PPP1R15A-expressing monocytic MDSCs promote immunosuppressive liver microenvironment in fibrosis-associated hepatocellular carcinoma.

Background & Aims: Recent studies demonstrated the importance of fibrosis in promoting an immunosuppressive liver microenvironment and thereby aggressive hepatocellular carcinoma (HCC) growth and resistance to immune checkpoint blockade (ICB), particularly via monocyte-to-monocytic myeloid-derived suppressor cell (M-MDSC) differentiation triggered by hepatic stellate cells (HSCs). We thus aimed to identify druggable targets in these immunosuppressive myeloid cells for HCC therapy. Methods: M-MDSC signature genes were identified by integrated transcriptomic analysis of a human HSC-monocyte culture system and tumor-surrounding fibrotic livers of patients with HCC. Mechanistic and functional studies were conducted using in vitro-generated and patient-derived M-MDSCs. The therapeutic efficacy of a M-MDSC targeting approach was determined in fibrosis-associated HCC mouse models. Results: We uncovered over-expression of protein phosphatase 1 regulatory subunit 15A (PPP1R15A), a myeloid cell-enriched endoplasmic reticulum stress modulator, in human M-MDSCs that correlated with poor prognosis and ICB non-responsiveness in patients with HCC. Blocking TGF-ß signaling reduced PPP1R15A expression in HSC-induced M-MDSCs, whereas treatment of monocytes by TGF-ß upregulated PPP1R15A, which in turn promoted ARG1 and S100A8/9 expression in M-MDSCs and reduced T-cell proliferation. Consistently, lentiviral-mediated knockdown of Ppp1r15a in vivo significantly reduced ARG1+S100A8/9+ M-MDSCs in fibrotic liver, leading to elevated intratumoral IFN-γ+GZMB+CD8+ T cells and enhanced anti-tumor efficacy of ICB. Notably, pharmacological inhibition of PPP1R15A by Sephin1 reduced the immunosuppressive potential but increased the maturation status of fibrotic HCC patient-derived M-MDSCs. Conclusions: PPP1R15A+ M-MDSC cells are involved in immunosuppression in HCC development and represent a novel potential target for therapies. Impact and implications: Our cross-species analysis has identified PPP1R15A as a therapeutic target governing the anti-T-cell activities of fibrosis-associated M-MDSCs (monocytic myeloid-derived suppressor cells). The results from the preclinical models show that specific inhibition of PPP1R15A can break the immunosuppressive barrier to restrict hepatocellular carcinoma growth and enhance the efficacy of immune checkpoint blockade. PPP1R15A may also function as a prognostic and/or predictive biomarker in patients with hepatocellular carcinoma.

Consensus statements on endoscopic ultrasound-guided tissue acquisition. Guidelines from the Asian Endoscopic Ultrasound Group.

OBJECTIVES: This consensus was developed by the Asian EUS Group (AEG), who aimed to formulate a set of practice guidelines addressing various aspects of endoscopic ultrasound-guided tissue acquisition (EUS-TA). METHODS: The AEG initiated the development of consensus statements and formed an expert panel comprising surgeons, gastroenterologists, and pathologists. Three online consensus meetings were conducted to consolidate the statements and votes. The statements were presented and discussed in the first two consensus meetings and revised according to comments. Final voting was conducted at a third consensus meeting. The Grading of Recommendations, Assessment, Development, and Evaluation system was adopted to define the strength of the recommendations and quality of evidence. RESULTS: A total of 20 clinical questions and statements regarding EUS-TA were formulated. The committee recommended that fine-needle biopsy (FNB) needles be preferred over conventional fine-needle aspiration (FNA) needles for EUS-TA of subepithelial lesions. For solid pancreatic masses, rapid on-site evaluation is not routinely recommended when FNB needles are used. For dedicated FNB needles, fork-tip and Franseen-tip needles have essentially equivalent performance. CONCLUSION: This consensus provides guidance for EUS-TA, thereby enhancing the quality of EUS-TA.

Deep Spatio-Temporal Network for Low SNR Cryo-EM Movie Frame Enhancement.

Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused by beam striking make the task very challenging. Video enhancement algorithms in computer vision shed new light on tackling such tasks by utilizing deep neural networks. However, they are designed for natural images with high SNR. Meanwhile, the lack of ground truth in cryo-EM movie seems to be one major limiting factor of the progress. Hence, we present a synthetic cryo-EM movie generation pipeline, which can produce realistic diverse cryo-EM movie datasets with low-SNR movie frames and multiple ground truth values. Then we propose a deep spatio-temporal network (DST-Net) for cryo-EM movie frame enhancement trained on our synthetic data. Spatial and temporal features are first extracted from each frame. Spatio-temporal fusion and high-resolution re-constructor are designed to obtain the enhanced output. For evaluation, we train our model on seven synthetic cryo-EM movie datasets and infer on real cryo-EM data. The experimental results show that DST-Net can achieve better enhancement performance both quantitatively and qualitatively compared with others.

Performance of continuous controlled attenuation parameter and liver stiffness measurement by the novel SmartExam in metabolic dysfunction-associated steatotic liver disease.

BACKGROUND & AIMS: FibroScan® Expert 630 and FibroScan® Mini+430 are novel vibration-controlled transient elastography devices equipped with the same SmartExam software, which allows continuous measurement of controlled attenuation parameter (CAP) during the entire examination. This study aims to compare the CAP variabilities and the quantification for liver fibrosis and steatosis between the conventional FibroScan and the SmartExam-equipped machines in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS: This retrospective study included 118 patients with biopsy-proven MASLD who underwent liver biopsy at two tertiary centres between 2021 and 2023. Liver stiffness and steatosis measurements were performed using both FibroScan machines and M and XL probes for each individual. Liver histology was used as the reference standard for liver fibrosis and steatosis staging. RESULTS: Standard deviations of continuous CAP (cCAP) were significantly lower than those of CAP for all probes (p < .0001). CAP variability was significantly associated with body mass index (p < .01), probe selection (p < .001) as well as the random effect of centre. Only the effect of probe selection (p < .001) was significantly associated with cCAP variability. No significant difference was found in the performance of staging liver fibrosis and steatosis between two types of machines at the same cut-offs. CONCLUSIONS: The SmartExam-based VCTE reduces the variability of CAP measurement and achieves a similar accuracy as the FibroScan 502 device for the estimation of both hepatic steatosis and fibrosis. Future studies should determine if cCAP is a better tool to monitor changes in steatosis than the original CAP.

Aspiration cytology of liver abscess uncovering metastatic rectal mucosal melanoma-A case report.

Aspirates of liver abscess are frequently encountered in routine practice and are often of a low index of suspicion. However, necrotic liver metastasis clinically and radiologically mimics liver abscesses, and malignant cells can be obscured in an inflammation-rich background on cytology. It is important to recognise malignant neoplasms in this scenario, in particular uncommon conditions such as metastatic mucosal melanoma.

Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis.

BACKGROUND & AIMS: aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance. METHODS: A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125). RESULTS: The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively). CONCLUSIONS: The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.

MAFLD fibrosis score: Using routine measures to identify advanced fibrosis in metabolic-associated fatty liver disease.

