Objective:To investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

Objective To analyze the characteristics of facial and tongue chromaticity parameters in patients with gastrointestinal tumors by setting the inspection image characteristics of patients with gastrointestinal tumors as the main research content;To establish an auxiliary diagnostic model for gastrointestinal tumors.Methods One-way ANOVA,t-test,Mann-whitney U test,canonical correlation analysis and Spearman statistical methods were used to analyze the characteristics of inspection image indexes and correlation of tumor markers of the 391 cases in the control group and 359 patients with gastrointestinal tumors.Machine learning methods such as SVM,Random Forest,KNN,Naive Bayes,XG Boost and Ada Boost were used to establish an auxiliary diagnostic model for gastrointestinal tumors.Results In terms of facial indicators,there were differences in F-R,F-G and F-B indicators among the control group,early-stage gastrointestinal cancer patients,and mid-to late-stage gastrointestinal cancer patients,in the comparison of tongue features among,TC-L,TB-L and TB-a of the control group,patients with early gastrointestinal tumors,and patients with intermediate and advanced gastrointestinal tumors showed a gradual downward trend;the AUC of the auxiliary diagnosis model of gastrointestinal tumor disease based on the chromaticity parameters of face tongue image constructed by Ada Boost algorithm was 0.930.Conclusion The auxiliary diagnostic model of gastrointestinal diseases constructed by facial and tongue images has good diagnostic effect,which can provide objective data support for in-depth exploration of the complex relationship between diagnosis and disease.

Objective:To investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To systematically evaluate the impact of internet-based patient decision aid (PtDA) on decision-making for prenatal screening and diagnosis among pregnant women.Methods:Randomized controlled trials (RCTs) on the application of internet-based PtDA for prenatal screening and diagnosis among pregnant women were searched in PubMed, Embase, Web of Science, CINAHL, Cochrane Library, China National Knowledge Infrastructure, Wanfang Date, VIP, and China Biology Medicine disc. The search period was from inception to August 11, 2024. Two researchers independently screened the literature, extracted data, and assessed the quality of included studies. Meta-analysis was performed using RevMan 5.4 software.Results:A total of 10 studies involving 3 838 pregnant women were included. The meta-analysis showed that internet-based PtDA significantly reduced decision conflict ( P<0.05), improved decision knowledge ( P<0.01), and decreased decision anxiety ( P<0.05) among pregnant women but had no significant impact on decision regret ( P>0.05) . Conclusions:Internet-based PtDA effectively reduce decision conflict and anxiety while improving decision knowledge during prenatal screening and diagnostic decision-making. Future studies should focus on developing culturally appropriate decision aids tailored to the Chinese context and explore the enhanced role of nurses in prenatal screening counseling.

Objective To explore the mediating role of intolerance of uncertainty(IU)and moderating role of the negative emotion differentiation in the influence of perceived stress on anxiety among college students from a cognitive perspective.Methods A total of 271 participants were surveyed using the perceived stress scale,intolerance of uncertainty scale,depression anxiety and stress scale(Chinese version),and the test on negative emotional differentiation.SPSS 22.0 was used to perform descriptive statistics and correlation analyses and to test the moderated mediation model.Results Perceived stress affected anxiety and IU played a mediating role-perceived stress could affect anxiety through influencing IU.At the same time,the influence of IU on anxiety could be adjusted through the negative emotion differentiation.The higher the degree of negative emotion differentiation,the lower the degree of anxiety increase(β=0.17,t=5.70,P＜0.01).Conclusion It may be effective to develop training programs to reduce anxiety by regulating perceived stress,increasing acceptance of uncertainty,and improving the negative emotion differentiation,which can help individuals reduce anxiety by perceiving and adjusting anxiety-related emotional or cognitive factors in a timely manner.

Ovulatory dysfunction (OD) is one of the main causes of infertility in women of childbearing age, which not only affects their reproductive ability, but also physical and mental health. Traditional treatment strategies have limited efficacies, and the emergence of biomedicines provides a promising alternative solution via the strategies of combining engineered design with modern advanced technology. This review explores the pathophysiological characteristics and related induction mechanisms of OD, and evaluates the current cutting-edge advances in its treatments. It emphasizes the potentials of biomedicines strategies such as hydrogels, nanoparticles and extracellular vesicles in improving therapeutic precision and efficacy. By mimicking natural physiological processes, and achieving controlled drug release, these advanced drug carriers are expected to address the challenges in ovarian microenvironment reprogramming, tissue repair, and metabolic and immune regulation. Despite the promising progress, there are still challenges in terms of biomedical complexity, differences between animal models and human physiology, and the demand for intelligent drug carriers in the therapy of OD. Future researches are mainly dedicated to developing precise personalized biomedicines in OD therapy through interdisciplinary collaboration, promoting the development of reproductive regenerative medicine.

Objective To analyze the characteristics of facial and tongue chromaticity parameters in patients with gastrointestinal tumors by setting the inspection image characteristics of patients with gastrointestinal tumors as the main research content;To establish an auxiliary diagnostic model for gastrointestinal tumors.Methods One-way ANOVA,t-test,Mann-whitney U test,canonical correlation analysis and Spearman statistical methods were used to analyze the characteristics of inspection image indexes and correlation of tumor markers of the 391 cases in the control group and 359 patients with gastrointestinal tumors.Machine learning methods such as SVM,Random Forest,KNN,Naive Bayes,XG Boost and Ada Boost were used to establish an auxiliary diagnostic model for gastrointestinal tumors.Results In terms of facial indicators,there were differences in F-R,F-G and F-B indicators among the control group,early-stage gastrointestinal cancer patients,and mid-to late-stage gastrointestinal cancer patients,in the comparison of tongue features among,TC-L,TB-L and TB-a of the control group,patients with early gastrointestinal tumors,and patients with intermediate and advanced gastrointestinal tumors showed a gradual downward trend;the AUC of the auxiliary diagnosis model of gastrointestinal tumor disease based on the chromaticity parameters of face tongue image constructed by Ada Boost algorithm was 0.930.Conclusion The auxiliary diagnostic model of gastrointestinal diseases constructed by facial and tongue images has good diagnostic effect,which can provide objective data support for in-depth exploration of the complex relationship between diagnosis and disease.

The treatment of hair loss often involves the use of oral medications, topical applications and hair transplantation. However, these methods come with drawbacks such as poor effectiveness, significant side effects, high recurrence rates, or high costs. In recent years, fractional laser therapy has emerged as a new choice for hair loss treatment due to its safety, effectiveness and high tolerance. Nonetheless, its mechanism of action in treating hair loss, application protocols and therapeutic outcomes need further clarification for broader clinical application. This article summarizes the treatment principles of fractional laser therapy, elaborates on the application effects and safety of different types of fractional lasers in treating hair loss, providing these for clinical references.