Search

The morphological changes based on deposition of secondary dentin and mineralization of the third molar have been proven to be related to chronological age. However, Kvaal’s method on the theory of deposition of secondary dentin was controversial with respect to dental age estimation in the recent research. The aim of this study was to combine the parameters of Kvaal’s method with relatively high correlation coefficients and mineralization stages of the third molar to improve the accuracy of predicting the dental age of subadults in northern China. A total of 340 digital orthopantomograms of subadults aged from 15 to 21 years were analysed. A training group was used to test the accuracy of the original Kvaal’s method and to establish novel methods for subadults in northern China. A testing group was used to compare the accuracy of the newly established methods with the Kvaal’s original method and with published method specifically used in northern China. To increase the feasibility of our estimation model, we combined the mineralization of the third molar to build a combined specific formula. The results showed that the combined specific model increased the coefficient of determination to 0.513, and the standard error of the estimate was reduced to 1.482 years. We concluded that the combined specific model based on the deposition of secondary dentin and mineralization of the third molar could improve the accuracy of dental age assessment of subadults in northern China. Key Points

Thrombin is associated with malignant tumors and promotes tumor development, metastasis, and angiogenesis, therefore its identification especially in lung cancer cells is crucial. Because the interference of in vivo biothiols caused false positive findings with prior gold fluorescent nanoprobes, in this manuscript, an Au-selenol(Se) nanoprobe (5-FAM-peptide-Se-AuNPs) that could specifically detect thrombin was designed and compared to traditional Au–S nanoprobes. For reaching this goal, fluorophore-bearing thrombin-specific peptide containing selenol at the end was synthesized. The nanoprobe may be broken by thrombin to regain its fluorescence in lung cancer cells, allowing for high-sensitivity thrombin detection. Since the Au–Se bond is more stable than the Au–S bond, the accuracy of the detection results can be guaranteed. The probe synthesis method is simple and cost-effective, as well as having high biocompatibility. Low concentrations of thrombin can be detected and imaged in lung cancer cells. The synthetic method of this probe opens up new avenues for the application of Au–Se bonds.

DNA profiling of short tandem repeats (STRs) is the primary method for genotyping forensic samples. However, degraded DNA and trace samples are still major problems for commercial 5- or 6-dye STR kits. In order to improve the performance of this method, we developed a novel 8-dye STR multiplex system containing 18 autosomal loci (D3S1358, D1S1656, TPOX, D16S539, vWA, D6S1043, D2S1338, CSF1PO, D19S433, D7S820, FGA, D8S1179, D5S818, D13S317, TH01, D21S11, D12S391, and PentaD) and the sex-determining locus Amelogenin, with all fragments smaller than 330 bases. Validation was carried out as recommended by the Scientific Working Group on DNA Analysis Methods. The results showed that complete profiles were obtainable when the input DNA was as low as 0.0625 ng. Full profiles were obtained even in the presence of inhibitors such as humic acid (

The complement cascade plays a "complementing" role in human immunity. However, the potential roles of complement system in impacting molecular and clinical features of hepatocellular carcinoma (HCC) remain unclear. In this study, eleven public datasets are analyzed to compare the complement status between normal and cancerous samples based on 18 classical complement-associated genes. The complement scores are constructed to quantify complement signatures of individual tumors. HCC patients in the The Cancer Genome Atlas (TCGA) cohort are focused to perform systematical analyses between complement status and immune infiltration, miRNA expression, DNA methylation, clinicopathological features, and drug response. The results show that the complement scores in normal tissues are dramatically higher than those of tumor tissues. Tumor samples in the TCGA cohort are classified into complement score-low and score-high groups. Pathway analysis reveals that tumor-promoting pathways are typically inhibited in complement score-high group. This study also shows that tumor-killing immune cells, such as CD8

DNA genotyping from trace and highly degraded biological samples is one of the most significant challenges of forensic DNA identification. There is a lack of simple and effective methods for genotyping highly degraded samples. In this study, a multiple loci insertion/deletion polymorphisms (Multi-InDels) panel was designed for detecting 18 autosomal Multi-InDels through capillary electrophoresis (CE) with amplicon sizes no longer than 125 bp. Studies of sensitivity, degradation, and species specificity were performed and a population study was carried out using 192 samples from Han populations in Hunan province in the south of China. The combined random match probability (CMP) of these 18 Multi-InDels was 3.23 × 10-12 and the cumulative probability of exclusion (CPE) was 0.9989, suggesting this panel could be used independently for human identification and could provide efficient supporting information for parentage testing. Complete profiles were obtained from as low as 62.5 pg of total input DNA after increasing the number of PCR cycles. Moreover, all alleles were detected from artificially highly degraded DNA after 80 min of boiling water bath treatment. This 18 Multi-InDels panel is simple, fast, and effective for the forensic analysis of highly degraded DNA.

