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Objective: The data regarding the mutation landscape in Chinese patients with thyroid cancer are limited. The diagnostic performance of thyroid nodul es by fine-needle aspiration (FNA) cytology needs optimization, especially in indeterminate nodules. Methods: A total of 1039 FNA and surgical resection samples tested using the targeted multigene next-generation sequencing (NGS) panel were retrospectively collected. The features of gene alterations in different thyroid tumors wer e analyzed, and the diagnostic efficacy was evaluated. Results: Among 1039 samples, there were 822 FNA and 217 surgical FFPE samples. Among 207 malignant thyroid resections, a total of 181 out of 193 papillary thyroid carcinomas (PTCs) were NGS-positive (93.8%), with a high prevalence of BRAF mutations (81.9%, 158/193) and a low prevalence of RAS (1.0%, 2/193) and TERT promoter mutations (3.6%, 7/193). Gene fusions, involving the RET and NTRK3 genes, were present in 20 PTCs (10.4%) and mutually exclusive with other driver mutations. Two of three follicular thyroid carcinomas harbored multiple mutations. RET gene point mutations were common in medullary thyroid carcinoma (8/11, 72.7%). The combination of cytology and DNA–RNA-based NGS analysis demonstrated superior diagnostic value (98.0%) in FNA samples. For indeterminate thyroid nodules, the diagnostic sens itivity and specificity of NGS testing were 79.2 (38/48) and 80.0% (8/10), respectively. Two mutation-positive benign cases harbored NRAS and TSHR mutations, respectively. Conclusions: Our study revealed the distinct molecular profile of thyroid tu mors in the Chinese population. The combination of NGS testing and FNA cytology could facilitate the accurate diagnosis of thyroid nodules, especially for indeterminate nodules.

Dissolved oxygen (DO) is essential for assessing and monitoring the health of marine ecosystems. The phenomenon of ocean deoxygenation is widely recognized. Nevertheless, the limited availability of observations poses a challenge in achieving a comprehensive understanding of global ocean DO dynamics and trends. The study addresses the challenge of unevenly distributed Argo DO data by developing time–space–depth machine learning (TSD-ML), a novel machine learning-based model designed to enhance reconstruction accuracy in data-sparse regions. TSD-ML partitions Argo data into segments based on time, depth, and spatial dimensions, and conducts model training for each segment. This research contrasts the effectiveness of partitioned and non-partitioned modeling approaches using three distinct ML regression methods. The results reveal that TSD-ML significantly enhances reconstruction accuracy in areas with uneven DO data distribution, achieving a 30% reduction in root mean square error (RMSE) and a 20% decrease in mean absolute error (MAE). In addition, a comparison with WOA18 and GLODAPv2 ship survey data confirms the high accuracy of the reconstructions. Analysis of the reconstructed global ocean DO trends over the past two decades indicates an alarming expansion of anoxic zones.

Highlights 1 Ovarian cancer patients have altered circulating lymphocyte profile 2 Low level of B cells might be related to disease aggressiveness, and it recovered after the removal of tumor.

Abstract Background To investigate changes in peripheral lymphocyte subsets after splenectomy during cytoreductive surgery for advanced or recurrent ovarian cancers. Methods We enrolled 83 patients with advanced or recurrent ovarian cancer who underwent cytoreductive surgery. Twenty patients who also underwent splenectomy were assigned to the splenectomy cohort and the rest were assigned to the non-splenectomy cohort. Flow cytometry was used to measure peripheral lymphocyte subsets consisting of T cells, regulatory T cells, natural killer cells, B cells, and activation antigens before and after surgery. Results There was no difference in the number and distribution of peripheral lymphocyte subsets between the two cohorts before surgery. After surgery, we observed elevated levels of T cells (CD3+, CD3+CD8+) in the splenectomy cohort compared to those in the non-splenectomy cohort, and the difference was statistically significant. CD8+CD28+ T cells had a significant decreasing tendency (P = 0.011) while CD3+/HLA-DR+ T cells showed the opposite trend (P = 0.001) in the splenectomy cohort. The proportion of Tregs (P = 0.005) and B cells (P

