Constructing a visual detection model for floc settling velocity using machine learning.

Optimizing the dosage of coagulant is a time-consuming process, and real-time evaluation of floc settling velocity can quickly predict the coagulation effect and optimize the dosage. This study used a convolutional neural network (CNN) model to analyze the accuracy of floc image recognition of settling velocity. Python-OpenCV was employed to develop a program that segments individual flocs and detects their settling velocity to constructing a dataset of floc images and settling velocity. The results showed that the accuracy of determining the settling velocity of flocs solely based on their particle size was 88%, indicating that the floc structure is complex and a single parameter is not sufficient to accurately identify settling velocity. The results of the CNN analysis indicated that using a relatively simple Lenet5 structure can quickly achieve an accuracy of 88%, while using a Resnet18 structure can achieve recognition accuracy of over 90%. These findings suggest that machine learning techniques applied to floc images can effectively evaluate floc settling velocity, providing theoretical guidance for optimizing coagulant dosage and regulating coagulation processes.

Dupilumab treatment reduces caregiver-reported skin pain in patients with moderate-to-severe atopic dermatitis aged 6 months to 5 years.

Background: Moderate-to-severe atopic dermatitis (AD) often has a profound impact on the quality of life of young children and their caregivers. One of the most burdensome symptoms reported by patients is skin pain. Methods: This post hoc analysis focuses on the impact of dupilumab treatment on skin pain in young children using data from the LIBERTY AD PRESCHOOL part B (NCT03346434), a 16-week randomized, double-blind, placebo-controlled, phase 3 study in 162 children aged 6 months to 5 years with moderate-to-severe AD receiving dupilumab or placebo, plus topical corticosteroids (TCS). Analyses were performed on the full analysis set and subgroups of patients who did not achieve an Investigator's Global Assessment score of 0 or 1 (IGA >1 subgroup), or who did not achieve a 75% improvement from baseline in the Eczema Area and Severity Index (1 and

Dupilumab versus mepolizumab for chronic rhinosinusitis with nasal polyposis: an indirect treatment comparison.

BACKGROUND: Dupilumab and mepolizumab have shown efficacy and safety in treating chronic rhinosinusitis with nasal polyps (CRSwNP). OBJECTIVE: Without available results from head-to-head randomized control trials (RCTs) comparing dupilumab with other biologics, we conducted an indirect treatment comparison (ITC) with mepolizumab. METHODS: A systematic literature review identified RCTs of biologics in CRSwNP. A Bucher ITC was performed, including nasal polyp score (NPS; range 0-8), nasal congestion (NC; 0-3), loss of smell (LOS; 0-3), University of Pennsylvania Smell Identification Test (UPSIT; 0-40), visual analog score (VAS; 0-10), Sino-Nasal Outcome Test (SNOT-22; 0-110), systemic corticosteroids (SCS) use or surgery for nasal polyps (NPs), and binary responder analyses for NPS and SNOT-22 improvement by ≥1/≥2 and ≥8.9, respectively. Matching-adjusted indirect comparisons (MAIC) were conducted as supporting analyses. RESULTS: SINUS-24/-52 (SYNAPSE-like subpopulation only) and SYNAPSE were identified for ITC. At 24 weeks, change from baseline in NPS and proportion of patients with a binary responder outcome of NPS improvement ≥1 were significantly (P<0.05) greater in patients receiving dupilumab versus mepolizumab. At 52 weeks, improvements in NPS, NC, LOS, UPSIT, and VAS were significantly (P<0.05) greater for dupilumab versus mepolizumab. Proportion of patients achieving binary responder outcomes of NPS and SNOT-22 improvement by ≥1/≥2 and ≥8.9, respectively, was significantly (P<0.05) higher, while SCS use was significantly (P<0.05) reduced, for dupilumab versus mepolizumab. Surgery rate was numerically reduced with dupilumab versus mepolizumab. The MAIC analyses confirmed these results. CONCLUSIONS: Dupilumab was associated with greater improvements in CRSwNP-related outcomes versus mepolizumab.

Expression of integrin α4ß1 and α4ß7 on B cells correlates with autoimmune responses in Graves' disease.

