Tag: nevin manimala

BMC Cancer. 2024 Dec 6;24(1):1506. doi: 10.1186/s12885-024-13214-5.

ABSTRACT

BACKGROUND: Breast cancer (BC) is the most prevalent cancer among women. Teachers play a crucial role in promoting healthy behaviors, including breast cancer screening (BCS). This study aimed to assess the impact of an Health Belief Model (HBM)-based educational intervention on BCS uptake, knowledge, and beliefs among female Yemeni teachers in Klang Valley, Malaysia.

METHODS: A cluster-randomized controlled trial was conducted with 180 participants from 12 schools, randomly assigned to intervention or control groups. The intervention group participated in a 90-minute educational session, with follow-up assessments at baseline, and at 1, 3, and 6 months’ post-intervention, using validated Arabic questionnaires. Data analysis was performed using SPSS version 22.0, with Generalized Estimating Equations (GEE) applied to assess differences within and between groups over time. Statistical significance was set at P < 0.05.

RESULTS: At baseline, there were no significant differences between groups. Post-intervention, the intervention group showed significantly higher rates of breast self-examination (BSE) and clinical breast examination (CBE) compared to the control group, with adjusted odds ratios (AOR) of 17.51 (CI: 8.22-37.29) for BSE and 2.75 (CI: 1.42-5.32) for CBE. Over six months, BSE performance in the intervention group increased, with AORs improving from 11.01 (CI: 5.05-24.04) to 18.55 (CI: 8.83-38.99). Similarly, CBE uptake rose from 1.60 (CI: 1.02-2.52) to 2.27 (CI: 1.44-3.58). Secondary outcomes revealed significant gains in knowledge and beliefs in the intervention group, including increased confidence in performing BSE and reduced perceived barriers.

CONCLUSIONS: The HBM-based educational intervention effectively enhanced BCS uptake, improved knowledge, and decreased barriers to BCS among Yemeni teachers in Malaysia, highlighting the potential of targeted educational programs to promote cancer screening behaviors in underserved populations.

CLINICAL TRIAL REGISTRATION: Retrospectively registered, ANZCTR (ACTRN12618000173291). Registered on February 02, 2018.

PMID:39643866 | DOI:10.1186/s12885-024-13214-5

Eur J Nutr. 2024 Dec 7;64(1):41. doi: 10.1007/s00394-024-03523-7.

ABSTRACT

PURPOSE: Dietary macronutrients significantly impact cardiometabolic health, yet research often focuses on individual macronutrient relationships. This study aimed to explore the associations between dietary macronutrient composition and cardiometabolic health.

METHODS: This study included 33,681 US adults (49.7 ± 18.3 years; 52.5% female) from the National Health and Nutrition Examination Survey during 1999-2014. Dietary data was derived from 1 to 2 separate 24-hour recalls and cardiometabolic health included lipid profile, glycemic control, blood pressure, and adiposity collected in a mobile examination center. Associations between dietary macronutrient composition and cardiometabolic health were examined using generalized additive models adjusted for age, socio-demographics, lifestyle, and diet quality.

RESULTS: In females, triglycerides (P < 0.01) and HDL cholesterol (P < 0.01) were the least optimal in diets containing lower fat (10%) and higher carbohydrate (75%). In males, HDL cholesterol was positively associated with fat (P < 0.01) and no association with triglycerides was detected. Total-C associations were male specific (P = 0.01) and highest in diets composed of 25% protein, 30% carbohydrate, and 45% fat. In both sexes, systolic blood pressure (P ≤ 0.02) was highest in diets containing lower fat (10%) coupled with moderate protein (25%). Diastolic blood pressure associations were female specific (P < 0.01) with higher values in those consuming the upper range of fat (55%). There were no associations of macronutrient composition with glycemic control or adiposity.

CONCLUSION: This study revealed sex-specific relationships between macronutrient composition and cardiometabolic health. Future research is needed to explore these relationships across age groups.

PMID:39643829 | DOI:10.1007/s00394-024-03523-7

Comput Methods Programs Biomed. 2024 Nov 30;260:108526. doi: 10.1016/j.cmpb.2024.108526. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Accurate medical image segmentation is crucial for diagnosis and treatment planning, particularly in tumor localization and organ measurement. Despite the success of Transformer models in various domains, they still struggle to capture high-frequency features, limiting their performance in medical image segmentation, especially in edge texture extraction. To overcome this limitation and improve segmentation accuracy, this study proposes a novel model architecture aimed at enhancing the Transformer’s ability to capture and integrate both high-frequency and low-frequency features.

