SciCombinator

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Journal: Zhonghua yi xue za zhi

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Objective: To compare the decolorization efficiency of lignin peroxidase (LiP), manganese peroxidase (MnP) and laccase on eumelanin and pheomelanin, and to investigate the effect of topical administration of LiP solution on hyperpigmented guinea pigs skin induced by 308 nm excimer light. Methods: Pheomelanin-enriched specimens were prepared from human hair and cutaneous melanoma tissue using alkaline lysis method.Synthetic eumelanin was purchased from a commercial supplier.The same amount (0.02%) of melanin was incubated with the equal enzyme activity (0.2 U/ml) of ligninolytic enzymes for 3 h respectively.The absorbance at 475 nm (A(475)) in the enzyme-catalyzed solution was measured using ELISA microplate reader.The experimental hyperpigmentation model was established in the dorsal skin of brownish guinea pigs using 308 nm excimer light radiation.LiP and heat-inactivated LiP solution were topically applied at each site.Meanwhile, 3% hydroquinone and vehicle cream were used as control.The skin color (L value) was recorded using a CR-10 Minolta chromameter.Corneocytes were collected using adhesive taping method.The amount and distribution of melanin in the corneocytes and skin tissues was visualized by Fontana-Masson staining. Results: All three ligninolytic enzymes showed various degree of eumelanin and pheomelanin decolorization activity.The decolorization activity of LiP, MnP and laccase was 40%-70%, 22%-42% and 9%-21%, respectively.The similar lightening was shown in the skin treated with LiP solution and 3% hydroquinone.The amount of melanin granules in the corneocytes was 199±11 by LiP, which was less than that in untreated control (923±12) and heat-inactive control (989±13). The amount of melanin was decreased in the whole epidermis treated with hydroquinone, the epidermis thickness was increased as well. In contrast, melanin of LiP group was decreased only in the superficial epidermis, the epidermis thickness seemed to be normal. Conclusion: LiP exerts a potent decolorization activity for hair- or skin-derived pheomelanin as well as eumelanin.It remains to be further investigated whether LiP serves as a substitute for hydroquinone in skin lightening products.

Concepts: Enzyme, Skin, Melanin, Epidermis, Human skin color, Melanocyte, Ligninase, Skin whitening

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Objective: To systematically compare the differences in the detection rate of prostate cancer with fusion targeting biopsy and systemic biopsy. Methods: A computer-based search of PubMed, Medline, China Biomedical Literature Database and Wanfang database (from the beginning of establishment of library to October 2016) on the detection rate of prostate cancer by fusion targeting biopsy and systemic biopsy was performed manually.The inclusion and exclusion criteria were formulated by 2 reviewers, and the data were extracted and evaluated respectively. RevMan5.3 software was used to analyze the detection rate of prostate cancer by two biopsy methods. Results: A total of 15 related clinical studies were included, 5 337 cases were enrolled in the study, including 2 667 cases of targeted fusion biopsy and 2 670 cases of routine systemic biopsy. The results showed that the overall detection rate of prostate cancer in targeting fusion biopsy was significantly higher than that of conventional systemic biopsy (OR=1.16, 95% CI 1.04-1.30, P=0.007). The detection rates of prostate cancer with different risk grades by two biopsy methods were conducted. We found that targeted fusion biopsy had a significant advantage compared with conventional system biopsy (OR=1.37, 95% CI 1.19-1.58, P<0.05) in middle and high risk prostate cancer with Gleason ≥ 7 points. In low-risk prostate cancer patients with Gleason score <7, the detection rate of prostate cancer by targeted fusion biopsy was lower (OR=0.76, 95% CI 0.65-0.89, P<0.05) than that of conventional systemic biopsy. Conclusions: Targeted fusion biopsy was significantly better than systemic biopsy in the overall detection rate of prostate cancer and the detection rate of the middle and high risk prostate cancer with Gleason ≥7 points. However, systemic biopsy performed better in patients with Gleason<7 points of low-risk prostate cancer.

Concepts: Cancer, Metastasis, Oncology, Obesity, Prostate cancer, Radiation therapy, BRCA2, Screening

