Search Results (2,608)

Autoantibodies have long been regarded as passive reflections of immune dysregulation in connective tissue diseases (CTDs). Recent advances in systems immunology and molecular pathology have fundamentally redefined them as active molecular fingerprints that delineate distinct disease endophenotypes with predictive power for clinical trajectories and therapeutic responses. Rather than mere epiphenomena, autoantibodies encode precise information about dominant immune pathways, organ tropism, and pathogenic mechanisms. This review synthesizes emerging evidence that autoantibody repertoires—defined by specificity, structural properties, and functional characteristics—stratify patients beyond traditional clinical taxonomy into discrete pathobiological subsets. Specific signatures such as anti-MDA5 in rapidly progressive interstitial lung disease, anti-RNA polymerase III in scleroderma renal crisis, and anti-Ro52/TRIM21 in systemic overlap syndromes illustrate how serological profiles predict outcomes with remarkable precision. Mechanistically, autoantibody pathogenicity is modulated by immunoglobulin isotype distribution, Fc glycosylation patterns, and tissue-specific receptor expression—variables that determine whether an antibody functions as a biomarker or pathogenic effector. The structural heterogeneity of autoantibodies, shaped by cytokine microenvironments and B-cell subset imprinting, creates a dynamic continuum between pro-inflammatory and regulatory states. The integration of serological, transcriptomic, and imaging data establishes a precision medicine framework: autoantibodies function simultaneously as disease classifiers and therapeutic guides. This endophenotype-driven approach is already influencing trial design and patient stratification in systemic lupus erythematosus, systemic sclerosis, and inflammatory myopathies, and is reshaping both clinical practice and scientific taxonomy in CTDs. Recognizing autoantibodies as endophenotypic determinants aligns disease classification with pathogenic mechanism and supports the transition towards immunologically informed therapeutic strategies. Full article

Deep learning models have advanced rapidly, leading to claims that they now match or exceed human performance. However, such claims are often based on closed-set conditions with fixed labels, extensive supervised training, and do not considering differences between the two systems. Recent findings also indicate that some models align more closely with human categorisation behaviour, whereas other studies argue that even highly accurate models diverge from human behaviour. Following principles from comparative psychology and imposing similar constraints on both systems, this study investigates whether these models can achieve human-level accuracy and human-like categorisation through three experiments using subsets of the ObjectNet dataset. Experiment 1 examined performance under varying presentation times and task complexities, showing that while recent models can match or exceed humans under conditions optimised for machines, they struggle to generalise to certain real-world categories without fine-tuning or task-specific zero-shot classification. Experiment 2 tested whether human performance remains stable when shifting from N-way categorisation to a free-naming task, while machine performance declines without fine-tuning; the results supported this prediction. Additional analyses separated detection from classification, showing that object isolation improved performance for both humans and machines. Experiment 3 investigated individual differences in human performance and whether models capture the qualitative ordinal relationships characterising human categorisation behaviour; only the multimodal CoCa model achieved this. These findings clarify the extent to which current models approximate human categorisation behaviour beyond mere accuracy and highlight the importance of incorporating principles from comparative psychology while considering individual differences. Full article

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F₁-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article

Cutaneous squamous cell carcinoma (cSCC) is an immunogenic malignancy with variable immune infiltration and inconsistent responses to checkpoint blockade. Tumor-infiltrating lymphocytes (TILs) influence tumor progression and therapeutic outcome, yet their phenotypic and functional diversity across disease contexts remains incompletely understood. This review systematically characterizes the TIL landscape in human cSCC. Following PRISMA 2020 guidelines, PubMed and Embase were searched up to May 2025 and restricted to studies evaluating tumor-infiltrating lymphocytes in human cSCC, using the modified Newcatle–Ottawa score to assess risk of bias. Data were synthesized qualitatively given methodological heterogeneity. 48 studies met inclusion criteria. cSCCs exhibited dense CD3⁺ infiltrates composed of cytotoxic (CD8⁺GzmB⁺, Ki-67⁺, CD69⁺) and regulatory (FOXP3⁺, CCR4⁺) subsets. Higher CD8⁺ activity correlated with smaller tumors and longer disease-free survival, whereas FOXP3⁺ enrichment and TGF-β2 signaling promoted immune evasion. Immunosuppressed patients demonstrated diminished CD8⁺ density and clonality. Immune modulation with PD-1/PD-L1 blockade, imiquimod, HPV vaccination, or OX40 stimulation enhanced effector function. The cSCC immune microenvironment reflects a balance between cytotoxic and suppressive factors. Harmonizing multimodal immune profiling and integrating spatial context with systemic immune status may advance both prognostic stratification and therapeutic design. Full article

