Biology subjects

Kido, K.

Publications and source records attributed to Kido, K..

6 recordsLinked to original sources

bioRxiv2026

HPV-Associated Tonsillar Intraepithelial Neoplasia and Oral High-Risk HPV mRNA as a Candidate Biomarker

Early detection of human papillomavirus (HPV)-associated oropharyngeal cancer is an unmet need, as its premalignant lesions remain unestablished. We hypothesized that such lesions exist in individuals with oral high-risk HPV (HR-HPV) mRNA. We preoperatively screened 1,180 individuals undergoing tonsillectomy due to benign conditions and detected five with oral HR-HPV mRNA. Serial sectioning of the entire excised tonsils identified HPV-associated intraepithelial lesions in three of these five individuals. A similar lesion was identified in an additional individual with oral HPV16 infection with unknown oral HR-HPV mRNA status. Intraepithelial lesions in two individuals were HPV16-associated and showed moderate-to-severe atypia, whereas those in the other two individuals were associated with other HR-HPV and showed minimal-to-mild atypia. Based on HPV life cycle, the former were classified as transforming lesions and the latter as productive-to-intermediate lesions. Spatial transcriptomic analysis revealed molecular profiles intermediate between those of normal epithelium and HPV-associated oropharyngeal cancer. These findings establish the first evidence for HPV-associated tonsillar premalignant lesions and suggest oral HR-HPV mRNA as a candidate biomarker.

cancer biology↗

bioRxiv2025

Computed Tomography-Derived Elastic Modulus as a Noninvasive Marker of Aortic Wall Integrity: Correlation with Histopathology in the Ascending Aorta

BackgroundAscending aortic aneurysms and dissections are life-threatening conditions often requiring prophylactic surgeries. Current guidelines rely primarily on aortic diameter for intervention; however, many dissections occur without severe dilation. Mechanical properties, such as elastic modulus have emerged as potential predictors of disease progression; nonetheless, noninvasive clinical applications remain limited. This study evaluated the relationship between the computed tomography (CT)-derived elastic modulus of the ascending aorta and the histopathological characteristics of the aortic media. MethodsThirty patients who underwent surgical ascending aorta replacement were included in this study. Preoperative CT was used to calculate the aortic elastic modulus based on geometric measurements and pulse pressure. Resected aortic specimens were subjected to histological and immunohistochemical analyses to assess elastin, collagen, vascular smooth muscle cells (VSMCs), and smoothelin expression. Correlation analyses between the CT-derived elastic modulus and aortic media composition were conducted after adjusting for age and aortic diameter. ResultsThe CT-derived elastic modulus exhibited a significant negative correlation with elastin area and a positive correlation with collagen area. Additionally, a moderate negative correlation was observed between the elastic modulus and elastin fiber waviness. A strong negative correlation was detected between the elastic modulus and the proportion of contractile-type (smoothelin-positive) VSMCs. These findings remained significant after adjusting for confounders. ConclusionsCT-derived elastic modulus of the ascending aorta reflects the underlying pathological changes, including extracellular matrix remodeling and VSMC phenotypic modulation. Noninvasive assessment of aortic mechanical properties may provide novel insights into aortic disease progression and therapeutic responses.

pathology↗

bioRxiv2022

Metformin improves glycemia independently of skeletal muscle AMPK via enhanced intestinal glucose clearance

Metformin is an inexpensive oral anti-hyperglycemic agent used worldwide as a first-choice drug for the prevention of type 2 diabetes mellitus (T2DM). Although current view suggests that metformin exerts its anti-hyperglycemic effect by lowering hepatic glucose production, it has been proposed that metformin also reduce hyperglycemia by increasing glucose uptake in skeletal muscle via activation of AMP-activated protein kinase (AMPK). Herein, we demonstrate in lean and diet-induced obese (DIO) male and female mouse models that the anti-hyperglycemic effect of metformin occurs independently of muscle AMPK, and instead relies on elevated intestinal glucose clearance. Furthermore, we report that the AMPK activity is elevated in skeletal muscle from patients with T2DM following chronic metformin treatment, but this is not associated with enhanced peripheral insulin sensitivity. These results argue against existing paradigms and emphasize the non-essential role of muscle AMPK but important role of the intestine for the anti-hyperglycemic effect of metformin.

physiology↗

bioRxiv2021

Myasthenia gravis-specific aberrant neuromuscular gene expression by medullary thymic epithelial cells in thymoma