METHODS: Our model combines the extraction of high-frequency features using a Laplacian pyramid with the capture of low-frequency features through a Local-Global Feature Aggregation Module. A Feature Interaction Fusion module is employed to integrate these features, focusing on target areas. Additionally, a new bridging module facilitates the transfer of spatial information between the encoder and decoder via layer-wise attention mechanisms. The model’s performance was evaluated using the Synapse dataset with statistical measures such as the Dice Similarity Coefficient and Hausdorff Distance. The code is available at https://github.com/chenyuxiao123/LGHF.

RESULTS: The proposed model demonstrated state-of-the-art performance in 2D medical image segmentation, achieving a Dice Similarity Coefficient of 84.10% and a Hausdorff Distance of 12.78. The evaluation metrics indicate significant improvements compared to existing methods.

CONCLUSION: This novel model architecture, with its enhanced capability to capture and integrate both high-frequency and low-frequency features, shows significant potential for advancing medical image segmentation. The results on the Synapse dataset demonstrate its effectiveness and suggest its application could improve diagnosis and treatment planning in clinical settings.

PMID:39642402 | DOI:10.1016/j.cmpb.2024.108526

J Breath Res. 2024 Dec 6. doi: 10.1088/1752-7163/ad9b46. Online ahead of print.

ABSTRACT

Volatolomics (or volatilomics), the study of volatile organic compounds, has emerged as a crucial field of metabolomics due&#xD;to its potential for non-invasive diagnostics and disease monitoring. However, analyzing high-resolution data generated by&#xD;mass spectrometry-based instrumentation remains challenging. This comprehensive guide provides an in-depth exploration&#xD;of volatolomics data analysis, highlighting the importance of subsequent steps, including data cleaning, pretreatment, and&#xD;statistical and machine learning techniques (dimensionality reduction, clustering, classification, and variable selection). The&#xD;choice of these methods, and the integration of data handling practices, such as missing data imputation, outlier detection,&#xD;model validation, and data integration, significantly impact the identification of meaningful metabolites and the accuracy of&#xD;diagnostic conclusions. This guide aims to familiarize the reader with the implications of various data analysis techniques in&#xD;volatolomics and their suitability for different applications. It emphasizes the necessity of understanding the strengths and&#xD;limitations of each method to make informed decisions that enhance the reliability of findings. By outlining these methodologies,&#xD;the guide aims to equip researchers with the knowledge needed to navigate the complexities of volatolomics data analysis. The&#xD;careful consideration of experimental design, data collection, and processing strategies is essential for the identification of&#xD;biomarkers, ultimately advancing the field and improving the understanding of metabolic processes in health and disease.

PMID:39642393 | DOI:10.1088/1752-7163/ad9b46

Prim Care Companion CNS Disord. 2024 Dec 3;26(6):24m03777. doi: 10.4088/PCC.24m03777.

ABSTRACT

Objective: To evaluate a novel curriculum aimed to increase nonpsychiatry providers’ confidence in treating mental health conditions.

Methods: The study sample consisted of a cohort of convenience of nonbehavioral health physicians and advanced practice providers. The authors administered pre and posttests to measure provider confidence in treating specific mental health diagnoses, utilizing specific classes of psychotropic medications, and managing specific clinical scenarios. Questions were ranked using a Likert scale from 1 (least comfortable) to 5 (very comfortable). Paired sample t-tests were utilized to compare the pre- and posttest survey results. A follow-up survey was administered 1 month following the completion of the seminar, and the results were analyzed qualitatively.

Results: Twenty nonbehavioral health care providers attended an educational 2-day seminar on August 3-4, 2023. There were statistically significant improvements between the pre- and posttest measures of confidence in all 31 items measured. At 1-month follow-up, 87.5% (N = 14) rated their overall impression of the seminar as “excellent” and 12.5% (N = 2) rated their impression as “very good.” At the 1- month follow-up, 15 participants reported treating patients for depression and anxiety, compared to 13 who had done so prior to the seminar.