BACKGROUND: Early screening may prevent fibrosis progression in metabolic-associated fatty liver disease (MAFLD). AIMS: We developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) among MAFLD patients. METHODS: This cross-sectional, multicentre study consecutively recruited MAFLD patients receiving tertiary care (Malaysia as training cohort [n = 276] and Hong Kong and Wenzhou as validation cohort [n = 431]). Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select 'highly important' predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula. RESULTS: MFS was composed of seven variables: age, body mass index, international normalised ratio, aspartate aminotransferase, gamma-glutamyl transpeptidase, platelet count, and history of type 2 diabetes. MFS demonstrated an area under the receiver-operating characteristic curve of 0.848 [95% CI 0.800-898] and 0.823 [0.760-0.886] in training and validation cohorts, significantly higher than aminotransferase-to-platelet ratio index (0.684 [0.603-0.765], 0.663 [0.588-0.738]), Fibrosis-4 index (0.793 [0.735-0.854], 0.737 [0.660-0.814]), and non-alcoholic fatty liver disease fibrosis score (0.785 [0.731-0.844], 0.750 [0.674-0.827]) (DeLong's test p < 0.05). MFS could include 92.3% of patients using dual cut-offs of 14 and 15, with a correct prediction rate of 90.4%, resulting in a larger number of patients with correct diagnosis compared to other scores. A two-step MFS-VCTE screening algorithm demonstrated positive and negative predictive values and overall diagnostic accuracy of 93.4%, 89.5%, and 93.2%, respectively, with only 4.0% of patients classified into grey zone. CONCLUSION: MFS outperforms conventional non-invasive scores in predicting advanced fibrosis, contributing to screening in MAFLD patients.

M-Denoiser: Unsupervised image denoising for real-world optical and electron microscopy data.

Real-world microscopy data have a large amount of noise due to the limited light/electron that can be used to capture images. The noise of microscopy data is composed of signal-dependent shot noise and signal-independent read noise, and the Poisson-Gaussian noise model is usually used to describe the noise distribution. Meanwhile, the noise is spatially correlated because of the data acquisition process. Due to the lack of clean ground truth, unsupervised and self-supervised denoising algorithms in computer vision shed new light on tackling such tasks by utilizing paired noisy images or one single noisy image. However, they usually make the assumption that the noise is signal-independent or pixel-wise independent, which contradicts with the actual case. Hence, we propose M-Denoiser for denoising real-world microscopy data in an unsupervised manner. Firstly, the shatter module is used to break the dependency and correlation before denoising. Secondly, a novelly designed unsupervised training loss based on a pair of noisy images is proposed for real-world microscopy data. For evaluation, we train our model on optical and electron microscopy datasets. The experimental results show that M-Denoiser achieves the best performance both quantitatively and qualitatively compared with all the baselines.

Targeting PPAR-gamma counteracts tumour adaptation to immune-checkpoint blockade in hepatocellular carcinoma.

OBJECTIVE: Therapy-induced tumour microenvironment (TME) remodelling poses a major hurdle for cancer cure. As the majority of patients with hepatocellular carcinoma (HCC) exhibits primary or acquired resistance to antiprogrammed cell death (ligand)-1 (anti-PD-[L]1) therapies, we aimed to investigate the mechanisms underlying tumour adaptation to immune-checkpoint targeting. DESIGN: Two immunotherapy-resistant HCC models were generated by serial orthotopic implantation of HCC cells through anti-PD-L1-treated syngeneic, immunocompetent mice and interrogated by single-cell RNA sequencing (scRNA-seq), genomic and immune profiling. Key signalling pathway was investigated by lentiviral-mediated knockdown and pharmacological inhibition, and further verified by scRNA-seq analysis of HCC tumour biopsies from a phase II trial of pembrolizumab (NCT03419481). RESULTS: Anti-PD-L1-resistant tumours grew >10-fold larger than parental tumours in immunocompetent but not immunocompromised mice without overt genetic changes, which were accompanied by intratumoral accumulation of myeloid-derived suppressor cells (MDSC), cytotoxic to exhausted CD8+ T cell conversion and exclusion. Mechanistically, tumour cell-intrinsic upregulation of peroxisome proliferator-activated receptor-gamma (PPARγ) transcriptionally activated vascular endothelial growth factor-A (VEGF-A) production to drive MDSC expansion and CD8+ T cell dysfunction. A selective PPARγ antagonist triggered an immune suppressive-to-stimulatory TME conversion and resensitised tumours to anti-PD-L1 therapy in orthotopic and spontaneous HCC models. Importantly, 40% (6/15) of patients with HCC resistant to pembrolizumab exhibited tumorous PPARγ induction. Moreover, higher baseline PPARγ expression was associated with poorer survival of anti-PD-(L)1-treated patients in multiple cancer types. CONCLUSION: We uncover an adaptive transcriptional programme by which tumour cells evade immune-checkpoint targeting via PPARγ/VEGF-A-mediated TME immunosuppression, thus providing a strategy for counteracting immunotherapeutic resistance in HCC.

aMAP Score and Its Combination With Liver Stiffness Measurement Accurately Assess Liver Fibrosis in Chronic Hepatitis B Patients.

