Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective:To investigate the feasibility and clinical value of artificial intelligence-assisted compressed sensing (ACS) technology in accelerating MR simulation (MR-sim) for radiotherapy of nasopharyngeal carcinoma (NPC).Methods:Thirty patients with NPC scheduled to receive radical radiotherapy at Sun Yat-sen University Cancer Center were prospectively enrolled. All patients underwent head and neck MR-sim on a 3.0 T scanner, with axial T 1 weighted imaging (WI), T 2WI, contrast-enhanced T 1WI, and fat-suppressed contrast-enhanced T 1WI images acquired using both ACS and parallel imaging (PI) techniques. Paired-sample t tests or rank-sum tests were used to compare scan time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of MR-sim images between the two techniques. A 5-point Likert scale was applied to evaluate tumor lesion visualization, lesion margin clarity, artifacts, and overall image quality, with chi-square tests used to compare subjective image quality scores between the two techniques. Tumor target volumes were delineated on MR-sim images obtained by both ACS and PI techniques after fusion with CT simulation images, and consistency was assessed using the Dice similarity coefficient (DSC). Results:For both individual sequences and overall protocols, ACS significantly reduced MR-sim acquisition time compared with PI ( P < 0.001). The total acquisition time with ACS was (378.60±17.07) s versus (694.93±17.07) s with PI, representing a 45.52% time reduction. SNR, CNR, tumor lesion identification, margin clarity, artifacts, and overall image quality scores of MR-sim images did not differ significantly between ACS and PI ( P > 0.05). Tumor target volumes delineated from ACS- and PI-based MR-sim images showed high consistency after fusion with CT simulation images ( P > 0.05), with mean DSC values of primary tumors and metastatic cervical lymph nodes approaching 1. Conclusion:Compared with conventional MR acceleration methods (PI), ACS enables faster MR-sim acquisition in NPC without compromising image quality or the accuracy of tumor target delineation.

Objective:To investigate the feasibility and clinical value of artificial intelligence-assisted compressed sensing (ACS) technology in accelerating MR simulation (MR-sim) for radiotherapy of nasopharyngeal carcinoma (NPC).Methods:Thirty patients with NPC scheduled to receive radical radiotherapy at Sun Yat-sen University Cancer Center were prospectively enrolled. All patients underwent head and neck MR-sim on a 3.0 T scanner, with axial T 1 weighted imaging (WI), T 2WI, contrast-enhanced T 1WI, and fat-suppressed contrast-enhanced T 1WI images acquired using both ACS and parallel imaging (PI) techniques. Paired-sample t tests or rank-sum tests were used to compare scan time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of MR-sim images between the two techniques. A 5-point Likert scale was applied to evaluate tumor lesion visualization, lesion margin clarity, artifacts, and overall image quality, with chi-square tests used to compare subjective image quality scores between the two techniques. Tumor target volumes were delineated on MR-sim images obtained by both ACS and PI techniques after fusion with CT simulation images, and consistency was assessed using the Dice similarity coefficient (DSC). Results:For both individual sequences and overall protocols, ACS significantly reduced MR-sim acquisition time compared with PI ( P < 0.001). The total acquisition time with ACS was (378.60±17.07) s versus (694.93±17.07) s with PI, representing a 45.52% time reduction. SNR, CNR, tumor lesion identification, margin clarity, artifacts, and overall image quality scores of MR-sim images did not differ significantly between ACS and PI ( P > 0.05). Tumor target volumes delineated from ACS- and PI-based MR-sim images showed high consistency after fusion with CT simulation images ( P > 0.05), with mean DSC values of primary tumors and metastatic cervical lymph nodes approaching 1. Conclusion:Compared with conventional MR acceleration methods (PI), ACS enables faster MR-sim acquisition in NPC without compromising image quality or the accuracy of tumor target delineation.

This study aims to compare the metabolic differences of baicalin and its analogues between Shuganning Injection and Scutellariae Radix extract. Twelve SD rats were randomly divided into a Shuganning Injection group and a Scutellariae Radix extract group, with 6 rats in each group. Their liver microsomes were incubated with the drugs, and then the samples were collected. Ultra performance liquid chromatography-quadrupole/electrostatic field orbitrap high resolution mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) was used to analyze the prototype components and metabolites of the drugs in liver microsomes of each group. Another 12 SD rats were also divided into a Shuganning Injection group and a Scutellariae Radix extract group, with 6 rats in each group. The rats were administrated with 4.2 mL·kg~(-1) Shuganning Injection or Scutellariae Radix extract by tail vein injection. After 48 h, the rat urine, feces, and bile were collected, and UPLC-Q-Exactive Orbitrap-MS was used to analyze the prototype components and metabolites in each biological sample. The results showed that 5 prototype components and 8 metabolites of Shuganning Injection and Scutellariae Radix extract were identified in liver microsomes. A total of 5 prototype components were identified in rat urine, feces, and bile separately. Fifteen metabolites were identified in the urine, 9 metabolites in the feces, and 12 metabolites in the bile. The differences of metabolic pathways and number of metabolites of baicalin were compared between Shuganning Injection and Scutellariae Radix extract. For both Shuganning Injection and Scutellariae Radix extract, the metabolites of baicalin or baicalein in rat liver microsomes, urine, bile, and feces were mainly formed glucuronic acid conjugates, and there were a small amount of glucose conjugates and methylation products. Differences were found in the number and types of metabolites of baicalin in urine samples between Shuganning Injection and Scutellariae Radix extract, indicating that differences existed in metabolism between the two. This suggests that the other components in the formula lead to changes of metabolites in vivo.
Animals ; Rats ; Rats, Sprague-Dawley ; Microsomes, Liver/chemistry* ; Drugs, Chinese Herbal/administration & dosage* ; Feces/chemistry* ; Scutellaria baicalensis/chemistry* ; Male ; Bile/chemistry* ; Flavonoids/metabolism* ; Urine/chemistry* ; Chromatography, High Pressure Liquid ; Mass Spectrometry ; Plant Extracts

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.