Association of AST/ALT ratio with 90-day outcomes in patients with acute exacerbation of chronic liver disease: a prospective multicenter cohort study in China.

Background and aim: A high aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio is associated with liver injury in liver disease; however, no data exist regarding its relationship with 90-day prognosis in patients with acute exacerbation of chronic liver disease. Methods: In this study, 3,758 participants (955 with advanced fibrosis and 2,803 with cirrhosis) from the CATCH-LIFE cohort in China were included. The relationships between different AST/ALT ratios and the risk of adverse 90-day outcomes (death or liver transplantation) were determined in patients with cirrhosis or hepatitis B virus (HBV)-associated advanced fibrosis, respectively. Results: In the patients with HBV-associated advanced fibrosis, the risk of 90-day adverse outcomes increased with AST/ALT ratio; after adjusting for all confounding factors, the risk of adverse 90-day outcomes was the highest when AST/ALT ratio was more than 1.08 (OR = 6.91 [95% CI = 1.789-26.721], p = 0.005), and the AST/ALT ratio of >1.9 accelerated the development of adverse outcomes. In patients with cirrhosis, an AST/ALT ratio > 1.38 increased the risk of adverse 90-day outcomes in all univariables (OR = 1.551 [95% CI = 1.216-1.983], p < 0.001) and multivariable-adjusted analyses (OR = 1.847 [95% CI = 1.361-2.514], p < 0.001), and an elevated AST/ALT ratio (<2.65) accelerated the incidence of 90-day adverse outcomes. An AST/ALT ratio of >1.38 corresponded with a more than 20% incidence of adverse outcomes in patients with cirrhosis. Conclusion: The AST/ALT ratio is an independent risk factor for adverse 90-day outcomes in patients with cirrhosis and HBV-associated advanced fibrosis. The cutoff values of the AST/ALT ratio could help clinicians monitor the condition of patients when making clinical decisions.

Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population.

Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes.

Predicting axillary metastasis in breast cancer using lymphatic contrast-enhanced ultrasound-guided fine-needle aspiration of one lymph node.

OBJECTIVES: Fine-needle aspiration (FNA) is a microinvasive method to diagnose lymph nodes. This study aims to determine the capability of lymphatic contrast-enhanced ultrasound (LCEUS)-guided FNA in predicting the axillary metastasis with the target of one lymph node (LN) in patients with breast cancer. METHODS: LCEUS was prospectively performed in 105 patients with breast cancer. The most suspicious LN was targeted based on the characters of LCEUS. FNA was performed in the LN, followed by localization using a guide wire. The detection of lymph cells and/or tumour cells was recognized as a puncture success. Cytologic diagnosis was compared with histologic diagnosis of wire-marked LN for diagnosing accuracy and compared with histologic diagnosis of axillary LNs for predicting accuracy. RESULTS: LCEUS-guided FNA was performed in all 105 female patients who underwent axillary dissection. The puncture success rates were 74.3%, 91.4%, and 97.1% for three sequential groups (P = .010). In diagnosing LN metastasis, the sensitivity, specificity, and accuracy values of LCEUS-guided FNA were 89.7%, 100%, and 95.7%, respectively. In predicting axillary metastasis, the sensitivity, specificity, and accuracy values of LCEUS-guided FNA were 81.4%, 100%, and 91.3%, respectively. CONCLUSIONS: The microinvasive LCEUS-guided FNA of one lymph node can be an accurate method and may help predict axillary metastasis in patients with breast cancer. ADVANCES IN KNOWLEDGE: This study presented that LCEUS combined with FNA would be practical in clinic. The characters of LCEUS could indicate the suspicious LNs and promote the accuracy in predicting axillary metastasis.

The clinical courses of HBV-related acute-on-chronic liver failure and a multi-state model to predict disease evolution.

