MetLife (United States)

To examine the prevalence of comorbidities and the use of psychiatric comedication among individuals in medical treatment for attention deficit hyperactivity disorder (ADHD), in comparison to a matched control group from the general population. This nationwide case-control study included 1,082,378 Danish individuals aged 7-64 of whom 98,398 had at least one prescription of ADHD medication during 2023. Data was collected over an observation period spanning from 2013 to 2023. Cases were matched to controls (1:10) from the general population on birth year and sex. Data were obtained and accessed through The Danish Health Data Authority. Associations were estimated using conditional logistic regression models. Somatic and particularly psychiatric comorbidities were more common among individuals in ADHD treatment across all age groups. Among those in ADHD treatment 46.7% had at least one comorbidity compared to 23.3% in the control group. The use of psychiatric comedications (besides ADHD medication) was likewise more common among individuals in ADHD treatment (32.7%) compared to the controls (7.2%). The association estimates from conditional logistic regression revealed a higher likelihood of somatic and psychiatric comorbidities among those in ADHD treatment in all age groups. Females in ADHD treatment had 4.48-4.50 times higher odds of comorbidities compared to females not in ADHD treatment (OR7 - 17 years = 4.48, 95% CI: 4.27-4.70; OR18 - 29 years = 4.50, 95% CI: 4.37-4.64). Similar patterns were observed for males but with slightly lower ORs (OR7 - 17 years = 2.35, 95% CI: 2.27-2.44; OR18 - 29 years = 3.38, 95% CI: 3.28-3.50). This study reveals that both prevalence of somatic and psychiatric comorbidities and the use of psychiatric comedication are significantly higher among individuals in ADHD treatment. The highest occurrence is seemingly among females aged 7-17 year and 18-29 years. The coexistence of ADHD with other somatic and psychiatric conditions can constitute a more complex disease burden, necessitating enhanced disease management strategies to reduce complications and enhance quality of life. Longitudinal studies are needed to confirm the temporal association of these results.

Saussurea involucrata, commonly known as snow lotus, is a rare medicinal plant that is traditionally used in several countries owing to its therapeutic properties. Snow lotus extracts have been shown to exert anti-inflammatory effects and reduce reactive oxygen species levels. Although various bioactive compounds have been identified in snow lotus, the biological activity and underlying mechanisms of DNA isolated from this plant remain unexplored. This study aimed to investigate the skin-regenerative properties of polydeoxyribonucleotide (PDRN) isolated from snow lotus. PDRN was extracted and purified from dried flowers of S. involucrata. Water-soluble tetrazolium salt 1, wound healing, and enzyme-linked immunosorbent assays were used to evaluate the effects of snow lotus PDRN on cell proliferation, cell migration, and collagen synthesis, respectively. We also measured matrix metalloproteinase 1 (MMP1) mRNA expression after snow lotus PDRN treatment. Snow lotus-derived PDRN was non-cytotoxic to human skin cells and significantly promoted cell proliferation and migration. Additionally, it enhanced collagen synthesis by suppressing the expression of MMP1. These findings demonstrate that snow lotus PDRN may be a promising anti-aging agent and may serve as a valuable ingredient in cosmeceutical formulations.

The rapid urbanization has enhanced the need to have an accurate, scalable, and automated analysis of high-resolution geospatial data. This paper outlines a combined method of semantic segmentation and three dimensional reconstruction of the urban structure based on LiDAR point clouds in the cloud computing platform. The key point of the strategy is the creation of ELFA-RandLA-Net, a more advanced deep learning architecture that utilizes local geometrical and semantic features with the help of a new Enhanced Local Feature Aggregation (ELFA) module, which is much more successful at classifying data than the current models. The system initially uses this network to precisely distinguish urban classes of buildings, vegetation and ground into large scale data throughout Dublin, Ireland. After segmentation, clustering algorithms segregate the occurrence of instances of buildings, which undergo a sequence of geometric and topological reconstruction phases. These are planar segmentation, roof surface recognition through cloth simulation filtering, vertical plane inference through geometric adjacency and adjacency graph construction to guarantee topologically consistent roof and wall models. The reconstructed 3D building models are further refined with the help of boundary extraction and corner point correction. The pipeline can be deployed on cloud processors and analytics service providers such as Google Earth Engine, making the entire pipeline a fit to provide urban analytics on a scale close to near-real-time. Both semantic classification (maximum overall accuracy and mean of 54.49% IoU) and 3D-reconstruction errors less than 0.3-0.4 meters are shown and assessed experimentally, proving the strength and utility of the system. The framework tackles some of the most troublesome issues faced when applying remote sensing to urban areas such as incompleteness of data and complicated roof forms, making it a useful instrument in urban planning, infrastructure surveying and evaluating the environment. The future directions will focus on improving the robustness of lower-density data, add semantic attributes to the reconstruction phase, and expand the functions of multi-temporal urban analysis.