BACKGROUND: Integrins are upregulated on endothelial cells and T-lymphocytes in autoimmune thyroid disease (AITD), potentially contributing to immune response localization. The role of integrins on B-cells in AITD remains unclear. METHODS: Peripheral blood samples were collected from healthy controls (n = 56), patients with Graves' disease (GD) (n = 37) and Hashimoto's thyroiditis (HT) (n = 52). Ultrasound-guided fine-needle aspiration (FNA) of the thyroid was performed in patients with non-autoimmune thyroid disease (nAITD) (n = 19), GD (n = 11), and HT (n = 40). Integrins α4ß7, α4ß1, and αEß7 in B cells were measured by flow cytometry. Serum zonulin levels were quantified via ELISA. Associations of integrins on B cells with thyroid hormones, thyroid autoantibodies, AITD duration, and zonulin were analyzed. RESULTS: HT patients exhibited lower α4ß7 and higher α4ß1 expression on B cells compared to healthy controls and GD patients. While α4ß7 was predominant on circulating B cells, the dominant integrin expressed on intrathyroidal B cells varied with specific thyroid diseases. In GD patients, α4ß7 and α4ß1 expression on circulating B cells correlated positively and negatively with thyroid function and thyroid stimulating immunoglobulins (TSI) levels, respectively. Intrathyroidal α4ß1+ B cells positively correlated with TSH levels in HT patients. Additionally, serum zonulin was elevated in HT patients, and intrathyroidal α4ß7+ B cells and α4ß1+ B cells correlated negatively and positively with zonulin levels, respectively. Integrin αEß7 on B cells showed no significant association with AITD. CONCLUSION: Integrins expressed on B cells potentially play a role in the pathogenesis of AITD and might serve as immune biomarkers for the disease.

Association Between Smell Loss, Disease Burden, and Dupilumab Efficacy in Chronic Rhinosinusitis with Nasal Polyps.

OBJECTIVE: To evaluate the association between smell loss and other aspects of disease, and evaluate dupilumab efficacy in patients with severe chronic rhinosinusitis with nasal polyps (CRSwNP) and moderate or severe smell loss. METHODS: This post-hoc analysis of the SINUS-24/52 studies (NCT02912468/NCT02898454) analyzed nasal polyp score (NPS, 0-8), nasal congestion/obstruction (NC, 0-3), Lund-Mackay CT-scan score (LMK-CT, 0-24), rhinosinusitis severity visual analog scale (RS-VAS, 0-10), and 22-item Sinonasal Outcome Test (SNOT-22, 0-110) according to baseline monthly average patient-reported loss of smell scores (LoS, 0-3) of >1 to 2 (moderate) or >2 to 3 (severe) in patients randomized to dupilumab 300 mg or placebo every 2 weeks. RESULTS: Of 724 patients randomized, baseline LoS was severe in 601 (83%) and moderate in 106 (15%). At baseline, severe versus moderate LoS was associated with 1-point greater severity of NC (odds ratio [OR] 6.01 [95% confidence interval, (CI) 3.95, 9.15]), 5-point greater severity of LMK-CT (OR 2.19 [1.69, 2.85]), and 8.9-point greater severity of SNOT-22 (OR 1.35 [1.20, 1.49]). At Week 24, least squares mean differences (95% CI) dupilumab versus placebo in change from baseline were: NPS -1.90 (-2.56, -1.25) and -1.95 (-2.20, -1.70) in the moderate and severe baseline LoS subgroups, respectively; NC -.35 (-.64, -.06) and -1.00 (-1.13, -.87); LMK-CT -6.30 (-7.88, -4.72) and -6.22 (-6.82, -5.63); RS-VAS -1.18 (-2.20, -.16) and -3.47 (-3.90, -3.03); and SNOT-22 -7.52 (-14.55, -.48) and -21.72 (-24.63, -18.82); all nominal P < .05 versus placebo. Improvements with dupilumab in NC, RS-VAS, and SNOT-22 were statistically greater in patients with severe versus moderate baseline LoS. CONCLUSION: Significant smell impairment in severe CRSwNP is associated with significant disease (NC, RS-VAS, LMK), health-related quality of life impairment (SNOT-22), asthma, and non-steroidal anti-inflammatory drug-exacerbated respiratory disease. Dupilumab significantly improved NPS, NC, LMK-CT, RS-VAS, and SNOT-22 in subjects with moderate and severe baseline smell loss.

