The discovery of immune checkpoint proteins such as PD-1/PDL-1 and CTLA-4 represents a significant breakthrough in the field of cancer immunotherapy. Therefore, humanized monoclonal antibodies, targeting these immune checkpoint proteins have been utilized successfully in patients with metastatic melanoma, renal cell carcinoma, head and neck cancers and non-small lung cancer. The US FDA has successfully approved three different categories of immune checkpoint inhibitors (ICIs) such as PD-1 inhibitors (Nivolumab, Pembrolizumab, and Cemiplimab), PDL-1 inhibitors (Atezolimumab, Durvalumab and Avelumab), and CTLA-4 inhibitor (Ipilimumab). Unfortunately, not all patients respond favourably to these drugs, highlighting the role of biomarkers such as Tumour mutation burden (TMB), PDL-1 expression, microbiome, hypoxia, interferon-γ, and ECM in predicting responses to ICIs-based immunotherapy. The current study aims to review the literature and updates on ICIs in cancer therapy.

Patients who suffer from inherited or acquired thrombocytopenia can be also affected by platelet function defects, which potentially increase the risk of severe and life-threatening bleeding complications. A plethora of tests and assays for platelet phenotyping and function analysis are available, which are, in part, feasible in clinical practice due to adequate point-of-care qualities. However, most of them are time-consuming, require experienced and skilled personnel for platelet handling and processing, and are therefore well-established only in specialized laboratories. This review summarizes major indications, methods/assays for platelet phenotyping, and in vitro function testing in blood samples with reduced platelet count in relation to their clinical practicability. In addition, the diagnostic significance, difficulties, and challenges of selected tests to evaluate the hemostatic capacity and specific defects of platelets with reduced number are addressed.

Human movement emerges from complex interactions between neural processes, musculoskeletal dynamics, and environmental constraints, resulting in behavior that is inherently nonlinear. Therefore, nonlinear dynamical systems approaches have been widely used to characterize variability, stability, and coordination in motor behavior. However, despite their conceptual value, these methods are often applied post hoc and remain limited in their ability to support prediction, control, and integration of high-dimensional multimodal data. Artificial intelligence (AI) provides a complementary modeling framework capable of addressing these limitations. Yet many current AI applications treat motor signals primarily as feature sets for classification or regression, leaving the underlying dynamical structure of movement underexplored. This review synthesizes recent research that integrates AI with nonlinear motor control analysis to model, interpret, and control human movement across neural, biomechanical, and behavioral domains. We organize related studies according to the type of nonlinear motor control problem addressed, including input–output mappings, temporal dynamics, and adaptive control policies under conditions of partial observability and nonstationarity. Across these examples, we show that AI becomes scientifically informative when constrained and evaluated by nonlinear dynamical constructs such as attractors, phase relationships, manifolds, and stability structures. Finally, we discuss current limitations and outline future directions toward theory-informed, explainable, and closed-loop AI models for motor control and human–actuator interaction.

The NLRP3 inflammasome pathway is central to host defense, but dysregulated activation of inflammasomes promotes diseases associated with metabolic syndrome (diabetes, obesity, CVD, MASLD), neurodegenerative diseases (Alzheimer's, Parkinson's), autoinflammatory conditions (CAPS, gout), and respiratory illnesses (asthma/COPD, COVID-19). Therapeutic modulation of NLRP3 is challenging as it requires selective blockade of detrimental inflammasome activation without broadly suppressing innate immunity. Here, we used a phenotypic screen in THP1-ASC-GFP monocytes to identify FDA-approved drugs that can block LPS-induced priming of the NLRP3 inflammasome or inhibit NLRP3 assembly without disrupting upstream priming. Various classes of drugs, such as antidepressants (Fluoxetine, Duloxetine), antihypertensives (Irbesartan, Amlodipine, Nebivolol), antidiabetics (Rosiglitazone), β-adrenergic agonists (Salmeterol), antimalarials (Mefloquine), antifungals (Azoles, Ciclopirox), and antivirals (Saquinavir, Remdesivir), were identified as potent blockers of either priming or assembly of the NLRP3 inflammasome. Secondary validation demonstrated that several compounds suppressed NF-κB activation, reduced LPS binding to immune cells, decreased pro-inflammatory cytokine production, enhanced efferocytic capacity of macrophages, and enhanced autophagy in vitro and in vivo. Mechanistic analyses further revealed drug-specific effects on lysosomal biogenesis, mitochondrial morphology, and mitochondrial reactive oxygen species. In murine models of acute inflammation and endotoxemia, selected compounds attenuated NLRP3 inflammasome activation, reduced systemic and local inflammatory cytokines, limited tissue injury, and improved survival following LPS-induced sepsis, with efficacy varying by sex. Further studies in primary human cells and in vivo disease models are needed to assess the repurposing and therapeutic relevance of identified drugs.