Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Learning Epipolar Line Window Attention for Stereo Image Super-Resolution Reconstruction

Transformer-based stereo image super-resolution reconstruction(Stereo SR)methods have significantly improved image quality.However,existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information.To address these challenges,this paper introduces a novel epipolar line window attention stereo image super-resolution network(EWASSR).For detail feature restoration,we design a feature extractor based on Transformer and convolutional neural network(CNN),which consists of(shifted)window-based self-attention((S)W-MSA)and feature distillation and enhancement blocks(FDEB).This combination effectively solves the problem of global image perception and local feature attention and captures more discriminative high-frequency features of the image.Furthermore,to address the problem of offset of complementary pixels in stereo images,we propose an epipolar line window attention(EWA)mechanism,which divides windows along the epipolar direction to promote efficient matching of shifted pixels,even in pixel smooth areas.More accurate pixel matching can be achieved using adjacent pixels in the window as a reference.Extensive experiments demonstrate that our EWASSR can reconstruct more realistic detailed features.Comparative quantitative results show that in the experimental results of our EWASSR on the Middlebury and Flickr1024 data sets for 2×SR,compared with the recent network,the Peak signal-to-noise ratio(PSNR)increased by 0.37 dB and 0.34 dB,respectively.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

DeepBio:A Deep CNN and Bi-LSTM Learning for Person Identification Using Ear Biometrics

The identification of individuals through ear images is a prominent area of study in the biometric sector.Facial recognition systems have faced challenges during the COVID-19 pandemic due to mask-wearing,prompting the exploration of supplementary biometric measures such as ear biometrics.The research proposes a Deep Learning(DL)framework,termed DeepBio,using ear biometrics for human identification.It employs two DL models and five datasets,including IIT Delhi(IITD-I and IITD-II),annotated web images(AWI),mathematical analysis of images(AMI),and EARVN1.Data augmentation techniques such as flipping,translation,and Gaussian noise are applied to enhance model performance and mitigate overfitting.Feature extraction and human identification are conducted using a hybrid approach combining Convolutional Neural Networks(CNN)and Bidirectional Long Short-Term Memory(Bi-LSTM).The DeepBio framework achieves high recognition rates of 97.97%,99.37%,98.57%,94.5%,and 96.87%on the respective datasets.Comparative analysis with existing techniques demonstrates improvements of 0.41%,0.47%,12%,and 9.75%on IITD-II,AMI,AWE,and EARVN1 datasets,respectively.

Effects of Drilling in Mastoid Cavity over Hearing in the Contralateral Ear

In advanced otological surgeries, powered instruments form an indispensable part. The risk of deterioration to hearing in the operated ear is a commonly discussed issue, however, there remains a possibility of affecting the hearing in the contralateral ear due to transcranial vibration. So in this study we aimed to assess the possibility of the non-operated ear being affected by the noise generated during ear surgeries and whether it is temporary or permanent in nature. Methodology: This study included 63 patients diagnosed with unilateral disease who underwent mastoid surgery. Preoperatively all the patients were subjected to Pure tone audiometry (PTA), Transient evoked otoacoustic emission (TEOAE) and Distortion product otoacoustic emission (DPOAE). Patients were operated using both cutting and diamond burrs of ranging from sizes 1 - 6 mm. Total drilling time was recorded. Results: Post-operative hearing evaluation was done at 1 week, 4 weeks and 12 weeks. The sound emitted by various burrs was recorded by Sound Level Meter. Out of the total 58 patients that followed up, 46 showed change in at least one of the hearing parameters. Patients showing changes had a higher drilling time as compared to those with no changes. Of these, the changes associated with the total drilling time and with cutting burr time were found to be significant. The hearing changes seen on PTA, TEOAE and DPOAE were transient in nature with only one patient having a persistent decreased high frequency threshold at the end of 12 weeks. It was also found that cutting burrs produce more sound as compared to diamond burrs and a larger size burr of a type produces more sound than a smaller one of its type. Conclusion: The drilling of mastoid bone during ear surgeries can transiently impair the hearing in the contralateral ear which is of great significance in patients with only one hearing ear.