BACKGROUND & AIMS: The changes in liver stiffness measurement (LSM) are unreliable to estimate regression of fibrosis during antiviral treatment for chronic hepatitis B (CHB) patients. The age-male-albumin-bilirubin-platelets score (aMAP), as an accurate hepatocellular carcinoma risk score, may reflect the liver fibrosis stage. Here, we aimed to evaluate the performance of aMAP for diagnosing liver fibrosis in CHB patients with or without treatment. METHODS: A total of 2053 patients from 2 real-world cohorts and 2 multicentric randomized controlled trials in China were enrolled, among which 2053 CHB patients were included in the cross-sectional analysis, and 889 CHB patients with paired liver biopsies before and after 72 or 104 weeks of treatment were included in the longitudinal analysis. RESULTS: In the cross-sectional analysis, the areas under the receiver operating characteristic curve of aMAP in diagnosing cirrhosis and advanced fibrosis were 0.788 and 0.757, which were comparable with or significantly higher than those of the fibrosis index based on 4 factors and the aspartate aminotransferase-platelet ratio. The stepwise approach using aMAP and LSM further improved performance in detecting cirrhosis and advanced fibrosis with the smallest uncertainty area (29.7% and 46.2%, respectively) and high accuracy (82.3% and 79.8%, respectively). In the longitudinal analysis, we established a novel model (aMAP-LSM model) by calculating aMAP and LSM results before and after treatment, which had satisfactory performance in diagnosing cirrhosis and advanced fibrosis after treatment (area under the receiver operating characteristic curve, 0.839 and 0.840, respectively), especially for those with a significant decrease in LSM after treatment (vs LSM alone, 0.828 vs 0.748; P < .001 [cirrhosis]; 0.825 vs 0.750; P < .001 [advanced fibrosis]). CONCLUSIONS: The aMAP score is a promising noninvasive tool for diagnosing fibrosis in CHB patients. The aMAP-LSM model could accurately estimate fibrosis stage for treated CHB patients.

Evaluation of a group-based sensorimotor intervention programme to improve Chinese handwriting of primary school students.

Objective: Sensorimotor performance is influential in Chinese handwriting, but few studies have examined the efficacy of sensorimotor-based interventions on Chinese handwriting among primary school students with poor handwriting performance. The study aims to evaluate a sensorimotor-based intervention to improve handwriting in the mainstream primary schools. Methods: This study adopted a two-group pretest-posttest design. An 8-session group-based sensorimotor intervention was delivered to school-aged children (mean age = 8.1, 68% male). Group A had 2 sessions every week, while Group B had 4 sessions every week. Analysis of variance with repeated measures was used to test the effects. Results: The intervention had a significant time effect (p < .05) in terms of improving handwriting process (d = 0.33-1.10), manual dexterity (d = 0.57), visual memory (d = 0.70), visual-spatial perception (d = 0.37), and motor and postural skills (d = 0.73). The effect sizes ranged from medium to large. For the handwriting process, time per character had a significant group × time interaction, with post hoc analysis showing that Group A had a significantly large effect (d = 1.89, p < .001) while Group B did not. Conclusions: The group-based sensorimotor intervention programme appeared to show improvements in students with fair skills in writing Chinese characters. It appears that the effect is better if the training sessions are spaced out in one month rather than intensively conducted within two weeks. It might be related to more involvement from parents, and students need more time for practice after the training sessions.

Detecting Early-Stage Liver Fibrosis Using Macromolecular Proton Fraction Mapping Based on Spin-Lock MRI: Preliminary Observations.

