Identification of Multi-Target Anti-AD Chemical Constituents From Traditional Chinese Medicine Formulae by Integrating Virtual Screening and In Vitro Validation

Alzheimer’s disease (AD) is a neurodegenerative disease that seriously threatens the health of the elderly. At present, no drugs have been proven to cure or delay the progression of the disease. Due to the multifactorial aetiology of this disease, the multi-target-directed ligand (MTDL) approach provides an innovative and promising idea in search for new drugs against AD. In order to find potential multi-target anti-AD drugs from traditional Chinese medicine (TCM) formulae, a compound database derived from anti-AD Chinese herbal formulae was constructed and predicted by the anti-AD multi-target drug prediction platform established in our laboratory. By analyzing the results of virtual screening, 226 chemical constituents with 3 or more potential AD-related targets were collected, from which 16 compounds that were predicted to combat AD through various mechanisms were chosen for biological validation. Several cell models were established to validate the anti-AD effects of these compounds, including KCl, Aβ, okadaic acid (OA), SNP and H2O2 induced SH-SY5Y cell model and LPS induced BV2 microglia model. The experimental results showed that 12 compounds including Nonivamide, Bavachromene and 3,4-Dimethoxycinnamic acid could protect model cells from AD-related damages and showed potential anti-AD activity. Furthermore, the potential targets of Nonivamide were investigated by molecular docking study and analysis with CDOCKER revealed the possible binding mode of Nonivamide with its predicted targets. In summary, 12 potential multi-target anti-AD compounds have been found from anti-AD TCM formulae by comprehensive application of computational prediction, molecular docking method and biological validation, which laid a theoretical and experimental foundation for in-depth study, also providing important information and new research ideas for the discovery of anti-AD compounds from traditional Chinese medicine.

Discovery and validation of novel methylated DNA markers of cervical cancer.

5526 Background: HR-HPV DNA testing, with or without cervical cytology, provides excellent sensitivity for detection of cervical cancer (CC) and its precursors; negative test results indicate that risk of disease is extremely low and enable women to undergo reduced screening with safety. However, management of women who screen positive remains challenging as many will prove to have self-limited HR-HPV infections. DNA methylation is an early event in carcinogenesis that could enhance CC screening specificity. Methods: For discovery, DNA from 70 FFPE CC (36 squamous, 34 adenocarcinoma) tissues that were reviewed microscopically, 18 fresh frozen benign cervicovaginal (BCV) tissues collected at the time of benign hysterectomy, and 18 buffy coats from cancer-free women underwent reduced representation bisulfite sequencing (RRBS) to identify MDMs associated with CC. Candidate MDM selection was based on area under the receiver operating characteristic curve (AUC) discrimination, methylation fold change, and low background methylation among benign controls. Candidate MDMs were re-tested using methylation-specific PCR (MSP) to confirm performance. Blinded biological validation was performed using MSP on DNA extracted from independent FFPE CC (38 squamous, 43 adenocarcinoma) and BCV (40) tissues. The performance of CC MDMs was also tested in DNA extracted from cervical dysplasia (36 adenocarcinoma in situ (AIS), 32 cervical intraepithelial neoplasia (CIN) 2/3, 11 CIN 1) FFPE tissues. Results: From RRBS discovery and technical validation via MSP, 30 candidate MDMs showed marked methylation fold changes (10 to >1000) across both CC histologies compared to BCV tissue from cancer-free women. Each of the 30 MDMs highly discriminated CC from BCV tissue with 9 MDMs having an AUC >0.90 (Table). CC MDMs also highly discriminated AIS from BCV but did not perform well in CIN 2/3 and CIN 1 (Table). Conclusions: Whole methylome sequencing, stringent filtering criteria, and biological validation have yielded outstanding candidate MDMs for CC that highly discriminate CC from BCV, notably with high specificity. Performance in cervical dysplasias varied with higher positivity rates in AIS than in CIN 2/3 and CIN 1. Translation to testing these novel MDMs in lower genital tract biospecimens and the addition of HR-HPV to the CC panel are warranted.[Table: see text]

GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation

Conventional magnetic resonance imaging (cMRI) in multiple sclerosis (MS) patients provides measures of focal brain damage and activity, which are fundamental for disease diagnosis, prognosis, and the evaluation of response to therapy. However, cMRI is insensitive to the damage to the microenvironment of the brain tissue and the heterogeneity of MS lesions. In contrast, the damaged tissue can be characterized by mathematical models on multishell diffusion imaging data, which measure different compartmental water diffusion. In this work, we obtained 12 diffusion measures from eight diffusion models, and we applied a deep-learning attention-based convolutional neural network (CNN) (GAMER-MRI) to select the most discriminating measures in the classification of MS lesions and the perilesional tissue by attention weights. Furthermore, we provided clinical and biological validation of the chosen metrics—and of their most discriminative combinations—by correlating their respective mean values in MS patients with the corresponding Expanded Disability Status Scale (EDSS) and the serum level of neurofilament light chain (sNfL), which are measures of disability and neuroaxonal damage. Our results show that the neurite density index from neurite orientation and dispersion density imaging (NODDI), the measures of the intra-axonal and isotropic compartments from microstructural Bayesian approach, and the measure of the intra-axonal compartment from the spherical mean technique NODDI were the most discriminating (respective attention weights were 0.12, 0.12, 0.15, and 0.13). In addition, the combination of the neurite density index from NODDI and the measures for the intra-axonal and isotropic compartments from the microstructural Bayesian approach exhibited a stronger correlation with EDSS and sNfL than the individual measures. This work demonstrates that the proposed method might be useful to select the microstructural measures that are most discriminative of focal tissue damage and that may also be combined to a unique contrast to achieve stronger correlations to clinical disability and neuroaxonal damage.

Association Between Stress and Bilateral Symmetrical Alopecia in Free-Ranging Formosan Macaques (Macaca Cyclopis) in A High-Population-Density Area

Since 2013, a high incidence of bilateral symmetrical alopecia has been observed in free-ranging Formosan macaques (Macaca cyclopis) in Mt. Longevity, Taiwan. We hypothesized that stress induces alopecia in this population. To verify our hypothesis, we evaluated the histopathological characteristics of skin biopsy and used a validated enzyme immunoassay (EIA) for fecal glucocorticoid metabolite (FGM) analysis, which act as an indicator of stress experienced by the individual. Follicular densities were lower (2.1–3.0 mm2) in individuals with symmetrical alopecia than in those with normal hair conditions (4.7 mm2). Furthermore, anagen to catagen/telogen ratios were lower in individuals with alopecia (0–1.4) than in those with normal hair (4.0). The histopathological characteristics of alopecia were similar to those of telogen effluvium, which indicates that stress is one of the possible etiologies. On the basis of the analytical and biological validation of EIAs for FGM analysis, 11β-hydroxyetiocholanolone was considered suitable for monitoring adrenocortical activity in both sexes of Formosan macaques. The mean concentrations (standard error; sample size) of 11β-hydroxyetiocholanolone were 2.02 (0.17; n = 10) and 1.41 (0.10; n = 31) μg/g for individuals with and without alopecia, respectively. Furthermore, the results of logistic regression analysis show that 11β-hydroxyetiocholanolone (p = 0.012) concentration was positively associated with alopecia. Thus, stress was the most likely to trigger symmetrical alopecia in Formosan macaques in Mt. Longevity. Although stress can decrease the fitness of an individual, it should not impact the total population of Formosan macaque in Taiwan. Nonetheless, stress-induced immunosuppression might increase zoonosis risk due to frequent human–macaque contact in Mt. Longevity.

A novel model to label delirium in an intensive care unit from clinician actions

Background In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. Methods EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation. Results Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes. Conclusions Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.