Shou-Zen Fan

Pain pathways and interpatient variability represent an ongoing challenge for nociception monitoring. Inter- and intrapatient variability require large patient data for training conventional nociception predictive models. In this paper, we propose a quantum multilayer perceptron (QMLP) model for nociception prediction utilizing quantum features such as entanglement which enables the capturing of complex parameter dependencies using less data, by representing intralayer connections between parameters. Our QMLP architecture encodes input physiological parameters into quantum states which are processed through entangled quantum circuits, and optimized using parameter shift rule for gradient estimation. Our nociception prediction model is trained and evaluated on surgical data collected from two hospitals, with features extracted from electrocardiogram, photoplethysmography, and electroencephalogram. Through systematic comparative analysis across multiple datasets using two sampling approaches (patient-wise and downsampling), we demonstrate that our QMLP model consistently outperforms different classical baselines including deep learning models, tree-based ensembles, and linear models across all evaluation metrics. Clinical evaluation on different populations of patients confirms the QMLP model’s superior ability to predict nociceptive changes during surgical events including Intubation, Incision, and Extubation. Expressivity analysis reveals that QMLP models achieve approximately twice the local effective dimension of classical baselines with identical parameter counts. Entanglement topology analysis demonstrates that circular configuration consistently achieves lower training loss compared to linear, pairwise, and non-entangled architectures, with quantum advantage driven by entanglement structure independent of data quantity. Our findings suggest quantum neural networks might offer advantages for nociception monitoring in anesthesia applications, particularly in data-limited scenarios where complex interrelated parameters influence clinical outcomes. The code used in this research can be accessed from: https://github.com/oyanoth/qmlp-nociception.

Depression during pregnancy is often overlooked and undertreated. Ketamine has been shown to exert prompt and sustained antidepressant effects in patients with depression, although concerns of potential neurotoxicity prohibit its use in pregnant women. Here, we aim to investigate the neurobehavioral effects of subanesthetic ketamine on pregnant mice and their offspring. We found that pregnant C57BL/6 mice receiving ketamine (10 mg/kg/day intraperitoneal) from gestation day 15 to 17 exhibited less depression-like behaviors. Prenatal ketamine treatment induced male-specific reduction in depression- and anxiety-like behaviors in adult offspring, without alterations in social and memory performance. These behavioral outcomes were associated with a male-specific increase in dendritic spine density of dentate gyrus granule cells, while neither dendritic architecture nor hippocampal neurogenesis was affected. N-methyl-D-aspartate receptor subunits GluN2A and GluN3A were expressed at significantly higher levels in the hippocampus of male as compared to female mouse embryos, suggesting sex-dependent actions of ketamine on developing brain. Overall, our study showed that prenatal exposure to subanesthetic ketamine could exert long-lasting neurobehavioral effects in a sex-dependent manner, with male offspring being more resilient to stress. These findings may have implications concerning ketamine use during pregnancy, and also provide clues about the developmental origins of emotional problems.

The gut microbiome has emerged as a key factor influencing a wide range of host physiological processes and behaviors, though the mechanisms behind these effects remain only partially understood. In this study, we explored the role of the gut microbiome in memory regulation using a parasitoid wasp-induced oviposition depression paradigm in Drosophila melanogaster . Our findings show that flies with depleted gut microbiota, either through axenic culture or antibiotic treatment, exhibited significant memory impairments. However, reintroducing the commensal bacterium Lactobacillus plantarum alone was sufficient to restore memory, while coinoculation with Acetobacter pomorum further enhanced memory performance. Hemolymph metabolomic analyses revealed reduced amino acid levels in antibiotic-treated flies, which were linked to impaired Drosophila target of rapamycin (dTOR) signaling. Additionally, genetic manipulation of dTOR or dietary supplementation with branched-chain amino acids either mimicked or rescued the memory deficits caused by antibiotic treatments. These results suggest that the gut microbiome is essential for regulating memory function by maintaining amino acid homeostasis and proper dTOR signaling, with profound implications for advancing knowledge of cognitive regulation.

Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preserving system that accurately classifies pain levels (low, medium, and high) by leveraging the intricate relationship between pain perception and autonomic nervous system responses captured in ECG signals. At the heart of our methodology lies a signal processing approach that transforms one-dimensional ECG signals into rich, two-dimensional Continuous Wavelet Transform (CWT) images. These transformations capture both temporal and frequency characteristics of pain-induced cardiac variations, providing a comprehensive representation of autonomic nervous system responses to different pain intensities. Our framework processes these CWT images through a sophisticated quantum–classical hybrid architecture, where edge devices perform initial preprocessing and feature extraction while maintaining data privacy. The cornerstone of our system is a Quantum Convolutional Hybrid Neural Network (QCHNN) that harnesses quantum entanglement properties to enhance feature detection and classification robustness. This quantum-enhanced approach is seamlessly integrated into a federated learning framework, allowing distributed training across multiple healthcare facilities while preserving patient privacy through secure aggregation protocols. The QCHNN demonstrated remarkable performance, achieving a classification accuracy of 94.8% in pain level assessment, significantly outperforming traditional machine learning approaches.

Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), photoplethysmography (PPG), and electrocardiogram (ECG) signals to predict nociception. We collected data from patients undergoing general anesthesia at two hospitals and developed and compared two deep learning models: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) network. Both models were trained on expert anesthesiologists’ assessments of nociception. We evaluated normalization strategies for offline and online usage and found that Min–Max normalization was most effective for our dataset. Our results demonstrate that the MLP model accurately captured nociceptive changes in response to painful surgical stimuli, whereas the LSTM model provided smoother predictions but with lower sensitivity to rapid changes. These findings underscore the potential of multimodal, deep learning-based solutions to improve real-time nociception monitoring in diverse clinical settings.

Objective: This study aims to assess the expansive effects of pterygomaxillary disjunction (PMD) in surgically assisted rapid maxillary expansion (SARME) surgery using a meta‐analysis approach.Materials and Methods: The study conducted a comprehensive literature search across five databases: PubMed, Scopus, Medline, Embase, and Cochrane, adhering to the PRISMA 2020 guidelines. Dental alterations were assessed using either cone‐beam computed tomography (CBCT) or dental casts, while skeletal changes were exclusively measured from CBCT scans. We analysed the dentoskeletal changes between PMD +/− groups and conducted a within‐group comparison. The primary focus of the results was on the mean differences observed in pre‐ and post‐operative measurements.Results: Dental expansion was larger in the PMD+ group but not statistically significant. Skeletal expansion showed a significantly larger expansion in the posterior region in the PMD+ group (P = .033). Without PMD, anterior palatal expansion was significantly larger (P = .03), and the buccal tipping of posterior teeth was also significantly larger (P = .011) to achieve acceptable dental expansion outcomes.Conclusions: Both PMD +/− groups of SARME surgery can achieve satisfactory dental expansion outcomes. However, bone expansion and tooth inclination are also important factors that influence orthodontic treatment and post‐expansion stability. By reducing the bony resistance with PMD, larger posterior palatal expansion and more parallel bony expansion are observed. In contrast, without PMD, there is smaller palatal expansion and greater tooth inclination in the posterior region. This could potentially lead to compromised periodontal conditions following expansion.

The term “anesthetic depth” refers to the extent to which a general anesthetic agent sedates the central nervous system with specific strength concentration at which it is delivered. The depth level of anesthesia plays a crucial role in determining surgical complications, and it is imperative to keep the depth levels of anesthesia under control to perform a successful surgery. This study used electroencephalography (EEG) signals to predict the depth levels of anesthesia. Traditional preprocessing methods such as signal decomposition and model building using deep learning were used to classify anesthetic depth levels. This paper proposed a novel approach to classify the anesthesia levels based on the concept of time series feature extraction, by finding out the relation between EEG signals and the bi-spectral Index over a period of time. Time series feature extraction on basis of scalable hypothesis tests were performed to extract features by analyzing the relation between the EEG signals and Bi-Spectral Index, and machine learning models such as support vector classifier, XG boost classifier, gradient boost classifier, decision trees and random forest classifier are used to train the features and predict the depth level of anesthesia. The best-trained model was random forest, which gives an accuracy of 83%. This provides a platform to further research and dig into time series-based feature extraction in this area.

Processed electroencephalogram (EEG) has been considered a useful tool for measuring the depth of anesthesia (DOA). However, because of its inability to detect the activities of the brain stem and spinal cord responsible for most of the vital signs, a new biomarker for measuring the multidimensional activities of the central nervous system under anesthesia is required. Detrended fluctuation analysis (DFA) is a new technique for detecting the scaling properties of nonstationary heart rate (HR) behavior. This study investigated the changes in fractal properties of heart rate variability (HRV), a nonlinear analysis, under intravenous propofol, inhalational desflurane, and spinal anesthesia. We compared the DFA method with traditional spectral analysis to evaluate its potential as an alternative biomarker under different levels of anesthesia. Eighty patients receiving elective procedures were randomly allocated different anesthesia. HRV was measured with spectral analysis and DFA short-term (4–11 beats) scaling exponent (DFAα1). An increase in DFAα1 followed by a decrease at higher concentrations during propofol or desflurane anesthesia is observed. Spinal anesthesia decreased the DFAα1 and low-/high-frequency ratio (LF/HF ratio). DFAα1 of HRV is a sensitive and specific method for distinguishing changes from baseline to anesthesia state. The DFAα1 provides a potential real-time biomarker to measure HRV as one of the multiple dimensions of the DOA.