Radiographic outcomes of lateral sinus floor elevation at sites without perforations and sites with perforations managed with a resorbable membrane: A retrospective study.

AIM: To evaluate the radiographic outcomes of lateral sinus floor elevation with simultaneous implant placement at sites without sinus membrane perforation (SMP) and sites with SMP managed with a resorbable membrane. MATERIALS AND METHODS: One hundred and thirty-nine patients and 170 implants (56 perforation, 114 non-perforation) were included. Cone-beam computed tomography (CBCT) images were taken before surgery (T0), immediately after surgery (T1) and 6 months after surgery (T2). Post-operative augmentation parameters, including endo-sinus bone gain (ESBG) along the implant axis, mean new bone height (NBH) surrounding the implant and augmentation volume (AV), were measured at T1 and T2. RESULTS: At T1, there were no significant differences in ESBG, NBH and AV between the two groups. At T2, although ESBG did not significantly differ between the two groups, NBH (8.50 ± 1.99 mm vs. 9.99 ± 2.52 mm, p = .039) and AV (519.37 ± 258.38 mm3 vs. 700.99 ± 346.53 mm3, p < .001) were significantly lower in the perforation group. The shrinkage of graft material from T1 to T2, including ΔESBG (p = .002), ΔNBH (p < .001) and ΔAV (p < .001), was higher in the perforation group. CONCLUSIONS: SMP during LSFE with simultaneous implant placement is associated with greater resorption of the grafted area at a 6-month follow-up.

Effect of oxide interactions on chromium speciation transformation during simulated municipal solid waste incineration.

Chromium released during municipal solid waste incineration (MSWI) is toxic and carcinogenic. The removal of chromium from simulated MSWI flue gas by four sorbents (CaO, bamboo charcoal (BC), powdered activated carbon (PAC), and Al2O3) and the effects of four oxides (SiO2, Al2O3, Fe2O3, and CaO) on chromium speciation transformation were investigated. The results showed that the removal rates of total Cr by the four sorbents were Al2O3 < CaO < PAC < BC, while the removal rates of Cr(VI) by the four sorbents were Al2O3 < PAC < BC < CaO. CaO had a strong oxidizing effect on Cr(III), while BC and PAC had a better-reducing effect on Cr(VI). SiO2 was better for the reduction of Na2CrO4 and K2CrO4 above 1000°C due to its strong acidity, and the addition of CaO significantly inhibited the reduction of Cr(VI). MgCrO4 decomposed above 700°C to form MgCr2O4, and the reaction between MgCrO4 and oxides also existed in the form of a more stable trivalent spinel. Furthermore, when investigating the effect of oxides on the oxidation of Cr(III) in CrCl3, it was discovered that CaO promoted the conversion of Cr(III) to Cr(VI), while the presence of chlorine caused chromium to exist in the form of Cr(V), and increasing the content of CaO and extending the heating time facilitated the oxidation of Cr(III). In addition, silicate, aluminate, and ferrite were generated after the addition of SiO2, Al2O3, and Fe2O3, which reduced the alkalinity of CaO and had an important role in inhibiting the oxidation of Cr(III). The acidic oxides can not only promote the reduction of Cr(VI) but also have an inhibitory effect on the oxidation of Cr(III) ascribed to alkali metals/alkaline earth metals, and the proportion of acidic oxides can be increased moderately to reduce the generation of harmful substances in the hazardous solid waste heat treatment.

Improving Cancer Survival Prediction via Graph Convolutional Neural Network Learning on Protein-Protein Interaction Networks.

Cancer is one of the most challenging health problems worldwide. Accurate cancer survival prediction is vital for clinical decision making. Many deep learning methods have been proposed to understand the association between patients' genomic features and survival time. In most cases, the gene expression matrix is fed directly to the deep learning model. However, this approach completely ignores the interactions between biomolecules, and the resulting models can only learn the expression levels of genes to predict patient survival. In essence, the interaction between biomolecules is the key to determining the direction and function of biological processes. Proteins are the building blocks and principal undertakings of life activities, and as such, their complex interaction network is potentially informative for deep learning methods. Therefore, a more reliable approach is to have the neural network learn both gene expression data and protein interaction networks. We propose a new computational approach, termed CRESCENT, which is a protein-protein interaction (PPI) prior knowledge graph-based convolutional neural network (GCN) to improve cancer survival prediction. CRESCENT relies on the gene expression networks rather than gene expression levels to predict patient survival. The performance of CRESCENT is evaluated on a large-scale pan-cancer dataset consisting of 5991 patients from 16 different types of cancers. Extensive benchmarking experiments demonstrate that our proposed method is competitive in terms of the evaluation metric of the time-dependent concordance index( Ctd) when compared with several existing state-of-the-art approaches. Experiments also show that incorporating the network structure between genomic features effectively improves cancer survival prediction.

