Measures Of Disease Severity Research Articles

Novel approach for quantification for severity estimation of blight diseases on leaves of tomato plant

AbstractThis study uses digital image processing and machine learning to quantify the infection patterns on tomato leaves due to blight diseases. Quantification, also known as severity measurement, is a technique to determine how much a leaf is diseased by calculating a numeric value. This value could be a fraction representing how much the diseased region is present on the leaf compared to the entire leaf region, or it can be a percentage value too. There are two main approaches to measuring disease severity; the first technique involves visual estimation using references like standard area diagrams. The second approach involves taking a digital image of the leaf, separating the diseased regions from the healthy regions, and then calculating the area of those two regions. The approach we took is similar. We first took the digital image and segmented the diseased and healthy regions. For quantification, we calculated the ratio of total pixels representing the diseased region to the total number of pixels representing the leaf. While finding ways to improve the accuracy of the segmentation algorithm, we also discovered our segmentation technique which automatically segments the diseased regions of the leaves from the healthy areas using k‐means clustering. The clustering‐segmentation algorithm did give good results for the sample images to which it was applied. The main thing about the clustering‐segmentation algorithm is that it tends to be automatic compared to some of the semi‐automatic segmentation approaches that have been discovered till now. We could reproduce the validated quantification results as other authors achieved in the recent work, which also validated our methodology.

Cerebrospinal fluid biomarkers for assessing Huntington disease onset and severity.

The identification of molecular biomarkers in CSF from individuals affected by Huntington disease may help improve predictions of disease onset, better define disease progression and could facilitate the evaluation of potential therapies. The primary objective of our study was to investigate novel CSF protein candidates and replicate previously reported protein biomarker changes in CSF from Huntington disease mutation carriers and healthy controls. Our secondary objective was to compare the discriminatory potential of individual protein analytes and combinations of CSF protein markers for stratifying individuals based on the severity of Huntington disease. We conducted a hypothesis-driven analysis of 26 pre-specified protein analytes in CSF from 16 manifest Huntington disease subjects, eight premanifest Huntington disease mutation carriers and eight healthy control individuals using parallel-reaction monitoring mass spectrometry. In addition to reproducing reported changes in previously investigated CSF biomarkers (NEFL, PDYN, and PENK), we also identified novel exploratory CSF proteins (C1QB, CNR1, GNAL, IDO1, IGF2, and PPP1R1B) whose levels were altered in Huntington disease mutation carriers and/or across stages of disease. Moreover, we report strong associations of select CSF proteins with clinical measures of disease severity in manifest Huntington disease subjects (C1QB, CNR1, NEFL, PDYN, PPP1R1B, and TTR) and with years to predicted disease onset in premanifest Huntington disease mutation carriers (ALB, C4B, CTSD, IGHG1, and TTR). Using receiver operating characteristic curve analysis, we identified PENK as being the most discriminant CSF protein for stratifying Huntington disease mutation carriers from controls. We also identified exploratory multi-marker CSF protein panels that improved discrimination of premanifest Huntington disease mutation carriers from controls (PENK, ALB and NEFL), early/mid-stage Huntington disease from premanifest mutation carriers (PPP1R1B, TTR, CHI3L1, and CTSD), and late-stage from early/mid-stage Huntington disease (CNR1, PPP1R1B, BDNF, APOE, and IGHG1) compared with individual CSF proteins. In this study, we demonstrate that combinations of CSF proteins can outperform individual markers for stratifying individuals based on Huntington disease mutation status and disease severity. Moreover, we define exploratory multi-marker CSF protein panels that, if validated, may be used to improve the accuracy of disease-onset predictions, complement existing clinical and imaging biomarkers for monitoring the severity of Huntington disease, and potentially for assessing therapeutic response in clinical trials. Additional studies with CSF collected from larger cohorts of Huntington disease mutation carriers are needed to replicate these exploratory findings.

