Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images.

The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of computational pipelines for image processing that guarantee segmentation reliability and a significant degree of automation. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images. Specifically, here we focused on the level set methodology and compared the performances of two standard approaches, a classical evolution model and a three-dimension geodesic model, with the performances of an original first-order modification of this latter one. The results of this analysis show that these gradient-based schemes guarantee reliability with respect to manual segmentation and that the first-order scheme requires a computational burden that is significantly smaller than the one needed by the second-order approach.

Distinct features of immune activation and exhaustion markers in people with perinatally-acquired HIV.

OBJECTIVE: The aim of this study was to characterize T cell activation, exhaustion, maturation and Treg frequencies in individuals who acquire perinatal HIV (PHIV), in individuals who acquired HIV as adult (AHIV), and in healthy controls (HC). DESIGN: This cross-sectional study included people with HIV ≥ 14 and <40 years, HIV-RNA < 50 copies/mL on antiretroviral therapy for at least 6 months, and HC. METHODS: We assessed the expression of PD-1, TIM-3, EOMES, CD38+ DR+, maturation status by CD4+ and CD8+T cells and the frequency of CD4+ and CD8+ Treg cells. Principal component analysis (PCA) and k-means cluster analysis investigated which combination of immunological parameters better associated with each group. RESULTS: 26 PHIV and 18 AHIV with median ages of 26 (8.0) and 28 (6.8) years were consecutively enrolled. PHIV showed significant higher frequency of naïve and lower frequency of terminal effector memory CD4+ and CD8+ T cells than AHIV. AHIV exhibited higher expression of exhaustion and activation markers. The statistical analysis returned two clusters with 94% of specificity and 88% of sensitivity identifying PHIV vs. AHIV. The 9 HC had a lower expression of exhaustion markers on both CD4+ and CD8+T lymphocytes than PHIV and AHIV. CONCLUSIONS: These data may exclude major alterations of lymphopoiesis in PHIV, with even lower state of immune-activation and exhaustion compared with AHIV. This suggests that recent lack of virological control, may affect immune activation and exhaustion of CD4+ and CD8+ T cells.

Longitudinal Cluster Analysis of Hemodialysis Patients with COVID-19 in the Pre-Vaccination Era.

Coronavirus disease 2019 (COVID-19) in hemodialysis patients (HD) is characterized by heterogeneity of clinical presentation and outcomes. To stratify patients, we collected clinical and laboratory data in two cohorts of HD patients at COVID-19 diagnosis and during the following 4 weeks. Baseline and longitudinal values were used to build a linear mixed effect model (LME) and define different clusters. The development of the LME model in the derivation cohort of 17 HD patients (66.7 ± 12.3 years, eight males) allowed the characterization of two clusters (cl1 and cl2). Patients in cl1 presented a prevalence of females, higher lymphocyte count, and lower levels of lactate dehydrogenase, C-reactive protein, and CD8 + T memory stem cells as a possible result of a milder inflammation. Then, this model was tested in an independent validation cohort of 30 HD patients (73.3 ± 16.3 years, 16 males) assigned to cl1 or cl2 (16 and 14 patients, respectively). The cluster comparison confirmed that cl1 presented a milder form of COVID-19 associated with reduced disease activity, hospitalization, mortality rate, and oxygen requirement. Clustering analysis on longitudinal data allowed patient stratification and identification of the patients at high risk of complications. This strategy could be suitable in different clinical settings.