Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

METAPHOR: Metabolic evaluation through phasor-based hyperspectral imaging and organelle recognition for mouse blastocysts and oocytes.

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology.

High-resolution dataset of manual claustrum segmentation.

The claustrum has a unique thin sheet-like structure that makes it hard to identify in typical anatomical MRI scans. Attempts have been made to identify the claustrum in anatomical images with either automatic segmentation techniques or using atlas-based approaches. However, the resulting labels fail to include the ventral claustrum portion, which consists of fragmented grey matter referred to as "puddles". The current dataset is a high-resolution label of the whole claustrum manually defined using an ultra-high resolution postmortem MRI image of one individual. Manual labelling was performed by four independent research trainees. Two trainees labelled the left claustrum and another two trainees labelled the right claustrum. For every hemisphere we created a union of the two labels and assessed the label correspondence using dice coefficients. We provide size measurements of the labels in MNI space by calculating the oriented bounding box size. These data are the first manual claustrum segmentation labels that include both the dorsal and ventral claustrum regions at such a high resolution in standard space. The label can be used to approximate the claustrum location in typical in vivo MRI scans of healthy individuals.

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

Label-free electrochemical biosensor based on dual amplification of gold nanoparticles and polycaprolactones for CEA detection.

Carcinoembryonic Antigen (CEA), an acidic glycoprotein with human embryonic antigen properties, is found on the surface of cancer cells that have differentiated from endodermal cells. This paper presents a label-free electrochemical immunoassay for the dual amplification detection of CEA using gold nanoparticles loaded with polypyrrole polydopamine (Au/PPy-PDA) and polymerized polycaprolactone (Ng-PCL) prepared by ring-opening polymerization (ROP). First, the composite Au/PPy-PDA was adhered to the electrode surface. Then, gold nanoparticles form a Au-S bond with the sulfhydryl group in Apt1 to secure it on the electrode surface. Subsequently, the non-specific binding sites on the electrodes surface are closed by bovine serum albumin (BSA). Next, CEA is dropped onto the electrode surface, which is immobilized by antigen-antibody specific recognition, and the carboxyl-functionalized Apt2 forms a "sandwich structure" of antibody-antigen-antibody by specific recognition. Polymeric Ng-PCL is adhered to the electrode surface, leading to an increase in the electrochemical impedance signal, resulting in a complete chain of signal analysis. Finally, the response signal is detected by electrochemical impedance spectroscopy (EIS). Under optimal experimental conditions, the method has the advantages of high sensitivity and wide linear range (1 pg mL-1∼100 ng mL-1), and the lower limit of detection (LOD) is 0.234 pg mL-1. And it has the same high sensitivity, selectivity and interference resistance for the real samples detection. Thus, it provides a new way of thinking about biomedical and clinical diagnosis.

Evaluation of nutritional, technological, oxidative, and sensory properties of low-sodium and phosphate-free mortadellas produced with bamboo fiber, pea protein, and mushroom powder.

This study examined the effects of replacing alkaline phosphate (AP) with bamboo fiber (BF), isolated pea protein (PP), and mushroom powder (MP) on the nutritional, technological, oxidative, and sensory characteristics of low-sodium mortadellas. Results indicated that this reformulation maintained the nutritional quality of the products. Natural substitutes were more effective than AP in reducing water and fat exudation. This led to decreased texture profile analysis (TPA) values such as hardness, cohesiveness, gumminess, and chewiness. The reformulation reduced the L* values and increased the b* values, leading to color modifications rated from noticeable to appreciable according to the National Bureau of Standards (NBS) index. Despite minor changes in oxidative stability indicated by increased values in TBARS (from 0.19 to 0.33 mg MDA/kg), carbonyls (from 2.1 to 4.4 nmol carbonyl/mg protein), and the volatile compound profile, the sensory profile revealed a beneficial increase in salty taste, especially due to the inclusion of MP, which was enhanced by the synergy with BF and PP. In summary, the results confirmed the potential of natural alternatives to replace chemical additives in meat products. Incorporating natural antioxidants into future formulations could address the minor oxidation issues observed and enhance the applicability of this reformulation strategy.

Label-free and highly sensitive detection of aflatoxin B1 by Ag IANPs via surface-enhanced Raman spectroscopy.

