Descurainiae Semen Lepidii Semen, also known as Tinglizi, originates from Brassicaceae plants Descurainia sophia or Lepidium apetalum. The former is commonly referred to as "Southern Tinglizi(Descurainiae Semen)", while the latter is known as "Northern Tinglizi(Lepidii Semen)". To scientifically and accurately identify the origin of Tinglizi medicinal materials and traditional Chinese medicine products, this study developed a specific DNA mini-barcode based on chloroplast genome sequences. By combining the DNA mini-barcode with DNA metabarcoding technology, a method for the qualitative and quantitative identification of Tinglizi medicinal materials and Chinese patent medicines was established. In this study, chloroplast genomes of Southern Tinglizi and Northern Tinglizi and seven commonly encountered counterfeit products were downloaded from the GenBank database. Suitable polymorphic regions were identified to differentiate these species, enabling the development of the DNA mini-barcode. Using DNA metabarcoding technology, medicinal material mixtures of Southern and Northern Tinglizi, as well as the most common counterfeit product, Capsella bursa-pastoris seeds, were analyzed to validate the qualitative and quantitative capabilities of the mini-barcode and determine its minimum detection limit. Additionally, the mini-barcode was applied to Chinese patent medicines containing Tinglizi to authenticate their botanical origin. The results showed that the developed mini-barcode(psbB) exhibited high accuracy and specificity, effectively distinguishing between the two authentic origins of Tinglizi and commonly encountered counterfeit products. The analysis of mixtures demonstrated that the mini-barcode had excellent qualitative and quantitative capabilities, accurately identifying the composition of Chinese medicinal materials in mixed samples with varying proportions. Furthermore, the analysis of Chinese patent medicines revealed the presence of the adulterant species(Capsella bursa-pastoris) in addition to the authentic species(Southern and Northern Tinglizi), indicating the occurrence of adulteration in commercially available Tinglizi-containing products. This study developed a method for the qualitative and quantitative identification of multi-origin Chinese medicinal materials and related products, providing a model for research on other multi-origin Chinese medicinal materials.
DNA Barcoding, Taxonomic/methods* ; Drugs, Chinese Herbal/chemistry* ; Drug Contamination ; Genome, Chloroplast ; Medicine, Chinese Traditional

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

Objective:To explore the longitudinal effect of learning stress on learning burnout in vocational college students, and the mediating role of academic procrastination.Methods:A total of 1 212 vocational college students were selected, and two follow-up surveys were conducted at 12-week intervals in September (T1) and November (T2) of 2024 using the basic situation questionnaire, the burnout inventory-student survey, the learning pressure questionnaire and the brief academic procrastination scale. SPSS 26.0 software was used to compare the demographic characteristics of students' depersonalization using t test and single factor analysis of variance. Bootstrap was used to analyze the relationship among learning stress, academic procrastination and learning burnout. Results:The scores of learning stress at T1 and T2 for vocational college students were 14.47±3.52 and 14.52 ±3.50, the scores of academic procrastination at T1 and T2 were 27.14±9.07 and 27.21±9.04, and the scores of learning burnout T1 and T2 were 39.38±8.76 and 39.69±8.79.The t-test showed that the score of learning burnout at T1 of students aged 18 and below (36.70±8.72) was lower than students aged 18 above (40.15±8.63, t=-5.81, P<0.01). The score of learning burnout for liberal arts students at T1(40.82±8.54) was higher than that of science students (37.68±8.72, t=6.31, P<0.01). Single factor analysis of variance showed that the score of learning burnout for grade 1 students at T1(35.19±8.45) was lower than that of grade 2 students (41.33±7.98) and grade 3 students (38.92±9.88), and learning burnout score of grade 2 students at T1 was higher than that of grades 3 students ( F=61.59, P<0.01). The score of learning burnout for high-achieving students at T1(36.23±8.34) was lower than that of middle-achieving students (39.82±8.52) and low-achieving students (45.42±9.14), and the score of learning burnout for middle-achieving students at T1 were lower than that of low-achieving students ( F=36.53, P<0.01). Bootstrap test showed that academic procrastination T2 played a partial mediating role in the relationship between learning stress T1 and learning burnout T2 (effect size=0.04, 95% CI=0.03-0.07). Academic procrastination T1 played a partial mediating role in the relationship between learning stress T1 and learning burnout T2 (effect size=0.05, 95% CI=0.04-0.07). Conclusion:Learning stress can directly affect learning burnout in vocational college students, and also can indirectly affect learning burnout through the mediating effect of academic procrastination.

Objective:To explore the longitudinal effect of learning stress on learning burnout in vocational college students, and the mediating role of academic procrastination.Methods:A total of 1 212 vocational college students were selected, and two follow-up surveys were conducted at 12-week intervals in September (T1) and November (T2) of 2024 using the basic situation questionnaire, the burnout inventory-student survey, the learning pressure questionnaire and the brief academic procrastination scale. SPSS 26.0 software was used to compare the demographic characteristics of students' depersonalization using t test and single factor analysis of variance. Bootstrap was used to analyze the relationship among learning stress, academic procrastination and learning burnout. Results:The scores of learning stress at T1 and T2 for vocational college students were 14.47±3.52 and 14.52 ±3.50, the scores of academic procrastination at T1 and T2 were 27.14±9.07 and 27.21±9.04, and the scores of learning burnout T1 and T2 were 39.38±8.76 and 39.69±8.79.The t-test showed that the score of learning burnout at T1 of students aged 18 and below (36.70±8.72) was lower than students aged 18 above (40.15±8.63, t=-5.81, P<0.01). The score of learning burnout for liberal arts students at T1(40.82±8.54) was higher than that of science students (37.68±8.72, t=6.31, P<0.01). Single factor analysis of variance showed that the score of learning burnout for grade 1 students at T1(35.19±8.45) was lower than that of grade 2 students (41.33±7.98) and grade 3 students (38.92±9.88), and learning burnout score of grade 2 students at T1 was higher than that of grades 3 students ( F=61.59, P<0.01). The score of learning burnout for high-achieving students at T1(36.23±8.34) was lower than that of middle-achieving students (39.82±8.52) and low-achieving students (45.42±9.14), and the score of learning burnout for middle-achieving students at T1 were lower than that of low-achieving students ( F=36.53, P<0.01). Bootstrap test showed that academic procrastination T2 played a partial mediating role in the relationship between learning stress T1 and learning burnout T2 (effect size=0.04, 95% CI=0.03-0.07). Academic procrastination T1 played a partial mediating role in the relationship between learning stress T1 and learning burnout T2 (effect size=0.05, 95% CI=0.04-0.07). Conclusion:Learning stress can directly affect learning burnout in vocational college students, and also can indirectly affect learning burnout through the mediating effect of academic procrastination.