PurposeTo seek novel diagnostic approaches, we improved the workflow of cell-free DNA (cfDNA) sequencing and evaluated its feasibility in vitreoretinal lymphoma (VRL) specimens; the profile of mutations was preliminarily analyzed for potential diagnostic value.MethodsThe study was a diagnostic trial. 23 eyes of 23 patients with VRL and 25 eyes of 25 patients with inflammatory eye diseases were enrolled. Approximate 500μl undiluted vitreous humor and 10ml diluted vitreous fluid was obtained through diagnostic vitrectomy and sent for cytopathological examinations. 500μl of the diluted vitreous fluid was spared for cfDNA sequencing. For cfDNA sequencing, DNA fragmentation procedure was added to the workflow to improve the extraction efficiency; mutations detected were analyzed for potential diagnostic model. The sensitivity and specificity of the cytopathology and cfDNA sequencing were compared. The clinical manifestations were preliminarily analyzed for potential correlations with the genotypes.ResultsCfDNA sequencing was accomplished in 23 eyes with VRL and 20 eyes with inflammatory eye diseases. VRL-related mutated genes included MYD88 (18 eyes, 78%), ETV6 (11 eyes, 48%), PIM1 (11 eyes,48%), BTG2 (7 eyes, 30%), IRF4 (7 eyes, 30%), CD79B (6 eyes, 26%), LRP1B (6 eyes, 26%), etc. Logistic regression based on the mutations of MYD88 and ETV6 was of the potential for the diagnosis of VRL (P

Deep learning-based super-resolution (SR) methods have been widely used in natural images; however, their applications in satellite-derived sea surface temperature (SST) have not yet been fully discussed. Hence, it is necessary to analyze the validity of deep learning-based SR methods in SST reconstruction. In this study, an SR model, including multiscale feature extraction and multireceptive field mapping, was first proposed. Then, the proposed model and four other existing SR models were applied to SST reconstruction and analyzed. First, compared with the bicubic interpolation method, the SR models can improve the reconstruction accuracy. Compared with four other SR models, the proposed model can achieve the lowest mean squared error (MAE) in the East China Sea (ECS), in the northwest Pacific (NWP) and in the west Atlantic (WA), the second-lowest MAE in the southeast Pacific (SEP); the lowest root mean squared error (RMSE) in ECS and WA, the second-lowest RMSE in NWP and SEP. Additionally, ODRE model can acquire the highest or the second-highest peak single-to-noise ratio and structural similarity index in ECS, NWP, and SEP. Moreover, the number of missing pixels and SST variety are two essential factors in the SR performance. The proposed multiscale feature extraction process can enhance the SR performance, especially for small regions and stable SST regions. Finally, while a deeper network can be helpful in achieving SR performance, the approach of simply adding more dilation convolutions may not enhance the reconstruction accuracy.

Introduction: Zinc-α2-glycoprotein (ZAG) is a novel adipokine, which is involved in metabolic syndrome (MetS). This study aimed to investigate the relationship between serum ZAG and MetS in Chinese adults, who diagnosed according to the 2005 International Diabetes Federation (IDF) criteria.Methods: A group of 151 MetS patients, 84 patients with central obesity and 70 healthy controls were enrolled. General clinical information, serum samples were obtained from all subjects and serum ZAG levels were determined via the commercial ELISA kits.Results: Serum ZAG levels were the highest in the control group, then gradually decreased with the severity of the metabolic abnormalities increased (8.78 ± 1.66 μg/mL for control vs. 8.37 ± 1.52 μg/mL for central obesity vs. 7.98 ± 0.94 μg/mL for MetS, P < 0.05). It was also decreased progressively with an increasing number of the MetS components (P for trend = 0.002). Additionally, serum ZAG/fat mass ratio was calculated and the similar changes were observed in the three groups (0.85 ± 0.53 μg/mL/kg for control vs. 0.39 ± 0.10 μg/mL/kg for central obesity vs. 0.36 ± 0.08 μg/mL/kg for MetS, P < 0.05). In the multiple regression analysis, group was a strong independent factor contributing to serum ZAG levels (P < 0.001). Furthermore, compared with subjects with the highest tertile of ZAG, subjects in the lowest tertile of ZAG had 1.946-fold higher risk of MetS (95% CI 1.419–6.117, P = 0.004). This phenomenon still existed after controlling for age, gender (Model 1), ALP, AST, Cr, UA, Urea based on Model 1 (Model 2), grip strength, smoking, drinking, birth place, current address, education level, manual labor, and exercise frequency based on Model 2 (Model 3). Receiver operation characteristic (ROC) curve analysis revealed that serum ZAG might serve as a candidate biomarker for MetS (sensitivity 57.6%, specificity 70.0% and area under the curve 0.655), and serum ZAG/fat mass ratio showed improved diagnosis value accuracy, with ROC curve area of 0.951 (95% CI, 0.920–0.983, P < 0.001), and 90.7% sensitivity and 88.6% specificity.Conclusions: Serum ZAG levels were lowered in patients with MetS and central obesity. The decreased serum ZAG levels were associated with the increased risks of MetS. Serum ZAG, especially serum ZAG/fat mass ratio might be the candidate diagnostic biomarkers for MetS.

