Tag: pubmed

Semin Oncol. 2025 Feb 25;52(1):1-6. doi: 10.1053/j.seminoncol.2025.01.001. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the relationship between obesity and Microsatellite Instability (MSI) in endometrial cancer (EC), determine which mismatch repair (MMR) protein loss is influenced by obesity, and assess the correlation between BMI and MSI probability.

METHODS: This retrospective cohort study included 89 endometrial cancer patients treated at the Gynaecologic oncology unit of the University of Campania “Luigi Vanvitelli” from August 2023 to October 2024, and stratified by BMI: normal weight (n = 26), overweight (n = 31), obese (n = 26), and severely obese (n = 6). Microsatellite instability (MSI) was determined through immunohistochemical assessment of mismatch repair (MMR) protein expression: MLH1, PMS2, MSH2, and MSH6. Tumors were considered MSI if at least one of the four MMR proteins showed loss of expression. Univariate and multivariate logistic regression models were constructed to evaluate the correlation between BMI and MSI RESULTS: 89 patients were enrolled. Obese and severely obese groups showed significantly higher MSI rates (50 % each) compared to normoweight (12 %) and overweight (29 %) groups (P = .013). MLH1 and PMS2 loss of expression were significantly higher in obese and severely obese women (MLH1: P = .003; PMS2: P = .014). Univariate logistic regression showed a significant positive correlation between BMI and MSI (OR 1.02, 95 % CI 1.01-1.04, P = .007). In multivariate analysis, adjusting for grading, stage, histotype, and age, BMI maintained a significant positive correlation with MSI (OR 1.02, 95 % CI 1.01-1.04, P = .048).

CONCLUSIONS: This study demonstrates a significant association between obesity and MSI in EC, particularly affecting MLH1 and PMS2 expression. The findings suggest that obesity may contribute to EC development also through MMR deficiency.

PMID:40009888 | DOI:10.1053/j.seminoncol.2025.01.001

West Afr J Med. 2024 Oct 30;41(10):1054-1065.

ABSTRACT

BACKGROUND: Obstetric fistulas are a high burden globally, especially in developing regions. Despite their experiences, many affected women have a high fertility desire.

OBJECTIVES: This study aimed to assess fertility desires and associated factors among women with obstetric fistulas in Northwest Nigeria and explore their experiences.

METHODOLOGY: Using a cross-sectional mixed-method approach, quantitative data was gathered from 420 women of reproductive age via an interviewer-administered questionnaire, and qualitative data from 24 women using focus group discussions. Participants were selected from three hospitals using a two-stage sampling technique. The quantitative data was analyzed using SPSS version 21, employing descriptive and inferential statistics to identify predictors of fertility desire. Thematic analysis was used for the qualitative data.

RESULTS: Respondents’ mean age (± SD) was 26.4 (±8.4) years. The majority (88.8%) had only vesico-vaginal fistulas, while 3.3% had both vesico-vaginal and recto-vaginal fistulas. Although less than half (43.8%) had no surviving children, 74.3% desired more children. No protective association was found in the multivariable analysis. Age, number of surviving children, and family influence significantly predicted fertility desires. Qualitative findings highlighted cultural norms contrib uting to delayed treatment and poor home-based services, with social stigma, divorce, and lack of family and social support being common. However, some women received encouragement from their maternal family and community members.

CONCLUSION: Obstetric fistulas not only significantly affect women’s fertility desires but their social and personal lives. Integrating fertility counselling and community-based support into comprehensive fistula care is crucial for addressing these challenges effectively.

PMID:40009865

Crit Care Nurs Q. 2025 Apr-Jun 01;48(2):172-185. doi: 10.1097/CNQ.0000000000000554. Epub 2025 Feb 27.