BACKGROUND AND AIMS: Acute-on-chronic liver failure (ACLF) is a highly dynamic syndrome. The objective of this study was to delineate the clinical course of patients with HBV-ACLF and to develop a model to estimate the temporal evolution of disease severity. METHODS: We enrolled eligible patients from 2 large, multicenter prospective cohorts. The ACLF grade, organ failures, and outcomes were assessed at multiple time points (days 1/4/7/14/21/28). Probabilities for ACLF transitions between these disease states and to death within 28 days were calculated using a multi-state model that used baseline information and updated ACLF status. The model was validated in independent patients. RESULTS: Among all the 445 patients with HBV-ACLF, 76 represented disease progression, 195 had a stable or fluctuating course, 8 with improvement, and the remaining 166 with resolution within 28-day follow-up. New coagulation (63.64%) or renal failure (45.45%) was frequently observed during early progression. Patients with disease progression had a higher incidence of new episodes of ascites [10 (13.16%) vs. 22 (5.96%), p = 0.027] and HE [13(17.11%) vs. 21 (5.69%), p = 0.001], and a significant increase in white blood cell count. The multi-state model represented dynamic areas under the receiver operating characteristic curves ranging from 0.71 to 0.84 for predicting all ACLF states and death at 4, 7, 14, 21, and 28 days post-enrollment and from 0.73 to 0.94 for predicting death alone, performing better than traditional prognostic scores. CONCLUSIONS: HBV-ACLF is a highly dynamic syndrome with reversibility. The multi-state model is a tool to estimate the temporal evolution of disease severity, which may inform clinical decisions on treatment.

Fecal Immunochemical Testing and the Risk of Advanced Colorectal Neoplasia: A Difference-In-Difference Analysis.

PURPOSE: To evaluate the effectiveness of fecal immunochemical testing (FIT) in colorectal cancer screening. METHODS: We conducted a prospective cohort study among 5,598 participants age 40-74 years between 2012 and 2020 in Tianjin, China. Inverse probability weighting was adopted to adjust for potential imbalanced factors between groups. A Cox proportional hazards model was used to estimate the weighted associations between FIT screening and advanced colorectal neoplasia. A difference-in-difference (DID) model was adopted to compare the incidence rates of advanced colorectal neoplasia between groups. RESULTS: In DID analysis, the rate of incidence was reduced by 0.34 cases per person-years in the screening group as compared with the historical FIT screening group (rate ratio [RR], 0.08 [95% CI, 0.07 to 0.10]) and by 0.06 cases per person-years in the non-FIT screening group as compared with the historical non-FIT screening group (RR, 0.37 [95% CI, 0.29 to 0.48]; P < .001 for both comparisons), with a relative reduction of 0.28. Similar benefit effect from FIT screening was observed in sex and age subgroups. CONCLUSION: FIT screening was associated with a reduction in incidence density from advanced colorectal neoplasia.

Severe anemia is associated with increased short-term and long-term mortality in patients hospitalized with cirrhosis.

INTRODUCTION AND OBJECTIVES: The relationship between anemia and the outcome of patients with cirrhosis is not completely clear. Therefore, we performed this large-scale epidemiological study to investigate the prevalence and severity of anemia in patients with cirrhosis and acute decompensation or liver injury and how anemia impacts short-term and long-term outcomes. PATIENTS AND METHODS: Patients with cirrhosis and acute decompensation (AD) or acute liver injury (ALI) were enrolled in the Chinese AcuTe on CHronic LIver FailurE (CATCH-LIFE) studies, which consisted of two large, multicenter, prospective, observational cohorts between January 2015 and December 2016 and July 2018 and January 2019. We conducted data analysis on the prevalence of anemia and determined the relationship between anemia and prognosis. RESULTS: Among 1979 patients, 1389 (70.2%) had anemia, among whom 599 (41.3%) had mild anemia, 595 (15.8%) had moderate anemia and 195 (2.4%) had severe anemia. A linear association between hemoglobin level and 90-day or 1-year LT-free mortality was shown, and a 10 g/L decrease in hemoglobin level was associated with a 6.8% extra risk of 90-day death and a 5.7% extra risk of 1-year death. Severe anemia was an independent risk factor for 90-day [HR=1.649 (1.100, 2.473), p=0.016] and 1-year LT-free mortality [HR=1.610 (1.159, 2.238), p=0.005]. Multinomial logistic regression analysis further identified that severe anemia was significantly associated with post-28-day mortality but not within-28-day mortality. CONCLUSIONS: Anemia is common in patients with cirrhosis admitted for acute events. Severe anemia was associated with poor 90-day and 1-year prognoses in these patients.

Metabolic biomarkers significantly enhance the prediction of HBV-related ACLF occurrence and outcomes.