The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal analysis should be more aspirational in terms of developing predictive capabilities, even if the interest is in inference and not in prediction per se. The theory developed in the paper provides practitioners guidance for developing causal models using new machine learning methods that have, so far, remained relatively underutilized in this context.

The study investigated the expression of monoacylglycerol lipase (MGL) during the skin-incised wound healing in mice and applicability of the time-dependent expression of MGL to wound age determination by immunofluorescent staining, Western blotting, and real-time PCR. Furthermore, cell types were identified by double immunofluorescence. A total of 45 BALB/c male mice were used in this study. After a 1.5-cm-long incision in the central dorsum skin, mice were killed at intervals ranging from 6 h to 14 days, followed by the sampling of wound margin. In the control, there was a low-level expression of MGL in the epidermis, hair follicles, and glandulae sebaceae. In the injured skin, MGL immunoreactivity was mainly detected in the neutrophils, macrophages, and myofibroblasts. Morphometrically, the average ratios of MGL-positive cells were more than 50% at 5 and 7 days post-wounding, whereas it was <50% at the other posttraumatic intervals. By Western blotting analysis, the average ratio of MGL protein expression was highest at 5 days after injury, which had a ratio of >2.30. Similarly, the relative quantity of MGL mRNA expression maximized at posttraumatic 5 days in comparison with control as detected by real-time PCR, with an average ratio of >2.54. In conclusion, MGL expression is detected in neutrophils, macrophages, and myofibroblasts and significantly up-regulated, suggesting that it may play roles in response to inflammation during skin-incised wound healing. From the viewpoint of forensic pathology, MGL detection is applicable to skin wound age determination. [ABSTRACT FROM AUTHOR]

Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) techniques have exhibited the ability to enhance spatial resolution, offering the potential to reconstruct the details of SST fields. Current SR research focuses mainly on improving the structure of the SR model instead of training dataset selection. Different from generating the low-resolution images by downscaling the corresponding high-resolution images, the high- and low-resolution SST are derived from different sensors. Hence, the structure similarity of training patches may affect the SR model training and, consequently, the SST reconstruction. In this study, we first discuss the influence of training dataset selection on SST SR performance, showing that the training dataset determined by the structure similarity index (SSIM) of 0.6 can result in higher reconstruction accuracy and better image quality. In addition, in the practical stage, the spatial similarity between the low-resolution input and the objective high-resolution output is a key factor for SST SR. Moreover, the training dataset obtained from the actual AMSR2 and MODIS SST images is more suitable for SST SR because of the skin and sub-skin temperature difference. Finally, the SST reconstruction accuracies obtained from different SR models are relatively consistent, yet the differences in reconstructed image quality are rather significant.

Abstract Background Improved carbohydrate-active enzymes (CAZymes) are needed to fulfill the goal of producing food, feed, fuel, chemicals, and materials from biomass. Little is known about how the diverse microbial communities in anaerobic digesters (ADs) metabolize carbohydrates or which CAZymes that are present, making the ADs a unique niche to look for CAZymes that can potentiate the enzyme blends currently used in industry. Results Enzymatic assays showed that functional CAZymes were secreted into the AD environments in four full-scale mesophilic Danish ADs fed with primary and surplus sludge from municipal wastewater treatment plants. Metagenomes from the ADs were mined for CAZymes with Homology to Peptide Patterns (HotPep). 19,335 CAZymes were identified of which 30% showed 50% or lower identity to known proteins demonstrating that ADs make up a promising pool for discovery of novel CAZymes. A function was assigned to 54% of all CAZymes identified by HotPep. Many different α-glucan-acting CAZymes were identified in the four metagenomes, and the most abundant family was glycoside hydrolase family 13, which contains α-glucan-acting CAZymes. Cellulytic and xylanolytic CAZymes were also abundant in the four metagenomes. The cellulytic enzymes were limited almost to endoglucanases and β-glucosidases, which reflect the large amount of partly degraded cellulose in the sludge. No dockerin domains were identified suggesting that the cellulytic enzymes in the ADs studied operate independently. Of xylanolytic CAZymes, especially xylanases and β-xylosidase, but also a battery of accessory enzymes, were present in the four ADs. Conclusions Our findings suggest that the ADs are a good place to look for novel plant biomass degrading and modifying enzymes that can potentiate biological processes and provide basis for production of a range of added-value products from biorefineries.

