Anatomical, histomorphological and molecular classification of cholangiocarcinoma

Cholangiocarcinoma constitutes a heterogeneous group of malignancies that can emerge at any point of the biliary tree. Cholangiocarcinoma is classified into intrahepatic, perihilar and distal based on its anatomical location. Histologically, conventional perihilar/distal cholangiocarcinomas are mucin-producing adenocarcinomas or papillary tumours; intrahepatic cholangiocarcinomas are more heterogeneous and can be sub-classified according to the level or size of the displayed bile duct. Cholangiocarcinoma develops through multistep carcinogenesis and is preceded by dysplastic and in situ lesions. Definition and clinical significance of precursor lesions, including biliary intraepithelial neoplasia, intraductal papillary neoplasms of the bile duct, intraductal tubulopapillary neoplasms and mucinous cystic neoplasm, are discussed in this review. A main challenge in diagnosing cholangiocarcinoma is the fact that tumour tissue for histological examination is difficult to obtain. Thus, a major clinical obstacle is the establishment of the correct diagnosis at a tumour stage that is amenable to surgery which still represents the only curable therapeutic option. Current standards, methodology and criteria for diagnosis are discussed. Cholangiocarcinoma represents a heterogeneous tumour with regard to molecular alterations. In intrahepatic subtype, mainly two distinctive morpho-molecular groups can currently be discriminated. Large-duct type intrahepatic cholangiocarcinoma shows a high mutation frequency of oncogenes and tumour suppressor genes, such as KRAS and TP53 while Isocitrate Dehydrogenase 1/2 mutations and Fibroblast Growth Factor Receptor 2-fusions are typically seen in small-duct type tumours. It is most important to ensure the separation of the given anatomical subtypes and to search for distinct subgroups within the subtypes on a molecular and morphological basis

Педагогическая эвристика в структуре личностно ориентированного образования

Цель статьи: провести анализ различных трактовок педагогической эвристики, определить ее задачи, основные структурные элементы и уровни в системе личностно ориентированного образования.This article is devoted to the pedagogical heurist in the personal oriented education that is the analyses of different approaches to the interpretation of pedagogical heurist is done, the tasks, methods and the main structural elements are determined. The peculiarities of theoretical, methodological, individual-practical levels of pedagogical heurist are examined. The functions of personality are determined on every level, principles of government with heuristic activity are revealed. Important conclusions are done

Predicting illness progression for children with lower respiratory infections (LRTI) presenting to primary care

Background Antibiotics are commonly prescribed for children with lower respiratory tract infections (LRTIs), fuelling antibiotic resistance, and there are few prognostic tools available to inform management. Aim To externally validate an existing prognostic model (STARWAVe) to identify children at low risk of illness progression, and if model performance was limited to develop a new internally validated prognostic model. Design and setting Prospective cohort study with a nested trial in a primary care setting. Method Children aged 6 months to 12 years presenting with uncomplicated LRTI were included in the cohort. Children were randomised to receive amoxicillin 50 mg/kg per day for 7 days or placebo, or if not randomised they participated in a parallel observational study to maximise generalisability. Baseline clinical data were used to predict adverse outcome (illness progression requiring hospital assessment). Results A total of 758 children participated (n= 432 trial, n= 326 observational). For predicting illness progression the STARWAVe prognostic model had moderate performance (area under the receiver operating characteristic [AUROC] 0.66, 95% confidence interval [CI] = 0.50 to 0.77), but a new, internally validated model (seven items: baseline severity; respiratory rate; duration of prior illness; oxygen saturation; sputum or a rattly chest; passing urine less often; and diarrhoea) had good discrimination (bootstrapped AUROC 0.83, 95% CI = 0.74 to 0.92) and calibration. A three-item model (respiratory rate; oxygen saturation; and sputum or a rattly chest) also performed well (AUROC 0.81, 95% CI = 0.70 to 0.91), as did a score (ranging from 19 to 102) derived from coefficients of the model (AUROC 0.78, 95% CI = 0.67 to 0.88): a score of &lt;70 classified 89% (n= 600/674) of children having a low risk (&lt;5%) of progression of illness. Conclusion A simple three-item prognostic score could be useful as a tool to identify children with LRTI who are at low risk of an adverse outcome and to guide clinical management.</p

Detection of primary Sjögren's syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning

Background: Primary Sjögren's Syndrome (pSS) is a rare autoimmune disease that is difficult to diagnose due to a variety of clinical presentations, resulting in misdiagnosis and late referral to specialists. To improve early-stage disease recognition, this study aimed to develop an algorithm to identify possible pSS patients in primary care. We built a machine learning algorithm which was based on combined healthcare data as a first step towards a clinical decision support system. Method: Routine healthcare data, consisting of primary care electronic health records (EHRs) data and hospital claims data (HCD), were linked on patient level and consisted of 1411 pSS and 929,179 non-pSS patients. Logistic regression (LR) and random forest (RF) models were used to classify patients using age, gender, diseases and symptoms, prescriptions and GP visits. Results: The LR and RF models had an AUC of 0.82 and 0.84, respectively. Many actual pSS patients were found (sensitivity LR = 72.3%, RF = 70.1%), specificity was 74.0% (LR) and 77.9% (RF) and the negative predictive value was 99.9% for both models. However, most patients classified as pSS patients did not have a diagnosis of pSS in secondary care (positive predictive value LR = 0.4%, RF = 0.5%). Conclusion: This is the first study to use machine learning to classify patients with pSS in primary care using GP EHR data. Our algorithm has the potential to support the early recognition of pSS in primary care and should be validated and optimized in clinical practice. To further enhance the algorithm in detecting pSS in primary care, we suggest it is improved by working with experienced clinicians

Is green space in the living environment associated with people's feelings of social safety?

