This study investigates the mechanism by which Xintong Granules improve myocardial ischemia-reperfusion injury(MIRI) through the regulation of gut microbiota and their metabolites, specifically short-chain fatty acids(SCFAs). Rats were randomly divided based on body weight into the sham operation group, model group, low-dose Xintong Granules group(1.43 g·kg~(-1)·d~(-1)), medium-dose Xintong Granules group(2.86 g·kg~(-1)·d~(-1)), high-dose Xintong Granules group(5.72 g·kg~(-1)·d~(-1)), and metoprolol group(10 mg·kg~(-1)·d~(-1)). After 14 days of pre-administration, the MIRI rat model was established by ligating the left anterior descending coronary artery. The myocardial infarction area was assessed using the 2,3,5-triphenyltetrazolium chloride(TTC) staining method. Apoptosis in tissue cells was detected by the terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling(TUNEL) assay. Pathological changes in myocardial cells and colonic tissue were observed using hematoxylin-eosin(HE) staining. The levels of tumor necrosis factor-α(TNF-α), interleukin-1β(IL-1β), interleukin-6(IL-6), creatine kinase MB isoenzyme(CK-MB), and cardiac troponin T(cTnT) in rat serum were quantitatively measured using enzyme-linked immunosorbent assay(ELISA) kits. The activities of lactate dehydrogenase(LDH), creatine kinase(CK), and superoxide dismutase(SOD) in myocardial tissue, as well as the level of malondialdehyde(MDA), were determined using colorimetric assays. Gut microbiota composition was analyzed by 16S rDNA sequencing, and fecal SCFAs were quantified using gas chromatography-mass spectrometry(GC-MS). The results show that Xintong Granules significantly reduced the myocardial infarction area, suppressed cardiomyocyte apoptosis, and decreased serum levels of pro-inflammatory cytokines(TNF-α, IL-1β, and IL-6), myocardial injury markers(CK-MB, cTnT, LDH, and CK), and oxidative stress marker MDA. Additionally, Xintong Granules significantly improved intestinal inflammation in MIRI rats, regulated gut microbiota composition and diversity, and increased the levels of SCFAs(acetate, propionate, isobutyrate, etc.). In summary, Xintong Granules effectively alleviate MIRI symptoms. This study preliminarily confirms that Xintong Granules exert their inhibitory effects on MIRI by regulating gut microbiota imbalance and increasing SCFA levels.
Animals ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Myocardial Reperfusion Injury/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Apoptosis/drug effects* ; Humans ; Tumor Necrosis Factor-alpha/metabolism* ; Interleukin-6/genetics* ; Malondialdehyde/metabolism*

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Objective:This study retrospectively analyzed the clinical characteristics of patients newly diagnosed with acute myeloid leukemia (AML) who were admitted to the hematology intensive care unit (HCU) with critical illness. It also examined factors associated with critical illness and early mortality in these patients.Methods:Clinical data were collected from 91 newly diagnosed AML patients admitted to the HCU of the Department of Hematology, First Affiliated Hospital of Soochow University, from October 2020 to 2024. Reasons for HCU admission, major therapeutic interventions, and risk factors for critical illness and early mortality were analyzed.Results:The median time from diagnosis to HCU admission was 3 days ( IQR: 3–9 days), and the median HCU stay was 10 days ( IQR: 3–23 days). Of the 91 patients, 71 were admitted to the HCU before induction chemotherapy, while 20 were transferred to the HCU after its initiation. The leading causes of HCU admission were pulmonary infection (78.0% ), respiratory failure (44.0% ), hepatic insufficiency (28.6% ), renal insufficiency (27.5% ), disseminated intravascular coagulation (DIC; 25.3% ), and sepsis (23.1% ). Median Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) and SOFA scores at HCU admission were 14 ( IQR: 11–18) and the median Sepsis Related Organ Failure Assessment (SOFA) score was 7 ( IQR: 4, 10). Major HCU interventions included vasoactive drugs, noninvasive and invasive mechanical ventilation, continuous renal replacement therapy, therapeutic leukocyte clearance, and cardiopulmonary resuscitation. Among patients receiving induction chemotherapy, the composite complete remission rate was 65.4%, and the overall remission rate was 88.5%. Thirty-five (38.5% ) patients died within 28 days of HCU admission. Independent risk factors for 28-day mortality were DIC ( OR=9.350, 95% CI 1.999–43.745, P=0.005), sepsis ( OR=6.817, 95% CI 1.571–29.582, P=0.010), and cardiac insufficiency ( OR=12.281, 95% CI 2.385–63.254, P=0.003) . Conclusion:The main reason for HCU admission in newly diagnosed critically ill AML patients was pulmonary infection. Nearly 40% of patients experisenced early death, and DIC, sepsis, and heart failure were factors influencing early mortatlity.

