Category: Nevin Manimala

Health Promot Int. 2023 Dec 1;38(6):daad166. doi: 10.1093/heapro/daad166.

ABSTRACT

Engagement in youth programs is a potential means to promote health and well-being across populations of young people. Safer spaces in these youth programs are likely critical in fostering positive health outcomes, but current research on the links between safer spaces and health is limited. In this exploratory study, we examined links between program safety in youth development programs and minoritized status, and health-related quality of life (HRQoL) and psychosomatic health complaints. Participants (N = 282; Mean age = 16.97 years; SD = 2.97) self-identified across various minority status groups, including LGBTQ (30%) and a range of perceived income levels. We tested a statistical model in which safer spaces, LGBTQ status and perceived income predicted HRQoL and health complaints in youth development program participants. LGBTQ status and lower perceived income were related to lower HRQoL and more health complaints, and safer space in youth development programs was related to better HRQoL. We also found an interaction effect, such that safer spaces in youth programs appeared to be especially beneficial for HRQoL for youth with higher incomes. Findings reinforce past research on LGBTQ status and income as factors for youth wellness and mental health. Findings also suggest that perceived safer spaces in youth development programs support better HRQoL and lower health complaints, across populations of participating youth.

PMID:38091620 | DOI:10.1093/heapro/daad166

Phys Med Biol. 2023 Dec 13. doi: 10.1088/1361-6560/ad1549. Online ahead of print.

ABSTRACT


The high production cost of commonly used lutetium-based fast scintillators and the development of silicon photomultipliers technology have made bismuth germanate (BGO) a promising candidate for time-of-flight positron emission tomography (TOF PET) detectors owing to its generation of prompt Cherenkov photons. However, using BGO as a hybrid scintillator is disadvantageous owing to its low photon statistics and distribution that does not conform well to a single Gaussian. To mitigate this, a proposal was made to increase the likelihood of detecting the first Cherenkov photons by positioning two photosensors in opposition at the entrance and exit faces of the scintillator and subsequently selectively picking an earlier timestamp. Nonetheless, the timing variation arising from the photon transit time remains affected by the entire length of the crystal, thereby presenting a possibility for further enhancement.
Approach: In this study, we aimed to improve the timing performance of the dual-ended BGO Cherenkov TOF PET detector by capitalizing on the synergistic advantages of applying depth-of-interaction (DOI) information and crystal surface finishes or reflector properties. A dual-ended BGO detector was implemented using a 3 × 3 × 15 mm3 BGO crystal. Coincidence events were acquired against a 3 × 3 × 3 mm3 LYSO:Ce:Mg reference detector. The timing performance of the dual-ended BGO detectors was analyzed using conventionally proposed timestamp methods before and after DOI correction.
Results:
Through a DOI-based correction of photon transit time spread, we demonstrated a further improvement in the timing resolution of the BGO-based Cherenkov TOF PET detector utilizing a dual-ended detector configuration and adaptive arrival time pickoff. We achieved further improvements in timing resolution by correcting the offset spread induced by the fluctuation of timing signal rise time in the dual-ended detector.
Significance: Although polishing the crystal surface was still favorable in terms of full-width-half-maximum (FWHM) value, incorporating DOI information from the unpolished crystal to compensate for photon travel time facilitated additional enhancement in the overall timing performance, thereby surpassing that achieved with the polished crystal.&#xD.

PMID:38091614 | DOI:10.1088/1361-6560/ad1549

Gigascience. 2022 Dec 28;12:giad106. doi: 10.1093/gigascience/giad106.

ABSTRACT

BACKGROUND: Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes.

FINDINGS: We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis.

CONCLUSIONS: The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

PMID:38091511 | DOI:10.1093/gigascience/giad106

Anesth Analg. 2023 Dec 8. doi: 10.1213/ANE.0000000000006802. Online ahead of print.

ABSTRACT

BACKGROUND: Trauma outcome prediction models have traditionally relied upon patient injury and physiologic data (eg, Trauma and Injury Severity Score [TRISS]) without accounting for comorbidities. We sought to prospectively evaluate the role of the American Society of Anesthesiologists physical status (ASA-PS) score and the National Surgical Quality Improvement Program Surgical Risk-Calculator (NSQIP-SRC), which are measurements of comorbidities, in the prediction of trauma outcomes, hypothesizing that they will improve the predictive ability for mortality, hospital length of stay (LOS), and complications compared to TRISS alone in trauma patients undergoing surgery within 24 hours.

METHODS: A prospective, observational multicenter study (9/2018-2/2020) of trauma patients ≥18 years undergoing operation within 24 hours of admission was performed. Multiple logistic regression was used to create models predicting mortality utilizing the variables within TRISS, ASA-PS, and NSQIP-SRC, respectively. Linear regression was used to create models predicting LOS and negative binomial regression to create models predicting complications.

