Background: Systemic autoimmune rheumatic diseases (SARDs), including Systemic Lupus Erythematosus (SLE) and Rheumatoid Arthritis (RA), are characterized by chronic inflammation and autoimmunity. Identifying reliable biomarkers is crucial for enhancing diagnosis, monitoring disease progression, and evaluating therapeutic responses. This study explores the potential of serum Syndecan-1 (SDC-1) as a biomarker for these conditions and concurrently examines its relationship with free light chain (FLC) expression levels in the same patients. Methods: A retrospective analysis was performed on serum samples from 120 patients (60 RA, 60 SLE), alongside 50 healthy donors (HD). Serum levels of κ and λ FLCs were determined by turbidimetric assay, while SDC-1 levels were measured using ELISA. Statistical analyses, including the Kruskal-Wallis test, Wilcoxon-Mann-Whitney U test, multivariable linear regression and Spearman’s correlation, were employed to compare biomarker levels across groups and to explore correlations among them. Results: Significantly increased levels of SDC-1, κ-FLC, and λ-FLC were observed in patients diagnosed with RA and SLE compared to HD (p < 0.001), while no significant differences in the κ/λ ratio were noted among the groups (p = 0.4). A statistically significant difference in subject age was also identified. However, multivariate regression analysis indicated that RA and SLE statuses are significantly associated with the levels of these markers, with minimal confounding by age. A significant correlation was observed separately in all groups between the FLC markers. Conversely, no correlation was detected between SDC-1 and FLCs, nor between these markers and age. Conclusion: Elevated serum levels of FLCs and SDC-1 in RA and SLE patients compared to healthy controls underscore their potential as biomarkers for SARDs. The findings also suggest sustained plasma cells activation, supporting the SDC-1 role in the pathogenesis of these conditions.

In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper the clustering techniques like Fuzzy C Means (FCM), Possibilistic Fuzzy C Means (PFCM), Possibilistic C Means (PCM), Sample Entropy features are extracted from normal and cirrhotic liver ultrasonic Images. The extracted features are classified as normal and Cirrhosis one through Gaussian Mixture Model(GMM), Softmax Dis(SDC), Harmonic Search Algorithm (HSA), SVM(Linear), SVM(RBF), SVM(Polynomial), Artificial Algae Optimization (AAO) and hybrid classifier Artificial Algae Optimization (AAO) with Gaussian Mixture Mode (GMM). The classifiers performances are compared based on Accuracy, F1 Score, MCC, F Measure, Error rate and Jaccard metric (JM).The hybrid classifier AAO GMM with PFCM feature outperforms than other classifiers and attained an accuracy of 99.03 % with MCC of 0.90

This study systematically addresses the challenge of accurately modeling memristors, focusing on four distinct types doped with tungsten, tin, chromium, and carbon, fabricated by Known Inc. A comprehensive characterization was performed by subjecting the devices to sinusoidal excitations with varying frequencies and amplitudes, followed by data averaging and high-frequency filtering. The resulting measurements were fitted using three prominent memristor models: VTEAM, MMS, and Yakopcic, with additional bespoke modifications assessed. These models, typically formulated as coupled algebraic-differential equations integrating electrical quantities (voltage and current) with internal state variables governing device dynamics, were optimized using two robust approaches: (1) interior-point optimization with gradient-based search, and (2) Nelder-Mead gradient-free optimization, both with box constraints applied. A thorough comparison and discussion of the optimized model parameters ensued, accompanied by an examination of sensitivity to diverse frequency and amplitude ranges. The findings inform conclusions and provide a foundation for future refinements, underscoring the importance of multi-model evaluation and advanced optimization strategies in precise memristor modeling. The presented methodology offers a valuable framework for elucidating optimal modeling paradigms tailored to specific memristor architectures and operating regimes, ultimately enhancing their integration in emerging neuromorphic and computational applications.

Steel damper columns (SDCs) are energy-dissipating members that are suitable for reinforced concrete (RC) buildings and are often used for multistory housing. The evaluation of the peak deformation and hysteretic dissipated energy of such building structures is essential for the rational seismic design of RC buildings with SDCs. In a previous study, the authors proposed an energy-based prediction procedure for the peak and cumulative response of an RC frame building with SDCs. In this procedure, the accuracy of the equivalent velocity of the maximum momentary input energy (V_ΔE1^*)–peak equivalent displacement (D₁^*_max) relationship is essential for high quality predictions. In this article, the V_ΔE1^*–D₁^*_max relationships of RC moment-resisting frames with and without SDCs are invested using a critical pseudo-double impulse (PDI) analysis based on a study by Takewaki and coauthors. The results show that the V_ΔE1^*–D₁^*_max relationship obtained from the critical PDI analysis agrees well with that calculated from the equations proposed in the previous study.

The response of structures under pulse-like ground motions is characterized by the large amount of energy input in a few cycles. Consequently, structures with insufficient strength may suffer severe damage owing to excessive deformation. In a previous paper, the energy-based prediction procedure for the peak and cumulative response of a reinforced concrete (RC) frame building with steel damper columns was proposed (Fujii and Shioda, 2023). Although this procedure was verified by comparison to the nonlinear time-history analysis (NTHA) results, the performance of the proposed procedure with pulse-like ground motion records has not been verified yet. In this study, the accuracy of the energy-based prediction procedure for an RC frame building with steel damper columns was investigated by considering pulse-like ground motions. The numerical analysis results reveal that the accuracy of the predicted peak response is satisfactory, which agrees with the results of the author’s previous study. However, the accuracy of the predicted total input energy to the building model depends on the ratio of the pulse period of the ground motion to the effective fundamental period of the building model. The reasons for this underestimation of the total input energy are discussed in this paper.