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 efficacy and safety of Venetoclax combined with multidrug chemotherapy in patients with relapsed or refractory early T-cell precursor acute lymphoblastic leukemia (R/R ETP-ALL) . Methods: This study retrospectively analyzed 15 patients with R/R ETP-ALL who received Venetoclax combined with multidrug chemotherapy from December 2018 to February 2022. Among them, eight cases were combined with demethylated drugs, four cases were combined with demethylated drugs and HAAG chemotherapy regimen, two cases were combined with demethylated drugs and CAG regimen, and one case was combined with Cladribine. Specific usage and dosage of Venetoclax: 100 mg on day 1, 200 mg on day 2, 400 mg on day 3-28, orally; when combined with azole antifungal drugs, dosage was reduced to 100 mg/d. Results: Fifteen patients (10 males and 5 females) with R/R ETP-ALL were treated with Venetoclax and multidrug chemotherapy with a median age of 35 (12-42) years old. Of 4 refractory and 11 relapsed patients, the efficacy was evaluated on the 21th day following combined chemotherapy: the overall response rate, the complete response (CR) rate, and the CR with incomplete hematological recovery (CRi) rate were 67.7% (10/15), 60.0% (9/15), and 6.7% (1/15), respectively. For the overall study population, the 12-month overall survival (OS) rate was 60.0%, and the median OS was 17.7 months. The disease-free survival (DFS) rate of all CR patients at 12 months was 60.0%, and the median DFS did not reach. About 14 patients had Ⅲ-Ⅳ hematological toxicity, but these adverse reactions were all controllable. No adverse reaction in the nervous system and tumor lysis syndrome occurred in this study, and no adverse reaction of organs above grade Ⅲ occurred. Conclusion: Venetoclax combined with multidrug chemotherapy may be a safe and promising treatment option for patients with R/R ETP-ALL.
Male ; Female ; Humans ; Adult ; Retrospective Studies ; Treatment Outcome ; Bridged Bicyclo Compounds, Heterocyclic/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols ; Precursor T-Cell Lymphoblastic Leukemia-Lymphoma/drug therapy* ; Precursor Cells, T-Lymphoid ; Leukemia, Myeloid, Acute/drug therapy*

OBJECTIVE: To evaluate the prospective association between cumulative resting heart rate (cumRHR) and rapid renal function decline (RRFD) in a cohort of individuals aged 60 and older. METHODS: In the Tianjin Chronic Kidney Disease Cohort Study, the individuals who underwent three consecutive physical examinations between 2014 and 2017, with estimated glomerular filtration rate (eGFR) greater than 60 mL/min per 1.73 m² and aged 60 years or older were enrolled. A total of 27,564 patients were prospectively followed up from January 1, 2017 to December 31, 2020. The 3-year cumRHR was calculated. The primary outcome was RRFD, defined as an annualized decline in eGFR of 5 mL/min per 1.73 m² or greater. Logistic and restricted spline regression models and subgroup analysis were used to investigate the association of cumRHR with RRFD after adjusting for all confounders. RESULTS: During a median follow-up of 3.2 years, a total of 4,347 (15.77%) subjects developed RRFD. In fully-adjusted models, compared with the lowest quartile of cumRHR, the odds ratio (OR) for the highest was 1.44 (1.28-1.61), P < 0.001. Furthermore, each 1-standard deviation (27.97 beats/min per year) increment in cumRHR was associated with a 17% (P < 0.001) increased risk of RRFD, with a linear positive correlation (P for non-linear = 0.803). Participants with a 3-year cumRHR ≥ 207 (beats/min) * year (equivalent to ≥ 69 beats/min per year in 3 years) were found to be at a higher risk of RRFD. CONCLUSIONS The cumRHR is significantly associated with a higher risk of RRFD among older adults. These results might provide an effective goal for managing and delaying the decline of renal function in the older adults.

