Age-related macular degeneration (AMD) is the leading cause of central visual impairment in the elderly. The exact pathophysiological mechanisms for AMD remain uncertain. Several studies suggest that choroidal abnormalities and alterations are critical in AMD progression. The transition from manual to automated segmentation and binarization techniques has resulted in accurate and precise measurements of different choroidal parameters. These qualitative and quantitative parameters, known as choroidal imaging biomarkers, have advanced from basic vertical subfoveal choroidal thickness to more intricate 3-dimensional choroidal reconstruction methods in the last decade. Therefore, a comprehensive evaluation of choroidal metrics may investigate valuable insights into AMD, potentially guiding the future development of customized therapeutic strategies and personalized patient care in AMD management. We describe the role of different choroidal biomarkers in evaluating patients with AMD and their contribution to management.

The global increase in the prevalence of type 2 diabetes has led to the development and implementation of new classes of antidiabetic medications, introducing advanced therapeutic options for the management of the disease. These new medications, though primarily designed to regulate blood glucose levels, also have applications in weight management, potentially transforming the current approaches to diabetes treatment. Newer medications, however, have ophthalmic side effects with controversies in trials and real-life data. We comprehensively assessed the ocular benefits and adverse effects of traditional and newer-generation anti-diabetic drugs. Our primary focus is on how these newer medications affect the stage of diabetic retinopathy. Additionally, we explore the associations between these medications and other ocular conditions, including age-related macular degeneration, glaucoma, orbital conditions, and diseases impacting the ocular surface. Furthermore, we provide contextual background by discussing the ocular effects of traditional anti-diabetic drugs.

Background: Gait disorders and as a consequence, robotic rehabilitation techniques are becoming increasingly prevalent as the population ages. In the area of rehabilitation robotics, using lightweight single hip joint exoskeletons are of significance. Considering no prior systematic review article on clinical outcomes, we aim to systematically review powered hip exoskeletons in terms of gait parameters and metabolic expenditure effects. Methods: Three databases of PubMed, Scopus, and Web of science were searched for clinical articles comparing outcomes of gait rehabilitation using hip motorized exoskeleton with conventional methods, on patients with gait disorder or healthy individuals. Of total number of 37 reviewed articles, 14 trials were quantitatively analyzed. Analyses performed in terms of gait spatiotemporal parameters like speed (self-speed and maximum speed), step length, stride length, cadence, and oxygen consumption. Results: Improved clinical outcomes of gait spatiotemporal parameters with hip joint exoskeletons are what our review’s findings show. In terms of gait values, meta-analysis indicates that rehabilitation with single hip joint exoskeleton enhanced parameters of maximum speed by 0.13 m/s (0.10–0.17) and step length by 0.06 m (0.05–0.07). For the remaining investigated gait parameters, no statistically significant difference was observed. Regarding metabolic parameters, oxygen consumption was lower in individuals treated with hip exoskeleton (− 1.23 ml/min/kg; range − 2.13 to − 0.32). Conclusion: Although the analysis demonstrated improvement with just specific gait measures utilizing powered hip exoskeletons, the lack of improvement in all parameters is likely caused by the high patient condition heterogeneity among the evaluated articles. We also noted in patients who rehabilitated with the hip exoskeleton, the oxygen cost was lower. More randomized controlled trials are needed to verify both the short- and long-term clinical outcomes, including patient-reported measures. Level of evidence: Level I (systematic review and meta-analysis).

