Intraventricular Thrombosis and Pulmonary Embolism Post-Nuss Procedure: A Rare Case of Chronic Bar Displacement in a 16-Year-Old Patient.

This novel report presents the first known case, to our knowledge, of a 16-year-old male patient who experienced intraventricular thrombosis and pulmonary embolism after a Nuss procedure for pectus excavatum, attributed to chronic bar displacement. Two years after the operation, the patient experienced post-exercise cough and hemoptysis, which led to his admission. Imaging revealed pulmonary embolism, thrombosis in the right ventricular outflow tract, and lung infiltrative lesions. We hypothesize that the chronic bar displacement led to its embedment in the right ventricle, resulting in thrombus formation, which subsequently contributed to partial pulmonary embolism. Surgery revealed the bars' intrusion into the right ventricle and lung. This case highlights the risk of severe complications from bar displacement in the Nuss procedure, which necessitates long-term follow-up evaluation, caution against strenuous activities after surgery, and use of thoracoscopic guidance during bar implantation and removal. It underscores the importance of vigilant evaluation for late-stage complications in patients with respiratory distress or thrombosis after a Nuss procedure.

Supramolecular Biomaterials for Cancer Immunotherapy.

Cancer immunotherapy has achieved tremendous successful clinical results and obtained historic victories in tumor treatments. However, great limitations associated with feeble immune responses and serious adverse effects still cannot be neglected due to the complicated multifactorial etiology and pathologic microenvironment in tumors. The rapid development of nanomedical science and material science has facilitated the advanced progress of engineering biomaterials to tackle critical issues. The supramolecular biomaterials with flexible and modular structures have exhibited unparalleled advantages of high cargo-loading efficiency, excellent biocompatibility, and diversiform immunomodulatory activity, thereby providing a powerful weapon for cancer immunotherapy. In past decades, supramolecular biomaterials were extensively explored as versatile delivery platforms for immunotherapeutic agents or designed to interact with the key moleculars in immune system in a precise and controllable manner. In this review, we focused on the crucial role of supramolecular biomaterials in the modulation of pivotal steps during tumor immunotherapy, including antigen delivery and presentation, T lymphocyte activation, tumor-associated macrophage elimination and repolarization, and myeloid-derived suppressor cell depletion. Based on extensive research, we explored the current limitations and development prospects of supramolecular biomaterials in cancer immunotherapy.

Primary Hepatic Gastrointestinal Stromal Tumor on 18 F-FDG PET/CT Images.

ABSTRACT: Primary hepatic gastrointestinal stromal tumor is an extremely rare type of liver tumor with an unknown origin and poor prognosis and usually lacking specific symptoms. This makes it difficult to make an accurate diagnosis. We report the case of a 56-year-old man with primary hepatic gastrointestinal stromal tumor demonstrating multiple heterogeneous lesions with intense FDG uptake in the liver on PET/CT mimicking hepatocellular carcinoma or sarcoma. Primary hepatic gastrointestinal stromal tumor should be considered among the differential diagnoses when multiple FDG-avid primary liver neoplasms are found in patients with malignant characteristic on PET/CT imaging.

Contralesional macrostructural plasticity in patients with frontal low-grade glioma: a voxel-based morphometry study.

PURPOSE: Neuroplasticity can partially compensate for the neurological deficits caused by brain tumors. However, the structural plasticity of the brain caused by brain tumors is not fully understood. This study aimed to assess the structural plasticity of the contralesional hemisphere in patients with frontal low-grade gliomas (LGGs). METHODS: A total of 25 patients with left frontal LGGs (LFLGGs), 19 patients with right frontal LGGs (RFLGGs), and 25 healthy controls (HCs) were enrolled in this study. High-resolution structural T1-weighted imaging and fluid attenuation inversion recovery were performed on all participants. Voxel-based morphometry (VBM) analysis was used to detect differences in the brain structural plasticity between patients with unilateral LGGs and HCs. RESULTS: VBM analysis revealed that compared with HCs, the gray matter volume (GMV) of the contralesional putamen and amygdala was significantly smaller and larger in the patients with RFLGGs and LFLGGs, respectively, while the GMVs of the contralesional cuneus and superior temporal gyrus (STG) were significantly larger in the patients with LFLGGs. The surviving clusters of the right hemisphere included 1357 voxels in the amygdala, 1680 voxels in the cuneus, 384 voxels in the STG, and 410 voxels in the putamen. The surviving clusters of the left hemisphere were 522 voxels in the amygdala and 320 voxels in the putamen. CONCLUSION: The unilateral frontal LGGs are accompanied by structural plasticity in the contralesional cortex and vary with tumor laterality. Contralesional structural reorganization may be one of the physiological basis for functional reorganization or compensation in the frontal LGGs.

