Predict Disease Severity Research Articles

Exploring the Clinical Diversity of Castleman Disease and TAFRO Syndrome: A Japanese Multicenter Study on Lymph Node Distribution Patterns.

Individuals diagnosed with Castleman disease (CD) and TAFRO syndrome (characterized by thrombocytopenia, anasarca, fever, bone marrow fibrosis, and organomegaly) displays a wide range of clinical symptoms, including varying patterns of lymph node enlargement, systemic inflammation, and impaired organ function. Some patients may present with both CD and TAFRO syndrome concurrently. A retrospective study conducted across multiple centers in Japan examined 321 cases to determine if the quantity and position of swollen lymph nodes could forecast the clinical progression and intensity of these conditions. Interestingly, the study revealed that patients with TAFRO syndrome exhibited lymphadenopathy across all ranges of lymph node region counts. Moreover, no specific clinical patterns were associated with the number of affected lymph node regions in CD patients, regardless of whether they also had TAFRO syndrome. These results enhance our understanding of the clinical variability in CD and TAFRO syndrome, suggesting that a comprehensive clinical evaluation, rather than relying solely on lymph node count, is crucial for effectively managing these conditions. Additional studies are required to establish reliable diagnostic markers and to predict disease severity at the time of diagnosis, ultimately improving patient outcomes.

P0456 Peripheral blood eosinophilia: A novel predictor of disease severity in Inflammatory Bowel Disease - A single-center retrospective study

Abstract Background Identifying early predictors of disease severity in inflammatory bowel diseases (IBD) remains crucial for optimizing therapeutic strategies. We investigated whether peripheral blood eosinophilia (PBE) at diagnosis could predict long-term outcomes in adult IBD patients. Methods We conducted a retrospective analysis of 128 IBD patients (78 Crohn’s disease [CD], 50 ulcerative colitis [UC]) diagnosed between 2003 and 2021, comparing them with 49 non-IBD controls. PBE was defined as an eosinophil count exceeding 0.5 × 109/L. We defined severe disease course as meeting any of these criteria: corticosteroid dependency, requirement for multiple biologic therapies (≥2 different classes), or surgical intervention. Disease location, behavior, and severity were classified according to Montreal classification. Outcomes were analyzed using Cox proportional hazard models. Results PBE was detected in 17 IBD patients (13.3%) compared to 2 controls (4.1%, p<0.001). UC patients showed higher PBE prevalence with 8 cases (16%) versus 9 CD patients (11.5%). Regarding disease location in CD patients with PBE, 5 patients (55.5%) had ileocolonic involvement, 3 (33.3%) had isolated ileal disease, and 1 (11.1%) had isolated colonic disease. In UC patients with PBE, 6 (75%) had extensive colitis. During the follow-up period (median 5.2 years), PBE-positive patients demonstrated significantly worse outcomes: 14 out of 17 (82.3%) developed severe disease course compared to 45 out of 111 (40.5%) PBE-negative patients. PBE-positive patients required more frequent hospitalizations (71% vs 35%), had higher rates of corticosteroid use (88% vs 45%), and increased need for biological therapy (76% vs 38%). Notably, 65% of PBE-positive patients developed corticosteroid dependency compared to 30% in the PBE-negative group. Multivariate analysis confirmed PBE as an independent predictor of severe disease course (HR = 1.49, 95% CI: 1.38-1.62, p<0.001). Conclusion This study establishes PBE as a valuable prognostic marker for IBD severity. The presence of eosinophilia at diagnosis strongly correlates with a more aggressive disease course, suggesting the need for early therapeutic optimization in these patients. These findings not only provide a simple and accessible prognostic tool but also highlight eosinophils as a potential therapeutic target in IBD management. Further prospective studies are warranted to validate these findings and explore eosinophil-targeted therapies.

Testing the Taguchi method to design and analyze integrated disease management strategies, for the control of late blight (Phytophthora infestans) on potato.

Identifying robust integrated pest management (IPM) strategies requires the testing of multiple factors at the same time and assessing their combined effects e.g., on disease control. This makes field-based experiments large, resource intensive and expensive. Hence, there are limits to the number of treatment combinations that can be practically tested under field conditions. Taguchi approach to design of experiments (DOE) or the Taguchi approach is commonly employed to enhance the quality of industrial products. It uses smaller experiments than classical DOE but its applicability to late blight research, and agricultural research, has not been widely evaluated. Two existing datasets, following the same protocol and investigating the effectiveness of different IPM treatments to control late blight, caused by Phytophthora infestans, on potato, were used to test the Taguchiapproach. Disease severity was quantified as area under the disease progress curve (AUDPC). The method could accurately predict the performance of a cultivar and fungicide-based integrated disease management strategy from a small dataset and identified cultivar as a key factor for disease control. Linear regression demonstrated a strong and statistically significant relationship between AUDPC values collected during the original experiments and the predicted disease severity values generated using the Taguchi method. The Taguchi approach can accurately predict disease severity, with predicted values similar to those collected during the original experiments. Moreover, associated analyses identified the most effective treatment combinations and the factors that exert the greatest influence on disease control. The relevance of this approach when designing and interpreting IPM strategies is discussed. © 2025 Society of Chemical Industry.

Mild behavioral impairment in idiopathic REM sleep behavior disorder and Lewy body disease continuum.

To investigate the clinical impact of mild behavioral impairment (MBI) in a predefined cohort with Lewy body disease (LBD) continuum. Eighty-four patients in the LBD continuum participated in this study, including 35 patients with video-polysomnography-confirmed idiopathic REM sleep behavior disorder (iRBD) and 49 clinically established LBD. Evaluations included the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), neuropsychological tests, and MBI Checklist (MBI-C). We examined the clinical associates of MBI-C and its diagnostic values in predicting disease severity and cognitive impairment across the LBD continuum. Participants were classified into 19 cognitively normal (CN), 45 mild cognitive impairment (MCI), and 20 dementia groups. Median MBI-C total scores were 1.0, 8.0, and 18.5 for CN, MCI, and dementia groups, respectively, with a significant increasing trend (p < 0.001). The MBI-C total score demonstrated significant correlations with the MDS-UPDRS part 1 (r = 0.504, p < 0.001) and total scores (r = 0.508, p < 0.001). Furthermore, significant correlations were observed between MBI-C and cognitive performances in frontal/executive (DSC: r = -0.314, p = 0.006; TMT-B: r = -0.338, p = 0.003) and attentional (TMT-A: r = -0.343, p = 0.002) domains. A cutoff 5.0 effectively differentiated the MCI from CN groups (area under the curve (AUC = 0.74). Furthermore, the MBI-C effectively discriminated the iRBD patients with high phenoconversion risk against those with low-risk (cut-off 4.0, AUC = 0.72). The MBI-C may be a useful screening questionnaire reflecting clinical severity across the LBD continuum. Longitudinal studies are needed to determine its value in monitoring disease progression in prodromal LBD.

Clinical predictors of disease severity in oral lichen planus

Abstract Background The limited understanding of factors influencing the disease progression of oral lichen planus (OLP) poses challenges in delivering effective and personalised treatment for this condition, known to increase the risk of oral cancer and adversely impact patient quality of life. Objective This study aimed to systematically identify clinical predictors of disease severity in OLP patients. Methods This cross-sectional and single-site prospective study was conducted between December 2021 and February 2024 in the Departments of Oral Medicine and Oral &amp; Maxillofacial Surgery, Aberdeen Royal Infirmary. Patients presenting with OLP aged 18 years or older diagnosed using Van der Meij and Van der Waal criteria were eligible to the study. Out of a total of 270 eligible OLP patients presenting consecutively to the outpatient clinics during the study period, 89 patients agreed to participate and were enrolled into the study. Participants demographic and relevant clinical data, namely medical history, smoking status, alcohol consumption, perceived stress levels, oral hygiene status and haematologic and biochemical parameters, including full blood count, haematinics, vitamin D were recorded. The outcome measure was OLP disease severity measured as the Oral Disease Severity Score (ODSS), Gingival ODSS and Reticular/hyperkeratotic, Erosive/erythematous, Ulcerative (REU scoring system). Results A total of 89 participants were recruited into the study. The median [IQRs] age of the study population was 66 [58 to 73] years, and 65 (73%) patients were females. The median total ODSS score was 10 (range: 0-44). After adjustment for confounding factors, patients with lichen planus affecting skin or other mucosal sites had a 5.76-unit higher OLP severity score (B=5.76, 95% CI=.74-10.78, P=.025) than those without extraoral involvement as measured by ODSS. Patients with insufficient vitamin D exhibited 5.49-unit increase in disease severity (B = 5.49, 95% CI = 1.13-9.84, P= 0.014) compared to those with adequate vitamin D levels. Conclusions This study identified the importance of cutaneous and/or genital lichen planus in phenotyping OLP disease severity. We also highlight the role of vitamin D as a significant predictor of disease severity of OLP, suggesting the importance of adequate vitamin D levels in lichen planus.

