Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm.

BACKGROUND: In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE. METHODS: A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively. RESULTS: The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001). CONCLUSIONS: CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.

Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.

BACKGROUND AND AIMS: Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to assess H. pylori infection by using endoscopic videos in real time. METHODS: Endoscopic data were retrospectively obtained from Zhejiang Cancer Hospital (ZJCH) for the development, validation, and testing of the system. Stored videos from ZJCH were used for assessing and comparing the performance of IDEA-HP with that of endoscopists. Prospective consecutive patients undergoing esophagogastroduodenoscopy were enrolled to assess the applicability of clinical practice. The urea breath test was used as the gold standard for diagnosing H. pylori infection. RESULTS: In 100 videos, IDEA-HP achieved a similar overall accuracy of assessing H. pylori infection to that of experts (84.0% vs. 83.6% [P = 0.729]). Nevertheless, the diagnostic accuracy (84.0% vs. 74.0% [P<0.001]) and sensitivity (82.0% vs. 67.2% [P<0.001]) of IDEA-HP were significantly higher than those of the beginners. In 191 prospective consecutive patients, IDEA-HP achieved accuracy, sensitivity, and specificity of 85.3% (95% CI: 79.0%-89.3%), 83.3% (95% CI: 72.8%-90.5%), and 85.8% (95% CI: 77.7%-91.4%), respectively. CONCLUSIONS: Our results show that IDEA-HP has great potential for assisting endoscopists in assessing H. pylori infection status during actual clinical work.

A mucosal recovery software tool for endoscopic submucosal dissection in early gastric cancer.

Background: Due to the limited diagnostic ability, the low detection rate of early gastric cancer (EGC) is a serious health threat. The establishment of the mapping between endoscopic images and pathological images can rapidly improve the diagnostic ability to detect EGC. To expedite the learning process of EGC diagnosis, a mucosal recovery map for the mapping between ESD mucosa specimen and pathological images should be performed in collaboration with endoscopists and pathologists, which is a time-consuming and laborious work. Methods: 20 patients at the Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College from March 2020 to July 2020 were enrolled in this study. We proposed the improved U-Net to obtain WSI-level segmentation results, and the WSI-level results can be mapped to the macroscopic image of the specimen. For the convenient use, a software pipeline named as "Pathology Helper" for integration the workflow of the construction of mucosal recovery maps was developed. Results: The MIoU and Dice of our model can achieve 0.955 ± 0.0936 and 0.961 ± 0.0874 for WSI-level segmentation, respectively. With the help of "Pathology Helper", we can construct the high-quality mucosal recovery maps to reduce the workload of endoscopists and pathologists. Conclusion: "Pathology Helper" will accelerate the learning of endoscopists and pathologists, and rapidly improve their abilities to detect EGC. Our work can also improve the detection rate of early gastric cancer, so that more patients with gastric cancer will be treated in a timely manner.

Application of a WeChat-based mini-app as a patient reminder in Helicobacter pylori eradication: a prospective multi-center randomized controlled study.

BACKGROUND: To improve the eradication rate of H. pylori, researchers have investigated the role of WeChat-based mini-app as an electronic reminding system in H. pylori treatment. METHODS: Subjects from three medical centers were divided into two groups. Patients in the daily mini-app-based notification system group received daily notifications via the WeChat mini-app. Patients in the control group received one-time verbal education on the first clinical visit. Both groups received a 14-day quadruple therapy to eradicate H. pylori infection. Eradication rate, compliance, adverse events and satisfaction were evaluated. RESULTS: Both intention-to-treat (ITT) and per-protocol (PP) analyses were conducted. The eradication rate in the daily mini-app-based notification system group was slightly higher compared with the control group (ITT analysis: 76.70% vs. 70.73%, p = 0.312; PP analysis: 85.87% vs. 82.86%, p = 0.562). The compliance was significantly higher in the daily mini-app-based notification system group (ITT analysis: 85.52% vs. 70.48%, p = 0.028; PP analysis: 92.39% vs. 81.90%, p = 0.030). The adverse event rates were similar between the two groups (PP analysis: 36.96% vs. 40.95%, p = 0.566). No significant difference in eradication rate was seen in each subgroup analysis by age, place of residence, grade of education, or endoscopic findings. CONCLUSION: The study showed that daily mini-app-based notification improved patient compliance but not H. pylori eradication rate. Trial registration The research was registered in the Chinese Clinical Trial Registry (ChiCTR2000031011, 21/03/2020).

