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Postoperative gastrointestinal dysfunction, including temporary nonmechanical suppression of gastrointestinal motility (known as postoperative ileus), occurs in about 10% surgeries of abdominal tumors. Since these complications can prolong hospitalization and affect eating, it is important to understand their risk factors and identify effective interventions to manage or prevent them. The present review comprehensively examined the relevant literature to describe risk factors for postoperative ileus and effective interventions. Risk factors include old age, open surgery, difficulty of surgery, surgery lasting longer than 3 hours, preoperative bowel treatment, infection, and blood transfusion. Factors that protect against postoperative ileus include early enteral nutrition, minimally invasive surgery, and multimodal pain treatment. Interventions that can shorten or prevent such ileus include minimally invasive surgery, early enteral nutrition as well as use of chewing gum, laxatives, and alvimopan. Most of these interventions have been integrated into current guidelines for enhanced recovery of gastrointestinal function after surgery. Future high-quality research is needed in order to clarify our understanding of efficacy and safety., Competing Interests: J.-H. Z received speaker fees for Bayer, BeiGene, AstraZeneca, MSD, Roche, Innovent, and HengRui. But none of them are related to this project at all. The other authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Weakly supervised group activity recognition (WSGAR) aims at identifying the overall behavior of multiple persons without any fine-grained supervision information (including individual position and action label). Traditional methods usually adopt a person-to-whole way: detect persons via off-the-shelf detectors, obtain person-level features, and integrate into the group-level features for training the classifier. However, these methods are unflexible due to serious reliance on the quality of detectors. To get rid of the detector, recent works learn several prototype tokens from noisy grid features with learnable weights directly, which treat all the local visual information equally and bring in redundant and ambiguous information to some extent. To this end, we propose a novel coarse-fine nested network (CFNN) to coarsely localize the key visual patches of activity and further finely learn the local features, as well as the global features. Specifically, we design a nested interactor (NI) to progressively model the spatiotemporal interactions of the learnable global token. According to the cue of spatial interaction in NI, we localize several key visual patches via a new coarse-grained spatial localizer (CSL). Then, we finally encode these localized visual patches with the help of global spatiotemporal dependency via a new fine-grained spatiotemporal selector (FSS). Extensive experiments on Volleyball and NBA datasets demonstrate the effectiveness of the proposed CFNN compared with the existing competitive methods. Code is available at: https://github.com/gexiaojingshelby/CFNN.

One fundamental problem in deep learning is understanding the excellent performance of deep Neural Networks (NNs) in practice. An explanation for the superiority of NNs is that they can realize a large family of complicated functions, i.e., they have powerful expressivity. The expressivity of a Neural Network with Piecewise Linear activations (PLNN) can be quantified by the maximal number of linear regions it can separate its input space into. In this paper, we provide several mathematical results needed for studying the linear regions of Convolutional Neural Networks with Piecewise Linear activations (PLCNNs), and use them to derive the maximal and average numbers of linear regions for one-layer PLCNNs. Furthermore, we obtain upper and lower bounds for the number of linear regions of multi-layer PLCNNs. Our results suggest that deeper PLCNNs have more powerful expressivity than shallow PLCNNs, while PLCNNs have more expressivity than fully-connected PLNNs per parameter, in terms of the number of linear regions.

Recently, deep metric learning (DML) has achieved great success. Some existing DML methods propose adaptive sample mining strategies, which learn to weight the samples, leading to interesting performance. However, these methods suffer from a small memory (e.g., one training batch), limiting their efficacy. In this work, we introduce a data-driven method, meta-mining strategy with semiglobal information (MMSI), to apply meta-learning to learn to weight samples during the whole training, leading to an adaptive mining strategy. To introduce richer information than one training batch only, we elaborately take advantage of the validation set of meta-learning by implicitly adding additional validation sample information to training. Furthermore, motivated by the latest self-supervised learning, we introduce a dictionary (memory) that maintains very large and diverse information. Together with the validation set, this dictionary presents much richer information to the training, leading to promising performance. In addition, we propose a new theoretical framework that can formulate pairwise and tripletwise metric learning loss functions in a unified framework. This framework brings new insights to society and facilitates us to generalize our MMSI to many existing DML methods. We conduct extensive experiments on three public datasets, CUB200-2011, Cars-196, and Stanford Online Products (SOP). Results show that our method can achieve the state of the art or very competitive performance. Our source codes have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/MMSI.