The effect of dupilumab on caregiver- and patient-reported outcomes in young children with moderate-to-severe atopic dermatitis: Results from a placebo-controlled, phase 3 study.

BACKGROUND: Moderate-to-severe atopic dermatitis (AD) greatly impacts children/caregivers. OBJECTIVE: Evaluate the impact of treatment with dupilumab on caregiver- and patient-reported AD symptoms and quality of life (QoL) in young children. METHODS: In the LIBERTY AD PRESCHOOL (randomized, placebo-controlled) study, children aged 6 months to 5 years with moderate-to-severe AD received dupilumab or placebo plus low-potency topical corticosteroids for 16 weeks. This posthoc analysis assessed the change from baseline to week 16 in caregiver-reported outcome measures of AD symptoms (eg, itch and sleep) and QoL of patients and their caregivers/families. RESULTS: Dupilumab (n = 83) vs placebo (n = 79) provided significant improvements in caregiver-reported AD symptoms and QoL. Significant improvements were seen as early as week 4 and sustained through the end of the study. Additionally, dupilumab vs placebo provided rapid and significant improvement in QoL measures for the patients' caregivers/families. LIMITATIONS: Few patients aged <2 years; significance only reported for prespecified endpoints; Infant's Dermatitis QoL Index severity strata adopted from Children's Dermatology Life Quality Index. CONCLUSION: Dupilumab improved AD symptoms and QoL in patients and their caregivers/families.

Comparative effectiveness of dupilumab and omalizumab on asthma exacerbations and systemic corticosteroid prescriptions: Real-world US ADVANTAGE study.

BACKGROUND: In the United States, dupilumab is approved for moderate-to-severe eosinophilic or oral corticosteroid-dependent asthma, and omalizumab is approved for managing moderate-to-severe allergic asthma uncontrolled by inhaled corticosteroids. However, limited comparative effectiveness data exist for these biologics due to differing patient characteristics and treatment histories. OBJECTIVE: This study assessed the real-world effectiveness of dupilumab and omalizumab for asthma in patients in the United States. METHODS: In this retrospective observational study, TriNetX Dataworks electronic medical record data were used to identify patients with asthma age ≥12 years who initiated (index) dupilumab or omalizumab between November 2018 and September 2020 and who had at least 12 months of pre- and post-index clinical information. Inverse probability of treatment weighting was applied to balance potential confounding in treatment groups. Asthma exacerbation rates and systemic corticosteroid (SCS) prescriptions were compared using a doubly robust negative binomial regression model, adjusting for baseline exacerbation/SCS rates and patient characteristics with ≥10% standardized differences after inverse probability of treatment weighting. RESULTS: All inclusion and exclusion criteria were met by 2138 dupilumab patients and 1313 omalizumab patients. After weighting, the majority of baseline characteristics were balanced (standard difference <10%) between the 2 groups. Dupilumab was associated with a 44% lower asthma exacerbation rate (P < .0001) versus omalizumab. Additionally, dupilumab treatment significantly (P < .05) reduced SCS prescriptions by 28% during the follow-up period compared with omalizumab treatment. CONCLUSIONS: The US ADVANTAGE real-world study demonstrated a significant reduction in severe asthma exacerbations and SCS prescriptions for patients prescribed dupilumab compared with patients prescribed omalizumab during 12 months of follow-up.

Real-world effectiveness of dupilumab versus benralizumab and mepolizumab.