SMG: self-supervised masked graph learning for cancer gene identification.

Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein-protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.

Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.

MOTIVATION: RNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability. RESULTS: In this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies. AVAILABILITY AND IMPLEMENTATION: The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images.

BACKGROUND AND OBJECTIVE: High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs. METHODS: Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantifies the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification. RESULTS: Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction. CONCLUSIONS: Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.

Survival analysis of implants placed simultaneously with lateral sinus floor elevation in severely atrophic maxilla: A 3- to 12-year retrospective cohort study.

OBJECTIVES: To retrospectively evaluate whether implants placed simultaneously with lateral sinus floor elevation (LSFE) in severely atrophic maxilla (residual bone height [RBH] ≤3 mm) could achieve long-term survival and comprehensively analyze the factors influencing their survival rates. MATERIALS AND METHODS: A total of 123 patients receiving LSFE and simultaneous implant placement from 2010 to 2019 and their 123 implants in sites with RBH ≤3 mm were included in this study. Basic characteristics of patients and implants were collected from the medical record system and cone-beam computed tomography (CBCT) images. Kaplan-Meier survival curves were applied to estimate cumulative survival rates (CSRs) and Cox proportional hazards regression models were used to detect factors influencing implant survival. RESULTS: The 6-year and 12-year CSR of implants placed in sites with RBH ≤3 mm were 95.7% (95% confidence interval [CI]: 92.1%-99.5%) and 76.6% (95% CI: 58.1%-100%), respectively. Eight patients presented late implant failure. Univariate and multivariate Cox regression analyses demonstrated that RBH ≤2 mm (hazard ratio [HR]: 20.63, p = 0.000) and smoking habit (HR: 6.055, p = 0.024) were significantly associated with long-term implant survival. Specifically, the 10-year CSR of implants in sites with RBH ≤2 mm (53.3%, 95% CI: 27.5%-100%) was dramatically lower than those in sites with RBH >2 mm (92.9%, 95% CI: 81.7%-100%, p = 0.000). CONCLUSIONS: Implants placed simultaneously with LSFE in sites with RBH ≤3 mm can achieve long-term survival. However, caution is required especially for implantation in sites with RBH ≤2 mm. Besides, the smoking habit is also considered a risk factor jeopardizing long-term implant survival.

Early diagnosis of coronary microvascular dysfunction by myocardial contrast stress echocardiography.

Coronary microvascular dysfunction (CMD) is one of the basic mechanisms of myocardial ischemia. Myocardial contrast echocardiography (MCE) is a bedside technique that utilises microbubbles which remain entirely within the intravascular space and denotes the status of microvascular perfusion within that region. Some pilot studies suggested that MCE may be used to diagnose CMD, but without further validation. This study is aimed to investigate the diagnostic performance of MCE for the evaluation of CMD. MCE was performed at rest and during adenosine triphosphate stress. ECG triggered real-time frames were acquired in the apical 4-chamber, 3-chamber, 2-chamber, and long-axis imaging planes. These images were imported into Narnar for further processing. Eighty-two participants with suspicion of coronary disease and absence of significant epicardial lesions were prospectively investigated. Thermodilution was used as the gold standard to diagnose CMD. CMD was present in 23 (28%) patients. Myocardial blood flow reserve (MBF) was assessed using MCE. CMD was defined as MBF reserve < 2. The MCE method had a high sensitivity (88.1%) and specificity (95.7%) in the diagnosis of CMD. There was strong agreement with thermodilution (Kappa coefficient was 0.727; 95% CI: 0.57-0.88, p < 0.001). However, the correlation coefficient (r = 0.376; p < 0.001) was not high.

Effects of zinc oxide and condensed tannins on the growth performance and intestinal health of weaned piglets in ETEC-challenged environment.

