• MeSH
• Algorithms MeSH
• Humans MeSH
• Computer-Assisted Instruction * MeSH
• Education, Medical methods   MeSH
• Decision Support Systems, Clinical * MeSH
• User-Computer Interface * MeSH
• Knowledge MeSH
• Check Tag
• Humans MeSH

• MeSH
• Ethics, Medical * education   MeSH
• Humans MeSH
• Education, Medical methods   MeSH
• Teaching MeSH
• Check Tag
• Humans MeSH

• MeSH
• Child MeSH
• Infant MeSH
• Adolescent MeSH
• Learning Disabilities MeSH
• Check Tag
• Child MeSH
• Infant MeSH
• Adolescent MeSH

• Conspectus
• Psychologie
• NML Fields
• psychologie, klinická psychologie
• pediatrie

Learning processes require individuals to find the courage to engage in challenging activities. While being highly personal, such processes always occur within a relational social system. Rehabilitation programs with adapted physical activity as the main intervention facilitate opportunities for young adults with the experience of living with a disability to explore their capacities and develop activity competence and agency. This study aims to explore the dynamic relationship between personal experiences and the social processes underpinning a learning process within a rehabilitation program based on adapted physical activity in groups. An ethnographic single case study generated in-depth descriptions illuminating personal experiences, as well as revealed insight into socio-cultural structures and interactional processes. Analysis of the single case demonstrated how the rehabilitation context was experienced as safe. However, the context also included sociocultural expectations guiding attention towards performance demands, limiting the sense of personal agency, and increasing the sense of risk. Trusting collaboration processes were essential in forming support and challenges sensitive to individual needs and sense of risk, and for translating activity experiences into personal learning and activity engagement outside the program context.

• MeSH
• Humans MeSH
• Young Adult psychology   MeSH
• Courage MeSH
• Motor Activity * MeSH
• Persons with Disabilities * psychology   rehabilitation   MeSH
• Rehabilitation methods   psychology   MeSH
• Social Interaction MeSH
• Learning MeSH
• Check Tag
• Humans MeSH
• Young Adult psychology   MeSH
• Female MeSH
• Publication type
• Case Reports MeSH

• MeSH
• Early Medical Intervention methods   utilization   MeSH
• Child MeSH
• Speech-Language Pathology * methods   organization & administration   trends   MeSH
• Humans MeSH
• Meta-Analysis as Topic MeSH
• Interdisciplinary Communication MeSH
• Child Behavior Disorders etiology   prevention & control   MeSH
• Personality Disorders prevention & control   therapy   MeSH
• Speech Disorders diagnosis   classification   therapy   MeSH
• Learning Disabilities * diagnosis   etiology   therapy   MeSH
• Child, Preschool MeSH
• Psychotherapy methods   MeSH
• Speech Therapy * methods   trends   MeSH
• Speech Intelligibility classification   MeSH
• Case-Control Studies MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Child, Preschool MeSH

Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.

• MeSH
• Deep Learning * MeSH
• Humans MeSH
• Microglia * MeSH
• Brain pathology   MeSH
• Mice MeSH
• Cell Count methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

This paper describes the interactive tools of the AKUTNE.CZ (part of MEFANET) and SEPSIS-Q portals for Problem Based Learning (PBL) sessions in medical education. The portals aim to be a comprehensive source of information and educational materials, covering all aspects of acute medicine for undergraduate medical students and health professionals. Our focus is mainly on simulation-based tools for teaching and learning algorithms in acute patient care, the backbone of the AKUTNE.CZ and SEPSIS-Q portals. Over the last five years, more than 30 interactive algorithms in the Czech and English languages (http:// www.akutne.eu) have been devel¨oped and published online, allowing users to test and improve their knowledge and skills in the field of acute medicine. Additionally, we have created six SEPSIS-Q interactive scenarios in the Czech version. The peerreviewed algorithms were used for conducting PBL-like sessions in General Medicine (First Aid, Anaesthesiology and Pain Management, Emergency Medicine) and in Nursing (Obstetric Analgesia and Anaesthesia for Midwives, Intensive Care Medicine). The interactive scenarios serve to demonstrate interesting cases, with preference for Intensive Care Medicine sessions in General Medicine and Nursing.

• MeSH
• Algorithms MeSH
• Humans MeSH
• Computer-Assisted Instruction methods   MeSH
• Problem-Based Learning * methods   trends   MeSH
• Sepsis * diagnosis   therapy   MeSH
• Education, Medical, Undergraduate methods   MeSH
• Education, Nursing, Baccalaureate methods   MeSH
• Emergency Medicine methods   instrumentation   education   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient's preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the need of active treatment?, and which attributes of VS development are important in decision making on active treatment? Using a machine-learning analysis of medical records of 93 patients, the objectives were addressed using two classification tasks: a time-independent case-based reasoning (CBR), where each medical record was treated as independent, and a personalized dynamic analysis (PDA), during which we analyzed the individual development of each patient's state in time. Using the CBR method we found that Koos classification of tumor size, speech reception threshold, and pure tone audiometry, collectively predict the need for active treatment with approximately 90% accuracy; in the PDA task, only the increase of Koos classification and VS size were sufficient. Our results indicate that VS treatment may be reliably predicted using only a small set of basic parameters, even without the knowledge of individual development, which may help to simplify VS treatment strategies, reduce the number of examinations, and increase cause effectiveness.