Glioblastoma, the most common and aggressive primary brain tumor, is highly invasive and neurologically destructive. The mean survival for glioblastoma patients is approximately 15 months and there is no effective therapy to significantly increase survival times to date. The development of effective therapy including mechanism-based therapies is urgently needed. At a molecular biology level, N6-methyladenine (m 6 A) mRNA modification is the most abundant posttranscriptional RNA modification in mammals. Recent studies have shown that m 6 A mRNA modifications affect cell survival, cell proliferation, invasion, and immune evasion of glioblastoma. In addition, m 6 A mRNA modifications are critical for glioblastoma stem cells, which could initiate the tumor and lead to therapy resistance. These findings implicate the function of m 6 A mRNA modification in tumorigenesis and progression, implicating its value in prognosis and therapies of human glioblastoma. This review focuses on the potential clinical significance of m 6 A mRNA modifications in prognostic and therapeutics of glioblastoma. With the identification of small-molecule compounds that activate or inhibit components of m 6 A mRNA modifications, a promising novel approach for glioblastoma therapy is emerging.

Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is typically represented by attribute descriptions shared between different classes, which act as strong priors for localizing object attributes that represent discriminative region features, enabling significant and sufficient visual-semantic interaction for advancing ZSL. Existing attention-based models have struggled to learn inferior region features in a single image by solely using unidirectional attention, which ignore the transferable and discriminative attribute localization of visual features for representing the key semantic knowledge for effective knowledge transfer in ZSL. In this paper, we propose a cross attribute-guided Transformer network, termed TransZero++, to refine visual features and learn accurate attribute localization for key semantic knowledge representations in ZSL. Specifically, TransZero++ employs an attribute → visual Transformer sub-net (AVT) and a visual → attribute Transformer sub-net (VAT) to learn attribute-based visual features and visual-based attribute features, respectively. By further introducing feature-level and prediction-level semantical collaborative losses, the two attribute-guided transformers teach each other to learn semantic-augmented visual embeddings for key semantic knowledge representations via semantical collaborative learning. Finally, the semantic-augmented visual embeddings learned by AVT and VAT are fused to conduct desirable visual-semantic interaction cooperated with class semantic vectors for ZSL classification. Extensive experiments show that TransZero++ achieves the new state-of-the-art results on three golden ZSL benchmarks and on the large-scale ImageNet dataset. The project website is available at: https://shiming-chen.github.io/TransZero-pp/TransZero-pp.html.

Generative (generalized) zero-shot learning [(G)ZSL] models aim to synthesize unseen class features by using only seen class feature and attribute pairs as training data. However, the generated fake unseen features tend to be dominated by the seen class features and thus classified as seen classes, which can lead to inferior performances under zero-shot learning (ZSL), and unbalanced results under generalized ZSL (GZSL). To address this challenge, we tailor a novel balanced semantic embedding generative network (BSeGN), which incorporates balanced semantic embedding learning into generative learning scenarios in the pursuit of unbiased GZSL. Specifically, we first design a feature-to-semantic embedding module (FEM) to distinguish real seen and fake unseen features collaboratively with the generator in an online manner. We introduce the bidirectional contrastive and balance losses for the FEM learning, which can guarantee a balanced prediction for the interdomain features. In turn, the updated FEM can boost the learning of the generator. Next, we propose a multilevel feature integration module (mFIM) from the cycle-consistency branch of BSeGN, which can mitigate the domain bias through feature enhancement. To the best of our knowledge, this is the first work to explore embedding and generative learning jointly within the field of ZSL. Extensive evaluations on four benchmarks demonstrate the superiority of BSeGN over its state-of-the-art counterparts.