BACKGROUND: Liver fibrosis is characterized by macromolecule depositions. Recently, a novel technology termed macromolecular proton fraction quantification based on spin-lock magnetic resonance imaging (MPF-SL) is reported to measure macromolecule levels. HYPOTHESIS: MPF-SL can detect early-stage liver fibrosis by measuring macromolecule levels in the liver. STUDY TYPE: Retrospective. SUBJECTS: Fifty-five participants, including 22 with no fibrosis (F0) and 33 with early-stage fibrosis (F1-2), were recruited. FIELD STRENGTH/SEQUENCE: 3 T; two-dimensional (2D) MPF-SL turbo spin-echo sequence, 2D spin-lock T1rho turbo spin-echo sequence, and multi-slice 2D gradient echo sequence. ASSESSMENT: Macromolecular proton fraction (MPF), T1rho, liver iron concentration (LIC), and fat fraction (FF) biomarkers were quantified within regions of interest. STATISTICAL TESTS: Group comparison of the biomarkers using Mann-Whitney U tests; correlation between the biomarkers assessed using Spearman's rank correlation coefficient and linear regression with goodness-of-fit; fibrosis stage differentiation using receiver operating characteristic curve (ROC) analysis. P-value < 0.05 was considered statistically significant. RESULTS: Average T1rho was 41.76 ± 2.94 msec for F0 and 41.15 ± 3.73 msec for F1-2 (P = 0.60). T1rho showed nonsignificant correlation with either liver fibrosis (ρ = -0.07; P = 0.61) or FF (ρ = -0.14; P = 0.35) but indicated a negative correlation with LIC (ρ = -0.66). MPF was 4.73 ± 0.45% and 5.65 ± 0.81% for F0 and F1-2 participants, respectively. MPF showed a positive correlation with liver fibrosis (ρ = 0.59), and no significant correlations with LIC (ρ = 0.02; P = 0.89) or FF (ρ = 0.05; P = 0.72). The area under the ROC curve was 0.85 (95% confidence interval [CI] 0.75-0.95) and 0.55 (95% CI 0.39-0.71; P = 0.55) for MPF and T1rho to discriminate between F0 and F1-2 fibrosis, respectively. DATA CONCLUSION: MPF-SL has the potential to diagnose early-stage liver fibrosis and does not appear to be confounded by either LIC or FF. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and fibrosis in MAFLD.

BACKGROUND AND AIMS: With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately screen high-risk MAFLD patients for liver inflammation and fibrosis. We aimed to develop a novel sequential algorithm based on N-terminal propeptide of type 3 collagen (PRO-C3) for disease risk stratification in patients with MAFLD. METHODS: A derivation and independent validation cohort of 327 and 142 patients with biopsy-confirmed MAFLD were studied. We compared the diagnostic performances of various non-invasive scores in different disease states, and a novel sequential algorithm was constructed by combining the best performing non-invasive scores. RESULTS: For patients with high-risk progressive steatohepatitis (i.e., steatohepatitis + NAFLD activity score ≥ 4 + F ≥ 2), the AUROC of FAST score was 0.801 (95% confidence interval (CI): 0.739-0.863), and the negative predictive value (NPV) was 0.951. For advanced fibrosis (≥ F3) and cirrhosis (F4), the AUROCs of ADAPT and Agile 4 were 0.879 (95%CI 0.825-0.933) and 0.943 (95%CI 0.892-0.994), and the NPV were 0.972 and 0.992. Sequential algorithm of ADAPT + Agile 4 combination was better than other combinations for risk stratification of patients with severe fibrosis (AUROC = 0.88), with similar results in the validation cohort. Meanwhile, in all subgroup analyses (stratifying by sex, age, diabetes, NAS, BMI and ALT), ADAPT + Agile 4 had a good diagnostic performance. CONCLUSIONS: The new sequential algorithm reliably identifies liver inflammation and fibrosis in MAFLD, making it easier to exclude low-risk patients and recommending high-risk MAFLD patients for clinical trials and emerging pharmacotherapies.

TP53 R249S mutation in hepatic organoids captures the predisposing cancer risk.

