Lateral Flow Assay Biotesting by Utilizing Plasmonic Nanoparticles Made of Inexpensive Metals─Replacing Colloidal Gold.

Nanoparticles (NPs) can be conjugated with diverse biomolecules and employed in biosensing to detect target analytes in biological samples. This proven concept was primarily used during the COVID-19 pandemic with gold-NP-based lateral flow assays (LFAs). Considering the gold price and its worldwide depletion, here we show that novel plasmonic NPs based on inexpensive metals, titanium nitride (TiN) and copper covered with a gold shell (Cu@Au), perform comparable to or even better than gold nanoparticles. After conjugation, these novel nanoparticles provided high figures of merit for LFA testing, such as high signals and specificity and robust naked-eye signal recognition. Since the main cost of Au NPs in commercial testing kits is the colloidal synthesis, our development with the Cu@Au and the laser-ablation-fabricated TiN NPs is exciting, offering potentially inexpensive plasmonic nanomaterials for various bioapplications. Moreover, our machine learning study showed that biodetection with TiN is more accurate than that with Au.

Non-steroidal anti-inflammatory drug target gene associations with major depressive disorders: a Mendelian randomisation study integrating GWAS, eQTL and mQTL Data.

Previous observational studies reported associations between non-steroidal anti-inflammatory drugs (NSAIDs) and major depressive disorder (MDD), however, these associations are often inconsistent and underlying biological mechanisms are still poorly understood. We conducted a two-sample Mendelian randomisation (MR) study to examine relationships between genetic variants and NSAID target gene expression or DNA methylation (DNAm) using publicly available expression, methylation quantitative trait loci (eQTL or mQTL) data and genetic variant-disease associations from genome-wide association studies (GWAS of MDD). We also assessed drug exposure using gene expression and DNAm levels of NSAID targets as proxies. Genetic variants were robustly adjusted for multiple comparisons related to gene expression, DNAm was used as MR instrumental variables and GWAS statistics of MDD as the outcome. A 1-standard deviation (SD) lower expression of NEU1 in blood was related to lower C-reactive protein (CRP) levels of -0.215 mg/L (95% confidence interval (CI): 0.128-0.426) and a decreased risk of MDD (odds ratio [OR] = 0.806; 95% CI: 0.735-0.885; p = 5.36 × 10-6). A concordant direction of association was also observed for NEU1 DNAm levels in blood and a risk of MDD (OR = 0.886; 95% CI: 0.836-0.939; p = 4.71 × 10-5). Further, the genetic variants associated with MDD were mediated by NEU1 expression via DNAm (ß = -0.519; 95% CI: -0.717 to -0.320256; p = 3.16 × 10-7). We did not observe causal relationships between inflammatory genetic marker estimations and MDD risk. Yet, we identified a concordant association of NEU1 messenger RNA and an adverse direction of association of higher NEU1 DNAm with MDD risk. These results warrant increased pharmacovigilance and further in vivo or in vitro studies to investigate NEU1 inhibitors or supplements for MDD.

Sex Hormone-Binding Globulin and Risk of Coronary Heart Disease in Men and Women.

BACKGROUND: The role of sex hormone-binding globulin (SHBG) levels in clinical risk stratification and intervention for coronary heart disease (CHD) remains uncertain. We aimed to examine whether circulating levels of SHBG are predictive of CHD risk in men and women. METHODS: We investigated the association between SHBG and the risk of incident CHD in 128 322 men and 135 103 women free of CHD at baseline in the prospective United Kingdom Biobank (UKB) cohort. The unconfounded associations were estimated using Mendelian randomization (MR) analysis. We further conducted a meta-analysis to integrate currently available prospective evidence. CHD events included nonfatal and fatal myocardial infarction and coronary revascularization. RESULTS: In the UKB, during a median of 11.7 follow-up years, 10 405 men and 4512 women developed CHD. Serum levels of SHBG were monotonically associated with a decreased risk of CHD in both men (adjusted hazard ratio [HR] per log nmol/L increase in SHBG: 0.88 [0.83-0.94]) and women (HR: 0.89 [0.83-0.96]). MR-based analyses suggested causality and a dose-response relationship of SHBG with CHD risk. A cumulative meta-analysis including 216 417 men and 138 282 women from 11 studies showed that higher levels of SHBG were prospectively associated with decreased CHD risk in men comparing the highest with the lowest quartile: pooled relative risk (RR) 0.81 (0.74-0.89) and women (pooled RR: 0.86 [0.78-0.94]). CONCLUSIONS: Higher circulating SHBG levels were directly and independently predictive of lower CHD risk in both men and women. The utility of SHBG for CHD risk stratification and prediction warrants further study.

