The incidence of tumors among the elderly is notably high, presenting significant challenges in terms of harm, complexity, and treatment.The processes of diagnosis and treatment often lack a precise clinical foundation and robust experimental evidence, resulting in numerous difficulties and dilemmas.A collaborative approach that integrates traditional Chinese and Western medicine, leveraging the strengths of both, can substantially alleviate these challenges and ensure that elderly cancer patients can undergo systematic, comprehensive, and intensive cancer treatments.Traditional Chinese medicine can play a vital role throughout the entire continuum of diagnosis and treatment for elderly cancer patients, whether by leading, assisting, or complementing other treatment modalities.This article employs the concepts of ′righteousness′and ′evil′from traditional Chinese medicine, where ′righteousness′signifies the ′state of a person′and ′evil ′denotes the ′state of a tumor′.We systematically explore collaborative diagnosis and treatment strategies that integrate traditional Chinese and Western medicine for tumor management in the elderly, categorizing the approaches into four conditions: strong righteousness with strong evil, strong righteousness with weak evil, weak righteousness with strong evil, and weak righteousness with weak evil.In cases where both righteousness and evil are strong, the treatment strategy is primarily dominated by Western medicine, with support from traditional Chinese medicine.Conversely, when righteousness is strong and evil is weak, the strategy entails a combination of both Chinese and Western medicine.In situations characterized by weak righteousness and strong evil, the treatment approach is largely guided by traditional Chinese medicine, while also considering equal attention to both modalities.Finally, when both righteousness and evil are weak, the coordinated diagnosis and treatment strategy is primarily based on Chinese medicine, with Western medicine adapting to the circumstances and intervening appropriately throughout the process.By accurately assessing the concepts of ′righteousness′and ′evil′and implementing collaborative diagnostic and treatment strategies that integrate both traditional Chinese and Western medicine, we can significantly enhance the physical condition of elderly cancer patients.This comprehensive approach not only boosts immunity and improves organ function but also increases tolerance to tumor treatments, alleviates complications, reduces adverse reactions, and ensures that elderly cancer patients can undergo systemic cancer treatment to its fullest extent.Ultimately, this strategy aims to improve prognosis, enhance quality of life, and extend the effective survival period.

Objective The objective of this study was to analyze the mortality and changing trend of head and neck cancer(nasopharyngeal cancer,laryngeal cancer,thyroid cancer,and oral cancer)in cancer registry areas of Inner Mongolia Autonomous Re-gion from 2010 to 2020,and to provide a scientific basis for the prevention and control of head and neck cancer in Inner Mongolia Au-tonomous Region.Methods The mortality data for head and neck cancers(nasopharyngeal cancer,laryngeal cancer,thyroid cancer,and oral cancers)in the tumor registration database of Inner Mongolia Autonomous Region from 2010 to 2020 were sorted out,and the China standard mortality of head and neck were calculated by gender,urban and rural areas,and cancer types.The average annual per-centage change(AAPC)was analyzed using Joinpoint 4.9.1.0 software to assess the trend of China standard mortality of head and neck cancers and cancer types.Results The China standard mortality of head and neck cancers in cancer registry areas of Inner Mongolia was 2.85/100,000.The China standard mortality of males(4.24/100,000)was higher than that of females(1.53/100,000),and the China standard mortality in rural areas(2.93/100,000)was higher than that in urban areas(2.79/100,000).The China standard mortality of oral cancer was the highest at 1.16/100,000,and the China standard mortality of nasopharyngeal cancer was the lowest at 0.42/100,000.From 2010 to 2020,the mortality of head and neck cancers increased by an average annual rate of 3.79%(95%CI:1.45%-6.17%),and the trend was statistically significant(P=0.005).The mortality of male head and neck canc-er increased by an annual rate of 7.27%(95%CI:3.05%-11.65%),and the trend was statistically significant(P=0.003).The mortality of females decreased by an average annual rate of 1.08%(95%CI:-4.51%-2.47%),and the trend was not statistically significant(P=0.500).The mortality of oral cancer showed an upward trend with an AAPC of 7.35%(P=0.040),and the mortality of laryngeal cancer,thyroid cancer and nasopharyngeal cancer showed no statistically significant trend(AAPC was3.36%,1.38%and-0.36%,respectively,P>0.05).Conclusion The mortality of head and neck cancer in cancer registry areas of Inner Mongolia Au-tonomous Region showed an upward trend from 2010 to 2020.The prevention and treatment of head and neck cancer should be paid to attention,with rural areas and male groups as the key prevention and control targets.The control measures should be strengthened for high-risk behaviors such as occupational exposure and alcohol consumption,oral cancer prevention and control should be focused on,and HPV vaccination and tobacco control policies should be strengthened.