BACKGROUND & AIMS: Acute-on-chronic liver failure (ACLF) is a clinical syndrome associated with high short-term mortality in patients with chronic liver disease. Chronic hepatitis B is the main cause of ACLF (HBV-ACLF) in China and other Asian countries. To improve disease management and survival for patients with ACLF, we aimed to discover novel biomarkers to enhance HBV-ACLF diagnosis and prognostication. METHODS: We performed a metabolomics profiling of 1,024 plasma samples collected from patients with HBV-related chronic liver disease with acute exacerbation at hospital admission in a multi-year and multi-center prospective study (367 ACLF and 657 non-ACLF). The samples were randomly separated into equal halves as a discovery set and a validation set. We identified metabolites associated with 90-day mortality in the ACLF group and the progression to ACLF within 28 days in the non-ACLF group (pre-ACLF) using statistical analysis and machine learning. We developed diagnostic algorithms in the discovery set and used these to assess the findings in the validation set. RESULTS: ACLF significantly altered the plasma metabolome, particularly in membrane lipid metabolism, steroid hormones, oxidative stress pathways, and energy metabolism. Numerous metabolites were significantly associated with 90-day mortality in the ACLF group and/or pre-ACLF in the non-ACLF group. We developed algorithms for the prediction of 90-day mortality in patients with ACLF (area under the curve 0.87 and 0.83 for the discovery set and validation set, respectively) and the diagnosis of pre-ACLF (area under the curve 0.94 and 0.88 for the discovery set and validation set, respectively). To translate our discoveries into practical clinical tests, we developed targeted assays using liquid chromatography-mass spectrometry. CONCLUSIONS: Based on novel metabolite biomarkers, we established tests for HBV-related ACLF with higher accuracy than existing methods. CLINICAL TRIAL NUMBER: NCT02457637 and NCT03641872. IMPACT AND IMPLICATIONS: Acute-on-chronic liver failure (ACLF) is a clinical syndrome associated with high short-term mortality affecting 25% of patients hospitalized with cirrhosis. Chronic hepatitis B is the main etiology of ACLF in China and other Asian counties. There is currently no effective therapy. Early diagnosis and accurate prognostication are critical for improving clinical outcomes in patients with ACLF. Based on novel metabolite biomarkers, we developed liquid chromatography-mass spectrometry tests with improved accuracy for the early diagnosis and prognostication of HBV-related ACLF. The liquid chromatography-mass spectrometry tests can be implemented in clinical labs and used by physicians to triage patients with HBV-related ACLF to ensure optimized clinical management.

Human understandable thyroid ultrasound imaging AI report system - A bridge between AI and clinicians.

Artificial intelligence (AI) enables accurate diagnosis of thyroid cancer; however, the lack of explanation limits its application. In this study, we collected 10,021 ultrasound images from 8,079 patients across four independent institutions to develop and validate a human understandable AI report system named TiNet for thyroid cancer prediction. TiNet can extract thyroid nodule features such as texture, margin, echogenicity, shape, and location using a deep learning method conforming to the clinical diagnosis standard. Moreover, it offers excellent prediction performance (AUC 0.88) and provides quantitative explanations for the predictions. We conducted a reverse cognitive test in which clinicians matched the correct ultrasound images according to TiNet and clinical reports. The results indicated that TiNet reports (87.1% accuracy) were significantly easier to understand than clinical reports (81.6% accuracy; p < 0.001). TiNet can serve as a bridge between AI-based diagnosis and clinicians, enhancing human-AI cooperative medical decision-making.

Association between use of antihypertensive drugs and the risk of cancer: a population-based cohort study in Shanghai.

BACKGROUND: Previously studies shown a potential risk of antihypertensive medicines in relation to cancer susceptibility, which creating significant debate in the scientific community and public concern. We sought to investigate the relationship between antihypertensive medicines and cancer risk, by drug type and class. METHODS: We conducted a population-based cohort study and enrolled patients diagnosed with hypertension from community healthcare centers in Changning District, Shanghai, China. Antihypertensive drug administration were classified as five common antihypertensive drugs. The main outcomes were incidence of total cancer and by major cancer type. RESULTS: Between January 2013 and December 2017, a total of 101,370 hypertensive patients were enrolled in this cohort. During a mean follow-up of 5.1 (SD 1.3) years, 4970 cancer cases were newly diagnosed in the cohort. CCBs were the most frequently used antihypertensives which were associated with a moderately increased risk of total cancer (hazard ratio, HR = 1.11, 95% CI: 1.05-1.18). The second commonly used drug ARBs were also associated with increased risk of total cancer (HR = 1.10, 95%CI: 1.03-1.17) as well as lung and thyroid cancers (HR = 1.21, 95%CI: 1.05-1.39; HR = 1.62 95%CI: 1.18-2.21, respectively). No significant association was found between cancer and other antihypertensives. Hypertensive patients who use more than one class of antihypertensives drugs had a higher risk of total cancer (HR: 1.22, 95%CI: 1.10-1.35 for two classes; HR: 1.22, 95%CI: 1.03-1.45 for three or more classes), and a possible dose-response relationship was suggested (P for trend < 0.001). The risk of thyroid cancer was higher in hypertensive patients prescribed with three or more antihypertensive classes. CONCLUSIONS: Use of ARBs or CCBs may be associated with an increased risk of total cancer. Taking more than one class of antihypertensives drugs appeared to have a higher risk for total cancer.