Background: Naegleria fowleri is a free-living amoeba that inhabits warm freshwater and causes primary amoebic meningoencephalitis (PAM), a rapidly fatal infection with >95% mortality. Due to the lack of early diagnosis and effective therapy, preventive vaccination represents a promising strategy. Methods: This study evaluated short- and long-term immune protection in BALB/c mice (20 mice per group) immunized intranasally with total N. fowleri extract co-administered with cholera toxin (CT). Mice were challenged with a lethal dose of trophozoites either 24 h (short-term) or three months (long-term) after the fourth immunization; the latter group received a booster 24 h before challenge. Serum and nasal washes were analyzed for IgA and IgG antibodies by immunoblot, and lymphocyte subsets from nasal-associated lymphoid tissue (NALT) and nasal passages (NPs) were characterized by flow cytometry. Results: Immunization conferred complete (100%) survival in the 24 h group and 60% protection in the 3-month group, whereas all control mice died. Immunoblotting showed that IgA and IgG antibodies recognized major N. fowleri antigens of 37, 45, 48 and 19, 37, and 100 kDa, respectively. Flow cytometry revealed increased activated and memory B lymphocytes, dendritic cells, and expression of CCR10, integrin α4β1, and FcγRIIB receptors, particularly in the 24 h group. Conclusions: Intranasal immunization with N. fowleri extract plus CT elicited both systemic and mucosal immune responses capable of short- and long-term protection. These findings highlight the potential of this immunization strategy as a foundation for developing effective vaccines against PAM. Full article

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents can formulate strategies without information about other agents’ actions so that system-level performance remains robust in the presence of communication failures. First, we use an algorithmic approach to study the scenario in which all agents lose the ability to communicate with one another, have a symmetric set of resources to choose from, and select actions independently according to a probability distribution over the resources. We show that the distribution that maximizes the expected system-level objective under this approach can be computed by solving a convex optimization problem, and we introduce a novel polynomial-time heuristic based on subset selection. Further, both of the methods are guaranteed to be within $1-1/e$ of the system’s optimal in expectation. Second, we use a learning-based approach to study how a system designer can employ neural networks to approximate optimal agent strategies in the presence of communication failures. The neural network, trained on system-level optimal outcomes obtained through brute-force enumeration, generates utility functions that enable agents to make decisions in a distributed manner. Empirical results indicate the neural network often outperforms greedy and randomized baseline algorithms. Collectively, these findings provide a broad study of optimal agent behavior and its impact on system-level performance when the information available to agents is extremely limited. Full article

B cells are key drivers of immune dysregulation across systemic autoimmune diseases. Among their progeny, plasmablasts occupy a uniquely revealing niche: short-lived, highly proliferative intermediates that mirror real-time B-cell activation. Their appearance in peripheral blood integrates antigenic stimulation, cytokine-driven differentiation, and aberrant germinal-center dynamics, transforming them into sensitive indicators of ongoing immunological activity. This review synthesizes current knowledge on plasmablast biology and highlights disease-specific phenotypes across systemic lupus erythematosus (SLE), primary Sjögren disease (pSjD), IgG4-related disease (IgG4-RD), ANCA-associated vasculitis (AAV), and rheumatoid arthritis (RA). We incorporate molecular insights from single-cell technologies that have uncovered previously unrecognized plasmablast subsets, metabolic states, and interferon-related signatures with prognostic and mechanistic value. Beyond descriptive immunology, plasmablasts are emerging as dynamic biomarkers capable of informing real-time clinical decisions. One of the most robustly supported applications is the prognostic interpretation of plasmablast kinetics following B-cell-depleting therapies, where early reconstitution patterns consistently predict relapse across multiple autoimmune conditions. As clinical immunology shifts from static serological markers toward kinetic, cell-based monitoring, plasmablast quantification offers a path toward precision immune surveillance. Integrating plasmablast dynamics into routine care may ultimately allow clinicians to anticipate disease flares, time therapeutic reinforcements, and transition from reactive management to preventive intervention. Full article