Study on Molecular Information Intelligent Diagnosis and Treatment of Bladder Cancer on Pathological Tissue Image.

Background: Molecular information about bladder cancer is significant for treatment and prognosis. The immunohistochemistry (IHC) method is widely used to analyze the specific biomarkers to determine molecular subtypes. However, procedures in IHC and plenty of reagents are time and labor-consuming and expensive. This study established a computer-aid diagnosis system for predicting molecular subtypes, p53 status, and programmed death-ligand 1 (PD-L1) status of bladder cancer with pathological images. Materials and Methods: We collected 119 muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from January 2016 to September 2018. All the pathological sections are scanned into digital whole slide images (WSIs), and the IHC results of adjacent sections were recorded as the label of the corresponding slide. The tumor areas are first segmented, then molecular subtypes, p53 status, and PD-L1 status of those tumor-positive areas would be identified by three independent convolutional neural networks (CNNs). We measured the performance of this system for predicting molecular subtypes, p53 status, and PD-L1 status of bladder cancer with accuracy, sensitivity, and specificity. Results: For the recognition of molecular subtypes, the accuracy is 0.94, the sensitivity is 1.00, and the specificity is 0.909. For PD-L1 status recognition, the accuracy is 0.897, the sensitivity is 0.875, and the specificity is 0.913. For p53 status recognition, the accuracy is 0.846, the sensitivity is 0.857, and the specificity is 0.750. Conclusion: Our computer-aided diagnosis system can provide a novel and simple assistant tool to obtain the molecular subtype, PD-L1 status, and p53 status. It can reduce the workload of pathologists and the medical cost.

Analysis of differential gene expression profiles uncovers mechanisms of Xuesaitong injection against cerebral ischemia-reperfusion injury.

BACKGROUND: Xuesaitong injection (XST), a well-known traditional Chinese patent medicine, has been widely used in the treatment of cardiovascular and cerebrovascular diseases. The exact mechanisms of XST in ischemic stroke remain to be thoroughly elucidated. PURPOSE: This study aims to characterize the candidate differentially expressed genes (DEGs) and pathways of XST in ischemic stroke by bioinformatics analysis, and to explore new clues for the underlying mechanisms of XST. METHODS: A dataset (GSE61616) was performed to screen out DEGs for deep analysis. Series Test of Cluster analysis for DEGs was carried out. For all DEGs, Gene Ontology (GO) annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for visualization. The screened hub gene expression characteristics were verified in middle cerebral artery occlusion (MCAO) rats. In vivo studies have demonstrated the mechanisms of XST against cerebral ischemia-reperfusion (CIR) injury. RESULTS: A total of 8066 DEGs were screened out and the expression of genes in profile 8 was suggested to have clinical significance. The MAPK signaling pathway was indicated as the most significantly enriched pathway in profile 8. Bdnf was identified as the most significant hub gene according to node degree. Animal experiments demonstrated that XST attenuated CIR injury. XST increased brain-derived neurotrophic factor (BDNF) and its high-affinity receptor tropomyosin-related kinase B (TrkB) levels in MCAO. Furthermore, the knockdown of BDNF by siRNA abolished the in vivo effects of XST on brain injury, neurodegeneration and apoptosis after CIR. CONCLUSION: The integrated strategy, based on bioinformatics analyses with experimental verification, provides a novel cellular mechanism by which XST alleviates CIR injury. The BDNF-TrkB pathway was highly thought to play a vital role in the neuroprotective effects of XST.

Correlation of the detection rate of upper GI cancer with artificial intelligence score: results from a multicenter trial (with video).