Objective:To establish machine learning models to predict visual improvement and anatomical response after anti-vascular endothelial growth factor (VEGF) treatment in patients with diabetic macular edema (DME).Methods:A multi-algorithm machine learning predictive modeling study based on retrospective clinical data was conducted.A total of 225 patients with DME who received their first intravitreal anti-VEGF injection at Fuzhou University Affiliated Provincial Hospital were enrolled between January 2023 and April 2025.According to data completeness, 204 cases were included in the visual recovery prediction model and 201 cases were included in the anatomical response prediction model.Baseline data included optical coherence tomography (OCT) features and blood biomarkers.The primary outcomes were defined as an improvement of ≥1 line in visual acuity and a reduction of ≥20% in central retinal thickness (CRT) after anti-VEGF treatment.Feature selection was performed using univariate logistic regression and Lasso regression.Four machine learning algorithms, logistic regression (LR), decision tree, multilayer perceptron, and random forest, were trained and validated.Model performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis.The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (No.K2025-03-064).Written informed consent was obtained from each subject.Results:Among the 37 baseline variables, five key predictors were identified for the outcome of ≥20% CRT reduction: baseline CRT, baseline CRT ≥400 μm, presence of subretinal fluid (SRF), disorganization of the retinal inner layers (DRIL), and integrity of the ellipsoid zone (EZ).Among the four models, the LR model had the best performance, with an accuracy of 0.88, sensitivity of 0.94, specificity of 0.70, and an AUC of 0.94 (95% confidence interval [ CI]: 0.87-1.00).SHAP analysis showed that baseline CRT ≥400 μm, DRIL, SRF, and baseline CRT contributed positively to the outcome, while EZ integrity was a negative predictor for CRT reduction.For the outcome of ≥1-line visual improvement, two key predictors were identified: baseline best corrected visual acuity (BCVA) and EZ integrity.Both baseline BCVA and EZ integrity were negative predictors for ≥1-line visual improvement.The LR model also had the best performance in the internal validation cohort, with an accuracy of 0.71, sensitivity of 0.67, specificity of 0.75, and an AUC of 0.76 (95% confidence interval [ CI]: 0.61-0.91).A visual nomogram was developed based on the selected predictors and the best-performing model.By converting patient-specific clinical characteristics into scores, clinicians can calculate a total score and estimate the probability of achieving a reduction of ≥20% in CRT and a ≥1-line improvement in visual acuity after anti-VEGF therapy. Conclusions:Machine learning-based model building can effectively predict visual and anatomical response following anti-VEGF treatment in DME patients.Logistic regression shows robust predictive performance for both outcomes.Identification of key predictors, especially OCT features such as EZ integrity, SRF, and DRIL, may aid in guiding treatment expectation assessment and personalized intervention strategies.Nomogram constructed in this study shows good clinical applicability and may serve as a decision-support tool to improve the precision of DME management.

