Objective: To explore the current status and influencing factors of eating behaviors in adolescents, so as to provide a theoretical foundation for health promotion education among adolescents. Methods: Based on the database from Survey of Chinese Family Health Index (2021), by a random number table method, 1 065 teenagers were selected from the provincial capitals of 22 provinces and 5 autonomous regions in China, as well as 4 municipalities directly under the central government. A general characteristic questionnaire, Patient Health Questionnaire-9 (PHQ-9), Short Form of the Family Health Scale (FHS-SF), 10-item Short Version of the Big Five Personality(BFP-10), Content-based Media Exposure Scale (CM-E) and Sakata Eating Behavior Scale Short Form(EBS-SF) were used to collect information. Multivariate stepwise linear regression analysis was employed to identify and analyze related factors of eating behaviors among adolescents. Receiver operating characteristic was used to validate the predictive ability of the EBS-SF score for overweight and obesity among adolescents. Results: The average scores of BFI-10,C-ME, FHS-SF, PHQ-9 and EBS-SF were (33.08±4.64)(19.20±4.55)(38.48±6.65)(6.09±5.63)(16.75±4.36), respectively. Multivariate linear regression showed that family type (other types), agreeableness, conscientiousness, family health and depression were the main related factors of EBS-SF scores among adolescents( B =2.61,-0.42,0.20,-0.11,0.23, P <0.05).The analysis of receiver operating characteristic curve showed that the EBS-SF scores had a good ability in predicting obesity among male adolescents ( AUC= 0.73, P <0.01). Conclusions Family type, big five personality, family health,depression are the related factors of eating behaviors among adolescents. EBS-SF scores are predictive of obesity in adolescents, which would provide a new perspective for promoting healthy eating habits among adolescents.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To investigate the application value of single nucleotide polymorphism(SNP)linkage analysis based on next-generation sequencing(NGS)technology in preimplantation genetic testing(PGT)of families with autosomal recessive polycystic kidney disease(ARPKD).Methods A family with ARPKD was selected,where the female member had a pregnancy ultrasound revealing polycystic kidney in the fetus.Genetic testing showed compound heterozygous mutations of the polycystic kidney/polycystic liver disease 1 gene(PKHD1),c.10444C>T(paternal)and c.4303del(maternal),with the c.4303del mutation being reported for the first time.Targeting the coding region of the PKHD1 gene,335 high-density tightly linked SNP sites were selected in the upstream and downstream 2M regions using multiplex polymerase chain reaction(PCR)and NGS.The couple′s SNP risk haplotypes carrying gene mutations were constructed.After in vitro fertilization,blastocyst culture was performed.Trophoblastic cells obtained from the biopsy were subjected to whole-genome amplification,and NGS was used for linkage analysis and low-depth chromosomal aneuploidy screening of the embryos.Sanger sequencing was used to verify the results of embryo linkage analysis.Results Among the 6 biopsied embryos,4 were mutation-free and euploid,1 exhibited heterozygous for the mutation and mosaic while another unstable sequencing data,making it impossible to judge.One of the mutation-free and developmentally healthy euploid embryos was implanted into the maternal uterus,resulting in the full-term delivery of a healthy baby.Conclusion Application of NGS-based SNP linkage analysis in PGT can effectively blocking the vertical transmission of ARPKD within families,while avoiding abortion issues caused by aneuploid embryos.This study is also the first PGT report target-ing the PKHD1 gene c.4303del mutation.

Objective:To explore the construction and application of an evidence-based practice plan for early postoperative activity in postoperative patients with large area cerebral infarction.Methods:Ninety-six postoperative patients with large area cerebral infarction admitted to Wenzhou Central Hospital from July 2021 to April 2023 were selected as the study subjects for a Clinical trial. They were divided into the control group (48 cases) and the observation group (48 cases) by random number table method. The control group received routine postoperative care for neurosurgery, while the observation group received evidence-based systematic early activity training. Both groups were followed up until 1 month after the patient was discharged from the hospital. The time of postoperative hospitalization, hospitalization expenses, vital signs and pain investigation 48 h after surgery, daily living ability before intervention and 7 d, 1 month after discharge, neurological function before intervention and 7 d and 1 month after surgery, and complications during follow-up between the two groups were compared.Results:In the control group, there were 29 males and 19 females, with an average age of 43-67(56.87 ± 1.76) years. In the observation group, there were 31 males and 17 females, with an average age of 43-68 (57.02 ± 1.82) years. The postoperative hospital stay in the observation group was (6.87 ± 0.65) d, in the control group was (9.06 ± 0.72) d, the difference between them was significant ( t=15.64, P<0.05). 48 hours after surgery, the heart rate, breathing rate and mean arterial pressure in the observation group were (71.65 ± 0, 45) times/min, (14.76 ± 0.36) times/min and (76.98 ± 5.65) mmHg(1 mmHg=0.133 kPa), which were different with those in the control group, (82.76 ± 2.65) times/min, (18.76 ± 2.87) times/min and (93.76 ± 5.93) mmHg ( t=28.64, 9.58 and 14.19, all P<0.05). Seven days after discharge, the score of Activities of Daily Living and the National Institutes of Health Neurological Impairment Scale in the observation group were (84.65 ± 2.45) and (23.65 ± 2.65), which were different than the (79.76 ± 1.97) and (28.54 ± 2.73) in the control group ( t=10.26, 8.91, both P<0.05). A month after discharge, the score of Activities of Daily Living and the National Institutes of Health Neurological Impairment Scale in the observation group were (95.45 ± 1.43) and (18.65 ± 1.98), while in the control group were (87.87 ± 1.39) and (21.54 ± 2.76), the difference between them were significant ( t=26.33, 5.90, both P<0.05). The total incidence of complications such as postoperative bleeding, postoperative infection and hypoxemia in the observation group was 20.83% (10/48), which was significant lower than the 68.75% (33/48) in the control group ( χ2=22.28, P<0.05). Conclusions:Evidence-based systematic early activity training could significantly alleviate postoperative pain in patients with large area cerebral infarction after surgery, improve their daily living ability and neurological function, further effectively shorten their hospitalization time, and reduce the occurrence of complications.

BACKGROUND:In vitro fertilization-embryo transfer is commonly used to solve infertility,but its success rate is not high,the more common reasons are poor endometrial receptivity,poor egg quality,etc.The follicular fluid melatonin can inhibit the aging of the ovary,to a certain extent,can promote the development of embryos,improve the probability of conception,but whether there is a correlation between the two is not known. OBJECTIVE:To explore the correlation between follicular fluid melatonin level and pregnancy rate of single-cycle in vitro fertilization-embryo transfer women. METHODS:A total of 112 female patients who received in vitro fertilization-embryo transfer treatment in the First Affiliated Hospital of Anhui Medical University from December 2020 to April 2021 were selected as the study subjects.They were divided into quartile array(Q1-Q5)according to the follicular fluid melatonin level from low to high.Among them,the melatonin level of group Q1 was<6.99 ng/L(n=18),that of group Q2 was 7.00-9.99 ng/L(n=26),that of group Q3 was 10.00-11.99 ng/L(n=27),and that of group Q4 was 12.00-13.99 ng/L(n=18);and melatonin levels in group Q5 were 14.00-19.99 ng/L(n=23).Clinical data characteristics of the five groups were compared.Multi-factor Logistic regression was used to analyze the correlation between follicular fluid melatonin level and pregnancy rate of women with single-cycle in vitro fertilization-embryo transfer and embryo transfer.A restricted cubic spline Logistic regression model was established to analyze the dose-response relationship,and the model was evaluated by clinical decision curve. RESULTS AND CONCLUSION:(1)Compared with the study population with the lowest melatonin quintile(Q1),with the increase of melatonin level(Q2-Q5),the levels of egg harvest and pregnancy success were gradually increased,and the body mass index was gradually decreased,and the differences were significant(P<0.05).(2)Multivariate Logistic regression analysis showed that after adjusting for confounding factors such as global mass index,number of eggs retrieved,luteinizing hormone,estradiol,progesterone and other confounding factors,follicular fluid melatonin level was still independently correlated with pregnancy rate of single-cycle in vitro fertilization-embryo transfer women(OR=1.538,95%CI:1.032-1.837,P<0.05),and there was significant difference in trend test of follicular fluid melatonin level from low to high quintile array(Ptrend<0.05).(3)The sensitivity test analysis showed that E value was 2.117.Subgroup analysis showed that the study population with higher levels of luteinizing hormone in follicular fluid had a more significant association between follicular fluid melatonin and pregnancy rate in single-cycle in vitro fertilization-embryo transfer women(P interaction=0.008).(4)The results of restricted cubic spline model analysis showed that there was a nonlinear dose-response relationship between follicular fluid melatonin level and pregnancy rate of single-cycle in vitro fertilization-embryo transfer women(P<0.05),and there was an overall positive correlation between follicular fluid melatonin level and pregnancy rate of single-cycle in vitro fertilization-embryo transfer women.(5)The results of clinical decision curve analysis showed that the follicular fluid melatonin level had important clinical value in predicting the pregnancy rate of single-cycle in vitro fertilization-embryo transfer women.(6)Follicular fluid melatonin level is closely related to the pregnancy rate of single-cycle in vitro fertilization-embryo transfer women,and with the decrease of follicular fluid melatonin level,the pregnancy rate of single-cycle in vitro fertilization-embryo transfer women also decreases.

Objective To investigate the factors influencing the pregnancy outcomes during frozen-thawed embryo transfer(FET)cycles in patients with polycystic ovary syndrome(PCOS).Methods A retrospective analysis was conducted on patients'data from 882 FET cycles.According to the pregnancy outcome,the patients were divided into non-implantation group(Group A),abortion group(Group B1)and live birth group(Group B2).Clinical data and laboratory parameters were compared among the three groups,and ordered Logistic regression analysis was used to study the factors influencing pregnancy outcomes after FET.Patients were also divided into four groups(C1-C4)based on the number of high-quality embryos obtained(0-3,4-6,7-10,≥11),and their clinical data and laboratory parameters were compared.Results The clinical pregnancy rate,live birth rate,and miscar-riage rate in the 882 treatment cycles were 71.09%(627/882),61.68%(544/882),and 13.24%(83/627),respectively.Single-factor analysis showed significant differences in body mass index(BMI),infertility type,hu-man chorionic gonadotropin(hCG)day estradiol(E2)level,number of retrieved oocytes,and number of high-quality embryos among Groups A,B1,and B2(P<0.05).Further multiple Logistic regression analysis revealed that BMI(OR=1.046,95%CI:1.001-1.093,P=0.044)and a history of previous pregnancy(OR=1.417,95%CI:1.030-1.950,P=0.032)were independent risk factors for successful FET in PCOS patients,while an in-creased number of high-quality embryos was an independent protective factor for successful pregnancy.Based on the results of Group B2,compared to Group A,OR=0.920,95%CI:0.880-0.962,P=0.000;compared to Group B1,OR=0.923,95%CI:0.862-0.988,P=0.022.Compared with the other three groups(C1-C3),the total amount of gonadotropin(Gn)in the C4 group was the lowest and the number of oocytes obtained was the high-est(P<0.05).Multiple comparisons showed that Group C4 had lower BMI,follicle-stimulating hormone(FSH),very low-density lipoprotein(vLDL)levels,a higher luteinizing hormone and follicle-stimulating hormone(LH/FSH)ratio compared to Group C1(P<0.05).Group C4 had lower fasting insulin(FINS)and homeostasis model assessment of insulin resistance(HOMA-IR)levels compared to Group C3,and higher high-density lipoprotein-cholesterol(HDL-C)and apolipoprotein A1(Apo A1)levels compared to Groups C2 and C3(P<0.05).Con-clusion BMI,the history of previous pregnancy and the number of high-quality embryos were both independent factors for predicting pregnancy outcomes in PCOS patients undergoing FET cycles.Patients with a higher number of high-quality embryos have a higher clinical pregnancy rate during FET cycles.

Objective To investigate the clinical effects and pregnancy outcomes of using luteal phase long protocol and GnRH antagonist protocol in patients with polycystic ovary syndrome(PCOS)who have failed their first GnRH antagonist protocol therapy.Methods The clinical data of 163 PCOS patients who underwent IVF/ICSI-ET were retrieved.After the failure of their first GnRH antagonist protocol treatment,they were divided into two groups in the second controlled ovarian hyperstimulation(COH)cycle:Luteal phase long protocol group(n=95)and Gn-RH antagonist protocol group(n=68).A retrospective analysis and comparison of basic clinical data,clinical and laboratory indicators,and pregnancy outcomes between two groups were conducted.Results ① There was no sta-tistically significant difference in basic clinical indicators between two group except LH.② Compared the first and second cycle treatments of patients in the luteal phase long protocol group,the initiation dose of gonadotropin(Gn),total number of Gn days,total Gn usage,estradiol(E2)on the day of hCG injection,number of retrieved eggs,oocyte maturation rate,2PN fertilization rate,2PN cleavage rate,blastocyst formation rate,high-quality blas-tocyst formation rate,and moderate to severe OHSS rate were significantly higher than those in the first GnRH an-tagonist cycle(P＜0.05).The GnRH antagonist protocol group also showed similar improvements.③ The com-parison of the second COH cycle between two groups showed that the total number of Gn days,total Gn usage,and total Gn cost in the luteal phase long protocol group were significantly higher(P＜0.05),while the E2 and LH on the day of hCG injection,and the maturation rate of eggs were significantly lower than those in the GnRH antagonist protocol group(P＜0.05).However,there was no statistically significant difference in the number of retrieved eggs,2PN fertilization,2PN cleavage,blastocyst formation rate,high-quality blastocyst formation rate,and OHSS rate between the two groups;④ The comparison of fresh transplantation cycles for the second COH cycle between the two groups showed that the luteal phase long protocol fresh transplantation rate,implantation rate,clinical preg-nancy rate,and live birth rate were slightly higher than those of the GnRH antagonist protocol group,but the differ-ence was not statistically significant.Comparing the outcomes of pregnancy following the initial frozen-thawed em-bryo transfer(FET)between two groups,the biochemical pregnancy rate and clinical pregnancy rate of the GnRH antagonist protocol group were higher than those of the luteal phase long protocol group(P＜0.05).However,no significant statistical variations were found in implantation rate,live birth rate,neonatal gestational age,and birth weight.Conclusion For PCOS patients who fail the first GnRH antagonist protocol,an appropriate increase in the initiating dose and usage of Gn can achieve satisfactory pregnancy outcomes with both protocols.Compared with change to a luteal phase long protocol,reusing the GnRH antagonist protocol still maintains its long-standing advan-tages,such as shorter total Gn days,lower costs,and better patient compliance.

The development of longitudinal cohorts has made the identification and surveillance of multiple biological markers and behavioral factors which influence disease course or health status become possible. However, traditional statistical methods typically use univariate longitudinal data for research, failing to fully exploit the information from multivariate longitudinal data. The group-based multi-trajectory model (GBMTM) emerged as a method to study the developmental trajectory of multivariate data in recent years. GBMTM has distinct advantages in analyzing multivariate longitudinal data by identifying potential subgroups of populations following similar trajectories by multiple indicators that influence the outcome of interest. In this study, we introduced the application of GBMTM by explaining the fundamental principles and using the data from a health management study in the elderly by using smart wearing equipment to investigate the relationship between multiple life-related variables and hypertension to promote the wider use of GBMTM in longitudinal cohort studies.