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Abstract Background Acute kidney injury (AKI) is common in critical cases of coronavirus disease 2019 (COVID-19) and associated with worse outcome. Dysregulated neutrophil extracellular trap (NET) formation is one of several suggested pathophysiological mechanisms involved in the development of COVID-19 associated AKI. The corticosteroid dexamethasone was implemented as a standard treatment for severe COVID-19 as of June 2020. A sub-analysis of a prospective observational single center study was performed to evaluate the effect of corticosteroid treatment on AKI development and NET markers in critical cases of COVID-19. Results Two hundred and ten adult patients admitted to intensive care at a tertiary level hospital due to respiratory failure or shock secondary to SARS-CoV-2-infection between March 13th 2020 and January 14th 2021 were included in the study. Ninety-seven of those did not receive corticosteroids. One hundred and thirteen patients were treated with corticosteroids [dexamethasone (n = 98) or equivalent treatment (n = 15)], but the incidence of AKI was assessed only in patients that received corticosteroids before any registered renal dysfunction (n = 63). Corticosteroids were associated with a lower incidence of AKI (19% vs 55.8%, p

Earlier research shows an arrest in reading and writing development among 9-12-year-old students with severe speech and physical impairment, SSPI. This article explores what five students with SSPI who have reached beyond beginner's phase without arrest in their literacy development have experienced as significant for their reading and writing development. The research design was explorative and case based. It contained researcher-participant longitudinal dialogues focusing on the students' experiences of literacy learning. Computer assisted email interviews were used. A semi-structured interview manual guided each dialogue. With the use of augmentative and alternative communication (AAC) systems and assistive technology devices, the participants could read and write (with alphabetical print in Swedish) independently. The analysis revealed four themes of great importance for the students' development of alphabetical print literacy skills: assistive technology use in writing and reading, continuity in long-term pedagogical relationships, mutual persistence in communication, and visions of nearer goals and future work life. The results are discussed in relation to the theoretical frameworks of self-efficacy and the capability approach.

BackgroundThe growing presence of digital technologies in health care requires the health workforce to have proficiency in subjects such as informatics. This has implications in the education of nursing students, as their preparedness to use these technologies in clinical situations is something that course administrators need to consider. Thus, students’ attitudes toward technology could be investigated to assess their needs regarding this proficiency. ObjectiveThis study aims to investigate attitudes (enthusiasm and anxiety) toward technology among nursing students and to identify factors associated with those attitudes. MethodsNursing students at 2 universities in Sweden and 1 university in Poland were invited to answer a questionnaire. Data about attitudes (anxiety and enthusiasm) toward technology, eHealth literacy, electronic device skills, and frequency of using electronic devices and sociodemographic data were collected. Descriptive statistics were used to characterize the data. The Spearman rank correlation coefficient and Mann-Whitney U test were used for statistical inferences. ResultsIn total, 646 students answered the questionnaire—342 (52.9%) from the Swedish sites and 304 (47.1%) from the Polish site. It was observed that the students’ technology enthusiasm (techEnthusiasm) was on the higher end of the Technophilia instrument (score range 1-5): 3.83 (SD 0.90), 3.62 (SD 0.94), and 4.04 (SD 0.78) for the whole sample, Swedish students, and Polish students, respectively. Technology anxiety (techAnxiety) was on the midrange of the Technophilia instrument: 2.48 (SD 0.96), 2.37 (SD 1), and 2.60 (SD 0.89) for the whole sample, Swedish students, and Polish students, respectively. Regarding techEnthusiasm among the nursing students, a negative correlation with age was found for the Swedish sample (P

Abstract Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors.

Abstract Introduction Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images. Methods A total of 1309 patients with cognitive impairment scanned with [11C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation. Results The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader’s classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R 2 = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status. Conclusion The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.

Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia.Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system’s efficiency.Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535.Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.

