Moderation effects of serotype on dengue severity across pregnancy status in Mexico.

BACKGROUND: Pregnancy increases a woman's risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. METHOD: Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. RESULTS: Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. CONCLUSION: The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico.

Multivariate group-level analysis for task fMRI data with canonical correlation analysis.

Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

3D spatially-adaptive canonical correlation analysis: Local and global methods.

Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data.

A family of locally constrained CCA models for detecting activation patterns in fMRI.

Canonical correlation analysis (CCA) has been used in Functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection. The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques. To evaluate the performance of this cCCA model, simulated data are generated with a Signal-To-Noise Ratio of 0.25, which is realistic to the noise level contained in episodic memory fMRI data. Receiver operating characteristic (ROC) methods are used to compare the performance of different models. The cCCA model with optimum parameters (called optimum-cCCA) obtains the largest area under the ROC curve. Furthermore, a novel validation method is proposed to validate the selected optimum-cCCA parameters based on ROC from simulated data and real fMRI data. Results for optimum-cCCA are then compared with conventional fMRI analysis methods using data from an episodic memory task. Wavelet-resampled resting-state data are used to obtain the null distribution of activation. For simulated data, accuracy in detecting activation increases for the optimum-cCCA model by about 43% as compared to the single voxel analysis with comparable Gaussian smoothing. Results from the real fMRI data set indicate a significant increase in activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI.

It has recently been shown that both high-frequency and low-frequency cardiac and respiratory noise sources exist throughout the entire brain and can cause significant signal changes in fMRI data. It is also known that the brainstem, basal forebrain and spinal cord areas are problematic for fMRI because of the magnitude of cardiac-induced pulsations at these locations. In this study, the physiological noise contributions in the lower brain areas (covering the brainstem and adjacent regions) are investigated and a novel method is presented for computing both low-frequency and high-frequency physiological regressors accurately for each subject. In particular, using a novel optimization algorithm that penalizes curvature (i.e. the second derivative) of the physiological hemodynamic response functions, the cardiac- and respiratory-related response functions are computed. The physiological noise variance is determined for each voxel and the frequency-aliasing property of the high-frequency cardiac waveform as a function of the repetition time (TR) is investigated. It is shown that for the brainstem and other brain areas associated with large pulsations of the cardiac rate, the temporal SNR associated with the low-frequency range of the BOLD response has maxima at subject-specific TRs. At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal.

Associations between e-cigarette use and sleep health among adults in the United States, NHANES 2015-2018.

OBJECTIVE: To examine the associations between e-cigarette use or dual (e-cigarette and combustible cigarette) use and short sleep duration and trouble sleeping among U.S. adults. METHODS: We used 2015-2018 data from the National Health and Nutrition Examination Survey (NHANES) (n = 11,659). E-cigarette use and dual use were categorized as current, former, and never use. Short sleep duration was defined as sleep duration ≤6 h. Trouble sleeping was self-reported. Weighted logistic regression analyses were performed. RESULTS: Among those with current e-cigarette use, 53.9 % were with current dual use and 23.8 % were with former dual use. Compared to never e-cigarette use, current e-cigarette use was associated with significantly higher odds of trouble sleeping (OR = 2.16, 95 % CI: 1.49-3.13), adjusting for potential confounders. Significant associations were also observed for former e-cigarette use versus never use with trouble sleeping (OR = 1.54, 95 % CI: 1.15-2.07) after full adjustment. Current cigarette use was associated with both short sleep duration (OR = 1.65, 95 % CI: 1.28-2.14) and trouble sleeping (OR = 1.36, 95 % CI: 1.03-1.79) after full adjustment. Additionally, the fully adjusted ORs for short sleep duration and trouble sleeping were 1.64 (95 % CI: 1.06-2.54) and 2.14 (95 % CI: 1.34-3.42) among those with current dual use, and 1.46 (95 % CI: 1.17-1.81) and 2.11 (95 % CI: 1.66-2.67) among those with former dual use, compared to those without dual use. CONCLUSIONS: Current cigarette use or dual use is associated with significantly higher odds of short sleep duration and trouble sleeping. Moreover, former e-cigarette use or dual use is associated with increased odds of trouble sleeping.