Chemotherapeutic agents remain the first-line treatment for solid tumors, including lung cancer, but chemotherapy resistance is hampering global efforts to treat this disease. CC-115 is a novel antitumoral compound used in phase I clinical trials. However, it is unclear whether CC-115 is effective against lung adenocarcinoma (LUAD). In the present study, we found that CC-115 induced lytic cell death in A549 and H1650 tumor cells via swelling of cells and formation of large bubbles on the plasma membrane that closely resembled those typical of pyroptosis, a type of programmed cell death linked to chemotherapy. We demonstrated that CC-115 exerts antitumor effects in LUAD through gasdermin E (GSDME)-mediated pyroptosis by acting as a dual inhibitor of DNA-PK and mTOR. CC-115 can inhibit Akt phosphorylation, impairing its inhibitory effect on Bax, thereby inducing pyroptosis via the Bax-mitochondrial intrinsic pathway. CC-115-induced pyroptosis was abrogated by treatment with the Akt activator SC79 or by depletion of Bax. Importantly, CC-115 significantly upregulated the expression of Bax and GSDME-N in a xenograft mouse model, with a reduction in tumor size. Our results revealed that CC-115 suppresses tumor growth by inducing GSDME-mediated pyroptosis through the Akt/Bax-mitochondrial intrinsic pathway, indicating CC-115 as a promising therapeutic agent for LUAD., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background: ALKBH5 is aberrantly activated and exerts critical roles in facilitating the development of glioblastoma. However, the underlying activation mechanism by which ALKBH5 protein is increased in glioblastoma is not completely understood. Our study aimed to elucidate the signaling pathways involved in mediating ALKBH5 protein stability., Methods: The contribution of deubiquitinating enzymes (DUB) to the fluctuation of ALKBH5 protein expression was globally profiled with western blot analysis. Mass spectrometry and immunoprecipitation were performed to identify the USP36 and ALKBH5 interaction. The effects of USP36 on the stability of ALKBH5 were detected with in vivo and in vitro ubiquitination assays. Cell proliferation assays, neurosphere formation, limited dilution assay, and intracranial tumor growth assays were implemented to assess the collaborative capacities of USP36 and ALKBH5 in tumorigenesis., Results: Ubiquitin-specific peptidase 36 (USP36), as a potential ALKBH5-activating DUB, played an essential role in stabilization of ALKBH5 and regulation of ALKBH5-mediated gene expression in glioblastoma. The depletion of USP36 drastically impaired cell proliferation deteriorated the self-renewal of GSCs and sensitized GSCs to temozolomide (TMZ) treatment. Furthermore, the deletion of USP36 substantially decreased the in vivo tumor growth when monitored by bioluminescence imaging. Our findings indicate that USP36 regulates the protein degradation and expression of ALKBH5, and the USP36-ALKBH5 axis orchestrates glioma tumorigenesis., Conclusion: Our findings identify USP36 as a DUB of ALKBH5 and its role in glioblastoma progression, which may serve as a potential therapeutic target for glioblastoma treatment., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Generative adversarial networks (GANs) for (generalized) zero-shot learning (ZSL) aim to generate unseen image features when conditioned on unseen class embeddings, each of which corresponds to one unique category. Most existing works on GANs for ZSL generate features by merely feeding the seen image feature/class embedding (combined with random Gaussian noise) pairs into the generator/discriminator for a two-player minimax game. However, the structure consistency of the distributions among the real/fake image features, which may shift the generated features away from their real distribution to some extent, is seldom considered. In this paper, to align the weights of the generator for better structure consistency between real/fake features, we propose a novel multigraph adaptive GAN (MGA-GAN). Specifically, a Wasserstein GAN equipped with a classification loss is trained to generate discriminative features with structure consistency. MGA-GAN leverages the multigraph similarity structures between sliced seen real/fake feature samples to assist in updating the generator weights in the local feature manifold. Moreover, correlation graphs for the whole real/fake features are adopted to guarantee structure correlation in the global feature manifold. Extensive evaluations on four benchmarks demonstrate well the superiority of MGA-GAN over its state-of-the-art counterparts.