Exploring Lead loci shared between schizophrenia and Cardiometabolic traits.

Individuals with schizophrenia (SCZ) have, on average, a 10- to 20-year shorter expected life span than the rest of the population, primarily due to cardiovascular disease comorbidity. Genome-wide association studies (GWAS) have previously been used to separately identify common variants in SCZ and cardiometabolic traits. However, genetic variants jointly influencing both traits remain to be fully characterised. To assess overlaps (if any) between the genetic architecture of SCZ and cardiometabolic traits, we used conditional false discovery rate (FDR) and local genetic correlation statistical framework analyses. A conjunctional FDR was used to identify shared genetic traits between SCZ and cardiometabolic risk factors. We identified 144 genetic variants which were shared between SCZ and body mass index (BMI), and 15 variants shared between SCZ and triglycerides (TG). Furthermore, we discovered four novel single nucleotide polymorphisms (SNPs) (rs3865350, rs9860913, rs13307 and rs9614186) and four proximate genes (DERL2, SNX4, LY75 and EFCAB6) which were shared by SCZ and BMI. We observed that the novel genetic variant rs13307 and the most proximate gene LY75 exerted potential effects on SCZ and BMI comorbidity. Also, we observed a mixture of concordant and opposite direction associations with shared genetic variants. We demonstrated a moderate to high genetic overlap between SCZ and cardiometabolic traits associated with a pattern of bidirectional associations. Our data suggested a complex interplay between metabolism-related gene pathways in SCZ pathophysiology.

Lateral Flow Assays Biotesting by Utilizing Plasmonic Nanoparticles Made of Inexpensive Metals - Replacing Colloidal Gold.

Nanoparticles (NPs) can be conjugated with diverse biomolecules and employed in biosensing to detect target analytes in biological samples. This proven concept was primarily used during the COVID-19 pandemic with gold NPs-based lateral flow assays (LFAs). Considering the gold price and its worldwide depletion, here we show that novel plasmonic nanoparticles (NPs) based on inexpensive metals, titanium nitride (TiN) and copper covered with a gold shell (Cu@Au), perform comparable or even better than gold nanoparticles. After conjugation, these novel nanoparticles provided high figures of merit for LFA testing, such as high signals and specificity and robust naked-eye signal recognition. To the best of our knowledge, our study represents the 1st application of laser-ablation-fabricated nanoparticles (TiN) in the LFA and dot-blot biotesting. Since the main cost of the Au NPs in commercial testing kits is in the colloidal synthesis, our development with TiN is very exciting, offering potentially very inexpensive plasmonic nanomaterials for various bio-testing applications. Moreover, our machine learning study showed that the bio-detection with TiN is more accurate than that with Au.

Stroke mortality prediction based on ensemble learning and the combination of structured and textual data.

For severe cerebrovascular diseases such as stroke, the prediction of short-term mortality of patients has tremendous medical significance. In this study, we combined machine learning models Random Forest classifier (RF), Adaptive Boosting (AdaBoost), Extremely Randomised Trees (ExtraTree) classifier, XGBoost classifier, TabNet, and DistilBERT to construct a multi-level prediction model that used bioassay data and radiology text reports from haemorrhagic and ischaemic stroke patients to predict six-month mortality. The performances of the prediction models were measured using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), precision, recall, and F1-score. The prediction models were built with the use of data from 19,616 haemorrhagic stroke patients and 50,178 ischaemic stroke patients. Novel six-month mortality prediction models for these patients were developed, which enhanced the performance of the prediction models by combining laboratory test data, structured data, and textual radiology report data. The achieved performances were as follows: AUROC = 0.89, AUPRC = 0.70, precision = 0.52, recall = 0.78, and F1 score = 0.63 for haemorrhagic patients, and AUROC = 0.88, AUPRC = 0.54, precision = 0.34, recall = 0.80, and F1 score = 0.48 for ischaemic patients. Such models could be used for mortality risk assessment and early identification of high-risk stroke patients. This could contribute to more efficient utilisation of healthcare resources for stroke survivors.

