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Background: Segmentation of medical image volumes is a time‐consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings. Purpose: To evaluate the performance of the open‐source Multi‐Planar U‐Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state‐of‐the‐art two‐dimensional (2D) U‐Net architecture on three clinical cohorts without extensive adaptation of the algorithms. Study Type: Retrospective cohort study. Subjects: A total of 253 subjects (146 females, 107 males, ages 57 ± 12 years) from three knee osteoarthritis (OA) studies (Center for Clinical and Basic Research [CCBR], Osteoarthritis Initiative [OAI], and Prevention of OA in Overweight Females [PROOF]) with varying demographics and OA severity (64/37/24/53/2 scans of Kellgren and Lawrence [KL] grades 0–4). Field Strength/Sequence: 0.18 T, 1.0 T/1.5 T, and 3 T sagittal three‐dimensional fast‐spin echo T1w and dual‐echo steady‐state sequences. Assessment: All models were fit without tuning to knee magnetic resonance imaging (MRI) scans with manual segmentations from three clinical cohorts. All models were evaluated across KL grades. Statistical Tests: Segmentation performance differences as measured by Dice coefficients were tested with paired, two‐sided Wilcoxon signed‐rank statistics with significance threshold α = 0.05. Results: The MPUnet performed superior or equal to KIQ and 2D U‐Net on all compartments across three cohorts. Mean Dice overlap was significantly higher for MPUnet compared to KIQ and U‐Net on CCBR (0.83±0.04 vs. 0.81±0.06 and 0.82±0.05), significantly higher than KIQ and U‐Net OAI (0.86±0.03 vs. 0.84±0.04 and 0.85±0.03), and not significantly different from KIQ while significantly higher than 2D U‐Net on PROOF (0.78±0.07 vs. 0.77±0.07, P=0.10, and 0.73±0.07). The MPUnet performed significantly better on N=22 KL grade 3 CCBR scans with 0.78±0.06 vs. 0.75±0.08 for KIQ and 0.76±0.06 for 2D U‐Net. Data Conclusion: The MPUnet matched or exceeded the performance of state‐of‐the‐art knee MRI segmentation models across cohorts of variable sequences and patient demographics. The MPUnet required no manual tuning making it both accurate and easy‐to‐use. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background: The skin's aging process involves a decreased biosynthesis of extracellular matrix proteins (predominantly collagen) compounded by damage from environmental and intrinsic stressors. The Indian population is susceptible to skin damage given its geography and increasing urbanization or a genetic disposition. Previous studies have investigated nutrients such as collagen peptides, vitamins and phytonutrient‐rich botanical extracts for their individual benefits on skin. Aims: This study examined the collective effect of a proprietary blend of these nutrients (in Nutrova Collagen+Antioxidants; NCA) on skin parameters, which has not been previously studied, especially in an Indian context. Patients/Methods: 34 healthy, Indian women (mean age = 39.5 years) were given a placebo daily for 30 days to establish a baseline, followed by NCA for two intervals of 30 days. 3D image reconstruction allowed the analysis of skin topography and blemishes. Instrumental measurements also included skin firmness, elasticity, hydration, and transepidermal water loss. Clinical evaluation was used to grade blemishes, wrinkles and periorbital hyperpigmentation. Results: Based on instrumental evaluation, NCA significantly reduced wrinkle width, open pores, skin roughness, and the colour of hyperpigmented blemishes, while improving skin hydration, firmness and barrier function from baseline to Day 30 and Day 60. NCA also increased elasticity at Day 30. Clinical evaluation showed that periorbital hyperpigmentation and wrinkles reduced significantly. Conclusion: NCA is effective for improving overall skin health in Indian women. These results show that targeted nutrient supplementation can improve skin health and further research over extended durations is merited. [ABSTRACT FROM AUTHOR]

