Category: Nevin Manimala

J Res Adolesc. 2023 Oct 14. doi: 10.1111/jora.12891. Online ahead of print.

ABSTRACT

Measurement alignment adjusts factor loadings and intercepts across different groups to achieve measurement invariance, which assumes the equal measurement model is validated across different groups. It should be achieved for validly conducting analysis and comparison in studies involving multiple groups, such as cross-cultural or cross-national studies. In this paper, I presented how to conduct measurement alignment with R. In addition to measurement alignment, I explained how to perform the Monte Carlo simulation to test the consistency and validity of alignment results and factor score calculation to facilitate further statistical analysis. A tutorial R code that implements all described procedures is freely shared via GitHub to inform readers who intend to use the alignment technique in their research projects.

PMID:37837225 | DOI:10.1111/jora.12891

Pediatr Transplant. 2023 Oct 13:e14623. doi: 10.1111/petr.14623. Online ahead of print.

ABSTRACT

BACKGROUND: Children at high risk for prolonged mechanical ventilation (PMV) after liver transplantation (LT) need to be identified early to optimize pulmonary support, allocate resources, and improve surgical outcomes. We aimed to develop and validate a metric that can estimate risk for Prolonged Ventilation After LT (PROVE-ALT).

METHODS: We identified preoperative risk factors for PMV by univariable analysis in a retrospective cohort of pediatric LT recipients between 2011 and 2017 (n = 205; derivation cohort). We created the PROVE-ALT score by mapping multivariable logistic regression coefficients as integers, with cutoff values using the Youden Index. We validated the score by C-statistic in a retrospectively collected separate cohort of pediatric LT recipients between 2018 and 2021 (n = 133, validation cohort).

RESULTS: Among total 338 patients, 21% (n = 72) were infants; 49% (n = 167) had cirrhosis; 8% (n = 27) required continuous renal replacement therapy (CRRT); and 32% (n = 111) required management in hospital (MIH) before LT. Incidence of PMV post-LT was 20% (n = 69) and 3% (n = 12) required tracheostomy. Independent risk factors (OR [95% CI]) for PMV were cirrhosis (3.8 [1-14], p = .04); age <1-year (8.2 [2-30], p = .001); need for preoperative CRRT (6.3 [1.2-32], p = .02); and MIH before LT (12.4 [2.1-71], p = .004). PROVE-ALT score ≥8 [Range = 0-21] accurately predicted PMV in the validation cohort with 73% sensitivity and 80% specificity (AUC: 0.81; 95% CI: 0.71-0.91).

CONCLUSION: PROVE-ALT can predict PMV after pediatric LT with a high degree of sensitivity and specificity. Once externally validated in other centers, PROVE-ALT will empower clinicians to plan patient-specific ventilation strategies, provide parental anticipatory guidance, and optimize hospital resources.

PMID:37837221 | DOI:10.1111/petr.14623

Int J Paediatr Dent. 2023 Oct 13. doi: 10.1111/ipd.13126. Online ahead of print.

ABSTRACT

BACKGROUND: Mineralization-promoting peptide-3 (MPP3) is a new biomimetic remineralization agent.

AIM: To assess the remineralization efficiency of MPP3, either alone or in combination with fluoride gel.

DESIGN: The samples were divided into four groups: control, 1.23% fluoride gel, 10% MPP3 gel, and 1.23% fluoride gel + 10% MPP3. Following the application of remineralization agents (4 min), the samples remained in a pH-cycling model (37°C, 4 weeks). Microhardness, microcomputed tomography (micro-CT), polarized light microscopy (PLM), and field emission scanning electron microscopy (FE-SEM) analysis were conducted. RM-ANOVA, one-way ANOVA, and intraclass correlation coefficient (ICC) were used for statistical analysis, and a significance level of p < .05 was employed.

RESULTS: Mineralization-promoting peptide 3 and fluoride gel + MPP3 increased the microhardness of the enamel compared with initial values in each group (p < .05). Mineralization-promoting peptide 3 successfully maintained the mineral density of enamel, although the cariogenic pH-cycling and PLM results indicated that the lesion depth (μm) was significantly lower in the fluoride gel + MPP3 group (27.0336 ± 12.53650) than in the control group (37.3907 ± 12.76002, p < .05).

CONCLUSION: The combined use of MPP3 with fluoride gel enhanced the caries-protective and mineralization-promoting effects of fluoride. Mineralization-promoting peptide 3 may be a potential agent that can be employed to improve the physical properties of enamel.

PMID:37837213 | DOI:10.1111/ipd.13126

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