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Introduction: Globally, the need for measuring exposure to algal toxins has become urgent due to ever-increasing reports of contamination in sea and freshwater, in shellfish and fish stocks and in aerosols. Methods: To address this issue, we evaluated the potential of determining autoantibodies to a panel of biomarkers known to be elevated following exposure to the hepatotoxin microcystin leucine-arginine. The presence of autoantibodies, specific to four selected stress-response, metabolomic and chaperone biomarkers, namely, Heat shock protein 1, Triosephosphate isomerase, Peroxiredoxin 1 and Peroxiredoxin 2 was employed in screening 371 serum samples from microcystin-exposed individuals in Tanzania. In addition, the capacity of the LightDeck fluorescence-based detector, a point-of-use device, to monitor these autoantibody responses in comparison to enzyme-linked immunosorbent assay was evaluated. Results: By using the determination of autoantibodies to this novel panel of biomarkers an altered response was observed following microcystin exposure, with levels generally upregulated. The presence of elevated levels of microcystin leucine-arginine in water, as well as in food sources in Tanzania, may potentially have significant health effects on the population. Discussion: This novel biomarker panel may have potential for the detection of microcystin leucine-arginine exposure as well as various microcystin exposure-associated cancers (e.g., hepatocellular cancer and colorectal cancer). In addition, the utilisation of the LightDeck point-of-use device proved successful for the rapid analysis of this biomarker panel. [ABSTRACT FROM AUTHOR]

In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such as urethane or N-nitroso-N-methylurea to test study validity based on the presence of the expected proliferative lesions in the transgenic mice. We hypothesized that artificial intelligence–based deep learning (DL) could provide decision support for the toxicologic pathologist by screening for the proliferative changes, verifying the expected pattern for the positive control groups. Whole slide images (WSIs) of the lungs, thymus, and stomach from positive control groups were used for supervised training of a convolutional neural network (CNN). A single pathologist annotated WSIs of normal and abnormal tissue regions for training the CNN-based supervised classifier using INHAND criteria. The algorithm was evaluated using a subset of tissue regions that were not used for training and then additional tissues were evaluated blindly by 2 independent pathologists. A binary output (proliferative classes present or not) from the pathologists was compared to that of the CNN classifier. The CNN model grouped proliferative lesion positive and negative animals at high concordance with the pathologists. This process simulated a workflow for review of these studies, whereby a DL algorithm could provide decision support for the pathologists in a nonclinical study. [ABSTRACT FROM AUTHOR]

Aims Materials and methods Results Conclusions To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis).Triagnexia Colorectal, a weakly supervised deep learning model, was developed for the classification of colorectal samples from haematoxylin and eosin (H&E)‐stained whole slide images. The model was trained on 24 983 digitised images and assessed by multiple pathologists in a simulated digital pathology environment. The AI application was implemented as part of a point and click graphical user interface to streamline decision‐making. Pathologists assessed the accuracy of the AI tool, its value, ease of use and integration into the digital pathology workflow.Validation of the model was conducted on two cohorts: the first, on 100 single‐slide cases, achieved micro‐average model specificity of 0.984, micro‐average model sensitivity of 0.949 and micro‐average model F1 score of 0.949 across all classes. A secondary multi‐institutional validation cohort, of 101 single‐slide cases, achieved micro‐average model specificity of 0.978, micro‐average model sensitivity of 0.931 and micro‐average model F1 score of 0.931 across all classes. Pathologists reflected their positive impressions on the overall accuracy of the AI in detecting colorectal pathology abnormalities.We have developed a high‐performing colorectal biopsy AI triage model that can be integrated into a routine digital pathology workflow to assist pathologists in prioritising cases and identifying cases with dysplasia/cancer versus non‐neoplastic biopsies. [ABSTRACT FROM AUTHOR]

Aims: To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis)., Materials and Methods: Triagnexia Colorectal, a weakly supervised deep learning model, was developed for the classification of colorectal samples from haematoxylin and eosin (H&E)-stained whole slide images. The model was trained on 24 983 digitised images and assessed by multiple pathologists in a simulated digital pathology environment. The AI application was implemented as part of a point and click graphical user interface to streamline decision-making. Pathologists assessed the accuracy of the AI tool, its value, ease of use and integration into the digital pathology workflow., Results: Validation of the model was conducted on two cohorts: the first, on 100 single-slide cases, achieved micro-average model specificity of 0.984, micro-average model sensitivity of 0.949 and micro-average model F1 score of 0.949 across all classes. A secondary multi-institutional validation cohort, of 101 single-slide cases, achieved micro-average model specificity of 0.978, micro-average model sensitivity of 0.931 and micro-average model F1 score of 0.931 across all classes. Pathologists reflected their positive impressions on the overall accuracy of the AI in detecting colorectal pathology abnormalities., Conclusions: We have developed a high-performing colorectal biopsy AI triage model that can be integrated into a routine digital pathology workflow to assist pathologists in prioritising cases and identifying cases with dysplasia/cancer versus non-neoplastic biopsies., (© 2024 John Wiley & Sons Ltd.)

