Computational Decision Support for PE Diagnosis based on Ventilation Perfusion Ratio.

AIM: The aim of this study is to investigate whether computer-aided, semi-automated 3D lung lobe quantification can support decision-making on PE diagnosis based on the ventilation-perfusion ratio in clinical practice. METHODS: A study cohort of 100 patients (39 male, 61 female, age 64.8±15.8 years) underwent ventilation/perfusion single photon emission computed tomography (V/Q-SPECT/CT) to exclude acute PE on SPECT/CT OPTIMA NM/CT 640 (GE Healthcare). Two 3D lung lobe quantification software tools (Q. Lung: Xeleris 4.0, GE Healthcare and LLQ: Hermes Hybrid 3D Lung Lobar Quantification, Hermes Medical Solutions) were used to evaluate the numerical lobar ventilation/perfusion ratio (VQR) and lobar volume/perfusion ratio (VPR). A test of linearity and equivalence of the two 3D software tools was performed using Pearson, Spearman, quadratic weighted kappa and the mean squared deviation for VPR/VQR. An algorithm was developed that identified PE candidates using ROC analysis. The agreement between the PE findings of an experienced nuclear medicine expert and the calculated PE candidates was represented by the magnitude of the YOUDEN index (J) and the size of the area under the receiver operating curve (AUC). RESULTS: Both 3D software tools showed good comparability. The YOUDEN index for QLUNG(VPR/VQR)/LLQ(VPR/VQR) was in the range from 0.2 to 0.5. The mean AUC averaged over all lung lobes for QLUNG(VPR) was 0.66, CI95%: ±14.0%, for QLUNG(VQR) 0.66, CI95%: ±13.3%, for LLQ(VPR) 0.64, CI95%: ±14.7% and for LLQ(VQR) 0.65, CI95%: ±13.1%. CONCLUSION: This study reveals that 3D software tools are feasible for numerical PE detection. The clinical decision can be supported by using a numerical algorithm based on ROC analysis.

European Joint Programme on Rare Diseases workshop: LAMA2-muscular dystrophy: paving the road to therapy March 17-19, 2023, Barcelona, Spain.

The European Joint Programme on Rare Diseases (EJPRD) funded the workshop "LAMA2-Muscular Dystrophy: Paving the road to therapy", bringing together 40 health-care professionals, researchers, patient-advocacy groups, Early-Career Scientists and other stakeholders from 14 countries. Progress in natural history, pathophysiology, trial readiness, and treatment strategies was discussed together with efforts to increase patient-awareness and strengthen collaborations. Key outcomes were (a) ongoing natural history studies in 7 countries already covered more than 350 patients. The next steps are to include additional countries, harmonise data collection and define a minimal dataset; (b) therapy development was largely complementary. Approaches included LAMA2-replacement and correction, LAMA1-reactivation, mRNA modulation, linker-protein expression, targeting downstream processes and identifying modifiers, using viral vectors, muscle stem cells, iPSC and mouse models and patient lines; (c) LAMA2-Europe will inform patients (-representatives) worldwide on standards of care and scientific progress, and enable sharing experiences. Follow-up monthly online meetings and research repositories have been established to create sustainable collaborations.

PKC regulates αKlotho gene expression in MDCK and NRK-52E cells.

Particularly expressed in the kidney, αKlotho is a transmembrane protein that acts together with bone hormone fibroblast growth factor 23 (FGF23) to regulate renal phosphate and vitamin D homeostasis. Soluble Klotho (sKL) is released from the transmembrane form and controls various cellular functions as a paracrine and endocrine factor. αKlotho deficiency accelerates aging, whereas its overexpression favors longevity. Higher αKlotho abundance confers a better prognosis in cardiovascular and renal disease owing to anti-inflammatory, antifibrotic, or antioxidant effects and tumor suppression. Serine/threonine protein kinase C (PKC) is ubiquitously expressed, affects several cellular responses, and is also implicated in heart or kidney disease as well as cancer. We explored whether PKC is a regulator of αKlotho. Experiments were performed in renal MDCK or NRK-52E cells and PKC isoform and αKlotho expression determined by qRT-PCR and Western Blotting. In both cell lines, PKC activation with phorbol ester phorbol-12-myristate-13-acetate (PMA) downregulated, while PKC inhibitor staurosporine enhanced αKlotho mRNA abundance. Further experiments with PKC inhibitor Gö6976 and RNA interference suggested that PKCγ is the major isoform for the regulation of αKlotho gene expression in the two cell lines. In conclusion, PKC is a negative regulator of αKlotho gene expression, an effect which may be relevant for the unfavorable effect of PKC on heart or kidney disease and tumorigenesis.

Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis.

Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.

Endothelin receptor B-deficient mice are protected from high-fat diet-induced metabolic syndrome.

OBJECTIVE: Endothelin receptor B (ETB) together with ETA mediates cellular effects of endothelin 1 (ET-1), an autocrine and endocrine peptide produced by the endothelium and other cells. It regulates vascular tone and controls kidney function. Metabolic syndrome is due to high caloric intake and is characterized by insulin resistance, dyslipidemia, and white adipose tissue (WAT) accumulation. ETA/ETB antagonism has been demonstrated to favorably influence insulin resistance. Our study explored the role of ETB in metabolic syndrome. METHODS: Wild type (etb+/+) and rescued ETB-deficient (etb-/-) mice were fed a high-fat diet, and energy, glucose, and insulin metabolism were analyzed, and hormones and lipids measured in serum and tissues. Cell culture experiments were performed in HepG2 cells. RESULTS: Compared to etb+/+ mice, etb-/- mice exhibited better glucose tolerance and insulin sensitivity, less WAT accumulation, lower serum triglycerides, and higher energy expenditure. Protection from metabolic syndrome was paralleled by higher hepatic production of fibroblast growth factor 21 (FGF21) and higher serum levels of free thyroxine (fT4), stimulators of energy expenditure. CONCLUSIONS: ETB deficiency confers protection from metabolic syndrome by counteracting glucose intolerance, dyslipidemia, and WAT accumulation due to enhanced energy expenditure, effects at least in part dependent on enhanced production of thyroid hormone/FGF21. ETB antagonism may therefore be a novel therapeutic approach in metabolic syndrome.

Unveiling Consumer Preferences and Intentions for Cocreated Features of a Combined Diet and Physical Activity App: Cross-Sectional Study in 4 European Countries.

BACKGROUND: Numerous mobile health apps are marketed globally, and these have specific features including physical activity tracking, motivational feedback, and recipe provision. It is important to understand which features individuals prefer and whether these preferences differ between consumer groups. OBJECTIVE: In this study, we aimed to identify consumers' most preferred features and rewards for a mobile app that targets healthy eating and physical activity and to reduce the number of individual mobile health app features to a smaller number of key categories as perceived by consumers. In addition, we investigated the impact of differences in consumers' BMI and self-efficacy on their intention to use and willingness to pay for such an app. Finally, we identified the characteristics of different target groups of consumers and their responses toward app features via cluster analysis. METHODS: A total of 212 participants from France, Italy, the United Kingdom, and Germany were recruited via the web to answer questions about app features, motivation, self-efficacy, demographics, and geographic factors. It is important to note that our study included an evenly distributed sample of people in the age range of 23 to 50 years (23-35 and 35-50 years). The app features in question were generated from a 14-day cocreation session by a group of consumers from the United Kingdom and the Republic of Ireland. RESULTS: "Home work out suggestions," "exercise tips," and "progress charts" were the most preferred app features, whereas "gift vouchers" and "shopping discounts" were the most preferred rewards. "Connections with other communication apps" was the least preferred feature, and "charitable giving" was the least preferred reward. Importantly, consumers' positive attitude toward the "social support and connectedness and mindfulness" app feature predicted willingness to pay for such an app (ß=.229; P=.004). Differences in consumers' health status, motivational factors, and basic demographics moderated these results and consumers' intention to use and willingness to pay for such an app. Notably, younger and more motivated consumers with more experience and knowledge about health apps indicated more positive attitudes and intentions to use and willingness to pay for this type of app. CONCLUSIONS: This study indicated that consumers tend to prefer app features that are activity based and demonstrate progress. It also suggested a potential role for monetary rewards in promoting healthy lifestyle behaviors. Moreover, the results highlighted the role of consumers' health status, motivational factors, and socioeconomic status in predicting their app use. These results provide up-to-date, practical, and pragmatic information for the future design and operation of mobile health apps.

Blood glucose forecasting from temporal and static information in children with T1D.

Background: The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods: In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data-(dilated) recurrent neural networks and a transformer-on our dataset for short-term (30 min) and long-term (2 h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results: Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30 min (RMSE of 1.66 mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50 mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion: We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.

A mixed-methods approach to understanding barriers and facilitators to healthy eating and exercise from five European countries: highlighting the roles of enjoyment, emotion and social engagement.

Healthy adults are consistently falling below national and international recommendations for physical activity and dietary intake across Europe. This study took a co-creative approach with adult samples from five European countries to qualitatively and quantitatively establish motivators, barriers and sustaining factors for positive health behaviour change. Stage 1 delivered a newly-designed online programme, creating a community who identified challenges, motivators and solutions to sustaining positive healthy eating and physical activity behaviours. Stage 2 administered an online survey (developed from Stage 1 findings) to a larger sample to quantify the relative importance of these motivators and barriers. Results from both stages indicated enjoyment, positive emotions, and reward as key motivators for both behaviours across all five countries. Barriers included habit-breaking difficulties, temptation and negative affective states. Those with a high BMI placed more importance on social pressure than those with healthy BMI. Participants' reports of motivators and barriers reflected relevant approaches from consumer science, behavioural economics, and psychology. Interventions supporting adults who are not chronically ill but would benefit from improved diet and/or physical activity should not focus exclusively on health as a motivating factor. Emphasis on enjoyable behaviours, social engagement and reward will likely improve engagement and sustained behaviour change.

Validating the early phototherapy prediction tool across cohorts.

Background: Hyperbilirubinemia of the newborn infant is a common disease worldwide. However, recognized early and treated appropriately, it typically remains innocuous. We recently developed an early phototherapy prediction tool (EPPT) by means of machine learning (ML) utilizing just one bilirubin measurement and few clinical variables. The aim of this study is to test applicability and performance of the EPPT on a new patient cohort from a different population. Materials and methods: This work is a retrospective study of prospectively recorded neonatal data from infants born in 2018 in an academic hospital, Regensburg, Germany, meeting the following inclusion criteria: born with 34 completed weeks of gestation or more, at least two total serum bilirubin (TSB) measurement prior to phototherapy. First, the original EPPT-an ensemble of a logistic regression and a random forest-was used in its freely accessible version and evaluated in terms of the area under the receiver operating characteristic curve (AUROC). Second, a new version of the EPPT model was re-trained on the data from the new cohort. Third, the predictive performance, variable importance, sensitivity and specificity were analyzed and compared across the original and re-trained models. Results: In total, 1,109 neonates were included with a median (IQR) gestational age of 38.4 (36.6-39.9) and a total of 3,940 bilirubin measurements prior to any phototherapy treatment, which was required in 154 neonates (13.9%). For the phototherapy treatment prediction, the original EPPT achieved a predictive performance of 84.6% AUROC on the new cohort. After re-training the model on a subset of the new dataset, 88.8% AUROC was achieved as evaluated by cross validation. The same five variables as for the original model were found to be most important for the prediction on the new cohort, namely gestational age at birth, birth weight, bilirubin to weight ratio, hours since birth, bilirubin value. Discussion: The individual risk for treatment requirement in neonatal hyperbilirubinemia is robustly predictable in different patient cohorts with a previously developed ML tool (EPPT) demanding just one TSB value and only four clinical parameters. Further prospective validation studies are needed to develop an effective and safe clinical decision support system.