The current study explores functioning in individuals with co-occurring Autism Spectrum Disorder and Down Syndrome (ASD+DS; n = 23), individuals with ASD and cognitive impairment (ASD+ID; n = 99) and individuals with idiopathic ID (n = 38). ANCOVA results revealed that individuals with ASD+DS showed strengths in behavioral functioning compared to individuals with ID and more similar behavioral functioning to those with ASD+ID ([eta][superscript 2] = 0.12), with the exception of disruptive behaviors. Cognitive functioning ([phi][subscript c] = 0.41) and ASD symptomatology ([eta][superscript 2] = 0.11) were more comparable for children with ASD+DS and ASD + ID than for individuals with ID. Individuals with ASD+DS had the lowest overall adaptive skills ([eta][superscript 2] = 0.11). Findings highlight similarities between ASD+DS and ASD+ID groups, emphasizing the importance of ASD identification within the DS population to provide access to specific interventions.

Older adults need to participate in the digital society, as societal and personal changes and what they do with the remaining time that they have in their older years has an undeniable effect on motivation, cognition and emotion. Changes in personality traits were investigated in older adults over the period 2019–2021. Technology enthusiasm and technology anxiety are attitudes that affect the relationship to the technology used. The changes in the score of technology enthusiasm and technology anxiety were the dependent variables. They were investigated with personality traits, age, gender, education, whether someone lives alone, cognitive function, digital social participation (DSP) and health literacy as predictors of the outcome. The Edwards-Nunnally index and logistic regression were used. The results indicated that DSP, lower age, lower neuroticism and higher education were indicative of less technology anxiety. High DSP and high extraversion are indicative of technology enthusiasm. DSP and attitude towards technology seem to be key in getting older adults to stay active online.

Abstract Background and aims eHealth literacy is important as it influences health-promoting behaviors and health. The ability to use eHealth resources is essential to maintaining health, especially during COVID-19 when both physical and psychological health were affected. This study aimed to assess the prevalence of eHealth literacy and its association with psychological distress and perceived health status among older adults in Blekinge, Sweden. Furthermore, this study aimed to assess if perceived health status influences the association between eHealth literacy and psychological distress. Methods This cross-sectional study (October 2021-December 2021) included 678 older adults’ as participants of the Swedish National Study on Aging and Care, Blekinge (SNAC-B). These participants were sent questionnaires about their use of Information and Communications Technology (ICT) during the COVID-19 pandemic. In this study, we conducted the statistical analysis using the Kruskal-Wallis one-way analysis of variance, Kendall’s tau-b rank correlation, and multiple linear regression. Results We found that 68.4% of the participants had moderate to high levels of eHealth literacy in the population. Being female, age $$< 75$$ < 75 years, and having a higher education are associated with high eHealth literacy ( $$p< 0.05$$ p < 0.05 ). eHealth literacy is significantly correlated ( $$\tau$$ τ =0.12, p-value=0.002) and associated with perceived health status ( $$\beta$$ β =0.39, p-value=0.008). It is also significantly correlated ( $$\tau$$ τ =-0.12, p-value=0.001) and associated with psychological distress ( $$\beta$$ β =-0.14, p-value=0.002). The interaction of eHealth literacy and good perceived health status reduced psychological distress ( $$\beta$$ β =-0.30, p-value=0.002). Conclusions In our cross-sectional study, we found that the point prevalence of eHealth literacy among older adults living in Blekinge, Sweden is moderate to high, which is a positive finding. However, there are still differences among older adults based on factors such as being female, younger than 75 years, highly educated, in good health, and without psychological distress. The results indicated that psychological distress could be mitigated during the pandemic by increasing eHealth literacy and maintaining good health status.

Background: The technical skills of a surgeon influence surgical outcome. Testing technical aptitude at point of recruitment of surgical residents is only conducted in a few countries. This study investigated the impact of visuospatial ability (VSA), background factors, and manual dexterity on performance in two different laparoscopic surgical simulators amongst applicants and 1st year surgical residents. Method: Applicants from general surgery, pediatric surgery, and urology were included from seven hospitals in Sweden between 2017 and 2021. Some 73 applicants were invited and 50 completed. Participants filled out a background form, and were tested for manual dexterity, and visuospatial ability. Two laparoscopic simulators were used, one 2D video box trainer and one 3D Virtual Reality Simulator. Results: A significant association was found between the visuospatial ability test and 2D video box laparoscopic performance (95 % CI: 1.028–1.2, p-value

Article highlights The body temperature can be measured using a powered toothbrush with an integrated temperature sensor. The values obtained from a temperature sensor–integrated powered toothbrush was comparable to those obtained from a traditional oral thermometer. The implication of the study is a way to facilitate the collection of temperature data, but the design of the temperature sensor’s integration in the brush head needs to be further developed, and the possibility of transferring the obtained data directly to a central server using WIFI must be explored.