Bilateral Middle Fossa Floor Meningiomas Invading the Middle Ear

This case is a 49-year-old female patient who presented with suspected eustachian tube dysfunction symptoms that were refractory to medical treatment. She presented with conductive hearing loss and aural fullness on the left. A physical exam revealed an epitympanic fleshy mass in the middle ear with effusion. Imaging with MRI and CT showed opacification of the epitympanum with surrounding bony demineralization bilaterally left greater than right and significant thinning versus dehiscence of the tegmen mastoideum and tympani on the left with only thinning of the tegmen on the right. An MR temporal bone with and without contrast, demonstrated enhancement of the left middle ear extending to the tegmen and corresponding enhancement of the dura along the floor of the left middle cranial fossa as well as extending along foramen ovale into the infratemporal fossa. There was to a lesser degree similar appearance on the right. A biopsy of the middle ear mass on the left revealed meningioma. This case highlights the need to widen your differential with common symptoms when they are refractory to treatment.

Endoscopic push through tragal cartilage tympanoplasty: A 10-year retrospective review of our technique and outcomes

BACKGROUND Endoscopic ear surgery(EES)provides a magnified,high-definition view of the otological surgical field.EES allows otologists to avoid surgical incisions and associated postoperative complications.It is an ideal technique for the perfor-mance and teaching of tympanoplasty.AIM To examine the efficacy of total Endoscopic Push Through Tragal Cartilage Tympanoplasty(EPTTCT),at our institution over a 10-year period.METHODS A retrospective analysis of 168 cases of EPTTCT for closure of small to medium tympanic membrane perforations from 2013-2023 was conducted.Patient sex,age range(pediatric vs adult),etiology of injury,success rate,complications,and postoperative hearing status were collected.RESULTS Graft uptake results indicated success in 94%of patients,with less than a 2%complication rate.Postoperative pure tone audiometry demonstrated hearing status improvement in 69%of patients.CONCLUSION EPTTCT has been shown to be effective in tympanic membrane perforation closures with minimal complications.This study further demonstrates the efficacy and safety of these procedures in a single-center review.

Classification and Comprehension of Software Requirements Using Ensemble Learning

The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

Evaluation of the Effect and Efficiency of Ear Tip Bloodletting Combined with Xianfang Huoming Yin in the Treatment of Early-Stage Wheals

Objective:To evaluate the effect and efficiency of ear tip bloodletting combined with Xianfang Huoming Yin(XFH)in the treatment of early-stage wheals.Methods:70 patients with early-stage wheals admitted from September 2020 to September 2022 were divided into two groups by randomized numerical table method,with 35 cases/group.Both groups received conventional treatment,the control group combined with XFH,and the observation group combined with ear tip bloodletting therapy and XFH.Conscious symptom scores,ocular indexes,therapeutic effects,and sleep quality between the two groups were compared.Results:After the treatment,the patients in the observation group had lower self-awareness symptom scores and sleep quality scores as compared to those in the control group;the diameter of the mass in the observation group was smaller than that of the control group after treatment;the average tear film rupture time and the time taken for the first non-contact tear film to rupture were longer in the observation group than those in the control group;the total effective rate of the treatment in the observation group was higher than that of the control group(P<0.05).Conclusion:Ear tip bloodletting combined with XFH was ideal for the treatment of early-stage wheals and has a high effective rate.

基于EAR算法的司机疲劳驾驶检测系统

为有效减少司机疲劳驾驶导致的交通事故发生率,设计了一套基于EAR算法的司机疲劳驾驶检测系统。系统由硬件和软件两部分构成。硬件部分包括STM32单片机、OLED12864液晶显示模块、DS18B20温度传感器模块、光电测速传感器模块、CN-TTS语音播报模块、ESP8266数据存储和传输模块以及K210人脸识别模块。软件部分采用EAR检测算法进行疲劳检测。通过单片机和手机上运行的APP双端监测,系统能够实时监测司机状态,在司机出现疲劳特征时发出提醒,从而有效避免疲劳驾驶带来的安全隐患。经测试,系统的综合准确率达到92.4%,能够有效提醒司机避免疲劳驾驶。

State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances.A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data.Based on the bilinear state observer,a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function.The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance.Furthermore,to improve the performance of the proposed algorithm,a dynamicmoving window is designed which can update the dynamical data by removing the oldest data and adding the newestmeasurement data.A numerical example of identification of bilinear systems is presented to validate the theoretical analysis.