Machine learning analysis of humoral and cellular responses to SARS-CoV-2 infection in young adults.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces B and T cell responses, contributing to virus neutralization. In a cohort of 2,911 young adults, we identified 65 individuals who had an asymptomatic or mildly symptomatic SARS-CoV-2 infection and characterized their humoral and T cell responses to the Spike (S), Nucleocapsid (N) and Membrane (M) proteins. We found that previous infection induced CD4 T cells that vigorously responded to pools of peptides derived from the S and N proteins. By using statistical and machine learning models, we observed that the T cell response highly correlated with a compound titer of antibodies against the Receptor Binding Domain (RBD), S and N. However, while serum antibodies decayed over time, the cellular phenotype of these individuals remained stable over four months. Our computational analysis demonstrates that in young adults, asymptomatic and paucisymptomatic SARS-CoV-2 infections can induce robust and long-lasting CD4 T cell responses that exhibit slower decays than antibody titers. These observations imply that next-generation COVID-19 vaccines should be designed to induce stronger cellular responses to sustain the generation of potent neutralizing antibodies.

A Case Report of Acute Onset and Rapid Resolution of Atrioventricular Block After Sugammadex: Is the Autonomic System Involved?

Administering sugammadex to reverse neuromuscular blockade can cause marked bradycardia and rarely asystole. In this case, a rapid onset, biphasic heart rate response; slowing then speeding, after administering sugammadex was noted while at steady state, 1.3% end-tidal sevoflurane. On review of the electrocardiogram (ECG), the heart rate slowing coincided with the onset of a second-degree, Mobitz type I block that lasted 45 seconds. No other events, drugs, or stimuli coincided with the event. The acute onset and transient nature of the atrioventricular block without evidence of ischemia implies a brief parasympathetic effect on the atrioventricular node after sugammadex administration.

Co-Culture of Mesenchymal Stem Cells and Ligamentocytes on Triphasic Embroidered Poly(L-lactide-co-ε-caprolactone) and Polylactic Acid Scaffolds for Anterior Cruciate Ligament Enthesis Tissue Engineering.

Successful anterior cruciate ligament (ACL) reconstructions strive for a firm bone-ligament integration. With the aim to establish an enthesis-like construct, embroidered functionalized scaffolds were colonized with spheroids of osteogenically differentiated human mesenchymal stem cells (hMSCs) and lapine (l) ACL fibroblasts in this study. These triphasic poly(L-lactide-co-ε-caprolactone) and polylactic acid (P(LA-CL)/PLA) scaffolds with a bone-, a fibrocartilage transition- and a ligament zone were colonized with spheroids directly after assembly (DC) or with 14-day pre-cultured lACL fibroblast and 14-day osteogenically differentiated hMSCs spheroids (=longer pre-cultivation, LC). The scaffolds with co-cultures were cultured for 14 days. Cell vitality, DNA and sulfated glycosaminoglycan (sGAG) contents were determined. The relative gene expressions of collagen types I and X, Mohawk, Tenascin C and runt-related protein (RUNX) 2 were analyzed. Compared to the lACL spheroids, those with hMSCs adhered more rapidly. Vimentin and collagen type I immunoreactivity were mainly detected in the hMSCs colonizing the bone zone. The DNA content was higher in the DC than in LC whereas the sGAG content was higher in LC. The gene expression of ECM components and transcription factors depended on cell type and pre-culturing condition. Zonal colonization of triphasic scaffolds using spheroids is possible, offering a novel approach for enthesis tissue engineering.

Multimodal Remote Home Monitoring of Lung Transplant Recipients during COVID-19 Vaccinations: Usability Pilot Study of the COVIDA Desk Incorporating Wearable Devices.

Background and Objectives: Remote patient monitoring (RPM) of vital signs and symptoms for lung transplant recipients (LTRs) has become increasingly relevant in many situations. Nevertheless, RPM research integrating multisensory home monitoring in LTRs is scarce. We developed a novel multisensory home monitoring device and tested it in the context of COVID-19 vaccinations. We hypothesize that multisensory RPM and smartphone-based questionnaire feedback on signs and symptoms will be well accepted among LTRs. To assess the usability and acceptability of a remote monitoring system consisting of wearable devices, including home spirometry and a smartphone-based questionnaire application for symptom and vital sign monitoring using wearable devices, during the first and second SARS-CoV-2 vaccination. Materials and Methods: Observational usability pilot study for six weeks of home monitoring with the COVIDA Desk for LTRs. During the first week after the vaccination, intensive monitoring was performed by recording data on physical activity, spirometry, temperature, pulse oximetry and self-reported symptoms, signs and additional measurements. During the subsequent days, the number of monitoring assessments was reduced. LTRs reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. Results: Ten LTRs planning to receive the first COVID-19 vaccinations were recruited. For the intensive monitoring study phase, LTRs recorded symptoms, signs and additional measurements. The most frequent adverse events reported were local pain, fatigue, sleep disturbance and headache. The duration of these symptoms was 5-8 days post-vaccination. Adherence to the main monitoring devices was high. LTRs rated usability as high. The majority were willing to continue monitoring. Conclusions: The COVIDA Desk showed favorable technical performance and was well accepted by the LTRs during the vaccination phase of the pandemic. The feasibility of the RPM system deployment was proven by the rapid recruitment uptake, technical performance (i.e., low number of errors), favorable user experience questionnaires and detailed individual user feedback.

