目的:探讨亚抗微生物剂量多西环素联合红蓝光治疗中重度痤疮的疗效及安全性。方法:于2020年1月-12月,选取笔者医院皮肤科收治的120例中重度痤疮患者,按照随机数字表法分为对照组与观察组,每组60例。对照组患者给予常规剂量(200 mg/d)多...目的:探讨亚抗微生物剂量多西环素联合红蓝光治疗中重度痤疮的疗效及安全性。方法:于2020年1月-12月,选取笔者医院皮肤科收治的120例中重度痤疮患者,按照随机数字表法分为对照组与观察组,每组60例。对照组患者给予常规剂量(200 mg/d)多西环素胶囊联合红蓝光照射治疗,观察组患者给予亚抗微生物剂量(40 mg/d)多西环素胶囊联合红蓝光照射治疗,连续治疗8周。比较两组的临床效果,炎症及非炎症皮损数目,痤疮严重程度,血清炎性介质[白细胞介素-4(Interleukin-4,IL-4)、白细胞介素-8(Interleukin-8,IL-8)、肿瘤坏死因子-α(Tumor necrosis factor-α,TNF-α)]水平及不良反应。结果:治疗后,观察组的总有效率明显高于对照组(91.67% vs 75.00%,P<0.05);观察组的炎症[(20.36±2.18)个vs(31.52±3.32)个]及非炎症[(12.70±2.60)个vs(20.16±3.38)个]皮损数目均明显少于对照组(P<0.05);观察组的血清IL-4[(53.18±12.75)ng/L vs (64.33±15.69)ng/L]、IL-8[(20.33±4.27)ng/L vs (25.80±6.45)ng/L]、TNF-α[(21.59±3.82)ng/L vs (26.63±5.24)ng/L]水平均明显低于对照组(P<0.05);观察组的不良反应发生率明显低于对照组(10.00% vs 21.67%,P<0.05)。结论:亚抗微生物剂量多西环素联合红蓝光治疗中重度痤疮的临床效果更加显著,能明显改善患者的痤疮症状及炎性反应,不良反应更少。展开更多
Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Dee...Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Deep Convolutional Neural Networks(CNNs)are now the most prevalent computer algorithms for the purpose of disease classification.As with all algorithms,CNNs are sensitive to noise from imaging devices,which often contaminates the quality of the images that are fed into them.In this paper,a deep CNN(Inception-v3)is used to study the effect of image noise on the classification of skin lesions.Gaussian noise,impulse noise,and noise made up of a compound of the two are added to an image dataset,namely the Dermofit Image Library from the University of Edinburgh.Evaluations,based on t-distributed Stochastic Neighbor Embedding(t-SNE)visualization,Receiver Operating Characteristic(ROC)analysis,and saliency maps,demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.展开更多
文摘目的:探讨亚抗微生物剂量多西环素联合红蓝光治疗中重度痤疮的疗效及安全性。方法:于2020年1月-12月,选取笔者医院皮肤科收治的120例中重度痤疮患者,按照随机数字表法分为对照组与观察组,每组60例。对照组患者给予常规剂量(200 mg/d)多西环素胶囊联合红蓝光照射治疗,观察组患者给予亚抗微生物剂量(40 mg/d)多西环素胶囊联合红蓝光照射治疗,连续治疗8周。比较两组的临床效果,炎症及非炎症皮损数目,痤疮严重程度,血清炎性介质[白细胞介素-4(Interleukin-4,IL-4)、白细胞介素-8(Interleukin-8,IL-8)、肿瘤坏死因子-α(Tumor necrosis factor-α,TNF-α)]水平及不良反应。结果:治疗后,观察组的总有效率明显高于对照组(91.67% vs 75.00%,P<0.05);观察组的炎症[(20.36±2.18)个vs(31.52±3.32)个]及非炎症[(12.70±2.60)个vs(20.16±3.38)个]皮损数目均明显少于对照组(P<0.05);观察组的血清IL-4[(53.18±12.75)ng/L vs (64.33±15.69)ng/L]、IL-8[(20.33±4.27)ng/L vs (25.80±6.45)ng/L]、TNF-α[(21.59±3.82)ng/L vs (26.63±5.24)ng/L]水平均明显低于对照组(P<0.05);观察组的不良反应发生率明显低于对照组(10.00% vs 21.67%,P<0.05)。结论:亚抗微生物剂量多西环素联合红蓝光治疗中重度痤疮的临床效果更加显著,能明显改善患者的痤疮症状及炎性反应,不良反应更少。
文摘Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Deep Convolutional Neural Networks(CNNs)are now the most prevalent computer algorithms for the purpose of disease classification.As with all algorithms,CNNs are sensitive to noise from imaging devices,which often contaminates the quality of the images that are fed into them.In this paper,a deep CNN(Inception-v3)is used to study the effect of image noise on the classification of skin lesions.Gaussian noise,impulse noise,and noise made up of a compound of the two are added to an image dataset,namely the Dermofit Image Library from the University of Edinburgh.Evaluations,based on t-distributed Stochastic Neighbor Embedding(t-SNE)visualization,Receiver Operating Characteristic(ROC)analysis,and saliency maps,demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.
