BACKGROUND:Ilizarov bone transport is very effective in the treatment of open large tibial bone defects,but there are still complications,among which the difficulty of junction healing is one of the difficult points in treatment. OBJECTIVE:To investigate the effect of Ilizarov bone transport combined with antibiotic bone cement on junction healing after operation of open large tibial bone defect. METHODS:Totally 51 patients with open large tibial bone defect(bone defect>4 cm)admitted to Binzhou Medical University Hospital from August 2010 to January 2022 were selected,of which 28 received Ilizarov bone transport alone(control group)and 23 received Ilizarov bone transport combined with antibiotic bone cement treatment(trial group).External fixation time,bone healing time,bone healing index,visual analog scale score during bone removal,bone defect limb function,junction healing and complications at the final follow-up were statistically compared between the two groups. RESULTS AND CONCLUSION:(1)All the 51 patients were followed up for a mean of(22.53±5.77)months.External fixation time,bone healing time,bone healing index,postoperative infection rate,and non-healing rate of junction were less in the trial group than those in the control group(P<0.05).There was no significant difference between the two groups in visual analog scale scores at 6 months after the second surgery and in the functional excellence and good rate of limb with bone defect at the final follow-up(P>0.05).(2)These findings indicate that compared with the Ilizarov bone transport alone,Ilizarov bone transport combined with antibiotic bone cement treatment can promote the healing of open tibial fracture junction and increase the rate of bone healing.

Objective:To investigated the relationship between magnetic resonance imaging(MRI)parameters and the molecular pathology of primary central nervous system lymphoma(PCNSL).Methods:We retrospectively analyzed 26 patients from The First Affiliated Hospital of Harbin Medical University between January 2020 and June 2023 classified into germinal center B-cell like(GCB)and non-germinal center B-cell like(non-GCB)groups based on cell origin,into Ki-67≥75%and<75%groups based on the Ki-67 index,into BCL-2+and BCL-2-groups based on BCL-2 expression,and into responsive and non-responsive groups based on their response to MAP+Bruton's tyrosine kinase inhib-itor(BTKi)treatment.We extracted and compared first-order parameters between the groups,including mean value,standard deviation,variance,coefficient of variation,skewness,kurtosis,and entropy from baseline MRI images.Results:Four parameters(variance,kurtosis,skewness,and coefficient of variation)showed no significant differences between groups.However,three parameters(mean,standard devi-ation,and entropy)significantly differed between the groups based on Ki-67 and BCL-2 expression.For the Ki-67 index,the three parameters'areas under the curve(AUC)were 0.731,0.831,and 0.913,respectively.For BCL-2 expression,the mean and standard deviation AUCs were 0.889 and 0.938,respectively.In addition,the mean and entropy parameters significantly differed between the groups categorized by cell origin and treatment responsiveness(P<0.05).Multi-parameter joint analysis demonstrated greater identification accuracy compared to util-izing individual quantitative parameters from texture analysis.Conclusions:The mean,standard deviation,and entropy MRI parameters can help predict Ki-67 and BCL-2 expression in patients with PCNSL and have evaluative functions for treatment.They are beneficial for preoper-ative non-invasive assessment of tumor malignancy,providing vidence for prognosis and treatment planning.

As confusion mounts over RNA isoforms involved in phenotypic plasticity, aberrant CpG methylation-mediated disruption of alternative splicing is increasingly recognized as a driver of intratumor heterogeneity (ITH). Protease serine 3 (PRSS3), possessing four splice variants (PRSS3-SVs; PRSS3-V1-V4), is an indispensable trypsin that shows paradoxical effects on cancer development. Here, we found that PRSS3 transcripts and their isoforms were divergently expressed in lung cancer, exhibiting opposing functions and clinical outcomes, namely, oncogenic PRSS3-V1 and PRSS3-V2 versus tumor-suppressive PRSS3-V3, by targeting different downstream genes. We identified an intragenic CpG island (iCpGI) in PRSS3. Hypermethylation of iCpGI was mediated by UHRF1/DNMT1 complex interference with the binding of myeloid zinc finger 1 (MZF1) to regulate PRSS3 transcription. The garlic-derived compound diallyl trisulfide cooperated with 5-aza-2'-deoxycytidine to exert antitumor effects in lung adenocarcinoma cells through site-specific iCpGI demethylation specifically allowing MZF1 to upregulate PRSS3-V3 expression. Epigenetic silencing of PRSS3-V3 via iCpGI methylation (iCpGIm) in BALF and tumor tissues was associated with early clinical progression in patients with lung cancer but not in those with squamous cell carcinoma or inflammatory disease. Thus, UHRF1/DNMT1-MZF1 axis-modulated site-specific iCpGIm regulates divergent expression of PRSS3-SVs, conferring nongenetic functional ITH, with implications for early detection of lung cancer and targeted therapies.

