Pflugers Archiv : European journal of physiology最新文献

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.

The rapid integration of artificial intelligence (AI) into surgical practice necessitates a comprehensive evaluation of its applications, challenges, and physiological impact. This systematic review synthesizes current AI applications in surgery, with a particular focus on machine learning (ML) and its role in optimizing preoperative planning, intraoperative decision-making, and postoperative patient management. Using PRISMA guidelines and PICO criteria, we analyzed key studies addressing AI's contributions to surgical precision, outcome prediction, and real-time physiological monitoring. While AI has demonstrated significant promise-from enhancing diagnostics to improving intraoperative safety-many surgeons remain skeptical due to concerns over algorithmic unpredictability, surgeon autonomy, and ethical transparency. This review explores AI's physiological integration into surgery, discussing its role in real-time hemodynamic assessments, AI-guided tissue characterization, and intraoperative physiological modeling. Ethical concerns, including algorithmic opacity and liability in high-stakes scenarios, are critically examined alongside AI's potential to augment surgical expertise. We conclude that longitudinal validation, improved AI explainability, and adaptive regulatory frameworks are essential to ensure safe, effective, and ethically sound integration of AI into surgical decision-making. Future research should focus on bridging AI-driven analytics with real-time physiological feedback to refine precision surgery and patient safety strategies.

The purpose was to investigate the changes in cytosolic Ca²⁺ and force output during post-tetanic potentiation (PTP) during pre-fatigue and during prolonged low-frequency force depression (PLFFD) following fatigue. Intact single myofibers from the flexor digitorum brevis of mice were electrically stimulated to record force (n = 8) and free cytosolic Ca²⁺ concentration ([Ca²⁺]_c) with FURA-2 (n = 6) at 32 °C. Initially, force and [Ca²⁺]_c were measured during brief (350 ms) trains of stimuli at 30, 50, 70, and 200 Hz at ~ 2 s intervals (Force-frequency protocol, FFP). Then, a conditioning stimulus (CS) of six 120 Hz stimuli, separated by ~ 3 s, was used to induce PTP, immediately followed by an FFP. Myofiber fatigue was produced by 150 Hz trains every 3 s until peak force decayed 70% of the initial. Thirty minutes after the fatigue, the CS was repeated to assess the effect of PTP on force and [Ca²⁺]_c during PLFFD. The CS in unfatigued myofibers induced PTP as the submaximal force was enhanced and accompanied by increased peak [Ca²⁺]_c with no change in myofilament Ca²⁺ sensitivity. After fatigue, PLFFD was due to lowered peak [Ca²⁺]_c. Inducing PTP during PLFFD enhanced submaximal force primarily through greater peak [Ca²⁺]_c, mitigating the submaximal force deficits. Despite the impaired force during PLFFD, myofibers remained sensitive to PTP, and this mitigated the submaximal force deficits through increased peak [Ca²⁺]_c without a change in myofilament Ca²⁺ sensitivity. Therefore, force adjustments of intact single myofibers due to activation history are principally accomplished by opposing adjustments in [Ca²⁺]_c.

Autosomal dominant polycystic kidney disease (ADPKD) is a debilitating disease characterized by renal cysts. It arises from mutations in proteins expressed in part in the primary cilia of renal epithelial cells. One of these, polycystin-2 (PC2), is an ion-conducting channel. To date, ion channels in the cilium have only been characterized in standard normosmolar external solutions, but the osmolality of the renal filtrate bathing the cilia varies widely. Here I report that urine, which better represents the filtrate, activates a large cation-conducting current in the cilia. With defined external solutions, hyperosmolality through addition of urea, NaCl, or D-mannitol activates a similar current. Most but not all of this current is conducted through TRPM4 channels. It is greatly reduced by internal MgATP or 9-phenanthrol, which inhibit TRPM4, or by shRNA knockdown of TRPM4. However, part of the current activated by urea conducts Ca²⁺ through channels that remain to be identified. External hyperosmolality also greatly increases the activity of ciliary PC2 channels; this is the first physiological stimulus identified for these channels. Possibilities are discussed for the mechanisms of channel activation and the roles for these activities in regulatory volume increase and cystogenesis.

