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Objectives. The bibliometric analysis uses the citation count of an article to measure its impact in the scientific community, yet there is still no comprehensive summary of gastric disease researches via bibliometric analysis. We aimed to evaluate the situations and trends of the most cited articles in gastric disease via bibliometric analysis and to provide physicians a practical guide in assessing the most influential articles written on this subject. Methods. The 100 top-cited articles in gastric disease were compiled using Web of Science. The articles selected were evaluated for their number of citations, year of publication, country of origin, type of study, and others. Results. The database had 484,281 articles published between 1965 and 2019. The most cited article received 4,017 citations and the least received 604, with a mean of 1,149 citations. We classified the articles into seven categories: gastric cancer (n = 53), Helicobacter pylori (n = 17), ulcer (n = 7), gastrointestinal stromal tumors (n = 6), gastritis (n = 5), gastric bypass surgery (n = 2), and others (n = 10). Altogether, 69 of the articles were from the USA (n = 41), the UK (n = 17), and Japan (n = 11). Among all the institutions, Royal Perth Hospital led the list with 5 articles. One-quarter of authors owned three or more of these top-cited articles. The 100 papers were published in 33 journals, and most of them were clinical researches (n = 47). Conclusions. Our study provides a historical perspective for the scientific progress of gastric disease, and the articles of significant findings that contributed great impact on the prevention and treatment of gastric disease had been identified. [ABSTRACT FROM AUTHOR]

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques. [ABSTRACT FROM AUTHOR]

Psychosocial distress and poorer quality of life after renal transplantation are common in children and young people. This has implications for medication adherence and survival. Posttransplant psychology annual reviews were introduced in one Paediatric Renal Service in the UK as a means of measuring psychological distress and quality of life, as well as facilitating identification of patients and parents/carers who would benefit from psychological intervention. The process of completing posttransplant psychology annual reviews is discussed within this paper. The posttransplant psychology annual review appointments identified patients experiencing depression and/or anxiety and problems in quality of life. These assessments have led to appropriate referrals to, and engagement with, the renal psychology service as well as with community tier 3 child and adolescent mental health services. The posttransplant psychology annual review will continue to be completed at this UK site and discussions will be undertaken with other paediatric renal transplant services to consider whether these could be introduced at a national level to facilitate collection of longitudinal data regarding long-term psychosocial impact of paediatric renal transplantation and its effect on quality of life. [ABSTRACT FROM AUTHOR]

This paper presents a computational model which estimates the postsynaptic conductance change of mammalian Type I afferent peripheral process when airborne acoustic waves impact on the tympanic membrane. A model of the human auditory periphery is used to estimate the inner hair cell potential change in response to airborne sound. A generic and tunable topology of the mammalian synaptic ribbon is generated and the voltage dependence of its substructures is used to calculate discrete and probabilistic neurotransmitter vesicle release. Results suggest an almost linear relationship between increasing sound level (in dB SPL) and the postsynaptic conductance for frequencies considered too high for neurons to phase lock with (i.e., a few kHz). Furthermore coordinated vesicle release is shown for up to 300–400 Hz and a mechanism of phase shifting the subharmonic content of a stimulating signal is suggested. Model outputs suggest that strong onset response and highly synchronised multivesicular release rely on compound fusion of ribbon tethered vesicles. [ABSTRACT FROM AUTHOR]