Idiopathic inflammatory myopathy(IIM)is a heterogeneous autoimmune disease of unknown etiology characterized by proximal limb muscle involvement and chronic inflammation.Multiple systems may be involved,with interstitial lung disease(ILD)being the most common manifestation of respiratory system involvement in IIM.This article summarizes the current methods of myositis animal models and myositis-associated ILD model construction methods,so as to provide reference for the subsequent construction and research of IIM-ILD models.

Objective:To investigate the clinical features and therapeutic efficacy of patients with hereditary leiomyomatosis and renal cell carcinoma(RCC) syndrome-associated RCC (HLRCC-RCC) in China.Methods:The clinical data of 119 HLRCC-RCC patients with fumarate hydratase (FH) germline mutation confirmed by genetic diagnosis from 15 medical centers nationwide from January 2008 to December 2021 were retrospectively analyzed. Among them, 73 were male and 46 were female. The median age was 38(13, 74) years. The median tumor diameter was 6.5 (1.0, 20.5) cm. There were 38 cases (31.9%) in stage Ⅰ-Ⅱand 81 cases (68.1%) in stage Ⅲ-Ⅳ. In this group, only 11 of 119 HLRCC-RCC patients presented with skin smooth muscle tumors, and 44 of 46 female HLRCC-RCC patients had a history of uterine fibroids. The pathological characteristics, treatment methods, prognosis and survival of the patients were summarized.Results:A total of 86 patients underwent surgical treatment, including 70 cases of radical nephrectomy, 5 cases of partial nephrectomy, and 11 cases of reductive nephrectomy. The other 33 patients with newly diagnosed metastasis underwent renal puncture biopsy. The results of genetic testing showed that 94 patients had FH gene point mutation, 18 had FH gene insertion/deletion mutation, 4 had FH gene splicing mutation, 2 had FH gene large fragment deletion and 1 had FH gene copy number mutation. Immunohistochemical staining showed strong 2-succinocysteine (2-SC) positive and FH negative in 113 patients. A total of 102 patients received systematic treatment, including 44 newly diagnosed patients with metastasis and 58 patients with postoperative metastasis. Among them, 33 patients were treated with tyrosine kinase inhibitor (TKI) combined with immune checkpoint inhibitor (ICI), 8 patients were treated with bevacizumab combined with erlotinib, and 61 patients were treated with TKI monotherapy. Survival analysis showed that the median progression-free survival (PFS) of TKI combined with ICI was 18 (5, 38) months, and the median overall survival (OS) was not reached. The median PFS and OS were 12 (5, 14) months and 30 (10, 32) months in the bevacizumab combined with erlotinib treatment group, respectively. The median PFS and OS were 10 (3, 64) months and 44 (10, 74) months in the TKI monotherapy group, respectively. PFS ( P=0.009) and OS ( P=0.006) in TKI combined with ICI group were better than those in bevacizumab combined with erlotinib group. The median PFS ( P=0.003) and median OS ( P=0.028) in TKI combined with ICI group were better than those in TKI monotherapy group. Conclusions:HLRCC-RCC is rare but has a high degree of malignancy, poor prognosis and familial genetic characteristics. Immunohistochemical staining with strong positive 2-SC and negative FH can provide an important basis for clinical diagnosis. Genetic detection of FH gene germ line mutation can confirm the diagnosis. The preliminary study results confirmed that TKI combined with ICI had a good clinical effect, but it needs to be confirmed by the results of a large sample multi-center randomized controlled clinical study.

