BMC Research Notes最新文献

Introduction: The attachment a mother feels for her fetus intensifies her duty to care for it, leading to a heightened desire to engage in behaviors that promote health. This research explored the association between maternal-fetal attachment (MFA) and adherence to health-related behaviors among pregnant women.

Methods: This cross-sectional study focused on 220 pregnant women in Jahrom City, and was conducted using a multi-stage random sampling strategy. The data were collected using the Maternal-Fetal Attachment Scale paired with a questionnaire that addressed health behaviors relevant to pregnancy. The data were analyzed using SPSS18 software, employing linear regression and the Pearson correlation test. A p-value of less than 0.05 was deemed significant.

Results: The mean age of participants was 28.06 ± 5.12 years. The adherence to health behaviors in pregnant women yielded a mean score of 174.51 ± 20.20. Pearson's correlation test revealed a significant statistical association between MFA and adherence to health behaviors (r = 0.54, p < 0.001). The linear regression analysis showed that the dimensions of interaction with the fetus (β = 0.19) and the act of surrendering to the fetus (β = 0.27) could explain 35% of the variance in adherence to health behaviors (F = 14.12, R2 = 0.35, p < 0.001).

Conclusion: This study highlights a significant association between MFA and adherence to health behaviors throughout pregnancy. Supportive measures may strengthen MFA, promoting self-care practices and behaviors, ultimately resulting in improved health for both the mother and her fetus.

Research progress and innovation are hindered by barriers, inequalities, and exclusions within academia. Embracing equality, diversity, and inclusion (EDI) is not only an ethical imperative but also essential for advancing knowledge and addressing global challenges. EDI principles ensure that researchers from all backgrounds have equitable opportunities to contribute to and benefit from research. Despite recent efforts to improve inclusivity, systemic barriers such as bias in funding, publication, and representation still persist. Strategies to address these include diverse recruitment, mentorship programmes, training to mitigate unconscious bias, and promoting data transparency. Institutional leadership plays a pivotal role in fostering an inclusive culture by setting clear goals and ensuring accountability. Promoting EDI in research enhances scientific excellence, aligns with human rights principles, and ensures equitable benefits for global populations, reflecting the richness of diverse perspectives in academic pursuits.

Background: Postharvest lemons are affected by several fungal infections, and as alternatives to chemical fungicides for combating these infections, different microbial biocontrol agents have been studied, with the Clavispora lusitaniae 146 strain standing out. Although strain 146 has proven to be an effective agent, the influence of a microbial biological control agent on the postharvest lemon microbiome has not been studied until now. Thus, this study aimed to evaluate how the epiphytic microbiome of postharvest lemons is affected by the application of the biocontrol yeast C. lusitaniae 146.

Results: In terms of bacterial composition, the most abundant genera were Sphingomonas, Pelomonas, and Bacillus and no significant differences in the composition were detected between the treated and control samples. Among fungi, Clavispora was predominant not only in the treated samples but also in the control, and statistics indicated differences, suggesting its significant role in modulating the epiphytic community composition of lemon. Understanding fruit microbiomes is vital for effective disease control, and this study provides insights into the microbial composition of the surface of lemon and the role of C. lusitaniae 146.

Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient. In addition, it is difficult for researchers to know which algorithm is appropriate for prediction as various regression and feature selection algorithms exist.

Methods: We compared and evaluated the performance of 13 representative regression algorithms using Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Three analyses was conducted to show the effect of feature selection methods, multiomics information, and drug categories on drug response prediction.

Results: In the experiments, Support Vector Regression algorithm and gene features selected with LINC L1000 dataset showed the best performance in terms of accuracy and execution time. However, integration of mutation and copy number variation information did not contribute to the prediction. Among the drug groups, responses of drugs related with hormone-related pathway were predicted with relatively high accuracy.

Conclusion: This study can help bioinformatics researchers design data processing steps and select algorithms for drug response prediction, and develop a new drug response prediction model based on the GDSC or other high-throughput sequencing datasets.

Background: The recent global pandemic posed extraordinary challenges for healthcare systems. Frontline healthcare workers required focused, immediate, practical, evidence-based instruction on optimal patient care modalities as knowledge evolved around disease management.

Objective: This course was designed to provide knowledge to protect healthcare workers; combat disease spread; and improve patient outcomes.

Methods: A team of global healthcare workers responded by rapidly creating a competency-based online course. To promote transcultural applicability, the course was developed by an international team of more than 45 educators from over 20 countries. Course delivery included a built-in language translation tool, routine updates, and several innovative course design elements. User feedback was collected to determine efficacy of course content, structure, unique delivery elements, and delivery options.

Results: An initial population of online learners (n = 147) living in 23 different countries and representing 22 languages completed the course and participated in post-course surveys. An additional population of learners (n = 505) attended an in-person offering of course materials. Course participants gave positive feedback and several requested additional courses in similar formats.

Conclusion: Global open access education courses may provide needed resources to empower healthcare professionals during health crises. Responsive course design can accommodate diverse learner resources and transcultural applicability.

Objectives: This data note presents a comprehensive geodatabase of cardiovascular disease (CVD) hospitalizations in Mashhad, Iran, alongside key environmental factors such as air pollutants, built environment indicators, green spaces, and urban density. Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions.

Data description: This dataset includes detailed epidemiologic and geospatial information on CVD hospitalizations in Mashhad, Iran, from January 1, 2016, to December 31, 2020. It contains 52,176 confirmed CVD cases and includes demographic information such as age, gender, admission date, ICD-10 codes, occurrence of death, and length of hospital stay. The median age of patients was 64 years, with 54.44% male. A notable 9.41% of patients died during hospitalization. In addition to the CVD hospitalization case file and its shape file created by joining with 1301 census tracts, this dataset includes environmental factors such as air quality indicators (SO₂, PM₂.₅, CO, etc.). It also incorporates socio-economic variables (population density, illiteracy, and unemployment rates), public infrastructure, and built environment data, providing a comprehensive view of cardiovascular health in Mashhad.

Background: Depressive symptom is the most common type of psychiatric co-morbidity among persons with epilepsy. Epilepsy patients are identified as at higher risk of suffering depressive symptom explicitly in low- and middle-income countries due to poor mental health care systems and financial burdens. The co-occurrence of depressive symptom among epilepsy patients deteriorates the prognosis of the disease and diminishes the quality of life of both the patients and their families. However, there is limited evidence on the prevalence of depressive symptom and associated factors in Ethiopia. Therefore, this study is intended to assess the prevalence of depressive symptom and associated factors among epilepsy patients attending in Amhara region, Ethiopia.

Method: A multi-center institution-based cross-sectional study was done among epilepsy patients attending at Amhara region, Ethiopia. The Hospital Anxiety Depression tool was used to assess depressive symptom. To determine the factors associated with depressive symptom, a binary logistic regression model was used. Adjusted Odds Ratio (AOR) with the 95% Confidence Interval (CI) was reported in the multivariable binary logistic regression analysis.

Results: About 406 participants were registered in the study with a response rate of 97.6%. The prevalence of depressive symptom among epilepsy patients was 53.9% [95%CI: 49.1%, 58.8%]. In the multivariable binary logistic regression analysis, taking polytherapy treatment [AOR = 1.87, 95% CI: 1.04, 3.36], perceived stigma [AOR = 5.73, 95%CI: 3.11, 10.55], poor antiepileptic medication adherence [AOR = 3.33, 95%CI: 1.30, 8.54] and having poor [AOR = 5.83, 95%CI: 2.44, 13.90] and moderate social support [AOR = 3.08, 95%CI: 1.34, 7.09] were significantly associated with depressive symptom.

Conclusions: This study revealed that the magnitude of depressive symptom among epilepsy patients in Ethiopia was relatively high and multiple factors determined the likelihood of depressive symptom. Thus, healthcare providers and concerned stakeholders should strengthen comprehensive health education to reduce the magnitude and consequences of depressive symptom among this segment of the population. Moreover, strong social support with special attention should be given to epilepsy patients.

Objective: Postoperative tracheostomy is a significant complication following medulloblastoma (MB) resection. This study aimed to develop a predictive model for postoperative tracheostomy requirement in children undergoing MB surgical resection. This model was derived as a side product of a larger research project analyzing surgical outcomes in pediatric MB patients.

Results: Forty-five patients (26%) required tracheostomy postoperatively. Using multivariable logistic regression, five models were developed, and the final model was selected based on performance and simplicity. The simplified version included two predictors: preoperative brainstem invasion and postoperative brainstem contusion, each contributing equally to the score. The model demonstrated an AUC of 0.845. Predicted risks of requiring a tracheostomy were 5.8%, 57.7%, and 75% for scores of 0, 1, and 2, respectively. This tool provides clinicians with a quantitative approach to assess tracheostomy risk, improving decision-making and patient management.

Objective: The World Health Organization (WHO) has declared antimicrobial resistance (AMR) as one of the top threats to global public health. While AMR surveillance of human clinical isolates is well-established in many countries, the increasing threat of AMR has intensified efforts to detect antibiotic resistance genes (ARGs) accurately and sensitively in environmental samples, wastewater, animals, and food. Using five ARGs and the 16S rRNA gene, we compared quantitative PCR (qPCR) and metagenomic sequencing (MGS), two commonly used methods to uncover the wastewater resistome. We compared both methods by evaluating ARG detection through a municipal wastewater treatment chain.

Results: Our results demonstrate that qPCR was more sensitive than MGS, particularly in diluted samples with low ARG concentrations such as oxidation pond water. However, MGS was potentially more specific and has less risk of off-target binding in concentrated samples such as raw sewage. MGS analysis revealed multiple subtypes of each gene which could not be distinguished by qPCR; these subtypes varied across different sample types. Our findings affect the conclusions that can be drawn when comparing different sample types, particularly in terms of inferring removal rates or origins of genes. We conclude that both methods appear suitable to profile the resistome of wastewater and other environmental samples, depending on the research question and type of sample.

Background: Immune reconstitution following the initiation of combination antiretroviral therapy (cART) significantly impacts the prognosis of individuals infected with human immunodeficiency virus (HIV). Our previous studies have indicated that the baseline CD4⁺ T cells count and percentage before cART initiation are predictors of immune recovery in TB-negative children infected with HIV, with TB co-infection potentially causing a delay in immune recovery. However, it remains unclear whether these predictors consistently impact immune reconstitution during long-term intensive cART treatment in TB-negative/positive children infected with HIV.

Results: We confirmed that the baseline CD4⁺ T cell count is a significant predictor of immune recovery following long-term intensive cART treatment among children aged 0 to 13 years. Children with lower CD4⁺ T cell count prior cART initiation did not show substantial immunological recovery during the follow-up period. Interestingly, children who were co-infected with TB and had higher baseline CD4⁺ T cell count eventually achieved good immunological recovery comparable to the TB-negative HIV-infected children. Hence, the baseline CD4⁺ T cell count at the onset of treatment serves as a reliable predictor of immunological reconstitution in HIV-infected children with or without TB co-infection. Taken together, this follow-up study validates our previous findings and further establishes that initiating cART early alongside early HIV testing can help prevent the diminished CD4⁺ T cell count associated with inadequate immunological reconstitution.