Objective To explore the risk factors for mineral and bone disorder in maintenance hemodialysis patients, and to construct and validate a nomogram prediction model. Methods A total of 306 patients undergoing maintenance hemodialysis at Shanghai Eighth People’s Hospital from January 2021 to May 2025 were selected as study subjects and randomly divided into a training set (n＝214) and a validation set (n＝92) in a 7∶3 ratio. In the training set, patients were divided into a normal bone mineral metabolism group and an abnormal bone mineral metabolism group, and related factors were compared between the two groups. The multivariate logistic regression analysis was used to identify the influencing factors of mineral and bone disorder in maintenance hemodialysis patients in the training set, and a nomogram prediction model was constructed. ROC curves were drawn to evaluate the ability of the nomogram model for predicting mineral and bone disorder in these patients. Calibration curves and Hosmer-Lemeshow goodness-of-fit test were used to analyze the consistency of the predictive probability of nomogram model and actual probability of mineral and bone disorder in these patients. The decision curve was used to assess the clinical benefit using nomogram prediction model. Results Among the 306 hemodialysis patients, 254 patients had mineral and bone disorder, accounting for 83.01%. Among the 214 patients in the training set, 177 had mineral and bone disorder, accounting for 82.71%. In the training set, age, gender, body mass index (BMI), hypertension rate, dialysis age, blood urea nitrogen (BUN), hemoglobin (Hb), albumin (ALB), alkaline phosphatase (ALP), serum creatinine (SCr), uric acid (UA), estimated glomerular filtration rate (eGFR), and rate of taking phosphate binders were statistically significant different between the two groups (P＜0.05). The multivariate logistic regression analysis showed higher age, female, hypertension, longer dialysis duration, decreased eGFR, and not taking phosphate binders were identified as risk factors for mineral and bone disorder in maintenance hemodialysis patients (P＜0.01). The nomogram prediction model was constructed. The area under the ROC curve of the model for mineral and bone disorder in the training set and validation set was 0.895 (95%CI 0.850-0.941) and 0.881 (95%CI 0.830-0.932), respectively, with maximum Youden indice of 0.650 and 0.600, sensitivity of 0.856 and 0.849, and specificity of 0.794 and 0.751. The Hosmer-Lemeshow test showed the nomogram prediction model had good consistency in predictive probabilities with actual probabilities in training set and validation set. The decision curve showed the nomogram model could bring clinical net benefits when the threshold probabilities in the training set and validation set were less than 0.96 and 0.91. Conclusions The nomogram prediction model constructed based on six independent risk factors including age, gender, hypertension, dialysis duration, eGFR, and using phosphate binders or not, shows good discrimination and calibration, with good clinical predictive ability, which could provide guidance for the management of maintenance hemodialysis patients.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

OBJECTIVE: To explore the co-morbid influencing factors of postmenopausal osteoporosis(PMOP) and dyslipidemia, and to provide evidence-based basis for clinical co-morbidity management. METHODS: Based on the 2017 to 2018 Beijing community cross-sectional survey data, PMOP patients were included and divided into the dyslipidemia group and the uncomplicated dyslipidemia group according to whether they were comorbid with dyslipidemia. Demographic characteristics, living habits and disease history were collected through questionnaires, and bone mineral density and bone metabolism biomarkers (osteocalcin, blood calcium, serum typeⅠprocollagen N-terminal prepeptide, etc.) were detected on site. Co-morbidity risk factors were analyzed using binary logistic regression. RESULTS: Three hundred and twenty patients with PMOP were included, including the comorbid group (75 patients) and the uncomplicated group (245 patients). The results showed that history of cardiovascular disease [OR=1.801, 95%CI(1.003, 3.236), P=0.049], history of cerebrovascular disease [OR=2.923, 95%CI(1.460, 5.854), P=0.002], frying and cooking methods[OR=5.388, 95%CI(1.632, 17.793), P=0.006], OST results[OR=0.910, 95%CI(0.843, 0.983), P=0.016], and blood Ca results [OR=60.249, 95%CI(1.862, 1 949.926), P=0.021] were the influencing factors of PMOP complicated with dyslipidemia. CONCLUSION Focus should be placed on the influencing factors of PMOP and dyslipidemia co-morbidities, with emphasis on multidimensional assessment, combining lifestyle interventions with bone metabolism marker monitoring to optimize co-morbidity management.
Humans ; Dyslipidemias/epidemiology* ; Female ; Middle Aged ; Osteoporosis, Postmenopausal/metabolism* ; Aged ; Cross-Sectional Studies ; Risk Factors ; Bone Density