A NOVEL EFFICIENT APPROACH TO ASSESSING ATOPIC DERMATITIS DISEASE SEVERITY: VIGAXBSA COMPARED TO EASI

<h3>Introduction</h3> Disease severity assessment is essential in determining the course of care and treatment for Atopic Dermatitis (AD). The Eczema Area Severity Index (EASI) is an extensive evaluation assessing the body for various eczematous symptoms: lichenification, erythema, edema/papulation, and excoriation. While a standard evaluation in the clinic trial setting, EASI scoring is time intensive and not feasible for most clinic visits. We investigated the validated Investigator Global assessment (vIGA) multiplied by Body Surface Area (BSA) as a more rapid alternative to EASI to evaluate AD severity and disease extent. <h3>Methods</h3> 65 difficult-to-control pediatric AD patients were longitudinally followed in a Multidisciplinary AD Clinic from July 2019 to May 2022. At each visit the following evaluations were collected: vIGA, BSA, and EASI scores. Correlation between evaluation type was analyzed via Pearson's r correlation for each individual visit. <h3>Results</h3> EASI had the strongest correlation to vIGAxBSA (r=0.934, p<0.001) for each visit compared to BSA (r=0.924, p<0.001) and vIGA (r=0.793, p<0.001). vIGAxBSA had the weakest correlation (r=0.633, p=<0.001) to EASI scores for scores less than 7 (clear to mild), but had the strongest correlation (r=0.891, p<0.001) for EASI scores greater than 7 (moderate to severe). <h3>Conclusion</h3> Given the limited time in traditional clinic settings, EASI evaluations are not viable for every patient even though it is a more comprehensive measurement of disease severity. Correlation of EASI to vIGAxBSA was significant and stronger than vIGA or BSA alone, showing potential for vIGAxBSA as an alternative to quantitatively evaluate severity of AD in the clinic setting.

Tender to touch-Prevalence and impact of concomitant fibromyalgia and enthesitis in spondyloarthritis: An ancillary analysis of the ASAS PerSpA study.

The primary objective was to evaluate the co-existence of fibromyalgia (FM) & enthesitis in individuals with spondyloarthritis (SpA). Secondary objectives were to identify clinical features associated with the presence of FM in enthesitis and analyse sex-specific differences. This was an ancillary analysis of the Assessment of SpondyloArthritis International Society Peripheral Involvement in SpA (PerSpA) study. Enthesitis was defined as the presence of enthesitis ever. Clinical FM was defined as the rheumatologist's confirmation of the presence of FM. A score of≥5/6 on the Fibromyalgia Rapid Screening Test (FiRST) defined a positive screening test for FM. Enthesitis ever and FM (EFM) co-existed in 10.3% (n=425) of the cohort using FiRST criteria and 5.3% using clinical diagnosis of FM. More individuals with FM by clinical diagnosis had imaging-confirmed enthesitis ever than by FiRST criteria. More females had EFM than males, defined clinically (76.9% vs 23.1%) or by FiRST criteria (62.6% vs 37.4%). Individuals with EFM had more severe disease across all measures compared to those with enthesitis only, with no significant difference between sexes. EFM was significantly associated with age, female sex, BMI, BASDAI and region. FM is an important comorbidity in the setting of enthesitis in SpA. While EFM is more common in females, it is not a rare condition in males. EFM is associated with worse disease severity measures in SpA in both males and females. Recognition of FM in the setting of enthesitis is essential to prevent overtreatment and optimise patient outcomes.

Task-based profiles of language impairment and their relationship to cognitive dysfunction in Parkinson's disease.

ObjectiveParkinson’s Disease (PD) is associated with both motor and non-motor problems, such as cognitive impairment. Particular focus in this area has been on the relationship between language impairment and decline in other cognitive functions, with the literature currently inconclusive on how the nature and degree of language impairment relate to cognition or other measures of disease severity. In addition, little information is available on how language problems identified in experimental task set-ups relate to competency in self-generated language paradigms such as picture description, monologues or conversations. This study aimed to inform clinical management of language impairment in PD by exploring (1) language performance across a range of experimental as well as self-generated language tasks, (2) how the relationship between these two aspects might be affected by the nature of the cognitive and language assessment; and (3) to what degree performance can be predicted across the language tasks.Methods22 non-demented people with PD (PwPD) and 22 healthy control participants performed a range of cognitive and language tasks. Cognitive tasks included a screening assessment in addition to tests for set shifting, short term memory, attention, as well as letter and category fluency. Language was investigated in highly controlled grammar tasks as well as a Sentence Generation and a Narrative.ResultsThe study highlighted impaired ability in set-shifting and letter fluency in the executive function tasks, and a higher rate of grammatical and lexical errors across all language tasks in the PD group. The performance in the grammar task was linked to set shifting ability, but error rates in Sentence Generation and Narrative were independent of this. There was no relevant relationship between performances across the three language tasks.ConclusionsOur results suggest that there is a link between executive function and language performance, but that this is task dependent in non-demented PwPD. This has implications for the management of language impairment in PD, both for assessment and for designing effective interventions.