Objective: To determine the inflammatory analytes that predict clinical progression and evaluate their performance against biomarkers of neurodegeneration. Methods: A longitudinal study of MCI‐AD patients in a Discovery cohort over 15 months, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort over 36 months. Fifty‐three inflammatory analytes were measured in the CSF and plasma with a RBM multiplex analyte platform. Inflammatory analytes that predict clinical progression on Clinical Dementia Rating Scale‐Sum of Boxes (CDR‐SB) and Mini Mental State Exam scores were assessed in multivariate regression models. To provide context, key analyte results in ADNI were compared against biomarkers of neurodegeneration, hippocampal volume, and CSF neurofilament light (NfL), in receiver operating characteristic (ROC) analyses evaluating highest quartile of CDR‐SB change over two years (≥3 points). Results: Cerebrospinal fluid inflammatory analytes in relation to cognitive decline were best described by gene ontology terms, natural killer cell chemotaxis, and endothelial cell apoptotic process and in plasma, extracellular matrix organization, blood coagulation, and fibrin clot formation described the analytes. CSF CCL2 was most robust in predicting rate of cognitive change and analytes that correlated to CCL2 suggest IL‐10 pathway dysregulation. The ROC curves for ≥3 points change in CDR‐SB over 2 years when comparing baseline hippocampal volume, CSF NfL, and CCL2 were not significantly different. Interpretation: Baseline levels of immune cell chemotactic cytokine CCL2 in the CSF and IL‐10 pathway dysregulation impact longitudinal cognitive and functional decline in MCI‐AD. CCL2's utility appears comparable to biomarkers of neurodegeneration in predicting rapid decline. [ABSTRACT FROM AUTHOR]

Background: The objective of this study is to investigate decline prediction of cognitive test scores in stable and converting mild cognitive impairment (MCI) subjects using both nonparametric and parametric Alzheimer's disease (AD) progression modeling methods trained on data including cognitive tests, CSF measures, and neuroimaging biomarkers. Method: The study dataset consisted of yearly visits (2005‐2017) for 782 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects with normal cognition, MCI, or AD, including FreeSurfer‐based T1‐weighted brain MRI volumes (ventricles, hippocampus, whole brain, fusiform, and entorhinal cortex, all normalized with intracranial volume), cognitive tests (MMSE, CDR‐SB, ADAS‐Cog‐13, FAQ, and RAVLT‐immediate‐recall), CSF measures (Amyloid‐beta and p‐tau), and FDG‐PET. Two state‐of‐the‐art disease progression modeling methods, a nonparametric using LSTMs (Ghazi, M. M., et al., Medical Image Analysis 53, 39‐46, 2019) and a parametric using regression (Jedynak, B. M., et al., Neurobiology of Aging 36, 178‐184, 2015), were trained on 632 subjects and subsequently applied to predict month 24 to 96 MMSE scores for 150 independent test subjects using only their baseline and month 12 data. Result: The predictive power and prognostic capability of the AD progression modeling methods were assessed using the per‐visit mean absolute error (MAE) and area under the ROC curve (AUC) of predicted MMSE scores for stable MCI (sMCI) and converting MCI (cMCI) test subjects. The average MAE results for month 24 to 96 MMSE scores were as follows: nonparametric 1.39 to 1.04 (sMCI), 2.41 to 3.62 (cMCI); parametric 1.46 to 4.00 (sMCI), 2.42 to 2.53 (cMCI). The average AUC results for month 24 to 96 obtained based on the predicted MMSE scores were as follows (two‐sample t‐test, p < 0.001 in all cases): nonparametric 0.79 to 0.73; parametric 0.83 to 0.69. Conclusion: In almost all cases, the nonparametric method outperforms the parametric model in predicting MMSE scores. Moreover, predictions from both nonparametric and parametric methods can significantly discriminate between sMCI and cMCI groups. Though, the discrimination capability of the nonparametric method is superior in long‐term prediction of cognitive decline. [ABSTRACT FROM AUTHOR]