Honglian Bai

BACKGROUND & AIMS: Current hepatocellular carcinoma (HCC) risk scores do not reflect changes in HCC risk resulting from liver disease progression/regression over time. We aimed to develop and validate two novel prediction models using multivariate longitudinal data, with or without cell-free DNA (cfDNA) signatures. METHODS: A total of 13,728 patients from two nationwide multicenter prospective observational cohorts, the majority of whom had chronic hepatitis B, were enrolled. aMAP score, as one of the most promising HCC prediction models, was evaluated for each patient. Low-pass whole-genome sequencing was used to derive multi-modal cfDNA fragmentomics features. A longitudinal discriminant analysis algorithm was used to model longitudinal profiles of patient biomarkers and estimate the risk of HCC development. RESULTS: We developed and externally validated two novel HCC prediction models with a greater accuracy, termed aMAP-2 and aMAP-2 Plus scores. The aMAP-2 score, calculated with longitudinal data on the aMAP score and alpha-fetoprotein values during an up to 8-year follow-up, performed superbly in the training and external validation cohorts (AUC 0.83-0.84). The aMAP-2 score showed further improvement and accurately divided aMAP-defined high-risk patients into two groups with 5-year cumulative HCC incidences of 23.4% and 4.1%, respectively (p = 0.0065). The aMAP-2 Plus score, which incorporates cfDNA signatures (nucleosome, fragment and motif scores), optimized the prediction of HCC development, especially for patients with cirrhosis (AUC 0.85-0.89). Importantly, the stepwise approach (aMAP -> aMAP-2 -> aMAP-2 Plus) stratified patients with cirrhosis into two groups, comprising 90% and 10% of the cohort, with an annual HCC incidence of 0.8% and 12.5%, respectively (p <0.0001). CONCLUSIONS: aMAP-2 and aMAP-2 Plus scores are highly accurate in predicting HCC. The stepwise application of aMAP scores provides an improved enrichment strategy, identifying patients at a high risk of HCC, which could effectively guide individualized HCC surveillance. IMPACT AND IMPLICATIONS: In this multicenter nationwide cohort study, we developed and externally validated two novel hepatocellular carcinoma (HCC) risk prediction models (called aMAP-2 and aMAP-2 Plus scores), using longitudinal discriminant analysis algorithm and longitudinal data (i.e., aMAP and alpha-fetoprotein) with or without the addition of cell-free DNA signatures, based on 13,728 patients from 61 centers across mainland China. Our findings demonstrated that the performance of aMAP-2 and aMAP-2 Plus scores was markedly better than the original aMAP score, and any other existing HCC risk scores across all subsets, especially for patients with cirrhosis. More importantly, the stepwise application of aMAP scores (aMAP -> aMAP-2 -> aMAP-2 Plus) provides an improved enrichment strategy, identifying patients at high risk of HCC, which could effectively guide individualized HCC surveillance.

Background Pulmonary infection is one of the common complications of long-term use of glucocorticoids. Severe infections not only increase the length of hospital stay and treatment costs but also cause progression or recurrence of the primary disease. Case description Herein, we reported a case of mixed pulmonary infection secondary to glucocorticoid use. Rare pathogens such as Nocardia nova, Mycobacterium tuberculosis, Aspergillus fumigatus , and cytomegalovirus were detected by metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid and lung puncture tissue. Combining the results of conventional pathogen detection and clinical symptoms, the patient was diagnosed with mixed pulmonary infection by multiple pathogens. After timely targeted medication, the patient was finally discharged with a good prognosis. Conclusion To our knowledge, this is the first case report on mixed pulmonary infection with pathogens including Nocardia nova, Mycobacterium tuberculosis, Aspergillus fumigatus , and human cytomegalovirus. As a new clinical diagnostic method, mNGS has great advantages in diagnosis of diseases such as mixed infections.

Background: The diagnosis of hepatocellular carcinoma (HCC) often experiences latency, ultimately leading to unfavorable patient outcomes due to delayed therapeutic interventions. Our study is designed to develop and validate a model that employs triple-phase computerized tomography (CT)-based deep learning radiomics and clinical variables for early warning of HCC in patients with cirrhosis. Methods: We studied 1858 patients with cirrhosis primarily from the PreCar cohort (NCT03588442) between June 2018 and January 2020 at 11 centres, and collected triple-phase CT images and laboratory results 3-12 months prior to HCC diagnosis or non-HCC final follow-up. Using radiomics and deep learning techniques, early warning model was developed in the discovery cohort (n = 924), and then validated in an internal validation cohort (n = 231), and an external validation cohort from 10 external centres (n = 703). Findings: We developed a hybrid model, named ALARM model, which integrates deep learning radiomics with clinical variables, enabling early warning of the majority of HCC cases. The ALARM model effectively predicted short-term HCC development in cirrhotic patients with area under the curve (AUC) of 0.929 (95% confidence interval 0.918-0.941) in the discovery cohort, 0.902 (0.818-0.987) in the internal validation cohort, and 0.918 (0.898-0.961) in the external validation cohort. By applying optimal thresholds of 0.21 and 0.65, the high-risk (n = 221, 11.9%) and medium-risk (n = 433, 23.3%) groups, which covered 94.4% (84/89) of the patients who developed HCC, had significantly higher rates of HCC occurrence compared to the low-risk group (n = 1204, 64.8%) (24.3% vs 6.4% vs 0.42%, P < 0.001). Furthermore, ALARM also demonstrated consistent performance in subgroup analysis. Interpretation: The novel ALARM model, based on deep learning radiomics with clinical variables, provides reliable estimates of short-term HCC development for cirrhotic patients, and may have the potential to improve the precision in clinical decision-making and early initiation of HCC treatments. Funding: This work was supported by National Key Research and Development Program of China (2022YFC2303600, 2022YFC2304800), and the National Natural Science Foundation of China (82170610), Guangdong Basic and Applied Basic Research Foundation (2023A1515011211).

BACKGROUND: Sofosbuvir/Velpatasvir (Epclusa, ECS) is the first pan-genotype direct-acting antiviral agent (DAA) for hepatitis C virus (HCV) infection, and Danoprevir (DNV) is the first DAA developed by a Chinese local enterprise, which is suitable for combined use with other drugs to treat genotype 1b chronic hepatitis C. However, previous reports have never compared the real-world data of ECS and DNV. PATIENTS AND METHODS: 178 chronic hepatitis C patients were retrospectively recruited, and 94cases were accepted with Sofosbuvir/Velpatasvir ± Ribavirin (ECS group), and others (n = 84 treated with DNV combination therapy (DNV group). The HCV genotype, virological response, adverse effects and some laboratory biochemical indexes were contrasted between above two groups in the real world study. RESULTS: DNV group had significantly lower level of alpha-fetoprotein (AFP), lower rates of decompensated cirrhosis ( P < 0.05). ECS group possessed more 6a (31.91% vs.13.10%) while DNV group was provided with more 1b (48.81% vs. 22.34%) patients. Significantly poor liver function was detected in ECS group at 4-week treatment (ALT and AST) and 12-week follow-up (AST) (all P < 0.05). The SVR12 undetectable rates of both groups were 100%, and no serious event was observed during the treatment and follow-up in both groups. CONCLUSION: In this retrospective real-world study, the efficacy of DNV combined therapy is similar to Sofosbuvir/Velpatasvir ± Ribavirin for chronic HCV infection, and the safety is comparable. DNV based therapy is a promising regimen for chronic hepatitis C.