Abstract. The authors investigate whether the percentage of green space in people's living environ- ment affects their feelings of social safety positively or negatively. More specifically they investigate the extent to which this relationship varies between urban and rural areas, between groups in the community that can be identified as more or less vulnerable, and the extent to which different types of green space exert different influences. The study includes 83736 Dutch citizens who were interviewed about their feelings of social safety. The percentage of green space in the living environment of each respondent was calculated, and data analysed by use of a three-level latent variable model, controlled for individual and environmental background characteristics. The analyses suggest that more green space in people's living environment is associated with enhanced feelings of social safetyöexcept in very strongly urban areas, where enclosed green spaces are associated with reduced feelings of social safety. Contrary to the common image of green space as a dangerous hiding place for criminal activity which causes feelings of insecurity, the results suggest that green space generally enhances feelings of social safety. The results also suggest, however, that green space in the most urban areas is a matter of concern with respect to social safety.

Dutch GP healthcare consumption in COVID-19 heterogeneous regions:An interregional time-series approach in 2020-2021

Background Many countries observed a sharp decline in the use of general practice services after the outbreak of the COVID-19 pandemic. However, research has not yet considered how changes in healthcare consumption varied among regions with the same restrictive measures but different COVID-19 prevalence.Aim To investigate how the COVID-19 pandemic affected healthcare consumption in Dutch general practice during 2020 and 2021, among regions with known heterogeneity in COVID-19 prevalence, from a pre-pandemic baseline in 2019.Design Population-based cohort study using electronic health records.Setting Dutch general practices involved in regional research networks.MethodsInterrupted time-series analysis of changes in healthcare consumption from before to during the pandemic. Descriptive statistics on the number of potential COVID-19 related contacts, reason for contact and type of contact.Results The study covered 3 627 597 contacts (425 639 patients), 3 532 693 contacts (433 340 patients), and 4 134 636 contacts (434 872 patients) in 2019, 2020, and 2021, respectively. Time-series analysis revealed a significant decrease in healthcare consumption after the outbreak of the pandemic. Despite interregional heterogeneity in COVID-19 prevalence, healthcare consumption decreased comparably over time in the three regions, before rebounding to a level significantly higher than baseline in 2021. Physical consultations transitioned to phone or digital over time.Conclusions Healthcare consumption decreased irrespective of the regional prevalence of COVID-19 from the start of the pandemic, with the Delta variant triggering a further decrease. Overall, changes in care consumption appeared to reflect contextual factors and societal restrictions rather than infection rates

Hepatocellular adenoma: When and how to treat? Update of current evidence

Hepatocellular adenoma (HCA) is a rare, benign liver tumor. Discovery of this tumor is usually as an incidental finding, correlated with the use of oral contraceptives, or pregnancy. Treatment options have focused on conservative management for the straightforward, smaller lesions (5 cm) that pose a greater risk of hemorrhage or malignant progression. In recent years, a new molecular subclassification of HCA has been proposed, associated with characteristic morphological features and loss or increased expression of immunohistochemical markers. This subclassification could possibly provide considerable benefits in terms of patient stratification, and the selection of treatment options. In this review we discuss the decision-making processes and associated risk analyses that should be made based on lesion size, and subtype. The usefulness of this subclassification system in terms of the procedures instigated as part of the diagnostic work-up of a suspected HCA will be outlined, and suitable treatment schemes proposed

A two-phase method for extracting explanatory arguments from Bayesian networks

Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a number of recent miscarriages of justice emphasises how severe these consequences can be. These cases, in which forensic evidence was misinterpreted, have ignited a scientific debate on how and when probabilistic reasoning can be incorporated in (legal) argumentation. One promising approach is to use Bayesian networks (BNs), which are well-known scientific models for probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated, they may appear as black box models. Argumentation models, on the contrary, can be used to show how certain results are derived in a way that naturally corresponds to everyday reasoning. In this paper we propose to explain the inner workings of a BN in terms of arguments. We formalise a two-phase method for extracting probabilistically supported arguments from a Bayesian network. First, from a Bayesian network we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the relation between hypotheses and evidence that is modelled in the Bayesian network

Vitamin G: effects of green space on health, well-being, and social safety

BACKGROUND: Looking out on and being in the green elements of the landscape around us seem to affect health, well-being and feelings of social safety. This article discusses the design of a research program on the effects of green space in the living environment on health, well-being and social safety. METHODS/DESIGN: The program consists of three projects at three different scales: at a macro scale using data on the Netherlands as a whole, at an intermediate scale looking into the specific effect of green space in the urban environment, and at micro scale investigating the effects of allotment gardens. The projects are observational studies, combining existing data on land use and health interview survey data, and collecting new data through questionnaires and interviews. Multilevel analysis and GIS techniques will be used to analyze the data. DISCUSSION: Previous (experimental) research in environmental psychology has shown that a natural environment has a positive effect on well-being through restoration of stress and attentional fatigue. Descriptive epidemiological research has shown a positive relationship between the amount of green space in the living environment and physical and mental health and longevity. The program has three aims. First, to document the relationship between the amount and type of green space in people's living environment and their health, well-being, and feelings of safety. Second, to investigate the mechanisms behind this relationship. Mechanisms relate to exposure (leading to stress reduction and attention restoration), healthy behavior and social integration, and selection. Third, to translate the results into policy on the crossroads of spatial planning, public health, and safety. Strong points of our program are: we study several interrelated dependent variables, in different ordinary settings (as opposed to experimental or extreme settings), focusing on different target groups, using appropriate multilevel methods