RESULTS: From 4 level I trauma centers, 1213 patients were included. The Brier Score for each model predicting mortality was found to improve accuracy in the following order: 0.0370 for ASA-PS, 0.0355 for NSQIP-SRC, 0.0301 for TRISS, 0.0291 for TRISS+ASA-PS, and 0.0234 for TRISS+NSQIP-SRC. However, when comparing TRISS alone to TRISS+ASA-PS (P = .082) and TRISS+NSQIP-SRC (P = .394), there was no significant improvement in mortality prediction. NSQIP-SRC more accurately predicted both LOS and complications compared to TRISS and ASA-PS.

CONCLUSIONS: TRISS predicts mortality better than ASA-PS and NSQIP-SRC in trauma patients undergoing surgery within 24 hours. The TRISS mortality predictive ability is not improved when combined with ASA-PS or NSQIP-SRC. However, NSQIP-SRC was the most accurate predictor of LOS and complications.

PMID:38091502 | DOI:10.1213/ANE.0000000000006802

West Afr J Med. 2023 Nov 30;40(11):1173-1180.

ABSTRACT

BACKGROUND: The most prevalent oral diseases, dental caries and periodontal disease, result in pain, discomfort, and loss of oral functions, often leading to poor nutrition.

OBJECTIVES: To assess the prevalence and relationship between oral health and nutritional status among children aged 2 to 5 years in Maiduguri, North-East Nigeria.

METHODS: A cross-sectional study that assessed caries experience, gingival status and nutritional status of children. Anthropometric measurements of weight-for-age and mid-upper arm circumference (MUAC) were used to assess nutritional status. Participants were randomly selected from three private schools. The MUAC was measured using a standardised tape rule. The weight-for-age by sex of the participants was extrapolated from the weight-for-age WHO chart. Oral health was assessed using the WHO Oral Health Survey Methods. Data were analysed using SPSS for Windows (version 23). Statistical significance was placed at 95% confidence and p ≤ 0.05.

RESULTS: There were 239 participants with a male:female ratio of 1.2:1 (SE=0.03). Mean dmft was 0.72 (SE 0.09) and 63.2% had healthy gingiva (SE 0.04). The mean weight was 16.8kg (SE=0.15) and mean MUAC was 15.3 cm. Caries prevalence was associated with nutritional status and positively correlated (r=0.03, P=0.64). Gingival status was associated but inversely correlated with MUAC (r= -0.02, P= 0.76).

CONCLUSION: Normal nutritional status was associated with no caries prevalence and healthy gingiva. Underweight was associated with caries prevalence. Adequate and healthy nutrition promotes good oral health in children.

PMID:38091448

Cutis. 2023 Nov;112(5):229-231. doi: 10.12788/cutis.0891.

ABSTRACT

Dermatology has long been recognized as a highly competitive field within medicine, with extremely limited spots available for aspiring dermatologists to secure residencies across the United States. We sought to evaluate the trends and factors influencing the match process in dermatology residencies, particularly given the changes brought on by the COVID-19 pandemic. Using data from publicly available match lists and regional categorizations, we studied the rates of internal and regional matches for dermatology applicants in the postpandemic era (2022-2023) compared with prepandemic statistics. Overall, the research sheds light on the evolving dynamics of dermatology residency matching in response to pandemic-induced changes and applicant preferences.

PMID:38091443 | DOI:10.12788/cutis.0891

PLoS One. 2023 Dec 13;18(12):e0289195. doi: 10.1371/journal.pone.0289195. eCollection 2023.

ABSTRACT

Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar’s statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.

PMID:38091358 | DOI:10.1371/journal.pone.0289195

J Physician Assist Educ. 2023 Dec 13. doi: 10.1097/JPA.0000000000000567. Online ahead of print.

ABSTRACT

PURPOSE: The primary aim of this study was the examination of relationships between students’ preadmission achievement, intraphysician assistant (PA) program achievement, and Physician Assistant National Certifying Examination (PANCE) performance using path analysis regression. Second, this study explored the extent to which the theoretical model differed based on several key demographic variables: sex and undergraduate major.

METHODS: This retrospective, single-institution study examined data from 2015 to 2022 (n = 322). Analysis included descriptive statistics, bivariate correlations, and path analysis using structural equation modeling to examine direct, indirect, and total effects of all predictors on the primary outcome variable, PANCE.

