High Performance Research Articles

Financial decision dynamics: Environmental, social, and governance factors and managerial choices in emerging markets

This study examines the linkage between the ESG performance of firms and managerial decision-making. It uses 8 years of ESG performance data of 110 Indian firms collected from the Thomson Reuters database. The study finds that high ESG performance mitigates a firm’s cost of debt more than equity cost. Furthermore, increases in ESG performance are associated with higher capital expenditures, lower investment cash flow sensitivity, higher cash holdings, and lesser payout of increased cash flows to shareholders. The findings are in sync with the Slack resources view, suggesting that the requirement of financial Slack for ESG-related innovations and risk-bearing is large and impacts the major managerial decisions of firms. While the effects of environmental and social performance on these decisions are similar to that of the composite ESG performance, governance performance shows an independent impact on the payout decisions of firms. The Granger Causality test reveals a bi-directional association between debt issues and ESG performance. The main results obtained from the multiple panel regressions are robust to additional tests involving lead-lag analysis. The overall findings of this study highlight the importance of ESG performance screening in the managerial decision-making process of investors who would not like to risk growth potential for a short-term increase in dividends.

Characterising and preventing the gut microbiota's inactivation of trifluridine, a colorectal cancer drug

The gut microbiome can metabolise hundreds of drugs, potentially affecting their bioavailability and pharmacological effect. As most gut bacteria reside in the colon, drugs that reach the colon in significant proportions may be most impacted by microbiome metabolism. In this study the anti-colorectal cancer drug trifluridine was used as a model drug for characterising metabolism by the colonic microbiota, identifying correlations between bacterial species and individuals’ rates of microbiome drug inactivation, and developing strategies to prevent drug inactivation following targeted colonic delivery. High performance liquid chromatography and ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry demonstrated trifluridine's variable and multi-route metabolism by the faecal microbiota sourced from six healthy humans. Here, four drug metabolites were linked to the microbiome for the first time. Metagenomic sequencing of the human microbiota samples revealed their composition, which facilitated prediction of individual donors’ microbial trifluridine inactivation. Notably, the abundance of Clostridium perfringens strongly correlated with the extent of trifluridine inactivation by microbiota samples after 2 hours (R2 = 0.8966). Finally, several strategies were trialled for the prevention of microbial trifluridine metabolism. It was shown that uridine, a safe and well-tolerated molecule, significantly reduced the microbiota's metabolism of trifluridine by acting as a competitive enzyme inhibitor. Further, uridine was found to provide prebiotic effects. The findings in this study greatly expand knowledge on trifluridine's interactions with the gut microbiome and provide valuable insights for investigating the microbiome metabolism of other drugs. The results demonstrate how protection strategies could enhance the colonic stability of microbiome-sensitive drugs.

Diagnosing skin cancer using social spider optimization (SSO) and error correcting output codes (ECOC) with weighted hamming distance

Skin cancer is a common disease resulting from genetic defects, and early detection is critical to improve treatment outcomes. Diagnostic programs that use computer aid especially those that use supervised learning are very useful in early diagnosis of skin cancer. This research therefore presents a new approach that integrates optimization methods with supervised learning to improve skin cancer diagnosis using machine vision approach. The presented method is initiated by data pre-processing that involves the removal of unnecessary data. Then, to segment the images, a combination of K-means clustering and social spider optimization technique is employed. The region of interest is then extracted from the segmented image and from this region a convolutional neural network extracts the significant features. To enhance the classification performance as compared with the standard classifiers, this research introduces a new concept of error correcting output codes coupled with a weighted Hamming distance in the group of gamma classifiers. The ability of the proposed approach in segmentation of skin lesions and classifying them was tested using samples from the ISIC-2017 and ISIC-2016 databases. The introduced method obtained state-of-the-art accuracy on both datasets (ISIC-2016: 97.10%, ISIC-2017: 95.17%). In particularly, the accuracy of the introduced approach for both these databases is at least 1.17% higher than the compared methods. This proves the high performance of the suggested method based on the usage of the convolutional neural networks for feature extraction and gamma classifiers with error correcting output codes for classification in skin cancer detection.