Objective The objective of this study was to analyze the mortality and changing trend of head and neck cancer(nasopharyngeal cancer,laryngeal cancer,thyroid cancer,and oral cancer)in cancer registry areas of Inner Mongolia Autonomous Re-gion from 2010 to 2020,and to provide a scientific basis for the prevention and control of head and neck cancer in Inner Mongolia Au-tonomous Region.Methods The mortality data for head and neck cancers(nasopharyngeal cancer,laryngeal cancer,thyroid cancer,and oral cancers)in the tumor registration database of Inner Mongolia Autonomous Region from 2010 to 2020 were sorted out,and the China standard mortality of head and neck were calculated by gender,urban and rural areas,and cancer types.The average annual per-centage change(AAPC)was analyzed using Joinpoint 4.9.1.0 software to assess the trend of China standard mortality of head and neck cancers and cancer types.Results The China standard mortality of head and neck cancers in cancer registry areas of Inner Mongolia was 2.85/100,000.The China standard mortality of males(4.24/100,000)was higher than that of females(1.53/100,000),and the China standard mortality in rural areas(2.93/100,000)was higher than that in urban areas(2.79/100,000).The China standard mortality of oral cancer was the highest at 1.16/100,000,and the China standard mortality of nasopharyngeal cancer was the lowest at 0.42/100,000.From 2010 to 2020,the mortality of head and neck cancers increased by an average annual rate of 3.79%(95%CI:1.45%-6.17%),and the trend was statistically significant(P=0.005).The mortality of male head and neck canc-er increased by an annual rate of 7.27%(95%CI:3.05%-11.65%),and the trend was statistically significant(P=0.003).The mortality of females decreased by an average annual rate of 1.08%(95%CI:-4.51%-2.47%),and the trend was not statistically significant(P=0.500).The mortality of oral cancer showed an upward trend with an AAPC of 7.35%(P=0.040),and the mortality of laryngeal cancer,thyroid cancer and nasopharyngeal cancer showed no statistically significant trend(AAPC was3.36%,1.38%and-0.36%,respectively,P>0.05).Conclusion The mortality of head and neck cancer in cancer registry areas of Inner Mongolia Au-tonomous Region showed an upward trend from 2010 to 2020.The prevention and treatment of head and neck cancer should be paid to attention,with rural areas and male groups as the key prevention and control targets.The control measures should be strengthened for high-risk behaviors such as occupational exposure and alcohol consumption,oral cancer prevention and control should be focused on,and HPV vaccination and tobacco control policies should be strengthened.

Objective:To establish machine learning models to predict visual improvement and anatomical response after anti-vascular endothelial growth factor (VEGF) treatment in patients with diabetic macular edema (DME).Methods:A multi-algorithm machine learning predictive modeling study based on retrospective clinical data was conducted.A total of 225 patients with DME who received their first intravitreal anti-VEGF injection at Fuzhou University Affiliated Provincial Hospital were enrolled between January 2023 and April 2025.According to data completeness, 204 cases were included in the visual recovery prediction model and 201 cases were included in the anatomical response prediction model.Baseline data included optical coherence tomography (OCT) features and blood biomarkers.The primary outcomes were defined as an improvement of ≥1 line in visual acuity and a reduction of ≥20% in central retinal thickness (CRT) after anti-VEGF treatment.Feature selection was performed using univariate logistic regression and Lasso regression.Four machine learning algorithms, logistic regression (LR), decision tree, multilayer perceptron, and random forest, were trained and validated.Model performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis.The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (No.K2025-03-064).Written informed consent was obtained from each subject.Results:Among the 37 baseline variables, five key predictors were identified for the outcome of ≥20% CRT reduction: baseline CRT, baseline CRT ≥400 μm, presence of subretinal fluid (SRF), disorganization of the retinal inner layers (DRIL), and integrity of the ellipsoid zone (EZ).Among the four models, the LR model had the best performance, with an accuracy of 0.88, sensitivity of 0.94, specificity of 0.70, and an AUC of 0.94 (95% confidence interval [ CI]: 0.87-1.00).SHAP analysis showed that baseline CRT ≥400 μm, DRIL, SRF, and baseline CRT contributed positively to the outcome, while EZ integrity was a negative predictor for CRT reduction.For the outcome of ≥1-line visual improvement, two key predictors were identified: baseline best corrected visual acuity (BCVA) and EZ integrity.Both baseline BCVA and EZ integrity were negative predictors for ≥1-line visual improvement.The LR model also had the best performance in the internal validation cohort, with an accuracy of 0.71, sensitivity of 0.67, specificity of 0.75, and an AUC of 0.76 (95% confidence interval [ CI]: 0.61-0.91).A visual nomogram was developed based on the selected predictors and the best-performing model.By converting patient-specific clinical characteristics into scores, clinicians can calculate a total score and estimate the probability of achieving a reduction of ≥20% in CRT and a ≥1-line improvement in visual acuity after anti-VEGF therapy. Conclusions:Machine learning-based model building can effectively predict visual and anatomical response following anti-VEGF treatment in DME patients.Logistic regression shows robust predictive performance for both outcomes.Identification of key predictors, especially OCT features such as EZ integrity, SRF, and DRIL, may aid in guiding treatment expectation assessment and personalized intervention strategies.Nomogram constructed in this study shows good clinical applicability and may serve as a decision-support tool to improve the precision of DME management.