BACKGROUND AND AIMS: The quality of EGD is a prerequisite for a high detection rate of upper GI lesions, especially early gastric cancer. Our previous study showed that an artificial intelligence system, named intelligent detection endoscopic assistant (IDEA), could help to monitor blind spots and provide an operation score during EGD. Here, we verified the effectiveness of IDEA to help evaluate the quality of EGD in a large-scale multicenter trial. METHODS: Patients undergoing EGD in 12 hospitals were consecutively enrolled. All hospitals were equipped with IDEA developed using deep convolutional neural networks and long short-term memory. Patients were examined by EGD, and the results were recorded by IDEA. The primary outcome was the detection rate of upper GI cancer. Secondary outcomes were part scores, total scores, and endoscopic procedure time, which were analyzed by IDEA. RESULTS: A total of 17,787 patients were recruited. The total detection rate of cancer-positive cases was 1.50%, ranging from .60% to 3.94% in each hospital. The total detection rate of early cancer-positive cases was .36%, ranging from .00% to 1.58% in each hospital. The average total score analyzed by IDEA ranged from 64.87 ± 16.87 to 83.50 ± 9.57 in each hospital. The cancer detection rate in each hospital was positively correlated with total score (r = .775, P = .003). Similarly, the early cancer detection rate was positively correlated with total score (r = .756, P = .004). CONCLUSIONS: This multicenter trial confirmed that the quality of the EGD result is positively correlated with the detection rate of cancer, which can be monitored by IDEA. (Clinical trial registration number: ChiCTR2000029001.).

Rib fracture detection system based on deep learning.

Rib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model's clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists' workload in the clinical practice.

"Pink Pattern" Visualized in Magnifying Endoscopy With Narrow-Band Imaging Is a Novel Feature of Early Differentiated Gastric Cancer: A Bridge Between Endoscopic Images and Histopathological Changes.

Background: A pink color change occasionally found by us under magnifying endoscopy with narrow-band imaging (ME-NBI) may be a special feature of early gastric cancer (EGC), and was designated the "pink pattern". The purposes of this study were to determine the relationship between the pink pattern and the cytopathological changes in gastric cancer cells and whether the pink pattern is useful for the diagnosis of EGC. Methods: The color features of ME-NBI images and pathological images of cancerous gastric mucosal surfaces were extracted and quantified. The cosine similarity was calculated to evaluate the correlation between the pink pattern and the nucleus-to-cytoplasm ratio of cancerous epithelial cells. Two diagnostic tests were performed by 12 endoscopists using stored ME-NBI images of 185 gastric lesions to investigate the diagnostic efficacy of the pink pattern for EGC. The diagnostic values, such as the area under the curve (AUC), the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), of test 1 and test 2 were compared. Results: The cosine similarity between the color values of ME-NBI images and pathological images of 20 lesions was at least 0.744. The median AUC, accuracy, sensitivity, specificity, PPV, and NPV of test 2 were significantly better than those of test 1 for all endoscopists and for the junior and experienced groups. Conclusions: The pink pattern observed in ME-NBI images correlated strongly with the change in the nucleus-to-cytoplasm ratio of gastric epithelial cells, and could be considered a useful marker for the diagnosis of differentiated EGC.

A novel model for predicting fatty liver disease by means of an artificial neural network.

BACKGROUND: The artificial neural network (ANN) emerged recently as a potent diagnostic tool, especially for complicated systemic diseases. This study aimed to establish a diagnostic model for the recognition of fatty liver disease (FLD) by virtue of the ANN. METHODS: A total of 7,396 pairs of gender- and age-matched subjects who underwent health check-ups at the First Affiliated Hospital, College of Medicine, Zhejiang University (Hangzhou, China) were enrolled to establish the ANN model. Indices available in health check-up reports were utilized as potential input variables. The performance of our model was evaluated through a receiver-operating characteristic (ROC) curve analysis. Other outcome measures included diagnostic accuracy, sensitivity, specificity, Cohen's k coefficient, Brier score, and Hosmer-Lemeshow test. The Fatty Liver Index (FLI) and the Hepatic Steatosis Index (HSI), retrained using our training-group data with its original designated input variables, were used as comparisons in the capability of FLD diagnosis. RESULTS: Eight variables (age, gender, body mass index, alanine aminotransferase, aspartate aminotransferase, uric acid, total triglyceride, and fasting plasma glucose) were eventually adopted as input nodes of the ANN model. By applying a cut-off point of 0.51, the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908 [95% confidence interval (CI), 0.901-0.915]-significantly higher (P < 0.05) than that of the FLI model (0.881, 95% CI, 0.872-0.891) and that of the HSI model (0.885; 95% CI, 0.877-0.893). Our ANN model exhibited higher diagnostic accuracy, better concordance with ultrasonography results, and superior capability of calibration than the FLI model and the HSI model. CONCLUSIONS: Our ANN system showed good capability in the diagnosis of FLD. It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.

Cost­effectiveness Analysis of Helicobacter pylori Eradication Therapy in First-Degree Relatives of Patients with Gastric Cancer.