Introduction: In the United States, this real-world study compared the effectiveness of dupilumab, benralizumab, and mepolizumab in reducing exacerbations and systemic corticosteroid (SCS) prescriptions among patients with asthma. Methods: Patients (≥12 years old) who initiated dupilumab, benralizumab, or mepolizumab (index) between November 2018 and September 2020 were identified by using electronic medical record data. Subjects were included if they had ≥ 12 months of data before and after the index date and two or more severe asthma-related exacerbations before the index date. Differences in baseline characteristics were addressed by using inverse probability treatment weighting (IPTW). Pairwise comparisons between dupilumab and benralizumab, or mepolizumab were conducted by using negative binomial regression, adjusting for baseline rates and unbalance characteristics (≥10% standardized differences) after IPTW. Results: Overall, a total of 1737 subjects met all criteria: 825 dupilumab, 461 benralizumab, and 451 mepolizumab initiators. In the postindex period, dupilumab was associated with a 24% and 28% significant reduction in the risk of severe asthma exacerbations versus benralizumab (incidence rate ratio [IRR] 0.76 [95% confidence interval {CI}, 0.67-0.86)] and mepolizumab (IRR 0.72 [95% CI, 0.63-0.82]), respectively. In addition, dupilumab treatment significantly reduced SCS prescriptions by 16% and 25% versus benralizumab and mepolizumab, respectively (p < 0.05). Conclusion: This study represents one of the largest real-world comparisons of biologics (dupilumab, benralizumab, and mepolizumab) for asthma in the United States to date. This analysis shows that the use of dupilumab was associated with a significantly greater reduction in both severe asthma exacerbations and SCS prescriptions compared with benralizumab and mepolizumab.

Enhanced predictive validity of integrative models for refractory hyperthyroidism considering baseline and early therapy characteristics: a prospective cohort study.

BACKGROUND: A subset of Graves' disease (GD) patients develops refractory hyperthyroidism, posing challenges in treatment decisions. The predictive value of baseline characteristics and early therapy indicators in identifying high risk individuals is an area worth exploration. METHODS: A prospective cohort study (2018-2022) involved 597 newly diagnosed adult GD patients undergoing methimazole (MMI) treatment. Baseline characteristics and 3-month therapy parameters were utilized to develop predictive models for refractory GD, considering antithyroid drug (ATD) dosage regimens. RESULTS: Among 346 patients analyzed, 49.7% developed ATD-refractory GD, marked by recurrence and sustained Thyrotropin Receptor Antibody (TRAb) positivity. Key baseline factors, including younger age, Graves' ophthalmopathy (GO), larger goiter size, and higher initial free triiodothyronine (fT3), free thyroxine (fT4), and TRAb levels, were all significantly associated with an increased risk of refractory GD, forming the baseline predictive model (Model A). Subsequent analysis based on MMI cumulative dosage at 3 months resulted in two subgroups: a high cumulative dosage group (average ≥ 20 mg/day) and a medium-low cumulative dosage group (average < 20 mg/day). Absolute values, percentage changes, and cumulative values of thyroid function and autoantibodies at 3 months were analyzed. Two combined predictive models, Model B (high cumulative dosage) and Model C (medium-low cumulative dosage), were developed based on stepwise regression and multivariate analysis, incorporating additional 3-month parameters beyond the baseline. In both groups, these combined models outperformed the baseline model in terms of discriminative ability (measured by AUC), concordance with actual outcomes (66.2% comprehensive improvement), and risk classification accuracy (especially for Class I and II patients with baseline predictive risk < 71%). The reliability of the above models was confirmed through additional analysis using random forests. This study also explored ATD dosage regimens, revealing differences in refractory outcomes between predicted risk groups. However, adjusting MMI dosage after early risk assessment did not conclusively improve the prognosis of refractory GD. CONCLUSION: Integrating baseline and early therapy characteristics enhances the predictive capability for refractory GD outcomes. The study provides valuable insights into refining risk assessment and guiding personalized treatment decisions for GD patients.

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice.

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients' electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0.7873, recall@10 of 0.9020 and hits@10 of 0.9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

Real-world effectiveness of dupilumab in patients with asthma: Findings from the US ADVANTAGE study.

