Objective To investigate the effects of open reading frame 1 protein(ORF1p),encoded by long interspersed nuclear element-1(LINE-1),on the proliferation,migration,and invasion of esophageal squamous cell carcinoma(ESCC)cells,and explore the underlying molecular mechanism.Methods① Western blotting was performed to compare the expression of ORF1p between normal esophageal squamous epithelial cells and ESCC cells.② Immunohistochemistry(IHC)assay was used to examine ORF1p expression in ESCC tissues and paired normal tissues adjacent to tumor.③ The effects of ORF1p knockdown and overexpression on malignant behaviors in ESCC cells were determined through functional assays.④ Xenograft tumor model in nude mice was established to evaluate the impact of ORF1p on tumor growth in vivo.⑤ Transcriptome sequencing combined with cell functional rescue experiments were conducted to identify downstream targets regulated by ORF1p.Results ① Western blot analysis demonstrated the expression of ORF1p was significantly higher in the ESCC cell lines than the normal esophageal squamous epithelial cells(P<0.05).② IHC confirmed remarkable up-regulation of ORF1p in ESCC tissues than paired adjacent normal tissues(P<0.000 1).③ Functional assays and experiments on xenograft tumor models revealed that ORF1p substantially enhanced the proliferation,migration,and invasion of ESCC cells,as well as tumorigenic potential in vivo(P<0.05).④ Functional rescue experiments showed that ORF1p facilitated the proliferation,migration,and invasion of ESCC cells by modulating AJUBA expression(P<0.05).Conclusion ORF1p is significantly up-regulated in ESCC and promotes the proliferation,migration,and invasion of ESCC cells by regulating AJUBA expression.

Objective To develop a machine learning model integrating preoperative chest CT radiomic features with clinical data for predicting 5-year postoperative recurrence risk in stage Ⅰ non-small cell lung cancer(NSCLC)patients undergoing surgical resection.Methods A total of 217 patients with pathologically confirmed stage Ⅰ NSCLC(selected from 778 initially screened cases based on our inclusion and exclusion criteria)treated in Army Medical Center of PLA between January 2014 and December 2019 were retrospectively enrolled,including 53 recurrence cases and 164 non-recurrence cases within 5-year follow-up.They were randomly divided into a training set(n=173)and a validation set(n=44)in a ratio of 8:2.Radiomic models were established based on extracted features from tumor-dominant regions of interest(ROI)on CT images,while clinical models were developed using demographic characteristics and preoperative laboratory examinations.A combined model was further constructed by integrating both feature sets,and model performance was compared to identify the optimal predictive model.Results This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model.Among 6 machine learning algorithms,the adaptive boosting(Adaboost)model demonstrated the best overall predictive performance,with an area under the curve(AUC)of 0.866(95%CI:0.808～0.923;accuracy:0.832,specificity:0.884)in the training set and of 0.806(95%CI:0.630～0.983;accuracy:0.795,specificity:0.971)in the validation set.Univariate and multivariate logistic regression analyses identified 4 clinical features for clinical model construction.The clinical model achieved an AUC value of 0.874(95%CI:0.821～0.928;accuracy:0.827,specificity:0.891)in the training set and 0.813(95%CI:0.677～0.948;accuracy:0.636,specificity:0.600)in the validation set.By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy,the combined model exhibited further improved predictive performance,with an AUC value of 0.953(95%CI:0.924～0.983;accuracy:0.884,specificity:0.860)and 0.852(95%CI:0.729～0.976;accuracy:0.682,specificity:0.629),respectively in the training set and the validation set.Conclusion The combined model integrating preoperative CT radiomic features with clinical risk factors may provide an evidence-based framework for evaluating 5-year postoperative recurrence risk in stage Ⅰ NSCLC patients.

Objective: To observe clinical value of MSCT and MR three-dimensional turbo field echo (3D-TFE) in diagnosis of children bilateral tracheal bronchi. Methods: Data of 14 cases of children bilateral tracheal bronchi examined with MSCT or MR were retrospectively reviewed. Minimum intensity projection was used to reconstruct the airway in 10 children who underwent MSCT scanning, while maximum intensity projection was used to appear the airway in 4 children underwent MR 3D-TFE. According to MSCT or MRI, bilateral tracheal bronchi were divided into standard type (bilateral tracheal bronchi originated from the upper part of tracheal eminence), critical type (bilateral tracheal bronchi originated from the beginning of tracheal eminence) or mixed type (bilateral tracheal bronchi originated from different locations). The characteristics and other heart anomalies of these children were recorded. Results: Bilateral tracheal bronchi were showed clearly on both MSCT and MR 3D-TFE images. Among 14 cases, 8 cases (8/14, 57.14%) were found with standard type bilateral tracheal bronchi, 5 cases (5/14, 35.71%) were found with borderline type, 1 case (1/14, 7.14%) was found with mixed type bilateral tracheal bronchi. All 14 children (14/14, 100%) were detected with asplenia syndrome. The most common cardiac malformations included common atrioventricular canal (12/14, 85.71%), pulmonary stenosis (11/14, 78.57%) and persistent left superior vena cava (11/14, 78.57%). Conclusion: MSCT and MR 3D-TFE sequences have good diagnostic value for assessing bilateral tracheal bronchi.

Objective To investigate the ultrasonographic features of normal rectal walls.Methods Ten removed rectal specimens were scanned with high frequency (4-13 MHz) linear array probe to obtain ultrasound images of various layers and having each layer marked,and separated,followed by histological examination respectively.Results Ultrasound demonstrated seven layers of structure which were identified by alternative high and low echoes.From innermost layer towards the outer layers,they were divided as:high-echo acoustic interface,low-echo mucous layer,high-echo sub-mucous layer,low-echo circular muscle layer,high-echo fibrous connective tissue layer,low-echo outer longitudinal muscle layer and high-echo outer membrane layer.All these findings were justified by histological examination.Conclusions High-frequency ultrasound demonstrated 7 layers of echoes in normal rectal walls.This provides imaging basis for diagnosis and judge the invasion degree of rectal cancers.