Transductive zero-shot learning (TZSL) extends conventional ZSL by leveraging (unlabeled) unseen images for model training. A typical method for ZSL involves learning embedding weights from the feature space to the semantic space. However, the learned weights in most existing methods are dominated by seen images, and can thus not be adapted to unseen images very well. In this paper, to align the (embedding) weights for better knowledge transfer between seen/unseen classes, we propose the virtual mainstay alignment network (VMAN), which is tailored for the transductive ZSL task. Specifically, VMAN is casted as a tied encoder-decoder net, thus only one linear mapping weights need to be learned. To explicitly learn the weights in VMAN, for the first time in ZSL, we propose to generate virtual mainstay (VM) samples for each seen class, which serve as new training data and can prevent the weights from being shifted to seen images, to some extent. Moreover, a weighted reconstruction scheme is proposed and incorporated into the model training phase, in both the semantic/feature spaces. In this way, the manifold relationships of the VM samples are well preserved. To further align the weights to adapt to more unseen images, a novel instance-category matching regularization is proposed for model re-training. VMAN is thus modeled as a nested minimization problem and is solved by a Taylor approximate optimization paradigm. In comprehensive evaluations on four benchmark datasets, VMAN achieves superior performances under the (Generalized) TZSL setting.

Feature representation learning, an emerging topic in recent years, has achieved great progress. Powerful learned features can lead to excellent classification accuracy. In this article, a selective and robust feature representation framework with a supervised constraint (SRSC) is presented. SRSC seeks a selective, robust, and discriminative subspace by transforming the original feature space into the category space. Particularly, we add a selective constraint to the transformation matrix (or classifier parameter) that can select discriminative dimensions of the input samples. Moreover, a supervised regularization is tailored to further enhance the discriminability of the subspace. To relax the hard zero-one label matrix in the category space, an additional error term is also incorporated into the framework, which can lead to a more robust transformation matrix. SRSC is formulated as a constrained least square learning (feature transforming) problem. For the SRSC problem, an inexact augmented Lagrange multiplier method (ALM) is utilized to solve it. Extensive experiments on several benchmark data sets adequately demonstrate the effectiveness and superiority of the proposed method. The proposed SRSC approach has achieved better performances than the compared counterpart methods.

Background: Due to the different treatments for low-volume metastatic prostate cancer (PCa) as well as high-volume ones, evaluation of bone metastatic status is clinically significant. In this study, we evaluated the correlation between pre-treatment plasma fibrinogen and the burden of bone metastasis in newly diagnosed PCa patients., Methods: A single-center retrospective analysis, focusing on prostate biopsies of newly diagnosed PCa patients, was performed. A total of 261 patients were enrolled in this study in a 4-year period. All subjects were submitted to single-photon emission computerized tomography-computed tomography to confirm the status of bone metastasis and, if present, the number of metastatic lesions would then be calculated. Clinical information such as age, prostate-specific antigen (PSA), fibrinogen, clinical T stage, and Gleason score were collected. Patients were divided into three groups: (i) a non-metastatic group, (ii) a high volume disease (HVD) group (>3 metastases with at least one lesion outside the spine), and (iii) a low volume disease (LVD) group (metastatic patients excluding HVD ones). The main statistical methods included non-parametric Mann-Whitney test, Spearman correlation, receiver operating characteristic (ROC) curves, and logistic regression., Results: Fibrinogen positively correlated with Gleason score (r = 0.180, P = 0.003), PSA levels (r = 0.216, P < 0.001), and number of metastatic lesions (r = 0.296, P < 0.001). Compared with the non-metastatic and LVD groups, the HVD group showed the highest PSA (104.98 ng/mL, median) and fibrinogen levels (3.39 g/L, median), as well as the largest proportion of Gleason score >7 (86.8%). Both univariate (odds ratio [OR] = 2.16, 95% confidential interval [CI]: 1.536-3.038, P < 0.001) and multivariate (OR = 1.726, 95% CI: 1.206-2.472, P = 0.003) logistic regressions showed that fibrinogen was independently associated with HVD. The ROC curve suggested that fibrinogen acts as a predictor of HVD patients, yielding a cut-off of 3.08 g/L, with a sensitivity of 0.684 and a specificity of 0.760 (area under the curve = 0.739, 95% CI: 0.644-0.833, P < 0.001)., Conclusions: Pre-treatment plasma fibrinogen is positively associated with bone metastatic burden in PCa patients. Our results indicate that fibrinogen might be a potential predictor of HVD.