Spiking neural networks fine-tuning for brain image segmentation.

Introduction: The field of machine learning has undergone a significant transformation with the progress of deep artificial neural networks (ANNs) and the growing accessibility of annotated data. ANNs usually require substantial power and memory usage to achieve optimal performance. Spiking neural networks (SNNs) have recently emerged as a low-power alternative to ANNs due to their sparsity nature. Despite their energy efficiency, SNNs are generally more difficult to be trained than ANNs. Methods: In this study, we propose a novel three-stage SNN training scheme designed specifically for segmenting human hippocampi from magnetic resonance images. Our training pipeline starts with optimizing an ANN to its maximum capacity, then employs a quick ANN-SNN conversion to initialize the corresponding spiking network. This is followed by spike-based backpropagation to fine-tune the converted SNN. In order to understand the reason behind performance decline in the converted SNNs, we conduct a set of experiments to investigate the output scaling issue. Furthermore, we explore the impact of binary and ternary representations in SNN networks and conduct an empirical evaluation of their performance through image classification and segmentation tasks. Results and discussion: By employing our hybrid training scheme, we observe significant advantages over both ANN-SNN conversion and direct SNN training solutions in terms of segmentation accuracy and training efficiency. Experimental results demonstrate the effectiveness of our model in achieving our design goals.

Evaluating the Causal Effects of Gestational Diabetes Mellitus, Heart Disease, and High Body Mass Index on Maternal Alzheimer's Disease and Dementia: Multivariable Mendelian Randomization.

Introduction: Gestational diabetes mellitus (GDM), heart disease (HD) and high body mass index (BMI) are strongly related to Alzheimer's disease (AD) dementia in pregnant women. Therefore, we aimed to determine the total effects of GDM, heart disease, and high BMI on maternal AD dementia. Methods: We used data from the genome-wide association studies of European populations including more than 30,000 participants. We performed two-sample Mendelian randomization (MR) and multivariable MR (MVMR) to systematically estimate the direct effects of GDM, HD, and high BMI on maternal AD and dementia. Multiple sensitivity analyses involving classical MR approaches and expanded MR-pleiotropy residual sum and outlier analysis. Results: In two-sample MR analysis, the inverse-variance weighted method in our study demonstrated no significant causality between GDM and maternal dementia (ß = -0.006 ± 0.0026, p = 0.82). This method also revealed no significant causality between high BMI and maternal dementia (ß = 0.0024 ± 0.0043, p = 0.57), and it was supported by the MR-Egger regression results, which showed no causal effect of high BMI on maternal Alzheimer's disease and dementia (ß = 0.0027 ± 0.0096, p = 0.78). The IVW method showed no significant causal relationship between maternal HD and maternal Alzheimer's disease and dementia (ß = -0.05 ± 0.0042, p = 0.117) and MR-Egger regression analysis gave a similar result (ß = -0.12 ± 0.0060, p = 0.079). In MVMR analysis, we found no significant causal relationship between GDM, high BMI, or HD and maternal Alzheimer's disease and dementia (p = 0.94, 0.82, and 0.13, respectively). Thus, the MVMR estimates were consistent with our results from the two-sample MR analysis. We confirmed that these results showed no horizontal pleiotropy and enhanced the robustness of our results through multiple sensitivity analyses. Conclusion: In two-sample MR analysis, we found no significant causal relationship between GDM, HD, high BMI and maternal AD and dementia. These results differed from previous observational studies showing HD is a significant predictor of dementia. MVMR analysis supported no significant causal relationship between GDM, HD, high BMI and maternal AD and dementia. Sensitivity analysis broadly increased the robustness of two-sample MR and MVMR analysis results.

Identifying Predictors of COVID-19 Mortality Using Machine Learning.

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84-0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality.

Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records.

