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Breast radiologists and nuclear medicine specialists have updated their previous recommendation/guidance at the 4th Hungarian Breast Cancer Consensus Conference. They suggest to adopt this actual protocol for the screening, diagnostics and treatment of breast tumors from now on. This recommendation includes the description of the newest technologies, the recent results of scientific research, as well as the role of imaging methods in the therapeutic processes and the followup. Suggestions for improvement of the current Hungarian practice and other related issues as forensic medicine, media connections, regulations, and reimbursement are also detailed. The guidance has been in agreement with the related medical disciplines.Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. Closed intervention sets also allow for a pruning strategy that is strictly more efficient than the traditional pruning strategy used by the Apriori algorithm. To implement our ideas, we introduce and compare five methods of estimating causal effect from observational data and rigorously evaluate them on synthetic data to mathematically prove (when possible) why they work. ML198 We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of 152000 patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).Reducing rates of early hospital readmission has been recognized and identified as a key to improve quality of care and reduce costs. There are a number of risk factors that have been hypothesized to be important for understanding re-admission risk, including such factors as problems with substance abuse, ability to maintain work, relations with family. In this work, we develop RoBERTa-based models to predict the sentiment of sentences describing readmission risk factors in discharge summaries of patients with psychosis. We improve substantially on previous results by a scheme that shares information across risk factors while also allowing the model to learn risk factor-specific information.Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools.A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy. Time-lapse microscopy provides clinicians with a wealth of information for selecting embryos. However, the resulting movies of embryos are currently analyzed manually, which is time consuming and subjective. Here, we automate feature extraction of time-lapse microscopy of human embryos with a machine-learning pipeline of five convolutional neural networks (CNNs). Our pipeline consists of (1) semantic segmentation of the regions of the embryo, (2) regression predictions of fragment severity, (3) classification of the developmental stage, and object instance segmentation of (4) cells and (5) pronuclei. Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.Millions of videos are ubiquitously generated and shared everyday. Releasing videos would be greatly beneficial to social interactions and the community but may result in severe privacy concerns. To the best of our knowledge, most of the existing privacy preserving techniques for video data focus on detecting and blurring the sensitive regions in the video. Such simple privacy models have two major limitations (1) they cannot quantify and bound the privacy risks, and (2) they cannot address the inferences drawn from the background knowledge on the involved objects in the videos. In this paper, we first define a novel privacy notion ϵ-Object Indistinguishability for all the predefined sensitive objects (e.g., humans and vehicles) in the video, and then propose a video sanitization technique VERRO that randomly generates utility-driven synthetic videos with indistinguishable objects. Therefore, all the objects can be well protected in the generated utility-driven synthetic videos which can be disclosed to any untrusted video recipient. We have conducted extensive experiments on three real videos captured for pedestrians on the streets. The experimental results demonstrate that the generated synthetic videos lie close to the original video for retaining good utility while ensuring rigorous privacy guarantee.Many processes in chemistry and biology involve interactions of a ligand with its molecular target. Interest in the mechanism governing such interactions has dominated theoretical and experimental analysis for over a century. The interpretation of molecular recognition has evolved from a simple rigid body association of the ligand with its target to appreciation of the key role played by conformational transitions. Two conceptually distinct descriptions have had a profound impact on our understanding of mechanisms of ligand binding. The first description, referred to as induced fit, assumes that conformational changes follow the initial binding step to optimize the complex between the ligand and its target. The second description, referred to as conformational selection, assumes that the free target exists in multiple conformations in equilibrium and that the ligand selects the optimal one for binding. Both descriptions can be merged into more complex reaction schemes that better describe the functional repertoire of macromolecular systems. This review deals with basic mechanisms of ligand binding, with special emphasis on induced fit, conformational selection, and their mathematical foundations to provide rigorous context for the analysis and interpretation of experimental data. We show that conformational selection is a surprisingly versatile mechanism that includes induced fit as a mathematical special case and even captures kinetic properties of more complex reaction schemes. These features make conformational selection a dominant mechanism of molecular recognition in biology, consistent with the rich conformational landscape accessible to biological macromolecules being unraveled by structural biology.[This corrects the article on p. 431 in vol. 47, PMID 31828239.].Lateral epicondylitis (LE) is a degenerative disease of the tendons, spurred by repetitive microtrauma leading to an attempt by the body to heal by upregulating local angiogenesis and fibroblast proliferation. Prolotherapy (PT) is the injection of dextrose around the injured tissues to stimulate their spontaneous regeneration. Herein, we have described a case of lateral epicondylitis, diagnosed with clinical and ultrasound (US) examination, where local steroid injections provided relief only for a limited time. We treated the patient with US-guided PT, following which the pain disappeared and the tendon was restored.An 84-year-old woman, who had been admitted to the emergency department (ED) several times because of dyspnoea, was treated for acute exacerbation of chronic respiratory failure without satisfactory clinical improvement. According to her medical history, 8 years earlier, she underwent a complicated cardiosurgical procedure that required tracheostomy and mechanical ventilation in the post-operative period for 45 days. Traditional X-Ray did not show any abnormal findings; however, high resolution thorax computed tomography (HRCT) scan revealed a severe tracheal stenosis, which was confirmed with bronchoscopy, and required immediate tracheostomy. Tracheal stenosis is a rare but severe complication that should be suspected when a patient with previous tracheostomy presents to the ED with dyspnoea even if tracheostomy had been closed many years before, because adaptive mechanism results in asymptomatic life for a long period.Placement of an epidural blood patch is the gold standard treatment for a postdural puncture headache when conservative measures have failed. If unsuccessful in relieving the symptoms, a second epidural blood patch may be warranted. However, when the accepted gold standard treatment has failed, alternative therapies may be pursued. A pterygopalatine ganglion block has been shown to be effective as an alternative to epidural blood patch placement. This case demonstrates the use of a suprazygomatic pterygopalatine ganglion block as a rescue technique for failed repeated epidural blood patch, with complete and permanent resolution of the headache.

The Helsinki Declaration on Patient Safety in Anaesthesiology is an important document for anaesthesiologists. This study aimed to evaluate the knowledge and experiences of anaesthesiologists in Turkey on the "Helsinki Declaration on Patient Safety."

After the ethics committee approval and participants' consent, electronic questionnaires were sent to anesthetists working in Turkey. The questionnaire included 48 questions.

The mean age of the participants was 44.28±8.01 years, and 52.1% were women (n=142). The mean time spent in the field of anesthesiology was 12.83±7.76 years. The percentage of participants working in private hospitals was 13.4%. A total of 58.5% of the participants were educated on patient safety out of whom 57% said that their knowledge was sufficient, 37.3% said that it was limited, and 5.6% felt that it was insufficient. The knowledge of participants about the Helsinki Declaration was sufficient in 31.7%, limited in 39.4%, insufficient in 9.2%, and 19.7% had no knowledge. A total of 27% of participants believed that implementation of the Helsinki Declaration improved patient safety.