Receiving a diagnosis of autism for their child can be a pivotal moment for parents, yet there is little research about how providers can predict parent reactions and adjust their feedback. We investigated factors related to parent reactions during the disclosure session using interviews with providers (n = 6), a parent focus group (n = 10), and a mixed-methods survey of parents (n = 189) of recently diagnosed children. Parents' prior knowledge of autism and anxiety about diagnosis predicted emotional reactions and readiness for next steps. Families anxious about receiving a diagnosis are most in need of information but may leave the session feeling lost and unprepared. Providers can promote positive emotional reactions for parents and prevent confusion by increasing their own positivity, warmth, respect, clarity, and confidence.

Abstract Background Dopamine transporter (DAT) imaging of striatum is clinically used in Parkinson’s disease (PD) and neurodegenerative parkinsonian syndromes (PS) especially in the early disease stages. The aim of the present study was to evaluate the diagnostic performance of the recently developed tracer for DAT imaging [18F]FE-PE2I PET/CT to the reference standard [123I]FP-CIT SPECT. Methods Ninety-eight unselected patients referred for DAT imaging were included prospectively and consecutively and evaluated with [18F]FE-PE2I PET/CT and [123I]FP-CIT SPECT on two separate days. PET and SPECT scans were categorized independently by two blinded expert readers as either normal, vascular changes, or mixed. Semiquantitative values were obtained for each modality and compared regarding effect size using Glass’ delta. Results Fifty-six of the [123I]FP-CIT SPECT scans were considered abnormal (52 caused by PS, 4 by infarctions). Using [18F]FE-PE2I PET/CT, 95 of the 98 patients were categorized identically to SPECT as PS or non-PS with a sensitivity of 0.94 [0.84–0.99] and a specificity of 1.00 [0.92–1.00]. Inter-reader agreement for [18F]FE-PE2I PET with a kappa of 0.97 [0.89–1.00] was comparable to the agreement for [123I]FP-CIT SPECT of 0.96 [0.76–1.00]. Semiquantitative values for short 10-min reconstructions of [18F]FE-PE2I PET/CT were comparable to longer reconstructions. The effect size for putamen/caudate nucleus ratio was significantly increased using PET compared to SPECT. Conclusions The high correspondence of [18F]FE-PE2I PET compared to reference standard [123I]FP-CIT SPECT establishes [18F]FE-PE2I PET as a feasible PET tracer for clinical use with favourable scan logistics.

BackgroundNormal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. ObjectiveThis study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. MethodsThis systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. ResultsIn the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network–based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. ConclusionsThis SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

Abstract Background Although several studies have found a high incidence of coexisting mental health problems among adolescents with substance use problems, follow-up studies addressing how these conditions change over time are rare. The study will describe and analyze indications of mental health problems and how various risk factors predict outcomes 1 year after initial treatment contact. In addition, gender-specific risk factors are explored. Methods A clinical sample of 455 adolescents (29% girls, median age 17 years) answered a structural interview at baseline and were followed up using official records 1 year after initiated treatment. Bivariate associations and logistic regressions were conducted to analyse the links between risk factors at the individual, social, and structural levels as well as links between various mental illness symptoms at treatment start and indications of mental health problems 1 year later were analysed. Results The results show that mental health problems among adolescents largely persisted 1 year after start of outpatient care for substance use problems. Forty-two per cent of the sample displayed indications of mental health problems at follow-up, and registrations for both outpatient treatment and psychiatric medication were more common among the girls. Girls also reported more mental illness symptoms at treatment start than boys did, especially anxiety. Depression and suicidal thoughts had predictive values regarding indications of mental health problems and small cumulative effects were found for 6–10 co-occurring risk factors. Conclusions Adolescents with depression and suicidal thoughts at treatment start should yield attention among clinicians as these general risk factors could predict indication of mental health problems at 1 year follow-up effectively. Also, patients with more than six co-occurring risk factors seem more vulnerable for continued mental health problems. Generally, girls displayed a greater mental health and psychosocial burden at treatment initiation and were more likely to show indication of mental health problems at follow-up. These results suggests that girls are more likely to get psychiatric out-treatment parallel to, or after, substance abuse treatment. We recommend further investigation of gender differences and gender-specific needs in substance use treatment.

PurposeConventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants.MethodsThe MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC.ResultsNinety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method.ConclusionWe evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.

Objectives To investigate differences in antibiotic prescription for patients with hard-to-heal ulcers assessed using a digital decision support system (DDSS) compared with those assessed without using a DDSS. A further aim was to examine predictors for antibiotic prescription.Design Register-based study.Setting In 2018–2019, healthcare staff in primary, community and specialist care in Sweden tested a DDSS that offers a mobile application for data and photograph transfer to a platform for multidisciplinary consultation and automatic transmission of data to the Registry of Ulcer Treatment (RUT). Register-based data from patients assessed and diagnosed using the DDSS combined with the RUT was compared with register-based data from patients whose assessments were merely registered in the RUT.Participants A total of 117 patients assessed using the DDSS combined with the RUT (the study group) were compared with 1784 patients whose assessments were registered in the RUT without using the DDSS (the control group).Primary and secondary outcome measures The differences in antibiotic prescription were analysed using the Pearson’s χ2 test. A logistic regression analysis was used to check for influencing factors on antibiotic prescription.Results Patients assessed using a DDSS in combination with the RUT had significantly lower antibiotic prescription than patients entered in the RUT without using the DDSS (8% vs 26%) (p=0.002) (only healed ulcers included). Predictors for antibiotic prescription were diabetes; long healing time; having an arterial, neuropathic or malignant ulcer.Conclusions A DDSS with data and photograph transfer that enables multidisciplinary communication appears to be a suitable tool to reduce antibiotic prescription for patients with hard-to-heal ulcers.

Purpose: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [18F]FE-PE2I and [11C]PiB PET/CT scans, simulating low injected activity or scanning time reduction, in combination with AI-assisted denoising. Methods: A total of 162 patients with clinically uncertain Alzheimer's disease underwent amyloid [11C]PiB PET/CT and 509 patients referred for clinically uncertain Parkinson's disease underwent dopamine transporter (DAT) [18F]FE-PE2I PET/CT. Simulated low-activity data were obtained by random sampling of 5% of the events from the list-mode file and a 5% time window extraction in the middle of the scan. A three-dimensional convolutional neural network (CNN) was trained to denoise the resulting PET images for each disease cohort. Results: Noise reduction of low-activity PET images was successful for both cohorts using 5% of the original activity with improvement in visual quality and all similarity metrics with respect to the ground-truth images. Clinically relevant metrics extracted from the low-activity images deviated

Abstract Coronavirus disease 19 (COVID-19) presents with disease severities of varying degree. In its most severe form, infection may lead to respiratory failure and multi-organ dysfunction. Here we study the levels and evolution of the damage associated molecular patterns (DAMPS) cell free DNA (cfDNA), extracellular histone H3 (H3) and neutrophil elastase (NE), and the immune modulators GAS6 and AXL in relation to clinical parameters, ICU scoring systems and mortality in patients (n = 100) with severe COVID-19. cfDNA, H3, NE, GAS6 and AXL were increased in COVID-19 patients compared to controls. These measures associated with occurrence of clinical events and intensive care unit acquired weakness (ICUAW). cfDNA and GAS6 decreased in time in patients surviving to 30 days post ICU admission. A decrease of 27.2 ng/mL cfDNA during ICU stay associated with patient survival, whereas levels of GAS6 decreasing more than 4.0 ng/mL associated with survival. The presence of H3 in plasma was a common feature of COVID-19 patients, detected in 38% of the patients at ICU admission. NETosis markers cfDNA, H3 and NE correlated well with parameters of tissue damage and neutrophil counts. Furthermore, cfDNA correlated with lowest p/f ratio and a lowering in cfDNA was observed in patients with ventilator-free days.

To assess the role of feral oilseed rape (OSR) plants as resources for pollinators and avenues for gene flow, we compared occurrence of feral populations in standardized agricultural landscapes, using a landscape ecological approach. The occurrence of feral and volunteer populations was investigated in relation to differences in road length and width, number of OSR fields, and landscape scale. The potential for pollen-mediated gene flow from crop to feral oilseed rape was investigated with fluorescent dye in a field experiment. Moreover, greenhouse estimates of pollen germination rate and pollen tube growth rate were performed to get an indication of siring success in crop and feral plants. Escaped OSR occurred in 14 out of the 16 investigated landscapes, and feral populations were more common alongside large roads than small roads in large-scale landscapes. The number of plants in a habitat ranged from 1-160 individuals, with 1-19 habitats per landscape. In the field experiment with fluorescent dye, no transfer of dye was detected during early flowering in May. At the end of the flowering period in June, transfer of dye was found in 71.4% of the feral plants, showing that significant transfer, most likely by pollinators, occurred from the field to the feral plants. There was no difference in pollen germination rate between crop and feral plants. Pollen tube growth rate was significantly higher in feral oilseed rape than in the crop (P < 0.001). Our results contribute to increased understanding of i) the utilization of feral populations by pollinators in an intensively farmed agricultural landscape, and ii) crop-feral gene flow within OSR.

Despite significant cost declines utility-scale solar projects have to perform well over their lifetime in order to return profits in a low-PPA environment of under $50/MWh. Using multivariate regression analysis this paper examines the 2014 Net Capacity Factors for 128 utility-scale projects that came online before the end of 2013 and explores the impact (in decreasing order of importance) of global horizontal irradiance, tracking technology, inverter loading ratios and project vintage plus three other interactive variables on performance. The model complies with the assumptions of OLS regressions and explains 93% of the observed NCF variation.

Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a χ2 statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model (χ2_RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model χ2_RF achieved the highest accuracy of 94.59%. The proposed model χ2_RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model χ2_RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module (χ2).

Introduction: This paper describes the development of “Swedish Guidelines for OSA treatment” and the underlying managed care process. The Apnea Hypopnea Index (AHI) is traditionally used as a single parameter for obstructive sleep apnea (OSA) severity classification, although poorly associated with symptomatology and outcome. We instead implement a novel matrix for shared treatment decisions based on available evidence. Methods: A national expert group including medical and dental specialists, nurses, and patient representatives developed the knowledge-driven management model. A Delphi round was performed amongst experts from all Swedish regions (N = 24). Evidence reflecting treatment effects was extracted from systematic reviews, meta-analyses, and randomized clinical trials. Results: The treatment decision in the process includes a matrix with five categories from a “very weak”” to “very strong” indication to treat, and it includes factors with potential influence on outcome, including (A) OSA-related symptoms, (B) cardiometabolic comorbidities, (C) frequency of respiratory events, and (D) age. OSA-related symptoms indicate a strong incitement to treat, whereas the absence of symptoms, age above 65 years, and no or well-controlled comorbidities indicate a weak treatment indication, irrespective of AHI. Conclusions: The novel treatment matrix is based on the effects of treatments rather than the actual frequency of respiratory events during sleep. A nationwide implementation of this matrix is ongoing, and the outcome is monitored in a prospective evaluation by means of the Swedish Sleep Apnea Registry (SESAR).

