Objective:To explore the clinical characteristics and healthcare burden in patients with McCune-Albright syndrome (MAS).Methods:A cross-sectional study was conducted at the Children′s Hospital, Zhejiang University School of Medicine. Clinical and healthcare burden data were systematically collected through structured questionnaires in 164 children with MAS from February 2022 to May 2023. According to the clinical characteristics, patients were categorized into 3 groups: monosymptomatic, bisymptomatic and trisymptomatic groups. Patients were also divided into 3 groups according to the age of <7, 7-<10 and 10-18 years. Comparative analyses of clinical characteristics and healthcare burden were conducted across age, sex, and symptom categories.Results:The cohort comprised 59 males (36.0%) and 105 females (64.0%) with an age of 4.6 (2.0, 7.4) years. Age stratification revealed 117 cases (71.3%) aged 0-<7 years, 29 cases (17.7%) aged 7-<10 years, and 18 cases (11.0%) aged 10-<18 years. Among monosymptomatic (67 cases, 40.9%), the cohort comprised 32 females (47.8%) and 35 males (52.2%), predominantly presenting with fibrous dysplasia (57 cases, 85.1%). This subgroup showed peak prevalence in the 0-<7 years age range (29 cases (50.9%)). The bisymptomatic cohort (56 cases, 34.1%) consisted of 39 females (69.6%) and 17 males (30.4%), predominantly manifesting fibrous dysplasia with skin hyperpigmentation (25 cases, 44.6%). Peak prevalence occurred in the 0-<7 years subgroup(16 cases (64.0%)). The trisymptomatic cohort (41 cases, 25.0%) consisted of 34 females (82.9%) and 7 males (17.1%), with peak prevalence occurring in the 0-<7 years subgroup (36 cases (87.8%)). The diagnostic journey analysis revealed 94 cases (57.3%) required 1-3 referrals, and 34 cases (20.7%) necessitated >3 referrals from symptom onset to definitive diagnosis. Healthcare expenditure analysis revealed 69 families (42.1%) incurred direct medical costs of 10 000-100 000 CNY, with 11 families (6.7%) exceeding >100 000 CNY. Direct non-medical costs reached of 10 000-100 000 CNY for 62 families (37.8%) and >100 000 CNY for 4 families (2.4%). Productivity loss affected 58 families (35.4%) at 10 000-100 000 CNY and 8 families (4.9%) above 100 000 CNY during the study period.Conclusion:MAS requires increased attention to skeletal manifestations, especially in children aged 0-<7 years. Moreover, the significant financial burden on families necessitates a society-wide support system.

Alzheimer's disease(AD)presents a notable gender disparity in prevalence among elderly individuals, with elderly women being a high-risk group for AD onset.Researches have confirmed the effects of estrogen on the nervous system and cognitive function through in vitro and animal models.Throughout their reproductive years, women undergo various life events such as menarche, menopause, pregnancy, childbirth, miscarriage, relevant surgeries, and medication, all of which can impact their endocrine status and subsequently influence brain function.As a result, there is a growing body of epidemiological evidence investigating the relationship between women's menstrual and reproductive factors and AD.Despite some controversies, this article offers a thorough review of current epidemiological research on the link between different menstrual and fertility factors in women and AD.

Objective: To investigate the current status and influencing factors for cognitive frailty among the elderly in community, so as to provide the evidence for early identification and prevention of cognitive frailty among the elderly. Methods: Residents aged 60 years and above with local household registration from 11 counties (cities, districts) in Zhejiang Province from 2021 to 2023 were selected as study participants using a multistage random sampling method. Demographic information, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were assessed using the Patient Health Questionnaire. Cognitive frailty was evaluated using the FRAIL Scale and the Mini-Mental State Examination. Factors affecting cognitive frailty among the elderly in community were identified using a multivariable logistic regression model. Results: A total of 16 613 individuals were surveyed, including 7 465 males (44.93%) and 9 148 females (55.07%). The average age was (70.97±7.29) years. A total of 784 individuals were detected with depressive symptoms, with a detection rate of 4.72%. A total of 724 individuals were detected with cognitive frailty, with a detection rate of 4.36%. Multivariable logistic regression analysis showed that females (OR=1.419, 95%CI: 1.179-1.708), aged ≥70 years (70-<80 years old, OR=1.869, 95%CI: 1.490-2.345; ≥80 years old, OR=5.017, 95%CI: 3.935-6.398), without a spouse (OR=1.495, 95%CI: 1.234-1.810), sedentary (OR=2.420, 95%CI: 1.829-3.202), chronic diseases (1 type, OR=1.456, 95%CI: 1.175-1.804; ≥2 types, OR=1.639, 95%CI: 1.314-2.045), and depressive symptoms (OR=4.191, 95%CI: 3.361-5.225) were associated with a higher risk of cognitive frailty among the elderly in community. Conversely, a lower risk of cognitive frailty was seen among the elderly in community who had primary school or above (primary school, OR=0.512, 95%CI: 0.389-0.676; junior high school or above, OR=0.464, 95%CI: 0.354-0.608), engaged in physical exercise (OR=0.396, 95%CI: 0.291-0.539), and were reported average or good self-rated health status (average, OR=0.641, 95%CI: 0.475-0.866; good, OR=0.150, 95%CI: 0.109-0.208). Conclusions The detection rate of cognitive frailty among the elderly in community is relatively low and is influenced by demographic factors such as gender, age, education level, as well as lifestyle like sedentary and physical exercise, and health status. It is recommended to reduce the risk of cognitive frailty among the elderly through multidimensional interventions, including health education, promotion of healthy lifestyles, and enhanced mental health support.

