Lower-limb rehabilitation exoskeletons have the potential to improve gait recovery after stroke by providing intensive and repetitive training. However, conventional control strategies often rely on fixed control parameters and exhibit limited adaptability to patient-specific characteristics, sensor noise, and dynamic uncertainties. This paper proposes an adaptive control framework that combines deep reinforcement learning (RL) with model-based impedance control for personalised lower-limb exoskeleton assistance. Patient-specific biological parameters are incorporated into the simulation environment and reward formulation to improve adaptability and robustness. Three state-of-the-art deep RL algorithms, Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC), are evaluated in a continuous control environment under varying signal-to-noise ratio (SNR) conditions ranging from 5 dB to noise-free conditions. Results demonstrate that TD3 achieves the most stable learning performance, obtaining a mean reward of -354.24 under noise-free conditions, while DDPG provides the highest joint-angle tracking accuracy with an RMSE of 0.0369 rad. SAC exhibits superior robustness in noisy environments, achieving the highest learning ratio of 0.51 at 5 dB SNR. Furthermore, the proposed personalised framework reduces tracking errors by up to 27% compared with non-personalised baseline approaches. The findings indicate that integrating patient-specific information with RL-based adaptive control can significantly enhance robustness, tracking performance, and personalisation in exoskeleton-assisted gait rehabilitation, providing a promising direction for future intelligent rehabilitation systems.

Deformable mirrors (DMs) serve as the core wavefront correction devices in wavefront sensorless adaptive optics (AO) systems, and their performance is predominantly determined by the convergence speed and stability of the control algorithm. Although the stochastic parallel gradient descent (SPGD) algorithm is extensively used for wavefront sensorless AO control, its slow convergence limits real-time wavefront correction. To address this issue, the RAD-SPGD algorithm is put forward by integrating the relativistic adaptive gradient descent (RAD) optimizer into the conventional SPGD algorithm. A wavefront sensorless AO system with a 97-element MEMS deformable mirror was established to evaluate the proposed algorithm under different turbulence levels, and physical experiments were carried out for verification. The convergence performance is evaluated by the number of iterations needed for the Strehl ratio (SR) to reach 80% of its maximum value. Simulation results demonstrate that the proposed algorithm improves the correction speed by approximately 56% on average compared with the conventional SPGD algorithm, while experimental results show an improvement of approximately 28.6%. Moreover, dynamic turbulence experiments demonstrate enhanced turbulence adaptability and correction stability. These results suggest that the proposed algorithm effectively enhances the closed-loop control efficiency of the 97-element MEMS deformable mirror, offering an effective solution for real-time wavefront sensorless adaptive optics systems.

The transmission of trauma across generations has traditionally been explained through genetic, epigenetic, familial, and attachment-based mechanisms. While these approaches have generated valuable insights, they remain insufficient for explaining the persistence of psychological vulnerability in contexts where the original traumatic event has disappeared but maladaptive patterns continue to emerge across generations. This paper introduces the Contextual Resonance Theory of Psychopathology (CRTP), a novel cognitive-systemic framework proposing that transgenerational trauma is maintained through the recursive reproduction of trauma-shaped environments rather than exclusively through biological inheritance. According to this model, traumatic experiences generate adaptive cognitive, emotional, and behavioral responses that become embedded within family narratives, relational structures, institutional practices, and cultural expectations. Over time, these adaptations evolve into self-maintaining contextual systems that continuously communicate threat, unpredictability, and hypervigilance to subsequent generations. The theory introduces the concepts of contextual traumatic inertia, distributed psychopathology, and recursive contextual resonance to explain how vulnerability becomes socially reproduced even in the absence of direct trauma exposure. Drawing on predictive processing, ecological psychology, systems theory, and contemporary trauma research, the paper proposes that psychopathology emerges from persistent interactions between individuals and pathogenic environments rather than solely from intrapsychic dysfunction. Furthermore, a complementary mechanism termed corrective recontextualization is proposed as a pathway for reversing transgenerational vulnerability through repeated exposure to safe, predictable, and psychologically restorative contexts. The framework offers an integrative perspective capable of bridging cognitive neuroscience, developmental psychopathology, trauma studies, and mental health intervention research. Clinical, educational, organizational, and societal implications are discussed, alongside future directions for empirical validation and neurocognitive investigation.

