Objective:To analyze the key factors in quality control for clinical laboratory instrument by constructing an analysis framework of the model that entropy weight method(EWM)combines with decision laboratory analysis(DEMATEL),so as to obtain the key factors of quality control for clinical laboratory instrument,and provide a basis in formulating targeted quality control strategies for clinical laboratory instruments.Methods:Based on EWM,the entropy weight values of various factors of quality control for clinical laboratory instruments that produced influence on quality control for clinical laboratory instruments were obtained,and the DEMATEL was combined to determine the value of influence degree among different factors.The combined weight values of each factor were calculated to construct the analysis framework of the model that EWM combined with DEMATEL.Then,the main influencing factors were determined to formulate the model of management system of quality control for clinical laboratory instruments.Results:The management system of quality control for clinical laboratory instruments included 23 key factors of 5 aspects:performance and design of instruments,operators'training and skills,environmental factors,maintenance management,and mechanism of quality feedback.The combined weights of 10 factors,which included maintenance for instrument,operators'experience and proficiency,repair for instrument,operators'understanding for instrument performance,accuracy of instrument,mechanism of quality control and continuous feedback,stability of instrument,data security,suggestions of analyzing and optimizing data,and calibration for instrument,has a relatively high weight,and they were extremely key factors in the quality control for clinical laboratory instruments.Conclusion:The analysis framework of EWM combined with DEMATEL model can improve the accuracy and reliability of analyzing key factors of quality control for clinical laboratory instrument,and provide scientific basis in formulating targeted quality control strategies for clinical laboratory instrument,and help to promote continuous improvement and enhancement of quality control for clinical laboratory instrument.

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent

Signal detection is a critical task in drug safety regulation. However, it inevitably generates irrelevant or false signals, posing challenges for resource allocation by marketing authorization holders. To reasonably assess these signals, different countries have established various principles for prioritizing the evaluation of risk signals. This study systematically compares these principles and finds that the U.S. Food and Drug Administration(FDA) focuses on practical issues, such as identifying drug confusion or drug interactions. However, China's Good Pharmacovigilance Practices and the European Medicines Agency(EMA) emphasize a comprehensive evaluation framework. The Council for International Organizations of Medical Sciences(CIOMS) emphasizes the consistency of multiple data sources, highlighting the reliability of signal evaluation. China practices a multidisciplinary approach combining traditional Chinese and western medicine, and the risk signals related to traditional Chinese medicine(TCM) have unique characteristics, including complex components, cumulative toxicity, specific theoretical foundations, and drug interactions. The different priorities in risk signal evaluation principles across countries suggest that China should strengthen clinical trial research, emphasize corroboration with evidence of multiple sources, and pay particular attention to the risks of drug interactions in the TCM regulatory science. Establishing the risk signal prioritization principles that align with the characteristics of TCM enables more precise and efficient scientific regulation of TCM.
Humans ; Medicine, Chinese Traditional/standards* ; China ; Drugs, Chinese Herbal/adverse effects* ; United States ; United States Food and Drug Administration

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.

