Category: Nevin Manimala

Int Endod J. 2023 Nov;56(11):1318. doi: 10.1111/iej.13964.

NO ABSTRACT

PMID:37837198 | DOI:10.1111/iej.13964

Mil Med. 2023 Oct 13:usad393. doi: 10.1093/milmed/usad393. Online ahead of print.

ABSTRACT

INTRODUCTION: Point-of-care ultrasound (POCUS) is a rapid, readily available, and cost-effective diagnostic and prognostic modality in a range of clinical settings. However, data to support its clinical application are limited. This project’s main goal was to assess the effectiveness of standardizing lung ultrasound (LUS) training for sonographers to determine if universal LUS adoption is justified.

MATERIALS AND METHODS: We describe the effectiveness of an implementation of a LUS research training program across eight international study sites in Asia, Africa, and North America as part of prospective Coronavirus Disease of 2019 (COVID-19) and sepsis study cohorts (Rapid Assessment of Infection with SONography research network). Within our network, point-of-care LUS was used to longitudinally evaluate radiographic markers of lung injury. POCUS operators were personnel from a variety of backgrounds ranging from research coordinators with no medical background to experienced clinicians.

RESULTS: Following a standardized protocol, 49 study sonographers were trained and LUS images from 486 study participants were collected. After training was completed, we compared before and after image qualities for interpretation. The proportion of acceptable images improved at each site between the first 25 scans and the second 25 scans, resulting in 80% or greater acceptance at each study site.

CONCLUSIONS: POCUS training and implementation proved feasible in diverse research settings among a range of providers. Standardization across ongoing cohort protocols affords opportunities for increased statistical power and generalizability of results. These results potentially support care delivery by enabling military medics to provide care at the point of injury, as well as aiding frontline clinicians in both austere and highly resourced critical care settings.

PMID:37837196 | DOI:10.1093/milmed/usad393

Oral Dis. 2023 Oct 13. doi: 10.1111/odi.14758. Online ahead of print.

ABSTRACT

OBJECTIVE: The present study evaluated the oral tissue expression of micro-RNA (miRNAs) linked to the potential malignant evolution of oral lichen planus (OLP). Furthermore, the correlation between OLP severity and miRNAs expression was assessed, and possible predictors of miRNAs in OLP patients were identified.

METHODS: The present study enrolled 41 patients with OLP (median age 58 years) and 42 healthy controls (median age 59 years). In each patient, miRNA levels (miR-7a-3p,-7a2-3p,-7a-5p,-21-3p,-21-5p,-100-3p,-100-5p,-125b-2-3p,-125b-5p,-200b-3p,-200b-5p) were assessed and analyzed through reverse transcription polymerase chain reaction. Clinical parameters and the eventual presence of OLP symptoms, signs, and disease severity scores in each patient were reported using an anamnestic questionnaire.

RESULTS: In comparison with healthy controls, OLP patients showed significantly higher miR-7a-3p,-7a-2-3p,-21-3p, miR-21-5p and miR-100-5p levels (p < 0.05) and significantly lower miR-125b-2-3p,-125b-5p,-200b-3p, and -200b-5p levels (p < 0.05). Furthermore, OLP symptoms and signs and disease severity scores were significantly correlated and were also predictors of all analyzed miRNAs (p < 0.05).

CONCLUSIONS: In comparison with healthy subjects, OLP patients exhibited unbalanced oral miRNAs expression linked to the risk of potential malignant evolution of OLP. Furthermore, some miRNAs were correlated with OLP extent and were significant predictors of OLP symptoms, signs, and disease severity scores.

PMID:37837187 | DOI:10.1111/odi.14758

Pediatr Blood Cancer. 2023 Oct 13:e30720. doi: 10.1002/pbc.30720. Online ahead of print.

ABSTRACT

BACKGROUND: Parents who lose a child are at increased risk of impaired mental health, which may negatively affect their work ability. The aims of this study were to examine the risk for reduced labor market affiliation in parents who lost a child with cancer compared to a matched parent cohort, and factors associated with the bereaved parents’ labor market affiliation.

METHODS: We conducted a nationwide population-based cohort study using Danish registry data. We followed bereaved parents (n = 1609) whose child died with cancer at age less than 30 during 1992-2020, and a matched, population-based sample of parents (n = 15,188) of children with no history of childhood cancer. Cox proportional hazard models and fractional logit models were performed separately for mothers and fathers.

RESULTS: Cancer-bereaved mothers had an overall increased risk of long-term sick leave (hazard ratio [HR] = 1.62; 95% confidence interval [CI]: 1.48-1.77), unemployment (HR = 1.53; CI: 1.37-1.70), and lower odds of working in the first 2 years following the loss (odds ratio [OR] = 0.44; CI: 0.39-0.49), while bereaved fathers had lower odds of working (OR = 0.65; CI: 0.53-0.79), and increased risk of permanently reduced work ability (HR = 1.29; 95% CI: 1.01-1.66), compared to the matched cohort of parents of cancer-free children. Younger parental age, lower education, and being a single parent were identified as the main determinants of the bereaved parents’ reduced labor market affiliation.

CONCLUSIONS: Cancer-bereaved parents are at increased risk of reduced labor market affiliation, compared with a matched, population-based sample of parents. Certain groups of bereaved parents may be at particularly high risk, and targeted bereavement interventions are warranted.

PMID:37837181 | DOI:10.1002/pbc.30720

Sensors (Basel). 2023 Oct 9;23(19):8330. doi: 10.3390/s23198330.

ABSTRACT

Characterizing motor subtypes of Parkinson’s disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.

PMID:37837160 | DOI:10.3390/s23198330

Sensors (Basel). 2023 Oct 8;23(19):8324. doi: 10.3390/s23198324.

ABSTRACT

An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.

PMID:37837153 | DOI:10.3390/s23198324

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