Pflugers Archiv : European journal of physiology最新文献

Cerebrocortical neurons receive glutamatergic inputs via thalamocortical projections, and their activities are simultaneously controlled by GABAergic interneurons. Few studies have demonstrated the difference in the amplitude of evoked excitatory postsynaptic currents (EPSCs) via thalamocortical projections onto glutamatergic excitatory (ENs) and GABAergic inhibitory neurons (INs); the strength of excitation among neural subtypes varies among sensory cortices. The present study aimed to reveal the profile of thalamocortical inputs to ENs and inhibitory neurons in the insular cortex (IC) by evaluating the amplitude and latency of EPSCs evoked in the connection from the ventroposteromedial (VPM) thalamic nucleus to the IC. Whole-cell patch-clamp recordings were prepared from ENs, fast-spiking neurons (FSNs), and non-fast-spiking neurons (NFSNs) in the middle layers (layer 4 and adjacent layers) of the IC. Photostimulation-induced EPSCs (pEPSCs) were evoked via the selective activation of thalamocortical axons via optogenetics. All the neuronal subtypes received direct excitatory inputs from the VPM, and pEPSCs recorded from FSNs had the greatest amplitude and shortest latency compared with those recorded from ENs and NFSNs. Under current-clamp conditions, FSNs almost invariably exhibited action potentials responding to photostimulation, whereas ENs and NFSNs often showed the failure of action potential induction. In addition to excitatory inputs, some neurons exhibited pEPSCs followed by outward GABA_A receptor-mediated currents, which curtailed the pEPSC peak and aligned the timing of the action potential to photostimulation. These results suggested that FSNs play a role in the feedforward inhibition of EN activity in the upper layer of the IC. (244 words).

Mechanical organ models are crucial for understanding organ function and clinical applications. These models rely on input data regarding smooth muscle properties, typically gathered from experiments involving stimulations at different muscle lengths. However, reproducibility of these experimental results is a major challenge due to rapid changes in active and passive smooth muscle properties during the measurement period. Usually, preconditioning of the tissue is employed to ensure reproducible behavior in subsequent experiments, but this process itself alters the tissue's mechanical properties. To address this issue, three protocols (P1, P2, P3) without preconditioning were developed and compared to preserve the initial mechanical properties of smooth muscle tissue. Each protocol included five repetitive experimental cycles with stimulations at a long muscle length, varying in the number of stimulations at a short muscle length (P1: 0, P2: 1, P3: 2 stimulations). Results showed that P2 and P3 successfully reproduced the initial active force at a long length over five cycles, but failed to maintain the initial passive forces. Conversely, P1 was most effective in maintaining constant passive forces over the cycles. These findings are supported by existing adaptation models. Active force changes are primarily due to the addition or removal of contractile units in the contractile apparatus, while passive force changes mainly result from actin polymerization induced by contractions, leading to cytoskeletal stiffening. This study introduces a new method for obtaining reproducible smooth muscle parameters, offering a foundation for future research to replicate the mechanical properties of smooth muscle tissue without preconditioning.

Traditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.

Recently, deep generative modelling has become an increasingly powerful tool with seminal work in a myriad of disciplines. This powerful modelling approach is supposed to not only have the potential to solve current problems in the medical field but also to enable personalised precision medicine and revolutionise healthcare through applications such as digital twins of patients. Here, the core concepts of generative modelling and popular modelling approaches are first introduced to consider the potential based on methodological concepts for the generation of synthetic data and the ability to learn a representation of observed data. These potentials will be reviewed using current applications in neuroimaging for data synthesis and disease decomposition in Alzheimer's disease and multiple sclerosis. Finally, challenges for further research and applications will be discussed, including computational and data requirements, model evaluation, and potential privacy risks.

Explainable artificial intelligence (XAI) has gained significant attention in various domains, including natural and medical image analysis. However, its application in spectroscopy remains relatively unexplored. This systematic review aims to fill this gap by providing a comprehensive overview of the current landscape of XAI in spectroscopy and identifying potential benefits and challenges associated with its implementation. Following the PRISMA guideline 2020, we conducted a systematic search across major journal databases, resulting in 259 initial search results. After removing duplicates and applying inclusion and exclusion criteria, 21 scientific studies were included in this review. Notably, most of the studies focused on using XAI methods for spectral data analysis, emphasizing identifying significant spectral bands rather than specific intensity peaks. Among the most utilized AI techniques were SHapley Additive exPlanations (SHAP), masking methods inspired by Local Interpretable Model-agnostic Explanations (LIME), and Class Activation Mapping (CAM). These methods were favored due to their model-agnostic nature and ease of use, enabling interpretable explanations without modifying the original models. Future research should propose new methods and explore the adaptation of other XAI employed in other domains to better suit the unique characteristics of spectroscopic data.