Objective:To screen the pivotal genes involved in the occurrence and development of HBV-associated HCC. Additionally, perform validation and biological function analysis to evaluate changes in the expression of pivotal genes and their prognostic value in patients with hepatocellular carcinoma.Methods:The GSE121248 gene expression profile data of HBV-HCC patients were searched and downloaded from the GEO database. The R language was used to compare the differences in gene expression between hepatocellular carcinoma and paracancerous tissues. KEGG and GO function enrichment analyses were performed on the differential genes. PPI plots and pivotal gene screening were carried out through online tools like STRING and Cytoscape software. 369 cases of hepatocellular carcinoma and 160 healthy controls in TCGA and GTEx were used as validation cohorts to verify the expression levels of the pivotal genes. A Kaplan-Meier plot was drawn to evaluate the prognostic value of the pivotal gene.Results:A total of 120 differentially expressed genes were screened, of which 89 were up-regulated and 31 were down-regulated. Differential genes were mainly enriched in the metabolic pathways related to retinol metabolism, cytochrome P450 metabolism, and the p53 signaling pathway. The top 10 differential genes were selected as pivotal genes by the Cytoscape plug-in cytoHubba. There were significant differences in the expression levels of four types of CCNB1, CDK1, RRM2, and TOP2A genes in the validation cohort. All four types of genes were up-regulated. Survival analysis showed that patients with elevated expression levels of four genes had a poorer prognosis, with statistical differences in results.Conclusion:Four types of genes, CCNB1, CDK1, RRM2, and TOP2A, have high expression levels in patients with HBV-HCC and are correlated to shorter survival times, making them a potential target for diagnosis, prognosis, and treatment.

Objective:To study the changes of urinary metabolic profile, screen metabolic ions characterization with clinical diagnostic value, and a disease differentiation model establishment, in an attempt to help the clinical diagnosis of hepatocellular carcinoma patients.Methods:A case-control study was conducted. Ultra-performance liquid chromatography/mass spectrometry (UPLC-MS) were used to analyze urine samples of 32 patients with hepatocellular carcinoma, 28 patients with liver cirrhosis and 28 healthy persons, respectively. The orthogonal partial least squares discriminant analysis (OPLS-DA) and the principal component analysis (PCA) model were constructed using MZmine2.0 and SIMCA-P + 12.0.1.0 software for preliminary screening of metabolites. The metabolic ions selected in the final test were analyzed by SPSS, and the markers were analyzed and screened by one-way analysis of variance. Finally, the sensitivity and specificity of the selected markers were analyzed by calculating the area under the receiver operating characteristic (ROC) curve. One-way analysis of variance was used to compare quantitative indicators between groups.Results:OPLS-DA model parameters were R2X = 35.3%, R2Y = 86.9%, and Q2 = 72.2%, which had a good identification value. A total of 26 characteristic ions were screened, of which 17 were identified. 14, 19-Dihydroaspidospermatine had a high value in distinguishing healthy person with hepatocellular carcinoma patients, and the area under the receiver operating characteristic curve was higher than 0.9. The area under the ROC curve for distinguishing liver cancer with liver cirrhosis patients was 0.88, which was higher than the ROC curve of alpha-fetoprotein (0.75).Conclusion:Based on the UPLC-MS platform, the PCA and OPLS-DA models were successfully constructed, and the characteristic metabolic ions in the urine were extracted and identified, which has a certain value in assisting clinical screening of hepatocellular carcinoma patients.

