Demographic Groups Research Articles

Difficulties of Teachers in the Contextualization of the Most Essential Learning Competencies

This descriptive study examined the teachers’ difficulties in contextualizing the most essential learning competencies (MELC) in a public elementary school under a medium-sized Division in an urbanized City in Central Philippines during the School Year 2022-2023. It used a validated and reliable self-made questionnaire to collect data from 86 teachers. The results showed that most teachers are younger, married, have taught longer, and hold bachelor’s degrees. Furthermore, data showed moderate teachers’ difficulties in contextualizing MELC according to curriculum and instruction, instructional materials, and assessment. When further grouped by four demographic groupings, teachers experienced moderate difficulties in contextualizing MELC based on the same three constructs above on curriculum and instruction, instructional materials, and assessment. Finally, no significant difference exists in teachers’ difficulties in contextualizing MELC based on the same four demographic groupings and three constructs. The findings suggest the need to equip teachers with the necessary tools and support to create a more engaging, relevant, and effective learning environment that enhances students’ understanding and appreciation of the subject. Keywords: Curriculum and instruction, instructional materials, assessment, Bacolod City, Negros Occidental, Philippines.

Uncertainty as a Fairness Measure

Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means for an ML model to be fair. This has been addressed by the ML community with various measures of fairness that depend on the prediction outcomes of the ML models, either at the group-level or the individual-level. These fairness measures are limited in that they utilize point predictions, neglecting their variances, or uncertainties, making them susceptible to noise, missingness and shifts in data. In this paper, we first show that a ML model may appear to be fair with existing point-based fairness measures but biased against a demographic group in terms of prediction uncertainties. Then, we introduce new fairness measures based on different types of uncertainties, namely, aleatoric uncertainty and epistemic uncertainty. We demonstrate on many datasets that (i) our uncertaintybased measures are complementary to existing measures of fairness, and (ii) they provide more insights about the underlying issues leading to bias.

The Intersecting Time, Administrative and Financial Burdens of a Cancer Diagnosis.

Cancer and its care create substantial financial, time, and administrative burdens both for patients and their loved ones. While cancer-related financial burdens have been well documented in the past decade, time and administrative burdens of cancer care have received substantially less attention. We define time burdens as the burden patients and caregivers experience due to the time needed to complete cancer-related treatment and tasks that take away from other life responsibilities. Relatedly, we conceptualize administrative burdens as those burdens patients and caregivers experience due to cancer-related, resource-consuming bureaucratic and logistical tasks. Finally, financial hardship can be conceptualized as problems patients experience related to the cost of medical care. These burdens do not exist in isolation; time, administrative, and financial burdens intersect with and compound each other. Currently, we have limited evidence-based measures on the objective (e.g., scheduling time, transportation, wait time) and subjective (e.g., mental, emotional and physical stress) measures of time and administrative burden. We have even more limited evidence of the risk factors for and outcomes from increased time and administrative burdens, and how they differentially impact populations across social and demographic groups. In this commentary, we present a research agenda to map, measure, evaluate, and mitigate the time, administrative, and financial burdens of cancer and its care.

Diverse perspectives on entrepreneurship: Analyzing gender and age-related differences in perceptions

Entrepreneurship is a key driver of economic growth, innovation, and social development, yet perceptions of entrepreneurship can vary significantly across different demographic groups. Understanding these variations is crucial for developing effective education and support systems. This study investigates the differences in perceptions of entrepreneurship among various gender and age groups, focusing on aspects such as career opportunities, financial readiness, the attractiveness of entrepreneurial ventures, skill requirements, economic impact, educational effectiveness, and gender-specific views on entrepreneurship. Using a quantitative research design, the study analyzed data collected from 300 entrepreneurs in Greater Noida through a structured questionnaire. Independent samples t-tests and ANOVA were employed to identify significant differences in perceptions across genders and age groups. The findings reveal notable differences in perceptions related to the effectiveness of entrepreneurship education and the role of women in entrepreneurship. Specifically, females demonstrated greater confidence in the ability of the education system to prepare individuals for entrepreneurial careers than males did. Additionally, significant differences in perceptions of entrepreneurial opportunities and support were observed between different age groups. These results highlight the need for tailored education programs and support systems that address the unique needs of diverse demographic segments. The study underscores the importance of promoting gender diversity and inclusivity in entrepreneurship to foster a more dynamic and innovative entrepreneurial ecosystem. By addressing the specific challenges and opportunities identified, policymakers and educators can develop more effective and inclusive strategies to support aspiring entrepreneurs. Future research should further explore the sociocultural and institutional factors influencing entrepreneurial perceptions to enhance the overall effectiveness of entrepreneurship initiatives.