Phenotypic variability and the gender paradox in the R363C variant of Fabry disease.

Fabry disease is an X-linked lysosomal disease caused by variants in the GLA gene. Although Fabry disease is X-linked, GLA gene variants in females can exhibit a wide range of symptoms, challenging the traditional view of Fabry as an X-linked recessive disease. A family is presented here with a 36-year-old female who is symptomatic with chronic kidney disease and her oligosymptomatic 70-year-old father, both of whom have a heterozygous and hemizygous GLA pathogenic variant, respectively, c.1087C>T (p.R363C). Interestingly, the proband's Lyso-GL-3 levels were lower than her father's despite her more severe clinical presentation. The discordance between clinical severity and Lyso-GL-3 levels, particularly in the context of migalastat therapy, raises questions about the appropriate interpretation and use of this biomarker. The earlier and more severe symptom onset in the female proband suggests the potential role of genetic modifiers or other factors influencing disease expression. This report underscores the complexity of Fabry disease phenotypes and the limitations of current biomarkers in predicting disease severity, particularly in females. The observed paradox between clinical symptoms and biomarker levels suggests the need for a deeper understanding of the underlying mechanisms driving phenotypic variability in Fabry disease.

Systemic Immune-Inflammation Index (SII) and Neutrophil-to-Lymphocyte Ratio (NLR): A Strong Predictor of Disease Severity in Large-Artery Atherosclerosis (LAA) Stroke Patients.

Systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) are novel inflammatory markers based on neutrophil, platelet and lymphocyte counts. Atherosclerosis is a chronic inflammatory vascular disease. This study aimed to verify the predictive value of the clinical parameters such as systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) for the severity in Large Artery Atherosclerosis (LAA) stroke patients. The SII is defined as platelet × (neutrophil count/lymphocyte count), the NLR is defined as neutrophil count/lymphocyte count. Univariate logistic regression was used to analyze the association between SII and NLR and NIHSS score in patients with LAA stroke. Multiple logistic regression was used to analyze the risk factors for the severity of LAA stroke. We plotted receiver operating characteristic curves to determine the diagnostic role of SII and NLR in differentiating stroke disease severity. We included 283 LAA stroke patients, the SII and NLR in the moderate-to-severe stroke group were significantly higher than the mild stroke group. Multiple logistic regression analysis showed that SII (OR 1.051 95% CI (1.035-1.066), P < 0.001), NLR (OR 1.077,95% CI (1.032-1.123), P < 0.001) were significantly associated with stroke severity. The SII values under the receiver operating characteristic curve (0.701, 95% CI (0.649-0.791, P < 0.001, cut-off value 912.97) and NLR values under the receiver operating characteristic curve (0.604,5% CI (0.519-0.689), P < 0.01, cut-off value 1.461), and SII values had high discrimination ability. Both SII and NLR had high diagnostic and predictive value for stroke severity, and SII was better than NLR. The higher SII and NLR, the more severity in LAA stroke patients. SII and NLR are independent risk factors for LAA stroke, and they can also effectively predict stroke severity; moreover, SII has a higher diagnostic efficacy than NLR. However, multicenter studies with large sample size are still needed to confirm this conclusion.

Pertussis Clinical Profile Shift, Severity, Prediction in a Tertiary Hospital: A Comparative Study Before, During, and After COVID-19 in Southern China

