Sumit Kaushik

Prevention of Fracture Risk among Older Long-term Care Residents: A Prospective Study from India

Older adults in long-term care (LTC) facilities are at high risk for fragility fractures due to bone loss, impaired mobility, cognitive decline and polypharmacy. Such fractures increase morbidity, mortality and healthcare costs. This prospective study evaluated the effectiveness of a multifaceted intervention programme for fracture prevention amongst older adults in LTC. A prospective, 24-month interventional study (January 2022–December 2023) was conducted in 10 LTC homes across three districts with 520 residents aged ≥65 years. Outcomes were compared with a matched historical control cohort drawn from the same LTC facilities during 2020–2022, using identical inclusion criteria and outcome definitions. The intervention included Vitamin D and calcium supplementation, structured exercise, medication review, fall prevention strategies and environmental modifications. Fracture incidence, fall rates and functional status were compared to a historical control. Hip and non-hip fractures decreased by 36% and 28%, respectively. Vitamin D supplementation improved serum 25(OH) D levels ( P < 0.01). Exercise enhanced balance and strength ( P < 0.05). Psychotropic use declined by 18% following polypharmacy review. Fall rates dropped by 31%. A comprehensive, multidisciplinary intervention significantly reduced fracture risk in LTC residents, supporting its broader implementation to improve musculoskeletal health and safety in geriatric care.

Self-supervised spectral-temporal denoising for dynamic deuterium metabolic imaging.

Dynamic deuterium metabolic imaging (DMI) enables time-resolved mapping of cerebral glucose metabolism in vivo, yet its intrinsically low SNR often renders voxel-wise metabolite quantification unstable-particularly at early repetitions. Low-rank denoising is widely used in MR spectroscopic imaging (MRSI)/DMI to improve robustness, but very low-SNR regimes and dynamic studies remain challenging. Self-supervised learning is promising for DMI/MRSI denoising, but its performance depends strongly on the representation domain and the noise assumptions underlying the training objective Here, we study a pragmatic denoising pipeline for dynamic DMI/MRSI that combines a mild low-rank stabilization with self-supervised learning in the spectral-temporal (f×T) domain. Exploiting metabolite-specific spectral structure and redundancy across repeated measurements, our approach relies on two mild assumptions: (i) additive, approximately zero-mean noise and (ii) approximate noise independence across repeated acquisitions. These conditions are expected to be satisfied across essentially any reconstruction and preprocessing pipeline. Our results indicate that the f×T domain is effective both with spatially correlated and approximately uncorrelated noise. We evaluate the approach in simulations and in vivo dynamic DMI data from healthy volunteers (n=6) and a brain tumor patient. The proposed pipeline improves the robustness of time-resolved metabolite estimates and increases LCModel fit stability relative to a state-of-the-art low-rank baseline (tMPPCA), with the largest gains for weak metabolites and early low-SNR repetitions. Together, this enables more reliable dynamic metabolite mapping in low-SNR regimes.

Nanomedicine targeting neuroinflammatory pathways in Alzheimer’s disease: a new frontier in inflammopharmacology