Antibody-based separation methods, such as immunoaffinity chromatography (IAC), are powerful purification and isolation techniques. Antibodies isolated using these techniques have proven highly efficient in applications ranging from clinical diagnostics to environmental monitoring. Immunoaffinity chromatography is an efficient antibody separation method which exploits the binding efficiency of a ligand to an antibody. Essential to the successful design of any IAC platform is the optimization of critical experimental parameters such as (a) the biological affinity pair, (b) the matrix support, (c) the immobilization coupling chemistry, and (d) the effective elution conditions. These elements and the practicalities of their use are discussed in detail in this review. At the core of all IAC platforms is the high affinity interactions between antibodies and their related ligands; hence, this review entails a brief introduction to the generation of antibodies for use in immunoaffinity chromatography and also provides specific examples of their potential applications.

Affinity chromatography permits the isolation of a target analyte from a complex mixture and can be utilized to purify proteins, carbohydrates, drugs, haptens, or any analyte of interest once an affinity pair is available. It involves the exploitation of specific interactions between a binding affinity pair, such as those between an antibody and its associated antigen, or between any ligand and its associated binding receptor/protein. With the discovery of protein A in 1970, and, subsequently protein G and L, immuno-affinity chromatography has grown in popularity and is now the standard methodology for the purification of antibodies which may be implemented for a selection of different applications such as immunodiagnostics. This chapter is designed to inform the researcher about the basic techniques involved in the affinity chromatography-based purification of monoclonal, polyclonal, and recombinant antibodies. Examples are provided for the use of protein A and G. In addition, tables are provided that allow the reader to select the most appropriate protein for use in the isolation of their antibody.

Colorectal cancer is one of the most common cancers worldwide with almost 700,000 deaths every year. Detection of colorectal cancer at an early stage significantly improves patient survival. Cancer-specific autoantibodies found in sera of cancer patients can be used for pre-symptomatic detection of the disease. In this study we assess the zinc finger proteins ZNF346, ZNF638, ZNF700 and ZNF768 as capture antigens for the detection of autoantibodies in colorectal cancer. Sera from 96 patients with colorectal cancer and 35 control patients with no evidence of cancer on colonoscopy were analysed for the presence of ZNF-specific autoantibodies using an indirect ELISA. Autoantibodies to individual ZNF proteins were detected in 10-20% of colorectal cancer patients and in 0-5.7% of controls. A panel of all four ZNF proteins resulted in an assay specificity of 91.4% and sensitivity of 41.7% for the detection of cancer patients in a cohort of non-cancer controls and colorectal cancer patients. Clinicopathological and survival analysis revealed that ZNF autoantibodies were independent of disease stage and did not correlate with disease outcome. Since ZNF autoantibodies were shared between patients and corresponding ZNF proteins showed similarities in their zinc finger motifs, we performed an in silico epitope sequence analysis. Zinc finger proteins ZNF700 and ZNF768 showed the highest sequence similarity with a bl2seq score of 262 (E-value 1E-81) and their classical C2H2 ZNF motifs were identified as potential epitopes contributing to their elevated immunogenic potential. Our findings show an enhanced and specific immunogenicity to zinc finger proteins, thereby providing a multiplexed autoantibody assay for minimally invasive detection of colorectal cancer.

Antibody-based separation methods, such as immunoaffinity chromatography (IAC), are powerful purification and isolation techniques. Antibodies isolated using these techniques have proven highly efficient in applications ranging from clinical diagnostics to environmental monitoring. IAC is an efficient antibody separation method which exploits the binding efficiency of a ligand to an antibody. Essential to the successful design of any IAC platform is the optimisation of critical experimental parameters such as: (a) the biological affinity pair, (b) the matrix support, (c) the immobilisation coupling chemistry, and (d) the effective elution conditions. These elements and the practicalities of their use are discussed in detail in this review. At the core of all IAC platforms is the high-affinity interactions between antibodies and their related ligands; hence, this review entails a brief introduction to the generation of antibodies for use in IAC and also provides specific examples of their potential applications.

Affinity chromatography permits the isolation of a target analyte from a complex mixture and can be utilised to purify proteins, carbohydrates, drugs, haptens, or any analyte of interest once an affinity pair is available. It involves the exploitation of specific interactions between a binding affinity pair, such as those between an antibody and its associated antigen, or between any ligand and its associated binding receptor/protein. With the discovery of protein A in 1970, and, subsequently proteins G and L, immuno-affinity chromatography has grown in popularity and is now the standard methodology for the purification of antibodies which may be implemented for a selection of different applications such as immunodiagnostics. This chapter is designed to inform the researcher about the basic techniques involved in the affinity chromatography-based purification of monoclonal, polyclonal, and recombinant antibodies. Examples are provided for the use of proteins A and G. In addition, tables are provided that allow the reader to select the most appropriate protein for use in the isolation of their antibody.