Automated diagnosis and management of follicular thyroid nodules based on the devised small-dataset interpretable foreground optimization network deep learning: a multicenter diagnostic study.

BACKGROUND: Currently, follicular thyroid carcinoma (FTC) has a relatively low incidence with a lack of effective preoperative diagnostic means. To reduce the need for invasive diagnostic procedures and to address information deficiencies inherent in a small dataset, we utilized interpretable foreground optimization network deep learning to develop a reliable preoperative FTC detection system. METHODS: In this study, a deep learning model (FThyNet) was established using preoperative ultrasound images. Data on patients in the training and internal validation cohort ( n =432) were obtained from Ruijin Hospital, China. Data on patients in the external validation cohort ( n =71) were obtained from four other clinical centers. We evaluated the predictive performance of FThyNet and its ability to generalize across multiple external centers and compared the results yielded with assessments from physicians directly predicting FTC outcomes. In addition, the influence of texture information around the nodule edge on the prediction results was evaluated. RESULTS: FThyNet had a consistently high accuracy in predicting FTC with an area under the receiver operating characteristic curve (AUC) of 89.0% [95% CI 87.0-90.9]. Particularly, the AUC for grossly invasive FTC reached 90.3%, which was significantly higher than that of the radiologists (56.1% [95% CI 51.8-60.3]). The parametric visualization study found that those nodules with blurred edges and relatively distorted surrounding textures were more likely to have FTC. Furthermore, edge texture information played an important role in FTC prediction with an AUC of 68.3% [95% CI 61.5-75.5], and highly invasive malignancies had the highest texture complexity. CONCLUSION: FThyNet could effectively predict FTC, provide explanations consistent with pathological knowledge, and improve clinical understanding of the disease.

Mitochondrial transfer from bone mesenchymal stem cells protects against tendinopathy both in vitro and in vivo.

BACKGROUND: Although mesenchymal stem cells (MSCs) have been effective in tendinopathy, the mechanisms by which MSCs promote tendon healing have not been fully elucidated. In this study, we tested the hypothesis that MSCs transfer mitochondria to injured tenocytes in vitro and in vivo to protect against Achilles tendinopathy (AT). METHODS: Bone marrow MSCs and H2O2-injured tenocytes were co-cultured, and mitochondrial transfer was visualized by MitoTracker dye staining. Mitochondrial function, including mitochondrial membrane potential, oxygen consumption rate, and adenosine triphosphate content, was quantified in sorted tenocytes. Tenocyte proliferation, apoptosis, oxidative stress, and inflammation were analyzed. Furthermore, a collagenase type I-induced rat AT model was used to detect mitochondrial transfer in tissues and evaluate Achilles tendon healing. RESULTS: MSCs successfully donated healthy mitochondria to in vitro and in vivo damaged tenocytes. Interestingly, mitochondrial transfer was almost completely blocked by co-treatment with cytochalasin B. Transfer of MSC-derived mitochondria decreased apoptosis, promoted proliferation, and restored mitochondrial function in H2O2-induced tenocytes. A decrease in reactive oxygen species and pro-inflammatory cytokine levels (interleukin-6 and -1ß) was observed. In vivo, mitochondrial transfer from MSCs improved the expression of tendon-specific markers (scleraxis, tenascin C, and tenomodulin) and decreased the infiltration of inflammatory cells into the tendon. In addition, the fibers of the tendon tissue were neatly arranged and the structure of the tendon was remodeled. Inhibition of mitochondrial transfer by cytochalasin B abrogated the therapeutic efficacy of MSCs in tenocytes and tendon tissues. CONCLUSIONS: MSCs rescued distressed tenocytes from apoptosis by transferring mitochondria. This provides evidence that mitochondrial transfer is one mechanism by which MSCs exert their therapeutic effects on damaged tenocytes.

