Internal flow field analysis of a dendritic pore scaffold for bone tissue engineering.

The effective reconstruction of osteochondral biomimetic structures is a key factor in guiding the regeneration of full-thickness osteochondral defects. Due to the avascular nature of hyaline cartilage, the greatest challenge in constructing this scaffold lies in both utilizing the biomimetic structure to promote vascular differentiation for nutrient delivery to hyaline cartilage, thereby enhancing the efficiency of osteochondral reconstruction, and effectively blocking vascular ingrowth into the cartilage layer to prevent cartilage mineralization. However, the intrinsic relationship between the planning of the microporous pipe network and the flow resistance in the biomimetic structure, and the mechanism of promoting cell adhesion to achieve vascular differentiation and inhibiting cell adhesion to block the growth of blood vessels are still unclear. Inspired by the structure of tree trunks, this study designed a biomimetic tree-like tubular network structure for osteochondral scaffolds based on Murray's law. Utilizing computational fluid dynamics, the study investigated the influence of the branching angle of micro-pores on the flow velocity, pressure distribution, and scaffold permeability within the scaffold. The results indicate that when the differentiation angle exceeds 50 degrees, the highest flow velocity occurs at the confluence of tributaries at the ninth fractal position, forming a barrier layer. This structure effectively guides vascular growth, enhances nutrient transport capacity, increases flow velocity to promote cell adhesion, and inhibits cell infiltration into the cartilage layer.

MALDI-mass spectrometry imaging as a new technique for detecting non-heme iron in peripheral tissues via caudal vein injection of deferoxamine.

As one of the most common iron-chelating agents, deferoxamine (DFO) rapidly chelates iron in the body. Moreover, it does not compete for the iron characteristic of hemoglobin in the blood cells, which is common in the clinical treatment of iron poisoning. Iron is a trace element necessary to maintain organism normal life activities. Iron deficiency can lead to anemia, whereas iron overload can cause elevated levels of cellular oxidative stress and cell damage. As a consequence, detection of the iron content in tissues and blood is of great significance. The traditional techniques for detecting the iron content include inductively coupled plasma-mass spectrometry and atomic absorption spectrometry, which cannot be used for imaging purposes. Laser ablation-ICP-MS and synchrotron radiation micro-X-ray fluorescence can map the concentration and distribution of iron in tissues. However, these methods can only be used to measure the total iron levels in blood or tissues. In recent years, due to the deepening understanding of iron metabolism, diseases related to iron overload have attracted increasing attention. Therefore, we took advantage of the properties of DFO in terms of chelating iron and investigated different sampling times following DFO injection in the tail vein of mice. We used mass spectrometry imaging (MSI) technology to detect the DFO and ferrioxamine content in the blood and different tissues to indirectly characterize the non-heme iron content.

Prognostic analysis and risk assessment based on RNA editing in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model's utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal.

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

Thermosensitive Microneedles Capable of On Demand Insulin Release for Precise Diabetes Treatment.

As a novel painless and minimally invasive transdermal drug delivery method, microneedles have solved the challenges of microbial infection and tissue necrosis associated with multiple subcutaneous injections in patients with diabetes. However, traditional soluble microneedles cannot switch drug release on and off according to the patient's needs during long-term use, which is one of the most critical elements of diabetes treatment. Herein, an insoluble thermosensitive microneedle (ITMN) that can control the release of insulin by adjusting the temperature, enabling the precise treatment of diabetes is designed. Thermosensitive microneedles are produced by in situ photopolymerization of the temperature-sensitive compound N-isopropylacrylamide with the hydrophilic monomer N-vinylpyrrolidone, which is encapsulated with insulin and bound to a mini-heating membrane. ITMN are demonstrated to have good mechanical strength and temperature sensitivity, can release significantly different insulin doses at different temperatures, and effectively regulate blood glucose in type I diabetic mice. Therefore, the ITMN provides a possibility for intelligent and convenient on-demand drug delivery for patients with diabetes, and when combined with blood glucose testing devices, it has the potential to form an integrated and precise closed-loop treatment for diabetes, which is of great importance in diabetes management.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study.

PURPOSE: Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection. METHODS: Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS: 127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03). CONCLUSION: ML radiomics models based on CECT are valuable in predicting ER in ICC.