Review of 6 Years of Middle Ear Surgery in the ENT-Laryngology Department of the Donka National Hospital and a Literature Review

Introduction: Middle ear surgery is increasingly performed in our department. Our goal was to take stock of this activity in the ENT department of the Donka National Hospital. Equipment and Methods: It was a descriptive and retrospective study;it involved 71 patients’ records operated from January 1, 2018 to December 31, 2023. We included the files of patients who had a middle ear surgery including the post-operative report and follow-up. Incomplete or illegible records were excluded. The parameters studied were epidemiological, otoscopic, tomodensitometric, surgical indications, surgical techniques and results. Results: We recorded 71 middle ear surgeries out of 548 ENT surgeries, the prevalence was 12.96%. The average age was 23 years (type-deviation = 17, 29 years) with extremes of 3 to 75 years. According to the preoperative audiometry, the deafness was medium (48.8%), mild (36.9%) and severe (14.3%). The operative indications were among others: perforation of the tympanic sequellar (52.5%), chronic otomastoiditis (23.8%), cholesteatoma (15%) and seromuqueous otitis (8.7%). We performed 70 tympanoplasties (90.9%), 50 mastoidectomies (64.9%) and 7 trans-tympanic aerators (9.1%). One month after surgery, the neotympanum was constituted in 86%, and the hearing gain was between 11 and 15 dB in 18.3% of cases. Conclusion: Middle ear surgery was infrequent. Young subjects are the most concerned. Tympanoplasty and mastoidectomy are the main surgical techniques. The anatomical and functional results are appreciable.

Evaluation of Ear and Facial Indices of Ibibio and Efik Female Children of Akwa Ibom and Cross River States, Nigeria

Background: The ear and face are indispensable and distinctive features for hearing and identification. Objectives: This study was designed to generate anthropometric data of the ear and facial indices of females of Efik and Ibibio children in Cross River and Akwa Ibom States, show morphological and aesthetic differences and ethnicity. Methods: A total of 600 female children (300 Efiks and 300 Ibibios) aged 2 to 10 years that met the inclusion criteria were chosen from selected primary schools in Calabar Municipality, Calabar South of Cross River State and from Uyo, Itu of Akwa Ibom State, Nigeria. Standardized measurements of face length, face width, ear length, and ear width were taken with a spreading caliper;the facial (proscopic) and ear (auricular) indices were determined. Results: Efik subjects presented a mean face length of 8.36 ± 0.06 cm, face width of 11.04 ± 0.04 cm, ear length of 4.92 ± 0.02 cm, and ear width of 3.06 ± 0.01 cm. Ibibio subjects had mean values for face length, face width, ear length, and ear width as 8.17 ± 0.05 cm, 10.75 ± 0.05 cm, 4.77 ± 0.03 cm, and 2.94 ± 0.02 cm respectively. The mean facial index and ear index for Efik subjects were 75.68 ± 0.31 and 62.16 ± 0.27 respectively;while the mean facial and ear indices for Ibibio subjects were 74.79 ± 0.36 and 61.80 ± 0.34 respectively. Statistical analysis demonstrated significant differences in face length, ear length, ear width and facial index, with the Efik subjects having higher values than Ibibio subjects (p Conclusion: The results showed hypereuryproscopic face as the prevalent face type among females of both ethnic groups, therefore can be of importance in sex, ethnic, and racial differentiation, and in clinical practice, aesthetics and forensic medicine.