• MeSH
• Adult MeSH
• Clinical Decision-Making * MeSH
• Middle Aged MeSH
• Humans MeSH
• Disease Management * MeSH
• Reproducibility of Results MeSH
• Supervised Machine Learning MeSH
• ROC Curve MeSH
• Decision Trees MeSH
• Aged MeSH
• Hearing MeSH
• Hearing Tests MeSH
• Machine Learning * MeSH
• Symptom Assessment MeSH
• Neuroma, Acoustic diagnosis   therapy   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Současný trend v edukačním procesu, zejména pak na vysokých školách, je mimo jiné charakterizován stále větším využíváním výpočetní techniky. Někdy se jedná o pouhé samostudium studentů, kdy třeba na internetu vyhledávají informace k tématům probíraným ve škole. Jindy jsou však počítače přímo součástí vlastní výuky. Například při rýsování obrázků různého charakteru, v laboratorních cvičeních, při používání specielního softwaru ve cvičeních apod. Kromě toho však existuje i výuka založená přímo na používání počítače. Tím je myšlen E-learning v různých jeho podobách a variantách (např. Blended learning). Při výuce jakožto činnosti učitele stejně tak i při učení se jakožto činnosti studujícího se příliš často zaměřujeme pouze na získávání a osvojování si určitých kompetencí apod., ale zapomínáme, že je třeba se věnovat i samotné práci u počítače, a to v komplexním pohledu. Tedy nejen ovládání softwaru, ale také třeba zdravotní hledisko studia u počítače. Příspěvek se věnuje právě této problematice. Popisuje návrhy, jak studujícího nenásilnou formou přimět k dodržování pracovní hygieny. Příspěvek se primárně zaměřuje na tvorbu E-learningových kurzů, ale mnohé z navržených podnětů lze aplikovat i při využívání počítačů v běžné výuce.

Computational technology has become a current trend in the educational process mainly in higher education. The technology sometimes helps students during their self-study time when they search for additional information concerning the topics discussed in the lesson. In other cases the computers are part of the instruction itself e.g. drawing different kinds of pictures, laboratory experiments, using special software in exercises. There also exists teaching based directly on the use of computer. The method, E-learning, exists in various forms and modifi cations e.g. Blended learning. We often focus on gaining and acquiring of certain skills and so on in teaching, carried out by teacher, as well as in learning, learner’s activity. However, we often forget to perceive the work on computer itself from the complex point of view. That means that not only the skill of work with software, but also health aspects of work on computer are important. This paper deals with the problem and suggests unforced ways leading to observing work hygiene. The paper primarily focuses on creation of E-learning courses; however most of the proposed suggestions are applicable to the use of computers in classic instruction.

• Keywords
• e-learning, opatření,
• MeSH
• Internet MeSH
• Humans MeSH
• Computer-Assisted Instruction * methods   utilization   MeSH
• Students MeSH
• Learning MeSH
• Health MeSH
• Check Tag
• Humans MeSH

Techniky strojového učení jsou metody, které umožní vytvořit z trénovací množiny případů model pro kategorie dat tak, že mohou být nové (neznámé) případy zařazeny do jedné nebo více kategorií schématem odpovídajícím modelu. Pro tento typ analýzy jsou velmi vhodná data ze studií sledujících určitou skupinu osob s opakovaným sběrem dat stejného typu. K vyhledávání znalostí z medicínských dat bylo užito různých algoritmů strojového učení. Bylo testováno několik algoritmů tak, aby bylo možno pokrýt většinu způsobů učení s učitelem. Byly provedeny dva typy pokusů. Jeden hledal vztahy mezi atributy, druhý testoval predikci budoucích příhod. Pro pokusy v tomto sdělení byla užita data z dvacet let trvající longitudinální primárně preventivní studie rizikových faktorů (RF) aterosklerózy u mužů středního věku. Studie se nazývá STULONG (LONGitudinal STUdy). Výsledky ukazují, že některé metody předpovídají některé poruchy lépe než jiné a že je tedy vhodné použít všechny algoritmy najednou a posuzovat spolehlivost výsledku na základě známého trendu každé metody. Algoritmy strojového učení byly také použity k předpovědi příčiny úmrtí. V tomto případě byly výsledky nevalné, pravděpodobně pro malé množství informace ve vstupních položkách v datového souboru.

Machine learning techniques are methods that given a training set of examples infer a model for the categories of the data, so that new (unknown) examples could be assigned to one or more categories by pattern matching within the model. The data from follow-up studies with repeated collection of the same type of data are very suitable for this analysis. Machine learning algorithms belonging to a variety of paradigms have been applied to knowledge discovery on medical data. All the used algorithms belong to the supervised learning paradigm. Several algorithms have been tested, trying to cover most of the kinds of supervised learning. Two kinds of experiments have been carried out. The first is intended to discover associations between attributes. The second kind is intended to test prediction of future disorders. For the experiments in this paper the data used was from the twenty years lasting primary preventive longitudinal study of the risk factors (RF) of atherosclerosis in middle aged men. Study is named STULONG (LONGitudinal STUdy). The results show that some methods predict some disorders better than others, so it is interesting to use all the algorithms at a time and consider the result confidence based upon the known tendency of each method. The machine learning algorithms have been also used in the prediction of death cause, obtaining poor results in this case, maybe due to the small amount of information (entries) of this type in the dataset.

• Keywords
• dobývání znalostí, strojové učení s učitelem, vytěžování z biomedicínských dat, rizikové faktory aterosklerózy,
• MeSH
• Algorithms MeSH
• Atherosclerosis diagnosis   MeSH
• Databases, Factual MeSH
• Financing, Organized MeSH
• Middle Aged MeSH
• Humans MeSH
• Decision Support Techniques MeSH
• Prognosis MeSH
• Risk Factors MeSH
• Decision Support Systems, Clinical MeSH
• Information Storage and Retrieval MeSH
• Knowledge Bases MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH