Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective To establish a high performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS)for the simultaneous determination of gefitinib,erlotinib,nillotinib and imatinib plasma concentrations and analyze the results.Methods The plasma samples were treated with acetonitrile precipitation and separated by Diamonsil C18 column(150 mm ×4.6 mm,3.5 μm)with mobile phase of 0.1％formic acid water(A)-0.1％formic acid acetonitrile(B).The flow rate of gradient elution was 0.7 mL·min-1,and the column temperature was 40 ℃ and the injection volume was 3 μL.Using arotinib as the internal standard,the scanning was carried out by using electrospray ionization source in positive ionization mode with multi-reaction monitoring.The specificity,standard curve,lower limit of quantitation,precision,accuracy,recovery rate,matrix effect and stability of the method were investigated.The concentrations of imatinib and erlotinib in 20 patients with chronic myelogenous leukemia(CML)and gefitinib and erlotinib in 3 patients with non-small cell lung cancer were measured.Results The standard curves of the four drugs were as follows,gefitinib:y=2.536 × 10-3x+9.362 × 10-3(linear range 20-2 000 ng·mL-1,R2=0.996 6);erlotinib:y=3.575× 10-3x+7.406 × 10-3(linear range 50-5 000 ng·mL-1,R2=0.994 9);nilotinib:y=1.945 x 10-3x+0.015 643(linear range 50-5 000 ng·mL-1,R2=0.990 6);imatinib:y=4.56 x 10-3x+0.010 451(linear range 100～104 ng·mL-1,R2=0.9963).RSD of intra-day and inter-day were less than 10％,and the accuracy ranged from 90％to 110％,and the recovery rates were 91.35％to 98.93％(RSD＜10％);the matrix effect ranged from 91.64％to 107.50％(RSD＜10％).Determination of 23 patients showed that the blood concentration of nilotinib ranged from 623.76 to 2 934.13 ng·mL-1,and the blood concentration of imatinib ranged from 757.77 to 2 637.71 ng·mL-1,and the blood concentration of gefitinib ranged from 214.76 to 387.40 ng·mL-1.The serum concentration of erlotinib was 569.57 ng·mL-1.Conclusion The method of this research is simple,fast,sensitive and dedicated,which can be monitored by the concentration of clinical blood.

Objective To study the factors influencing the blood concentration of caspofungin(CPFG),construct a prediction model,and validate the predictive effect of the model,so as to provide reference for the individualised dosing of patients with fungal infections in haematology.Methods Seventy-five patients admitted to the Department of Haematology,General Hospital of Tianjin Medical University,who were treated with CPFG for antifungal therapy during the period of March 2021 to June 2022 were selected as the study subjects,and CPFG blood concentration monitoring was carried out to explore the influencing factors of CPFG blood concentration and to construct a prediction model accordingly.Hosmer-Lemeshow(H-L)was used to test the goodness-of-fit of the model,and another 30 patients were selected as the verification group,and the predictive effect of the model was verified by the receiver's operating characteristics(ROC)curve.Results The mean blood concentrations of the patients at 0.5,9 and 24 h were(12.54±4.38),(6.80±2.76),(4.13±2.16)μg·mL-1,and the mean AUC0-24h were(152.05±57.60)μg·mL-1·h.AUC0-24h was lower than the reference value(98 μg·mL-1·h)in two patients.The results of correlation analysis showed that gender showed a correlation with 0.5 h blood concentration(P＜0.05),and there was no correlation with the rest of the two time points blood concentration and AUC0-24h(P＞0.05).Body weight and albumin(Alb)concentration showed correlation with 0.5,9,24 h blood drug concentration and AUC0-24 h(P＜0.05),and the rest of the indicators showed no correlation with blood drug concentration and AUC0_24h at each time point(P＞0.05).The results of multifactorial analysis showed that the factors influencing the patients'0.5 h blood concentration were gender,Alb concentration and body weight,and the factors influencing the 9 and 24 h blood concentration and AUC0-24h were Alb concentration and body weight(P＜0.05).Correlation analysis showed that the daily dose was positively correlated with the plasma concentration of CPFG at 0.5,9 and 24 h and AUC0-24h(P＜0.05).The results of multivariate analysis showed that the daily dose was also one of the influencing factors of the plasma concentration of CPFG(P＜0.05).ROC curve shows that the model has good prediction ability.Conclusion Body weight and Alb are significantly associated with CPFG blood concentrations and area under the drug-time curve,which can be used as a basis for preventive risk avoidance.