The Notch signaling pathway is crucial for skeletal muscle development, regeneration, inflammation, and aging. This study investigated the association between interleukin-10 (IL-10) and the Notch pathway in C2C12 cells, as well as explored the effects of combined endurance and resistance exercise on the Notch and autophagy pathways in the skeletal muscle of senescence-accelerated mouse-resistant 1 Sedentary (SAMR1 CT), SAMR1 exercised (SAMR1 EX), senescence-accelerated prone mouse 8 Sedentary (SAMP8 CT), and SAMP8 exercised (SAMP8 EX). C2C12 myoblasts were transfected with siIL-10. Histological analysis, reverse transcription-quantitative polymerase chain reaction, and immunoblotting were performed on the quadriceps and tibialis anterior muscles. A publicly available dataset was analyzed to assess the Notch pathway in older men. In summary, IL-10 knockdown in myoblasts reduced the Notch pathway gene and protein expression. In SAMP8 mice, combined exercise improved muscle fiber organization, enhanced balance and coordination, and increased Notch2 and Hes1 mRNA levels. NOTCH2 mRNA levels were also higher in older men compared to young subjects with similar physical activity levels. These findings suggest that combined physical exercise enhances muscle regeneration via the Notch pathway in aged muscle.

The prevalence of panic disorder is two to four times higher in women compared to that in men, and hormonal changes during the menstrual cycle play a role in the occurrence of panic attacks. Here, we investigated the effect of the estrous cycle on the ventilatory and behavioral responses to CO₂ in mice. Female mice in proestrus, estrus, metestrus, or diestrus were exposed to 20% CO₂, and their escape behaviors, brain monoamines, and plasma levels of 17β-estradiol (E₂) and progesterone (P₄) were measured. Pulmonary ventilation (V̇_E), oxygen consumption (V̇O₂), and body core temperature (T_B) were also measured during normocapnia followed by CO₂. Females exposed to 20% CO₂ exhibited an escape behavior, but the estrous cycle did not affect this response. Females in all phases of the estrous cycle showed higher V̇_E and lower T_B during hypercapnia. In diestrus, there was an attenuation of CO₂-induced hyperventilation with no change in V̇O₂, whereas in estrus, this response was accompanied by a reduction in V̇O₂. Hypercapnia also increased the concentration of plasma P₄ and central DOPAC, the main dopamine metabolite, in all females. There was an estrous cycle effect on brainstem serotonin, with females in estrus showing a higher concentration than females in the metestrus and diestrus phases. Therefore, our data suggest that hypercapnia induces panic-related behaviors and ventilatory changes that lead to an increase in P₄ secretion in female mice, likely originating from the adrenals. The estrous cycle does not affect the behavioral response but interferes in the ventilatory and metabolic responses to CO₂ in mice.

Plasma thyroid hormone (TH) binding proteins (THBPs), including thyroxine-binding globulin (TBG), transthyretin (TTR), and albumin (ALB), carry THs to extrathyroidal sites, where THs are unloaded locally and then taken up via membrane transporters into the tissue proper. The respective roles of THBPs in supplying THs for tissue uptake are not completely understood. To investigate this, we developed a spatial human physiologically based kinetic (PBK) model of THs, which produces several novel findings. (1) Contrary to postulations that TTR and/or ALB are the major local T4 contributors, the three THBPs may unload comparable amounts of T4 in Liver, a rapidly perfused organ; however, their contributions in slowly perfused tissues follow the order of abundances of T4TBG, T4TTR, and T4ALB. The T3 amounts unloaded from or loaded onto THBPs in a tissue acting as a T3 sink or source respectively follow the order of abundance of T3TBG, T3ALB, and T3TTR regardless of perfusion rate. (2) Any THBP alone is sufficient to maintain spatially uniform TH tissue distributions. (3) The TH amounts unloaded by each THBP species are spatially dependent and nonlinear in a tissue, with ALB being the dominant contributor near the arterial end but conceding to TBG near the venous end. (4) Spatial gradients of TH transporters and metabolic enzymes may modulate these contributions, producing spatially invariant or heterogeneous TH tissue concentrations depending on whether the blood-tissue TH exchange operates in near-equilibrium mode. In summary, our modeling provides novel insights into the differential roles of THBPs in local TH tissue distribution.