The incidence of tumors among the elderly is notably high, presenting significant challenges in terms of harm, complexity, and treatment.The processes of diagnosis and treatment often lack a precise clinical foundation and robust experimental evidence, resulting in numerous difficulties and dilemmas.A collaborative approach that integrates traditional Chinese and Western medicine, leveraging the strengths of both, can substantially alleviate these challenges and ensure that elderly cancer patients can undergo systematic, comprehensive, and intensive cancer treatments.Traditional Chinese medicine can play a vital role throughout the entire continuum of diagnosis and treatment for elderly cancer patients, whether by leading, assisting, or complementing other treatment modalities.This article employs the concepts of ′righteousness′and ′evil′from traditional Chinese medicine, where ′righteousness′signifies the ′state of a person′and ′evil ′denotes the ′state of a tumor′.We systematically explore collaborative diagnosis and treatment strategies that integrate traditional Chinese and Western medicine for tumor management in the elderly, categorizing the approaches into four conditions: strong righteousness with strong evil, strong righteousness with weak evil, weak righteousness with strong evil, and weak righteousness with weak evil.In cases where both righteousness and evil are strong, the treatment strategy is primarily dominated by Western medicine, with support from traditional Chinese medicine.Conversely, when righteousness is strong and evil is weak, the strategy entails a combination of both Chinese and Western medicine.In situations characterized by weak righteousness and strong evil, the treatment approach is largely guided by traditional Chinese medicine, while also considering equal attention to both modalities.Finally, when both righteousness and evil are weak, the coordinated diagnosis and treatment strategy is primarily based on Chinese medicine, with Western medicine adapting to the circumstances and intervening appropriately throughout the process.By accurately assessing the concepts of ′righteousness′and ′evil′and implementing collaborative diagnostic and treatment strategies that integrate both traditional Chinese and Western medicine, we can significantly enhance the physical condition of elderly cancer patients.This comprehensive approach not only boosts immunity and improves organ function but also increases tolerance to tumor treatments, alleviates complications, reduces adverse reactions, and ensures that elderly cancer patients can undergo systemic cancer treatment to its fullest extent.Ultimately, this strategy aims to improve prognosis, enhance quality of life, and extend the effective survival period.

Objective To explore the value of automatic segmentation technique combined with neurite dispersion and density imaging(NODDI)for displaying volume and microstructure changes of hippocampal subregion in patients with hippocampal sclerosis medial temporal lobe epilepsy(mTLE-HS).Methods MRI data of 33 patients with left mTLE-HS(mTLE-HS group)and 35 healthy adults(control group)were retrospectively analyzed.The hippocampal subregions were automatically segmented using FreeSurfer software,the volume of cornu Ammonis(CA)1,CA2-3,CA4,granulose cell-dentate gyrus(GC-DG)and subiculum were measured,then the NODDI parameters of each subregion were obtained through post-processing.The intra-and inter-groups hippocampal subregion volumes and NODDI parameters were compared,and the correlations of parameters being significantly different with the onset age and disease courses were analyzed.Results The volume of hippocampal subregions in mTLE-HS group were all lower than those in control group(all P＜0.05).In mTLE-HS group,the neurite density index(NDI)of left CA1 and CA4 subregions were both lower,while the free-water isotropic volume fraction(fiso)of the left CA1 subregion was higher than those of the right side(all P＜0.05).The orientation dispersion index(ODI)of left CA1,CA2-3 and CA4 subregions,as well as NDI of left CA1,CA4 and GC-DG subregions in mTLE-HS group were all lower than those in control group(all P＜0.05),while fiso of left CA1,GC-DG and subiculum subregions in mTLE-HS group were all higher than those in control group(all P＜0.05).The volume of left hippocampal subregions in patients with mTLE-HS were all moderately positively correlated with the onset age(r=0.540-0.667,all P＜0.001)but weakly negatively correlated with disease courses(r=-0.492--0.386,all P＜0.05).NDI of left CA4 and GC-DG subregions in patients with mTLE-HS were both weakly negatively correlated with disease courses(r=-0.418,-0.388,both P＜0.05).Conclusion Automatic segmentation technique combined with NODDI could be used to display the volume and microstructure changes of mTLE-HS.NDI might be a biomarker of mTLE-HS being sensitive to progressive neuronal damage.

