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Comparative performance of disability measures
Researchers and federal agencies are currently discussing the best way to measure disability in US federal surveys. One idea being discussed is expanding/supplementing the question sets commonly used to capture disability status in order to better capture a broader segment of the disabled population. We used data from the 2010–2018 National Health Interview Survey to examine the performance of questions commonly used to measure disability in the US–the ACS-6 and Washington Group Short Set questions–in capturing intellectual and developmental disabilities, mental health disabilities, and physical health disabilities. We found that neither set of disability questions was sufficient to fully capture these disability statuses. We contend that current disability questions used in US population surveys must be expanded/supplemented in order to increase the inclusiveness of disability measurement, and thereby, improve efforts to reduce disparities experienced by the disabled population.
Identification of an additional periplanone receptor family gene preferentially expressed in the male antennae of the American cockroach
Early detection of anxiety symptoms in Autism Spectrum Disorder: An exploratory study in a Spanish sample of 3–6 year old children
Current research often overlooks anxiety symptoms in preschoolers with Autism Spectrum Disorder (ASD), focusing on older children. This study examines anxiety symptomatology primarily in young children with ASD by analyzing data from 82 Spanish children aged 3- to 6 years. Parents completed the Anxiety Scale for Children with ASD and the Social Communication Questionnaire. Results indicate that 30.48% of the children exhibit significant anxiety symptoms, “with anxious uncertainty” being the most prevalent. Factor analysis reveals three principal components of the Anxiety Scale, supporting its reliability. Regression analysis highlights stereotypical behaviors as significant predictors of anxiety levels. These findings underscore the importance of early assessment and adaptation of assessment tools for preschoolers with ASD. Stereotypical behaviors are identified as key predictors of anxiety in this population.
Exploring the potential impacts of anthropogenic heating on urban climate during heatwaves
Impact of digital breast tomosynthesis on screening performance and interval cancer rates compared to digital mammography: A meta-analysis
Background The performance of digital breast tomosynthesis (DBT) alone, digital mammography (DM) plus DBT, and synthesized mammography (SM) plus DBT, in comparison to DM in breast cancer screening, remains a topic of ongoing debate. The effectiveness of these modalities in reducing interval cancer rates (ICR) is particularly contentious. Materials and methods A database of data was searched for articles published until July 2024. Initially, the pooled sensitivity and specificity of DBT (DBT alone, DM/DBT, and SM/DBT) and DM were estimated. Additionally, the sensitivity of breast cancer screening and ICR for DBT alone, DM/DBT, and SM/DBT compared to DM. The characteristics of interval breast cancer were compared with those screening BC, alongside differences across various screening methods. Results Eleven studies comparing DBT and DM were included. The sensitivity of DBT was higher than that of DM, with rates of 86% (95%CI: 81, 90) and 80% (95%CI: 76, 84), respectively. The specificities of both modalities were similar, recorded at 96% (95%CI: 95, 98) and 96% (95%CI: 95, 97), respectively. In comparison to DM, the screening sensitivities of DBT, DM/DBT, and SM/DBT were increased by 4.33% (95% CI: 1.52, 7.13), 6.29% (95% CI: 2.55, 10.03), and 5.22% (95% CI: 1.35, 9.10), respectively; however, the difference in the ICR was not statistically significant. Conclusion DBT offers advantages in enhancing the sensitivity of breast cancer screening; however, its impact on ICR remains uncertain. Consequently, further research is necessary to comprehensively evaluate both the effectiveness of screening and the potential risks associated with DBT.
Revealing the critical state and identifying individualized dynamic network biomarker for type 2 diabetes through advanced analysis methods on individual basis
Automated recognition and segmentation of lung cancer cytological images based on deep learning
Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. However, traditional cytological diagnosis requires an experienced pathologist to evaluate all sections individually under a microscope, which is a time-consuming process with low interobserver consistency. With the development of deep neural networks, the You Only Look Once (YOLO) object-detection model has been recognized for its impressive speed and accuracy. Thus, in this study, we developed a model for intraoperative cytological segmentation of pulmonary lesions based on the YOLOv8 algorithm, which labels each instance by segmenting the image at the pixel level. The model achieved a mean pixel accuracy and mean intersection over union of 0.80 and 0.70, respectively, on the test set. At the image level, the accuracy and area under the receiver operating characteristic curve values for malignant and benign (or normal) lesions were 91.0% and 0.90, respectively. In addition, the model was deemed suitable for diagnosing pleural fluid cytology and bronchoalveolar lavage fluid cytology images. The model predictions were strongly correlated with pathologist diagnoses and the gold standard, indicating the model’s ability to make clinical-level decisions during initial diagnosis. Thus, the proposed method is useful for rapidly localizing lung cancer cells based on microscopic images and outputting image interpretation results.