Substrate preference, RNA binding and active site versatility of Stenotrophomonas maltophilia nuclease SmNuc1, explained by a structural study.

Nucleases of the S1/P1 family have important applications in biotechnology and molecular biology. We have performed structural analyses of SmNuc1 nuclease from Stenotrophomonas maltophilia, including RNA cleavage product binding and mutagenesis in a newly discovered flexible Arg74-motif, involved in substrate binding and product release and likely contributing to the high catalytic rate. The Arg74Gln mutation shifts substrate preference towards RNA. Purine nucleotide binding differs compared to pyrimidines, confirming the plasticity of the active site. The enzyme-product interactions indicate a gradual, stepwise product release. The activity of SmNuc1 towards c-di-GMP in crystal resulted in a distinguished complex with the emerging product 5'-GMP. This enzyme from an opportunistic pathogen relies on specific architecture enabling high performance under broad conditions, attractive for biotechnologies.

A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans

BackgroundLiver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.MethodsWe designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model’s robustness.ResultsThe H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9.ConclusionThe proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.

Fine-grained Fidgety Movement Classification using Active Learning.

Typically developing infants, between the corrected age of 9-20 weeks, produce fidgety movements. These movements can be identified with the General Movement Assessment, but their identification requires trained professionals to conduct the assessment from video recordings. Since trained professionals are expensive and their demand may be higher than their availability, computer vision-based solutions have been developed to assist practitioners. However, most solutions to date treat the problem as a direct mapping from video to infant status, without modeling fidgety movements throughout the video. To address that, we propose to directly model infants' short movements and classify them as fidgety or non-fidgety. In this way, we model the explanatory factor behind the infant's status and improve model interpretability. The issue with our proposal is that labels for an infant's short movements are not available, which precludes us to train such a model. We overcome this issue with active learning. Active learning is a framework that minimizes the amount of labeled data required to train a model, by only labeling examples that are considered "informative" to the model. The assumption is that a model trained on informative examples reaches a higher performance level than a model trained with randomly selected examples. We validate our framework by modeling the movements of infants' hips on two representative cohorts: typically developing and at-risk infants. Our results show that active learning is suitable to our problem and that it works adequately even when the models are trained with labels provided by a novice annotator.

Fully Automated Detection of the Appendix Using U-Net Deep Learning Architecture in CT Scans

Background: The accurate segmentation of the appendix with well-defined boundaries is critical for diagnosing conditions such as acute appendicitis. The manual identification of the appendix is time-consuming and highly dependent on the expertise of the radiologist. Method: In this study, we propose a fully automated approach to the detection of the appendix using deep learning architecture based on the U-Net with specific training parameters in CT scans. The proposed U-Net architecture is trained on an annotated original dataset of abdominal CT scans to segment the appendix efficiently and with high performance. In addition, to extend the training set, data augmentation techniques are applied for the created dataset. Results: In experimental studies, the proposed U-Net model is implemented using hyperparameter optimization and the performance of the model is evaluated using key metrics to measure diagnostic reliability. The trained U-Net model achieved the segmentation performance for the detection of the appendix in CT slices with a Dice Similarity Coefficient (DSC), Volumetric Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), Hausdorff Distance 95 (HD95), Precision (PRE) and Recall (REC) of 85.94%, 23.29%, 1.24 mm, 5.43 mm, 86.83% and 86.62%, respectively. Moreover, our model outperforms other methods by leveraging the U-Net’s ability to capture spatial context through encoder–decoder structures and skip connections, providing a correct segmentation output. Conclusions: The proposed U-Net model showed reliable performance in segmenting the appendix region, with some limitations in cases where the appendix was close to other structures. These improvements highlight the potential of deep learning to significantly improve clinical outcomes in appendix detection.

How relational and collective identification shape the relationship between individual ambidexterity and job performance

AsbtractIn the contemporary business landscape, employees are increasingly expected to demonstrate a wide range of competencies, which require a combination of exploration‐based and exploitation‐based individual abilities. In this vein, the present study investigates how individual ambidextrous abilities affect job performance. Furthermore, by bridging the gap between individual ambidexterity literature and dynamic self‐concept theory, we argue that identification with both peers and leaders plays a contingency role, thus moderating the relationship between individual ambidexterity and job performance. To test our hypotheses, we conducted a multi‐source field study of 380 employees operating in manufacturing firms. The collected data combines objective measures of individual ambidextrous abilities, supervisory‐rated job performance, and identification measures. Our hypotheses received empirical support. Overall, this study provides practical guidance for organizations by highlighting the importance of fostering individual ambidexterity to maintain high job performance through effective cooperation with both peers and leaders at work.

