Quantitative electroencephalography in predicting on outcome of awakening in long-term unconscious patients after severe traumatic brain injury

Objective To explore quantitative electroencephalography in unconscious patients after severe traumatic brain injury (TBI) to predict awakening. Methods All cases were divided into two groups(the awake group 19 cases and the unfavourable prognosis group 22 cases).Two weeks after admission the original EEGs were preformed in 41 patients suffering from severe TBI with duration of disturbance of

Development and Validation of a New Prognostic Model for Predicting Survival Outcomes in Patients with Acute-onchronic Liver Failure

Background and Aims:Early determination of prognosis in patients with acute-on-chronic liver failure(ACLF)is crucial for optimizing treatment options and liver allocation.This study aimed to identify risk factors associated with ACLF and to develop new prognostic models that accurately predict patient outcomes.Methods:We retrospectively selected 1,952 hospitalized patients diagnosed with ACLF between January 2010 and June 2018.This cohort was used to develop new prognostic scores,which were subsequently validated in external groups.Results:The study included 1,386 ACLF patients and identified six independent predictors of 28-day mortality through multivariate analysis(all p<0.05).The new score,based on a multivariate regression model,demonstrated superior predictive accuracy for both 28-day and 90-day mortalities,with Areas under the ROC curves of 0.863 and 0.853,respectively(all p<0.05).This score can be used to stratify the risk of mortality among ACLF patients with ACLF,showing a significant difference in survival between patients categorized by the cut-off value(log-rank(Mantel-Cox)χ^(2)=487.574 and 606.441,p=0.000).Additionally,the new model exhibited good robustness in two external cohorts.Conclusions:This study presents a refined prognostic model,the Model for end-stage liver disease-complication score,which accurately predicts short-term mortality in ACLF patients.This model offers a new perspective and tool for improved clinical decision-making and short-term prognostic assessment in ACLF patients.

Circulating tumor DNA as prognostic markers of relapsed breast cancer:a systematic review and meta-analysis

Objective: Circulating tumor DNA (ctDNA) is increasingly being used as a potential prognosis biomarker in patients of breast cancer. This review aims to assess the clinical value of ctDNA in outcome prediction in breast cancer patients throughout the whole treatment cycle. Methods: PubMed, Web of Science, Embase, Cochrane Library, Scopus, and clinical trials.gov were searched from January 2016 to May 2022. Conference abstracts published in last three years were also included. The following search terms were used: ctDNA OR circulating tumor DNA AND breast cancer OR breast carcinoma. Only studies written in English languages were included. The following pre-specified criteria should be met for inclusion: (1) observational studies (prospective or retrospective), randomized control trials, case-control studies and case series studies;(2) patients with breast cancer;(3) ctDNA measurement;(4) clinical outcome data such as objective response rate (ORR), pathological complete response (pCR), relapse-free survival (RFS), overall survival (OS), and so on. The random-effect model was preferred considering the potential heterogeneity across studies. The primary outcomes included postoperative short-term outcomes (ORR and pCR) and postoperative long-term outcomes (RFS, OS, and relapse). Secondary outcomes focused on ctDNA detection rate. Results: A total of 30 studies, comprising of 19 cohort studies, 2 case-control studies and 9 case series studies were included. The baseline ctDNA was significantly negatively associated with ORR outcome (Relative Risk [RR] = 0.65, 95% confidence interval [CI]: 0.50–0.83), with lower ORR in the ctDNA-positive group than ctDNAnegative group. ctDNA during neoadjuvant therapy (NAT) treatment was significantly associated with pCR outcomes (Odds Ratio [OR] = 0.15, 95% CI: 0.04–0.54). The strong association between ctDNA and RFS or relapse outcome was significant across the whole treatment period, especially after the surgery (RFS: Hazard Ratio [HR] = 6.74, 95% CI: 3.73–12.17;relapse outcome: RR = 7.11, 95% CI: 3.05–16.53), although there was heterogeneity in these results. Pre-operative and post-operative ctDNA measurements were significantly associated with OS outcomes (pre-operative: HR = 2.03, 95% CI: 1.12–3.70;post-operative: HR = 6.03, 95% CI: 1.31–27.78). Conclusions: In this review, ctDNA measurements at different timepoints are correlated with evaluation indexes at different periods after treatment. The ctDNA can be used as an early potential postoperative prognosis biomarker in breast cancer, and also as a reference index to evaluate the therapeutic effect at different stages.