Interaction between sleep duration and trouble sleeping on depressive symptoms among U.S. adults, NHANES 2015-2018.

BACKGROUND: This study aims to examine the associations and interaction effects of sleep duration and trouble sleeping on depressive symptoms among U.S. adults. METHODS: National Health and Nutrition Examination Survey (NHANES) data from 2015 to 2018 were analyzed (N = 10,044). Trouble sleeping and sleep duration were self-reported. Sleep duration was defined as short (≤6 h) or long (≥9 h), compared with normal (>6 and < 9 h). Depressive symptoms were determined by the Patient Health Questionnaire-9 score ≥ 10. Both multiplicative interaction and additive interaction were reported. RESULTS: There was a significant positive additive interaction between short sleep duration and trouble sleeping on depressive symptoms in the fully adjusted model (Relative excess risk due to interaction, RERIOR = 4.42, 95 % CI: 1.12, 7.73), with 43 % of the association with depressive symptoms attributed to the interaction (attributable proportion of interaction, AP = 0.43, 95 % CI: 0.22, 0.64). Similarly, a significant positive additive interaction between long sleep duration and trouble sleeping on depressive symptoms was found (RERIOR = 4.17, 95 % CI: 0.96, 7.38), with 41 % of the association with depressive symptoms attributed to the interaction (AP = 0.41, 95 % CI: 0.21, 0.60). No multiplicative interaction was detected between short or long sleep duration and trouble sleeping. LIMITATIONS: The cross-sectional design limits the ability to draw causal inferences. CONCLUSIONS: Findings suggest that different aspects of sleep health interact synergistically, accounting for a substantial portion of the association with depressive symptoms. This underscores the importance of simultaneously considering multiple dimensions of sleep health in relation to depressive symptoms.

Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Background: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data. Objective: Train machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features. Methods: AD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women). Results: Our models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status. Conclusions: Our models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.

Bayesian approach for clinical trial safety data using an Ising prior.

In drug safety, development of statistical methods for multiplicity adjustments has exploited potential relationships among adverse events (AEs) according to underlying medical features. Due to the coarseness of the biological features used to group AEs together, which serves as the basis for the adjustment, it is possible that a single adverse event can be simultaneously described by multiple biological features. However, existing methods are limited in that they are not structurally flexible enough to accurately exploit this multi-dimensional characteristic of an adverse event. In order to preserve the complex dependencies present in clinical safety data, a Bayesian approach for modeling the risk differentials of the AEs between the treatment and comparator arms is proposed which provides a more appropriate clinical description of the drug's safety profile. The proposed procedure uses an Ising prior to unite medically related AEs. The proposed method and an existing Bayesian method are applied to a clinical dataset, and the signals from the two methods are presented. Results from a small simulation study are also presented.

Trends in prevalence of short sleep duration and trouble sleeping among US adults, 2005-2018.

STUDY OBJECTIVES: To determine trends in prevalence of short sleep duration and trouble sleeping among US adults from 2005 to 2018, and to assess how sleep trends vary by sex and race/ethnicity. METHODS: Seven cycles of the National Health and Nutrition Examination Survey data between 2005-2006 and 2017-2018 were analyzed. Trouble sleeping and sleep duration were self reported. Short sleep duration was defined as sleep duration ≤6 hr. Age-standardized prevalence of reporting trouble sleeping to a health care provider and short sleep duration were estimated among the overall US adult population, and by sex and race/ethnicity. RESULTS: From 2005 to 2014, the age-adjusted prevalence of short sleep duration remained similar in the overall population (p for trend >0.05). Non-Hispanic Black people had the highest prevalence of short sleep duration among all race/ethnicity groups in all seven cycles. The prevalence of short sleep duration appears lower in 2015-2018 than in 2005-2014 due to different measurement methods applied. However, from 2005 to 2018, there were increasing trends in age-adjusted prevalence of reporting trouble sleeping to a health care provider in the overall population, among both men and women, and all race/ethnicity groups (p for trend <0.05). Among all the race/ethnicity groups, non-Hispanic White people had the highest prevalence of reporting trouble sleeping to a healthcare provider. CONCLUSION: Findings depict the persistence of sleep-related issues in the United States and possible risk factors, as well as racial disparities.

