Validity of an isometric mid-thigh pull dynamometer in male youth athletes

The purpose of the present study was to investigate the validity of an isometric mid-thigh pull dynamometer against a criterion measure (i.e., 1,000 Hz force platform) for assessing muscle strength in male youth athletes. Twenty-two male adolescent (age 15.3 ± 0.5 years) rugby league players performed four isometric mid-thigh pull efforts (i.e., two on the dynamometer and two on the force platform) separated by 5 minutes rest in a randomised and counterbalanced order. Mean bias, typical error of estimate (TEE) and Pearson correlation coefficient for peak force (PF) and peak force minus body weight (PFBW) from the force platform were validated against peak force from the dynamometer (DynoPF). When compared to PF and PFBW, mean bias (with 90% Confidence limits) for DynoPF was very large (-32.4 [-34.2 to -30.6] %) and moderate (-10.0 [-12.8 to -7.2] %), respectively. The TEE was moderate for both PF (8.1 [6.3 to 11.2] %) and PFBW (8.9 [7.0 to 12.4]). Correlations between DynoPF and PF (r 0.90 [0.79 to 0.95]) and PFBW (r 0.90 [0.80 to 0.95] were nearly perfect. The isometric mid-thigh pull assessed using a dynamometer underestimated PF and PFBW obtained using a criterion force platform. However, strong correlations between the dynamometer and force platform suggest that a dynamometer provides an appropriate alternative to assess isometric mid-thigh pull strength when a force platform is not available. Therefore, practitioners can use an isometric mid-thigh pull dynamometer to assess strength in the field with youth athletes but should be aware that it underestimates peak force

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Advances in genetics: widening our understanding of prostate cancer

Prostate cancer is a leading cause of cancer-related death in Western men. Our understanding of the genetic alterations associated with disease predisposition, development, progression, and therapy response is rapidly improving, at least in part, owing to the development of next-generation sequencing technologies. Large advances have been made in our understanding of the genetics of prostate cancer through the application of whole-exome sequencing, and this review summarises recent advances in this field and discusses how exome sequencing could be used clinically to promote personalised medicine for prostate cancer patients.</ns4:p

Influence of ocean–atmospheric oscillations on lake ice phenology in eastern North America

Our results reveal long-term trends in ice out dates (1836–2013) for twelve lakes in Maine, New Brunswick and New Hampshire, in eastern North America. The trends are remarkably coherent between lakes (rs = 0.462–0.933, p < 0.01) and correlate closely with the March–April (MA) instrumental temperature records from the region (rs = 0.488–0.816, p < 0.01). This correlation permits use of ice out dates as a proxy to extend the shorter MA instrumental record (1876–2013). Mean ice out dates trended progressively earlier during the recovery from the Little Ice Age through to the 1940s, and gradually became later again through to the late 1970s, when ice out dates had returned to values more typical of the late nineteenth century. Post-1970’s ice out dates resumed trending toward earlier dates, with the twenty-first century being characterized by the earliest ice out dates on record. Spectral and wavelet time series analysis indicate that ice out is influenced by several teleconnections including the Quasi-biennial Oscillation, El Niño-Southern Oscillation, North Atlantic Oscillation, as well as a significant correlation between inland lake records and the Atlantic Multidecadal Oscillation. The relative influence of these teleconnections is variable with notable shifts occurring after ~1870, ~1925, and ~1980–2000. The intermittent expression of these cycles in the ice out and MA instrumental record is not only influenced by absolute changes in the intensity of the various teleconnections and other climate drivers, but through phase interference between teleconnections, which periodically damps the various signals