ObjectiveTo analyze the epidemiological characteristics, clinical manifestations, antimicrobial resistance and develop a predictive model for severe pertussis spanning five years – before, during, and after the COVID-19 pandemic – in Shenzhen children’s hospital in southern China, aiming to provide insights into the pandemic impact and control measures on the pertussis disease profile. MethodsDemographic, clinical, vaccination, and laboratory data were collected for patients who tested positive for pertussis by polymerase chain reaction and/or culture from January 1, 2019, to March 30, 2024. Analysis included changes in demographic and clinical features, indicators of severe cases, and resistance patterns over the study period. ResultsDuring this period, 3963 patients were diagnosed, with 79 severe (PICU admitted) cases; 1433 isolates underwent antimicrobial susceptibility testing. In late 2023, pertussis cases began to increase. From 2019 to 2024, the proportion of cases among 4-6 year olds rose from 4.9% to 28.6%, and among 7-11 year olds from 0.7% to 21.2%. Macrolide resistance surged from 46.5% in 2019 to 97.3% in 2024, with 80% of resistant hospitalized cases initially treated with macrolides. Clinical severity and co-infection increased post-pandemic, with a larger number of cases necessitating antibiotic changes and enhanced supportive care. Vaccination protected against severe disease. Indicators such as white blood cell count, lymphocyte to neutrophil ratio, platelet count, cyanosis and pneumonia predicted disease severity. ConclusionsPost-pandemic, pertussis cases shifted from infants to school-aged children, with increased clinical severity and high macrolide resistance. Urgent measures are needed to optimize vaccination schedules and develop management strategies addressing and changing epidemiological patterns.

Evaluation of Sepsis Severity Using Combined High-Density Lipoprotein and Red Cell Distribution Width Indicators.

Aims/Background Sepsis is a life-threatening condition resulting from dysregulated immune responses to infection, leading to organ dysfunction. High-density lipoprotein (HDL) and red cell distribution width (RDW) have shown significant correlations with sepsis severity, yet the combined prognostic value of HDL and RDW in evaluating sepsis severity and outcomes remains unclear. This study examines the relationship between HDL and RDW levels and sepsis severity, as well as evaluates the combined utility of these markers in predicting disease severity and patient outcomes. Methods This retrospective study included 103 patients diagnosed with sepsis. Clinical data, including HDL and RDW levels, were collected for analysis. Patients were divided into shock and non-shock groups based on the presence of septic shock and into survival and death groups based on 30-day in-hospital mortality. Multivariate logistic regression was used to identify factors influencing sepsis severity and prognosis, while the predictive value of HDL in combination with RDW was evaluated using receiver operating characteristic (ROC) curve analysis. Results Multivariate analysis identified sequential organ failure assessment (SOFA) score (OR = 6.566), interleukin-6 (IL-6) (OR = 2.568), HDL (OR = 0.864), and RDW (OR = 4.052) as independent predictors of sepsis severity (p < 0.05 for all). ROC analysis demonstrated that HDL combined with RDW yielded the highest diagnostic accuracy for sepsis severity, with an area under curve (AUC) of 0.962, sensitivity of 97.56%, and specificity of 91.94%. Additionally, SOFA score (OR = 2.354), interleukin-6 (IL-6) (OR = 1.446), HDL (OR = 0.870), and RDW (OR = 3.502) were independent prognostic indicators (p < 0.05 for all). ROC analysis for prognosis showed that HDL combined with RDW had the highest predictive efficacy for the prognosis of sepsis, with an AUC of 0.922, sensitivity of 79.31%, and specificity of 93.24%. Conclusion The combination of HDL and RDW is a robust indicator for the evaluation of sepsis severity and is a valuable prognostic tool for assessing 30-day mortality risk in sepsis patients.

Spatiotemporal analysis of meteorological drivers of major foliar fungal diseases in rubber budwood gardens in Cote D'Ivoire

Latex production in Côte d'Ivoire is significantly influenced by meteorological conditions and various diseases, particularly foliar fungal diseases affecting Hevea brasiliensis, which can cause yield losses of up to 100%. This study aims to identify the climatic factors driving the severity of these diseases across different ecological zones. Surveys were conducted in budwood gardens (JBG) across six agroecological zones, with meteorological data collected on-site. The analysis identified Helminthosporium heveae, Colletotrichum gloeosporioides, and Corynespora cassiicola as the most prevalent fungi, exhibiting high incidence and severity in key production areas such as Tabou and Nouamou. Multiple regression analyses revealed that rainfall and relative humidity (correlation coefficients ranging from 0.4 to 0.55, p 0.0001) and leaf wetness duration (correlation coefficient 0.7, p 0.0001) are critical predictors of disease severity. Specifically, each additional unit of leaf wetness increased infection severity by 0.642 for H. heveae, 0.435 for C. gloeosporioides, and 0.832 for C. cassiicola. These findings underscore the importance of meteorological factors in disease development and could support the establishment of early warning systems to manage foliar fungal diseases of H. brasiliensis in Côte d'Ivoire.