Generalized contact matrices allow integrating socioeconomic variables into epidemic models.

Variables related to socioeconomic status (SES), including income, ethnicity, and education, shape contact structures and affect the spread of infectious diseases. However, these factors are often overlooked in epidemic models, which typically stratify social contacts by age and interaction contexts. Here, we introduce and study generalized contact matrices that stratify contacts across multiple dimensions. We demonstrate a lower-bound theorem proving that disregarding additional dimensions, besides age and context, might lead to an underestimation of the basic reproductive number. By using SES variables in both synthetic and empirical data, we illustrate how generalized contact matrices enhance epidemic models, capturing variations in behaviors such as heterogeneous levels of adherence to nonpharmaceutical interventions among demographic groups. Moreover, we highlight the importance of integrating SES traits into epidemic models, as neglecting them might lead to substantial misrepresentation of epidemic outcomes and dynamics. Our research contributes to the efforts aiming at incorporating socioeconomic and other dimensions into epidemic modeling.

Evaluation of human and ecological health risks associated with the potentially toxic heavy metals in groundwater of Vellore city, Tamil Nadu, India

Groundwater pollution by heavy metals is a significant and alarming threat globally, through its chronic effect on human health and ecosystem. The current study evaluated the identification and risk assessment of heavy metals in groundwater of the Vellore city, Tamilnadu, India. Groundwater samples were collected from 32 locations over a continuous period of 2022–2023 for every three months on seasonal basis. The samples were analysed for pH, Total Dissolved Solids (TDS), Hardness and heavy metals (Al, As, Ba, Cd, Cu, Pb, Al and Zn). The analysed mean concentrations of these heavy metals were found in the order: As > Cd > Pb > Ba >Zn >Al> Cu. Elevated concentrations of heavy metals were observed in 2022, likely influenced by rainfall and land use patterns. Heavy metals such as Al, As, Pb, and Cd exceeded the BIS limits and whereas Ba, Cu, and Zn concentrations remain within desirable limits. Groundwater Quality Index (GWQI) classified 41% as 'unfit for drinking'. Ecological risk indices (ERI) indicate significant contamination in 34% of samples, with Al and Pb being primary contributors. Non-carcinogenic health risk assessments reveal, chronic hazards, where children facing higher risks followed by men and women groups. Carcinogenic risk assessments indices such as Heavy metal pollution Index (HPI) shown 25% of samples in critical quality, Heavy metal Evaluation Index (HEI) evaluated all samples were in low contamination and Contamination Index (CI) shown 72% samples under high contamination posing high risk to humans. Probable Cancer Risk (PCR) values for As, Pb, and Cd exceed acceptable ranges across all demographic groups posing cancer risk to humans particularly children. The present study highlights the need for groundwater treatment and regulatory interventions to mitigate health and ecological risks due to the contamination of heavy metals in Vellore city.

Investigating inequalities in patient outcomes for first-episode psychosis.

