Tag: pubmed

Sensors (Basel). 2023 Oct 8;23(19):8310. doi: 10.3390/s23198310.

ABSTRACT

Molecular tests for infectious diseases and genetic anomalies, which account for significant global morbidity and mortality, are central to nucleic acid analysis. In this study, we present a digital droplet LAMP (ddLAMP) platform that offers a cost-effective and portable solution for such assays. Our approach integrates disposable 3D-printed droplet generator chips with a consumer smartphone equipped with a custom image analysis application for conducting ddLAMP assays, thereby eliminating the necessity for expensive and complicated photolithographic techniques, optical microscopes, or flow cytometers. Our 3D printing technique for microfluidic chips facilitates rapid chip fabrication in under 2 h, without the complications of photolithography or chip bonding. The platform’s heating mechanism incorporates low-powered miniature heating blocks with dual resistive cartridges, ensuring rapid and accurate temperature modulation in a compact form. Instrumentation is further simplified by integrating miniaturized magnification and fluorescence optics with a smartphone camera. The fluorescence quantification benefits from our previously established RGB to CIE-xyY transformation, enhancing signal dynamic range. Performance assessment of our ddLAMP system revealed a limit of detection at 10 copies/μL, spanning a dynamic range up to 10⁴ copies/μL. Notably, experimentally determined values of the fraction of positive droplets for varying DNA concentrations aligned with the anticipated exponential trend per Poisson statistics. Our holistic ddLAMP platform, inclusive of chip production, heating, and smartphone-based droplet evaluation, provides a refined method compatible with standard laboratory environments, alleviating the challenges of traditional photolithographic methods and intricate droplet microfluidics expertise.

PMID:37837140 | DOI:10.3390/s23198310

Sensors (Basel). 2023 Oct 8;23(19):8308. doi: 10.3390/s23198308.

ABSTRACT

The in-hole multipoint traveling wave decomposition (MPTWD) method is developed for detecting and characterizing the damage of cast in situ reinforced concrete (RC) piles. Compared with the results of MPTWD, the results of the in-hole MPTWD reconstruction technique are found ideal for evaluating the lower-part pile integrity and are further utilized to establish a data-driven machine-learning framework to detect and quantify the degree of damage. Considering the relatively small number of field test samples of the in-hole MPTWD method at this stage, an analytical solution is employed to generate sufficient samples to verify the feasibility and optimize the performance of the machine learning modeling framework. Two types of features extracted by the distributed sampling and statistical and signal processing techniques are applied to three machine-learning classifiers, i.e., logistic regression (LR), extreme gradient boosting (XGBoost) and multilayer perceptron (MLP). The performance of the data-driven machine-learning framework is then evaluated through a specific case study. The results demonstrate that all three classifiers perform better when employing the statistical and signal processing techniques, and the total of 24 extracted features are sufficient for the machine-learning algorithms.

PMID:37837138 | DOI:10.3390/s23198308

Sensors (Basel). 2023 Oct 7;23(19):8291. doi: 10.3390/s23198291.

ABSTRACT

The wireless communication system is used to provide dispatching, control, communication and other services for rail transit operations. In practice, interference from other wireless communication systems will affect the normal operation of trains, so it is an urgent problem to study the interference detection algorithms for rail transit applications. In this paper, the fourth-order cyclic cumulant (FOCC) of signals with different modulation modes is analyzed for the narrow-band wireless communications system of rail transit. Based on the analysis results, an adjacent-frequency interference detection algorithm is proposed according to the FOCC of the received signal within the predetermined cyclic frequency range. To detect interference with the same carrier frequency, a same-frequency interference detection algorithm using the relationship between the FOCC and the received power is proposed. The performance of the proposed detection algorithms in terms of correct rate and computational complexity is analyzed and compared with the traditional second-order statistical methods. Simulation results show that when an interference signal coexists with the expected signal, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the signal-to-noise ratio (SNR) is higher than 2 dB and -4 dB, respectively. Under the practical rail transit wireless channel with multipath propagation and the Doppler effect, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the SNR is higher than 3 dB and 7 dB, respectively. Compared with the existing second-order statistical methods, the proposed method can detect both the adjacent-frequency and the same-frequency interference when the interference signals coexist with the expected signal. Although the computational complexity of the proposed method is increased, it is acceptable in the application of rail transit wireless communication interference detection.

PMID:37837120 | DOI:10.3390/s23198291

Sensors (Basel). 2023 Oct 6;23(19):8281. doi: 10.3390/s23198281.

ABSTRACT

Various statistical data indicate that mobile source pollutants have become a significant contributor to atmospheric environmental pollution, with vehicle tailpipe emissions being the primary contributor to these mobile source pollutants. The motion shadow generated by motor vehicles bears a visual resemblance to emitted black smoke, making this study primarily focused on the interference of motion shadows in the detection of black smoke vehicles. Initially, the YOLOv5s model is used to locate moving objects, including motor vehicles, motion shadows, and black smoke emissions. The extracted images of these moving objects are then processed using simple linear iterative clustering to obtain superpixel images of the three categories for model training. Finally, these superpixel images are fed into a lightweight MobileNetv3 network to build a black smoke vehicle detection model for recognition and classification. This study breaks away from the traditional approach of “detection first, then removal” to overcome shadow interference and instead employs a “segmentation-classification” approach, ingeniously addressing the coexistence of motion shadows and black smoke emissions. Experimental results show that the Y-MobileNetv3 model, which takes motion shadows into account, achieves an accuracy rate of 95.17%, a 4.73% improvement compared with the N-MobileNetv3 model (which does not consider motion shadows). Moreover, the average single-image inference time is only 7.3 ms. The superpixel segmentation algorithm effectively clusters similar pixels, facilitating the detection of trace amounts of black smoke emissions from motor vehicles. The Y-MobileNetv3 model not only improves the accuracy of black smoke vehicle recognition but also meets the real-time detection requirements.

PMID:37837111 | DOI:10.3390/s23198281

Sensors (Basel). 2023 Sep 29;23(19):8178. doi: 10.3390/s23198178.

ABSTRACT

The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer-Douglas-Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.

PMID:37837008 | DOI:10.3390/s23198178

Semergen. 2023 Oct 11;50(1):102086. doi: 10.1016/j.semerg.2023.102086. Online ahead of print.

ABSTRACT

Logistic regression is a group of statistical techniques that aim to test hypotheses or causal relationships between a categorical dependent variable and other independent variables that can be categorical and quantitative. Through this model we intend to study the probability that the event studied will occur based on some variables that we assume are relevant or influential. In this method it is necessary to detect effect modifier and confounding variables. Its parameters are estimated with the maximum likelihood method through a process with successive iterations.

PMID:37832165 | DOI:10.1016/j.semerg.2023.102086

Cytokine. 2023 Oct 11;172:156401. doi: 10.1016/j.cyto.2023.156401. Online ahead of print.

ABSTRACT

Interleukin-2 (IL-2) is a cytokine secreted from T helper type 1 cells and released after induction of T helper cells with major histocompatibility complexes or antigens presented by antigen presenting cells. IL-2 activity and gene polymorphisms have been studied in both solid and hematological malignancies. In the present study, it was aimed to examine the effects of IL-2RA rs2104286, IL-2 rs2069762 and rs2069763 polymorphisms on multiple myeloma (MM) susceptibility, progression-free survival (PFS) and overall survival (OS). A total of 300 patients diagnosed with MM in our clinic between January 2010 and January 2021, and 170 healthy individuals were included. In addition to the demographic data of the patients, MM subtypes, initial stages, prognostic index scores, laboratory results, treatment preferences, and survival data were recorded. The genotypes of the IL-2RA rs2104286, IL-2 rs2069762 and rs2069763 polymorphisms were statistically compared between patients and healthy controls to reveal their effects on MM susceptibility and survival. In the statistical analysis performed to examine the effect of IL-2RA rs2104286, IL-2 rs2069762 and rs2069763 polymorphisms on disease susceptibility, no significant difference was found between the patient and healthy control groups. Patients with the TG genotype of IL-2 rs2069762 had a significantly shorter median PFS and OS compared to others. Patients with the GG genotype of IL-2 rs2069763 had a significantly shorter median PFS compared to others. Having the TG genotype of IL-2 rs2069762 has been shown to be protective for short PFS and OS. Our study results will be guiding in terms of IL-2 based therapies, the future for MM and MM epigenetics.

PMID:37832160 | DOI:10.1016/j.cyto.2023.156401

Issues Ment Health Nurs. 2023 Oct 13:1-7. doi: 10.1080/01612840.2023.2258219. Online ahead of print.

