OBJECTIVE: The objective of this study was to explore the association of longitudinal CA-125 measurements with overall survival (OS) time by developing a flexible model for patient-specific CA-125 profiles, and to provide a simple and reliable prediction of OS. METHODS: A retrospective study was performed on 275 patients with ovarian cancer who underwent at least one cycle of primary chemotherapy in our institute. Serial measurements of patients' CA-125 levels were performed at different frequencies according to their clinical plans. A statistical model coupling the Cox proportional hazards and the mixed-effects models was applied to determine the association of OS with patient-specific longitudinal CA-125 values. Stage and residual tumor size were additional variables included in the analysis. RESULTS: A total of 1,601 values of CA-125 were included. Longitudinal CA-125 levels, stage, and the residual tumor size were all significantly associated with OS. A patient-specific survival probability could be calculated. Validation showed that, in average, 85.4% patients were correctly predicted to have a high or low risk of death at a given time point. Comparison with a traditional model using CA-125 half-life and time to reach CA-125 nadir showed that the longitudinal CA-125 model had an improved predicative value. CONCLUSION: Longitudinal CA-125 values, measured from the diagnosis of ovarian cancer to the completion of primary chemotherapy, could be used to reliably predict OS after adjusting for the stage and residual tumor disease. This model could be potentially useful in clinical counseling of patients with ovarian cancer.
Counseling ; Diagnosis ; Drug Therapy ; Half-Life ; Humans ; Models, Statistical ; Neoplasm, Residual ; Ovarian Neoplasms* ; Retrospective Studies

Background: Compared with the Western population, central demyelinating disorders are relatively rare while the data on the prognostic value of autoantibodies together with clinical characteristics and cognitive dysfunction has rarely been explored in neuromyelitis optica (NMO) and multiple sclerosis (MS). Methods: Nineteen patients with MS and 14 with NMO underwent clinical profiling and cognitive assessment. According to serology tests, they are divided into four subgroups for further analysis. Results: There was higher frequency of aquaporin-4 immunoglobulin G. sero-positivity (64.3% vs. 10.5%; p=0.003) and antinuclear antibodies (ANA) and/or antibodies to extractable nuclear antigens (anti-ENA) in NMO compared to MS (42.9% vs. 5.2%; p=0.026). The presence of anti-ENA represented a unique clinical phenotype, with longer segment of myelitis (p=0.049), female preponderance, and an inverse correlation between age-of-onset and annual relapse rate (ρ= -0.88, p=0.021). Among patients with anti-ENA positivity, comprehensive serology panels revealed Sjögren’s syndrome A antibodies as the most common (83%), in contrast to limited clinical documentation of Sjögren’s syndrome (16%). There was no significant difference in cognitive assessment by anti-ENA status. MS and NMO represent two different serologic entities. Conclusions: Anti-ENA may have prognostic value for its linkage to a unique clinical phenotype, which has longer initial segment of myelitis, female preponderance, and higher annual relapse rate on earlier age-of-onset, but has limited clinical impact on cognition. Further studies are warranted to investigate whether anti-ENA represents an epiphenomenon of myelitis or simply a systemic inflammatory state.

Background:Reports on the aquaporin-4 immunoglobulin G (AQP4-IgG) status for cognitive performance and neuroimaging correlations are limited in neuromyelitis optica (NMO) and multiple sclerosis (MS) literature. Methods: Cognitive results of 19 MS and 15 NMO patients were compared with 47 agematched controls. Apparent diffusion coeffi cient (ADC) values were used to delineate gray matter and white matter damages and correlate with neuropsychological results. Results: Verbal memory test showed signifi cant differences between MS and NMO in the late registration, early and delay recall (p<0.05), while their retention rates were even. In MS, ADC values were signifi cantly elevated in the dorsolateral prefrontal and occipital gray matter which was in contrast with NMO group that showed elevation in the dorsolateral prefrontal gray matter and parieto-occcipital white matter. AQP4-IgG status exerted a limited effect on ADC values and neuropsychological results. Conclusions: Verbal memory test might be helpful in differentiating NMO and MS. ADC values can be used as a surrogate marker for tissue injury in NMO and MS since they were in line with the cognition scores. Anatomical regions with elevated ADC values were different in NMO and MS.

BACKGROUND/OBJECTIVES: Coffee is a complex chemical mixture, with caffeine being the most well-known bioactive substance. The immunomodulatory and anti-inflammatory properties of coffee and caffeine impact health in various aspects, including the respiratory system. The objective is to investigate the effects of coffee and caffeine on airway hyperresponsiveness and allergic reactions, as well as to analyze and compare associated cytokine profiles.MATERIALS/METHODS: BALB/c mice were intraperitoneally sensitized with ovalbumin (OVA) and given OVA inhalation to induce airway hypersensitivity. Two weeks after sensitization, they were intragastrically gavaged with coffee or caffeine, both containing 0.3125 mg caffeine, daily for 4 weeks. Control mice were fed with double-distilled water. Serum OVAspecific antibody levels were measured beforehand and 5 weeks after the first gavage. Airway hyperresponsiveness was detected by whole body plethysmography after gavage. Cytokine levels of bronchoalveolar lavage and cultured splenocytes were analyzed. RESULTS: Coffee effectively suppressed T helper 2-mediated specific antibody response.Airway responsiveness was reduced in mice treated with either coffee or caffeine. Compared to the control, coffee significantly reduced OVA-specific immunoglobulin (Ig) G, IgG1 and IgE antibody responses (P < 0.05). Caffeine also attenuated specific IgG and IgG1 levels, though IgE level was unaffected. Coffee significantly reduced interleukin (IL)-4 and increased IL-10 concentration in spleen cells and bronchoalveolar lavage fluid (P < 0.05). CONCLUSIONS Coffee effectively attenuated airway hyperresponsiveness and systemic allergic responses induced by OVA food allergen in mice. As a complex composition of bioactive substances, coffee displayed enhanced immunomodulatory and anti-inflammatory effects than caffeine.