Objective:To evaluate the clinical application of urine sediment score (USS) in early diagnosis, etiological differentiation, staging and prognosis of acute kidney injury (AKI), and to investigate the diagnostic efficacy of independent USS and its combination with blood urea nitrogen(Bun) serum creatinine(sCr) and uric acid(UA) in AKI.Methods:From August 23 to September 28, 2023, 9 020 morning urine samples of hospitalized patients in the First Hospital of Jilin University were detected by Sysmex UF5000.A total of 3 226 ssamples with small and round cell (SRC) > 1/μl and/or CAST>1/μl were screened for microscopic examination, and 404 cases with positive renal tubular epithelial cells and/or cast were enrolled in this study. There were 218 males and 186 females, aged 59.5 (49.0, 71.0) years. The 404 cases were divided into the USS AKI group (345 cases) and the USS non-AKI group (59 cases) according to the USS results based on the microscopic findings. According to Kidney Disease: Improving Global Outcomes (KDIGO) criteria, they were divided into KDIGO criteria AKI group (63 cases) and KDIGO criteria non-AKI group (341 cases), and the AKI group was divided into renal AKI group (33 cases) and non-renal AKI group (30 cases). According to the clinical diagnosis recorded in the medical records, they were divided into clinically diagnosed AKI group (29 cases) and clinically diagnosed non-AKI group (375 cases).The χ 2 test or Fisher exact test was used to compare USS in different AKI causes and stages. Logistic regression was used to calculate the odds ratio of renal AKI and stage 3 AKI. The area under the receiver operating characteristic curve was used to evaluate the sensitivity and specificity of USS, sCr, UA and Bun alone and in combination in the diagnosis of AKI, and the best cut-off value, sensitivity and specificity in the diagnosis of AKI were calculated. P < 0.05 was considered statistically significant. Results:The USS was used to identify the etiology of KDIGO standard AKI group,and there were significant differences in USS between renal AKI group and non-renal AKI group (χ 2=11.070, P<0.001). Compared to USS=1, the odds ratio of renal AKI was 8.125 when USS≥2 (95% CI 2.208—29.901). There was a statistically significant difference in the comparison of USS between groups in each stage of the AKI staging study based on USS (χ 2=15.724, P<0.05). Compared to USS=1, the odds ratio of stage 3 AKI was 9.714 when USS≥2 (95% CI 1.145-82.390). The AUC of independent USS in the diagnosis of AKI was 0.687 (95% CI 0.618-0.757, P<0.001), the specificity was 65.7% and the sensitivity was 61.9%. The AUC of USS combined with Bun, sCr, UA in the diagnosis of AKI was 0.794 (95% CI 0.608-0.980, P<0.05), the specificity was 82.4%, and the sensitivity was 88.9%. Conclusions:There wasan increased likelihood of renal AKI or stage 3 AKI while USS≥2,and whose combination with Bun, sCr and UA will improve the diagnostic efficiency of AKI.

BACKGROUND:Animal models of diabetic encephalopathy that have been studied mainly include streptozotocin-induced model,high-sugar and high-fat diet-induced model and spontaneous animal model.Establishing a simple,easy,short-cycle,safe and effective model of diabetic encephalopathy can help to explore the subsequent pathogenesis and screen therapeutic drugs. OBJECTIVE:To further explore and evaluate the method of building diabetic encephalopathy rat models. METHODS:Twenty Sprague-Dawley rats were randomly divided into control(n=10)and model(n=10)groups.Rats in the model group were given a single injection of 45 mg/kg streptozotocin in the left lower abdominal cavity,and those in the control group were given the same amount of citrate buffer.During the experiment,the body mass,feed intake,water intake and blood glucose were measured.After 8 weeks,the glucose tolerance and oxidative stress levels were measured,and the pathological changes of brain tissue and the expression of apoptotic proteins were compared between groups. RESULTS AND CONCLUSION:Compared with the control group,the food intake,water intake,encephalization quotient,blood glucose and area under the blood glucose curve were significantly increased in the model group,while the body mass decreased significantly(P<0.01).Histopathological examination of the brain showed that compared with the control group,the number of surviving nerve cells was significantly reduced in the model group(P<0.01),with more significant pathological damage of nerve cells.Compared with the control group,the activities of serum superoxide dismutase,catalase and glutathione in the model group were significantly decreased(P<0.01),and the content of oxidative malondialdehyde was significantly increased(P<0.05).The expression levels of apoptosis-related proteins Bax and Caspase-3 in brain tissue increased in the model group compared with the control group,while the expression of Bcl-2 decreased(P<0.01).In conclusion,an 8-week injection of 45 mg/kg streptozotocin can cause obvious pathological damage to the brain tissue of diabetic rats,to successfully establish the rat model of diabetic encephalopathy.