In some criminal cases, the identity of suspect is unknown and there is no matching DNA profile in the DNA database. Forensic DNA Phenotyping can provide useful investigative information for these cases. Most forensic studies focus on visible characteristics rather than behavioral characteristics. However, smoking is prevalent in the Chinese population, and DNA methylation is the most promising biomarker for smoking. We collected 204 whole blood samples from the Chinese population and measured methylation levels of 9 smoking-related CpG loci using the methylation-sensitive single-nucleotide primer extension method (Ms-SnuPE). But the single-base extension primers of loci cg12803068 and cg21566642 contained other CpG sites, which may introduce bias, and only the other 7 CpG loci were included in subsequent statistical analysis. The methylation level of locus cg05575921 near the aromatic hydrocarbon receptor repressor (AHRR) gene was much lower in the current smoker group than in the never smoker group. To evaluate the ability of each of 7 CpG loci to predict smoking status, the logistic regression (LR) models were established separately, and locus cg05575921 had the best ability to predict smoking status compared with the other 6 loci. Then, combined (including loci cg19572487, cg05575921, cg23480021, cg23576855, cg21161138, cg01940273, and cg09935388) and stepwise (including loci cg05575921 and cg01940273) multinomial logistic regression (MLR) models were also established. Both combined and stepwise MLR models had good efficiencies in predicting smoking status, and outperformed the above 7 LR models. However, the accuracy, specificity and area under the curve (AUC) of stepwise MLR model in the testing dataset were slightly higher than those of combined MLR model, and the stepwise MLR model required less loci information. Therefore, the stepwise MLR model based on 2 significant CpG loci was more recommended model for predicting smoking status in the Chinese population, and the formula was as follow: P = 1/(1 +e

Monocarboxylate transporter (MCT) 4 is a high-affinity lactate transporter that is primarily involved in the maintenance of intracellular pH homeostasis and highly expressed in different tumors. However, the role of MCT4 in modulating immune responses against HCC remains unknown.In this study, we demonstrated that MCT4 was overexpressed in HCC, which was associated with poor prognosis in patients. Genetic or pharmacological inhibition of MCT4 using VB124 (a highly potent MCT4 inhibitor) suppressed HCC tumor growth in immunocompetent mice model by enhancing CD8Our results revealed that lactate exportation by MCT4 has a tumor-intrinsic function in generating an immunosuppressive HCC environment and demonstrated the proof of the concept of targeting MCT4 in tailoring HCC immunotherapeutic approaches.

Background Early-stage hepatocellular carcinoma (HCC) is the ideal indication for liver resection. High recurrence rate limits the radical possibility. Current clinicopathological determinants are insufficient to reliably evaluate the recurrence risk after surgery. To address this global issue, we aimed to use deep learning to explore novel pathological signatures based on histological slides for predicting early-stage HCC recurrence and to evaluate the relationship between histological features and multi-omics information. Methods 576 pathological images collected from 547 patients with BCLC stage 0-A HCC who underwent hepatectomy from 2006 to 2015 were randomly divided into the training cohort and validation cohort. The external validation cohort was composed of 147 TNM I patients from TCGA database. Weakly supervised convolutional neural networks were used to identify six classes of HCC tissues. Pathological signatures were extracted and two novel risk scores were constructed by LASSO Cox to predict recurrence. The forecast performance of the scores and patients' prognosis were evaluated. The relationship between histological score (HS) and immune infiltrating cells was estimated by clustering analysis. Results The classification accuracy of HCC tissue was 94.17%. The C-indexes of histological score in the training, validation and TCGA cohorts were 0.804, 0.739 and 0.708, respectively. Multivariate analysis showed that microvascular invasion (HR = 1.46, 95% CI: 1.09-1.95) and HS (HR = 4.05, 95% CI: 3.40-4.84) were independent risk factors for recurrence-free survival. Patients in HS high-risk group had elevated alpha fetoprotein, worse tumor differentiation and higher proportion of microvascular invasion. HS was positively correlated with the expression of CD14 in adjacent normal liver tissue (P = 0.013), and negatively correlated with the expression of CD8 in tumor (P Conclusions This study established and validated two novel risk scores based on the histological slides using deep learning. HS performed well in recurrence prediction for early-stage HCC patients and indication of important clinicopathological features.

