Purpose: This study evaluated the feasibility and effectiveness of autologous adipose-derived mesenchymal stem cell (ASC) transplantation into the lacrimal gland for treating aqueous-deficient dry eye disease (DED) associated with Sjögren syndrome. Methods: Patients with Sjögren syndrome–related DED underwent autologous adipose tissue harvest via liposuction. ASCs were isolated, cultured, and injected into the lacrimal gland (volume ≤50% of estimated gland volume). Clinical evaluations— including Ocular Surface Disease Index (OSDI), tear osmolarity, tear film breakup time (TBUT), Oxford corneal staining, and Schirmer test I—were conducted at 1-, 4-, 16-, and 24-weeks after injection. Visual quality assessments included contrast sensitivity and higher-order aberrations (HOAs). Results: Six patients (mean age 56.1 ± 7.2 years) completed the study. Mean OSDI scores significantly decreased from 48.6 ± 8.4 to 28 ± 2.1. TBUT improved in both eyes (right, 3.3 ± 1.0 to 5.6 ± 1.2 seconds; left, 3.6 ± 1.0 to 6.1 ± 1.6 seconds). Schirmer test I values increased (right, 4.1 ± 0.7 to 7.8 ± 0.7 mm; left, 4.0 ± 0.6 to 7.6 ± 0.5 mm). Oxford staining scores decreased (right, 1.6 ± 0.5 to 0.67 ± 0.2; left, 1.3 ± 0.5 to 0.67 ± 0.2). Tear osmolarity also improved (right, 311.6 ± 6.1 to 299.1 ± 5.8 mOsm/L; left, 309 ± 7.6 to 298.3 ± 7 mOsm/L). HOAs were reduced in one eye. No significant change in contrast sensitivity or visual acuity was observed. No adverse events were reported. Conclusions Autologous ASC transplantation into the lacrimal gland appears to be a safe and promising therapeutic option for aqueous-deficient DED in Sjögren syndrome, offering significant improvement in both objective measures and patient-reported symptoms over a 6-month period.

In the unrelenting race to strive to dominate type 2 diabetes mellitus (T2DM) care better, this review paper sets out on a significant discovery trip across recent advancements in treatment and the blooming era of artificial intelligence (AI) utilities. Given the considerable global burden of T2DM, innovative therapeutic approaches to improve patient outcomes remain a public health priority. This review first provides an in-depth analysis of the current state of therapy, from novel pharmacotherapy to lifestyle interventions and new treatment methods. At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. This critical role is shown by integrating recent therapeutic advances and AI with overall showcasing better screening, diagnosis, and therapeutics decision-making to outcome prediction in T2DM. The review emphasizes how AI applications in insulin therapy have transformative potential in diabetes care. These person-centred approaches to T2DM management, which are more effective and personalized than some traditional strategies, only work because of the often-hidden synergies between AI algorithms in areas such as diagnostic criteria, predictive methods, and familiar classification tools for subgroups with relevant aspects/predictors on prognosis or treatment responsiveness.

In the unrelenting race to strive to dominate type 2 diabetes mellitus(T2DM)care better,this review paper sets out on a significant discovery trip across recent advancements in treatment and the blooming era of artificial intelligence(AI)utilities.Given the considerable global burden of T2DM,innovative therapeutic approaches to improve patient outcomes remain a public health priority.This review first provides an in-depth analysis of the current state of therapy,from novel pharmacotherapy to lifestyle interventions and new treatment methods.At the same time,the rapidly increasing role of AI in diabetes care is woven into the story,mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling.It leaves a deep review of their pre-existing synergies,which helps understand how collaborative opportunities will unlock the future of T2DM care.This critical role is shown by integrating recent therapeutic advances and AI with overall showcasing better screening,diagnosis,and therapeutics decision-making to outcome prediction in T2DM.The review emphasizes how AI applications in insulin therapy have transformative potential in diabetes care.These person-centred approaches to T2DM management,which are more effective and personalized than some traditional strategies,only work because of the often-hidden synergies between AI algorithms in areas such as diagnostic criteria,predictive methods,and familiar classification tools for subgroups with relevant aspects/predictors on prognosis or treatment responsiveness.