Myasthenia gravis (MG) is a neurological disease caused by autoantibodies against neuromuscular-associated proteins. While MG is frequently developed in thymoma patients, the etiologic factors for MG are not well understood. Here, by constructing a comprehensive atlas of thymoma using bulk and single-cell RNA-seq, we identified ectopic expression of neuromuscular molecules in MG-associated thymoma (MG-thymoma). These molecules were originated from a distinct subpopulation of medullary thymic epithelial cells (mTECs), which we named neuromuscular mTECs (nmTECs). MG-thymoma also exhibited microenvironments dedicated to autoantibody production, including ectopic germinal center formation, T follicular helper cell accumulation, and type 2 conventional dendritic cell migration. Cell-cell interaction analysis also predicted the interaction between nmTECs and T/B cells via CXCL12-CXCR4. The enrichment of nmTECs presenting neuromuscular molecules within MG-thymoma was further confirmed by immunohistochemically and by cellular composition estimation from MG-thymoma transcriptome. Altogether, this study suggests that nmTECs play a significant role in MG pathogenesis via ectopic expression of neuromuscular molecules.

immunology↗

bioRxiv2019

The Feasibility of Predicting Impending Malignant Ventricular Arrhythmias on the basis of Signal Complexity of Heartbeat Intervals

Malignant ventricular arrhythmias (MAs), such as ventricular tachycardia (VT) that presages cardiac arrest, present the highest hurdle for the healthcare community to overcome. Given that MAs occur unpredictably and lead to emergencies, convenient tracking devices, e.g. photoplethysmogram (PPG), that could predict MAs would be irreplaceably valuable. Since the use of heartbeat intervals (HbI) to predict the occurrence of arrhythmias is becoming more feasible, a further attempt to establish a new convenient approach for predicting impending MAs with HbI is worth trying. Assuming that intrinsic characteristics of MAs (VT and ventricular fibrillation: VF) can be revealed by a suitable approach on the basis of signal complexity, we propose an approach that first expresses the physiological status of the heart by HbI; then delineates the patterns of HbI by a new complexity metric (refined composite multi-scale entropy: RCMsEn); and finally trains a nonlinear machine learning model (random forest: RF) to learn the specific patterns of MAs so as to differentiate them from the normal sinus heart rhythm(N) and other prevalent arrhythmias (atrial fibrillation: AF, and premature ventricular contraction: V). For calculating entropy values and predicting MAs as early as possible (which is the aim of this study), two specifications are of interest: the minimal length of HbI needed to delineate the MAs patterns sufficiently (lenmin), and the maximum time length at which our model can predict impending MAs (timemax). We compared the RF model with support vector machine (SVM) models based on linear and Gaussian kernels. Results show that the RF model performs the best, reaching a 99.24% recall and a 99.87% precision for a HbI of 500 heartbeats (the lenmin) 374 seconds (the timemax) preceding the occurrence of MAs. The HbI samples in this study were extracted from an electrocardiograph (ECG). However, given the subtle difference (0.1 ms typically) between the R-R interval of ECG and the P-P interval of PPG, this approach could be extended to HbI acquired by the PPG sensor and thus should be of substantial theoretical and practical significance in cardiac arrest prevention.

bioinformatics↗

bioRxiv2019

The feasibility of atrial and ventricular arrhythmias recognition using metrics of signal complexity for heartbeat intervals

It is well known that the cardiac system is controlled by the complex nonlinear self-regulation and the heartbeat variability (HRV) is an independent indicator of the autonomic regulation. With the assumption that intrinsic differences of atrial fibrillation (A) and ventricular ectopic arrhythmias (V) can be unveiled by a proper approach based on of signal complexity, we examine the feasibility of detecting these arrhythmias of different pathological origins using metrics of complexity for heartbeat intervals (HRI). Specifically, the normal sinus rhythm (N), the A type and the V type are used as the targeted types of heartbeat. By extracting the entropy-based features from HRI of different lengths, i.e., from 300 heartbeats to 1000 heartbeats, we examined the distinguishability of these 3 types of heartbeat. By applying the features to the random forest model, the HRI signal of 600-heartbeat-length can be used to detect the A and V completely, i.e., with 100% of type-wise recall and precision. What is more, this approach is sensitive to the existence of the corresponding arrhythmias. The results substantiate our assumption about the intrinsic difference of the A and V type. A further investigation applying this approach to a wider spectrum and a finer stratification of arrhythmias/ cardiac diseases and may lead to the systematic understanding in the context of complexity and better insight for its practical use for wearable/unconstrained monitors.

physiology↗