RESULTS: PACKRAT-I demonstrated the largest total effect size on PANCE total score (β = .45). Total effect size on PANCE was small yet significant for prerequisite grade point average (GPA), Graduate Record Exam verbal and quantitative subscores, a comprehensive didactic cardiology examination, didactic and clinical year GPAs, and End of Rotation examination mean score (β < .25). The relationship between mean preceptor evaluation score and PANCE was nonsignificant. Subgroup analyses showed differences between female and male in the relationship between several didactic variables and preceptor evaluations. No differences were detected between groups based on undergraduate major.

CONCLUSION: This PANCE analysis revealed relationships among pre-PA and intra-PA performance metrics that may subsequently support data-informed strategies for programs to identify at-risk students, aid student success, and support the assessment of curriculum. Future studies should replicate the approach using a larger, multi-institution sample that examines additional preprogram and intraprogram achievement variables.

PMID:38091357 | DOI:10.1097/JPA.0000000000000567

West Afr J Med. 2023 Nov 30;40(11):1164-1172.

ABSTRACT

BACKGROUND: Metabolic syndrome is a constellation of abnormalities which includes central obesity, dyslipidaemia, elevated blood pressure and hyperglycemia. Hypertension, (which is a very common component of metabolic syndrome), and diabetes mellitus, are independently associated. Also, studies examining metabolic syndrome inAbuja, a city with affluence-driven lifestyle, are not available. This study aimed to investigate the prevalence of metabolic syndrome among hypertensive patients in Abuja, Nigeria, as well as to examine the associations between metabolic syndrome and certain factors in that cohort of hypertensive patients.

METHODS: This was a retrospective study that used data from hypertensive patients who attended clinic over a period of five years. Eight hundred and fifty-eight, (858-combined), case files of pre-treated, (previously known hypertensive patients) and newly diagnosed hypertensive participants were used for the study. The student t-tests were used to compare continuous variables, while Chi-square (χ2) tests were used for relationship between qualitative variables. The likelihood ratio test was employed to further confirm the statistical significance of certain independent variables relating with metabolic syndrome. A P-value of < 0.05 was considered statistically significant.

RESULTS: The mean ages were 48.70±12.18, 49.19±11.06 and 48.2±13.3 years for combined group, the pre-treated and the newly-diagnosed groups respectively. The pre-treated, group consists of those previously known hypertensive patients, while the new group consists of those who were newly diagnosed hypertensive patients and were treatment naïve. The prevalence of metabolic syndrome in this study was 45.5% in the combined group, 47.23% in the pre-treated group and 37.3% in the newly diagnosed group. The commonest component of metabolic syndrome was reduced high density lipoprotein cholesterol, HDL-C.

CONCLUSION: Metabolic syndrome is prevalent among hypertensive patients in Abuja, Nigeria. Some correlates of metabolic syndrome include; elevated BMI, truncal obesity, elevated total cholesterol, the use of thiazide diuretics and beta blockers as antihypertensives.

PMID:38091343

PLOS Glob Public Health. 2023 Dec 13;3(12):e0002679. doi: 10.1371/journal.pgph.0002679. eCollection 2023.

ABSTRACT

Brazil was one of the countries most affected during the first year of the COVID-19 pandemic, in a pre-vaccine era, and mathematical and statistical models were used in decision-making and public policies to mitigate and suppress SARS-CoV-2 dispersion. In this article, we intend to overview the modeling for COVID-19 in Brazil, focusing on the first 18 months of the pandemic. We conducted a scoping review and searched for studies on infectious disease modeling methods in peer-reviewed journals and gray literature, published between January 01, 2020, and June 2, 2021, reporting real-world or scenario-based COVID-19 modeling for Brazil. We included 81 studies, most corresponding to published articles produced in Brazilian institutions. The models were dynamic and deterministic in the majority. The predominant model type was compartmental, but other models were also found. The main modeling objectives were to analyze epidemiological scenarios (testing interventions’ effectiveness) and to project short and long-term predictions, while few articles performed economic impact analysis. Estimations of the R0 and transmission rates or projections regarding the course of the epidemic figured as major, especially at the beginning of the crisis. However, several other outputs were forecasted, such as the isolation/quarantine effect on transmission, hospital facilities required, secondary cases caused by infected children, and the economic effects of the pandemic. This study reveals numerous articles with shared objectives and similar methods and data sources. We observed a deficiency in addressing social inequities in the Brazilian context within the utilized models, which may also be expected in several low- and middle-income countries with significant social disparities. We conclude that the models were of great relevance in the pandemic scenario of COVID-19. Nevertheless, efforts could be better planned and executed with improved institutional organization, dialogue among research groups, increased interaction between modelers and epidemiologists, and establishment of a sustainable cooperation network.

PMID:38091336 | DOI:10.1371/journal.pgph.0002679