5-delta32, CCR5-A55029 G, CXCR4-C138T and CCR2-V64I gene polymorphisms with COVID-19 severity to further unveil the immunological pathways leading to disease severity and death. Polymerase chain reaction(PCR)/Sanger sequencing analysis was performed for detection of the variations in 60 asymptomatic and 119 severe COVID-19 patients. In our study, we found that the frequencies of MCP-1 of GA genotype and G allele carriers were significantly higher in severe COVID-19 patients than the asymptomatic COVID-19 patients (p < .0001 and p: .005, respectively). However, no significant association was found for any of the other polymorphisms with the severity of COVID-19. Our findings suggest that there is a positive association between MCP-1-A2518 G gene variants with the severity of COVID-19. However, larger studies in different population which will focus on gene expression levels will help us to understand the capability of the mechanism role.

Congenital Heart Disease: Growth Evaluation and Sport Activity in a Paediatric Population.

(1) Objective: To evaluate: (i) the associations of age and disease severity with anthropometric indices and weight status, (ii) the difference in the frequency of sports activity among different levels of disease severity in paediatric patients with congenital heart disease (CHD). (2) Methods: Clinical data of Caucasian children (aged 2–18 years) diagnosed with CHD (2005–2018) were retrospectively collected from the electronic register of outpatient visits. Of the 475 children with CHD, 368 children and their 1690 complete anthropometric measurements were eligible for inclusion in our analysis. (3) Results: Significant increase with age was observed for weight z-score [beta (95%CI): 0.03 (0.02, 0.05) for one-unit of age] and BMI z-score [0.06 (0.03, 0.08)] but not for height z-score. The probability of being underweight and overweight/obese increased and decreased with disease severity, respectively. The obesity probability of patients with mild CHD (0.06 [95%CI: 0.03, 0.08]) was not statistically distinguishable from that of patients with moderate CHD (0.03 [95%CI: 0.02, 0.05]), whereas it was lower in patients with severe CHD (0.004 [95%CI: 0.0, 0.009]). No obese patients with a univentricular heart defect were observed. Days spent in sport activities were equal to 1.9 [95%CI: 1.6, 2.2] days/week, 1.9 [1.5, 2.2], 1.4 [1.1, 1.7] and 0.7 [0.1, 1.3] in patients with mild, moderate, severe and univentricular CHD, respectively. (4) Conclusions: The risk of being overweight and obese should not be underestimated in paediatric patients diagnosed with CHD, especially in children with mild or moderate heart defects. It could be prevented or reduced by promoting a healthy lifestyle.

Clustering of EORTC QLQ-C30 health-related quality of life scales across several cancer types: Validation study

IntroductionThe European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30) measures 15 health-related quality of life (HRQoL) scales relevant to the disease and treatment of patients with cancer. A study by Martinelli (2011) demonstrated that these scales could be grouped into three main clusters: physical, psychological and gastrointestinal. This study aims to validate Martinelli's findings in an independent dataset and evaluate whether these clusters are consistent across cancer types and patient characteristics. MethodsPre-defined criteria for successful validation were three main clusters should emerge with a minimum R-squared value of 0.51 using pooled baseline-data. A cluster analysis was performed on the 15 QLQ-C30 HRQoL-scales in the overall dataset, as well as by cancer type and selected patient characteristics to examine the robustness of the results. ResultsThe dataset consisted of 20,066 patients pooled across 17 cancer types. Overall, three main clusters were identified (R2 = 0.61); physical-cluster included role-functioning, physical-functioning, social-functioning, fatigue, pain, and global-health status; psychological-cluster included emotional-functioning, cognitive-functioning, and insomnia; gastro-intestinal-cluster included nausea/vomiting and appetite loss. The results were consistent across different levels of disease severity, socio-demographic and clinical characteristics with minor variations by cancer type. Global-health status was found to be strongly linked to the scales included in the physical-functioning-related cluster. ConclusionThis study successfully validated prior findings by Martinelli (2011): the QLQ-C30 scales are interrelated and can be grouped into three main clusters. Knowing how these multidimensional HRQoL scales are related to each other can help clinicians and patients with cancer in managing symptom burden, guide policymakers in defining social-support plans and inform selection of HRQoL scales in future clinical trials.

Living in a rural area: a potential risk factor for falls in people with Parkinson disease.

Falls are prevalent in patients with Parkinson disease (PD). Previous work focused on the impact of motor and non-motor symptoms on falls and ignored the impact of environmental factors, such as residence, economic level, and nursing status. The aim of this study was to investigate the prevalence and risk factors of falls in patients with PD and explore the impact of residence on falls. A cross-sectional study of 100 patients with PD was carried out. Patients were recruited from Anhui Provincial Hospital (Hefei, Anhui province, China) between July 2017 and December 2020. Participants were grouped based on whether they had fallen in the previous 3 months, and demographic information was collected through detailed interviews. In addition, severity of motor symptoms, cognitive function, and self-care abilities were assessed with the Unified Parkinson's Disease Rating Scale part III (UPDRS-III), the Hoehn-Yahr (H&Y) scale, the Mini-Mental State Examination (MMSE), and the Barthel Index. The results were analyzed using student t-test, Mann-Whitney U-test, χ2 test and multivariate binary logistic regression analyses. A total of 42% of the patients had fallen in the previous 3 months. The patients who had fallen were older and with a longer disease period, a higher UPDRS-III score, a higher H&Y stage, a lower MMSE score, and a lower Barthel Index score (all p<0.05). According to the logistic regression analysis, living in a rural area (odds ratio (OR)=3.34, 95% confidence interval (CI) 1.15-9.65), MMSE<24 (OR=4.79, 95%CI 1.17-19.65), having sleep disorders (OR=4.97, 95%CI 1.74-14.2), and having a high UPDRS-III score (OR=1.07, 95%CI 1.02-1.11) were independent risk factors for falls. The incidence of falls was higher in rural areas. Urban and rural patients showed different levels of disease severity; rural patients had higher H&Y stages, higher UPDRS-III scores and lower Barthel Index scores. Falls are caused by a variety of factors in people with PD. Multidimensional factors should be considered comprehensively to develop a personalized plan to prevent falls in PD patients.

Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19

The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic’s trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant.

Clinical efficacy and safety of high-flow nasal cannula (HFNC) in acute hypoxaemic patients with COVID-19: a protocol for a systematic review and meta-analysis.

IntroductionWhen COVID-19 patients develop hypoxaemic respiratory failure, they often undergo early intubation. Such a potentially aerosol-generating approach places caregivers at increased risk of contracting COVID-19. This protocol aims to evaluate the clinical efficacy and safety of a high-flow nasal cannula (HFNC) for the treatment of COVID-19 patients with acute hypoxaemic respiratory failure.Methods and analysisWe intend to search MEDLINE, Embase, Web of Science and Cochrane Library to identify all randomised controlled trials (RCTs) on the use of HFNC in COVID-19 patients with acute respiratory failure. We will screen the RCTs against eligibility criteria for inclusion in our review. Two reviewers will independently undertake RCT selection, data extraction and risk of bias assessment. Primary outcome will be the rate of intubation, and secondary outcomes will be intensive care unit (ICU)/hospital mortality, ICU/hospital length of stay and risks of infection transmission. We will conduct meta-analyses to determine the risk ratio for dichotomous data and the mean difference (MD) or standardised MD for continuous data. Subgroup analyses will be performed based on the different quality of studies, different levels of disease severity, and the age and sex of participants.Ethics and disseminationEthical approval is not required for this study considering this is a systematic review protocol that uses only published data. The findings of this study will be disseminated through peer-reviewed publications and conference presentations.PROSPERO registration numberCRD42021236519.

Performance Assessment of a Rapid Molecular Respiratory Syncytial Virus Point-of-Care Test: A Prospective Community Study in Older Adults.

BackgroundRespiratory syncytial virus (RSV) causes a substantial burden in older adults. Viral load in RSV-infected adults is generally lower compared to young children, which could result in suboptimal sensitivity of RSV diagnostics. Although the Xpert® Xpress Flu/RSV assay has been used in routine clinical care, its sensitivity to diagnose RSV infection in older adults is largely unknown. We aimed to compare the performance of the Xpert® Xpress Flu/RSV assay with real-time reverse-transcription polymerase chain reaction (RT-PCR) in home-dwelling older adults (≥60 years of age).MethodsNasopharyngeal swabs were tested with Xpert® Xpress Flu/RSV and compared to RSV RT-PCR in older adults with acute respiratory tract infections with different levels of disease severity.ResultsWe studied 758 respiratory samples from 561 older adults from 2 consecutive RSV seasons. Thirty-five (4.6%) samples tested positive for RSV by at least 1 of the assays, of which 2 samples were negative by Xpert® Xpress Flu/RSV and 3 samples by real-time RT-PCR. The positive percentage agreement (PPA) was 90.9% (95% confidence interval [CI], 76.4%–96.8%) and negative percentage agreement was 99.7% (95% CI, 99.0%–99.9%). Viral loads were low (≤103 copies/mL or cycle threshold value ≥34) in all cases with discordant results for the 2 assays.ConclusionsThe PPA of Xpert® Xpress Flu/RSV compared to routine RT-PCR is high for RSV detection in home-dwelling older adults. The assay is fast and easy to use at the point of care.Clinical Trials RegistrationNCT03621930.

A novel self-learning framework for bladder cancer grading using histopathological images

In recent times, bladder cancer has increased significantly in terms of incidence and mortality. Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer (MIBC). In this work, we focus on the MIBC subtype because it has the worst prognosis and can spread to adjacent organs. We present a self-learning framework to grade bladder cancer from histological images stained by immunohistochemical techniques. Specifically, we propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows for the classification of histological patches into different levels of disease severity, according to established patterns in the literature. The proposed DCEAC model follows a fully unsupervised two-step learning methodology to discern between non-tumour, mild and infiltrative patterns from high-resolution 512 × 512 pixel samples. Our system outperforms previous clustering-based methods by including a convolutional attention module, which enables the refinement of the features of the latent space prior to the classification stage. The proposed network surpasses state-of-the-art approaches by 2–3% across different metrics, reaching a final average accuracy of 0.9034 in a multi-class scenario. Furthermore, the reported class activation maps evidence that our model is able to learn by itself the same patterns that clinicians consider relevant, without requiring previous annotation steps. This represents a breakthrough in MIBC grading that bridges the gap with respect to training the model on labelled data.