Conclusions: An educational seminar hosted by psychiatrists is an effective intervention for increasing provider confidence in treating mental health conditions and could serve as a valuable method for expanding the mental health workforce.

Prim Care Companion CNS Disord2024;26(6):24m03777.

Author affiliations are listed at the end of this article.

PMID:39642384 | DOI:10.4088/PCC.24m03777

J Neurosurg. 2024 Dec 6:1-12. doi: 10.3171/2024.7.JNS241102. Online ahead of print.

ABSTRACT

OBJECTIVE: Maximum extent of resection in glioma yields enhanced survival outcomes. The contemporary literature presents contradictory results regarding the benefit of intraoperative MRI (iMRI). This meta-analysis aimed to investigate the efficacy and safety of iMRI-guided surgery.

METHODS: The authors searched the PubMed, Embase, and Cochrane Reviews databases for eligible prospective randomized controlled trials through the end of February 2024. Endpoints were extent of resection, progression-free survival (PFS), overall survival, neurological functioning, and surgical complications. Individual patient data regarding PFS were reconstructed using the R package IPDfromKM.

RESULTS: From 1923 identified results, 3 randomized controlled trials with 384 patients met the inclusion criteria. Extended resections after iMRI were performed in 29.2% of the iMRI cases. Intraoperative MRI-guided glioma surgery (OR 5.40, 95% CI 3.25-8.98; p < 0.00001) outperformed conventional navigation-guided surgery in attaining gross-total resection (GTR). In patients in whom a GTR was achieved, the median time to progression was 16.0 months (95% CI 12.3-19.7 months), while the median PFS in patients with a subtotal resection was 9.7 months (95% CI 6.9-12.5 months) (p < 0.001). Despite increased GTR rates, postoperative neurological deterioration was equal among the iMRI and control groups (OR 1.0, 95% CI 0.6-1.7; p = 0.91, I2 = 0%). Intraoperative MRI use prolongs surgery by 42 minutes on average (95% CI 3.3-80.7 minutes; p = 0.03, I2 = 56%). The risk of postoperative intracranial hemorrhage (OR 1.9, 95% CI 0.2-16.9; p = 0.55, I2 = 0%) was not increased, while in one study significantly increased infections were observed in the iMRI arm.

CONCLUSIONS: Intraoperative MRI outperforms conventional surgery in achieving complete glioma resections of all contrast-enhancing tumor portions, enhancing PFS without added risk. Intraoperative MRI is a tool that facilitates these aims without reducing safety in terms of neurological deficits and surgical complications.

PMID:39642374 | DOI:10.3171/2024.7.JNS241102

J Med Internet Res. 2024 Dec 6;26:e55635. doi: 10.2196/55635.

ABSTRACT

BACKGROUND: Although significant research has explored the digital phenotype in mood disorders, the time-lagged and bidirectional relationship between mood and global positioning system (GPS) mobility remains relatively unexplored. Leveraging the widespread use of smartphones, we examined correlations between mood and behavioral changes, which could inform future scalable interventions and personalized mental health monitoring.

OBJECTIVE: This study aims to investigate the bidirectional time lag relationships between passive GPS data and active ecological momentary assessment (EMA) data collected via smartphone app technology.

METHODS: Between March 2020 and May 2022, we recruited 45 participants (mean age 42.3 years, SD 12.1 years) who were followed up for 6 months: 35 individuals diagnosed with mood disorders referred by psychiatrists and 10 healthy control participants. This resulted in a total of 5248 person-days of data. Over 6 months, we collected 2 types of smartphone data: passive data on movement patterns with nearly 100,000 GPS data points per individual and active data through EMA capturing daily mood levels, including fatigue, irritability, depressed, and manic mood. Our study is limited to Android users due to operating system constraints.

RESULTS: Our findings revealed a significant negative correlation between normalized entropy (r=-0.353; P=.04) and weekly depressed mood as well as between location variance (r=-0.364; P=.03) and depressed mood. In participants with mood disorders, we observed bidirectional time-lagged associations. Specifically, changes in homestay were positively associated with fatigue (β=0.256; P=.03), depressed mood (β=0.235; P=.01), and irritability (β=0.149; P=.03). A decrease in location variance was significantly associated with higher depressed mood the following day (β=-0.015; P=.009). Conversely, an increase in depressed mood was significantly associated with reduced location variance the next day (β=-0.869; P<.001). These findings suggest a dynamic interplay between mood symptoms and mobility patterns.

