Multiple Risk Factors Research Articles

Pharmaco-Economic Assessment of Screening Strategies for High-Risk MASLD in Primary Care.

Several scientific associations recommend a sequential combination of non-invasive tests (NITs) to identify high-risk MASLD patients but their cost-effectiveness is unknown. A cost-utility model was developed to assess the incremental cost-effectiveness ratio (ICER) of recommended screening strategies for patients with clinically suspected MASLD, specifically those with type 2 diabetes (T2D) and obesity with multiple cardiometabolic risk factors which will be initiated in primary care. Six screening strategies were assessed, using either vibration-controlled transient elastography (VCTE) or the enhanced liver fibrosis (ELF) test as a second-line test following an initial Fibrosis-4 (FIB-4) assessment as the first line NIT. The model included treatment effects of resmetirom for metabolic dysfunction-associated steatohepatitis (MASH) patients with F2 or F3 fibrosis. All screening strategies for high-risk MASLD in US incurred additional costs compared to no screening, ranging from $13 587 to $14 730 per patient with T2D and $14 274 to $15 661 per patient with obesity. However, screening reduced long-term costs, ranging from $22 150 to $22 279 per patient with T2D and $13 704 to $13 705 per patient with obesity, compared to $24 221 and $14 956 for no screening, respectively. ICERs ranged from $26 913 to $27 884 per QALY for T2D patients and $23 265 to $24 992 per QALY for patients with obesity. While ICERs were influenced by VCTE availability, they remained cost-effective when using ELF as the second-line test. Our findings remain robust across a range of key parameters. Screening for high-risk MASLD is cost-effective according to recent guidelines. Implementing these screening strategies in primary care should be considered.

Clinical, Diagnostic, and Metabolic Characteristics Associated with Nephrolithiasis in the Black Women's Health Study.

Background/Objectives: Nephrolithiasis (kidney stones) is a frequently occurring urologic condition resulting in significant patient morbidity and healthcare costs. Despite the higher prevalence of metabolic risk factors for nephrolithiasis among Black women, there have been few epidemiologic studies of kidney stones focusing on this group. Methods: We describe demographic and health characteristics, diagnostics, and metabolic profiles of US Black women with self-reported kidney stones. The women were participants in the Black Women's Health Study (BWHS), a large prospective cohort of US Black women (median age 38 years) begun in 1995. Results: Among the 2750 BWHS participants who completed an online supplemental questionnaire assessing urologic health, 201 women reported nephrolithiasis. Of this number, 62% had completed ≥ 16 years of education, and 82% reported access to health care. Overall, 39% reported experiencing ≥ 2 stones in their lifetime, and 29% required surgery to treat the condition. Thirty-two percent reported having completed a metabolic evaluation, while 70% had undergone a CT scan to diagnose nephrolithiasis. The frequency of metabolic evaluation increased with the number of metabolic components reported: 3% (0 components) to 43% (3-4 components). Conclusions: Our findings are consistent with reports of lower rates of metabolic evaluation among Black patients despite their having multiple risk factors for nephrolithiasis. Further study is needed to identify the barriers and facilitators of metabolic and diagnostic workup of nephrolithiasis in Black women.

Risk of recurrence after a first unprovoked seizure with different risk factors: A 10-year prospective cohort study

