Sarcopenia-related Traits, Body Mass Index and Ovarian Cancer Risk: Investigation of Causal Relationships Through Multivariable Mendelian Randomization Analyses

Objective: This study was aimed at exploring the causal relationships of four sarcopenia-related traits (appendicular lean mass, usual walking pace, right hand grip strength, and levels of moderate to vigorous physical activity) with body mass index (BMI) and ovarian cancer risk, by using univariable and multivariable Mendelian randomization (MR) methods. Materials and Methods: Univariable and multivariable MR was performed to estimate causal relationships among sarcopenia-related traits, BMI, and ovarian cancer risk, in aggregated genome-wide association study (GWAS) data from the UK Biobank. Genetic variants associated with each variable (P < 5 × 10−8) were identified as instrumental variables. Three methods—inverse variance weighted (IVW) analysis, weighted median analysis, and MR-Egger regression—were used. Results: Univariable MR analyses revealed positive causal effects of high appendicular lean mass (P = 0.02) and high BMI (P = 0.001) on ovarian cancer occurrence. In contrast, a genetically predicted faster usual walking pace was associated with lower risk of ovarian cancer (P = 0.03). No evidence was found supporting roles of right hand grip strength and levels of moderate to vigorous physical activity in ovarian cancer development (P = 0.56 and P = 0.22, respectively). In multivariable MR analyses, the association between a genetically predicted faster usual walking pace and lower ovarian cancer risk remained significant (P = 0.047). Conclusions: Our study highlights a role of slower usual walking pace in the development of ovarian cancer. Further studies are required to validate our findings and understand the underlying mechanisms

Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models

Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue, but also increases false positives, resulting in unnecessary biopsies. This study investigated the use of Google AutoML Vision (Mountain View, CA), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound. Methods: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model were evaluated, while also investigating training strategies to enhance model performances. Pathology from the patient’s biopsy were used as a reference standard. Results: The image classification models trained under different conditions demonstrated areas under the precision recall curve (AUC) ranging from 0.85 to 0.96 during internal validation. Once deployed, the model with highest internal performance demonstrated a sensitivity of 100% (95% confidence interval (CI) of 73.5-100%), specificity of 83.3% (CI=51.6-97.9%), positive predictive value (PPV) of 85.7% (CI=62.9-95.5%), and negative predictive value (NPV) of 100% (CI non-evaluable) in an independent dataset. The object detection model demonstrated lower performance internally during development (AUC=0.67) and during prediction in the independent dataset (sensitivity=75.0% (CI=42.8-94.5), specificity=80.0% (CI=51.9-95.7), PPV=75.0% (CI=50.8-90.0), NPV=80.0% (CI=59.3-91.7%)), but was able to demonstrate the location of the lesion within the image. Conclusions: Two models appear to be useful tools for identifying and classifying suspicious areas on B-mode images of indeterminate breast lesions

Inhibition of Jak-STAT3 pathway enhances bufalin-induced apoptosis in colon cancer SW620 cells

Abstract Background The purpose of the research is to investigate the roles of Jak-STAT3 signaling pathway in bufalin-induced apoptosis in colon cancer SW620 cells. Methods The inhibitory effects of bufalin on cell proliferation were determined by MTT (Methyl thiazolyltetrazolium) assay. The morphological changes of cells were measured by Wright-Giemsa staining. The cell cycle arrest and apoptosis were tested by flow cytometry analysis. Western Blot was used to determine the protein expression of the apoptosis inhibitors livin and caspase-3, the apoptosis-related proteins Bax and Bcl-2, as well as the key protein kinases in the Jak-stat3 signaling pathway, stat3 and p-stat3. Results (1) Bufalin inhibited the proliferation of SW620 cells. IC50 at 24 h, 48 h and 72 h were 76.72 ± 6.21 nmol/L, 34.05 ± 4.21 nmol/L and 16.7 ± 6.37 nmol/L. (2) Bufalin induced SW620 cell cycle arrest and apoptosis, indicated by the appearance of apoptotic bodies; (3) The results from flow cytometry demonstrated that there was cell cycle G2/M phase arrest in 20 nmol/L bufalin treatment group (36.29 ± 2.11% vs 18.39 ± 1.74%, P Conclusions Bufalin not only inhibited the growth of colon cancer SW620 cells, but also induced apoptosis of SW620 cells. Activation of caspase-3, up-regulation of Bax, down-regulation of livin and Bcl-2, as well as inhibition of Jak-stat3 signaling pathway might be the important mechanisms for the bufalin-induced apoptosis.</p