Relationship Between Valence and Arousal for Subjective Experience in a Real-life Setting for Supportive Housing Residents: Results From an Ecological Momentary Assessment Study.

BACKGROUND: The circumplex model of affect posits that valence and arousal are the principal dimensions of affect. The center of the 2D space represents a neutral state of valence and a medium state of arousal. The role of valence and arousal in human emotion has been studied extensively. However, no consistent relationship between valence and arousal has been established. Most of the prior studies investigating the relationship have been conducted in relatively controlled laboratory settings. OBJECTIVE: Ecological momentary assessment (EMA) of affect from participants residing in permanent supportive housing was used to study the relationship between valence and arousal in real-life settings. The goal of this study was to explore the relationship between valence and arousal in a person's natural environment. METHODS: Participants were recruited from housing agencies in Fort Worth, Texas, United States. All participants had a history of chronic homelessness and reported at least one mental health condition. A subset of participants completed daily (morning) EMAs of emotions and other behaviors. The sample comprised 78 women and 77 men, and the average age was 52 (SD 8) years. From the circumplex model of affect, the EMA included 9 questions related to the participant's current emotional state (happy, frustrated, sad, worried, restless, excited, calm, bored, and sluggish). The responses were used to calculate 2 composite scores for valence and arousal. RESULTS: Statistical models uniformly showed a dominant linear relation between valence and arousal and a significant difference in the slopes among races. None of the other effects were statistically significant. Compared with previous studies, the effects were quite robust. CONCLUSIONS: Our findings may provide a window to the fundamental structure of affect. We found a strong positive linear relationship between valence and arousal at the nomothetic level, which may provide insight into a universal structure of affect. However, the study needs to be replicated for different populations to determine whether our findings can be generalized beyond the population studied here.

Optimization of contrast detection power with probabilistic behavioral information.

Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects' responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data.

Optimizing the performance of local canonical correlation analysis in fMRI using spatial constraints.

The benefits of locally adaptive statistical methods for fMRI research have been shown in recent years, as these methods are more proficient in detecting brain activations in a noisy environment. One such method is local canonical correlation analysis (CCA), which investigates a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel for convenience. The method without constraints is prone to artifacts, especially in a region of localized strong activation. To compensate for these deficiencies, the impact of different spatial constraints in CCA on sensitivity and specificity are investigated. The ability of constrained CCA (cCCA) to detect activation patterns in an episodic memory task has been studied. This research shows how any arbitrary contrast of interest can be analyzed by cCCA and how accurate P-values optimized for the contrast of interest can be computed using nonparametric methods. Results indicate an increase of up to 20% in detecting activation patterns for some of the advanced cCCA methods, as measured by ROC curves derived from simulated and real fMRI data.

Longitudinal Changes in Cognitive Functioning and Brain Structure in Professional Boxers and Mixed Martial Artists After They Stop Fighting.

