Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU). STUDY DESIGN: This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis. RESULTS: A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96. CONCLUSIONS: The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.

Combinatorial discovery of antibacterials via a feature-fusion based machine learning workflow.

The discovery of new antibacterials within the vast chemical space is crucial in combating drug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). However, the traditional approach of screening the entire chemical library in an ergodic manner can be laborious and time-consuming. Machine learning-assisted screening of antibacterials alleviates the exploration effort but suffers from the lack of reliable and related datasets. To address these challenges, we devised a combinatorial library comprising over 110 000 candidates based on the Ugi reaction. A focused library was subsequently generated through uniform sampling of the entire library to narrow down the preliminary screening scale. A novel feature-fusion architecture called the latent space constraint neural network was developed which incorporated both fingerprint and physicochemical molecular descriptors to predict the antibacterial properties. This integration allowed the model to leverage the complementary information provided by these descriptors and improve the accuracy of predictions. Three lead compounds that demonstrated excellent efficacy against MRSA while alleviating drug resistance were identified. This workflow highlights the integration of machine learning with the combinatorial chemical library to expedite high-quality data collection and extensive data mining for antibacterial screening.

Neonatal Filaggrin Genetic Screening and Counseling to Prevent Atopic Dermatitis in High-Risk Infants.

Background: Mutations in filaggrin (FLG), the gene that codes for the skin barrier protein, have been shown to be associated with atopic dermatitis (AD). Objective: The objectives of this study were to determine the effects of genetic counseling and parental education on infants at a high risk of AD. Methods: We enrolled 7521 newborns in Taiwan from January 1, 2016, to March 30, 2020, and all of them received genetic testing encompassing 20 known FLG mutations. The genetic counseling and AD prevention and care team consisted of pediatricians, dermatologists, social workers, and genetic counselors. The counseling was arranged for at least 30 minutes within 45 days after delivery. Results: A total of 2963 high-risk infants (39.4%) were identified. Homozygous c.1432C>T was the most commonly identified mutation. A total of 418 neonates' parents were stratified into counseling and noncounseling groups, where the effect of parental education was evaluated. The genetically stratified parental education program was effective in preventing AD development by 63.3% in high-risk infants before 12 months of life (P < 0.0001). Conclusion: Genetic stratification and parental education are effective in preventing the development of AD in high-risk infants before 12 months of life.

Study found increasing use of core outcome sets in Cochrane systematic reviews and identified facilitators and barriers.

OBJECTIVES: In 2019, only 7% of Cochrane systematic reviews (SRs) cited a core outcome set (COS) in relation to choosing outcomes, even though a relevant COS existed but was not mentioned (or cited) for a further 29% of SRs. Our objectives for the current work were to (1) examine the extent to which authors are currently considering COS to inform outcome choice in Cochrane protocols and completed SRs, and (2) understand author facilitators and barriers to using COS. STUDY DESIGN AND SETTING: We examined all completed Cochrane SRs published in the last 3 months of 2022 and all Cochrane protocols published in 2022 for the extent to which they: (a) cited a COS, (b) searched for COS, (c) used outcomes from existing COS, and (d) reported outcome inconsistency among included studies and/or noted the need for COS. One investigator extracted information; a second extractor verified all information, discussing discrepancies to achieve consensus. We then conducted an online survey of authors of the included SRs to assess awareness of COS and identify facilitators and barriers to using COS to inform outcome choice. RESULTS: Objective 1: We included 294 SRs of interventions (84 completed SRs and 210 published SR protocols), of which 13% cited specific COS and 5% did not cite but mentioned searching for COS. A median of 83% of core outcomes from cited COS (interquartile range [IQR] 57%-100%) were included in the corresponding SR. We identified a relevant COS for 39% of SRs that did not cite a COS. A median of 50% of core outcomes from noncited COS (IQR 35%-72%) were included in the corresponding SR. Objective 2: Authors of 236 (80%) of the 294 eligible SRs completed our survey. Seventy-seven percent of authors noted being aware of COS before the survey. Fifty-five percent of authors who did not cite COS but were aware of them reported searching for a COS. The most reported facilitators of using COS were author awareness of the existence of COS (59%), author positive perceptions of COS (52%), and recommendation in the Cochrane Handbook regarding COS use (48%). The most reported barriers related to matching of the scope of the COS and the SR: the COS target population was too narrow/broad relative to the SR population (29%) or the COS target intervention was too narrow/broad relative to the SR intervention (21%). Most authors (87%) mentioned that they would consider incorporating missing core outcomes in the SR/update. CONCLUSION: Since 2019, there is increasing consideration and awareness of COS when choosing outcomes for Cochrane SRs of interventions, but uptake remains low and can be improved further. Use of COS in SRs is important to improve outcome standardization, reduce research waste, and improve evidence syntheses of the relevant effects of interventions across health research.