The insurance industry, particularly in life and annuities sectors, processes millions of image -based documents annually. Traditionally, these processes have relied heavily on manual indexing and proprietary vendor tools, leading to operational inefficiencies, high costs, data security concerns, and limited agility. This paper presents the development of a homegrown AI -powered image processing framework built using open -source tools. By leveraging technologies such as Tesseract OCR, OpenNLP, and OpenCV, insurance providers can fully automate document classification, text extraction, and metadata tagging without relying on external vendors. The solution reduces manual effort, improves compliance, accelerates throughput, and lowers operational overhead, setting a precedent for scalable and secure document processing in the insurance industry.

Abstract BackgroundLong COVID is a major public health burden causing a diverse array of debilitating symptoms in tens of millions of patients globally. In spite of this overwhelming disease prevalence and staggering cost, its severe impact on patients’ lives and intense global research efforts, study of the disease has proved challenging due to its complexity. Genome-wide association studies (GWAS) have identified only four loci potentially associated with the disease, although these results did not statistically replicate between studies. A previous combinatorial analysis study identified a total of 73 genes that were highly associated with two long COVID cohorts in the predominantly (>91%) white European ancestry Sano GOLD population, and we sought to reproduce these findings in the independent and ancestrally more diverse All of Us (AoU) population.MethodsWe assessed the reproducibility of the 5,343 long COVID disease signatures from the original study in the AoU population. Because the very small population sizes provide very limited power to replicate findings, we initially tested whether we observed a statistically significant enrichment of the Sano GOLD disease signatures that are also positively correlated with long COVID in the AoU cohort after controlling for population substructure.ResultsFor the Sano GOLD disease signatures that have a case frequency greater than 5% in AoU, we consistently observed a significant enrichment (77% - 83%,p< 0.01) of signatures that are also positively associated with long COVID in the AoU cohort. These encompassed 92% of the genes identified in the original study. At least five of the disease signatures found in Sano GOLD were also shown to be individually significantly associated with increased long COVID prevalence in the AoU population. Rates of signature reproducibility are strongest among self-identified white patients, but we also observe significant enrichment of reproducing disease associations in self-identified black/African-American and Hispanic/Latino cohorts. Signatures associated with 11 out of the 13 drug repurposing candidates identified in the original Sano GOLD study were reproduced in this study.ConclusionThese results demonstrate the reproducibility of long COVID disease signal found by combinatorial analysis, broadly validating the results of the original analysis. They provide compelling evidence for a much broader array of genetic associations with long COVID than previously identified through traditional GWAS studies. This strongly supports the hypothesis that genetic factors play a critical role in determining an individual’s susceptibility to long COVID following recovery from acute SARS-CoV-2 infection. It also lends weight to the drug repurposing candidates identified in the original analysis. Together these results may help to stimulate much needed new precision medicine approaches to more effectively diagnose and treat the disease.This is also the first reproduction of long COVID genetic associations across multiple populations with substantially different ancestry distributions. Given the high reproducibility rate across diverse populations, these findings may have broader clinical application and promote better health equity. We hope that this will provide confidence to explore some of these mechanisms and drug targets and help advance research into novel ways to diagnose the disease and accelerate the discovery and selection of better therapeutic options, both in the form of newly discovered drugs and/or the immediate prioritization of coordinated investigations into the efficacy of repurposed drug candidates.

