BACKGROUND: Tissue engineering has a promising prospect in corneal transplantation. Scaffolds are always restricting the development of tissue-engineered cornea. OBJECTIVE: To analyze application of different seed cells and scaffolds, and to summarize the progress of tissue-engineered cornea in recent years.METHODS: First author searched literature from CNKI (2000/2010-10) and PubMed database (2000/2010-10). The key words are tissue engineering, corneal transplantation in Chinese or English. A total of 223 literatures were seized by computers, according to the inclusion criteria, papers concerning the advance, application and reconstruction of tissue-engineered cornea were analyzed. Finally, 33 papers were included for further analysis. The present study was to analyze the seed cells, scaffolds, organ building and clinical applications of tissue-engineered cornea and investigate the development direction in the future. RESULTS AND CONCLUSION: The results show that seed cells and scaffolds are the focus of the studies now. Corneal transplantation has a high success rate in organ transplantation since the immune privilege of eye and avascular cornea, and it will be able to be a tissue engineering organ that can be largely built, easy to transplant. Reconstruction of cornea has reached first base, but every kind of scaffold has certain drawbacks, so the next goal is to find an ideal scaffold material.

Objective: This study aims to investigate the impact of individual and family factors on anxiety and loneliness of middle school students during quarantine in the COVID-19 pandemic and to provide reference for discussing the possible effective intervention paths in the stress process of public health emergencies. Methods: A total of 11 596 students from junior and high schools in Shanghai were selected to complete the questionnaire with the stratified random cluster sampling method. Generalized Anxiety Disorder(GAD-7) and UCLA Loneliness Scale, University of California at Los Angeles(ULS-6) were used for the survey. Results: The prevalence of anxiety and loneliness were 34.78% and 52.44% respectively. The rate of anxiety and loneliness in girls (36.13%, 54.62%) was higher than that of boys (33.44%, 50.28%)(χ 2=17.03, 21.82, P<0.05). Logistic regression analysis showed that graduates (OR=1.34, 95%CI=1.18-1.52), students ranking in the middle (OR=1.25, 95%CI=1.08-1.45) or in the bottom 25% (OR=1.76, 95%CI=1.50-2.05), non only children in the family (OR=1.25, 95%CI=1.09-1.43) and low socioeconomic status (OR=1.42, 95%CI=1.23-1.65) were associated with increased risk of moderate and severe level of anxiety(P<0.05). Girls(OR=1.20, 95%CI=1.12-1.30), graduates(OR=1.13, 95%CI=1.04-1.24), non residents(OR=1.20, 95%CI=1.09-1.33), lower level of grades(OR=1.72, 95%CI=1.55-1.91) and not living with parents(OR=1.32, 95%CI=1.18-1.48) were associated with increased risk of loneliness(P<0.05). Conclusion During the quarantine period caused by the COVID-19 pandemic, negative emotions such as anxiety and loneliness among adolescents need attention, especially females, graduates, only children and students with low rankings or low socioeconomic status.

Objective:To evaluate the surgery combined chemotherapy and radiation in locally advanced neuroendocrine carcinoma of the cervix (NECC) .Methods:This is a single-center retrospective cohort study. Locally advanced NECC patients admitted to Peking Union Medical College Hospital, Chinese Acadmy of Medical Sciences from January 2011 to April 2022 were enrolled. They were divided into concurrent chemoradiotherapy group, and surgery combined with chemotherapy and radiation group. The Kaplan-Meier method was used to analyze the progression free survival (PFS), overall survival (OS), recurrence rate, and mortality rate.Results:(1) Forty-six cases were included, 22 in concurrent chemoradiotherapy group, 24 in surgery combined chemotherapy and radiation group. With 16 patients (35%, 16/46) received neoadjuvant chemotherapy (NACT), the NACT effective rate was 15/16. (2) The median follow-up time was 27.5 months (range: 10-106 months), with 26 (57%, 26/46) experienced recurrences. There were 4 (9%, 4/46) pelvic recurrences and 25 (54%, 25/46) distant recurrences, and 3 (7%, 3/46) both pelvic and distant recurrences. Compared with concurrent chemoradiotherapy group, surgery combined chemotherapy and radiation group had lower pelvic recurrence rate [14% (3/22) vs 4% (1/24); χ2=1.296, P=0.255] but without statistic difference. Both groups had similar distant recurrence rate [55% (12/22) vs 54% (13/24); χ2=0.001, P=0.979] and overall recurrence rate [59% (13/22) vs 54% (13/24); χ2=0.113, P=0.736]. (3) During the follow-up period, 22 cases (48%, 22/46) died, with 11 cases (50%, 11/22) in concurrent chemoradiotherapy group and 11 cases (46%, 11/24) in surgery combined chemotherapy and radiation group, without significant difference ( χ2=0.080, P=0.777). The postoperative 3-year and 5-year OS rates were 62.3% and 36.9%. Compared with concurrent chemoradiotherapy group, the patients in surgery combined chemotherapy and radiation group showed an extended trend in PFS (17.0 vs 32.0 months) and OS (37.0 vs 50.0 months) but without statistic differences ( P=0.287, P=0.125). Both groups had similar 3-year OS rate (54.2% vs 69.9%; P=0.138) and 5-year OS rate (36.1% vs 38.8%; P=0.217). Conclusions:Our study supports the multi-modality treatment strategy (including surgery, chemotherapy and radiation) as an important component in the treatment of locally advanced NECC. The combination of surgery, chemotherapy and radiation seems to have advantages in the treatment of locally advanced NECC, but needs to be confirmed by further multicenter studies.

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.
Algorithms ; Computational Biology ; methods ; Diagnostic Imaging ; Genomics ; methods ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Machine Learning ; Neural Networks (Computer) ; Protein Structure, Secondary ; Proteins ; metabolism