Understanding inequalities in outcomes between demographic groups is a necessary step in addressing them in clinical care. Inequalities in treatment uptake between demographic groups may explain disparities in outcomes in people with first-episode psychosis (FEP). To investigate disparities between broad demographic groups in symptomatic improvement in patients with FEP and their relationship to treatment uptake. We used data from 6813 patients from the 2021-2022 National Clinical Audit of Psychosis data-set. Data were grouped by category type to obtain mean outcomes before adjustment to see whether disparities in outcomes remained after differences in treatment uptake had been accounted for. After matching, the average effect of each demographic variable in terms of outcome change was calculated. Moderator effects on specific treatments were investigated using interaction terms in a regression model. Observational results showed that patients aged 18-24 years were less likely to improve in outcome, unless adjusted for intervention uptake. Patients classified as Black and Black British were less likely to improve in outcome (moderation effect 0.04, 95% CI 0-0.07) after adjusting for treatment take-up and demographic factors. Regression analysis showed the general positive effect of supported employment interventions in improving outcomes (coefficient -0.13, 95% CI -0.07 to -0.18, P < 0.001), and moderator analysis suggested targeting particular groups for interventions. Inequalities in treatment uptake and psychotic symptom outcome of FEP by social and demographic factors require monitoring over time. Our analysis provides a framework for monitoring health inequalities across national clinical audits in the UK.

Skipping Breakfast and Academic Grades, Persistent Feelings of Sadness or Hopelessness, and School Connectedness Among High School Students - Youth Risk Behavior Survey, United States, 2023.

Breakfast consumption is positively associated with academic achievement and diet quality among students, whereas skipping breakfast has been linked with poor mental health. Data from CDC's 2023 nationally representative Youth Risk Behavior Survey were used to describe how often high school students ate breakfast in the past 7 days and the associations between skipping breakfast every day (ate breakfast on 0 of the past 7 days), experiencing persistent feelings of sadness or hopelessness, school connectedness, and self-reported grades. Prevalence estimates and corresponding 95% CIs were calculated, and t-tests were used to identify differences within demographic groups (e.g., sex, race and ethnicity, and sexual identity). Logistic regression analyses were conducted to calculate prevalence ratios describing breakfast skipping, adjusting for demographics, and stratified by sex and race and ethnicity. Most students missed breakfast ≥1 time in the past 7 days (72.6%), and 17.9% of students skipped breakfast every day, with differences by sex, sexual identity, and race and ethnicity. Overall, and among both males and females, students who experienced persistent feelings of sadness or hopelessness were more likely to skip breakfast every day. The association between feelings of sadness and hopelessness and skipping breakfast was generally consistent across racial and ethnic groups. In contrast, greater levels of school connectedness and earning mostly As or Bs were inversely associated with skipping breakfast. Students who had higher school connectedness were approximately 30% less likely to skip breakfast on all 7 days. Skipping breakfast and poor mental health co-occur among many adolescents and might impede students' readiness to learn. School efforts to make breakfast accessible and appealing to high school students might yield multiple benefits and help reinforce school administrators' efforts to recover student learning losses that occurred during the COVID-19 pandemic. Parents, school decision-makers, and organizations that partner with schools and families can use these findings to guide efforts to promote breakfast consumption.

Building employability in PASS leaders through peer-assisted study sessions (PASS) programs

PurposeThe peer-assisted study sessions program (PASS) – variously known as supplemental instruction, peer mentoring and peer-assisted learning – is regarded as a global phenomenon in academic support interventions and has well established presence in higher education. The purpose of this study was to add to the body of knowledge of how PASS may add value to the employability of the student leaders, in relation to the CareerEDGE model.Design/methodology/approachThis qualitative research was based on interviews from a sample of 18 former PASS leaders who have since obtained employment.FindingsFindings demonstrate that being a PASS leader enhanced many of their competencies as defined by CareerEDGE model, for example subject related knowledge, interpersonal skills and some generic skills. However, many of the reported competency enhancements led to unexpected future career advancement.Research limitations/implicationsThis study contributes to the body of knowledge relating to how PASS may add value to the employability of student leaders in relation to the CareerEDGE model using a sample of leaders who have already graduated and have obtained employment. Limitations of this study include: lack of generalisability of small geographically isolated sample; coding and analsysis not comparing findings from different demographic groups; only explores individual perceptions and relies on self-reported data.Practical implicationsThe results of this study have implications for the development of future PASS programs and other similar academic support interventions. Given the global prevalence of such programs in higher education institutions, this research may have wide-scale transferability. Insights from this paper will allow future PASS leaders to proactively reflect and evaluate their experience and increase their confidence in pursuing their employment opportunities.Originality/valueWhile there is much understanding on the role that PASS has in improving students grades and in improving retention rates, less is known about the value of this experience on improving student employability, and little attempt has been made in examining this within relevant employability models post-employment.

