An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification.

The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes.

A Phase II Study of Neoadjuvant PLD/Cyclophosphamide and Sequential nab-Paclitaxel Plus Dual HER2 Blockade in HER2-Positive Breast Cancer.

BACKGROUND: Neoadjuvant trastuzumab/pertuzumab (HP) plus chemotherapy for HER2-positive breast cancer (BC) achieved promising efficacy. The additional cardiotoxicity still existed. Brecan study evaluated the efficacy and safety of neoadjuvant pegylated liposomal doxorubicin (PLD)/cyclophosphamide and sequential nab-paclitaxel based on HP (PLD/C/HP-nabP/HP). PATIENTS AND METHODS: Brecan was a single-arm phase II study. Eligible patients with stages IIA-IIIC HER2-positive BC received 4 cycles of PLD, cyclophosphamide, and HP, followed by 4 cycles of nab-paclitaxel and HP. Definitive surgery was scheduled after 21 days for patients completing treatment or experiencing intolerable toxicity. The primary endpoint was the pathological complete response (pCR). RESULTS: Between January 2020 and December 2021, 96 patients were enrolled. Ninety-five (99.0%) patients received 8 cycles of neoadjuvant therapy and all underwent surgery with 45 (46.9%) breast-conserving surgery and 51 (53.1%) mastectomy. The pCR was 80.2% (95%CI, 71.2%-87.0%). Four (4.2%) experienced left ventricular insufficiency with an absolute decline in LVEF (43%-49%). No congestive heart failure and ≥grade 3 cardiac toxicity occurred. The objective response rate was 85.4% (95%CI, 77.0%-91.1%), including 57 (59.4%) complete responses and 25 (26.0%) partial responses. The disease control rate was 99.0% (95%CI, 94.3%-99.8%). For overall safety, ≥grade 3 AEs occurred in 30 (31.3%) and mainly included neutropenia (30.2%) and asthenia (8.3%). No treatment-related deaths occurred. Notably, age of >30 (P = .01; OR = 5.086; 95%CI, 1.44-17.965) and HER2 IHC 3+ (P = .02; OR = 4.398; 95%CI, 1.286-15.002) were independent predictors for superior pCR (ClinicalTrials.gov Identifier NCT05346107). CONCLUSION: Brecan study demonstrated the encouraging safety and efficacy of neoadjuvant PLD/C/HP-nabP/HP, suggesting a potential therapeutic option in HER2-positive BC.

Citrus dataset for image classification.

Citrus fruits are a specialty of the subtropical region and are currently the most widely cultivated and produced fruits in Taiwan. They contain a wealth of vitamins and minerals, including vitamin C, potassium, magnesium, and more, offering anti-inflammatory and antioxidant benefits. Citrus plants are among the economically significant fruits in Taiwan, and there are several citrus species that share a similar appearance. We have constructed a database containing the four most commonly purchased citrus varieties in the market. This database comprises a total of 1379 original images, which have been expanded to 7584 images using six different data augmentation methods. We have chosen three Convolutional Neural Network (CNN) models that have achieved an accuracy rate exceeding 95 % in classifying the four varieties of citrus fruits.

Factors Affecting the Effectiveness of Discharge Planning Implementation: A Case-Control Cohort Study.

BACKGROUND: In many hospitals, a discharge planning team works with the medical team to provide case management to ensure high-quality patient care and improve continuity of care from the hospital to the community. However, a large-scale database analysis of the effectiveness of overall discharge planning efforts is lacking. PURPOSE: This study was designed to investigate the clinical factors that impact the efficacy of discharge planning in terms of hospital length of stay, readmission rate, and survival status. METHODS: A retrospective study was conducted based on patient medical records and the discharge plans applied to patients hospitalized in a regional medical center between 2017 and 2018. The medical information system database and the care service management information system maintained by the Ministry of Health and Welfare were used to collect data and explore patients' medical care and follow-up status. RESULTS: Clinical factors such as activities of daily living ≤ 60, having indwelling catheters, having poor control of chronic diseases, and insufficient caregiver capacity were found to be associated with longer hospitalization stays. In addition, men and those with indwelling catheters were found to have a higher risk of readmission within 30 days of discharge. Moreover, significantly higher mortality was found after discharge in men, those ≥ 75 years old, those with activities of daily living ≤ 60, those with indwelling catheters, those with pressure ulcers or unclean wounds, those with financial problems, those with caregivers with insufficient capacity, and those readmitted 14-30 days after discharge. CONCLUSIONS: The findings of this study indicate that implementing case management for discharge planning does not substantially reduce the length of hospital stay nor does it affect patients' readmission status or prognosis after discharge. However, age, underlying comorbidities, and specific disease factors decrease the efficacy of discharge planning. Therefore, active discharge planning interventions should be provided to ensure transitional care for high-risk patients.

