Fermented Wheat Germ Protein with Histone Deacetylase Inhibitor AR42 Demonstrates Enhanced Cytotoxicity against Lymphoma Cells In Vitro and In Vivo.

Current treatments for lymphoma are plagued by substantial toxicity and the inability to overcome drug resistance, leading to eventual relapse and rationalizing the development of novel, less toxic therapeutics and drug combinations. Histone deacetylase inhibitors (HDACis) are a broad class of epigenetic modulators that have been studied in multiple tumor types, including lymphoma. Currently, HDACis are FDA-approved for treating relapsed T-cell lymphomas and multiple myeloma, with ongoing trials in other lymphomas and solid tumors. As single agents, HDACis frequently elicit toxic side effects and have limited efficacy; therefore, many current treatment strategies focus on combinations to boost efficacy while attempting to minimize toxicity. Fermented wheat germ extract (FWGE) is a complementary agent that has shown efficacy in several malignancies, including lymphoma. Here, we utilize a more potent FWGE derivative, known as fermented wheat germ protein (FWGP), in combination with the HDACi AR42, to assess for enhanced activity. We report increased in vitro killing, cell cycle arrest, and in vivo efficacy for this combination compared to each agent alone with minimal toxicity, suggesting a potentially new, minimally toxic treatment modality for lymphoma.

Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning.

Due to acromegaly's insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning on the data of 2148 photographs at different severity levels. Each photograph was given a score reflecting its severity (range 1~3). Our developed model achieved a prediction accuracy of 90.7% on the internal test dataset and outperformed the performance of ten junior internal medicine physicians (89.0%). The prospect of applying this model to real clinical practices is promising due to its potential health economic benefits.

Artificial intelligence: a key to relieve China's insufficient and unequally-distributed medical resources.

In this manuscript, we firstly reviewed the challenges faced by China in its health care reform. Though Chinese governments have made tremendous efforts, problems like the difficulties and high expense in medical care and the nervous doctor-patient relationship have been reported a lot, whose key problem is the insufficiency of high-quality medical resource and the supply-demand imbalance. Presently, it's almost old news: artificial intelligence will overturn the existing medical model. Artificial intelligence technology will transform the medical sector and trigger an estimated $147 billion market during the next 20 years. We hereby pointed out the strengths of medical artificial intelligence and its potentials to relieve China's insufficient and unequally-distributed medical resources. Also, we analyzed China's advantages in developing medical AI due to its huge medical big data and China government's powerful promotion policy. Finally, we put forward some challenges for China to practice this.

Opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) and postoperative nausea and vomiting.

BACKGROUND: OPRM1-A118G polymorphism (A > G, rs1799971) is associated with interindividual variability in both response to postoperative pain and opioid treatment. The aim of this meta-analysis is to identify the predictive strength in the current literature of OPRM1-A118G polymorphism to postoperative anesthetic reactions, including nausea, vomiting, pruritus and dizziness. METHODS: PubMed, EMBASE, Cochrane Library, Web of Knowledge, Google Scholar and CNKI database were searched to find gene-association researches exploring the impacts of OPRM1-A118G polymorphism on postoperative side effects (time: up to July 2016). Odd ratios (ORs) with 95% confidence intervals (95% CIs) were estimated in allele model, homozygote model, heterozygote model, dominant model and recessive model. Sensitivity analysis and potential bias were also assessed. RESULTS: 137 articles were retrieved from databases. 17 eligible studies, including 4690 patients were considered in the meta-analysis. The ORs with 95% CIs of postoperative nausea, vomiting, nausea and vomiting (PONV), pruritus and dizziness in the five genetic models mentioned above were determined. Postoperative vomiting was significantly associated with OPRM1-A118G polymorphism in homozygote (OR: 0.422; 95% CI: 0.254, 0.701; P = 0.001), dominant (OR: 0.765; 95% CI: 0.592, 0.987; P = 0.040) and recessive (OR: 0.439; 95% CI: 0.268, 0.717; P = 0.001) models. The 118G allele was associated with a reduced risk of vomiting. No other associations were detected. There was no evidence of publication bias. CONCLUSIONS: OPRM1-A118G polymorphism (A > G) is associated with a reduced risk of postoperative vomiting, but not nausea, pruritus and dizziness. The results should be interpreted with caution due to limited sample and possible heterogeneity between the included studies. Well-designed and large-scale studies are necessary to confirm our results.

Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

BACKGROUND: Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. METHODS: In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. RESULTS: The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. CONCLUSIONS: Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity.

Roles and expression profiles of long non-coding RNAs in triple-negative breast cancers.

Triple-negative breast cancer (TNBC) refers to the breast cancers that express little human epidermal growth factor receptor 2 (HER2), progesterone receptor (PR) and oestrogen receptor (ER). When compared to other types of breast cancers, TNBC behaves more aggressively with relatively poorer prognosis. Moreover, except chemotherapy, no targeted treatments have been approved yet until now. Although the molecular-biological mechanisms of the initiation and development of TNBC have been explored a lot, the exact details underlying its progressions are still not clear. Long non-coding RNAs (lncRNAs), with the length greater than 200 nucleotides, are non-protein coding transcripts. Previous researches have shown that lncRNAs are significantly involved in a variety of pathophysiological processes such as cell migration, invasion, proliferation, differentiation and development. lncRNAs' dysregulated expressions have been observed in many types of tumours including TNBCs. This article will review the functional roles and dysregulations of lncRNAs in TNBCs. These lncRNAs are worthy of exploitation regarding their potential application values of TNBC's diagnosis and treatment.

Expression signatures and roles of MicroRNAs in human oesophageal adenocarcinomas.