OBJECTIVE: We conducted a comprehensive evaluation of features associated with stroke records. METHODS: We screened the dietary nutrients, blood biomarkers, and clinical information from the National Health and Nutrition Examination Survey (NHANES) 2015-16 database to assess a self-reported history of all strokes (136 strokes, n = 4381). We computed feature importance, built machine learning (ML) models, developed a nomogram, and validated the nomogram on NHANES 2007-08, 2017-18, and the baseline UK Biobank. We calculated the odds ratios with/without adjusting sampling weights (OR/ORw). RESULTS: The clinical features have the best predictive power compared to dietary nutrients and blood biomarkers, with 22.8% increased average area under the receiver operating characteristic curves (AUROC) in ML models. We further modeled with ten most important clinical features without compromising the predictive performance. The key features positively associated with stroke include age, cigarette smoking, tobacco smoking, Caucasian or African American race, hypertension, diabetes mellitus, asthma history; the negatively associated feature is the family income. The nomogram based on these key features achieved good performances (AUROC between 0.753 and 0.822) on the test set, the NHANES 2007-08, 2017-18, and the UK Biobank. Key features from the nomogram model include age (OR = 1.05, ORw = 1.06), Caucasian/African American (OR = 2.68, ORw = 2.67), diabetes mellitus (OR = 2.30, ORw = 1.99), asthma (OR = 2.10, ORw = 2.41), hypertension (OR = 1.86, ORw = 2.10), and income (OR = 0.83, ORw = 0.81). CONCLUSIONS: We identified clinical key features and built predictive models for assessing stroke records with high performance. A nomogram consisting of questionnaire-based variables would help identify stroke survivors and evaluate the potential risk of stroke.

A Mendelian randomization-based exploration of red blood cell distribution width and mean corpuscular volume with risk of hemorrhagic strokes.

Red blood cell distribution width (RCDW) and mean corpuscular volume (MCV) are associated with different risk factors for hemorrhagic stroke. However, whether RCDW and MCV are causally related to hemorrhagic stroke remains poorly understood. Therefore, we explored the causality between RCDW/MCV and nontraumatic hemorrhagic strokes using Mendelian randomization (MR) methods. We extracted exposure and outcome summary statistics from the UK Biobank and FinnGen. We evaluated the causality of RCDW/MCV on four outcomes (subarachnoid hemorrhage [SAH], intracerebral hemorrhage [ICH], nontraumatic intracranial hemorrhage [nITH], and a combination of SAH, cerebral aneurysm, and aneurysm operations) using univariable MR (UMR) and multivariable MR (MVMR). We further performed colocalization and mediation analyses. UMR and MVMR revealed that higher genetically predicted MCV is protective of ICH (UMR: odds ratio [OR] = 0.89 [0.8-0.99], p = 0.036; MVMR: OR = 0.87 [0.78-0.98], p = 0.021) and nITH (UMR: OR = 0.89 [0.82-0.97], p = 0.005; MVMR: OR = 0.88 [0.8-0.96], p = 0.004). There were no strong causal associations between RCDW/MCV and any other outcome. Colocalization analysis revealed a shared causal variant between MCV and ICH; it was not reported to be associated with ICH. Proportion mediated via diastolic blood pressure was 3.1% (0.1%,14.3%) in ICH and 3.4% (0.2%,15.8%) in nITH. The study constitutes the first MR analysis on whether genetically elevated RCDW and MCV affect the risk of hemorrhagic strokes. UMR, MVMR, and mediation analysis revealed that MCV is a protective factor for ICH and nITH, which may inform new insights into the treatments for hemorrhagic strokes.

Assessment of Bidirectional Relationships between Leisure Sedentary Behaviors and Neuropsychiatric Disorders: A Two-Sample Mendelian Randomization Study.

(1) Background: Increasing evidence shows that sedentary behaviors are associated with neuropsychiatric disorders (NPDs) and thus may be a modifiable factor to target for the prevention of NPDs. However, the direction and causality for the relationship remain unknown; sedentary behaviors could increase or decrease the risk of NPDs, and/or NPDs may increase or decrease engagement in sedentary behaviors. (2) Methods: This Mendelian randomization (MR) study with two samples included independent genetic variants related to sedentary behaviors (n = 408,815), Alzheimer's disease (AD; n = 63,926), schizophrenia (SCZ; n = 105,318), and major depressive disorder (MDD; n = 500,199), which were extracted from several of the largest non-overlapping genome-wide association studies (GWASs), as instrumental variables. The summarized MR effect sizes from each instrumental variable were combined in an IVW (inverse-variance-weighted) approach, with various approaches (e.g., MR-Egger, weighted median, MR-pleiotropy residual sum and outlier), and sensitivity analyses were performed to identify and remove outliers and assess the horizontal pleiotropy. (3) Results: The MR evidence and linkage disequilibrium score regression revealed a consistent directional association between television watching and MDD (odds ratio (OR), 1.13 for MDD per one standard deviation (SD) increase in mean television watching time; 95% CI, 1.06-1.20; p = 6.80 × 10-5) and a consistent relationship between computer use and a decrease in the risk of AD (OR, 0.52 for AD per one SD increase in mean computer use time; 95% CI, 0.32-0.84; p = 8.20 × 10-3). In the reverse direction, MR showed a causal association between a reduced risk of SCZ and an increase in driving time (ß, -0.016; 95% CI, -0.027--0.004; p = 8.30 × 10-3). (4) Conclusions: Using genetic instrumental variables identified from large-scale GWASs, we found robust evidence for a causal relationship between long computer use time and a reduced risk of AD, and for a causal relationship between long television watching time and an increased risk of MDD. In reverse analyses, we found that SCZ was causally associated with reduced driving time. These findings fit in with our observations and prior knowledge as well as emphasizing the importance of distinguishing between different domains of sedentary behaviors in epidemiologic studies of NPDs.

