Tubal ciliary movement is one of the essential transport mechanisms for female fertility, playing a key role in facilitating oocyte pickup and transporting the fertilized ovum. This movement is mediated by multiciliated cells and regulated by specific proteins and hormones that modulate ciliary number, length, polarity, beat frequency, and amplitude to ensure proper function. Genetic mutations, inflammatory stimuli, and hormonal fluctuations can impair ciliary activity or induce ciliary apoptosis, leading to ciliary dysfunction. Disorders of tubal ciliary movement are frequently observed in primary ciliary dyskinesia, pelvic inflammatory disease, polycystic ovary syndrome, and endometriosis, conditions commonly associated with female infertility. These disorders manifest as structural abnormalities of cilia, disrupted polarity, shortened ciliary length, reduced ciliary count, and decreased beat frequency and amplitude. Understanding the role of tubal ciliary movement in female infertility-related diseases, through immunohistochemistry and ultrastructural analysis, helps clarify underlying infertility mechanisms. Identifying abnormal inflammatory factors, hormonal environments, and gene expression, combined with advanced techniques for measuring ciliary protein and beat frequency, may offer novel clinical targets for early prevention and treatment of female infertility.
Humans ; Female ; Infertility, Female/etiology* ; Cilia/physiology* ; Polycystic Ovary Syndrome/physiopathology* ; Fallopian Tubes/physiopathology* ; Endometriosis/complications* ; Pelvic Inflammatory Disease/complications*

Objective:To explore the application effect of the modified sandwich teaching method based on constructivism theory in clinical teaching of neurology nursing interns.Methods:A total of 29 nursing interns who practiced in the Department of Neurology of the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital) from June 2020 to May 2021 were selected as the control group, using the conventional clinical nursing teaching method, and 28 nursing interns who practiced from June 2021 to May 2022 were selected as the observation group, using the modified sandwich teaching method based on constructivism theory. Before graduating from the Department of Neurology, nursing interns in the two groups were compared in terms of the teaching effects, such as the score of learning initiative, theoretical examination, operation examination, and nursing round report. SPSS 25.0 was used for t test and chi-square test. Results:The score for each dimension (learning driving force, learning objectives, in-depth learning, controlled learning, and solid learning) and total score in the observation group were significantly higher than those in the control group ( P<0.05). There was no significant difference between the two groups in the scores of theoretical examination and operation examination (89.11±3.58 vs. 88.97±2.74, 93.79±2.48 vs. 93.86±2.20; P>0.05); the scores of nursing rounds in the observation group were significantly higher than those in the control group (88.61±2.60 vs. 83.38±3.97, P<0.05). Conclusions:The modified sandwich teaching method based on constructivism theory can enhance the learning initiative and comprehensive analysis ability of nursing interns in the Department of Neurology. It is suitable for clinical nursing teaching in the Department of Neurology and is worth popularizing.

Objective To investigate the epidemiological characteristics and mutation spectrum of monogenic diseases in Chinese population through a large-scale,multicenter carrier screening.Methods This study was conducted among a total of 33 104 participants(16 610 females)from 12 clinical centers across China.Carrier status for 223 genes was analyzed using high-throughput sequencing and different PCR methods.Results The overall combined carrier frequency was 55.58%for 197 autosomal genes and 1.84%for 26 X-linked genes in these participants.Among the 16 669 families,874 at-risk couples(5.24%)were identified.Specifically,584 couples(3.50%)were at risk for autosomal genes,306(1.84%)for X-linked genes,and 16 for both autosomal and X-linked genes.The most frequently detected autosomal at-risk genes included GJB2(autosomal recessive deafness type 1A,393 couples),HBA1/HBA2(α-thalassemia,36 couples),PAH(phenylketonuria,14 couples),and SMN1(spinal muscular atrophy,14 couples).The most frequently detected X-linked at-risk genes were G6PD(G6PD deficiency,236 couples),DMD(Duchenne muscular dystrophy,23 couples),and FMR1(fragile X syndrome,17 couples).After excluding GJB2 c.109G>A,the detection rate of at-risk couples was 3.91%(651/16 669),which was lowered to 1.72%(287/16 669)after further excluding G6PD.The theoretical incidence rate of severe monogenic birth defects was approximately 4.35‰(72.5/16 669).Screening for a battery of the top 22 most frequent genes in the at-risk couples could detect over 95%of at-risk couples,while screening for the top 54 genes further increased the detection rate to over 99%.Conclusion This study reveals the carrier frequencies of 223 monogenic genetic disorders in the Chinese population and provides evidence for carrier screening strategy development and panel design tailored to the Chinese population.In carrier testing,genetic counseling for specific genes or gene variants can be challenging,and the couples need to be informed of these difficulties before testing and provided with options for not screening these genes or gene variants.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Mild cognitive impairment(MCI)is often regarded as a prodromal stage of dementia,primarily characterized by mild decline in cognitive function.Due to the mild nature of its symptoms,many MCI cases miss the opportunity for intervention.Research indicates that linguistic changes in MCI may precede other cognitive symptoms,providing an opportunity for early identification and intervention.These linguistic changes include abnormalities in speech rate,pronunciation,and intonation.Speech repetition tasks,such as the five-word test,delayed digit matching,and sentence repetition tests,are effective methods for assessing the speech characteristics of MCI patients.These tasks involve asking patients to repeat specific content,and analyzing the accuracy of their repetitions to assess their speech function.The application of machine learning and deep learning techniques enables automatic extraction of MCI-related features from speech data,improving diagnostic accuracy.The combined application of these techniques contributes to the early detection of MCI,providing a basis for timely intervention.

