Search Results (3,089)

Difficult-to-treat systemic lupus erythematosus (D2T-SLE) remains a major unmet challenge in contemporary lupus care, yet it continues to be defined predominantly by clinical non-response rather than underlying biology. Current biomarkers largely quantify inflammatory burden, immune complex activity, or organ damage and do not reliably capture persistent activation of pathogenic pathways under therapy. Emerging multi-omics, single-cell, and longitudinal studies suggest that, in a subset of patients, apparent treatment failure may reflect incomplete attenuation of dominant immune circuits rather than uniformly elevated inflammation. We propose molecular refractoriness in systemic lupus erythematosus (SLE) as sustained, pathway-level immune activity despite apparently adequate, mechanism-directed therapy. We outline the major immune programs implicated in this process—including interferon-enriched, B-cell/plasmablast-associated, neutrophil extracellular trap (NET)-related, cytotoxic T-cell, and cytokine-associated states—and discuss their relevance for biomarker development and precision trial design. Importantly, we emphasize that interferon gene signatures (IGS) should be interpreted as context-dependent and non-specific markers of interferon responsiveness, reflecting combined activity of type I, II, and III interferons, and functioning primarily as predictive rather than mechanistic biomarkers. We further highlight critical limitations of a purely endotype-based model, including the need to distinguish true molecular refractoriness from damage-dominant and pseudo-refractory states, as well as the emerging role of immune-reset strategies such as cluster of differentiation 19 (CD19)-directed chimeric antigen receptor T-cell (CAR-T) therapy, which may overcome refractoriness independently of specific pathway dominance. These observations suggest that difficult-to-treat SLE encompasses biologically heterogeneous states that may not be fully captured by pathway-resolved stratification alone. Reframing D2T-SLE as a biologically heterogeneous state of incomplete immune attenuation may help bridge the gap between clinical treatment failure and mechanism-informed precision medicine in systemic lupus erythematosus. Full article

Background: Chimeric antigen receptor (CAR) T cells achieve cure in the therapy of hematological malignancies. In solid tumors, however, CAR-T cells face an immunosuppressive tumor microenvironment (TME) which crucially impedes their cytotoxic capacities. Citrate accumulating in the TME is a crucial metabolite in mediating immune suppression and is consumed by cancer cells promoting growth of various tumors, including melanoma; blocking the citrate transporter pmCiC with gluconate abrogates citrate-mediated tumor growth. Methods: To bolster treatment of melanoma, we explored gluconate as adjuvant for CAR-T cell therapy. Results: First, gluconate did not impair CAR-T cell functional capacities with regard to cytotoxicity, cytokine secretion, and persistence in a “stress test” based on repetitive antigen stimulation with cognate cancer cells. The addition of gluconate antagonized the citrate-mediated enhanced proliferation of melanoma cells. As a consequence, the elimination of citrate-boosted melanoma cells by CSPG4-specific CAR-T cells was augmented in the presence of gluconate. Conclusions: Taken together, these data suggest that counteracting citrate-mediated enhanced tumor growth with gluconate may improve the cytotoxic activity of CAR-T cells against melanoma. Full article

(1) Background: Despite the success of combination antiretroviral therapy (cART), immune recovery in treated HIV infection remains heterogeneous, and discordant immune–virologic responses persist in a substantial proportion of people living with HIV (PLWH). These patterns may reflect ongoing immune dysregulation despite effective viral suppression. This study aimed to characterize discordant treatment classifications, evaluate immune imbalance using the CD4/CD8 ratio, identify associated clinical predictors, and assess opportunistic infection burden in a Romanian cohort of people living with HIV receiving long-term cART. (2) Methods: A retrospective cross-sectional study was conducted in 462 adults with HIV-1 infection receiving cART at the “Prof. Dr. Matei Balș” National Institute of Infectious Diseases, Bucharest (2018–2021). PLWH were classified as concordant responders (CR), immunological discordant responders (ID), or virological discordant responders (VD) based on plasma HIV-1 RNA and CD4+ T-cell count thresholds. Immune dysregulation was assessed using the CD4/CD8 ratio. Multinomial logistic, logistic, and negative binomial regression models were used to identify predictors of discordant responses, severe CD4/CD8 ratio inversion, and opportunistic infection burden. (3) Results: Discordant responses were observed in 30.7% of PLWH (14.5% ID, 16.2% VD). CD4/CD8 ratio inversion occurred in 71.2% and severe inversion in 40.0%. Significant differences across clinical classification groups were found for CD4+T-cell counts (H = 153.62, p < 0.001, ε² = 0.33) and CD4/CD8 ratio (H = 115.10, p < 0.001, ε² = 0.25), while CD8+ counts were similar (p = 0.571). Male sex was associated with both ID and VD, and severe CD4/CD8 inversion was strongly associated with ID. Opportunistic infection burden was associated with duration of HIV infection and CDC stage. (4) Conclusions: Discordant immune–virologic responses remain frequent during long-term cART and are characterized by persistent immune imbalance reflected by CD4/CD8 ratio inversion. The CD4/CD8 ratio may provide clinically relevant information on immune recovery beyond CD4+ T-cell counts. Full article