Objective: To establish a nomogram prediction model for Alzheimer's disease (AD) among the elderly in community, so as to provide the evidence for early screening and prevention of AD. Methods: Based on the Zhejiang Healthy Aging Cohort Study, the elderly aged 60-90 years who completed the baseline survey were selected as the study subjects. Follow-up surveys were conducted from 2015 to 2016 and from 2019 to 2021. Sociodemographic characteristics, lifestyle factors, medical history, and waist circumference were collected through questionnaire surveys and physical examinations. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and a diagnosis of AD was made based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale and medical history. The participants were randomly divided into training and validation sets at 8∶2 ratio. LASSO regression was used to screen for predictive factors. Multivariable logistic regression model was used to analyze predictive factors and construct a nomogram. The model was analyzed and evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 6 988 elderly were included at baseline, with a mean age of (68.19±6.63) years. There were 3 438 males (49.20%), and 3 550 females (50.80%). The median follow-up duration was 4.90 (interquartile range, 3.80) years, with 817 new cases of AD were identified, yielding an incidence of 11.69%. LASSO regression and multivariable logistic regression showed that age (OR=1.017, 95%CI: 1.005-1.030), gender (female, OR=1.820, 95%CI: 1.533-2.165), educational level (primary school, OR=0.813, 95%CI: 0.673-0.980), physical exercise (not active, OR=1.572, 95%CI: 1.260-1.980), dining companions (spouse and children, OR=0.771, 95%CI: 0.598-0.995), baseline MMSE score (OR=0.843, 95%CI: 0.821-0.866), and waist circumference (OR=0.981, 95%CI: 0.973-0.989) were risk predictors for AD among the elderly in community. The prediction model demonstrated an area under the ROC curve of 0.740 (95%CI: 0.698-0.783) in the validation set, with a sensitivity of 0.731 and a specificity of 0.667. DCA indicated that when the probability threshold was 0.060 to 0.325, the clinical net benefit was relatively high. Conclusion The AD risk prediction model constructed in this study has good discrimination and clinical practicability, can be used for early screening of AD among the elderly in the community.

BACKGROUND: Alpha-glucosidase inhibitors or dipeptidyl peptidase-4 inhibitors are both hypoglycemia agents that specifically impact on postprandial hyperglycemia. We compared the effects of acarbose and sitagliptin add on to metformin on time in range (TIR) and glycemic variability (GV) in Chinese patients with type 2 diabetes mellitus through continuous glucose monitoring (CGM). METHODS: This study was a randomized, open-label, active-con-trolled, parallel-group trial conducted at 15 centers in China from January 2020 to August 2022. We recruited patients with type 2 diabetes aged 18-65 years with body mass index (BMI) within 19-40 kg/m 2 and hemoglobin A1c (HbA1c) between 6.5% and 9.0%. Eligible patients were randomized to receive either metformin combined with acarbose 100 mg three times daily or metformin combined with sitagliptin 100 mg once daily for 28 days. After the first 14-day treatment period, patients wore CGM and entered another 14-day treatment period. The primary outcome was the level of TIR after treatment between groups. We also performed time series decomposition, dimensionality reduction, and clustering using the CGM data. RESULTS: A total of 701 participants received either acarbose or sitagliptin treatment in combination with metformin. There was no statistically significant difference in TIR between the two groups. Time below range (TBR) and coefficient of variation (CV) levels in acarbose users were significantly lower than those in sitagliptin users. Median (25th percentile, 75th percentile) of TBR below target level <3.9 mmol/L (TBR 3.9 ): Acarbose: 0.45% (0, 2.13%) vs . Sitagliptin: 0.78% (0, 3.12%), P = 0.042; Median (25th percentile, 75th percentile) of TBR below target level <3.0 mmol/L (TBR 3.0 ): Acarbose: 0 (0, 0.22%) vs . Sitagliptin: 0 (0, 0.63%), P = 0.033; CV: Acarbose: 22.44 ± 5.08% vs . Sitagliptin: 23.96 ± 5.19%, P <0.001. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV (group with small wave, moderate wave and big wave). No significant difference was found in the complexity of glucose time series index (CGI) between acarbose users and sitagliptin users. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV. CONCLUSIONS: Acarbose had slight advantages over sitagliptin in improving GV and reducing the risk of hypoglycemia. Time series analysis of CGM data may predict GV and the risk of hypoglycemia. TRIAL REGISTRATION Chinese Clinical Trial Registry: ChiCTR2000039424.
Humans ; Metformin/therapeutic use* ; Sitagliptin Phosphate/therapeutic use* ; Acarbose/therapeutic use* ; Diabetes Mellitus, Type 2/blood* ; Middle Aged ; Male ; Female ; Adult ; Blood Glucose/drug effects* ; Hypoglycemic Agents/therapeutic use* ; Aged ; Glycated Hemoglobin/metabolism* ; Adolescent ; Young Adult ; China ; East Asian People

Alzheimer's disease(AD)presents a notable gender disparity in prevalence among elderly individuals, with elderly women being a high-risk group for AD onset.Researches have confirmed the effects of estrogen on the nervous system and cognitive function through in vitro and animal models.Throughout their reproductive years, women undergo various life events such as menarche, menopause, pregnancy, childbirth, miscarriage, relevant surgeries, and medication, all of which can impact their endocrine status and subsequently influence brain function.As a result, there is a growing body of epidemiological evidence investigating the relationship between women's menstrual and reproductive factors and AD.Despite some controversies, this article offers a thorough review of current epidemiological research on the link between different menstrual and fertility factors in women and AD.

Objective:To explore the clinical characteristics and healthcare burden in patients with McCune-Albright syndrome (MAS).Methods:A cross-sectional study was conducted at the Children′s Hospital, Zhejiang University School of Medicine. Clinical and healthcare burden data were systematically collected through structured questionnaires in 164 children with MAS from February 2022 to May 2023. According to the clinical characteristics, patients were categorized into 3 groups: monosymptomatic, bisymptomatic and trisymptomatic groups. Patients were also divided into 3 groups according to the age of <7, 7-<10 and 10-18 years. Comparative analyses of clinical characteristics and healthcare burden were conducted across age, sex, and symptom categories.Results:The cohort comprised 59 males (36.0%) and 105 females (64.0%) with an age of 4.6 (2.0, 7.4) years. Age stratification revealed 117 cases (71.3%) aged 0-<7 years, 29 cases (17.7%) aged 7-<10 years, and 18 cases (11.0%) aged 10-<18 years. Among monosymptomatic (67 cases, 40.9%), the cohort comprised 32 females (47.8%) and 35 males (52.2%), predominantly presenting with fibrous dysplasia (57 cases, 85.1%). This subgroup showed peak prevalence in the 0-<7 years age range (29 cases (50.9%)). The bisymptomatic cohort (56 cases, 34.1%) consisted of 39 females (69.6%) and 17 males (30.4%), predominantly manifesting fibrous dysplasia with skin hyperpigmentation (25 cases, 44.6%). Peak prevalence occurred in the 0-<7 years subgroup(16 cases (64.0%)). The trisymptomatic cohort (41 cases, 25.0%) consisted of 34 females (82.9%) and 7 males (17.1%), with peak prevalence occurring in the 0-<7 years subgroup (36 cases (87.8%)). The diagnostic journey analysis revealed 94 cases (57.3%) required 1-3 referrals, and 34 cases (20.7%) necessitated >3 referrals from symptom onset to definitive diagnosis. Healthcare expenditure analysis revealed 69 families (42.1%) incurred direct medical costs of 10 000-100 000 CNY, with 11 families (6.7%) exceeding >100 000 CNY. Direct non-medical costs reached of 10 000-100 000 CNY for 62 families (37.8%) and >100 000 CNY for 4 families (2.4%). Productivity loss affected 58 families (35.4%) at 10 000-100 000 CNY and 8 families (4.9%) above 100 000 CNY during the study period.Conclusion:MAS requires increased attention to skeletal manifestations, especially in children aged 0-<7 years. Moreover, the significant financial burden on families necessitates a society-wide support system.

Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) represent predominant histological subtypes of cervical cancer. To improve screening efficacy, we leveraged RNA sequencing data from 4 cervical SCC samples, 4 cervical ADC samples, and 8 normal cervix samples and conducted a comprehensive mRNA and long noncoding RNA (lncRNA) profiling analysis followed with a multi-phase study comprising 556 samples. Validating the RNA sequencing data in a clinical sample set comprising 45 normal cervix tissues, 45 SCC tissues, and 45 ADC tissues, we identified 9 mRNAs (SMC1B, OTX1, GRP, CELSR3, HOXC6, ITGB6, WDR62, SEPT3, and KLHL34) and 4 lncRNAs (FEZF1-AS1, LINC01305, LINC00857, and LINC00673) differentially expressed in both SCC and ADC samples. Utilizing quantitative reverse transcription polymerase chain reaction analysis and receiver operating characteristic (ROC) curve analysis in a training set (45 normal, 126 SCC, and 82 ADC tissues), we refined a novel mRNA-lncRNA-based panel (SMC1B/CELSR3/FEZF1-AS1/LINC01305). Employing logistic regression model and ROC analysis, this panel exhibited significant distinctions and promising area under the curve (AUC) values in both SCC (AUC=0.9520, p<0.0001) and ADC (AUC=0.9748, p<0.0001) tissues. Subsequent validation in an independent set (11 normal, 32 SCC, and 20 ADC tissues) demonstrated its diagnostic accuracy in both SCC (AUC=0.9659, p<0.0001) and ADC (AUC=0.9636, p<0.0001) patients. Notably, this tissue-based biomarker panel robustly discriminated precancerous lesion and cervical cancer patients from nondisease controls in a blood-based validation set (30 normal, 25 HSIL and 50 cervical cancer) with an AUC value of 0.9320. This study presents a non-invasive, efficient diagnostic panel for cervical cancer screening.

Long non-coding RNAs(lncRNAs)play an indispensable role in the occurrence and development of ovarian cancer(OC).However,the potential involvement of lncRNAs in the progression of OC is largely unknown.To investigate the detailed roles and mechanisms of RAD51 homolog B-antisense 1(RAD51B-AS1),a novel lncRNA in OC,reverse transcription-quantitative polymerase chain reaction(RT-qPCR)was performed to verify the expression of RAD51B-AS1.Cellular proliferation,metastasis,and apoptosis were detected using the cell counting kit-8(CCK-8),colony-formation,transwell,and flow cytometry assays.Mouse xenograft models were established for the detection of tumorigenesis.The results revealed that RAD51B-AS1 was significantly upregulated in a highly metastatic human OC cell line and OC tissues.RAD51B-AS1 significantly increased the proliferation and metastasis of OC cells and enhanced their resistance to anoikis.Biogenetics prediction analysis revealed that the only target gene of RAD51B-AS1 was RAD51B.Subsequent gene function experiments revealed that RAD51B exerts the same biological effects as RAD51B-AS1.Rescue experiments demonstrated that the malignant biological behaviors promoted by RAD51B-AS1 overexpression were partially or completely reversed by RAD51B silencing in vitro and in vivo.Thus,RAD51B-AS1 promotes the malignant biological behaviors of OC and activates the protein kinase B(Akt)/B cell lymphoma protein-2(Bcl-2)signaling pathway,and these effects may be associated with the positive regulation of RAD51B expression.RAD51B-AS1 is expected to serve as a novel molecular biomarker for the diagnosis and prediction of poor prognosis in OC,and as a potential therapeutic target for disease management.

Objective:The present study used single-cell RNA sequencing(scRNA-seq)to characterize the cellular composition of ovarian carcinosarcoma(OCS)and identify its molecular characteristics.Methods:scRNA-seq was performed in resected primary OCS for an in-depth analysis of tumor cells and the tumor microenvironment.Immunohistochemistry staining was used for validation.The scRNA-seq data of OCS were compared with those of high-grade serous ovarian carcinoma(HGSOC)tumors and other OCS tumors.Results:Both malignant epithelial and malignant mesenchymal cells were observed in the OCS patient of this study.We identified four epithelial cell subclusters with different biological roles.Among them,epithelial subcluster 4 presented high levels of breast cancer type 1 susceptibility protein homolog(BRCA1)and DNA topoisomerase 2-α(TOP2A)expression and was related to drug resistance and cell cycle.We analyzed the interaction between epithelial and mesenchymal cells and found that fibroblast growth factor(FGF)and pleiotrophin(PTN)signalings were the main pathways contributing to communication between these cells.Moreover,we compared the malignant epithelial and mesenchymal cells of this OCS tumor with our previous published HGSOC scRNA-seq data and OCS data.All the epithelial subclusters in the OCS tumor could be found in the HGSOC samples.Notably,the mesenchymal subcluster C14 exhibited specific expression patterns in the OCS tumor,characterized by elevated expression of cytochrome P450 family 24 subfamily A member 1(CYP24A1),collagen type XXIII α1 chain(COL23A1),cholecystokinin(CCK),bone morphogenetic protein 7(BMP7),PTN,Wnt inhibitory factor 1(WIF1),and insulin-like growth factor 2(IGF2).Moreover,this subcluster showed distinct characteristics when compared with both another previously published OCS tumor and normal ovarian tissue.Conclusions:This study provides the single-cell transcriptomics signature of human OCS,which constitutes a new resource for elucidating OCS diversity.