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As distinct cancer biomarkers have been discovered in recent years, a need to reclassify tumors by more than their histology has been proposed, and therapies are now tailored to treat cancers based on specific molecular aberrations and immunologic markers. In fact, multiple histology-agnostic therapies are currently adopted in clinical practice for treating patients regardless of their tumor site of origin. In parallel with this new model for drug development, in the past few years, several novel antibody–drug conjugates (ADCs) have been approved to treat solid tumors, benefiting from engineering improvements in the conjugation process and the introduction of novel linkers and payloads. With the recognition that numerous surface targets are expressed across various cancer histologies, alongside the remarkable activity of modern ADCs, this drug class has been increasingly evaluated as suitable for a histology-agnostic expansion of indication. For illustration, the anti-HER2 ADC trastuzumab deruxtecan has demonstrated compelling activity in HER2-overexpressing breast, gastric, colorectal, and lung cancer. Examples of additional novel and potentially histology-agnostic ADC targets include trophoblast cell-surface antigen 2 (Trop-2) and nectin-4, among others. In the current review article, the authors summarize the current approvals of ADCs by the US Food and Drug Administration focusing on solid tumors and discuss the challenges and opportunities posed by the multihistological expansion of ADCs.

Multi-object Tracking (MOT) is one of the most fundamental computer vision tasks that contributes to various video analysis applications. Despite the recent promising progress, current MOT research is still limited to a fixed sampling frame rate of the input stream. They are neither as flexible as humans nor well-matched to industrial scenarios which require the trackers to be frame rate insensitive in complicated conditions. In fact, we empirically found that the accuracy of all recent state-of-the-art trackers drops dramatically when the input frame rate changes. For a more intelligent tracking solution, we shift the attention of our research work to the problem of Frame Rate Agnostic MOT (FraMOT), which takes frame rate insensitivity into consideration. In this paper, we propose a Frame Rate Agnostic MOT framework with a Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time. Specifically, we propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information to aid identity matching across multi-frame-rate inputs, improving the capability of the learned model in handling complex motion-appearance relations in FraMOT. Moreover, the association gap between training and inference is enlarged in FraMOT because those post-processing steps not included in training make a larger difference in lower frame rate scenarios. To address it, we propose Periodic Training Scheme to reflect all post-processing steps in training via tracking pattern matching and fusion. Along with the proposed approaches, we make the first attempt to establish an evaluation method for this new task of FraMOT. Besides providing simulations and evaluation metrics, we try to solve new challenges in two different modes, i.e., known frame rate and unknown frame rate, aiming to handle a more complex situation. The quantitative experiments on the challenging MOT17/20 dataset (FraMOT version) have clearly demonstrated that the proposed approaches can handle different frame rates better and thus improve the robustness against complicated scenarios.

The politicisation of biblical language has become more acute with the rise of Christian nationalism and right-wing movements in many countries. This article explores the American political scene and its rhetorical appropriation of biblical language. Fightin’ words drawn from presumed Pauline texts, have become staple rhetoric among candidates seeking to attract Christian voters to their cause. The article then discusses the use of such discourse by Republican and Democrat politicians in recent decades. Next, it attempts to reduce, if possible, combative speech by examining three verses: 1 Timothy 1:18, 6:12, and 2 Timothy 4:7. It asks whether Bible translators have accurately conveyed the Greek text of these verses in English. The article argues that modern translations have overlooked the contextual meaning and lexical background found in the material culture of the Graeco-Roman world. It is hoped that the proposed new translations will produce less toxic debate in future political discourse.

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model works with the aim of making ML models more transparent and increasing the trust of end‐users in their output. SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanation (LIME) are two widely used XAI methods, particularly with tabular data. In this perspective piece, the way the explainability metrics of these two methods are generated is discussed and a framework for the interpretation of their outputs, highlighting their weaknesses and strengths is proposed. Specifically, their outcomes in terms of model‐dependency and in the presence of collinearity among the features, relying on a case study from the biomedical domain (classification of individuals with or without myocardial infarction) are discussed. The results indicate that SHAP and LIME are highly affected by the adopted ML model and feature collinearity, raising a note of caution on their usage and interpretation. SHapley Additive exPlanations and Local Interpretable Model Agnostic Explanation are two widely used eXplainable artificial intelligence methods. However, they have limitations related to model‐dependency and the presence of collinearity among the features which result in unrealistic explanations. This perspective discusses these two issues through two case studies and provides possible solutions to overcome and eliminate their impacts