An Algorithm of Nonparametric Quantile Regression.

Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem. Regular linear quantile regression uses an L 1 loss function [Koenker in Quantile regression, Cambridge University Press, Cambridge, 2005], and the optimal solution of linear programming for estimating coefficients of regression. A problem with linear quantile regression is that the estimated curves for different quantiles can cross, a result that is logically inconsistent. To overcome the curves crossing problem, and to improve high quantile estimation in the nonlinear case, this paper proposes a nonparametric quantile regression method to estimate high conditional quantiles. A three-step computational algorithm is given, and the asymptotic properties of the proposed estimator are derived. Monte Carlo simulations show that the proposed method is more efficient than linear quantile regression method. Furthermore, this paper investigates COVID-19 and blood pressure real-world examples of extreme events by using the proposed method.

GCS-YOLOV4-Tiny: A lightweight group convolution network for multi-stage fruit detection.

Fruits require different planting techniques at different growth stages. Traditionally, the maturity stage of fruit is judged visually, which is time-consuming and labor-intensive. Fruits differ in size and color, and sometimes leaves or branches occult some of fruits, limiting automatic detection of growth stages in a real environment. Based on YOLOV4-Tiny, this study proposes a GCS-YOLOV4-Tiny model by (1) adding squeeze and excitation (SE) and the spatial pyramid pooling (SPP) modules to improve the accuracy of the model and (2) using the group convolution to reduce the size of the model and finally achieve faster detection speed. The proposed GCS-YOLOV4-Tiny model was executed on three public fruit datasets. Results have shown that GCS-YOLOV4-Tiny has favorable performance on mAP, Recall, F1-Score and Average IoU on Mango YOLO and Rpi-Tomato datasets. In addition, with the smallest model size of 20.70 MB, the mAP, Recall, F1-score, Precision and Average IoU of GCS-YOLOV4-Tiny achieve 93.42 ± 0.44, 91.00 ± 1.87, 90.80 ± 2.59, 90.80 ± 2.77 and 76.94 ± 1.35%, respectively, on F. margarita dataset. The detection results outperform the state-of-the-art YOLOV4-Tiny model with a 17.45% increase in mAP and a 13.80% increase in F1-score. The proposed model provides an effective and efficient performance to detect different growth stages of fruits and can be extended for different fruits and crops for object or disease detections.

Traditional chinese god image dataset: A glimpse of chinese culture.

This data article describes a dataset of images of common Chinese deities. The dataset is divided into five categories according to the types of deities, and a total of 1314 original images were captured by smart phones from Chinese temples and through Google search engine. Each category were split into training, validation and test subsets in a ratio of 70:20:10. We rotated the pictures by 30°, 60°, 90°, 120°, 150°, and 180°; and zoomed in and out to augment the images for training and validation sets. After data enhancement, the total number of images reaches 10,786. Two models, EfficientNet-B0 and MobileNetV2, are used to identify five kinds of god images. After data augmentation, the accuracy, precision, recall, specificity and F1-score of EfficientNet-B0 were 96.15%, 96.44%, 96.18%, 96.16% and 97.60%, respectively; the accuracy, precision recall, specificity and F1-score of MobileNetV2 were 92.31%, 92.89%, 92.37%, 92.33% and 95.19%, respectively. This dataset can be used as a reference for traditional Chinese god statue images, and can also be used for object detection and image classification through machine learning and deep learning methods.

Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19.