The liver is a metabolically active organ and is also 'tolerogenic', exhibiting sophisticated mechanisms of immune regulation that prevent pathogen attacks and tumorigenesis. How metabolism impacts the tumor microenvironment (TME) in hepatocellular carcinoma (HCC) remains understudied.We investigated the role of the metabolic regulator SIRT5 in HCC development by conducting metabolomic analysis, gene expression profiling, flow cytometry and immunohistochemistry analyses in oncogene-induced HCC mouse models and human HCC samples.We show that SIRT5 is downregulated in human primary HCC samples and that Sirt5 deficiency in mice synergizes with oncogenes to increase bile acid (BA) production, via hypersuccinylation and increased BA biosynthesis in the peroxisomes of hepatocytes. BAs act as a signaling mediator to stimulate their nuclear receptor and promote M2-like macrophage polarization, creating an immunosuppressive TME that favors tumor-initiating cells (TICs). Accordingly, high serum levels of taurocholic acid correlate with low SIRT5 expression and increased M2-like tumor-associated macrophages (TAMs) in HCC patient samples. Finally, administration of cholestyramine, a BA sequestrant and FDA-approved medication for hyperlipemia, reverses the effect of Sirt5 deficiency in promoting M2-like polarized TAMs and liver tumor growth.This study uncovers a novel function of SIRT5 in orchestrating BA metabolism to prevent tumor immune evasion and suppress HCC development. Our results also suggest a potential strategy of using clinically proven BA sequestrants for the treatment of patients with HCC, especially those with decreased SIRT5 and abnormally high BAs.Hepatocellular caricinoma (HCC) development is closely linked to metabolic dysregulation and an altered tumor microenvironment. Herein, we show that loss of the metabolic regulator Sirt5 promotes hepatocarcinogenesis, which is associated with abnormally elevated bile acids and subsequently an immunosuppressive microenvironment that favors HCC development. Targeting this mechanism could be a promising clinical strategy for HCC.

Intrahepatic cholangiocarcinoma (ICC) remains a highly heterogeneous disease with poor prognosis. Tumor‐infiltrating lymphocytes were predictive in various cancers, but their prognostic value in ICC is less clear. A total of 168 ICC patients who had received liver resection were enrolled and assigned to the derivation cohort. Sixteen immune markers in tumor and peritumor regions were examined by immunohistochemistry. A least absolute shrinkage and selection operator model was used to identify prognostic markers and to establish an immune signature for ICC (ISICC). An ISICC‐applied prediction model was built and validated in another independent dataset. Five immune features, including CD3peritumor (P), CD57P, CD45RAP, CD66bintratumoral (T) and PD‐L1P, were identified and integrated into an individualized ISICC for each patient. Seven prognostic predictors, including total bilirubin, tumor numbers, CEA, CA19‐9, GGT, HBsAg and ISICC, were integrated into the final model. The C‐index of the ISICC‐applied prediction model was 0.719 (95% CI, 0.660‐0.777) in the derivation cohort and 0.667 (95% CI, 0.581‐0.732) in the validation cohort. Compared with the conventional staging systems, the new model presented better homogeneity and a lower Akaike information criteria value in ICC. The ISICC‐applied prediction model may provide a better prediction performance for the overall survival of patients with resectable ICC in clinical practice., Using tissue microarray, we examined the density of 16 immune biomarkers in 280 ICC patients who underwent hepatectomy, and established a novel ISICC‐based prediction model (IPM) to predict patients’ overall survival with bilirubin, tumor numbers, CEA, CA19‐9, γ‐glutamyl transferase (GGT), HBsAg and ISICC. The new model may provide a better prediction performance for the overall survival of patients with resectable ICC in clinical practice.

The key problem for picking robots is to locate the picking points of fruit. A method based on the moment of inertia and symmetry of apples is proposed in this paper to locate the picking points of apples. Image pre-processing procedures, which are crucial to improving the accuracy of the location, were carried out to remove noise and smooth the edges of apples. The moment of inertia method has the disadvantage of high computational complexity, which should be solved, so convex hull was used to improve this problem. To verify the validity of this algorithm, a test was conducted using four types of apple images containing 107 apple targets. These images were single and unblocked apple images, single and blocked apple images, images containing adjacent apples, and apples in panoramas. The root mean square error values of these four types of apple images were 6.3, 15.0, 21.6 and 18.4, respectively, and the average location errors were 4.9°, 10.2°, 16.3° and 13.8°, respectively. Furthermore, the improved algorithm was effective in terms of average runtime, with 3.7 ms and 9.2 ms for single and unblocked and single and blocked apple images, respectively. For the other two types of apple images, the runtime was determined by the number of apples and blocked apples contained in the images. The results showed that the improved algorithm could extract symmetry axes and locate the picking points of apples more efficiently. In conclusion, the improved algorithm is feasible for extracting symmetry axes and locating the picking points of apples.

Accurate segmentation of apple targets is one of the most important problems to be solved in the vision system of apple picking robots. This work aimed to solve the difficulties that background targets often bring to foreground targets segmentation, by fusing the visual attention mechanism and the growth rule of seed points. Background targets could be eliminated by extracting the ROI (region of interest) of apple targets; the ROI was roughly segmented on the HSV color space, and then each of the pixels was used as a seed growing point. The growth rule of the seed points was adopted to obtain the whole area of apple targets from seed growing points. The proposed method was tested with 20 images captured in a natural scene, including 54 foreground apple targets and approximately 84 background apple targets. Experimental results showed that the proposed method can remove background targets and focus on foreground targets, while the k-means algorithm and the chromatic aberration algorithm cannot. Additionally, its average segmentation error rate was 13.23%, which is 2.71% higher than that of the k-means algorithm and 2.95% lower than that of the chromatic aberration algorithm. In conclusion, the proposed method contributes to the vision system of apple-picking robots to locate foreground apple targets quickly and accurately under a natural scene