Mitigating Bias in AI-Driven Recruitment : The Role of Explainable Machine Learning (XAI)

This article explores the critical role of Explainable Artificial Intelligence (XAI) in mitigating bias within AI-driven recruitment processes. As AI becomes increasingly prevalent in hiring practices, concerns about algorithmic bias and fairness have emerged. The article discusses how XAI techniques, such as SHAP and LIME, can be used to detect and interpret potential biases in recruitment algorithms. It examines the implementation of XAI for feature importance analysis, algorithmic bias detection, and disparate impact analysis across different demographic groups. The article addresses the challenges of balancing model complexity with explainability and the limitations of XAI in identifying systemic biases. By implementing XAI strategies, organizations can enhance the fairness and transparency of their hiring practices, ultimately fostering more diverse and equitable workplaces.

How Time, Living Situation, and Stress Related to Technology Influence User Acceptance and Usability of a Socialization Service for Older Adults and Their Formal and Informal Caregivers: Six-Month Pilot Study.

Considering the growing population of older adults, addressing the influence of loneliness among this demographic group has become imperative, especially due to the link between social isolation and deterioration of mental and physical well-being. Technology has the potential to be used to create innovative solutions to increase socialization and potentially promote healthy aging. This 6-month study examined the usability and acceptability of a technology-based socialization service and explored how stress and living situation affect older adults' and their ecosystem's perceptions of technology, investigating cross-sectional and longitudinal differences among and across user groups. Participants were recruited in Tuscany and Apulia (Italy) through a network of social cooperatives and a research hospital, respectively. A total of 20 older adults were provided with the same technology installed on a tablet and on a smart television. The technology has three functionalities: video calling, playing games, and sharing news. Additionally, 20 informal caregivers (IC) and 13 formal caregivers (FC) connected to the older adults were included in the study. After both initial training in the use of the system (T0) and 6 months of using the system (T6), questionnaires on usability, acceptability, and technostress were filled in by older adults, IC, and FC. Nonparametric or parametric tests were conducted to investigate group differences at both time points and changes over time. Additional analyses on older adults were done to assess whether differences in usability and acceptability were related to living situation (ie, alone or with someone). Furthermore, correlation analyses were performed between usability, acceptability, and stress toward technology at T0 and T6. At both T0 and T6, older adults had lower usability scores than IC and FC and higher anxiety than IC. Over time, there was a significant decrease in older adults' attitudes toward technology score, depicting a negative attitude over time (T0 median 4.2, IQR 0.5; T6 median 3.7, IQR 0.8; Cohen d=0.7), while there was no change for IC and FC. At T0, those living alone had lower acceptability than those living with someone but this difference disappeared at T6. People or participants living with someone had a decline in anxiety, attitudes toward technology, enjoyment, and perceived usefulness. Stress toward technology affected usability and acceptability in the older adult group entering the study (ρ=-.85) but this was not observed after 6 months. In the IC group, stress affected trust at T0 (ρ=-.23) but not at T6. At the start of the study, older adults judged the system to be less usable and more stressful than did the caregivers. Indeed, at first, technostress was correlated with usability and acceptability; however, with repeated use, technostress did not influence the perception of technology. Overall, getting accustomed to technology decreased anxiety and stress toward technology.

Behavioral and Psychosocial Dynamics of Engagement: The Digital Divide in Artificial Intelligence [AI]-Driven Sports Podcasts.

The digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICTs) across different demographic groups, focusing on gender, age, and religious level. The advent of the commercial web has heightened the significance of these divides, as the first-level digital divide concerns access to the internet, while the second-level digital divide pertains to the ability to use technology proficiently. The existing literature has consistently highlighted persistent inequalities in these areas, which significantly impact the extent to which students from various backgrounds can engage with AI sport podcasts effectively. Understanding these dynamics is crucial for developing strategies to bridge the gap and ensure equitable access to digital learning resources.

Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R-squared = 0.31; placebo: R-squared = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Comparing public health-related material in print and web page versions of legacy media.

The objectives of this study were to create a database of public health content from a sample of legacy media, and to compare the prevalence of public health themes in print and web-based versions over time. A database was created from eleven nationally published magazines as a sample of legacy media content. Relevant material was extracted and coded by the title of the article, periodical, print or web edition, month of publication, item type, and 1-3 public health theme codes. Theme codes emerged as the documents were reviewed based on the primary discussion in each piece. A total of 2558 unique documents were extracted from print issues and 6440 from web-based issues. Seventeen public health themes were identified. Individual coded documents were saved with file names identical to the code string, thus creating a searchable database. Legacy media are those that existed before the internet and social media. Publishers target readership groups defined by age, gender, race, sexual orientation, and other commonalities. Although legacy media have been identified as trusted sources of health information, they have not been examined as sources of public health communication. Because both print and web-based versions exist as unstructured textual data, these are rarely examined with informatics methods. The process described can serve as a model for application of informatics approaches to similar data and assist development of targeted public health communications. Having a better understanding of what types of health content is distributed through legacy media can help to target health messages to specific demographic and interest groups in ways that are understandable and appealing to them.

Improvement of Elderly Welfare Through Food Program

The general background increasing number of elderly in society requires effective welfare programs to meet their specific needs. Specific background in Indonesia, the Food Program of the Ministry of Social Affairs aims to provide essential nutritional support for this demographic group. Knowledge gap although this program has been implemented, there is still a lack of comprehensive analysis regarding its effectiveness and reach, especially in the Sidoarjo District. This study aims to fill this knowledge gap by evaluating the program through descriptive qualitative methods, including observation and interviews. Results findings indicate that the implementers' understanding of the program has improved due to socialization activities. Positive changes have been recorded among beneficiaries, demonstrating the program's potential to improve the welfare of the elderly. The novelty of this study lies in the comprehensive assessment of the components of the Food Program. Implications these findings can serve as a guide for improving and adapting social welfare programs in the future, emphasizing the importance of addressing the welfare of the elderly in community development initiatives. Highlights: Improved understanding of the Food Program through socialization activities. Need to increase the number of eligible elderly beneficiaries. Program meets health standards, indicating successful food provision. Keywords: Program, Food, Social, Effectiveness, Welfare

Impact of Socio-Demographic Factors on Quality of Life and Coping Strategies of Children with Different Disabilities.

Background and Objectives: Children with disabilities face unique challenges that can affect their well-being and quality of life (QOL). This study aimed to assess the QOL and coping strategies adopted by children with disabilities and explore how socio-demographic factors influence QOL and coping strategies. Materials and Methods: This cross-sectional study, which was conducted in Saudi Arabia with children aged 6 to 18 years, used a stratified random sample to ensure representation from a variety of demographic groups. Short Form-12 (SF-12) was used to assess the QOL in the sample population. The Brief COPE Inventory was used to examine coping strategies among the children. One-way analysis of variance was applied to examine differences in the QOL, coping strategies scores, and demographic variables. Multiple regression analyses were performed to examine the role of demographic variables in predicting QOL and p value was considered statistical significance at p < 0.05. Results: The results of the study clearly revealed significant differences between the mean scores of QOL for gender, age, and type of disability, duration of disability, education qualification, family status, family occupation, and housing status. Female participants showed better QOL in physical functioning than their male counterparts. Children with intellectual disability reported better QOL in general health, vitality, social functioning, and mental health. Participants with seven to eight years of disability reported higher scores in physical functioning, vitality, and mental health. Children whose parents were working in private agencies and living in rented houses scored higher on the general health aspects of quality of life. The findings also revealed that the duration of the disability was a significant predictor of the QOL. The mean scores of different dimensions of coping strategies clearly revealed that male participants used dysfunctional coping (p < 0.01), as compared to problem-focused and emotional-focused coping while female children relied more on emotional-focused coping. Emotion-focused coping was significantly higher in participants with visual (p < 0.01), learning (p < 0.01), and intellectual disabilities (p < 0.01). Dysfunctional coping scores were higher among children with auditory disabilities (p < 0.01) and multiple disabilities (p < 0.01). Conclusions: This study highlights the significance of demographic factors in understanding and improving the well-being of a diverse population of disabled juveniles. It offers valuable insights into the subtle factors affecting quality of life. Future interventions and policies can leverage these findings to enhance the quality of life of individuals with disabilities and to foster a more supportive and inclusive approach.