BACKGROUND: Helicobacter pylori (H. pylori) eradication therapy has been shown to reduce the risk of gastric cancer in patients who have a family history of gastric cancer in first-degree relatives. The aim of this study was to assess the cost-effectiveness of H. pylori eradication therapy in a select population in the People's Republic of China. METHODS: A Markov model was applied to evaluate the cost-effectiveness of H. pylori eradication therapy. The long-term costs of H. pylori eradication therapy were calculated from the Chinese perspective. Health outcomes were measured by quality-adjusted life years (QALYs). Epidemiological information and health utilities used in the model were collected from published literatures or statistical bureaus. A sensitivity analysis was conducted to explore the influence of parameters on the uncertainty of the model. RESULTS: Compared with the no eradication therapy group, H. pylori eradication therapy prolonged an average of 4.52 QALYs (32.64 QALYs vs 28.12 QALYs) and saved $3227.07 ($2472.83 vs $5699.90). The cost-effectiveness analysis demonstrated that no H. pylori eradication therapy cost more and produced less QALYs. It was dominated by H. pylori eradication therapy. The one-way sensitive analyses proved that the results were robust to the fluctuations of the input parameters. CONCLUSION: H. pylori eradication therapy not only reduced the risk of gastric cancer in first-degree relatives of patients with gastric cancer but also was an economical strategy with lower costs and greater efficacy.

Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time.

BACKGROUND: Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important; however, there is a lack of effective evaluation tools. AIMS: To develop an artificial intelligence (AI)-assisted EGD system able to automatically monitor blind spots in real-time. METHODS: An AI-based system, called the Intelligent Detection Endoscopic Assistant (IDEA), was developed using a deep convolutional neural network (DCNN) and long short-term memory (LSTM). The performance of IDEA for recognition of gastric sites in images and videos was evaluated. Primary outcomes included diagnostic accuracy, sensitivity, and specificity. RESULTS: A total of 170,297 images and 5779 endoscopic videos were collected to develop the system. As the test group, 3100 EGD images were acquired to evaluate the performance of DCNN in recognition of gastric sites in images. The sensitivity, specificity, and accuracy of DCNN were determined as 97.18%,99.91%, and 99.83%, respectively. To assess the performance of IDEA in recognition of gastric sites in EGD videos, 129 videos were used as the test group. The sensitivity, specificity, and accuracy of IDEA were 96.29%,93.32%, and 95.30%, respectively. CONCLUSIONS: IDEA achieved high accuracy for recognition of gastric sites in real-time. The system can be applied as a powerful assistant tool for monitoring blind spots during EGD.

Novel Polymeric Hybrid Nanocarrier for Curcumin and Survivin shRNA Co-delivery Augments Tumor Penetration and Promotes Synergistic Tumor Suppression.

A major barrier for co-delivery of gene medicine with small molecular chemotherapeutic drugs in solid tumors is the inadequate tumor penetration and transfection. In this study, a novel polymeric nanocarrier with integrated properties of tumor penetration, nuclear targeting, and pH-responsive features was designed, and further used to achieve the synergistic anti-tumor effect of curcumin (CUR) and survivin shRNA (pSUR). The polymeric hybrid nanocarrier was constructed from the FDA-approved polymer PLGA and a novel conjugated triblock polymer W5R4K-PEG2K-PHIS (WPH). CUR and pSUR were simultaneously encapsulated in the dual-drug-loaded nanoparticles (CUR/pSUR-NPs) by a modified double-emulsion solvent evaporation (W/O/W) method. The obtained nanoparticles exhibited better pharmaceutical properties with a uniform spherical morphology and sustained release manners of CUR and pSUR. Excellent features including preferable cellular uptake, efficient endosomal escape, enhanced tumor penetration, and elevated transfection efficiency were further proven. Additionally, a markedly enhanced anti-tumor efficacy for CUR/shRNA-NPs was achieved on SKOV-3 and Hela cells. The synergistic anti-tumor effect involved the inhibition of tumor cell proliferation, induction of cell apoptosis, and the activation of caspase-3 pathways. This work sets up an innovative co-delivery nanosystem to suppress tumor growth, contributing to the development of a comprehensive nanoparticulate strategy for future clinical applications.

An efficient controlled release strategy for hypertension therapy: Folate-mediated lipid nanoparticles for oral peptide delivery.

Hypertension is an important cardiovascular disease, which need long-term medication. Thus, oral drug delivery system is a preferred route for hypertension patients due to the convenience and compliance. Val-Leu-Pro-Val-Pro (VLPVP, VP5) is an angiotensin converting enzyme inhibitory peptide with antihypertensive effects. However, the oral peptide delivery is faced with obstacles, such as gastric acid, enzyme degradation and intestine barriers. Herein, we developed a controlled release system consisting of a PLGA core encapsulated with VP5 and a folate-decorated lipid shell (FA-VP5-LNPs) for the oral delivery of antihypertensive peptide. The results found that FA-VP5-LNPs exhibited high stability and possessed a controlled release behavior. Besides, FA-VP5-LNPs improved the cellular uptake both in Caco-2 and HT29 cells and enhanced in situ intestinal absorption in SD rats. The in vivo bioavailability study showed a superior oral absorption of FA-VP5-LNPs, and the AUC0-72 h of FA-VP5-LNPs was 30.71-fold higher than that of free VP5. The pharmacodynamics study exhibited that FA-VP5-LNPs maintained strong antihypertensive effect for six days compared with free VP5, which may reduce the frequency of administration and improve patient compliance. In addition, the nano-formulations showed no toxicity to cells and tissues. These promising results suggested that FA-VP5-LNPs could overcome the intestinal barrier and provide a potential strategy for enhancing peptide delivery and improve the antihypertensive effects.

Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging.

BACKGROUND: Magnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI. METHODS: A total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: The sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts. CONCLUSIONS: Our CNN system showed high accuracy, sensitivity and specificity in the diagnosis of early gastric cancer. It is anticipated that more progress will be made in optimization of the CNN diagnostic system and further development of artificial intelligence in the medical field.

Cancer Targeted Gene Therapy for Inhibition of Melanoma Lung Metastasis with eIF3i shRNA Loaded Liposomes.

Eukaryotic translation initiation factors 3i (eIF3i) is a proto-oncogene that is overexpressed in various tumors, reducing its expression by eIF3i shRNA is a promising strategy to inhibit tumor growth or metastasis. Tumor cell is the target of eIF3i shRNA so that tumor-site accumulation could be important for fulfilling its therapeutic effect. Thus, the iRGD modified liposome (R-LP) was rationally synthesized to enhance the antitumor effect by active targeted delivery of eIF3i shRNA to B16F10 melanoma cells. R-LP encapsulating eIF3i shRNA gene (R-LP/sheIF3i) were prepared by a film dispersion method. The transfection experiment proves that R-LP could effectively transfect B16F10 cells. R-LP/sheIF3i notably restrained the migration, invasion, and adhesion of melanoma cells in vitro. In a mouse model of lung metastasis, R-LP/sheIF3i administered by intravenous injection suppressed pulmonary metastasis of melanoma by dramatically downregulated eIF3i expression and subsequently inhibiting tumor neovascularization and tumor cells proliferation in vivo. Our results provide a basis for tumor cells targeting strategies to reduce the expression of eIF3i by RNAi in the treatment of tumor metastasis.

Cost-effectiveness analysis of gemcitabine plus cisplatin versus fluorouracil plus cisplatin in the first-line setting for Chinese patients with metastatic nasopharyngeal carcinoma.

PURPOSE: A randomized phase III trial demonstrated that gemcitabine plus cisplatin (GP) prolonged progression-free survival and overall survival compared with fluorouracil plus cisplatin (FP) as first-line chemotherapy in patients with metastatic nasopharyngeal carcinoma (NPC). The cost-effectiveness analysis was designed to identify the economic option for metastatic NPC from a Chinese societal perspective. METHODS: We established a Markov model that involved three health states representing the stages of disease to simulate therapy. Survival data of clinical outcomes were derived from the trial and adjusted to quality-adjusted life years (QALYs). Transition probabilities and health utilities were obtained from the clinical trial and published literatures. The cost-effective strategy was estimated for these treatments using a willing-to-pay (WTP) threshold. A one-way sensitivity analysis was conducted to study the influences of parameters. RESULTS: GP treatment group produced a gain of 0.37 QALYs with an incremental cost of $2520.80, yielding an incremental cost-effectiveness ratio (ICER) of $6812.97 per QALY, compared with FP treatment ($15,530.96 versus $13,010.16). The ICER was lower than the accepted WTP threshold, which was 3 times gross domestic product per capita of China ($25,749 per QALY). CONCLUSION: GP regimen is more cost-effective compared with FP regimen as the first-line treatment for Chinese patients with metastatic NPC.