BACKGROUND:Combined radiation and wound injury appeared mainly in patients with tumor radiotherapy and nuclear radiation accidents.The radiation destroys the repair mechanism,resulting in delayed or prolonged wound healing.It still lacks an effective therapeutic strategy currently. OBJECTIVE:To prepare multifunctional wound dressings based on the multiple clinical symptoms of combined radiation and wound injury,which are designed to be antibacteria,promoted healing and analgesics. METHODS:Using levofloxacin,fibroin and lidocaine hydrochloride as raw materials,3D bioprinting technology was applied to prepare the multifunctional wound dressing.(1)The multifunctional dressing was placed on a fixed culture plate coated with Staphylococcus aureus,Escherichia coli and Pseudomonas aeruginosa,and incubated at 37 ℃ overnight to detect the diameter of the antibacterial zone.(2)40 Kunming mice were randomly divided into trauma group,radiation and trauma model group,treatment group and positive drug group,with 10 mice in each group.Mice in the radiation and trauma model group,treatment group and positive drug group were irradiated by 60Co gamma rays.After 1 hour of radiation,a full-layer skin defect wound with a diameter of 1 cm was made on the back of each mouse in the four groups.Normal saline was applied to the wounds of the trauma group and the radiation and trauma model group.Trethanolamine cream was applied to the wounds of the positive drug group.Multifunctional dressing was applied to the wounds of the treatment group.The dressing was changed every 2 days,and the treatment was continued for 14 days.Wound healing rate and serum interleukin-6 level were measured at 3,7 and 14 days after wound modeling.14 days after the wound modeling,the skin tissue of the wound was obtained and received hematoxylin-eosin staining,Masson staining and cytokeratin-14 immunohistochemical staining. RESULTS AND CONCLUSION:(1)3D-printed multifunctional wound dressing had good antibacterial activity.The antibacterial zone diameters against Staphylococcus aureus,Escherichia coli and Pseudomonas aeruginosa were(4.15±0.09),(4.18±0.23)and(4.35±0.13)cm,respectively.(2)With the extension of modeling time,the wound healed gradually.The wound healing rate of the treatment group and the positive drug group was higher than that of the radiation and trauma model group at 3,7 and 14 days after modeling(P<0.01,P<0.001).The wound healing rate of the treatment group was higher than that of the positive drug group.With the extension of modeling time,the serum interleukin level of mice increased first and then decreased.The serum interleukin level in the treatment group at 3,7 and 14 days after modeling was lower than that in the radiation and trauma model group.Hematoxylin-eosin staining and Masson staining exhibited that inflammatory cells infiltrated the granuloma tissue in the trauma group,and the dermal collagen fibers were densely arranged.The normal structure of epidermis and dermis was destroyed and inflammatory cells were infiltrated in the radiation and trauma model group.In the treatment group,normal skin mucosal tissue was observed,the epidermis was arranged closely,and the sweat glands,hair follicles and dermal collagen fibers were arranged regularly.In the positive drug group,the arrangement of epidermal layer was tight,and the arrangement of sweat glands,hair follicles and dermal collagen fibers was regular.Cytokeratin-14 immunohistochemical staining displayed that the epidermal tissue thickness in the treatment group was lower than that in the other three groups(P<0.01,P<0.001).(3)The results confirm that the 3D-printed multifunctional dressing has multiple functions of local anesthesia,anti-infection and promoting healing.