• Hepatitis B-related cirrhosis
• Machine learning
• Portal vein thrombosis
• Risk

• anticoagulation
• emerging therapeutics
• hepatocellular carcinoma
• interventional radiology
• liver transplantation
• portal vein thrombosis

• Traumatic splenic rupture
• anticoagulation
• caesarean section
• multiple venous thrombosis
• splenectomy

• Humans
• Female
• Splenectomy
• Venous Thrombosis/etiology
• Venous Thrombosis/surgery
• Venous Thrombosis/drug therapy
• Adult
• Splenic Rupture/etiology
• Splenic Rupture/surgery
• Splenic Rupture/diagnosis
• Pregnancy
• Cesarean Section/adverse effects
• Postpartum Period
• Anticoagulants/therapeutic use
• Heparin, Low-Molecular-Weight/therapeutic use
• Warfarin/therapeutic use

• hepatolenticular degeneration
• nomogram
• portal vein system thrombosis
• predictive variables
• splenectomy

• Liver fibrosis
• logistic regression analysis
• random forest algorithm
• serum
• ultrasound

For patients with portal hypertension (PH), portal vein thrombosis (PVT) is a fatal complication after splenectomy. Postoperative platelet elevation is considered the foremost reason for PVT. However, the value of postoperative platelet elevation rate (PPER) in predicting PVT has never been studied.

To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.

We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018, and they were randomized into either a training (n = 338) or a validation (n = 145) cohort. The generalized linear (GL) method, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were used to construct models. The receiver operating characteristic curves (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the robustness and clinical practicability of the GL model (GLM), LASSO model (LSM), and RF model (RFM).

Multivariate analysis exhibited that the first and third days for PPER (PPER1, PPER3) were strongly associated with PVT [odds ratio (OR): 1.78, 95% confidence interval (CI): 1.24-2.62, P = 0.002; OR: 1.43, 95%CI: 1.16-1.77, P < 0.001, respectively]. The areas under the ROC curves of the GLM, LSM, and RFM in the training cohort were 0.83 (95%CI: 0.79-0.88), 0.84 (95%CI: 0.79-0.88), and 0.84 (95%CI: 0.79-0.88), respectively; and were 0.77 (95%CI: 0.69-0.85), 0.83 (95%CI: 0.76-0.90), and 0.78 (95%CI: 0.70-0.85) in the validation cohort, respectively. The calibration curves showed satisfactory agreement between prediction by models and actual observation. DCA and CIC indicated that all models conferred high clinical net benefits.

PPER1 and PPER3 are effective indicators for postoperative prediction of PVT. We have successfully developed PPER-based practical models to accurately predict PVT, which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT, and thus guide the adoption of timely interventions.

• Portal hypertension
• Splenectomy
• Portal vein thrombosis
• Postoperative platelet elevation rate
• Practical model
• Machine learning

• Humans
• Hypertension, Portal/diagnosis
• Hypertension, Portal/etiology
• Hypertension, Portal/surgery
• Liver Cirrhosis/pathology
• Machine Learning
• Portal Vein/diagnostic imaging
• Portal Vein/pathology
• Portal Vein/surgery
• Retrospective Studies
• Risk Factors
• Splenectomy/adverse effects
• Splenectomy/methods
• Venous Thrombosis/complications
• Venous Thrombosis/etiology

Venous thromboembolism (VTE) is a fatal disease and has become a burden on the global health system. Recent studies have suggested that artificial intelligence (AI) could be used to make a diagnosis and predict venous thrombosis more accurately. Thus, we performed a meta-analysis to better evaluate the performance of AI in the prediction and diagnosis of venous thrombosis. PubMed, Web of Science, and EMBASE were used to identify relevant studies. Of the 741 studies, 12 met the inclusion criteria and were included in the meta-analysis. Among them, 5 studies included a training set and test set, and 7 studies included only a training set. In the training set, the pooled sensitivity was 0.87 (95% CI 0.79-0.92), the pooled specificity was 0.95 (95% CI 0.89-0.97), and the area under the summary receiver operating characteristic (SROC) curve was 0.97 (95% CI 0.95-0.98). In the test set, the pooled sensitivity was 0.87 (95% CI 0.74-0.93), the pooled specificity was 0.96 (95% CI 0.79-0.99), and the area under the SROC curve was 0.98 (95% CI 0.97-0.99). The combined results remained significant in the subgroup analyzes, which included venous thrombosis type, AI type, model type (diagnosis/prediction), and whether the period was perioperative. In conclusion, AI may aid in the diagnosis and prediction of venous thrombosis, demonstrating high sensitivity, specificity and area under the SROC curve values. Thus, AI has important clinical value.

• artificial intelligence
• diagnosis
• prediction
• venous thrombosis

N⁶-methyladenosine (m⁶A) plays an important role in various biological processes. Identifying m⁶A site is a key step in exploring its biological functions. One of the biggest challenges in identifying m⁶A sites is how to extract features comprising rich categorical information to distinguish m⁶A and non-m⁶A sites. To address this challenge, we propose bidirectional dinucleotide and trinucleotide position-specific propensities, respectively, in this paper. Based on this, we propose two feature-encoding algorithms: Position-Specific Propensities and Pointwise Mutual Information (PSP-PMI) and Position-Specific Propensities and Pointwise Joint Mutual Information (PSP-PJMI). PSP-PMI is based on the bidirectional dinucleotide propensity and the pointwise mutual information, while PSP-PJMI is based on the bidirectional trinucleotide position-specific propensity and the proposed pointwise joint mutual information in this paper. We introduce parameters α and β in PSP-PMI and PSP-PJMI, respectively, to represent the distance from the nucleotide to its forward or backward adjacent nucleotide or dinucleotide, so as to extract features containing local and global classification information. Finally, we propose the M6A-BiNP predictor based on PSP-PMI or PSP-PJMI and SVM classifier. The 10-fold cross-validation experimental results on the benchmark datasets of non-single-base resolution and single-base resolution demonstrate that PSP-PMI and PSP-PJMI can extract features with strong capabilities to identify m⁶A and non-m⁶A sites. The M6A-BiNP predictor based on our proposed feature encoding algorithm PSP-PJMI is better than the state-of-the-art predictors, and it is so far the best model to identify m⁶A and non-m⁶A sites.

• N6-methyladenosine (m6A)
• feature representation
• nucleotide position-specific propensities
• pointwise joint mutual information
• predictive model

• Algorithms
• Artificial Intelligence
• Biomarkers/blood
• Child
• Computational Biology
• Data Mining
• Databases, Factual
• Deep Learning
• Diagnosis, Computer-Assisted
• Humans
• Ileus/diagnosis
• Ileus/surgery
• Intestinal Obstruction/blood
• Intestinal Obstruction/diagnosis
• Intestinal Obstruction/surgery
• Retrospective Studies

• Endoscopic therapy
• Idiopathic non-cirrhotic portal hypertension
• Splenectomy plus esophagogastric devascularization
• Survival
• Transjugular intrahepatic portosystemic shunt