OBJECTIVE: This study compares longitudinal changes in cognitive functioning and brain structures in male fighters who transitioned to an inactive fighting status without any further exposure to repetitive head impacts (RHI) and fighters remaining active with continual exposure to RHI. METHODS: Participants were recruited from the Professional Fighters Brain Health Study. At time point 1 (TP1), all fighters were active, with continual exposure to RHI. At time point 2 (TP2), fighters were considered "transitioned" if they had no sanctioned professional fights and had not been sparring for the past 2 years. Fighters were considered "active" if they continued to train and compete. All fighters underwent cognitive testing and 3T magnetic resonance imaging (MRI) at both TPs. A subset of our fighters (50%) underwent blood sampling for characterization of neurofilament light (NfL) levels at both TPs. Linear mixed effect models were applied to investigate the potentially different longitudinal trajectories (interaction effect between group and time) of cognitive function measures, NfL levels and regional thickness measures (derived from structural MRI) between transitioned and active fighters. RESULTS: 45 male transitioned fighters (31.69±6.27 years old (TP1), 22 boxers, 22 mixed martial artists, 1 martial artist) and 45 demographically matched male active fighters (30.24±5.44 years old (TP1); 17 boxers, 27 mixed martial artists, 1 martial artist) were included in the analyses. Significantly different longitudinal trajectories between transitioned and active fighters were observed in verbal memory (p FDR =4.73E-04), psychomotor speed (p FDR =4.73E-04), processing speed (p FDR =3.90E-02) and NfL levels (p=0.02). Transitioned fighters demonstrated longitudinally improved cognitive functioning and decreased NfL levels, and active fighters demonstrated declines in cognitive performance and stable NfL levels. Out of 68 cortical regions inspected, 54 regions demonstrated a consistently changing trajectory, with thickness measures stabilizing on a group level for transitioned fighters and subtly declining over time for active fighters. CONCLUSION: After fighters' cessation of RHI exposure, cognitive function and brain thickness measures may stabilize and blood NfL levels may decline. This study could be a starting point to identify potential predictors of individuals who are at a higher risk of RHI-related long-term neurological conditions.

Higher Expression of Annexin A2 in Metastatic Bladder Urothelial Carcinoma Promotes Migration and Invasion.

In this study, we aim to evaluate the significance of AnxA2 in BLCA and establish its metastatic role in bladder cancer cells. Analysis of TCGA data showed that AnxA2 mRNA expression was significantly higher in BLCA tumors than in normal bladder tissues. High mRNA expression of AnxA2 in BLCA was significantly associated with high pathological grades and stages, non-papillary tumor histology, and poor overall survival (OS), progression-free survival (PFS), and diseases specific survival (DSS). Similarly, we found that AnxA2 expression was higher in bladder cancer cells derived from high-grade metastatic carcinoma than in cells derived from low-grade urothelial carcinoma. AnxA2 expression significantly mobilized to the surface of highly metastatic bladder cancer cells compared to cells derived from low-grade tumors and associated with high plasmin generation and AnxA2 secretion. In addition, the downregulation of AnxA2 cells significantly inhibited the proliferation, migration, and invasion in bladder cancer along with the reduction in proangiogenic factors and cytokines such as PDGF-BB, ANGPT1, ANGPT2, Tie-2, bFGF, GRO, IL-6, IL-8, and MMP-9. These findings suggest that AnxA2 could be a promising biomarker and therapeutic target for high-grade BLCA.

Country-Level Governance Indicators as Predictors of COVID-19 Morbidity, Mortality, and Vaccination Coverage: An Exploratory Global Analysis.

As the COVID-19 pandemic continues to affect all countries across the globe, this study seeks to investigate the relationship between nations' governance, COVID-19 national data, and nation-level COVID-19 vaccination coverage. National-level governance indicators (corruption index, voice and accountability, political stability, and absence of violence/terrorism), officially reported COVID-19 national data (cases, death, and tests per one million population), and COVID-19 vaccination coverage was considered for this study to predict COVID-19 morbidity and mortality. Results indicate a strong relationship between nations' governance and officially reported COVID-19 data. Countries were grouped into three clusters using only the governance data: politically stable countries, average countries or "less corrupt countries," and corrupt countries or "more corrupt countries." The clusters were then tested for significant differences in reporting various aspects of the COVID-19 data. According to multinomial regression, countries in the cluster of politically stable nations reported significantly more deaths, tests per one million, total cases per one million, and higher vaccination coverage compared with nations both in the clusters of corrupt countries and average countries. The countries in the cluster of average nations reported more tests per one million and higher vaccination coverage than countries in the cluster of corrupt nations. Countries included in the corrupt cluster reported a lower death rate and morbidity, particularly compared with the politically stable nations cluster, a trend that can be attributed to poor governance and inaccurate COVID-19 data reporting. The epidemic evaluation indices of the COVID-19 cases demonstrate that the pandemic is still evolving on a global level.

Characteristic Deviations of Auditory Evoked Potentials in Individuals with Autism Spectrum Disorder.