Puerarin 6″-O-xyloside suppressed HCC via regulating proliferation, stemness, and apoptosis with inhibited PI3K/AKT/mTOR.

Puerarin 6″-O-xyloside is a tumor suppressive derivate of Puerarin that is recently characterized as a lysine-specific demethylase 6B inhibitor. Here we investigated the effects of Puerarin 6″-O-xyloside in hepatocellular carcinoma (HCC) cell lines SMMC-7721 and HepG2. Cell viability, proliferation, stemness, protein expression, and autophagy were tested by CCK-8, colony formation, sphere formation, western blotting, and LC3B GFP puncta per cell, respectively. Apoptosis, CD133-positive cells, and JC-1-labeled mitochondrial membrane potential were measured by flow cytometry. The effects of Puerarin 6″-O-xyloside in vivo were explored in HepG2 xenograft mice. Puerarin 6″-O-xyloside inhibited cell viability, proliferation, and stemness, and promoted apoptosis in both SMMC-7721 and HepG2 cells. Further experiments showed promoted autophagy and decreased mitochondrial membrane potential, and decreased expression of p-PI3K, p-AKT, and p-mTOR in HepG2 cells. Co-administration of 3-MA with Puerarin 6″-O-xyloside obviously augmented these effects including inhibited protein expression of p-PI3K, p-AKT, and p-mTOR, and inhibited proliferation, promoted apoptosis, and decreased stemness. In HepG2 xenograft mice, 100 mg/kg/d Puerarin 6″-O-xyloside significantly suppressed tumor growth, stemness, and apoptosis. In conclusion, our study indicated that Puerarin 6″-O-xyloside decreased cell viability, proliferation, and stemness, and promoted autophagy and mitochondria-dependent apoptosis of HCC, at least partly through inhibiting PI3K/AKT/mTOR. These results highlighted Puerarin 6″-O-xyloside as a promising prodrug that could inhibit both PI3K/AKT/mTOR and epigenetic demethylation.

Effects of RANKL, osteoprotegerin, calcium and phosphorus on survival and activation of Muscovy duck osteoclasts in vitro.

The aim of this study was to determine whether receptor activator of nuclear factor NF-kappaB ligand (RANKL), osteoprotegerin (OPG) and a calcium:phosphorus (Ca:P) ratio of 2:1 could affect survival and activation of Muscovy duck osteoclasts (OCs). Bone marrow cells were obtained from 5-day-old Muscovy ducks and cultured with (Group A) No added factors, (B) 30ng/mL soluble RANKL (sRANKL), (C) 30ng/mL sRANKL and 10ng/mL OPG, (D) 10ng/mL OPG, (E) 50ng/mL OPG, (F) 100ng/mL OPG and (G) 30ng/mL sRANKL, 6mmol/L Ca and 3mmol/L P. sRANKL promoted the survival of OCs on day 2, whereas the number of OCs decreased with addition of OPG in a dose-dependent manner. OPG and Ca:P (2:1) both inhibited OC survival induced by RANKL. RANKL stimulated bone resorption by OCs, whereas OPG, but not Ca:P (2:1), inhibited the activity of OCs induced by RANKL. RANKL promotes the survival and activation of OCs from Muscovy ducks, whereas OPG and, to a lesser extent, Ca:P (2:1) reduce the life span and inhibited the activation of OCs induced by RANKL.

Green tea extract for treatment of cancers: A systematic review protocol.

