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Chip-scale optical waveguide-assisted surface-enhanced Raman spectroscopy (SERS) that used nanoparticles (NPs) was demonstrated. The Raman signals from Raman reporter (RR) molecules on NPs can be efficiently excited by the waveguide evanescent field when the molecules are in proximity to the waveguide surface. The Raman signal was enhanced by plasmon resonance due to the NPs close to the waveguide surface. The optical waveguide mode and the NP-induced field enhancement were calculated using a finite difference method (FDM). The sensing performance of the waveguide-assisted SERS device was experimentally characterized by measuring the Raman scattering from various RRs, including 4-mercaptobenzoic acid (4-MBA), 5,5'-dithio-bis-(2-nitrobenzoic acid) (DTNB), and malachite green isothiocyanate (MGITC). The observed Raman spectral features were identified and assigned to the complex vibrational modes associated with different reporters. A low detection limit of 1 nM was achieved. In addition, the device sensing method was applied to the detection of the biomarker cardiac troponin I (cTnI) using an aptamer sandwich assay immobilized on the device surface. Overall, the optical waveguides integrated with SERS show a miniaturized sensing platform for the detection of small molecules and large proteins, potentially enabling multiplexed detection for clinically relevant applications.

Deep-red fluorescence was implemented in this fully injectable, nonenzymatic glucose biosensor design to allow for better light penetration through the skin, particularly for darker skin tones. In this work, a novel method was developed to synthesize Cy5.5 labeled mannose conjugates (Cy5.5-mannobiose, Cy5.5-mannotriose, and Cy5.5-mannotetraose) to act as the fluorescent competing ligand in a competitive binding assay with the protein Concanavalin A acting as the recognition molecule. Using fluorescence anisotropy (FA) data, a computational model was developed to determine optimal concentration ratios of the assay components to allow for sensitive glucose measurements within the physiological range. The model was experimentally validated by measuring the glucose response via FA of the three Cy5.5-labeled mannose conjugates synthesized with Cy5.5-mannotetraose demonstrating the most sensitive response to glucose across the physiological range. The developed method may be broadly applied to a vast range of commercially available fluorescent dyes and opens up opportunities for glucose measurements using nonenzymatic assays.

Fireflies were believed to originally evolve their novel bioluminescence as warning signals to advertise their toxicity to predators, which was later adopted in adult mating. Although the evolution of bioluminescence has been investigated extensively, the warning signal hypothesis of its origin has not been tested. In this study, we test this hypothesis by systematically determining the presence or absence of firefly toxin lucibufagins (LBGs) across firefly species and inferring the time of origin of LBGs. We confirm the presence of LBGs in the subfamily Lampyrinae, but more importantly, we reveal the absence of LBGs in other lineages, including the subfamilies of Luciolinae, Ototretinae, and Psilocladinae, two incertae sedis lineages, and the Rhagophthalmidae family. Ancestral state reconstructions for LBGs based on firefly phylogeny constructed using genomic data suggest that the presence of LBGs in the common ancestor of the Lampyrinae subfamily is highly supported but unsupported in more ancient nodes, including firefly common ancestors. Our results suggest that firefly LBGs probably evolved much later than the evolution of bioluminescence. We thus conclude that firefly bioluminescence did not originally evolve as direct warning signals for toxic LBGs and advise that future studies should focus on other hypotheses. Moreover, LBG toxins are known to directly target and inhibit the α subunit of Na + , K + -ATPase (ATPα). We further examine the effects of amino acid substitutions in firefly ATPα on its interactions with LBGs. We find that ATPα in LBG-containing fireflies is relatively insensitive to LBGs, which suggests that target-site insensitivity contributes to LBG-containing fireflies' ability to deal with their own toxins., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Generic drugs are essential for affordable medicine and improving accessibility to treatments. Bioequivalence (BE) is typically demonstrated by assessing a generic product's pharmacokinetics (PK) relative to a reference-listed drug (RLD). Accurately estimating cutaneous PK (cPK) at or near the site of action can be challenging for locally acting topical products. Certain cPK approaches are available for assessing local bioavailability (BA) in the skin. Stimulated Raman scattering (SRS) microscopy has unique capabilities enabling continuous, high spatial and temporal resolution and quantitative imaging of drugs within the skin. In this paper, we developed an approach based on SRS and a polymer-based standard reference for the evaluation of topical product BA and BE in human skin ex vivo. BE assessment of tazarotene-containing formulations was achieved using cPK parameters obtained within different skin microstructures. The establishment of BE between the RLD and an approved generic product was successfully demonstrated. Interestingly, within the constraints of the current study design the results suggest similar BA between the tested gel formulation and the reference cream formulation, despite the differences in the formulation/dosage form. Another formulation containing polyethylene glycol as the vehicle was demonstrated to be not bioequivalent to the RLD. Compared to using the SRS approach without a standard reference, the developed approach enabled more consistent and reproducible results, which is crucial in BE assessment. The abundant information from the developed approach can help to systematically identify key areas of study design that will enable a better comparison of topical products and support an assessment of BE., Competing Interests: Declaration of competing interest CLE is an inventor on patents for CARS microscopy that were licensed to multiple microscope manufacturers. All other authors have no conflicts of interest to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

A variety of innovative point-of-care (POC) solutions using Raman systems have been explored. However, the vast effort is in assay development, while studies of the characteristics required for Raman spectrometers to function in POC applications are lacking. In this study, we tested and compared the performance of eight commercial Raman spectrometers ranging in size from benchtop Raman microscopes to portable and handheld Raman spectrometers using paper fluidic cartridges, including their ability to detect cardiac troponin I and heart fatty acid binding protein, both of which are well-established biomarkers for evaluating cardiovascular health. Each spectrometer was evaluated in terms of excitation wavelength, laser characteristics, and ease of use to investigate POC utility. We found that the Raman spectrometers equipped with 780 and 785 nm laser sources exhibited a reduced background signal and provided higher sensitivity compared to those with 633 and 638 nm laser sources. Furthermore, the spectrometer equipped with the single acquisition line readout functionality showed improved performance when compared to the point scan spectrometers and allowed measurements to be made faster and easier. The portable and handheld spectrometers also showed similar detection sensitivity to the gold standard instrument. Lastly, we reduced the laser power for the spectrometer with single acquisition line readout capability to explore the system performance at a laser power that change the classification from a Class 3B laser device to a Class 3R device and found that it showed comparable performance. Overall, these findings show that portable Raman spectrometers have the potential to be used in POC settings with accuracy comparable to laboratory-grade instruments, are relatively low-cost, provide fast signal readout, are easy to use, and can facilitate access for underserved communities., Competing Interests: Declaration of Conflicting InterestsThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Jonathan Faircloth is an employee of Wasatch Photonics, which manufactures and sells spectrometers related to the subject of the study. Some of the spectrometers (633 X Series, 785 X Series, 785 XM Series, WP-XS-633, WP-XS-785, and WP-XS-785-SERS) were provided by Wasatch Photonics. This affiliation with Wasatch Photonics could be perceived as a potential conflict of interest, as it may influence the objectivity of the research findings in this paper. However, the study design, data collection, analysis, and interpretation of the results were conducted independently by the authors based at Texas A&M University. The decision to include Wasatch Photonics products in the study was based solely on their suitability and availability, and not on any directive from the company. The remaining authors declare no conflicts of interest. The authors have taken precautions to ensure the integrity and scientific rigor of this study.

The field of glucose biosensors for diabetes management has been of great interest over the past 60 years. Continuous glucose monitoring (CGM) is important to continuously track the glucose level to provide better management of the disease. Concanavalin A (ConA) can reversibly bind to glucose and mannose molecules and form a glucose biosensor via competitive binding. Here, we developed a glucose biosensor using ConA and a fluorescent probe, which generated a fluorescent intensity change based on solvatochromism, the reversible change in the emission spectrum dependent on the polarity of the solvent. The direction in which the wavelength shifts as the solvent polarity increases can be defined as positive (red-shift), negative (blue-shift), or a combination of the two, referred to as reverse. To translate this biosensor to a subcutaneously implanted format, Cyanine 5.5 (Cy5.5)-labeled small mannose molecules were used, which allows for the far-red excitation wavelength range to increase the skin penetration depth of the light source and returned emission. Three Cy5.5-labeled small mannose molecules were synthesized and compared when used as the competing ligand in the competitive binding biosensor. We explored the polarity-sensitive nature of the competing ligands and examined the biosensor's glucose response. Cy5.5-mannotetraose performed best as a biosensor, allowing for the detection of glucose from 25 to 400 mg/dL. Thus, this assay is responsive to glucose within the physiologic range when its concentration is increased to levels needed for an implantable design. The biosensor response is not statistically different when placed under different skin pigmentations when comparing the percent increase in fluorescence intensity. This shows the ability of the biosensor to produce a repeatable signal across the physiologic range for subcutaneous glucose monitoring under various skin tones.

Deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation; however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised domain adaptation (UDA) is a promising solution to tackle this problem. In this work, we present a novel UDA method, named dual adaptation-guiding network (DAG-Net), which incorporates two highly effective and complementary structural-oriented guidance in training to collaboratively adapt a segmentation model from a labelled source domain to an unlabeled target domain. Specifically, our DAG-Net consists of two core modules: 1) Fourier-based contrastive style augmentation (FCSA) which implicitly guides the segmentation network to focus on learning modality-insensitive and structural-relevant features, and 2) residual space alignment (RSA) which provides explicit guidance to enhance the geometric continuity of the prediction in the target modality based on a 3D prior of inter-slice correlation. We have extensively evaluated our method with cardiac substructure and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our DAG-Net greatly outperforms the state-of-the-art UDA approaches for 3D medical image segmentation on unlabeled target images.

Nicotinamide adenine dinucleotide cofactor (NAD(P)H) is regarded as an important energy carrier and charge transfer mediator. Enzyme-catalyzed NADPH production in natural photosynthesis proceeds via a hydride transfer mechanism. Selective and effective regeneration of NAD(P)H from its oxidized form by artificial catalysts remains challenging due to the formation of byproducts. Herein, electrocatalytic NADH regeneration and the reaction mechanism on metal and carbon electrodes are studied. We find that the selectivity of bioactive 1,4-NADH is relatively high on Cu, Fe, and Co electrodes without forming commonly reported NAD 2 byproducts. In contrast, more NAD 2 side product is formed with the carbon electrode. ADP-ribose is confirmed to be a side product caused by the fragmentation reaction of NAD + . Based on H/D isotope effects and electron paramagnetic resonance analysis, it is proposed that the formation of NADH on these metal electrodes proceeds via a hydrogen atom-coupled electron transfer (H ad CET) mechanism, in contrast to the direct electron-transfer and NAD˙ radical pathway on carbon electrodes, which leads to more by-product, NAD 2 . This work sheds light on the mechanism of electrocatalytic NADH regeneration, which is different from biocatalysis., Competing Interests: The authors declare no competing financial interest., (This journal is © The Royal Society of Chemistry.)

Significance: Point-of-care (POC) platforms utilizing optical biosensing strategies can achieve on-site detection of biomarkers to improve the quality of care for patients in low-resource settings., Aim: We aimed to develop a portable, multi-modal spectroscopic platform capable of performing Raman and fluorescence measurements from a single sample site., Approach: We designed the spectroscopic platform in OpticStudio using commercial optical components and built the system on a portable optical breadboard. Two excitation and collection arms were utilized to detect the two optical signals. The multi-modal functionality was validated using ratiometric Raman/fluorescence samples, and the potential utility was demonstrated using a model bioassay for cardiac troponin I., Results: The designed spectroscopic platform achieved a spectral resolution of 0.67 ± 0.2 nm across the Raman detection range (660 to 770 nm). The ratiometric Raman/fluorescence samples demonstrated no crosstalk between the two detector arms across a gradient of high molar concentrations. Testing of the model bioassay response showed that the integrated approach improved the linearity of the calibration curve from (R 2 = 0.977) for the Raman only and (R 2 = 0.972) for the fluorescence only to (R2 = 0.988) for the multi-modal approach., Conclusion: These findings demonstrate the potential impact of a multi-modal POC spectroscopic platform to improve the sensitivity and robustness necessary for biomarker detection.

Background: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance., Methods: Spirometry PDF files retrieved from one hospital between October 2017 and October 2020 were labeled according to ATS/ERS 2019 criteria and divided into training and internal test sets. Additional files from three hospitals were used for external testing. A deep learning-based model was constructed and assessed to determine acceptability, usability, and quality rating for FEV 1 and FVC. System warning messages and patient instructions were also generated for general practitioners (GPs)., Results: A total of 16,502 files were labeled. Of these, 4592 curves were assigned to the internal test set, the remaining constituted the training set. In the internal test set, the model generated 95.1%, 92.4%, and 94.3% accuracy for FEV 1 acceptability, usability, and rating. The accuracy for FVC acceptability, usability, and rating were 93.6%, 94.3%, and 92.2%. With the assistance of the model, the performance of GPs in terms of monthly percentages of good quality (A, B, or C grades) tests for FEV 1 and FVC was higher by ~ 21% and ~ 36%, respectively., Conclusion: The proposed model assisted GPs in spirometry quality assurance, resulting in enhancing the performance of GPs in quality control of spirometry., (© 2022. The Author(s).)

Indosasa hispida 'Rainbow' is a new horticultural plant variety for anthocyanin production, which has great ornamental value and huge market potential. The chloroplast genome is 139,690 bp in length, containing a large single-copy region (LSC) of 83,268 bp, a small single-copy region (SSC) of 12,830 bp, and a pair of 21,796 bp inverted repeats region (IR). The GC content of chloroplast genome is 38.9%. There are 130 genes in the cp genome, including 83 protein-coding genes, 8 ribosomal RNA genes, and 39 transfer RNA genes. In addition, phylogenetic analysis firmly supported that I. hispida 'Rainbow' constituted that a sister species with Pleioblastus maculatus ., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Ternary blending based on an alloy-like model has been proved as an efficient strategy for high-efficiency organic solar cells (OSCs). However, the third component that possesses excellent miscibility with host materials in the alloy-like model may trigger adverse effects for the active layer, especially at a high doping ratio. In this work, we propose a new concept of nonalloy model for the ternary OSCs in which the third component presents moderate miscibility with the acceptor and distributes at the interspace between donor and acceptor domains. The nonalloy model is constructed based on the PM6:Y6 system, and a Y6 analogue (BTP-MCA) is synthesized as the third component. The BTP-MCA can maintain initial excellent morphology of the active layer and enhance the morphological stability by acting as a frame around the host materials. As a result, ternary OSCs based on the PM6:Y6:BTP-MCA blend exhibit an impressive efficiency of 17.0% with a high open-circuit voltage of 0.87 V. Moreover, the devices present a high doping tolerance (keeping high efficiency with a doping ratio of 50%) and improved stability. This work indicates that the nonalloy model can be a promising method to fabricate efficient and stable ternary OSCs apart from the conventional alloy-like model.

Multiplexed assays are essential for the detection of biomarker panels. Differentiating signals from different biomarkers in a single test zone makes the detection more efficient. In this paper, a new method is designed for the synthesis of gap-enhanced nanoparticles (GeNPs) using Raman reporter molecules (RRM) and 6-amino-1-hexanethiol (6-AHT) as the spacer. The GeNPs show a nanometer-size gap, generate strong surface-enhanced Raman scattering (SERS) attributed to the gap, and exhibit discriminative spectral peaks. The strong Au-S bonds on both core and shell sides and the covalent bond between RRM and 6-AHT led to a stable structure, which ensured the stable SERS signal generation from the GeNPs. Using the GeNPs, a spectrally multiplexed assay for the detection of a biomarker panel is developed. The biomarker panel is composed of cardiac troponin I (cTnI), copeptin, and heart-type fatty acid-binding protein (h-FABP), which improves myocardial infarction (MI) diagnostic performance. A paper-based platform that is more amenable to point-of-care diagnostic analysis is used. The developed single biomarker assay achieves limits of detection of 0.01 ng mL -1 , 0.86 ng mL -1 , 0.004 ng mL -1 for cTnI, h-FABP, and copeptin in buffer solutions. The dynamic range of the assay in human serum samples also covers the clinically relevant range of the biomarkers. The cross interference in the multiplexed assay is low. These results show the strong potential of the developed GeNPs in multiplexed detection of biomarkers and the developed simple-to-use multiplexed assay in the diagnosis of MI at the point of care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians., Methods: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flow-volume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data., Results: Of 45,831 patients' spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5-52.5%), with poor agreements (κ = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001)., Conclusions: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians' correct diagnosis of upper airway obstruction using spirometry., (© 2022 S. Karger AG, Basel.)

[This corrects the article DOI: 10.1038/s42256-021-00421-z.]., (© The Author(s) 2022.)