An ultrasound-based histogram analysis model for prediction of tumour stroma ratio in pleomorphic adenoma of the salivary gland.

OBJECTIVES: Preoperative identification of different stromal subtypes of pleomorphic adenoma (PA) of the salivary gland is crucial for making treatment decisions. We aimed to develop and validate a model based on histogram analysis (HA) of ultrasound (US) images for predicting tumour stroma ratio (TSR) in salivary gland PA. METHODS: A total of 219 PA patients were divided into low-TSR (stroma-low) and high-TSR (stroma-high) groups and enrolled in a training cohort (n = 151) and a validation cohort (n = 68). The least absolute shrinkage and selection operator regression algorithm was used to screen the most optimal clinical, US, and HA features. The selected features were entered into multivariable logistic regression analyses for further selection of independent predictors. Different models, including the nomogram model, the clinic-US (Clin + US) model, and the HA model, were built based on independent predictors using logistic regression. The performance levels of the models were evaluated and validated on the training and validation cohorts. RESULTS: Lesion size, shape, cystic areas, vascularity, HA_mean, and HA_skewness were identified as independent predictors for constructing the nomogram model. The nomogram model incorporating the clinical, US, and HA features achieved areas under the curve of 0.839 and 0.852 in the training and validation cohorts, respectively, demonstrating good predictive performance and calibration. Decision curve analysis and clinical impact curves further confirmed its clinical usefulness. CONCLUSIONS: The nomogram model we developed offers a practical tool for preoperative TSR prediction in PA, potentially enhancing clinical decision-making.

Metagenomic analysis of individual mosquito viromes reveals the geographical patterns and drivers of viral diversity.

Mosquito transmitted viruses are responsible for an increasing burden of human disease. Despite this, little is known about the diversity and ecology of viruses within individual mosquito hosts. Here, using a meta-transcriptomic approach, we determined the viromes of 2,438 individual mosquitoes (81 species), spanning ~4,000 km along latitudes and longitudes in China. From these data we identified 393 viral species associated with mosquitoes, including 7 (putative) species of arthropod-borne viruses (that is, arboviruses). We identified potential mosquito species and geographic hotspots of viral diversity and arbovirus occurrence, and demonstrated that the composition of individual mosquito viromes was strongly associated with host phylogeny. Our data revealed a large number of viruses shared among mosquito species or genera, enhancing our understanding of the host specificity of insect-associated viruses. We also detected multiple virus species that were widespread throughout the country, perhaps reflecting long-distance mosquito dispersal. Together, these results greatly expand the known mosquito virome, linked viral diversity at the scale of individual insects to that at a country-wide scale, and offered unique insights into the biogeography and diversity of viruses in insect vectors.

A nomogram based on ultrasound scoring system for differentiating between immunoglobulin G4-related sialadenitis and primary Sjögren syndrome.

OBJECTIVES: Accurate distinguishing between immunoglobulin G4-related sialadenitis (IgG4-RS) and primary Sjögren syndrome (pSS) is crucial due to their different treatment approaches. This study aimed to construct and validate a nomogram based on the ultrasound (US) scoring system for the differentiation of IgG4-RS and pSS. METHODS: A total of 193 patients with a clinical diagnosis of IgG4-RS or pSS treated at our institution were enrolled in the training cohort (n = 135; IgG4-RS = 28, pSS = 107) and the validation cohort (n = 58; IgG4-RS = 15, pSS = 43). The least absolute shrinkage and selection operator regression algorithm was utilized to screen the most optimal clinical features and US scoring parameters. A model for the differential diagnosis of IgG4-RS or pSS was built using logistic regression and visualized as a nomogram. The performance levels of the nomogram model were evaluated and validated in both the training and validation cohorts. RESULTS: The nomogram incorporating clinical features and US scoring parameters showed better predictive value in differentiating IgG4-RS from pSS, with the area under the curves of 0.947 and 0.958 for the training cohort and the validation cohort, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: A nomogram based on the US scoring system showed favourable predictive efficacy in differentiating IgG4-RS from pSS. It has the potential to aid in clinical decision-making.