Prediction of chronic severe intestinal failure-associated liver disease by current criteria in adults: A descriptive cohort study.

Intestinal failure-associated liver disease covers a spectrum of conditions from mild to end-stage disease. Currently, there are 9 diagnostic criteria divided to four categories: cholestasis, steatosis, fibrosis, and unclassified. Our aim was to evaluate the application of these criteria to patients with chronic severe liver disease in patients with intestinal failure. This was a cross-sectional study of patients attending the home parenteral nutrition clinic of a national UK reference intestinal failure center from March 2015 to December 2019. Exclusion criteria included active malignancy, home parenteral nutrition for <6 months duration, and liver transplantation. Clinically significant intestinal failure-associated liver disease was defined as moderate-severe fibrosis or cirrhosis on liver biopsy and/or radiological imaging compatible with liver cirrhosis. Two hundred and twenty-one patients were included (age at home parenteral nutrition initiation: 50 ± 16.0 years; 63.6% female). There was a wide range of intestinal failure-associated liver disease point prevalence depending on the established criteria used (2.9%-35.1%). Twenty-three patients (9.5%) were diagnosed with clinically significant intestinal failure-associated liver disease, but no patient with clinically significant intestinal failure-associated liver disease met all diagnostic criteria, and 6 of 23 (26.1%) did not fit any of the established criteria. Intestinal failure-associated liver disease is a poorly defined medical condition, and current noninvasive diagnostic methods are unreliable in predicting disease severity. Further studies are needed to develop the definition to reflect that intestinal failure-associated liver disease is a spectrum of disease that includes chronic severe liver disease and improve methods of disease diagnosis.

Non-Invasive Tests of Non-Alcoholic Fatty Liver Disease: Perspective Review

Cirrhosis and hepatocellular carcinoma often develop from Non-Alcoholic Fatty Liver Disease (NAFLD), a progressive condition characterized by hepatic steatosis, inflammation and varying degrees of fibrosis. The increasing incidence of non-alcoholic steatohepatitis (NASH) highlights the need for effective diagnostic and treatment methods. Non-invasive tests (NITs) have emerged as promising tools for evaluating and monitoring NAFLD, offering safer and more cost-effective alternatives to invasive liver biopsies. This review focuses on the current state of NITs in NAFLD management, emphasizing their diagnostic accuracy, prognostic significance, and role in guiding treatment. NITs discussed include transient elastography, magnetic resonance imaging (MRI), magnetic resonance elastography (MRE) and serum biomarker panels such as the NAFLD fibrosis score, Fibro Test, and Enhanced Liver Fibrosis (ELF) test. Recent advancements in MRI and MRE have greatly improved the non-invasive assessment of liver fibrosis and steatosis, enhancing sensitivity and specificity for early detection and risk stratification. Additionally, combined serum biomarker panels have shown promise in predicting disease severity and progression, aiding in the identification of high-risk patients who could benefit from early intervention. By enabling prompt diagnosis, comprehensive risk assessment and monitoring of disease progression, the integration of NITs into standard clinical practice offers significant potential for improving NAFLD management. Ongoing research efforts are focused on developing new NITs and optimizing existing methods to enhance diagnostic precision and prognostic evaluation in NAFLD patients. This evolving landscape of non-invasive assessment tools could revolutionize the approach to managing this increasingly prevalent liver disease. Keywords: Fatty liver, Liver biopsy, Transient Elastography, Serum biomarkers, Hepatic Cell Carcinoma.

LncRNA six3os1 diagnoses acute stroke, predicts disease severity, and predicts post-stroke cognitive impairment.

Stroke is the main cause of death and disability. Post-stroke cognitive impairment (PSCI) is one of the most severe complications of stroke, which lacks effective biomarkers for its early detection. This study evaluated the significance of lncRNA SIX3OS1 in acute stroke and PSCI aiming to identify a novel biomarker. The study enrolled 138 patients with acute stroke and 80 healthy individuals. By comparing the serum SIX3OS1 in acute stroke and healthy individuals, the significance of SIX3OS1 in diagnosing acute stroke, assessing disease severity, and predicting the risk of PSCI was revealed. Significant upregulation of SIX3OS1 in acute stroke was observed, which discriminated patients with acute stroke from healthy individuals and indicated severe disease conditions of patients. There were 72 acute stroke patients who had PSCI accounting for 52.17% that showed a higher serum SIX3OS1 level than post-stroke cognitive normal patients. The increasing serum SIX3OS1 level was also identified as a risk factor for PSCI and could distinguish PSCI patients. Additionally, SIX3OS1 showed a negative correlation with the MoCA score of PSCI patients. Serum SIX3OS1 level can be considered a biomarker for screening acute stroke and a predictor for PSCI.

