Aqueous Extract of Shi-Liu-Wei-Liu-Qi-Yin Induces G2/M Phase Arrest and Apoptosis in Human Bladder Carcinoma Cells via Fas and Mitochondrial Pathway

Shi-Liu-Wei-Liu-Qi-Yin (SLWLQY) was traditionally used to treat cancers. However, scientific evidence of the anticancer effects still remains undefined. In this study, we aimed to clarify the possible mechanisms of SLWLQY in treating cancer. We evaluated the effects of SLWLQY on apoptosis-related experiments inducing in TSGH-8301 cells by (i) 3-(4,5-dimethylthiazol-zyl)-2,5-diphenylterazolium bromide (MTT) for cytotoxicity; (ii) cell-cycle analysis and (iii) western blot analysis of the G2/M-phase and apoptosis regulatory proteins. Human bladder carcinoma TSGH-8301 cells were transplanted into BALB/c nude mice as a tumor model for evaluating the antitumor effect of SLWLQY. Treatment of SLWLQY resulted in the G2/M phase arrest and apoptotic death in a dose-dependent manner, accompanied by a decrease in cyclin-dependent kinases (cdc2) and cyclins (cyclin B1). SLWLQY stimulated increases in the protein expression of Fas and FasL, and induced the cleavage of caspase-3, caspase-9 and caspase-8. The ratio of Bax/Bcl2 was increased by SLWLQY treatment. SLWLQY markedly reduced tumor size in TSGH-8301 cells-xenografted tumor tissues. In the tissue specimen, SLWLQY up-regulated the expression of Fas, FasL and Bax proteins, and down-regulated Bcl2 as well as in in vitro assay. Our results showed that SLWLQY reduced tumor growth, caused cell-cycle arrest and apoptosis in TSGH-8301 cells via the Fas and mitochondrial pathway

NMD-12: A New Machine-Learning Derived Screening Instrument to Detect Mild Cognitive Impairment and Dementia

Introduction Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia. Methods With the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in the test group. Results The screening model (NMD-12) was constructed with the 12 items that were ranked the highest in feature selection. The receiver-operator characteristic (ROC) analysis showed that the area under the curve (AUC) in the test group was 0.94 for discriminating NC vs. MCI, 0.88 for MCI vs. VMD, 0.97 for MCI vs. dementia, and 0.96 for VMD vs. dementia, respectively. Discussion The NMD-12 model has been developed and validated in this study. It provides healthcare professionals with a simple and practical screening tool which accurately differentiates NC, MCI, VMD, and dementia

Machine Learning for the Preliminary Diagnosis of Dementia

Objective: The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods: We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results: Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion: The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia

Machine learning for the preliminary diagnosis of dementia

Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia

Analyze informant-based questionnaire for the early diagnosis of senile dementia using deep learning

OBJECTIVE: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire. METHODS: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score. RESULTS: Compared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), mild cognitive impairment (MCI) (F1-score = 0.87), very mild dementia (VMD) (F1-score = 0.77) and Severe dementia (F1-score = 0.94). CONCLUSION: The deep neural network (DNN) classification model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe)

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT

BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners.MethodsTwo hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACμ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACμ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACμ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-ACμ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods.ResultsThe NMSE for Chang's method, DL-ACμ, DL-AC, cDL-ACμ, cDL-AC, eDL-ACμ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACμ are closer to CT-AC, Followed by DL-AC, eDL-ACμ, cDL-ACμ, cDL-AC, eDL-AC, Chang's method, and NAC.ConclusionAll DL-based AC methods are superior to Chang-AC. DL-ACμ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT

Performance of Thallium-201 Electrocardiography-gated Myocardial Perfusion Single Photon Emission Computed Tomography to Assess Left Ventricular Function

This study evaluated the performance of gated single photon emission computed tomography (SPECT) with thallium-201 (201Tl) in assessing left ventricular ejection fraction (LVEF), end-diastolic volume (EDV), and end-systolic volume (ESV) in Taiwanese by determining repeatability and correlation with two-dimensional (2D) echocardiography. A total of 18 patients underwent two sequential gated SPECT acquisitions within 30 minutes in the resting state to assess repeatability. Another 28 patients who underwent gated SPECT and 2D echocardiography within 7 days were included for comparison. The two sequential measurements were well correlated with respect to LVEF, EDV, and ESV (r = 0.97, 0.95, and 0.97, respectively, all p < 0.0001). Bland-Altman analysis revealed that two standard deviations of the absolute difference between the two sequential measurements for LVEF, EDV, and ESV were 6.4%, 16.8 mL, and 8.6 mL, respectively. For LVEF, EDV, and ESV, correlations between redistribution 201Tl-gated SPECT and echocardiography were also excellent (all r = 0.83, p < 0.0001). LVEF was similar with 201Tl-gated SPECT and echocardiography, but EDV and ESV were significantly higher with echocardiography (p < 0.05). Our study revealed that 201Tl-gated SPECT has high repeatability and excellent correlation with echocardiography for the assessment of LVEF and volumes in Taiwanese. These results support the clinical application of gated SPECT in routine 201Tl myocardial perfusion imaging in Taiwanese