Artificial dermis combined with skin grafting for the treatment of hand skin and soft tissue defects and exposure of bone and tendon

BACKGROUND The recovery time of hand wounds is long,which can easily result in chronic and refractory wounds,making the wounds unable to be properly repaired.The treatment cycle is long,the cost is high,and it is prone to recurrence and disability.Double layer artificial dermis combined with autologous skin transplantation has been used to repair hypertrophic scars,deep burn wounds,exposed bone and tendon wounds,and post tumor wounds.AIM To investigate the therapeutic efficacy of autologous skin graft transplantation in conjunction with double-layer artificial dermis in treating finger skin wounds that are chronically refractory and soft tissue defects that expose bone and tendon.METHODS Sixty-eight chronic refractory patients with finger skin and soft tissue defects accompanied by bone and tendon exposure who were admitted from July 2021 to June 2022 were included in this study.The observation group was treated with double layer artificial dermis combined with autologous skin graft transplantation(n=49),while the control group was treated with pedicle skin flap transplantation(n=17).The treatment status of the two groups of patients was compared,including the time between surgeries and hospital stay.The survival rate of skin grafts/flaps and postoperative wound infections were evaluated using the Vancouver Scar Scale(VSS)for scar scoring at 6 mo after surgery,as well as the sensory injury grading method and two-point resolution test to assess the recovery of skin sensation at 6 mo.The satisfaction of the two groups of patients was also compared.RESULTS Wound healing time in the observation group was significantly longer than that in the control group(P<0.05,27.92±3.25 d vs 19.68±6.91 d);there was no significant difference in the survival rate of skin grafts/flaps between the two patient groups(P>0.05,95.1±5.0 vs 96.3±5.6).The interval between two surgeries(20.0±4.3 d)and hospital stay(21.0±10.1 d)in the observation group were both significantly shorter than those in the control group(27.5±9.3 d)and(28.4±17.7 d),respectively(P<0.05).In comparison to postoperative infection(23.5%)and subcutaneous hematoma(11.8%)in the control group,these were considerably lower at(10.2%)and(6.1%)in the observation group.When comparing the two patient groups at six months post-surgery,the excellent and good rate of sensory recovery(91.8%)was significantly higher in the observation group than in the control group(76.5%)(P<0.05).There was also no statistically significant difference in two point resolution(P>0.05).The VSS score in the observation group(2.91±1.36)was significantly lower than that in the control group(5.96±1.51),and group satisfaction was significantly higher(P<0.05,90.1±6.3 vs 76.3±5.2).CONCLUSION The combination of artificial dermis and autologous skin grafting for the treatment of hand tendon exposure wounds has a satisfactory therapeutic effect.It is a safe,effective,and easy to operate treatment method,which is worthy of clinical promotion.

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide,and the earlier identification of skin lesions using dermoscopic images can raise the survival rate.Classification of the skin lesion from those dermoscopic images will be a tedious task.The accuracy of the classification of skin lesions is improved by the use of deep learning models.Recently,convolutional neural networks(CNN)have been established in this domain,and their techniques are extremely established for feature extraction,leading to enhanced classification.With this motivation,this study focuses on the design of artificial intelligence(AI)based solutions,particularly deep learning(DL)algorithms,to distinguish malignant skin lesions from benign lesions in dermoscopic images.This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoen-coder(OSSAE)based feature extractor with backpropagation neural network(BPNN),named the OSSAE-BPNN technique.The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region.In addition,the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis.Moreover,the parameter tuning of the SSAE model is carried out by the use of sea gull optimization(SGO)algo-rithm.To showcase the enhanced outcomes of the OSSAE-BPNN model,a comprehensive experimental analysis is performed on the benchmark dataset.The experimentalfindings demonstrated that the OSSAE-BPNN approach outper-formed other current strategies in terms of several assessment metrics.