BACKGROUND: Dupilumab is approved as an add-on maintenance therapy for patients (≥6 years) with moderate-to-severe asthma. Better understanding of real-world effectiveness is needed. OBJECTIVE: To characterize the real-world effectiveness of dupilumab in asthma management. METHODS: This retrospective study included patients (≥12 years of age) diagnosed with asthma, initiating dupilumab between November 2018 and September 2020. The study used a US electronic medical record database (TriNetX Dataworks, Cambridge, Massachusetts). Asthma exacerbation rates before and after the initiation of dupilumab were analyzed using generalized estimating equations models with Poisson probabilistic link to estimate incidence rate ratios (IRRs). Sensitivity analyses were conducted based on previous exacerbation data, eosinophil levels, history of atopic dermatitis or chronic rhinosinusitis with nasal polyps, previous use of biologics, and presence of SARS-CoV-2 (COVID-19). RESULTS: A total of 2400 patients initiating dupilumab met all study criteria. After initiation of dupilumab, risk of asthma exacerbation was reduced by 44% (IRR, 0.56; 95% CI, 0.47-0.57; P = <0.0001) and systemic corticosteroid prescriptions by 48% (IRR, 0.52; 95% CI, 0.48, 0.56; P = <0.0001) compared with those before initiation of dupilumab. Adjustment for COVID-19 showed a greater reduction in asthma exacerbations (IRR, 0.50; 95% CI, 0.45-0.55; P = <0.0001). CONCLUSION: Current real-world efficacy evidence indicates that dupilumab reduces asthma exacerbations and total systemic corticosteroid prescriptions in clinical practice. The effectiveness of dupilumab was observed independent of exacerbation history, eosinophil levels, or COVID-19 impact.

Key protein identification by integrating protein complex information and multi-biological features.

Identifying key proteins based on protein-protein interaction networks has emerged as a prominent area of research in bioinformatics. However, current methods exhibit certain limitations, such as the omission of subcellular localization information and the disregard for the impact of topological structure noise on the reliability of key protein identification. Moreover, the influence of proteins outside a complex but interacting with proteins inside the complex on complex participation tends to be overlooked. Addressing these shortcomings, this paper presents a novel method for key protein identification that integrates protein complex information with multiple biological features. This approach offers a comprehensive evaluation of protein importance by considering subcellular localization centrality, topological centrality weighted by gene ontology (GO) similarity and complex participation centrality. Experimental results, including traditional statistical metrics, jackknife methodology metric and key protein overlap or difference, demonstrate that the proposed method not only achieves higher accuracy in identifying key proteins compared to nine classical methods but also exhibits robustness across diverse protein-protein interaction networks.

H3K18 lactylation of senescent microglia potentiates brain aging and Alzheimer's disease through the NFκB signaling pathway.

Cellular senescence serves as a fundamental and underlying activity that drives the aging process, and it is intricately associated with numerous age-related diseases, including Alzheimer's disease (AD), a neurodegenerative aging-related disorder characterized by progressive cognitive impairment. Although increasing evidence suggests that senescent microglia play a role in the pathogenesis of AD, their exact role remains unclear. In this study, we quantified the levels of lactic acid in senescent microglia, and hippocampus tissues of naturally aged mice and AD mice models (FAD4T and APP/PS1). We found lactic acid levels were significantly elevated in these cells and tissues compared to their corresponding counterparts, which increased the level of pan histone lysine lactylation (Kla). We aslo identified all histone Kla sites in senescent microglia, and found that both the H3K18 lactylation (H3K18la) and Pan-Kla were significantly up-regulated in senescent microglia and hippocampus tissues of naturally aged mice and AD modeling mice. We demonstrated that enhanced H3K18la directly stimulates the NFκB signaling pathway by increasing binding to the promoter of Rela (p65) and NFκB1(p50), thereby upregulating senescence-associated secretory phenotype (SASP) components IL-6 and IL-8. Our study provides novel insights into the physiological function of Kla and the epigenetic regulatory mechanism that regulates brain aging and AD. Specifically, we have identified the H3K18la/NFκB axis as a critical player in this process by modulating IL-6 and IL-8. Targeting this axis may be a potential therapeutic strategy for delaying aging and AD by blunting SASP.

Integrating Medical Domain Knowledge for Early Diagnosis of Fever of Unknown Origin: An Interpretable Hierarchical Multimodal Neural Network Approach.