BACKGROUND AND AIMS: Major genomic drivers of hepatocellular carcinoma (HCC) are nowadays well recognized, although models to establish their roles in human HCC initiation remain scarce. Here, we used human liver organoids in experimental systems to mimic the early stages of human liver carcinogenesis from the genetic lesions of TP53 loss and L3 loop R249S mutation. In addition, chromatin immunoprecipitation sequencing (ChIP-seq) of HCC cell lines shed important functional insights into the initiation of HCC consequential to the loss of tumor-suppressive function from TP53 deficiency and gain-of-function activities from mutant p53. APPROACH AND RESULTS: Human liver organoids were generated from surgical nontumor liver tissues. CRISPR knockout of TP53 in liver organoids consistently demonstrated tumor-like morphological changes, increased in stemness and unrestricted in vitro propagation. To recapitulate TP53 status in human HCC, we overexpressed mutant R249S in TP53 knockout organoids. A spontaneous increase in tumorigenic potentials and bona fide HCC histology in xenotransplantations were observed. ChIP-seq analysis of HCC cell lines underscored gain-of-function properties from L3 loop p53 mutants in chromatin remodeling and overcoming extrinsic stress. More importantly, direct transcriptional activation of PSMF1 by mutant R249S could increase organoid resistance to endoplasmic reticulum stress, which was readily abrogated by PSMF1 knockdown in rescue experiments. In a patient cohort of primary HCC tumors and genome-edited liver organoids, quantitative polymerase chain reaction corroborated ChIP-seq findings and verified preferential genes modulated by L3 mutants, especially those enriched by R249S. CONCLUSIONS: We showed differential tumorigenic effects from TP53 loss and L3 mutations, which together confer normal hepatocytes with early clonal advantages and prosurvival functions.

Steatosis, HBV-related HCC, cirrhosis, and HBsAg seroclearance: A systematic review and meta-analysis.

BACKGROUND AND AIMS: NAFLD and chronic hepatitis B (CHB) infection are common etiologies of HCC. The impact of hepatic steatosis on HCC in CHB, as well as its relationship with the development of cirrhosis, fibrosis, and HBsAg seroclearance, remains controversial. APPROACH AND RESULTS: Data from observational studies were collected through PubMed, EMBASE, and the Cochrane Library from inception to February 1, 2022. Outcomes of interest included the association of hepatic steatosis with HCC, cirrhosis, advanced fibrosis, and HBsAg seroclearance, expressed in terms of pooled ORs. Additional subgroup and sensitivity analyses were performed to validate the robustness of findings. A total of 34 studies with 68,268 patients with CHB were included. Hepatic steatosis was associated with higher odds of HCC (OR, 1.59; 95% CI, 1.12-2.26; I2  = 72.5%), with the association remaining consistent in Asia (OR, 1.56; 95% CI, 1.08-2.25), studies with a median follow-up duration of ≥5 years (OR, 2.82; 95% CI, 1.57-5.08), exclusion of alcohol use (OR, 1.71; 95% CI, 1.01-2.91), and biopsy-proven steatosis (OR, 2.86; 95% CI, 1.61-5.06), although no significant association was noted among nucleos(t)ide analogue-treated patients (OR, 1.05; 95% CI, 0.62-1.77). Steatosis was associated with the development of cirrhosis (OR, 1.52; 95% CI, 1.07-2.16; I2  = 0%) and HBsAg seroclearance (OR, 2.22; 95% CI, 1.58-3.10; I2  = 49.0%). CONCLUSIONS: Hepatic steatosis was associated with an increased risk of HCC and cirrhosis among patients with CHB but with a higher chance of achieving a functional cure, highlighting the importance of identifying concomitant steatosis in CHB.

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction.

Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.

Nestin as a diagnostic and prognostic marker for combined hepatocellular-cholangiocarcinoma.