Clinical and neurophysiological biomarkers of disease progression in amyotrophic lateral sclerosis.

Rate of disease progression (ΔFS), measured as change in the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) and body mass index (BMI), are predictors of survival in amyotrophic lateral sclerosis (ALS). Our aim in this study was to assess the utility of these clinical biomarkers along with neurophysiological measures, such as the split hand index (SI), in monitoring disease progression. Clinical trial data were collected from 107 patients recruited into the Tecfidera in ALS trial. The prognostic utility of clinical and neurophysiological measures, including ΔFS, BMI, SI, and neurophysiological index (NPI), were assessed cross-sectionally and longitudinally (40 weeks). The outcome measures of disease severity and progression included: (i) ALSFRS-R score; (ii) Medical Research Council (MRC) score; and (iii) forced vital capacity and sniff nasal inspiratory pressure. Fast-progressor ALS patients (ΔFS ≥1.1) exhibited significantly lower ALSFRS-R and total MRC scores at baseline. A baseline ΔFS score ≥1.1 was associated with a greater reduction in ALSFRS-R (P=.002) and MRC (P=.002) scores over 40 weeks. Baseline BMI <25 was also associated with faster reduction of ALSFRS-R and MRC scores. SI and NPI were associated with disease severity at baseline, but not with subsequent rate of disease progression. Implementation of the assessed clinical and neurophysiological biomarkers may assist in patient management and stratification into clinical trials.

Sexual Dissatisfaction after Hand Surgery.

Background:Using the hand questionnaire (HAND-Q) patient-reported outcome measure, the effects of upper extremity surgery on patients’ perception of their sex life were explored. The hand is a uniquely sexual organ, and we hypothesized that self-reported measures of disease severity, quality of life, and emotional impact would correlate with sexual dissatisfaction among patients receiving treatment for hand/upper extremity conditions.Methods:Patients were prospectively enrolled for hand questionnaire participation. Patients with valid responses to the following questions were included: functionality, hand appearance satisfaction, symptom severity, emotional dissatisfaction, sexual dissatisfaction, and treatment satisfaction. Composite scores were created and scored. Sexual dissatisfaction composite scores were compared through Spearman correlation coefficient analysis to quality of life, emotional dissatisfaction, hand appearance, symptom severity, and hand functionality.Results:High levels of diminished quality of life correlated with sexual dissatisfaction (rs = 0.748, P < 0.001). Increased emotional dissatisfaction correlated with sexual dissatisfaction (rs = 0.827, P < 0.001). Increased satisfaction with hand appearance negatively correlated with sexual dissatisfaction (rs = –0.648, P = 0.001). Increased levels of dissatisfaction with hand functionality correlated with sexual dissatisfaction (rs = 0.526, P = 0.005).Conclusions:The correlation between sex life and quality of life may allow surgeons to improve patient satisfaction when treating hand/upper extremity issues. The relationship between sex life and emotional dissatisfaction emphasizes the impact that sexual dissatisfaction has on patients’ lives. Evaluating the relationship between hand appearance and sexual dissatisfaction may indicate that patient self-perception of hand attractiveness plays a role in sex life.

The Validity of Health-Related Quality of Life Instruments in Patients With Late-Stage Parkinson's Disease.

To examine the validity of health-related quality of life (Hr-QoL) measures in patients with late-stage Parkinson's disease (PD). We analysed data from patients with late-stage PD and their carers who were assessed with a range of clinical measures and the EQ-5D-3L. The DEMQOL-Proxy was completed for 157 patients with a diagnosis of dementia and the PDQ-8 by 401 patients without dementia. Convergent validity was assessed using correlations with measures of Parkinson's severity, independence and cognitive function, and construct validity using correlations with patients' own EQ-5D-3L scores. In addition, we assessed divergent validity using correlations with carers' own EQ-5D index, EQ-VAS and Zarit caregiver burden scores. In patients without dementia, both the PDQ-8 and EQ-5D-3L correlated with measures of disease severity, dependence and carer burden scores, and PDQ-8 scores moderately with EQ-5D-3L and EQ-5D-3L VAS scores. In patients with dementia, EQ-5D-3L scores correlated with disease severity, cognition and dependence scores, but DEMQOL-Proxy scores were moderately associated only with patients' dependence and carers' own EQ-5D-3L scores but not patients' disease severity, EQ-5D-3L or cognitive scores. The PDQ-8 and EQ-5D-3L have adequate validity in late stage PD without dementia, but in those with PD and dementia the EQ-5D-3L may be preferable to the DEMQOL-Proxy.

