Southern States University

This bibliometric analysis examines the evolving trends of digital transformation (DT) and information systems (IS) in U.S. businesses. It explores how technology—focused on strategy, efficiency, innovation, and customer engagement—is reshaping organizations and workplaces. Using a PRISMA-based systematic review and data from Scopus (2016–2026), the study applies the Bibliometrix R package to assess publication patterns. Results show significant growth, with 2,692 documents reflecting a 43.58% annual increase and 18.39% involving international collaboration. Key themes include AI/ML integration in business processes, digital sustainability, and IS as a strategic driver for business model evolution. U.S. businesses are increasingly aligning digital transformation with sustainability goals. This study addresses a key research gap by offering detailed insights into DT and IS impacts on operations and sustainability practices. It underscores the need for integrated socio-technical strategies, responsible data governance, and global collaboration to foster innovation and bridge digital divides.

Variability in cognitive decline in preclinical Alzheimer's disease (AD) presents a significant challenge in evaluating treatment effects in clinical trials. This study developed models to forecast cognitive progression and assessed their potential to improve the precision of treatment effect estimates in future trials. Data were from the Phase III Anti-Amyloid Treatment trial of Solanezumab in amyloid-positive asymptomatic preclinical AD. Due to no significant cognitive decline differences between groups, the Solanezumab arm (n=549) was used for training, and the placebo arm (n=559) for validation. Cognitive decline was assessed as 24-week changes in Preclinical Alzheimer's Cognitive Composite (PACC) score over 216 weeks. A predictive model for PACC decline was developed using demographics, APOE ε4 status, and baseline clinical assessments (PACC composite score, its components, and CDR-SB), employing Stochastic Gradient Boosting. The added value of amyloid PET Centiloid (CL), plasma pTau217 (electrochemiluminescence assay), MRI morphometrics, and tau PET measures (375-participant substudy) were evaluated. Model performance was optimized via cross-validation and validated in the placebo group. The impact of baseline PACC decline predictions as an Alzheimer's Prognostic Covariate (APC) on treatment effect assessments was evaluated in trial simulations. A model with demographics, CL, and clinical assessments explained 16% of PACC decline at week 216 (R²=0.16), improving over CL or clinical assessments alone (p<0.05). Adding MRI increased R² to 0.23 (p<0.05); replacing CL with plasma pTau217 or tau PET raised it to 0.25 and 0.42, respectively. Without tau PET in the model, key baseline predictors were pTau217, CL, PACC, MMSE, inferior temporal area, and entorhinal volume. When tau PET was included, it became the strongest predictor, with fusiform, inferior temporal, and supramarginal regions being the most predictive. Simulations showed baseline prediction of PACC decline as APC can reduce treatment effect variance by 20.3%, increase power from 80% to 88%, or reduce sample size by 21.5%. Incorporating tau PET further reduces variance by 37%, increases power to 94%, and decreases sample size by 35%. Baseline tau PET was the strongest predictor of PACC decline. Using baseline-predicted PACC decline as APC can enhance treatment effect estimates and trial efficiency in preclinical AD.

Progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) are severe neurodegenerative disorders lacking disease-modifying treatments and validated biomarkers. Clinical trials face challenges due to phenotypic overlap and imperfect clinicopathological correlations. MRI-derived models have shown to accurately predict PSP and CBD pathology in a large autopsy-confirmed cohort (Illán-Gala et al., JAMA Network Open, 2022). This study examines how participant selection based on MRI models and imaging outcomes impacts sample size estimations in hypothetical clinical trials. Eighty-four participants from the 4 Repeat Tauopathy Neuroimaging Initiative (4RTNI) with baseline and longitudinal MRI data and clinical assessments were included. Diagnoses comprised Richardson syndrome (RS, 61%) and corticobasal syndrome (CBS, 30%) without Alzheimer's disease. MRI-derived models predicted PSP, CBD, or other pathologies (MRI-PSP, MRI-CBD, MRI-Other) according to baseline MRI. Cortical thickness and volume measures were derived from MRI data using Freesurfer and employed to identify an optimal MRI-signature of regions showing the highest effect size on atrophy over 12 months using linear mixed-effects models. Disease progression was also measured with PSP Rating Scale (PSPRS). Sample sizes required to detect a 30% reduction in mean change at 12 months were calculated for hypothetical clinical trials. MRI predicted PSP, CBD, and other pathologies in 46%, 26%, and 27% of participants, respectively. Among RS diagnoses, 31 (61%) were classified as MRI-PSP; among CBS, 6 (24%) were MRI-CBD. MRI-signature regions for PSP progression included midbrain, superior-temporal, and rostral-middle-frontal thickness. For CBD, key regions included midbrain and pons volumes, superior-frontal, and entorhinal thickness. In a hypothetical PSP trial, selection criteria based on clinical diagnosis required 336 participants using PSPRS as outcome, while MRI-based diagnosis with MRI-signature as outcome reduced the sample size to 121 (64% decrease). For a CBS trial, the sample size reduced from 1301 participants if inclusion was based on clinical diagnosis to 160 using MRI-based selection and outcomes. Selecting participants with increased diagnostic certainty for PSP and CBD based on baseline MRI, combined with using MRI measures as outcomes could enhance the efficiency of future phase 2 clinical trials for 4R tauopathies. We plan to replicate these results in the Davunetide trial cohort.