This report presents the management of a critically ill 36-year-old woman diagnosed with Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph +ALL) at 28 weeks of gestation. The patient rapidly deteriorated, developing disseminated intravascular coagulation (DIC) , diffuse alveolar hemorrhage (DAH) , septic shock, and multi-organ dysfunction, necessitating admission to the hematological intensive care unit. Given her critical condition and advanced pregnancy, a chemotherapy-free induction regimen comprising imatinib and dexamethasone was initiated, alongside comprehensive supportive measures, including mechanical ventilation, continuous renal replacement therapy (CRRT) , broad-spectrum antibiotics, and high-dose corticosteroids. During treatment, intrauterine fetal demise occurred, and a stillborn was delivered following obstetric intervention. With aggressive treatment, the patient's respiratory failure, DIC, and DAH gradually resolved, and she achieved complete remission. She subsequently received consolidation chemotherapy, CAR-T cell therapy, and allogeneic hematopoietic stem cell transplantation, achieving sustained complete molecular remission on long-term follow-up. This case demonstrates that for critically ill pregnant patients with Ph + ALL, a chemotherapy-free regimen of targeted therapy and corticosteroids, when combined with intensive supportive care, is a safe and effective approach that may offer a therapeutic option for similar cases.

Objective:To establish daily quality assurance workflow based on self-developed phantom to ensure MR-linac performance such as beam accuracy, MR image guidance accuracy, and the clinical treatment workflow to enhance the efficiency of daily quality assurance (QA).Methods:The self-developed phantom was made by 3D printer and used in conjunction with Daily QA-MR detector array. After CT-sim scanning, a treatment plan was designed and transmitted to the accelerator, tests were performed such as image guidance accuracy, beam output and beam quality, the differences in daily QA results between the self-developed phantom and standard phantom recommended by the manufacturer were analyzed by using paired t-test. Results:A total of 24 sets results were collected, the image guide accuracy in the X, Y and Z directions between standard and self-made phantom were (0.1±0.4), (-0.14±0.16), (0.07±0.05) mm and (0±0.02), (-0.02±0.02), (0.02±0.01) mm, respectively, and the differences were statistically significant ( P<0.001, =0.001 and <0.001). Daily QA-MR detector array beam measurement results including output, symmetry, beam quality and field size were -0.11%±0.20%, -0.10%±0.19%, -0.01%±0.08%, (0.4±0.1) mm and (0.2±0.1) mm, respectively. The new process saved 25% (approximately 9 min) of the time compared to the standard process. Conclusions:The new daily QA process for MR-linac is performed based on self-developed phantom and Daily QA-MR detector array. The accuracy and sensitivity meet the requirements and can improve the QA efficiency.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Objective:This study retrospectively analyzed the clinical characteristics of patients newly diagnosed with acute myeloid leukemia (AML) who were admitted to the hematology intensive care unit (HCU) with critical illness. It also examined factors associated with critical illness and early mortality in these patients.Methods:Clinical data were collected from 91 newly diagnosed AML patients admitted to the HCU of the Department of Hematology, First Affiliated Hospital of Soochow University, from October 2020 to 2024. Reasons for HCU admission, major therapeutic interventions, and risk factors for critical illness and early mortality were analyzed.Results:The median time from diagnosis to HCU admission was 3 days ( IQR: 3–9 days), and the median HCU stay was 10 days ( IQR: 3–23 days). Of the 91 patients, 71 were admitted to the HCU before induction chemotherapy, while 20 were transferred to the HCU after its initiation. The leading causes of HCU admission were pulmonary infection (78.0% ), respiratory failure (44.0% ), hepatic insufficiency (28.6% ), renal insufficiency (27.5% ), disseminated intravascular coagulation (DIC; 25.3% ), and sepsis (23.1% ). Median Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) and SOFA scores at HCU admission were 14 ( IQR: 11–18) and the median Sepsis Related Organ Failure Assessment (SOFA) score was 7 ( IQR: 4, 10). Major HCU interventions included vasoactive drugs, noninvasive and invasive mechanical ventilation, continuous renal replacement therapy, therapeutic leukocyte clearance, and cardiopulmonary resuscitation. Among patients receiving induction chemotherapy, the composite complete remission rate was 65.4%, and the overall remission rate was 88.5%. Thirty-five (38.5% ) patients died within 28 days of HCU admission. Independent risk factors for 28-day mortality were DIC ( OR=9.350, 95% CI 1.999–43.745, P=0.005), sepsis ( OR=6.817, 95% CI 1.571–29.582, P=0.010), and cardiac insufficiency ( OR=12.281, 95% CI 2.385–63.254, P=0.003) . Conclusion:The main reason for HCU admission in newly diagnosed critically ill AML patients was pulmonary infection. Nearly 40% of patients experisenced early death, and DIC, sepsis, and heart failure were factors influencing early mortatlity.