Opinion analysis and aspect understanding during covid-19 pandemic using BERT-Bi-LSTM ensemble method

Social media platforms significantly increase general information about disease severity and inform preventive measures among community members. To identify public opinion through tweets on the subject of Covid-19 and investigate public sentiment in the country over the period. This article proposed a novel method for sentiment analysis of coronavirus-related tweets using bidirectional encoder representations from transformers (BERT) bi-directional long short-term memory (Bi-LSTM) ensemble learning model. The proposed approach consists of two stages. In the first stage, the BERT model gains the domain knowledge with Covid-19 data and fine-tunes with sentiment word dictionary. The second stage is the Bi-LSTM model, which is used to process the data in a bi-directional way with context sequence dependency preserving to process the data and classify the sentiment. Finally, the ensemble technique combines both models to classify the sentiment into positive and negative categories. The result obtained by the proposed method is better than the state-of-the-art methods. Moreover, the proposed model efficiently understands the public opinion on the Twitter platform, which can aid in formulating, monitoring and regulating public health policies during a pandemic.

Genetic association of agronomic traits with partial resistance to gray leaf spot in elite maize germplasm

AbstractStudies on gray leaf spot (GLS) of maize have reported inconsistencies in the relationship between partial disease resistance and agronomic traits. Understanding this variation could facilitate the use of agronomic traits as a basis for selection to improve partial resistance to GLS. Coinheritance of nine agronomic traits with partial resistance to GLS was examined among 48 maize (Zea mays L.) inbred lines artificially infected with Cercospora zeina in field evaluations across nine environments in western Kenya in 2013 and 2014. Five measures of disease severity and two disease resistance components were evaluated for their association with agronomic traits. Standardized area under disease progress curve (SAUDPC) was the most efficient in delineating differences in GLS severity between genotypes, whereas latent period (LP) was the least effective. Among maize genotypes, values of SAUDPC ranged from 29.3 to 97.9 across environments. Genotypic and phenotypic correlations were strongest between SAUDPC and the absolute rate of disease increase (ρ; r = .71), final percent diseased leaf area (r = .66) and International Maize and Wheat Improvement Center (CIMMYT) disease severity grade (r = .60), but weakest between SAUDPC and LP (r = −.19). Correlations of SAUDPC were significant (P = .05) with eight of the 11 agronomic traits examined, with the strongest being between SAUDPC and the stay‐green characteristic (SGR; r = −.87), days to maturity (DTM; r = −.60) and ear/plant height ratio (r = −.52). Genotypic and phenotypic coefficients of correlation of SAUDPC with agronomic traits were all negative. Overall, absolute genotypic correlations were numerically larger than the corresponding coefficients of phenotypic correlation with the magnitude and direction of coheritability estimates mimicking trends in genotypic and phenotypic correlations. Causal mediation analysis indicated that covariation of GLS resistance with agronomic traits was mainly due to direct effects of days to anthesis and DTM and indirect effects of SGR and silking–maturity interval.

Size matters - redefining sex differences among patients with transthyretin amyloid cardiomyopathy – have we been wrong all along?