OBJECTIVE: To examine the mechanism of action of tanshinone II A (Tan II A) in promoting chemosensitization of osteosarcoma cells to cisplatin (DDP). METHODS: The effects of different concentrations of Tan II A (0-80 µ mol/L) and DDP (0-2 µ mol/L) on the proliferation of osteosarcoma cell lines (U2R, U2OS, 143B, and HOS) at different times were examined using the cell counting kit-8 and colony formation assays. Migration and invasion of U2R and U2OS cells were detected after 24 h treatment with 30 µ mol/L Tan II A, 0.5 µ mol/L DDP alone, and a combination of 10 µ mol/L Tan II A and 0.25 µ mol/L DDP using the transwell assay. After 48 h of treatment of U2R and U2OS cells with predetermined concentrations of each group of drugs, the cell cycle was analyzed using a cell cycle detection kit and flow cytometry. After 48 h treatment, apoptosis of U2R and U2OS cells was detected using annexin V-FITC apoptosis detection kit and flow cytometry. U2R cells were inoculated into the unilateral axilla of nude mice and then the mice were randomly divided into 4 groups of 6 nude mice each. The 4 groups were treated with equal volume of Tan II A (15 mg/kg), DDP (3 mg/kg), Tan II A (7.5 mg/kg) + DDP (1.5 mg/kg), and normal saline, respectively. The body weight of the nude mice was weighed, and the tumor volume and weight were measured. Cell-related gene and signaling pathway expression were detected by RNA sequencing and Kyoto Encyclopedia of Genes and Genomes pathway analysis. p38 MAPK signaling pathway proteins and apoptotic protein expressions were detected by Western blot. RESULTS: In vitro studies have shown that Tan II A, DDP and the combination of Tan II A and DDP inhibit the proliferation, migration and invasion of osteosarcoma cells. The inhibitory effect was more pronounced in the Tan II A and DDP combined treatment group (P<0.05 or P<0.01). Osteosarcoma cells underwent significantly cell-cycle arrest and cell apoptosis by Tan II A-DDP combination treatment (P<0.05 or P<0.01). In vivo studies demonstrated that the Tan II A-DD combination treatment group significantly inhibited tumor growth compared to the Tan II A and DDP single drug group (P<0.01). Additionally, we found that the combination of Tan II A and DDP treatment enhanced the p38 MAPK signaling pathway. Western blot assays showed higher p-p38, cleaved caspase-3, and Bax and lower caspase-3, and Bcl-2 expressions with the combination of Tan II A and DDP treatment compared to the single drug treatment (P<0.01). CONCLUSION Tan II A synergizes with DDP by activating the p38/MAPK pathway to upregulate cleaved caspase-3 and Bax pro-apoptotic gene expressions, and downregulate caspase-3 and Bcl-2 inhibitory apoptotic gene expressions, thereby enhancing the chemosensitivity of osteosarcoma cells to DDP.
Abietanes/therapeutic use* ; Osteosarcoma/enzymology* ; Cisplatin/therapeutic use* ; Humans ; Cell Line, Tumor ; Animals ; Apoptosis/drug effects* ; Mice, Nude ; Cell Proliferation/drug effects* ; Cell Movement/drug effects* ; p38 Mitogen-Activated Protein Kinases/metabolism* ; MAP Kinase Signaling System/drug effects* ; Bone Neoplasms/enzymology* ; Cell Cycle/drug effects* ; Xenograft Model Antitumor Assays ; Mice ; Drug Resistance, Neoplasm/drug effects* ; Neoplasm Invasiveness ; Mice, Inbred BALB C

Hypoxemia is a common pathological state characterized by low oxygen saturation in the blood. This condition compromises mucosal barrier integrity particularly in the gut and oral cavity. However, the mechanisms underlying this association remain unclear. This study used periodontitis as a model to investigate the role of platelet activation in oral mucosal immunopathology under hypoxic conditions. Hypoxia upregulated methyltransferase-like protein 4 (METTL4) expression in platelets, resulting in N⁶-methyladenine modification of mitochondrial DNA (mtDNA). This modification impaired mitochondrial transcriptional factor A-dependent cytosolic mtDNA degradation, leading to cytosolic mtDNA accumulation. Excess cytosolic mt-DNA aberrantly activated the cGAS-STING pathway in platelets. This resulted in excessive platelet activation and neutrophil extracellular trap formation that ultimately exacerbated periodontitis. Targeting platelet METTL4 and its downstream pathways offers a potential strategy for managing oral mucosa immunopathology. Further research is needed to examine its broader implications for mucosal inflammation under hypoxic conditions.
DNA, Mitochondrial/metabolism* ; Mouth Mucosa/pathology* ; Hypoxia/immunology* ; Methyltransferases/metabolism* ; Blood Platelets/metabolism* ; Animals ; Periodontitis/immunology* ; Humans ; Platelet Activation ; Mice

Objective To explore the regularity of prescriptions for epidemic disease in Taiping Huimin Heji Ju Fang based on the latent structure model and association rules analysis,and to provide references for modern epidemic treatment.Methods Prescriptions for epidemic diseases were extracted from Taiping Huimin Heji Ju Fang.A high-frequency herb matrix(frequency≥10)was constructed,and high-frequency herbs were analyzed using MicrosoftExcel 2016,Lantern 5.0,and IBM SPSS Modeler 18.0 for efficacy classification,property/flavor/channel tropism statistics,latent structure modeling,and association rule analysis.Results Among the 200 collected herbal prescriptions,46 high-frequency medicinal materials were identified,such as licorice,fresh ginger,dried ginger,poriae,and ginseng.The top 3 drugs efficacy were tonifying deficiency,relieving surface and warming inner.The medicinal properties were mainly warm,and the medicinal flavors were pungent,bitter,and sweet.The meridian tropisms mainly included the spleen meridian,lung meridian and stomach meridian.The analysis of latent structure model suggested that there were 8 types of common symptoms of epidemic disease,such as wind evil attack exterior,wind-cold-dampness,wind-heat,excess-heat in triple energizer,dampness inhibits qi stagnation,yang deficiency,blockage and spleen-qi deficiency.Theanalysis of association rules obtained licorice-ephedra and licorice-atractylodes,which with a core of licorice medicine,and the 16 association rules such as dried ginger-cinnamon,pericarpium citri reticulatae-mangnolia officinalis and poriae-ginseng-atractylodes macrocephala after eliminating ginger,jujube and licorice.Conclusion Most of the medicines used in the treatment of epidemic diseases in Taiping Huimin Heji Ju Fang are pungent,bitter and dispelling evil,the treatment should follow the principle of dispelling evil,warming yang to dissipate cold,dispelling dampness,clearing heat and expelling fire,inducing resuscitation,supporting the right and supplementing deficiency,which embodies the treatment principle of dispelling evil and supporting right,and provides reference and ideas for the treatment of clinical diseases based on syndrome differentiation.

The advantages of deep learning(DL)over the traditional imaging evaluation methods were introduced for tumor prognosis prediction and efficacy assessment.The research progress of DL applied in prognosis and efficacy prediction of head and neck squamous cell carcinoma was reviewed in terms of survival prediction,local recurrence and distant metastasis,cervical lymph node metastasis,therapeutic efficacy and molecular markers of tumors.The defficiencies of DL for prognosis and efficacy prediction of head and neck squamous cell carcinoma were explored,and the future research directions were envisioned.[Chinese Medical Equipment Journal,2025,46(6):97-104]

Objective:To construct and evaluate a radiomics model for predicting perineural invasion in patients with intrahepatic cholangiocarcinoma (ICC).Methods:Clinical data of 144 patients with ICC undergoing surgery in the People’s Hospital of Zhengzhou University ( n=113) and the Affiliated Cancer Hospital of Zhengzhou University ( n=31) from January 2018 to June 2023 were retrospectively analyzed, including 80 males and 64 females, aged (58.8±10.1) years. The patients were randomly divided into a training set ( n=100) and a test set ( n=44) at a ratio of 7: 3. The former set was used to build the model for predicting perineural invasion, and the latter was used to evaluate the model. Enhanced CT images and clinical data of the patients were collected, and features related to perineural invasion were screened. A light gradient boosting machine was used to construct an imaging genomics model. The model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results:Univariate and multivariate logistic regression analysis showed that none of the clinical features were associated with neural invasion in ICC patients (all P>0.05). Six, 25, 32, and 37 radiomics features were obtained by screening the intratumoral, 2 mm peritumoral, 5 mm peritumoral, and 8 mm peritumoral regions, respectively. The area under the ROC curve for predicting perineural invasion in ICC patients was 0.849 (95% CI: 0.774-0.923) in the training set and 0.745 (95% CI: 0.597-0.894) in the test set for the intratumoral model, 0.966 (95% CI: 0.938-0.995) and 0.750 (95% CI: 0.604-0.896) for the 5mm peritumoral model, 0.936 (95% CI: 0.892-0.980) and 0.792 (95% CI: 0.644-0.939) for the 2mm peritumoral model, and 0.961 (95% CI: 0.929-0.992) and 0.689 (95% CI: 0.526-0.853) for the 8mm peritumoral model. The area under the ROC curve, accuracy, sensitivity, and specificity of the combined intratumoral and 5mm peritumoral model for predicting perineural invasion were 0.927 (95% CI: 0.878-0.976), 88.0%, 84.5%, and 89.8% in the training set, and 0.849 (95% CI: 0.737-0.960), 77.3%, 85.2%, and 72.0% in the test set, respectively. The calibration curve showed a deviation between the calibration curve of the combined intratumoral and 5mm peritumoral model and the ideal line, but it could achieve basic consistency. DCA showed that when the threshold was between 0.18 and 0.70, the combined intratumoral and 5mm peritumoral model could bring clinical net benefit to patients when predicting neural invasion. Conclusion:The intratumoral and 5mm peritumoral imaging genomics model based on enhanced CT features can effectively predict neural invasion and offer clinical benefits in patients with ICC.