Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery

BackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation.ResultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p < 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.ConclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.

Broad-spectrum mono- and combinational drug therapies for global viral pandemic preparedness

Broadly effective antiviral therapies must be developed to be ready for clinical trials, which should begin soon after the emergence of new life-threatening viruses. Here, we pave the way towards this goal by analyzing conserved druggable virus-host interactions, mechanisms of action and immunomodulatory properties of broad-spectrum antivirals (BSAs), routes of BSA delivery, and BSA interactions with other antivirals. Based on the analysis we developed scoring systems, which allowed us to predict novel BSAs and BSA-containing drug combinations (BCCs). Thus, we have developed a new strategy to broaden the spectrum of BSA indications and predict novel mono- and combinational therapies that can help better prepare for imminent future viral outbreaks.

The Role of Advanced Imaging in Gout Management

Gout is a common form of inflammatory arthritis where urate crystals deposit in joints and surrounding tissues. With the high prevalence of gout, the standardized and effective treatment of gout is very important, but the long-term treatment effect of gout is not satisfied because of the poor adherence in patients to the medicines. Recently, advanced imaging modalities, including ultrasonography (US), dual-energy computed tomography (DECT), and magnetic resonance imaging (MRI), attracted more and more attention for their role on gout as intuitive and non-invasive tools for early gout diagnosis and evaluation of therapeutic effect. This review summarized the role of US, DECT, and MRI in the management of gout from four perspectives: hyperuricemia, gout attacks, chronic gout, and gout complications described the scoring systems currently used to quantify disease severity and discussed the challenges and limitations of using these imaging tools to assess response to the gout treatment.

MAFLD/NAFLD Biopsy-Free Scoring Systems for Hepatic Steatosis, NASH, and Fibrosis Diagnosis

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as nonalcoholic fatty liver disease, is the most prevalent liver disorder worldwide. Historically, its diagnosis required biopsy, even though the procedure has a variable degree of error. Therefore, new non-invasive strategies are needed. Consequently, this article presents a thorough review of biopsy-free scoring systems proposed for the diagnosis of MAFLD. Similarly, it compares the severity of the disease, ranging from hepatic steatosis (HS) and nonalcoholic steatohepatitis (NASH) to fibrosis, by contrasting the corresponding serum markers, clinical associations, and performance metrics of these biopsy-free scoring systems. In this regard, defining MAFLD in conjunction with non-invasive tests can accurately identify patients with fatty liver at risk of fibrosis and its complications. Nonetheless, several biopsy-free scoring systems have been assessed only in certain cohorts; thus, further validation studies in different populations are required, with adjustment for variables, such as body mass index (BMI), clinical settings, concomitant diseases, and ethnic backgrounds. Hence, comprehensive studies on the effects of age, morbid obesity, and prevalence of MAFLD and advanced fibrosis in the target population are required. Nevertheless, the current clinical practice is urged to incorporate biopsy-free scoring systems that demonstrate adequate performance metrics for the accurate detection of patients with MAFLD and underlying conditions or those with contraindications of biopsy.

The use of a tablet-based app for investigating the influence of autistic and ADHD traits on performance in a complex drawing task

This paper describes a smart tablet-based drawing app to digitally record participants’ engagement with the Rey-Osterrieth complex figure (ROCF) task, a well-characterised perceptual memory task that assesses local and global memory. Digitisation of the tasks allows for improved ecological validity, especially in children attracted to tablet devices. Further, digital translation of the tasks affords new measures, including accuracy and computation of the fine motor control kinematics employed to carry out the drawing Here, we report a feasibility study to test the relationship between two neurodevelopmental conditions: autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). The smart tablet app was employed with 39 adult participants (18-35) characterised for autistic and ADHD traits, and scored using the ROCF perceptual and organisational scoring systems. Trait scores and conditions were predictor variables in linear regression models. Positive correlations were found between the attention-to-detail, attention-switching and communication subscales of the autistic trait questionnaire and organisational scores on the ROCF task. These findings suggest that autistic traits might be linked to differential performance on the ROCF task. Novelty and future applications of the app are discussed.

Review: Colon Capsule Endoscopy in Inflammatory Bowel Disease

The COVID-19 pandemic has caused considerable disruption in healthcare services and has had a substantial impact on the care of patients with chronic diseases, such as inflammatory bowel disease. Endoscopy services were significantly restricted, resulting in long waiting lists. There has been a growing interest in the use of capsule endoscopy in the diagnostic pathway and management of these patients. This review explores the published literature on the role of colon capsule endoscopy in ulcerative colitis and Crohn’s disease as a method for mucosal assessment of extent, severity, and response to treatment. Colon capsule preparation regimens and scoring systems are reported. The studies indicate that, despite inherent limitations of minimally invasive capsule endoscopy, there is increasing evidence to support the use of the second-generation colon capsule in inflammatory bowel disease evaluation, providing an additional pathway to expedite investigation of appropriate patients especially during and after the pandemic.