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Many pupils with disabilities receive schooling in segregated contexts, such as special classes or special schools. Furthermore, the percentage of pupils educated in segregated settings has increased in many European countries. Studies suggest that there is high commitment to the general ideology of inclusive education among teachers in 'regular' education in many countries. This survey study investigates the views of teachers in segregated types of school about education. A questionnaire was sent out, in 2016, to all Swedish teachers (N = 2871, response rate 57.7%) working full time in special classes for pupils with intellectual disability (ID). On a general level results show that there is a strong commitment to preserving a segregated school setting for pupils with ID, a limited desire to cooperate with colleagues from 'regular schools' and a view that schooling and teaching are not quite compatible with the idea of inclusive education. The results highlight the importance of investigating processes of resistance within segregated schools to the development of inclusive schools and education systems. We argue that, while research and debate about inclusive education are important, both are insufficient without analyses of existing types of segregated schooling.

While individual and family factors behind students' school absenteeism are well-researched, fewer studies have addressed school climate factors. This study investigated the association between school climate in Swedish schools and students' absenteeism. A multi-informant survey of school climate was conducted in 101 schools and analysed in relation to the history of absenteeism of 2770 students attending those schools in the 7th grade at inception, with follow-up until completion of the compulsory school (9th grade). Data on absenteeism was extracted from schools' registers. Student (but not teacher) positive ratings of school climate were associated with lower absenteeism between the age of 13 and the age of 16. The associations between student rated school climate and absenteeism appeared stronger among students with highly educated parents.

Migration to Europe in recent years has made the issue of educational support for recently arrived immigrant pupils critically important, and, especially for pupils who are in need of special educational support. Schools in Sweden have special needs educators (SNEs) supporting pupils in need of special education, but little is known about the work of SNEs in regard to special educational support for recently arrived immigrants. This article reports the results of 483 SNEs answering a survey on their views of their role and work tasks in relation to recently arrived immigrant pupils in need of special educational support in Swedish compulsory schools. SNEs report that their actual tasks include compiling individual educational plans (IEPs), developing learning environments, and supervising staff. Less than 50% of the participants responded that they work directly with the pupils, and around 19% of the SNEs wanted to do more of such work. Class and subject teachers and mother tongue tutors are the occupational groups seen as providing the most support for recently arrived immigrant pupils. The empirical results are discussed from different theoretical perspectives on special education (Emanuelsson, 2001; Persson, 1998), Hughes' (1958) concept of 'dirty work' and Abbott's theory of profession (1988).

Background: The integration of artificial intelligence (AI) into healthcare has opened new avenues for enhancing patient care and clinical research. In gastroenterology, the potential of AI tools, specifically large language models like ChatGPT, is being explored to understand their utility and effectiveness. Objectives: The primary goal of this systematic review is to assess the various applications, ascertain the benefits, and identify the limitations of utilizing ChatGPT within the realm of gastroenterology. Design: Through a systematic approach, this review aggregates findings from multiple studies to evaluate the impact of ChatGPT on the field. Data sources and methods: The review was based on a detailed literature search of the PubMed database, targeting research that delves into the use of ChatGPT for gastroenterological purposes. It incorporated six selected studies, which were meticulously evaluated for quality using the Joanna Briggs Institute critical appraisal instruments. The data were then synthesized narratively to encapsulate the roles, advantages, and drawbacks of ChatGPT in gastroenterology. Results: The investigation unearthed various roles of ChatGPT, including its use in patient education, diagnostic self-assessment, disease management, and the formulation of research queries. Notable benefits were its capability to provide pertinent recommendations, enhance communication between patients and physicians, and prompt valuable research inquiries. Nonetheless, it encountered obstacles in decoding intricate medical questions, yielded inconsistent responses at times, and exhibited limitations in generating novel content. The review also considered ethical implications. Conclusion: ChatGPT has demonstrated significant potential in the field of gastroenterology, especially in facilitating patient–physician interactions and managing diseases. Despite these advancements, the review underscores the necessity for ongoing refinement, customization, and ethical regulation of AI tools. These findings serve to enrich the dialog concerning AI’s role in healthcare, with a specific focus on ChatGPT’s application in gastroenterology.