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrous interstitial lung disease of unknown etiology. IPF is also considered to be among the independent risk factors for lung cancer, increasing the risk of lung cancer by 7% and 20%. The incidence of IPF complicated with lung cancer, especially non-small cell lung cancer (NSCLC), is increasing gradually, but there is no consensus on unified management and treatment. IPF and NSCLC have similar pathological features. Both appear in the surrounding area of the lung. In pathients with IPF complicated with NSCLC, NSCLC often develops from the honeycomb region of IPF, but the mechanism of NSCLC induced by IPF remains unclear. In addition, IPF and NSCLC have similar genetic, molecular and cellular processes and common signal transduction pathways. The universal signal pathways targeting IPF and NSCLC will become potential therapeutic drugs for IPF complicated with NSCLC. This article examines the main molecular mechanisms involved in IPF and NSCLC and the research progress of drugs under development targeting these signal pathways.
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Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; Idiopathic Pulmonary Fibrosis/drug therapy* ; Lung Neoplasms/genetics* ; Lung/pathology* ; Signal Transduction

Objective:To investigate the effect of albumin to fibrinogen ratio on the prognosis of patients undergoing radical resection for colorectal cancer.Methods:Clinical and pathological data of 216 patients who underwent laparoscopic radical resection of colorectal cancer at the General Surgery Department of Taizhou People's Hospital from Aug 2015 to Jul 2017 were retrospectively analyzed. Albumin and fibrinogen results within 7 days before surgery was collected. The optimal cut-off point of AFR was determined by Youden index of ROC curve. Kaplan-Meier analysis, univariate and multivariate COX regression models were used to analyze the prognostic factors of OS and DFS.Results:The best postoperative OS threshold of AFR for patients undergoing laparoscopic radical resection of colorectal cancer was 9.43. Univariate analysis and multivariate COX regression analysis showed that age ≤65 years, TNM stage Ⅰ-Ⅱ, and AFR≥9.43 had better OS and DFS (all P<0.05). Conclusions:Preoperative AFR level had a good predictive value on postoperative survival of patients undergoing laparoscopic radical resection of colorectal cancer, and AFR<9.43 was an independent risk factor for postoperative OS and DFS.

Objective:To compare the ability of deep convolutional neural network-crop (DCNN-C) and deep convolutional neural network-whole (DCNN-W), 2 artificial intelligence systems based on different training methods to dignose early gastric cancer (EGC) diagnosis under magnifying image-enhanced endoscopy (M-IEE).Methods:The images and video clips of EGC and non-cancerous lesions under M-IEE under narrow band imaging or blue laser imaging mode were retrospectively collected in the Endoscopy Center of Renmin Hospital of Wuhan University, for the training set and test set for DCNN-C and DCNN-W. The ability of DCNN-C and DCNN-W in EGC identity in image test set were compared. The ability of DCNN-C, DCNN-W and 3 senior endoscopists (average performance) in EGC identity in video test set were also compared. Paired Chi-squared test and Chi-squared test were used for statistical analysis. Inter-observer agreement was expressed as Cohen′s Kappa statistical coefficient (Kappa value).Results:In the image test set, the accuracy, sensitivity, specificity and positive predictive value of DCNN-C in EGC diagnosis were 94.97%(1 133/1 193), 97.12% (202/208), 94.52% (931/985), and 78.91%(202/256), respectively, which were higher than those of DCNN-W(86.84%, 1 036/1 193; 92.79%, 193/208; 85.58%, 843/985 and 57.61%, 193/335), and the differences were statistically significant ( χ2=4.82, 4.63, 61.04 and 29.69, P=0.028, =0.035, <0.001 and <0.001). In the video test set, the accuracy, specificity and positive predictive value of senior endoscopists in EGC diagnosis were 67.67%, 60.42%, and 53.37%, respectively, which were lower than those of DCNN-C (93.00%, 92.19% and 87.18%), and the differences were statistically significant ( χ2=20.83, 16.41 and 11.61, P<0.001, <0.001 and =0.001). The accuracy, specificity and positive predictive value of DCNN-C in EGC diagnosis were higher than those of DCNN-W (79.00%, 70.31% and 64.15%, respectively), and the differences were statistically significant ( χ2=7.04, 8.45 and 6.18, P=0.007, 0.003 and 0.013). There were no significant differences in accuracy, specificity and positive predictive value between senior endoscopists and DCNN-W in EGC diagnosis (all P>0.05). The sensitivity of senior endoscopists, DCNN-W and DCNN-C in EGC diagnosis were 80.56%, 94.44%, and 94.44%, respectively, and the differences were not statistically significant (all P>0.05). The results of the agreement analysis showed that the agreement between senior endoscopists and the gold standard was fair to moderate (Kappa=0.259, 0.532, 0.329), the agreement between DCNN-W and the gold standard was moderate (Kappa=0.587), and the agreement between DCNN-C and the gold standard was very high (Kappa=0.851). Conclusion:When the training set is the same, the ability of DCNN-C in EGC diagnosis is better than that of DCNN-W and senior endoscopists, and the diagnostic level of DCNN-W is equivalent to that of senior endoscopists.