Compact hash code learning has been widely applied to fast similarity search owing to its significantly reduced storage and highly efficient query speed. However, it is still a challenging task to learn discriminative binary codes for perfectly preserving the full pairwise similarities embedded in the high-dimensional real-valued features, such that the promising performance can be guaranteed. To overcome this difficulty, in this paper, we propose a novel scalable supervised asymmetric hashing (SSAH) method, which can skillfully approximate the full-pairwise similarity matrix based on maximum asymmetric inner product of two different non-binary embeddings. In particular, to comprehensively explore the semantic information of data, the supervised label information and the refined latent feature embedding are simultaneously considered to construct the high-quality hashing function and boost the discriminant of the learned binary codes. Specifically, SSAH learns two distinctive hashing functions in conjunction of minimizing the regression loss on the semantic label alignment and the encoding loss on the refined latent features. More importantly, instead of using only part of similarity correlations of data, the full-pairwise similarity matrix is directly utilized to avoid information loss and performance degeneration, and its cumbersome computation complexity on n ×n matrix can be dexterously manipulated during the optimization phase. Furthermore, an efficient alternating optimization scheme with guaranteed convergence is designed to address the resulting discrete optimization problem. The encouraging experimental results on diverse benchmark datasets demonstrate the superiority of the proposed SSAH method in comparison with many recently proposed hashing algorithms.

Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu's, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searching for optimal thresholds, the method of quantum lossy-encoding-based entropy maximization (QLEEM) is presented, in which the eigenvalues of density matrices can give direct clues for thresholding, and then, the process of optimal searching can be avoided. Meanwhile, the QLEEM algorithm achieves two additional effects: (1) the upper bound of the thresholding level can be implicitly determined according to the eigenvalues; and (2) the proposed approaches ensure that the local information in images is retained as much as possible, and simultaneously, the inter-class separability is maximized in the segmented images. Both of them contribute to the structural characteristics of images, which the human visual system is highly adapted to extract. Experimental results show that the proposed methods are able to achieve a competitive quality of thresholding and the fastest computation speed compared with the state-of-the-art methods.

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

Background: The diagnostic value of current prostate-specific antigen (PSA) tests is challenged by the poor detection rate of prostate cancer (PCa) in repeat prostate biopsy. In this study, we proposed a novel PSA-related parameter named PSA density variation rate (PSADVR) and designed a clinical trial to evaluate its potential diagnostic value for detecting PCa on a second prostate biopsy., Methods: Data from 184 males who underwent second ultrasound-guided prostate biopsy 6 months after the first biopsy were included in the study. The subjects were divided into PCa and non-PCa groups according to the second biopsy pathological results. Prostate volume, PSA density (PSAD), free-total PSA ratio, and PSADVR were calculated according to corresponding formulas at the second biopsy. These parameters were compared using t-test or Mann-Whitney U-test between PCa and non-PCa groups, and receiver operating characteristic analysis were used to evaluate their predictability on PCa detection., Results: PCa was detected in 24 patients on the second biopsy. Mean values of PSA, PSAD, and PSADVR were greater in the PCa group than in the non-PCa group (8.39 μg/L vs. 7.16 μg/L, 0.20 vs. 0.16, 14.15% vs. -1.36%, respectively). PSADVR had the largest area under the curve, with 0.667 sensitivity and 0.824 specificity when the cutoff was 10%. The PCa detection rate was significantly greater in subjects with PSADVR >10% than PSADVR ≤10% (28.6% vs. 6.5%, P< 0.001). In addition, PSADVR was the only parameter in this study that showed a significant correlation with mid-to-high-risk PCa (r = 0.63, P = 0.03)., Conclusions: Our results demonstrated that PSADVR improved the PCa detection rate on second biopsies, especially for mid-to-high-risk cancers requiring prompt treatment.