Agreement of three CT perfusion software packages in patients with acute ischemic stroke: A comparison with RAPID.

PURPOSE: To compare ischemic core volume (ICV) and penumbra volume (PV) measured by MIStar, F-STROKE, and Syngo.via with that measured by RAPID in acute ischemic stroke (AIS), and their concordance in selecting patients for endovascular thrombectomy (EVT). METHODS: Computed tomography perfusion (CTP) data were processed with four software packages. Bland-Altman analysis and intraclass correlation coefficient (ICC) were performed to evaluate their agreement in quantifying ICV and PV. Kappa test was conducted to assess consistency in the selection of EVT candidates. The correlation between predicted ICV and segmented final infarct volume (FIV) on follow-up images was investigated. RESULTS: A total of 91 patients were retrospectively included. F-STROKE had the best consistency with RAPID (ICV: ICC = 0.97; PV: ICC = 0.84) and Syngo.via had the worst consistency (ICV: ICC = 0.77; PV: ICC = 0.66). F-STROKE had the narrowest limits of agreements both in ICV (-27.02, 24.40 mL) and PV (-85.59, 101.80 mL). When selecting EVT candidates, MIStar (kappa = 0.71-0.88) and F-STROKE (kappa = 0.84-0.90) had good to excellent consistency with RAPID, while Syngo.via had poor consistency (kappa = 0.20-0.41). ICV predicted by MIStar was correlated strongest with FIV (r = 0.77). CONCLUSIONS: F-STROKE is most consistent with RAPID in quantitative ICV and PV. F-STROKE and MIStar exhibit similar EVT candidate selection to RAPID. Syngo.via, for its part, seems to have overestimated ICV and underestimated PV, leading to an overly restrictive selection of EVT candidates.

Synergistic Effect of Combined Sub-Tenon Triamcinolone and Intravitreal Anti-VEGF Therapy for Uveitic Macular Edema.

Purpose: To investigate effects of intravitreal anti-VEGF in combination therapy with sub-Tenon triamcinolone acetonide (STA) injection for uveitic macular edema (UME). Design: A single-center, retrospective cohort study. Methods: The medical records were obtained for 65 eyes of 65 patients with UME. Of which, 32 eyes received combined anti-VEGF with STA injection, and 33 eyes received 40 mg of STA injection alone. The primary outcome was the reduction of central macular thickness (CMT) measured with optical coherence tomography (OCT). Resolution rate of clinical UME and changes of best corrected visual acuity (BCVA) over 24 weeks were secondary outcomes. Results: There was a significantly greater reduction of CMT with the combination treatment than with STA alone at 1-week (ß = -157.9, P < 0.001) and 1-month (ß = -53.1, P = 0.019) after injection. The cumulative incidence of macular edema resolution of all eyes was 87.7%, with 90.6% (29/32) in the combined group and 84.8% (28/33) in the STA group, respectively. More incidence of UME resolution was observed in the combined group than the STA group after 1 week (71.9% vs 15.2%, P < 0.001) and 4 weeks (84.4% vs 54.5%, P = 0.009), respectively. BCVA was better for the combination treatment than STA alone at 1-week (ß = -0.085, P = 0.070) and 1-month (ß = -0.108, P = 0.019) after injection, respectively. Increased intraocular pressure (>25 mmHg) was observed in 4 eyes (12.5%) in the combined group and 5 eyes (15.2%) in the STA group, respectively. Conclusion: Combined intravitreal anti-VEGF and STA is superior to STA alone for reduction of UME and visual restoration. Addition of anti-VEGF did not increase risk for steroid-induced elevation of intraocular pressure over 6 months.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity.

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further in vitro and in vivo experimental validation.

Genetic Polymorphisms of Long Non-coding RNA Linc00312 Are Associated With Susceptibility and Predict Poor Survival of Nasopharyngeal Carcinoma.