Background/Objectives: Chimeric antigen receptor (CAR) T cell therapy has profoundly transformed the cancer treatment landscape, achieving unprecedented clinical success in patients with hematological malignancies. However, challenges such as cytokine release syndrome, neurotoxicity, antigen escape, and limited efficacy in solid tumors remain, underscoring the need for robust preclinical modeling to evaluate novel CAR T cell products. Methods: This review provides a comprehensive overview of in vitro and in vivo preclinical modeling for CAR T cell functionality and toxicity assessment. We examine traditional experimental approaches and their limitations, discuss emerging technologies, and highlight how these strategies can be integrated to advance future CAR T cell therapies. Results: In vitro assays provide insights into efficacy but fail to model trafficking, dynamic immune cell interactions, and complex tumor microenvironments. In vivo mouse models allow for more complex physiological evaluation but are limited by species differences. Next generation platforms, such as patient-derived tumor organoids and organ- or multi-organ-on-a-chip microfluidics are emerging as potential tools to model CAR T cell therapy in physiologically relevant contexts and computational approaches are being increasingly used to develop novel CAR designs and predict patient responses. Conclusions: By integrating traditional experimental approaches with innovative technologies, the CAR T cell field is poised to generate more clinically relevant and predictive data thereby accelerating the development of safer, more effective, and personalized CAR T cell therapies. Full article

Accurate vehicle counts from traffic videos are fundamental to traffic measurement and to estimating roadway demand for infrastructure planning and maintenance. However, many vision-based traffic datasets and pretrained models under-represent vehicle types that are prevalent in developing countries, such as the keke (globally known as auto-rickshaw/three-wheeler), which can bias traffic composition estimates and downstream workload indicators. This paper presents a keke-aware vehicle detection and counting pipeline that combines fine-tuned YOLO-based detectors with BoT-SORT/ByteTrack tracking and ROI-based counting, together with a newly curated and publicly released traffic-video dataset that includes a dedicated keke class. The detectors are fine-tuned from pretrained weights on a six-class dataset (bicycle, bus, car, motorcycle, truck, keke) and evaluated on held-out roadside test videos with a manual counting baseline. On the validation split (2088 images; 8400 instances), the fine-tuned YOLO11l model achieves $P=0.752$, $R=0.696$, $\mathrm{mAP}@0.5=0.766$, and $\mathrm{mAP}@0.5:0.95=0.578$, with the keke class attaining $\mathrm{mAP}@0.5=0.772$, while YOLO26l achieves slightly higher overall precision ($P=0.766$) and stronger keke recall and $\mathrm{mAP}@0.5:0.95$. In system-level counting, the selected tuned ROI-based variants produce the most reliable results on the Yola Road downward flow, where keke counts remain close to the manual baseline, but performance is strongly direction- and scene-dependent, with substantially larger errors in the Yola upward flow and the more challenging Mubi Road scene. Flow-rate and ESAL-rate analyses further show that class misclassification can severely distort pavement-loading estimates even when total traffic flow appears close to baseline, underscoring the need for localized class ontologies and robust heavy-vehicle discrimination in mixed-traffic ITS deployments. The released dataset and baseline pipeline provide a practical reference for keke-aware traffic monitoring and for infrastructure-relevant traffic measurement in developing-country contexts. Full article