AbstractThe present work aims to evaluate the influence of varieties and malting conditions on the liquefying and sweetening capacities of germinated flours of two new maize varieties (Atp-Y and Coca-sr). Five (5) factors such as soaking time (18–42 h), vegetable salt concentration (0.5–1.2%), soaking temperature (25–41°C), germination time (80–195 h) and maturation time (17.5–42 h) were used to potentiate the liquefying and sweetening capacities of the two flours. The dehulled flours of the two maize varieties were also used as a substrate for the evaluation of these responses. The optimal flours were then subjected to physicochemical analysis. From these analyses, it follows that the responses were affected (p < 0.05) by the malting conditions, the variety of germinated flours and the variety of substrate flours. Overall, the sprouted flours respectively from the Coca-sr variety showed the highest liquefying (190 and 282.50 mm/30 s on the gruels from the Atp-Y and Coca-sr dehulled flours) and sweetening (7193.10 and 8735.63 mg/100 mL on the gruels from the Atp-Y and Coca-sr dehulled flours) potential of the two dehulled flours. A positive correlation was observed between the sweetening and liquefying potential of the germinated flours. The combination of these different flours (malted and dehulled flours) allowed to potentiate the responses up to 3.5 times compared to the previous data. The optimal conditions obtained in this study could be applied to reduce the energy and nutrient deficiency and improve the fluidity of infant formula.

Skin is the largest organ and a multifunctional interface between the body and its environment. It acts as a barrier against cold, heat, injuries, infections, chemicals, radiations or other exogeneous factors, and it is also known as the mirror of the soul. The skin is involved in body temperature regulation by the storage of fat and water. It is an interesting tissue in regard to the local and transdermal application of active ingredients for prevention or treatment of pathological conditions. Topical and transdermal delivery is an emerging route of drug and cosmetic administration. It is beneficial for avoiding side effects and rapid metabolism. Many pharmaceutical, technological and cosmetic innovations have been described and patented recently in the field. In this review, the main features of skin morphology and physiology are presented and are being followed by the description of classical and novel nanoparticulate dermal and transdermal drug formulations. The biophysical aspects of the penetration of drugs and cosmetics into or across the dermal barrier and their investigation in diffusion chambers, skin-on-a-chip devices, high-throughput measuring systems or with advanced analytical techniques are also shown. The current knowledge about mathematical modeling of skin penetration and the future perspectives are briefly discussed in the end, all also involving nanoparticulated systems.

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.

Introduction Drug prior authorization (PA) imposes a bureaucratic and economic burden on healthcare service providers and payers. A novel automated PA system may improve these drawbacks. Methods An historical cohort study from a large health maintenance organization in Israel, comparing manual versus automated PA mechanisms for diabetes mellitus (DM) drugs: sodium-glucose co-transporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 analogs (GLP1-A). We compared patients with DM, whose first drug applications were approved using the automated system, with similar patients whose first drug applications were approved by manual PA. The primary endpoint was the time elapsed from application approval to prescription filling (accessibility time). Secondary endpoints included the prescription filling rate at 7 and 30 days. Results In total, 1371 automated approved prescriptions and 1240 manually approved prescriptions were included in the analysis. Median accessibility time was one day (interquartile range (IQR) 0–5) with automated PA for both GLP1-A and SGLT2i, compared with four days (IQR 1–9) and three days (IQR 1–8), respectively, with the manual PA ( p

IntroductionThe utilization of sprouted meals in beer production and enhancing the physicochemical properties of supplementary foods is widespread in Africa. This work aimed to determine the influence of soaking, germination, maturation and variety conditions on the physicochemical properties, proteolytic activity, free amino nitrogen (FAN) and soluble protein contents of Coca-sr and Atp-Y maize varieties.MethodsTo achieve this, the central composite design (CCD) was used for the optimization of five parameters, namely soaking time (18–42 h), plant salt concentration (0.5–1.2%), soaking temperature (25–41°C), sprouting time (80–195 h) and ripening time (17.50–42 h), and following dependent variables were investigated: proteolytic activity, FAN content and soluble protein. Optimal samples flours obtained were then subsequently subjected to physicochemical and functional analysis.ResultsThe analysis of results showed that the linear, interactive and quadratic effects of the factors significantly (p

Abstract Objectives Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in‐hospital mortality among this population. Methods Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient‐boosting machine learning model to identify in‐hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held‐out data from the fifth hospital. Results A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in‐hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden’s index, the model had a sensitivity of 0.77 (95% CI: 0.67–0.86) with a false positive rate of 1:9. Conclusion A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning–based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701–0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737–0.991) and 0.943 (95% CI = 0.889–0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections. [ABSTRACT FROM AUTHOR]