BACKGROUND: Currently, more and more infertility couples are opting for combined acupuncture to improve success rate of in vitro fertilization (IVF). However, evidence from acupuncture for improving IVF pregnancy outcomes remains a matter of debate. OBJECTIVE: To quantitatively summarized the evidence of the efficacy of acupuncture among women undergoing IVF by means of systematic review and meta-analysis. METHODS: Four English (PubMed, Web of Science, EMBASE, and Cochrane Register of Controlled Clinical Trials) and Four Chinese databases (Wanfang Databases, Chinese National Knowledge Infrastructure, Chinese Science and Technology Periodical Database, and SinoMed) were searched from database inception until July 2, 2023. Randomized controlled trials (RCTs) that evaluated the acupuncture's effects for women undergoing IVF were included. The subgroup analysis was conducted with respect to the age of participants, different acupuncture types, type of control, acupuncture timing, geographical origin of the study, whether or not repeated IVF failure, and acupuncture sessions. Sensitivity analyses were predefifined to explore the robustness of results. The primary outcomes were clinical pregnancy rate (CPR) and live birth rate (LBR), and the secondary outcomes were ongoing pregnancy rate and miscarriage rate. Random effects model with I² statistics were used to quantify heterogeneity. Publication bias was estimated by funnel plots and Egger's tests. RESULTS: A total of 58 eligible RCTs representing 10,968 women undergoing IVF for pregnant success were identifified. Pooled CPR and LBR showed a signifificant difference between acupuncture and control groups [69 comparisons, relative risk (RR) 1.19, 95% confifidence intervals (CI) 1.12 to 1.25, I²=0], extremely low evidence; 23 comparisons, RR 1.11, 95%CI 1.02 to 1.21, I²=14.6, low evidence, respectively). Only transcutaneous electrical acupoint stimulation showed a positive effect on both CPR (16 comparisons, RR 1.17, 95%CI 1.06 to 1.29; I²=0, moderate evidence) and LBR (9 comparisons, RR 1.20, 95%CI 1.04 to 1.37; I²=8.5, extremely low evidence). Heterogeneity across studies was found and no studies were graded as high-quality evidence. CONCLUSION Results showed that the convincing evidence levels on the associations between acupuncture and IVF pregnant outcomes were relatively low, and the varied methodological design and heterogeneity might inflfluence the fifindings. (Registration No. PROSPERO CRD42021232430).
Pregnancy ; Female ; Humans ; Live Birth ; Fertilization in Vitro/methods* ; Pregnancy Outcome ; Abortion, Spontaneous ; Acupuncture Therapy

Objective:To explore the efficacy and safety of Rasburicase therapy in critically ill children su-ffering from advanced Burkitt′s lymphoma.Methods:A retrospective analysis of children with advanced Burkitt′s lymphoma was admitted to Pediatric Intensive Care Unit, the First Affiliated Hospital of Sun Yat-Sen University, from January 2015 to May 2020 and accepted treatment.According to the uric acid-lowering therapies, patients were divided into 2 groups, namely Rasburicase group (Group R) and traditional treatment group (Group T), to compare the effects of hypouricemic treatment and the prognosis between the 2 groups.Results:Twenty-nine children with advanced Burkitt′s lymphoma were included in this study, with 13 cases (44.83%) of stage Ⅲ and 16 cases (55.17%) of stage Ⅳ.Abdominal mass/ abdominal distension (13 cases, 44.83%) and abdominal pain (7 cases, 24.14%) were the main reasons of initial medical visit attendance.The most common primary tumor site was abdominal/ pelvic cavity (21 cases, 72.41%), followed by head or neck (6 cases, 20.69%). There were 15 cases in Group R and 14 cases in group T. No significant differences in serum creatinine, lactate dehydrogenase and uric acid were detected between the 2 groups (all P>0.05). The proportion of serum uric acid recovery rate of 24 hours and 72 hours after initial treatment in Group R were significantly higher than those in T group (85.71% vs.25.00%, 100.00% vs.25.00%, all P<0.01). Although there were no obvious differences in the incidence of tumor lysis syndrome between the 2 groups (33.33% vs.64.29%, P=0.096), the incidence of acute renal injury, renal replacement therapy requirement, serious complications and the 28 day mortality in Group R were remarkably lower than those in Group T (33.33% vs.85.71%, 13.33% vs.64.29%, 20.00% vs.78.57%, 0 vs.35.71%, all P< 0.05). Conclusions:Rasburicase can effectively reduce the serum uric acid level and decrease the incidence of acute kidney injury and other severe complications, thus improving the prognosis of children experiencing advanced Burkitt′s lymphoma.

The exacerbation of progressive multiple sclerosis (MS) is closely associated with obstruction of the differentiation of oligodendrocyte progenitor cells (OPCs). To discover novel therapeutic compounds for enhancing remyelination by endogenous OPCs, we screened for myelin basic protein expression using cultured rat OPCs and a library of small-molecule compounds. One of the most effective drugs was pinocembrin, which remarkably promoted OPC differentiation and maturation without affecting cell proliferation and survival. Based on these in vitro effects, we further assessed the therapeutic effects of pinocembrin in animal models of demyelinating diseases. We demonstrated that pinocembrin significantly ameliorated the progression of experimental autoimmune encephalomyelitis (EAE) and enhanced the repair of demyelination in lysolectin-induced lesions. Further studies indicated that pinocembrin increased the phosphorylation level of mammalian target of rapamycin (mTOR). Taken together, our results demonstrated that pinocembrin promotes OPC differentiation and remyelination through the phosphorylated mTOR pathway, and suggest a novel therapeutic prospect for this natural flavonoid product in treating demyelinating diseases.
Animals ; Cell Differentiation ; Flavanones ; Mice ; Mice, Inbred C57BL ; Myelin Sheath/metabolism* ; Oligodendroglia/metabolism* ; Rats ; Remyelination ; Signal Transduction ; TOR Serine-Threonine Kinases/metabolism*