Somatic SF3B1 hotspot mutation in prolactinomas.

The genetic basis and corresponding clinical relevance of prolactinomas remain poorly understood. Here, we perform whole genome sequencing (WGS) on 21 patients with prolactinomas to detect somatic mutations and then validate the mutations with digital polymerase chain reaction (PCR) analysis of tissue samples from 227 prolactinomas. We identify the same hotspot somatic mutation in splicing factor 3 subunit B1 (SF3B1R625H) in 19.8% of prolactinomas. These patients with mutant prolactinomas display higher prolactin (PRL) levels (p = 0.02) and shorter progression-free survival (PFS) (p = 0.02) compared to patients without the mutation. Moreover, we identify that the SF3B1R625H mutation causes aberrant splicing of estrogen related receptor gamma (ESRRG), which results in stronger binding of pituitary-specific positive transcription factor 1 (Pit-1), leading to excessive PRL secretion. Thus our study validates an important mutation and elucidates a potential mechanism underlying the pathogenesis of prolactinomas that may lead to the development of targeted therapeutics

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes

Is occupation a good predictor of self-rated health in China?

China's rapidly changing economic landscape has led to widening social inequalities. Occupational status in terms of occupational type and prestige may reflect these socio-structural shifts of social position and be more predictive of self-rated health status than income and education, which may only reflect more gradual acquisitions of social status over time. The goals of this study were to understand the role of occupational status in predicting self-rated health, which is well known to be associated with long-term mortality, as well as compare the occupational status to the other major socioeconomic indicators of income and education.Data from the 2010 baseline surveys of the China Family Panel Studies, which utilized multi-stage probability sampling with implicit stratification was used. Logistic regression was used to examine the relationship of various socioeconomic indicators (i.e. occupational status, income, and education) with self-rated health as the primary outcome of interest. A series of models considered the associations of occupational category or occupational prestige with self-rated health.The final sample consisted of 14,367 employed adults aged 18-60, which was nationally representative of working adults in China. We found that occupation was not a major predictor of self-rated health in China when age, ethnicity, location, marital status, physical and mental health status were controlled for, with the exception of women working in lower grade management and professional jobs (OR = 1.82, 95% CI: 1.03-3.22). In comparison, income followed by education exhibited greater association with self-rated health. The highest income group had the least probability to report poor health (In men: OR = 0.30, 95% CI: 0.21-0.43. In women: OR = 0.44, 95% CI: 0.26-0.73). People educated with junior high school had better self-rated health than those with primary and below education level (In men: OR = 0.62, 95% CI: 0.50-0.75. In women: OR = 0.53, 95% CI: 0.42-0.68). Income, education and occupation were correlated with each other.Within the context of rapid societal changes in China, income and its implications for greater healthcare access and benefits had the greatest association with self-rated health followed by education. Occupational status was not associated. Occupational categories and prestige should be better adapted to reflect China's unique sociopolitical and historical context

In Vitro Degradation Behavior, Mechanical Properties, and Cytocompatibility of Biodegradable Mg-1Zn-xSn Alloys

The application of biodegradable alloys in orthopedic implants has gained widespread attention globally. Magnesium alloys with controllable degradation rate and suitable mechanical properties have been regarded as potential orthopedic implant material. In this paper, a Mg-1Zn-xSn (x = 0, 1.0, 1.5, 2.0 wt.%) ternary alloy was designed and its performance was investigated. Compared with the Mg-1Zn alloy, the Mg-1Zn-xSn alloys showed enhanced mechanical properties and in vitro degradation performance. Above all, the extruded Mg-1Zn-1.0Sn alloy exhibited an extremely low corrosion rate of 0.12 mm/y with a low hydrogen release of 0.021 mL/cm2/day, which can be attributed to the hydrogen release suppression effect caused by Sn and SnO2 formation in the surface of the alloy. The cytotoxicity of the Mg-1Zn-1.0Sn alloy was evaluated by the cell counting kit-8 (CCK-8) method, the results of which show that its cytotoxicity grade is zero, and the MC3T3-E1 cells spread well on the alloy surface. The findings in this paper demonstrated that Mg-1Zn-1.0Sn is a potential candidate for biodegradable material in the orthopedic implant field

Corrosion Behavior and Osteogenic Activity of a Biodegradable Orthopedic Implant Mg–Si Alloy with a Gradient Structure

A gradient Mg-8 wt % Si alloy, which was composed of the agglomerated Mg2Si crystals coating (GMS8-1) and the eutectic Mg–Si alloy matrix (GMS8-2), was designed for biodegradable orthopedic implant materials. The bio-corrosion behavior was evaluated by the electrochemical measurements and the immersion tests. The results show that a significant improvement of bio-corrosion resistance was achieved by using the gradient Mg–Si alloy, as compared with the traditional Mg-8 wt % Si alloy (MS8), which should be attributed to the compact and insoluble Mg2Si phase distributed on the surface of the material. Especially, GMS8-1 exhibits the highest polarization resistance of 1610 Ω, the lowest corrosion current density of 1.7 × 10−6 A·cm−2, and the slowest corrosion rate of 0.10 mm/year. In addition, GMS8-1 and GMS8-2 show better osteogenic activity than MS8, with no cytotoxicity to MC3T3-E1 cells. This work provides a new way to design a gradient biodegradable Mg alloys with some certain biological functions

In Vitro Degradation Behavior, Mechanical Properties, and Cytocompatibility of Biodegradable Mg-1Zn-xSn Alloys

The application of biodegradable alloys in orthopedic implants has gained widespread attention globally. Magnesium alloys with controllable degradation rate and suitable mechanical properties have been regarded as potential orthopedic implant material. In this paper, a Mg-1Zn-xSn (x = 0, 1.0, 1.5, 2.0 wt.%) ternary alloy was designed and its performance was investigated. Compared with the Mg-1Zn alloy, the Mg-1Zn-xSn alloys showed enhanced mechanical properties and in vitro degradation performance. Above all, the extruded Mg-1Zn-1.0Sn alloy exhibited an extremely low corrosion rate of 0.12 mm/y with a low hydrogen release of 0.021 mL/cm2/day, which can be attributed to the hydrogen release suppression effect caused by Sn and SnO2 formation in the surface of the alloy. The cytotoxicity of the Mg-1Zn-1.0Sn alloy was evaluated by the cell counting kit-8 (CCK-8) method, the results of which show that its cytotoxicity grade is zero, and the MC3T3-E1 cells spread well on the alloy surface. The findings in this paper demonstrated that Mg-1Zn-1.0Sn is a potential candidate for biodegradable material in the orthopedic implant field

Concentration in the international construction market

With the involvement of an increasing number of contractors in the international construction market, venturing overseas has been fraught with fierce competition and considerable uncertainties. This underscores the importance of examining the extent to which the intensity of competition (IoC) for international construction business is moderate. Concentration is a conventional IoC measure. By synthesizing four major concentration methods including concentration ratio, entropy, Gini coefficient and the Herfindahl index, a new model for measuring moderate IoC is thus proposed. Using the 28 years ENR‐based data of the top 225 international contractors (TIC 225) in the proposed model, the IoC for international construction business is found to be moderate, if the magnitude of the market shares occupied by the top four international contractors is 0.2735. The finding revises a popular view that international construction competition has been intensifying only since 2002. It is implied that the proposed model favours a better understanding of competition characteristics in the international construction market and provides new insights into the theories about concentration.International construction market, business concentration, intensity of competition, moderate competition,