Background: The early diagnosis and treatment of rheumatoid arthritis (RA) are essential to prevent joint damage and enhance patient outcomes. Diagnosing RA in its early stages is challenging due to the nonspecific and variable clinical signs and symptoms. Our study aimed to identify the most predictive features of hand ultrasound (US) for RA development and assess the performance of machine learning models in diagnosing preclinical RA. Methods: We conducted a prospective cohort study with 326 adults who had experienced hand joint pain for less than 12 months and no clinical arthritis. We assessed the participants clinically and via hand US at baseline and followed them for 24 months. Clinical progression to RA was defined according to the ACR/EULAR criteria. Regression modeling and machine learning approaches were used to analyze the predictive US features. Results: Of the 326 participants (45.10 ± 11.37 years/83% female), 123 (37.7%) developed clinical RA during follow-up. At baseline, 84.6% of the progressors had US synovitis, whereas 16.3% of the non-progressors did (p < 0.0001). Only 5.7% of the progressors had positive PD. Multivariate analysis revealed that the radiocarpal synovial thickness (OR = 39.8), PIP/MCP synovitis (OR = 68 and 39), and wrist effusion (OR = 12.56) on US significantly increased the odds of developing RA. ML confirmed these US features, along with the RF and anti-CCP levels, as the most important predictors of RA. Conclusions: Hand US can identify preclinical synovitis and determine the RA risk. The radiocarpal synovial thickness, PIP/MCP synovitis, wrist effusion, and RF and anti-CCP levels are associated with RA development.

Objective: The objective of this study isto assess the association between ankylosing spondylitis (AS) and risk of heart conduction disorders and arrhythmia. Methods: PubMed, Embase, and Web of Science databases were systematically searched for observational studies that investigated the association between AS and risk of heart conduction disorders and arrhythmia with no language or date restrictions until September 16, 2019. We used randomand fixed-effects models to pool the results of the studies. Publication bias was assessed by Egger’s test. Subgroup analysis was carried out based on the study design. A p-value less than 0.05 was considered significant. Comprehensive Meta-Analysis (CMA) software was used to perform meta-analysis. Results: After removing duplicates, we reviewed 135 articles. Finally, we included seven articles in our meta-analysis, of which four studies reported AV block and any conductive abnormality and three focused on atrial fibrillation and any arrhythmia. Based on our meta-analysis, an increased risk of atrial fibrillation (RR: 1.85, 95%CI: 1.15-2.98) and atrioventricular block (OR: 3.46, 95%- CI: 1.09-10.93) was found in AS subjects compared to the general population. In a subgroup analysis based on study design, we found a greater association between AS and atrioventricular block in cohort studies (RR: 5.14, 95%CI: 1.001-26.50) compared to cross-sectional ones. However, we did not find any association between AS and any arrhythmia (OR=3.36, 95% CI: 0.93-12.15), or conduction disorders (OR: 0.64, 95%CI: 0.38-1.06). No publication bias was found. Conclusion: Our results support an association between AS and a higher risk of atrial fibrillation and atrioventricular block.

Background: Plantar fasciitis (PF) is a common enthesopathy in patients with ankylosing spondylitis (AS). Shear wave elastography (SWE) and the Belgrade ultrasound enthesitis score (BUSES) may detect PF, but their comparative diagnostic performance is unclear. Objective: To compare SWE with the BUSES for identifying PF in individuals with and without AS. Methods: In this cross-sectional study, 96 participants were stratified into AS and non-AS populations, each further divided based on the presence or absence of clinical PF. Demographic data, the American Orthopedic Foot and Ankle Society Score (AOFAS), and the BASDAI score were recorded. All subjects underwent grayscale ultrasonography, the BUSES scoring, and SWE assessment of the plantar fascia. Logistic regression models were constructed for each population, controlling for age, body mass index (BMI), and fascia–skin distance. ROC curve analyses were performed to evaluate diagnostic accuracy. Results: In both AS and non-AS groups, SWE and the BUSES were significant predictors of PF (p < 0.05). SWE demonstrated slightly higher diagnostic accuracy, with area under the curve (AUC) values of 0.845 (AS) and 0.837 (non-AS), compared to the BUSES with AUCs of 0.785 and 0.831, respectively. SWE also showed stronger adjusted odds ratios in regression models. The interobserver agreement was good to excellent for both modalities. Conclusions: Both SWE and the BUSES are effective for PF detection, with SWE offering marginally superior diagnostic performance, particularly in AS patients. SWE may enhance the early identification of biomechanical changes in the plantar fascia.