Combined simple adaptive and integral terminal sliding mode control for industrial feed drive systems

In the manufacturing industry, feed drive systems are utilized for various applications in industrial machines. These systems are crucial components for ensuring precision in motion control and are, therefore, expected to offer high tracking performance. This study focuses on application of integral terminal sliding mode control (ITSMC) to a feed drive system because of its robustness against disturbances and convergence of the tracking error in finite-time. However, its design requires plant dynamics model. To alleviate this concern, a solution of modifying the original ITSMC law to adaptive ITSMC law based on simple adaptive control (SAC) technique is used. To implement SAC-like adaptive law to a real plant, a property of “almost strictly positive real (ASPR)” is required to be satisfied. ASPR property is satisfied using velocity-based augmented output signal that ensures the stability. In experimental results, the adaptive controller follows the reference trajectory more precisely than conventional methods.

B-155 Analysis of Monoclonal Antibodies Using a Quantification LC-MS/MS Kit - Spotlight on Infliximab

Abstract Background The use of immunoassays for measuring mAbs is well established but has some limitations, including poor performance, lack of standardization, a high cost when processing a limited number of samples, limited dynamic range, and the potential for cross-reactivity. Using LC-MS/MS associated with the ready-to-use commercial mAbXmise Kit is a simple way to implement mAbs measurement for infliximab, providing high analytical performance, ease of use, and flexibility. Methods The Promise Proteomics Inflammation mAbXmise Kit contains all the calibrators, QCs, internal standard and sample preparation consumables to run the LC-MS/MS method for infliximab. Briefly, 20µL samples are dispensed into the mAbXmise plates containing lyophilized stable labelled infliximab. Samples are transferred to the PuriXmise plate to perform the immunocapture of infliximab and allow washing of the samples. Samples are eluted into a collection plate, evaporated to dryness, resuspended, and the protease (CutXmise) is added to the sample to digest infliximab overnight. The digestion is quenched, with the samples immediately ready for analysis. Using an ACQUITY™ UPLC™ I-Class PLUS FL System, samples were injected onto a Waters™ XSelect™ Premier HSS T3 Column, eluted using a water/acetonitrile/formic acid gradient profile and quantified with both a Waters Xevo™ TQ-XS and Xevo TQ-S micro Mass Spectrometer. Results The method was shown to be linear from 2 - 100 µg/mL. Analytical sensitivity at lowest calibrator of 2µg/mL provided S/N (PtP) ≥15:1 for all tryptic peptides on both mass spectrometers. Coefficients of variation (CV) at 4 and 25µg/mL QC concentrations were all ≤ 10.0% (n = 5). The relative concentrations of the tryptic peptides were compared across 29 anonymized samples and good agreement was observed for GLE, SAV, DIL and ASA peptides. Conclusions We have demonstrated a clinical research method for quantifying Infliximab in plasma using a commercially available kit for sample preparation followed by LC-MS/MS analysis. The Promise Proteomics mAbXmise Kit aids in the day-to-day reproducibility of the immunocapture and tryptic digestion of IFX for targeted LC-MS/MS analysis with the correct peptide selection. Compared to a direct digest surrogate peptide workflow, the process used in the kit reduces matrix interference, improves analytical sensitivity, and enables the use of lower sample volumes. The method can be run on both the Xevo TQ-XS and Xevo TQ-S micro Mass Spectrometers which provide the dynamic range to quantify IFX across the expected range, and the selectivity and analytical sensitivity to obtain low-level quantification of the IFX surrogate peptide in plasma samples. The data presented combines the use of a kit dedicated to the preparation of samples and the use of liquid chromatography and mass spectrometry instrumentation to perform the quantitative analyses. The mAbXmise Kit described has not been cleared by any regulatory entity for diagnostic purposes outside of Europe. Analytical performance depends on the instrument characteristics and its settings. The end user is responsible for completion of the method development and validation. Proteomics mAbXmise Kits are not available for sale in all countries. For Research Use Only. Not for use in diagnostic procedures.