Objective To investigate the epidemiological characteristics and mutation spectrum of monogenic diseases in Chinese population through a large-scale,multicenter carrier screening.Methods This study was conducted among a total of 33 104 participants(16 610 females)from 12 clinical centers across China.Carrier status for 223 genes was analyzed using high-throughput sequencing and different PCR methods.Results The overall combined carrier frequency was 55.58%for 197 autosomal genes and 1.84%for 26 X-linked genes in these participants.Among the 16 669 families,874 at-risk couples(5.24%)were identified.Specifically,584 couples(3.50%)were at risk for autosomal genes,306(1.84%)for X-linked genes,and 16 for both autosomal and X-linked genes.The most frequently detected autosomal at-risk genes included GJB2(autosomal recessive deafness type 1A,393 couples),HBA1/HBA2(α-thalassemia,36 couples),PAH(phenylketonuria,14 couples),and SMN1(spinal muscular atrophy,14 couples).The most frequently detected X-linked at-risk genes were G6PD(G6PD deficiency,236 couples),DMD(Duchenne muscular dystrophy,23 couples),and FMR1(fragile X syndrome,17 couples).After excluding GJB2 c.109G>A,the detection rate of at-risk couples was 3.91%(651/16 669),which was lowered to 1.72%(287/16 669)after further excluding G6PD.The theoretical incidence rate of severe monogenic birth defects was approximately 4.35‰(72.5/16 669).Screening for a battery of the top 22 most frequent genes in the at-risk couples could detect over 95%of at-risk couples,while screening for the top 54 genes further increased the detection rate to over 99%.Conclusion This study reveals the carrier frequencies of 223 monogenic genetic disorders in the Chinese population and provides evidence for carrier screening strategy development and panel design tailored to the Chinese population.In carrier testing,genetic counseling for specific genes or gene variants can be challenging,and the couples need to be informed of these difficulties before testing and provided with options for not screening these genes or gene variants.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:To investigate the behavior and emotion of trainees (residents) in the standardized residency training under the novel coronavirus pneumonia (NCP) epidemic, and adjust the training plan and mode of standardized residency training during the NCP epidemic according to the survey results.Methods:A real-name closed questionnaire survey was conducted among 40 residents (27 ultrasound majors and 13 other majors) in ultrasound department using the "Questionnaire Star" WeChat applet, including basic personal information, questions related to NCP, behavioral change survey, behavioral attitude survey and emotional status survey. All the collected data were further statistically analyzed by Chi-square test and rank sum test using SPSS 19.0Results:There were forty valid questionnaires and the participation rate was 100.0%. The accuracy of 24 NCP-related questions was not significantly different between residents majoring in ultrasound and other specialties [92.0%(596/648) vs. 93.6%(292/312), P>0.05]. All the residents had a good understanding of NCP. The main behaviors of going out were significantly reduced, while various behaviors of being at home were significantly increased. Their interest in learning declined. When the epidemic happened (or possibly) in the community or village they lived in, their negative emotions, such as nervousness, fear pessimism and depression, were obviously expressed. Network teaching and evaluation through WeChat public number were adopted and we guided residents to adjust their home behaviors and emotions according to the survey results. Conclusion:Under the NCP epidemic, the behaviors and emotions of residents had been extensively changed. The training center should adjust the teaching plan and mode of training in time, and provide more counselling service in psychology and learning for residents during the epidemic.

Dementia is one of the age-related diseases with the fastest increase in incidence. With the increasingly severe aging problem of the population, it further exacerbates the demand and burden of the healthcare system. Red blood cell distribution width (RDW) is an easily obtainable blood routine indicator that reflects the variability of red blood cell size. As an early marker of dementia risk, researches have shown an association between white matter hyperintensities (WMHs) and RDW. This article reviews the relationship between RDW and WMHs.

Self-organized blastoids from extended pluripotent stem (EPS) cells possess enormous potential for investigating postimplantation embryo development and related diseases. However, the limited ability of postimplantation development of EPS-blastoids hinders its further application. In this study, single-cell transcriptomic analysis indicated that the "trophectoderm (TE)-like structure" of EPS-blastoids was primarily composed of primitive endoderm (PrE)-related cells instead of TE-related cells. We further identified PrE-like cells in EPS cell culture that contribute to the blastoid formation with TE-like structure. Inhibition of PrE cell differentiation by inhibiting MEK signaling or knockout of Gata6 in EPS cells markedly suppressed EPS-blastoid formation. Furthermore, we demonstrated that blastocyst-like structures reconstituted by combining the EPS-derived bilineage embryo-like structure (BLES) with either tetraploid embryos or tetraploid TE cells could implant normally and develop into live fetuses. In summary, our study reveals that TE improvement is critical for constructing a functional embryo using stem cells in vitro.
Pregnancy ; Female ; Animals ; Mice ; Tetraploidy ; Blastocyst ; Embryo, Mammalian ; Cell Differentiation ; Embryonic Development