Background/Aims: Data from Asia regarding the short-term and long-term outcomes for acute severe ulcerative colitis (ASUC) are limited. We assessed the outcomes of ASUC, identified the risk factors for colectomy, and compared colectomy rates between the pre-biologics and post-biologics eras in Taiwan. Methods: The patients with an ASUC diagnosis between January 2013 and March 2022 at 5 tertiary medical centers were retrospectively analyzed. Results: In total, 98 patients were enrolled, with 68.4% diagnosed in the post-biologics era. In 78.6% of the ASUC patients initially received intravenous steroid therapy, for which the success rate was 74.1%. As for rescue therapy, 15 patients (93.8%) received biologics and 1 (6.3%) received cyclosporin. Biologics rescue therapy had a 93.3% success rate. One (1%) mortality due to septic shock occurred. The colectomy rate for index ASUC admission was 11.2%. Patients receiving colectomy were predominantly male (P= 0.012) and at older age (P= 0.016). Higher C-reactive protein (P= 0.035), lower albumin (P= 0.017), and hemoglobin (P= 0.023) levels were associated with colectomy risk. During a median follow-up of 24 months, 13 patients (15.1%) had recurrent ASUC and 23.1% of patients received colectomy. The accumulated colectomy rate at 3 years did not differ between the pre- and post-biologics eras (16.1% vs. 13.4%, P= 0.270). Conclusions This is the first Asian study on ASUC to compare colectomy rates between the prebiologics and post-biologics eras, revealing no significant difference. The recurrent ASUC had a higher colectomy rate than the index ASUC.

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.

The patient was a boy aged 1 year and 9 months who presented with 46,XY disorder of sex development (DSD), with severe undermasculinization of the external genitalia. Laboratory tests and ultrasound examinations showed normal functions of Leydig cells and Sertoli cells in the testes. Genetic testing revealed a novel pathogenic heterozygous variant, c.1186dupA (p.T396Nfs*17), in the PPP1R12A gene. Thirteen cases of PPP1R12A gene variants have been reported previously. These variants may cause isolated involvement of the genitourinary or neurological systems, or affect other systems/organs including the digestive tract, eyes, heart, etc. Patients with DSD typically present with a 46,XY karyotype and variable degrees of undermasculinization involving the external genitalia, gonads, and reproductive tract. This article reports a child with 46,XY DSD accompanied by growth retardation caused by a heterozygous variant in the PPP1R12A gene, which expands the clinical disease spectrum associated with PPP1R12A gene variants.
Humans ; Male ; Infant ; Disorder of Sex Development, 46,XY/etiology* ; Protein Phosphatase 1/genetics*

OBJECTIVES: To investigate the clinical features and prognosis of children with non-Down-syndrome-related acute megakaryoblastic leukemia (non-DS-AMKL). METHODS: A retrospective analysis was conducted on the medical data of 17 children with non-DS-AMKL who were admitted to Children's Hospital of The First Affiliated Hospital of Zhengzhou University from January 2013 to December 2023, and their clinical features, treatment, and prognosis were summarized. RESULTS: Among the 17 children with non-DS-AMKL, there were 8 boys and 9 girls. Fourteen patients had an onset age of less than 36 months, with a median age of 21 months (range:13-145 months). Immunophenotyping results showed that 16 children were positive for CD61 and 13 were positive for CD41. The karyotype analysis was performed on 16 children, with normal karyotype in 6 children and abnormal karyotype in 9 children, among whom 5 had complex karyotype and 1 had no mitotic figure. Detected fusion genes included EVI1, NUP98-KDM5A, KDM5A-MIS18BP1, C22orf34-BRD1, WT1, and MLL-AF9. Genetic alterations included TET2, D7S486 deletion (suggesting 7q-), CSF1R deletion, and PIM1. All 17 children received chemotherapy, among whom 16 (94%) achieved complete remission after one course of induction therapy, and 1 child underwent hematopoietic stem cell transplantation (HSCT) and remained alive and disease-free. Of all children, 7 experienced recurrence, among whom 1 child received HSCT and died of graft-versus-host disease. At the last follow-up, six patients remained alive and disease-free. CONCLUSIONS Non-DS-AMKL primarily occurs in children between 1 and 3 years of age. The patients with this disorder have a high incidence rate of chromosomal abnormalities, with complex karyotypes in most patients. Some patients harbor fusion genes or gene mutations. Although the initial remission rate is high, the long-term survival rate remains low.
Humans ; Male ; Female ; Leukemia, Megakaryoblastic, Acute/etiology* ; Child, Preschool ; Infant ; Child ; Retrospective Studies ; Prognosis ; Down Syndrome/complications*

OBJECTIVE: To investigate the effect of TBL1XR1 mutation on cell biological characteristics of diffuse large B-cell lymphoma (DLBCL). METHODS: The TBL1XR1 overexpression vector was constructed and DNA sequencing was performed to determine the mutation status. The effect of TBL1XR1 mutation on apoptosis of DLBCL cell line was detected by flow cytometry and TUNEL fluorescence assay; CCK-8 assay was used to detect the effect of TBL1XR1 mutation on cell proliferation; Transwell assay was used to detect the effect of TBL1XR1 mutation on cell migration and invasion; Western blot was used to detect the effect of TBL1XR1 mutation on the expression level of epithelial-mesenchymal transition (EMT) related proteins. RESULTS: The TBL1XR1 overexpression plasmid was successfully constructed. The in vitro experimental results showed that TBL1XR1 mutation had no significant effect on apoptosis of DLBCL cells. Compared with the control group, TBL1XR1 mutation enhanced cell proliferation, migration and invasion of DLBCL cells. TBL1XR1 gene mutation significantly increased the expression of N-cadherin protein, while the expression of E-cadherin protein decreased. CONCLUSION TBL1XR1 mutation plays a role in promoting tumor cell proliferation, migration and invasion in DLBCL. TBL1XR1 could be considered as a potential target for DLBCL therapy in future research.
Humans ; Lymphoma, Large B-Cell, Diffuse/pathology* ; Cell Proliferation ; Mutation ; Receptors, Cytoplasmic and Nuclear/genetics* ; Apoptosis ; Cell Line, Tumor ; Epithelial-Mesenchymal Transition ; Cell Movement ; Repressor Proteins/genetics* ; Nuclear Proteins/genetics* ; Cadherins/metabolism*

OBJECTIVE: To analyze the efficacy and safety of decitabine-based myeloablative preconditioning regimen for allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with acute myeloid leukemia (AML). METHODS: The clinical characteristics and efficacy of 115 AML patients who underwent allo-HSCT at the First Medical Center of Chinese PLA General Hospital from August 2018 to August 2022 were retrospectively analyzed, including 37 patients treated with decitabine conditioning regimen (decitabine group) and 78 patients without decitabine conditioning regimen (non-decitabine group). The cumulative incidence of relapse (CIR), overall survival (OS), leukemia-free survival (LFS), non-relapse mortality (NRM) and graft versus host disease (GVHD) were analyzed. RESULTS: For the patients in first complete remission (CR1) state before allo-HSCT, the 1-year relapse rates of decitabine group(22 cases) and non-decitabine group(69 cases) were 9.1% and 29.6%, respectively, the difference was statistically significant(P =0.042). The 1-year cumulative incidence of acute graft-versus-host disease (aGVHD) in decitabine group and non-decitabine group was 62.2% and 70.5%, respectively, and the 1-year cumulative incidence of chronic inhibitor-versus-host disease (cGVHD) was 18.9% and 14.1%, respectively, there were no significant differences in the incidence of aGVHD and cGVHD between the two groups (P >0.05). Of the 115 patients, there were no significantly differences in the 1-year CIR(21.7% vs 28.8%, P =0.866), NRM(10.9% vs 3.9%, P =0.203), OS(75.2% vs 83.8%, P =0.131) and LFS(74.6% vs 69.1%, P =0.912) between the decitabine group(37 cases) and the non-decitabine group(78 cases). CONCLUSION Decitabine-based conditioning regimen could reduce the relapse rate of AML CR1 patients with good safety.
Humans ; Leukemia, Myeloid, Acute/therapy* ; Hematopoietic Stem Cell Transplantation/methods* ; Decitabine/therapeutic use* ; Transplantation Conditioning/methods* ; Retrospective Studies ; Graft vs Host Disease ; Transplantation, Homologous ; Male ; Female ; Adult ; Middle Aged ; Adolescent ; Young Adult

ObjectiveNon-invasive magnetic stimulation technology has been widely used in the treatment of Alzheimer’s disease (AD), but there is a lack of convenient and timely methods for evaluating and providing feedback on the effectiveness of the stimulation, which can be used to guide the adjustment of the stimulation protocol. This study aims to explore the possibility of heart rate variability (HRV) in diagnosing AD and guiding AD magnetic stimulation intervention techniques. MethodsIn this study, we used a 40 Hz, 10 mT pulsed magnetic field to expose AD mouse models to whole-body exposure for 18 d, and detected the behavioral and electroencephalographic signals before and after exposure, as well as the instant electrocardiographic signals after exposure every day. ResultsUsing one-way ANOVA and Pearson correlation coefficient analysis, we found that some HRV indicators could identify AD mouse models as accurately as behavioral and electroencephalogram(EEG) changes (P<0.05) and significantly distinguish the severity of the disease (P<0.05), including rMSSD, pNN6, LF/HF, SD1/SD2, and entropy arrangement. These HRV indicators showed good correlation and statistical significance with behavioral and EEG changes (r>0.3, P<0.05); HRV indicators were significantly modulated by the magnetic field exposure before and after the exposure, both of which were observed in the continuous changes of electrocardiogram (ECG) (P<0.05), and the trend of the stimulation effect was more accurately observed in the continuous changes of ECG. ConclusionHRV can accurately reflect the pathophysiological changes and disease degree, quickly evaluate the effect of magnetic stimulation, and has the potential to guide the pattern of magnetic exposure, providing a new idea for the study of personalized electromagnetic neuroregulation technology for brain diseases.

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.