Cortical functional mechanisms in emotional cognitive tasks in first-episode, drug-naïve with major depressive disorder: A fNIRS study.

Background: Previous research has revealed that patients with major depressive disorder (MDD) have negative biases in various aspects of information processing, and these biases are mainly manifested in recognizing facial expressions. However, the link between this emotional cognitive inhibition and neural activation mechanisms in cortical brain regions remains poorly understood. Therefore, this study employed functional near-infrared spectroscopy (fNIRS) to explore the potential impaired regions and neural mechanisms associated with facial emotion cognition in MDD patients.

RBM24 Suppresses the Tumorigenesis of Glioblastoma by Stabilizing LATS1 mRNA.

The ribose nucleic acid (RNA)-binding motif protein 24 (RBM24) has been recognized as a critical regulatory protein in various types of tumors. However, its specific role in glioblastoma (GBM) has not been thoroughly investigated. The objective of this study is to uncover the role of RBM24 in GBM and understand the underlying mechanism.

Lateral Flow Biosensor for On-Site Multiplex Detection of Viruses Based on One-Step Reverse Transcription and Strand Displacement Amplification.

Respiratory pathogens pose a huge threat to public health, especially the highly mutant RNA viruses. Therefore, reliable, on-site, rapid diagnosis of such pathogens is an urgent need. Traditional assays such as nucleic acid amplification tests (NAATs) have good sensitivity and specificity, but these assays require complex sample pre-treatment and a long test time.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism.

A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease.

Coronary artery disease is one of the most common types of cardiovascular disease. Death from coronary heart disease is influenced by genetic factors in both women and men. In this article, we propose a novel Bayesian variable selection framework for the identification of important genetic variants associated with coronary artery disease disease status.

Model detection for semiparametric accelerated failure additive model with right-censored data.

Censored data frequently appeared in applications across a variety of different areas like epidemiology or medical research. Traditionally statistical inference on this data mechanism was based on some pre-assigned models that will suffer from the risk of model-misspecification. This article proposes a two-folded shrinkage procedure for simultaneous structure identification and variable selection of the semiparametric accelerated failure additive model with right-censored data, in which the nonparametric functions are addressed by spline approximation.

A bibliometric analysis of publications on obsessive-compulsive disorder using VOSviewer.

Background: Obsessive-compulsive disorder (OCD) is one of the top ten disabling diseases seriously affecting the health of population. Recently, studies on this disease significantly increased. However, only a few bibliometric analyses concerning this area have been reported.

Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data.

In this paper, we consider a class of partially linear transformation models with interval-censored competing risks data. Under a semiparametric generalized odds rate specification for the cause-specific cumulative incidence function, we obtain optimal estimators of the large number of parametric and nonparametric model components via maximizing the likelihood function over a joint B-spline and Bernstein polynomial spanned sieve space. Our specification considers a relatively simpler finite-dimensional parameter space, approximating the infinite-dimensional parameter space as → ∞, thereby allowing us to study the almost sure consistency, and rate of convergence for all parameters, and the asymptotic distributions and efficiency of the finite-dimensional components.

Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks.

Wastewater-based SARS-CoV-2 surveillance enables unbiased and comprehensive monitoring of defined sewersheds. We performed real-time monitoring of hospital wastewater that differentiated Delta and Omicron variants within total SARS-CoV-2-RNA, enabling correlation to COVID-19 cases from three tertiary-care facilities with >2100 inpatient beds in Calgary, Canada. RNA was extracted from hospital wastewater between August/2021 and January/2022, and SARS-CoV-2 quantified using RT-qPCR.

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver.

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) coexpression network analyses focusing on correlations between genes and (ii) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: A slight change of parameterization or dataset could lead to marked alterations of outcomes. Here, we propose Stabilized COre gene and Pathway Election (SCOPE), a tool integrating bootstrapped least absolute shrinkage and selection operator and coexpression analysis, leading to robust outcomes insensitive to variations in data.

Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning.

Background: Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients.

Methods: Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke.

Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater.

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data.

Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population.

Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.44 million).

A signal-enhanced and sensitive lateral flow aptasensor for the rapid detection of PDGF-BB.

Platelet-derived growth factor BB (PDGF-BB) is a potential biomarker of tumor angiogenesis. For the first time, we developed a highly sensitive aptasensor for PDGF-BB with an enhanced test line signal by using two different gold nanoparticles (AuNPs). Herein, we describe a highly sensitive biosensor for PDGF-BB detection that combines biotinylated aptamer on a sample pad and poly thymine-Cy3-AuNP-monoclonal antibody complexes against PDGF-BB immobilized on conjugate pad A.