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health., Competing Interests: Competing interests The authors declare no competing interests.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Surface-enhanced Raman scattering (SERS) is a sensitive analytical technique capable of magnifying the vibrational intensity of molecules adsorbed onto the surface of metallic nanostructures. Various solution-based SERS-active metallic nanostructures have been designed to generate substantial SERS signal enhancements. However, most of these SERS substrates rely on the chemical aggregation of metallic nanostructures to create strong signals. While this can induce high SERS intensities through plasmonic coupling, most chemically aggregated assemblies suffer from poor signal reproducibility and reduced long-term stability. To overcome these issues, here we report for the first time the synthesis of gold core-satellite nanoparticles (CSNPs) for robust SERS signal generation. The novel CSNP assemblies consist of a 30 nm spherical gold core linked to 18 nm satellite particles via linear heterobifunctional thiol-amine terminated PEG chains. We explore the effects that the varying chain lengths have on SERS hot-spot generation, signal reproducibility and long-term activity. The chain length was varied by using PEGs with different molecular weights (1000 Da, 2000 Da, and 3500 Da). The CSNPs were characterized via UV-Vis spectrophotometry, transmission electron microscopy (TEM), ζ -potential measurements, and lastly SERS measurements. The versatility of the synthesized SERS-active CSNPs was revealed through characterization of optical stability and SERS enhancement at 0, 1, 3, 5, 7 and 14 days., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages CT scans and the clinical data of outpatients and achieves an AUC of 0.920 in the single-center study. We also propose a domain adaptation approach to improve the generalization of our model and achieve an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

A microfluidic paper-based analytical device (μPAD) is a cost-effective platform to implement assays, especially for point-of-care testing. Developing μPADs with fluidic control is important to implement multistep assays and provide high sensitivities. However, current localized delays in μPADs made of sucrose have a limited ability to decrease the flow rate. In addition, existing μPADs for automatic multistep assays are limited by their need for auxiliary instruments, their false activation, or their unavoidable tradeoff between available fluid volumes and temporal differences between steps. Here, a novel μPAD composed of a localized dissolvable delay and a horizontal motion mechanical valve for use as an automatic multistep assay is reported. A mixture of fructose and sucrose was used in the localized dissolvable delay and it provided an effective decrease in the flow rate to ensure adequate sensitivity in an assay. The dissolvable delay effectively doubled the flow time. A mechanical valve using a horizontal movement was developed to automatically implement a multistep process. Two-step and four-step processes were enabled with the μPAD. Cardiac troponin I (cTnI), a gold-standard biomarker for myocardial infarction, was used as a model analyte to show the performance of the developed μPAD in an assay. The designed μPAD, with the simple-to-make localized dissolvable delay and the robust mechanical valve, provides the potential to automatically implement high-performance multistep assays toward a versatile platform for point-of-care diagnostics.

N-type semiconducting polymers are attractive for organic electronics, but desirable electron-deficient units for synthesizing such polymers are still lacking. As a cousin of rylene diimides such as naphthalene diimide (NDI) and perylene diimide (PDI), anthracene diimide (ADI) is a promising candidate; its polymers, however, have not been achieved yet because of synthetic challenges for its polymerizable monomers. Herein, we present ingenious synthesis of two dibromide ADI monomers with dibromination at differently symmetrical positions of the ADI core, which are further employed to construct ADI polymers. More interestingly, the two obtained ADI polymers possess the same main-chain and alkyl-chain structures but different backbone conformations owing to varied linking positions between repeating units. This feature enables their different optoelectronic properties and film-state packing behavior. The ADI polymers offer first examples of conjugated polymer conformational isomers and are highly promising as a new class of n-type semiconductors for various organic electronics applications., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Cardiovascular diseases (CVDs) cause significant mortality globally. Notably, CVDs disproportionately negatively impact underserved populations, such as those that are economically disadvantaged and often located in remote regions. Devices to measure cardiac biomarkers have traditionally been focused on large instruments in a central laboratory but the development of affordable, portable devices that measure multiple cardiac biomarkers at the point-of-care (POC) are needed to improve clinical outcomes for patients, especially in underserved populations. Considering the enormity of the global CVD problem, complexity of CVDs, and the large candidate pool of biomarkers, it is of great interest to evaluate and compare biomarker performance and identify potential multiplexed panels that can be used in combination with affordable and robust biosensors at the POC toward improved patient care. This review focuses on describing the known and emerging CVD biosensing technologies for analysis of cardiac biomarkers from blood. Initially, the global burden of CVDs and the standard of care for the primary CVD categories, namely heart failure (HF) and acute coronary syndrome (ACS) including myocardial infarction (MI) are discussed. The latest United States, Canadian and European society guidelines recommended standalone, emerging, and add-on cardiac biomarkers, as well as their combinations are then described for the prognosis, diagnosis, and risk stratification of CVDs. Finally, both commercial in vitro biosensing devices and recent state-of-art techniques for detection of cardiac biomarkers are reviewed that leverage single and multiplexed panels of cardiac biomarkers with a view toward affordable, compact devices with excellent performance for POC diagnosis and monitoring., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images. Materials and Methods Head CT angiography images were retrospectively retrieved from two hospital databases acquired across four different scanners between January 2015 and June 2019. The data were divided into training and validation sets; 400 additional independent CT angiograms acquired between July and December 2019 were used for external validation. A deep learning-based algorithm was constructed and assessed. Both internal and external validation were performed. Jackknife alternative free-response receiver operating characteristic analysis was performed. Results A total of 1068 patients (mean age, 57 years ± 11 [standard deviation]; 660 women) were evaluated for a total of 1068 CT angiograms encompassing 1337 cerebral aneurysms. Of these, 534 CT angiograms (688 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5% (633 of 649; 95% CI: 96.0, 98.6). Moreover, eight new aneurysms that had been overlooked in the initial reports were detected (1.2%, eight of 649). With the aid of the algorithm, the overall performance of radiologists in terms of area under the weighted alternative free-response receiver operating characteristic curve was higher by 0.01 (95% CI: 0.00, 0.03). Conclusion The proposed deep learning algorithm assisted radiologists in detecting cerebral aneurysms on CT angiography images, resulting in a higher detection rate. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kallmes and Erickson in this issue.

Significance: Cardiac troponin I (cTnI) is a primary biomarker for diagnosis of myocardial infarction (MI). In contrast to central laboratory tests for cTnI, point-of-care (POC) testing has the advantage of providing results when the patient is first encountered, which helps high-risk patients to be treated more rapidly and low-risk patients to be released in a timely fashion. A paper fluidic platform is good for POC testing because the paper is abundant, low cost, and disposable. However, current cTnI assays on paper platforms use antibodies as the recognition element, which has limitations due to the high cost of production and antibody stability issues at the POC., Aim: To develop an aptamer-based assay on a paper strip using surface-enhanced resonance Raman spectroscopy (SERRS) for detection of cTnI in the clinically relevant range at the POC., Approach: Gold nanoparticles (AuNPs) were functionalized with a Raman reporter molecule, malachite green isothiocyanate. The functionalized AuNPs were encapsulated in a silica shell and provided a SERRS signal using a handheld Raman system with a 638-nm excitation wavelength. A primary aptamer and a secondary aptamer of cTnI were used in a sandwich assay format to bind the cTnI on a test line of a paper fluidic platform. By measuring the SERRS signal from the test line, the concentration of cTnI was quantitatively determined., Results: The aptamer-based SERRS assay on a paper strip had a detection range of 0.016 to 0.1  ng  /  ml for cTnI, had good selectivity for cTnI compared to three other markers, had good stability over 10 days, and had good performance in the more complex serum sample matrix., Conclusions: The aptamer-based SERRS assay on a paper strip has the potential to provide a sensitive, selective, stable, repeatable, and cost-effective platform for the detection of cTnI toward eventual use in diagnosis of MI at the POC.

Raman spectroscopy using aluminum nitride (AlN) optical waveguides was demonstrated for organic compound analysis. The AlN waveguide device was prepared by reactive sputtering deposition and complementary-metal-oxide semiconductor (CMOS) processes. A fundamental waveguide mode was observed over a broad visible spectrum and the waveguide evanescent wave was used to excite the Raman signals of the test analytes. The performance of the waveguide sensor was characterized by measuring the Raman spectra of the benzene derivative mixtures consisting of benzene, anisole, and toluene. The compositions and concentrations were resolved by correlating the obtained Raman spectrum with the characteristic Raman peaks associated with C-C, C-H, and C-O functional groups. With the advantages of real-time detection and enhanced Raman signal intensity, the AlN waveguides provided a sensor platform for nondestructive and online chemical compound monitoring.

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world., Competing Interests: Competing interests The authors declare no competing interests.

