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How TGF-b1-mediated signaling pathways are finely tuned to orchestrate the generation of carcinoma-associated fibroblasts (CAFs) is poorly understood. Here, we demonstrate that miR-21 and the signaling of its target Smad 7 determine TGF-b1-induced CAF formation. In primary cultured fibroblasts, mature miR-21 increases after TGF-b1 treatment, whereas the Smad 7 protein level decreases. MiR-21 binds to the 39 UTR of Smad7 mRNA and inhibits its translation, rather than causing its degradation. Most importantly, Smad 7 is bound to Smad 2 and 3, which are thought to competitively bind to TGFBR1, and prevents their activation upon TGF-b1 stimulation. The depletion of miR-21 or the overexpression of Smad 7 blocks TGF-b1-induced CAF formation, whereas the overexpression of miR-21 or the depletion of Smad 7 promotes CAF formation, even without TGF-b1 stimulation. Collectively, these findings clearly demonstrate that miR-21 and Smad7 are critical regulators of TGF-b1 signaling during the induction of CAF formation. [ABSTRACT FROM AUTHOR]

Background • Type 2 diabetes (T2D) and osteoporosis are both diseases with a high clinical incidence. Among the population with diabetes, T2D accounts for approximately 90%. With the change in people’s eating habits and lifestyles, the incidence rate is gradually increasing. Aim • We aimed to explore the relationship between the change in the Geriatric Nutritional Risk Index (GNRI) and the change in bone metabolism index parameters in elderly male patients with T2D and the occurrence of osteoporosis. Methods • A total of 290 elderly male patients with type 2 diabetes (T2D) diagnosed in North China University of Science and Technology Affiliated Hospital from October 2019 to February 2022 were selected for GNRI evaluation. Of these patients, 148 with a GNRI > 98 (the normal group) and 142 with a GNRI ≤ 98 (the risk group) were selected for the study. The levels of 1,25-hydroxyvitamin D3 [1,25 (OH) 2D3], type 1 collagen N-terminal propeptide (P1NP), serum type 1 collagen C-terminal peptide hinge (S-CTX), osteocalcin (OC) and serum bone alkaline phosphatase (BALP) in the 2 groups were detected and compared. A dual-energy bone mineral density instrument was used to detect the bone mineral density (BMD) in the 2 groups. The logistic regression model was used to analyze the relationship between the occurrence of osteoporosis and indicators such as GNRI, and the receiver operating characteristic (ROC) curve was drawn to analyze the value of GNRI in predicting osteoporosis in elderly patients with T2D. Results • The 1,25(OH)2D3 and P1NP levels in the risk group were lower than in the normal group, and the serum S-CTX and BALP levels in the risk group were higher than in the normal group; the differences were statistically significant (P <.05). The average BMD values of femoral neck, femur trochanter, Ward triangle and lumbar spine in the risk group were lower than in the normal group; the differences were statistically significant (P < .05). There were 70 patients with osteoporosis in the risk group and 9 patients with osteoporosis in the normal group. The difference in the detection rate of osteoporosis between the 2 groups was statistically significant (χ2 = 68.281; P = .000 < .05). The area under the curve (AUC) value under the ROC curve predicted by the GNRI for osteoporosis in elderly patients with T2D was 0.719, the sensitivity was 51.43% and the specificity was 97.26%. The logistic regression model showed that duration of diabetes, glycated hemoglobin A1c (HbA1c), S-CTX and BALP were independent risk factors for osteoporosis in elderly male patients with T2D (P < .05). Increased 1,25(OH)2D3, ALB and GNRI can reduce the risk for osteoporosis in elderly male patients with T2D (P<.05). Conclusion • GNRI can reflect the nutritional status of elderly male patients with T2D, which is related to some extent to osteoporosis caused by loss of bone mass. [ABSTRACT FROM AUTHOR]

Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries. [ABSTRACT FROM AUTHOR]