Feline eosinophilic sclerosing fibroplasia (FESF) is a proliferative, inflammatory disease of the gastrointestinal tract and other sites, uncommonly diagnosed in the cat. This entity of uncertain etiology typically presents as a progressive mass lesion, mimicking a neoplastic process. In this case series, we present 17 cases of FESF associated with intralesional lymphoma. Histologic and immunohistochemical characterization of this unique lymphoma revealed that the neoplastic lymphocytes were immunopositive for CD56 and/ or CD3, suggesting a natural killer cell, natural killer T-cell, or T-cell origin. This case series represents the first description of this lymphoma subtype, for which the term eosinophilic sclerosing lymphoma is proposed. [ABSTRACT FROM AUTHOR]

Background: Large language models are increasingly evaluated for use in healthcare. However, concerns about their impact on disparities persist. This study reviews current research on demographic biases in large language models to identify prevalent bias types, assess measurement methods, and evaluate mitigation strategies. Methods: We conducted a systematic review, searching publications from January 2018 to July 2024 across five databases. We included peer-reviewed studies evaluating demographic biases in large language models, focusing on gender, race, ethnicity, age, and other factors. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools. Results: Our review included 24 studies. Of these, 22 (91.7%) identified biases. Gender bias was the most prevalent, reported in 15 of 16 studies (93.7%). Racial or ethnic biases were observed in 10 of 11 studies (90.9%). Only two studies found minimal or no bias in certain contexts. Mitigation strategies mainly included prompt engineering, with varying effectiveness. However, these findings are tempered by a potential publication bias, as studies with negative results are less frequently published. Conclusion: Biases are observed in large language models across various medical domains. While bias detection is improving, effective mitigation strategies are still developing. As LLMs increasingly influence critical decisions, addressing these biases and their resultant disparities is essential for ensuring fair artificial intelligence systems. Future research should focus on a wider range of demographic factors, intersectional analyses, and non-Western cultural contexts. [ABSTRACT FROM AUTHOR]

Recent advancements in generative artificial intelligence have enabled analysis of text with visual data, which could have important implications in healthcare. Diagnosis in ophthalmology is often based on a combination of ocular examination, and clinical context. The aim of this study was to evaluate the performance of multimodal GPT-4 (GPT-4 V) in an integrated analysis of ocular images and clinical text. This retrospective study included 40 patients seen in our institution with images of their ocular examinations. Cases were selected by a board-certified ophthalmologist, to represent various pathologies. We provided the model with each patient image, without and then with the clinical context. We also asked two non-ophthalmology physicians to write diagnoses for each image, without and then with the clinical context. Answers for both GPT-4 V and the non-ophthalmologists were evaluated by two board-certified ophthalmologists. Performance accuracies were calculated and compared. GPT-4 V provided the correct diagnosis in 19/40 (47.5%) cases based on images without clinical context, and in 27/40 (67.5%) cases when clinical context was provided. Non-ophthalmologist physicians provided the correct diagnoses in 24/40 (60.0%), and 23/40 (57.5%) of cases without clinical context, and in 29/40 (72.5%) and 27/40 (67.5%) with clinical context. For all study participants adding context improved accuracy (p = 0.033). GPT-4 V is currently able to simultaneously analyze and integrate visual and textual data, and arrive at accurate clinical diagnoses in the majority of cases. Multimodal large language models like GPT-4 V have significant potential to advance both patient care and research in ophthalmology. [ABSTRACT FROM AUTHOR]

This article presents findings from an ongoing project focused on teaching in Schools for Students with Intellectual Disabilities (SSIDs) in Sweden. Methodologically, the project employs professional development circles as a collaborative working method, aiming to bridge the gap between research and practice by facilitating dialog between teachers and researchers. Through qualitative content analysis of data collected from these circles, the study delves into professionals' instructional priorities and didactic choices, offering insights into how teachers adapt instruction to meet students' diverse needs and prerequisites. By fostering knowledge exchange and shared knowledge production, the project aims to support school development and enhance teacher professionalism in SSIDs. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the Collaborative and Proactive Solutions (CPS) intervention in an alternative educational setting for students with behavioural problems. The effect of the CPS intervention on students’ off-task behaviour was studied using a single-subject experimental design that included two students with behavioural problems via systematic direct observations and direct behaviour ratings. The usability of the CPS intervention was investigated through questionnaires and interviews with the participating students and teacher assistant. The results revealed no significant effects of the CPS intervention on students’ off-task behaviour. The students and teacher assistant viewed the CPS intervention as acceptable but the teacher assistant’s ratings of the feasibility of the intervention were lower, together with the ratings of the extent to which the intervention matched the socio-political climate of the educational setting.