Chronic atrophic gastritis （CAG）， a common digestive system disease， has an unclear pathogenesis. Currently， it is mostly believed to be related to Helicobacter pylori （Hp） infection， immune factors， dietary factors， bile reflux， long-term use of antibiotics and anti-inflammatory drugs， and other factors. Ferroptosis is a regulated cell death mechanism that is iron-dependent and characterized by disruption of iron metabolism and accumulation of lipid peroxides. More and more studies have found that ferroptosis is closely related to the onset of CAG. Professor LI Diangui， a master of traditional Chinese medicine， first proposed the turbid toxin theory， which holds that spleen deficiency and turbid toxin is the main pathogenic mechanism of CAG. Abnormal iron metabolism regulation is a prerequisite for the accumulation of turbid toxin in CAG， and ferroptosis is in accordance with the pathogenic mechanism （spleen deficiency and turbid toxin） of CAG. This article explores the pathological mechanism of spleen deficiency and turbid toxin in CAG from the perspectives of iron metabolism， oxidative stress， and lipid peroxidation， providing theoretical support of traditional Chinese medicine for the modern research on CAG. It enriches the modern scientific connotation of the turbid toxicity theory and provides new ideas and breakthrough points for the clinical treatment of CAG.

Chronic atrophic gastritis （CAG）， a common digestive system disease， has an unclear pathogenesis. Currently， it is mostly believed to be related to Helicobacter pylori （Hp） infection， immune factors， dietary factors， bile reflux， long-term use of antibiotics and anti-inflammatory drugs， and other factors. Ferroptosis is a regulated cell death mechanism that is iron-dependent and characterized by disruption of iron metabolism and accumulation of lipid peroxides. More and more studies have found that ferroptosis is closely related to the onset of CAG. Professor LI Diangui， a master of traditional Chinese medicine， first proposed the turbid toxin theory， which holds that spleen deficiency and turbid toxin is the main pathogenic mechanism of CAG. Abnormal iron metabolism regulation is a prerequisite for the accumulation of turbid toxin in CAG， and ferroptosis is in accordance with the pathogenic mechanism （spleen deficiency and turbid toxin） of CAG. This article explores the pathological mechanism of spleen deficiency and turbid toxin in CAG from the perspectives of iron metabolism， oxidative stress， and lipid peroxidation， providing theoretical support of traditional Chinese medicine for the modern research on CAG. It enriches the modern scientific connotation of the turbid toxicity theory and provides new ideas and breakthrough points for the clinical treatment of CAG.

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.

ObjectiveTo investigate the main risk factors for liver cirrhosis in chronic hepatitis B （CHB） patients with high metabolic risk， to establish a noninvasive predictive model， and to compare the diagnostic efficiency of this model and other models including fibrosis-4 （FIB-4）， aspartate aminotransferase-to-platelet ratio index （APRI）， gamma-glutamyl transpeptidase-to-platelet ratio （GPR）， and Forns index. MethodsA total of 527 CHB patients with high metabolic risks who were admitted to The Second Affiliated Hospital of Guangxi Medical University from September 1， 2017 to October 31， 2022 were enrolled as subjects， and they were randomly divided into modeling group with 368 patients and validation group with 159 patients at a ratio of 7∶3. The LASSO regression analysis and the multivariate Logistic regression analysis were performed for the modeling group to identify independent risk factors， and a nomogram model was established. The receiver operating characteristic （ROC） curve， the calibration curve， and the decision curve analysis were used to validate the nomogram prediction model in the modeling group and the validation group and assess its discriminatory ability， calibration， and clinical practicability. The Delong test was used to compare the area under the ROC curve （AUC） of the nomogram prediction model and other models. ResultsThe multivariate Logistic regression analysis showed that prealbumin （odds ratio ［OR］ = 0.993， 95% confidence interval ［CI］： 0.988 — 0.999， P= 0.019）， thrombin time （OR=1.182， 95% CI： 1.006 — 1.385， P=0.047）， log₁₀ total bilirubin （TBil） （OR=1.710， 95%CI： 1.239 — 2.419， P=0.001）， and log₁₀ alpha-fetoprotein （AFP） （OR=1.327， 95%CI： 1.052 — 1.683， P=0.018） were independent influencing factors for liver cirrhosis in CHB patients with high metabolic risks. A nomogram model for risk prediction was established based on the multivariate analysis， which had an AUC of 0.837 （95%CI： 0.788 — 0.888）， a specificity of 73.5%， and a sensitivity of 84.7%， as well as a significantly higher diagnostic efficiency than the models of FIB-4 （0.739）， APRI （0.802）， GPR （0.800）， and Forns index （0.709） （Z=2.815， 2.271， 1.989， and 2.722， P=0.005， 0.017， 0.045， and 0.006）. ConclusionThe nomogram model established based on prealbumin， thrombin time， log₁₀ TBil， and log₁₀ AFP has a certain clinical application value.