This experiment was conducted to evaluate effects of zine oxide (ZnO) and condensed tannins (CT), independently or in combination, on the growth performance and intestinal health of weaned piglets in enterotoxigenic Escherichia coli (ETEC-K88)-challenged environment. Randomly divided 72 weaned piglets into 4 groups. Dietary treatments included the following: basic diet group (CON), 1,500 mg/kg zinc oxide group (ZnO), 1,000 mg/kg condensed tannins group (CT), and 1,500 mg/kg zinc oxide +1,000 mg/kg condensed tannins group (ZnO + CT). Dietary ZnO supplementation decreased diarrhea rate from 0 to 14 days, 15 to 28 days, and 0 to 28 days (p < 0.05) and no significant on growth performance. The effect of CT on reducing diarrhea rate and diarrhea index was similar to the results of ZnO. Compared with the CON group, ZnO increased the ileum villus height and improved intestinal barrier function by increasing the content of mucin 2 (MUC-2) in jejunum and ileum mucosa and the mRNA expression of zonula occludens-1 (ZO-1) in jejunum (p < 0.05) and the expression of Occludin in duodenum and ileum (p < 0.05). The effects of CT on intestinal barrier function genes were similar to that of ZnO. Moreover, the mRNA expression of cystic fibrosis transmembrane conductance regulator (CFTR) in jejunum and ileum was reduced in ZnO group (p < 0.05). And CT was also capable of alleviating diarrhea by decreasing CFTR expression and promote water reabsorption by increasing AQP3 expression (p < 0.05). In addition, pigs receiving ZnO diet had higher abundance of phylum Bacteroidetes, and genera Prevotella, and lower phylum Firmicutes and genera Lactobacillus in colonic contents. These results indicated that ZnO and CT can alleviate diarrhea and improve intestinal barrier function of weaned pigs in ETEC-challenged environment. In addition, the application of ZnO combined with CT did not show synergistic effects on piglet intestinal health and overall performance. This study provides a theoretical basis for the application of ZnO in weaning piglet production practices, we also explored effects of CT on the growth performance and intestinal health of weaned piglets in ETEC-challenged environment.

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

Repositioning of the bone window in lateral sinus floor elevation with simultaneous implant placement: A retrospective radiographic study.

OBJECTIVES: To retrospectively evaluate whether repositioning the bone window leads to a better outcome of three-dimensional sinus augmentation in lateral sinus floor elevation (LSFE) with simultaneous implant placement. METHODS: 34 patients with a total of 40 implants (14: test group, 26: control group) receiving LSFE with simultaneous implant placement were included in this retrospective research. CBCT images were taken before surgery, immediately and 6 months after surgery. The two-dimensional augmentation parameters, including apical bone height (ABH), endo-sinus bone gain (ESBG), and palatal/buccal bone height (PBH/BBH), and three-dimensional parameters, including augmentation volume (AV) and palatal/buccal augmentation volume (PAV/BAV), were measured. The lateral defect length (LDL) and lateral window length (LWL) were also measured to evaluate the lateral antrostomy recovery. RESULTS: At the 6-month follow-up, the reduction rates at ABH, ESBG, and BBH of the test group (ABH: 10.41% ± 30.30%, ESBG: 2.55% ± 8.91%, BBH: 2.50% ± 8.65%) were significantly lower than those of the control group (ABH: 25.10% ± 22.02%, ESBG: 11.47% ± 9.79%, BBH: 7.10% ± 5.37%; p < .05). In addition, the test group showed better three-dimensional augmentation stability on the buccal side (BAV reduction: 15.51% ± 10.86% vs. 27.15% ± 12.61%; p < .05). Moreover, the LDL/LWL ratio of the test group was significantly lower than that of the control group (p < .05). CONCLUSION: Within the limitations of this study, repositioning of the bone window in LSFE with simultaneous implant placement could contribute to endo-sinus augmentation stability on the buccal side at the 6-month follow-up. Moreover, it would also facilitate recovery of the lateral antrostomy defect.

Methylation quantitative locus rs3758653 in the DRD4 gene is associated with duration from first heroin exposure to addiction.