Abstract Background Transthyretin amyloid cardiomyopathy (ATTR-CM) is most often diagnosed in men (1–5). The few available studies suggest affected women have a more favourable cardiac phenotype (5–8), but remain unclear regarding differences in outcomes. Objectives and methods To characterise sex differences among consecutive patients with non-hereditary and two prevalent forms of hereditary ATTR-CM diagnosed over a 20-year period at our specialist centre through analysis of deep phenotyping at presentation, changes on serial echocardiography and overall prognosis. Results In total, 1732 patients were studied, comprising: 1095 with wild-type (wt)ATTR-CM; 206 with T60A-hATTR-CM; and 431 with V122I-hATTR-CM. Female prevalence was greater in T60A-hATTR-CM (29.6%) and V122I-hATTR-CM (27.8%) compared to wtATTR-CM (6%). At presentation, females were 3.3 years older than males (81.9 vs 77.8 years for wtATTR-CM; 68.7 vs 65.1 years for T60A-hATTR-CM; 77.1 vs 74.9 years for V122I-hATTR-CM). At diagnosis, non-indexed measures of wall thickness were significantly greater in males (interventricular septum in diastole (IVSd) of 17.13mm in males &amp; 16.15mm in females; p&amp;lt;0.001). When indexed for body surface area (BSA), we observed that the mean indexed IVSd was fairly constant in males throughout the study period, but in females, had a tendency to decrease over the same study period. Furthermore, BSA significantly influenced measures of disease severity. When indexed for BSA, overall structural and functional phenotype was similar between sexes; the few observed significant differences including indexed IVSd (9.62mm/m2 in females &amp; 8.88mm/m2 in males; p&amp;lt;0.001), indexed left ventricular (LV) end-diastolic volume (35.07ml/m2 in females &amp; 41.05ml/m2 in males; p&amp;lt;0.001) and indexed LV end-systolic volume (17.95ml/m2 in females &amp; 21.74ml/m2 in males; p&amp;lt;0.001) suggested a mildly worse phenotype in females. No significant differences were observed in disease progression on serial echocardiography and mortality across the overall population (p=0.459) and when divided by genotype (p=0.730 for wtATTR-CM; p=0.161 for T60A-hATTR-CM; p=0.056 for V122I-hATTR-CM). Conclusion This study of a well-characterized large cohort of ATTR-CM patients, contrary to previous dogmas, did not demonstrate overall differences between sexes in either clinical phenotype, when indexed, or with respect to disease progression and prognosis. The analysis highlighted the deficiencies in using non-indexed values which can not only lead to the inaccurate perception of a milder clinical phenotype in women compared to men, but has been shown to result in female patients presenting at an older age and with a worse phenotype compared to men. These findings indicate the need for revision of existing clinical guidelines regarding awareness and diagnosis of ATTR-CM in women, and modification of clinical trials which currently use single non-indexed threshold for wall thickness as key inclusion criterion. Funding Acknowledgement Type of funding sources: Foundation.

The feasibility and value of assessing patient-reported outcomes in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a progressive pulmonary vascular disease that negatively impacts health‐related quality of life (HRQOL). The PAH‐symptoms and impact (PAH‐SYMPACT) questionnaire is a validated disease‐specific patient‐reported outcome (PRO) instrument that assesses a patient's symptoms and the impact of PAH and its treatment on well‐being. We performed a single‐center prospective cohort study of patients with PAH to determine the feasibility of assessing PROs in clinical practice and to determine the association between PAH‐SYMPACT domains and clinical characteristics and outcomes. One hundred and ten patients completed the 1‐day version of the PAH‐SYMPACT questionnaire which consists of 22 Likert‐scale questions that assess HRQOL across four domains: cardiopulmonary (CP) symptoms, cardiovascular (CV) symptoms, physical impact (PI), and cognitive and emotional (CE) impact. Higher scores indicate worse HRQOL. Patients were predominantly female (n = 86, 78%) with a mean age of 57.8 ± 16.2 years. While several patient characteristics were associated with CP and PI domains, few were associated with CV and CE domains. PI and CE impact scores were associated with recent hospitalizations and mortality and CE impact score was independently associated with an increased risk of death after adjustment for disease severity (hazard ratio: 3.29, 95% confidence interval: 1.56–6.91, p = 0.002). In conclusion, the assessment of PROs in clinical practice using the PAH‐SYMPACT questionnaire is both feasible and valuable. PAH‐SYMPACT scores have independent prognostic value and are not adequately reflected by traditional measures of disease severity. These findings underscore the importance of assessing HRQOL in clinical practice.

S1053 Characteristics, Medication and Healthcare Utilization of Crohn’s Disease Patients Stratified by Short-CDAI Status: Descriptive Results From the IBD PLEXUS and IQVIA Linked Dataset