The Role of Inflammatory and Hemostatic Markers in the Prediction of Severe Acute Pancreatitis: An Observational Cohort Study.

Acute pancreatitis (AP) is a serious inflammatory disease of the pancreas that can lead to significant morbidity and increased mortality. The special role of inflammation and disruption of the hemostatic system in the development of severe forms of the disease is known, however, the relationship between inflammatory and anti-inflammatory cytokines and thromboelastogram parameters has not been sufficiently studied. The aim of this study is to assess the prognostic significance of thromboelastogram parameters, interleukin-6, and interleukin-22 levels in assessing the risk for developing severe forms of acute pancreatitis. Data from 149 patients with acute pancreatitis were included in the analysis. The classification of AP was performed according to the 2012 Revision of the Atlanta Classification. Data including gender, age, lab tests, radiological information, and prognosis were included. The following scales were used to assess severity: SOFA scale and BISAP scale. IL-6 and IL-22 were analyzed at 24 h and 48 h after the onset of symptoms. The collected TEG parameters included K-time, R-value, and Maximum amplitude value at admission. All patients were divided into three groups: mild, moderate-severe, and severe pancreatitis. Statistically significant differences were found between the groups in the IL-6 level at the first measurement and on day 2 of the study. IL-22 values were also higher in the group with severe pancreatitis, however, on day 2, its level became lower compared to the group of patients with moderate and mild pancreatitis. Statistically significant differences were found in the level of K-time, R-value, Maximum amplitude, fibrinogen concentration, and platelets count, demonstrating a hypercoagulation state in severe pancreatitis at admission. The conducted logistic regression showed that the factors associated with the development of severe forms are the number of points on the BISAP scale, the level of interleukin-6 in the first 24 hours of the disease, delta IL-22, and K-time. (AUC = 0.948). The study highlights that both IL-6 and IL-22 play crucial roles in the inflammatory cascade of severe acute pancreatitis. Their levels, along with specific hemostasis parameters like K-time and BISAP score, serve as reliable early predictors of disease severity.

Circulatory microRNAs as potential biomarkers for different aspects of COVID-19.

The coronavirus disease of 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can alter the expression levels of host microRNAs (miRNAs). Increasing evidence suggests that circulating miRNAs can potentially play an important role in the diagnosis and prognosis of respiratory infectious diseases, especially COVID-19, and might serve as sensitive indicators of disease before the emergence of clinical symptoms. Here, we review the potential of circulatory microRNAs as novel biomarkers for different aspects of COVID-19. Recent studies have suggested that they can be useful not only for COVID-19 prognosis but also for prediction of disease severity and mortality among intensive care unit (ICU) and ward patients. Moreover, extracellular vesicle (EV) miRNAs can be associated with antibody titer after COVID-19 vaccination. This review provides an overview of miRNA-based biomarkers.

DINOV2-FCS: a model for fruit leaf disease classification and severity prediction.

The assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability. In light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. DINOV2-FCS employs the DINOv2-B (distilled) backbone feature extraction network to enhance the extraction of features from fruit disease leaf images. In fruit leaf disease classification, for the problem that leaf spots of different diseases have great similarity, we have proposed Class-Patch Feature Fusion Module (C-PFFM), which integrates the local detailed feature information of the spots and the global feature information of the class markers. For the problem that the model ignores the fine spots in the segmentation process, we propose Explicit Feature Fusion Architecture (EFFA) and Alterable Kernel Atrous Spatial Pyramid Pooling (AKASPP), which improve the segmentation effect of the model. To verify the accuracy and generalizability of the model, two sets of experiments were conducted. First, the labeled leaf disease dataset of five fruits was randomly divided. The trained model exhibited an accuracy of 99.67% in disease classification, an mIoU of 90.29%, and an accuracy of 95.68% in disease severity classification. In the generalizability experiment, four disease data sets were used for training and one for testing. The mIoU of the trained model reached 83.95%, and the accuracy of disease severity grading was 95.24%. The results demonstrate that the model exhibits superior performance compared to other state-of-the-art models and that the model has strong generalization capabilities. This study provides a new method for leaf disease classification and leaf disease severity prediction for a variety of fruits. Code is available at https://github.com/BaiChunhui2001/DINOV2-FCS.