Abstract Primary and metastatic brain tumors are among the deadliest and treatment-resistant cancers, mainly because of their inherent resistance to chemoradiation and limited drug delivery across the blood–brain barrier (BBB). Identifying molecules that can cross the BBB and serve as sonosensitizers is crucial for developing noninvasive, targeted therapies such as sonodynamic therapy (SDT). To overcome the limitations of traditional low-throughput screening, a New Approach Methodology (NAM) was developed, starting with AI-driven molecular discovery. A positive–unlabeled neural network, trained on over 200 molecular descriptors, was used to predict compounds likely to respond to focused ultrasound (FUS), penetrate the BBB, and mediate therapeutic sonodynamic activity. While AI helps prioritize potent candidates, effective SDT also depends on clinically scalable FUS delivery. Low-intensity FUS enables noninvasive activation of small molecules in the brain and has been validated in several clinical settings. Therefore, AI-guided predictions were combined with ultrasound-based sonodynamic testing using parameters relevant to human therapy. A major challenge in SDT development is the lack of rapid, physiologically relevant tumor models, as traditional 3D organoids take weeks to mature, delaying validation. To address this, a magnetic bioprinting platform was used to produce uniform 3D tumor spheroids within hours, enabling high-throughput screening of SDT. These spheroids replicate key microenvironmental gradients, supporting reliable ultrasound-driven cytotoxicity testing. Through this combined NAM pipeline, AI identified NEO100—an ultrapure, pharmaceutical-grade perillyl alcohol currently in Phase 2a clinical trials—as a promising sonosensitizer candidate. These predictions were validated in rapidly 3D bioprinted tumor models representing glioblastoma, Group 3 pediatric medulloblastoma, meningioma, and breast-to-brain and lung-to-brain metastases. In all tumor types, ultrasound activation significantly increased NEO100’s cytotoxicity. Given its established safety profile in humans and ability to cross the BBB, NEO100 demonstrates how integrating AI-based molecular discovery, accelerated 3D bioprinting, and clinically relevant ultrasound parameters can rapidly advance precision sonodynamic therapies for various primary and metastatic brain cancers.

Kelp forests are under threat worldwide due to a variety of biological and physical factors that include climate change, marine heat waves, storms, coastal development, and overgrazing by sea urchins. This is leading to widespread losses of the important services they provide and an overall reduction in ecosystem value. Chief among these are reductions in the biodiversity of many ecologically and commercially important fishes and invertebrates; lower rates of primary production; altered patterns of seawater chemistry and carbon sequestration; and the loss of biomass that is used for human food, medicine, and industrial and cultural purposes. Therefore, understanding how kelp loss affects these important services and identifying ways to better conserve and restore these iconic forests is of significant economic, social, and ecological concern. This review examines the ecosystem services kelp forests provide and how the loss of these forests affects them. It discusses kelp aquaculture as a potential solution to restore many of these services and explores how advances in our understanding of the kelp microbiome can enhance farming and restoration efforts. It ends with a call for international collaboration in the conservation and restoration of these iconic forests.

Burnout and declining fulfillment are prevalent among facial plastic surgeons (FPS), yet the organizational drivers of these challenges remain underexplored. Ownership structure, workplace autonomy, and collective representation may influence surgeon well-being and workforce sustainability.To assess how autonomy, ownership, and unionization relate to satisfaction and stress among FPS. We hypothesized that private practitioners would report greater autonomy and satisfaction, that academic and hospital-employed surgeons would experience more stress and diminished control, and that prior union exposure would correspond with more favorable views of collective representation.Cross-sectional observational survey (STROBE).A 26-item anonymous survey was distributed to all 1,041 members of the American Academy of Facial Plastic and Reconstructive Surgery; 104 responded (10%). Variables included practice type, ownership, union history, autonomy, compensation satisfaction, stressors, and openness to unionization. Analyses employed chi-square tests and qualitative content analysis.Surgeons in private practice reported the highest autonomy and compensation satisfaction and the lowest rates of feeling rushed. Hospital-employed and academic surgeons were more likely to report diminished autonomy, administrative stress, and lack of influence over workplace decisions. Only 11% of respondents expressed interest in unionizing, though prior union members generally described positive experiences. Surgeons' beliefs about whether unionization affects patient care did not vary significantly by practice setting.Structural factors, particularly the autonomy and ownership model, are strongly linked to satisfaction and stress among FPS. Organizational reforms that enhance autonomy and support collective voice may reduce burnout and improve workforce sustainability.

The emergence of micro frontend architectures has revolutionized the way organizations approach frontend application development, enabling distributed teams to work independently while maintaining system consistency. However, performance optimization in these distributed systems presents unique challenges that differ significantly from traditional monolithic approaches. This study examines performance strategies for micro frontend-based applications through a comprehensive analysis of 30 applications across six key performance metrics. The research reveals significant performance variations across micro frontend implementations, with bundle sizes ranging from 345KB to 550KB and API response times ranging from 155ms to 300ms. Our analysis demonstrates strong correlations between optimization strategies and application performance, particularly highlighting the critical role of lazy loading implementations. Applications achieving lazy loading rates above 50% consistently outperformed those below 40%, with performance score improvements of up to 37 points. The study uses XGBoost regression models to predict key performance metrics, identifying challenges in CPU usage prediction due to overfitting concerns, while achieving exceptional accuracy for bundle size prediction (R² = 0.9647).The performance patterns indicate that successful micro frontend applications require integrated optimization across multiple dimensions, including composition strategies, dependency management, and inter-service communication protocols. The research identifies threshold values for optimal performance, including maintaining bundle sizes below 400KB and implementing aggressive lazy loading strategies. These findings provide actionable insights for development teams working with micro frontend architectures, providing data-driven guidance for architectural decisions and performance strategies in distributed frontend systems.Objective: This study examines performance optimization in micro-frontend-based applications. It analyzes 30 settings across six key metrics, focusing on bundle size, lazy loading, CPU utilization, and response times. The research highlights optimization constraints and a predictive model to guide architectural decisions for scalable, efficient distributed frontends using XGBoost regression.