ObjectiveTo evaluate the recurrence risk following a first unprovoked seizure using both single-factor and multiple-factor approaches, as well as to further analyze the potential risk factors associated with recurrence. MethodsIn a prospective cohort study, a total of 201 individuals who experienced their initial unprovoked seizure were recruited from January 2010 to December 2019. The cumulative recurrence rates were calculated by Kaplan–Meier survival curves. Multivariate analyses for recurrence risk were conducted utilizing the Cox regression model. Additionally, interaction effects were evaluated by quantifying the attributable proportion due to interaction (AP). ResultsThe cumulative recurrence rates were as follows: 29.4 % at 6 months, 35.8 % at 1 year, 41.1 % at 2 years, 47.9 % at 5 years, and 57.5 % at 10 years. Notably, the majority of recurrences, specifically 61.2 %, manifested within the initial 6 months following the onset, with 74.4 % occurring within the first year, and 82.6 % within the initial 2 years. The recurrence risk of patients with epileptic abnormal discharges on VEEG, nocturnal seizure, abnormal MRI, prior brain insult and focal seizure was 71.9 %, 61.4 %, 61.5 %, 75.0 %, and 69.7 %, respectively. Epileptiform discharges (RR 2.5, 95 % CI 1.4–4.3, P=0.001) and prior brain insult (RR 2.1, 95 % CI 1.2–3.7, P=0.007) were predictors of recurrence. Interaction analysis showed the combination of epileptiform discharges and prior brain insult was associated with a 7-fold increased risk of recurrence (RR 7.0, 95 %CI 3.5–14.2)，with AP estimated at 0.34, the combination of epileptiform discharges and nocturnal seizure was associated with a 4-fold increased risk of recurrence(RR 4.3, 95 %CI 2.4–7.4), with AP estimated at −0.25,and the combination of prior brain insult and nocturnal seizures was associated with a 4-fold increased risk of recurrence(RR 4.1, 95 %CI 1.9–8.9), with AP estimated at −0.03. ConclusionsPatients with epileptiform discharges VEEG, nocturnal seizures, abnormal MRI findings, prior brain insult, or focal seizures exhibited a substantial recurrence rate. Specifically, the presence of epileptiform discharges in VEEG recordings, and a history of prior brain insult were identified as independent risk factors associated with recurrence following an initial unprovoked seizure. Notably, individuals with multiple risk factors exhibited a significantly higher recurrence risk compared to those with no or a single risk factor.

7725 Could COVID-19 Be the Trigger for Type 1 Diabetes?

Abstract Disclosure: E. Ahsan: None. F.F. Foo: None. A. Ravin: None. M. Akula: None. K. Hu: None. R. Ong: None. S. Holland: None. M. Hossain: None. J. Cheng: None. Introduction: COVID-19 disrupts glucose homeostasis, which may result in hyperglycemia and new-onset diabetes mellitus. It has been hypothesized to be caused by an increased inflammatory response, increased insulin resistance, and a degree of acute viral pancreatic damage. A meta-analysis showed that the incidence of new-onset diabetes was found to be as high as 14.4% (95% CI: [5.9%-25.8%]). Here, we report a case of COVID-19-induced diabetes to spread awareness among healthcare providers and encourage early detection to prevent long-term complications. Clinical Case: A 54-year-old African American female with a past medical history of hyperlipidemia, obesity, and hypothyroidism presented to our endocrinology office for evaluation and management of diabetes mellitus (DM). She presented with symptoms of 66-pound weight loss, polydipsia, polyphagia, and polyuria. Her labs on diagnosis were significant for blood glucose of 550 mg/dl (70-100 mg/dl), hemoglobin A1c >15.5% (<5.7%), and bicarbonate 23 mmol/L (20-31 mmol/L). She started on insulin and then switched to semaglutide after six months. Her fasting glucose on the follow-up visit was 103 mg/dl, the anti-glutamic acid decarboxylase 65 (GAD-65) antibody was 8,558.9 units/ml (<5 units/ml), C peptide was 1.9 ng/ml (0.5-2ng/ml), and Islet Antigen-2 (IA-2) autoantibody was <7.5 U/ml (0-7.49 U/ml). Despite positive antibodies, her C-peptide suggested enough beta-cell function, and she was switched from insulin to semaglutide. The patient was diagnosed with autoimmune diabetes, which may be from the COVID-19 infection, and was educated about regular follow-up and new medications like teplizumab that may help prevent the progression to needing insulin. Clinical Lesson: The exact mechanism of new-onset COVID-19-induced diabetes is still unknown. However, multiple risk factors are theorized to play a role in COVID-induced DM. These include defects in insulin production and glucose clearance, obesity, stress hyperglycemia, insulin resistance, and steroid therapy. Our patient had a history of obesity but no history of diabetes, steroid use, or any personal or family history of autoimmune processes. Compared to COVID-19 patients with normoglycemia or hyperglycemia, patients with preexisting diabetes and newly diagnosed diabetes experienced more severe complications, such as shock, ARDS, acute kidney injury, hypoalbuminemia, and severe COVID-19 complications. Additional research is required to investigate the various pathophysiologies and treatment options, including new medications that may prevent the disease's further progression. Diabetes caused by COVID-19 should be treated cautiously, and prompt diagnosis and care are essential to preventing further complications. Presentation: 6/3/2024

Iatrogenic urinary injuries in colorectal surgery: outcomes and risk factors from a nationwide cohort.