Influence of ear tags on the results of body composition analysis in mice

Background:The aim of this study was to investigate the influence of marking meth-ods on the outcomes of body composition analysis and provide guidance for the se-lection of marking methods in mouse body composition analysis.Methods:Male C57BL/6J mice aged 6 weeks were randomly assigned for pre-and post-ear tagging measurements.The body composition of the mice was measured using a small animal body composition analyzer,which provided measurements of the mass of fat,lean,and free fluid.Then,the mass of fat,lean and free fluid to body weight ratio was gained.Further data analysis was conducted to obtain the range and coeffi-cient of variation in body composition measurements for each mouse.The distribution of fat and lean tissue in the mice was also analyzed by comparing the fat-to-lean ratio.Results:(1)The mass of all body composition components in the ear tagging group was significantly lower than that in the control group.(2)There was a significant in-crease in the range and coefficient of variation of body composition measurements between the ear tagging group and the control group.(3)The fat-to-lean ratio in the ear tagging group was significantly lower than that in the control group.Conclusions:Ear tagging significantly lowered the results of body composition analy-sis in mice and higher the results of measurement error.Therefore,ear tagging should be avoided as much as possible when conducting body composition analysis experi-ments in mice.

The environment,especially the minimum temperature,affects summer maize grain yield by regulating ear differentiation and grain development

Ear differentiation,grain development and their interaction with factors in the growing environment,such as temperature,solar radiation and precipitation,greatly influence grain number and grain weight,and ultimately affect summer maize production.In this study,field experiments involving different sowing dates were conducted over three years to evaluate the effects of temperature factors,average solar radiation and total precipitation on the growth process,ear differentiation,fertilization characteristics,grain filling and yield of summer maize varieties with different growth durations.Four hybrids were evaluated in Huang-Huai-Hai Plain(HHHP),China from 2018 to 2020 with five different sowing dates.The results showed that the grain yield formation of summer maize was strongly impacted by the environment from the silking(R1)to milking(R3)stage.Average minimum temperature(AT_(min))was the key environmental factor that determined yield.Reductions in the length of the growing season(r=–0.556,P<0.01)and the total floret number on ear(R^(2)=0.200,P<0.001)were found when AT_(min) was elevated from the emerging(VE)to R1 stage.Both grain-filling rate(R^(2)=0.520,P<0.001)and the floret abortion rate on ear(R^(2)=0.437,P<0.001)showed quadratic relationships with AT_(min) from the R1 to physiological maturity(R6)stage,while the number of days after the R1 stage(r=–0.756,P<0.01)was negatively correlated with AT_(min).An increase in AT_(min) was beneficial for the promotion of yield when it did not exceeded a certain level(above 23°C during the R1–R3 stage and 20–21°C during the R1-R6 stage).Enhanced solar radiation and precipitation during R1–R6 increased the grain-filling rate(R^(2)=0.562,P<0.001 and R^(2)=0.229,P<0.05,respectively).Compared with short-season hybrids,full-season hybrids showed much greater suitability for a critical environment.The coordinated regulation of AT_(min),ear differentiation and grain development at the pre-and post-silking stages improved maize yield by increasing total floret number and grain-filling rate,and by reducing the floret abortion rate on ear.

Squamous Cell Carcinoma of the Middle Ear: Case Report and Literature Review

Malignant tumors originating from the middle ear are rare. The literature identifies chronic inflammation and Human Papillomavirus (HPV) infection as the most common risk factors. A CT scan to assess bony invasion and an MRI to evaluate soft tissue involvement, depth of invasion, and perineural invasion, followed by a biopsy, are indispensable for diagnosis and treatment. There is no standard treatment for squamous cell carcinoma of the middle ear, however, most reported cases are treated with surgical resection followed by postoperative radiotherapy. Given the challenges of achieving complete surgical excision, radiotherapy plays a crucial role in controlling middle ear cancers, as demonstrated in our case. We present a case of squamous cell carcinoma of the middle ear in a 63-year-old female with a history of chronic suppurative otitis media. The patient underwent a right subtotal petrosectomy without lymph node dissection followed by concurrent chemoradiotherapy. At the one-year follow visit, no recurrence or metastasis was detected.