BACKGROUND: Neurological, structural, and behavioral abnormalities are widely reported in individuals with autism spectrum disorder (ASD); yet there are no objective markers to date. We postulated that by using dominant and nondominant ear data, underlying differences in auditory evoked potentials (AEPs) between ASD and control groups can be recognized. PURPOSE: The primary purpose was to identify if significant differences exist in AEPs recorded from dominant and nondominant ear stimulation in (1) children with ASD and their matched controls, (2) adults with ASD and their matched controls, and (3) a combined child and adult ASD group and control group. The secondary purpose was to explore the association between the significant findings of this study with those obtained in our previous study that evaluated the effects of auditory training on AEPs in individuals with ASD. RESEARCH DESIGN: Factorial analysis of variance with interaction was performed. STUDY SAMPLE: Forty subjects with normal hearing between the ages of 9 and 25 years were included. Eleven children and 9 adults with ASD were age- and gender-matched with neurotypical peers. DATA COLLECTION AND ANALYSIS: Auditory brainstem responses (ABRs) and auditory late responses (ALRs) were recorded. Adult and child ASD subjects were compared with non-ASD adult and child control subjects, respectively. The combined child and adult ASD group was compared with the combined child and adult control group. RESULTS: No significant differences in ABR latency or amplitude were observed between ASD and control groups. ALR N1 amplitude in the dominant ear was significantly smaller for the ASD adult group compared with their control group. Combined child and adult data showed significantly smaller amplitude for ALR N1 and longer ALR P2 latency in the dominant ear for the ASD group compared with the control group. In our earlier study, the top predictor of behavioral improvement following auditory training was ALR N1 amplitude in the dominant ear. Correspondingly, the ALR N1 amplitude in the dominant ear yielded group differences in the current study. CONCLUSIONS: ALR peak N1 amplitude is proposed as the most feasible AEP marker in the evaluation of ASD.

MicroRNA-940 as a Potential Serum Biomarker for Prostate Cancer.

Prostate cancer is one of the leading causes of death despite an astoundingly high survival rate for localized tumors. Though prostate specific antigen (PSA) test, performed in conjunction with digital rectal examinations, is reasonably accurate, there are major caveats requiring a thorough assessment of risks and benefits prior to conducting the test. MicroRNAs, a class of small non-coding RNAs, are stable molecules that can be detected in circulation by non-invasive methods and have gained importance in cancer prognosis and diagnosis in the recent years. Here, we investigate circulating miR-940, a miRNA known to play a role in prostate cancer progression, in both cell culture supernatants as well as patient serum and urine samples to determine the utility of miR-940 as a new molecular marker for prostate cancer detection. We found that miR-940 was significantly higher in serum from cancer patients, specifically those with clinically significant tumors (GS ≥ 7). Analysis of receiver operating characteristic curve demonstrated that miR-940 in combination with PSA had a higher area under curve value (AUC: 0.818) than the miR-940 alone (AUC: 0.75) for the diagnosis of prostate cancer. This study provides promising results suggesting the use of miR-940 for prostate cancer diagnosis.

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.

Assessment of Knowledge, Attitudes, and Practices Regarding Dengue among Physicians: A Web-Based Cross-Sectional Survey.

Dengue fever is one of the most important viral infections transmitted by Aedes mosquitoes and a major cause of morbidity and mortality globally. Accurate identification of cases and treatment of dengue patients at the early stages can reduce medical complications and dengue mortality rate. This survey aims to determine the knowledge, attitude, and practices (KAP) among physicians in dengue diagnosis and treatment. This study was conducted among physicians in Turkey as one nonendemic country and Bangladesh, India, and Malaysia as three dengue-endemic countries. The dosing frequencies, maximum doses, and contraindications in dengue fever were examined. The results found that physicians from Bangladesh, India, and Malaysia have higher KAP scores in dengue diagnosis and treatment compared to physicians in Turkey. This may be due to a lack of physician's exposure to a dengue patient as Turkey is considered a nonendemic country. This assessment may help establish a guideline for intervention strategies among physicians to have successful treatment outcomes and reduce dengue mortality.