Iatrogenic urinary injury (IUI) can lead to significant complications after colorectal surgery, especially when diagnosis is delayed. This study analyzes risk factors associated with IUI and delayed IUI among patients undergoing colorectal procedures. Adults undergoing colorectal surgery between 2012 and 2021 were identified in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP®) database. Multivariable regression analysis was used to determine risk factors and outcomes associated with IUI and delayed IUI. Among 566,036 patients, 5836 patients (1.0%) had IUI after colorectal surgery, of whom 236 (4.0%) had delayed IUI. Multiple preoperative risk factors for IUI and delayed IUI were identified, with disseminated cancer [adjusted odds ratio (aOR) 1.4, 95% confidence interval (CI) 1.2-1.5; p < 0.001] and diverticular disease [aOR 1.1, 95% CI 1.0-1.2; p = 0.009] correlated with IUI and increased body mass index [aOR 1.6, 95% CI 1.2-2.1; p = 0.003] and ascites [aOR 5.6, 95% CI 2.1-15.4; p = 0.001] associated with delayed IUI. Laparoscopic approach was associated with decreased risk of IUI [aOR 0.4, 95% CI 0.4-0.5; p < 0.001] and increased risk of delayed IUI [aOR 1.8, 95% CI 1.4-2.5; p < 0.001]. Both IUI and delayed IUI were associated with significant postoperative morbidity, with severe multiorgan complications seen in delayed IUI. While IUI occurs infrequently in colorectal surgery, unrecognized injuries can complicate repair and cause other negative postoperative outcomes. Patients with complex intra-abdominal pathology are at increased risk of IUI, and patients with large body habitus undergoing laparoscopic procedures are at increased risk of delayed IUI.

Machine Learning-Based Prediction of Neurodegenerative Disease in Patients With Type 2 Diabetes by Derivation and Validation in 2 Independent Korean Cohorts: Model Development and Validation Study.

Several machine learning (ML) prediction models for neurodegenerative diseases (NDs) in type 2 diabetes mellitus (T2DM) have recently been developed. However, the predictive power of these models is limited by the lack of multiple risk factors. This study aimed to assess the validity and use of an ML model for predicting the 3-year incidence of ND in patients with T2DM. We used data from 2 independent cohorts-the discovery cohort (1 hospital; n=22,311) and the validation cohort (2 hospitals; n=2915)-to predict ND. The outcome of interest was the presence or absence of ND at 3 years. We selected different ML-based models with hyperparameter tuning in the discovery cohort and conducted an area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. The study dataset included 22,311 (discovery) and 2915 (validation) patients with T2DM recruited between 2008 and 2022. ND was observed in 133 (0.6%) and 15 patients (0.5%) in the discovery and validation cohorts, respectively. The AdaBoost model had a mean AUROC of 0.82 (95% CI 0.79-0.85) in the discovery dataset. When this result was applied to the validation dataset, the AdaBoost model exhibited the best performance among the models, with an AUROC of 0.83 (accuracy of 78.6%, sensitivity of 78.6%, specificity of 78.6%, and balanced accuracy of 78.6%). The most influential factors in the AdaBoost model were age and cardiovascular disease. This study shows the use and feasibility of ML for assessing the incidence of ND in patients with T2DM and suggests its potential for use in screening patients. Further international studies are required to validate these findings.

Trends and determinants of clustering for non-communicable disease risk factors in women of reproductive age in Nepal.