As HF progresses into advanced HF, patients experience a poor quality of life, distressing symptoms, intensive care use, social distress, and eventual hospital death. We aimed to investigate the relationship between morality and potential prognostic factors among in-patient and emergency patients with HF. A case series study: Data are collected from in-hospital and emergency care patients from 2014 to 2021, including their international classification of disease at admission, and laboratory data such as blood count, liver and renal functions, lipid profile, and other biochemistry from the hospital's electrical medical records. After a series of data pre-processing in the electronic medical record system, several machine learning models were used to evaluate predictions of HF mortality. The outcomes of those potential risk factors were visualized by different statistical analyses. In total, 3871hF patients were enrolled. Logistic regression showed that intensive care unit (ICU) history within 1week (OR: 9.765, 95% CI: 6.65, 14.34; p-value < 0.001) and prothrombin time (OR: 1.193, 95% CI: 1.098, 1.296; <0.001) were associated with mortality. Similar results were obtained when we analyzed the data using Cox regression instead of logistic regression. Random forest, support vector machine (SVM), Adaboost, and logistic regression had better overall performances with areas under the receiver operating characteristic curve (AUROCs) of >0.87. Naïve Bayes was the best in terms of both specificity and precision. With ensemble learning, age, ICU history within 1week, and respiratory rate (BF) were the top three compelling risk factors affecting mortality due to HF. To improve the explainability of the AI models, Shapley Additive Explanations methods were also conducted. Exploring HF mortality and its patterns related to clinical risk factors by machine learning models can help physicians make appropriate decisions when monitoring HF patients' health quality in the hospital.

Abstract Background In the breast cancer treatment, there may be a gap between patients’ information needs and physicians’ perceptions. To address this issue, we conducted a comprehensive questionnaire survey aimed to assess the specific information needs of patients regarding the adverse events (AEs) associated with treatment. Methods A web-based questionnaire survey (UMIN000049280: Registered on October 31, 2022) was conducted in patients with a history of breast cancer treatment. Responses were obtained regarding AEs experienced, AEs for which remedies were identified, AEs patients sought to prevent, and pre-treatment information on AEs patients desired to have. Results Data from 435 breast cancer patients were analyzed. The most common AEs reported included hair loss (93.3%), malaise/fatigue (89.4%), nail changes (83.2%), dysgeusia (69.0%), leukopenia/white blood cell decreased (65.1%), neuropathy (62.3%), and nausea/vomiting (61.4%). Financial anxiety was reported in 35.2% of the participants. AEs for which a minority of patients found effective solutions included neuropathy (20.3%), financial anxiety (21.6%), edema (24.3%), joint pain (26.0%), and malaise/fatigue (26.7%). Patients expressed the greatest desire to avoid hair loss (34.7%), followed by nausea/vomiting (23.7%), interstitial lung disease/pneumonitis (5.5%), malaise/fatigue (5.1%), and dysgeusia (5.1%). The most commonly requested pre-treatment information regarding AEs was their duration, followed by prevention methods, management strategies, time to onset, and the impact on daily life. Conclusions This survey highlights the existence of significant unmet medical needs among breast cancer patients, due to the inadequate solutions available for managing AEs associated with various therapeutic agents. In addition, the survey revealed that patients have different information needs regarding different types of AEs.

PurposeNonfamily farms are responsible for a disproportionate amount of US agriculture production. The importance of these operations to the volume of agriculture production in the United States has led researchers and policymakers to understand nonfamily farms as large commercial operations. This paper examines whether the distinction between family and nonfamily helps explain the financial outcomes of farm operations and households.Design/methodology/approachWe test for differences in financial outcomes of the household and operations of family and nonfamily farms using an Oaxaca-Blinder decomposition. We compare these results to a decomposition of other possible typologies.FindingsWe present evidence that nonfamily farms are a heterogeneous group with a majority of small operations that are dominated by a small number of large operations. We discover that differences associated with the family-nonfamily distinction are largely explained by observable farm and operator characteristics that arise mechanically from the definition. However, we find suggestive evidence that family-nonfamily classification captures differences in economic behavior that lead to higher profitability measures to nonfamily farms. We find little evidence of any inherent structural differences between family and nonfamily farms that helps explain financial outcomes related to leverage or household finances.Practical implicationsWe conclude that including nonfamily farms in official statistics of farm households may provide a more comprehensive overview of the farm sector, as our results suggest that family and nonfamily farms do not have innate differences that help explain many of their financial outcomes.Originality/valueWe incorporate previously unused data on nonfamily farm households and test the difference in mean financial outcomes between family and nonfamily farms.