Feature Model Research Articles

Issues in Learning Labial Consonants of English by Arabic Speaking Students in Saudi Arabia: A Test of Feature Model

Issues in Learning Labial Consonants of English by Arabic Speaking Students in Saudi Arabia: A Test of Feature Model

Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction.

Human motion prediction is challenging due to the complex spatiotemporal feature modeling. Among all methods, graph convolution networks (GCNs) are extensively utilized because of their superiority in explicit connection modeling. Within a GCN, the graph correlation adjacency matrix drives feature aggregation, and thus, is the key to extracting predictive motion features. State-of-the-art methods decompose the spatiotemporal correlation into spatial correlations for each frame and temporal correlations for each joint. Directly parameterizing these correlations introduces redundant parameters to represent common relations shared by all frames and all joints. Besides, the spatiotemporal graph adjacency matrix is the same for different motion samples, and thus, cannot reflect samplewise correspondence variances. To overcome these two bottlenecks, we propose dynamic spatiotemporal decompose GC (DSTD-GC), which only takes 28.6% parameters of the state-of-the-art GC. The key of DSTD-GC is constrained dynamic correlation modeling, which explicitly parameterizes the common static constraints as a spatial/temporal vanilla adjacency matrix shared by all frames/joints and dynamically extracts correspondence variances for each frame/joint with an adjustment modeling function. For each sample, the common constrained adjacency matrices are fixed to represent generic motion patterns, while the extracted variances complete the matrices with specific pattern adjustments. Meanwhile, we mathematically reformulate GCs on spatiotemporal graphs into a unified form and find that DSTD-GC relaxes certain constraints of other GC, which contributes to a better representation capability. Moreover, by combining DSTD-GC with prior knowledge like body connection and temporal context, we propose a powerful spatiotemporal GCN called DSTD-GCN. On the Human3.6M, Carnegie Mellon University (CMU) Mocap, and 3D Poses in the Wild (3DPW) datasets, DSTD-GCN outperforms state-of-the-art methods by 3.9%-8.7% in prediction accuracy with 55.0%-96.9% fewer parameters. Codes are available at https://github.com/Jaakk0F/DSTD-GCN.

A combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound in the Kupffer phase for the diagnosis of well-differentiated hepatocellular carcinoma and atypical focal liver lesions: a prospective, multicenter study.

The objective of this study was to develop a combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound (CEUS) during the Kupffer phase and to evaluate its value in differentiating well-differentiated hepatocellular carcinoma (w-HCC) from atypical benign focal liver lesions (FLLs). A total of 116 patients with preoperatively Sonazoid-CEUS confirmed w-HCC or benign FLL were selected from a prospective multiple study on the clinical application of Sonazoid in FLLs conducted from August 2020 to March 2021. According to the randomization principle, the patients were divided into a training cohort and a test cohort in a 7:3 ratio. Seventy-nine patients were used for establishing and training the radiomics model and combined model. In comparison, 37 patients were used for validating and comparing the performance of the models. The diagnostic efficacy of the models for w-HCC and atypical benign FLLs was evaluated using ROCs curves and decision curves. A combined model nomogram was created to assess its value in reducing unnecessary biopsies. Among the patients, there were 55 cases of w-HCC and 61 cases of atypical benign FLLs, including 28 cases of early liver abscess, 16 cases of atypical hepatic hemangioma, 8 cases of hepatocellular dysplastic nodules (DN), and 9 cases of focal nodular hyperplasia (FNH). The radiomics model and combined model we established had AUCs of 0.905 and 0.951, respectively, in the training cohort, and the AUCs of the two models in the test cohort were 0.826 and 0.912, respectively. The combined model outperformed the radiomics feature model significantly. Decision curve analysis demonstrated that the combined model achieved a higher net benefit within a specific threshold probability range (0.25 to 1.00). A nomogram of the combined model was developed. The combined model based on the radiomics features of Sonazoid-CEUS in the Kupffer phase showed satisfactory performance in diagnosing w-HCC and atypical benign FLLs. It can assist clinicians in timely detecting malignant FLLs and reducing unnecessary biopsies for benign diseases.