Prediction of Response to Lenvatinib Monotherapy for Unresectable Hepatocellular Carcinoma by Machine Learning Radiomics: A Multicenter Cohort Study.

PURPOSE: We aimed to construct machine learning (ML) radiomics models to predict response to lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC). EXPERIMENTAL DESIGN: Patients with HCC receiving lenvatinib monotherapy at three institutions were retrospectively identified and assigned to training and external validation cohorts. Tumor response after initiation of lenvatinib was evaluated. Radiomics features were extracted from contrast-enhanced CT images. The K-means clustering algorithm was used to distinguish radiomics-based subtypes. Ten ML radiomics models were constructed and internally validated by 10-fold cross-validation. These models were subsequently verified in an external validation cohort. RESULTS: A total of 109 patients were identified for analysis, namely, 74 in the training cohort and 35 in the external validation cohort. Thirty-two patients showed partial response, 33 showed stable disease, and 44 showed progressive disease. The overall response rate (ORR) was 29.4%, and the disease control rate was 59.6%. A total of 224 radiomics features were extracted, and 25 significant features were identified for further analysis. Two distant radiomics-based subtypes were identified by K-means clustering, and subtype 1 was associated with a higher ORR and longer progression-free survival (PFS). Among the 10 ML algorithms, AutoGluon displayed the highest predictive performance (AUC = 0.97), which was relatively stable in the validation cohort (AUC = 0.93). Kaplan-Meier analysis showed that responders had a better overall survival [HR = 0.21; 95% confidence interval (CI): 0.12-0.36; P < 0.001] and PFS (HR = 0.14; 95% CI: 0.09-0.22; P < 0.001) than nonresponders. CONCLUSIONS: Valuable ML radiomics models were constructed, with favorable performance in predicting the response to lenvatinib monotherapy for unresectable HCC.

Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment.

OBJECTIVE: To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. MAIN RESULTS: The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent.

A low-loss zero-index photonic crystal slab based on toroidal dipole mode.

Zero-index medium has profound application for light manipulation. Certain types of dielectric photonic crystals (PCs) may have zero effective index since they form Dirac cone at the Γ point of their band structure. Although zero index photonic crystals provide a solution to impedance mismatch in photonic integrated circuits, its propagation modes strongly radiate to the surrounding environment, which hampers their application for high-density integration. In this paper, by an appropriate design of PC's unit cell, toroidal dipole mode is excited at Dirac-point frequency through coupled Mie resonance to suppress radiative losses of other multipoles. The PCs with the Dirac-like dispersion at the Γ point can be mapped to an effective zero-index medium. The physical mechanism was utterly investigated by means of multipole decomposition and band structure analysis. Due to the non-radiation property of the toroidal dipole mode, the proposed photonic crystal slab process is low-loss based on numerical simulation. Moreover, its relatively simple design facilitates integration with future quantum photonic devices.

Characterization of cancer-related fibroblasts (CAF) in hepatocellular carcinoma and construction of CAF-based risk signature based on single-cell RNA-seq and bulk RNA-seq data.

Background: Cancer-associated fibroblasts (CAFs) are involved in tumor growth, angiogenesis, metastasis, and resistance to therapy. We sought to explore the CAFs characteristics in hepatocellular carcinoma (HCC) and establish a CAF-based risk signature for predicting the prognosis of HCC patients. Methods: The signal-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of HCC were obtained from the TCGA and GEO databases respectively. Seurat R package was applied to process scRNA-seq data and identify CAF clusters according to the CAF markers. Differential expression analysis was performed to screen differentially expressed genes (DEGs) between normal and tumor samples in TCGA dataset. Then Pearson correlation analysis was used to determine the DEGs associated with CAF clusters, followed by the univariate Cox regression analysis to identify CAF-related prognostic genes. Lasso regression was implemented to construct a risk signature based on CAF-related prognostic genes. Finally, a nomogram model based on the risk signature and clinicopathological characteristics was developed. Results: Based on scRNA-seq data, we identified 4 CAF clusters in HCC, 3 of which were associated with prognosis in HCC. A total of 423 genes were identified from 2811 DEGs to be significantly correlated with CAF clusters, and were narrowed down to generate a risk signature with 6 genes. These six genes were primarily connected with 39 pathways, such as angiogenesis, apoptosis, and hypoxia. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for HCC, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of HCC. Conclusion: CAF-based risk signatures can effectively predict the prognosis of HCC, and comprehensive characterization of the CAF signature of HCC may help to interpret the response of HCC to immunotherapy and provide new strategies for cancer treatment.

Phase mismatch induced suppression of eigenmode resonance in terahertz metasurfaces.

In this paper, we observe the distinguishable modulation of the different eigenmodes by lattice mode in terahertz U-shaped metasurfaces, and a remarkable lattice induced suppression of the high order eigenmode resonance is demonstrated. With the quantitative analysis of Q factor and loss of the resonances, we clarify that the peculiar phenomenon of suppression is originated from the phase mismatch of the metasurfaces via introducing the phase difference between the neighboring structures. These results provide new insights into the phase mismatch mediated transmission amplitude of eigenmode resonance in metasurfaces and open a new path to developing terahertz multifunctional devices.

Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach.

The non-invasive fetal electrocardiogram (FECG) derived from abdominal surface electrodes has been widely used for fetal heart rate (FHR) monitoring to assess fetal well-being. However, the accuracy of FECG-based FHR estimation heavily depends on the quality of FECG signal itself, which can generally be affected by several interference sources such as maternal heart activities and fetal movements. Hence, FECG signal quality assessment (SQA) is an essential task to improve the accuracy of FHR estimation by removing or interpolating low-quality FECG signals. In recent research, various SQA methods based on supervised learning have been proposed. Although these methods could perform accurate SQA, they require large labeled datasets. Nevertheless, the labeled datasets for the FECG SQA are very limited. In this paper, to address this limitation, we propose an unsupervised learning-based SQA method for identifying high and low-quality FECG signal segments. Specifically, a fully convolutional network (FCN)-based autoencoder (AE) is trained for reconstructing a spectrogram derived from FECG. An AE-based feature related to reconstruction error is then calculated to identify high and low-quality FECG segments. In addition, entropy-based features, statistical features, and ECG signal quality indices (SQIs) are also extracted. The high and low-quality segments are identified by feeding the extracted features into self-organizing map (SOM). The experimental results showed that our proposal achieved an accuracy of 98% in high and low-quality signal classification.

The Chemopreventive Role of ß-Elemene in Cholangiocarcinoma by Restoring PCDH9 Expression.

Background: ß-Elemene, an effective anticancer component isolated from the Chinese herbal medicine Rhizoma Zedoariae, has been proved to have therapeutic potential against multiple cancers by extensive clinical trials and experimental research. However, its preventive role in cholangiocarcinoma (CCA) and the mechanisms of action of ß-elemene on CCA need to be further investigated. Methods: A thioacetamide (TAA)-induced pre-CCA animal model was well-established, and a low dosage of ß-elemene was intragastrically (i.g.) administered for 6 months. Livers were harvested and examined histologically by a deep-learning convolutional neural network (CNN). cDNA array was used to analyze the genetic changes of CCA cells following ß-elemene treatment. Immunohistochemical methods were applied to detect ß-elemene-targeted protein PCDH9 in CCA specimens, and its predictive role was analyzed. ß-Elemene treatment at the cellular or animal level was performed to test the effect of this traditional Chinese medicine on CCA cells. Results: In the rat model of pre-CCA, the ratio of cholangiolar proliferation lesions was 0.98% ± 0.72% in the control group, significantly higher than that of the ß-elemene (0. 47% ± 0.30%) groups (p = 0.0471). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the top 10 pathways affected by ß-elemene treatment were associated with energy metabolism, and one was associated with the cell cycle. ß-Elemene inactivated a number of oncogenes and restored the expression of multiple tumor suppressors. PCDH9 is a target of ß-elemene and displays an important role in predicting tumor recurrence in CCA patients. Conclusions: These findings proved that long-term use of ß-elemene has the potential to interrupt the progression of CCA and improve the life quality of rats. Moreover, ß-elemene exerted its anticancer potential partially by restoring the expression of PCDH9.

Pyrazinamide enhances persistence of T-cell memory induced by tuberculosis subunit vaccine LT70.