BACKGROUND: Previous clinical studies suggested that green tea extract (GTE) may benefit patients with a variety of cancers. However, its efficacy is still inconclusive. Thus, the objective of this study will systematically collate the clinical studies testing its efficacy and safety for cancers. METHODS: We will perform a systematic review of clinical studies assessing the efficacy of GTE in variety of cancers. We will search Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, MEDILINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Allied and Complementary Medicine Database (AMED), and Chinese Biomedical Literature Database (CBM) using a comprehensive strategy. We will also screen the reference lists of relevant studies to identify any additional studies for potential inclusion. All databases will be searched up to February 1, 2019. All eligible case-control studies and randomized controlled trials will be included in this study. Two independent authors will review all searched literature. Upon inclusion of trials, we will extract data by using a predefined standardized form. The risk of bias assessment will be evaluated by using Cochrane risk of bias tool. We will use RevMan 5.3 software to pool the data and carry out meta-analysis. RESULTS: The primary outcome includes overall response rate. The secondary outcomes comprise of overall survival, progression-free survival, the disease control rate, and any adverse events. CONCLUSIONS: The results of this study will contribute to the understanding of the efficacy of GTE in the setting of cancers and promote future research of GTE in patients with cancers. DISSEMINATION AND ETHICS: The results of this systematic review are expected to be published through peer-reviewed journals. This study does not need ethic approval, because it does not utilize individual patient data. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019125111.

Effect of high WDR5 expression on the hepatocellular carcinoma prognosis.

WD repeat domain 5 (WDR5) serves an important role in various biological functions through the epigenetic regulation of gene transcription. Aberrant expression of WDR5 has been observed in various types of human cancer, including prostate cancer, breast cancer and leukemia. However, the role of WDR5 expression and its clinical implications in hepatocellular carcinoma (HCC) remain largely unknown. The present study investigated the WDR5 expression pattern in HCC. It was demonstrated that the mRNA and protein levels of WDR5 were upregulated in HCC cancer tissues compared with normal adjacent tissues using reverse transcription-quantitative polymerase chain reaction and western blotting. Furthermore, the elevated WDR5 protein level was significantly associated with the histological grade (P=0.038), tumor size (P=0.023), tumor-node-metastasis stage (P=0.035) and reduced long-term survival time. Additionally, it was demonstrated through the shRNA-mediated knockdown of WDR5 in HCC cells in vitro that WDR5 expression promotes cell proliferation using an MTT assay. Taken together, the results suggested that WDR5 overexpression may have an oncogenic effect in HCC, and may be a promising biomarker for the diagnosis and prognosis of HCC.

Distance-informed metric learning for Alzheimer's disease staging.

Identifying intermediate biomarkers of Alzheimer's disease (AD) is of great importance for diagnosis and prognosis of the disease. In this study, we develop a new AD staging method to classify patients into Normal Controls (NC), Mild Cognitive Impairment (MCI), and AD groups. Our solution employs a novel metric learning technique that improves classification rates through the guidance of some weak supervisory information in AD progression. More specifically, those information are in the form of pairwise constraints that specify the relative Mini Mental State Examination (MMSE) score disparity of two subjects, depending on whether they are in the same group or not. With the imposed constraints, the common knowledge that MCI generally sits in between of NC and AD can be integrated into the classification distance metric. Subjects from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; 56 AD, 104 MCI, 161 controls) were used to demonstrate the improvements made comparing with two state-of-the-art metric learning solutions: large margin nearest neighbors (LMNN) and relevant component analysis (RCA).

Robust separation of visceral and subcutaneous adipose tissues in micro-CT of mice.

One of the common practices in obesity and diabetes studies is to measure the volumes and weights of various adipose tissues, among which, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) play critical yet different physiological roles in mouse aging. In this paper, a robust two-stage VAT/SAT separation framework for micro-CT mouse data is proposed. The first stage is to distinguish adipose from other tissue types, including background, soft tissue and bone, through a robust mixture of Gaussian model. Spatial recognition relevant to anatomical locations is carried out in the second step to determine whether the adipose is visceral or subcutaneous. We tackle this problem through a novel approach that relies on evolving the abdominal muscular wall to keep VAT/SAT separated. The VAT region of interest (ROI) is also automatically set up through an atlas based skeleton matching procedure. The results of our method are compared with VAT/SAT delineations by human experts, and a high classification accuracy is demonstrated on eight micro-CT mouse volume sets.