Seton Hall University

Anarthria is a lack of verbal communication caused by physiological disturbances in the motor pathway. While affected individuals retain the ability to comprehend and produce speech, orofacial paralysis renders them unable to execute speech. Anarthria can be caused by amyotrophic lateral sclerosis, stroke, traumatic brain injury, and other etiologies that affect the descending motor pathway. A wide range of technologies has been developed and tested to improve communication efficiency for patients with anarthria and accompanying paralysis. This review evaluates three key eras of communication device development. First, before implantation devices gained traction, many communication devices revolved around blinks, head and eye tracking, and non-invasive brain recording. Second, implanted cortical neuroprosthetics were designed to improve accuracy and speed of communication. Finally, the review analyzes the future era, where accessibility, patient comfort, and broader applications of neural analysis elevate communication for patients with anarthria to match fluid communication. Restoring speech communication in patients with anarthria is vital to improve their quality of life. Therefore, understanding communication device efficiency and its future trajectory is of utmost clinical importance.

Background: Dialysis disequilibrium syndrome (DDS) is a rare but serious neurologic complication of hemodialysis caused by rapid osmotic shifts leading to cerebral edema. Although well recognized in nephrology, DDS is underappreciated in neurosurgical practice, where it often presents only after significant intracranial hypertension has developed. As dialysis use expands, early neurosurgical awareness is increasingly important.Methods: This narrative review synthesizes current evidence on DDS pathophysiology, clinical features, diagnostic considerations, and management, with a specific focus on neurosurgical implications. Data from case reports, pooled analyses, and neurocritical care studies were examined to characterize intracranial pressure (ICP) changes associated with renal replacement therapy and to identify points for multidisciplinary intervention.Results: DDS most frequently occurs during early or rapid hemodialysis in patients with markedly elevated urea levels. A pooled analysis shows ICP elevation in up to 73% of neurosurgical patients receiving intermittent hemodialysis versus 38% treated with continuous modalities. Despite these risks, standardized neurosurgical protocols for prevention or monitoring remain lacking. Evidence supports gradual urea clearance, early use of continuous therapies, and timely hyperosmolar treatment when cerebral edema develops. However, delayed recognition and limited cross-disciplinary coordination continue to hinder effective management.Conclusion: DDS is a preventable neurocritical complication that requires stronger integration into neurosurgical practice. Improved interdisciplinary communication, structured monitoring strategies, and incorporation of DDS into neurocritical care training could support earlier recognition and safer dialysis initiation. Reframing DDS as a shared neurosurgical–nephrology responsibility is essential to moving from reactive treatment toward prevention and improving neurologic outcomes.

Many drugs prescribed to children have not been formally studied or approved for pediatric use, leaving clinicians without age-specific, high-quality clinical trial data to inform dosing, safety, and efficacy.1–3 The 2003 Pediatric Research Equity Act (PREA) authorizes the US Food and Drug Administration (FDA) to require pediatric studies for certain drug and biological products.4,5 Since its enactment, PREA has been impactful: between September 2007 and December 2019, 937 studies were completed under PREA, catalyzing pediatric research and contributing to important prescribing information across a range of products.6 However, delays in fulfilling PREA requirements are well documented.1–3,7 Up to 64% of new drugs lack pediatric labeling 5 years after approval, and only one-third of required studies are completed after a median of nearly 7 years.1,2To enforce PREA, the FDA has the authority to issue noncompliance (NC) letters when sponsors fail to meet deadlines specified in postmarketing requirements (PMRs). An NC letter may be issued when a sponsor fails to (1) submit a required pediatric assessment within the specified timeframe (ie, provide pediatric study data); (2) request, with adequate justification, a waiver or deferral of the required studies; or (3) submit a request for approval of a required pediatric formulation.4 The effectiveness of NC letters has not been well studied. We examined all PREA-related NC letters issued between 2015 and 2023 and identified patterns in sponsor responses and postmarketing study status.We conducted a retrospective review of NC letters issued under PREA between January 1, 2015, and December 31, 2023, using 4 publicly available FDA resources (Supplemental Table 1). Sponsors were classified as brand, generic, or other; each PMR was classified by status, study type, and age group (Table 1; Supplemental Table 2). Sponsor-reported explanations for NC were categorized into themes using inductive content analysis with iterative refinement and independent verification. Data were extracted by one author and verified by another, and discrepancies were resolved by consensus. Expanded methods are provided in the Supplemental Materials.Between 2015 and 2023, the FDA issued 100 NC letters to 74 sponsors referencing 74 products and 122 PMRs (Supplemental Table 3). PMRs spanned all pediatric age groups, with required study types including safety (32%), pharmacokinetic/pharmacodynamic (26%), and efficacy (20%). At the time of data collection, 58 noncompliant PMRs (48%) remained delayed, 16 (13%) were fulfilled, 9 (7%) were submitted, and 13 (11%) were released (Table 1). Seven letters referencing nine PMRs received no response. Median time from product approval to NC letter issuance was 6.8 years (interquartile range [IQR], 4.6–9.4) and median time to sponsor response was 41 days (IQR, 28–47).Common reasons for NC included protocol or study design challenges (n = 25), no explanation provided (n = 23), and enrollment difficulties (n = 19). Seventeen sponsors attributed delays to the COVID-19 pandemic (Table 2; Supplemental Table 4).Although PREA has advanced pediatric research, our analysis highlights persistent gaps in enforcement. Nearly half of PMRs remained delayed even after NC letter issuance, indicating that the FDA’s current enforcement power is insufficient to ensure the timely completion of mandated pediatric studies. These delays leave patients and clinicians without age-specific, high-quality clinical trial data to guide prescribing.Noncompliant PMRs spanned a wide range of products, study types, and age groups, underscoring established systemic obstacles in pediatric drug development.1–3,8,9 Sponsor explanations for NC most often reflected feasibility challenges rather than regulatory resistance. This pattern highlights opportunities for targeted solutions such as innovative trial designs, investment in pediatric formulations, and strategies to improve enrollment.This work also highlights a lack of available data. The total number of delayed or otherwise incomplete pediatric PMRs is not publicly reported, limiting the ability to place NC letters in the broader context of PREA enforcement. Greater data transparency and enhanced enforcement authority would strengthen FDA oversight and help ensure timely access to safe and effective therapies for children.The authors appreciate assistance in data collection provided by Jay Crawford, Samantha Girschick, and Addie Morgan.

