The Antitumor Activities of Marsdenia tenacissima

Marsdenia tenacissima (MT), a traditional Chinese herbal medicine, has long been used for thousands of years to treat asthma, tracheitis, rheumatism, etc. An increasing number of recent studies have focused on the antitumor effects of MT. The effects of MT on cancer are the result of various activated signaling pathways and inhibiting factors and the high expression levels of regulatory proteins. MT can inhibit different cancer types including non-small cell lung cancer (NSCLC), malignant tumors, hepatic carcinoma, and so on. This article mainly focuses on the activities and mechanisms of MT. In addition, the efficacy and toxicity of MT are also discussed. Further studies of MT are required for improved medicinal utilization

Non-coding RNAs: The recently accentuated molecules in the regulation of cell autophagy for ovarian cancer pathogenesis and therapeutic response

Autophagy is a self-recycling and conserved process, in which the senescent cytoplasmic components are degraded in cells and then recycled to maintain homeostatic balance. Emerging evidence has suggested the involvement of autophagy in oncogenesis and progression of various cancers, such as ovarian cancer (OC). Meanwhile, the non-coding RNAs (ncRNAs) frequently regulate the mRNA transcription and other functional signaling pathways in cell autophagy, displaying promising roles in human cancer pathogenesis and therapeutic response. This article mainly reviews the cutting-edge research advances about the interactions between ncRNAs and autophagy in OC. This review not only summarizes the underlying mechanisms of dynamic ncRNA-autophagy association in OC, but also discusses their prognostic implications and therapeutic biomarkers. The aim of this review was to provide a more in-depth knowledge framework exploring the ncRNA-autophagy crosstalk and highlight the promising treatment strategies for OC patients

SNCA Is a Functionally Low-Expressed Gene in Lung Adenocarcinoma

There is increasing evidence for the contribution of synuclein alpha (SNCA) to the etiology of neurological disorders, such as Parkinson’s disease (PD). However, little is known about the detailed role of SNCA in human cancers, especially lung cancers. Here, we evaluated the effects of SNCA on the occurrence and prognosis of lung adenocarcinoma (ADC). Comprehensive bioinformatics analyses of data obtained from the Oncomine platform, the human protein atlas (HPA) project and the cancer cell line encyclopedia (CCLE) demonstrated that SNCA expression was significantly reduced in both ADC tissues and cancer cells. The results of relevant clinical studies indicated that down-regulation of SNCA was statistically correlated with shorter overall survival time and post-progression survival time. Through analysis of datasets obtained from the Gene Expression Omnibus database, significant low levels of SNCA were identified in cisplatin-resistant ADC cells. Moreover, small interfering RNA (siRNA)-mediated knockdown of protein tyrosine kinase 7 (PTK7) elevated the expression of SNCA in the ADC cell lines H1299 and H2009. Our work demonstrates that low levels of SNCA are specifically found in ADC and that this gene may be a potential therapeutic target for this subset of lung cancers. Determination of the role of SNCA in ADC biology would give us some insightful information for further investigations

Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review

In order to meet the requirements of high-tech enterprises for high power quality, high-quality operation and maintenance (O&M) in smart distribution networks (SDN) is becoming increasingly important. As a significant element in enhancing the high-quality O&M of SDN, situation awareness (SA) began to excite the significant interest of scholars and managers, especially after the integration of intermittent renewable energy into SDN. Specific to high-quality O&M, the paper decomposes SA into three stages: detection, comprehension, and projection. In this paper, the state-of-the-art knowledge of SND SA is discussed, a review of critical technologies is presented, and a five-layer visualization framework of the SDN SA is constructed. SA detection aims to improve the SDN observability, SA comprehension is associated with the SDN operating status, and SA projection pertains to the analysis of the future SDN situation. The paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of SDN SA

Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review

In order to meet the requirements of high-tech enterprises for high power quality, high-quality operation and maintenance (O&M) in smart distribution networks (SDN) is becoming increasingly important. As a significant element in enhancing the high-quality O&M of SDN, situation awareness (SA) began to excite the significant interest of scholars and managers, especially after the integration of intermittent renewable energy into SDN. Specific to high-quality O&M, the paper decomposes SA into three stages: detection, comprehension, and projection. In this paper, the state-of-the-art knowledge of SND SA is discussed, a review of critical technologies is presented, and a five-layer visualization framework of the SDN SA is constructed. SA detection aims to improve the SDN observability, SA comprehension is associated with the SDN operating status, and SA projection pertains to the analysis of the future SDN situation. The paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of SDN SA

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system (RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction (M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search (CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models

Commentary: Lico A causes ER stress and apoptosis via up-regulating miR-144-3p in human lung cancer cell line H292

Natural compound licochalcone A (lico A), a main constituent of flavonoids, has been proved to induce ER stress and apoptosis in cancer cells. However, the underlying anti-tumor mechanism of lico A remains to be clarified in detail. A recently-published report from the journal Frontiers in Pharmacology evaluated the miRNA-based mechanism for lico A on its anti-cancer effect. They found that lico A significantly promoted the tumor-suppressor miR-144-3p expression, so as to up-regulate ER stress-response protein CHOP (CCAAT/-enhancer-binding protein homologous protein) by down-regulating nuclear factor E2-related factor 2 (Nrf2), finally inducing apoptotic cell death in lung cancer. Their findings pave the path toward a more understanding of miRNA-based drugs for cancer treatment in the future. Keywords: Lico A, ER stress, Apoptosis, miR-144-3p, Lung cancer, Anti-cancer therapeutic