Aflatoxin B1 (AFB1), a pernicious constituent of the aflatoxin family, predominantly contaminates cereals, oils, and their derivatives. Acknowledged as a Class I carcinogen by the World Health Organization (WHO), the expeditious and quantitative discernment of AFB1 remains imperative. This investigation delineates that aluminum ions can precipitate the coalescence of iodine-modified silver nanoparticles, thereby engendering hot spots conducive for label-free AFB1 identification via Surface-Enhanced Raman Spectroscopy (SERS). This methodology manifests a remarkable limit of detection (LOD) at 0.47 fg/mL, surpassing the sensitivity thresholds of conventional survey techniques. Moreover, this method has good anti-interference ability, with a relative error of less than 10% and a relative standard deviation of less than 6% in quantitative results. Collectively, these findings illuminate the substantial application potential and viability of this approach in the quantitative analysis of AFB1, underpinning a significant advancement in food safety diagnostics.

Design, synthesis, antimicrobial activity, and mechanism of novel 3-(2,4-dichlorophenyl)-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazole derivatives.

BACKGROUND: Plant pathogens cause substantial crop losses annually, posing a grave threat to global food security. Fungicides have usually been used for their control, but the rapid development of pesticide resistance renders many ineffective, therefore the search for novel and efficient green pesticides to prevent and control plant diseases has become the top priority in crop planting. RESULTS: The results of bioassay studies indicated that most of the target compounds showed certain antimicrobial activity in vitro. In particular, compound X7 showed high inhibitory activity against Xanthomonas oryzae pv. oryzae (Xoo), with an EC50 value of 27.47 µg mL-1, surpassing conventional control agents such as thiazole zinc (41.55 µg mL-1) and thiodiazole copper (53.39 µg mL-1). Further studies on molecular docking showed that X7 had a strong binding affinity with 2FBW. The morphological change observed by scanning electron microscopy indicated that the surface of Xoo appears wrinkled and cracked under X7 treatment and a total of 2662 proteins were identified by label-free proteomic analysis. Three experiments have elucidated the mechanism whereby X7 induced considerable changes in the physiological and biochemical properties of Xoo, which in turn affected the reproduction and growth of bacteria. CONCLUSION: This work represents a pivotal advancement, offering important reference for the research and development therapeutics in combating plant pathogens. © 2024 Society of Chemical Industry.

Label-free and ultrasensitive detection of environmental lead ions based on spatially localized DNA nanomachines driven by hyperbranched hybridization chain reaction.

A label-free fluorescent sensing strategy for the rapid and highly sensitive detection of Pb2+ was developed by integrating Pb2+ DNAzyme-specific cleavage activity and a tetrahedral DNA nanostructure (TDN)-enhanced hyperbranched hybridization chain reaction (hHCR). This strategy provides accelerated reaction rates because of the highly effective collision probability and enriched local concentrations from the spatial confinement of the TDN, thus showing a higher detection sensitivity and a more rapid detection process. Moreover, a hairpin probe based on a G-triplex instead of a G-quadruplex or chemical modification makes hybridization chain reaction more controlled and flexible, greatly improving signal amplification capacities and eliminating labeled DNA probes. The enhanced reaction rates and improved signal amplification efficiency endowed the biosensors with high sensitivity and a rapid response. The label-free detection of Pb2+ based on G-triplex combined with thioflavin T can be achieved with a detection limit as low as 1.8 pM in 25 min. The proposed Pb2+-sensing platform was also demonstrated to be applicable for Pb2+ detection in tap water, river water, shrimp, rice, and soil samples, thus showing great potential for food safety and environmental monitoring.

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

An open-label pilot study of losmapimod to evaluate the safety, tolerability, and changes in biomarker and clinical outcome assessments in participants with facioscapulohumeral muscular dystrophy type 1.

INTRODUCTION: Facioscapulohumeral muscular dystrophy (FSHD) is a genetic disease caused by aberrant DUX4 expression, leading to progressive muscle weakness. No effective pharmaceutical treatment is available. Losmapimod, a small molecule selective inhibitor of p38 α/ß MAPK, showed promising results in a phase 1 trial for the treatment of FSHD, prompting additional studies. We report the findings of an open-label phase 2 trial (NCT04004000) investigating the safety, tolerability, pharmacokinetics, pharmacodynamics, and exploratory efficacy of losmapimod in participants with FSHD1. METHODS: This study was conducted at a single site in the Netherlands from August 2019 to March 2021, with an optional, ongoing open-label extension. Participants aged 18 to 65 years with FSHD1 took 15 mg of losmapimod twice daily for 52 weeks. Primary endpoints were measures of losmapimod safety and tolerability. Secondary endpoints were assessments of losmapimod pharmacokinetics and pharmacodynamics. RESULTS: Fourteen participants were enrolled. No deaths, serious treatment-emergent adverse events (TEAEs), or discontinuations due to TEAEs were reported. Losmapimod achieved blood concentrations and target engagements that were previously associated with decreased DUX4 expression in vitro. Clinical outcome measures showed a trend toward stabilization or improvement. CONCLUSIONS: Losmapimod was well tolerated and may be a promising new treatment for FSHD; a larger phase 3 study is ongoing.

Identification of heparin-binding amino acid residues in antibody HS4C3 with the potential to design antibodies against heparan sulfate domains.

Heparan sulfate (HS) is a linear polysaccharide with high structural and functional diversity. Detection and localization of HS in tissues can be performed using single chain variable fragment (scFv) antibodies. Although several anti-HS antibodies recognizing different sulfation motifs have been identified, little is known about their interaction with HS. In this study the interaction between the scFv antibody HS4C3 and heparin was investigated. Heparin-binding lysine and arginine residues were identified using a protect and label methodology. Site-directed mutagenesis was applied to further identify critical heparin-binding lysine/arginine residues using immunohistochemical and biochemical assays. In addition, computational docking of a heparin tetrasaccharide towards a 3-D homology model of HS4C3 was applied to identify potential heparin-binding sites. Of the 12 lysine and 15 arginine residues within the HS4C3 antibody, 6 and 9, respectively, were identified as heparin-binding. Most of these residues are located within one of the complementarity determining regions (CDR) or in their proximity. All basic amino acid residues in the CDR3 region of the heavy chain were involved in binding. Computational docking showed a heparin tetrasaccharide close to these regions. Mutagenesis of heparin-binding residues reduced or altered reactivity towards HS and heparin. Identification of heparin-binding arginine and lysine residues in HS4C3 allows for better understanding of the interaction with HS and creates a framework to rationally design antibodies targeting specific HS motifs.

Label-free three-dimensional imaging and quantitative analysis of living fibroblasts and myofibroblasts by holotomographic microscopy.

Holotomography (HT) is a cutting-edge fast live-cell quantitative label-free imaging technique. Based on the principle of quantitative phase imaging, it combines holography and tomography to record a three-dimensional map of the refractive index, used as intrinsic optical and quantitative imaging contrast parameter of biological samples, at a sub-micrometer spatial resolution. In this study HT has been employed for the first time to analyze the changes of fibroblasts differentiating towards myofibroblasts - recognized as the main cell player of fibrosis - when cultured in vitro with the pro-fibrotic factor, namely transforming growth factor-ß1. In parallel, F-actin, vinculin, α-smooth muscle actin, phospho-myosin light chain 2, type-1 collagen, peroxisome proliferator-activated receptor-gamma coactivator-1α expression and mitochondria were evaluated by confocal laser scanning microscopy. Plasmamembrane passive properties and transient receptor potential canonical channels' currents were also recorded by whole-cell patch-clamp. The fluorescence images and electrophysiological results have been compared to the data obtained by HT and their congruence has been discussed. HT turned out to be a valid approach to morphologically distinguish fibroblasts from well differentiated myofibroblasts while obtaining objective measures concerning volume, surface area, projection area, surface index and dry mass (i.e., the mass of the non-aqueous content inside the cell including proteins and subcellular organelles) of the entire cell, nuclei and nucleoli with the major advantage to monitor outer and inner features in living cells in a non-invasive, rapid and label-free approach. HT might open up new research opportunities in the field of fibrotic diseases. RESEARCH HIGHLIGHTS: Holotomography (HT) is a label-free laser interferometric imaging technology exploiting the intrinsic optical property of cells namely refractive index (RI) to enable a direct imaging and analysis of whole cells or intracellular organelles. HT turned out a valid approach to distinguish morphological features of living unlabeled fibroblasts from differentiated myofibroblasts. HT provided quantitative information concerning volume, surface area, projection area, surface index and dry mass of the entire fibroblasts/myofibroblasts, nuclei and nucleoli.

Modifying the Health Star Rating nutrient profiling algorithm to account for ultra-processing.

AIM: To modify the Australian and New Zealand Health Star Rating to account for ultra-processing and compare the alignment of the modified ratings with NOVA classifications and the current Australian Dietary Guidelines classifications of core (recommended foods) and discretionary (foods to limit). METHODS: Data was cross-sectionally analysed for 25 486 products. Four approaches were compared to the original Health Star Rating: (1) five 'negative' points added to ultra-processed products (modification 1; inclusion approach); (2) ultra-processed products restricted to a maximum of 3.0 Health Stars (modification 2; capping approach); (3 and 4) same approach used for modifications 1 and 2 but only applied to products that already exceeded 10 'negative' points from existing Health Star Rating attributes (modifications 3 and 4, respectively; hybrid approaches). Alignment occurred when products (i) received <3.5 Health Stars and were NOVA group 4 (for NOVA comparison) or discretionary (for Dietary Guidelines comparison), or (ii) received ≥3.5 Health Stars and were NOVA groups 1-3 or core. RESULTS: All Health Star Rating modifications resulted in greater alignment with NOVA (ranging from 69% to 88%) compared to the original Health Star Rating (66%). None of the modifications resulted in greater alignment to the Dietary Guidelines classifications overall (69% to 76%, compared with 77% for the original Health Star Rating), but alignment varied considerably by food category. CONCLUSIONS: If ultra-processing were incorporated into the Australian and New Zealand Health Star Rating, consideration of ultra-processing within the broader dietary guidance framework would be essential to ensure coherent dietary messaging to Australians.

Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Perceptions and experiences of community pharmacists with off-label prescribing in the pediatric population.

OBJECTIVES: This study aimed to investigate and provide insight into the prevalence and patterns of off-label drug use in the pediatric population from the perspective of community pharmacists, addressing the existing data gap in a developing setting. METHODS: A questionnaire-based cross-sectional study was conducted on Albanian community pharmacists in June 2021. The online administered survey explored the participants' demographic details, perceptions, and experiences with off-label prescriptions in pediatric patients. The statistical analysis conducted on the survey data comprised the construction of frequency tables and the application of the chi-square test for independence. KEY FINDINGS: Three hundred and thirty-six community pharmacists nationwide completed the survey, out of which 186 (55.3%) were practiced in Tirana, the capital of Albania. Over 80% of surveyed pharmacists (n = 275) had encountered off-label drug prescriptions in pediatric patients, yet only 40% of participants reported dispensing medicines for off-label use. Community pharmacists reported that general pediatricians tended to prescribe off-label medications more frequently than pediatric subspecialists or general practitioners. It was found that off-label prescriptions were more frequently observed among children aged between 2 and 11 years. Antibiotics were the most reported medicines for off-label use in this study mentioned in almost all off-label categories. CONCLUSIONS: Prescribing medicines for unapproved uses for the treatment of pediatric patients is present in community settings in Albania. This indicates the need for further data collection and analysis to understand off-label practices in our country's pediatric population comprehensively.

Enzyme-powered, label-free DNA walker for Uracil-DNA glycosylase detection at single-cell level.

Uracil-DNA glycosylase (UDG) plays a crucial role in the removal of damaged uracil bases, thereby upholding genetic stability and integrity. An enzyme-powered, label-free DNA walker was devised for UDG activity detection. Initially, a label-free DNA track, incorporating a gold nanoparticle (AuNP), multiple hairpin structures, and various swing arms, was engineered for walking mechanism. The hairpin structure was meticulously crafted to include a G-quadruplex sequence, enabling the generation of a label-free fluorescence signal. The swing arm remained inert in the absence of UDG, but became activated upon the introduction of UDG, thereby initiating the enzyme-powered walking process and generating significant dissociative G-quadruplex sequences. By integrating a selective fluorescent dye into the design, an enhanced label-free fluorescence response was achieved. The proposed DNA walker presented a direct and label-free approach for UDG detection, demonstrating exceptional sensitivity with a detection limit of 0.00004 U/mL. Using the uracil glycosylase inhibitor (UGI) as an inhibitory model, inhibitor assay was conducted with satisfactory precision. Furthermore, successful analysis of cellular UDG at the single-cell level was accomplished. Consequently, the developed DNA walker serves as a label-free, selective, and sensitive tool for UDG activity assessment, showing great potential for applications in disease diagnosis, inhibitor screening, and biomedical investigations.

A wave delay neural network for solving label-constrained shortest route query on time-varying communication networks.

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy.