OBJECTIVE To investigate the application status of prescription pre-review systems in healthcare institutions of Yunnan province， evaluate their system functions and management capabilities， and provide a practical basis for promoting rational drug use. METHODS A questionnaire survey was conducted among public healthcare institutions at or above the secondary level in Yunnan province to investigate the deployment status of the systems. A capability maturity assessment framework was constructed， encompassing 6 dimensions and 39 indicators， including real-time prescription review， prescription correlation review， rule setting， evidence-based information support， prescription authority management， and system operation management. This framework was then used to evaluate the institutions that had implemented the pre-review systems. RESULTS A total of 100 valid questionnaires were collected， with 37 institutions having adopted prescription pre-review systems， mainly tertiary hospitals. The system predominantly adopted a modular architecture and was embedded into the hospital information system through application programming interfaces and middleware， providing certain capabilities for real-time prescription risk identification. Evaluation results indicated that basic functions such as reviewing indications， contraindications， and drug compatibility performed well， while deficiencies remained in functions related to parenteral nutrition prescription， review of drug dosage for specific diseases， individual patient characteristic recognition， and rule setting. Moreover， the construction of review centers and establishment of management systems were also not well-developed. CONCLUSIONS The overall application rate of prescription pre-review systems in Yunnan province remains low. System functions and management mechanisms require further improvement. It is recommended to enhance information infrastructure in lower-level institutions and explore regionally unified review models to promote standardized and intelligent development of prescription review practices.

ObjectiveTo evaluate the clinical efficacy and safety of Jingmaiyan granules （composed of Lonicerae Japonicae Flos， Sedi Herba， Paeoniae Radix Rubra， Moutan Cortex， Rhei Radix et Rhizoma Praeparata， and Glycyrrhizae Radix et Rhizoma） combined with external application of Jinhuang Ointment in treating acute-stage blood heat stasis type superficial thrombophlebitis （ST） of lower extremities， and to explore their effects on hemorheology and serum inflammatory factors. MethodsA randomized， double-blind， placebo-controlled clinical trial was conducted. A total of 124 patients with lower extremity ST were randomized into two groups（62 cases in each group）. The control group received external application of Jinhuang ointment and oral placebo treatment， while the observation group received external application of Jinhuang ointment and oral Jingmaiyan granules. Both groups were treated for 2 weeks. The clinical symptom scores， therapeutic efficacy of traditional Chinese medicine （TCM） syndrome， pain visual analog scale （VAS） scores， hemorheological indices ［including whole blood high-shear， medium-shear， and low-shear viscosity， as well as plasma viscosity （PV）］， and inflammatory factors ［C-reactive protein （CRP）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）］ were compared before and after treatment. ResultsAfter 2 weeks of treatment， the total effective rate in the observation group （98.3%， 60/62） was significantly higher than that in the control group （83.8%， 52/62）， with a statistically significant difference （Z=3.512 1， P<0.05）. Compared with pre-treatment scores， the scores for skin color， skin temperature， swelling， pain， and cord or nodules were significantly reduced in both groups （P<0.05）， with more pronounced improvement in the observation group （P<0.05）. Additionally， compared with pre-treatment levels， the whole blood viscosity （low-， medium-， and high-shear） significantly improved in both groups after treatment （P<0.05）， with more marked improvement in the observation group （P<0.05）. Furthermore， the plasma viscosity， CRP， IL-6， and TNF-α levels were significantly reduced in both groups after treatment （P<0.05）， with more pronounced improvement observed in the observation group （P<0.05）. ConclusionThe combination of external application of Jinhuang ointment and oral Jingmaiyan granules effectively improves clinical symptoms， hemorheological abnormalities， and inflammatory responses in patients with acute stage blood heat stasis type ST of lower extremities. The treatment is safe and holds clinical promotion value.

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 evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

OBJECTIVE To study the effects and mechanism of Portulaca oleracea cream on skin pruritus and barrier function in allergic contact dermatitis （ACD） mice. METHODS Low-concentration and high-concentration P. oleracea creams were prepared， with the P. oleracea extract solution （1 g/mL， calculated by crude drug） concentrations of 10% and 20%. Sixty BALB/c mice were randomly allocated into blank group， model group， Mometasone furoate cream group （positive control）， blank matrix cream group， P. oleracea low-concentration and high-concentration cream groups. Except for blank group， ACD model was induced in each group using 2，4-dinitrochlorobenzene solution. The blank group and model groups received normal saline， while the remaining groups were treated with their respective creams， once a day， at a dose of approximately 0.5 g per application， continuously for 14 days. At 24 h post-final administration， skin lesions of mice were observed and scored； pathological changes of skin tissue were observed； serum levels of immunoglobulin E（IgE） and tumor necrosis factor-α （TNF-α） were quantified. mRNA expression of MAS-related G protein-coupled receptors （including MrgprA3， MrgprC11， and MrgprD） in dorsal root ganglion （DRG） was assessed； while protein expressions of skin barrier function-related proteins Claudin-1 and Occludin in skin tissues were determined. RESULTS Compared with blank group， mice in the model group exhibited severe skin damage， characterized by loss of epidermal architecture， hyperkeratosis of the skin tissue， and the infiltration of a large number of inflammatory cells. The skin injury scores， as well as the serum levels of IgE and TNF-α， and the mRNA expression levels of MrgprA3， MrgprC11， and MrgprD in DRG， were all significantly elevated compared to the blank group （P＜0.05 or P＜0.01）； in contrast， the protein expression levels of Claudin-1 and Occludin in the skin tissue were markedly reduced （P＜0.05）. Compared with model group， mice in P. oleracea low-concentration and high- concentration cream groups demonstrated significant alleviation of skin damage， as evidenced by reduced epidermal hyperplasia， mitigated spongiosis in the dermis， and decreased infiltration of inflammatory cells； these quantitative indicators were almost significantly reversed （P＜0.05 or P＜0.01）. No significant differences were observed in the aforementioned skin injuries， pathological alterations， or quantitative indicators between the blank matrix cream group and the model group. CONCLUSIONS P. oleracea may ameliorate skin lesions and restore skin barrier function of ACD mice， the mechanism of which may be associated with downregulating mRNA expressions of MrgprA3， MrgprC11 and MrgprD in DRG， and up-regulating the protein expressions of Claudin-1 and Occludin in skin tissue.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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.