Explainability in deep learning (XDL) for healthcare is increasingly portrayed as essential for addressing the “black box” problem in clinical artificial intelligence. However, this universal transparency mandate may create unintended consequences, including cognitive overload, spurious confidence, and workflow disruption. This paper examines a fundamental question: Is explainability a panacea that resolves AI’s trust deficit, or a Pandora’s box that introduces new risks? Drawing on general systems theory we demonstrate that the answer is profoundly context dependent. Through systemic analysis of current XDL methods, Saliency Maps, LIME, SHAP, and attention mechanisms, we reveal systematic disconnects between technical transparency and clinical utility. This paper argues that XDL is a context-dependent systemic property rather than a universal requirement. It functions as a panacea when proportionately applied to high-stakes reasoning tasks (cancer treatment planning, complex diagnosis) within integrated socio-technical architectures. Conversely, it becomes a Pandora’s box when superficially imposed on routine operational functions (scheduling, preprocessing) or time-critical emergencies (e.g., cardiac arrest) where comprehensive explanation delays lifesaving intervention. The paper proposes a risk-stratified framework recognizing that a specific subset of healthcare AI applications—those involving high-stakes clinical reasoning—require comprehensive explainability, while other applications benefit from calibrated transparency appropriate to their clinical context. We conclude that explainability is neither a cure-all nor an inevitable harm, but rather a dynamic equilibrium requiring continuous rebalancing across technical, cognitive, and organizational dimensions. Full article

Accurate modeling and parameter estimation of photovoltaic (PV) systems are vital for advancing energy sustainability and achieving global decarbonization goals. Reliable PV models enable better integration of solar resources into smart grids, improve system efficiency, and reduce maintenance costs. This aligns with the vision of sustainable energy systems that combine intelligent optimization with environmental responsibility. The recently introduced Black-Winged Kite Algorithm (BWKA) has shown promise by emulating the predatory and migratory behaviors of black-winged kites; however, it still suffers from issues of slow convergence, limited population diversity, and imbalance between exploration and exploitation. To address these limitations, this paper proposes an Improved Black-Winged Kite Algorithm (IBWKA) that integrates two novel strategies: (i) a Soft-Rime Search (SRS) modulation in the attacking phase, which introduces a smoothly decaying nonlinear factor to adaptively balance global exploration and local exploitation, and (ii) a Quadratic Interpolation (QI) refinement mechanism, applied to a subset of elite individuals, that accelerates local search by fitting a parabola through representative candidate solutions and guiding the search toward promising minima. These dual enhancements reinforce both global diversity and local accuracy, preventing premature convergence and improving convergence speed. The effectiveness of the proposed IBWKA in contrast to the standard BWKA is validated through a comprehensive experimental study for accurate parameter identification of PV models, including single-, double-, and three-diode equivalents, using standard datasets (RTC France and STM6_40_36). The findings show that IBWKA delivers higher accuracy and faster convergence than existing methods, with its improvements confirmed through statistical analysis. Compared to BWKA and others, it proves to be more robust, reliable, and consistent. By combining adaptive exploration, strong diversity maintenance, and refined local search, IBWKA emerges as a versatile optimization tool. Full article