Association of continuous BMI with health-related quality of life in the United States by age and sex.

The objective of this study is to estimate health-related quality of life (HRQoL) by continuous BMI by age, sex, and demographic group in the United States. We estimated HRQoL (overall and by domain) by continuous BMI using SF-6D (Short-Form Six-Dimension) data from 182,778 respondents ages 18 years and older from the repeated cross-sectional Medical Expenditure Panel Survey (MEPS) 2008 to 2016. We adjusted for BMI self-report bias and for potential confounding between BMI and HRQoL. We found an inverse J-shaped curve of HRQoL by BMI, with lower values for female individuals and the highest health utilities occurring at BMI of 20.4 kg/m2 (95% CI: 20.32-20.48) for female individuals and 26.5 kg/m2 (95% CI: 26.45-26.55) for male individuals. By BMI category, excess weight contributed to HRQoL loss of 0.0349 for obesity overall, rising to 0.0724 for class III obesity. By domain, pain was the largest cause of HRQoL loss for obesity (26%), followed by role limitations (22%). HRQoL is lower for people with excess body weight across a broad range of ages and BMI levels, especially at high levels of BMI, with pain being the largest driver of HRQoL loss. These findings highlight the importance of promoting a healthy weight for the entire population while also targeting efforts to prevent extreme weight gain over the life course.

Prevalence Trends and Treatment Patterns of Autism Spectrum Disorder Among Children and Adolescents in the United States from 2017 to 2020.

Autism spectrum disorder (ASD) poses a significant challenge due to its diverse impact on individuals, emphasizing the need for personalized treatment plans. The financial burden of ASD-related healthcare is substantial, necessitating a comprehensive understanding of its prevalence and evolving trends. This study aims to analyze the prevalence and trends of ASD, treatment patterns, gender differences, and racial-ethnic disparities in the United States from 2017 to 2020, utilizing nationally representative data from the National Survey of Children's Health (NSCH). The NSCH, a leading annual national survey, provided rich data on child health. A total of 108,142 participants aged 3-17 years were included, with ASD prevalence assessed based on self-reported diagnoses. Between 2017 and 2020, ASD prevalence in children aged 3-17 was 2.94% (95% confidence interval: 2.68-3.18). Significant disparities were observed: older age and male gender correlated with higher prevalence, while family income-to-poverty ratio and insurance coverage influenced prevalence. Racial/ethnic disparities existed, with Hispanics showing the highest prevalence. Treatment trends showed stability overall, but age influenced behavioral and medication interventions. The prevalence remained stable from 2017 to 2020, with variations in age groups and a significant increase among non-Hispanic Whites. This study highlights a higher but stable overall ASD prevalence, with nuanced disparities among different demographic groups. Gender differences persist, emphasizing the need for tailored interventions. Racial-ethnic disparities call for targeted healthcare strategies. The stability in treatment trends underscores the persistent challenge of addressing core ASD symptoms.

