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ObjectiveIn order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density.MethodsThis retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands’ density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity.ResultsIn total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001).ConclusionsDeep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.

Breast cancer and osteoporosis are common diseases that affect the survival and quality of life in postmenopausal women. Women with breast cancer are more likely to develop osteoporosis than women without breast cancer due to certain factors that can affect both diseases simultaneously. For instance, estrogen and the receptor activator of nuclear factor-κB ligand (RANKL) play important roles in the occurrence and development of these two diseases. Moreover, chemotherapy and hormone therapy administered to breast cancer patients also increase the incidence of osteoporosis, and in recent years, neuropeptide Y (NPY) has also been found to impact breast cancer and osteoporosis.Y1 and Y5 receptors are highly expressed in breast cancer, and Y1 and Y2 receptors affect osteogenic response, thus potentially highlighting a potential new direction for treatment strategies. In this paper, the relationship between breast cancer and osteoporosis, the influence of NPY on both diseases, and the recent progress in the research and treatment of these diseases are reviewed.

Background/Aims: Non-invasive measurement of intracranial pressure (ICP) using ultrasound has garnered increasing attention. This study aimed to compare the reliability of ultrasonographic measurement of optic nerve sheath diameter (ONSD) and transcranial Doppler (TCD) in detecting potential ICP elevations.Methods: Patients who needed lumbar puncture (LP) in the Department of Neurology were recruited from December 2016 to July 2017. The ONSD and TCD measurements were completed before LP.Results: One hundred sixty-five participants (mean age, 41.96 ± 14.64 years; 80 men; 29 patients with elevated ICP) were included in this study. The mean ICP was 170 ± 52 mmH2O (range, 75–400 mmH2O). Univariate analyses revealed that ICP was non-significantly associated with TCD parameters and significantly associated with ONSD (r = 0.60, P < 0.001). The mean ONSD of the elevated ICP group was significantly higher than that of the normal ICP group (4.53 ± 0.40 mm vs. 3.97 ± 0.23 mm; P < 0.001). Multivariate linear regression determined that the difference between ICP and ONSD is significant.Conclusions: In the early stage of intracranial hypertension, ONSD is more reliable for evaluating ICP than TCD.

Parkinson’s disease (PD) is a neurodegenerative disease that is generally thought to be caused by multiple factors, including environmental and genetic factors. Emerging evidence suggests that intestinal disturbances, such as constipation, are common non-motor symptoms of PD. Gut inflammation may be closely associated with pathogenesis in PD. This review aims to discuss the cross-talk between gut inflammation and PD pathology initiation and progression. Firstly, we will highlight the studies demonstrating how gut inflammation is related to PD. Secondly, we will analyze how gut inflammation spreads from the gastro-intestine to the brain. Here, we will mainly discuss the neural pathway of pathologic α-syn and the systemic inflammatory routes. Thereafter, we will address how alterations in the brain subsequently lead to dopaminergic neuron degeneration, in which oxidative stress, glutamate excitotoxicity, T cell driven inflammation and cyclooxygenase-2 (COX-2) are involved. We conclude a model of PD triggered by gut inflammation, which provides a new angle to understand the mechanisms of the disease.

Accumulating data demonstrated that hepatic endoplasmic reticulum (ER) stress was involved in the pathogenesis of liver fibrosis. Long-term chronic hepatocyte death contributed to liver fibrosis initiation and progression. Previous researches reported that ER stress sensor inositol-requiring enzyme 1 alpha (IRE1α) was first activated in the process of liver fibrosis. STF-083010 was an IRE1α RNase specific inhibitor. This study aimed to explore the effects of STF-083010 on carbon tetrachloride (CCl4)-induced liver injury and subsequent liver fibrosis. Mice were intraperitoneally (i.p.) injected with CCl4 (0.15 ml/kg) for 8 weeks. In STF-083010+CCl4 group, mice were injected with STF-083010 (30 mg/kg, i.p.), twice a week, beginning from the 6th week after CCl4 injection. CCl4 treatment markedly enhanced the levels of serum ALT, TBIL, DBIL and TBA, and STF-083010 had obviously extenuated CCl4-induced exaltation of ALT, DBIL, and TBA levels. CCl4-induced hepatic hydroxyproline and collagen I, major indicators of liver fibrosis, were alleviated by STF-083010. Additionally, CCl4-induced α-smooth muscle actin, a marker for hepatic stellate cells activation, was obviously attenuated in STF-083010-treated mice. Moreover, CCl4-induced upregulation of inflammatory cytokines was suppressed by STF-083010. Mechanistic exploration found that hepatic miR-122 was downregulated in CCl4-treated mice. Hepatic MCP1, CTGF, P4HA1, Col1α1, and Mmp9, target genes of miR-122, were upregulated in CCl4-treated mice. Interestingly, STF-083010 reversed CCl4-induced hepatic miR-122 downregulation. Correspondingly, STF-083010 inhibited CCl4-induced upregulation of miR-122 target genes. This study provides partial evidence that STF-083010 alleviated CCl4-induced liver injury and thus protected against liver fibrosis associated with hepatic miR-122.