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Aims: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD‐code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. Methods and results: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP‐HF) and non‐HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF‐related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP‐HF as cases (PPV, 0.86; sensitivity, 0.86). From the 'HF ICD‐code universe', we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). Conclusions: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation. [ABSTRACT FROM AUTHOR]

Aims: According to the Kidney Disease: Improving Global Outcomes (KDIGO) guideline, the definition of chronic kidney disease (CKD) requires the presence of abnormal kidney structure or function for >3 months with implications for health. CKD in patients with heart failure (HF) has not been defined using this definition, and less is known about the true health implications of CKD in these patients. The objective of the current study was to identify patients with HF who met KDIGO criteria for CKD and examine their outcomes. Methods and results: Of the 1 419 729 Veterans with HF not receiving kidney replacement therapy, 828 744 had data on ≥2 ambulatory serum creatinine >90 days apart. CKD was defined as estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 (n = 185 821) or urinary albumin‐to‐creatinine ratio (uACR) >30 mg/g (n = 32 730) present twice >3 months apart. Normal kidney function (NKF) was defined as eGFR ≥60 ml/min/1.73 m2, present for >3 months, without any uACR >30 mg/g (n = 365 963). Patients with eGFR <60 ml/min/1.73 m2 were categorized into four stages: 45–59 (n = 72 606), 30–44 (n = 74 812), 15–29 (n = 32 077), and <15 (n = 6326) ml/min/1.73 m2. Five‐year all‐cause mortality occurred in 40.4%, 57.8%, 65.6%, 73.3%, 69.7%, and 47.5% of patients with NKF, four eGFR stages, and uACR >30mg/g (albuminuria), respectively. Compared with NKF, hazard ratios (HR) (95% confidence intervals [CI]) for all‐cause mortality associated with the four eGFR stages and albuminuria were 1.63 (1.62–1.65), 2.00 (1.98–2.02), 2.49 (2.45–2.52), 2.28 (2.21–2.35), and 1.22 (1.20–1.24), respectively. Respective age‐adjusted HRs (95% CIs) were 1.13 (1.12–1.14), 1.36 (1.34–1.37), 1.87 (1.84–1.89), 2.24 (2.18–2.31) and 1.19 (1.17–1.21), and multivariable‐adjusted HRs (95% CIs) were 1.11 (1.10–1.12), 1.24 (1.22–1.25), 1.46 (1.43–1.48), 1.42 (1.38–1.47), and 1.13 (1.11–1.16). Similar patterns were observed for associations with hospitalizations. Conclusion: Data needed to define CKD using KDIGO criteria were available in six out of ten patients, and CKD could be defined in seven out of ten patients with data. HF patients with KDIGO‐defined CKD had higher risks for poor outcomes, most of which was not explained by abnormal kidney structure or function. Future studies need to examine whether CKD defined using a single eGFR is characteristically and prognostically different from CKD defined using KDIGO criteria. [ABSTRACT FROM AUTHOR]

Introduction: According to the US Renal Data System (USRDS), patients with end-stage kidney disease (ESKD) on maintenance dialysis had higher mortality during early COVID-19 pandemic. Less is known about the effect of the pandemic on the delivery of outpatient maintenance hemodialysis and its impact on death. We examined the effect of pandemic-related disruption on the delivery of dialysis treatment and mortality in patients with ESKD receiving maintenance hemodialysis in the Veterans Health Administration (VHA) facilities, the largest integrated national healthcare system in the USA. Methods: Using national VHA electronic health records data, we identified 7,302 Veterans with ESKD who received outpatient maintenance hemodialysis in VHA healthcare facilities during the COVID-19 pandemic (February 1, 2020, to December 31, 2021). We estimated the average change in the number of hemodialysis treatments received and deaths per 1,000 patients per month during the pandemic by conducting interrupted time-series analyses. We used seasonal autoregressive moving average (SARMA) models, in which February 2020 was used as the conditional intercept and months thereafter as conditional slope. The models were adjusted for seasonal variations and trends in rates during the pre-pandemic period (January 1, 2007, to January 31, 2020). Results: The number (95% CI) of hemodialysis treatments received per 1,000 patients per month during the pre-pandemic and pandemic periods were 12,670 (12,525–12,796) and 12,865 (12,729–13,002), respectively. Respective all-cause mortality rates (95% CI) were 17.1 (16.7–17.5) and 19.6 (18.5–20.7) per 1,000 patients per month. Findings from SARMA models demonstrate that there was no reduction in the dialysis treatments delivered during the pandemic (rate ratio: 0.999; 95% CI: 0.998–1.001), but there was a 2.3% (95% CI: 1.5–3.1%) increase in mortality. During the pandemic, the non-COVID hospitalization rate was 146 (95% CI: 143–149) per 1,000 patients per month, which was lower than the pre-pandemic rate of 175 (95% CI: 173–176). In contrast, there was evidence of higher use of telephone encounters during the pandemic (3,023; 95% CI: 2,957–3,089), compared with the pre-pandemic rate (1,282; 95% CI: 1,241–1,324). Conclusions: We found no evidence that there was a disruption in the delivery of outpatient maintenance hemodialysis treatment in VHA facilities during the COVID-19 pandemic and that the modest rise in deaths during the pandemic is unlikely to be due to missed dialysis. [ABSTRACT FROM AUTHOR]