We investigated the effects of alpha-fetoprotein (AFP) gene silencing on Survivin expression in HepG2 cells. Small interfering RNA technology was used to downregulate AFP expression in HepG2 cells. An enzyme-linked immunosorbent assay was used to measure AFP concentration in the supernatant before and after transfection. An MTT assay was used to detect cell proliferation activity before and after transfection. We performed flow cytometric analysis to detect the cell apoptosis rate, and reverse transcription-polymerase chain reaction to detect Survivin mRNA levels before and after transfection. Forty-eight hours after transfection, AFP concentration in the supernatant of the experimental group significantly decreased, hepatocellular carcinoma cell growth was inhibited by 43.1%, and the apoptosis rate increased by 24.3%. Survivin mRNA expression was reduced by 78.0% in HepG2 cells. These indicators in the control group and in the blank group did not change significantly. Silencing of AFP expression in HepG2 cells can effectively inhibit the growth of hepatoma cells and promote apoptosis, which may be useful for reducing intracellular Survivin mRNA levels.

Background: Hepatocarcinogenesis is a complex process that may be influenced by many factors, including polymorphism in microsomal epoxide hydrolase (mEH). Previous work suggests an association between the Tyr113His and His139Arg mEH polymorphisms and susceptibility to hepatocellular carcinoma (HCC), but the results have been inconsistent., Methods: PubMed, EMBASE, Google Scholar and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. A meta-analysis was performed to examine the association between Tyr113His and His139Arg mEH polymorphism and susceptibility to HCC. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated., Results: Eleven studies were included in the meta-analysis, involving 1,696 HCC cases and 3,600 controls. The 113His- mEH allele was significantly associated with increased risk of HCC based on allelic contrast (OR = 1.35, 95% CI = 1.04-1.75, p = 0.02), homozygote comparison (OR = 1.65, 95% CI = 1.07-2.54, p = 0.02) and a recessive genetic model (OR = 1.54, 95% CI = 1.21-1.96, p<0.001), while individuals carrying the Arg139Arg mEH genotype had no association with increased or decreased risk of HCC., Conclusion: The 113His- allele polymorphism in mEH may be a risk factor for hepatocarcinogenesis, while the mEH 139Arg- allele may not be a risk or protective factor. There is substantial evidence that mEH polymorphisms interact synergistically with other genes and the environment to modulate risk of HCC. Further large and well-designed studies are needed to confirm these conclusions.

Aim: To evaluate the chemopreventive efficacy of vitamin K2 (VK2) analog in patients with hepatocellular carcinoma (HCC) after curative hepatic resection or local ablation, since a recent randomized control trial (RCT) and systematic review have given contradictory results., Methods: MEDLINE, EMBASE and Cochrane library databases were systematically searched through the end of May 2012. Meta-analysis of RCTs and cohort studies was performed to estimate the effects of the VK2 analog on tumor recurrence rate and overall survival (OS). Risk ratios (RRs) and 95% confidence intervals (95% CIs) were calculated., Results: Six RCTs and one cohort study involving a total of 930 patients were included. VK2 analog therapy did not reduce the 1-year recurrence rate, with a pooled RR of 0.67 (95% CI 0.39-1.13, p = 0.13). However, VK2 analog therapy was associated with a significant reduction in the 2- and 3-year tumor recurrence rates, with respective pooled RRs of 0.65 (95% CI 0.51-0.83, p<0.001) and 0.70 (95% CI = 0.58-0.85, p<0.001). The therapy was also associated with a significant improvement in 1-, 2-, and 3-year OS, with respective pooled RRs of 1.03 (95% CI 1.01-1.05, p = 0.02), 1.11 (95% CI 1.03-1.19, p = 0.005) and 1.14 (95% CI 1.02-1.28, p = 0.02). None of the studies reported adverse effects attributable to VK2 analog therapy., Conclusion: The VK2 analog may reduce recurrence rate after 1 year and improve OS in HCC patients as early as 1 year. However, these findings should be considered preliminary since the majority of patients came from an RCT with survival data out to only 1 year. More extensive studies with larger sample sizes and longer follow-up are needed.