ABSTRACT

This study aims to assess the crisis management and problem-solving skills of nurses caring for patients with COVID-19. The participants of this descriptive cross-sectional were 132 nurses who cared for patients with COVID-19 in a public hospital. The crisis management scale (CMS), problem-solving inventory (PSI), and Nurse Introduction Form were used to collect data. In this study, the nurses’ CMS total score average was 3.75 ± 0.442, the average PSI total score was 86.32 ± 24.420, and it was determined that their crisis management ability was at a good level and their problem-solving skills were at a medium level. A significant difference was found between the nurses’ descriptive characteristics of having children (P = .029), being informed about crisis management (P = .035), and their total average score on the CMS (P < .05). A statistically significant negative relationship was found between the nurses’ total CMS and PSI scores (P < .05).This study showed that the problem-solving skill levels of nurses caring for patients with COVID-19 affected their crisis management skills.

PMID:40009863 | DOI:10.1097/CNQ.0000000000000554

Crit Care Nurs Q. 2025 Apr-Jun 01;48(2):143-150. doi: 10.1097/CNQ.0000000000000544. Epub 2025 Feb 27.

ABSTRACT

It has been suggested that the use of statin pills beforehand could potentially influence the outcomes when individuals are hospitalized with COVID-19. In this study, we investigated how the prior use of statin medication could influence the COVID-19 severity parameters. In this retrospective cohort study, we categorized COVID-19 patients into 2 groups: statin users and non-users. Then, various data including age, gender, the patient’s need for ventilation support, the lowest oxygen blood saturation level, the length of hospitalization, receiving remdesivir treatment, and their COVID-19 vaccination status were collected. Out of 168 patients, 62 had taken statin medication before being admitted. Using statins decreased the patient’s need for ventilation support, length of hospitalization, ventilation duration, and oxygen saturation level (P < .001). Interaction effect analysis showed that receiving remdesivir statically affected the length of hospitalization, ventilation duration, and oxygen saturation level but did not significantly affect the association between statins and needing to ventilator. The use of statin pills before COVID-19 admission reduced the requirement for ventilator support.

PMID:40009860 | DOI:10.1097/CNQ.0000000000000544

JMIR Aging. 2025 Feb 26;8:e68957. doi: 10.2196/68957.

ABSTRACT

BACKGROUND: Injuries from falls are a major concern among older adults. Targeted exercise has been shown to improve fall risk, and recommendations for identifying and referring older adults for exercise-based interventions exist. However, even when very inexpensive or free, many do not use available fall prevention programs, citing barriers related to convenience and safety. These issues are even greater among older adults residing in rural areas where facilities are less abundant. These realities highlight the need for different approaches to reducing falls in novel ways that increase reach and are safe and effective. Web-based delivery of exercise interventions offers some exciting and enticing prospects.

OBJECTIVE: Our objective was to assess the efficacy of the Strong Foundations exercise program to change markers of physical function, posture, balance, strength, and fall risk.

METHODS: Strong Foundations is a once weekly (60 minutes), 12-week iterative program with 3 core components: postural alignment and control, balance and mobility, and muscular strength and power. We used a quasi-experimental design to determine changes in physical function specific to balance, postural control, and muscular strength among older adults at low or moderate risk of falling.

RESULTS: A total of 55 low-risk and 37 moderate-risk participants were recruited. Participants significantly improved on the 30-second Chair Stand (mean change of 1, SD 3.3 repetitions; P=.006) and Timed Up and Go (mean change of 0.2, SD 0.7 seconds; P=.004), with the moderate-risk group generally improving to a greater degree than the low-risk group. Additionally, Short Physical Performance Battery performance improved significantly in the moderate-risk category (P=.02). The majority of postural measures showed statistically significant improvement for both groups (P<.05). Measures of “relaxed” posture showed improvements between 6% and 27%. When an “as tall as possible” posture was adopted, improvements were ~36%.

CONCLUSIONS: In this 12-week, iterative, web-based program, we found older adults experienced improvement not only in measures used in clinical contexts, such as the 30-second Chair Stand and Timed Up and Go, but also contextualized gains by providing deeper phenotypical measurement related to posture, strength, and balance. Further, many of the physical improvements were attenuated by baseline fall risk level, with those with the highest level of risk having the greater gains, and, thus, the most benefit from such interventions.

PMID:40009847 | DOI:10.2196/68957

J Med Internet Res. 2025 Feb 26;27:e57730. doi: 10.2196/57730.