[Objective] To summarize the diagnosis and treatment of Antopol Goldman lesion (AGL) in clinical practice. [Methods] Clinical data and diagnosis and treatment process of one AGL case treated in our hospital were retrospectively analyzed, and relevant literature was reviewed. [Results] The patient presented with painless gross hematuria and right-sided lower back pain.Imaging examination suggested swelling of the right kidney, blood accumulation in the right calyx, renal pelvis and lower ureter, blood clot in the bladder, and multiple small stones in the left kidney.After multidisciplinary consultation, close imaging follow-up, interventional and flexible ureterdscope examination, tumors of hematological diseases, renal hematuria, arteriovenous fistula and collection system were excluded.After conservative treatment, the patient gradually recovered.During the follow-up of 1 year, no hematuria or low back pain recurred. [Conclusion] There is no clear diagnostic standard for AGL.Diagnosis relies on imaging, interventional methods and ureteroscopy.It is necessary to exclude other diseases and adopt conservative treatment.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective:To investigate the expression level of UBE2C in liver cancer tissues and its effect on the proliferation and invasion of hepatocellular carcinoma cells HepG2 after UBE2C silencing.Methods:The data set of liver cancer was downloaded from the TCGA database.Ac-cording to the median expression level of UBE2C mRNA in liver cancer tissues,all liver cancer pa-tients were divided into UBE2C higher(n=169)and lower expression group(n=205),respectively.The expression level of UBE2C mRNA in liver cancer tissues and its relationship with the patients prognosis was analyzed.COX regression was used to analyze the influencing factors of the liver cancer patients prognosis.The human hepatocellular carcinoma cell lines(HepG2,Huh7 and SMMC-7721)and human nromal hepatic epithelial cell line(THLE-3)were selected,and the ex-pression level of UBE2C in the four cell lines were detected by Western blot and real-time fluores-cence quantitative PCR,respectively.The HepG2 cell line was protein and mRNA expression leves divided into control group,NC group and si-UBE2C group according to UBE2C silencing.The ef-fects of UBE2C silencing on proliferation and invasion of HepG2 cell line were analyzed.Results:The expression level of UBE2C mRNA in liver cancer tissues and adjacent normal liver tissues were 4.342(3.239,5.635)and 0.905(0.587,1.230),respectively.Compared with adjacent normal liver tissues,UBE2C mRNA levels in liver cancer tissues were significantly higher(P＜0.001).The UBE2C mRNA expression levels in liver cancer tissues and paired adjacent normal liver tissues were 4.266(3.342,5.054)and 0905(0.587,1.230),respectively.Compared with paired adjacent normal liver tissues,UBE2C mRNA expression levels in liver cancer tissues were significantly higher(P＜0.001).The median survival time of UBE2C mRNA higher and lower expression groups was 48.85 months and 69.38 months.Compared with the lower expression group,the median survival time of UBE2C mRNA higher expression group was significantly shortened(P=0.045).T staging(T3/T4)and UBE2C expression(higher expression)were independent risk factors for poor prognosis in patients with liver cancer(P＜0.05).Compared with human liver epithelial cell line THLE-3,UBE2C protein and mRNA were significantly higher expressed in human hepatocellular carcinoma cell line HepG2,Huh7 and SMMC-7721(P＜0.05).The expression level of UBE2C protein and mRNA expression was the most significant in human hepatocellular carcinoma cell line HepG2 relative to cell line Huh7 and SMMC-7721.The CCK-8 results show that the cell proliferation rate in si-UBE2C group were significantly decreased protein and mRNA expression levels compared to control group and NC group at 72 h and 96 h,and the differences were significant(P＜0.05).The number of invasive cells in control group,NC group and si-UBE2C group were(23.12±3.45),(24.33±2.83)and(10.21±1.14),respectively.Compared with control group and NC group,the number of invasive cells in si-UBE2C group was significantly decreased(P＜0.05).Conclusion:UBE2C was higher expressed in liver cancer,and can be used as a biomarker for poor prognosis of patients with liver cancer.After silencing of UBE2C gene can significantly inhibit proliferation and invasion of HepG2.

Objective:To explore the factors influencing clinicians'adoption of new medical technologies and provide data and strategic support to promote the clinical application of innovative medical technologies.Methods:Based on the Unified Theory of Acceptance and Use of Technology ( UTAUT),a model was constructed to examine the factors influencing clinicians'adoption of new medical technologies.The model was revised through expert interviews,and data were collected via a survey questionnaire.Structural equation modeling ( SEM) was conducted using AMOS software to test both the direct and mediating effects.Results:Clinical performance expectancy ( P<0.001 ),DRG/DIP policy impact ( P=0.009 ),patient willingness ( P<0.001 ),and facilitating conditions ( P<0.001 ) had significant direct effects on usage intention.Usage intention ( P=0.005 ),patient willingness ( P=0.021 ),and facilitating conditions ( P<0.001 ) had significant direct effects on usage behavior.Usage intention mediated the relationship between clinical performance expectancy ( P<0.001 ),DRG/DIP policy impact ( P=0.030 ),patient willingness ( P<0.001 ),facilitating conditions ( P<0.001 ),and usage behavior.Conclusions:Clinical performance expectancy is the primary factor influencing clinicians'willingness to adopt new medical technologies.DRG/DIP payment methods somewhat reduce usage intention,while facilitating conditions are the key factor influencing usage behavior.Policy recommendations should focus on these key factors to promote the clinical application of innovative medical technologies.

Objective:To investigate the expression of TMSB10 mRNA in primary liver can-cer and its clinical value.Methods:The primary liver cancer data set was downloaded from the TCGA database and to analyze the expression of TMSB10 mRNA in primary liver cancer.The as-sociation between TMSB10 mRNA expression and primary liver cancer patients survival rate,im-mune cell infiltration were analyzed.A total of 150 primary liver cancer tissues and adjacent tis-sues were enrolled in our hospital,and the expression of TMSB10 mRNA in primary liver cancer tissues and adjacent tissues were analyzed via immunohistochemistry.All patients with primary liver cancer were followed up and divide into poor prognosis(n=81)and good prognosis group(n=69).The Logistic regression analysis was used to analyze the risk factors for poor prognosis in pa-tients with primary liver cancer.Results:The expression of TMSB10 mRNA in primary liver can-cer tissue and normal liver tissue were 1111.239(601.842,2154.575),396.998(324.379,524.741),respectively.When compared with normal liver tissue,the TMSB10 mRNA was significantly over-expressed in primary liver cancer tissue(P＜0.001).The median survival time of the TMSB10 mRNA lower expression group was 84.4 months,while the median survival time was 49.7 months in TMSB10 mRNA higher expression group.Compared with the TMSB10 lower expression group,the survival rate of the TMSB10 higher expression group was significantly decreased(HR=1.55,P=0.039).The expression of TMSB10 mRNA in primary liver cancer tissue was significantly positively correlated with the infiltration degree of B lymphocytes,CD8+T lymphocytes,CD4+T lympho-cytes,macrophages,neutrophils,and dendritic cells.The high expression rate of TMSB10 in pri-mary liver cancer tissues was significantly higher than that in adjacent tissues(x2=103.419,P＜0.001).The Logistic analysis showed that vascular invasion,AJCC stage with Ⅲ-Ⅳ,and higher ex-pression of TMSB were all independent risk factors for the poor prognosis in patients with primary liver cancer.In poor prognosis group,when compared with TMSB10 lower expression group,the time of recurrence and metastasis in TMSB10 higher expression group was significantly shortened(t=4.456,P＜0.001).Conclusion:TMSB10 was significantly over-expressed in primary liver cancer,and TMSB can serve as a biological marker for poor prognosis in patients with primary liver cancer.

Objective:To explore the factors influencing clinicians'adoption of new medical technologies and provide data and strategic support to promote the clinical application of innovative medical technologies.Methods:Based on the Unified Theory of Acceptance and Use of Technology ( UTAUT),a model was constructed to examine the factors influencing clinicians'adoption of new medical technologies.The model was revised through expert interviews,and data were collected via a survey questionnaire.Structural equation modeling ( SEM) was conducted using AMOS software to test both the direct and mediating effects.Results:Clinical performance expectancy ( P<0.001 ),DRG/DIP policy impact ( P=0.009 ),patient willingness ( P<0.001 ),and facilitating conditions ( P<0.001 ) had significant direct effects on usage intention.Usage intention ( P=0.005 ),patient willingness ( P=0.021 ),and facilitating conditions ( P<0.001 ) had significant direct effects on usage behavior.Usage intention mediated the relationship between clinical performance expectancy ( P<0.001 ),DRG/DIP policy impact ( P=0.030 ),patient willingness ( P<0.001 ),facilitating conditions ( P<0.001 ),and usage behavior.Conclusions:Clinical performance expectancy is the primary factor influencing clinicians'willingness to adopt new medical technologies.DRG/DIP payment methods somewhat reduce usage intention,while facilitating conditions are the key factor influencing usage behavior.Policy recommendations should focus on these key factors to promote the clinical application of innovative medical technologies.