The maximum fire smoke temperature beneath tunnel ceilings using longitudinal ventilation was studied by both small-scale experiments and numerical simulations for a small heat release rate (HRR) fire. And then, the accuracy of the numerical simulation is verified. A numerical simulation is subsequently employed to modify the Kurioka model for cases in large HRR. Then, the modified Kurioka model is verified by various on-site high HRR fire experimental results conducted by other authors.

The red-emitting perovskite material has received widespread attention as a long-wavelength optical gain media. But the easy phase change in the air limits its practical application. Herein, red CsPbBr x I3âˆ' x /SiO2 quantum dots (QDs) are prepared by a ligand-mediated hot injection method in which 3-aminopropyl-triethoxysilane (APTES) is used instead of the usual oleylamine (OAm) ligand. Through the hydrolysis of amino groups, a thin silicon layer is formed on the QD surface, improving the stability and without causing the aggregation of QDs. We find that the ratio of I/Br and the size of QDs can be tuned by adjusting the APTES amount. Moreover, this ligand-mediated synthesis effectively passivates the surface defects, so the photoluminescence quantum yield is remarkably improved, and the carrier lifetime is prolonged. The amplified spontaneous emission is achieved under 532 nm nanosecond laser excitation. Compared with the original CsPbBrI2-OAm QD films, the threshold of CsPbBr x I3âˆ' x /SiO2 QD films is reduced from 403.5 to 98.7 ÎĽ J cmâˆ'2, and the radiation stability is significantly enhanced. Therefore, this material shows great potential in the random laser field. [ABSTRACT FROM AUTHOR]

All inorganic cesium lead halide perovskite (CsPbX3; X = Cl, Br, or I) quantum dots (QDs) have received widespread attention as a class of favorable optical gain materials. The key issue of CsPbX3 perovskites is their instability under light or exposure to air. Notably, the inherent phase instability of CsPbI3 QDs severely restricts the development of red-light emission. Herein, we propose a simple method to post-process CsPbBr3 QDs with a ZnI2 solution. The prepared CsPbBryI3−y (named CPBI-z, where z corresponds to the photoluminescence peaks' position) QDs exhibit adjustable photoluminescence in the range of 550–640 nm. The photoluminescence quantum yield is increased from 73.3% to 97.2% as the surface defects of QDs are passivated by I− and Zn2+. Compared with the pristine CsPbBr3 QDs, the post-processed QDs are more stable under ultraviolet irradiation and in the air. Aside from that, under 532 nm nanosecond laser excitation, CPBI-z QD films exhibit excellent amplified spontaneous emission performance. The CPBI-640 QD film shows a low threshold, 102 μJ cm−2 for red stable amplified spontaneous emission. [ABSTRACT FROM AUTHOR]

This paper presents the design of triple-band terahertz metamaterial absorber, whose surface structure is a nested-type metallic pattern composed of two pairs of splitting arcs, of which the inner two splitting arcs have the same but smaller size, while the outer two possess the same but larger dimension. The surface patterned structure can strongly interact with the incident terahertz beam, resulting in three narrow-band and discrete resonance peaks having higher than 94% absorption rates. Near-field patterns of the three absorption peaks are discussed to insight into the physical mechanism of these peaks, it is revealed that the first two absorption peaks result from the dipole resonance of the inner two splitting arcs and outer pair of splitting arcs, respectively, while the last absorption peak is caused by surface propagation resonance of the surface patterned resonator. The effect of structure parameters, including the sizes of splitting arcs and the spacing between two pairs of splitting arcs, on the absorption properties, is further analyzed. The absorber suggested here could provide guidance for the design of multiple-band metamaterial absorbers, and could find great application prospects in terahertz technology-related areas. [ABSTRACT FROM AUTHOR]

Objective: Hydroxychloroquine (HCQ) may prolong the QT interval, a risk factor for torsade de pointes, a potentially fatal ventricular arrhythmia. This study was undertaken to examine the cardiovascular safety of HCQ in patients with rheumatoid arthritis (RA). Methods: We conducted an active comparator safety study of HCQ in a propensity score–matched cohort of 8,852 US veterans newly diagnosed as having RA between October 1, 2001 and December 31, 2017. Patients were started on HCQ (n = 4,426) or another nonbiologic disease‐modifying antirheumatic drug (DMARD; n = 4,426) after RA diagnosis, up to December 31, 2018, and followed up for 12 months after therapy initiation, up to December 31, 2019. Results: Patients had a mean ± SD age of 64 ± 12 years, 14% were women, and 28% were African American. The treatment groups were balanced with regard to 87 baseline characteristics. There were 3 long QT syndrome events (0.03%), 2 of which occurred in patients receiving HCQ. Of the 56 arrhythmia‐related hospitalizations (0.63%), 30 occurred in patients in the HCQ group (hazard ratio [HR] associated with HCQ 1.16 [95% confidence interval (95% CI) 0.68–1.95]). All‐cause mortality occurred in 144 (3.25%) and 136 (3.07%) of the patients in the HCQ and non‐HCQ groups, respectively (HR associated with HCQ 1.06 [95% CI, 0.84–1.34]). During the first 30 days of follow‐up, there were no long QT syndrome events, 2 arrhythmia‐related hospitalizations (none in the HCQ group), and 13 deaths (6 in the HCQ group). Conclusion: Our findings indicate that the incidence of long QT syndrome and arrhythmia‐related hospitalization is low in patients with RA during the first year after the initiation of HCQ or another nonbiologic DMARD. We found no evidence that HCQ therapy is associated with a higher risk of adverse cardiovascular events or death. [ABSTRACT FROM AUTHOR]

Abstract Background The purpose of this study was to evaluate the effectiveness of a patient education program developed to facilitate statin adherence. Methods A controlled trial was designed to test the effectiveness of a multifaceted patient education program to facilitate statin adherence. The program included a brief, in-office physician counseling kit followed by patient mailings. The primary end point was adherence to filling statin prescriptions during a 120-day period. Patients new to statins enrolled and completed a survey. Data from a national pharmacy claims database were used to track adherence. Results Patients new to statin therapy exposed to a patient counseling and education program achieved a 12.4 higher average number of statin prescription fill days and were 10% more likely to fill prescriptions for at least 120 days (p = .01). Conclusion Brief in-office counseling on cardiovascular risk followed by patient education mailings can be effective in increasing adherence. Physicians found a one-minute counseling tool and pocket guidelines useful in counseling patients.

Abstract Background The internet has had a strong impact on how physicians access information and on the development of continuing medical education activities. Evaluation of the effectiveness of these activities has lagged behind their development. Methods To determine the effectiveness of a group of 48 internet continuing medical education (CME) activities, case vignette surveys were administered to US physicians immediately following participation, and to a representative control group of non-participant physicians. Responses to case vignettes were analyzed based on evidence presented in the content of CME activities. An effect size for each activity was calculated using Cohen's d to determine the amount of difference between the two groups in the likelihood of making evidence-based clinical decisions, expressed as the percentage of non-overlap, between the two groups. Two formats were compared. Results In a sample of 5621 US physicians, of the more than 100,000 physicians who participated in 48 internet CME activities, the average effect size was 0.75, an increased likelihood of 45% that participants were making choices in response to clinical case vignettes based on clinical evidence. This likelihood was higher in interactive case-based activities, 51% (effect size 0.89), than for text-based clinical updates, 40% (effect size 0.63). Effectiveness was also higher among primary care physicians than specialists. Conclusion Physicians who participated in selected internet CME activities were more likely to make evidence-based clinical choices than non-participants in response to clinical case vignettes. Internet CME activities show promise in offering a searchable, credible, available on-demand, high-impact source of CME for physicians.