Clinical translation of gene medicine.

Gene therapy has recently witnessed accelerated progress as a new therapeutic strategy with the potential to treat a range of inherited and acquired diseases. Billions of dollars have been invested in basic and clinical research on gene medicine, with ongoing clinical trials focused on cancer, monogenic diseases, cardiovascular diseases and other refractory diseases. Advances addressing the inherent challenges of gene therapy, particularly those related to retaining the delivery efficacy and minimizing unwanted immune responses, provide the basis for the widespread clinical application of gene medicine. Several types of genes delivered by viral or non-viral delivery vectors have demonstrated encouraging results in both animals and humans. As augmented by clinical indications, gene medicine techniques have rapidly become a promising alternative to conventional therapeutic strategies because of their better clinical benefit and lower toxicities. Their application in the clinic has been extensive as a result of the approval of many gene therapy drugs in recent years. In this review, we provide a comprehensive overview of the clinical translation of gene medicine, focusing on the key events and latest progress made regarding clinical gene therapy products. We also discuss the gene types and non-viral materials with respect to developing gene therapeutics in clinical trials.

Polymorphism of cytosolic serine hydroxymethyltransferase and breast cancer risk: evidence from a meta-analysis.

One-carbon metabolism has been presumed to influence cancer pathogenesis. Cytosolic serine hydroxymethyltransferase (cSHMT) is a critical enzyme in one-carbon metabolism pathway. Until now, many studies have investigated the association between cSHMT (also named SHMT1) C1420T polymorphism and breast cancer risk. However, the results remained conflicting rather than conclusive. Literature search was conducted using PubMed, Embase, and China National Knowledge Infrastructure (CNKI) databases until December 2013 to identify studies investigating the association of SHMT1 C1420T polymorphism with breast cancer risk. The strength of the association was assessed by the summary odd ratios (ORs) and their corresponding 95 % confidence interval (CI). Seven studies with a total of 5,534 cases and 6,581 controls were included. Overall, no association was detected between SHMT1 C1420T polymorphism and breast cancer risk (T vs. C, OR = 0.97, 95 % CI = 0.92-1.03). In the subgroup analysis based on ethnicity, SHMT1 C1420T polymorphism has shown a protective effect on breast cancer in Asians (T vs. C, OR = 0.78, 95 % CI = 0.66-0.93) but not in Caucasian (T/T vs. C/C, OR = 0.98, 95 % CI = 0.86-1.12). Significant heterogeneity across studies could be observed in some genetic comparison models in the overall estimation (C/T vs. C/C, P het = 0.004; T/T+C/T vs. C/C, P het = 0.006) but turned to be mild in all comparison models when stratified by ethnicity. Our meta-analysis failed to detect association between SHMT1 C1420T polymorphism and breast cancer risk. However, we found evidence for association of SHMT1 C1420T polymorphism with significantly reduced risk of breast cancer in Asians. Further well-designed studies with larger sample size and better selected controls are warranted.

CYP-nsSNP: a specialized database focused on effect of non-synonymous SNPs on function of CYPs.

The cytochrome P450 (CYP) enzymes play the central role in synthesis of endogenous substances and metabolism of xenobiotics. The substitution of single amino acid caused by non-synonymous single nucleotide polymorphism (nsSNP) will lead to the change in enzymatic activity of CYP isozymes, especially the drugmetabolizing ability. CYP-nsSNP is a specialized database focused on the effect of nsSNPs on enzymatic activity of CYPs. Its unique feature lies in providing the qualitative and quantitative description of the CYP variants in terms of enzymatic activity. In addition, the database also offers the general information about nsSNP and compounds that are involved in corresponding enzymatic reaction. The current CYP-nsSNP can be accessible at http://cypdatabase.sjtu.edu.cn/ and includes more than 300 genetic variants of 12 CYP isozymes together with about 100 compounds. In order to keep the accuracy of information within database, all experimental data were collected from the scientific literatures, and the users who conducted research to identify the novel CYP variants are encouraged to contribute their data. Therefore, CYP-nsSNP can be considered as a valuable source for experimental and computational studies of impact of genetic polymorphism on the function of CYPs.