Synchronous Double Primary Malignant Tumors and their Possible Shared Genes: A Rare Clinical Entity.

Objective: This study sought to analyze the 18F-FDG PET/CT and contrast-enhanced computed tomography (CT) images of synchronous colorectal cancer (CRC) and renal clear cell carcinoma (ccRCC) and identify the shared genes between these two types of cancer through bioinformatic analysis. Methods: A retrospective analysis was conducted on a patient with synchronous CRC and ccRCC who underwent 18F-FDG PET/CT and contrast-enhanced CT before treatment. Databases were analyzed to identify differentially expressed genes between CRC and ccRCC, and co-expression genes were extracted for RCC and CRC. Results: 18F-FDG PET/CT revealed intense metabolic activity in the primary colorectal lesion (SUVmax 13.2), while a left renal mass (diameter = 35 mm) was observed with no significant uptake. Contrast-enhanced CT during the arterial phase showed heterogeneous intense enhancement of the renal lesion, and the lesion washed out earlier than in the renal cortex in the nephrographic and excretory phases, indicating ccRCC. The histopathological results confirmed synchronous double primary malignant tumors. Our bioinformatic analysis results showed that synchronous occurrence of CRC and ccRCC may correlate with simultaneous expression of Carbonic Anhydrase 9 (CA9), integrin-binding sialoprotein (IBSP), and Fibrinogen γ chain (FGG). Conclusion: 18F-FDG PET/CT combined with contrast-enhanced CT is an effective diagnostic tool in evaluating synchronous CRC and RCC. By analyzing this clinical case and conducting bioinformatic analysis, we improved our current understanding of the mechanisms underlying synchronous tumors.

Predicting anti-SARS-CoV-2 activities of chemical compounds using machine learning models.

To accelerate the discovery of novel drug candidates for Coronavirus Disease 2019 (COVID-19) therapeutics, we reported a series of machine learning (ML)-based models to accurately predict the anti-SARS-CoV-2 activities of screening compounds. We explored 6 popular ML algorithms in combination with 15 molecular descriptors for molecular structures from 9 screening assays in the COVID-19 OpenData Portal hosted by NCATS. As a result, the models constructed by k-nearest neighbors (KNN) using the molecular descriptor GAFF+RDKit achieved the best overall performance with the highest average accuracy of 0.68 and relatively high average area under the receiver operating characteristic curve of 0.74, better than other ML algorithms. Meanwhile, The KNN model for all assays using GAFF+RDKit descriptor outperformed using other descriptors. The overall performance of our developed models was better than REDIAL-2020 (R). A web server (https://clickff.org/amberweb/covid-19-cp) was developed to enable users to predict anti-SARS-CoV-2 activities of arbitrary compounds using the COVID-19-CP (P) models. Besides the descriptor-based machine learning models, we also developed graph-based Attentive FP (A) models for the 9 assays. We found that the Attentive FP models achieved a comparable performance to that of COVID-19-CP and outperformed the REDIAL-2020 models. The consensus prediction utilizing both COVID-19-CP and Attentive FP can significantly boost the prediction accuracy as assessed by comparing its performance with other three individual models (R, P, A) utilizing the Wilcoxon signed-rank test, thus can ultimately improve the success rate of COVID-19 drug discovery.

Whole-Genome Analysis of Termite-Derived Bacillus velezensis BV-10 and Its Application in King Grass Silage.