A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology, from two perspectives: traditional recognition methods and deep learning-based recognition methods. Firstly, the complexity of Chinese character recognition is pointed out, including its numerous categories, complex structure, and the problem of similar characters, especially the variability of handwritten Chinese characters. Subsequently, recognition methods based on feature optimization, model optimization, and fusion techniques are highlighted. The fusion studies between feature optimization and model improvement are further explored, and these studies further enhance the recognition effect through complementary advantages. Finally, the article summarizes the current challenges of Chinese character recognition technology, including accuracy improvement, model complexity, and real-time problems, and looks forward to future research directions.

Radiomics and Clinical Features for Distinguishing Kidney Stone-Associated Urinary Tract Infection: A Comprehensive Analysis of Machine Learning Classification.

This study investigated the abilities of radiomics and clinical feature models to distinguish kidney stone-associated urinary tract infections (KS-UTIs) using computed tomography. A retrospective analysis was conducted on a single-center dataset comprising computed tomography (CT) scans and corresponding clinical information from 461 patients with kidney stones. Radiomics features were extracted from CT images and underwent dimensionality reduction and selection. Multiple machine learning (Three types of shallow learning and four types of deep learning) algorithms were employed to construct radiomics and clinical models in this study. Performance evaluation and optimal model selection were done using receiver operating characteristic (ROC) curve analysis and Delong test. Univariate and multivariate logistic regression analyzed clinical and radiomics features to identify significant variables and develop a clinical model. A combined model integrating radiomics and clinical features was established. Model performance was assessed by ROC curve analysis, clinical utility was evaluated through decision curve analysis, and the accuracy of the model was analyzed via calibration curve. Multilayer perceptron (MLP) showed higher classification accuracy than other classifiers (area under the curve (AUC) for radiomics model: train 0.96, test 0.94; AUC for clinical model: train 0.95, test 0.91. The combined radiomics-clinical model performed best (AUC for combined model: train 0.98, test 0.95). Decision curve and calibration curve analyses confirmed the model's clinical efficacy and calibration. This study showed the effectiveness of combining radiomics and clinical features from CT scans to identify KS-UTIs. A combined model using MLP exhibited strong classification abilities.

Belief rule learning and reasoning for classification based on fuzzy belief decision tree

The belief rules which extend the classical fuzzy IF-THEN rules with belief consequent parts have been widely used for classifier design due to their capabilities of building linguistic models interpretable to users and addressing various types of uncertainty. However, in the rule learning process, a high number of features generally results in a belief rule base with large size, which degrades both the classification accuracy and the model interpretability. Motivated by this challenge, the decision tree building technique which implements feature selection and model construction jointly is introduced in this paper to learn a compact and accurate belief rule base. To this end, a new fuzzy belief decision tree (FBDT) with fuzzy feature partitions and belief leaf nodes is designed: a fuzzy information gain ratio is first defined as the feature selection criterion for node fuzzy splitting and then the belief distributions are introduced to the leaf nodes to characterize the class uncertainty. Based on the initial rules extracted from the constructed FBDT, a joint optimization objective considering both classification accuracy and model interpretability is then designed to further reduce the rule redundancy. Experimental results based on real datasets show that the proposed FBDT-based classification method has much smaller rule base and better interpretability than other rule-based methods on the premise of competitive accuracy.

Ultrasound-based Radiomics Analysis for Assessing Risk Factors Associated With Early Recurrence Following Surgical Resection of Hepatocellular Carcinoma