Best practices for cryopreserving sperm in Nonhuman Primates: a systematic review and meta-analysis
The prevalence of chronic kidney disease in people with severe mental illness: A systematic review protocol
Background People with severe mental illness (SMI) are more likely to develop long-term physical health conditions, including type 2 diabetes and cardiovascular disease, compared to people without SMI. This contributes to an inequality in life expectancy known as the ‘mortality gap’. Chronic kidney disease (CKD) is a growing global health concern set to be the 5th leading cause of life-years lost by 2040. However, there is limited research exploring the relationship between CKD and SMI. This systematic review will aim to examine the prevalence and incidence of CKD among people with SMI. Methods We will search Medline, Embase, PsycINFO, CINAHL, Scopus and Web of Science for primary epidemiological research reporting the prevalence or incidence of CKD among people with SMI in any setting. Retrieved records will be managed in Covidence and screened by two independent reviewers. Data will be extracted from included studies using a piloted data extraction form, and the quality of studies will be evaluated using the appropriate JBI Critical Appraisal Checklist. The certainty of evidence will be assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Data from the included studies will be narratively synthesised. Meta-analyses will be conducted using random effects models to examine the prevalence and incidence of CKD among people with SMI. Discussion There is limited research exploring the relationship between CKD and SMI, and this proposed systematic review will be the first to examine the prevalence of CKD among people with SMI. This review will highlight the extent of the problem and provide a foundation for future research to improve health outcomes for people with SMI.
Temperature and repeated catecholamine surges modulate regional wall motion abnormalities in a rodent takotsubo syndrome model
Abstract Transient regional wall motion abnormalities (RWMA) after stress are a hallmark of experimental and clinical Takotsubo Syndrome (TS). The mechanisms driving RWMA remain incompletely understood. This study investigates these mechanisms in a small-animal model of TS, validating the model’s complication profile, recovery dynamics, and recurrence, while assessing the predictive value of echocardiography. Male, 7-9-week-old rats underwent TS induction via isoprenaline infusion. The effects of body temperature and repeated catecholaminergic surges on RWMA were assessed using high-resolution speckle-tracking echocardiography. Complications were evaluated through ECG, autopsy, and blood gas analysis. The TS phenotype showed reduced longitudinal strain with RWMA characterized by apical end-systolic akinesia/dyskinesia, a contracting base, and a narrow transition zone, resulting in apical ballooning. RWMA originated from the apex and recovered in the opposite direction. Hyperthermia increased RWMA incidence, while hypothermia attenuated RWMA (p = 0.008). A repeated catecholamine surge exacerbated RWMA in the acute phase (p = 0.042) but not during recovery (p = 0.308), with reduced RWMA susceptibility after recovery (RR 0.45, 95%CI; 0.27–0.76). Systolic function at initial echocardiography predicted worse outcomes (odds ratio 0.73; 95%CI 0.57–0.85), and RWMA extent correlated with recovery time (Rho = 0.772). TS rats developed complications similar to patients, including heart failure, arrhythmias, and thrombus formation. This study validates the small-animal TS model by replicating patient findings, and emphasizing the importance of controlling body temperature and limiting adrenergic drug use in managing TS. RWMA analysis offers promise as a diagnostic and prognostic tool in TS, and the delayed adaptive response presents a potential pathway for therapeutic intervention.