Accurate and interpretable differential diagnostic technologies are crucial for supporting clinicians in decision-making and treatment-planning for patients with fever of unknown origin (FUO). Existing solutions commonly address the diagnosis of FUO by transforming it into a multi-classification task. However, after the emergence of COVID-19 pandemic, clinicians have recognized the heightened significance of early diagnosis in patients with FUO, particularly for practical needs such as early triage. This has resulted in increased demands for identifying a wider range of etiologies, shorter observation windows, and better model interpretability. In this article, we propose an interpretable hierarchical multimodal neural network framework (iHMNNF) to facilitate early diagnosis of FUO by incorporating medical domain knowledge and leveraging multimodal clinical data. The iHMNNF comprises a top-down hierarchical reasoning framework (Td-HRF) built on the class hierarchy of FUO etiologies, five local attention-based multimodal neural networks (La-MNNs) trained for each parent node of the class hierarchy, and an interpretable module based on layer-wise relevance propagation (LRP) and attention mechanism. Experimental datasets were collected from electronic health records (EHRs) at a large-scale tertiary grade-A hospital in China, comprising 34,051 hospital admissions of 30,794 FUO patients from January 2011 to October 2020. Our proposed La-MNNs achieved area under the receiver operating characteristic curve (AUROC) values ranging from 0.7809 to 0.9035 across all five decomposed tasks, surpassing competing machine learning (ML) and single-modality deep learning (DL) methods while also providing enhanced interpretability. Furthermore, we explored the feasibility of identifying FUO etiologies using only the first N-hour time series data obtained after admission.

Long-Term Effectiveness of Dupilumab in Patients with Atopic Dermatitis: Results up to 3 Years from the RELIEVE-AD Study.

INTRODUCTION: Atopic dermatitis (AD) can require long-term therapy. Few real-world studies have evaluated long-term effectiveness from the patients' perspective. The aim of this study was to evaluate patient-reported outcomes (PROs) during long-term dupilumab treatment. METHODS: Adults with moderate-to-severe AD who initiated dupilumab through the US manufacturer patient support program and participated in RELIEVE-AD (a prospective patient survey study with a 12-month follow-up) were recontacted 30-36 months post-initiation regardless of current dupilumab use. The online questionnaire consisted of PROs, including the Atopic Dermatitis Control Tool (ADCT), use of concomitant AD therapies, satisfaction with current therapy, global change in itch relative to before dupilumab initiation, non-itch skin symptoms (skin pain/soreness, hot/burning feeling, and sensitivity to touch), flares, Dermatology Life Quality Index, sleep problems, and the AD-specific Work Productivity and Activity Impairment Questionnaire. RESULTS: Of 698 patients who initiated dupilumab (baseline) and were recontacted, 425 completed the 30-36-month survey. Significant reductions from baseline were reported in concomitant AD therapy use (P < 0.05); 54.4% reported not using other AD medications vs. 12.8% at baseline. At 30-36 months, all results (non-itch skin symptoms, flares, sleep problems, health-related quality of life work/activity impairment, disease control, and treatment satisfaction) were similar to or incrementally better than the 12-month timepoint, with significant improvements vs. baseline (P < 0.001). Global change in itch was reported as "very much better" by 75.3% of respondents. Adequate disease control (score < 7 on ADCT) was reported by 80.7% of respondents, and 86.8% were satisfied with the treatment. CONCLUSIONS: In clinical practice settings, patient-reported benefits of dupilumab were maintained in survey respondents during long-term treatment up to 36 months while the use of concomitant AD therapies reduced.