Understanding the clustering of two or more risk factors of non-communicable disease, such as smoking, overweight/obesity, and hypertension, among women of reproductive age could facilitate the design and implementation of strategies for prevention and control measures. This study examined the factors associated with smoking, overweight/obesity, and hypertension among Nepalese women of reproductive age (15-49 years). This study used the Nepal Demographic and Health Surveys (NDHS) 2016 (6,079 women for smoking and overweight/obesity, 6076 for hypertension) and 2022 (6,957 women for overweight/obesity and smoking status and 3,749 women for hypertension) for comparison of trends of NCD risk factors among women aged 15-49 years. Additionally, for each participant, risk factors score (range of 0 to 3) was created by summing individual risk factors. We assessed the determinants of risk factor clustering using multivariable Poisson regression models with robust sandwich variance estimator to calculate adjusted prevalence ratios using NDHS 2022. The national prevalence of overweight/obesity increased from 22.2% in 2016 to 29.2% in 2022 among women of reproductive age. In 2022, the prevalence for smoking, overweight/obesity, and hypertension were 3.8%, 29.2%, and 9.6%, respectively. More than one in four women (28.7%) had one NCD risk factor, while 6.5% had two such risk factors. Higher aged women (40-49 years) were more likely to have multiple NCD risk factors than those aged 15-29 years (APR: 3.19; 95% CI: 2.68-3.80). Those in the richest wealth quintile (APR: 1.52; 95% CI: 1.24-1.85), as well as married (APR: 3.02; 95% CI: 2.43-3.76) and widowed/divorced (APR: 2.85; 95% CI: 2.14-3.80) were more likely to have multiple NCD risk factors. Women from Koshi province (APR: 1.74; 95% CI: 1.41-2.15) had more NCD risk factors than those from the Sudurpaschim province. Working women also had a higher prevalence of NCD risk factors compared to non-working women (APR: 1.23; 95% CI: 1.06-1.43). Additionally, Hill Janajatis (APR: 1.44; 95% CI: 1.21-1.72) and Dalits (APR: 1.42; 95% CI: 1.15-1.75) women were more likely to have NCD risk factors compared to women of Brahmin hill origin. Clustering of two or more NCD risk factors was higher among women aged ≥30 years, those who are currently married or widowed/divorced/separated, working women, and individuals from the wealthiest socioeconomic groups. A higher burden of risk factors underscores the importance of targeted public health interventions, particularly among women from advantaged socio-economic groups, those of affluent regions, and in the workplace.

Multimodal data-driven, vertical visualization prediction model for early prediction of atherosclerotic cardiovascular disease in patients with new-onset hypertension.

: Hypertension is an important contributing factor to atherosclerotic cardiovascular disease (ASCVD), and multiple risk factors, many of which are implicated in metabolic disorders, contribute to the cause of hypertension. Despite the promise of multimodal data-driven prediction model, no such prediction model was available to predict the risk of ASCVD in Chinese individuals with new-onset hypertension and no history of ASCVD. : A total of 514 patients were randomly allocated to training and verification cohorts (ratio, 7 : 3). We employed Boruta feature selection and conducted multivariate Cox regression analyses to identify variables associated with ASCVD in these patients, which were subsequently utilized for constructing the predictive model. The performance of prediction model was assessed in terms of discriminatory power (C-index), calibration (calibration curves), and clinical utility [decision curve analysis (DCA)]. : This model was derived from four clinical variables: 24-h SBP coefficient of variation, 24-h DBP coefficient of variation, urea nitrogen and the triglyceride-glucose (TyG) index. Bootstrapping with 500 iterations was conducted to adjust the C-indexes were C-index = 0.731, 95% confidence interval (CI) 0.620-0.794 and C-index: 0.799, 95% CI 0.677-0.892 in the training and verification cohorts, respectively. Calibration plots with 500 bootstrapping iterations exhibited a strong correlation between the predicted and observed occurrences of ASCVD in both the training and verification cohorts. DCA analysis confirmed the clinical utility of this prediction model. The constructed nomogram demonstrated significant additional prognostic utility for ASCVD, as evidenced by improvements in the C-index, net reclassification improvement, integrated discrimination improvement, and DCA compared with the overall ASCVD risk assessment. The developed longitudinal prediction model based on multimodal data can effectively predict ASCVD risk in individuals with an initial diagnosis of hypertension. : The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300074392).

Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic and modifiable risk factors influence disease susceptibility are under intense investigation, with APOE being the major genetic risk factor for late onset AD. Yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear.To model multiple risk factors, including APOE genotype, age, sex, diet, and immunity we used a cross sectional design, leveraging mice expressing human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. We used network topological and GraphClass analyses of brain connectomes derived from accelerated diffusion-weighted MRI to assess the global and local impact of risk factors, in the absence of AD pathology.Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles. Sparse Canonical Correlation Analysis (CCA) including spatial memory as a risk factor resulted in a network comprising 80 edges, showing significant overlap with risk-associated networks from GraphClass. The largest overlaps were observed with networks impacted by diet (47 edges), immunity (39 edges), APOE3 vs 4 (26 edges), sex (23 edges), and age (19 edges), the resulting networks supporting the use of sensory cues in spatial memory retrieval.These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

Acute infections of the central nervous system in children and adults: diagnosis and management.