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Objective: To explore the minimally invasive techniques and outcome of carotid ophthalmic artery aneurysms clipping via a frontolateral approach. Methods: The clinical data of 95 patients with carotid ophthalmic artery aneurysms treated via frontolateral approach in the last 1.5 years in Beijing Tiantan Hospital and Beijing Anzhen Hospital were analyzed retrospectively.Before the operation, digital subtraction angiogram (DSA) was performed among all patients.The patients were divided into two groups by the lateral approach.According to preoperative classification, surgical characteristics and prognosis were summarized. Results: Ninety-five cases of ophthalmic aneurysms were divided into type Ⅰ of 44 cases (46.3%), type Ⅱ of 34 cases (35.7%) and type Ⅲ of 17cases (17.9%), according to the results of DSA.The diameter of aneurysm was <10 mm (35 cases), 10-25 mm (34 cases), and >25 mm (26 cases). In the 17 cases of subarachnoid hemorrhage (SAH), 8 cases were ruptured carotid-ophthalmic artery aneurysms.Among those 95 patients, 93 were clipped successfully, 2 was trapped.Multiple aneurysms in 5 cases were treated in one surgical session through the same approach.No aneurysm residual was found after postoperative CTA review.Ipsilateral vision of 3 cases were decline.Cerebral infarction was appeared in 9 cases.All the others had a good recovery. Conclusions: The carotid-ophthalmic artery aneurysms could be well exposed. Microsurgery through frontolateral approach has the advantages such as minimal invasion, less effect on the patients' look and simple procedure.The frontolateral approach is safe and effective in surgery for ophthalmic segment of the internal carotid artery aneurysms.

Concepts: Atherosclerosis, Stroke, Surgery, Internal carotid artery, Common carotid artery, Subarachnoid hemorrhage, Arteries of the head and neck, Ophthalmic artery

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Objective: To investigate the therapeutic efficacy of tonsillectomy for patients with recurrence of IgA nephropathy (IgAN) after kidney transplantation. Methods: From May 2014, tonsillectomy was performed in 11 renal transplant patients with biopsy-proved recurrent IgAN. In a median follow-up of 14 (4-38) months, data of proteinuria, hematuria, estimated glomerular filtration rate (eGFR), and serum levels of IgA in these patients were compared before and after tonsillectomy.Patient’s survival and renal graft survival were also summarized. Results: A remission of proteinuria was observed in 8 patients after tonsillectomy, and this status maintained well in the subsequent follow-up.Three patients had no or minimal reduction of proteinuria after tonsillectomy and returned to dialysis within 1 year after tonsillectomy.Possible causes could be severe primary IgAN of crescentric glomerulonephritis, IgAN recurrence in kidney retransplantation, and late tonsillectomy after IgAN recurrence.Serum levels of IgA significant decreased and no patients developed acute rejection or infection after tonsillectomy.In the 1-year follow-up, no patients died and grafts survived well in 8 out of 11 patients. Conclusions: Tonsillectomy may represent an effective and reliable way to treat recurrence IgAN after kidney transplantation, and may be applied widely in the future clinical management. However, early intervention is critical and effects may depend on the pathological features of primary IgAN.

Concepts: Renal failure, Chronic kidney disease, Kidney, Nephrology, Organ transplant, Kidney transplantation, Glomerulonephritis, IgA nephropathy

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Artificial intelligence (AI) is a hot point in clinical medicine research. In recent years, AI has played an important role in recognizing the lesion, improving the diagnostic accuracy and assessing the diagnostic efficacy. To accelerate the pace of AI industry, it should be a first thing to improve relevant industrial policies and regulations and to build a transformation platform for industry-university-research. All these will contribute to the standardization in further development of medical imaging AI industry.

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Objective: To investigate the diagnostic value of radiomics model based on plain CT scan of peripheral coronary artery adipose tissue for non-calcified plaque. Methods: The image data of 461 patients undergoing coronary CT angiography (CCTA) in the Department of Radiology of the First Affiliated Hospital of Suzhou University from August 1,2019 to July 31,2020 were retrospectively analyzed. Two hundred and six cases (355 branches) with non-calcified plaques, and 255 cases (510 branches) with no coronary artery disease were detected by CCTA. The regions of interest (ROI) of the pericoronary adipose tissue were segmented on the plain CT scan images (coronary calcification score (CCS) sequence). The coronary ROI was determined by selecting the coronary artery with a length of 40 mm and starting at 10 mm from the opening of the coronary artery, and the pericoronary adipose ROI was generated automatically. The pericoronary fat attenuation index (FAI) was then performed, and the radiomics features were extracted. The 865 coronary arteries were divided into the training group (n=606) and the testing group (n=259) at a ratio of 7∶3, and the radiomics model was carried out. The receiver operating characteristic (ROC) analysis was used to assess the FAI value and the diagnostic efficacy of the radiomics model for non-calcified plaque. Results: A total of 1 692 features were extracted from images of pericoronary adipose based on plain scan. All features were screened by using max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), and 14 features were selected for the establishment of the radiomics model. The accuracy, sensitivity, specificity and area under the curve (AUC) of the model in distinguishing patients with non-calcified plaque and those without coronary stenosis in the testing group were 70.3%, 63.2%, 75.2% and 0.75, respectively. Conclusion: The radiomics model based on plain CT scan of the pericoronary adipose tissue had good diagnostic efficacy for non-calcified plaque.

