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Objective To investigate the effect of 3 laparoscopic surgical procedures (hysterectomy, oophorectomy and fallectomy) on anti-Mullerian hormone (AMH) level. Methods A total of 162 women who were treated with laparoscopic surgical procedures (56 cases of hysterectomy, 52 cases of oophorectomy and 54 cases of fallectomy) in our department from November 2019 to February 2021 were enrolled in this study. The AMH level of each patient was compared preoperatively, and 1, 3 and 6 months postoperatively. The changes in AMH for the same type, different surgical procedures, were also compared. Results No statistical difference was observed in preoperative serum AMH level among the 3 groups of patients (P>0.05), and the level was significantly decreased in the ovarian and hysterectomy groups than the fallopian tube group in 1 month postoperatively (P=0.04). At 3 and 6 months postoperatively, there was no difference in serum AMH level in the 3 groups (P=0.1, P=0.33), and △AMH level was decreased gradually. For the same type, different surgical procedures, when compared with subtotal hysterectomy, serum AMH level was decreased more significantly in patients with total hysterectomy at 1, 3 and 6 months postoperatively than preoperative level (P=0.016, P=0.021, P=0.021), and the decrease in AMH was more obviously in bilateral than in unilateral oophorectomy. Conclusion All these 3 types of common laparoscopic gynecological surgeries affect ovarian function, with ovarian surgery most significant, followed by hysterectomy and then salpingo-oophorectomy. Total uterine resection has more obvious effect on postoperative decline in ovarian reserve than sub-total uterine resection, and bilateral salpingo-oophorectomy than unilateral salpingo-oophorectomy, especially in 1 month postoperatively, while no such effect is observed in unilateral salpingo-oophorectomy.

Short-video mobile apps are an emergent media form and play a significant role in young children's everyday lives. We explored the parental perspectives of 2-to-6-year-old Chinese children's use of short-video mobile apps. We distributed an online questionnaire that received 266 valid responses from parents and conducted in-depth interviews with 20 parents to understand children's usage and parents' attitudes, roles and mediations. Results revealed the most frequently watched genres were animated stories and reviews of toys. The affordances of short-video apps appeared to break children's normal media-use routine. Most parents acknowledged both the educational and intimate/family value as well as the psychological risks of short-video apps and tended to employ restrictive mediations to regulate children. We discuss different parental and children's attitudes towards using short-video apps. We also suggest parents initiating various mediated strategies according to dynamic contexts. We propose strategies for parents/caregivers, policy makers and technology designers for creating a vibrant and trustworthy media environment for young children.

Children with autism spectrum disorder (ASD) require long-term interventions with professional tutors. In this paper, we explore the design space of virtual tutors in digital learning systems that may facilitate effective ASD interventions. We conducted a classroom observational study to examine how children with ASD learn and interact with tutors, organized a design workshop with the children to understand their visual preferences for virtual tutors, and held a focus group with tutors and individual interviews with parents to investigate their teaching methods, techniques, and challenges at school and at home. We identified three types of interactions and five types of learning behaviors in children with ASD. We also found tutors and parents followed a structured training approach and often used personalized rewards to mediate the intervention. Based on previous research and our empirical results, we proposed 12 design considerations for designing virtual tutors' visual, audio, kinesthetic, and interaction features. We also presented a design case for developing a virtual tutor in a language learning app to help children with ASD learn social communication skills. We conclude with a discussion of the advantages and challenges of designing and employing virtual tutors in educational contexts for children with ASD.

In this study, the composition of volatile flavor substances during the fermentation of cream was analyzed by solid phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS). A total of 32 volatile substances were identified at five different periods, among which acids were the most abundant. Principal component analysis (PCA) revealed that the composition of volatile compounds differed at different fermentation periods, and samples from different periods could be clearly distinguished. Nine key flavor components, including capric acid, caprylic acid, δ-decalactone, 2-nonanone and 3-hydroxy-2-butanone, were identified by calculating odor activity values (OAVs). Five sensory attributes of the samples were scored by descriptive sensory analysis. The correlations between key flavor substances and sensory attributes were analyzed by partial least squares regression (PLSR) analysis, and it was found that the fermented and creamy flavor had strong correlations with capric acid, capric acid, δ-decalactone, 2-decanone, 2-nonanone and 3-hydroxy-2-butanone. This study is of great significance to regulating the production of fermented cream.

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.