Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Ambient Pressure XPS Investigation of Thermally Stable Halide Perovskite Solar Cells via Post-Treatment.

In ACS applied materials & interfaces ; h5-index 147.0

Long-term thermal stability is one limiting factor that impedes the commercialization of perovskite solar cell. Inspired by our prior results from machine learning, we discover that coating a thin layer of 4,4'-dibromotriphenylamine (DBTPA) on top of a CH3NH3PbI3 (MAPbI3) thin film can improve the stability of the resultant perovskite solar cells. The passivated perovskite solar cells retained more than 96% of their initial power conversion efficiency over 1000 h at 85 ℃ in N2 atmosphere without encapsulation. Near-ambient pressure x-ray photoelectron spectroscopy (NAP-XPS) was employed to investigate the evolution of composition and evaluate thermal and moisture stability by in-situ studies. A comparison between pristine MAPbI3 films and DBTPA-treated films shows the DBTPA treatment suppresses the escape of iodide and methylamine up to 150 oC under 5 mbar humidity. Furthermore, we have used ATR-FTIR and XPS to probe the interactions between DBTPA and MAPbI3 surface. The results prove that DBTPA coordinate with perovskite by Lewis acid-base and cation-π interaction. Compared with the 19.9% efficiency of pristine sample, the champion efficiency of passivated sample reaches 20.6%. Our results reveal DBTPA as a new post-treating molecule that not only leads to the improvement of photovoltaic efficiency but also thermal and moisture stability.

Ning Shougui, Zhang Songwei, Sun Jiaonan, Li Congping, Zheng Jingfeng, Khalifa Yehia, Zhou Shouhuan, Cao Jing, Wu Yiying

2020-Sep-04

Pathology Pathology

Chronic Lung Allograft Dysfunction Small Airways Reveal A Lymphocytic Inflammation Gene Signature.

In American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Chronic lung allograft dysfunction (CLAD) is the major barrier to long-term survival following lung transplantation, and new mechanistic biomarkers are needed. Lymphocytic bronchitis (LB) precedes CLAD and has a defined molecular signature. We hypothesized that this LB molecular signature would be associated with CLAD in small airway brushings independent of infection. We quantified RNA expression from small airway brushings and transbronchial biopsies, using RNAseq and digital RNA counting, respectively, for 22 CLAD cases and 27 matched controls. LB metagene scores were compared across CLAD strata by Wilcoxon rank sum test. We performed unbiased host transcriptome pathway and microbial metagenome analysis in airway brushes and compared machine-learning classifiers between the two tissue types. This LB metagene score was increased in CLAD airway brushes (P = 0.002) and improved prediction of graft failure (P = 0.02). Gene expression classifiers based on airway brushes outperformed those using transbronchial biopsies. While infection was associated with decreased microbial alpha-diversity (P ≤0.04), neither infection nor alpha-diversity was associated with LB gene expression. In summary, CLAD was associated with small airway gene expression changes not apparent in transbronchial biopsies in this cohort. Molecular analysis of airway brushings for diagnosing CLAD merits further examination in multicenter cohorts.

Dugger Daniel T, Fung Monica, Hays Steven R, Singer Jonathan P, Ellen Kleinhenz Mary, Leard Lorriana E, Golden Jeffrey A, Shah Rupal J, Lee Joyce S, Deiter Fred, Greenland Nancy Y, Jones Kirk D, Langelier Chaz R, Greenland John R

2020-Sep-03

Internal Medicine Internal Medicine

Patient-Centered Appointment Scheduling: a Call for Autonomy, Continuity, and Creativity.

In Journal of general internal medicine ; h5-index 57.0

When making an appointment, patients are generally unaware of how much clinician time is available to address their concerns. Similarly, the primary care clinician is often unaware of what the patient expects to accomplish during the visit, leading to uncertainty about how much time they can allot to each sequentially appearing concern, and whether they can reasonably expect to address necessary preventive services and chronic disease management. Neither patient nor clinician expectations can be adequately managed through standardized scheduling templates, which assign a fixed appointment length based on a single stated reason for the visit. As such, standardized appointment scheduling may contribute to inefficient use of valuable face-to-face time, patient and clinician dissatisfaction, and low-value care. Herein, we suggest several potential mechanisms for improving the scheduling process, including (1) entrusting scheduling to the primary care team; (2) advance visit planning; (3) pro-active engagement of ancillary team members including behavioral health, nursing, social work, and pharmacy; and (4) application of innovative, technologically advanced solutions such as telehealth and artificial intelligence to the scheduling process. These changes have the potential to improve efficiency, patient and clinician satisfaction, and health outcomes, while decreasing low-value testing and return visits for unaddressed concerns.

Matulis John C, McCoy Rozalina

2020-Sep-03

appointments and schedules, call center, healthcare quality, primary healthcare, professional autonomy

Public Health Public Health

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

In International journal of environmental research and public health ; h5-index 73.0

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

Nguyen Quynh C, Huang Yuru, Kumar Abhinav, Duan Haoshu, Keralis Jessica M, Dwivedi Pallavi, Meng Hsien-Wen, Brunisholz Kimberly D, Jay Jonathan, Javanmardi Mehran, Tasdizen Tolga

2020-Sep-01

COVID-19, GIS, big data, built environment, computer vision, machine learning

Pathology Pathology

Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.

In Viruses ; h5-index 58.0

Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

Rohaim Mohammed A, Clayton Emily, Sahin Irem, Vilela Julianne, Khalifa Manar E, Al-Natour Mohammad Q, Bayoumi Mahmoud, Poirier Aurore C, Branavan Manoharanehru, Tharmakulasingam Mukunthan, Chaudhry Nouman S, Sodi Ravinder, Brown Amy, Burkhart Peter, Hacking Wendy, Botham Judy, Boyce Joe, Wilkinson Hayley, Williams Craig, Whittingham-Dowd Jayde, Shaw Elisabeth, Hodges Matt, Butler Lisa, Bates Michelle D, La Ragione Roberto, Balachandran Wamadeva, Fernando Anil, Munir Muhammad

2020-Sep-01

LAMP, SARS-CoV-2, artificial intelligence, diagnosis, point of care

Surgery Surgery

Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA.

In European radiology ; h5-index 62.0

OBJECTIVES : A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA.

METHODS : In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression.

RESULTS : Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (p < 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (p < 0.001), the diameter of the descending aorta at the diaphragmatic level (p < 0.001) and weight (p = 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation.

CONCLUSIONS : CoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered.

KEY POINTS : • CTA is routinely ordered for infants with coarctation of the aorta; however, whether anatomical variations observed with CTA could be used to assess the severity of CoA remains unknown. • Using the diameter and area of the coarctation site adjusted to body growth as features, the LDA model achieved an accuracy of 88.6% and 90.2% in differentiating between the mild and severe CoA patients in the non-PDA group and PDA group, respectively. • The narrowest aortic diameter (CoA diameter) indexed to weight has a hazard ratio of 10.29 for re-coarctation.

Yu Yiming, Wang Yubo, Yang Maoqing, Huang Meiping, Li Jun, Jia Qianjun, Zhuang Jian, Huang Liyu

2020-Sep-03

Aortic coarctation, Computed tomography angiography, Machine learning, Re-coarctation, Risk assessment