ABSTRACT
BACKGROUND: Rectal mucinous adenocarcinoma (MAC) is a rare pathological type of rectal cancer with unique pathological features and a poor prognosis. It is difficult to diagnose and treat early because of the lack of specific manifestations in some aspects of the disease. The common metastatic organs of rectal cancer are the liver and lung; however, rectal carcinoma with metastasis to subcutaneous soft tissue is a rare finding. CASE SUMMARY: In this report, the clinical data, diagnosis and treatment process, and postoperative pathological features of a patient with left waist subcutaneous soft tissue masses were retrospectively analyzed. The patient underwent surgical treatment after admission and recovered well after surgery. The final pathological diagnosis was rectal MAC with left waist subcutaneous soft tissue metastasis. CONCLUSION: Subcutaneous soft tissue metastasis of rectal MAC is rare, and it can suggest that the tumor is disseminated, and it can appear even earlier than the primary malignant tumor, which is occult and leads to a missed diagnosis and misdiagnosis clinically. When a subcutaneous soft tissue mass of unknown origin appears in a patient with rectal cancer, a malignant tumor should be considered.
ABSTRACT
BACKGROUND: Prediction of drug-drug interactions (DDIs) can reveal potential adverse pharmacological reactions between drugs in co-medication. Various methods have been proposed to address this issue. Most of them focus on the traditional link prediction between drugs, however, they ignore the cold-start scenario, which requires the prediction between known drugs having approved DDIs and new drugs having no DDI. Moreover, they're restricted to infer whether DDIs occur, but are not able to deduce diverse DDI types, which are important in clinics. RESULTS: In this paper, we propose a cold start prediction model for both single-type and multiple-type drug-drug interactions, referred to as CSMDDI. CSMDDI predict not only whether two drugs trigger pharmacological reactions but also what reaction types they induce in the cold start scenario. We implement several embedding methods in CSMDDI, including SVD, GAE, TransE, RESCAL and compare it with the state-of-the-art multi-type DDI prediction method DeepDDI and DDIMDL to verify the performance. The comparison shows that CSMDDI achieves a good performance of DDI prediction in the case of both the occurrence prediction and the multi-type reaction prediction in cold start scenario. CONCLUSIONS: Our approach is able to predict not only conventional binary DDIs but also what reaction types they induce in the cold start scenario. More importantly, it learns a mapping function who can bridge the drugs attributes to their network embeddings to predict DDIs. The main contribution of CSMDDI contains the development of a generalized framework to predict the single-type and multi-type of DDIs in the cold start scenario, as well as the implementations of several embedding models for both single-type and multi-type of DDIs. The dataset and source code can be accessed at https://github.com/itsosy/csmddi .
Subject(s)
Pharmaceutical Preparations , Software , Drug InteractionsABSTRACT
An ethanol-soluble pigment extract was separated from fermented Zijuan Pu-erh tea. The compositions of the ethanol soluble pigment extract were analyzed by high-performance liquid chromatography-tandem mass spectroscopy (HPLC-MS/MS). The extract was prepared into a series of ethanol solutions and analyzed for free radical-scavenging activities (against two free radicals: 1,1-diphenyl-2-picrylhydrazyl (DPPH) and (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO)) and in vitro anti-oxidative properties. Electron spin resonance spectroscopy showed that the peaks of DPPH and TEMPO decreased with increasing extract concentration, suggesting that the extract had excellent free radical-scavenging activities. In vitro cell culture suggested that, at 50-200 mg/L, the extract had no measurable effect on the viability of vascular endothelial cells (ECV340) but produced significant protective effects for cells that underwent oxidative injuries due to hydrogen peroxide (H2O2) treatment. Compared with the H2O2 treatment alone cells group, 200 mg/L of the extract increased the activity of superoxide dismutase (SOD) in cells by 397.3%, and decreased the concentration of malondialdehyde (MDA) and the activity of lactate acid dehydrogenase (LDH) by 47.8% and 69.6%, respectively. These results suggest that the extract has excellent free radical scavenging and anti-oxidative properties.