Bacillus velezensis (B. velezensis) is a cellulose-degrading strain that has the potential as an additive in fermented feed. B. velezensis BV-10 was isolated and screened from the termite gut. We sequenced the whole genome of this new source of B. velezensis to reveal its potential for use in cellulose degradation. Whole-genome sequencing of B. velezensis BV-10 showed that it has a circular chromosome of 3929792 bp containing 3873 coding genes with a GC content of 45.51% and many genes related to cellulose, hemicellulose, and lignin degradation. King grass silage was inoculated with B. velezensis BV-10 and mixed with other feed additives to assess the effect of B. velezensis BV-10 on the fermentation quality of silage. Six treatment groups were established: the control, B. velezensis BV-10, molasses, cellulase, B. velezensis BV-10 plus molasses, and B. velezensis BV-10 plus cellulase groups. After 30 days of silage-fermentation testing, B. velezensis BV-10 was found to rapidly reduce the silage pH value and significantly reduce the acid-detergent fiber (ADF) content (p < 0.05). The addition of B. velezensis BV-10 plus molasses and cellulase in fermented feed significantly reduced the silage neutral-detergent fiber and ADF content and promoted organic-acid accumulation (p < 0.05). The above results demonstrate that B. velezensis BV-10 promotes the fermentation quality of silage and that this effect is greater when other silage-fermentation additives are included. In conclusion, genes involved in cellulose degradation in B. velezensis BV-10 were identified by whole-genome sequencing and further experiments explored the effects of B. velezensis BV-10 and different feed additives on the fermentation quality of king grass silage, revealing the potential of Bacillus velezensis as a new silage additive.

CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease.

Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.

Risk factors for death associated with severe influenza in children and the impact of the COVID-19 pandemic on clinical characteristics.

Background: To summarize the clinical features of severe influenza in children and the high-risk factors for influenza-related deaths and to raise awareness among pediatricians. Methods: A retrospective study of clinical manifestations, laboratory tests, and diagnosis and treatment of 243 children with severe influenza admitted to Shenzhen Children's Hospital from January 2009 to December 2022 was conducted. Univariate logistic regression analysis and Boruta analysis were also performed to identify potentially critical clinical characteristics associated with death, and clinically significant were used in further multivariate logistic regression analysis. Subject receiver operating characteristic (ROC) curves were applied to assess the efficacy of death-related independent risk factors to predict death from severe influenza. Results: There were 169 male and 74 female patients with severe influenza, with a median age of 3 years and 2 months and 77.4% of patients under six. There were 46 cases (18.9%) in the death group. The most common pathogen was Influenza A virus (IAV) (81.5%). The most common complication in the death group was influenza-associated acute necrotizing encephalopathy (ANE [52.2%]). Severe influenza in children decreased significantly during the COVID-19 pandemic, with a median age of 5 years, a high predominance of neurological symptoms such as ANE (P = 0.001), and the most common pathogen being H3N2 (P < 0.001). D-dimer, acute respiratory distress syndrome (ARDS), and acute necrotizing encephalopathy (ANE) were significant independent risk factors for severe influenza-associated death. Furthermore, the ROC curves showed that the combined diagnosis of independent risk factors had significant early diagnostic value for severe influenza-related deaths. Conclusion: Neurological disorders such as ANE are more significant in children with severe influenza after the COVID-19 pandemic. Influenza virus infection can cause serious multisystem complications such as ARDS and ANE, and D-dimer has predictive value for early diagnosis and determination of the prognosis of children with severe influenza.

Metagenomic analysis of individual mosquitos reveals the ecology of insect viruses.

Mosquito transmitted viruses are responsible for an increasing burden of human disease. Despite this, little is known about the diversity and ecology of viruses within individual mosquito hosts. Using a meta-transcriptomic approach, we analysed the virome of 2,438 individual mosquitos (79 species), spanning ~4000 km along latitudes and longitudes in China. From these data we identified 393 core viral species associated with mosquitos, including seven (putative) arbovirus species. We identified potential species and geographic hotspots of viral richness and arbovirus occurrence, and demonstrated that host phylogeny had a strong impact on the composition of individual mosquito viromes. Our data revealed a large number of viruses shared among mosquito species or genera, expanding our knowledge of host specificity of insect-associated viruses. We also detected multiple virus species that were widespread throughout the country, possibly facilitated by long-distance mosquito migrations. Together, our results greatly expand the known mosquito virome, linked the viral diversity at the scale of individual insects to that at a country-wide scale, and offered unique insights into the ecology of viruses of insect vectors.

Analysis of the Promoter Regions of gga-miR-31 and Its Regulation by RA and C-jun in Chicken.