Circulating succinate-modifying metabolites accurately classify and reflect the status of fumarate hydratase-deficient renal cell carcinoma.

Germline or somatic loss-of-function mutations of fumarate hydratase (FH) predispose patients to an aggressive form of renal cell carcinoma (RCC). Since other than tumor resection there is no effective therapy for metastatic FH-deficient RCC, an accurate method for early diagnosis is needed. Although MRI or CT scans are offered, they cannot differentiate FH-deficient tumors from other RCCs. Therefore, finding noninvasive plasma biomarkers suitable for rapid diagnosis, screening, and surveillance would improve clinical outcomes. Taking advantage of the robust metabolic rewiring that occurs in FH-deficient cells, we performed plasma metabolomics analysis and identified 2 tumor-derived metabolites, succinyl-adenosine and succinic-cysteine, as excellent plasma biomarkers for early diagnosis. These 2 molecules reliably reflected the FH mutation status and tumor mass. We further identified the enzymatic cooperativity by which these biomarkers are produced within the tumor microenvironment. Longitudinal monitoring of patients demonstrated that these circulating biomarkers can be used for reporting on treatment efficacy and identifying recurrent or metastatic tumors.

Multi-omics profiling of papillary thyroid microcarcinoma reveals different somatic mutations and a unique transcriptomic signature.

BACKGROUND: Papillary thyroid microcarcinoma (PTMC) incidence has significantly increased, and some cases still exhibit invasive traits. The entire molecular landscape of PTMC, which can offer hints for the etiology of cancer, is currently absent. METHODS: We compared our findings with those for PTMC in the TCGA by analyzing the largest study at the current stage of whole exome sequencing and RNA-sequencing data from 64 patients with PTMC. Then, we systematically demonstrated the differences between the two PTMC subtypes based on multi-omics analyses. Additionally, we created a molecular prediction model for the PTMC subtypes and validated them among TCGA patients for individualized integrative assessment. RESULTS: In addition to the presence of BRAF mutations and RET fusions in the TCGA cohort, we also discovered a new molecular signature named PTMC-inflammatory that implies a potential response to immune intervention, which is enriched with AFP mutations, IGH@-ext fusions, elevated immune-related genes, positive peroxidase antibody, and positive thyroglobulin antibody. Additionally, a molecular prediction model for the PTMC-inflammatory patients was created and validated among TCGA patients, while the prognosis for these patients is poor. CONCLUSIONS: Our findings comprehensively define the clinical and molecular features of PTMC and may inspire new therapeutic hypotheses.

Integrated gene profiling of fine-needle aspiration sample improves lymph node metastasis risk stratification for thyroid cancer.

BACKGROUND: Lymph node metastasis risk stratification is crucial for the surgical decision-making of thyroid cancer. This study investigated whether the integrated gene profiling (combining expression, SNV, fusion) of Fine-Needle Aspiration (FNA) samples can improve the prediction of lymph node metastasis in patients with papillary thyroid cancer. METHODS: In this retrospective cohort study, patients with papillary thyroid cancer who went through thyroidectomy and central lymph node dissection were included. Multi-omics data of FNA samples were assessed by an integrated array. To predict lymph node metastasis, we built models using gene expressions or mutations (SNV and fusion) only and an Integrated Risk Stratification (IRS) model combining genetic and clinical information. Blinded histopathology served as the reference standard. ROC curve and decision curve analysis was applied to evaluate the predictive models. RESULTS: One hundred and thirty two patients with pathologically confirmed papillary thyroid cancer were included between 2016-2017. The IRS model demonstrated greater performance [AUC = 0.87 (0.80-0.94)] than either expression classifier [AUC = 0.67 (0.61-0.74)], mutation classifier [AUC = 0.61 (0.55-0.67)] or TIRADS score [AUC = 0.68 (0.62-0.74)] with statistical significance (p < 0.001), and the IRS model had similar predictive performance in large nodule [>1 cm, AUC = 0.88 (0.79-0.97)] and small nodule [≤1 cm, AUC = 0.84 (0.74-0.93)] subgroups. The genetic risk factor showed independent predictive value (OR = 10.3, 95% CI:1.1-105.3) of lymph node metastasis in addition to the preoperative clinical information, including TIRADS grade, age, and nodule size. CONCLUSION: The integrated gene profiling of FNA samples and the IRS model developed by the machine-learning method significantly improve the risk stratification of thyroid cancer, thus helping make wise decisions and reducing unnecessary extensive surgeries.