The neurotrophic tyrosine receptor kinase (NTRK) gene family encodes three tropomyosin receptor kinases (TRKA, TRKB, TRKC) that contribute to central and peripheral nervous system development and function. NTRK gene fusions are oncogenic drivers of various adult and paediatric tumours. Several methods have been used to detect NTRK gene fusions including immunohistochemistry, fluorescence in situ hybridisation, reverse transcriptase polymerase chain reaction, and DNA- or RNA-based next-generation sequencing. For patients with TRK fusion cancer, TRK inhibition is an important therapeutic target. Following the FDA approval of the selective TRK inhibitor, larotrectinib, as well as the ongoing development of multi-kinase inhibitors with activity in TRK fusion cancer, testing for NTRK gene fusions should become part of the standard diagnostic process. In this review we discuss the biology of NTRK gene fusions, and we present a testing algorithm to aid detection of these gene fusions in clinical practice and guide treatment decisions.

Predicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data are available on a subset of individuals and nonlinear relationships between variables are common. Here we provide a guide to the latest advances in statistical machine learning to construct pathogen‐risk models that automatically incorporate complex nonlinear relationships with minimal statistical assumptions from ecological data with missing data. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus. Our modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualize strong nonlinear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years. When combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure that are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns. The authors provide a practical guide to integrating the latest advances in data science and machine learning to better quantify disease risk. Importantly, many of the methods employed in the guide can be easily adjusted to address broader ecological questions involving, but not limited to, big data or complex non‐linear interactions.

Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically creates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g., linear classifier) around the prediction through generating simulated data around the instance by random perturbation, and obtaining feature importance through applying some form of feature selection. While LIME and similar local algorithms have gained popularity due to their simplicity, the random perturbation methods result in shifts in data and instability in the generated explanations, where for the same prediction, different explanations can be generated. These are critical issues that can prevent deployment of LIME in sensitive domains. We propose a deterministic version of LIME. Instead of random perturbation, we utilize Agglomerative Hierarchical Clustering (AHC) to group the training data together and K-Nearest Neighbour (KNN) to select the relevant cluster of the new instance that is being explained. After finding the relevant cluster, a simple model (i.e., linear model or decision tree) is trained over the selected cluster to generate the explanations. Experimental results on six public (three binary and three multi-class) and six synthetic datasets show the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME), where we quantitatively determine the stability and faithfulness of DLIME compared to LIME.

The concept of agnostic biomarkers—molecular modifications that guide therapy irrespective of tumor origin—has gained increasing relevance in oncology, including colorectal cancer (CRC). This review aims to critically evaluate the role of such biomarkers in CRC, highlighting their clinical significance as therapeutic targets and indicators of prognosis. Through a PubMed search using the terms “agnostic treatment AND colorectal cancer,” eight key studies were identified and qualitatively analyzed. We focus on several biomarkers commonly regarded as agnostic across tumor types, including BRAF V600E mutation, receptor tyrosine kinase (RTK) and PI3K fusions, the CpG island methylator phenotype (CIMP), high tumor mutational burden (TMB), and microsatellite instability (MSI). These markers are inspected for their prevalence in CRC, underlying pathophysiological mechanisms of cancer promotion, and predictive or prognostic implications. Moreover, we integrate findings from broader oncologic studies to contextualize the evolving role of agnostic biomarkers beyond organ-specific paradigms. Emerging evidence suggests that leveraging these molecular signatures may inform the use of targeted and immunotherapeutic agents as first-line options in select CRC populations. Collectively, agnostic biomarkers represent an auspicious avenue for personalizing CRC treatment, particularly in advanced-stage disease where traditional treatment options remain limited.

Sulfur fluoride exchange (SuFEx) has emerged as the new generation of click chemistry. We report here a SuFEx-enabled, agnostic approach for the discovery and optimization of covalent inhibitors of human neutrophil elastase (hNE). Evaluation of our ever-growing collection of SuFExable compounds toward various biological assays unexpectedly revealed a selective and covalent hNE inhibitor: benzene-1,2-disulfonyl fluoride. Synthetic derivatization of the initial hit led to a more potent agent, 2-(fluorosulfonyl)phenyl fluorosulfate with IC50 0.24 μM and greater than 833-fold selectivity over the homologous neutrophil serine protease, cathepsin G. The optimized, yet simple benzenoid probe only modified active hNE and not its denatured form.

Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer’s disease (AD). Adhering to PRISMA and Kitchenham’s guidelines, we identified 23 relevant articles and investigated these frameworks’ prospective capabilities, benefits, and challenges in depth. The results emphasise XAI’s crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.