Software engineering education must be guided by developments in software engineering and aim for professional and educational development of students. This position paper explores the historical and conceptual evolution of software engineering as a discipline central to modern integrated design, software systems, and process science, tracing the trajectory from early structured models, through waterfall, to Agile and AI-augmented paradigms, and looks at how changes in application mix and modes of use, technological complexity, and business and enterprise needs have shaped development methodologies. Special attention is paid to the role of generative AI on the software engineering process and the role of the software engineer, altering workflows, collaboration, and enterprise architectures, and further enabling automation, digital transformation, and autonomous systems, shifting responsibilities and skill sets upwards. The article then considers corresponding shifts in software engineering education, highlighting the need for curricula that intertwine process thinking, systems theory, and ethical engagement, integrate with generative AI technologies, and place greater emphasis on “soft skills”, in courses that combine development of professional expertise with conceptual capabilities and an enhanced capacity for life-long learning. It then presents a set of requirements, options, and guidelines for software engineering education in the era of generative AI.

Antimicrobial resistance has emerged as a significant global public health concern, necessitating prompt and comprehensive attention. In response to this pressing challenge, antimicrobial peptides (AMPs), which are innate immune system components naturally synthesized by diverse organisms, hold potential as alternative therapeutic agents. However, a primary obstacle resides in the absence of effective experimental methodologies capable of investigating transient behaviors of membrane active peptides at the atomic scale. In this study, we have employed all-atom molecular dynamics (MD) simulations to elucidate the mechanisms underlying the behavior of two magainin peptides and their equimolar mixture, namely PGLa and magainin 2 (Mag2). Despite extensive investigation spanning decades, the fundamental mechanisms governing these peptides are still not fully understood. Our findings demonstrate that simulation time scales over 10 μs can capture the formation of pores within bacterial model membrane mimics and obtain comprehensive insights into the spontaneous self-assembly of magainin peptides. An hourglass pore model is proposed to explain their transient membrane perforation, where peptides self-assemble to support the pore structure from both membrane leaflets as double stacks. Atomistic MD simulations provide an unprecedented direct view on selective transmembrane binding, pore formation, pore fluctuation and ultimately pore closure. These insights offer a new promising path for the development of peptide-based therapeutics tailored to combat bacterial infections.

SummaryBackgroundChronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality globally. Effective management hinges on early diagnosis, which is often impeded by non-specific symptoms and resource-intensive diagnostic methods. This study assesses the effectiveness of electrocardiograms (ECGs) analysed via deep learning as a tool for early COPD detection.MethodsWe utilised a Convolutional Neural Network model to analyse ECGs for detecting COPD. The primary outcome was the accuracy of a new clinical COPD diagnosis as determined by ICD codes. Performance was evaluated using Area-Under-the-Curve (AUC) metrics derived by testing against ECGs from a set of holdout patients, ECGs from patients from another hospital, and ECGs of patients with COPD within the UK BioBank (UKBB).FindingsWe analysed a total of 208,231 ECGs from 18,225 COPD cases, matched to 49,356 controls by age, sex, and race. The model exhibited robust performance across diverse populations with an AUC of 0⋅80 (0⋅80–0⋅80) in internal testing, 0⋅82 (0⋅81–0⋅82) in external validation and 0⋅75 (0⋅71–0⋅78) in the UKBB cohort. Subsequent analyses linked ECG-derived model predictions with spirometry data, and model explainability highlighted P-wave changes as indicative of COPD.InterpretationAI-powered ECG analysis offers a promising path for early COPD detection, potentially facilitating earlier and more effective management. Implementing such tools in clinical settings could significantly enhance COPD screening and diagnostic accuracy, thereby improving patient outcomes and addressing the global health burden of the disease.FundingThis work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the 10.13039/100007277Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the 10.13039/100006108National Center for Advancing Translational Sciences; and R01HL167050-02 from the 10.13039/100000050National Heart, Lung, and Blood Institute.