Transposon insertion in Rothia dentocariosa.

Objectives: Rothia spp. are emerging as significant bacteria associated with oral health, with Rothia dentocariosa being one of the most prevalent species. However, there is a lack of studies examining these properties at the genetic level.

Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus.

Background: Evaluation of new treatment policies is often costly and challenging in complex conditions, such as hepatitis C virus (HCV) treatment, or in limited-resource settings. We sought to identify hypothetical policies for HCV treatment that could best balance the prevention of cirrhosis while preserving resources (financial or otherwise).

Methods: The cohort consisted of 3792 HCV-infected patients without a history of cirrhosis or hepatocellular carcinoma at baseline from the national Veterans Health Administration from 2015 to 2019.

Adapted time-varying covariates Cox model for predicting future cirrhosis development performs well in a large hepatitis C cohort.

Background: Patients with hepatitis C virus (HCV) frequently remain at risk for cirrhosis after sustained virologic response (SVR). Existing cirrhosis predictive models for HCV do not account for dynamic antiviral treatment status and are limited by fixed laboratory covariates and short follow up time. Advanced fibrosis assessment modalities, such as transient elastography, remain inaccessible in many settings.

Assessing Clinical Disease Recurrence Using Laboratory Data in Surgically Resected Patients From the TOPPIC Trial.

Background: Machine learning methodologies play an important role in predicting progression of disease or responses to medical therapy. We previously derived and validated a machine learning algorithm to predict response to thiopurines in an inflammatory bowel disease population. We aimed to apply a modified algorithm to predict postsurgical treatment response using clinical trial data.

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

Importance: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission.

Objective: To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment.

Machine learning models to predict disease progression among veterans with hepatitis C virus.

Background: Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data.

Predicting Corticosteroid-Free Biologic Remission with Vedolizumab in Crohn's Disease.

Background And Aims: Vedolizumab (VDZ) is effective for Crohn's disease (CD) but costly and is slow to produce remission. Early knowledge of whether vedolizumab is likely to succeed is valuable for physicians, patients, and insurers.

Methods: Phase 3 clinical trial data on VZD for CD were used to predict outcomes.

Predicting Hospitalization and Outpatient Corticosteroid Use in Inflammatory Bowel Disease Patients Using Machine Learning.

Background: Inflammatory bowel disease (IBD) is a chronic disease characterized by unpredictable episodes of flares and periods of remission. Tools that accurately predict disease course would substantially aid therapeutic decision-making. This study aims to construct a model that accurately predicts the combined end point of outpatient corticosteroid use and hospitalizations as a surrogate for IBD flare.

Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

Objective: Assessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort.

Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines.

Background And Aims: Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease [IBD] has proved difficult. Current methods using 6-thioguanine nucleotide [6-TGN] metabolites have failed in randomized controlled trials [RCTs], and have not been used to predict objective remission [OR].

Handheld multispectral fluorescence lifetime imaging system for in vivo applications.

There is an increasing interest in the application of fluorescence lifetime imaging (FLIM) for medical diagnosis. Central to the clinical translation of FLIM technology is the development of compact and high-speed clinically compatible systems. We present a handheld probe design consisting of a small maneuverable box fitted with a rigid endoscope, capable of continuous lifetime imaging at multiple emission bands simultaneously.