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Healthcare facilities produce millions of tons of waste annually, with a significant portion consisting of diagnostic plasticware. Here, we introduce a new detection platform that completely replaces traditional assay plates with a piece of membrane, offering a much greener and more sustainable alternative. The membrane, integrated within the portable vortex fluidic device (P-VFD), enables rapid detection of a clinically relevant protein biomarker, urinary p75ECD. This biomarker is utilized to evaluate the prognosis, disease severity, and progression of amyotrophic lateral sclerosis (ALS). This assay has a limit-of-detection (LOD) of 4.03 pg, which is comparable to the plate-based assay (2.24 pg) and has been optimised through a full factorial design of experiments (DOE) and response surface methodology (RSM). P-VFD has great potential in quantifying p75ECD in human biofluids and can significantly reduce the assay time to 5 min compared to the current plate-based p75ECD ELISA assay (3 days), with at least a 4.4-fold reduction in the usage of the detection antibody.

BackgroundAsthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well. ObjectiveTo enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions. MethodsWe will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation. ResultsWe have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years. ConclusionsOur results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. International Registered Report Identifier (IRRID)PRR1-10.2196/27065

BackgroundAsthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired. ObjectiveThis study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model. MethodsOur data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit. ResultsOur method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year. ConclusionsThis study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.

BackgroundAs a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes. ObjectiveThe goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients. MethodsSecondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building. ResultsThe model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model. ConclusionsOur model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation. International Registered Report Identifier (IRRID)RR2-10.2196/resprot.5039

Abstract Passive solar membrane distillation (MD) is an emerging technology to alleviate water scarcity. Recently, its performance has been enhanced by multistage design, though the gains are marginal due to constrained temperature and vapor pressure gradients across the device. This makes condenser cooling enhancement a questionable choice. We argue that condenser heating could suppress the marginal effect of multistage solar MD by unlocking the moisture transport limit in all distillation stages. Here, we propose a stage temperature boosting (STB) concept that directs low-temperature heat to the condensers in the last stages, enhancing moisture transport across all stages. Through STB in the last two stages with a heat flux of 250 W m−2, a stage-averaged distillation flux of 1.13 L m−2 h−1 S−1 was demonstrated using an 8-stage MD device under one-sun illumination. This represents an 88% enhancement over the state-of-the-art 10-stage solar MD devices. More notably, our analysis indicates that 16-stage STB-MD devices driven by solar energy and waste heat can effectively compete with existing photovoltaic reverse osmosis (PV-RO) systems, potentially elevating freshwater production with low-temperature heat sources.

Abstract Sorption-based atmospheric water harvesting (SAWH) has received unprecedented attention as a future water and energy platform. However, the water productivity of SAWH systems is still constrained by the slow sorption kinetics at material and component levels and inefficient condensation. Here, we report a facile method to prepare hygroscopic interconnected porous gel (HIPG) with fast sorption-desorption kinetics, high scalability and stability, and strong adhesion property for highly efficient SAWH. We further design a solar-wind coupling driven SAWH device with collaborative heat and mass enhancement achieving continuous water production. Concentrated sunlight contributes to enhancing the desorption and condensation synergistically, and natural wind is introduced to drive the device operation and improve the sorption kinetics. The device demonstrated record high working performance of 14.9 Lwater m−2 day−1 and thermal efficiency of 25.7% in indoor experiments and 3.5–8.9 Lwater m−2 day−1 in outdoor experiments by solar concentration without any other energy consumption. This work provides an up-and-coming pathway to realize highly efficient and sustainable clean water supply for off-grid and arid regions.

Since the application of electromagnetic technology，the investigation of microwave absorbing materials has been widespread concerned in the world. With the development of technology，the effect of microwave absorption technology is constantly increasing in the world. Up to now，the structural design and electrical performance simulation calculation of microwave absorption coatings play an increasingly important role in the preparation of microwave absorption coatings. The design and optimization of coating structure can directly improve the absorption performance. Nowadays，microwave absorbing materials usually have multi-layer structures such as impedance matching layer，absorption layer and bonding layer. Their reflection coefficients are determined by the thickness，magnetic permeability，and dielectric constant. Only relying on the experiments to study the influence of reflection coefficient must be inevitably resulted in a relatively broad and blind workload. Based on this， three kinds of microwave absorption coating absorbers were selected to carry out electromagnetic parameter testing and analysis. The electromagnetic properties of single layer，single layer and double layer structure combined with two kinds of absorbers were simulated and calculated under the 1.2 mm thickness of the coating. Four optimal coating design schemes were obtained through the simulation calculation，and the electrical performance experiment were further completed for comparison. The results show that the electrical performance verified by experiment is in good agreement with the trend of simulation calculation results. Finally，the optimal coating is the single-layer coating with the Absorbent A∶ Absorbent C=2∶1. At this time，the simulation calculation and experimental results of the microwave absorption coating both show relatively high absorption performance.