To provide new ideas for clinical diagnosis and treatment of coronavirus disease 2019 (COVID-19), this study explore the expression level and prognostic value of platelet parameters in mild, moderate and severe COVID-19. This is a retrospective analysis. From January to May 2020, a total of 69 patients who were diagnosed with COVID-19 in the Third Central Hospital and the Jinnan Hospital (both situated in Tianjin) were enrolled in the disease group. According to the severity, these patients were divided into mild group (15 cases), moderate group (46 cases), and severe group (8 cases). In the same period, 70 non-infected patients were enrolled in control group. The level of white blood cell count (WBC), absolute neutrophil count (NEU#), absolute lymphocyte count (LY#), neutrophil-lymphocyte ratio (NLR), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet-large contrast ratio (P-LCR) before and after treatment were analyzed. Binary logistic regression analysis is used to establish a mathematical model of the relationship between these indexes and the outcome of severe COVID-19 patients. The receiver operating characteristic(ROC) curve is used to further explore the prognosis value of MPV, P-LCR, NLR separately and jointly in COVID-19 patients. Compare to the control group, WBC and NE# increase ( Z=-5.63, P<0.01; Z=-9.19, P<0.01) and LY# decrease ( Z=-9.34, P<0.01) in the severe group; NLR increase with the aggravation of the disease, there is significant difference between groups ( Z=17.61, P<0.01); PLT, PDW, MPV and P-LCR decrease with the aggravation of the disease, there is significant difference between groups ( Z=9.47, P<0.01; Z=11.41, P<0.01; Z =16.76, P<0.01; Z=13.97, P<0.01). Binary logistic regression analysis shows MPV, P-LCR and NLR have predictive value for severe COVID-19 patients. There is a negative correlation between MPV, P-LCR and severe COVID-19 patients ( OR=1.004, P=0.034; OR=1.097, P=0.046). There is a positive correlation between NLR and severe COVID-19 patients ( OR=1.052, P=0.016). MPV and P-LCR of patients with good prognosis after treatment were significantly higher than those before treatment ( Z=-6.47, P<0.01; Z=-5.36, P<0.01). NLR was significantly lower than that before treatment ( Z=-8.13, P<0.01). MPV and P-LCR in poor prognosis group were significantly lower than those before treatment ( Z=-9.46, P<0.01; Z=-6.81, P<0.01). NLR was significantly higher than that before treatment ( Z=-3.24, P<0.01). There were significant differences between good and poor prognosis groups before and after treatment in MPV, P-LCR and NLR ( P<0.01). Combination of these three indexes, ROC shows the AUC is 0.931, the sensitivity is 91.5%, the specificity is 94.1%, the positive predictive value is 88.9%, and the negative predictive value is 87.4%, which is better than any of these indexes separately. Changes in these parameters are closely related to clinical stage of COVID-19 patients. MPV, P-LCR and NLR are of great value in the prediction and prognosis of severe COVID-19 patients.

To provide new ideas for clinical diagnosis and treatment of coronavirus disease 2019 (COVID-19), this study explore the expression level and prognostic value of platelet parameters in mild, moderate and severe COVID-19. This is a retrospective analysis. From January to May 2020, a total of 69 patients who were diagnosed with COVID-19 in the Third Central Hospital and the Jinnan Hospital (both situated in Tianjin) were enrolled in the disease group. According to the severity, these patients were divided into mild group (15 cases), moderate group (46 cases), and severe group (8 cases). In the same period, 70 non-infected patients were enrolled in control group. The level of white blood cell count (WBC), absolute neutrophil count (NEU#), absolute lymphocyte count (LY#), neutrophil-lymphocyte ratio (NLR), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet-large contrast ratio (P-LCR) before and after treatment were analyzed. Binary logistic regression analysis is used to establish a mathematical model of the relationship between these indexes and the outcome of severe COVID-19 patients. The receiver operating characteristic(ROC) curve is used to further explore the prognosis value of MPV, P-LCR, NLR separately and jointly in COVID-19 patients. Compare to the control group, WBC and NE# increase ( Z=-5.63, P<0.01; Z=-9.19, P<0.01) and LY# decrease ( Z=-9.34, P<0.01) in the severe group; NLR increase with the aggravation of the disease, there is significant difference between groups ( Z=17.61, P<0.01); PLT, PDW, MPV and P-LCR decrease with the aggravation of the disease, there is significant difference between groups ( Z=9.47, P<0.01; Z=11.41, P<0.01; Z =16.76, P<0.01; Z=13.97, P<0.01). Binary logistic regression analysis shows MPV, P-LCR and NLR have predictive value for severe COVID-19 patients. There is a negative correlation between MPV, P-LCR and severe COVID-19 patients ( OR=1.004, P=0.034; OR=1.097, P=0.046). There is a positive correlation between NLR and severe COVID-19 patients ( OR=1.052, P=0.016). MPV and P-LCR of patients with good prognosis after treatment were significantly higher than those before treatment ( Z=-6.47, P<0.01; Z=-5.36, P<0.01). NLR was significantly lower than that before treatment ( Z=-8.13, P<0.01). MPV and P-LCR in poor prognosis group were significantly lower than those before treatment ( Z=-9.46, P<0.01; Z=-6.81, P<0.01). NLR was significantly higher than that before treatment ( Z=-3.24, P<0.01). There were significant differences between good and poor prognosis groups before and after treatment in MPV, P-LCR and NLR ( P<0.01). Combination of these three indexes, ROC shows the AUC is 0.931, the sensitivity is 91.5%, the specificity is 94.1%, the positive predictive value is 88.9%, and the negative predictive value is 87.4%, which is better than any of these indexes separately. Changes in these parameters are closely related to clinical stage of COVID-19 patients. MPV, P-LCR and NLR are of great value in the prediction and prognosis of severe COVID-19 patients.

