The purpose of this study was to investigate the relationships, by linear regression, between internal and external pelvic landmarks identified by two techniques: manual digitization or skin markers. It was hypothesized that the body mass index or the skinfold thickness are significant variables in these relationships. The internal pelvic landmarks were obtained with a stereoradiographic method. Results showed that the external coordinates are generally statistically different from the internal ones; manual digitization of the landmark reduces the soft tissue artifacts compared to the use of skin markers. Different regression models were obtained according to the external acquisition method. Body mass index or skinfold thickness was generally included as a significant variable in models along the direction of the soft tissue thickness: postero-anterior direction for the anterior-superior iliac spine, medio-lateral direction for the apex of the iliac crests. With the use of skin markers, models obtained for a specific internal landmark coordinate include generally many variables, such as the other two coordinates of the landmark, body mass index, or skinfold measurements. This study presented preliminary results on the relationships between internal and external pelvic landmark coordinates. More research is needed before the full relationships are understood and adequate models are developed.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1109/TBME.2006.886619 | DOI Listing |
Background: There is continuous demand for safe, effective cosmetic ingredients to treat the signs of aging skin, including fine lines, wrinkles, brown spots, discoloration, laxity, and sagging. While there are a plethora of cosmeceutical peptides, few combine anti-aging and anti-inflammatory benefits with small size.
Methods: Preclinical and clinical studies evaluated the anti-inflammatory properties, anti-aging benefits, and tolerability of acetyl dipeptide-31 amide (AP31), a novel, small, anti-aging micropeptide, to understand its impact as a multifaceted, cosmetic, anti-aging, and anti-inflammaging ingredient.
Background: Injectable biostimulator treatments stimulate endogenous collagen in aging skin, but whether they act through similar pathways is unknown. This study evaluates two biostimulatory agents' effects on genes, expressed proteins, and respective pathways as potential aging biomarkers and treatment outcomes.
Methods: This 13-week, randomized, single-center, comparative study compared volume change and gene expression stimulated by poly-L-lactic acid (PLLA-SCATM) and calcium hydroxylapatite (CaHA-R) via punch biopsy in the nasolabial fold (NLF).
Allergy
January 2025
Department of Dermatology, Icahn School of Medicine at the Mount Sinai, New York, New York, USA.
Introduction: Chronic hand eczema (CHE) is a highly prevalent inflammatory skin condition which is often resistant to conventional treatments. Molecular insights of CHE remain limited. Tape stripping combined with high-throughput RNA sequencing can now provide a better insight into CHE pathogenesis in a minimally invasive fashion.
View Article and Find Full Text PDFFASEB J
January 2025
HSS Research Institute, Hospital for Special Surgery, New York, New York, USA.
Aging is a risk factor for several chronic conditions, including intervertebral disc degeneration and associated back pain. Disc pathologies include loss of reticular-shaped nucleus pulposus cells, disorganization of annulus fibrosus lamellae, reduced disc height, and increased disc bulging. Sonic hedgehog, cytokeratin 19, and extracellular matrix proteins are markers of healthy disc.
View Article and Find Full Text PDFNat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!