Objective: To explore the association between digital devices usage and body weight overestimation in children and adolescents aged 7-18, in order to provide a scientific basis for body weight overestimation prevention in children and adolescents. Methods: Based on the data of the Research Special Project for Public Welfare Industry of Health using stratified cluster sampling method in 2012, a tatal of 40 073 children and adolescents from 7 provinces with complete information were chosen. Ordinal multivariable Logistic regression model estimated the association between digital devices usage and body weight overestimation. Results: A total of 4 276(11.8%) students with overestimation of body weight were detected, who spent >300 min/d time in digital devices(5.12%) than others (3.84%)( χ 2=19.14, P <0.01). Univariate analysis showed that students with time spent on digital devices >300 min/d had a higher risk in overestimation of body weight ( OR=1.36,95%CI=1.18-1.57,P <0.01) compared with students who spent on digital devices≤120 min/d. There was still a significant association after confounder adjustment ( OR=1.28, 95%CI= 1.10-1.48,P <0.05). Stratified analysis showed that the association between digital devices usage and overestimation of body weight were only observed in girls, 11-18 years old and non single child( P <0.05). Conclusion The time usage of digital devices is associated with overestimation of body weight in children and adolescents. It may helpful for children and adolescents to prevent overestimation of body weight by reducing time spent on digital devices.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

OBJECTIVETo study the potential factors in influencing the performance of immunohistochemical testing for HER2 protein in gastric cancers.

METHODSThe HER2 protein expression status of 1 471 surgically resected archival gastric cancer cases in Drum Tower Hospital collected during two different periods was retrospectively analyzed. The materials included 957 cases tested during the period from 2007 to 2009 (group 1) and 514 cases from 2012 to 2013 (group 2). The test procedures and results observed during these two periods were compared.

RESULTSThe percentages of score 3 HER2 protein expression (14.4%, 74/514 versus 9.5%, 91/957) and score 2 or score 3 HER2 protein expression (27.2%, 140/514 versus 21.7%, 208/957) were both higher in group 2 than in group 1 (P < 0.05). In group 1, the cancer tissue was fixed in 10% formalin, stained manually with HER2 antibody A0485 (Dako) and assessed by different pathologists.In group 2, the tissue was fixed in 10% neutral buffered formalin (pH 7.2), stained using automated immunostaining system (Roche Benchmark XT) with HER2 antibody 4B5 (Ventana) and assessed by a specialized team of pathologists.

CONCLUSIONThe results of HER2 immunostaining in gastric cancer are influenced by a number of factors including type of fixative, clone number of primary antibody, staining methods and experience of pathologists.

Antibodies, Monoclonal ; Fixatives ; Formaldehyde ; Gene Expression Regulation, Neoplastic ; Humans ; Immunohistochemistry ; Receptor, ErbB-2 ; metabolism ; Retrospective Studies ; Staining and Labeling ; Stomach Neoplasms ; metabolism

Stem cell transplantation holds a promising future for central nervous system repair. Current challenges, however, include spatially and temporally defined cell differentiation and maturation, plus the integration of transplanted neural cells into host circuits. Here we discuss the potential advantages of neuromodulation-based stem cell therapy, which can improve the viability and proliferation of stem cells, guide migration to the repair site, orchestrate the differentiation process, and promote the integration of neural circuitry for functional rehabilitation. All these advantages of neuromodulation make it one potentially valuable tool for further improving the efficiency of stem cell transplantation.

Hearing loss has become increasingly prevalent and causes considerable disability, thus gravely burdening the global economy. Irreversible loss of hair cells is a main cause of sensorineural hearing loss, and currently, the only relatively effective clinical treatments are limited to digital hearing equipment like cochlear implants and hearing aids, but these are of limited benefit in patients. It is therefore urgent to understand the mechanisms of damage repair in order to develop new neuroprotective strategies. At present, how to promote the regeneration of functional hair cells is a key scientific question in the field of hearing research. Multiple signaling pathways and transcriptional factors trigger the activation of hair cell progenitors and ensure the maturation of newborn hair cells, and in this article, we first review the principal mechanisms underlying hair cell reproduction. We then further discuss therapeutic strategies involving the co-regulation of multiple signaling pathways in order to induce effective functional hair cell regeneration after degeneration, and we summarize current achievements in hair cell regeneration. Lastly, we discuss potential future approaches, such as small molecule drugs and gene therapy, which might be applied for regenerating functional hair cells in the clinic.
Infant, Newborn ; Humans ; Hair Cells, Auditory, Inner/physiology* ; Ear, Inner/physiology* ; Hair Cells, Auditory/physiology* ; Regeneration/genetics* ; Stem Cells