Repurposing anti-tuberculosis drugs with adjuvant properties in vaccination has double benefits for the control of tuberculosis. In this study, to verify the immunomodulatory effect of the tuberculosis drug pyrazinamide (PZA) on tuberculosis subunit vaccine-induced memory T cell response, we treated mice with PZA during the course of vaccination and then monitored the vaccine-specific T cell memory responses. Compared with the mice that received LT70 alone, we found that the mice co-administrated with PZA and LT70 did not produce a higher frequency of multifunctional CD4+ T lymphocytes at 8-week post-vaccination, but the T lymphocytes produced stronger long-term IL-2 response rather than IFN-γ recall response and had higher long-term proliferating potential upon antigen stimulation at 28-week post-vaccination. In addition, the memory T cells from PZA-treated mice showed superior IFN-γ recall response after twice antigen stimulations in vivo and in vitro respectively. Together, the findings show that PZA treatment during the course of vaccination contributes to inducing TCM-like cells and enhances vaccine-induced T-cell long-term immunological memory, which would be helpful for designing novel vaccination and therapeutic strategies for tuberculosis.

Comprehensive Evaluation and Application of a Novel Method to Isolate Cell-Free DNA Derived From Bile of Biliary Tract Cancer Patients.

Cell-free DNA (cfDNA) exists in various types of bodily fluids, including plasma, urine, bile, and others. Bile cfDNA could serve as a promising liquid biopsy for biliary tract cancer (BTC) patients, as bile directly contacts tumors in the biliary tract system. However, there is no commercial kit or widely acknowledged method for bile cfDNA extraction. In this study, we established a silica-membrane-based method, namely 3D-BCF, for bile cfDNA isolation, exhibiting effective recovery of DNA fragments in the spike-in assay. We then compared the 3D-BCF method with four other commercial kits: the BIOG cfDNA Easy Kit (BIOG), QIAamp DNA Mini Kit (Qiagen), MagMAXTM Cell-Free DNA Isolation Kit (Thermo Fisher), and NORGEN Urine Cell-Free Circulating DNA Purification Mini Kit (Norgen Biotek). The proposed 3D-BCF method exhibited the highest cfDNA isolation efficiency (p < 0.0001) from patient bile samples, and bile cfDNA of short, medium or long fragments could all be extracted effectively. To test whether the extracted bile cfDNA from patients carries tumor-related genomic information, we performed next-generation sequencing on the cfDNA and verified the gene-mutation results by polymerase chain reaction (PCR)-Sanger chromatograms and copy-number-variation (CNV) detection by fluorescence in situ hybridization (FISH) of tumor tissues. The 3D-BCF method could efficiently extract cfDNA from bile samples, providing technical support for bile cfDNA as a promising liquid biopsy for BTC patient diagnosis and prognosis.

Machine-learning-assisted inverse design of scattering enhanced metasurface.

The scattering enhancement technique has shown prominent potential in various regimes such as satellite communication, Radar Cross Section (RCS) camouflage, and remote sensing. Currently, the scattering enhancement devices based on the metasurface have shown advantages in light weight and better performance. These metasurfaces always possess complex structure, it is hard to achieve through the tradition trial-and-error method which relies on the full-wave numerical simulation. In this paper, a new method combining the machine learning and the evolution optimization algorithm is proposed to design the metasurface retroreflector (MRF) for arbitrary direction incident wave. In this method, a predicting model and a generative inverse design model are constructed and trained, the predicting model is used to evaluate the fitness of each offspring in the genetic algorithm (GA), the generative model is used to initialize the first offspring of the GA by inverse generate the MRF based on the requirements of the designer. With the assistance of these two machine learning models, the evolution optimization algorithm is employed to find the optimal design of the MRF. This approach enables automatic solution of electromagnetic inverse design problems and opens the way to facilitate the optimization of other metadevices.

The Systematic Review and Meta-Analysis on the Immunogenicity and Safety of the Tuberculosis Subunit Vaccines M72/AS01E and MVA85A.