Glypican-3 (GPC3)-targeted chimeric antigen receptor T (CAR-T) cell therapy is a promising approach for hepatocellular carcinoma (HCC), but marked interpatient variability and antigen heterogeneity limit its broader application. Here, we established a patient-derived organoid (PDO)-based platform to functionally evaluate autologous GPC3-targeted CAR-T cell activity in HCC. HCC PDOs preserved key histologic features and heterogeneous GPC3 expression patterns of the original tumors. In co-culture assays, CAR-T cell cytotoxicity was associated with GPC3 expression levels and was accompanied by IFN-γ and IL-2 release, supporting the feasibility of using PDOs for functional assessment of CAR-T cell sensitivity. We further found that matrix conditions strongly influenced organoid architecture, viral transduction, CAR-T cell infiltration, and killing efficiency, with lower Matrigel concentrations providing a more permissive setting for functional assessment. Importantly, in GPC3-low PDOs, pretreatment with the DNA methyltransferase inhibitor 5-azacytidine (5-AZA) reduced DNA methyltransferase 3 alpha (DNMT3A) expression, increased surface GPC3 expression, and significantly enhanced CAR-T-mediated cytotoxicity. Together, these findings provide proof-of-concept evidence supporting the use of HCC PDOs as a patient-derived platform for modeling selected determinants of GPC3-targeted CAR-T cell activity and for exploring combination strategies to improve therapeutic efficacy. Full article

Background: Trichosporon japonicum is a rare but highly lethal pathogen causing fungemia in immunocompromised patients. With the expanding use of chimeric antigen receptor T (CAR-T) cell therapy, the spectrum of opportunistic fungal infections is changing, yet data on T. japonicum infections in this setting remain scarce. Case Presentation: A 69-year-old man with diffuse large B-cell lymphoma developed catheter-associated fungemia after CAR-T cell reinfusion. He initially presented with neck pain and white oral mucosal patches, followed by fever four days later. T. japonicum was isolated from both peripheral blood and central venous catheter tip cultures, identified by microscopic examination, mass spectrometry, and molecular sequencing. Antifungal prophylaxis was initiated before fever onset based on close monitoring of white blood cell count, procalcitonin, interleukin-6, and C-reactive protein; treatment was subsequently adjusted according to species identification and antifungal susceptibility results. Infection was controlled within two weeks after catheter removal and immune recovery. The patient remained well at six-month follow-up. Conclusion: This case adds to the limited literature on T. japonicum fungemia in patients receiving CAR-T therapy. Our experience, together with a review of the literature, underscores that successful management requires prompt catheter removal, immune restoration, and combination therapy with voriconazole and amphotericin B, as echinocandin monotherapy should be avoided. Awareness of this pathogen in immunocompromised patients is critical. Full article

Despite prolonged viral inhibition with combination antiretroviral therapy (ART), HIV-1 survives as genetically intact, replication-capable proviruses within durable CD4+ T-cell fractions, involving central memory, transitional memory, and stem cell-like memory populations, as well as within tissue-resident compartments including lymphoid follicles and gut-associated lymphoid tissue. Reservoir stability is preserved via clonal growth of infected cells and epigenetic processes that impose proviral transcriptional silencing. As a result, current therapeutic approaches seek to either directly alter proviral survival or to improve immune-driven elimination of infected cells. At the molecular level, investigational strategies such as CRISPR–Cas9 and CRISPR–Cas12 gene-editing systems are intended to remove or induce inactivating mutations inside embedded proviral DNA, as well as alter host entrance co-receptors such as CCR5 to provide cellular resistance to infection. In addition, pharmacologic latency regulation is being studied via histone deacetylase inhibitors, protein kinase C agonists, and bromodomain inhibitors to reverse latency, along with Tat inhibitors and other transcriptional repressors aimed to persistently silence proviral expression. Moreover, immunological techniques aim to counteract inefficient endogenous antiviral defenses. Broadly neutralizing antibodies with tailored Fc-driven effector functions are under examination for both neutralization and antibody-dependent cellular cytotoxicity. Therapeutic vaccine approaches seek to elevate polyfunctional HIV-specific CD8+ T-cell responses, while adoptive cellular approaches, involving CAR-T cells aiming HIV envelope epitopes, remain in early clinical research. Immune checkpoint blockade is also being investigated to reverse T-cell depletion inside reservoir-rich tissues. Nevertheless, the key obstacles continue to be the diverse reservoir composition, restricted tissue penetration, viral escape, and safety limitations. The molecular and translational obstacles that characterize attempts toward an HIV cure must be addressed through ongoing multidisciplinary research. Full article

Background/Objectives: CD19-directed chimeric antigen receptor T (CAR-T) cell therapy induces profound immune remodeling. Nonetheless, biomarkers predicting complete remission (CR) remain poorly defined. We characterized longitudinal cytokine and immune-cell dynamics after CAR-T infusion and identified early immunological features associated with CR. Methods: Longitudinal immune profiling was performed in 18 patients with non-Hodgkin lymphoma, including 14 with relapsed/refractory diffuse large B-cell lymphoma treated with anti-CD19 CAR-T cells. Peripheral blood was collected at the baseline and days 7, 14, 21, 28, and 60 post-infusion. Multiparameter flow cytometry quantified lymphoid and myeloid subsets and Toll-like receptor (TLR)2 and TLR4 expression. Serum cytokines were measured by multiplex assays. Machine-learning-based feature selection identified variables associated with CR. Results: Two inflammatory waves were observed. The first, at day 7, featured elevated IL-6, IL-10, IFN-α, IFN-γ, and TNF-α, accompanied by increased CD4⁺ T cells, HLA-DR^high classical monocytes, and non-classical monocytes. The second, at days 21–28, showed increased IL-5, IL-6, IL-12, IFN-γ, and GM-CSF, with expansion of CD4⁺ and CD8⁺ T cells, regulatory T cells, NK-T cells, and non-classical monocytes. TLR2 expression was significantly upregulated at day 7 on T-cell subsets and on classical and intermediate monocytes. An exploratory feature-selection analysis identified baseline and day-7 TLR2 and TLR4 expression on lymphoid and myeloid cells, early IFN-γ levels, and monocyte frequencies as variables associated with CR. Conclusions: Together, these data show that anti-CD19 CAR-T therapy induces two coordinated waves of cytokine release and immune-cell activation. Moreover, the findings suggest that early modulation of innate immune features, particularly TLR2 expression, is associated with complete remission, although these biomarker relationships remain exploratory and require validation in larger cohorts. Full article

Background/Objectives: B-cell maturation antigen (BCMA)-directed immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies (BsAbs), have improved clinical outcomes in multiple myeloma. However, cytokine release syndrome (CRS) remains a major safety concern, and comparative real-world evidence across BCMA-directed agents remains limited. This study aimed to evaluate and compare CRS reporting patterns associated with BCMA-targeted CAR-T and BsAb therapies using the FDA Adverse Event Reporting System (FAERS) data and to identify predictors of CRS reporting using machine learning-based approaches. Methods: A pharmacovigilance analysis was conducted using FAERS reports from 2021 Q1 to 2025 Q3. Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC), and signals were considered present when predefined thresholds were met. Multivariable logistic regression was applied to estimate adjusted odds ratios (aORs) for CRS reporting while adjusting for demographic and reporting characteristics. Machine learning models, including XGBoost, LightGBM, and random forest were developed to predict CRS reporting. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Among 4046 reports included in the final dataset, CAR-T therapies showed higher CRS reporting odds than BsAbs (aOR: 2.55, 95% CI: 2.16–3.01). Disproportionality analyses identified significant CRS signals for CAR-T therapies across all indices, whereas BsAbs did not meet signal detection thresholds. At the agent level, idecabtagene vicleucel was the only agent meeting all predefined signal detection criteria and exhibited the strongest reporting pattern in multivariable analysis (aOR: 6.96, 95% CI: 5.53–8.75). Among the evaluated models, LightGBM achieved the highest predictive test AUROC (0.762). SHAP analysis identified idecabtagene vicleucel, United States region, and reporting year as the most influential predictors of CRS reporting. Conclusions: CAR-T therapies, particularly idecabtagene vicleucel, exhibited higher CRS reporting odds than BsAbs, with substantial agent-level heterogeneity observed across BCMA-directed immunotherapies. Integrating pharmacovigilance and machine learning approaches may facilitate more individualized safety monitoring by identifying agent-specific differences in CRS risk among BCMA-targeted therapies. Full article

Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free rapid optical imaging (ROI) biosensor with automated machine learning analysis for direct quantification of CAR T-cells from whole blood. This microfluidic platform integrates red blood cell (RBC) removal, CAR T-cell capture, and imaging-based quantification on a single chip, eliminating the need for centrifugation, staining, and operator-dependent interpretation. For validation, 50 μL whole blood samples spiked with Jurkat cells expressing CD19 CARs underwent RBC depletion by agglutination and microfiltration. The remaining blood components were then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by brightfield microscopy and automatically enumerated using a machine learning algorithm trained on fluorescence-validated cells. The CD-19 cells’ capture performance was validated by flow cytometry and fluorescence imaging. The trained machine learning model validated at 88% sensitivity and 96% specificity. Buffer and whole blood calibration curves were established across clinically relevant concentrations (1–1000 cells/µL) with triple replicates. The results showed high correlation (0.975 and 0.990 R²) between the spiked concentration and the detected CAR T-cells, with a 95% certainty limit of detection (LOD) and quantification (LOQ) of 0.6 and 1.1 cells/µL for spiked buffer, and 14 and 67 cells/µL for spiked whole-blood, respectively. Full article

