Licochalcone E is a chalcone isolated from Glycyrrhiza uralensis and G. inflata Batal. This study explored the effect of licochalcone E on improving hepatic steatosis in obese mice and evaluated the role of licochalcone E in regulating lipid accumulation in hepatocytes. In vitro, oleic acid–induced hepatocytes were treated with licochalcone E to investigate its effect on lipid metabolic pathways. In animal experiments, male C57BL/6 mice were fed with a high-fat diet (HFD) and treated with licochalcone E by intraperitoneal injection for 12 weeks to assess its effects on biochemical indexes and hepatic steatosis. Furthermore, mice were fed a methionine/choline-deficient (MCD) diet and administered licochalcone E, followed by evaluation of liver fibrosis. Licochalcone E effectively reduced body weight, epididymal and inguinal fat weight, and adipocyte size in HFD-induced obese mice. Licochalcone E treatment of obese mice also reduced hepatic lipid accumulation and improved hepatocyte steatosis. Licochalcone E regulated the expression of lipogenesis- and lipolysis-related genes in the livers of obese mice and increased AMPK phosphorylation and Sirt1 expression in the liver. Licochalcone E also attenuated hepatic inflammation and oxidative stress in obese mice. Furthermore, treatment of MCD-induced mice with licochalcone E reduced the number of lipid vacuoles and the extent of fibrosis and inhibited liver inflammation. In FL83B hepatocytes, licochalcone E could regulate lipogenesis and lipolysis, and increase the phosphorylation of AMPK and ACC. These findings provide new insights into the role of licochalcone E in regulating lipid metabolism and preventing hepatic steatosis.

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.

In order to investigate whether the effect of Yuxuebi Tablets on the peripheral and central inflammation resolution of mice with inflammatory pain is related to their regulation of G protein-coupled receptor 37(GPR37), an inflammatory pain model was established by injecting complete Freund's adjuvant(CFA) into the paws of mice, with a sham-operated group receiving a similar volume of normal saline. The mice were assigned randomly to the sham-operated group, model group, ibuprofen group(91 mg·kg~(-1)), and low-, medium-, and high-dose groups of Yuxuebi Tablets(60, 120, and 240 mg·kg~(-1)). The drug was administered orally from days 1 to 19 after modeling. Von Frey method and the hot plate test were used to detect mechanical pain thresholds and heat hyperalgesia. The levels of interleukin-10(IL-10) and transforming growth factor-beta(TGF-β) in the spinal cord were quantified using enzyme-linked immunosorbent assay(ELISA), and the mRNA and protein expression of GPR37 in the spinal cord was measured by real-time quantitative reverse transcription PCR(qRT-PCR) and Western blot. Additionally, immunofluorescence was used to detect the expression of macrosialin antigen(CD68), mannose receptor(MRC1 or CD206), and GPR37 in dorsal root ganglia, as well as the expression of calcium-binding adapter molecule 1(IBA1), CD206, and GPR37 in the dorsal horn of the spinal cord. The results showed that compared with those of the sham-operated group, the mechanical pain thresholds and hot withdrawal latency of the model group significantly declined, and the expression of CD68 in the dorsal root ganglia and the expression of IBA1 in the dorsal horn of the spinal cord significantly increased. The expression of CD206 and GPR37 significantly decreased in the dorsal root ganglion and dorsal horn of the spinal cord, and IL-10 and TGF-β levels in the spinal cord were significantly decreased. Compared with those of the model group, the mechanical pain thresholds and hot withdrawal latency of the high-dose group of Yuxuebi Tablets significantly increased, and the expression of CD68 in the dorsal root ganglion and IBA1 in the dorsal horn of the spinal cord significantly decreased. The expression of CD206 and GPR37 in the dorsal root ganglion and dorsal horn of the spinal cord significantly increased, as well as IL-10 and TGF-β levels in the spinal cord. These findings indicated that Yuxuebi Tablets may reduce macrophage(microglial) infiltration and foster M2 macrophage polarization by enhancing GPR37 expression in the dorsal root ganglia and dorsal horn of the spinal cord of CFA-induced mice, so as to improve IL-10 and TGF-β levels, promote resolution of both peripheral and central inflammation, and play analgesic effects.
Inflammation/genetics* ; Pain/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Mice ; Freund's Adjuvant/pharmacology* ; Ibuprofen ; Pain Threshold/drug effects* ; Hyperalgesia/genetics* ; Ganglia, Spinal ; Interleukin-10/genetics* ; Transforming Growth Factor beta/genetics* ; Reverse Transcriptase Polymerase Chain Reaction ; Tablets ; Receptors, G-Protein-Coupled

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 evaluate and integrate evidence on the management of sleep disorders in children with attention deficit hyperactivity disorder (ADHD). METHODS: Literature was retrieved based on the 6S model, and evidence related to sleep disorder management in children with ADHD was extracted from the included references. RESULTS: A total of 17 studies were included, from which 16 pieces of evidence were extracted. Of these, 6 were classified as Level 1 evidence and 10 as Level 5. The evidence covered screening, assessment, non-pharmacological interventions, pharmacological interventions, follow-up, and multidisciplinary collaboration. CONCLUSIONS This study integrated evidence on the management of sleep disorders in children with ADHD using an evidence-based approach, providing an evidence-based foundation for managing sleep disorders in this population.
Humans ; Attention Deficit Disorder with Hyperactivity/complications* ; Sleep Wake Disorders/etiology* ; Child ; Evidence-Based Medicine

OBJECTIVE: To detect and analyze the expression and clinical significance of long non-coding RNA tyrosine kinase non-catalytic region adaptor protein 1-antisense RNA1 (NCK1-AS1) in patients with acute myeloid leukemia (AML). METHODS: 89 AML patients and 23 healthy controls were included from the People's Hospital Affiliated to Jiangsu University. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of NCK1-AS1 and NCK1 in bone marrow samples. The relationship between the expression of NCK1-AS1 and the clinical characteristics of patients were analyzed, as well as the correlation between NCK1-AS1 and NCK1. RESULTS: The expression level of NCK1-AS1 in all AML, non-M3 AML and cytogenetically normal AML (CN-AML) patients was significantly higher than that in the control group (P < 0.01, P < 0.05, P < 0.01, respectively). In non-M3 AML, patients with high NCK1-AS1 expression had a significantly lower hemoglobin level than those with low NCK1-AS1 expression (P =0.036), furthermore, NCK1-AS1 ^high patients had shorter overall survival than NCK1-AS1^low patients (P =0.0378). Multivariate analysis showed that NCK1-AS1 expression was an independent adverse factor in patients with non-M3 AML ( HR =2.392, 95% CI :1.089-5.255, P =0.030). In addition, NCK1 expression was also significantly upregulated in all AML, non-M3 AML and CN-AML patients compared with controls (P < 0.01, P < 0.01, P < 0.001, respectively). There was a certain correlation between NCK1-AS1 and NCK1 expression (r =0.37, P =0.0058). CONCLUSION High expression of NCK1-AS1 in AML indicates poor prognosis of AML patients.
Humans ; Leukemia, Myeloid, Acute/genetics* ; RNA, Long Noncoding/genetics* ; Oncogene Proteins/genetics* ; Adaptor Proteins, Signal Transducing/genetics* ; Prognosis ; Male ; Female ; Middle Aged ; Adult ; Case-Control Studies ; Clinical Relevance

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.