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ObjectiveAlthough Balance Evaluation Systems Test (BESTest) is an important balance assessment tool to differentiate balance deficits, it is time consuming and tiring for hemiparetic patients. Using artificial neural networks (ANNs) to estimate balance status can be a practical and useful tool for clinicians. The aim of this study was to compare manual BESTest results and ANNs predictive results and to determine the highest contributions of BESTest sections by using ANNs predictive results of BESTest sections. Methods66 hemiparetic individuals were included in the study. Balance status was evaluated using the BESTest. 70% (n = 46), of the dataset was used for learning, 15% (n = 10) for evaluation, and 15%(n = 10) for testing purposes in order to model ANNs. Multiple linear regression models (MLRs) were used to compare with ANNs. ResultsThe results of the study showed that ANNs(root mean square error-RMSE4.993) were better than MLR (RMSE7.031) model to estimate balance status of patients with hemiparesis. The BESTest sections making lowest and highest contribution to BESTest total score was found to be "Stability Limits/Verticality" and "Stability in Gait" sections, respectively. As the highest and the lowest contribution of sections items were investigated it was found that error(RMSE) values were small indicating the success of ANN modeling. DiscussionThe results obtained from this study showed that RMSE values of ANNs were better than the ones found in literature. It is believed that this study can lead to constitute a shorter, more sensitive and more practical mini subset of BESTest for physiotherapists to differentiate balance problems while carrying the whole philosophy of the full BESTest.

Childhood cancer survivors should be routinely screened for psychological distress. However, existing screening tools promoted by cancer care institutions, such as the Distress Thermometer (DT) generate high rates of errors. The aim of this study is to help refining strategies of screening psychological distress in this population by exploring two-step methods combining the DT on step #1 with one question on step #2.

Data from 255 survivors of childhood acute lymphoblastic leukemia aged 13-40 years were analyzed (38% 13-18 years, 62% 19+ years, 53% females). We used the DT on step #1 and the individual emotion items from the Pediatric Quality of Life Questionnaire (PedsQL) on step #2, to detect distress, depression and anxiety as measured by standard instruments. We compared sensitivity, specificity, negative and positive predictive values, Youden index, and clinical utility indices, in newly developed two-step strategies.

The best two-step strategies to screen anxious-depressive distress were DT ≥ 2 on step #1, with the item of Sadness on step #2, and DT ≥ 2 combined with the item of Concerns. Two-step strategies outperformed the DT alone on the correct identification of distressed survivors. However, two-step strategies did not outperform the DT used alone on the correct detection of no distressed survivors. Results were similar when predicting depression or anxiety alone.

Completing the DT with one single question on emotions from the PedsQL may minimize the number of participants falsely identified as distressed, which could be particularly pertinent in resource-limited clinics.

Completing the DT with one single question on emotions from the PedsQL may minimize the number of participants falsely identified as distressed, which could be particularly pertinent in resource-limited clinics.In this research, new magnetic nanocomposites that consist of NH2-MIL53 (Al) and Fe3O4 nanoparticles functionalized with cysteine were synthesized and characterized. The application of these nanocomposites was investigated to remove lead ions from the wastewater model. The concentration of metal ions was measured by the utilization of flame atomic absorption spectroscopy (FAAS). Also, XRD, SEM, EDX, and FTIR instruments were used to identification and characterization of the synthesized nanocomposites. The effect of operating parameters such as; pH, contact time and adsorbent dosage were investigated on lead removal. The synthesized nanocomposite showed great potential for lead removal. The maximum adsorption capacity of the nanocomposite was about 361.53 mg/g. Adsorption kinetic parameters well fitted with the pseudo-second order kinetic model. The reusability test of the synthesized magnetic absorbent showed good adsorption efficiency for at least three consecutive cycles.Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI). Smart assistants are finding their way into the world of knowledge work and require cooperation with humans. Here, trust is an essential factor that determines the success of human-AI cooperation. Within this article, an analysis within production management identifies possible antecedent variables on trust in AI and evaluates these due to interaction scenarios with AI. The results of this research are five antecedents for human trust in AI within production management. From these results, preliminary design guidelines are derived for a socially sustainable human-AI interaction in future production management systems. see more Practitioner summary In the future, artificial intelligence will assist cognitive tasks in production management. In order to make good decisions, humans trust in AI has to be well calibrated. For trustful human-AI interactions, it is beneficial that humans subjectively perceive AI as capable and comprehensible and that they themselves are digitally competent.

Although lung cancer screening (LCS) with low-dose computed tomography (LDCT) is now recommended for those meeting standard risk factor-based eligibility criteria, the role of comorbidity in the uptake of LCS with LDCT in an older real-world U.S. population is not well established.

To examine the relationships between comorbidity, functional status and LCS utilization in the United States.

Using population-based data from the 2017-2019 Behavioral Risk Factor Surveillance System (BRFSS), we examined the association of comorbid conditions and functional limitations regarding activities of daily living with LCS utilization among participants that met the LCS criteria based on the US Preventive Service Taskforce guidelines. We employed multivariable weighted logistic regression models to evaluate these associations, both overall and within subgroups defined by age (<65 vs. ≥65 years), gender, and smoking history.

Of 11,214 participants that met the eligibility criteria for LCS, 1731 (16%) underwent LCS with LDCT.