Controlled synthesis of the M doped (M = Fe, Cu, Zn, Mn)-Co4S3/Ni3S2 catalyst with high electrocatalytic performance for hydrogen evolution reaction in seawater and urea

Electrolysis of seawater is a promising method for producing hydrogen, but sluggish reaction kinetics and electrolyte containing corrosive ions limit its development. In this work, a series of M doped (M = Fe, Cu, Zn, Mn)-Co4S3/Ni3S2 catalyst has been prepared on Ni foam by hydrothermal method with excellent seawater and urea splitting performance. The unique 3D porous structure provides a large number of active sites for the catalyst, which has strong catalytic activity. Fe-Co4S3/Ni3S2 electrode showed excellent hydrogen evolution reaction (HER) catalytic activity in 1 M KOH + seawater and 1 M KOH + 0.5 M urea, driving a current density of 10 mA cm−2 at a low overpotential of 81 and 66 mV. In addition, Fe-Co4S3/Ni3S2 electrode also shows strong stability in 15 h stability test. The results show that although the impurity ions in seawater affect the activity of catalyst, the corrosion resistance of catalyst also improves the catalytic activity and stability. Density functional theory calculations show that this Fe-Co4S material exhibits the optimal Gibbs free energy of hydrogen, the introduction of this Ni3S2 material enhances the electrical conductivity of the material, and the synergistic catalysis of the two materials promotes the optimal hydrogen production performance of the Fe-Co4S3/Ni3S2 material. This work provides a novel understanding for the research of catalysts used in the electrolysis of seawater and urea.

B-047 A Rapid Host-Response Test Supports Antimicrobial Stewardship at a Micro-Hospital Emergency Department

Abstract Background The management of patients with suspected infections requires identification of the infectious etiology to determine appropriate use of antibiotics. However, differentiating viral from bacterial infection (and co-infection) is often challenging as clinical presentations can be similar and existing diagnostics sometimes fail to identify a clinically relevant pathogen. A host-response test (MeMed BV®, MMBV) that relies on computational integration of three proteins (TRAIL, IP-10 and CRP) measured from blood or serum has demonstrated high diagnostic performance for differentiating bacterial from viral infections, with a negative predictive value >95% across multiple studies. This report evaluates real-world use of MMBV at a micro-hospital Emergency Department (ED) and associated antibiotic prescribing. Methods The study is a retrospective analysis of real-world data collected between January and June 2023. MMBV was ordered by providers at a micro-hospital ED in Tucson, AZ at provider discretion as part of routine care. Prescription among cases with MMBV score <35 was analyzed. This cutoff is indicated by the manufacturer to indicate a viral or other non-bacterial etiology. If an antibiotic was prescribed, the authors conducted a chart review to adjudicate whether the prescription was warranted. Results Data was evaluated between January and June 2023; 116 MMBV tests were ordered by six providers. Most tests (81.9% (95%CI: ±7.0%)) had an MMBV score <35 (mean score 11.3 with standard deviation 10.8). Among these, 92.6% (95%CI: ±5.3%) of cases with viral results were not prescribed antibiotics. MMBV use increased throughout the study period from 12 tests in the first month to 25 in the last month (r = 0.88; p<0.001). There were seven cases where providers chose to prescribe despite a viral MMBV result. Upon chart review, 2/7 were unwarranted prescriptions. Conclusions In the emergency settings, integrating MMBV can effectively guide clinical decision-making, potentially reducing unnecessary antibiotic use. Further research in diverse healthcare settings is needed to validate these findings.