ABSTRACT

BACKGROUND: In this era of digitalization, eHealth interventions are used to engage patients in health care and help them manage their health. Previous studies showed that this can be particularly interesting for chronic disease self-management and self-care in older adults. Despite older adults becoming increasingly active on the internet, they continue to struggle in using eHealth information due to inadequate eHealth literacy. Thus, assessing and monitoring eHealth literacy is critical to support eHealth interventions.

OBJECTIVE: This study aimed to describe the translation, adaptation, and validation process of the eHealth Literacy Scale (eHEALS) in Portuguese older adults.

METHODS: The cross-cultural adaption followed the steps of forward and blinded backward translations, evaluation of the translations by a committee of judges, pilot-testing, and full psychometric testing. We tested the psychometric properties of the eHEALS by carrying out two studies: general psychometric analysis (study 1) and confirmatory factor analysis (study 2). Study 1 included 80 older adults conveniently selected from a Health Family Unit. Data were collected by in-person questionnaires between May and July 2022. Study 2 included 301 older adults randomly selected from two distinct Health Family Units. Data were collected by in-person questionnaires between May and July 2023.

RESULTS: We tested stability, reliability, construct validity (exploratory and confirmatory factor analyses and known groups), and model fit. Study 1 had 58.8% (47/80) male and 41.3% (33/80) female respondents (mean age 71.20, SD 5.26 years). Study 2 had 56.5% (170/301) male and 43.5% (131/301) female respondents (mean age 71.77, SD 5.15 years). Moderate and strong correlations were identified in the scale items (study 1: 0.42≤r≤0.91 and study 2: 0.81≤r≤0.96; P<.001). The scale showed good internal consistency for study 1 (α=.92) and study 2 (α=.98), with high correlations between items. The exploratory factor analysis yielded a single-factor structure, explaining 58.3% of the variance in study 1 and 86.4% in study 2. In the confirmatory analysis (study 2), the model fit was mixed (χ²₂₀=265, P<.001; comparative fit index=0.94; Tucker-Lewis Index=0.91; root mean square error of approximation=0.20). Thus, we compared 1-, 2-, and 3-factor structures, deciding on the unidimensional one. In study 1, the eHEALS-PT24 (Portuguese version of the eHealth Literacy Scale for older adults) mean score was 27.25 (SD 5.61), with 43.8% (35/80) and 11.3% (9/80) of participants showing low and high eHealth literacy levels, respectively. In study 2, the eHEALS-PT24 mean score was 23.31 (SD 9.53), with 38.2% (115/301) and 23.6% (71/301) of participants showing low and high eHealth literacy levels, respectively. The known-groups analysis showed statistically significant differences between eHealth literacy and demographic variables (P<.001).

CONCLUSIONS: The findings suggest that the eHEALS-PT24 is a reliable and valid tool to assess eHealth literacy in Portuguese older adults. Therefore, this instrument can be integrated to support the implementation process of eHealth interventions.

PMID:40009846 | DOI:10.2196/57730

JMIR Res Protoc. 2025 Feb 26;14:e51502. doi: 10.2196/51502.

ABSTRACT

BACKGROUND: Optimal anticoagulation using vitamin K antagonists prevents strokes associated with atrial fibrillation and heart valve replacements. Preparedness for self-monitoring and self-management could improve outcomes, but this remains a challenge.

OBJECTIVE: This study aimed to outline the methodology for developing and validating the PERSONAE scale, a self-report measure designed to assess the preparedness for self-monitoring and self-management of oral anticoagulation in adult patients.

METHODS: This study comprises 2 main phases, and it adheres to the “COnsensus-based Standards for the selection of health Measurement INstruments” (COSMIN) guidelines for instrument development. The first phase involved the conceptualization of the PERSONAE scale, where a comprehensive literature review and a consensus meeting among experts were conducted to draft the initial items. Face and content validity were then established through an expert panel review. In the second phase (ongoing), a detailed sampling methodology will be used, targeting adult Italian patients on long-term oral anticoagulation. According to a performed simulation-based power analysis, the study aims to recruit a sample size of approximately 500 participants by using a combination of convenience and snowball sampling. Data collection will be facilitated through web-based surveys distributed through social media and patient networks, ensuring a wide and representative sample. Analytical procedures will include Mokken scaling analysis for item selection and confirmatory factor analysis to validate the scale’s structure. In addition, internal consistency will be assessed using Molenaar Sijtsma statistics.