ObjectiveThe aim of this study was to explore the value of ultrasound-based radiomics analysis for early recurrence (ER) after surgical resection of hepatocellular carcinoma (HCC). MethodsThis retrospective study included 127 patients who underwent primary surgical resection for HCC between October 2019 and November 2021. The patients were subsequently divided into training and validation sets (7:3 ratio). All patients received preoperative routine ultrasound and contrast-enhanced ultrasound (CEUS), with postoperative pathological confirmation of HCC. Radiomics features were extracted from maximum section of two-dimensional ultrasound image. The least absolute shrinkage and selection operation (LASSO) logistic regression algorithm with 10-fold cross-validation was employed to establish ultrasonic radiomics features. Logistic regression modeling was employed to build models based on clinical and ultrasonic features (model 1, clinical-ultrasonic model), radiomics signature (model 2, ultrasonic radiomics model), and the combination (model 3, clinical-ultrasonic-radiomics model). Then a nomogram model was established to predict the risk of ER, and the application value of nomogram through internal verification was evaluated. ResultsModel 3 showed optimal diagnostic performance in both training set (AUC=0.907) and validation set (AUC=0.925), followed by the model 1 in training set (AUC=0.846) and validation set (AUC=0.855), both above two models performed better than model 2 in training set (AUC=0.751) and validation set (AUC=0.702) (p<0.05). In training set and validation set of model 3, the sensitivity were 83.3%, 77.8%, the specificity ware 95.8%, 100.0% and the C-index were 0.791, 0.778. ConclusionThe preoperative clinical-ultrasonic-radiomics model is anticipated to be a reliable tool for predicting the ER of surgical resection of HCC.

Zebrafish Models for Skeletal and Extraskeletal Osteogenesis Imperfecta Features: Unveiling Pathophysiology and Paving the Way for Drug Discovery.

In the last decades, the easy genetic manipulation, the external fertilization, the high percentage of homology with human genes and the reduced husbandry costs compared to rodents, made zebrafish a valid model for studying human diseases and for developing new therapeutical strategies. Since zebrafish shares with mammals the same bone cells and ossification types, it became widely used to dissect mechanisms and possible new therapeutic approaches in the field of common and rare bone diseases, such as osteoporosis and osteogenesis imperfecta (OI), respectively. OI is a heritable skeletal disorder caused by defects in gene encoding collagen I or proteins/enzymes necessary for collagen I synthesis and secretion. Nevertheless, OI patients can be also characterized by extraskeletal manifestations such as dentinogenesis imperfecta, muscle weakness, cardiac valve and pulmonary abnormalities and skin laxity. In this review, we provide an overview of the available zebrafish models for both dominant and recessive forms of OI. An updated description of all the main similarities and differences between zebrafish and mammal skeleton, muscle, heart and skin, will be also discussed. Finally, a list of high- and low-throughput techniques available to exploit both larvae and adult OI zebrafish models as unique tools for the discovery of new therapeutic approaches will be presented.

Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity.

Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting the specificity at the individual level. Recently, there has been a growing interest in individual differences in brain connectivity. Investigating individual-specific connectivity is important for understanding the mechanisms of major depressive disorder (MDD) and the variations among individuals. To integrate individualized functional connectivity and structural connectivity with machine learning techniques to distinguish people with MDD and healthy controls (HCs). Prospective. A total of 182 patients with MDD and 157 HCs and a verification cohort including 54 patients and 46 HCs. 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and diffusion tensor imaging with single-shot spin echo. Functional and structural brain networks from rs-fMRI and DTI data were constructed, respectively. Based on these networks, individualized functional connectivity (IFC) and individualized structural connectivity (ISC) were extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation analysis combined with joint independent component analysis (mCCA + jICA) was conducted to fusion analysis to identify the joint and unique independent components (ICs) across multiple modes. These ICs were utilized to generate features, and a support vector machine (SVM) model was implemented for the classification of MDD. The differences in individualized connectivity between patients and controls were compared using two-sample t test, with a significance threshold set at P < 0.05. The established model was tested and evaluated using the receiver operating characteristic (ROC) curve. The classification performance of the constructed individualized connectivity feature model after multisequence fusion increased from 72.2% to 90.3%. Furthermore, the prediction model showed significant predictive power for assessing the severity of depression in patients with MDD (r = 0.544). The integration of IFC and ISC through multisequence fusion enhances our capacity to identify MDD, highlighting the advantages of the individualized approach and underscoring its significance in MDD research. 1 TECHNICAL EFFICACY: Stage 2.

Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm.

The aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study. This study was a retrospective cohort study. The study population was 1855 participants who underwent consecutive physical examinations at the First Affiliated Hospital of Soochow University between 2018 and 2022.The dataset included medical history, physical examination, and biochemical index test results. The cohort was randomly divided into a training dataset and a validation dataset in a ratio of 8:2. The machine learning algorithms used in this study include Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Naive Bayes, Decision Trees (DT), and traditional Logistic Regression (LR). Feature selection, parameter optimization, and model construction were performed in the training set, while the validation set was used to evaluate the predictive performance of the models. The performance of these models is evaluated by an area under the receiver operating characteristic (ROC) curves (AUC), calibration curves and decision curve analysis (DCA). To interpret the best-performing model, the Shapley Additive exPlanation (SHAP) Plots was used in this study. The training/validation dataset consists of 1,855 individuals from the First Affiliated Hospital of Soochow University, yielded significant variables following selection by the Boruta algorithm and logistic multivariate regression analysis. These significant variables included systolic blood pressure (SBP), fatty liver, waist circumference (WC) and serum creatinine (Scr). The XGBoost model outperformed the other models, demonstrating an AUC of 0.7391 in the validation set. The XGBoost model was composed of SBP, fatty liver, WC and Scr may assist doctors with the early identification of IFG in middle-aged and elderly people.

Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study.

Develop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunotherapy (NICT), by leveraging whole slide images (WSIs). This retrospective study examined pre-treatment WSIs from 186 patients with non-small cell lung cancer (NSCLC), using a weakly supervised learning framework. We employed advanced deep learning architectures, including DenseNet121, ResNet50, and Inception V3, to analyze WSIs on both micro (patch) and macro (slide) levels. The training process incorporated innovative data augmentation and normalization techniques to bolster the robustness of the models. We evaluated the performance of these models against traditional clinical predictors and integrated them with a novel pathomics signature, which was developed using multi-instance learning algorithms that facilitate feature aggregation from patch-level probability distributions. Univariate and multivariable analyses confirmed histology as a statistically significant prognostic factor for MPR (P-value< 0.05). In patch model evaluations, DenseNet121 led in the validation set with an area under the curve (AUC) of 0.656, surpassing ResNet50 (AUC = 0.626) and Inception V3 (AUC = 0.654), and showed strong generalization in external testing (AUC = 0.611). Further evaluation through visual inspection of patch-level data integration into WSIs revealed XGBoost's superior class differentiation and generalization, achieving the highest AUCs of 0.998 in training and robust scores of 0.818 in validation and 0.805 in testing. Integrating pathomics features with clinical data into a nomogram yielded AUC of 0.819 in validation and 0.820 in testing, enhancing discriminative accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) and feature aggregation methods notably boosted the model's interpretability and feature modeling. The application of weakly supervised deep learning to WSIs offers a powerful tool for predicting MPR in NSCLC patients treated with NICT.

MODEL&amp;CO: exoplanet detection in angular differential imaging by learning across multiple observations

ABSTRACT Direct imaging of exoplanets is particularly challenging due to the high contrast between the planet and the star luminosities, and their small angular separation. In addition to tailored instrumental facilities implementing adaptive optics and coronagraphy, post-processing methods combining several images recorded in pupil tracking mode are needed to attenuate the nuisances corrupting the signals of interest. Most of these post-processing methods build a model of the nuisances from the target observations themselves, resulting in strongly limited detection sensitivity at short angular separations due to the lack of angular diversity. To address this issue, we propose to build the nuisance model from an archive of multiple observations by leveraging supervised deep learning techniques. The proposed approach casts the detection problem as a reconstruction task and captures the structure of the nuisance from two complementary representations of the data. Unlike methods inspired by reference differential imaging, the proposed model is highly non-linear and does not resort to explicit image-to-image similarity measurements and subtractions. The proposed approach also encompasses statistical modelling of learnable spatial features. The latter is beneficial to improve both the detection sensitivity and the robustness against heterogeneous data. We apply the proposed algorithm to several data sets from the VLT/SPHERE instrument, and demonstrate a superior precision-recall trade-off compared to the PACO algorithm. Interestingly, the gain is especially important when the diversity induced by ADI is the most limited, thus supporting the ability of the proposed approach to learn information across multiple observations.