BACKGROUND & AIMS: Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) associated with a poor prognosis. Given the challenges in its identification and its clinical implications, biomarkers are critically needed. We aimed to investigate the diagnostic and prognostic value of the immunohistochemical expression of Nestin, a progenitor cell marker, in a large multicentric series of PLCs. METHODS: We collected 461 cHCC-CCA samples from 32 different clinical centers. Control cases included 368 hepatocellular carcinomas (HCCs) and 221 intrahepatic cholangiocarcinomas (iCCAs). Nestin immunohistochemistry was performed on whole tumor sections. Diagnostic and prognostic performances of Nestin expression were determined using receiver-operating characteristic curves and Cox regression modeling. RESULTS: Nestin was able to distinguish cHCC-CCA from HCC with AUCs of 0.85 and 0.86 on surgical and biopsy samples, respectively. Performance was lower for the distinction of cHCC-CCA from iCCA (AUCs of 0.59 and 0.60). Nestin, however, showed a high prognostic value, allowing identification of the subset of cHCC-CCA ("Nestin High", >30% neoplastic cells with positive staining) associated with the worst clinical outcome (shorter disease-free and overall survival) after surgical resection and liver transplantation, as well as when assessment was performed on biopsies. CONCLUSION: We show in different clinical settings that Nestin has diagnostic value and that it is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy. LAY SUMMARY: There are different types of primary liver cancers (i.e. cancers that originate in the liver). Accurately identifying a specific subtype of primary liver cancer (and determining its associated prognosis) is important as it can have a major impact on treatment allocation. Herein, we show that a protein called Nestin could be used to refine risk stratification and improve treatment allocation for patients with combined hepatocellular carcinoma, a rare but highly aggressive subtype of primary liver cancer.

Validation model of fibrosis-8 index score to predict significant fibrosis among patients with nonalcoholic fatty liver disease.

BACKGROUND: Identifying hepatic fibrosis is crucial for nonalcoholic fatty liver disease (NAFLD) management. The fibrosis-8 (FIB-8) score, recently developed by incorporating four additional variables into the fibrosis-4 (FIB-4) score, showed better performance in predicting significant fibrosis in NAFLD. AIM: To validate the FIB-8 score in a biopsy-proven NAFLD cohort and compare the diagnostic performance of the FIB-8 and FIB-4 scores and NAFLD fibrosis score (NFS) for predicting significant fibrosis. METHODS: We collected the data of biopsy-proven NAFLD patients from three Asian centers in three countries. All the patients with available variables for the FIB-4 score (age, platelet count, and aspartate and alanine aminotransferase levels) and FIB-8 score (the FIB-4 variables plus 4 additional parameters: The body mass index (BMI), albumin to globulin ratio, gamma-glutamyl transferase level, and presence of diabetes mellitus) were included. The fibrosis stage was scored using nonalcoholic steatohepatitis CRN criteria, and significant fibrosis was defined as at least fibrosis stage 2. RESULTS: A total of 511 patients with biopsy-proven NAFLD and complete data were included for validation. Of these 511 patients, 271 (53.0%) were female, with a median age of 51 (interquartile range: 41, 58) years. The median BMI was 29 (26.3, 32.6) kg/m2, and 268 (52.4%) had diabetes. Among the 511 NAFLD patients, 157 (30.7%) had significant fibrosis (≥ F2). The areas under the receiver operating characteristic curves of the FIB-8 and FIB-4 scores and NFS for predicting significant fibrosis were 0.774, 0.743, and 0.680, respectively. The FIB-8 score demonstrated significantly better performance for predicting significant fibrosis than the NFS (P = 0.001) and was also clinically superior to FIB-4, although statistical significance was not reached (P = 0.073). The low cutoff point of the FIB-8 score for predicting significant fibrosis of 0.88 showed 92.36% sensitivity, and the high cutoff point of the FIB-8 score for predicting significant fibrosis of 1.77 showed 67.51% specificity. CONCLUSION: We demonstrated that the FIB-8 score had significantly better performance for predicting significant fibrosis in NAFLD patients than the NFS, as well as clinically superior performance vs the FIB-4 score in an Asian population. A novel simple fibrosis score comprising commonly accessible basic laboratories may be beneficial to use for an initial assessment in primary care units, excluding patients with significant liver fibrosis and aiding in patient selection for further hepatologist referral.