Objective:To develop an artificial intelligence-based system for measuring the size of gastrointestinal lesions under white light endoscopy in real time.Methods:The system consisted of 3 models. Model 1 was used to identify the biopsy forceps and mark the contour of the forceps in continuous pictures of the video. The results of model 1 were submitted to model 2 and classified into open and closed forceps. And model 3 was used to identify the lesions and mark the boundary of lesions in real time. Then the length of the lesions was compared with the contour of the forceps to calculate the size of lesions. Dataset 1 consisted of 4 835 images collected retrospectively from January 1, 2017 to November 30, 2019 in Renmin Hospital of Wuhan University, which were used for model training and validation. Dataset 2 consisted of images collected prospectively from December 1, 2019 to June 4, 2020 at the Endoscopy Center of Renmin Hospital of Wuhan University, which were used to test the ability of the model to segment the boundary of the biopsy forceps and lesions. Dataset 3 consisted of 302 images of 151 simulated lesions, each of which included one image of a larger tilt angle (45° from the vertical line of the lesion) and one image of a smaller tilt angle (10° from the vertical line of the lesion) to test the ability of the model to measure the lesion size with the biopsy forceps in different states. Dataset 4 was a video test set, which consisted of prospectively collected videos taken from the Endoscopy Center of Renmin Hospital of Wuhan University from August 5, 2019 to September 4, 2020. The accuracy of model 1 in identifying the presence or absence of biopsy forceps, model 2 in classifying the status of biopsy forceps (open or closed) and model 3 in identifying the presence or absence of lesions were observed with the results of endoscopist review or endoscopic surgery pathology as the gold standard. Intersection over union (IoU) was used to evaluate the segmentation effect of biopsy forceps in model 1 and lesion segmentation effect in model 3, and the absolute error and relative error were used to evaluate the ability of the system to measure lesion size.Results:(1)A total of 1 252 images were included in dataset 2, including 821 images of forceps (401 images of open forceps and 420 images of closed forceps), 431 images of non-forceps, 640 images of lesions and 612 images of non-lesions. Model 1 judged 433 images of non-forceps (430 images were accurate) and 819 images of forceps (818 images were accurate), and the accuracy was 99.68% (1 248/1 252). Based on the data of 818 images of forceps to evaluate the accuracy of model 1 on judging the segmentation effect of biopsy forceps lobe, the mean IoU was 0.91 (95% CI: 0.90-0.92). The classification accuracy of model 2 was evaluated by using 818 forceps pictures accurately judged by model 1. Model 2 judged 384 open forceps pictures (382 accurate) and 434 closed forceps pictures (416 accurate), and the classification accuracy of model 2 was 97.56% (798/818). Model 3 judged 654 images containing lesions (626 images were accurate) and 598 images of non-lesions (584 images were accurate), and the accuracy was 96.65% (1 210/1 252). Based on 626 images of lesions accurately judged by model 3, the mean IoU was 0.86 (95% CI: 0.85-0.87). (2) In dataset 3, the mean absolute error of systematic lesion size measurement was 0.17 mm (95% CI: 0.08-0.28 mm) and the mean relative error was 3.77% (95% CI: 0.00%-10.85%) when the tilt angle of biopsy forceps was small. The mean absolute error of systematic lesion size measurement was 0.17 mm (95% CI: 0.09-0.26 mm) and the mean relative error was 4.02% (95% CI: 2.90%-5.14%) when the biopsy forceps was tilted at a large angle. (3) In dataset 4, a total of 780 images of 59 endoscopic examination videos of 59 patients were included. The mean absolute error of systematic lesion size measurement was 0.24 mm (95% CI: 0.00-0.67 mm), and the mean relative error was 9.74% (95% CI: 0.00%-29.83%). Conclusion:The system could measure the size of endoscopic gastrointestinal lesions accurately and may improve the accuracy of endoscopists.