A new approach to K-nearest neighbors distance metrics on sovereign country credit rating

This study introduces feature importance K-nearest neighbors (FIKNN), an innovative adaptation of the K-nearest neighbors (KNN) algorithm tailored for classifying sovereign country credit ratings. The primary objective is to enhance KNN's predictive accuracy by integrating a feature importance mechanism derived from the random forest algorithm, which prioritizes significant features and reduces the impact of less relevant ones, refining the distance computation within KNN. Utilizing a comprehensive dataset of sovereign credit ratings, the performance of FIKNN was assessed against traditional KNN using various feature sets and bootstrap samples. The FIKNN model consistently outperformed the standard KNN by approximately 1% in classification accuracy, attributed to the weighted distance metric adjusting feature influence based on importance. Key findings indicate that FIKNN effectively manages datasets with varying feature relevance and demonstrates a positive correlation between feature diversity and model performance. Future research will explore other distance metrics and refine the feature importance weighting mechanism to broaden FIKNN's applicability in diverse predictive tasks.

Effort in Hindustani Dhrupad Singing: Using Interviews to Develop Effort Inference Models of Acoustic and Movement Features

Abstract This article sets out the results of research into effort in Dhrupad, a genre of Hindustani music. I examine gestural interactions with imaginary objects that vocalists outline when engaging with melodies. The study takes an embodied cognition stance and applies thematic analysis and inductive coding to original interviews with Dhrupad musicians in India, the UK, and the Netherlands. My findings demonstrate that from the singers’ cognitive perspectives, their hand movements are deeply connected to their voices. I also offer insights on the relationships between bodily movement, sound, and imagery that are used to inform the development of effort inference models.

Deep learning for surgical workflow analysis: a survey of progresses, limitations, and trends

Automatic surgical workflow analysis, which aims to recognize the ongoing surgical events in videos, is fundamental for developing context-aware computer-assisted systems. This paper reviews representative surgical workflow recognition algorithms based on deep learning, outlining their merits, limitations, and future research directions. The literature survey was performed on three large bibliographic databases, covering 67 lary sources, which were comparatively analyzed in terms of spatial feature modeling, spatio-temporal feature modeling, input pre-processing, regularization and post-processing algorithms, as well as learning strategies. Then, common public datasets and evaluation metrics for surgical workflow recognition are also described in detail. Finally, we discuss all literature from different perspectives, and point out the challenges, possible solutions and future trends. The need for more diverse and larger datasets, the potential of unsupervised and semi-supervised learning approaches, comprehensive and equitable metrics, establishing complete regulatory and data standards, and interoperability will be key challenges in translating models to clinical operating rooms. And we propose that surgical activity anticipation and employing large language model as training assistant are interesting research directions in surgical workflow analysis.

Bayesian Optimized Machine Learning Model for Automated Eye Disease Classification from Fundus Images

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound

ObjectivesTo establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS).Methods527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists.ResultsIn the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205).ConclusionsA CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

Research on Critical Quality Feature Recognition and Quality Prediction Method of Machining Based on Information Entropy and XGBoost Hyperparameter Optimization

To address the problem of predicting machining quality for critical features in the manufacturing process of mechanical products, a method that combines information entropy and XGBoost (version 2.1.1) hyperparameter optimization is proposed. Initially, machining data of mechanical products are analyzed based on information entropy theory to identify critical quality characteristics. Subsequently, a quality prediction model for these critical features is established using the XGBoost machine learning framework. The model’s hyperparameters are then optimized through Bayesian optimization. This method is applied as a case study to a medium-speed marine diesel engine piston. After the critical quality characteristics in the machining process are identified, the machining quality of these vital characteristics is predicted, and the results are compared with those obtained from a machine learning model without hyperparameter optimization. The findings demonstrate that the proposed method effectively predicts the machining quality of mechanical products.

