MicroRNAs (miRNAs) play important roles in regulating tumour development and progression. Here we show that miR-647 is repressed in gastric cancer (GC), and associated with GC metastasis. Moreover, we identify that miR-647 can suppress GC cell migration and invasion in vitro. Mechanistically, we confirm miR-647 directly binds to the 3' untranslated regions of SRF mRNA, and SRF binds to the CArG box located at the MYH9 promoter. CCG-1423, an inhibitor of RhoA/SRF-mediated gene transcription, inhibits the expression of MYH9, especially in SRF downregulated cells. Overexpression of miR-647 inhibits MGC 80-3 cells' metastasis in orthotropic GC models, but increasing SRF expression in these cells reverses this change. Importantly, we found the synergistic inhibition effect of CCG-1423 and agomir-647, an engineered miRNA mimic, on cancer metastasis in orthotropic GC models. Our study demonstrates that miR-647 functions as a tumor metastasis suppressor in GC by targeting SRF/MYH9 axis.

Our findings suggest that SILS + 1 might be safe and feasible for rectosigmoid cancer when performed by experienced surgeons. It offers minimal invasiveness without compromising oncologic treatment principles. Trial Registration This trial was registered on ClinicalTrials.gov (NCT02117557).

Retroperitoneoscopic partial adrenalectomy is technically safe. It has therapeutic results similar to those of total adrenalectomy in patients with primary aldosteronism due to aldosteronoma.

Casticin, a compound purified from the Chinese herb Viticis Fructus, has been proven effective in preventing tumor progression in previous studies. Ulcerative colitis (UC) is a common inflammatory bowel disease that affects millions of people worldwide, but no effective and safe drugs are available. In this study, we aimed to study how did casticin affect UC by evaluating its effects on dextran sulfate sodium (DSS)-induced colitis in mice. Our data suggested that casticin attenuated body weight loss, colon length shortening, and pathological damage in the colon of DSS-treated mice. Casticin decreased reactive oxygen species level and chemocytokines (IL-1β, IL-6, TNF-α) productions in colon tissue. The decreased reactive oxygen species level and suppressed proinflammatory cytokines productions were also confirmed in casticin-treated LPS-stimulated RAW264.7 cells and hydrogen peroxide-treated CACO-2 cells in vitro. Mechanistically, casticin treatment prevented the profound activation of AKT signaling caused by DSS administration. And casticin inhibited the productions of proinflammatory chemocytokines through downregulating AKT/NF-κB pathway in macrophages. Meanwhile, data revealed that casticin increased expressions of endogenous antioxidants peroxiredoxin 3 and MnSOD were through activation in FOXO3α signaling by downregulating AKT signaling in colon epithelium cells. Our findings demonstrated that casticin alleviated DSS-induced UC by increasing the antioxidant enzyme peroxiredoxin 3 and MnSOD expressions, and decreasing the production of proinflammatory chemocytokines through inhibition of AKT signaling.

Nicotine has long been considered a potent therapeutic agent for neuroprotection. This study reported the positive effect of nicotine treatment on cognitive dysfunction after partial hepatectomy in aged rats. Furthermore, the underlying mechanism may involve activation of the BDNF/TrkB signaling pathway and inhibition of the NF-κB signaling pathway in the hippocampus.

No abstract

Beyond detecting brain damage or tumors with magnetic resonance brain imaging, little success has been attained on identifying individual differences, e.g., sex or brain disorders. The current study aims to build an industrial-grade brain imaging-based classifier to infer individual differences using deep learning/transfer learning on big data. We pooled 34 datasets to constitute the largest brain magnetic resonance image sample to date (85,721 samples from 50,876 participants), and then applied a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, to build an industrial-grade sex classifier. We achieved 94.9% accuracy in cross-dataset-validation, i.e., the model can classify the sex of a participant with brain structural imaging data from anybody and any scanner with about 95% accuracy. We then explored the potential of a deep convolutional network to objectively diagnose brain disorders. Using transfer learning, the model fine-tuned to Alzheimers Disease (AD) achieved 88.4% accuracy in leave-sites-out five-fold cross-validation on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset and 86.1% accuracy for a direct test on an unseen independent dataset (OASIS). When directly testing this AD classifier on brain images of mild cognitive impairment (MCI) patients, 64.2% who finally converted into AD were predicted as AD, versus 25.9% who did not convert into AD (during the ADNI data collection period, but might convert in the future) were predicted as AD. The AD classifier also achieved high specificity in direct testing on other brain disorder datasets. Occlusion tests showed that hypothalamus, superior vermis, thalamus, amygdala and limbic system areas played critical roles in predicting sex, and hippocampus, parahippocampal gyrus, putamen and insula were crucial for predicting AD. Finally, the transfer learning framework failed to achieve practical accuracy for psychiatric disorders, which remain open questions for future studies. We openly shared our preprocessed data, trained model, code and framework, as well as built an online predicting website (http://brainimagenet.org:8088) for whomever is interested in testing our classifier with brain imaging data from anybody and from any scanner.

No abstract