P05 Evaluation of changes in serum cytokine (TNFα, IFNα2, IFNγ, IL-1β, IL-6, IL-8, IL-10, IL-10, IL-12p70, IL-17A, IL-18, IL-23, IL-23, IL-33, and MCP-1) values in biologic-naive psoriasis patients during biologic agent use

Abstract Psoriasis is an immune-mediated chronic inflammatory skin disease. Proinflammatory cytokines have an important role in the pathogenesis. Current treatments mainly act on TNF-α, IL-12, IL-17 or IL-23 pathways. It is thought that determination of cytokine levels before treatment can be used as useful biomarkers for monitoring disease activity and personalized treatment selection. The aim of this study is to contribute to the establishment of biomarkers that can help predict disease severity, treatment response and disease prognosis in patients with psoriasis and also compare healthy controls. We analysed the changes in serum levels of cytokines (TNFα, IFNα2, IFNγ, IL-1β, IL-6, IL-8, IL-10, IL-10, IL-12p70, IL-17A, IL-18, IL-23, IL-23, IL-33, MCP-1) before and 4 months after treatment with biological agents in 40 bio-naive psoriasis patients and 15 healthy subjects. Flow cytometry method [LEGENDplex™ Multi-Analyte Flow Assay Kit Human Inflammation Panel 1, 13 Plex with V-bottom Plate (BioLegend, San Diego, CA, USA)] was used for the determination of cytokine levels. Clinical and demographic information, including PASI (psoriasis area and severity index) and DQLS (Dermatology Quality of Life Scale) scores were recorded. In addition, acute phase reactants (CRP, ESR, ferritin, albümin) were measured before and after 4 months of treatment. The data obtained were analysed using NCSS 2007 software. PASI and DQLS scores at the fourth month of treatment were found to be significantly lower compared with the onset (P = 0.000, P = 0.0001, respectively). When cytokine values were analysed; IFN-γ levels of the patients increased after treatment (P = 0.007). Pre-treatment IL-33 and IL-1β levels of the patients were lower than healthy subjects (P = 0.045), but there was no difference between the mean levels of these cytokines before and after treatment. Among the biologic agents, IL-17A levels were found to be lower only in patients using secukinumab (P = 0.022). Pre-treatment and post-treatment IL-17A difference of the secukinumab group was higher than the other drugs (P = 0.004, P = 0.009), while no statistical difference was observed between the other biologic agents (P &amp;gt; 0.05). There was no correlation between age at disease onset, PASI and DQLS and cytokine levels. The ESR, CRP, and ferritin values of the patients were higher than the healthy controls before treatment (P = 0.011, P = 0.022, and P = 0.037, respectively). However, only ESR decreased after treatment in psoriasis patients (P = 0.027). In this study, no significant differences were found in serum cytokine levels. However, the literature shows that while serum levels may remain unchanged, tissue levels can be significantly different. Future studies should include cytokine analyses in both serum and tissue levels.

FC07 Transcriptomic profiling across tissues and machine learning reveal molecular endotypes of psoriasis phenotypes and signatures of disease severity during biologic therapy