An ultrasensitive and specific point-of-care CRISPR/Cas12 based lateral flow biosensor for the rapid detection of nucleic acids.

CRISPR/Cas systems have displayed remarkable potential in developing novel biosensing applications for nucleic acid detection owing to the collateral cleavage activity of Cas effector proteins (Cas12, Cas13, etc.). Despite tremendous progress in recent years, the existing CRISPR/Cas based biosensing platforms have several limitations, including reliance on proper amplification methods, expensive fluorescence detection equipment, or lateral flow biosensor (LFB).

A highly sensitive and specific lateral flow aptasensor for the detection of human osteopontin.

The rapid determination of human osteopontin (OPN) protein, a potential cancer biomarker, holds substantial promise for point-of-care diagnostics and biomedical applications. To date, most reported platforms for OPN detection are apparatus-dependent, time-consuming, and expensive. Herein, we established a lateral flow biosensor (LFB) for OPN detection.

Improved performance of lateral flow immunoassays for alpha-fetoprotein and vanillin by using silica shell-stabilized gold nanoparticles.

The sensitivity of lateral flow assays (LFA) was increased 30-fold by making use of spherical core-shell gold-silica nanoparticles (AuNP@SiO NPs). They can be prepared by silylation of surfactant stabilized AuNPs. The AuNP@SiO NPs are highly stable and can be used to label antibodies at virtually any concentration.

Real-time quantitative PCR detection of circulating tumor cells using tag DNA mediated signal amplification strategy.

The level of circulating tumor cell (CTCs) is a reliable marker for tumor burden and malignant progression. Quantification of CTCs remains technically challenging due to the rarity of these cells in peripheral blood. In the present study, we established a real-time quantitative PCR (Q-PCR) based method for sensitive detection of CTCs without DNA extraction.

Lateral flow biosensor for multiplex detection of nitrofuran metabolites based on functionalized magnetic beads.

The use of potential mutagenic nitrofuran antibiotic in food animal production has been banned world-wide. Common methods for nitrofuran detection involve complex extraction procedures. In the present study, magnetic beads functionalized with antibody against nitrofuran derivative were used as both the extraction and color developing media in lateral flow biosensor.

Strand displacement amplification for ultrasensitive detection of human pluripotent stem cells.

Human pluripotent stem cells (hPSCs), such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), provide a powerful model system for studies of cellular identity and early mammalian development, which hold great promise for regenerative medicine. It is necessary to develop a convenient method to discriminate hPSCs from other cells in clinics and basic research. Herein, a simple and reliable biosensor for stem cell detection was established.

A sensitive lateral flow biosensor for Escherichia coli O157:H7 detection based on aptamer mediated strand displacement amplification.

Foodborne diseases caused by pathogens are one of the major problems in food safety. Convenient and sensitive point-of-care rapid diagnostic tests for food-borne pathogens have been a long-felt need of clinicians. Commonly used methods for pathogen detection rely on conventional culture-based tests, antibody-based assays and polymerase chain reaction (PCR)-based techniques.

Lateral flow biosensor for DNA extraction-free detection of Salmonella based on aptamer mediated strand displacement amplification.

Convenient and sensitive point-of-care rapid diagnostic tests for food-borne pathogens have been a long-felt need of clinicians. Traditional approaches such as culture-based methods have good sensitivity and specificity, but they tend to be tedious and time-consuming. Herein we present a simple and sensitive aptamer based biosensor for rapid detection of Salmonella enteritidis (S.

Assessment of leptomeningeal collaterals using dynamic CT angiography in patients with acute ischemic stroke.

Whole-brain dynamic time-resolved computed tomography angiography (CTA) is a technique developed on the new 320-detector row CT scanner capable of generating time-resolved cerebral angiograms from skull base to vertex. Unlike a conventional cerebral angiogram, this technique visualizes pial arterial filling in all vascular territories, thereby providing additional hemodynamic information. Ours was a retrospective study of consecutive patients with ischemic stroke and M1 middle cerebral artery +/- intracranial internal carotid artery occlusions presenting to our center from June 2010 and undergoing dynamic time-resolved CTA and perfusion CT within 6 hours of symptom onset.