A-021 Evaluation of a Novel N Terminal Pro B Type Natriuretic Peptide Assay as an aid in the Diagnosis of Acute Heart Failure in the Emergency Department

Abstract Background The signs and symptoms of acute heart failure (AHF) may overlap with other conditions and lead to diagnostic difficulty in the undifferentiated patient with dyspnea. The particularly high negative predictive value (NPV) of natriuretic peptides (NPs) in excluding AHF have rendered these assays especially useful in evaluating dyspnea in acute care setting. This study was conducted to assess the diagnostic performance of a novel N-terminal pro-B type natriuretic peptide (NT-proBNP) assay in the intended use patient population in the emergency department (ED). Methods A multicenter study was conducted in the U.S. on individuals presenting to the ED with dyspnea and a suspicion of AHF. Blood specimens were collected and tested using the Beckman Coulter Access NT-proBNP assay on the DxI 9000 Immunoassay Analyzer. Test results were compared to an adjudicated diagnosis clinical endpoint performed by an independent Clinical Events Committee blinded to the Access NT-proBNP result. Performance included NPV and sensitivity of an age-independent cut point of <300 ng/L to exclude AHF, and the positive predictive value (PPV) of the age-dependent cut points of >450, >900, and >1800 ng/L for ages <50 years, 50-75 years, and >75 years, respectively, for the diagnosis of AHF. The sample size required to achieve a 95% two tailed confidence interval (CI) was 150 subjects per age group to provide clinical performance at 90% (±10%) sensitivity. Sample size was based upon CLSI EP24-A2, Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves. Sensitivity and specificity were calculated at a series of cutoff points and plotted in separate receiver operation curves (ROC) for an overall age-independent rule-out analysis and for individual age-dependent groups separately. Area under the receiver operator characteristic curve (AUROC) was reported with 95% 2-sided CI. NPV and PPV with respective 95% 2-sided score CI was computed for the overall age-independent cut point and for each age group cohort. Results Of 490 enrolled patients, 41% were adjudicated with AHF with a median (Q1, Q3) age of 60 (47-76) years, and distributions of 46.1% female, 60.8% White and 38.2% Black. The assay had an AUROC for AHF of 0.88 (P<0.001), comparable to other commercially available NT-proBNP assays. The rule-out cut point of <300 ng/L had 96% sensitivity and NPV of 95%. Age-dependent cut points had sensitivity of 84%, 90%, and 87%, specificity of 81%, 70% and 61%, and PPV of 72%, 62%, and 74%, respectively. Conclusions The Access NT-proBNP assay demonstrated high clinical performance in the diagnosis and exclusion of acute HF in the undifferentiated dyspneic patient population and performed similarly well to validated assays used in current clinical practice. Given the continued increase in importance and reach of natriuretic peptide testing, these results are useful to extend availability of this assay to a larger audience.

Bioinspired 4D Printed Tubular/Helicoidal Shape Changing Metacomposites for Programmable Structural Morphing

AbstractBiological structures combine passive shape‐changing with force generation through intricate composite architectures. Natural fibers, with their tubular‐like structures and responsive components, have inspired the design of pneumatic tubular soft composite actuators. However, no development of passive structural actuation is available despite the recent rise of 4D printing. In this study, a biomimicry approach is proposed with inspiration from natural fiber architecture to create a novel concept of thermally active 4D printed tubular metacomposites. These metacomposites exhibit high mechanical performance and 3D‐to‐3D shape‐changing ability triggered by changes in temperature. A rotative printer is proposed for winding a continuous carbon fibers reinforced PolyAmide 6.I composite on a PolyAmide 6.6 polymer mandrel in a similar manner to the structure of cellulose microfibrils within the polysaccharide matrix of natural fiber cell‐walls. The resulting 4D printed tubular metacomposites exhibit programmable rotation and torque in response to thermal variations thanks to the control of their mesostructure and the overall geometry. Energy density values representing a trade‐off between the rotation and the torque are comparable to shape memory alloys when normalized by stiffness. Finally, a proof of concept for an autonomous solar tracker is presented, showcasing its potential for designing autonomous assemblies for structure morphing.

Highly Conductive Supramolecular Salt Gel Electrolyte for Flexible Supercapacitors.

Conductive gels have greatly facilitated the development of flexible energy storage devices, including supercapacitors, batteries, and triboelectric nanogenerators. However, it is challenging for gel electrolytes to tackle the trade-off issues between mechanical properties and conductivity. Herein, a strategy of all inorganic salt-driven supramolecular networks is presented to construct gel electrolytes with high conductivity and reliable mechanical performance for flexible supercapacitors. The salt gel is successfully fabricated by combining a salt supramolecular network constructed by NH4Mo7O24·4H2O and FeCl3·6H2O and a polymer network of poly(vinyl alcohol). The inorganic salt supramolecular network serves as a rigid self-supporting framework in the hydrogel system for improving the mechanical properties and providing abundant active sites for accelerating ion transport. Furthermore, the salt gel-enabled supercapacitors are equipped and exhibit a high specific capacitance (199.4 mF cm-2) and excellent energy density (27.69 μWh cm-2). Moreover, the flexible supercapacitors not only present remarkable cyclic stability after 3000 charging/discharging cycles but also exhibit good electrochemical stability even under severe deformation conditions. The strategy of salt-gel-driven flexible supercapacitors would provide fresh thinking for the development of advanced flexible energy storage fields.

B-037 Zeon original coating technology COP microplates for cell imaging

Abstract Background Zeon has developed high quality microplate made of Cyclo Olefin Polymer (COP) and special coat reagent for cell culture for Aurora microplates. The new 96 well-microplates have best-in-class properties for analysis of cultured cells can reduce the background of fluorescence images absolutely and excellent bottom flatness for observation of target. This enables to obtain high-precision data for cell analysis. Also, we developed Zeon original coating (ZOC) reagent specialized for COP surface. It is suitable for various cells including neurons derived from human induced pluripotent stem cells (iPSC), primary cells, general cell lines, and so on. ZOC coated COP microplates (ZOC plates) are fully-precoated and ready-to-use. Methods We evaluated the following points regarding ZOC plates. The cells used were derived from human iPSC. [1. Storage stability] ZOC plates were stored at room temperature for a long time. After that, cardiomyocyte cells were cultured for 3 days on the plates. The cell adhesion and beat rate were evaluated. [2. Neurite extension] Dopaminergic neurons were cultured for 14 days, then the neurite extension was evaluated. For comparison, a conventional poly-D-lysine* (PDL) coating was used. [3. Lot-to-lot variation] COP microplates coated with different lots of ZOC reagents were prepared. Glutamatergic neurons were cultured for 14 days on those plates, then the neurite extension and cell morphology were evaluated. *The typical coating reagent applied for neuronal culture. Results [1.] ZOC plates stored over 12 months at room temperature showed excellent cell adhesion, and the beat rate was equivalent to prepared just before use. [2.] ZOC plates tended to extend neurites more than PDL coated plates. [3.] No variation between lots was observed on ZOC plates regarding the neurite extension and cell morphology. Conclusions We suggest applying the combination of high accuracy for cell imaging using COP microplates and high performance for cell culture using ZOC can provide high precision data at bioassay research. This would be a significant value for development of drug screening processes at pharmaceutical company.

B-253 Using biomarkers in recurrent renal stone formers to detect silent stones

Abstract Background Glycosaminoglycans (GAGs) protect cells from binding of calcium oxalate (CaOx) crystals to prevent CaOx stone formation. Hyaluronan (HA) is a non-sulfated GAG with crystallization-promoting activity during renal stone formation. The excretion of urinary GAGs and HA were measured in stone-formers (SF), post-treated SF, and normal controls to reveal their relationship with renal stone disease. Methods Three groups were included - active SF, post-treated SF (SF after extracorporeal shock wave lithotripsy) and normal controls with 40 subjects in each group. Early morning urine was collected for each subject. The hexuronate content of the GAGs were measured by carbazole reaction and values for GAGs were standardized against creatinine. Individual GAGs extracts were then digested sequentially with Streptomyces hyaluronidase and chondroitinase ABC to yield the HA disaccharides for analysis by high performance liquid chromatography. Results Hexuronate content of GAGs were in the order of post-treated SF < SF << normal controls. SF had an enhanced urinary excretion of HA with no increase of urinary HA in post-treated SF group. However, both the SF and post-treated SF groups had increased proportion of HA (of total GAGs) compared to normal controls. Active-SF and post-treated SF groups had lower total GAGs content but increased proportion of HA than that of the normal controls indicating that urinary GAGs and HA are probably protective/risk factors, respectively, for renal stone disease. Conclusions The higher recurrence rate of renal stone disease may be due to the presence of sub-species of GAGs, HA, that becomes an accidental participant for renal stone formation. Concomitantly, other species of charged sulfated GAGs, which are protective to the cells and prevent crystal binding, are found to be in lesser content in active SF and post-treated SF. Clinicians can use these two markers, which are easily detectable (by ELISA) for prevention and monitoring of silent renal stone formation.

Additive engineering for Sb2S3 indoor photovoltaics with efficiency exceeding 17%

Indoor photovoltaics (IPVs) have attracted increasing attention for sustainably powering Internet of Things (IoT) electronics. Sb2S3 is a promising IPV candidate material with a bandgap of ~1.75 eV, which is near the optimal value for indoor energy harvesting. However, the performance of Sb2S3 solar cells is limited by nonradiative recombination, which is dependent on the quality of the absorber films. Additive engineering is an effective strategy to fine tune the properties of solution-processed films. This work shows that the addition of monoethanolamine (MEA) into the precursor solution allows the nucleation and growth of Sb2S3 films to be controlled, enabling the deposition of high-quality Sb2S3 absorbers with reduced grain boundary density, optimized band positions, and increased carrier concentration. Complemented with computations, it is revealed that the incorporation of MEA leads to a more efficient and energetically favorable deposition for enhanced heterogeneous nucleation on the substrate, which increases the grain size and accelerates the deposition rate of Sb2S3 films. Due to suppressed carrier recombination and improved charge-carrier transport in Sb2S3 absorber films, the MEA-modulated Sb2S3 solar cell yields a power conversion efficiency (PCE) of 7.22% under AM1.5 G illumination, and an IPV PCE of 17.55% under 1000 lux white light emitting diode (WLED) illumination, which is the highest yet reported for Sb2S3 IPVs. Furthermore, we construct high performance large-area Sb2S3 IPV minimodules to power IoT wireless sensors, and realize the long-term continuous recording of environmental parameters under WLED illumination in an office. This work highlights the great prospect of Sb2S3 photovoltaics for indoor energy harvesting.

B-215 Evaluating RT-qPCR for Dengue Virus Detection: Outcomes of a Brazilian External Quality Assessment Program

Abstract Background Dengue virus (DENV), responsible for a prevalent arthropod-borne viral illness, poses a substantial public health challenge worldwide. Clinically, DENV's manifestation often resembles that of other arboviruses, resulting in frequent healthcare visits and complicating the early diagnosis, which is critical for patient management. While virus isolation is a viable method, RT-qPCR assays yield significantly quicker results. Rapid immunochromatographic tests are available; yet, RT-qPCR not only matches their sensitivity but also provides distinct advantages in clinical laboratory settings. These include higher throughput and integrated, traceable quality control within each routine, offering less subjective and more dependable results. Maintaining high diagnostic standards for DENV is imperative. External quality assessment programs (EQAP) play a key role in enhancing laboratory diagnostic capabilities by monitoring and assessing diagnostic methods. This study evaluates the diagnostic efficacy of RT-qPCR for DENV using data from an EQAP conducted by a Brazilian provider, accredited by ABNT NBR ISO/IEC 17043:2011. Methods The quality control samples comprised lyophilized suspensions containing viable DENV particles, grown on vero cells (BCRJ 0245/ATCC CCL-81) under BSL-3 conditions. From February 2017 to November 2022, twenty-four EQAP survey rounds were carried out, each encompassing three samples, culminating in 1,153 data points. The evaluation metrics included participant numbers, adequacy percentage, sensitivity, specificity, and the incidence of false positives and negatives. The Mann-Kendall test assessed trends in these metrics over the survey rounds. The RT-qPCR kits employed by participants were classified either as in-vitro diagnostic (IVD) or as laboratory-developed tests (LDT). Kits that demonstrated high performance in terms of adequacy, sensitivity, and specificity were identified and listed. Results The number of laboratories participating has increased. Starting with four in 2017, the program expanded to 28 by 2022. Laboratory accuracy was high during the evaluation period, with a median of 99% (97.8% in 2017 and 99.2% in 2022). The Mann-Kendall test indicated a significant trend of increased accuracy (p<0.001). Specificity remained stable at 98.8%, while sensitivity improved, with a median of 99.4%—rising from 93.3% in 2017 to 100% in 2022 (p<0.001). Only three false negatives were reported over three separate rounds, and six false positives were reported across four rounds. Both LDT and IVD RT-qPCR kits demonstrated high accuracy (99.4% and 98.4%, respectively). Biomanguinhos ZDC, Viasure ZDC, and ZDC Multiplex kits all showed 100% adequacy, sensitivity, and specificity. Conclusions This study highlights the increasing adoption of RT-qPCR for DENV detection, as evidenced by the growing number of laboratories in the EQAP program and the consistently high adequacy rates from 2017 to 2022. Nonetheless, the number of participating laboratories remains relatively small for a vast tropical country like Brazil. The high sensitivity and specificity, along with the infrequency of false results, underscore its effectiveness. The superior performance of certain kits suggests possible advancements in DENV diagnosis, contributing to global public health initiatives against this arboviral disease.