Central nervous system infections are due to different microorganisms such as viruses, bacteria, mycobacteria, fungi, amoebas, and other parasites. The etiology depends on multiple risk factors, and it defines the infection location because some microorganisms prefer meninges, brain tissue, cerebellum, brain stem or spinal cord. The microorganisms induce diseases in the nervous system through direct invasion, neurotoxin production, and the triggered immune response. To determine the infection etiology, there are several diagnostic tests which may be conducted with cerebrospinal fluid, blood, respiratory and stool samples. These tests include but are not limited to direct microscopic examination of the sample, stains, cultures, antigenic tests, nucleic acid amplification tests, metagenomic next-generation sequencing, immunologic biomarker and neuroimaging, especially contrast-enhanced magnetic resonance imaging. The treatment may consist of specific antimicrobial treatment and supportive standard care. Since viruses have no specific antiviral treatment, antimicrobial treatment is mainly targeted at non-viral infections. This article will focus on diagnosis and treatment of acute acquired infections of the central nervous system beyond the neonatal period. The discussion defines the disease, provides the clinical presentation, explains the etiology and risk factors, and briefly mentions potential complications. This updated review aims to provide the reader with all the elements needed to adequately approach a patient with a central nervous system infection. Mycobacterium tuberculosis infection, Cryptococcus spp. infection and vaccines are not within the scope of this article.

Long-term efficacy and safety of dose-dense and dose-intense ABVD without consolidation radiotherapy in patients with advanced Hodgkin lymphoma: A 15-year follow-up of the ABVDDD-DI phase II study.

We demonstrated that dose-densified and dose-intensified ABVD (doxorubicin, bleomycin, vinblastine, dacarbazine; ABVDDD-DI) was safe and effective. Here, we present a post hoc long-term analysis of the 82 patients enrolled in the original study. The median observation time was 175 months (IQR 159-197). At 15 years, progression-free and overall survival rates were 81.2% (95% CI, 69.9%-88.7%) and 92.7% (95% CI, 82.6%-97.0%), respectively. Four patients with multiple cardiovascular risk factors experienced delayed G3 cardiac events. The cumulative incidence of second malignancies at 20 years was 6.1%. Fertility and childbearing potential were unaffected. Data support an ongoing benefit for ABVDDD-DI without uneven late toxicities.

The cumulative impact of clinical risk on brain networks and associations with executive function impairments in adolescents with congenital heart disease.

Patients with congenital heart disease (CHD) demonstrate altered structural brain network connectivity. However, there is large variability between reported results and little information is available to identify those patients at highest risk for brain alterations. Thus, we aimed to investigate if network connectivity measures were associated with the individual patient's cumulative load of clinical risk factors and with family-environmental factors in a cohort of adolescents with CHD. Further, we investigated associations with executive function impairments. In 53 adolescents with CHD who underwent open-heart surgery during infancy, and 75 healthy controls, diffusion magnetic resonance imaging and neuropsychological assessment was conducted at a mean age of 13.2 ± 1.3 years. Structural connectomes were constructed using constrained spherical deconvolution tractography. Graph theory and network-based statistics were applied to investigate network connectivity measures. A cumulative clinical risk (CCR) score was built by summing up binary risk factors (neonatal, cardiac, neurologic) based on clinically relevant thresholds. The role of family-environmental factors (parental education, parental mental health, and family function) was investigated. An age-adjusted executive function summary score was built from nine neuropsychological tests. While network integration and segregation were preserved in adolescents with CHD, they showed lower edge strength in a dense subnetwork. A higher CCR score was associated with lower network segregation, edge strength, and executive function performance. Edge strength was particularly reduced in a subnetwork including inter-frontal and fronto-parietal-thalamic connections. There was no association with family-environmental factors. Poorer executive functioning was associated with lower network integration and segregation. We demonstrated evidence for alterations of network connectivity strength in adolescents with CHD - particularly in those patients who face a cumulative exposure to multiple clinical risk factors over time. Quantifying the cumulative load of risk early in life may help to better predict trajectories of brain development in order to identify and support the most vulnerable patients as early as possible.

