Background/Aims: Data from Asia regarding the short-term and long-term outcomes for acute severe ulcerative colitis (ASUC) are limited. We assessed the outcomes of ASUC, identified the risk factors for colectomy, and compared colectomy rates between the pre-biologics and post-biologics eras in Taiwan. Methods: The patients with an ASUC diagnosis between January 2013 and March 2022 at 5 tertiary medical centers were retrospectively analyzed. Results: In total, 98 patients were enrolled, with 68.4% diagnosed in the post-biologics era. In 78.6% of the ASUC patients initially received intravenous steroid therapy, for which the success rate was 74.1%. As for rescue therapy, 15 patients (93.8%) received biologics and 1 (6.3%) received cyclosporin. Biologics rescue therapy had a 93.3% success rate. One (1%) mortality due to septic shock occurred. The colectomy rate for index ASUC admission was 11.2%. Patients receiving colectomy were predominantly male (P= 0.012) and at older age (P= 0.016). Higher C-reactive protein (P= 0.035), lower albumin (P= 0.017), and hemoglobin (P= 0.023) levels were associated with colectomy risk. During a median follow-up of 24 months, 13 patients (15.1%) had recurrent ASUC and 23.1% of patients received colectomy. The accumulated colectomy rate at 3 years did not differ between the pre- and post-biologics eras (16.1% vs. 13.4%, P= 0.270). Conclusions This is the first Asian study on ASUC to compare colectomy rates between the prebiologics and post-biologics eras, revealing no significant difference. The recurrent ASUC had a higher colectomy rate than the index ASUC.

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.

Objective: To explore the clinical application value of neural network technology,and to screen the clinical early warning indicators of severe hand-foot-mouth disease(HFMD) by constructing a back propagation neural network(BPNN) model. Methods: The clinical data of children with HFMD admitted to the Department of Infectious Diseases and Pediatrics of the First Affiliated Hospital of Xinxiang Medical University from January2019 to January 2023 were collected.The data were divided into 70% training samples and 30 % test samples using SPSS Modeler 18.0.The BPNN model and Logistic model were constructed to compare and evaluate the prediction accuracy and screening effect of the model. Results: The clinical data of 589 children were collected and analyzed,including 324 cases in the mild group and 265 cases in the severe group.The prediction accuracy of the test set(n=178) of BP neural network model and Logistic regression model was 82.02% and 84.83%,respectively.The area under the ROC curve(95% CI) was 0.791(0.749-0.834) and 0.625(0.577-0.674),respectively.Among the predictive variables output by the BPNN model,the top five factors that had the greatest impact on the grouping were highest body temperature,duration of fever,glutamyl transpeptidase,aspartate aminotransferase,and globulin.There were 3 of the top 10 overlaps in the importance of the predictive variables output by the two models,which were the highest body temperature,duration of fever and limb shaking. Conclusion The BPNN model and Logistic regression model perform well in screening and verifying the risk factors of severe hand-foot-mouth disease,but the comprehensive prediction performance of BP neural network model is better.The top five influencing factors of severe HFMD screened by the BPNN model are the highest body temperature,duration of fever,glutamyl transpeptidase,aspartate aminotransferase and globulin.

Objective Sequencing depth is a key parameter in next generation sequencing,which is closely related to the accuracy of sequencing results.Forensic biological evidence examination requires extremely high accuracy.It is crucial to scientifically and reasonably set the sequencing depth analysis threshold for forensic next generation sequencing testing.Methods This study used targeted sequencing data of microhaplotypes from 50 samples with known genotypes.By calculating the accuracy,precision,recall,and F1 score of each locus under various threshold conditions,two types of analysis threshold setting methods,which were based on fixed read count and fixed sequencing depth ratio,were studied extensively.Results The results showed that false positives were observed when the analysis threshold was set at 50×or 100×.When the analysis threshold was set at 200×,false negatives were observed.When the analysis threshold was set at 1.5%,3.0%,or 4.5%,false positives were observed.This study further proposed a third type of analysis threshold setting method,which was based on sequencing depth ratio scatter plots.With this method,no false positive or false negative was observed in the results.This article then explored four factors that lead to significant differences in the sequencing depth of forensic next generation sequencing experiments,compared with the analysis threshold setting method for capillary electrophoresis technology,and discussed the correlation between analysis thresholds and the ability to distinguish mixed DNA.Conclusion Employing the sequencing depth ratio scatter plot method to set analysis threshold has significant application value in next generation sequencing-based forensic genetic marker genotyping.