B-038 A Novel Ultrasensitive Single-Molecule Counting Technology for Quantitation of Low Abundance Biomarkers

Abstract Background There is a need for sensitive, robust, and scalable analytical methods for accurate, early detection of disease using less invasive sampling methods. Often, smaller volumes and low marker concentrations present challenges to achieving sufficient sensitivity and accuracy with traditional immunoassay methods. This is particularly the case for measuring markers for Alzheimer's and other neurological diseases in blood, where levels are typically at much lower concentrations than in CSF. Here we introduce a novel ultrasensitive technology for flow-based single molecule detection in an optofluidic chip. We have developed an Ultrasensitive Detection Module (UDM) device and optofluidic cartridge that enable easy integration of this technology into existing immunoassay systems. UDM sensitivity data is shown for three prototype biomarker assays in comparison to the Lumipulse G1200 (Fujirebio Inc., Japan). We also present analytical validation data for three Alzheimer’s Disease plasma biomarker assays developed on a fully automated benchtop analyzer with integrated UDM detector. Methods Ultrasensitive detection is achieved by integrated low-loss waveguide in an optofluidic chip that enables maximal excitation of fluorescent molecules traveling through a narrow fluidic channel. The resulting high-intensity emission is detected as a single digital event, enabling individual molecules to be counted without need for amplification. Fluorescence-based immunoassays were developed for detection with the UDM using high performance monoclonal antibodies and reagents (Fujirebio Inc., Japan). Capture antibodies were immobilized on magnetic particles and detection antibodies conjugated to a fluorescent reporter construct (“reporter-Ab”). Multiple incubation and wash steps were followed by dissociation of immune complexes and separation of reporter-Ab from magnetic particles. Released reporter-Ab molecules were injected into the UDM device for digital detection. Signal to noise performance was evaluated in the UDM using reporter construct alone (no assay), as well as for prototype PSA, IL-6, and TSH assays, to establish baseline performance for analytical sensitivity. Plasma assays for pTau181, Aβ1-40, and Aβ1-42 were developed and optimized for the automated benchtop analyzer and validated for analytical sensitivity (LOD/LOQ), linearity, parallelism, precision and repeatability using clinical samples. Results Detection signal-to-noise testing of the reporter construct alone (no immunoassay) demonstrated up to 1000-fold higher sensitivity than the Lumipulse G1200. Prototype assays for PSA, TSH, and IL-6 showed up to 118-fold, 36-fold, and 80-fold higher signal to noise ratio, respectively, when compared to the Lumipulse G1200. Plasma assays for pTau181, Aβ1-40, and Aβ1-42 showed preliminary LLOQs calculated at 0.03 pg/mL, 0.17 pg/mL and 0.37 pg/mL, respectively. Conclusions We have developed a new single molecule counting platform able to detect neurological biomarkers at sub-pg/ml levels in plasma, with the potential to provide sensitive and accurate diagnostic testing of low abundance biomarkers in blood. Such technologies can create new clinical value through higher detection sensitivity and accuracy in areas like Alzheimer’s and other neurological diseases, supporting clinical research and translation to clinical practice.