A model of community youth mental health support services in the global South: A description and use of school-based services delivered by a non-governmental organisation in South Africa

ObjectiveYoung people in low- and middle-income countries (LMICs), including South Africa, are disproportionately affected by multiple risk factors for developing mental health problems and there are numerous barriers to accessing mental health care. In such contexts non-governmental organisations (NGOs) play a key role in providing support. We report on the mental health care services provided by NGO Community Keepers in South Africa. MethodCommunity Keepers (CK) offers psychosocial support to young people, parents, and teachers, during regular school hours, and free of charge. At the end of 2019, CK was based at 23 schools in 12 areas in the Western Cape Province, South Africa. In this paper we describe the model of service provision and present descriptive statistics from retrospective observational service data using routinely collected quantitative data from 2019, including details about referrals received, referral sources and reasons, and feedback received from children, parents, and teachers on the psychosocial support CK delivered. ResultsDuring 2019, 34 676 individuals accessed 18169 psychosocial support sessions rendered by CK. The most common referral source of learners to CK was self-referrals (40.6%), and the main referral reasons were emotional (psychological) problems, behaviour problems, or family (community) problems. At termination of therapy 320 (79.2%) learners said they strongly agreed that they would recommend CK to someone at their school. At termination of therapy with learners, 95 (39.3%) teachers rated CK’s services as excellent, 139 (57.4%) teachers gave a rating of good, and 204 (79.7%) parents reported an improvement in their relationship with their children. ConclusionsCK is an example of how psychosocial support can be embedded in schools in a LMIC. CK’s multifaceted model of service delivery could be used by other school-based mental health care service providers in other LMICs to inform and/or refine the services that they wish to offer.

Harnessing uncertainty: A deep mechanistic approach for cautious diagnostic and forecast of Bovine Respiratory Disease

Bovine Respiratory Disease (BRD) is a prevalent infectious disease of respiratory tract in cattle, presenting challenges in accurate diagnosis and forecasting due to the complex interactions of multiple risk factors. Common methods, including mathematical epidemiological models and data-driven approaches such as machine learning models, face limitations such as difficult parameter estimation or the need for data across all potential outcomes, which is challenging given the scarcity and noise in observing BRD processes. In response to these challenges, we introduce a novel approach known as the Bayesian Deep Mechanistic method. This method couples a data-driven model with a mathematical epidemiological model while accounting for uncertainties within the processes. By utilising 265 lung ultrasound videos as sensor data from 163 animals across 9 farms in France, we trained a Bayesian deep learning model to predict the infection status (infected or non-infected) of an entire batch of 12 animals, also providing associated confidence levels. These predictions, coupled with their confidence levels, were used to filter out highly uncertain diagnoses and diffuse uncertainties into the parameterisation of a mathematical epidemiological model to forecast the progression of infected animals. Our findings highlight that considering the confidence levels (or uncertainties) of predictions enhances both the diagnosis and forecasting of BRD. Uncertainty-aware diagnosis reduced errors to 32 %, outperforming traditional automatic diagnosis. Forecast relying on veterinarian diagnoses, considered the most confident, had a 23 % error, whilst forecast taking into account diagnosis uncertainties had a close 27.2 % error. Building upon uncertainty-awareness, our future research could explore integrating multiple types of sensor data, such as video surveillance, audio recordings, and environmental parameters, to provide a comprehensive evaluation of animal health, employing multi-modal methods for processing this diverse data.

A group prenatal care intervention reduces gestational weight gain and gestational diabetes in American Samoan women.

The objective of this study was to determine the preliminary effectiveness of an intervention to mitigate adverse pregnancy outcomes associated with pre-pregnancy obesity in American Samoa. We enrolled n = 80 low-risk pregnant women at <14 weeks' gestation. A complete case analysis was conducted with randomized group assignment (group prenatal care-delivered intervention vs. one-on-one usual care) as the independent variable. Primary outcomes were gestational weight gain and postpartum weight change. Secondary outcomes included gestational diabetes screening and exclusive breastfeeding at 6 weeks post partum. Other outcomes reported include gestational diabetes incidence, preterm birth, mode of birth, infant birth weight, and macrosomia. Gestational weight gain was lower among group versus usual care participants (mean [SD], 9.46 [7.24] kg vs. 14.40 [8.23] kg; p = 0.10); postpartum weight change did not differ between groups. Although the proportion of women who received adequate gestational diabetes screening (78.4% group; 65.6% usual care) was similar, there were clinically important between-group differences in exclusive breastfeeding (44.4% group; 25% usual care), incidence of gestational diabetes (27.3% group; 40.0% usual care), and macrosomia (8.3% group; 29.0% usual care). It may be possible to address multiple risk factors related to intergenerational transmission of obesity in this high-risk setting using a group care-delivered intervention.

