Recent advancements in artificial intelligence–enabled medical gas sensing have led to enhanced accuracy, safety, and efficiency in healthcare. Medical gases, including oxygen, nitrous oxide, and carbon dioxide, are essential for various treatments but pose health risks if improperly managed. This review highlights the integration of artificial intelligence in medical gas sensing, enhancing traditional sensors through advanced data processing, pattern recognition, and real-time monitoring capabilities. Artificial intelligence improves the ability to detect harmful gas levels, enabling immediate intervention to prevent adverse health effects. Moreover, developments in nanotechnology have resulted in advanced materials, such as metal oxides and carbon-based nanomaterials, which increase sensitivity and selectivity. These innovations, combined with artificial intelligence, support continuous patient monitoring and predictive diagnostics, paving the way for future breakthroughs in medical care.

Oxidative stress, characterized by an imbalance between the production of reactive oxygen species (ROS) and the body’s antioxidant defenses, significantly affects cellular function and viability. It plays a pivotal role in modulating membrane potentials, particularly action potentials (APs), essential for properly functioning excitable cells such as neurons, smooth muscles, pancreatic beta cells, and myocytes. The interaction between oxidative stress and AP dynamics is crucial for understanding the pathophysiology of various conditions, including neurodegenerative diseases, cardiac arrhythmias, and ischemia-reperfusion injuries. This review explores how oxidative stress influences APs, focusing on alterations in ion channel biophysics, gap junction, calcium dynamics, mitochondria, and Interstitial Cells of Cajal functions. By integrating current research, we aim to elucidate how oxidative stress contributes to disease progression and discuss potential therapeutic interventions targeting this interaction.

The vagina is an essential component of the female reproductive system and is responsible for providing female sexual satisfaction. Vaginal smooth muscle contraction plays a crucial role in various physiological processes, including sexual arousal, childbirth, and urinary continence. In pathophysiological conditions, such as pelvic floor disorders, aberrations in vaginal smooth muscle function can lead to urinary incontinence and pelvic organ prolapse. A set of cellular and sub-cellular physiological mechanisms regulates the contractile properties of the vaginal smooth muscle cells. Calcium influx is a crucial determinant of smooth muscle contraction, facilitated through voltage-dependent calcium channels and calcium release from intracellular stores. Comprehensive reviews on smooth muscle biophysics are relatively scarce within the scientific literature, likely due to the complexity and specialized nature of the topic. The objective of this review is to provide a comprehensive description of alterations in the cellular physiology of vaginal smooth muscle contraction. The benefit associated with this particular approach is that conducting a comprehensive examination of the cellular mechanisms underlying contractile activation will enable the creation of more targeted therapeutic agents to control vaginal contraction disorders.

We developed a mathematical model to simulate the dynamics of background synaptic noise in non-neuronal cells. By employing the stochastic Ornstein–Uhlenbeck process, we represented excitatory synaptic conductance and integrated it into a whole-cell model to generate spontaneous and evoke cellular electrical activities. This single-cell model encompasses numerous biophysically detailed ion channels, depicted by a set of ordinary differential equations in Hodgkin–Huxley and Markov formalisms. Consequently, this approach effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potentials (sAPs), resembling electrical activity observed in vitro. The input resistance decreased significantly, while the firing rate of spontaneous action potentials increased. Moreover, alterations in the ability to reach the action potential threshold were observed. Background synaptic activity can modify the input/output characteristics of non-neuronal excitatory cells. Hence, suppressing these baseline activities could aid in identifying new pharmaceutical targets for various clinical diseases.

Oxidative stress, characterized by an imbalance between the production of reactive oxygen species (ROS) and the body's antioxidant defenses, significantly affects cellular function and viability. It plays a pivotal role in modulating action potentials (APs), essential for properly functioning excitable cells such as neurons, smooth muscles, pancreatic beta cells, and myocytes. The interaction between oxidative stress and AP dynamics is crucial for understanding the pathophysiology of various conditions, including neurodegenerative diseases, cardiac arrhythmias, and ischemia-reperfusion injuries. This review explores how oxidative stress influences APs, focusing on alterations in ion channel functionality, signaling pathways, and membrane properties. By integrating current research, we aim to elucidate how oxidative stress contributes to disease progression and discuss potential therapeutic interventions targeting this interaction.

Background: Bladder pathophysiology encompasses a wide array of disorders, including bladder cancer, interstitial cystitis, overactive and underactive bladder, and bladder outlet obstruction. It also involves conditions such as neurogenic bladder, bladder infections, trauma, and congenital anomalies. Each of these conditions presents unique challenges for diagnosis and treatment. Recent advancements in artificial intelligence (AI) have shown significant potential in revolutionizing diagnostic methodologies within this domain. Objective: This review provides an updated and comprehensive examination of the integration of AI into the diagnosis of bladder pathophysiology. It highlights key AI techniques, including machine learning and deep learning, and their applications in identifying and classifying bladder conditions. The review also assesses current AI-driven diagnostic tools, their accuracy, and clinical utility. Furthermore, it explores the challenges and limitations confronted in the implementation of AI technologies, such as data quality, interpretability, and integration into clinical workflows, among others. Finally, the paper discusses future directions and advancements, proposing pathways for enhancing AI applications in bladder pathophysiology diagnosis. This review aims to provide a valuable resource for clinicians, researchers, and technologists, fostering an in-depth understanding of AI’s roles and potential in transforming bladder disease diagnosis. Conclusion: While AI demonstrates considerable promise in enhancing the diagnosis of bladder pathophysiology, ongoing progresses in data quality, algorithm interpretability, and clinical integration are essential for maximizing its potential. The future of AI in bladder disease diagnosis holds great promise, with continued innovation and collaboration opening the possibility of more accurate, efficient, and personalized care for patients.