7949 Unraveling a Unique Presentation of Paget’s Disease of Bone

Abstract Disclosure: S. Puri: None. L. Bovee: None. R. Narla: None. Background: Paget's Disease of Bone (PDB) is a disorder characterized by rapid bone destruction and repair, which results in distortion of bone architecture. PDB occurs most commonly in adults over 55 and of European descent. PDB presents with both lytic and sclerotic lesions, predominantly in the axial skeleton. Serum alkaline phosphatase (ALP) levels and bone turnover markers (BTMs) are typically elevated in PDB. Clinical Case: A 53-year-old African-American woman with a history of breast cancer, PTSD, depression, and obesity (BMI 37) presented with worsening back pain over the past six months. Her pain would start at the center of her back and radiate to her left lower extremity. She endorsed rapid weight loss and night sweats and had lumbar bony tenderness on exam, prompting a spinal MRI. Spinal MRI was significant for diffuse sclerosis of the L3 vertebral body only. The radiology report provided a broad differential for this finding, including metastases, lymphoma, infection, PDB, and hemangioma. Due to progression of the patient’s back pain and concern for possible malignancy, a whole-body SPECT was completed which showed isolated tracer uptake into the L3 vertebral body extending into the pedicles and spinous process. Similar findings were noted on bone scans dating back to 2001. Per the radiology report, the presence of sclerosis with minimal expansion is most suggestive of PDB. Endocrinology was consulted for guidance on treatment and surveillance imaging for this patient. The endocrinology team discussed the case with radiology, who confirmed that the patient’s imaging findings had classic features of PDB. Bone specific ALP (B-ALP) and procollagen type 1 amino-terminal propeptide (P1NP) as bone turnover markers (BTMs) were within normal limits. A DEXA scan is pending to evaluate for concurrent osteoporosis. Clinical Lessons: PDB was favored as the most likely diagnosis primarily due to the sclerotic appearance of the patient’s vertebral lesion on imaging, radicular symptoms, and exam finding of bony point tenderness. Other diagnoses, such as metastatic disease, were considered and were thought to be unlikely given that the vertebral lesion is monostotic and stable. This case highlights the necessity of carefully evaluating radiographic findings and clinical history in the diagnosis of PDB. In this case, the patient was from a demographic group unlikely to have PDB and, unexpectedly, did not have elevated BTMs. It is important to consider PDB on the differential diagnosis in cases such as this because PDB can present in myriad ways and in a variety of patient populations. Presentation: 6/2/2024

Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence.

Diabetic foot ulcers (DFUs) are a severe complication of diabetes mellitus (DM), which often lead to hospitalization and non-traumatic amputations in the United States. Diabetes prevalence estimates in South Texas exceed the national estimate and the number of diagnosed cases is higher among Hispanic adults compared to their non-Hispanic white counterparts. San Antonio, a predominantly Hispanic city, reports significantly higher annual rates of diabetic amputations compared to Texas. The late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk. The aim of this study was to identify molecular factors related to the severity of DFUs by leveraging a multimodal approach. We first utilized electronic health records (EHRs) from two large demographic groups, encompassing thousands of patients, to identify blood tests such as cholesterol, blood sugar, and specific protein tests that are significantly associated with severe DFUs. Next, we translated the protein components from these blood tests into their ribonucleic acid (RNA) counterparts and analyzed them using public bulk and single-cell RNA sequencing datasets. Using these data, we applied a machine learning pipeline to uncover cell-type-specific and molecular factors associated with varying degrees of DFU severity. Our results showed that several blood test results, such as the Albumin/Creatinine Ratio (ACR) and cholesterol and coagulation tissue factor levels, correlated with DFU severity across key demographic groups. These tests exhibited varying degrees of significance based on demographic differences. Using bulk RNA-Sequenced (RNA-Seq) data, we found that apolipoprotein E (APOE) protein, a component of lipoproteins that are responsible for cholesterol transport and metabolism, is linked to DFU severity. Furthermore, the single-cell RNA-Seq (scRNA-seq) analysis revealed a cluster of cells identified as keratinocytes that showed overexpression of APOE in severe DFU cases. Overall, this study demonstrates how integrating extensive EHRs data with single-cell transcriptomics can refine the search for molecular markers and identify cell-type-specific and molecular factors associated with DFU severity while considering key demographic differences.