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.

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.

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

Coptis chinensis Franch. and Panax ginseng C. A. Mey. are traditional herbal medicines with millennia of documented use and broad therapeutic applications, including anti-diabetic properties. However, the synergistic effect of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng on type 2 diabetes mellitus (T2DM) and its underlying mechanism remain unclear. The research demonstrated that the optimal ratio of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng was 4∶1, exhibiting maximal efficacy in improving insulin resistance and gluconeogenesis in primary mouse hepatocytes. This combination demonstrated significant synergistic effects in improving glucose tolerance, reducing fasting blood glucose (FBG), the weight ratio of epididymal white adipose tissue (eWAT), and the homeostasis model assessment of insulin resistance (HOMA-IR) in leptin receptor-deficient (db/db) mice. Subsequently, a T2DM liver-specific network was constructed based on RNA sequencing (RNA-seq) experiments and public databases by integrating transcriptional properties of disease-associated proteins and protein-protein interactions (PPIs). The network recovery index (NRI) score of the combined treatment group with a 4∶1 ratio exceeded that of groups treated with individual components. The research identified that activated adenosine 5'-monophosphate-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) signaling in the liver played a crucial role in the synergistic treatment of T2DM, as verified by western blot experiment in db/db mice. These findings demonstrate that the 4∶1 combination of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng significantly improves insulin resistance and glucose and lipid metabolism disorders in db/db mice, surpassing the efficacy of individual treatments. The synergistic mechanism correlates with enhanced AMPK/ACC signaling pathway activity.
Animals ; Panax/chemistry* ; Ginsenosides/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Mice ; Male ; Alkaloids/pharmacology* ; Coptis/chemistry* ; Drug Synergism ; Insulin Resistance ; Mice, Inbred C57BL ; Humans ; Transcriptome/drug effects* ; Blood Glucose/metabolism* ; Hypoglycemic Agents/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Hepatocytes/metabolism*

Surgical operation is the main treatment of oral and maxillofacial tumors.Dysphagia is a common postoperative complication.Swal-lowing disorder can not only lead to mis-aspiration,malnutrition,aspiration pneumonia and other serious consequences,but also may cause psychological problems and social communication barriers,affecting the quality of life of the patients.At present,there is no systematic evalua-tion and rehabilitation management plan for the problem of swallowing disorder after oral and maxillofacial tumor surgery in China.Combining the characteristics of postoperative swallowing disorder in patients with oral and maxillofacial tumors,summarizing the clinical experience of ex-perts in the field of tumor and rehabilitation,reviewing and summarizing relevant literature at home and abroad,and through joint discussion and modification,a group of national experts reached this consensus including the core contents of the screening of swallowing disorders,the phased assessment of prognosis and complications,and the implementation plan of comprehensive management such as nutrition management,respiratory management,swallowing function recovery,psychology and nursing during rehabilitation treatment,in order to improve the evalua-tion and rehabilitation of swallowing disorder after oral and maxillofacial tumor surgery in clinic.