Background/Objectives: Stroke-related aphasia significantly impairs communication and quality of life (QoL). Constraint-induced aphasia therapy (CIAT) and repetitive transcranial magnetic stimulation (rTMS) are evidence-based approaches targeting language recovery. No combined protocol exists for Greek, a morphologically complex language. The present study was designed as a feasibility pilot, with the primary objective of determining if a modified CIAT (mCIAT) protocol, with or without continuous theta burst stimulation (cTBS), could be delivered safely and acceptably in a Cypriot clinical context, while also reporting the first mCIAT protocol adapted for Greek. Methods: A single-subject experimental design (SSED) was employed. Two participants (PAs) with chronic anomic aphasia underwent a 10-day treatment protocol. PA1 received sham cTBS plus mCIAT-GR; PA2 received real cTBS (to the right inferior frontal gyrus) plus mCIAT. Multiple language, cognitive, and QoL measures were administered across three baselines and two follow-up time points. Results: Both PAs completed the full protocol with 100% session adherence and no adverse effects, establishing the feasibility of delivery. Neither participant showed improvements in standardized expressive language measures following mCIAT with or without cTBS; Standard Error of Measurement (SEM) analysis confirmed that observed score fluctuations on most measures fell within expected measurement noise. PA2 showed improvement in QoL (SAQOL-39g) domains at the two-year follow-up, though this finding requires cautious interpretation given the follow-up interval and regression-to-mean considerations. Conclusions: The Greek mCIAT protocol is feasible and safe to deliver in a Cypriot clinical setting. The absence of clear language improvements may reflect several factors, including ceiling effects, limited assessment sensitivity, insufficient treatment dosage, or limited intervention effects. These findings should be interpreted cautiously and require confirmation in larger, adequately powered studies.

Background: The Six Sigma metric benchmarks analytical performance, but most reports assess one platform at one time point, merging trueness with imprecision. Using routine peer-group internal quality control (IQC), we profiled two clinical chemistry platforms over five months. Because peer-group IQC compares imprecision directly but positions trueness only against same-method peers, we characterise rather than rank cross-platform trueness. Methods: Bio-Rad Unity peer-group IQC (January-May 2026) spanned two Siemens Dimension EXL analysers and two Roche cobas pro channels, two QC levels, and 30 analytes. TEa followed the CLIA 2024 Final Rule (25 analytes; provisional EFLM goals for five non-CLIA analytes). The Standard Deviation Index (SDI) referenced trueness to same-method peers, the CV ratio (CVR) imprecision, Sigma = (TEa - |bias|)/CV. Analyte-level comparison used Wilcoxon signed-rank testing, a confirmatory mixed-effects model, and Westgard SQC mapping. Results: Across 25 CLIA-regulated analytes, median Sigma was 5.9 (IQR 4.8-8.2) for cobas pro versus 4.0 (2.3-6.1) for Dimension EXL: a Hodges-Lehmann difference of 2.4 (95% CI 1.7-3.2; p < 0.001), confirmed by CLIA-only (2.51) and mixed-effects (2.7) analyses. Both aligned with their peers (SDI near zero); the gap reflected imprecision (median CV 1.7% vs. 2.5%; CVR 0.83 vs. 1.02), stable across months and instruments. The ranking reversed for iron, GGT and amylase; sodium, chloride, urea and creatinine were TEa-limited. The SQC redesign averaged N = 6 versus N = 3 controls/run (Dimension vs. cobas pro). Conclusions: A precision-driven difference separated the platforms as implemented here. Cross-platform trueness could not be ranked, yet decomposing Sigma localised precision limits, isolated calibration outliers, and produced analyte-specific SQC strategies needing prospective validation.