ABSTRACT Termites forage on a range of substrates, and it has been suggested that diet shapes the composition and function of termite gut bacterial communities. Through comparative analyses of gut metagenomes in nine termite species with distinct diets, we characterize bacterial community compositions and use peptide-based functional annotation method to determine biomass-degrading enzymes and the bacterial taxa that encode them. We find that fungus-growing termite guts have relatively more fungal cell wall-degrading enzyme genes, while wood-feeding termite gut communities have relatively more plant cell wall-degrading enzyme genes. Interestingly, wood-feeding termite gut bacterial genes code for abundant chitinolytic enzymes, suggesting that fungal biomass within the decaying wood likely contributes to gut bacterial or termite host nutrition. Across diets, the dominant biomass-degrading enzymes are predominantly coded for by the most abundant bacterial taxa, suggesting tight links between diet and gut community composition, with the most marked difference being the communities coding for the mycolytic capacity of the fungus-growing termite gut. IMPORTANCE Understanding functional capacities of gut microbiomes is important to improve our understanding of symbiotic associations. Here, we use peptide-based functional annotation to show that the gut microbiomes of fungus-farming termites code for a wealth of enzymes that likely target the fungal diet the termites eat. Comparisons to other termites showed that fungus-growing termite guts have relatively more fungal cell wall-degrading enzyme genes, whereas wood-feeding termite gut communities have relatively more plant cell wall-degrading enzyme genes. Across termites with different diets, the dominant biomass-degrading enzymes are predominantly coded for by the most abundant bacterial taxa, suggesting tight links between diet and gut community compositions.

Alginate is an anionic polysaccharide abundantly present in the cell walls of brown macroalgae. The enzymatic depolymerization is performed solely by alginate lyases (EC 4.2.2.x), categorized as polysaccharide lyases (PLs) belonging to 12 different PL families. Until now, the vast majority of the alginate lyases have been found in bacteria. We report here the first extensive characterization of four alginate lyases from a marine fungus, the ascomycete Paradendryphiella salina, a known saprophyte of seaweeds. We have identified four polysaccharide lyase encoding genes bioinformatically in P. salina, one PL8 (PsMan8A), and three PL7 alginate lyases (PsAlg7A, -B, and -C). PsMan8A was demonstrated to exert exo-action on polymannuronic acid, and no action on alginate, indicating that this enzyme is most likely an exo-acting polymannuronic acid specific lyase. This enzyme is the first alginate lyase assigned to PL8 and polymannuronic acid thus represents a new substrate specificity in this family. The PL7 lyases (PsAlg7A, -B, and -C) were found to be endo-acting alginate lyases with different activity optima, substrate affinities, and product profiles. PsAlg7A and PsMan8A showed a clear synergistic action for the complete depolymerization of polyM at pH 5. PsAlg7A depolymerized polyM to mainly DP5 and DP3 oligomers and PsMan8A to dimers and monosaccharides. PsAlg7B and PsAlg7C showed substrate affinities towards both polyM and polyG at pH 8, depolymerizing both substrates to DP9-DP2 oligomers. The findings elucidate how P. salina accomplishes alginate depolymerization and provide insight into an efficient synergistic cooperation that may provide a new foundation for enzyme selection for alginate degradation in seaweed bioprocessing.

Tianjin is the largest open city along the coastline in Northern China, which has several important wetland ecosystems. However, no systematic study has assessed the water body changes over the past few decades for Tianjin, not to mention their response to human activities and climate change. Here, based on the water change tracking (WCT) algorithm, we proposed an improved water change tracking (IWCT) algorithm, which could remove built-up shade noise (account for 0.4%~6.0% of the final water area) and correct omitted water pixels (account for 1.1%~5.1% of the final water area) by taking the time-series data into consideration. The seasonal water product of the Global Surface Water Data (GSWD) was used to provide a comparison with the IWCT results. Significant changes in water bodies of the selected area in Tianjin were revealed from the time-series water maps. The permanent water area of Tianjin decreased 282.5 km2 from 1984 to 2019. Each time after the dried-up period, due to government policies, the land reclamation happened in Tuanbo Birds Nature Reserve (TBNR), and, finally, 12.6 km2 of the lake has been reclaimed. Meanwhile, 488.6 km2 of land has been reclaimed from the sea along the coastal zone in the past 16 years at a speed of 28.74 km2 yr−1 in the Binhai New Area (BHNA). The method developed in this study could be extended to other sensors which have similar band settings with Landsat; the products acquired in this study could provide fundamental reference for the wetland management in Tianjin.

Aspergillus hancockii sp. nov., classified in Aspergillus subgenus Circumdati section Flavi, was originally isolated from soil in peanut fields near Kumbia, in the South Burnett region of southeast Queensland, Australia, and has since been found occasionally from other substrates and locations in southeast Australia. It is phylogenetically and phenotypically related most closely to A. leporis States and M. Chr., but differs in conidial colour, other minor features and particularly in metabolite profile. When cultivated on rice as an optimal substrate, A. hancockii produced an extensive array of 69 secondary metabolites. Eleven of the 15 most abundant secondary metabolites, constituting 90% of the total area under the curve of the HPLC trace of the crude extract, were novel. The genome of A. hancockii, approximately 40 Mbp, was sequenced and mined for genes encoding carbohydrate degrading enzymes identified the presence of more than 370 genes in 114 gene clusters, demonstrating that A. hancockii has the capacity to degrade cellulose, hemicellulose, lignin, pectin, starch, chitin, cutin and fructan as nutrient sources. Like most Aspergillus species, A. hancockii exhibited a diverse secondary metabolite gene profile, encoding 26 polyketide synthase, 16 nonribosomal peptide synthase and 15 nonribosomal peptide synthase-like enzymes.

Landsat and MODIS data have been widely utilized in many remote sensing applications, however, the trade-off between the spatial resolution and temporal frequency has limited their capacities in monitoring detailed spatio-temporal dynamics. Spatio-temporal fusion methods based on a linear model that considers the differences between fine- and coarse-spatial-resolution images as linear can effectively solve this trade-off problem, yet the existing linear fusion methods either regard the coefficients of the linear model as constants or have adopted regression methods to calculate the coefficients, both of which may introduce some errors in the fusion process. In this paper, we proposed an enhanced linear spatio-temporal fusion method (ELSTFM) to improve the data fusion accuracy. In the ELSTFM, it is not necessary to calculate the slope of the linear model, and the intercept, which can be deemed as the residual caused by systematic biases, is calculated based on spectral unmixing theory. Additionally, spectrally similar pixels in a given fine-spatial-resolution pixel’s neighborhood and their corresponding weights were used in the proposed method to mitigate block effects. Landsat-7/ETM+ and 8-day composite MODIS reflectance data covering two study sites with heterogeneous and homogenous landscapes were selected to validate the proposed method. Compared to three other typical spatio-temporal fusion methods visually and quantitatively, the predicted images obtained from ELSTFM could acquire better results for the two selected study sites. Furthermore, the resampling methods used to resample MODIS to the same spatial resolution of Landsat could slightly, but did not significantly influence the fusion accuracy, and the distributions of slopes of different bands for the two study sites could all be deemed as normal distributions with a mean value close to 1. The performance of ELSTFM depends on the accuracy of residual calculation at fine-resolution and large landscape changes may influence the fusion accuracy.

Background and objective It has been proven that endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) was a new minimally invasive method in the diagnosis for mediastinal lymph nodes. The aim of this study is to evaluate the diagnostic and staging yield of EBUS-TBNA for lung cancer. Methods Seventy-five patients with tumors and enlarged mediastinal lymph nodes found by CT underwent the diagnosis by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) from April 1, 2009 to Febuary 8, 2010. The sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of EBUS-TBNA were evaluated. Results Seventy-five patients with 177 lymph node groups (2.4 groups in average) were studied. Histopathological samples were found in 49.33% patients and in 28.81% groups. Overall sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of EBUS-TBNA for diagnostic were 98.43%, 100.00%, 100.00%, 91.67% and 98.66%, respectively, in per patient analysis and were 95.10%, 100%, 100.00%, 82.93%, and 96.05%, respectively, in per group analysis, higher than CT examination (P < 0.05) expect for sensitivity (P=0.435). Staging changed in 19 (26.03%) patients after EBUS-TBNA. Conclusion EBUS-TBNA proved to be a safe procedure with a high yield for the diagnosis of lung cancer.

