Oligomerization of human islet amyloid polypeptide (hIAPP) is toxic and contributes to progressive reduction of β cell mass in patients with type 2 diabetes mellitus. Autophagy is a highly conserved homeostatic mechanism in eukaryotes. Previous studies have confirmed that hIAPP can promote autophagy in β cells, but the underlying molecular mechanism and cellular regulatory pathway of hIAPP-induced autophagy remains not fully elucidated. Accumulation of reactive oxygen species (ROS) causes hIAPP induced-β cell death. At present, little is known about the association between hIAPP-induced oxidative stress and autophagy in β cells. Therefore, the present study investigated the underlying molecular mechanism and regulatory pathway of hIAPP-induced autophagy. Transmission electron microscopy was used to observe the number of autophagosome in cells. Cell viability was determined by an MTT test. A 2′,7′-dichlorofluorescin diacetate assay was used to measure the relative levels of reactive ROS. Western blotting was used to detect expression of adenosine monophosphate-activated protein kinase (AMPK) and autophagic markers p62 and microtubule associated protein 1 light chain 3. The results demonstrated that hIAPP induces autophagy through ROS-mediated AMPK signaling pathway in INS-1 cells. Upregulation of autophagy by AMPK activator 5-aminoimidazole-4-carboxamide1-β-D-ribofuranoside decreased ROS and malondialdehyde generation, whereas inhibition of autophagy by 3-methyladenine and AMPK inhibitor compound C aggravated hIAPP-induced oxidative stress and toxicity in INS-1 cells. Taken together, the present study suggested that hIAPP induces autophagy via a ROS-mediated AMPK signaling pathway. Furthermore, autophagy serves as a cell-protective mechanism against hIAPP-induced toxicity and chemical promotion of autophagy through AMPK signaling pathway attenuates hIAPP induced cytotoxicity and oxidative stress in INS-1 cells.

Conversion of lignin feedstocks into aromatic chemicals is a highly desirable target for biorefineries, whose depolymerization often requires high temperatures and harsh conditions.

BACKGROUND Background: Machine learning algorithms well-suited in cancer research, especially in breast cancer for the investigation and development of riTo assess the performance of available machine learning-based breast cancer risk prediction model. OBJECTIVE Objective: To assess the performance of available machine learning-based breast cancer risk prediction model. METHODS Methods: As of June 9, 2021, articles on breast cancer risk prediction models by machine learning were searched in PubMed, Embase, and Web of Science. Studies describing the development or validation of risk prediction models for predicting future breast cancer risk were included. Pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. RESULTS Result: A total of 8 studies with 10 datasets were included. Neural network was the most common machine learning method for the development of risk prediction models. The pooled AUC of machine learning-based optimal risk prediction model reported in each study was 0.73 (95%CI: 0.66-0.80), which was higher than that of traditional risk factor-based risk prediction models (all Pheterogeneity < 0.001). The pooled AUC of neural network-based risk prediction model was higher than that of non-neural network-based optimal risk prediction model (0.71 vs. 0.68). Subgroup analysis showed that incorporation of imaging features risk models had a higher pooled AUC than model of non-incorporation of imaging features (0.73 vs. 0.61; Pheterogeneity =0.001). CONCLUSIONS Conclusions: The pooled machine learning-based breast cancer risk prediction model yield a good prediction performance and promising results.

On the basis of experimentally reported series of novel imide-functionalized ladder-type heteroarenes (fused bithiophene imide oligomers BTIn, n = 1–5), expanded BTIn up to 24 rings with 8 imide groups (n = 6–8) as well as some heteroarenes derived from BTI are designed. Charge transfer and delocalization properties of BTIn (n = 1–8) are studied theoretically by non-empirically optimal tuned range-separated density functional theory (DFT) (LC-BLYP*), LC-BLYP* combining polarizable continuum model (PCM) [LC-BLYP* (PCM, solid)], and traditional B3LYP functional. LC-BLYP* provides a good balance between localized and delocalized effects and reduces the electron self-interaction error of the traditional DFT method, whereas combining PCM model with tuned range-separated DFT could accurately describe the properties of single crystals. The relationships between geometries, electronic properties, and semiconductor performances are explored. Because of the good planarity induced by the fused thienothiophene rings with the imide, BTI oligomers show better performance than their derivatives with a similar number of thiophene rings. It was found that the BTI oligomers have very good planarity, large conjugation extent, fully delocalized polaron (over 16T units), and good semiconductor properties for both p- and n-types. BTI8 is good potential ambipolar semiconductor with a very small charge-injection barrier and large hole and electron mobilities of 16.60 and 3.02 cm2 V–1 s–1, respectively. Different roles of thiophene and imide rings in making BTI series a good semiconductor are also revealed by comparing BTI2 and BTI3 with thiophene-containing derivatives.

Background Several studies have explored the predictive performance of machine learning–based breast cancer risk prediction models and have shown controversial conclusions. Thus, the performance of the current machine learning–based breast cancer risk prediction models and their benefits and weakness need to be evaluated for the future development of feasible and efficient risk prediction models. Objective The aim of this review was to assess the performance and the clinical feasibility of the currently available machine learning–based breast cancer risk prediction models. Methods We searched for papers published until June 9, 2021, on machine learning–based breast cancer risk prediction models in PubMed, Embase, and Web of Science. Studies describing the development or validation models for predicting future breast cancer risk were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and the clinical applicability of the included studies. The pooled area under the curve (AUC) was calculated using the DerSimonian and Laird random-effects model. Results A total of 8 studies with 10 data sets were included. Neural network was the most common machine learning method for the development of breast cancer risk prediction models. The pooled AUC of the machine learning–based optimal risk prediction model reported in each study was 0.73 (95% CI 0.66-0.80; approximate 95% prediction interval 0.56-0.96), with a high level of heterogeneity between studies (Q=576.07, I2=98.44%; P<.001). The results of head-to-head comparison of the performance difference between the 2 types of models trained by the same data set showed that machine learning models had a slightly higher advantage than traditional risk factor–based models in predicting future breast cancer risk. The pooled AUC of the neural network–based risk prediction model was higher than that of the nonneural network–based optimal risk prediction model (0.71 vs 0.68, respectively). Subgroup analysis showed that the incorporation of imaging features in risk models resulted in a higher pooled AUC than the nonincorporation of imaging features in risk models (0.73 vs 0.61; Pheterogeneity=.001, respectively). The PROBAST analysis indicated that many machine learning models had high risk of bias and poorly reported calibration analysis. Conclusions Our review shows that the current machine learning–based breast cancer risk prediction models have some technical pitfalls and that their clinical feasibility and reliability are unsatisfactory.