OBJECTIVE: To explore the diagnostic value of the serum metabolites identified by high-performance liquid chromatography-mass spectrometry (HPLC/MS) for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODS: A total of 126 patients admitted to Tianjin Third Central Hospital were enrolled, including 27 patients with HBV-related hepatitis with negative viral DNA (DNA-N), 24 with HBV-related hepatitis with positive viral DNA, 24 with HBV-related liver cirrhosis, 27 with HBV-related HCC undergoing surgeries or radiofrequency ablation, and 24 with HBV-related HCC receiving interventional therapy, with 25 healthy volunteers as the normal control group. Serum samples were collected from all the subjects for HPLC/MS analysis, and the data were pretreated to establish an orthogonal partial least- squares discriminant analysis (OPLS-DA) model. The differential serum metabolites were preliminarily screened by comparisons between the HBV groups and the control group, and the characteristic metabolites were identified according to the results of non-parametric test. The potential clinical values of these characteristic metabolites were evaluated using receiver operator characteristic curve (ROC) analysis. RESULTS: A total of 25 characteristic metabolites were identified in the HBV- infected patients, including 9 lysophosphatidylcholines, 2 fatty acids, 17α-estradiol, sphinganine, 5-methylcytidine, vitamin K2, lysophosphatidic acid, glycocholic acid and 8 metabolites with few reports. The patients with HBV- related HCC showed 22 differential serum metabolites compared with the control group, 4 differential metabolites compared with patients with HBV-related liver cirrhosis; 10 differential metabolites were identified in patients with HBV-related HCC receiving interventional therapy compared with those receiving surgical resection or radiofrequency ablation. From the normal control group to HBV-related HCC treated by interventional therapy, many metabolites underwent variations following a similar pattern. CONCLUSIONS We identified 25 characteristic metabolites in patients with HBV-related HCC, and these metabolites may have potential clinical values in the diagnosis of HBV-related HCC. The continuous change of some of these metabolites may indicate the possibility of tumorigenesis, and some may also have indications for the choice of surgical approach.
Carcinoma, Hepatocellular ; blood ; diagnosis ; virology ; Case-Control Studies ; Chromatography, High Pressure Liquid ; DNA, Viral ; blood ; Hepatitis B virus ; genetics ; Hepatitis B, Chronic ; blood ; virology ; Humans ; Liver Cirrhosis ; virology ; Liver Neoplasms ; blood ; diagnosis ; virology ; Mass Spectrometry ; Metabolome ; Metabolomics ; ROC Curve

The original version of this article unfortunately contained a mistake. One of the authors of this article has been misspelled. Xiaoyang Zhang should be Xiaoyan Zhang. The update is also provided here.

Objectives This research explored the characteristics of changes in the serum metabolic profile of preeclampsia pregnancy(PE) to establish the disease distinguish model and screen characteristic metabolic markers with potential diagnostic value for preeclampsia.Methods From August 2014 to January 2016,samples in three groups were collected at Tianjin Third Central Hospital.Thirty-one clinically diagnosis patients with preeclampsia,25 normal pregnancy women and 29 healthy volunteers of childbearing age were enrolled.Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze serum metabolites of PE group (31 patients with preeclampsia),P group (25 normal pregnancy women) and Normal group (29 healthy volunteers of childbearing age).Nonparametric test analyzes were used to analyze the data and find the specific metabolites.Results This research established the principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) disease distinguish model for PE group,P group and Normal group.To distinguish PE group,P group and Normal group,15 characteristic metabolites were identified.Eight kinds of glycerol phospholipid (including 7 kinds of hemolysis phosphatidyl choline and 1 kind of lysophospholipids acid) and 1 kind of sphingomyelin in PE group were higher than that of normal pregnancy group.The difference had statistically significant(Z of the metabolites were 2.32,3.34,3.21,2.60,2.22,3.40,3.58,5.84,2.70 respectively,all P<0.05).1,25-Dihydroxyvitamin D3-26,23-lactone and 24-Oxo-1alpha,23,25-trihydroxyvitamin D3 in PE group were higher than that of P group and Normal group,which had a statistics difference (Z of the metabolites were 2.01,3.89,3.26,2.34 respectively,all P<0.05).Conclusions Metabolomics distinguish model has a good ability to distinguish PE group,P group and Normal group.Serum characteristic metabolites can successfully reflect the status of fat,calcium and phosphorus metabolism of preeclampsia patients and provide high value for prediction,diagnosis and treatment.