The role of gga-miR-31 in chicken germ cell differentiation and spermatogenesis is of significant importance. The transcriptional properties of gga-miR-31 are crucial in establishing the foundation for the formation of chicken spermatogonia stem cells and spermatogenesis. In this study, a series of recombinant vectors including varying lengths of the gga-miR-31 promoter were predicted and constructed. Through the utilization of the dual luciferase reporting system, the upstream -2180~0 bp region of gga-miR-31 was identified as its promoter region. Furthermore, it was predicted and confirmed that the activity of the gga-miR-31 promoter is increased by retinoic acid (RA). The binding of RA to the gga-miR-31 and Stra8 promoter regions was found to be competitive. Through the deletion of C-jun binding sites and the manipulation of C-jun expression levels, it was determined that C-jun inhibits the activity of the gga-miR-31 promoter. Furthermore, the combined treatment of C-jun and RA demonstrated that the positive regulatory effect of RA on the gga-miR-31 promoter is attenuated in the presence of high levels of C-jun. Overall, this study establishes a foundation for further investigation into the regulatory mechanisms of gga-miR-31 action, and provides a new avenue for inducing chicken embryonic stem cells (ESC) to differentiate into spermatogonial stem cells (SSC), and sperm formation.

A paradigm for high-throughput screening of cell-selective surfaces coupling orthogonal gradients and machine learning-based cell recognition.

The combinational density of immobilized functional molecules on biomaterial surfaces directs cell behaviors. However, limited by the low efficiency of traditional low-throughput experimental methods, investigation and optimization of the combinational density remain daunting challenges. Herein, we report a high-throughput screening set-up to study biomaterial surface functionalization by integrating photo-controlled thiol-ene surface chemistry and machine learning-based label-free cell identification and statistics. Through such a strategy, a specific surface combinational density of polyethylene glycol (PEG) and arginine-glutamic acid-aspartic acid-valine peptide (REDV) leads to high endothelial cell (EC) selectivity against smooth muscle cell (SMC) was identified. The composition was translated as a coating formula to modify medical nickel-titanium alloy surfaces, which was then proved to improve EC competitiveness and induce endothelialization. This work provided a high-throughput method to investigate behaviors of co-cultured cells on biomaterial surfaces modified with combinatorial functional molecules.

DFR-U-Net: Dual residual and feature fusion network for ulna and radius segmentation on dual-energy X-ray absorptiometry images.

BACKGROUND: Ulna and radius segmentation of dual-energy X-ray absorptiometry (DXA) images is essential for measuring bone mineral density (BMD). OBJECTIVE: To develop and test a novel deep learning network architecture for robust and efficient ulna and radius segmentation on DXA images. METHODS: This study used two datasets including 360 cases. The first dataset included 300 cases that were randomly divided into five groups for five-fold cross-validation. The second dataset including 60 cases was used for independent testing. A deep learning network architecture with dual residual dilated convolution module and feature fusion block based on residual U-Net (DFR-U-Net) to enhance segmentation accuracy of ulna and radius regions on DXA images was developed. The Dice similarity coefficient (DSC), Jaccard, and Hausdorff distance (HD) were used to evaluate the segmentation performance. A one-tailed paired t-test was used to assert the statistical significance of our method and the other deep learning-based methods (P < 0.05 indicates a statistical significance). RESULTS: The results demonstrated our method achieved the promising segmentation performance, with DSC of 98.56±0.40% and 98.86±0.25%, Jaccard of 97.14±0.75% and 97.73±0.48%, and HD of 6.41±11.67 pixels and 8.23±7.82 pixels for segmentation of ulna and radius, respectively. According to statistics data analysis results, our method yielded significantly higher performance than other deep learning-based methods. CONCLUSIONS: The proposed DFR-U-Net achieved higher segmentation performance for ulna and radius on DXA images than the previous work and other deep learning approaches. This methodology has potential to be applied to ulna and radius segmentation to help doctors measure BMD more accurately in the future.

Jun-mediated lncRNA-IMS promotes the meiosis of chicken spermatogonial stem cells via gga-miR-31-5p/stra8.

Meiosis, a key step in spermatogenesis, is affected by many factors. Current studies have shown that long noncoding RNAs (lncRNAs) are potential factors regulating meiosis, and their regulatory mechanisms have received much attention. However, little research has been done on its regulatory mechanism in the spermatogenesis of roosters. Here, we found that lncRNA involved in meiosis and spermatogenesis (lncRNA-IMS) was involved in the regulation of Stra8 by gga-miR-31-5p and hindered the inhibition of Stra8 by gga-miR-31-5p. The acquisition and loss of function experiments demonstrated that lncRNA-IMS was involved in meiosis and spermatogenesis. In addition, we predicted and determined the core promoter region of lncRNA-IMS. Prediction of transcription factors, deletion/overexpression of binding sites, knockdown/overexpression of Jun, and dual-luciferase reporter analysis confirmed that Jun positively activated transcription of lncRNA-IMS. Our findings further enrich the TF-lncRNA-miRNA-mRNA regulatory network during male meiosis and provide new ideas for studying the molecular mechanism of meiosis and spermatogenesis in chicken spermatogonial stem cells.

