Exploring the Impact of C-Terminal Based Pentapeptides on the Disassembly of Aß42 Fibrils.

An effective therapeutic strategy to suppress Alzheimer's disease (AD) progression is to disrupt ß-sheet rich neurotoxic soluble amyloid-ß (Aß) aggregates. Previously, we identified new pentapeptides (RVVPI and RIAPA) with notably enhanced ability to block Aß42 aggregation as compared to Aß42 C-terminal derived peptide RIIGL using integrated computational protocol. In this work, the potential of RIIGL, RVVPI, and RIAPA for the structural destabilization of Aß42 protofibril was assessed by molecular dynamics (MD) simulations and in vitro studies. The binding free energy analysis depicts that charged residues influence Aß42 protofibril-pentapeptide interactions. Notably, RVVPI displays a more pronounced destabilization effect than other peptides due to higher conformational fluctuations, and disruption of salt bridge (K28-A42) interactions in Aß42 protofibril. RVVPI exhibited highest inhibitory activity (Inhibition= 66.2%, IC50= 5.57 ± 0.83 µM) against Aß42 aggregation consistent with computational results. Remarkably, RVVPI displayed ~4.5 fold lower IC50 value as compared to RIIGL. ThT and TEM studies highlighted the enhanced efficiency of RVVPI (62.4%) in the disassembly of pre-formed Aß42 fibrils than RIIGL and RIAPA. The combined in silico and in vitro studies identified a new peptide, RVVPI, as an efficient inhibitor of Aß42 fibrillation and disassembly of Aß42 aggregates.

Evaluation of AI-based Gleason grading algorithms "in the wild".

The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)-based algorithms employing deep learning have shown their ability to match pathologists' performance in assigning Gleason scores, with the potential to enhance pathologists' grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark datasets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data is not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aim of this study is to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse dataset of whole-slide prostate biopsy images through crowdsourcing, containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from five top-ranked public algorithms from the PANDA challenge and from two commercial Gleason grading algorithms. Additionally, ten pathologists evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms.

Predicting prostate cancer grade reclassification on active surveillance using a deep Learning-Based grading algorithm.

Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to re-grade initial prostate biopsies in two independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio 3.3, p = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio 1.7, p = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS.

Bells and Whistles on Fertilizers: Molecular Hands to Hang Nanoporous Foliar Fertilizer Reservoirs.

Porous materials are highly explored platforms for fertilizer delivery. Among porous materials, metal-organic frameworks (MOFs) are an important class of coordination polymers in which metal ions and organic electron donors as linkers are assembled to form crystalline structures with stable nanoporosity. Selected amino acids were inherently found to have the capacity to hold the leaf cuticle. Hence, MOF synthesis was attempted in the presence of amino acids, which can act as surface terminators and can assist as hands to hold to the leaf for a controlled nutrient supply. By serendipity, the amino acids were found to act as modulators, resulting in well-stabilized porous MOF structures with iron metal nodes, which are often noted to be unstable. Thus, the composite, i.e., (MOF@aa) MOF modulated with amino acids, has efficient nutrient-feeding ability through the foliar route when compared to the control.

Phosphoenolpyruvate carboxykinase-1 targeted siRNA promotes wound healing in type 2 diabetic mice by restoring glucose homeostasis.

It is well-accepted that the liver plays a vital role in the metabolism of glucose and its homeostasis. Dysregulated hepatic glucose production and utilization, leads to type 2 diabetes (T2DM). In the current study, RNA sequencing and qRT-PCR analysis of nanoformulation-treated T2DM mice (TGthr group) revealed beneficial crosstalk of PCK-1 silencing with other pathways involved in T2DM. The comparison of precise genetic expression profiles of the different experimental groups showed significantly improved hepatic glucose, fatty acid metabolism and several other T2DM-associated crucial markers after the nanoformulation treatment. As a result of these improvements, we observed a significant acceleration in wound healing and improved insulin signaling in vascular endothelial cells in the TGthr group as compared to the T2DM group. Enhanced phosphorylation of PI3K/Akt pathway proteins in the TGthr group resulted in increased angiogenesis as observed by the increased expression of endothelial cell markers (CD31, CD34) thereby improving endothelial dysfunctions in the TGthr group. Additionally, therapeutic nanoformulation has been observed to improve the inflammatory cytokine profile in the TGthr group. Overall, our results demonstrated that the synthesized therapeutic nanoformulation referred to as GPR8:PCK-1siRNA holds the potential in ameliorating hyperglycemia-associated complications such as delayed wound healing in diabetes.