Contrasting Effects of Oxytocin on MK801-Induced Social and Non-Social Behavior Impairment and Hyperactivity in a Genetic Rat Model of Schizophrenia-Linked Features.

Social withdrawal in rodents is a measure of asociality, an important negative symptom of schizophrenia. The Roman high- (RHA) and low-avoidance (RLA) rat strains have been reported to exhibit differential profiles in schizophrenia-relevant behavioral phenotypes. This investigation was focused on the study of social and non-social behavior of these two rat strains following acute administration of dizocilpine (MK801, an NMDA receptor antagonist), a pharmacological model of schizophrenia-like features used to produce asociality and hyperactivity. Also, since oxytocin (OXT) has been proposed as a natural antipsychotic and a potential adjunctive therapy for social deficits in schizophrenia, we have evaluated the effects of OXT administration and its ability to reverse the MK801-impairing effects on social and non-social behavior and MK801-induced hyperactivity. MK801 administration produced hyperlocomotion and a decrease in social and non-social behavior in both rat strains, but these drug effects were clearly more marked in RHA rats. OXT (0.04 mg/kg and 0.2 mg/kg) attenuated MK801-induced hyperlocomotion in both rat strains, although this effect was more marked in RHA rats. The MK801-decreasing effect on exploration of the "social hole" was moderately but significantly attenuated only in RLA rats. This study is the first to demonstrate the differential effects of OXT on MK801-induced impairments in the two Roman rat strains, providing some support for the potential therapeutic effects of OXT against schizophrenia-like symptoms, including both a positive-like symptom (i.e., MK801-induced hyperlocomotion) and a negative-like symptom (i.e., MK801 decrease in social behavior), while highlighting the importance of the genetic background (i.e., the rat strain) in influencing the effects of both MK801 and oxytocin.

Establishment of Nano-ITO induced rat model of indium lung disease and exploration of its pathological characteristics

Objective: To study the dynamic pathological characteristics of lung tissue in a Nano-ITO induced rat model of indium lung disease and to guide clinical and basic scientific research to further explore the mechanisms of pulmonary interstitial injury and pulmonary alveolar proteinosis (PAP). Methods: Dose-response (three divided doses) and time-course studies (six exposure periods) were performed to investigate the pulmonary toxicity induced by Nano-ITO. At the end of the experiment, cytokine levels and oxidative stress were analyzed in the bronchoalveolar lavage fluid. Rat lung tissues were also collected for staining with H&E, PAS, Masson's, Oil Red O, and Sirius Red. Ultrastructure of lung tissue cells was observed by transmission electron microscopy. Expression of IL-1β, HO-1, SP-A was observed by immunohistochemistry, and the expression of α-SMA was observed by immunofluorescence. Results: Nano-ITO intratracheal instillation caused pulmonary toxicity by inducing acute inflammation at 3 days, granuloma (nodule) formation and collagen hyperplasia at 14 days, and alveolar proteinosis at 56 days post-exposure. Pathological features of lung tissue included typical alveolar exudates, cellular fibrous nodules, enlarged alveolar fat droplet fusion, cholesterol crystal granuloma and pulmonary alveolar proteinosis. The intra-alveolar eosinophilic material (multilamellated, lattice-shaped, and myelin-like structure) showed abnormal lamellar bodies (features of alveolar type Ⅱ epithelial cells) and abundant rough endoplasmic reticulum and mitochondria (features of fibroblasts) on transmission electron microscopy of the lung tissue from rats exposed to Nano-ITO on the 84th day. Cellular pathology revealed that a large amount of amorphous PAS stain-positive substances appear in BALF at 28 days post-exposure, and pink granular protein-like substances can be seen in alveolar macrophages. Conclusions: There are three characteristic developmental stages in Nano-ITO induced pulmonary injury in rats, acute inflammation, granuloma (nodule) formation and collagen proliferation, and pulmonary alveolar proteinosis, which provide a reference feature model for the pathogenesis of indium lung disease.