Surgical robot navigation in a restricted pelvic workspace requires accurate target localization, directional consistency and robot-feasible execution. This study proposes a physically constrained dual-branch front-end network (PCD-Net) for DDH-oriented navigation using public CT-derived pelvis geometries. PCD-Net maps a 21-dimensional input comprising the target geometry, current joint state, workspace bounds and constraint parameters to a base-frame target position, principal insertion direction and seven-joint correction vector for MoveIt2 and OMPL planning. Training combines supervised pretraining with direction consistency, joint limit, correction magnitude and workspace gap penalties. Evaluation comprised 15 complete simulation trials per method and target sequence-level fivefold cross-validation of 999 samples from five complete sequences. PCD-Net achieved a position error of 0.736±0.202 mm, a direction error of 0.428±0.130°, a planning time of 0.0162±0.0040 s and successful execution in all 15 trials. In cross-validation, the complete constraint setting produced the lowest joint correction MAE (0.755±0.034 rad) and temporal correction variation (3.270±0.589 rad) while maintaining sub-millimeter position and sub-degree direction errors. Removing the joint limit penalty increased the violation rate by 42.35%. These results support PCD-Net as a lightweight, planner-compatible front end that balances geometric accuracy and joint-level feasibility. All evidence is simulation-based; phantom experiments, physical robot validation and evaluation using clinically characterized DDH cases remain necessary before surgical translation.

The traditional glucose-centric paradigm of type 2 diabetes management is being superseded by growing evidence that certain agents, sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and pioglitazone, confer cardiovascular, renal, and hepatic benefits that extend well beyond glycemic control. Building on the previously proposed "diabetes/disease-modifying drugs (DMDs)" concept introduced through the SIMPLE framework, we propose a more mechanistically precise term: Disease-Modifying Anti-Diabetic Drugs (DMADDs). DMADDs are defined by four criteria: glucose-independent clinical benefit, durable organ-specific protection, mechanistic evidence of anti-inflammatory or anti-fibrotic activity, and sustained legacy effects or remission potential. Using these criteria, SGLT2i, GLP-1RA, and pioglitazone emerge as prototypical agents, while metformin, dipeptidyl peptidase-4 (DPP-4) inhibitors, insulin, and sulfonylureas do not meet the full threshold. We explicitly explain why metformin and DPP-4 inhibitors are excluded as DMADDs, situate the DMADD framework within the heterogeneity of type 2 diabetes and against what non-pharmacological interventions can achieve, and state the limitations of the proposal. Recent evidence reinforces this framework: the SOUL trial established cardiovascular superiority for oral semaglutide, extending disease-modifying properties beyond injectable formulations, while Phase 3 TRIUMPH data position retatrutide as an emerging triple-agonist candidate awaiting cardiovascular outcome data. The 2026 American Diabetes Association Standards of Care now recommend SGLT2i and GLP-1RA independent of baseline glycated hemoglobin (HbA1c), an institutional shift that mirrors the disease-interception logic proposed here. We argue that adopting the DMADD framework could reorient diabetes therapeutics from glycemic normalization toward early disease interception, potentially preserving β-cell function and expanding the clinical horizon toward remission. This terminology offers a unifying scaffold for future trials, regulatory classification, and clinical guidelines, redefining therapeutic success in type 2 diabetes beyond glucose control alone.

This proof-of-concept study developed a novel in vitro biomechanical evaluation platform for quantitatively detecting altered mechanical responses in small joints under controlled structural perturbations. The platform was designed to apply repeatable flexion-extension cycles to ex vivo rabbit hindlimb specimens while enabling synchronized measurement of global reaction force and localized periarticular deformation. Four specimens were tested under baseline and post-perturbation conditions, with controlled bone resections of increasing magnitude introduced following standard surgical procedures and quantified using the Relative Incision Length (RIL). To characterize perturbation-induced mechanical changes, two dimensionless indices were defined: the Relative Amplitude Difference (RAD) to represent changes in global dynamic force response and the Strain Alteration Index (SAI) to quantify changes in marker-derived apparent in-plane strain. Force attenuation was observed in all four specimens under both the 0-35° and 0-70° motion profiles. Under the 0-35° profile, RAD showed progressively greater attenuation with increasing RIL, ranging from -32% to -93%, whereas the ordering was less uniform under the 0-70° profile. The strain-based response also varied with motion range and strain metric. The smallest perturbation exhibited among the largest relative strain increases under both profiles, while the intermediate and larger perturbations did not show a uniform graded ordering. These findings indicate that global force and regional apparent in-plane strain provide complementary, motion-dependent quantitative readouts of mechanically altered joint behavior rather than a single angle-independent relationship with perturbation magnitude. Overall, the proposed platform establishes an accessible and controllable in vitro biomechanical framework for comparing mechanical responses across controlled structural and motion conditions and provides a foundation for future evaluation of artificial finger joint designs under conditions such as implant undersizing, geometric mismatch, or altered joint congruence.