Opioid addiction is a chronic brain disease with a high heritability. However, the genetic underpinnings remain uncertain. DNA methylation is involved in the adaptive changes in neuroplasticity after prolonged drug use. The dopamine receptor D4 (DRD4) has an essential role in the reward processes associated with addictive drugs. To further elucidate the potential role and mechanism of the DRD4 gene variants in heroin addiction, we detected the methylation level of 46 CpG sites in the promoter region and the genotypes of three SNPs in the DRD4 gene. Correlations between the SNPs and methylation levels of the CpG sites, i.e., the analysis of methylation quantitative trait loci (mQTLs) was conducted. Following the identification of mQTLs that are unique in the heroin addiction group, we performed an association study between the mQTLs and traits of heroin addiction. Our results revealed that there were several correlations of SNPs rs3758653 and rs11246226 with the methylation levels of some CpG sites in the DRD4 gene. Among these SNP-CpG pairs, rs3758653-DRD4_04, rs3758653-DRD4_05, rs3758653-DRD4_13 and rs3758653-DRD4_03 were unique in the heroin addiction group. Moreover, we found that mQTL rs3758653 was associated with duration from first heroin exposure to addiction, and the expression level of the DRD4 gene in human brain regions of the frontal cortex and hippocampus. Our findings suggested that some mQTLs in the genome may be associated with traits of opioid addiction through implicating the processes of DNA methylation and gene expression.

Atomic-Scale Dispersed Fe-Based Catalysts Confined on Nitrogen-Doped Graphene for Li-S Batteries: Polysulfides with Enhanced Conversion Efficiency.

Lithium-sulfur batteries have been considered as potential electrochemical energy-storage devices owing to their satisfactory theoretical energy density. Nonetheless, the inferior conversion efficiency of polysulfides in essence leads to fast capacity decay during the discharge/charge cycle. In this work, it is successfully demonstrated that the conversion efficiency of lithium polysulfides is remarkably enhanced by employing a well-distributed atomic-scale Fe-based catalyst immobilized on nitrogen-doped graphene (Fe@NG) as a coating of separator in lithium-sulfur batteries. The quantitative electrocatalytic efficiency of the conversion of lithium polysulfides is determined through cyclic voltammetry. It is also proven that the Fe-NX configuration with highly catalytic activity is quite beneficial for the conversion of lithium polysulfides. In addition, the adsorption and permeation experiments distinctly indicate that the strong anchoring effect, originated from the charge redistribution of N doping into the graphene matrix, inhibits the movement of lithium polysulfides. Thanks to these advantages, if the as-prepared Fe@NG catalyst is combined with polypropylene and applied as a separator (Fe@NG/PP) in Li-S batteries, a high initial capacity (1616 mA h g-1 at 0.1 C), excellent capacity retention (93 % at 0.2 C, 70 % at 2 C), and superb rate performance (820 mA h g-1 at 2 C) are achieved.

Circulating Tumor Cells as a Screening and Diagnostic Marker for Early-Stage Non-Small Cell Lung Cancer.

BACKGROUND: Circulating tumor cells (CTCs) have become potential diagnostic biomarker for several types of cancer, including lung cancer. In this study, we aim to determine whether CTCs detected by CellCollector can be used for early-stage diagnosis of lung cancer. METHODS: In this study, we recruited 64 volunteers, among whom 44 were suspected lung cancer patients requiring surgical treatment and 20 were healthy volunteers. We simultaneously analyzed PD-L1 expression in CTCs isolated using the GILUPI CellCollector and copy number variation by next-generation sequencing (NGS). RESULTS: We enrolled a total of 44 patients with suspected lung cancer who required surgery and 20 healthy volunteers. The patients were classified into 4 groups based on their pathological results: benign disease, in situ cancer, microinvasive, and invasive. The CTCs detection rate for each group was 10.00% (1/10), 45% (5/11), 50% (7/14), and 67% (6/9), respectively. Among the patients with lung cancer, the CTCs detection rate increased with disease progression. The rate of CTCs positivity was 52.94% (18/34) in patients who were diagnosed with lung cancer by pathology and 10% (1/10) in patients with benign disease. CTCs were not detected in the control group. The area under the receiver operating characteristic (ROC) curve, a measure for distinguishing patients with primary lung cancer, was 0.715 (95% CI 0.549-0.880, P=0.041). The sensitivity and specificity of the in vivo CTCs detection strategy for the diagnosis of early-stage lung cancer were 52.94% and 90%, respectively. CTCs were associated with clinical pathology but not with the size and location of the nodules. CONCLUSION: CTCs isolation using the CellCollector in vivo detection method might be effective for distinguishing between benign and malignant nodules and may be used for early-stage diagnosis of lung cancer.