BACKGROUND: Linc00312 is dysregulated in nasopharyngeal carcinoma (NPC) and participates in the initiation and progression of NPC. Our previous studies suggested that linc00312 was able to enhance the sensitivity of NPC cells to irradiation and NPC patients with higher expression of linc00312 was associated with better short-term curative effect and overall survival. The single nucleotide polymorphisms (SNPs) of lncRNAs may influence the disease course and outcome by affecting the expression, secondary structure or function of lncRNAs. However, the role of SNPs in linc00312 on the occurrence and survival of NPC remains unknown. METHODS: We recruited 684 NPC patients and 823 healthy controls to evaluate the association between linc00312 SNPs and NPC susceptibility by using multivariate logistic regression analysis. Kaplan-Meier analysis and Cox proportional hazards regression were applied to assess the effect of linc00312 SNPs on the survival of NPC patients. The relative expression of linc00312 in NPC tissues was determined by real-time PCR. The interaction between linc00312 and mir-411-3p was explored by luciferase reporter assay. In silico prediction of the changes on linc00312 folding structure was conducted by RNAfold WebServer. RESULT: We demonstrated that rs12497104 (G > A) GA genotype carriers had a higher risk than others for suffering from NPC (GA vs GG, OR = 1.437, P = 0.003). Besides, patients with rs12497104 AA genotype showed a poorer overall survival in contrast to GG genotype (AA vs GG, HR = 2.117, P = 0.011). In addition, the heterozygous carriers of rs15734 (G > A) and rs164966 (A > G) were correlated with decreased risk of NPC (GA vs GG, OR = 0.778, P = 0.031; GA vs AA, OR = 0.781, P = 0.033, respectively). We found that the three SNPs might influence the expression of linc00312 in a genotype specific feature. The local centroid secondary structure as well as the minimum free energy of linc00312 were changed following the candidate SNPs alterations. Besides, we revealed that the G to A alteration at rs12497104 disrupted the binding between mir-411-3p and linc00312. CONCLUSION: Our results indicated genetic polymorphisms of linc00312 might serve as potential biomarkers for NPC carcinogenesis and prognosis.

Changes of serum IL-10, IL-1ß, IL-6, MCP-1, TNF-α, IP-10 and IL-4 in COVID-19 patients.

BACKGROUND AND PURPOSE: Studies have shown that some cytokines in COVID-19 patients were elevated. This study aims to assess whether IL-10, IL-1ß, IL-6, MCP-1, TNF-α, IP-10 and IL-4 serve as potential diagnostic biomarkers of COVID-19. METHODS: The above serum cytokines in COVID-19 patients and non-COVID-19 patients were detected by ELISA and SARS-CoV-2 IgM and IgG were detected by the chemiluminescence method. The independent-sample Mann-Whitney U test was utilised to compare cytokine levels in different groups and courses, the Levene T-test and T'-test were utilised to compare they in different genders and the Spearman correlation test was utilised to analyse the correlation between the cytokine levels with ages and SARS-CoV-2 IgG and IgM. RESULTS: Serum levels of IL-10, IL-1ß, MCP-1, TNF-α and IL-4 in COVID-19 patients were significantly higher than those in non-COVID-19 patients, while IL-6 were only significantly higher than in healthy people, IP-10 were significantly lower than in other diseases patients. AUCs of COVID-19 diagnosed by IL-10, IL-1ß, IL-6, MCP-1, TNF-α, IP-10 and IL-4 were 0.735, 0.775, 0.595, 0.821, 0.848, 0.38 and 0.682, respectively. In the COVID-19 patients' serum, the levels of IL-10 and MCP-1 of male were noticeably higher than those of female, and all cytokines were significantly positively correlated with age, IL-1ß and IL-4 were significantly negatively correlated with SARS-CoV-2 IgM, while IL-10, IL-1ß, IL-6, TNF- and IP-10 were significantly negatively correlated with SARS-CoV-2 IgG. IL-10 on 43-56 days was significantly lower than at 29-42 days, TNF-α at 15-42 days was significantly higher than at 0-14 days, IP-10 at 0-14 days was the highest and IL-4 at 29-42 days was significantly higher than at 0-14 days. CONCLUSIONS: The detection of IL-10, IL-1 ß, IL-6, MCP-1, TNF-α and IL-4 would assist the clinical study of COVID-19, and IP-10 may be the cytokine of early elevation in COVID-19 patients.

Nerve Guidance Conduits with Hierarchical Anisotropic Architecture for Peripheral Nerve Regeneration.

Nerve guidance conduits with multifunctional features could offer microenvironments for improved nerve regeneration and functional recovery. However, the challenge remains to optimize multiple cues in nerve conduit systems due to the interplay of these factors during fabrication. Here, a modular assembly for the fabrication of nerve conduits is utilized to address the goal of incorporating multifunctional guidance cues for nerve regeneration. Silk-based hollow conduits with suitable size and mechanical properties, along with silk nanofiber fillers with tunable hierarchical anisotropic architectures and microporous structures, are developed and assembled into conduits. These conduits supported improves nerve regeneration in terms of cell proliferation (Schwann and PC12 cells) and growth factor secretion (BDNF, brain-derived neurotrophic factor) in vitro, and the in vivo repair and functional recovery of rat sciatic nerve defects. Nerve regeneration using these new conduit designs is comparable to autografts, providing a path towards future clinical impact.