Comments on:Perioperative von Willebrand factor dynamics are associated with liver regeneration and predict outcome after liver resection

Recentlythearticle"PerioperativevonWillebrandfactordynamics are associated with liver regeneration and predict outcome afterliver resection" was published in Hepatology[1].Prof.Starlinger et al. aimed to assess the association of von Willebrand factor (vWF) levels and clinical outcome in patients with liver cancers post-liverresection(LR).Basedonthemechanismthatplatelets accumulation in the liver may promote liver regeneration after partial LR in mice, they found the vWF-dependent pattern of platelets accumulationduringliverregenerationinpatientsaftersurgery.

Multi-outcome predictive modelling of anesthesia patients

Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures.Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures.This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.

Value of colonoscopy for prediction of prognosis in patients with ulcerative colitis

Ulcerative colitis (UC) is a chronic inflammatory bowel disorder characterized by exacerbations and remissions. Some UC patients remain refractory to conventional medical treatment while, in others, the effectiveness of drugs is limited by side-effects. Recently, cyclosporine and leukocyte removal therapy have been used for refractory UC patients. To predict the efficacy of these therapies is important for appropriate selection of treatment options and for preparation for colectomy. Endoscopy is the cornerstone for diagnosis and evaluation of UC. Endoscopic parameters in patients with severe or refractory UC may predict a clinical response to therapies, such as cyclosporine or leukocyte removal therapy. As for the patients with quiescent UC, relapse of UC is difficult to predict by routine colonoscopy. Even when routine colonoscopy suggests remission and a normal mucosal appearance, microscopic abnormalities may persist and relapse may occur later. To more accurately identify disease activity and to predict exacerbations in UC patients with clinically inactive disease is important for deciding whether medical treatment should be maintained. Magnifying colonoscopy is useful for the evaluation of disease activity and for predicting relapse in patients with UC.

Development and Validation of a Nomogram for Predicting the Probability of Live Birth in Infertile Women

Objective:To develop a nomogram to predict the probability of live birth on the basis of the association of patient characteristics in subfertile individuals or couples.Methods:A retrospective study was conducted from January 2014 to December 2015.A nomogram was built from a training cohort and tested on an independent validation cohort.A total of 2,257 patients who had undergone their first nondonor cycle of in vitro fertilization(IVF)(including intracytoplasmic sperm injection)were randomly split 2:1 into training(n=1,527)and validation(n=730)cohorts.Results:There were no statistically significant differences in the patients’baseline and cycle characteristics between the training and validation cohorts.On multiple logistic regression analysis,female age,antral follicle count,tubal factor,anovulation,ethnicity,unexplained fertility,and male factor were significantly associated with live birth.The nomogram had a C-index of 0.700(95%confidence interval[CI]:0.698-0.701)in the training cohort and 0.684(95%CI:0.681-0.687)in the validation cohort.Conclusions:Our nomogram can predict the probability of live birth for infertile women and can be used to guide clinicians and couples to decide on an IVF treatment option.

Machine Learning for Brain Imaging Genomics Methods:A Review

In the past decade,multimodal neuroimaging and genomic techniques have been increasingly developed.As an interdiscip-linary topic,brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotyp-ic measures derived from structural and functional brain imaging.This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans.It is well known that machine learn-ing is a powerful tool in the data-driven association studies,which can fully utilize priori knowledge(intercorrelated structure informa-tion among imaging and genetic data)for association modelling.In addition,the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is ex-plored.In this paper,the related background and fundamental work in imaging genomics are first reviewed.Then,we show the univari-ate learning approaches for association analysis,summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning,and present methods for joint association analysis and outcome prediction.Finally,this paper discusses some prospects for future work.

Artificial intelligence in radiotherapy:a technological review

Radiation therapy(RT)is widely used to treat cancer.Technological advances in RT have occurred in the past 30 years.These advances,such as three-dimensional image guidance,intensity modulation,and robotics,created challenges and opportunities for the next breakthrough,in which artificial intelligence(AI)will possibly play important roles.AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others,particularly those with increased complexity because of technological advances.The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society.Furthermore,AI may provide new functionalities that facilitate satisfactory RT.The functionalities include superior images for real-time intervention and adaptive and personalized RT.AI may effectively synthesize and analyze big data for such purposes.This review describes the RT workflow and identifies areas,including imaging,treatment planning,quality assurance,and outcome prediction,that benefit from AI.This review primarily focuses on deep-learning techniques,although conventional machine-learning techniques are also mentioned.

Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of education.This study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic year.We have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these outcomes.Our goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures promptly.Experimental results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.