Objective:To evaluate deep learning in improving the diagnostic rate of adenomatous and non-adenomatous polyps.Methods:Non-magnifying narrow band imaging (NBI) polyp images obtained from Endoscopy Center of Renmin Hospital, Wuhan University were divided into three datasets. Dataset 1 (2 699 adenomatous and 1 846 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used for model training and validation of the diagnosis system. Dataset 2 (288 adenomatous and 210 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used to compare the accuracy of polyp classification between the system and endoscopists. At the same time, the accuracy of 4 trainees in polyp classification with and without the assistance of this system was compared. Dataset 3 (203 adenomatous and 141 non-adenomatous non-magnifying NBI polyp images from November 2020 to January 2021) was used to prospectively test the system.Results:The accuracy of the system in polyp classification was 90.16% (449/498) in dataset 2, superior to that of endoscopists. With the assistance of the system, the accuracy of colorectal polyp diagnosis was significantly improved. In the prospective study, the accuracy of the system was 89.53% (308/344).Conclusion:The colorectal polyp classification system based on deep learning can significantly improve the accuracy of trainees in polyp classification.

Objective:To study the efficacy of different systemic chemotherapy regimens as first-line and second-line therapy and to determine the prognostic factors for patients with advanced biliary tract cancer.Methods:The clinical data of patients with advanced biliary tract cancer who underwent systemic chemotherapy in Cancer Hospital of Chinese Academy of Medical Sciences from January 2011 to December 2018 were studied. The efficacy of chemotherapy on objective response rate (ORR) and disease control rate (DCR) were evaluated. Potential prognostic factors for survival were studied using the Cox proportional hazards models.Results:Of 151 patients enrolled into this study, there were 75 males and 76 females, with ages ranging from 31 to 77 years (median 58 years). Two treatment protocols were used: (1) 104 patients received a gemcitabine-based regimen (combined with platinums or fluorouracils) or a combination of platinums and fluorouracils, while (2) 47 patients received a combination of albumin-bound paclitaxel and S-1. The corresponding ORR for each group were 15.4%(16/104) and 27.6%(13/47), respectively, and the DCR were 65.4%(68/104) and 72.3%(34/47), respectively. Of 58 evaluable patients who received chemotherapy as a second-line therapy, 31 patients received the regimen containing gemcitabine, platinums or fluorouracils with an ORR of 3.2% (1/31) and a DCR of 35.5%(11/31); a total of 18 patients received the taxanes-based regimen with an ORR of 11.1%(2/18) and a DCR of 38.9%(7/18); 9 patients received the irinotecan-based regimen with an ORR of 22.2%(2/9) and a DCR of 44.4%(4/9). Univariate analysis showed positive liver metastasis and elevated carbohydrate antigen (CA)19-9 level to be significantly correlated with worse survival outcomes ( HR=1.540, 95% CI: 1.019-2.328, P=0.040 and HR=1.892, 95% CI: 1.123-3.188, P=0.017). Conclusion:For patients with advanced biliary tract cancer, in addition to the conventional regimens containing gemcitabine, platinums and fluorouracils, the combination of albumin-bound paclitaxel and S-1 was shown to be an effective chemotherapeutic regimen for these patients. Second-line chemotherapy was insufficient and ineffective, and an irinotecan-based regimen deserves to be further investigated. Liver metastasis and elevated CA19-9 level were worse prognosis after chemotherapy for patients with advanced biliary tract cancer.