Background: AMP-activated protein kinase (AMPK) is a key enzyme for cellular energy homeostasis and improves metabolic disorders. Brown and beige adipose tissues exert thermogenesis capacities to dissipate energy in the form of heat. Here, we investigated the beneficial effects of the antioxidant alpha-lipoic acid (ALA) in menopausal obesity and the underlying mechanisms. Methods: Female Wistar rats (8 weeks old) were subjected to bilateral ovariectomy (Ovx) and divided into four groups: Sham (n=8), Ovx (n=11), Ovx+ALA2 (n=10), and Ovx+ALA3 (n=6) (ALA 200 and 300 mg/kg/day, respectively; gavage) for 8 weeks. 3T3-L1 cells were used for in vitro study. Results: Rats receiving ALA2 and ALA3 treatment showed significantly lower levels of body weight and white adipose tissue (WAT) mass than those of the Ovx group. ALA improved plasma lipid profiles including triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Hematoxylin & eosin staining of inguinal WAT showed that ALA treatment reduced Ovx-induced adipocyte size and enhanced uncoupling protein 1 (UCP1) expression. Moreover, plasma levels of irisin were markedly increased in ALA-treated Ovx rats. Protein expression of brown fat-specific markers including UCP1, PRDM16, and CIDEA was downregulated by Ovx but markedly increased by ALA. Phosphorylation of AMPK, its downstream acetyl-CoA carboxylase, and its upstream LKB1 were all significantly increased by ALA treatment. In 3T3-L1 cells, administration of ALA (100 and 250 μM) reduced lipid accumulation and enhanced oxygen consumption and UCP1 protein expression, while inhibition of AMPK by dorsomorphin (5 μM) significantly reversed these effects. Conclusion ALA improves estrogen deficiency-induced obesity via browning of WAT through AMPK signaling.

Background: AMP-activated protein kinase (AMPK) is a key enzyme for cellular energy homeostasis and improves metabolic disorders. Brown and beige adipose tissues exert thermogenesis capacities to dissipate energy in the form of heat. Here, we investigated the beneficial effects of the antioxidant alpha-lipoic acid (ALA) in menopausal obesity and the underlying mechanisms. Methods: Female Wistar rats (8 weeks old) were subjected to bilateral ovariectomy (Ovx) and divided into four groups: Sham (n=8), Ovx (n=11), Ovx+ALA2 (n=10), and Ovx+ALA3 (n=6) (ALA 200 and 300 mg/kg/day, respectively; gavage) for 8 weeks. 3T3-L1 cells were used for in vitro study. Results: Rats receiving ALA2 and ALA3 treatment showed significantly lower levels of body weight and white adipose tissue (WAT) mass than those of the Ovx group. ALA improved plasma lipid profiles including triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Hematoxylin & eosin staining of inguinal WAT showed that ALA treatment reduced Ovx-induced adipocyte size and enhanced uncoupling protein 1 (UCP1) expression. Moreover, plasma levels of irisin were markedly increased in ALA-treated Ovx rats. Protein expression of brown fat-specific markers including UCP1, PRDM16, and CIDEA was downregulated by Ovx but markedly increased by ALA. Phosphorylation of AMPK, its downstream acetyl-CoA carboxylase, and its upstream LKB1 were all significantly increased by ALA treatment. In 3T3-L1 cells, administration of ALA (100 and 250 μM) reduced lipid accumulation and enhanced oxygen consumption and UCP1 protein expression, while inhibition of AMPK by dorsomorphin (5 μM) significantly reversed these effects. Conclusion ALA improves estrogen deficiency-induced obesity via browning of WAT through AMPK signaling.

Background: AMP-activated protein kinase (AMPK) is a key enzyme for cellular energy homeostasis and improves metabolic disorders. Brown and beige adipose tissues exert thermogenesis capacities to dissipate energy in the form of heat. Here, we investigated the beneficial effects of the antioxidant alpha-lipoic acid (ALA) in menopausal obesity and the underlying mechanisms. Methods: Female Wistar rats (8 weeks old) were subjected to bilateral ovariectomy (Ovx) and divided into four groups: Sham (n=8), Ovx (n=11), Ovx+ALA2 (n=10), and Ovx+ALA3 (n=6) (ALA 200 and 300 mg/kg/day, respectively; gavage) for 8 weeks. 3T3-L1 cells were used for in vitro study. Results: Rats receiving ALA2 and ALA3 treatment showed significantly lower levels of body weight and white adipose tissue (WAT) mass than those of the Ovx group. ALA improved plasma lipid profiles including triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Hematoxylin & eosin staining of inguinal WAT showed that ALA treatment reduced Ovx-induced adipocyte size and enhanced uncoupling protein 1 (UCP1) expression. Moreover, plasma levels of irisin were markedly increased in ALA-treated Ovx rats. Protein expression of brown fat-specific markers including UCP1, PRDM16, and CIDEA was downregulated by Ovx but markedly increased by ALA. Phosphorylation of AMPK, its downstream acetyl-CoA carboxylase, and its upstream LKB1 were all significantly increased by ALA treatment. In 3T3-L1 cells, administration of ALA (100 and 250 μM) reduced lipid accumulation and enhanced oxygen consumption and UCP1 protein expression, while inhibition of AMPK by dorsomorphin (5 μM) significantly reversed these effects. Conclusion ALA improves estrogen deficiency-induced obesity via browning of WAT through AMPK signaling.

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.