The most common forms of oesophageal cancers are adenocarcinomas and squamous cell carcinoma (SCC). Although the incidence of SCC in the United States tends to be declining, the adenocarcinoma incidence caused by Barrett's oesophagus has been increasing. Oesophageal cancer is regarded as one of the most fatal malignancies with a short prognosis. Systemic manifestations of patients with PCNSL keep backward in spite of recent development of chemoradiotherapy. MicroRNAs are small non-coding RNAs that can post-transcriptionally down-regulate the expression of genes by targeting mRNAs, causing their translational repression as well as degradation. MicroRNAs exert critical functions in many malignancy-related biological processes, including cell apoptosis, metabolism, proliferation and differentiation. Many deregulated miRNAs have been identified in oesophageal adenocarcinomas, but their biological importance has not yet been fully elucidated. In this study, we review present evidence regarding the potential applications of oesophageal adenocarcinomas associated microRNAs for prognosis and diagnosis of this lethal disease.

Survivin overexpression is potentially associated with pituitary adenoma invasiveness.

BACKGROUND AND OBJECTIVE: Survivin is an inhibitor of apoptosis. Its role in guiding the treatment of neoplasms, making diagnosis and predicting prognosis has been reported. However, there is little information on the implications and uses of survivin in predicting pituitary adenoma (PA) invasiveness. Existing information is unclear and controversial. We thus conducted this meta-analysis to explore whether the surviving expression levels in invasive PAs (IPA) and regular PAs are different or not. We considered both non-secreting and secreting tumors together. METHODS: A global search strategy was systematically applied among five databases including Cochrane Library, Embase, PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) up to June 18th, 2017. With a specially designed form including PAs' invasive features, etc., data was collected. The included studies should present the data representing the surviving levels in IPA groups and regular PA groups, respectively. Differences were expressed as standard mean differences (SMDs) or odds ratios (ORs) with 95% confidence interval (CI). To estimate the heterogeneities, I2 test, Cochran's Q-test and Galbr figure were all conducted. A sensitivity-analysis and potential-publication bias were also performed. RESULTS: In the present meta-analysis, 9 studies containing 489 patients were included. Seven studies with dichotomous-data showed that survivin over-expression in PA tissue was closely associated with a high invasive tendency (OR 6.226, 95% CI 3.970, 9.765; P<0.001), but 2 continuous-data studies revealed that there was no significant association (SMD -5.043, 95% CI-10.965, 0.878; p=0.095). A sensitivity-analysis suggested a statistically stable result. We did not find publication bias. CONCLUSION: We suggest that survivin overexpression is potentially associated with PA invasiveness. More research based on medical big data is needed to confirm this finding.

Neuroprotective effects of allicin on ischemia-reperfusion brain injury.

BACKGROUND: Ischemia-reperfusion brain injury (IRBI) is an important cause for mortality and morbidity. Studies on humans and animals showed that oxidative stress (OS) plays a crucial role in ischemic stroke with or without reperfusion. Allicin is reported to be able to attenuate OS and has neuroprotective effects on rabbits' ischemia-reperfusion spinal cord injury. AIM: To explore whether Allicin pretreatment has neuroprotective effects on IRBI in mice. METHODS AND RESULTS: Transient middle cerebral artery occlusion (MCAO) was conducted to induce IRBI in mice. The mice were pretreated with either Allicin (MCAOA) or normal saline in the same volume (MCAONS). Sham-operated groups [Allicin group (SOA) and normal saline group (SONS)] were also set. Blood pressure and cerebral blood flow measurements revealed comparable hemodynamics. Via brain MRI and neuronal nuclear antigen (NeuN) immune-histochemical staining, MCAOA mice had a significantly reduced stroke size than MCAONS mice (P < 0.05, n = 15). Allicin pretreatment could attenuate the OS, the activity of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, inflammation, dysfunction of mitochondrial respiratory chain, and apoptosis (all P < 0.05, n = 15). Furthermore, Allicin also increased the activities of endogenous antioxidant enzymes, including catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPX), and glutathione S-transferase (GST), and promoted the angiogenesis in the peri-infarct zone (all P < 0.05, n = 15). CONCLUSION: We showed that Allicin could protect mice from IRBI through a series of mechanisms. Allicin represents a new therapeutic direction of IRBI.

Association of opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) with nicotine dependence.

BACKGROUND AND OBJECT: Whether opioid-receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) is associated with nicotine dependence is controversial. We analyzed the combined results from published studies of this possibility. METHODS: Literature reviews were performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Web of Science, Chinese National Science Infrastructure (CNKI), PubMed, Embase and Google Scholar database searches using MeSH terms were conducted to find all relevant researches up to October 2016. Odds ratios (ORs) and their 95% confidence intervals (95% CIs) were calculated in allele, homozygote, heterozygote, dominant and recessive models. Ethnicity-specific subgroup meta-analysis, heterogeneity, sensitivity analysis and publication bias were considered. RESULTS: Seven eligible studies with 3313 patients were included. The ORs in the five genetic models mentioned above were 1.000 (95% CI: 0.906, 1.104; p = 0.999), 1.032 (95% CI: 0.771, 1.381; p = 0.834), 0.963 (95% CI: 0.799, 1.162; p = 0.696), 1.006 (95% CI: 0.916, 1.104; p = 0.907), 0.967 (95% CI: 0.715, 1.309; p = 0.830), respectively. Only in dominant model is the association significant. Upon ethnicity-specific subgroup analysis, there is no statistical significance. CONCLUSION: OPRM1-A118G polymorphism (A>G) is not associated with nicotine dependence.