ABSTRACT

Suicide is a serious public health problem for adolescents. Based on the framework of ideation-to-action, it is important to examine the factors associated with the translation from suicide ideation to suicide attempt. The present study aimed to investigate the risk factors of suicide attempts among adolescents with suicide ideation in low-income and middle-income countries (LMICs). We analyzed data of students aged 12-18 years who participated in the 2009-2013 Global School-based Health Surveys (GSHS) in 39 LMICs. The Chi-square test was used to compare the prevalence of suicide attempts among participants with suicide ideation, the multilevel logistic regression model was used to identify significant factors associated with suicide attempts among suicide ideators. Among 22,655 adolescents with suicide ideation, 55.1% of them reported having made a suicide attempt in the past year. Loneliness, anxiety, alcohol use, and drug use were risk factors for suicide attempts among suicide ideators. Strategies should be implemented to reduce the likelihood of adolescents acting on their suicidal thoughts, such as community psychological crisis line, school-based mental health and skills training programs, and family support for adolescents with psychological problems.

PMID:37832147 | DOI:10.1080/01612840.2023.2258219

Plast Reconstr Surg. 2023 Oct 13. doi: 10.1097/PRS.0000000000011141. Online ahead of print.

ABSTRACT

BACKGROUND: The driver of secondary lymphedema (SL) progression is chronic inflammation, which promotes fibrosis. Despite advances in preclinical research, a specific effector cell subpopulation as a biomarker for therapy response or stage progression is still missing for SL.

METHODS: Whole skin samples of 35 murine subjects of a microsurgical-induced SL model and 12 patients with SL were collected and their fibroblasts were isolated. These lymphedema-derived fibroblasts (LAF) were cultured in a collagen I-poly-D-Lysine 3D hydrogel to mimic skin conditions. Fibroblasts from non-lymphedema skin were used as negative control and TGF-β-stimulated fibroblasts were used to recreate profibrotic myofibroblasts. Quantitative immunocytofluorescence confocal microscopy analysis and invasion functional assays were performed in all subpopulations and statistically compared.

RESULTS: In contrast to normal skin fibroblasts, LAF exhibit α-SMA-positive stress fibers and a reduced number of tight junctions in 3D hydrogel conditions. The switch from normal E-cadherin high phenotype to an N-cadherin high-E-cadherin low morphology suggests epithelial to mesenchymal transition for expansion and proliferation. This pathological behavior of LAF was confirmed by live cell imaging analysis of invasion assays. The significant activation of markers of the TGFBR2-Smad pathway and collagen synthesis (HSP-47) in LAF supports EMT phenotypic changes and previous findings relating to TGF-β1 and fibrosis with lymphedema.

CONCLUSIONS: A characteristic SL myofibroblast subpopulation was identified and translationally related to fibrosis and TGF-β1-associated stage progression. This SL-related subpopulation was termed lymphedema-associated fibroblasts. A comprehensive stage-related characterization is required to validate LAF as a reliable biomarker for SL disease progression.

PMID:37832143 | DOI:10.1097/PRS.0000000000011141

Stat Methods Med Res. 2023 Oct 13:9622802231197977. doi: 10.1177/09622802231197977. Online ahead of print.

ABSTRACT

With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association test, which is a widely used variant-set test, have been proposed for unrelated samples and extended for family data. Family data have been shown to be powerful when analyzing rare variants. However, most of such methods capture familial relatedness using a random effect component within the generalized linear mixed model framework. Therefore, there is a need to develop unified and flexible methods to study the association between a set of genetic variants and a trait, especially for a binary outcome. Copulas are multivariate distribution functions with uniform margins on the $[0,1]$ interval and they provide suitable models to capture familial dependence structure. In this work, we propose a flexible family-based association test for both rare and common variants in the presence of binary traits. The method, termed novel rare variant association test (NRVAT), uses a marginal logistic model and a Gaussian Copula. The latter is employed to model the dependence between relatives. An analytic score-type test is derived. Through simulations, we show that our method can achieve greater power than existing approaches. The proposed model is applied to investigate the association between schizophrenia and bipolar disorder in a family-based cohort consisting of 17 extended families from Eastern Quebec.

PMID:37832140 | DOI:10.1177/09622802231197977