RESULTS: The scale’s content derived from phase 1 (process completed in December 2023) is grounded in a comprehensive literature review and based on the assessments of a panel of 12 health care expert professionals. The PERSONAE scale derived from phase 1 encompasses 20 items reflecting essential behaviors needed to assess the preparedness for self-monitoring and self-management of oral anticoagulation. Each item obtained a content validity ratio higher than 0.67, which is the critical content validity ratio indicating the minimum level of agreement among the experts for an item to be considered essential beyond the level of chance at a significance level of .05 for a 1-tailed test. From January 2024 to May 2024, we conducted the initial round of data collection and use Mokken scaling analysis to select items. A second round of data collection for confirmatory factor analysis was scheduled from June 2024 to September 2024, which will validate the scale’s unidimensional structure. We expect to achieve robust psychometric properties, including high internal consistency and validated constructs.

CONCLUSIONS: The PERSONAE scale will be a valuable tool to assess patients’ preparedness for self-monitoring and self-management of oral anticoagulation. The study’s insights into technology-assisted learning preferences will inform the design of future educational interventions to enhance preparedness in adult patients.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05973240; https://clinicaltrials.gov/study/NCT05973240.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/51502.

PMID:40009845 | DOI:10.2196/51502

JMIR Aging. 2025 Feb 26;8:e64148. doi: 10.2196/64148.

ABSTRACT

BACKGROUND: The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASCVD remain unclear, necessitating the development of accurate prediction models.

OBJECTIVE: The aim of the study is to develop a machine learning model for use in patients with ASCVD to predict dementia risk using available clinical and sociodemographic data.

METHODS: This prognostic study included patients with ASCVD between 2006 and 2010, with registration of follow-up data ending on April 2023 based on the UK Biobank. We implemented a data-driven strategy, identifying predictors from 316 variables and developing a machine learning model to predict the risk of incident dementia, Alzheimer disease, and vascular dementia within 5, 10, and longer-term follow-up in patients with ASCVD.

RESULTS: A total of 29,561 patients with ASCVD were included, and 1334 (4.51%) developed dementia during a median follow-up time of 10.3 (IQR 7.6-12.4) years. The best prediction model (UK Biobank ASCVD risk prediction model) was light gradient boosting machine, comprising 10 predictors including age, time to complete pairs matching tasks, mean time to correctly identify matches, mean sphered cell volume, glucose levels, forced expiratory volume in 1 second z score, C-reactive protein, forced vital capacity, time engaging in activities, and age first had sexual intercourse. This model achieved the following performance metrics for all incident dementia: area under the receiver operating characteristic curve: mean 0.866 (SD 0.027), accuracy: mean 0.883 (SD 0.010), sensitivity: mean 0.637 (SD 0.084), specificity: mean 0.914 (SD 0.012), precision: mean 0.479 (SD 0.031), and F₁-score: mean 0.546 (SD 0.043). Meanwhile, this model was well-calibrated (Kolmogorov-Smirnov test showed goodness-of-fit P value>.99) and maintained robust performance across different temporal cohorts. Besides, the model had a beneficial potential in clinical practice with a decision curve analysis.

CONCLUSIONS: The findings of this study suggest that predictive modeling could inform patients and clinicians about ASCVD at risk for dementia.

PMID:40009844 | DOI:10.2196/64148

Interact J Med Res. 2025 Feb 26;14:e66286. doi: 10.2196/66286.

ABSTRACT

BACKGROUND: Ferroptosis, as a novel modality of cell death, holds significant potential in elucidating the pathogenesis and advancing therapeutic strategies for breast cancer.

OBJECTIVE: This study aims to comprehensively analyze current ferroptosis research and future trends, guiding breast cancer research advancements and innovative treatment strategies.