Diagnostic efficiency on ultrasound shear wave elastography in evaluation of steatosis severity for non-alcoholic fatty liver disease: a rat model.

BACKGROUND: The pathological feature of steatosis affects the elasticity values measured by shear wave elastography (SWE) is still controversial in non-alcoholic fatty liver disease (NAFLD). The aim of this study is to demonstrate the influence of steatosis on liver stiffness measured by SWE on a rat model with NAFLD and analyze feasibility of SWE for grading steatosis in absence of fibrosis. METHODS: Sixty-six rats were fed with methionine choline deficient diet or standard diet to produce various stages of steatosis; 48 rats were available for final analysis. Rats underwent abdominal ultrasound SWE examination and pathological assessment. Liver histopathology was analyzed to assess the degree of steatosis, inflammation, ballooning, and fibrosis according to the non-alcoholic fatty liver disease activity score. The diagnostic performance of SWE for differentiating steatosis stages was estimated according to the receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was conducted to determine clinical usefulness and the areas under DCA (AUDCAs) calculated. RESULTS: In multivariate analysis, steatosis was an independent factor affecting the mean elastic modules (B = 1.558, P < 0.001), but not inflammation (B = - 0.031, P = 0.920) and ballooning (B = 0.216, P = 0.458). After adjusting for inflammation and ballooning, the AUROC of the mean elasticity for identifying S ≥ S1 was 0.956 (95%CI: 0.872-0.998) and the AUDCA, 0.621. The AUROC for distinguishing S ≥ S2 and S = S3 was 0.987 (95%CI: 0.951-1.000) and 0.920 (95%CI: 0.816-0.986) and the AUDCA was 0.506 and 0.256, respectively. CONCLUSIONS: Steatosis is associated with liver stiffness and SWE may have the feasibility to be introduced as an assistive technology in grading steatosis for patients with NAFLD in absence of fibrosis.

Case report: Hepatic tuberculosis mimicking hepatocellular carcinoma in a patient with cirrhosis induced by hepatitis B virus.

Hepatic tuberculosis (TB), which is secondary to post-hepatitis B cirrhosis, is extremely rare. We report the case of a 69-year-old man with post-hepatitis B cirrhosis complicated by primary isolated hepatic TB who was initially misdiagnosed as having hepatocellular carcinoma (HCC). The patient was hospitalized with complaints of 2 weeks of fever. He had a 20-year history of post-hepatitis B cirrhosis. The laboratory tests suggested that his serum alpha-fetoprotein (AFP) level was markedly elevated to 1210 ng/ml. From the abdominal ultrasound (US) and magnetic resonance imaging (MRI) images, we confirmed the presence of cirrhosis and discovered a space-occupying lesion of the hepatic left lobe as well as portal vein-filling defects. These results led us to consider primary liver cancer and portal vein tumor thrombus combined with decompensated cirrhosis. Biopsy and histology may be considered the ultimate diagnostic tests, but we excluded needle biopsy because of his high risk of bleeding, in addition, the patient declined the procedure. To cope with his fever, the patient was given broad-spectrum antibiotic treatment initially, followed by intravenous vancomycin. After antibiotic treatment had failed, the patient was treated with anti-TB for 10 days; after that, the patient maintained a normal temperature. The patient continued to receive tuberculostatic therapy for 6 months following his discharge. AFP completely returned to the normal level, and the aforementioned mass disappeared. Finally, hepatic TB secondary to post-hepatitis B cirrhosis with portal vein thrombosis (PVT) was considered to be the final diagnosis. More than two imaging techniques discover a space-occupying liver lesion and that the serum alpha-fetoprotein (AFP) level is extremely elevated, which means that hepatocellular carcinoma (HCC) could be diagnosed. However, some exceedingly rare diseases should not be excluded. This case illustrated that the non-invasive diagnostic criteria for liver cancer should be considered carefully when discovering a space-occupying liver lesion in a patient with cirrhosis and an elevated AFP level. In addition, primary hepatic TB should be considered and included in the differential diagnosis, and a biopsy should be performed promptly.