A hierarchical nanostructural coating of amorphous silicon nitride on polyetheretherketone with antibacterial activity and promoting responses of rBMSCs for orthopedic applications.

Silicon nitride (SN) with good osteoconductivity has been introduced as an implantable biomaterial for joint replacement and interbody fusion devices. In this study, SN was coated on a polyetheretherketone (PEEK) surface by inductively coupled plasma-enhanced chemical vapor deposition (ICPECVD). The results showed that a dense coating (thickness of about 500 nm) of amorphous SN was closely combined with a PEEK substrate (PKSN) with a binding strength of 6.88 N. In addition, the coating surface showed hierarchical nanostructures containing many spherical bulges (sizes about 150 nm), which were composed of many small humps (sizes about 10 nm). Moreover, the roughness, hydrophilicity, surface energy, surface charge and adsorption of bovine serum albumin (BSA) of PKSN were obviously higher than those of PEEK. After immersion into simulated body fluid (SBF), the Si ions were gradually released from PKSN into SBF and a weak alkaline environment was created. Antibacterial experiments showed that PKSN exhibited a greater antibacterial activity than that of PEEK. Moreover, compared with PEEK, PKSN significantly promoted adhesion, proliferation, differentiation and expression of osteogenic related genes of the rat bone marrow stromal cells (rBMSCs). In conclusion, the SN coating of PKSN with hierarchical nanostructures exhibited excellent antibacterial activity and cytocompatibility, which would make it a great candidate for orthopedic applications.

Rapid Identification of Dipeptidyl Peptidase-IV (DPP-IV) Inhibitory Peptides from Ruditapes philippinarum Hydrolysate.

Dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides were rapidly identified from Ruditapes philippinarum hydrolysate. The hydrolysate was fractionated by ethanol precipitation and preparative reverse phase high-performance liquid chromatography (RP-HPLC). The fraction which showed the highest DPP-IV inhibitory activity was then analyzed by a high-throughput nano-liquid chromatography electrospray ionization tandem mass spectrometry (nano-LC ESI-MS/MS) method, and the sequences of peptides were identified based on the MS/MS spectra against the Mollusca protein data from the UniProt database. In total, 50 peptides were identified. Furthermore, molecular docking was used to identify potential DPP-IV inhibitors from the identified peptides. Docking results suggested that four peptides: FAGDDAPR, LAPSTM, FAGDDAPRA, and FLMESH, could bind pockets of DPP-IV through hydrogen bonds, π-π bonds, and charge interactions. The four peptides were chemically synthesized and tested for DPP-IV inhibitory activity. The results showed that they possessed DPP-IV inhibitory activity with IC50 values of 168.72 µM, 140.82 µM, 393.30 µM, and >500 µM, respectively. These results indicate that R. philippinarum-derived peptides may have potential as functional food ingredients for the prevention of diabetes.

Prognostic role of platelet to lymphocyte ratio in non-small cell lung cancers: A meta-analysis including 3,720 patients.

Platelet to lymphocyte ratio (PLR) was recently reported as a useful index in predicting the prognosis of lung cancer. However, the prognostic role of PLR in lung cancer remains controversial. The aim of this study was to evaluate the association between PLR and clinical outcome of lung cancer patients through a meta-analysis. Relevant literatures were retrieved from PubMed, Ovid, the Cochrane Library and Web of Science databases. Meta-analysis was performed using hazard ratio (HR) and 95% confidence intervals (CIs) as effect measures. A total of 5,314 patients from 13 studies were finally enrolled in the meta-analysis. The summary results showed that elevated PLR predicted poorer overall survival (OS) (HR: 1.526, 95%CI: 1.268-1.836, p < 0.001) in patients with lung cancer and OS (HR: 1.631, 95%CI: 1.447-1.837, p < 0.001) in patients with nonsmall cell lung cancer (NSCLC). Subgroup analysis revealed that increased PLR was also associated with poor OS in NSCLC treated by surgical resection (HR: 1.884, 95%CI: 1.308-2.714, P < 0.001) and non-surgery (HR: 1.570, 95%CI: 1.323-1.863, P < 0.001). In addition, PLR Cut-off value ≤ 160 (HR: 1.506, 95%CI: 1.292-1.756, P < 0.001) and PLR Cut-off value>160 (HR: 1.842, 95%CI: 1.523-2.228, P < 0.001). In contrast, elevated PLR was not associated with OS (HR: 1.117, 95%CI: 0.796-1.569, P > 0.05) in patients with small cell lung cancer (SCLC).This meta-analysis result suggested that elevated PLR might be a predicative factor of poor prognosis for NSCLC patients.