Introduction: Crohn’s disease (CD) is a life-long chronic inflammatory disorder. CD studies in real-world settings usually do not include disease severity measures since few longitudinal databases with robust clinical data and long-term administrative claims exist. We describe the demographics, clinical characteristics, medications and healthcare utilization of patients with CD stratified by the short Crohn's Disease Activity Index (sCDAI) with a unique CCF and IQVIA linked dataset. Methods: This retrospective analysis used data from the Crohn’s & Colitis Foundation (CCF) SPARC IBD and IBD QORUS data linked with IQVIA’s prescription and medical claims (LRx/Dx), to identify a cohort of CD patients with up to 24 months pre-enrollment and at least 18 months of follow-up. Enrollment date into CCF studies was used as the Index date and CD patients were stratified into three disease activity cohorts based on enrollment sCDAI scores: Remitters (R) sCDAI < 150, Non-remitters and non-moderate-severe (nRnMS, a.k.a. ‘Mild’) sCDAI = 150-219, and Moderate-Severe (MS) sCDAI ≥220. Medication use for biologics, immunomodulators and 5-ASAs was based on a hierarchy of highest step achieved. Descriptive analyses were performed for baseline period clinical characteristics, medications and healthcare utilization. Results: Of the 1756 CD patients included, 1027 were in R, 361 were nRnMS and 368 were in MS disease activity. Mean age for the three groups was around 43 years. Time since CD diagnosis was more than 10yrs in 54.5%, 52.9% and 58.4% of the R, nRnMS and MS groups, respectively. Mean (±SD) sCDAI scores at enrollment were 96.2 (±28.1) for R, 182.0 (±20.3) for nRnMS and 302.0 (±77.7) for the MS group. Biologics were used in 37.5% of R, 39.3% of nRnMS and 33.4% of MS patients. High use of corticosteroids and opioids was seen across all three groups. Emergency room visits increased across the R (7.1%), nRnMS (12.2%) and MS (27.2%) cohorts. Hospitalizations were highest in the MS group. (Table) Conclusion: We utilized a unique longitudinal dataset linked to administrative claims, to provide an initial description of characteristics between three severity-based CD cohorts. We observed generally more severe disease features, medication and healthcare utilization in CD patients who were not in remission. Future research will evaluate relationships between disease characteristics, treatment patterns and poor outcomes during follow-up. Table 1. - Demographics, Disease Characteristics, Medications and Healthcare Utilization* Characteristic Remitters (n = 1027) nRnMS (n = 361) Mod/Sev (n = 368) Female (n, %) 545 (53.1%) 230 (63.7%) 250 (67.9%) Age in years (mean +/- SD) 42.9 (15.2) 43.2 (15.2) 43.1 (13.9) Age at First Diagnosis (n, %) < =11 60 (5.8%) 25 (6.9%) 25 (6.8%) 12-17 161 (15.7%) 50 (13.9%) 50 (13.6%) 18-39 557 (54.2%) 206 (57.1%) 210 (57.1%) >=40 202 (19.7%) 66 (18.3%) 63 (17.1%) unknown/missing/other 47 (4.6%) 14 (3.9%) 20 (5.4%) Time since diagnosis (Mean Yrs +/- SD) 14.2 (10.8) 14.7 (11.4) 15.6 (12.2) Race (n, %) African American (Black or African American) 79 (7.7%) 43 (11.9%) 29 (7.9%) Asian 69 (6.7%) 24 (6.6%) 22 (6.0%) Other 103 (10.0%) 37 (10.2%) 42 (11.4%) White 776 (75.6%) 257 (71.2%) 275 (74.7%) CD Phenotype (n, %) Both stricturing and penetrating 105 (10.2%) 36 (10.0%) 42 (11.4%) Non-stricturing and non-penetrating 371 (36.1%) 124 (34.3%) 115 (31.3%) Penetrating and non-stricturing 129 (12.6%) 47 (13.0%) 47 (12.8%) vStricturing and non-penetrating 218 (21.2%) 77 (21.3%) 92 (25.0%) vUnknown / Missing 204 (19.9%) 77 (21.3%) 72 (19.6%) Other Current IBD Manifestations (n, %) Intestinal inflammation 43 (5.6%) 21 (7.4%) 36 (12.1%) Extra-intestinal manifestations 24 (3.1%) 14 (4.9%) 23 (7.7%) None / unknown / missing 711 (92.2%) 254 (89.8%) 255 (85.6%) sCDAI at Enrollment (mean +/- SD) 96.2 (28.1) 182 (20.3) 302 (77.7) Abdominal Pain (mean +/- SD) 0.3 (0.5) 1.0 (0.7) 1.7 (0.8) Stool Frequency (mean +/- SD) 2.6 (1.4) 4.4 (2.4) 8.1 (4.6) General Well-Being (mean +/- SD) 0.1 (0.3) 0.9 (0.5) 1.7 (0.9) Medication Utilization (n, %) Current Biologic users 385 (37.5%) 142 (39.3%) 123 (33.4%) Current IMS users (Never Biologic) 173 (16.8%) 63(17.5%) 62 (16.8%) Current 5-ASA (Never Biologic or IMS) 57 (5.6%) 23(6.4%) 12 (3.3%) Any Corticosteroid user 278 (27.1%) 133 (36.8%) 173 (47.0%) Any Opioid user 202 (19.7%) 120(33.2%) 163 (44.3%) No therapy (at least 6 months) 249 (24.2%) 71 (19.7%) 62 (16.8%) Healthcare Utilization (n, %) ER Visits (in 12 months prior to enrollment) 73 (7.1%) 44 (12.2%) 100 (27.2%) Hospitalization Visits (in 6 months prior to enrollment) 49 (4.8%) 17 (4.7%) 38 (10.3%) Prior CD-related Surgery 18 (1.8%) 5 (1.4%) 2 (0.5%) Office visits (in 24 months prior to enrollment) 853 (83.1%) 315 (87.3%) 330 (89.7%) Table: Percentages may not always add up to 100% due to missing/unknown/none values for some variables

