Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective To examine the causes of incision infections following lower extremity open fractures and develop a predictive model for assessing the risk.Methods A total of 104 patients with open fractures of the lower extremity,who received internal fixation from January 2022 to August 2023.According to whether there was incision infection after the operation,the patients were divided into infection group and non-infection group.The aim of the study was to analyze the distribution of pathogenic bacteria causing postoperative incision infections.Single-factor and multifactor Logistic regression analyses were employed to examine the factors influencing postoperative incisional infections.Subsequently,a risk prediction model for these infections was developed.The predictive capacity of this model was assessed using ROC curves.Results In the cohort of 104 patients with open fractures of the lower limb,the occurrence rate of postoperative incision infections was 19.23％.A total of 45 non-repeated pathogenic bacteria were isolated,among which gram-positive bacteria accounted for 53.33％,gram-negative bacteria 42.22％,fungi 4.44％.Gram-positive bacteria showed 100％resistance to ampicillin/sulbactam and penicillin,while resistance rates for erythromycin and clindamycin exceeded 90％.Among gram-negative bacteria,resistance rates to cefazolin,sulfamethoxazole/trimethoprim,levofloxacin,ampicillin/sulbactam,ciprofloxacin,and gentamicin were all above 67％.Notably,resistance rates for cefazolin,sulfamethoxazole,and trimethoprim surpassed 90％.Univariate and multifactorial logistic stepwise regression analysis highlighted that time elapsed from injury to surgery,duration of surgery,length of hospital stay,perioperative prophylactic medication,and Gustilo classification were significant risk factors for postoperative incisional infections in patients with the condition(P＜0.05).The ROC curves illustrated that the risk prediction model accurately forecasted the incidence of postoperative incisional infections in patients with open fractures of the lower extremity,with an area under the curve of 0.861(95％ CI:0.811 to 0.911),boasting a sensitivity of 90.50％and a specificity of 72.92％.Conclusion The main pathogen of wound infection after open fracture of lower extremity is Gram-negative,the time from injury to operation,operation time,hospitalization time,prophylactic medication during perioperative period and GUSTILO classification were the influencing factors of postoperative wound infection.In addition,the establishment of risk prediction model has a good prediction effect on the incidence of postoperative wound infection in patients with this disease.

Objective:To evaluate the value of multi-slice spiral CT (MSCT) in the diagnosis of liver metastases with rich blood supply.Methods:The clinical data and imaging data of 19 patients with liver metastases with rich blood supply admitted to the 921st Hospital of the Joint Logistics Support Force of Chinese People′s Liberation Army from September 2018 to September 2023 were retrospectively analyzed, and the number, location, shape, size of the lesions and the images of CT plain scan and dynamic enhanced scan were analyzed.Results:Among the 19 patients, there were 18 multiple cases and 1 single case. A total of 108 lesions were found. There were 62 cases (57.4%) in the right lobe of liver and 87 cases (80.6%) in the peripheral part of liver. The form of circular or quasi-circular, there were 99, irregular shape or lobed 9. The focal diameter was 0.6-6.8 cm. CT plain scan showed that 99 lesions showed slightly low density, and the other 9 lesions showed equal density relative to the background liver. In the dynamic enhanced scan, 108 lesions in arterial stage showed high-density enhancement, 97 lesions showed circular enhancement, and 11 lesions showed nodular enhancement. Among them, 77 lesions had moderate to obvious intensification density. Of the 108 lesions in the portal vein stage, 31 lesions showed moderate to obvious enhancement density, 49 lesions showed slightly low clearance density, and 28 lesions showed continuous enhancement density. In the delayed stage, all 108 lesions showed slightly low density.Conclusions:The main features of liver metastases with rich blood supply are: low density on plain CT scan, annular or nodular enhancement in the arterial phase of enhanced CT scan, and the peak of enhanced density can be in the arterial phase or the portal vein phase. Combined with clinical data, CT can make a correct diagnosis.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective:To develop and design a during-treatment records auto-review program to comply the quality assurance(QA)requirement of radiotherapy chart auditing,and thereby improve the review efficiency and accuracy.Methods:Based on the items the guideline required,the Aria Oncology Information System database backup files was analyzed by Java,Vue,and etc.languages and the corresponding review logic was formulated.A total of 530 treatment records generated at Shanghai Concord Cancer Center from January to March 2024(10 weeks)were auto-reviewed and compared with the manual results for evaluating the accuracy and efficiency of the program.Results:The auto-review program was running smoothly.Overall with the above data,the sensitivity,specificity,accuracy and the error-miss rate were 73.4%,14.3%,87.7%and 12.3%respectively.For sub-set items,the source-skin distance(SSD)error detecting rate was 100%,the wrong session reporting was 100%correlated with the plans switching and the wrong fraction reporting was 100%related to plan revision.For the other items,auto and manual reviews gave out the same accuracy.Conclusion:The none-error results from the program are all true,so the manual rechecking could limit to those auto-review error records,which can reduce the workload by 73.4%,therefore improve the effectiveness and accuracy of the radiotherapy data review.

BACKGROUND:Circadian rhythms are closely related to the life activities of most mammals and insects.Timless gene plays a crucial role in the generation of circadian rhythms as key components encoding the Timeless/Period gene complex.However,its specific mechanism in circadian rhythms is still unclear.OBJECTIVE:To study the relationship between Timless protein gene,Period gene,circadian rhythm,environment and cryptochrome gene,so as to have a more comprehensive understanding of the nucleation and accumulation mechanism of circadian cycle and the influence of environment on circadian rhythm.METHODS:Literature retrieval was conducted in the Web of science Core Collection database,PubMed and CNKI,and the relevant literature was searched,consulted and screened after the keyword was set as"Timless,Period,circadian rhythm,environment"in English and Chinese.Non-relevant literature was progressively excluded through full-text reading,and 126 papers were finally included for the review.RESULTS AND CONCLUSION:In the circadian clock,the circadian spontaneous output of the cyclins kaput and CYCLE activate the Timeless/Period gene,Timeless gene regulates the nucleation mechanism and stability of Period gene,and Period gene can also be nucleated individually by a number of mechanisms.Casein kinase 2,Shaggy protein kinase and double time genes can regulate circadian rhythms and participate in transcription by phosphorylating Timeless gene/Period gene.Cryptochrome gene-mediated degradation of Timeless gene has a very important role in transcriptional integrity.External factors such as environmental factors and dietary patterns can influence circadian rhythms through the Timeless gene/Period gene.Interestingly,time-restricted eating can be used as an effective way to improve circadian rhythm disturbances.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.