A derivatizaton method combined with gas chromatography-mass spectrometry(GC-MS)was established for detection of isobutyl chloroformate(IBCF)residue in active pharmaceutical ingredient of agatroban.The extraction and derivatization reagents,derivatization time,qualitative and quantitative ions were selected and optimized,respectively.The possible mechanism of derivatization and characteristic fragment ions fragmentation were speculated.The agatroban samples were dissolved and extracted by methanol,and the residual IBCF was derived with methanol to generate methyl isobutyl carbonate(MIBCB).After 24 h static derivatization at room temperature,IBCF was completely transformed into MIBCB,which could be used to indirectly detect IBCF accurately.The results showed that the linearity of this method was good in the range of 25-500 ng/mL(R2=0.9999).The limit of detection(LOD,S/N=3)was 0.75 μg/g,and the limit of quantification(LOQ,S/N=10)was 2.50 μg/g.Good recoveries(95.2%-97.8%)and relative standard deviations(RSDs)less than 3.1%(n=6)were obtained from agatroban samples at three spiked levels of IBCF(2.50,25.00,50.00 μg/g),which showed good accuracy of this method.Good precision of detection results was obtained by different laboratory technicians at different times,the mean value of spiked sample solution(25.00 μg/g)was 24.28 μg/g,and the RSD was 2.1%(n=12).The durability was good,minor changes of detection conditions had little effect on the results.Under the original condition and conditions with initial column temperature±5℃,heating rate±2℃/min,column flow rate±0.1 mL/min,the IBCF content of spiked sample solution(25.00 μg/g)was detected,the mean value of detection results was 24.16 μg/g,and the RSD was 2.2%(n=7).Eight batches of agatroban samples from two manufacturers were detected using the established method,and the results showed that no IBCF residue was detected in any of these samples.The agatroban samples could be dissolved by methanol,and then the IBCF residue could be simultaneously extracted and derived with methanol as well.This detection method had the advantages of simple operation,high sensitivity,low matrix effect and accurate quantification,which provided a new effective method for detection of IBCF residue in agatroban.

Objective To investigate the infection status and changing trend of hepatitis C virus(HCV)infection in hospitalized patients in medical institutions,and provide reference for formulating HCV infection prevention and control strategies.Methods HCV infection surveillance results from cross-sectional survey data reported to China Healthcare-associated Infection(HAI)Surveillance System in 2020 were summarized and analyzed,HCV positive was serum anti-HCV positive or HCV RNA positive,survey result was compared with the survey results from 2003.Results In 2020,1 071 368 inpatients in 1 573 hospitals were surveyed,738 535 of whom underwent HCV test,4 014 patients were infected with HCV,with a detection rate of 68.93％and a HCV positive rate of 0.54％.The positive rate of HCV in male and female patients were 0.60％and 0.48％,respectively,with a statistically sig-nificant difference(x2=47.18,P＜0.001).The HCV positive rate in the 50-＜60 age group was the highest(0.76％),followed by the 40-＜50 age group(0.71％).Difference among all age groups was statistically signifi-cant(x2=696.74,P＜0.001).In 2003,91 113 inpatients were surveyed.35 145 of whom underwent HCV test,resulting in a detection rate of 38.57％;775 patients were infected with HCV,with a positive rate of 2.21％.In 2020,HCV positive rates in hospitals of different scales were 0.46％-0.63％,with the highest in hospital with bed numbers ranging 600-899.Patients'HCV positive rates in hospitals of different scales was statistically signifi-cant(X2=35.34,P＜0.001).In 2020,12 provinces/municipalities had over 10 000 patients underwent HCV-rela-ted test,and HCV positive rates ranged 0.19％-0.81％,with the highest rate from Hainan Province.HCV posi-tive rates in different departments were 0.06％-0.82％,with the lowest positive rate in the department of pedia-trics and the highest in the department of internal medicine.In 2003 and 2020,HCV positive rates in the depart-ment of infectious diseases were the highest,being 7.95％and 3.48％,respectively.Followed by departments of orthopedics(7.72％),gastroenterology(3.77％),nephrology(3.57％)and general intensive care unit(ICU,3.10％)in 2003,as well as departments of gastroenterology(1.35％),nephrology(1.18％),endocrinology(0.91％),and general intensive care unit(ICU,0.79％)in 2020.Conclusion Compared with 2003,HCV positive rate decreased significantly in 2020.HCV infected patients were mainly from the department of infectious diseases,followed by departments of gastroenterology,nephrology and general ICU.HCV infection positive rate varies with gender,age,and region.