Maternal Outcomes Among Pregnant Women With Congenital Heart Disease-Associated Pulmonary Hypertension.

BACKGROUND: Studies focused on pregnant women with congenital heart disease (CHD)-associated pulmonary hypertension (PH) are scarce and limited by small sample sizes and single-center design. This study sought to describe the pregnancy outcomes in women with CHD with and without PH. METHODS: Outcomes for pregnant women with CHD were evaluated retrospectively from 1993 to 2016 and prospectively from 2017 to 2019 from 7 tertiary hospitals. PH was diagnosed on the basis of echocardiogram or catheterization. The incidence of maternal death, cardiac complications, and obstetric and offspring complications was compared for women with CHD and no PH, mild, and moderate-to-severe PH. RESULTS: A total of 2220 pregnant women with CHD had completed pregnancies. PH associated with CHD was identified in 729 women, including 398 with mild PH (right ventricle to right atrium gradient 30-50 mm Hg) and 331 with moderate-to-severe PH (right ventricle to right atrium gradient >50 mm Hg). Maternal mortality occurred in 1 (0.1%), 0, and 19 (5.7%) women with CHD and no, mild, or moderate-to-severe PH, respectively. Of the 729 patients with PH, 619 (85%) had CHD-associated pulmonary arterial hypertension, and 110 (15%) had other forms of PH. Overall, patients with mild PH had better maternal outcomes than those with moderate-to-severe PH, including the incidence of maternal mortality or heart failure (7.8% versus 39.6%; P<0.001), other cardiac complications (9.0% versus 32.3%; P<0.001), and obstetric complications (5.3% versus 15.7%; P<0.001). Brain natriuretic peptide >100 ng/L (odds ratio, 1.9 [95% CI, 1.0-3.4], P=0.04) and New York Heart Association class III to IV (odds ratio, 2.9 [95% CI, 1.6-5.3], P<0.001) were independently associated with adverse maternal cardiac events in pregnancy with PH, whereas follow-up with a multidisciplinary team (odds ratio, 0.4 [95% CI, 0.2-0.6], P<0.001) and strict antenatal supervision (odds ratio, 0.5 [95% CI, 0.3-0.7], P=0.001) were protective. CONCLUSIONS: Women with CHD-associated mild PH appear to have better outcomes compared with women with CHD-associated moderate-to-severe PH, and with event rates similar for most outcomes with women with CHD and no PH. Multimodality risk assessment, including PH severity, brain natriuretic peptide level, and New York Heart Association class, may be useful in risk stratification in pregnancy with PH. Follow-up with a multidisciplinary team and strict antenatal supervision during pregnancy may also help to mitigate the risk of adverse maternal cardiac events.

N6 -methyladenosine-modified circRNA RERE modulates osteoarthritis by regulating ß-catenin ubiquitination and degradation.

OBJECTIVES: N6 -methyladenosine (m6A) is one of the most abundant internal RNA modifications. We investigated the role of m6A-modified circRERE in osteoarthritis (OA) and its mechanism. MATERIALS AND METHODS: CircRERE and IRF2BPL were screened by microarrays. The role of m6A-modification in circRERE was examined by methylated RNA precipitation and morpholino oligo (MOs) treatment. The axis of circRERE/miR-195-5p/IRF2BPL/ß-catenin was determined using flow cytometry, western blotting and immunofluorescence in human chondrocytes (HCs) and corroborated using a mouse model of destabilization of medial meniscus (DMM) with intra-articular (IA) injection of adeno-associated viruses (AAV). RESULTS: CircRERE was decreased in OA cartilage and chondrocytes compared with control. CircRERE downregulation was likely attributed to its increased m6A modification prone to endoribonucleolytic cleavage by YTHDF2-HRSP12-RNase P/MRP in OA chondrocytes. MOs transfection targeting HRSP12 binding motifs in circRERE partially reversed decreased circRERE expression and increased apoptosis in HCs treated with IL-1ß for 6 h. CircRERE exerted chondroprotective effects by targeting miR-195-5p/IRF2BPL, thus regulating the ubiquitination and degradation of ß-catenin. CircRere (mouse homologue) overexpression by IA-injection of AAV-circRere into mice attenuated the severity of DMM-induced OA, whereas AAV-miR-195a-5p or AAV-sh-Irf2bpl reduced the protective effects. The detrimental effects of AAV-sh-Irf2bpl on DMM-induced OA were substantially counteracted by ICG-001, an inhibitor of ß-catenin. CONCLUSIONS: Our study is a proof-of-concept demonstration for targeting m6A-modified circRERE and its target miR-195-5p/IRF2BPL/ß-catenin as potential therapeutic strategies for OA treatment.

Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation.

Background: The initial dose of tacrolimus after liver transplantation (LT) is critical for rapidly achieving the steady state of the drug concentration, minimizing the potential adverse reactions and warranting long-term patient prognosis. We aimed to develop and validate a genotype-guided model for determining personalized initial dose of tacrolimus. Methods: By combining pharmacokinetic modeling, pharmacogenomic analysis and multiple statistical methods, we developed a genotype-guided model to predict individualized tacrolimus initial dose after LT in the discovery (n = 150) and validation cohorts (n = 97) respectively. This model was further validated in a prospective, randomized and single-blind clinical trial from August, 2021 to February, 2022 (n = 40, ChiCTR2100050288). Findings: Our model included donor's and recipient's genotypes, recipient's weight and total bilirubin, which achieved an area under the curve of receiver operating characteristic curve (AUC of ROC) of 0.88 and 0.79 in the discovery and validation cohorts, respectively. We found that patients who were given tacrolimus within the recommended concentration range (RCR) (4-10 ng/mL), the new-onset metabolic syndromes are lower, especially for new-onset diabetes (p = 0.043). In the clinical trial, compared to those in experience-based (EB) group, patients in the model-based (MB) group were more likely to achieving the RCR (75% vs 40%, p = 0.025) with a more variable individualized dose (0.023-0.096 mg/kg/day vs 0.045-0.057 mg/kg/day). Moreover, significantly fewer medication adjustments were required for the MB group than the EB group (2.75 ± 2.01 vs 6.05 ± 3.35, p = 0.001). Interpretation: Our genotype-based model significantly improved the initial dosing accuracy of tacrolimus and reduced the number of medication adjustments, which are critical for improving the prognosis of LT patients. Funding: National Natural Science Foundation of China, Shanghai three-year action plan, National Science and Technology Major Project of China.

Prognosis prediction performs better in patients with non-cirrhosis hepatitis B virus-related acute-on-chronic liver failure than those with cirrhosis.

Background: The accurate prediction of the outcome of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is impeded by population heterogeneity. The study aimed to assess the impact of underlying cirrhosis on the performance of clinical prediction models (CPMs). Methods: Using data from two multicenter, prospective cohorts of patients with HBV-ACLF, the discrimination, calibration, and clinical benefit were assessed for CPMs predicting 28-day and 90-day outcomes in patients with cirrhosis and those without, respectively. Results: A total of 919 patients with HBV-ACLF were identified by Chinese Group on the Study of Severe Hepatitis B (COSSH) criteria, including 675 with cirrhosis and 244 without. COSSH-ACLF IIs, COSSH-ACLFs, Chronic Liver Failure-Consortium Acute-on-Chronic Liver Failure score (CLIF-C ACLFs), Tongji Prognostic Predictor Model score (TPPMs), Model for End-Stage Liver Disease score (MELDs), and MELD-Sodium score (MELD-Nas) were all strong predictors of short-term mortality in patients with HBV-ACLF. In contrast to a high model discriminative capacity in ACLF without cirrhosis, each prognostic model represents a marked decline of C-index, net reclassification index (NRI), and integrated discrimination improvement (IDI) in predicting either 28-day or 90-day prognosis of patients with cirrhosis. The hazard analysis identified largely overlapping risk factors of poor outcomes in both subgroups, while serum bilirubin was specifically associated with short-term mortality in patients with cirrhosis and blood urea nitrogen in patients without cirrhosis. A subgroup analysis in patients with cirrhosis showed a decline of discrimination of CPMS in those with ascites or infections compared to that in those without. Conclusion: Predicting the short-term outcome of HBV-ACLF by CPMs is optimal in patients without cirrhosis but limited in those with cirrhosis, at least partially due to the complicated ascites or infections.