Identification of circulating metabolites associated with wooden breast and white striping.

Current diagnostic methods for wooden breast and white striping, common breast muscle myopathies of modern commercial broiler chickens, rely on subjective examinations of the pectoralis major muscle, time-consuming microscopy, or expensive imaging technologies. Further research on these disorders would benefit from more quantitative and objective measures of disease severity that can be used in live birds. To this end, we utilized untargeted metabolomics alongside two statistical approaches to evaluate plasma metabolites associated with wooden breast and white striping in 250 male commercial broiler chickens. First, mixed linear modeling was employed to identify metabolites with a significant association with these muscle disorders and found 98 metabolites associated with wooden breast and 44 metabolites associated with white striping (q-value < 0.05). Second, a support vector machine was constructed using stepwise feature selection to determine the smallest subset of metabolites with the highest categorization accuracy for wooden breast. The final support vector machine achieved 94% accuracy using only 6 metabolites. The metabolite 3-methylhistidine, which is often used as an index of myofibrillar breakdown in skeletal muscle, was the top metabolite for both wooden breast and white striping in our mixed linear model and was also the metabolite with highest marginal prediction accuracy (82%) for wooden breast in our support vector machine. Overall, this study identified a candidate set of metabolites for an objective measure of wooden breast or white striping severity in live birds and expanded our understanding of these muscle disorders.

Cannabis use and related clinical variables in patients with obsessive-compulsive disorder.

Limited studies have investigated cannabis use in patients with obsessive-compulsive disorder (OCD), despite its widespread use by patients with psychiatric illnesses. The aim of this study was to assess the frequency, correlates, and clinical impact of cannabis use in an Italian sample of patients with OCD. Seventy consecutive outpatients with OCD were recruited from a tertiary specialized clinic. To assess cannabis-related variables, patients completed a questionnaire developed for the purpose of this study, investigating cannabis use-related habits and the influence of cannabis use on OCD symptoms and treatments. A set of clinician and self-reported questionnaires was administered to measure disease severity. The sample was then divided into three subgroups according to the pattern of cannabis use: "current users" (CUs), "past-users" (PUs), and "non-users" (NUs). Approximately 42.8% of patients reported lifetime cannabis use and 14.3% reported current use. Approximately 10% of cannabis users reported an improvement in OCD symptoms secondary to cannabis use, while 23.3% reported an exacerbation of anxiety symptoms. CUs showed specific unfavorable clinical variables compared to PUs and NUs: a significant higher rate of lifetime use of tobacco, alcohol, and other substances, and a higher rate of pre-OCD onset comorbidities. Conversely, the three subgroups showed a similar severity of illness. A considerable subgroup of patients with OCD showed a predisposition towards cannabis use and was associated with some specific clinical characteristics, suggesting the need for targeted consideration and interventions in this population.

Validity of the Central Sensitization Inventory compared with traditional measures of disease severity in fibromyalgia.

The goal of this study was to explore additional evidence of convergent and discriminant validity of the Central Sensitization Inventory (CSI) in a large sample of subjects with fibromyalgia (FM). Patients were consecutively enrolled for a cross-sectional assessment comprehensive of three FM-specific measures (the revised Fibromyalgia Impact Questionnaire [FIQR], the modified Fibromyalgia Assessment Status [modFAS], and the Polysymptomatic Distress Scale [PDS]) and of CSI. To test the convergent validity, the Spearman's rho was used to measure the degree of correlation between the variables CSI and the FM-specific measures. To assess discriminant validity, CSI scores were grouped according to FIQR disease severity states, and differences between these groups studied with the Kruskal-Wallis test. Interpretative cutoffs were established with the interquartile reconciliation approach. The study included 562 FM patients, 199 (35.4%) were classified as having central sensitization syndrome (CSI ≥40). CSI was largely correlated with modFAS (ρ=0.580; p < 0.0001), FIQR (ρ=0.542; p < 0.0001), and PDS (ρ=0.518; p < 0.0001). The differences between the CSI scores in accordance with the FIQR were significant (p < 0.000001). CSI cutoffs proposed for FM: 21 between remission and mild severity, 30 between mild and moderate severity, 37 between moderate and severe disease, and 51 between severe and very severe disease. The current study successfully showed additional evidence of the convergent and discriminant validity of the CSI in FM patients.