RATIONALE AND OBJECTIVES: Early detection and treatment of COVID-19 patients is crucial. Convolutional neural networks have been proven to accurately extract features in medical images, which accelerates time required for testing and increases the effectiveness of COVID-19 diagnosis. This study proposes two classification models for multiple chest diseases including COVID-19. MATERIALS AND METHODS: The first is Stacking-ensemble model, which stacks six pretrained models including EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, EfficientNetV2-B3, EfficientNetV2-S and EfficientNetV2-M. The second model is self-designed model ECA-EfficientNetV2 based on ECA-Net and EfficientNetV2. Ten-fold cross validation was performed for each model on chest X-ray and CT images. One more dataset, COVID-CT dataset, was tested to verify the performance of the proposed Stacking-ensemble and ECA-EfficientNetV2 models. RESULTS: The best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.21%, Precision of 99.23%, Recall of 99.25%, F1-score of 99.20%, and (area under the curve) AUC of 99.51% on chest X-ray dataset; the best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.81%, Precision of 99.80%, Recall of 99.80%, F1-score of 99.81%, and AUC of 99.87% on chest CT dataset. The differences for five metrics between Stacking-ensemble and ECA-EfficientNetV2 models are not significant. CONCLUSION: Ensemble model achieves better performance than single pretrained models. Compared to the SOTA, Stacking-ensemble and ECA-EfficientNetV2 models proposed in this study demonstrate promising performance on classification of multiple chest diseases including COVID-19.

Emerging potential of ubiquitin-specific proteases and ubiquitin-specific proteases inhibitors in breast cancer treatment.

Breast cancer is the most frequently diagnosed cancer in women, accounting for 30% of new diagnosing female cancers. Emerging evidence suggests that ubiquitin and ubiquitination played a role in a number of breast cancer etiology and progression processes. As the primary deubiquitinases in the family, ubiquitin-specific peptidases (USPs) are thought to represent potential therapeutic targets. The role of ubiquitin and ubiquitination in breast cancer, as well as the classification and involvement of USPs are discussed in this review, such as USP1, USP4, USP7, USP9X, USP14, USP18, USP20, USP22, USP25, USP37, and USP39. The reported USPs inhibitors investigated in breast cancer were also summarized, along with the signaling pathways involved in the investigation and its study phase. Despite no USP inhibitor has yet been approved for clinical use, the biological efficacy indicated their potential in breast cancer treatment. With the improvements in phenotypic discovery, we will know more about USPs and USPs inhibitors, developing more potent and selective clinical candidates for breast cancer.

[Psychological and behavioral problems in children and adolescents during the coronavirus disease 2019 epidemic: a Scoping review]. / æ–°åž‹å† çŠ¶ç— æ¯’è‚ºç‚Žæµè¡ŒæœŸé—´å„¿ç«¥å’Œé’å°‘å¹´çš„å¿ƒç†è¡Œä¸ºé—®é¢˜ï¼šScoping综述.

OBJECTIVES: To investigate the psychological and behavioral problems and related influencing factors in children and adolescents during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: China National Knowledge Infrastructure, Wanfang Data, PubMed, and Web of Science were searched using the method of subject search for articles published up to March 31, 2022, and related data were extracted for Scoping review. RESULTS: A total of 3 951 articles were retrieved, and 35 articles from 12 countries were finally included. Most of the articles were from the journals related to pediatrics, psychiatry, psychology, and epidemiology, and cross-sectional survey was the most commonly used research method. Psychological and behavioral problems in children and adolescents mainly included depression/anxiety/stress, sleep disorder, internet behavior problems, traumatic stress disorder, and self-injury/suicide. Influencing factors were analyzed from the three aspects of socio-demographic characteristics, changes in living habits, and ways of coping with COVID-19. CONCLUSIONS: During the COVID-19 epidemic, the psychological and behavioral problems of children and adolescents in China and overseas are severe. In the future, further investigation and research can be carried out based on relevant influencing factors to improve the psychological and behavioral problems.

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.

BACKGROUND AND OBJECTIVES: The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease. METHODS: This study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT). RESULTS: On chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image. CONCLUSIONS: Compared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games.

(1) Background and Objective: Major League Baseball (MLB) is one of the most popular international sport events worldwide. Many people are very interest in the related activities, and they are also curious about the outcome of the next game. There are many factors that affect the outcome of a baseball game, and it is very difficult to predict the outcome of the game precisely. At present, relevant research predicts the accuracy of the next game falls between 55% and 62%. (2) Methods: This research collected MLB game data from 2015 to 2019 and organized a total of 30 datasets for each team to predict the outcome of the next game. The prediction method used includes one-dimensional convolutional neural network (1DCNN) and three machine-learning methods, namely an artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). (3) Results: The prediction results show that, among the four prediction models, SVM obtains the highest prediction accuracies of 64.25% and 65.75% without feature selection and with feature selection, respectively; and the best AUCs are 0.6495 and 0.6501, respectively. (4) Conclusions: This study used feature selection and optimized parameter combination to increase the prediction performance to around 65%, which surpasses the prediction accuracies when compared to the state-of-the-art works in the literature.

Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network.

Tree blossoms have been widely used on the prevention and treatment of a variety of diseases in traditional Chinese medicine for thousand years [1,2]. The growth of flowers is not only for their ornamental value, but also for nutritional, medicinal, cooking, cosmetic and aromatic properties. They are a rich source of many compounds, which play an important role in various metabolic processes of the human body [3]. Edible flowers can promote the global demand for more attractive and delicious food, and can improve the nutritional content of gourmet food [4]. Flowers are beneficial for anti-anxiety, anti-cancer, anti-inflammatory, antioxidant, diuretic and immune-modulator, etc. It is very important to identify edible flowers correctly, because only a few are edible [5]. The shapes or colors of different flowers may be very similar. Visual evaluation is one of the classification methods, but it is error-prone and time-consuming [6]. Flowers are divided into flowers from herbaceous plants (flower) and flower trees (blossom). Now there is a public herbaceous flower dataset [7], but lack of dataset for Chinese medicinal blossoms. This article presents and establishes the dataset for twelve most commonly and economically valuable blossoms used in traditional Chinese medicine. The dataset provide a collection of blossom images on traditional Chinese herbs help Chinese pharmacist to classify the categories of Chinese herbs. In addition, the dataset can serve as a resource for researchers who use different algorithms of machine learning or deep learning for image segmentation and image classification.

A dataset of fortunella margarita images for object detection of deep learning based methods.

Crops require appropriate planting techniques at different growth stages. Judgments on crop maturity affect the yield of crops. The planting and management of crops rely heavily on experienced farmers, which can reduce planting costs and increase yields. With the advancement of smart agriculture [1], images of crops can be used to accurately determine the growth stage of crops and estimate crop yields [2]. This can be combined with drones or smartphones to predict the growth stage and yield of Fortunella margarita for farmers in the future. This article presents an F. margarita image dataset. We classified F. margarita into three growth stages: mature, immature, and growing. In this dataset, an image may contain plants in several growth stages. The images were divided into seven categories according to growth stage. The dataset contains a total of 1031 original images. The total number of images was increased to 6611 through data augmentation. In addition, the dataset includes 6611 annotations with 7 categories of manually marked positions of F. margarita. Field images were captured in Jiaoxi, Yilan County, Taiwan, using smartphones. The dataset can serve as a resource for researchers who use different algorithms of machine learning or deep learning for object detection, image segmentation, and multiclass classification.

A Method for Confidence Intervals of High Quantiles.

The high quantile estimation of heavy tailed distributions has many important applications. There are theoretical difficulties in studying heavy tailed distributions since they often have infinite moments. There are also bias issues with the existing methods of confidence intervals (CIs) of high quantiles. This paper proposes a new estimator for high quantiles based on the geometric mean. The new estimator has good asymptotic properties as well as it provides a computational algorithm for estimating confidence intervals of high quantiles. The new estimator avoids difficulties, improves efficiency and reduces bias. Comparisons of efficiencies and biases of the new estimator relative to existing estimators are studied. The theoretical are confirmed through Monte Carlo simulations. Finally, the applications on two real-world examples are provided.

Dataset of breast mammography images with masses.

Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawbacks in breast mammography is breast cancer masses are more difficult to be found in extremely dense breast tissue. We select 106 breast mammography images with masses from INbreast database. Through data augmentation, the number of breast mammography images was increased to 7632. We utilize data augmentation on breast mammography images, and then apply the Convolutional Neural Networks (CNN) models including AlexNet, DenseNet, and ShuffleNet to classify these breast mammography images.

Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network.

