Resilience intervention for families of autistic children : reviewing the literature

: Given the rising diagnostic rates of autism, it is imperative to investigate the well-being of families with autistic children. Families of autistic children report more mental health difficulties than families of typically developing children. Resilience is identified as a key protective factor for mental health difficulties in many populations, and research suggests that this construct is effective for coping with mental health difficulties in families of autistic children. However, reviews on resilience interventions for families of autistic children are lacking. Accordingly, this paper aims to report (a) common mental health difficulties that families of autistic children experience, (b) how resilience may reduce mental health difficulties, (c) interventions to enhance resilience in families of autistic children, and (d) discuss implications for practice and future research. Our review identified that mental distress resulting from feelings of uncertainty and helplessness following a diagnosis, in addition to caregiving stressors, is especially common among families of autistic children. Enhancing resilience is suggested to reduce those difficulties by tapping into strengths related to various positive psychological resources such as internal locus of control, positive cognitive appraisal, acceptance and self-efficacy. Interventions such as Dance Movement Psychotherapy and spirituality-based approaches, are deemed especially helpful to families of autistic children. However, research in this area is still underdeveloped, and there is a pressing need to build a more rigorous evidence base. Findings reviewed in the current work can aid families of autistic children, healthcare practitioners, and researchers to support the mental wellbeing of families of autistic children, which in turn would support the wellbeing of autistic children

Leukocyte classification for acute lymphoblastic leukemia timely diagnosis by interpretable artificial neural network

Leukemia is a blood cancer characterized by leukocyte overproduction. Clinically, the reference for acute lymphoblastic leukemia diagnosis is a blood biopsy that allows obtain microscopic images of leukocytes, whose early-stage classification into leukemic (LEU) and healthy (HEA) may be disease predictor. Thus, the aim of this study is to propose an interpretable artificial neural network (ANN) for leukocyte classification to timely diagnose acute lymphoblastic leukemia. The “ALL_IDB2” dataset was used. It contains 260 microscopic images showing leukocytes acquired from 130 LEU and 130 HEA subjects. Each microscopic image shows a single leukocyte that was characterized by 8 morphological and 4 statistical features. An ANN was developed to distinguish microscopic images acquired from LEU and HEA subjects, considering 12 features as inputs and the local-interpretable model-agnostic explanatory (LIME) algorithm as an interpretable post-processing algorithm. The ANN was evaluated by the leave-one-out cross-validation procedure. The performance of our ANN is promising, presenting a testing area under the curve of the receiver operating characteristic equal to 87%. Being implemented using standard features and having LIME as a post-processing algorithm, it is clinically interpretable. Therefore, our ANN seems to be a reliable instrument for leukocyte classification to timely diagnose acute lymphoblastic leukemia, guaranteeing a high clinical interpretability level

Pancreatic surgery outcomes: multicentre prospective snapshot study in 67 countries

Background: Pancreatic surgery remains associated with high morbidity rates. Although postoperative mortality appears to have improved with specialization, the outcomes reported in the literature reflect the activity of highly specialized centres. The aim of this study was to evaluate the outcomes following pancreatic surgery worldwide.Methods: This was an international, prospective, multicentre, cross-sectional snapshot study of consecutive patients undergoing pancreatic operations worldwide in a 3-month interval in 2021. The primary outcome was postoperative mortality within 90 days of surgery. Multivariable logistic regression was used to explore relationships with Human Development Index (HDI) and other parameters.Results: A total of 4223 patients from 67 countries were analysed. A complication of any severity was detected in 68.7 percent of patients (2901 of 4223). Major complication rates (Clavien-Dindo grade at least IIIa) were 24, 18, and 27 percent, and mortality rates were 10, 5, and 5 per cent in low-to-middle-, high-, and very high-HDI countries respectively. The 90-day postoperative mortality rate was 5.4 per cent (229 of 4223) overall, but was significantly higher in the low-to-middle-HDI group (adjusted OR 2.88, 95 per cent c.i. 1.80 to 4.48). The overall failure-to-rescue rate was 21 percent; however, it was 41 per cent in low-to-middle-compared with 19 per cent in very high-HDI countries.Conclusion: Excess mortality in low-to-middle-HDI countries could be attributable to failure to rescue of patients from severe complications. The authors call for a collaborative response from international and regional associations of pancreatic surgeons to address management related to death from postoperative complications to tackle the global disparities in the outcomes of pancreatic surgery (NCT04652271; ISRCTN95140761)