Protective Role of a New Polysaccharide Extracted from Lonicera japonica Thunb in Mice with Ulcerative Colitis Induced by Dextran Sulphate Sodium.

Lonicera japonica Thunb is a traditional Chinese herbal medicine for treating intestinal inflammation. The extraction method of Lonicera japonica Thunb polysaccharide (LJP) has been developed previously by our research group. In this study, a Fourier transform infrared spectrometer (FT-IR) was used to perform a qualitative analysis of LJP and a precolumn derivatization high-performance liquid chromatography (HPLC) ((Palo Alto, CA, USA) method was used to explore the monosaccharide composition of LJP. Then, we studied the effect of LJP on the intestinal flora and immune functions of dextran sulfate sodium- (DSS-) induced colitis ulcerative mouse models. The results showed that LJP was consisted of 6 types of monosaccharides and had the characteristic absorption of typical polysaccharides. LJP can increase significantly the weight, organ index, serum cytokines (interleukin, tumor necrosis factor, and interferon-γ), secretory immunoglobulin A (SIgA) concentration, and natural killer (NK) cell and cytotoxic lymphocyte (CTL) activities in DSS-treated mice. The results of intestinal flora showed that a high dose (150 mg kg-1) of LJP had the best effects on improving the intestinal probiotics (Bifidobacterium and Lactobacilli) and antagonizing the pathogenic bacteria (Escherichia coli and Enterococcus). In addition, the measurement results of the spleen lymphocyte apoptosis confirmed from another perspective that LJP had protective effects of immune cells for DSS-treated mice.

DeepLRHE: A Deep Convolutional Neural Network Framework to Evaluate the Risk of Lung Cancer Recurrence and Metastasis From Histopathology Images.

It is critical for patients who cannot undergo eradicable surgery to predict the risk of lung cancer recurrence and metastasis; therefore, the physicians can design the appropriate adjuvant therapy plan. However, traditional circulating tumor cell (CTC) detection or next-generation sequencing (NGS)-based methods are usually expensive and time-inefficient, which urge the need for more efficient computational models. In this study, we have established a convolutional neural network (CNN) framework called DeepLRHE to predict the recurrence risk of lung cancer by analyzing histopathological images of patients. The steps for using DeepLRHE include automatic tumor region identification, image normalization, biomarker identification, and sample classification. In practice, we used 110 lung cancer samples downloaded from The Cancer Genome Atlas (TCGA) database to train and validate our CNN model and 101 samples as independent test dataset. The area under the receiver operating characteristic (ROC) curve (AUC) for test dataset was 0.79, suggesting a relatively good prediction performance. Our study demonstrates that the features extracted from histopathological images could be well used to predict lung cancer recurrence after surgical resection and help classify patients who should receive additional adjuvant therapy.

Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data.

Patients with carcinoma of unknown primary (CUP) account for 3-5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.

Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method.

It is urgent to find an effective antiviral drug against SARS-CoV-2. In this study, 96 virus-drug associations (VDAs) from 12 viruses including SARS-CoV-2 and similar viruses and 78 small molecules are selected. Complete genomic sequence similarity of viruses and chemical structure similarity of drugs are then computed. A KATZ-based VDA prediction method (VDA-KATZ) is developed to infer possible drugs associated with SARS-CoV-2. VDA-KATZ obtained the best AUCs of 0.8803 when the walking length is 2. The predicted top 3 antiviral drugs against SARS-CoV-2 are remdesivir, oseltamivir, and zanamivir. Molecular docking is conducted between the predicted top 10 drugs and the virus spike protein/human ACE2. The results showed that the above 3 chemical agents have higher molecular binding energies with ACE2. For the first time, we found that zidovudine may be effective clues of treatment of COVID-19. We hope that our predicted drugs could help to prevent the spreading of COVID.

Assessing the Impact of Data Preprocessing on Analyzing Next Generation Sequencing Data.