Electrocardiogram (ECG) technology plays a vital role in detecting arrhythmia. Numerous achievements have been marked in ECG-related research. Most methods first pre-process ECG signals, then extract features, and finally classify them. Most of the ECG signals used in the related studies were analyzed in specific time intervals or using a fixed number of samples. However, it is not always possible to see significant changes in a short term, and the symptoms of some patients are relatively short-lived. Misjudgments are possible because the ECG signal was not accurately extracted. This study proposes a computer-aided diagnosis (CAD) system for classification of Atrial Fibrillation and Normal Sinus Rhythm based on ECG signals through convolutional neural network. The proposed system considers a single heartbeat, rather than a specific number of seconds. This study eschews the one-dimensional digital ECG signal used in previous studies and uses convolutional neural networks to analyze two-dimensional ECG image. This study explores whether two-dimensional image ECG requires signal filtering. The final classification results in filtered ECG signals is accuracy of 99.23%, sensitivity of 99.71%, and specificity of 98.66%. The best result in non-filtered ECG signals achieves accuracy of 99.18%, sensitivity of 99.31%, and specificity of 99.03%. With no cumbersome artificial settings, the results of this study are comparable to the related studies. The proposed CAD system has high generalizability; it can help doctors to diagnose diseases effectively and reduce misdiagnosis.

Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network.

BACKGROUND AND OBJECTIVE: A feed-forward neural network (FNN) is a type of artificial neural network that has been widely used in medical diagnosis, data mining, stock market analysis, and other fields. Many studies have used FNN to develop medical decision-making systems to assist doctors in clinical diagnosis. The aim of the learning process in FNN is to find the best combination of connection weights and biases to achieve the minimum error. However, in many cases, FNNs converge to the local optimum but not the global optimum. Using open disease datasets, the purpose of this study was to optimize the connection weights and biases of the FNN to minimize the error and improve the accuracy of disease diagnosis. METHOD: In this study, the chronic kidney disease (CKD) and mesothelioma (MES) disease datasets from the University of California Irvine (UCI) machine learning repository were used as research objects. This study applied the FNN to learn the features of each datum and used particle swarm optimization (PSO) and a gravitational search algorithm (GSA) to optimize the weights and biases of the FNN classifiers based on the algorithms inspired by the observation of natural phenomena. Moreover, fuzzy rules were used to optimize the parameters of the GSA to improve the performance of the algorithm in the classifier. RESULTS: When applied to the CKD dataset, the accuracies of PSO and GSA were 99%. By using fuzzy rules to optimize the GSA parameter, the accuracy of fuzzy-GSA was 99.25%. The accuracies of the combined algorithms PSO-GSA and fuzzy-PSO-GSA reached 100%. In the MES disease dataset, all methods exhibited good performance with 100% accuracy. CONCLUSIONS: This study used PSO, GSA, fuzzy-GSA, PSO-GSA, and fuzzy-PSO-GSA on CKD and MES disease datasets to identify the disease, and the performance of different algorithms was explored. Compared with other methods in the literature, our proposed method achieved higher accuracy.

A nonparametric approach for quantile regression.

Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular quantile regression (QR) method often designs a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantiles. This approach may be restricted by the linear model setting. To overcome this problem, this paper proposes a direct nonparametric quantile regression method with five-step algorithm. Monte Carlo simulations show good efficiency for the proposed direct QR estimator relative to the regular QR estimator. The paper also investigates two real-world examples of applications by using the proposed method. Studies of the simulations and the examples illustrate that the proposed direct nonparametric quantile regression model fits the data set better than the regular quantile regression method.

[Effect of intense pulsed light on Trichophyton rubrum growth in vitro].

OBJECTIVE: To investigate the inhibitory effect of 420 nm intense pulsed light on Trichophyton rubrum growth in vitro and explore the mechanism. METHODS: The fungal conidia were divided into treatment group with intense pulse light irradiation and control group without irradiation. The surface areas of the fungal colonies were photographed before irradiation and on the 2nd and 3rd days after irradiation to observe the changes in fungal growth. The viability of the fungus in suspension was detected at 6 h after irradiation using MTT assay. The intracellular reactive oxygen species (ROS) level in the fungus was determined using DCFH-DA fluorescent probe, and the MDA content was detected using TBA method. RESULTS: Intense pulse light (420 nm) irradiation caused obvious injuries in Trichophyton rubrum with the optimal effective light dose of 12 J/cm2 in 12 pulses. At 6 h after the irradiation, the fungus in suspension showed a 30% reduction of viability (P<0.05), and the fungal colonies showed obvious growth arrest without further expansion. Compared to the control group, the irradiated fungus showed significant increases in ROS level and MDA content (P<0.05). CONCLUSION: Intense pulse light (420 nm) irradiation can induce oxidative stress in Trichophyton rubrum to lead to fungal injuries and death.