Background: Tuberculosis (TB) is a severe infectious disease with devastating effects on global public health. No TB vaccine has yet been approved for use on latent TB infections and healthy adults. In this study, we performed a systematic review and meta-analysis to evaluate the immunogenicity and safety of the M72/AS01E and MVA85A subunit vaccines. The M72/AS01E is a novel peptide-based vaccine currently in progress, which may increase the protection level against TB infection. The MVA85A was a viral vector-based TB subunit vaccine being used in the clinical trials. The vaccines mentioned above have been studied in various phase I/II clinical trials. Immunogenicity and safety is the first consideration for TB vaccine development. Methods: The PubMed, Embase, and Cochrane Library databases were searched for published studies (until October 2019) to find out information on the M72/AS01E and MVA85A candidate vaccines. The meta-analysis was conducted by applying the standard methods and processes established by the Cochrane Collaboration. Results: Five eligible randomized clinical trials (RCTs) were selected for the meta-analysis of M72/AS01E candidate vaccines. The analysis revealed that the M72/AS01E subunit vaccine had an abundance of polyfunctional M72-specific CD4+ T cells [standardized mean difference (SMD) = 2.37] in the vaccine group versus the control group, the highest seropositivity rate [relative risk (RR) = 5.09]. The M72/AS01E vaccinated group were found to be at high risk of local injection site redness (RR = 2.64), headache (RR = 1.59), malaise (RR = 3.55), myalgia (RR = 2.27), fatigue (RR = 2.16), pain (RR = 3.99), swelling (RR = 5.09), and fever (RR = 2.04) compared to the control groups. The incidences of common adverse events of M72/AS01E were local injection site redness, headache, malaise, myalgia, fatigue, pain, swelling, fever, etc. Six eligible RCTs were selected for the meta-analysis on MVA85A candidate vaccines. The analysis revealed that the subunit vaccine MVA85A had a higher abundance of overall pooled proportion polyfunctional MVA85A-specific CD4+ T cells SMD = 2.41 in the vaccine group vs. the control group, with the highest seropositivity rate [estimation rate (ER) = 0.55]. The MVA85A vaccinated group were found to be at high risk of local injection site redness (ER = 0.55), headache (ER = 0.40), malaise (ER = 0.29), pain (ER = 0.54), myalgia (ER = 0.31), and fever (ER = 0.20). The incidences of common adverse events of MVA85A were local injection site redness, headache, malaise, pain, myalgia, fever, etc. Conclusion: The M72/AS01E and MVA85A vaccines against TB are safe and had immunogenicity in diverse clinical trials. The M72/AS01E and MVA85A vaccines are associated with a mild adverse reaction. The meta-analysis on immunogenicity and safety of M72/AS01E and MVA85A vaccines provides useful information for the evaluation of available subunit vaccines in the clinic.

The assessment of the quality of reporting of systematic reviews/meta-analyses in diagnostic tests published by authors in China.

BACKGROUND: The quality of reporting in systematic reviews (SRs)/meta-analyses (MAs) of diagnostic tests published by authors in China has not been evaluated. The aims of present study are to evaluate the quality of reporting in diagnostic SRs/MAs using the PRISMA statement and determine the changes in the quality of reporting over time. METHODS: According to the inclusion and exclusion criteria, we searched five databases including Chinese Biomedical Literature Database, PubMed, EMBASE, the Cochrane Library, and Web of knowledge, to identify SRs/MAs on diagnostic tests. The searches were conducted on July 14, 2012 and the cut off for inclusion of the SRs/MAs was December 31(st) 2011. The PRISMA statement was used to assess the quality of reporting. Analysis was performed using Excel 2003, RevMan 5. RESULTS: A total of 312 studies were included. Fifteen diseases systems were covered. According to the PRISMA checklist, there had been serious reporting flaws in following items: structured summary (item 2, 22.4%), objectives (item 4, 18.9%), protocol and registration (item 5, 2.6%), risk of bias across studies (item 15, 26.3%), funding (item 27, 28.8%). The subgroup analysis showed that there had been some statistically significant improvement in total compliance for 9 PRISMA items after the PRISMA was released, 6 items were statistically improved regarding funded articles, 3 items were statistically improved for CSCD articles, and there was a statistically significant increase in the proportion of reviews reporting on 22 items for SCI articles (P<0.050). CONCLUSION: The numbers of diagnostic SRs/MAs is increasing annually. The quality of reporting has measurably been improved over the previous years. Unfortunately, there are still many deficiencies in the reporting including protocol and registration, search, risk of bias across studies, and funding. Future Chinese reviewers should address issues on these aspects.