Atopic dermatitis (also known as eczema) is a chronic skin disease that can have a profoundly negative effect on patients' quality of life. To control disease symptoms, patients often need long-term treatment. Dupilumab is a treatment that has shown benefits in adults with moderate-to-severe atopic dermatitis (AD) when used in long-term (under 4 years) clinical trials; however, few studies have evaluated patients' experiences of long-term dupilumab treatment outside of a clinical trial setting. This study was conducted to do so: 425 adults with moderate-to-severe AD who received dupilumab through a US manufacturer patient support program filled in an online questionnaire 30­36 months after starting treatment. The questionnaire included items on use of additional AD therapies, AD symptoms, quality of life, disease control, and satisfaction with treatment. Patients' responses showed that, at 30­36 months after starting dupilumab treatment, 54% of patients reported not using any other medications for AD vs. 13% of patients when starting dupilumab treatment. In addition, since starting dupilumab, 75% of patients reported one of the most burdensome AD symptoms, itch, as being "very much better" vs. before starting treatment; 81% reported control of AD symptoms; 85% reported a meaningful improvement in quality of life; and 76% were "extremely" or "very" satisfied with the treatment. In summary, this study showed that long-term dupilumab treatment provides continued improvement in symptoms, treatment satisfaction, disease control, and quality of life in adults with moderate-to-severe AD while reducing the need for other AD treatments. Video abstract: How do patients with atopic dermatitis perceive long-term dupilumab treatment in the real world? (MP4 31888 kb).

Interaction between the GCKR rs1260326 variant and serum HDL cholesterol contributes to HOMA-ß and ISIMatusda in the middle-aged T2D individuals.

This study aims to investigate the correlations between islet function/ insulin resistance and serum lipid levels, as well as to assess whether the strength of such correlations is affected by the GCKR rs1260326 variant in healthy and T2D individuals. We performed an oral glucose tolerance test (OGTT) on 4889 middle-aged adults, including 3135 healthy and 1754 T2D individuals from the REACTION population study in the Nanjing region. We also measured their serum lipid levels and genotyped for rs1260326. We found that serum high-density lipoprotein (HDL) cholesterol and triglyceride (TG) levels were independently correlated with indexes of islet function (HOMA-ß and IGI [insulinogenic index]) and insulin resistance (HOMO-IR and ISIMatsuda) in both healthy and T2D individuals. The correlations were significantly decreased in T2D individuals, with significant heterogeneities compared to healthy controls (I2 > 75%, Phet < 0.05). Although no correlation was observed between serum total cholesterol (TC) level and islet function/ insulin resistance in healthy controls, significant correlations were found in T2D individuals, with significant heterogeneity to healthy controls in the correlation with ISIMatsuda(I2 = 85.3%, Phet = 0.009). Furthermore, we found significant interactions of the GCKR rs1260326 variant for the correlations between serum HDL cholesterol and HOMA-ß/ISIMatsuda in T2D subjects (P = 0.015 and 0.038, respectively). These findings illustrate that distinct correlations between serum lipid levels and islet function/ insulin resistance occurred in T2D subjects compared to healthy individuals. Common gene variants, such as rs1260326, might interact substantially when studied in specific populations, especially T2D disease status.

Epileptic detection in single and multi-lead EEG signals using persistent homology based on bi-directional weighted visibility graphs.

Epilepsy is a widespread neurological disorder, and its recurrence and suddenness are making automatic detection of seizure an urgent necessity. For this purpose, this paper performs topological data analysis (TDA) of electroencephalographic (EEG) signals by the medium of graphs to explore the potential brain activity information they contain. Through our innovative method, we first map the time series of epileptic EEGs into bi-directional weighted visibility graphs (BWVGs), which give more comprehensive reflections of the signals compared to previous existing structures. Traditional graph-theoretic measurements are generally partial and mainly consider differences or correlations in vertices or edges, whereas persistent homology (PH), the essential part of TDA, provides an alternative way of thinking by quantifying the topology structure of the graphs and analyzing the evolution of these topological properties with scale changes. Therefore, we analyze the PH for BWVGs and then obtain the two indicators of persistence and birth-death for homology groups to reflect the topology of the mapping graphs of EEG signals and reveal the discrepancies in brain dynamics. Furthermore, we adopt neural networks (NNs) for the automatic detection of epileptic signals and successfully achieve a classification accuracy of 99.67% when distinguishing among three different sets of EEG signals from seizure, seizure-free, and healthy subjects. In addition, to accommodate multi-leads, we propose a classifier that incorporates graph structure to distinguish seizure and seizure-free EEG signals. The classification accuracies of the two subjects used in the classifier are as high as 99.23% and 94.76%, respectively, indicating that our proposed model is useful for the analysis of EEG signals.