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.

Systemic lupus erythematosus （SLE） may lead to secondary gynecological and obstetric disorders such as decreased ovarian reserve function， menstrual abnormalities， and adverse pregnancy outcomes. Based on "bi （痹） of both body and viscera" theory， this paper proposed that the core mechanism of SLE secondary gynecological and obstetric diseases lies in the mutual transformation between "body bi" and "viscera bi"， which together affect the uterus. Physiologically， uterus forms an internal-external network with the body and viscera through the meridians and blood vessels. Pathologically， when the healthy qi is deficient， nourishment of the body and viscera is impaired； when toxins and stasis accumulate， pathogenic factors disturb the uterus through the chong （冲） and ren （任） meri-dians. The resulting obstruction in the uterus can， in turn， manifest externally and aggravate damage to the body and viscera. Therefore， the pathogenesis of SLE secondary gynecological and obstetric diseases follows a dynamic trajectory of "body bi first， body bi affecting viscera， and then bi of both body and viscera". In treatment， the principle of harmonizing and balancing the healthy qi is emphasized. The main approach is to regulate the viscera， stabilize the body， and nourish the uterus， with the coordination of nourishing the viscera through the body， thereby achieving simultaneous treatment of both body and viscera. This highlights the guiding significance of the "bi of both body and viscera" theory in preventing and treating SLE secondary gynecological and obstetric diseases.

Diabetic nephropathy(DN)is a significant causative factor of end-stage renal disease globally,and its pathogenesis involves dysregulation of multiple cellular and hormonal pathways.Podocytes play crucial roles in the process of DN,with the extent of podocyte injury closely associated with key pathological manifestations of renal damage,such as proteinuria,glomerular filtration rate,and glomerulosclerosis.However,due to the complexity and interplay of mechanisms contributing to podocyte injury,such as oxidative stress,abnormal lipid metabolism,and mitochondrial damage,the precise mechanisms underlying podocyte injury remain incompletely understood.This review integrated the latest research findings from both domestic and international studies on the core mechanisms of podocyte injury in DN.Furthermore,this article summarized the implications of these mechanisms for DN treatment,particularly focusing on potential therapeutic targets and the development of related pharmacological interventions derived from targeting podocyte injury pathways,so as to provide a theoretical foundation for the development of clinical therapeutic strategies for DN.

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 construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective To observe the effects of Hedysarum Polybotrys polysaccharide(HPS)on the glucose metabolism of small intestinal smooth muscle in rats with diabetic gastroparesis(DGP)of spleen qi deficiency syndrome;To explore its mechanism based on AMPK/GLUT4 signaling pathway.Methods Totally 72 male Wistar rats were randomly divided into 12 in the blank group and the remaining 60 rats were assigned to the model group.The DGP spleen qi deficiency syndrome model was replicated by intraperitoneal injection of streptozotocin+irregular feeding with high-fat and high sugar feed combined with swimming exhaustion method.The model rats were randomly divided into model group,metformin group and HPS high-,medium-and low-dosage groups.The metformin group was given 100 mg/kg metformin hydrochloride by gavage,while the HPS high-,medium-and low-dosage groups were given 200,100 and 50 mg/kg HPS by gavage,respectively.The blank group and model group were given purified water by gavage once a day for 8 consecutive weeks.The gastric emptying rate and small intestine propulsion rate in rats were detected,HE staining was used to observe the smooth muscle morphology of gastric antrum and ileal tissue,immunofluorescence staining was used to detect the expression of glucagon like peptide-1(GLP-1)in ileal tissue,ELISA was used to detect the contents of adiponectin(APN),glucagon(Glu)and insulin(INS)in serum,Western blot was used to detect the protein expressions of glucose transporter(GLUT)4,GLUT1,AMP-activated protein kinase(AMPK)and p-AMPK in ileal tissue.Results Compared with the blank group,the model group rats showed significantly increased random blood glucose,significantly decreased body mass,gastric emptying rate and small intestinal propulsion rate(P<0.01);the edema in the submucosal layer,loose arrangement of connective tissue,infiltration of a small number of lymphocytes,granulocytes and macrophages,reduced number of goblet cells in the ileal tissue,shedding of intestinal villous epithelial cells and widening of the gap between the submucosal layer and the lamina propria;the expression of GLP-1 in ileal tissue was significantly decreased(P<0.05),the contents of serum APN and INS were significantly decreased,and the content of Glu significantly increased(P<0.01),the expressions of GLUT4 and p-AMPK/AMPK proteins in ileal tissue were significantly decreased,while the expression of GLUT1 protein significantly increased(P<0.01).Compared with the model group,rats in metformin group and HPS high-dosage group showed a random decrease in blood glucose,an increase in body mass,gastric emptying rate,and small intestine propulsion rate(P<0.05,P<0.01);a more regular arrangement of gastric antral tissue cells,a reduction of shedding cells and edema,the smooth muscle structure of the ileal tissue was relatively intact,with evenly distributed cells and reduced infiltration of inflammatory cells;the expression of GLP-1 in ileal tissue increased,the contents of serum APN and INS increased,and the content of Glu decreased,the expressions of GLUT4 and p-AMPK/AMPK proteins in ileal tissue increased,while the expression of GLUT1 protein decreased(P<0.05,P<0.01).Conclusion HPS may up-regulate GLUT4 and down-regulate GLUT1 expression through activates AMPK,promotes glucose uptake and utilization by small intestinal smooth muscle,and improves glucose metabolism of DGP rats with spleen qi deficiency syndrome.

Objective:To investigate the iodine nutrition status of children aged 8 - 10 and pregnant women in Xining City, Qinghai Province.Methods:From 2019 to 2021, a stratified cluster sampling method was used to divide 7 counties (districts) under the jurisdiction of Xining City, Qinghai Province into 5 sampling areas according to east, west, south, north, and center each year. One township (town, street) was selected from each area. Forty non boarding students aged 8 to 10 from each primary school (half male and half female, age balanced) and 20 pregnant women from each township (town, street) location were selected to collect edible salt samples at home and a random urine sample to measure salt iodine and urinary iodine level. B-ultrasound was used to measure thyroid volume in children and the goiter rate was calculated.Results:A total of 6 534 samples of household edible salt were collected from children and pregnant women, with an average salt iodine concentration of 25.58 mg/kg. The coverage rate of iodized salt was 97.50% (6 371/6 534), and the qualified iodized salt consumption rate was 89.46% (5 845/6 534). A total of 4 362 urine samples were collected from children, with a median urinary iodine level of 183.10 μg/L. The difference between different years was statistically significant ( H = 20.27, P < 0.001). A total of 2 169 urine samples were collected from pregnant women, with a median urinary iodine level of 168.90 μg/L. The difference between different years was statistically significant ( H = 107.09, P < 0.001). A total of 3 336 cases of thyroid gland examination were conducted in children, including 33 cases of thyroid enlargement, with a goiter rate of 0.99%. There was a statistically significant difference between different years (χ 2 = 15.00, P < 0.001). Conclusion:From 2019 to 2021, children aged 8 to 10 and pregnant women in Xining City are at an appropriate level of iodine, and the achievements in prevention and treatment of iodine deficiency disorders still need to be continuously consolidated.

Objective:To investigate the mechanism of Juanbi Capsules in the intervention of knee osteoarthritis by inhibiting lipid peroxidation and chondrocyte ferroptosis through activating nuclear factor E2 related factor (Nrf2)/glutathione peroxidase 4 (GPx4) signaling pathway.Methods:Totally 45 rats were randomly divided into blank group, model group and Juanbi Capsules low-, medium- and high-dosage groups, with 9 rats in each group. Except the blank group, the other groups were injected with sodium monoiodoacetate (MIA) to establish knee osteoarthritis model. From the third week of modeling, rats in Juanbi Capsules low-, medium- and high-dosage groups were gavaged with 108.05, 216.09, and 432.18 mg/kg of Juanbi Capsules suspension, and rats in the blank group and model group were gavaged with equal volume of normal saline, once a day, for 28 consecutive days. HE and safranine fast green staining were used to observe the pathological changes of cartilage tissue, and Mankin's score was performed; the levels of MDA, Fe 2+, glutathione (GSH) in articular cartilage were detected by biochemical kit; Western blot was used to detect the protein expressions of SLC7A11, GPx4, acsll4 and Nrf2 in rat articular cartilage. Results:Compared with the model group, the Mankin's score was significantly lower in the Juanbi Capsules middle- and high-dosage groups ( P<0.01); the MDA level decreased in Juanbi Capsules low-, medium- and high-dosage groups ( P<0.01), GSH level increased ( P<0.01), and and Fe 2+ level decreased Juanbi Capsules middle- and high-dosage groups ( P<0.01, P<0.05); the protein expressions of Nrf2, SLC7A11 and GPx4 in cartilage tissue of Juanbi Capsules middle- and high-dosage groups increased ( P<0.01 or P<0.05), the expression of ACSl4 protein decreased ( P<0.01 or P<0.05), and SLC7A11 protein expression increased in Juanbi Capsules low-dosage group ( P<0.05). Conclusion:Juanbi Capsule may inhibit the ferroptosis of rat articular cartilage by activating the Nrf2/GPX4 signaling pathway.