The spectrum of cardiorenal and metabolic diseases comprises many disorders, including obesity, type 2 diabetes (T2D), chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), dyslipidemias, hypertension, and associated comorbidities such as pulmonary diseases and metabolism dysfunction-associated steatotic liver disease and metabolism dysfunction-associated steatohepatitis (MASLD and MASH, respectively, formerly known as nonalcoholic fatty liver disease and nonalcoholic steatohepatitis [NAFLD and NASH]). Because cardiorenal and metabolic diseases share pathophysiologic pathways, two or more are often present in the same individual. Findings from recent outcome trials have demonstrated benefits of various treatments across a range of conditions, suggesting a need for practice recommendations that will guide clinicians to better manage complex conditions involving diabetes, cardiorenal, and/or metabolic (DCRM) diseases. To meet this need, we formed an international volunteer task force comprising leading cardiologists, nephrologists, endocrinologists, and primary care physicians to develop the DCRM 2.0 Practice Recommendations, an updated and expanded revision of a previously published multispecialty consensus on the comprehensive management of persons living with DCRM. The recommendations are presented as 22 separate graphics covering the essentials of management to improve general health, control cardiorenal risk factors, and manage cardiorenal and metabolic comorbidities, leading to improved patient outcomes.
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- diabetes mellitus 2. typu terapie MeSH
- lidé MeSH
- metabolické nemoci * terapie MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- směrnice pro lékařskou praxi MeSH
BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.
OBJECTIVE: A survey among medical students of all medical schools in the Czech Republic was conducted to investigate attitudes and views of psychiatry and career choice of psychiatry. METHODS: A Czech version of the Attitudes to Psychiatry Scale (APS) and a questionnaire surveying demographic characteristics and choices of future specialty were distributed to all medical students of eight medical schools in the Czech Republic via the schools' internal communication systems in the form of an anonymous online questionnaire. RESULTS: Out of a total of 10,147 medical students in the Czech Republic (academic year 2019/2020), 2418 students participated in the survey (response rate 23.8%). Psychiatry as a non-exclusive career choice was considered by 31.3% respondents; child and adolescent psychiatry was considered by 15.4% respondents. Psychiatry as the only choice was considered by 1.6%, and child and adolescent psychiatry was not considered at all. The interest in both specialties was declining since the first year of study. The status of psychiatry among other medical specialties was perceived as low; students were rather discouraged from entering psychiatry by their families. They did not feel encouraged by their teachers to pursue career in psychiatry despite the fact that they were interested in psychiatry. They also felt uncomfortable with patients with mental illness. CONCLUSIONS: Despite high enthusiasm for psychiatry in the first year of medical school, only a small proportion of medical students consider to choose psychiatry, and especially child and adolescent psychiatry, as a career at the end of medical school.
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- dítě MeSH
- lidé MeSH
- mladiství MeSH
- postoj zdravotnického personálu MeSH
- postoj MeSH
- průzkumy a dotazníky MeSH
- psychiatrie * MeSH
- studenti lékařství * MeSH
- volba povolání MeSH
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- dítě MeSH
- lidé MeSH
- mladiství MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
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- kurikulum MeSH
- kurzy a stáže v nemocnici metody MeSH
- lidé MeSH
- paliativní péče * MeSH
- péče o umírající MeSH
- studium lékařství pregraduální metody MeSH
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- lidé MeSH
- Publikační typ
- úvodníky MeSH