Synthetically Tunable Suprahybrid Nanoparticle Platform for the Efficacious Delivery of Therapeutics.

The discovery of lipid-hybrid nanosystems has offered potential solutions to various drug delivery and theranostic challenges. However, in many instances, the commonly used lipids and other components in these systems often pose challenges related to their solubility, physicochemical properties, immune compatibility, and limited synthetic tunability. In this work, we introduce a synthetically tunable supramolecular scaffold with amphiphilic characteristics based on the calix[4]arene macrocyclic system. We designed and synthesized two novel calix[4]arene-polyethylene glycol (PEG) conjugates, termed Cal-P1 and Cal-P2, and these were characterized utilizing a wide range of spectroscopic and analytical methods. The rational design of Cal-P1 and Cal-P2 demonstrates their utility in forming stable blended nanospheres with sustained drug release characteristics. The synergistic blending of PLGA and the calixarene scaffold (Cal-P1 and Cal-P2) in constructing long-lasting and controlled-release nanoparticles (NPs), which are optimized for encapsulating Nile Red dye, and their successful internalization and retention in HeLa cancer cells are demonstrated through in vitro assays. The potential of these NPs as sustained therapeutic carriers is investigated in vivo, showing improved retention compared to free dye with negligible toxicity. The successful design and construction of Cal-P1 and Cal-P2 nanosystems represent a new paradigm for addressing drug loading challenges, opening up opportunities for the development of highly efficient, synthetically tunable alternative adjuvants for drug encapsulation and delivery.

Trace elements dyshomeostasis in liver and brain of weanling mice under altered dietary selenium conditions.

BACKGROUND: A balanced diet containing selenium (Se) and other trace elements is essential for normal development and growth. Se has been recognized as an essential trace element; however, its interaction with other elements has not been fully investigated. In the present study, sodium (Na), magnesium (Mg), potassium (K), calcium (Ca), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), copper (Cu), zinc (Zn), Se and rubidium (Rb), were analysed in liver and brain regions under altered dietary Se intake in weanling mice to identify major discriminatory elements. METHODS: The study investigated the effects of different levels of Se intake on the elemental composition in liver and brain tissues of weaned mice. After 24 weeks of feeding with Se adequate, deficient, and excess diets, elemental analysis was performed on the harvested tissues using Inductively coupled plasma mass spectrometry (ICP-MS). Statistical analysis that included analysis of covariance (ANCOVA), correlation coefficient analysis, principal component analysis, and partial least squares discriminant analysis were performed. RESULTS: The ANCOVA showed statistically significant changes and correlations among the analysed elements under altered dietary Se status. The multivariate analysis showed differential changes in elements in liver and brain regions. The results suggest that long-term dietary Se alternations lead to dyshomeostasis in trace elements that are required in higher concentrations compared to Se. It was observed that changes in the Fe, Co, and Rb levels were similar in all the tissues studied, whereas the changes in Mg, Cr, and Mn levels were different among the tissues under altered dietary Se status. Additionally, the changes in Rb levels correlated with the dietary Se intake but had no relation with the tissue Se levels. CONCLUSIONS: The findings suggest interactions between Mg, Cr, Mn, Fe, Co, and Se under altered Se status may impact cellular functions during postnatal development. However, the possible biological significance of alterations in Rb levels under different dietary Se paradigms needs to be further explored.

Nano-Biotechnology for Bacteria Identification and Potent Anti-bacterial Properties: A Review of Current State of the Art.

Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.

Multiepitope glycan based laser assisted fluorescent nanocomposite with dual functionality for sensing and ablation of Pseudomonas aeruginosa.

Pseudomonas aeruginosa (P. aeruginosa) infection is becoming a severe health hazard and needs early diagnosis with high specificity. However, the non-specific binding of a biosensor is a challenge to the current bacterial detection system. For the first time, we chemically synthesized a galactose tripod (GT) as a P. aeruginosa-specific ligand. We conjugated GT to a photothermally active fluorescent nanocomposite (Au@SiO2-TCPP). P. aeruginosa can be detected using Au@SiO2-TCPP-GT, and additionally ablated as well using synergistic photothermal and photodynamic therapy. Molecular dynamics and simulation studies suggested better binding of GT (binding energy = -6.6 kcal mol-1) with P. aeruginosa lectin than that of galactose monopod (GM) (binding energy = -5.9 kcal mol-1). Furthermore, a binding study was extended to target P. aeruginosa, which has a galactose-binding carbohydrate recognition domain receptor. The colorimetric assay confirmed a limit of detection (LOD) of 104 CFU mL-1. We also looked into the photosensitizing property of Au@SiO2-TCPP-GT, which is stimulated by laser light (630 nm) and causes photoablation of bacteria by the formation of singlet oxygen in the surrounding media. The cytocompatibility of Au@SiO2-TCPP-GT was confirmed using cytotoxicity assays on mammalian cell lines. Moreover, Au@SiO2-TCPP-GT also showed non-hemolytic activity. Considering the toxicity analysis and efficacy of the synthesized glycan nanocomposites, these can be utilized for the treatment of P. aeruginosa-infected wounds. Furthermore, the current glycan nanocomposites can be used for bacterial detection and ablation of P. aeruginosa in contaminated food and water samples as well.