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 explore the distant metastatic characteristics of metastatic hormone-sensitive prostate cancer (mHSPC) and metastatic castration-resistant prostate cancer (mCRPC) based on prostate-specific membrane antigen (PSMA) PET-CT.Methods:This is a retrospective cohort study. Ultimately, data from 227 patients with metastatic prostate cancer who underwent PSMA PET-CT examinations at Xiangya Hospital, Central South University between March 2016 and May 2025 were retrospectively reviewed, including 117 mHSPC patients with an age of (68.8±7.6) years (range:53 to 89 years) and 110 mCRPC patients with an age of (69.4±7.5) years (range: 49 to 88 years). Clinical and pathological data, along with metastatic characteristics identified via PSMA PET-CT, were collected and compared. Intergroup comparisons were performed using χ 2 tests. Results:The incidence rates of lymph node metastasis, bone metastasis, and visceral metastasis in the mHSPC group were 71.8% (84/117), 89.7% (105/117), and 11.1% (13/117), respectively, while those in the mCRPC group were 52.7% (58/110), 91.8% (101/110), and 15.5% (17/110), respectively. The incidence of lymph node metastasis in the mHSPC group was significantly higher than that in the mCRPC group ( χ2=8.800, P=0.003). Among patients with bone metastasis, the rates of osteoblastic metastasis, osteolytic metastasis, and mixed metastasis in the mHSPC group were 76.2% (80/105), 8.6% (9/105), and 15.2% (16/105), respectively, while the corresponding rates in the mCRPC group were 74.3% (75/101), 7.3% (8/101), and 16.4% (18/101), respectively, all indicating a relatively high probability of osteolytic and mixed bone metastases ( χ2=0.260, P=0.878). Among patients with mHSPC and mCRPC who tested positive for visceral metastasis, lung metastasis (9/13 and 8/17) and liver metastasis (4/13 and 9/17) were the most common sites of metastasis, but there was no significant difference in the composition of visceral metastasis between the two groups ( χ2=0.933, P=0.564). In this study, among 20 patients who progressed from mHSPC to mCRPC, 35.0% (7/20) had persistent or progressive activity at the original metastatic site, 35.0% (7/20) developed new metastatic lesions, and 30.0% (6/20) showed inhibitory changes in the original metastatic lesions. Among patients with imaging progression, 1/14 of patients with osteoblastic metastatic lesions at the mHSPC stage exhibited osteolytic changes upon progression to mCRPC. Conclusion:Compared with the mCRPC group, the mHSPC group has a higher lymph node metastasis rate,and both groups have common rates of osteolytic and mixed bone metastases and visceral metastasis.

OBJECTIVE: Ulcerative colitis is closely associated with intestinal stem cell (ISC) loss and impaired intestinal mucus barrier. Sinisan (SNS), a compound Chinese herbal medicine, has a long history in the treatment of intestinal dysfunction, yet whether SNS can relieve acute experimental colitis by modulating ISC proliferation and secretory cell differentiation has not been studied. Our study tested the effect of SNS against acute colitis and focused on the mechanisms involving intestinal barrier recovery. METHODS: Network pharmacology analysis and blood entry component analysis of SNS were used to explore the underlying mechanism by which SNS affects the acute dextran sulfate sodium (DSS)-induced murine colitis model. RNA-sequencing was used to demonstrate the mechanism. Further, reverse transcription-quantitative polymerase chain reaction, immunofluorescence staining, and alcian blue and periodic acid-Schiff staining were performed in vivo and in the colonic organoids to investigate the cell lineage differentiation-related mechanism of SNS. Furthermore, potential active ingredients from SNS were predicted by network pharmacology analysis. RESULTS: SNS dramatically suppressed DSS-induced acute colonic inflammation in mice. RNA-sequencing analysis revealed downregulation of inflammation and apoptosis-related genes, and upregulation of lipid metabolism and proliferation-related genes, such as Irf7, Pparα, Clspn and Hspa5. Additionally, ISC renewal and intestinal secretory cell lineage commitment were significantly promoted by SNS both in vivo and in vitro in colonic organoids, leading to enhanced mucin expression. Furthermore, potential active ingredients from SNS that mediated inflammation, lipid metabolism, proliferation, apoptosis, stem cells and secretory cells were predicted using a network pharmacology approach. CONCLUSION Our study shed light on the underlying mechanism of SNS in attenuating acute colitis from the perspective of ISC renewal and secretory lineage cell differentiation, suggesting a of novel therapeutic strategy against colitis. Please cite this article as: Cai YJ, Lan JH, Li S, Feng YN, Li FH, Guo MY, et al. Sinisan, a compound Chinese herbal medicine, alleviates acute colitis by facilitating colonic secretory cell lineage commitment and mucin production. J Integr Med. 2025; 23(4): 429-444.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Mice ; Colon/pathology* ; Mucins/metabolism* ; Mice, Inbred C57BL ; Cell Differentiation/drug effects* ; Male ; Colitis/metabolism* ; Cell Lineage/drug effects* ; Dextran Sulfate ; Stem Cells/drug effects* ; Disease Models, Animal