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 evaluate the clinical value of CD4⁺ cell percentage (CD4⁺%) and NK cell percentage (NK%) in predicting treatment outcomes in children with aplastic anemia (AA), providing a reference for precise diagnosis and treatment. METHODS: This retrospective study analyzed the clinical data of AA children treated with cyclosporine A at the First Affiliated Hospital of Xinjiang Medical University from January 2019 to April 2024. The study involved 48 AA children as the observation group and 50 children undergoing medical check-ups during the same period as the control group. Lymphocyte subset data were collected from both groups to analyze differences and their relationship with treatment efficacy. Based on hematological responses, the observation group was divided into an effective group of 18 patients (HR group, including complete and partial remission) and an ineffective group of 30 patients (NHR group, including non-remission). RESULTS: Univariate analysis showed that NK% in the observation group was significantly lower than that in the control group (P<0.05). The observation group was followed up for 3 months. The HR group had a lower CD4⁺% than the NHR group (P=0.018) and a higher NK% than the NHR group (P=0.029). Multivariate logistic regression analysis indicated that a high CD4⁺% was a risk factor for poor treatment efficacy (OR=1.062), whereas a high NK% was a protective factor (OR=0.820). The area under the curve for the prediction of HR in pediatric AA by combining CD4⁺% and NK% was 0.812. CONCLUSIONS A higher CD4⁺% at diagnosis is a predictor of poor treatment response, whereas a higher NK% is associated with better outcomes.
Humans ; Anemia, Aplastic/blood* ; Male ; Female ; Killer Cells, Natural ; Child ; Retrospective Studies ; Child, Preschool ; Prognosis ; Adolescent ; CD4-Positive T-Lymphocytes ; Infant

OBJECTIVE: To evaluate the efficacy and safety of DCAG (decitabine, cytarabine, anthracyclines, and granulocyte colony-stimulating factor) regimen in the treatment of patients with relapsed/refractory (R/R) acute myeloid leukemia (AML). METHODS: The clinical data of 64 R/R AML patients received treatment at Chinese PLA General Hospital from January 1st, 2012 to December 31st, 2022 were retrospectively analyzed. Primary endpoints included efficacy measured by overall response rate (ORR) and safety. Secondary endpoints included overall survival (OS), event-free survival (EFS) and duration of response (DOR). The patients were followed from enrollment until death, or the end of last follow-up (June 1st, 2023), whichever occurred first. RESULTS: Sixty-four patients who failed prior therapy were enrolled and completed 1 cycle, and 26 and 5 patients completed 2 and 3 cycles, respectively. Objective response rate was 67.2% ［39: complete remission (CR)/CR with incomplete hematologic recovery (CRi), 4: partial remission (PR)］. With a median follow-up of 62.0 months (1.0-120.9), the median overall survival (OS) was 23.3 and event-free survival was 10.6 months. The median OS was 51.7 months (3.4-100.0) in responders (CR/CRi/PR) while it was 8.4 months (6.1-10.7) in nonresponders ( P <0.001). Grade 3-4 hematologic toxicities were observed in all patients. Four patients died from rapid disease progression within 8 weeks after chemotherapy. CONCLUSION The DCAG regimen represents a feasible and effective treatment for R/R AML.
Humans ; Leukemia, Myeloid, Acute/drug therapy* ; Cytarabine/administration & dosage* ; Granulocyte Colony-Stimulating Factor/administration & dosage* ; Retrospective Studies ; Male ; Female ; Decitabine ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Anthracyclines/administration & dosage* ; Middle Aged ; Adult ; Treatment Outcome ; Aged ; Recurrence

