Consensus on thromboprophylaxis in gynecological surgery

Update of the PANCCO clinical practice guidelines for the treatment of ulcerative colitis in the adult population

Ulcerative colitis (US) is a chronic disease of unknown etiology. It is incurable and its clinical course is intermittent, characterized by periods of remission and relapse. The prevalence and incidence of the disease has been increasing worldwide. The update presented herein includes the participation of healthcare professionals, decision-makers, and a representative of the patients, all of whom declared their conflicts of interest. Answerable clinical questions were formulated, and the outcomes were graded. The information search was conducted on the Medline/PubMed, Embase, Epistemonikos, and LILACS databases, and covered grey literature sources, as well. The search was updated on November 30, 2020, with no restrictions regarding date or language. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) classification system was implemented to establish the strength of the recommendation and quality of evidence. A formal consensus was developed, based on the RAND/UCLA methodology and the document was peer reviewed. The short version of the Clinical Practice Guidelines for the Treatment of Ulcerative Colitis in the Adult Population is presented herein, together with the supporting evidence and respective recommendations. In mild-to-moderate UC, budesonide MMX is an option when treatment with 5-ASA fails, and before using systemic steroids. In moderate-to-severe UC, infliximab, adalimumab, vedolizumab, ustekinumab, and tofacitinib can be used as first-line therapy. If there is anti-TNF therapy failure, ustekinumab and tofacitinib provide the best results. In patients with antibiotic-refractory pouchitis, anti-TNFs are the treatment of choice.

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.