Explainable artificial intelligence (XAI) is gaining importance in physiological research, where artificial intelligence is now used as an analytical and predictive tool for many medical research questions. The primary goal of XAI is to make AI models understandable for human decision-makers. This can be achieved in particular through providing inherently interpretable AI methods or by making opaque models and their outputs transparent using post hoc explanations. This review introduces XAI core topics and provides a selective overview of current XAI methods in physiology. It further illustrates solved and discusses open challenges in XAI research using existing practical examples from the medical field. The article gives an outlook on two possible future prospects: (1) using XAI methods to provide trustworthy AI for integrative physiological research and (2) integrating physiological expertise about human explanation into XAI method development for useful and beneficial human-AI partnerships.

This special issue presents a collection of reviews on the recent advancements and applications of artificial intelligence (AI) in medicine and physiology. The topics covered include digital histopathology, generative AI, explainable AI (XAI), and ethical considerations in AI development and implementation. The reviews highlight the potential of AI to transform medical diagnostics, personalized medicine, and clinical decision making, while also addressing challenges such as data quality, interpretability, and trustworthiness. The contributions demonstrate the growing importance of AI in physiological research and medicine, the need for multi-level ethics approaches in AI development, and the potential benefits of generative AI in medical applications. Overall, this special issue showcases some of the the pioneering aspects of AI in medicine and physiology, covering technical, applicative, and ethical viewpoints, and underlines the remarkable impact of AI on these fields.

With rapid advances of deep neural networks over the past decade, artificial intelligence (AI) systems are now commonplace in many applications in biomedicine. These systems often achieve high predictive accuracy in clinical studies, and increasingly in clinical practice. Yet, despite their commonly high predictive accuracy, the trustworthiness of AI systems needs to be questioned when it comes to decision-making that affects the well-being of patients or the fairness towards patients or other stakeholders affected by AI-based decisions. To address this, the field of explainable artificial intelligence, or XAI for short, has emerged, seeking to provide means by which AI-based decisions can be explained to experts, users, or other stakeholders. While it is commonly claimed that explanations of artificial intelligence (AI) establish the trustworthiness of AI-based decisions, it remains unclear what traits of explanations cause them to foster trustworthiness. Building on historical cases of scientific explanation in medicine, we here propagate our perspective that, in order to foster trustworthiness, explanations in biomedical AI should meet the criteria of being scientific explanations. To further undermine our approach, we discuss its relation to the concepts of causality and randomized intervention. In our perspective, we combine aspects from the three disciplines of biomedicine, machine learning, and philosophy. From this interdisciplinary angle, we shed light on how the explanation and trustworthiness of artificial intelligence relate to the concepts of causality and robustness. To connect our perspective with AI research practice, we review recent cases of AI-based studies in pathology and, finally, provide guidelines on how to connect AI in biomedicine with scientific explanation.

Recent advancements in generative approaches in AI have opened up the prospect of synthetic tabular clinical data generation. From filling in missing values in real-world data, these approaches have now advanced to creating complex multi-tables. This review explores the development of techniques capable of synthesizing patient data and modeling multiple tables. We highlight the challenges and opportunities of these methods for analyzing patient data in physiology. Additionally, it discusses the challenges and potential of these approaches in improving clinical research, personalized medicine, and healthcare policy. The integration of these generative models into physiological settings may represent both a theoretical advancement and a practical tool that has the potential to improve mechanistic understanding and patient care. By providing a reliable source of synthetic data, these models can also help mitigate privacy concerns and facilitate large-scale data sharing.

Artificial intelligence systems (ai-systems) (e.g. machine learning, generative artificial intelligence), in healthcare and medicine, have been received with hopes of better care quality, more efficiency, lower care costs, etc. Simultaneously, these systems have been met with reservations regarding their impacts on stakeholders' privacy, on changing power dynamics, on systemic biases, etc. Fortunately, healthcare and medicine have been guided by a multitude of ethical principles, frameworks, or approaches, which also guide the use of ai-systems in healthcare and medicine, in one form or another. Nevertheless, in this article, I argue that most of these approaches are inspired by a local isolationist view on ai-systems, here exemplified by the principlist approach. Despite positive contributions to laying out the ethical landscape of ai-systems in healthcare and medicine, such ethics approaches are too focused on a specific local healthcare and medical setting, be it a particular care relationship, a particular care organisation, or a particular society or region. By doing so, they lose sight of the global impacts ai-systems have, especially environmental impacts and related social impacts, such as increased health risks. To meet this gap, this article presents a global approach to the ethics of ai-systems in healthcare and medicine which consists of five levels of ethical impacts and analysis: individual-relational, organisational, societal, global, and historical. As such, this global approach incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.