Molecular characterization of Richter syndrome identifies de novo diffuse large B-cell lymphomas with poor prognosis.

Richter syndrome (RS) is the transformation of chronic lymphocytic leukemia (CLL) into aggressive lymphoma, most commonly diffuse large B-cell lymphoma (DLBCL). We characterize 58 primary human RS samples by genome-wide DNA methylation and whole-transcriptome profiling. Our comprehensive approach determines RS DNA methylation profile and unravels a CLL epigenetic imprint, allowing CLL-RS clonal relationship assessment without the need of the initial CLL tumor DNA. DNA methylation- and transcriptomic-based classifiers were developed, and testing on landmark DLBCL datasets identifies a poor-prognosis, activated B-cell-like DLBCL subset in 111/1772 samples. The classification robustly identifies phenotypes very similar to RS with a specific genomic profile, accounting for 4.3-8.3% of de novo DLBCLs. In this work, RS multi-omics characterization determines oncogenic mechanisms, establishes a surrogate marker for CLL-RS clonal relationship, and provides a clinically relevant classifier for a subset of primary "RS-type DLBCL" with unfavorable prognosis.

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population. Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations. Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN. Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke.

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.

Now you see it, now you don't: Relevance of threat enhances social anxiety-linked attentional bias to angry faces, but relevance of neutral information attenuates it.

Temporary goals modulate attention to threat. We examined whether attentional bias to angry faces differs depending on whether a temporary background goal is neutral, or threat related, whilst also measuring social anxiety. Participants performed a dot probe task combined with a separate task that induced a temporary goal. Depending on the phase in this goal task, the goal made angry faces or neutral stimuli (i.e., houses) relevant. The dot probe task measured attention to combinations of angry faces, neutral but goal-relevant stimuli (i.e., houses), and neutral control stimuli. Attention was allocated to angry faces when an angry goal was active. This was more pronounced for people scoring high on social phobia. The neutral goal attenuated attention to angry faces and effects of social phobia were no longer apparent. These findings suggest that individual differences in social anxiety interact with current and temporary goals to affect attentional processes.

Amyloid fibril-UiO-66-NH2 aerogels for environmental remediation.

A sustainable hybrid aerogel based on ß-lactoglobulin amyloid fibril/UiO-66-NH2 is developed for environmental remediation. The hybrid aerogel's CO2 capture and water purification performances were investigated. The hybrid aerogel can achieve CO2 capture and possesses excellent adsorption capacities for several heavy metals, dyes, and organic solvents.

Re-focusing explainability in medicine.

Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma and how much explanation is required to use artificial intelligence safely in healthcare. A key issue is the lack of consensus on the definition of explainability by experts, regulators, and healthcare professionals, resulting in a wide variety of terminology and expectations. This paper aims to fill the gap by defining minimal explainability standards to serve the views and needs of essential stakeholders in healthcare. In that sense, we propose to define minimal explainability criteria that can support doctors' understanding, meet patients' needs, and fulfill legal requirements. Therefore, explainability need not to be exhaustive but sufficient for doctors and patients to comprehend the artificial intelligence models' clinical implications and be integrated safely into clinical practice. Thus, minimally acceptable standards for explainability are context-dependent and should respond to the specific need and potential risks of each clinical scenario for a responsible and ethical implementation of artificial intelligence.

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis.

BACKGROUND: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. STUDY DESIGN: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. RESULTS: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. CONCLUSIONS: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics.