Twelve compounds were isolated from the rice fermentation extracts of Penicillium expansum GY618 by silica column chromatography, Sephadex gel column chromatography, ODS column chromatography and semi preparative HPLC methods. They were determined as 11-hydroxyl-penicitrinone F (1), penicitrinone F (2), betulin (3), erythrodiol (4), ergosterol (5), ergost-5α,8α-epidioxy-6,22-dien-3β-ol (6), (5α,8α-epidioxy-(22E,24R)-ergosta-6,9(11),22-trien-3β-ol) (7), 5α,8α-epidioxy-(22E,24R)-23-methylergosta-6,22-dien-3β-ol (8), 5α,8α-epidioxy- 23,24(R)-dimethylcholesta-6,9(11),22-trien-3β-ol (9), dankasterone A (10), (17R)-4-hydroxy-17-methylincisterol (11) and ergosta-4,6,8(14),22-tetraen-3-one (12), through mass spectrometer, nuclear magnetic resonance (NMR) and comparison with the literature. Compound 1 was a new compound and compounds 2-4, 6-12 were isolated from Penicillium expansum fungus for the first time. The tyrosinase inhibitory activity experiment showed that compounds 1, 3 and 12 showed certain inhibitory activity against tyrosinase with IC₅₀ values of (75 ± 9), (69 ± 8) and (64 ± 2) μmol·L^-1, respectively. The IC₅₀ of other compounds were all greater than 100 μmol·L^-1, while IC₅₀ of the positive control kojic acid was (46 ± 4) μmol·L^-1.

BACKGROUND:Exercise has a regulatory effect on intestinal flora and glucose metabolism,but the effects of high-intensity intermittent exercise on intestinal flora and glucose metabolism in patients with type 2 diabetes mellitus are unclear. OBJECTIVE:To investigate the effects of high-intensity intermittent exercise on glucose metabolism and intestinal flora in patients with type 2 diabetes mellitus. METHODS:Eleven patients with type 2 diabetes mellitus were recruited,among which,two were lost to the follow-up and nine were finally enrolled.High-intensity intermittent exercise intervention was conducted 3 times per week for 6 continuous weeks.Fasting blood and fecal samples were collected before and after the intervention.Glucose metabolism indexes were detected in the blood samples,and intestinal flora was detected in the fecal samples.Changes in glucose metabolism indexes and intestinal flora indexes of the patients with type 2 diabetes mellitus before and after the intervention were compared. RESULTS AND CONCLUSION:After 6 weeks of high-intensity intermittent exercise intervention,fasting blood glucose and glycosylated serum protein levels in patients were significantly reduced(P<0.05),and fasting insulin,although not significantly changed,was decreased compared with before intervention.Alpha diversity analysis showed that the diversity(Shannon index),richness(Chao index)and coverage(Coverage index)did not change significantly.Venn diagrams showed that the relative abundance of Bacteroidetes,Actinobacteria,Proteobacteria,and Fusobacteria in the intestinal flora of the patients increased,and the relative abundance of Firmicutes decreased,and a significant decrease was seen in Ruminococcus_torques and Ruminococcus_gnavus in the Firmicutes,which were both positively correlated with the abnormalities of the glycemic metabolism-related indicators,as well as with other disease development.All these findings indicate that high-intensity intermittent exercise intervention has an improvement effect on the glycemic metabolism-related indexes of patients with type 2 diabetes mellitus,and the abundance of beneficial flora in the intestinal tract increases,and the abundance of harmful flora decreased,enhancing the stability of the intestinal flora in patients.

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.

Objective: To explore the clinical characteristics and diagnosis and treatment plans of neurofibromatosis type 1 (NF1), and to provide references for clinical diagnosis and treatment. Methods : The clinical manifestations and treatment of an 8-year-old female patient with NF1 was reported. A literature review was conducted to summarize the clinical characteristics and therapeutic strategies of NF1. Multiple NF1s occurred on the right cheek, orbit, and eyelid, and recurred after surgical resection. The tumor caused ptosis, incomplete closure, and vision loss in the upper eyelid of the right eye. After a multidisciplinary assessment determined that radical resection was not feasible, selumetinib sulfate targeted therapy was adopted (25 mg, Po, bid), 28 days constitute one treatment course, and 14 courses have been completed, combined with symptomatic ocular treatments, such as Befusu. Result: The follow-up showed that the tumor volume did not continue to increase (stable disease), the uncorrected vision of the right eye improved (0.05 vs 0.1), and no drug-related adverse reactions occurred during the treatment period. The literature review summarizes the diverse clinical manifestations of NF1, with café-au-lait macules, multiple neurofibromas, and Lisch nodules being hallmark features. Currently, surgical intervention remains the most commonly employed and primary therapeutic approach for NF1; however, for patients who do not meet the criteria for surgery, alternative treatment strategies should be considered. MEK inhibitors, such as selumetinib, demonstrate significant efficacy in inhibiting the growth of NF1-associated plexiform neurofibromas, with tumor volume reductions of at least 20% observed in 70% of pediatric patients in the SPRINT clinical trial. Furthermore, these inhibitors exhibit favorable long-term safety profiles. Conclusion Café-au-lait macules, multiple neurofibromas, and Lisch nodules are hallmark features of NF1. Selumetinib is safe and effective for NF1 in the head and neck of children, and it is the preferred treatment option for patients who are not suitable for surgery. Long-term follow-up monitoring of tumor changes and drug safety is required.

BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*

Anxiety disorder is a highly prevalent psychological illness, and research has shown that obesity is a significant risk factor for its development. This study explored the ameliorative effects and mechanisms of saponins from Panax japonicus(SPJ) on anxiety disorder in mice fed a high-fat diet(HFD). Fifty C57BL/6J mice were randomly divided into normal control diet(NCD) group, HFD group, and low-and high-dose SPJ groups. At week 12, six mice from the HFD group were further divided into a control group(treated with DMSO) and an exogenous fibroblast growth factor 21(FGF21) group(administered rFGF21). The anxiety-like behavior of the mice was assessed using the open field test and elevated plus maze test. Hematoxylin-eosin(HE) staining and oil red O staining were performed to observe pathological changes in the liver and adipose tissue. Glucose metabolism was evaluated through the glucose tolerance test(GTT) and insulin tolerance test(ITT). Western blot analysis was performed to detect the expression of FGF21 and its downstream-related proteins in the liver and cortex, along with the expression of brain-derived neurotrophic factor(BDNF), disks large homolog 4(DLG4), and synaptophysin(SYP) in the cortex. Real-time quantitative fluorescent PCR(qPCR) was used to detect the expression of FGF21 and its receptor genes in the liver and cortex. Immunofluorescence staining was employed to examine the expression of neuronal activator c-Fos, FGF21, and the FGF21 co-receptor β-klotho in the cerebral cortex. The results showed that SPJ significantly improved the frequency of activity in the open arms of the elevated plus maze and the central area of the open field in HFD mice, up-regulated the expression of BDNF, DLG4, and SYP, and effectively alleviated anxiety-like behaviors in HFD mice. Compared with the NCD group, HFD mice exhibited up-regulated expression of FGF21 in the liver and cerebral cortex, while the expression of fibroblast growth factor receptor 1(FGFR1) and β-klotho was significantly down-regulated, suggesting that HFD mice exhibited FGF21 resistance. SPJ markedly up-regulated the β-klotho levels in HFD mice, reversing FGF21 resistance. Further comparison with exogenously administered FGF21 revealed that SPJ activates brain cortical regions in a consistent manner, and additionally, SPJ promotes the number and colocalization of c-Fos and β-klotho positive cells in the brain cortex. In summary, SPJ effectively alleviates anxiety-like behaviors in HFD mice. Its mechanism is associated with up-regulation of β-klotho expression in the brain, reversal of FGF21 resistance, and subsequent activation of neurons in the cerebral cortex and amygdala.
Animals ; Diet, High-Fat/adverse effects* ; Fibroblast Growth Factors/genetics* ; Mice ; Male ; Panax/chemistry* ; Mice, Inbred C57BL ; Anxiety/etiology* ; Saponins/administration & dosage* ; Brain-Derived Neurotrophic Factor/genetics* ; Humans ; Liver/metabolism* ; Drugs, Chinese Herbal/administration & dosage*