Functional and psychosocial impact of COVID-19 pandemic on rheumatic patients’ quality of life in Saudi Arabia

Quality of Life Research - The COVID-19 pandemic might add to the stressors experienced by people living with rheumatic diseases. This study aimed to examine rheumatic patients’ functional...     

Treatment of multiple adjacent RT 1 gingival recessions with the modified coronally advanced tunnel (MCAT) technique and a collagen matrix or palatal connective tissue graft: 9-year results of a split-mouth randomized clinical trial

Clinical Oral Investigations - To evaluate t he long-term outcomes following treatment of RT 1 multiple adjacent gingival recessions (MAGR) using the modified coronally advanced tunnel (MCAT) with...     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Emerging therapeutic options in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic disease that requires chronic treatment throughout the evolution of the disease, with a complex physiopathology that entails great challenges for the development of new and specific treatments for ulcerative colitis and Crohn´s disease. The anti-tumor necrosis factor alpha therapy has impacted the clinical course of IBD in those patients who do not respond to conventional treatment, so there is a need to develop new therapies and markers of treatment response. Various pathways involved in the development of the disease are known and the new therapies have focused on blocking the inflammatory process at the gastrointestinal level by oral, intravenous, subcutaneous, and topical route. All these new therapies can lead to more personalized treatments with higher success rates and fewer relapses. These treatments have not only focused on clinical remission, but also on achieving macroscopic changes at the endoscopic level and microscopic changes by achieving mucosal healing. These treatments are mainly based on modifying signaling pathways, by blocking receptors or ligands, reducing cell migration and maintaining the integrity of the epithelial barrier. Therefore, this review presents the efficacy and safety of the new treatments that are currently under study and the advances that have been made in this area in recent years.