This study aimed to assess cytokine levels in blood plasma and serum, and saliva of juvenile pigs in response to acute systemic inflammation. The objectives were to: (1) validate an analytical method for quantifying cytokines in serum; (2) assess the reliability of serum compared to plasma for cytokine quantification; and (3) explore the potential of saliva as a non-invasive alternative for cytokine measurement. Changes in 13 cytokines (IFN-γ, TNF-α, IL-1α, IL-1β, IL-1ra, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18 and GM-CSF) were analyzed in serum and saliva samples collected over a 72 h period following lipopolysaccharide (LPS) infusion to induce an acute inflammatory response in 10 juvenile pigs (~28 kg BW). EDTA plasma was collected over the same time period, and a subset of four cytokines (IL-1β, IL-6, IL-10 and IFN-γ) was analyzed to assess correlations with serum concentrations. A strong positive correlation was observed between serum and EDTA plasma levels of IL-1β, IL-6, IL-10 and IFN-γ (r = 0.91–1.00, p < 0.001), indicating that both serum and EDTA plasma can be used to obtain reliable measurements of cytokine concentrations in blood of juvenile pigs. Among the 13 analyzed cytokines in serum, TNF-α and IL-6 appeared as the most reliable cytokines during acute inflammation, peaking at 1 h and between 2 and 3 h post LPS infusion, respectively. In general, saliva did not correlate with serum for most cytokines, suggesting limited application of such a non-invasive matrix for systemic cytokine monitoring. However, IL-1α was detected at higher concentrations in saliva than in serum, suggesting that saliva may be useful for monitoring specific cytokines under certain inflammatory conditions. Further research is needed to clarify the origin and physiological role of salivary cytokines following LPS stimulation. Serum and plasma were suitable for cytokine analysis; however, serum may offer practical advantages by facilitating blood sample handling. Saliva may be useful for monitoring specific cytokines under certain inflammatory conditions. Full article

The manufacturing process contributes significantly to the proliferation, metabolic state, and functional persistence of chimeric antigen receptor (CAR)-T cells. However, how different culture systems regulate CAR-T cell metabolism and thereby influence their long-term antitumor activity remains poorly understood. In this study, we compared dynamic cultivation using a wave bioreactor with static expansion systems (gas-permeable and conventional T-flasks) for the production of CD99-specific CAR-T cells. CAR-T cells expanded by the wave bioreactor exhibited faster proliferation and stronger cytotoxicity during culture. Upon repeated antigen stimulation, they retained these enhanced functional properties and showed the reduced expression of immune checkpoint molecules, preferentially preserved memory-like subsets, and displayed transcriptional features consistent with memory maintenance and exhaustion resistance. Targeted metabolomic profiling revealed enhanced Tricarboxylic Acid (TCA) cycle activity and features consistent with sustained oxidative phosphorylation, supporting mitochondrial-centered metabolic reprogramming. In a Ewing sarcoma xenograft model, wave bioreactor-cultured CAR-T cells showed a greater percentage of memory-like tumor-infiltrating lymphocytes. Collectively, these results indicate that wave bioreactor-based dynamic cultivation promotes mitochondrial metabolic reprogramming, which is characterized by an enhanced TCA cycle and sustained oxidative phosphorylation, thereby sustaining CAR-T cell functionality and providing a robust platform for the manufacturing of potent and durable cellular therapeutics. Full article

Human adult cardiomyocytes (CMs) have limited regenerative capacity, posing a significant challenge in restoring cardiac function following substantial CM loss due to an acute ischemic event or chronic hemodynamic overload. Nearly half of patients show no improvement in left ventricular ejection fraction during recovery from acute myocardial infarction. At baseline, both humans and mice exhibit low but continuous cell turnover originating from the existing CMs. Moreover, myocardial infarction can induce endogenous CM cell cycling. Consequently, research has focused on identifying drivers of CM rejuvenation and proliferation from pre-existing CMs. High-throughput screening has facilitated the discovery of novel pro-proliferative targets through small molecules, microRNAs, and pathway-specific interventions. More recently, omics-based approaches such as single-nucleus RNA sequencing and spatial transcriptomics have expanded our understanding of cardiac cellular heterogeneity. The big-data strategies provide critical insights into why only a subset of CMs re-enter the cell cycle while most remain quiescent. In this review, we compare several high-throughput screening strategies used to identify novel targets for CM proliferation. We also summarize the benefits and limitations of various screening models—including zebrafish embryos, rodent CMs, human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), and cardiac organoids—underscoring the importance of integrating multiple systems to uncover new regenerative mechanisms. Further work is needed to identify translatable and safe targets capable of inducing functional CM expansion in clinical settings. By integrating high-throughput screening findings with insights into CM heterogeneity, this review provides a comprehensive framework for advancing cardiac regeneration research and guiding future therapeutic development. Full article

This paper presents a novel method based on a Hybrid Multi-Objective Evolutionary strategy for the automatic design of a lightweight convolutional neural network used for coronary stenosis classification. The hybrid methodology consists of two search stages, starting with the Multi-Objective Evolutionary Algorithm based on Decomposition, to generate a set of optimal solutions focused on the minimization of two objectives: the accuracy classification error and the number of learning parameters in the convolutional neural network. Subsequently, the Simulated Annealing algorithm is applied to improve a subset of the solutions produced in the previous step. After the method was complete, a convolutional neural network model consisting of 3498 learning parameters was found by the proposed hybrid strategy, which is a considerably low number compared with the other architectures reported in the literature. Consequently, the found model achieved the highest classification performance rate in terms of the Accuracy and Jaccard Similarity Coefficient metrics with values of $0.94$ and $0.89$, respectively, using a database consisting of 608 images of regions with positive and negative coronary stenosis cases. On a second test, the model was tested using a database consisting of 2788 instances of natural and synthetic images of coronary stenosis cases. Corresponding maximum classification rates of $0.97$ and $0.93$ for the Accuracy and Jaccard Similarity Coefficient metrics, respectively, were achieved. In addition, the average required time to classify a single instance was $0.009$ seconds. The obtained results showed that the proposed method is feasible for the automatic design of lightweight convolutional neural networks that can be used as a part of decision-making systems in clinical practice. Full article

Aging reduces the tissue regenerative capacity, promotes chronic inflammation, and contributes to neurodegenerative diseases, including age-related macular degeneration (AMD). AMD is a leading cause of vision loss in older adults and manifests as dry (atrophic) or wet (neovascular) disease. Although dry AMD is more prevalent, neovascular AMD (nAMD) causes the most severe vision impairment and remains a major public health burden. Oxidative stress-mediated inflammation and dysfunction of retinal pigment epithelium (RPE) cells and choriocapillaris drive early AMD. Neovascular AMD is marked by pathologic choroidal neovascularization (CNV), driven largely by dysregulated VEGF signaling. Anti-VEGF therapies are the current standard of care for nAMD but require frequent intravitreal injections, carry procedure-related risks, and are ineffective in a substantial subset of patients, underscoring the need for new therapeutic approaches. Caffeine, a widely consumed and well-tolerated adenosine receptor antagonist, has emerging relevance in vascular regulation and inflammatory signaling. Extracellular ATP and its metabolites, including adenosine, accumulate under stress and act through purinergic receptors to influence angioinflammatory processes. We recently showed that systemic caffeine administration suppressed CNV in vivo, an effect partly reproduced by the adenosine receptor A_2A antagonist Istradefylline. Here, we investigated the cell-autonomous effects of caffeine on mouse choroidal endothelial cells, focusing on its role as an adenosine receptor antagonist and its potential to inhibit pathological neovascularization. Full article

In the context of the convergence of multiple energy systems, the risk of power quality degradation across different stages of energy generation and distribution has become increasingly significant. Accurate identification of power quality disturbances is crucial for improving power quality and ensuring the stable operation of power grids. However, existing disturbance identification methods struggle to balance accuracy and computational efficiency, limiting their applicability in real-time monitoring scenarios. To address this issue, this paper proposes a novel disturbance recognition framework called ST-mRMR-RF. The method first applies the S-transform to convert the time-domain signal into the time-frequency domain. It then extracts spectrum, low-frequency, mid-frequency, and high-frequency components as frequency-domain features from this domain. These are fused with time-domain features to form a multi-scale feature set. To reduce feature redundancy, the Maximum Relevance Minimum Redundancy (mRMR) algorithm is applied to select the optimal feature subset, ensuring maximum category relevance and minimal redundancy. Based on this foundation, four classifiers—Random Forest (RF), Partial Least Squares (PLS), Extreme Learning Machine (ELM), and Convolutional Neural Network (CNN)—are employed for disturbance identification. Experimental results show that the feature subset selected via mRMR reduces the model’s training time by 88.91%. When tested in a white noise environment containing 21 types of power quality disturbance signals, the ST-mRMR-RF method achieves a recognition accuracy of 99.24% at a 20dB signal-to-noise ratio. Overall, this framework demonstrates outstanding performance in noise resistance, classification accuracy, and computational efficiency. Full article