The Usefulness of the Artificial Intelligence Data in Analyzing the Skin in the Era of the Fourth Industrial Revolution

In the World economy forum Global Challenge Insight Report titled “The Future of Jobs-Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution (FIR) in 2016”, a new industrial revolution was predicted to occur in the near future. This is followed by the opinion that it would be mandatory to prepare for the FIR because it will definitely change people’s way of working, consuming and thinking. There is a controversy as to the potential of AI in health care. To date, however, remarkable achievements have been made in the field of medicine, particularly entailing dermatology. Therefore, this study explored the usefulness of the AI data in analyzing the skin in the era of the FIR. The current study finally included a total of 300 subjects, for whom a self-reporting questionnaire survey was performed between June 09 and July 18, 2020. The current study proposed the following hypothesis: The AI data might be useful in analyzing the skin in the era of the FIR. This hypothesis was accepted. In conclusion, the current study suggests that the AI data might be useful in analyzing the skin in the era of the FIR. But this deserves further study.

Effect of artificial dermis combined with jinfu ning on skin healing and bacterial detection rate of finger abdomen

Objective: To explore the effect of artificial dermis combined with rhGM-CSF(Jinfuning) on healing of soft tissue defect of finger ventral skin and the influence of bacterial detection rate. Methods: Totally 110 patients with finger injury admitted to the rehabilitation department of our department from January 2017 to June 2018 were collected and divided into control group and observation group according to the random number table method with 55 cases in each group. The control group received direct artificial derma lrepairing after thorough debridement, while the observation group received recombinant gm-csf gel coating on the wound surface before artificial dermal repairing, Wound healing, wound inflammation, bacterial detection rate, inflammatory factor expression, follow-up and adverse reactions were compared between the two groups. Results: The wound healing rate of the observation group at 7, 14, 21 and 28 days after treatment was significantly higher than that of the control group (t= 11.211, P =0.000).( T = 14.895, P =0.000;T = 25.346, P=0.000;T =8.247, P=0.000). The wound healing time of the observation group was (19.7±2.3) d, and that of the control group was (27.4±3.3) d. The average wound healing time of the observation group was significantly shorter than that of the control group, and the difference was statistically significant (t=14.197, P= 0.000). Observation group wound inflammation at each time point score was significantly lower than the control group, the group rooms, time points, ·point interaction effect between the comparison, the differences were statistically significant (P <0.05), the observation group wound bacteria detection rate of 7.27% (4 cases) : the control bacteria detection rate was 21.81% (12 cases), difference was statistically significant (chi-square = 4.68, P= 0.0305), the observation group of bacteria detection rate was significantly lower than the control group;The bacteria detected in the two groups were mainly e. coli, tetanus bacillus and fungi. There was no significant difference in the indicators between the two groups before treatment, and the values of inflammatory cytokines il-1 and TNF- IOD in the two groups were significantly decreased after treatment, and the observation group was significantly lower than the control group, with statistically significant differences (P < 0.05). No serious adverse reactions occurred in either group during the treatment. Conclusion: the application of artificial dermals combined with jinfuning can promote wound healing of skin and soft tissue defect of finger abdomen, effectively inhibit bacterial infection of wound surface, reduce inflammation and infection,reducing bacterial detection rate.

A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.

A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

Melanoma or skin cancer is the most dangerous and deadliest disease.As the incidence and mortality rate of skin cancer increases worldwide,an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer.In this study,a Hybrid Artificial Intelligence Model(HAIM)is designed for skin cancer classification.It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron(EWHMLP)for the classification.Though the wavelet transform is a powerful tool for signal and image processing,it is unable to detect the intermediate dimensional structures of a medical image.Thus the proposed HAIM uses Curvelet(CurT),Contourlet(ConT)and Shearlet(SheT)transforms as feature extraction techniques.Though MLP is very flexible and well suitable for the classification problem,the learning of weights is a challenging task.Also,the optimization process does not converge,and the model may not be stable.To overcome these drawbacks,EWHMLP is developed.Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33%in a multi-class approach on PH2 database.