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

To investigate the prevalence of general and abdominal obesity and associated factors in Bouyei and Han peoples.A cross-sectional study was carried out in Guizhou province, southwest China in 2012, with multi-stage sampling to enroll 4551 participants aged 20 to 80 years. General and abdominal obesity were defined by World Health Organization (WHO) for Chinese. A design-based analysis was performed to evaluate prevalence of obesity and its related factors.Bouyei people had a significantly lower prevalence of general obesity (4.8% vs. 10.9%, p < 0.05) and abdominal obesity (13.6% vs. 26.8%, p < 0.05) than that in Han people. Prevalence of obesity increased with age until middle-age period and declined thereafter. Men aged 40-49 years group and women aged 50-59 years group have the highest prevalence of general obesity. Prevalence of abdominal obesity was higher than that of general obesity. Middle-age, Higher income, Han people were significantly associated with an increased risk of General/abdominal obesity.Bouyei people had a lower prevalence of general and abdominal obesity than the Han people. Etiological studies should be conducted to determine underlying genetic factors and dietary factors.

BACKGROUND: Hepatocellular carcinoma (HCC) can be diagnosed by noninvasive approaches with serum α-fetoprotein (AFP) levels >200 ng/ml and/or a radiological imaging study of tumor mass >2 cm in patients with chronic liver disease. Percutaneous fine needle aspiration (FNA) under ultrasound (US) guidance has a diagnostic specificity of 95% and is superior to radiological imaging studies. AIM: The aim of this study is to elucidate the effectiveness and complications of fine needle aspiration in a Chinese population with primary liver cancer and AFP levels ≤200 ng/ml. MATERIALS AND METHODS: A retrospective study was conducted over a period of 28 years. This selection period included patients with a suspected diagnosis of primary liver cancer whose AFP levels were ≤200 ng/ml and who underwent US-FNA. This data was then analyzed with cytomorphological features correlating with medical history, radiological imaging, AFP, and follow-up information. RESULTS: Of the 1,929 cases with AFP ≤200 mg/ml, 1,756 underwent FNA. Of these, 1,590 cases were determined malignant and the remaining 166 were determined benign. Further, 1,478 malignant cases were diagnosed by FNA alone, and of these, 1,138 were diagnosed as PLC. The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of the diagnoses were 92.96%, 100%, 100%, 59.71%, and 93.62% respectively. There was no significant difference in the sensitivity, specificity, PPV and NPV between the subgroups with tumor size

The cellulose-degrading fungal enzymes are glycoside hydrolases of the GH families and lytic polysaccharide monooxygenases. The entanglement of glycoside hydrolase families and functions makes it difficult to predict the enzymatic activity of glycoside hydrolases based on their sequence. In the present study we further developed the method Peptide Pattern Recognition to an automatic approach not only to find all genes encoding glycoside hydrolases and lytic polysaccharide monooxygenases in fungal genomes but also to predict the function of the genes. The functional annotation is an important feature as it provides a direct route to predict function from primary sequence. Furthermore, we used Peptide Pattern Recognition to compare the cellulose-degrading enzyme activities encoded by 39 fungal genomes. The results indicated that cellobiohydrolases and AA9 lytic polysaccharide monooxygenases are hallmarks of cellulose-degrading fungi except brown rot fungi. Furthermore, a high number of AA9, endocellulase and β-glucosidase genes were identified, not in what are known to be the strongest, specialized lignocellulose degraders but in saprophytic fungi that can use a wide variety of substrates whereas only few of these genes were found in fungi that have a limited number of natural, lignocellulotic substrates. This correlation suggests that enzymes with different properties are necessary for degradation of cellulose in different complex substrates. Interestingly, clustering of the fungi based on their predicted enzymes indicated that Ascomycota and Basidiomycota use the same enzymatic activities to degrade plant cell walls.