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Biologic Therapy Is Associated With Improved Oncologic Outcomes in Crohn's Disease-Associated Colorectal Cancer.
Dis Colon Rectum
February 2025
Department of Colorectal Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Ohio.
Background: Patients with Crohn's disease face an elevated risk of colorectal cancer, in part due to underlying chronic inflammation. Biologic therapy is the mainstay of medical treatment; however, the impact of treatment on colorectal cancer-related outcomes remains unclear.
Objective: To investigate the association between prior exposure to biologic treatment and colorectal cancer-related outcomes in patients with underlying Crohn's disease.
Incidence and Risk Factors for Nonreversal of Stoma in Patients With Crohn's Disease: A Single-Center Study.
Dis Colon Rectum
February 2025
Department of General Surgery, Jinling Medical School of Nanjing Medical University, Nanjing, China.
Background: Even in the biological era, permanent stoma is not uncommon in patients with Crohn's Disease.
Objective: This study aimed to investigate the incidence and risk factors of permanent stoma in Crohn's disease patients and provide clinical evidence for reducing this disabling outcome.
Design: Consecutive patients with Crohn's disease who underwent ostomies in the past decade were reviewed.
Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.
PLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFMultilevel gene expression changes in lineages containing adaptive copy number variants.
Mol Biol Evol
January 2025
Center for Genomics and Systems Biology, Department of Biology, New York University.
Copy-number variants (CNVs) are an important class of genetic variation that can mediate rapid adaptive evolution. Whereas CNVs can increase the relative fitness of the organism, they can also incur a cost due to the associated increased gene expression and repetitive DNA. We previously evolved populations of Saccharomyces cerevisiae over hundreds of generations in glutamine-limited (Gln-) chemostats and observed the recurrent evolution of CNVs at the GAP1 locus.
View Article and Find Full Text PDFImaging Findings, Complications, and Mimics after Common and Advanced Dental Procedures.
Radiographics
February 2025
From the Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba 271-8587, Japan (K.I., K.O., T.K.); Department of Diagnostic Radiology, National Cancer Center Hospital East, Chiba, Japan (H.K.); Department of Radiology, VA Boston Health Care System, Boston, Mass (V.C.A.A.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (O.S.).
Various new dental treatment methods have been introduced in dental clinics, and many new materials have been used in recent years for dental treatments. Dentistry is divided into several specialties, each offering unique treatments, such as endodontics, implantology, oral surgery, and orthodontics. CT and MR images after dental treatment reveal a variety of hard- and soft-tissue changes and dental materials, which often cause image artifacts.
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