ABSTRACT
OBJECTIVES: The aims of the study were to investigate the relationship between sarcopenia and renin-angiotensin system-related disorders and to explore the effects of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on muscle mass/function and physical performance. DESIGN: This multicenter, cross-sectional study was performed using ISarcoPRM algorithm for the diagnosis of sarcopenia. RESULTS: Of the 2613 participants (mean age = 61.0 ± 9.5 yrs), 1775 (67.9%) were hypertensive. All sarcopenia-related parameters (except chair stand test in males) were worse in hypertensive group than in normotensive group (all P < 0.05). When clinical/potential confounders were adjusted, hypertension was found to be an independent predictor of sarcopenia in males (odds ratio = 2.403 [95% confidence interval = 1.514-3.813]) and females (odds ratio = 1.906 [95% confidence interval = 1.328-2.734], both P < 0.001). After adjusting for confounding factors, we found that all sarcopenia-related parameters (except grip strength and chair stand test in males) were independently/negatively related to hypertension (all P < 0.05). In females, angiotensin-converting enzyme inhibitors users had higher grip strength and chair stand test performance values but had lower anterior thigh muscle thickness and gait speed values, as compared with those using angiotensin II receptor blockers (all P < 0.05). CONCLUSIONS: Hypertension was associated with increased risk of sarcopenia at least 2 times. Among antihypertensives, while angiotensin-converting enzyme inhibitors had higher muscle function values, angiotensin II receptor blockers had higher muscle mass and physical performance values only in females.
Subject(s)
Hypertension , Sarcopenia , Male , Female , Humans , Middle Aged , Aged , Sarcopenia/diagnosis , Muscle Strength/physiology , Cross-Sectional Studies , Hand Strength/physiology , Angiotensin-Converting Enzyme Inhibitors/adverse effects , Angiotensin Receptor Antagonists/pharmacologyABSTRACT
BACKGROUND: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). CONCLUSIONS: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.