Health-related quality of life in patients with multiple system atrophy using the EQ-5D-5L.

BackgroundMultiple system atrophy (MSA) is an incurable neurodegenerative disease. We aimed to investigate the health‐related quality of life (HRQoL) and the determinants of HRQoL in patients with MSA.MethodsThe five‐level EuroQol five‐dimensional questionnaire (EQ‐5D‐5L) was used to evaluate patients’ HRQoL. The results of HRQoL were indicated by the EQ‐5D‐5L index values and visual analog scale (EQ VAS) scores. Specific scales were used to measure disease severity, cognition, frontal lobe function, anxiety, depression, fatigue, and sleep disorders. The beta mixture model and the linear regression model were used to explore the determinants of HRQoL in patients with MSA.ResultsA total of 205 patients with cerebellar variants (MSA‐C; 53.9%) and 175 patients with parkinsonian variants (MSA‐P; 46.1%) were included in this cross‐sectional study. The mean values of the EQ‐5D‐5L index values and EQ VAS scores were .558 and 59.5, respectively. Problem with mobility was the problem reported by the highest proportion of patients (92.1%), followed by problems with usual activities (88.7%), self‐care (81.3%), anxiety/depression (72.1%), and pain/discomfort (53.9%). The determinants of the lower EQ‐5D‐5L index values in patients with MSA were greater disease severity, fatigue, Parkinson's disease‐related sleep problems (PD‐SP), depressive mood, and anxious mood. Greater disease severity, fatigue, and depressive mood were associated with lower EQ VAS scores.ConclusionThe problem reported most frequently by Chinese individuals with MSA was mobility. In addition to the greater disease severity of MSA, fatigue, PD‐SP, depression, and anxiety were determinants of poor HRQoL.

Signposts to the Promised Land in Alopecia Areata

Alopecia areata (AA) is an autoimmune disease of the hair and nails. Although genotype alters the risk, the etiology remains unclear. AA typically presents as one or more hairless patches; however, full loss of scalp hair occurs in about 15% of patients (Cranwell et al., 2019; Ikeda, 1965). The relative ease in semiobjectively quantifying the area of bald scalp has resulted in the Severity of Alopecia Tool (SALT) becoming the main measure of disease severity among physicians (Olsen et al, 2004).

Antigenic drift and epidemiological severity of seasonal influenza in Canada

Seasonal influenza epidemics circulate globally every year with varying levels of severity. One of the major drivers of this seasonal variation is thought to be the antigenic drift of influenza viruses, resulting from the accumulation of mutations in viral surface proteins. In this study, we aimed to investigate the association between the genetic drift of seasonal influenza viruses (A/H1N1, A/H3N2 and B) and the epidemiological severity of seasonal epidemics within a Canadian context. We obtained hemagglutinin protein sequences collected in Canada between the 2006/2007 and 2019/2020 flu seasons from GISAID and calculated Hamming distances in a sequence-based approach to estimating inter-seasonal antigenic differences. We also gathered epidemiological data on cases, hospitalizations and deaths from national surveillance systems and other official sources, as well as vaccine effectiveness estimates to address potential effect modification. These aggregate measures of disease severity were integrated into a single seasonal severity index. We performed linear regressions of our severity index with respect to the inter-seasonal antigenic distances, controlling for vaccine effectiveness. We did not find any evidence of a statistical relationship between antigenic distance and seasonal influenza severity in Canada. Future studies may need to account for additional factors, such as co-circulation of other respiratory pathogens, population imprinting, cohort effects and environmental parameters, which may drive seasonal influenza severity.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

BackgroundMultiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.MethodsSystematic searches through eight databases were conducted for literature published in 2014–2020 on MS and specified ML algorithms.ResultsOf the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.ConclusionsML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.