Data quality control and preprocessing are often the first step in processing next-generation sequencing (NGS) data of tumors. Not only can it help us evaluate the quality of sequencing data, but it can also help us obtain high-quality data for downstream data analysis. However, by comparing data analysis results of preprocessing with Cutadapt, FastP, Trimmomatic, and raw sequencing data, we found that the frequency of mutation detection had some fluctuations and differences, and human leukocyte antigen (HLA) typing directly resulted in erroneous results. We think that our research had demonstrated the impact of data preprocessing steps on downstream data analysis results. We hope that it can promote the development or optimization of better data preprocessing methods, so that downstream information analysis can be more accurate.

A New Method for CTC Images Recognition Based on Machine Learning.

Circulating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs. First, we collected the CTC test results of 600 patients. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python's openCV scheme. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. We took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. We will further revise our models, hoping to achieve a higher sensitivity and specificity.

TOOme: A Novel Computational Framework to Infer Cancer Tissue-of-Origin by Integrating Both Gene Mutation and Expression.

Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiple-label classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene mutation (53%) alone. In conclusion, TOOme provides a quick yet accurate alternative to traditional medical methods in inferring cancer tissue-of-origin. In addition, the methods combining somatic mutation and gene expressions outperform those using gene expression or mutation alone.

Fragment Enrichment of Circulating Tumor DNA With Low-Frequency Mutations.

Human blood contains cell-free DNA (cfDNA), with circulating tumor-derived DNAs (ctDNAs) widely used in cancer diagnosis and treatment. However, it is still difficult to efficiently and accurately identify and distinguish specific ctDNAs from normal cfDNA in cancer patient blood samples. In this study, ctDNA fragment length distribution analysis showed that ctDNA fragments are frequently shorter than the normal cfDNAs, which is consistent with previous findings. Interestingly, the ctDNA fragment length was found to be partially associated with the mutant allele frequency, with a low mutant allele frequency (< ~0.6%) associated with a longer ctDNA fragment length when compared to normal cfDNAs. The findings of this study contribute to improving the detection of low-frequency tumor mutations.

Comparison of diagnostic accuracy of Midkine and AFP for detecting hepatocellular carcinoma: a systematic review and meta-analysis.

OBJECTIVE: Midkine (MDK) has been proposed as one of the most promising markers for hepatocellular carcinoma (HCC). This meta-analysis was conducted to compare the diagnostic accuracy of MDK and α-fetoprotein (AFP) for HCC. METHODS: We systematically searched PubMed/MEDLINE, Ovid/EMBASE, and the Cochrane Library for all relevant studies up to 18 May 2019. The Revised Quality Assessment for Studies of Diagnostic Accuracy tool (QUADAS-2) was used to assess the methodological quality of the included studies. The sensitivity, specificity, and the area under the curve (AUC) of MDK and AFP for detecting HCC were pooled using random-effects model. RESULTS: Seventeen studies from five articles with a total of 1122 HCC patients and 2483 controls were included. The summary estimates using MDK and AFP for detecting HCC were as follows: sensitivity, 85 vs 52%, specificity, 82 vs 94%, and AUC, 0.90 vs 0.83. The summary estimates using MDK and AFP for detecting hepatitis virus-related HCC as follows: sensitivity, 93 vs 74%, specificity, 85 vs 97%, and AUC, 0.95 vs 0.97. The summary estimates using MDK and AFP for detecting early-stage HCC were as follows: sensitivity, 83.5 vs 44.4%, specificity, 81.7 vs 84.8%, and AUC, 0.87 vs 0.52. The summary estimates using MDK for detecting AFP-negative HCC as follows: sensitivity, 88.5%, specificity, 83.9%, and AUC, 0.91. CONCLUSION: MDK is more accurate than AFP in diagnosing HCC, especially for early-stage HCC and AFP-negative HCC. Both MDK and AFP had excellent diagnostic performance for hepatitis virus-related HCC.

Genomic Profiling of Driver Gene Mutations in Chinese Patients With Non-Small Cell Lung Cancer.

Worldwide, especially in China, lung cancer accounts to a major cause of mortality related to cancer. Treatment decisions mainly depend on oncogenic driver mutations, which offer novel therapeutic targets for anticancer therapy. However, studies of genomic profiling of driver gene mutations in mainland China are rare. Hence, this is an extensive study of these mutations in Non-small-cell lung cancer (NSCLC) Chinese patients. Comparison of driver gene mutations of lung adenocarcinoma with other races showed that the mutational frequencies were similar within the different East Asian populations, while there were differences between East Asian and non-Asian populations. Further, four promising candidates for druggable mutations of epidermal growth factor receptor (EGFR) were revealed that open up avenues to develop and design personal therapeutic approaches for patients harboring mutations. These results will help to develop personalized therapy targeting NSCLC.