Investigating the Anticancer Potential of Biochanin A in KB Oral Cancer Cells Through the NFκB Pathway.

Squamous cell carcinoma (SCC) is a malignancy primarily affecting squamous cells. Its development is linked to multiple risk factors, such as alcohol and tobacco consumption, human papillomavirus (HPV) infection, and Epstein-Barr Virus (EBV) infection. Biochanin A (BCA), a phytoestrogen extracted from red clover, has been extensively researched for its therapeutic properties. It spans antioxidant activity, anti-inflammatory effects, neuroprotection, cardioprotection, and anticancer potential in different bodily systems. However, its impact on oral cancer remains unexplored. Therefore, this investigation aims to assess the potential anticancer effects of BCA, specifically on KB oral cancer cells. This study utilized KB cells to evaluate the impact of BCA on various cellular parameters, including cell viability, apoptosis, intracellular ROS production, mitochondrial membrane potential, and cell migration. BCA treatment induced several notable effects on KB cells, including reduced cell viability, altered morphology suggestive of apoptosis, heightened oxidative stress, and alterations in mitochondrial membrane potential. Moreover, BCA treatment demonstrated an inhibitory effect on cell migration. The study further investigated the impact of BCA on antioxidant enzyme activities and lipid peroxidation, revealing decreased antioxidant enzyme activities and increased lipid peroxidation across different BCA concentrations (IC50 and IC90). Immunocytochemistry and qRT-PCR analyses unveiled that BCA treatment at varying doses (IC50 and IC90) downregulated the expression of nuclear factor-κB (NF-κB) subunits p50 and p65, pivotal players in cancer progression. In summary, this study sheds light on the promising potential of BCA as an anticancer therapeutic agent for treating oral cancer. Its demonstrated ability to induce apoptosis, perturb cellular functions, and modulate gene expression within cancer cells underscores its significance. Nonetheless, further research, particularly following animal studies, is imperative to comprehensively grasp the breadth of BCA's effects and its viability for clinical applications.

Assessment of the clinical and laboratory risk factors for thrombosis in neonates admitted to neonatal intensive care unit (two Egyptian tertiary centers experience).

In neonates admitted to the neonatal intensive care unit (NICU), arterial and venous thromboembolism is a major cause of morbidity and death which could be attributed to multiple risk factors exposure. This study aimed to evaluate the clinical characteristics, laboratory and radiological assessments, predisposing risk factors, and outcomes of thrombosis in neonates admitted to NICU. This prospective cohort study was conducted at NICU, Minia, and Alexandria University Children's Hospital. Screening of 886 patients admitted to NICU over one year with different clinical presentations, patients were classified into the thrombotic and non-thrombotic groups based on the presence or absence of thrombosis. Thrombosis was diagnosed based on clinical, laboratory and different radiologic assessments. Genetic testing for factor V Leiden mutations G1691A, prothrombin mutation G20210A, protein C, protein S, and antithrombin III gene mutations were performed for patients with a family history of thrombosis. Out of a total of 886 neonatal admissions, 36 patients were diagnosed with evident thrombosis (40 per 1000 NICU admissions). The sites of venous thrombosis detection were Portal vein thrombosis in 11 patients (30.6%), superior vena cava thrombosis in 7 patients (19.4%), deep venous thrombosis in 5 patients (13.9%), central venous thrombosis in 5 patients (13.9%), intra-cardiac thrombosis in 3 patients (8.3%) and necrotic skin patches in one patient (2.8%). Only 69% of enrolled thrombosis patients showed genetic mutations the most common of which was factor V Leiden mutation (52.3%). Sepsis, central venous line (CVL) insertion, C reactive protein (CRP), and duration of NICU admission were significantly more common in the thrombotic group (p < 0.001) and were associated with a higher risk of thrombosis (ORs: 1.02, 7.7, and 1.11, respectively) (p < 0.001). Higher mortality occurred in thrombosis neonates compared with a non-thrombotic group (52.8% versus 17.4%) (p < 0.001). NICU-admitted neonates are exposed to multiple overlapped risk factors, the detection of which is important for preventing potential thrombosis and improving the patient's outcomes. The complexity of sepsis pathogenesis and management could potentiate multiple acquired risk factors. inherited thrombophilia detection is required for prevention of further morbidities.

EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN MANAGING EMERGING RISKS: AN IN-DEPTH STUDY OF AI APPLICATIONS IN FINANCIAL INSTITUTIONS' RISK FRAMEWORKS

This research focuses on using approaches such as ML and ANNs in FRM while will look at and try to analyze their effectiveness compared to logistic regression, random forest, and support vector machine. Training and testing of the models were done using accuracy, precision, recall and F1-score with a sample database comprising of 15000 financial records. Imputation of missing values; selection of informative variables; and data scaling, were performed to enhance the reliability of the models used. Analysis of the results revealed that ANNs and more so DNNs surpassed conventional approaches in the prediction of financial risks. Still, the integration of traditional and AI-based approaches resulted in improved performance outcomes as well as proved to be more resilient to multiple risk factors. Thus, the work concludes that the enhancement of the integration of AI in the management of financial risk can enhance the accuracy of risk assessment. The future work should include improvements regarding the interpretability of the model, testing on a more substantial number of data and experimenting with reinforcement learning to apply it to decision making in the financial risk cases.

High BMI Is Associated with Changes in Peritumor Breast Adipose Tissue That Increase the Invasive Activity of Triple-Negative Breast Cancer Cells

Breast cancer is the most common cancer in women with multiple risk factors including smoking, genetics, environmental factors, and obesity. Smoking and obesity are the top two risk factors for the development of breast cancer. The effect of obesity on adipose tissue mediates the pathogenesis of breast cancer in the context of obesity. Triple-negative breast cancer (TNBC) is a breast cancer subtype within which the cells lack estrogen, progesterone, and HER2 receptors. TNBC is the deadliest breast cancer subtype. The 5-year survival rates for patients with TNBC are 8–16% lower than the 5-year survival rates for patients with estrogen-receptor-positive breast tumors. In addition, TNBC patients have early relapse rates (3–5 years after diagnosis). Obesity is associated with an increased risk for TNBC, larger TNBC tumors, and increased breast cancer metastasis compared with lean women. Thus, novel therapeutic approaches are warranted to treat TNBC in the context of obesity. In this paper, we show that peritumor breast adipose-derived secretome (ADS) from patients with a high (&gt;30) BMI is a stronger inducer of TNBC cell invasiveness and JAG1 expression than peritumor breast ADS from patients with low (&lt;30) BMI. These findings indicate that patient BMI-associated changes in peritumor AT induce changes in peritumor ADS, which in turn acts on TNBC cells to stimulate JAG1 expression and cancer cell invasiveness.

LL37-DNA Complex Drives Vitiligo Progression Through TLR9-MyD88 Signaling Pathways.

Vitiligo is an autoimmune disorder characterized by chronic depigmentation and milk-white patches on the skin. Skin infiltration by autoreactive CD8+ T cells causes melanocyte destruction in vitiligo. Multiple risk factors, particularly immune-related inflammatory factors, are involved in the disappearance of melanocytes. LL37 is a classic damage-associated molecular pattern molecule that is involved in the development of various autoimmune diseases. An enhanced expression of LL37 in vitiligo is known; however, the exact role of LL37 in melanocyte loss has not yet been elucidated. In the present study, we detected increased LL37 expression in vitiligo serum and lesions. Furthermore, we confirmed that cultured keratinocytes released LL37 after treatment with H2O2. Moreover, the LL37-DNA complex enhanced the secretion of CXCL9, CXCL10, and CXCL16 from keratinocytes via the TLR9-MyD88 signaling pathway and facilitated the migration of CD8+ T cells. Altogether, our study demonstrates that LL37 released from keratinocytes binds to DNA and contributes to melanocyte destruction under oxidative stress-induced autoimmunity in vitiligo.