OBJECTIVE: To analyze the distribution characteristics of glucose-6-phosphate-dehydrogenase (G6PD) mutations and their enzyme activity in newborns patients with G6PD deficiency in Guilin region. METHODS: From July 2022 to July 2024, umbilical cord blood samples from 4 554 newborns in Guilin were analyzed for G6PD mutations using fluorescence PCR melting curve analysis. Enzyme activity was detected in 4 467 cases using the rate assay. RESULTS: Among 4 467 newborns who underwent G6PD activity testing, 162 newborns (3.63%) were identified as G6PD-deficient, including 142 males (6.04%) and 20 females (0.94%), the prevalence of G6PD deficiency was significantly higher in males than in females (P < 0.001). Genetic analysis of 4 554 newborns detected G6PD mutations in 410 cases (9%), including 171 males (7.13%) and 239 females (11.09%), with a significantly higher mutation detection rate in females than in males (P < 0.001). A total of nine single mutations and four compound heterozygous mutations were identified. The most common mutations were c.1388G>A (33.66%), c.1376G>T (23.66%) and c.95A>G (16.34%). Among newborns who underwent both enzyme activity and genetic mutation testing, males with G6PD mutations had significantly lower enzyme activity than that of females with G6PD mutations(P < 0.001). Specifically, among newborns carrying the mutations c.1388G>A, c.1376G>T, c.95A>G, c.1024C>T or c.871G>A, males consistently exhibited lower enzymatic activity than females with the same mutations (P < 0.001). Furthermore, in male G6PD-deficient newborns, the enzyme activity levels in those carrying c.1388G>A, c.1376G>T, c.95A>G, c.1024C>T, or c.871G>A were lower than those in both the control group and the c.519C>T group (P < 0.05). CONCLUSION This study provides a comprehensive profile of G6PD deficiency incidence and mutation spectrum in the Guilin region. By analyzing enzyme activity and genetic mutation results, this study provides insights into potential intervention strategies and personalized management approaches for the prevention and treatment of neonatal G6PD deficiency in the region.
Humans ; Infant, Newborn ; Glucosephosphate Dehydrogenase Deficiency/epidemiology* ; Glucosephosphate Dehydrogenase/genetics* ; Female ; Male ; Mutation ; China/epidemiology*

Episodic memory, our ability to recall past experiences, is supported by structures in the medial temporal lobe (MTL) particularly the hippocampus, and its interactions with fronto-parietal brain regions. Understanding how these brain regions coordinate to encode, consolidate, and retrieve episodic memories remains a fundamental question in cognitive neuroscience. Non-invasive brain stimulation (NIBS) methods, especially transcranial magnetic stimulation (TMS), have advanced episodic memory research beyond traditional lesion studies and neuroimaging by enabling causal investigations through targeted magnetic stimulation to specific brain regions. This review begins by delineating the evolving understanding of episodic memory from both psychological and neurobiological perspectives and discusses the brain networks supporting episodic memory processes. Then, we review studies that employed TMS to modulate episodic memory, with the aim of identifying potential cortical regions that could be used as stimulation sites to modulate episodic memory networks. We conclude with the implications and prospects of using NIBS to understand episodic memory mechanisms.
Humans ; Memory, Episodic ; Transcranial Magnetic Stimulation/methods* ; Brain/physiology* ; Nerve Net/physiology* ; Mental Recall/physiology* ; Neural Pathways/physiology*

Objective To investigate the effect of varying blood pressure stratification on renal function in the diabetic population.Methods A prospective cohort study was conducted,enrolling 9 489 diabetic patients from a total of 101 510 Kailuan Group employees who underwent health examinations between July 2006 and October 2007.The follow-up period was(8.6±4.0)years.Participants were categorized into four groups based on their baseline blood pressure levels:normal blood pressure(systolic blood pressure<120 mmHg and diastolic blood pressure<80 mmHg),elevated blood pressure(systolic blood pressure 120-130 mmHg and diastolic blood pressure<80 mmHg),stage 1 hypertension(systolic blood pressure 130-140 mmHg and/or diastolic blood pressure 80-90 mmHg),and stage 2 hypertension(systolic blood pressure≥140 mmHg and/or diastolic blood pressure≥90 mmHg).The incidence density of chronic kidney disease(CKD)was compared among these groups.A multivariate Cox proportional hazards regression model was employed to assess the effects of different blood pressure levels on renal function in diabetic patients,with the stability of the results confirmed using a multivariate time-dependent Cox proportional hazards model.Sensitivity analysis was conducted after excluding cases of cardiovascular disease(CVD)during follow-up,and cases using antihypertensive and antidiabetic medications at baseline.Results(1)At baseline,stage 1 hypertension patients demonstrated statistically significant higher differences with age and body mass index(BMI)compared to normal blood pressure group(P<0.05).(2)By the end of the follow-up,2 294 cases of CKD were identified,including 1 117 cases of estimated glomerular filtration rate(eGFR)decline and 1 575 cases of urinary protein.The incidences density of CKD,eGFR decline and urinary protein for stage 1 hypertension group were 39.4,16.3 and 25.5 per thousand person-years,respectively,all of which were statistically significant different from normal blood pressure group(log-rank test,P<0.01).(3)Multivariate Cox regression analysis revealed that,compared to the normal blood pressure group,stage 1 hypertension was associated with a 29%increased risk of CKD(HR=1.29,95%CI 1.09-1.52)and a 40%increased risk of eGFR decline(HR=1.40,95%CI 1.08-1.80)in diabetic individuals.Conclusion Stage 1 hypertension significantly increases the risk of CKD and eGFR decline in diabetic individuals,with a particularly notable effect on the risk of eGFR decline.