Late initiation of antenatal care visit amid implementation of new antenatal care model in Sub-Saharan African countries: A multilevel analysis of multination population survey data
Introduction Although late initiation of antenatal care has been linked with adverse pregnancy outcomes, a significant number of pregnant women in resource-limited regions are seeking this care late. There has been a lack of information on the extent and factors influencing late initiation of antenatal care in the African context following the implementation of the new antenatal care model in 2016. Thus, we aimed to determine the pooled prevalence of late antenatal care visit and its determinants among women in Sub-Saharan Africa (SSA) using national surveys conducted after the implementation of new guidelines. Methods We analyzed data from the recent demographic and health survey (DHS) conducted in 16 SSA between 2018 and 2022 using a weighted sample of 101,983 women who had antenatal care follow-up during their index pregnancy. A multilevel logistic regression models were fitted and likelihood and deviance values were used for model selection. In the regression model, we used adjusted odds ratios along with their corresponding 95% confidence intervals to determine the factors associated with late antenatal care visit. Results The pooled prevalence of late antenatal care visit among pregnant women in SSA was 53.8% [95% CI: 46.2% - 61.3%], ranging from 27.8% in Liberia to 75.8% in Nigeria. Women’s age and education, working status, partner’s education, health insurance coverage, birth order, household wealth, age at marriage, decision on health care, residence, and community-level women’s illiteracy were the factors associated with late antenatal care visit. Conclusion More than half of pregnant women in SSA initiated attending antenatal care during late pregnancy, with significant differences seen among countries. These results underscore the necessity for focused interventions to tackle these issues and enhance prompt access to antenatal services for better maternal and child health outcomes in the area. Moreover, creating tailored interventions for younger women, those with multiple previous births, employed women, and those who experienced early marriage to address their specific challenges and obstacles in seeking care is crucial.
Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture
A new electoral bottom-up model of institutional governance
Abstract The sustainable governance of Global Risky Commons (GRC)—global commons in the presence of a sizable risk of overall failure—is ubiquitous and requires a global solution. A prominent example is the mitigation of the adverse effects of global warming. In this context, the Collective Risk Dilemma (CRD) provides a convenient baseline model which captures many important features associated with GRC type problems by formulating them as problems of cooperation. Here we make use of the CRD to develop, for the first time, a bottom-up institutional governance framework of GRC. We find that the endogenous creation of local institutions that require a minimum consensus amongst group members—who, in turn, decide the nature of the institution (reward/punishment) via an electoral process—leads to higher overall cooperation than previously proposed designs, especially at low risk, proving that carrots and sticks implemented through local voting processes are more powerful than other designs. The stochastic evolutionary game theoretical model framework developed here further allows us to directly compare our results with those stemming from previous models of institutional governance. The model and the methods employed here are relevant and general enough to be applied to a variety of contemporary interdisciplinary problems.
Application of Hansen solubility parameters in the eutectic mixtures: difference between empirical and semi-empirical models
Synthesis of chitosan/PVA/copper oxide nanocomposite using Anacardium occidentale extract and evaluating its antioxidant, antibacterial, anti-inflammatory and cytotoxic activities
Author Correction: Magnetic and pH sensitive nanocomposite microspheres for controlled temozolomide delivery in glioblastoma cells
A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection
Detector development for spin-echo SANS techniques using ZnS:Ag/6LiF and 6Li glass scintillators
Abstract Neutron spin-echo techniques exploit Larmor precession of the neutron spin to encode either the scattering angle or energy. These are powerful means to extend the measurable momentum transfer (Q) and energy (E) range in neutron scattering measurements. Standard small-angle neutron scattering (SANS) instruments are sensitive in a range of $$\approx$$ ≈ 10–200 nm, whereas these techniques allow the study of structures in materials on length scales of tens of nm up to tens of $$\upmu$$ μ m. The Larmor instrument at ISIS is equipped to operate in spin-echo modulated SANS (SEMSANS) and spin-echo SANS (SESANS) mode. Two separate detectors were developed to cope with the performance demands set by these techniques. The first is a position sensitive ZnS:Ag/6LiF scintillator-based detector coupled with wavelength shifting fibres that can be used for both SEMSANS and SESANS. A detector prototype using GS20 glass scintillator directly coupled to a multi-anode photomultiplier was developed as an alternative for SESANS measurements at higher incident neutron fluxes. The designs and results obtained with the two detectors are presented together with future improvements to both technologies. These, in addition to promising development routes, demonstrate the potential for utilising a WLSF ZnS:Ag/6LiF scintillator detector and a pixelated GS20 detector for SEMSANS and SESANS applications.