Abstract Despite advances in understanding the pathogenesis of psoriasis, important knowledge gaps exist including (i) whether molecular endotypes of stable plaque psoriasis underlie distinct clinical phenotypes (e.g. subjects with specific comorbidities) and (ii) the positive drivers and negative molecular regulators of disease severity across tissue compartments. We performed a comprehensive RNA-seq study of skin and blood samples (n = 718) from deeply phenotyped discovery (n = 89) and refinement (n = 57) psoriasis cohorts, during targeted treatment with a TNF inhibitor (adalimumab) or an interleukin (IL)-12/23 inhibitor (ustekinumab). As gene expression is controlled by multiple factors across different cell types, network analysis of bulk RNAseq data across large numbers of clinical samples takes into account the underlying latent factors and modules that result in the coordination of gene expression and can more accurately reflect the relationship between transcriptional signatures and clinical phenotypes compared with analysis of individual genes. Using two complementary methods for dimensionality reduction, weighted gene correlation network analysis (WGCNA) and Independent Component Analysis (ICA), we defined distinct but interconnected gene modules and factors within skin and blood that were significantly positively or negatively associated with disease severity as measured by Psoriasis Area Severity Index (PASI). To understand which modules and factors in lesional psoriasis skin contribute to the prediction of disease severity, we applied Gaussian process regression followed by SHAP (SHapley Additive exPlanations). This analysis highlighted 2 principal gene modules [cytokine signalling (267 genes) and Wnt signalling (207 genes)] that explained ∼50% of the variance of PASI during clinical response to both adalimumab and ustekinumab. We derived a further gene signature (14 genes), linked to body mass index in nonlesional skin at baseline, that in lesional skin was significantly negatively associated with PASI. Notably, PASI-associated signatures in blood were only seen following exposure to adalimumab whereas ustekinumab PASI-associated signatures were confined to skin. These highly drug-specific tissue compartmentalization effects indicate a differential systemic impact of adalimumab compared with ustekinumab, in line with differences in side-effect profiles. In contrast, a gene signature in blood closely linked to HLA-C*06:02 status was independent of disease severity (PASI). The systematic nature of the dataset across skin and blood and its integration with key clinical features and outcomes to therapy provides a comprehensive and global perspective of psoriasis that are not apparent from previous studies. Specifically, our findings delineate, across skin and blood, distinct gene-environmental and genetic effects on the psoriasis transcriptome linked to clinical phenotypes and disease severity endotypes.

Haematological parameters as predictors of severe dengue: a study from northern districts of West Bengal.

A hike in dengue cases was recorded in last two years, resulting from both single and multiple-serotypes of dengue virus (DENV) and secondary infections, culminating in significant hospitalizations and deaths in India. This study focuses on evaluating symptomatic and haematological parameters in acute dengue patients of the northern part of West Bengal to predict disease severity early on and also to analyze the correlation between circulating DENV serotypes with severity. Dengue patients (N=540) diagnosed as NS1 positives were categorized into 13.7% severe DHF (N=74) and 86.3% mild DF (N=466) and prediction of risk was done using logistic regression. DENV RNA was isolated from blood, converted to cDNA, and detected/serotyped via RT-qPCR by using DENV specific primers. Only 14.48% (N=11) patients showed single serotypic (DENV2 or DENV3) infection of dengue. In contrast, multi-serotypic infections (N=65) with the prevalence of DENV2 and DENV3 co-infections were found among the dengue patients, affecting severe changes in the most critical haematological parameters such as hematocrit and platelet count. The multivariate binary logistic regression model revealed that only six parameters viz., age (p=0.032), presence of joint pain (p=0.015), Haemoglobin level (p<0.001), total RBC count (p=0.024), total WBC count (p=0.003), lymphocyte% (p=0.019) were found to be significantly associated with the risk of DHF. Most prevalent DENV2 and DENV3 infections significantly impact hematocrit and platelet counts in the study region. Our prediction model, incorporating age, joint pain, hemoglobin, RBC, WBC, and lymphocyte, may effectively predict dengue severity.

Systematic quantitative modeling of the natural history of Aicardi syndrome: A cross sectional study of 245 published cases

PurposeAicardi syndrome is a rare epileptic encephalopathy, almost exclusively affecting girls. It was first described as a triad of infantile spasms, chorioretinal defects and agenesis of the corpus callosum. The etiology remains unknown and there is uncertainty on best practice therapy and outcome. We aimed at defining quantitative clinical endpoints that will inform future research and clinical trials.MethodsQuantitative natural history modeling of cases with Aicardi syndrome from published clinical reports. Main outcome measures were age at disease onset, survival and diagnostic delay. Phenotypic features of affected individuals as well as neuroradiological and ophthalmological features were descriptively stated. STROBE criteria were respected.ResultsTwo hundred forty-five cases were available for analysis. Median age at disease onset was 2.2 months. Median diagnostic delay was 1 month. Mortality was estimated with 6% at 1 and 17% at 5 years of age. 60% of children showed the classic clinical features, while 40% met the revised diagnostic criteria. We delineate possible predictors of disease severity and of seizure control.ConclusionWe provide natural history data including geographical localization of 245 published patients with Aicardi syndrome. Quantitative history modeling in rare epileptic encephalopathies will help to raise disease awareness and facilitate future clinical trials as one core element of quantitative systems pharmacology.