CONCLUSIONS: This study demonstrates the potential of utilizing active EMA data to assess mood levels and passive GPS data to analyze mobility behaviors, with implications for managing disease progression in patients. Monitoring location variance and homestay can provide valuable insights into this process. The daily use of smartphones has proven to be a convenient method for monitoring patients’ conditions. Interventions should prioritize promoting physical movement while discouraging prolonged periods of staying at home.

PMID:39642364 | DOI:10.2196/55635

Ann Am Thorac Soc. 2024 Dec 6. doi: 10.1513/AnnalsATS.202407-799OC. Online ahead of print.

ABSTRACT

RATIONALE: Multiple clinical practice guidelines lack recommendations pertaining to non-invasive ventilation (NIV) in acute asthma exacerbations due to a paucity of evidence. However, the evidence syntheses for these guidelines were performed years ago and more recent randomized controlled trials (RCTs) and observational studies have been published.

OBJECTIVE: Update the evidence syntheses from previous guidelines to further clarify the effects of NIV in acute asthma exacerbations.

METHODS: A systematic search of Medline, Embase and the Cochrane Library was conducted, studies comparing NIV plus standard medical therapy to standard medical therapy alone in adults with acute asthma exacerbation were selected using a priori selection criteria, and relevant data were extracted. Weighted aggregation (meta-analysis) was performed to summarize effects, which were appraised using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach.

RESULTS: Eight RCTs and five observational studies were selected. NIV was associated with a reduced intubation rate (RCTs RR 0.46, CI 0.16-1.29 and observational studies RR 0.55, CI 0.45-0.68), admission rate (RR 0.57, CI 0.34-0.98), and time to improvement in accessory muscle use (Mean difference -1.13 hours, CI -1.28 – -0.99). Additional outcomes favored NIV plus standard medical therapy but didn’t reach statistical significance including dyspnea measures and spirometry measures. There were too few deaths to reliably assess mortality. The quality of evidence ranged from low to very low for all outcomes.

CONCLUSION: All statistically significant outcomes favored NIV plus standard medical therapy over standard medical therapy alone in adults with acute asthma exacerbation. Our aggregate data suggests that intubation rate may be reduced with NIV plus SMT, though the overall quality of the evidence is low. If this is a true effect, it may be clinically important because intubation has been shown to correlate with mortality in multiple observational trials. Given these findings, patients with acute asthma exacerbations may benefit from a trial of NIV in addition to standard medical therapy.

PMID:39642363 | DOI:10.1513/AnnalsATS.202407-799OC

JMIR Res Protoc. 2024 Dec 6;13:e65043. doi: 10.2196/65043.

ABSTRACT

BACKGROUND: Almost 40% of persons living with dementia make an emergency department (ED) visit each year. One of the most impactful and costly elements of their ED care is the decision to discharge or admit them to the hospital-the “disposition” decision. When more than one reasonable option exists regarding a health care decision, such as the decision to admit or not, it often requires a complex conversation between patients, care partners, and ED providers, ideally involving shared decision-making. However, little is known about how these conversations are conducted and the real-world context in which they take place. Best practices in ED communication and shared decision-making for persons living with dementia and their care partners are limited.

OBJECTIVE: This study aims to characterize current practices in ED disposition conversations for persons living with dementia and their care partners, informed by perspectives from patient and care partner participants.

METHODS: This study will use an ethnographic design, including direct observation methods with a semistructured data collection tool to capture the ED encounter for up to 20 patient and care partner dyads, including all discussions about dispositions. Follow-up qualitative, semistructured interviews will be conducted with persons living with dementia and their care partners to explore specific observations made during their ED encounter, and to gain insight into their perspective on their role and elements of decision support used during that conversation.

RESULTS: Data collection was initiated in October 2023, with 13 dyads recruited and observed as of July 2024. This study is expected to be completed by December 2024.

CONCLUSIONS: Novel methods can offer novel insights. By combining direct observation and follow-up interviews about an ED visit, our study design will provide insights into how ED disposition occurs in real-world settings for persons living with dementia. Findings can inform more patient-centered interventions for disposition decision-making.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65043.

PMID:39642361 | DOI:10.2196/65043