OBJECTIVE To investigate the ameliorative effect and potential mechanism of imperatorin(IMP)on doxorubicin(DOX)resistance in breast cancer.METHODS The effects of maximum non-toxic concentration(100 μg/mL)of IMP combined with different concentrations of DOX(12.5,25,50,75,100 μg/mL)on the proliferation of MCF-7/DOX cells were determined by MTT method.MCF-7/DOX cells were divided into blank control group(1‰ dimethyl sulfoxide),DOX group(50 μg/mL),IMP+DOX group(100 μg/mL IMP+50 μg/mL DOX)and IMP group(100 μg/mL).mRNA and protein expressions of multidrug resistance protein 1(MDR1)and multidrug resistance-associated protein l in each group were measured.The relevant pathways and targets involved in the improvement of DOX resistance in breast cancer cells by IMP were screened and validated by using transcriptome sequencing technology,along with gene ontology(GO)enrichment analyses and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses.RESULTS Compared with DOX alone,the combination of IMP and DOX reduced the half inhibitory concentration of DOX on MCF-7/DOX cells from 81.965 μg/mL to 43.170 μg/mL,the reverse fold was 1.90,and the mRNA expression of MDR1 was significantly down-regulated(P＜0.05).The results of GO enrichment analyses and KEGG pathway enrichment analyses indicated that the reversal of DOX resistance in breast cancer by IMP was mainly associated with the regulation of biological processes such as detoxification,multiple biological processes,and cell killing.The main pathway involved was the p53 signaling pathway,and the key targets mainly included constitutively photomorphogenic protein 1(COP1),cyclin E1(CCNE1),growth arrest and DNA damage-inducible protein 45A(GADD45A)and GADD45B.The results of the verification experiments showed that compared with DOX group,there was a trend of up-regulation of COP1 mRNA,and significant down-regulation of CCNE1,GADD45A,and GADD45B mRNA expression in IMP+DOX group(P＜0.05).CONCLUSIONS The effect of IMP in ameliorating DOX resistance in breast cancer is related to its regulation of COP1,CCNE1,GADD45A and GADD45B targets in the p53 signaling pathway.

This report presents the management of a critically ill 36-year-old woman diagnosed with Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph +ALL) at 28 weeks of gestation. The patient rapidly deteriorated, developing disseminated intravascular coagulation (DIC) , diffuse alveolar hemorrhage (DAH) , septic shock, and multi-organ dysfunction, necessitating admission to the hematological intensive care unit. Given her critical condition and advanced pregnancy, a chemotherapy-free induction regimen comprising imatinib and dexamethasone was initiated, alongside comprehensive supportive measures, including mechanical ventilation, continuous renal replacement therapy (CRRT) , broad-spectrum antibiotics, and high-dose corticosteroids. During treatment, intrauterine fetal demise occurred, and a stillborn was delivered following obstetric intervention. With aggressive treatment, the patient's respiratory failure, DIC, and DAH gradually resolved, and she achieved complete remission. She subsequently received consolidation chemotherapy, CAR-T cell therapy, and allogeneic hematopoietic stem cell transplantation, achieving sustained complete molecular remission on long-term follow-up. This case demonstrates that for critically ill pregnant patients with Ph + ALL, a chemotherapy-free regimen of targeted therapy and corticosteroids, when combined with intensive supportive care, is a safe and effective approach that may offer a therapeutic option for similar cases.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.