Natural killer/T-cell lymphoma (NKTCL) is a rare and aggressive Epstein–Barr virus (EBV)-associated lymphoma characterized by intrinsic chemoresistance and an immunosuppressive tumor immune microenvironment (TIME). EBV-driven immune dysregulation provides a biological rationale for immunotherapy. This review summarizes current advances in immunotherapeutic strategies for NKTCL, integrating molecular mechanisms, clinical evidence, and resistance mechanisms within the context of TIME remodeling and immune reprogramming. We synthesize evidence from clinical trials, translational studies, and preclinical investigations evaluating immune checkpoint inhibitors, antibody-based therapies, adoptive cellular therapies, immune engagers, EBV-directed immunotherapies, and multimodal combination strategies in NKTCL. Among these strategies, PD-1/PD-L1 inhibitors are the most extensively studied immunotherapies in NKTCL and demonstrate clinically meaningful activity across different clinical settings. However, therapeutic responses remain heterogeneous, and primary or acquired resistance is common, driven by EBV-associated immune suppression, defective antigen presentation, metabolic reprogramming, and multi-checkpoint co-expression. Beyond immune checkpoint blockade, emerging approaches—including dual-checkpoint inhibition, epigenetic and metabolic combinations, antibody–drug conjugates, EBV-specific cytotoxic T lymphocytes, chimeric antigen receptor (CAR)-based platforms, immune engagers, and EBV vaccines—have shown encouraging signals in early-phase studies. Increasing evidence also supports multimodal strategies integrating immunotherapy with radiotherapy and other immune-modulatory interventions to enhance immune reprogramming and improve response durability. Overall, immunotherapy has substantially expanded the therapeutic landscape of NKTCL but remains constrained by complex EBV–TIME interactions and interpatient heterogeneity. Future progress will rely on biologically informed patient stratification, rational multimodal combination strategies, and integration of innovative immune platforms to establish a durable, immune-reprogramming-centered treatment paradigm for EBV-driven NKTCL. Full article

Traffic accident detection is a critical component of intelligent transportation systems (ITS), enabling timely incident response and traffic management. While most existing approaches rely on temporal information from video sequences, such methods are not always applicable in resource-constrained surveillance environments. This study investigates the feasibility of detecting traffic accidents from single static images by formulating the task as a binary classification problem. Representative architectures, including Vision Transformer (ViT), Swin Transformer, and ResNet-50, are systematically evaluated on the Car Crash Dataset (CCD) under multiple training configurations. To assess generalization capability, cross-domain evaluation is conducted using an external crash video dataset (ECVD) constructed to approximate real-world deployment conditions. Experimental results show that all models achieve strong performance under in-domain evaluation. However, cross-domain testing reveals substantial performance degradation, particularly in recall, indicating limited generalization capability under domain shift. Qualitative analysis further shows that missed detections are associated with weak visual cues, occlusion, and complex traffic environments, while false positives are caused by visually ambiguous patterns resembling accident scenarios. Unlike prior studies that primarily report performance improvements, this work provides empirical evidence that model behavior in static-image-based accident detection is governed by dataset composition rather than architectural design. Therefore, static-image-based accident detection should be interpreted as a coarse-level screening tool rather than a fully reliable decision-making system. This study highlights the importance of data-centric design and cross-domain evaluation for improving real-world applicability. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article

The treatment of chronic lymphocytic leukemia (CLL) has significantly shifted from chemoimmunotherapy to targeted therapies like Bruton’s tyrosine kinase and BCL2 inhibitors. Despite these advancements, CLL remains an incurable disease characterized by immune dysregulation, therapeutic resistance, and cumulative toxicities. To overcome these challenges, novel immunotherapeutic strategies are emerging as fundamentally different approaches that target immune–tumor interactions. These innovations include novel monoclonal antibodies, bispecific antibodies that redirect T cell cytotoxicity, chimeric antigen receptor (CAR) T-cell therapies, and natural killer (NK) cell-based platforms. By actively engaging cellular cytotoxicity, these approaches show promise in high-risk and treatment-resistant scenarios where standard pathway inhibition is inadequate. Establishing optimal use, toxicity management, and combination strategies for these cell-engaging immunotherapies is now a critical priority in contemporary CLL research. Full article