OBJECTIVE To analyze the specific risks and long-term toxicities of four BCR-ABL1 tyrosine kinase inhibitors （TKIs）（imatinib， dasatinib， nilotinib， and bosutinib） in pediatric patients with hematological malignancies. METHODS Adverse drug event （ADE） reports submitted to the the United States FDA Adverse Event Reporting System （FAERS） from January 2012 to December 2024， with imatinib， dasatinib， nilotinib， and bosutinib as the primary suspect drugs， were collected. Data mining was performed using the reporting odds ratio method and proportional reporting ratio method. ADE terms were classified and summarized by system organ class （SOC） and preferred term （PT） according to the Medical Dictionary for Drug Regulatory Activities （MedDRA， version 26.0）. Meanwhile， the ADE reports were divided by age into the adult group （≥18 years） and the pediatric group （＜18 years） to compare the differences in ADE between the two groups. RESULTS A total of 1 512 pediatric ADE reports were included： 993 for imatinib， 391 for dasatinib， 112 for nilotinib， and 16 for bosutinib. Among the reported ADEs， the patients were mainly aged 12-＜18 years； the reports mainly originated from the United States， France， and Japan； and the primary indications were chronic myeloid leukemia and acute lymphoblastic leukemia. A total of 5 256 ADE signals were mined， among which 235 were positive signals， involving 1 103 PT across 27 SOC. The top five PT ranked by the number of positive signals were nausea， febrile neutropenia， abdominal pain， neutropenia， and anemia. The top two SOC were general disorders and administration site conditions， and gastrointestinal disorders. Compared with the adult group， the pediatric group had relatively higher proportions of events related to infections and infestations as well as blood and lymphatic system disorders. Pediatric long-term toxicity signals primarily included growth retardation， accompanied by signals related to endocrine system abnormalities and bone metabolism abnormalities. Specific signals included imatinib-associated septic shock， dasatinib-associated chylothorax， and nilotinib-associated electrocardiographic QT interval prolongation. CONCLUSIONS When pediatric patients use BCR-ABL1 TKIs， priority monitoring of infection risk and hematologic parameters is required， along with long-term follow-up of height， endocrine， and bone metabolism parameters. Targeted screening and management of drug-specific signals should be performed to ensure the long-term safety of pediatric medication.

The chemical constituents in dried roots of Atractylodes macrocephala were investigated in this study. Through utilizing normal-phase silica gel and ODS column chromatography, TLC, and semi-preparative HPLC, 4 new sesquiterpenoids were purified from the ethyl acetate extract of A. macrocephala. By various spectroscopic techniques, such as 1D and 2D NMR, HR-ESI-MS, IR, UV, and CD, their structures were identified as atractylmacron A (1), 9β-hydroxyasterolide (2), atractylenolide H (3), atractylenolide J (4). Compound 1 possesses a rare 6/7 bicyclic skeleton.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