A 2D conjugated polyelectrolyte (CPE), PBDTTh-TT-NBr, having the same backbone as the highly efficient donor polymer PTB7-Th and the quaternary ammonium pendant, is synthesized as a cathode interlayer (CIL) material for PTB7-Th-based fullerene and non-fullerene solar cells. The quaternary ammonium group is connected to the 2D conjugated backbone by a long, flexible alkyl chain, facilitating the modification of cathode via forming interface dipoles. Moreover, compared with another CPE analogue to PTB7, PBDT-TT-NBr, without the 2D conjugated backbone, the PBDTTh-TT-NBr presents a higher similarity in polymer structure to the donor polymer PTB7-Th. This feature makes it more compatible with the PTB7-Th-based active-layer film, improving the electron transport. With the PBDTTh-TT-NBr as the CIL, devices afford higher performances than those using the PBDT-TT-NBr in both fullerene and non-fullerene systems. This work offers guidance on choosing the CIL material that ought to possess a highly similar structure to the active-layer component., (© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Methoxydiphenylamine-substituted carbazole (MODPACz) is widely used to construct hole-transporting materials (HTMs) for perovskite solar cells (PSCs), whose performances rely highly on the linking way of the MODPACz units and the simplicity of the π-bridge. In this paper, we report a new HTM, p Ph-2MODPACz, using one of the simplest π-bridges p -phenylene to link the MODPACz units. The structural feature endows p Ph-2MODPACz with high hole mobility and conductivity, efficient hole extraction ability, and good film-forming property. MAPbI 3 -based PSCs using doped and undoped p Ph-2MODPACz as the HTM offer efficiencies of ∼20% and 16.07%, respectively; both are better than those of the devices with spiro-OMeTAD as the HTM. The device stability of p Ph-2MODPACz-based PSCs is also greatly enhanced. This work demonstrates that the simplest p -phenylene bridge for linking MODPACz can derive a promising HTM with a high device performance, providing a distinctive pathway to develop new HTMs.

A polymeric ionic liquid (PIL) was synthesized from a small-molecule one (MIL, the monomer), and they were used as cathode interlayers in organic solar cells. Compared with the MIL, the PIL can form a more uniform film for full coverage of an active layer and efficient cathode modification, resulting in higher device efficiency and reproducibility.

Bis(2-ethylhexyl) phthalate (DEHP) is an endocrine disruptor commonly present in plastic products, such as PVC tubes and water bottles. In this work, a surface enhanced Raman spectroscopy (SERS) based aptasensor was developed and utilized for rapid, easy, sensitive, and specific detection of trace DEHP. A DEHP aptamer was immobilized on magnetic particles. Raman reporter molecule conjugated silver nanoparticles were clustered and coated with silica to provide a stable SERS signal. The SERS silica particle was then functionalized with 1,2,4-benzenetricarboxylic acid 1,2-bis(2-ethylhexyl) ester to increase its affinity to the DEHP aptamer. In the presence of a sample with DEHP, the high-affinity SERS silica particle competes with the DEHP molecule to bind with the aptamer on the magnetic particle. By measuring the signal of free SERS silica particles in the supernatant after magnetic separation, the concentration of DEHP in the sample was quantitatively determined. The developed DEHP aptasensor had a detection range from 0.008 to 182 nM and a limit of detection (LOD) of 8 pM. The aptasensor also showed high selectivity when exposed to interferents with analogous structures. The aptasensor was successfully tested for the detection of DEHP spiked in tap water, bottled water, and a carbonate beverage. The developed SERS-based aptasensor provides a rapid, sensitive, and easy-to-use method for the quantitative detection of DEHP in environmental and food analysis., Competing Interests: Conflicts of interest There are no conflicts to declare.

Inspired by the structural feature of the classical hole-transport material (HTM), Spiro-OMeTAD, many analogues based on a highly symmetrical spiro-core were reported for perovskite solar cells (PSCs). However, these HTMs were prone to crystallize because of the high molecular symmetry, forming non-uniform films, unfavorable for the device stability and large-area processing. By lowering the symmetry of spiro-core, we report herein a novel spirobisindane-based HTM, Spiro-I, which could form amorphous films with high uniformity and morphological stability. Compared to the Spiro-OMeTAD-based PSCs, those containing Spiro-I exhibit similar efficiencies for small area but higher ones for large area (1 cm 2 ), and especially much higher air stability (retaining 80 % of initial PCE after 2400 h storage without encapsulation). Moreover, the Spiro-I can be synthesized from a cheap starting material bisphenol A and used with a small amount for the device fabrication., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

New synthetic procedures for the pyrrolo[2,3-b]indole skeleton have been developed via intramolecular annulation of indole derivatives under iodine(III). A series of indole derivatives with different protecting groups or substitutions were explored to facilitate the corresponding pyrrolo[2,3-b]indole compounds in excellent yields.