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Objective: To investigate the role of artificial intelligence-based coronary CT blood flow reserve score (FFRCT) in assessing hemodynamic relevance in patients with deep myocardial bridge (MB) of the left anterior descending coronary artery. Methods: A total of 113 patients diagnosed with deep MB of the left anterior descending coronary artery by coronary CT angiography (CCTA) at the Department of Radiology of Tongji Hospital Affiliated to Tongji University from January 2017 to December 2019 were retrospectively analyzed. The location, length, depth, and degree of systolic compression of the MB were measured. The artificial intelligence-based coronary FFRCT software was employed to calculate the FFRCT value of the deep MB of the left anterior descending coronary artery. With the boundary of 0.80, all patients were divided into FFRCT normal group (FFRCT>0.80) and FFRCT abnormal group (FFRCT≤0.80), and the relationship between FFRCT abnormality and the location, length, depth, and degree of systolic stenosis of the deep MB of the left anterior descending branch was analyzed. The effectiveness of the receiver operating characteristic (ROC) curve in predicting FFRCT abnormalities was measured by using ROC curve to analyze the length, depth, and degree of systolic stenosis of MB. Results: There were no significant differences in age, gender and high-risk factors between FFRCT normal group (n=79) and FFRCT abnormal group (n=34) (P>0.05). In terms of clinical symptoms, unstable angina, asymptomatic myocardial ischemia, stable angina in the FFRCT normal group were 15.2%, 41.8%, 32.9%,respectively, while 32.4%, 23.5%, 35.3% in the FFRCT abnormal group,respectively. Except for unstable angina (χ²=4.32,P=0.038), there were no significant differences in asymptomatic myocardial ischemia and stable angina between the two groups (χ²=3.42, 0.06, P>0.05). The length of deep MB was about (36±5) mm in the FFRCT normal group and (44±5) mm in the FFRCT abnormal group, respectively. The difference between the two groups was statistically significant (t=-7.703, P<0.001). The ROC curve showed that the optimal critical value of the length of the deep MB was 39.7 mm, the area under the curve was 0.88 (95%CI:0.81-0.95, P<0.001), and the accuracy rate of diagnosing FFRCT ≤0.80 was 82.3%. Conclusion: FFRCT value is of great value in the evaluation of hemodynamics in patients with deep myocardial bridge of left anterior descending coronary artery, and the length of deep myocardial bridge is an important factor affecting FFRCT value.

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Objective: To investigate the segmentation effects of the deep learning method on CT in the arterial phase and venous phase respectively by using subjective and objective evaluation system, and to investigate the factors that affect the difference between arterial phase and venous phase pancreas segmentation and the related factors affecting the venous pancreas segmentation. Method: A total of 218 cases of pancreatic CT scan data in the Department of Radiology of Peking Union Medical College Hospital from January to November 2019 were retrospectively collected. Each case contained images of arterial and venous phases, and the data were randomly divided into training set (139 cases), validation set (20 cases) and test set (59 cases) according to the ratio of the training and verification set to the test set of 7∶3. The two-stage global local progressive fusion network was trained on the training set, the model parameters of the optimal segmentation effect were found on the validation set, and the test set was predicted and the results were evaluated subjectively and objectively. The Likert 5-point scale was used for subjective evaluation based on the critical regions between pancreas and peripheral organs, while the Dice similarity coefficient (DSC) was used for objective evaluation. The paired t test or Wilcoxon paired rank test was used to compare the differences of subjective and objective scores of the arterial phase and venous phase. Results: For the critical regions of the pancreas at the duodenum, duodenal jejunal flexure, left adrenal gland, portal vein, superior mesenteric vein, splenic artery and splenic vein, the median number of subjective scores in arterial phase were 4(4, 5), 5(4, 5), 5(4, 5), 4(4, 5), 5(4, 5), 5(5, 5) and 4(3, 5)points respectively, the median number(first quartile, third quartile) of subjective scores in venous phase were 4(4, 4), 5(4, 5), 5(4, 5), 5(4, 5), 5(5, 5), 4(3, 4) and 5(5, 5) points respectively,there were statistically significant differences of the median number(first quartile, third quartile) of the subjective scores between the arterial and venous phase for the critical regions of the pancreas at the organs described above (all P<0.05). DSC in the venous phase was slightly higher than that in the arterial phase and the difference was not statistically significant (DSC: 0.932 vs 0.921, P=0.952). Subjective scores in venous phase of the pancreas and duodenal jejunum, stomach, and left adrenal gland with fat gaps were 4.64,4.68 and 4.63 points respectively, and those of the group without fat gaps were 4.56,4.62 and 4.56 points respectively, there were statistically significant differences of the subjective scores in venous phase of the groups with fat gaps or not between the pancreas and the organs described above (t=2.147, 2.112, 2.277, all P<0.05). Except the spleen, the density differences between the critical regions of the pancreas and other surrounding organs were statistically significant in arterial phase and venous phase segmentation (all P<0.05). Conclusion: Dual-phase CT was used to construct a deep learning automatic pancreas segmentation model, and the segmentation effect was evaluated subjectively and objectively. Subjective evaluation was helpful to improve the ability to segment the critical regions of the pancreas in the future.