Objective: To present the experience in a technique used to treat intractable postherpetic neuralgia (PHN)-percutaneous dorsal root ganglion (DGR) radiofrequency thermocoagulation guided by CT scanning., Methods: Sixteen PHN patients underwent puncture of radiohealing needle into the superior 1/3 of the corresponding intervertebral foramen guided by CT. Electric stimulation test and impedance test were conducted to confirm the right spot. Radiofrequency heat coagulation therapy under the condition of 90 degrees C 90 s was performed for 3 cycles. Then mixture of betamethasone and lidocain was injected and the needle was pulled out. CT was conducted to observe if pneumothorax occurred. The patients were followed up for 2-16 months., Results: Radiofrequency thermocoagulation was performed on 45 target dorsal root ganglia in 16 PHN patients with the effect of immediate disappearance of hyperalgesia. Remaining spontaneous pain was seen in 5 cases, however, with decreases of attack frequency and severity. The visual analogue pain scale score was decreased from 7-9 before the procedure to 2-3 after the procedure. No relapse was found during the follow-up., Conclusion: Selective percutaneous DRG radiofrequency thermocoagulation guided by CT scanning is effective in treatment of PHN.

Background: Recent studies suggest that circulating DNA may be a potential tumor marker for lung cancer, but most of these studies are conducted between healthy controls and lung cancer patients, with few or no benign lung disease patients included. The objective of this study was to evaluate the performance of plasma DNA quantification in discriminating lung cancer from the healthy and benign lung disease., Methods: Plasma DNA was extracted with a QIAamp DNA Blood Midi kit and quantified by a PicoGreen dsDNA quantitation kit in 44 healthy individuals, 36 benign lung disease patients and 67 lung cancer patients. Discrimination power was evaluated by the receiver operating characteristic curve., Results: Plasma DNA values were significantly increased in lung cancer patients, especially in those with metastases, and in benign lung disease patients compared with that in the healthy individuals (P < 0.001, respectively). The values in lung cancer patients were significantly increased compared with that in the benign lung disease patients (P < 0.001). The area under the curve was 0.96 [95% confidence interval (CI) 0.92 - 0.99] for the healthy versus lung cancer, 0.73 (95% CI 0.64 - 0.83) for lung cancer versus benign lung disease, and 0.86 (95% CI 0.80 - 0.91) for lung cancer versus the healthy and benign lung disease., Conclusions: Plasma DNA quantification has a strong power to discriminate lung cancer from the healthy and from the healthy and benign lung disease, less power to discriminate lung cancer from benign lung disease. Plasma DNA quantification may be useful as a screening tool for lung cancer.

Background: Diffuse panbronchiolitis (DPB) was originally and is still primarily reported in Japan, rarely in other countries. As macrolide therapy is effective for this disease with once dismal prognosis, familiarity with its clinical features is urgently needed, especially for clinicians outside Japan. The objectives of this study were to investigate the clinical features of DPB in a Chinese population and propose diagnostic procedures that will lead to increased awareness of this treatable disease among clinicians, ultimately allowing for more rapid diagnosis., Methods: After a literature review, the clinical features of DPB were histopathologically confirmed in a series of 9 cases either by open lung biopsy or video-assisted thoracic surgical biopsy, resulting in the largest series of confirmed DPB cases in a non-Japanese population. Here, the cases are retrospectively described and diagnostic procedures are discussed., Results: Persistent cough, sputum, and exertion dyspnea occurred in 89% of patients, a history of or current chronic sinusitis in 78%, centrilobular micronodules appearing on chest CT scans in 100%, coarse crackles in 78%, FEV1/FVC < 70% in 44%, PaO2 < 80 mmHg in 56%, and titer of cold hemagglutinin > or = 1:64 in 11%. According to its clinical diagnostic criteria, diagnosis was definitive in 44%, suggested in 33%, and excluded in 23% at the time of diagnosis. However, DPB was clinically considered before confirmation in only 22% of patients, with the remaining 78% of cases missed or mistaken for other diseases. Of the 9 cases, 8 received transbronchial biopsies before confirmation of the diagnosis, but all showed non-specific inflammation., Conclusions: Although its clinical features may vary with disease course and ethnic populations, most cases of DPB can be diagnosed or suggested according to clinical diagnostic criteria. However, underdiagnosis as a result of unfamiliarity with its clinical features and diagnostic criteria prevails. If difficulty in diagnosis arises, the diagnosis should be based on clinicopathological features and the exclusion of other diseases. Few cases can be confirmed by transbronchial biopsies; in these cases, either an open-lung biopsy or a video-assisted thoracic surgical lung biopsy should be recommended.