m^(6)A modification of lncRNA in middle ear cholesteatoma

Objective:Middle ear cholesteatoma is a non-tumorous condition that typically leads to hearing loss,bone destruction,and other severe complications.Despite surgery being the primary treatment,the recurrence rate remains high.Therefore,exploring the molecular mechanisms underlying cholesteatoma is crucial for discovering new therapeutic approaches.This study aims to explore the involvement of N6-methyladenosine(m^(6)A)methylation in long non-coding RNAs(lncRNAs)in the biological functions and related pathways of middle ear cholesteatoma.Methods:The m^(6)A modification patterns of lncRNA in middle ear cholesteatoma tissues(n=5)and normal post-auricular skin tissues(n=5)were analyzed using an lncRNA m^(6)A transcriptome microarray.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses were conducted to identify potential biological functions and signaling pathways involved in the pathogenesis of middle ear cholesteatoma.Methylated RNA immunoprecipitation(MeRIP)-PCR was used to validate the m^(6)A modifications in cholesteatoma and normal skin tissues.Results:Compared with normal skin tissues,1525 lncRNAs were differentially methylated in middle ear cholesteatoma tissues,with 1048 showing hypermethylation and 477 showing hypomethylation[fold change(FC)≥3 or<1/3,P<0.05].GO enrichment analysis indicated that hypermethylated lncRNAs were involved in protein phosphatase inhibitor activity,neuron-neuron synapse,and regulation ofα-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid(AMPA)receptor activity.Hypomethylated lncRNAs were associated with mRNA methyltransferase activity,secretory granule membrane,and mRNA methylation.KEGG analysis revealed that hypermethylated lncRNAs were mainly associated with 5 pathways:the Hedgehog signaling pathway,viral protein interaction with cytokines and cytokine receptors,mitogen-activated protein kinase(MAPK)signaling pathway,cytokine-cytokine receptor interaction,and adrenergic signaling in cardiomyocytes.Hypomethylated lncRNAs were mainly involved in 4 pathways:Renal cell carcinoma,tumor necrosis factor signaling pathway,transcriptional misregulation in cancer,and cytokine-cytokine receptor interaction.Additionally,MeRIP-PCR confirmed the changes in m^(6)A methylation levels in NR_033339,NR_122111,NR_130744,and NR_026800,consistent with microarray analysis.Real-time PCR also confirmed the significant upregulation of MAPK1 and NF-κB,key genes in the MAPK signaling pathway.Conclusion:This study reveals the m^(6)A modification patterns of lncRNAs in middle ear cholesteatoma,suggests a direction for further research into the role of lncRNA m^(6)A modification in the etiology of cholesteatoma.The findings provide potential therapeutic targets for the treatment of middle ear cholesteatoma.

Ear keloid and epidermal cyst following auricular cartilage harvest for rhinoplasty:A case report

BACKGROUND This case report highlights a rare instance of concurrent keloid and epidermal cyst development at an ear cartilage harvest site following rhinoplasty in a 25-year-old woman.Both conditions,which typically stem from skin trauma,seldom occur together,demonstrating the exceptional characteristics of this case.CASE SUMMARY The patient underwent successful surgical removal of both the keloid and the epidermal cyst.Postoperative treatment included the use of silicone sheets,gel,and oral tranilast to reduce scarring.No recurrence was observed over a 6-mo follow-up period,indicating effective management of the condition.CONCLUSION The effective management of complex skin trauma cases underscores the need for individualized treatment strategies in plastic surgery.

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”

Tissue optical clearing enhances efficacy of vascular targeted photodynamic therapy of mouse dorsal skin

Vascular-targeted photodynamic therapy(V-PDT)is an effective treatment for port wine stains(PWS).However,repeated treatment is usually needed to achieve optimal treatment outcomes,possibly due to the limited treatment light penetration depth in the PWS lesion.The optical clearing technique can increase light penetration in depth by reducing light scattering.This study aimed to investigate the V-PDT in combination with an optical clearing agent(OCA)for the therapeutic enhancement of V-PDT in the rodent skinfold window chamber model.Vascular responses were closely monitored with laser speckle contrast imaging(LSCI),optical coherence tomography angiography,and stereo microscope before,during,and after the treatment.We further quantitatively demonstrated the effects of V-PDT in combination with OCA on the blood flow and blood vessel size of skin microvasculature.The combination of OCA and V-PDT resulted in significant vascular damage,including vasoconstriction and the reduction of blood flow.Our results indicate the promising potential of OCA for enhancing V-PDT for treating vascular-related diseases,including PWS.