Role of precipitants in transition of acute decompensation to acute-on-chronic liver failure in patients with HBV-related cirrhosis.

Background & Aims: Pre-acute-on-chronic liver failure (ACLF) is a distinct intermediate stage between acute decompensation (AD) and ACLF. However, identifying patients with pre-ACLF and predicting progression from AD to ACLF is difficult. This study aimed to identify pre-ACLF within 28 days, and to develop and validate a prediction model for ACLF in patients with HBV-related decompensated cirrhosis. Methods: In total, 1,736 patients with HBV-related cirrhosis and AD were enrolled from 2 large-scale, multicenter, prospective cohorts. ACLF occurrence within 28 days, readmission, and 3-month and 1-year outcomes were collected. Results: Among 970 patients with AD without ACLF in the derivation cohort, the 94 (9.6%) patients with pre-ACLF had the highest 3-month and 1-year LT-free mortality (61.6% and 70.9%, respectively), which was comparable to those with ACLF at enrollment (57.1% and 67.1%); the 251 (25.9%) patients with unstable decompensated cirrhosis had mortality rates of 22.4% and 32.1%, respectively; while the 507 (57.9%) patients with stable decompensated cirrhosis had the best outcomes (1-year mortality rate of 2.6%). Through Cox proportional hazard regression, specific precipitants, including hepatitis B flare with HBV reactivation, spontaneous hepatitis B flare with high viral load, superimposed infection on HBV, and bacterial infection, were identified to be significantly associated with ACLF occurrence in the derivation cohort. A model that incorporated precipitants, indicators of systemic inflammation and organ injuries reached a high C-index of 0.90 and 0.86 in derivation and validation cohorts, respectively. The optimal cut-off value (0.22) differentiated high-risk and low-risk patients, with a negative predictive value of 0.95. Conclusions: Three distinct clinical courses of patients with AD are validated in the HBV-etiology population. The precipitants significantly impact on AD-ACLF transition. A model developed by the precipitant-systemic inflammation-organ injury framework could be a useful tool for predicting ACLF occurrence. Clinical trial number: NCT02457637 and NCT03641872. Lay summary: It was previously shown that patients with decompensated cirrhosis could be stratified into 3 groups based on their short-term clinical prognoses. Herein, we showed that this stratification applies to patients who develop cirrhosis as a result of hepatitis B virus infection. We also developed a precipitant-based model (i.e. a model that incorporated information about the exact cause of decompensation) that could predict the likelihood of these patients developing a very severe liver disease called acute-on-chronic liver failure (or ACLF).

Panoramic quality assessment tool for investigator initiated trials.

Objectives: Quality can be a challenge for Investigator initiated trials (IITs) since these trials are scarcely overseen by a sponsor or monitoring team. Therefore, quality assessment for departments managing clinical research grants program is important and urgently needed. Our study aims at developing a handy quality assessment tool for IITs that can be applied by both departments and project teams. Methods: The framework of the quality assessment tool was developed based on the literature studies, accepted guidelines and the Delphi method. A total of 272 ongoing IITs funded by Shanghai non-profit organizations in 2015 and 2016 were used to extract quality indexes. Confirmatory factor analysis (CFA) was used to further evaluate the validity and feasibility of the conceptual quality assessment tool. Results: The tool consisted of 4 critical quality attributes, including progress, quality, regulation, scientificity, and 13 observed quality indexes. A total of 257 IITs were included in the validity and feasibility assessment. The majority (60.29%) were Randomized Controlled Trial (RCT), and 41.18% were multi-center studies. In order to test the validity and feasibility of IITs quality assessment tool, CFA showed that the model fit the data adequately. (CMIN/DF = 1.868, GFI = 0.916; CFI = 0.936; TLI = 0.919; RMSEA = 0.063; SRMR = 0.076). Different types of clinical studies fit well in the tool. However, RCT scored lower than prospective cohort and retrospective study in enrollment progress (7.02 vs. 7.43, 9.63, respectively). Conclusion: This study established a panoramic quality assessment tool based on the Delphi method and CFA, and the feasibility and effectiveness of the tool were verified through clinical research examples. The use of this tool can help project management departments effectively and dynamically manage research projects, rationally allocate resources, and ensure the quality of IITs.