This study presents the results of a longitudinal research project focusing on long-term outcomes among young people after initiation of outpatient treatment for substance use problems (SUP) in Sweden. Young people are defined with the age group 13–25 years. A clinical sample of 451 young people (29% girls, median age 17 years) completed a structured interview at baseline and was followed using official records one, two, and 3 years after initiation of treatment. Gender-specific patterns at intake were described and bivariate associations and logistic regressions were calculated to analyse the links between risk factors at treatment start and indications of substance use problems 3 years later. Significantly more boys than girls displayed indications of continued SUP at 3-year follow-up. More specifically, 49% of the boys vs. 35% of the girls were identified through records as still having problems with substance use. Predictive risk factors also displayed gender-specific patterns. Primary drug use frequency and age at intake predicted indications of SUP among boys but not among girls. Placement in foster care/residential homes, depression, and early drug debut had significant predictive value regarding indications of SUP among females but not among males. Girls also displayed a greater psychosocial burden at treatment start, but a more favorable treatment outcome at follow-up. Youths with a heavy risk load at treatment start (i.e., over six risk factors) did not display a greater risk of SUP at 3-year follow-up, although our results suggest that this subgroup has indications of continued problems with mental health. Consequently, future studies should further explore gender-specific treatment pathways for young people with substance use problems. Since women and girls seem to have different risk factors, co-occurring psychiatric problems and more experiences of trauma compared to men, they might need multidimensional and more comprehensive treatment interventions that run over a longer period of time.

BackgroundEarly diagnosis of cognitive disorders is becoming increasingly important. Limited resources for specialist assessment and an increasing demographical challenge warrants the need for efficient methods of evaluation. In response, CoGNIT, a tablet app for automatic, standardized, and efficient assessment of cognitive function, was developed. Included tests span the cognitive domains regarded as important for assessment in a general memory clinic (memory, language, psychomotor speed, executive function, attention, visuospatial ability, manual dexterity, and symptoms of depression). ObjectiveThe aim of this study was to assess the feasibility of automatic cognitive testing with CoGNIT in older patients with symptoms of mild cognitive impairment (MCI). MethodsPatients older than 55 years with symptoms of MCI (n=36) were recruited at the research clinic at the Blekinge Institute of Technology (BTH), Karlskrona, Sweden. A research nurse administered the Mini-Mental State Exam (MMSE) and the CoGNIT app on a tablet computer. Technical and testing issues were documented. ResultsThe test battery was completed by all 36 patients. One test, the four-finger–tapping test, was performed incorrectly by 42% of the patients. Issues regarding clarity of instructions were found in 2 tests (block design test and the one finger-tapping test). Minor software bugs were identified. ConclusionsThe overall feasibility of automatic cognitive testing with the CoGNIT app in patients with symptoms of MCI was good. The study highlighted tests that did not function optimally. The four-finger–tapping test will be discarded, and minor improvements to the software will be added before further studies and deployment in the clinic.

Abstract Background Young people with substance use problems face a high risk of co-occurring mental health problems, something that may involve a more difficult life situation, social problems as well as worse treatment outcomes. The aim of this study is to analyse self-reported mental health problems among young people receiving outpatient treatment for substance use problems in Sweden. We explore what types of mental health problems are more or less predominant, and whether there are significant differences between boys and girls. In addition, we analyse how various mental health problems covary with indicators of substance abuse severity. Methods The study is based on structured interviews with 1970 young people enrolled at outpatient clinics in 11 Swedish cities. The data was analysed through frequency- and averages-calculations, Chi-square tests and multivariate logistic regression analyses. Results Self-reported mental health problems were common among the young people in the study. A relatively large percentage of the total group (34–54%) reported problems such as concentration difficulties, sleeping difficulties, anxiety and depression. At the same time, many of the young people did not report any symptoms and only a small group, about 20%, reported diagnosed mental health disorders. The results show substantial gender differences, with girls reporting significantly higher levels of mental health problems. Multivariate logistic regression analyses demonstrated significant associations between severity of drug use problems and anxiety, concentration difficulties, aggression, hallucinations and mental stress caused by experiences of trauma. Conclusions Treatment needs are diverse within this group of young people who use drugs. Since girls report higher levels of all mental health problems, and a larger burden of psychosocial risk factors than boys, they are likely to require more comprehensive treatment interventions. The link between more severe drug problems and mental health problems points to the importance of exploring this relationship in treatment. A multidisciplinary approach, in which co-occurring problems can be addressed simultaneously, may be the best treatment form for many young people with drug problems.