The effect of ballistic potentiation protocols on elite sprint swimming: Optimizing performance.

Warming up prior to an athletic event is important for performance, however in some competition scenarios there is a long wait between completing the warm up and the event. Thus potentiation protocols are becoming increasingly popular in a competition environment. The aim of the study was to determine the effects of practical potentiation protocols on 15m start performance in national level swimmers. Eleven national level swimmers participated in the study. Using a randomized cross over design participants completed a 15m swimming start following 4 different experimental conditions (swim specific control, resisted band squat, weighted counter movement jumps, drop jumps from a 45cm box) each separated by at least 48h. A repeated measures ANOVA showed a significant difference in 15-m swimming start performance following different warm-up protocols (F (1.646, 14.810) =6.968, p=0.01) A Post hoc Bonferroni test indicated that 15-m start time was significantly quicker with the band squat protocol compared to the swim specific protocol (6.65 ± 0.43 v 6.78 ± 0.43 s respectively, p = 0.04). The results conclude that practical potentiation protocols are able to enhance 15-m swim start performance when combined with a swim specific warm-up and supports the use of post activation potentiation (PAP) during competitive swimming environments

A digital pathology workflow for the segmentation and classification of gastric glands: Study of gastric atrophy and intestinal metaplasia cases.

Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric atrophy (GA) and gastric intestinal metaplasia (IM) of the mucosa of the stomach have been found to increase the risk of gastric cancer and are considered precancerous lesions. Therefore, the early detection of GA and IM may have a valuable role in histopathological risk assessment. However, GA and IM are difficult to confirm endoscopically and, following the Sydney protocol, their diagnosis depends on the analysis of glandular morphology and on the identification of at least one well-defined goblet cell in a set of hematoxylin and eosin (H&E) -stained biopsy samples. To this end, the precise segmentation and classification of glands from the histological images plays an important role in the diagnostic confirmation of GA and IM. In this paper, we propose a digital pathology end-to-end workflow for gastric gland segmentation and classification for the analysis of gastric tissues. The proposed GAGL-VTNet, initially, extracts both global and local features combining multi-scale feature maps for the segmentation of glands and, subsequently, it adopts a vision transformer that exploits the visual dependences of the segmented glands towards their classification. For the analysis of gastric tissues, segmentation of mucosa is performed through an unsupervised model combining energy minimization and a U-Net model. Then, features of the segmented glands and mucosa are extracted and analyzed. To evaluate the efficiency of the proposed methodology we created the GAGL dataset consisting of 85 WSI, collected from 20 patients. The results demonstrate the existence of significant differences of the extracted features between normal, GA and IM cases. The proposed approach for gland and mucosa segmentation achieves an object dice score equal to 0.908 and 0.967 respectively, while for the classification of glands it achieves an F1 score equal to 0.94 showing great potential for the automated quantification and analysis of gastric biopsies

Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset

Gastric cancer is one of the most common cancers and a leading cause of cancer-related death worldwide. Among the risk factors of gastric cancer, the gastric intestinal metaplasia (IM) has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM could allow risk stratification regarding the possibility of progression to cancer. To this end, accurate classification of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. To date, although many gland segmentation approaches have been proposed, no general model has been proposed for the identification of IM glands. Thus, in this paper, we propose a model for gastric glands’ classification. More specifically, we propose a multi-scale deformable transformer-based network for glands’ classification into normal and IM gastric glands. To evaluate the efficiency of the proposed methodology we created the IMGL dataset consisting of 1000 gland images, including both intestinal metaplasia and normal cases received from 20 Whole Slide Images (WSI). The results showed that the proposed approach achieves an F1 score equal to 0.94, showing great potential for the gastric glands’ classification

Preprint: Mutation Rate Evolution Drives Immune Escape In Mismatch Repair-Deficient Cancer

SUMMARY Mutation rate optimisation drives evolution and immune evasion of bacteria and lentiviral strains, including HIV. Whether evolving cancer lineages similarly adapt mutation rates to increase tumour cell fitness is unknown. Here, by mapping the clonal topography of mismatch repair-deficient (MMRd) colorectal cancer, we show that genomic MMRd mutability co-evolves with neoantigen selection to drive intratumour diversification and immune escape. Mechanistically, we find that microsatellite instability modulates subclonal DNA repair by toggling two hypermutable mononucleotide homopolymer runs in the mismatch repair genes MSH6 and MSH3 (C8 and A8, respectively) through stochastic frameshift switching. Spontaneous mutation and reversion at these evolvability switches modulates subclonal mutation rate, mutation bias, and clonal HLA diversity during MMRd cancer evolution. Combined experimental and simulation studies demonstrate that subclonal immune selection favours incremental MMR mutations. MMRd cancers thus fuel intratumour heterogeneity by adapting subclonal mutation rate and mutation bias to immune selection, revealing a conserved co-evolutionary arms race between neoantigen selection and adaptive genomic mutability. Our work reveals layers of mutational complexity and microsatellite biology in MMRd cancer evolution previously hidden in bulk analyses

Homopolymer switches mediate adaptive mutability in mismatch repair-deficient colorectal cancer

Mismatch repair (MMR)-deficient cancer evolves through the stepwise erosion of coding homopolymers in target genes. Curiously, the MMR genes MutS homolog 6 (MSH6) and MutS homolog 3 (MSH3) also contain coding homopolymers, and these are frequent mutational targets in MMR-deficient cancers. The impact of incremental MMR mutations on MMR-deficient cancer evolution is unknown. Here we show that microsatellite instability modulates DNA repair by toggling hypermutable mononucleotide homopolymer runs in MSH6 and MSH3 through stochastic frameshift switching. Spontaneous mutation and reversion modulate subclonal mutation rate, mutation bias and HLA and neoantigen diversity. Patient-derived organoids corroborate these observations and show that MMR homopolymer sequences drift back into reading frame in the absence of immune selection, suggesting a fitness cost of elevated mutation rates. Combined experimental and simulation studies demonstrate that subclonal immune selection favors incremental MMR mutations. Overall, our data demonstrate that MMR-deficient colorectal cancers fuel intratumor heterogeneity by adapting subclonal mutation rate and diversity to immune selection