Abstract Background Investigators are actively testing interventions intended to increase lifespan and wish to test whether the interventions increase maximum lifespan. Based on the fact that one cannot be assured of observing population maximum lifespans in finite samples, in previous work, we constructed and validated several tests of difference in the upper parts of lifespan distributions between a treatment group and a control group by testing whether the probabilities that observations are above some threshold defining 'old' or being in the tail of the survival distribution are equal in the two groups. However, a limitation of these tests is that they do not consider how much above the threshold any particular observation is. Methods In this article we propose new methods which improve upon our previous tests by considering not only whether an observation is above some threshold, but also the magnitudes by which observations exceed the threshold. Results Simulations show that the new methods control type I error rates quite well and that the power of the new methods is usually higher than that of the tests we previously proposed. In illustrative analyses of two real datasets involving rodents, when setting the threshold equal to 110 (100) weeks for the first (second) datasets, the new methods detected differences in 'maximum lifespan' between groups at nominal alpha levels of 0.01 (0.05) for the first (second) datasets and provided more significant results than competitor tests. Conclusion The new methods not only have good performance in controlling the type I error rates but also improve the power compared with the tests we previously proposed.

The real‐time and full concentration analysis of ethanol during the fermentation reaction allows the precise control of the microbial metabolism to promote the conversion rate; however, only few techniques can directly detect the fermentation broth without pretreatment. To address this issue, a screen‐printed biosensing microchip (SPBM) was proposed to analyze the ethanol concentration in fermentation, relying on designing a well‐defined nanocubic structure of an Au nanoparticles/nickel hexacyanoferrate nanocomposite. This nanocomposite was then made into a printing ink for the direct fabrication of a SPBM. The as‐prepared microchip exhibits an ultrahigh electrocatalysis on nicotinamide adenine dinucleotide with a high sensitivity of 96.2 ± 2.9 μA·mM−1·cm−2. Moreover, after the immobilization of alcohol dehydrogenase, the accurate and rapid monitoring of the ethanol concentration during the real fermentation reaction can be realized within an ultrawide linear range of 0.1–10 M in the broth without any pretreatment. [ABSTRACT FROM AUTHOR]

Utilizing natural wetlands to remove phosphorus (P) from agricultural drainage is a feasible approach of protecting receiving waterways from eutrophication. However, few studies have been carried out about how these wetlands, which act as buffer zones of pollutant sinks, can be operated to achieve optimal pollutant removal and cost efficiency. In this study, cores of sediments and water were collected from a lacustrine wetland of Lake Xiaoxingkai region in Northeastern China, to produce a number of lab-scale wetland columns. Ex situ experiments, in a controlled environment, were conducted to study the effects of aeration, vegetation, and iron (Fe) input on the removal of total P (TP) and values of dissolved oxygen (DO) and pH of the water in these columns. The results demonstrated the links between Fe, P and DO levels. The planting of Glyceria spiculosa in the wetland columns was found to increase DO and pH values, whereas the Fe:P ratio was found to inversely correlate to the pH values. The TP removal was the highest in aerobic and planted columns. The pattern of temporal variation of TP removals matched first-order exponential growth model, except for under aerobic condition and with Fe:P ratio of 10:1. It was concluded that Fe introduced into a wetland by either surface runoff or agricultural drainage is beneficial for TP removal from the overlying water, especially during the growth season of wetland vegetation. [ABSTRACT FROM AUTHOR]

In this paper, we propose and develop a small-scale research platform for intelligent transportation systems (ITSs). Our platform has four main parts, i.e., an arena, an indoor localization system, automated radio-controlled (RC) cars, and roadside monitoring facilities. First, to mimic traffic environments, we build an arena with a wooden floor, mock buildings, and streets. Second, to facilitate feedback control for trajectory following, an indoor localization system is set up to track the RC cars. Third, both autonomous driving RC cars and human driving RC cars are developed, based on an automated RC car design. The automated RC cars can receive control signals from a computer through an Xbee RF module and control the front and rear wheels through motors. A new control algorithm is developed to allow the RC cars to track predefined trajectories. Finally, we implement an example of roadside monitoring, which uses a fish-eye camera associated with advanced video processing for image segmentation, object identification, and tracking. Experiments are performed to demonstrate the effectiveness of the designed platform. We also discuss possible ITS research problems that can be studied in this testbed. [ABSTRACT FROM PUBLISHER]

With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize electric appliances' operation schedules, this paper considers how to quantitatively evaluate a customer's comfort satisfaction in energy-saving programs, and how to formulate the optimal energy-saving model based on this satisfaction evaluation. First, the paper categorizes the utility functions of current electric appliances into two types; time-sensitive utilities and temperature-sensitive utilities, which cover nearly all kinds of electric appliances in HEMS. Furthermore, considering the bounded rationality of customers, a novel concept called the energy-saving cost is defined by incorporating prospect theory in behavioral economics into general utility functions. The proposed energy-saving cost depicts the comfort loss risk for customers when their HEMS schedules the operation status of appliances, which is able to be set by residents as a coefficient in the automatic energy-saving program. An optimization model is formulated based on minimizing energy consumption. Because the energy-saving cost has already been evaluated in the context of the satisfaction of customers, the formulation of the optimization program is very simple and has high computational efficiency. The case study included in this paper is first performed on a general simulation system. Then, a case study is set up based on real field tests from a pilot project in Guangdong province, China, in which air-conditioners, lighting, and some other popular electric appliances were included. The total energy-saving rate reached 65.5% after the proposed energy-saving program was deployed in our project. The benchmark test shows our optimal strategy is able to considerably save electrical energy for residents while ensuring customers' comfort satisfaction is maintained. [ABSTRACT FROM AUTHOR]

The energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach employs the internet, Internet of Things (IoT), data mining, and other advanced technologies. The network aims to share energy information to realize distributed smart energy management, which can, for example, save energy while ensuring the reliability and quality of electricity for customers. Based on the network, a series of smart energy management theories are proposed to support the smart energy network. The core idea of these theories is to allow the network to teach itself in order to learn from the massive amounts of collected energy data, by using machine learning algorithms. The concept of power utility is proposed to quantitatively assess load energy efficiency. Then, a data-driven consumer energy activity recognition method is proposed based on a hidden Markov model (HMM). A test system is generated using field data from a pilot project in Guangdong Province, China. The energy saving rate in our test is 37.9%, which means that the smart energy network and the proposed algorithms perform well for automatic and intelligent energy efficiency management. [ABSTRACT FROM AUTHOR]

Background: The internet has had a strong impact on how physicians access information and on the development of continuing medical education activities. Evaluation of the effectiveness of these activities has lagged behind their development.Methods: To determine the effectiveness of a group of 48 internet continuing medical education (CME) activities, case vignette surveys were administered to US physicians immediately following participation, and to a representative control group of non-participant physicians. Responses to case vignettes were analyzed based on evidence presented in the content of CME activities. An effect size for each activity was calculated using Cohen's d to determine the amount of difference between the two groups in the likelihood of making evidence-based clinical decisions, expressed as the percentage of non-overlap, between the two groups. Two formats were compared.Results: In a sample of 5621 US physicians, of the more than 100,000 physicians who participated in 48 internet CME activities, the average effect size was 0.75, an increased likelihood of 45% that participants were making choices in response to clinical case vignettes based on clinical evidence. This likelihood was higher in interactive case-based activities, 51% (effect size 0.89), than for text-based clinical updates, 40% (effect size 0.63). Effectiveness was also higher among primary care physicians than specialists.Conclusion: Physicians who participated in selected internet CME activities were more likely to make evidence-based clinical choices than non-participants in response to clinical case vignettes. Internet CME activities show promise in offering a searchable, credible, available on-demand, high-impact source of CME for physicians. [ABSTRACT FROM AUTHOR]