OBJECTIVES: To investigate the genomic characteristics and prognostic factors of juvenile myelomonocytic leukemia (JMML) with RAS mutations. METHODS: A retrospective analysis was conducted on the clinical data of JMML children with RAS mutations treated at the Hematology Hospital of Chinese Academy of Medical Sciences, from January 2008 to November 2022. RESULTS: A total of 34 children were included, with 17 cases (50%) having isolated NRAS mutations, 9 cases (27%) having isolated KRAS mutations, and 8 cases (24%) having compound mutations. Compared to children with isolated NRAS mutations, those with NRAS compound mutations showed statistically significant differences in age at onset, platelet count, and fetal hemoglobin proportion (P<0.05). Cox proportional hazards regression model analysis revealed that hematopoietic stem cell transplantation (HSCT) and hepatomegaly (≥2 cm below the costal margin) were factors affecting the survival rate of JMML children with RAS mutations (P<0.05); hepatomegaly was a factor affecting survival in the non-HSCT group (P<0.05). CONCLUSIONS Children with NRAS compound mutations have a later onset age compared to those with isolated NRAS mutations. At initial diagnosis, children with NRAS compound mutations have poorer peripheral platelet and fetal hemoglobin levels than those with isolated NRAS mutations. Liver size at initial diagnosis is related to the prognosis of JMML children with RAS mutations. HSCT can improve the prognosis of JMML children with RAS mutations.
Humans ; Leukemia, Myelomonocytic, Juvenile/therapy* ; Mutation ; Male ; Female ; Child, Preschool ; Retrospective Studies ; Child ; Infant ; GTP Phosphohydrolases/genetics* ; Membrane Proteins/genetics* ; Adolescent ; Hematopoietic Stem Cell Transplantation ; Proportional Hazards Models ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective:To explore the optimization potential of clinical target volume (CTV) delineation proposed in the guidelines of the Oncology Group (RTOG), the Francophone Group of Urological Radiotherapy (GFRU), and the European Society for Radiotherapy and Oncology (ESTRO) based on prostate bed local occurrence patterns after radical prostatectomy identified using prostate-specific membrane antigen positron emission tomography (PSMA PET).Methods:A retrospective analysis was conducted on patients with local prostate bed recurrence after radical prostatectomy who underwent PSMA PET at the Department of Nuclear Medicine, Peking University First Hospital from September 2021 to February 2024. The central point of each recurrence was marked. A six-zone method was established based on prostate bed anatomy and the characteristics of cross-sectional imaging. Then, the positional relationships (within or outside) were recorded with respect to recurrences and CTV defined by the RTOG, GFRU, and ESTRO (CTV RTOG, CTV GFRU, and CTV ESTRO), followed the analysis of the recurrence rates and distribution characteristics of various zones. Results:A total of 63 patients with prostate bed recurrence after radical prostatectomy were enrolled in this study, including 97 recurrences. The recurrence rates in the six zones were as follows: 10% of zone 1, 22% of zone 2, 29% of zone 3, 2% of zone 4, 12% of zone 5a, 18% of zone 5b, and 7% of zone 6. Among these zones, zones 2 and 3 showed the highest and second-highest recurrence rates, respectively. CTV GFRU and CTV ESTRO completely covered zones 2 and 3, while CTV RTOG covered zone 2 completely and zone 3 partially. Zone 4, characterized by a low recurrence rate, was not covered by CTV GFRU and CTV ESTRO but was entirely covered by CTV RTOG. Zone 5a, with a recurrence rate of 12%, was completely covered by CTV RTOG but was partially covered by CTV GFRU and CTV ESTRO. The range of 1.3 cm in front of the posterior wall of the bladder covered all recurrences in zone 5a. Conclusions:For CTV delineation of the prostate cancer surgical bed, zone 4, the anterior half of the bladder above the pubic symphysis midpoint, should be contracted due to the low recurrence rate in this zone. In contrast, the anterior boundary above the pubic symphysis midpoint should extend to 1.3 cm in front of the posterior wall of the bladder to completely cover the recurrence zones.

Objective To understand the distribution and antimicrobial resistance profiles of bacterial strains isolated from blood cultures at Yunyang County People's Hospital from 2019 to 2023.Methods The data of bacterial isolates from blood samples and the results of antimicrobial susceptibility testing were analyzed retrospectively from 2019 to 2023 at Yunyang County People's Hospital.Results A total of 3 789 bacterial strains were isolated from blood culture,including 1 931(51.0％)strains of Gram negative bacteria and 1 858(49.0％)strains of Gram positive bacteria.Coagulase negative Staphylococcus(33.3％),Escherichia coli(25.4％),Klebsiella pneumoniae(13.7％),Staphylococcus aureus(9.9％),and Enterobacter cloacae(1.8％)were the top five bacterial pathogens.Antimicrobial susceptibility testing showed that the prevalence of methicillin-resistant strains was 27.1％in S.aureus,34.5％in S.epidermidis,and 49.9％in other coagulase-negative Staphylococcus.Methicillin resistant strains(MRSA,MRSE,and other MRCNS)showed significantly higher resistance rates to most antibiotics than corresponding methicillin-susceptible strains(MSSA,MSSE,and other MSCNS).No staphylococcal isolates were resistant to vancomycin,teicoplanin,linezolid,or tigecycline.Enterococcus faecium showed significantly higher resistance rate to antibiotics than Enterococcus faecalis.No enterococcal strains were resistant to vancomycin,teicoplanin,linezolid,or tigecycline.No streptococcal isolates were found resistant to vancomycin or linezolid.Serratia marcescens strains had a resistance rate of 25.0％to carbapenems.All other Enterobacterales species showed a resistance rate of less than 10.0％to carbapenems.No Enterobacterales isolates were found resistant to tigecycline.The resistance rates of P.aeruginosa to imipenem and meropenem were 5.7％and 3.8％,respectively.No P.aeruginosa isolates were found resistant to colistin.The resistance rates of Acinetobacter baumannii to imipenem and meropenem were 41.4％and 38.0％,respectively.Conclusions The proportion of Gram negative bacteria is slightly higher than that of Gram positive bacteria in the bacterial isolates from blood samples at Yunyang County People's Hospital.The prevalence of MRSA and MRCNS is relatively high,while A.baumannii and S.marcescens showed high resistance rates to carbapenems.Antimicrobial resistance surveillance should be strengthened for the bacterial isolates from blood samples in order to learn the changing resistance profiles,use antibiotics reasonably,and prevent the spread of drug-resistant bacteria.