Predicting Prostate Cancer Molecular Subtype With Deep Learning on Histopathologic Images.

Microscopic examination of prostate cancer has failed to reveal a reproducible association between molecular and morphologic features. However, deep-learning algorithms trained on hematoxylin and eosin (H&E)-stained whole slide images (WSI) may outperform the human eye and help to screen for clinically-relevant genomic alterations. We created deep-learning algorithms to identify prostate tumors with underlying ETS-related gene (ERG) fusions or PTEN deletions using the following 4 stages: (1) automated tumor identification, (2) feature representation learning, (3) classification, and (4) explainability map generation. A novel transformer-based hierarchical architecture was trained on a single representative WSI of the dominant tumor nodule from a radical prostatectomy (RP) cohort with known ERG/PTEN status (n = 224 and n = 205, respectively). Two distinct vision transformer-based networks were used for feature extraction, and a distinct transformer-based model was used for classification. The ERG algorithm performance was validated across 3 RP cohorts, including 64 WSI from the pretraining cohort (AUC, 0.91) and 248 and 375 WSI from 2 independent RP cohorts (AUC, 0.86 and 0.89, respectively). In addition, we tested the ERG algorithm performance in 2 needle biopsy cohorts comprised of 179 and 148 WSI (AUC, 0.78 and 0.80, respectively). Focusing on cases with homogeneous (clonal) PTEN status, PTEN algorithm performance was assessed using 50 WSI reserved from the pretraining cohort (AUC, 0.81), 201 and 337 WSI from 2 independent RP cohorts (AUC, 0.72 and 0.80, respectively), and 151 WSI from a needle biopsy cohort (AUC, 0.75). For explainability, the PTEN algorithm was also applied to 19 WSI with heterogeneous (subclonal) PTEN loss, where the percentage tumor area with predicted PTEN loss correlated with that based on immunohistochemistry (r = 0.58, P = .0097). These deep-learning algorithms to predict ERG/PTEN status prove that H&E images can be used to screen for underlying genomic alterations in prostate cancer.

Salmonella typhimurium detection and ablation using OmpD specific aptamer with non-magnetic and magnetic graphene oxide.

Foodborne diseases have increased in the last few years due to the increased consumption of packaged and contaminated food. Major foodborne bacteria cause diseases such as diarrhea, vomiting, and sometimes death. So, there is a need for early detection of foodborne bacteria as pre-existing detection techniques are time-taking and tedious. Aptamer has gained interest due to its high stability, specificity, and sensitivity. Here, aptamer has been developed against Salmonella Typhimurium through the Cell-Selex method, and to further find the reason for specificity and sensitivity, OmpD protein was isolated, and binding studies were done. Single molecular FRET experiment using aptamer and graphene oxide studies has also been done to understand the mechanism of FRET and subsequently used for target bacterial detection. Using this assay, Salmonella Typhimurium can be detected up to 10 CFU/mL. Further, Magnetic Graphene oxide was used to develop an assay to separate and ablate bacteria using 808 nm NIR where temperature increase was more than 60 °C within 30 s and has been shown by plating as well as a confocal live dead assay. Thus, using various techniques, bacteria can be detected and ablated using specific aptamer and Graphene oxide.

Triazole-Peptide Conjugate as a Modulator of Aß-Aggregation, Metal-Mediated Aß-Aggregation, and Cytotoxicity.

Amyloid-ß (Aß) aggregation plays a key role in the pathogenesis of Alzheimer's disease (AD). Along with this, the presence of redox-active metals like Cu2+ further enhances Aß aggregation, oxidative stress, and cellular toxicity. In this study, we have rationally designed, synthesized, and evaluated a series of triazole-peptide conjugates as potential promiscuous ligands capable of targeting different pathological factors of AD. In particular, peptidomimetic DS2 showed the best inhibitory activity against Aß aggregation with an IC50 value of 2.43 ± 0.05 µM. In addition, DS2 disaggregates preformed Aß42 fibrils, chelates metal ions, inhibits metal-mediated Aß aggregation, significantly controls reactive oxygen species production, and reduces oxidative stress. DS2 exhibited very low cytotoxicity and significantly ameliorated the Aß-induced toxicity in differentiated neuroblastoma cells, SH-SY5Y. In addition, alteration in the fibrillary architecture of Aß42 in the absence and presence of DS2 was validated by transmission electron microscopy (TEM) images. To shed light on the inhibitory mechanism of DS2 against Aß aggregation and disassembly of the protofibril structure, molecular dynamics (MD) simulations have been performed. DS2 binds preferentially with the central hydrophobic core (CHC) residues of Aß42 monomer and chains D-E of Aß42 protofibril. The dictionary of secondary structure of proteins analysis indicated a noteworthy increase in the helix content from 38.5 to 61% and, notably, a complete loss of ß-sheet content of Aß42 monomer when DS2 is added to it. DS2 suppressed Aß42 monomer aggregation by preserving helical conformations and was able to reduce the production of aggregation-prone ß-sheet structures, which are consistent with ThT, circular dichroism, and TEM assay that indicate a reduction in the formation of toxic Aß42 aggregated species on the addition of DS2. Moreover, DS2 destabilized the Aß42 protofibril structure by significantly reducing the binding affinity between chains D-E of protofibril, which highlighted the disruption of interchain interactions and subsequent deformation of the protofibril structure. The results of the present study demonstrate that triazole-peptide conjugates may be valuable chemotypes for the development of promising multifunctional AD therapeutic candidates.

Glyco-conjugated metal-organic framework biosensor for fluorescent detection of bacteria.

Metal-organic frameworks (MOFs) are hybrid materials constructed by the linkage between an inorganic secondary building unit and an organic linker. A number of MOFs are luminescent in nature and can be structurally tuned for desirable geometry, surface functionality, and porosity. Luminescent MOFs have been endorsed for various biosensing applications. Lectins and carbohydrates have been used for the development of simple and convenient biosensing and bioimaging tools. Lectins are mostly present on the surface of microorganisms where they aid in pathogenesis. Due to this, they can be potential targets for a microbial biosensor. The present study, for the first time, explores the usage of a carbohydrate-conjugated FeMOF (Glyco-MOF) bioprobe for the selective determination of Pseudomonas aeruginosa and Escherichia coli. NH2-MIL-53(Fe) MOF was synthesized via a room temperature protocol and separately conjugated with galactose and mannose sugars via glutaraldehyde chemistry. The synthesized bioprobe is validated for structural integrity, luminescent nature, stability, and analyte assay. Electron microscopy studies validated the unhindered MOF's morphology and structural integrity, after bioconjugation. The synthesized bioprobes were able to detect P. aeruginosa and E. coli up to respective detection limits of 202 and 8 CFU/mL, respectively. The bioprobes are selective even in co-presence of possible interferants as well as being environmentally stable.

Mechanobactericidal, Gold Nanostar Hydrogel-Based Bandage for Bacteria-Infected Skin Wound Healing.

The emergence of multidrug resistant (MDR) microorganisms has led to the development of alternative approaches for providing relief from microbial attacks. The mechano-bactericidal action as a substitute for antimicrobials has become the focus of intensive research. In this work, nanostructure-conjugated hydrogel are explored as a flexible dressing against Staphylococcus aureus (S. aureus)-infected skin wounds. Herein gold nanostars (AuNst) with spike lengths reaching 120 nm are probed for antibacterial action. The bacterial killing of >95% is observed for Pseudomonas aeruginosa (P. aeruginosa) and Escherichia coli (E. coli), while up to 60% for Gram-positive S. aureus. AuNst conjugated hydrogel (AuNst120@H) reduced >80% colonies of P. aeruginosa and E. coli. In comparison, around 35.4% reduction of colonies are obtained for S. aureus. The viability assay confirmed the presence of about 85% of living NIH-3T3 cells when grown with hydrogels. An animal wound model is also developed to assess the efficiency of AuNst120@H. A significant reduction in wound size is observed on the 10th day in AuNst120@H treated animals with fully formed epidermal layers, hair follicles, new blood vessels, and arrector muscles. These findings suggest that novel dressing materials can be developed with antimicrobial nanotextured surfaces.

Rethinking ImageNet Pre-training for Computational Histopathology.

Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models on a Histopathology dataset. However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on a Histopathology dataset may provide better initialization. To prove this hypothesis, we train two commonly used Deep Learning model architectures - ResNet and DenseNet on a complex Histopathology classification dataset, and compare transfer learning performance with ImageNet pretrained weights. Based on the fine-tuning on three histopathology datasets including two different stains (H&E and IHC), we show that the domain specific pretrained weights are better suited for transfer learning. This is reflected by higher performance, lower training time as well as better feature reuse. Clinical Relevance - The paper establishes merit of using Histopathology domain specific pretrained weights rather than ImageNet pretrained weights.

ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images.

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Insight into the antifungal effect of chitosan-conjugated metal oxide nanoparticles decorated on cellulosic foam filter for water filtration.

Worldwide, fungal contamination of water resources has become a major threat to both human health and the environment. The adaptation of nanotechnology in conventional water processes is significant to offer new breakthroughs in water treatment, especially fungal contaminants. Chitosan conjugated metal oxide nanoparticles can affect the antimicrobial properties of cellulosic foam. In the present study, three different types of biocompatible nanoconjugates (i.e., ZnO/chitosan, CuO/chitosan, and Ag2O/chitosan) were synthesized for functionalization of five differently processed cellulose foam filters for resisting fungal spores during water treatment. To evaluate the antifungal effect of these nanoconjugates against prevalent strains of Aspergillus niger (A. niger), Aspergillus flavus (A. flavus), and Rhizopus oryzae (R. oryzae), the stable coating was introduced on different cellulose filter papers through impregnation. The statistical analysis of antifungal experiment was carried out by two-way factorial ANOVA test. Cellulose filter containing ZnO/chitosan displayed a stronger antifungal behavior in disc diffusion method than those impregnated with CuO/chitosan, and Ag2O/chitosan nanoconjugates. Besides the choice of nanoconjugates, the variation in cellulose foam filters (in terms of concentration of their raw materials and/or processing methodology) can also affect their antifungal performance. Further, the assessment of cytotoxic nature of such nanocomposites-modified cellulose foam filters is a fundamental step towards their real field applications.

A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies.

Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. Deep Learning (DL) systems have shown promise in enhancing the objectivity and efficiency of Gleason grading. However, DL networks exhibit domain shift and reduced performance on Whole Slide Images (WSI) from a source other than training data. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. 3741 core needle biopsies (CNBs) received from two centers were used for training. The κquad (quadratic-weighted kappa) and AUC were measured for grade group comparison and core-level detection accuracy, respectively. Accuracy of 89.4% and κquad of 0.92 on the internal test set of 425 CNB WSI and accuracy of 85.3% and κquad of 0.96 on an external set of 1201 images, was observed. The system showed an accuracy of 83.1% and κquad of 0.93 on 1303 WSI from the third institution (blind evaluation). Our DL system, used as an assistive tool for CNB review, can potentially improve the consistency and accuracy of grading, resulting in better patient outcomes.

Nanoglycocluster based diagnostic platform for colorimetric detection of bacteria; A comparative study analysing the effect of AuNPs size, linker length, and glycan diversity.

Nanoglycoclusters, an upcoming class of functional nanomaterial are known to drive various processes like detection, imaging, targeting proteins, cells, and bacteria. Nanoglycoclusters are a type of nanomaterial functionalized with various glycans. The array of glycan in multiple copies enhances binding affinity with proteins. Selective and sensitive bacteria/lectin interactions using nanomaterials are an emerging area of research. The measurement of different ligand receptor interactions require sophisticated analytical tools that limit the application in biosensor domain. Recently, colorimetric biosensors gained importance in the field of the biosensor for the detection of bacteria/lectins. Herein we have demonstrated that different size of gold nanoparticles (AuNPs) along with various polyethylene glycol (PEG) linkers, functionalized with synthesized monopod and tripod of mannose and galactose that have different bacteria/lectins specificity. The newly synthesized nanoglycoclusters were able to discriminate between different lectins and bacteria. The aggregation of specific nanoglycocluster upon interaction with specific bacteria/lectins revealed that mannose monopod (MM) and mannose tripod (MT) are specific to Escherichia coli and concanavalin A (ConA) lectin, while galactose monopod (GM) and galactose tripod (GT) are specific to Pseudomonas aeruginosa and Peanut agglutinin (PNA) lectin. Further, the binding events depict the affinity of tripod glycans is more with respect to its corresponding monopod glycans. Our findings explored the potential of colorimetric sensing depending upon the size of AuNPs, linker length, specificity, along with glycans density to develop user friendly diagnostic system for the detection of bacteria.