TP53-mutated myeloid neoplasms, including acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS), remain among the hardest-to-treat hematologic malignancies, with short survival despite hypomethylating agents +/- venetoclax, and allogeneic transplantation. Despite encouraging early-phase data, Phase 3 trials of mutant-p53 reactivators to date have not yet translated into clear, practice-changing survival benefits, highlighting a need to explore complementary strategies that aim to directly restore wild-type p53 function in the leukemic niche. Building on clinical experience with the adenoviral p53 product (rAd-p53) in solid tumors, where intratumoral rAd-p53 achieves high cumulative response rates with manageable toxicity, we investigate the feasibility of translating p53 replacement into select TP53-mutated myeloid neoplasms using antigen-directed delivery systems. Advances in AML surface proteomics and immunotherapy identify many antigens of interest, in particular, CD33, CD123, and CD209, as internalizing myeloid antigens that are broadly expressed on leukemic blasts and stem/progenitor cells. In parallel, emerging data on in vivo gene delivery via lipid nanoparticles (LNP) and adenovirus platforms that are capable of efficient marrow transduction demonstrate that systemic gene transfer to hematopoietic compartments is feasible. We outline a framework in which CD33-, CD123-, and CD209-directed mRNA/LNP systems or leukemia-adapted rAd-p53 vectors could potentially be used to restore wild-type TP53 in TP53-mutant myeloid clones and discuss key preclinical questions in xenograft models. We acknowledge that the strategy is built on converging but largely indirect lines of evidence rather than direct experimental data in TP53-mutated AML/MDS, and we explicitly discuss key preclinical validation requirements and biological barriers, including dominant-negative stoichiometry, liver sequestration, and by conceivable toxicities, that must be systematically addressed before clinical translation.

Use of quantitative polymerase chain reaction (qPCR) to measure viral DNA in plasma has promise for the detection and/or monitoring of high-risk human papillomavirus (hrHPV)-associated cancers. We examined Roche's cobas 6800 HPV test for detection of hrHPV in plasma or peripheral blood mononuclear cells (PBMC) of patients with HPV-associated dysplasia or cancer. In our pilot study, this assay detected hrHPV DNA in 1 mL pre-treatment plasma of patients with advanced (stage II-IV) cervical cancer, but not stage I or dysplasia cases, or in PBMC. Plasma of oropharyngeal cancer patients also tested positive in most cases. Seeking to enhance sensitivity, we re-tested these pilot samples with qPCR employing three published primer sets (E7, BR E1-5, HR E5L2-4) selected for improved detection of HPV16 in cancer. This triplex assay detected quantifiable levels of HPV16 in pre-treatment plasma of advanced (stage II-IV) cervical cancer cases, and lower levels in some stage I and dysplasia cases. In serial sampling of HPV16 + cervical cancer patients post-treatment with no evidence of disease over ~2 years, no reproducible signal was detected by cobas HPV testing, whereas the triplex qPCR sporadically detected HPV DNA in plasma and PBMC samples of a subset of these patients.

Radiation-associated dysphagia (RAD) is a major short and long-term complication of treatment in head and neck cancer (HNC) patients associated with malnutrition, aspiration pneumonia, and markedly reduced quality of life. This review focuses exclusively on the management of RAD for the surgeon, emphasizing surgical techniques, functional rehabilitation, and emerging technologies. Post-operative dysphagia following surgical disease management (tumor resection, reconstruction, nerve injury) is an important but separate entity and is beyond the scope of this review. We discuss the pathophysiology of RAD, common clinical presentations, evidence-based surgical interventions, and future directions for study in optimizing post-radiotherapy swallowing function.

Enhancing contact compliance and task flexibility is essential for expanding the real-world use of robotic systems. This paper presents a modular collaborative robot system that combines antagonistic actuation with hybrid soft-rigid variable-stiffness components, including a modular variable-stiffness bending joint (mvsBJ), a modular variable-stiffness rotational joint (mvsRJ), and a modular variable-stiffness link (mvsL), supported by an integrated control framework. Experimental characterization shows that the mvsBJ achieves a 55.96° bending range and nearly threefold stiffness variation, from 9.16 to 24.72 Nm/rad, through fluidic pressure adjustment. The mvsRJ achieves 145° bidirectional rotation. The platform is benchmarked across repeated trials in healthcare assistance and industrial support scenarios. In an assistive feeding task, a 3-DoF RBBL configuration operates in a low-stiffness mode, maintaining bounded force-tracking variation with mean error drift below 0.16 N. Collision tests across 40-60 mm/s show peak interaction forces increasing predictably from 3.93 to 5.38 N. In an industrial chamfering task, the 2-DoF RB configuration achieves pressurized rigidity comparable to a conventional mechanical workbench. These results demonstrate plug-and-play reconfigurability with reliable adjustable compliance, providing a novel architecture for diverse physical human-robot collaboration applications.

Deep learning has significantly advanced medical image analysis, yet the relative strengths of convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid CNN-Transformer models remain unclear. We present a systematic comparison of these model families across six 2D MedMNIST classification tasks under a controlled parameter budget of 20 to 30 million parameters, using both full fine-tuning and linear-probing. Among all evaluated models, the self-supervised CNN DINO-S achieves the highest average accuracy and AUC, while hybrid models such as MedViT-S perform comparably by combining local feature extraction with global context modeling. In contrast, compact ViTs generally underperform CNNs and hybrids under full fine-tuning when model size is controlled, and their performance depends strongly on pretraining strategy. Supervised medical pretraining on NIH ChestX-ray14 and CheXpert yields modest, task-dependent gains but does not alter the overall ranking of model families. Large-scale foundation models, including MedMAE, Rad-DINO, and CheXFound, also underperform smaller fully fine-tuned models under linear-probing, underscoring the importance of domain and task alignment. Overall, our results show that larger models and larger pretraining datasets do not automatically lead to better downstream performance. Instead, transfer effectiveness in medical imaging is driven primarily by architectural inductive biases, pretraining strategy, and domain relevance.

Dysphagia is commonly linked to advanced age and coexisting health conditions, but population-level data, especially for younger adults, are limited. This study sought to determine the prevalence of dysphagia symptoms in the general population of Cyprus and examine how it varies across demographic and clinical groups. A large-scale survey was carried out among adult permanent residents of Cyprus aged 18 years and older. Dysphagia symptoms were evaluated using the Eating Assessment Tool-10 (EAT-10). In the primary analysis of 1,163 participants, 18.3% scored ≥ 3 on the EAT-10, indicating a substantial prevalence of dysphagia symptoms in the adult population, with age and health status emerging as the primary associated factors. The association between age and dysphagia risk was non-linear, with higher prevalence observed in both older adults (≥ 65 years) and younger adults (18-25 years). Given the unexpectedly high prevalence of dysphagia in the 18-25 age group, a supplementary analysis was conducted in a cohort of 199 young adults recruited from the student population of the Cyprus University of Technology (CUT). The findings were consistent with those of the primary analysis, showing no meaningful differences in EAT-10 scores between subgroups. Overall, dysphagia symptoms are common among young adults in the Cypriot population and are not confined to older individuals. These findings suggest that swallowing-related symptoms may arise from diverse underlying mechanisms and etiologies (e.g., reflux, pill-swallowing difficulties, etc.) across age groups. As the EAT-10 is a symptom-based screening tool that does not distinguish between underlying causes, the reported dysphagia symptom-based prevalence may be inflated, highlighting the need for more comprehensive assessment approaches to improve etiological differentiation and clinical identification of swallowing disorders.