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Bioengineered skin organoids:from development to applications

Signifcant advancements have been made in recent years in the development of highly sophisticated skin organoids.Serving as three-dimensional(3D)models that mimic human skin,these organoids have evolved into complex structures and are increasingly recognized as efective alternatives to traditional culture models and human skin due to their ability to overcome the limitations of two-dimensional(2D)systems and ethical concerns.The inherent plasticity of skin organoids allows for their construction into physiological and pathological models,enabling the study of skin development and dynamic changes.This review provides an overview of the pivotal work in the progression from 3D layered epidermis to cyst-like skin organoids with appendages.Furthermore,it highlights the latest advancements in organoid construction facilitated by state-of-the-art engineering techniques,such as 3D printing and microfuidic devices.The review also summarizes and discusses the diverse applications of skin organoids in developmental biology,disease modelling,regenerative medicine,and personalized medicine,while considering their prospects and limitations.

Ultraconformable Integrated Wireless Charging Micro-Supercapacitor Skin

Conformable and wire-less charging energy storage devices play important roles in enabling the fast development of wearable,non-contact soft electronics.However,current wire-less charging power sources are still restricted by limited flexural angles and fragile connection of components,resulting in the failure expression of performance and constraining their fur-ther applications in health monitoring wearables and moveable artificial limbs.Herein,we present an ultracompatible skin-like integrated wireless charging micro-supercapacitor,which building blocks(including electrolyte,electrode and substrate)are all evaporated by liquid precursor.Owing to the infiltration and permeation of the liquid,each part of the integrated device attached firmly with each other,forming a compact and all-in-one configuration.In addition,benefitting from the controllable volume of electrode solution precursor,the electrode thickness is easily regulated varying from 11.7 to 112.5μm.This prepared thin IWC-MSC skin can fit well with curving human body,and could be wireless charged to store electricity into high capacitive micro-supercapacitors(11.39 F cm-3)of the integrated device.We believe this work will shed light on the construction of skin-attachable electronics and irregular sensing microrobots.

Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Supporting Skin Structure and Its Barrier Functions with Evidence-Based Skin Care Ingredients

The epidermis, and in particular its outermost layer, the stratum corneum, contributes much of the barrier function of the skin and is a readily visible representation of skin health. Maintaining the health of the skin barrier has arguably become more important than ever in the modern world, in which a large majority of people are exposed to environmental insults. These external factors can damage the integrity of the skin barrier and prematurely age the skin. Therefore, it has become increasingly important to maintain and protect the stratum corneum. Here, we briefly review the complex, multilayered structure of the skin and relate it to clinically translatable function, with an emphasis on the stratum corneum. In the context of epidermal structure and function, the formulation and clinical data for Phelityl® Reviving Cream will be reviewed. Phelityl Reviving Cream was shown to be associated with improvements in both immediate- and long-term parameters, including a significant positive effect on the skin barrier and immediate and long-lasting hydration.

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

Post-earthquake rescue missions are full of challenges due to the unstable structure of ruins and successive aftershocks.Most of the current rescue robots lack the ability to interact with environments,leading to low rescue efficiency.The multimodal electronic skin(e-skin)proposed not only reproduces the pressure,temperature,and humidity sensing capabilities of natural skin but also develops sensing functions beyond it—perceiving object proximity and NO2 gas.Its multilayer stacked structure based on Ecoflex and organohydrogel endows the e-skin with mechanical properties similar to natural skin.Rescue robots integrated with multimodal e-skin and artificial intelligence(AI)algorithms show strong environmental perception capabilities and can accurately distinguish objects and identify human limbs through grasping,laying the foundation for automated post-earthquake rescue.Besides,the combination of e-skin and NO2 wireless alarm circuits allows robots to sense toxic gases in the environment in real time,thereby adopting appropriate measures to protect trapped people from the toxic environment.Multimodal e-skin powered by AI algorithms and hardware circuits exhibits powerful environmental perception and information processing capabilities,which,as an interface for interaction with the physical world,dramatically expands intelligent robots’application scenarios.