METHODS: This research used the R package Bibliometrix (Department of Economic and Statistical Sciences at the University of Naples Federico II), VOSviewer (Centre for Science and Technology Studies at Leiden University), and CiteSpace (Drexel University’s College of Information Science and Technology), to conduct a bibliometric analysis of 387 papers on breast cancer and ferroptosis from the Web of Science Core Collection. The analysis covers authors, institutions, journals, countries or regions, publication volumes, citations, and keywords.

RESULTS: The number of publications related to this field has surged annually, with China and the United States collaborating closely and leading in output. Sun Yat-sen University stands out among the institutions, while the journal Frontiers in Oncology and the author Efferth T contribute significantly to the field. Highly cited papers within the domain primarily focus on the induction of ferroptosis, protein regulation, and comparisons with other modes of cell death, providing a foundation for breast cancer treatment. Keyword analysis highlights the maturity of glutathione peroxidase 4-related research, with breast cancer subtypes emerging as motor themes and the tumor microenvironment, immunotherapy, and prognostic models identified as basic themes. Furthermore, the application of nanoparticles serves as an additional complement to the basic themes.

CONCLUSIONS: The current research status in the field of ferroptosis and breast cancer primarily focuses on the exploration of relevant theoretical mechanisms, whereas future trends and mechanisms emphasize the investigation of therapeutic strategies, particularly the clinical application of immunotherapy related to the tumor microenvironment. Nanotherapy has demonstrated significant clinical potential in this domain. Future research directions should deepen the exploration in this field and accelerate the clinical translation of research findings to provide new insights and directions for the innovation and development of breast cancer treatment strategies.

PMID:40009842 | DOI:10.2196/66286

J Med Internet Res. 2025 Feb 26;27:e55492. doi: 10.2196/55492.

ABSTRACT

BACKGROUND: Sepsis is an organ dysfunction caused by a dysregulated host response to infection. Early detection is fundamental to improving the patient outcome. Laboratory medicine can play a crucial role by providing biomarkers whose alteration can be detected before the onset of clinical signs and symptoms. In particular, the relevance of monocyte distribution width (MDW) as a sepsis biomarker has emerged in the previous decade. However, despite encouraging results, MDW has poor sensitivity and positive predictive value when compared to other biomarkers.

OBJECTIVE: This study aims to investigate the use of machine learning (ML) to overcome the limitations mentioned earlier by combining different parameters and therefore improving sepsis detection. However, making ML models function in clinical practice may be problematic, as their performance may suffer when deployed in contexts other than the research environment. In fact, even widely used commercially available models have been demonstrated to generalize poorly in out-of-distribution scenarios.

METHODS: In this multicentric study, we developed ML models whose intended use is the early detection of sepsis on the basis of MDW and complete blood count parameters. In total, data from 6 patient cohorts (encompassing 5344 patients) collected at 5 different Italian hospitals were used to train and externally validate ML models. The models were trained on a patient cohort encompassing patients enrolled at the emergency department, and it was externally validated on 5 different cohorts encompassing patients enrolled at both the emergency department and the intensive care unit. The cohorts were selected to exhibit a variety of data distribution shifts compared to the training set, including label, covariate, and missing data shifts, enabling a conservative validation of the developed models. To improve generalizability and robustness to different types of distribution shifts, the developed ML models combine traditional methodologies with advanced techniques inspired by controllable artificial intelligence (AI), namely cautious classification, which gives the ML models the ability to abstain from making predictions, and explainable AI, which provides health operators with useful information about the models’ functioning.

RESULTS: The developed models achieved good performance on the internal validation (area under the receiver operating characteristic curve between 0.91 and 0.98), as well as consistent generalization performance across the external validation datasets (area under the receiver operating characteristic curve between 0.75 and 0.95), outperforming baseline biomarkers and state-of-the-art ML models for sepsis detection. Controllable AI techniques were further able to improve performance and were used to derive an interpretable set of diagnostic rules.

CONCLUSIONS: Our findings demonstrate how controllable AI approaches based on complete blood count and MDW may be used for the early detection of sepsis while also demonstrating how the proposed methodology can be used to develop ML models that are more resistant to different types of data distribution shifts.

PMID:40009841 | DOI:10.2196/55492