Diagnostic value of SHOX2 DNA methylation in lung cancer: a meta-analysis.

The diagnostic value of SHOX2 DNA methylation in patients with lung cancer remains controversial. Thus, we performed a systematic review and meta-analysis to assess diagnostic accuracy of SHOX2 DNA methylation in the lymph node, bronchial aspirates, pleural effusion, plasma, and tumor tissue for lung cancer. We conducted a comprehensive literature search in PubMed, Ovid, the Cochrane library, and Web of Science databases in May 2015. The diagnostic sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve were pooled using STATA 12.0 software. A total of 2,296 subjects included 1,129 lung cancer patients in eight studies were recruited in this meta-analysis. The summary estimates for SHOX2 DNA methylation in the diagnosis of lung cancer in these studies were pooled SEN =0.70 (95% confidence interval [CI]: 0.46-0.87), SPE =0.96 (95% CI: 0.91-0.99), PLR 20.01 (95% CI: 6.96-57.52), NLR 0.31 (95% CI: 0.15-0.64), and DOR 65.11 (95% CI: 13.10-323.61), and the area under the curve (AUC) was 0.96 (95% CI: 0.94-0.97). SHOX2 DNA methylation has greater diagnostic value in detecting lung cancer. In addition, considering the potential publication bias and high heterogeneity, further research studies with more well-designed and large sample sizes are needed in the future.

Prognostic role of neutrophil to lymphocyte ratio in lung cancers: a meta-analysis including 7,054 patients.

BACKGROUND: Neutrophil to lymphocyte ratio (NLR) has recently been reported to be a poor prognostic indicator in lung cancer. However, the prognostic value of the NLR in patients with lung cancer still remains controversial. We performed a meta-analysis to evaluate the prognostic value of NLR in patients with lung cancer. METHODS: We performed a comprehensive literature search in PubMed, Ovid, the Cochrane Library, and Web of Science databases in May 2015. Studies were assessed for quality using the Newcastle-Ottawa Scale. RESULTS: Twenty-two studies with a total of 7,054 patients were included in this meta-analysis. The meta-analysis was performed to generate combined hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS). Our analysis results indicated that high NLR predicted poorer OS (HR, 1.51; 95% confidence interval [CI], 1.33-1.71; P<0.001) and PFS (HR, 1.33; 95% CI, 1.07-1.67; P=0.012) in patients with lung cancer. High NLR was also associated with poor OS in lung cancer treated by surgical resection (HR, 1.59; 95% CI, 1.26-1.99; P<0.001) and chemotherapy (HR, 1.15; 95% CI, 1.08-1.22; P<0.001). In addition, NLR cut-off value =5 (HR, 1.57; 95% CI, 1.16-2.12; P=0.003) and NLR cut-off value <5 (HR, 1.47; 95% CI, 1.28-1.69; P<0.001). CONCLUSION: This meta-analysis result suggested that NLR should have significant predictive ability for estimating OS and PFS in patients with lung cancer and may be as a significant biomarker in the prognosis of lung cancer.

[Detections of the Focal Regions Temperature for High Intensity Focused Ultrasound].

As a tumor thermal ablation technology in cancer therapy, HIFU (High Intensity Focused Ultrasound) has been developed rapidly in recent years. With the technology becoming more and more mature, it's clinical application is becoming more and more widely. In HIFU therapy, the high-intensity ultrasound energy is focused in the target tumor tissue, generating heat within very short time, causing coagulation necrosis, so that the effect of the treatment is achieved. To ensure safe and therapeutic efficacy, HIFU therapy needs to be properly monitored by medical imaging, and temperature in the target has to be precisely measured, this article is based on the current domestic and foreign detection methods of the focal region temperature.