The efficacy of a novel tomato extracts formulation on skin aging and pigmentation:A randomized,double-blind,parallel-controlled trial

Background:Oxidative stress is a significant factor in skin aging and pigmentation,which can be precipitated by various circumstances.Antioxidants and tyrosinase inhibitors,such as carotenoids,yeast extract(glutathione),sodium hyaluronate,astaxanthin,and niacin,can individually protect the skin against aging through distinct mechanisms.These mechanisms potentially enhance the skin barrier and improve signs of aging and pigmentation.However,the synergistic effects of these compounds,as found in a golden tomato extract formulation,have been scarcely explored.Objective:To evaluate the effects of an orally administered formulation on the skin aging and pigmentation.Material and Methods:In this study,a randomized,double-blind,parallel-controlled trial was conducted,utilizing the WONDERLAB?Tomato Niacinamide beverage.Out of all participants,62 volunteers completed the experiment and were included in the statistical analysis.Results:The results indicated that after eight weeks of consuming the research product,there were no significant changes in the skin indicators within the placebo group.In contrast,the treatment group receiving the sample formulation exhibited a 35.63%increase in stratum corneum hydration and a 29.39%reduction in transepidermal water loss(TEWL),suggesting enhanced skin hydration.Visual assessments revealed improvements in skin color and gloss index by 15.03%and 11.41%,respectively,in the treatment group.Furthermore,the skin gloss and individual typology angle(ITA)value increased by 18.59%and 6.36%,respectively,leading to a lighter skin tone.Significant enhancements were also observed in skin pigmentation,color uniformity,and redness.After eight weeks of intervention with the sample,blood levels of superoxide dismutase(SOD)and glutathione peroxidase(GPx)increased,while malondialdehyde(MDA)levels decreased.Conclusion:These findings confirm that continuous intake of the tomato extract formulation over eight weeks effectively improved the volunteers'skin whitening and hydration,and visibly brightened skin tone through an antioxidant mechanism.

A Discussion of Artificial Intelligence in Visual Art Education

Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.

The Application of Digital Optical 3D Image Analyzer Evaskin in The Evaluation of Wrinkles

To verify the effectiveness of digital optical 3D image analyzer EvaSKIN in the objective and quantitative evaluation of wrinkles.A total of 115 subjects were recruited,the facial images of the subjects were collected by digital optical 3D image analyzer and manual camera,the changes of crow’s feet with age were analyzed.Pictures obtained by manual photography can be directly used for observation and preliminary grading of wrinkles.However,the requirements for evaluators are high,and the results are prone to errors,which will affect the accuracy of the evaluation.Therefore,skilled raters are needed.Compared with the manual photography method,the digital optical 3D image analyzer EvaSKIN can realize three-dimensional extraction of wrinkles,and obtain the change trend of crow’s feet with age.20~30 years old,wrinkles begin to appear slowly;wrinkles will increase rapidly at the age of 30~50;The length of 50~60 year old wrinkles is basically fixed,the wrinkles develop longitudewise,gradually widen and deepen,and the area,depth and volume increase is obvious,and the skin aging condition is intensified.the digital optical 3D image analyzer EvaSKIN realizes the 3D extraction of wrinkles,quantifies the circumference,area,average depth,maximum depth and volume of wrinkles,realizes the objective and quantitative evaluation of wrinkle state,is more accurate in the measurement of wrinkles,and provides a new instrument and method for the evaluation of wrinkles.it is a perfect and supplement to the traditional evaluation methods,and to a certain extent,it helps the research and development and evaluation institutions of cosmetics to obtain more abundant and three-dimensional data support.

Sensitive skin syndrome: Research progress on mechanisms and applications

Sensitive skin is a clinical syndrome characterized by a hyper-reactive state of the skin,primarily on the face.It is accompanied by subjective symptoms such as burning,stinging,itching,and tightness when exposed to physical,chemical,or psychological stimuli.Objective signs,such as erythema,scales,and dilated blood vessels,may or may not be present.The discomfort associated with sensitive skin can be triggered by various endogenous and exogenous factors,which usually have no significant effect on the individual and do not induce irritant reactions.Sensitive skin often presents as a subjective state without clinical signs and exhibits diversity,posing challenges in sensitive skin research and care.This review summarizes the prevalence,key factors,pathophysiological mechanisms,diagnosis,and progress in daily care for sensitive skin.The aim is to provide a clearer and more systematic understanding of sensitive skin and offer guidance for sensitive skin care.