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Objective: To evaluate the diagnostic value of the lung nodule classification and segmentation algorithm based on deep learning among different CT reconstruction algorithms. Methods: Chest CT of 363 patients from June 2019 to September 2019 in Radiology Department of Tianjin Medical University Chu Hsien-I Memorial Hospital were retrospectively collected in this study, each of which consisted of images by three different reconstruction methods (lung reconstruction, mediastinal reconstruction, bone reconstruction).These collected data were used as testing set and a total of 4 185 Chest CTs including the public data set and the constructed private data set were used as the training set. A model combines 3D deep convolutional neural network and recurrent neural network under a multi-task joint learning algorithm for lung nodule classification and segmentation were constructed. The well-trained method was tested on 363 test cases using two metrics, i.e., the accuracy of the density classification and the Dice coefficient of nodule segmentation. The performances under three reconstruction methods were statistically analyzed according to the variance analysis among three different reconstruction methods. Results: The average classification accuracies of the nodule under three reconstruction methods were 98.67%±5.70%, 98.38%±6.61% and 97.89%±7.32%. Specifically, the accuracies of the solid nodules under three reconstruction methods were 98.79%±5.58%, 98.49%±6.89% and 97.90%±7.41% and the accuracies of the sub-solid nodules were 97.57%±10.19%, 98.52%±7.77% and 98.52%±7.77%. There was no significant difference in the classification accuracy of pulmonary nodules under three different reconstruction algorithms (all P>0.05). The average Dice coefficients of nodule segmentation was 79.87%±5.78%, 79.02%±6.04% and 79.31%±5.95%. There was no significant difference in the average Dice coefficients of nodule segmentation under three different reconstruction algorithms (all P>0.05). Conclusion: Deep learning algorithm which combined with 3D convolutional neural network and recurrent neural network has demonstrated relatively stable in classification and segmentation of lung nodules under different CT reconstruction method.

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Objective: To investigate the diagnostic efficacy and potential application value of deep learning-based chest CT auxiliary diagnosis system in emergency trauma patients. Methods: A total of 403 patients, including 254 males and 149 females aged from 16 to 100 (50±19) years, who received emergency treatment for trauma and chest CT examination in the Eastern Theater General Hospital from September 2019 to November 2019 were retrospectively analyzed. Dr. Wise Lung Analyzer’s chest CT auxiliary diagnosis system was applied to detect 5 types of injuries, including pneumothorax, pleural effusion/hemothorax, pulmonary contusion (shown as consolidation and ground glass opacity), rib fractures, and other fractures (including thoracic vertebrae, sternum, scapula and clavicle, etc.) and 6 other abnormalities (bullae, emphysema, pulmonary nodules, stripe, reticulation, pleural thickening). The diagnostic reference standards were labeled by two radiologists independently. The sensitivity and specificity of the auxiliary diagnosis system were evaluated. The imaging diagnostic reports were compared with the results of the auxiliary diagnosis system, and the diagnostic consistency between the two was calculated by using the Kappa test. Results: According to the reference standards, among the 403 patients, 29 were pneumothorax, 75 were pleural effusion/hemothorax, 131 were pulmonary contusion, 124 were rib fractures, and 63 were other fractures. The sensitivity and specificity of the auxiliary diagnosis system for detection of pneumothorax, pleural effusion/hemothorax, rib fractures, and other fractures were 96.6%, 97.6%, 80.0%, 99.7%, 99.2%, 83.9%, 84.1%, and 99.7%, respectively. The sensitivity of detecting lung contusion was 97.7%. There was a high consistency between the auxiliary diagnosis system and imaging diagnosis in the diagnosis of injuries, in which the kappa values of pneumothorax, pleural effusion, rib fracture and other fractures were 0.783, 0.821, 0.706 and 0.813, respectively (all P<0.001). Two cases of pneumothorax, three cases of pleural effusion/hemothorax, nine cases of rib fractures, and six cases of other fractures missed by imaging diagnosis were all detected by the auxiliary diagnosis system. The detection sensitivity of the auxiliary diagnosis system was higher for emphysema, pulmonary nodules and stripe (all>85%), but lower for bullae, reticulation and pleural thickening. Conclusions: The deep learning-based chest CT auxiliary diagnosis system could effectively assist chest CT to detect injuries in emergency trauma patients, which was expected to optimize the clinical workflow.