OBJECTIVE To provide a reference for standardizing the conduct of positron-emitting radiopharmaceuticals’ phase 0 clinical trials （hereinafter referred to as “phase 0 clinical trials”） and advancing the development of innovative drug by medical institutions. METHODS Initiated by the First Affiliated Hospital of Jinan University， a panel of experts consisting of pharmacy， clinical medicine and medical ethics from multiple institutions was established to investigate the current landscape， and discuss the necessary conditions， procedures， and other aspects for conducting phase 0 clinical trials in medical institutions by integrating relevant national policies， regulations and expert consensus. Finally， an agreement was reached to formulate this consensus. RESULTS & CONCLUSIONS Currently， most medical institutions have deficiencies in pharmaceutical care during the management of radiopharmaceuticals and the phase 0 clinical trials. In conjunction with the Expert Consensus on the Establishment of Nuclear Pharmacist Positions， this consensus explicitly defines the responsibilities of nuclear pharmacists in the phase 0 clinical trials on the basis of the Expert Consensus for the Application of Positron Emission Tomography Radioligands for Translational Study in the Phase 0 Clinical Trials （2020 edition）， providing a guidance for high-quality participation of nuclear pharmacists from medical institutions in China in phase 0 clinical research. Additionally， in consideration of some constraints imposed by current relevant regulations， this consensus also proposes strategic recommendations， such as encouraging medical institutions to form a consortium， leading to the establishment of dedicated bases or industrial parks， holding significant implications to strengthen institutional capacity for advancing radiopharmaceutical innovation through phase 0 clinical trials.

Objective To evaluate the surgical efficacy of unilateral pneumonectomy for the treatment of tuberculous destroyed lung, analyze the causes of severe postoperative complications, and explore clinical management strategies. Methods A retrospective analysis was conducted on the clinical data of patients with tuberculous destroyed lung who underwent unilateral pneumonectomy at the Public Health Clinical Center of Chengdu from 2017 to 2023. Postoperative severe complications were statistically analyzed. Patients were divided into a non-severe complication group and a severe-complication group, and the causes, management, and outcomes of complications were analyzed. Results A total of 134 patients were included, comprising 69 males and 65 females, with a mean age of 17-73 (40.43±12.69) years. There were 93 patients undergoing left pneumonectomy and 41 patients undergoing right pneumonectomy. Preoperative sputum smear was positive in 35 patients, all of which converted to negative postoperatively. There were 58 patients with hemoptysis preoperatively, and none experienced hemoptysis postoperatively. Postoperative incisional infection occurred in 8 (5.97%) patients, and postoperative pulmonary infection in 26 (19.40%) patients. Severe postoperative complications occurred in 17 (12.69%) patients, including empyema in 9 (6.72%) patients, bronchopleural fistula with empyema in 1 (0.75%) patient, severe pneumonia in 3 (2.24%) patients, postpneumonectomy syndrome in 1 (0.75%) patient, chylothorax in 1 (0.75%) patient, ketoacidosis in 1 (0.75%) patient, and heart failure with severe pneumonia in 1 (0.75%) patient. Perioperative mortality occurred in 2 (1.49%) patients, both of whom underwent right pneumonectomy. Multivariate logistic regression analysis revealed that a history of ipsilateral thoracic surgery, concomitant Aspergillus infection, and greater blood loss were independent risk factors for severe complications following unilateral pneumonectomy for tuberculous destroyed lung (P<0.05). Conclusion Unilateral pneumonectomy for patients with tuberculous destroyed lung can significantly improve the clinical cure rate, sputum conversion rate, and hemoptysis cessation rate. However, there is a certain risk of severe perioperative complications and mortality, requiring thorough perioperative management and appropriate management of postoperative complications.

Background::Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them.Methods::This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated.Results::When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899–0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796–0.995) and 0.870 (95% CI, 0.769–0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model.Conclusions::DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo.