Metformin Attenuates TGF-ß1-Induced Fibrosis in Salivary Gland: A Preliminary Study.

Fibrosis commonly arises from salivary gland injuries induced by factors such as inflammation, ductal obstruction, radiation, aging, and autoimmunity, leading to glandular atrophy and functional impairment. However, effective treatments for these injuries remain elusive. Transforming growth factor-beta 1 (TGF-ß1) is fundamental in fibrosis, advancing fibroblast differentiation into myofibroblasts and enhancing the extracellular matrix in the salivary gland. The involvement of the SMAD pathway and reactive oxygen species (ROS) in this context has been postulated. Metformin, a type 2 diabetes mellitus (T2DM) medication, has been noted for its potent anti-fibrotic effects. Through human samples, primary salivary gland fibroblasts, and a rat model, this study explored metformin's anti-fibrotic properties. Elevated levels of TGF-ß1 (p < 0.01) and alpha-smooth muscle actin (α-SMA) (p < 0.01) were observed in human sialadenitis samples. The analysis showed that metformin attenuates TGF-ß1-induced fibrosis by inhibiting SMAD phosphorylation (p < 0.01) through adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK)-independent pathways and activating the AMPK pathway, consequently suppressing NADPH oxidase 4 (NOX4) (p < 0.01), a main ROS producer. Moreover, in rats, metformin not only reduced glandular fibrosis post-ductal ligation but also protected acinar cells from ligation-induced injuries, thereby normalizing the levels of aquaporin 5 (AQP5) (p < 0.05). Overall, this study underscores the potential of metformin as a promising therapeutic option for salivary gland fibrosis.

Computer-aided diagnosis of early-stage colorectal cancer using nonmagnified endoscopic white-light images (with videos).

BACKGROUND AND AIMS: Differentiation of colorectal cancers (CRCs) with deep submucosal invasion (T1b) from CRCs with superficial invasion (T1a) or no invasion (Tis) is not straightforward. This study aimed to develop a computer-aided diagnosis (CADx) system to establish the diagnosis of early-stage cancers using nonmagnified endoscopic white-light images alone. METHODS: From 5108 images, 1513 lesions (Tis, 1074; T1a, 145; T1b, 294) were collected from 1470 patients at 10 academic hospitals and assigned to training and testing datasets (3:1). The ResNet-50 network was used as the backbone to extract features from images. Oversampling and focal loss were used to compensate class imbalance of the invasive stage. Diagnostic performance was assessed using the testing dataset including 403 CRCs with 1392 images. Two experts and 2 trainees read the identical testing dataset. RESULTS: At a 90% cutoff for the per-lesion score, CADx showed the highest specificity of 94.4% (95% confidence interval [CI], 91.3-96.6), with 59.8% (95% CI, 48.3-70.4) sensitivity and 87.3% (95% CI, 83.7-90.4) accuracy. The area under the characteristic curve was 85.1% (95% CI, 79.9-90.4) for CADx, 88.2% (95% CI, 83.7-92.8) for expert 1, 85.9% (95% CI, 80.9-90.9) for expert 2, 77.0% (95% CI, 71.5-82.4) for trainee 1 (vs CADx; P = .0076), and 66.2% (95% CI, 60.6-71.9) for trainee 2 (P < .0001). The function was also confirmed on 9 short videos. CONCLUSIONS: A CADx system developed with endoscopic white-light images showed excellent per-lesion specificity and accuracy for T1b lesion diagnosis, equivalent to experts and superior to trainees. (Clinical trial registration number: UMIN000037053.).

Computer-aided diagnosis of serrated colorectal lesions using non-magnified white-light endoscopic images.

PURPOSE: Computer-aided diagnosis systems for polyp characterization are commercially available but cannot recognize subtypes of sessile lesions. This study aimed to develop a computer-aided diagnosis system to characterize polyps using non-magnified white-light endoscopic images. METHODS: A total of 2249 non-magnified white-light images from 1030 lesions including 534 tubular adenomas, 225 sessile serrated adenoma/polyps, and 271 hyperplastic polyps in the proximal colon were consecutively extracted from an image library and divided into training and testing datasets (4:1), based on the date of colonoscopy. Using ResNet-50 networks, we developed a classifier (1) to differentiate adenomas from serrated lesions, and another classifier (2) to differentiate sessile serrated adenoma/polyps from hyperplastic polyps. Diagnostic performance was assessed using the testing dataset. The computer-aided diagnosis system generated a probability score for each image, and a probability score for each lesion was calculated as the weighted mean with a log10-transformation. Two experts (E1, E2) read the identical testing dataset with a probability score. RESULTS: The area under the curve of classifier (1) for adenomas was equivalent to E1 and superior to E2 (classifier 86%, E1 86%, E2 69%; classifier vs. E2, p < 0.001). In contrast, the area under the curve of classifier (2) for sessile serrated adenoma/polyps was inferior to both experts (classifier 55%, E1 68%, E2 79%; classifier vs. E2, p < 0.001). CONCLUSION: The classifier (1) developed using white-light images alone compares favorably with experts in differentiating adenomas from serrated lesions. However, the classifier (2) to identify sessile serrated adenoma/polyps is inferior to experts.

Automatic Measurement of Endometrial Thickness From Transvaginal Ultrasound Images.

Purpose: Endometrial thickness is one of the most important indicators in endometrial disease screening and diagnosis. Herein, we propose a method for automated measurement of endometrial thickness from transvaginal ultrasound images. Methods: Accurate automated measurement of endometrial thickness relies on endometrium segmentation from transvaginal ultrasound images that usually have ambiguous boundaries and heterogeneous textures. Therefore, a two-step method was developed for automated measurement of endometrial thickness. First, a semantic segmentation method was developed based on deep learning, to segment the endometrium from 2D transvaginal ultrasound images. Second, we estimated endometrial thickness from the segmented results, using a largest inscribed circle searching method. Overall, 8,119 images (size: 852 × 1136 pixels) from 467 cases were used to train and validate the proposed method. Results: We achieved an average Dice coefficient of 0.82 for endometrium segmentation using a validation dataset of 1,059 images from 71 cases. With validation using 3,210 images from 214 cases, 89.3% of endometrial thickness errors were within the clinically accepted range of ±2 mm. Conclusion: Endometrial thickness can be automatically and accurately estimated from transvaginal ultrasound images for clinical screening and diagnosis.