Objective To explore the application of metagenomic next-generation sequencing(mNGS)technology in the investigation of healthcare-associated infection(HAI)outbreaks of carbapenem-resistant Acinetobacter bau-mannii(CRAB).Methods Pathogenic detection by mNGS and conventional pathogen culture were performed on 5 patients in the intensive care unit(ICU)of a hospital from June 8 to 22,2023 from whom CRAB were detected.Microbial sampling was carried out in potentially contaminated environment.Bacterial culture,identification,and antimicrobial susceptibility testing were conducted.Comprehensive control measures were taken,and the effect was evaluated.Results The time required for reporting results by mNGS was shorter than the culture time([3.92± 1.05]days vs[6.24±0.25]days,P＜0.001).CRAB was isolated from the specimens of 5 patients.mNGS de-tected OXA-23 resistance genes from all patients.After comprehensive assessment by experts,4 patients were HAI and 1 patient was due to specimen contamination.According to the definition from Guidelines for HAI outbreak control,this event was considered an outbreak of HAI.The monitoring results of environmental hygiene showed that the detection rate of CRAB in the environment during the outbreak was 51.30％(59/115),mainly from the hands of health care workers and the surface of ventilators.After implementing multidisciplinary infection control measures,clinicians'hand hygiene compliance rate and implementation rate of ventilator disinfection increased from 40.83％(49/120)and 33.33％(16/48)to 82.61％(95/115)and 83.33％(30/36),respectively.The prognosis of patients was good,and no new case emerged during subsequent monitoring.The outbreak of HAI in this hospital has been effectively controlled.Conclusion mNGS is characterized by high precision,less time consumption,and high accuracy,and can be applied to the prevention and control of HAI outbreak and the study of antimicrobial-re-sistant genomes.It is of great significance for the anti-infection treatment of patients with multidrug-resistant orga-nism infection as well as the formulation of HAI prevention and control measures.Continuous improving disinfec-tion effectiveness and hand hygiene compliance is important for preventing and controlling CRAB infection.

A 65-year-old male patient was admitted for recurrent lymph node enlargement for 5 years and elevated creatinine for 6 months. This patient was diagnosed with angioimmunoblastic T-cell lymphoma 5 years ago and underwent multiple lines of anti-tumor therapy, including cytotoxic chemotherapy; epigenetic modifying drugs such as chidamide and azacitidine; the immunomodulator lenalidomide; and targeted therapy such as rituximab, a CD20-targeting antibody, and brentuximab vedotin, which targets CD30. Although the tumor was considered stable, multiple virus activation (including BK virus, JC virus, and cytomegalovirus) accompanied by the corresponding organ damage (polyomavirus nephropathy, cytomegalovirus retinitis, and progressive multifocal leukoencephalopathy) occurred during anti-tumor treatment. Anti-tumor therapy was suspended and ganciclovir was used. The serum viral load decreased and organ functions were stabilized. The purpose of this report was to raise clinicians′ awareness of opportunistic virus reactivation during anti-tumor treatment.