Maternal malnourishment induced upregulation of fetuin-B blunts nephrogenesis in the low birth weight neonate.

Maternal undernutrition during pregnancy (MUN) often leads to low birth weight (LBW) neonates that have a reduced total nephron endowment, leaving these neonates susceptible to kidney disease throughout their lives. For reasons unknown, these LBW neonates have impaired kidney development due to a severe reduction in renal SIX2+ stem cells during nephrogenesis. Using a mouse model of MUN, we investigated SIX2+ stem cell reduction in the LBW neonate. Significant upregulation of the protein fetuin-B (measured by PCR and immunoblotting) in the MUN mother's placenta, organs and circulation yielded a 3-fold increase of this protein in the embryonic kidney. Recombinant fetuin-B, administered to healthy pregnant mothers at the concentration equivalent to that in the MUN mother, crossed the placenta and reduced both SIX2+ stem cells by 50% and nephron formation by 66% in embryonic kidneys (measured by immunofluorescence and the physical dissector/fractionator stereological method). Administration of fetuin-B to kidney explants yielded similar reductions in renal SIX2+ stem cells and nephron formation. Fetuin-B treatment of isolated embryonic renal SIX2+ stem cell primary cultures 1) increased NF-kB activity and apoptosis, 2) reduced cell proliferation due to upregulated p21 nuclear activity and subsequent cell cycle arrest, and 3) enhanced generation of reactive oxygen species (measured by fluorescence microscopy). In conclusion, MUN increases fetuin-B in the developing embryonic kidney. The increase in fetuin-B blunts nephrogenesis by reducing SIX2+ stem cells by promoting their apoptosis (via NF-kB upregulation), blunting their proliferative renewal (via p21 upregulation) and enhancing oxidative stress.

Sonolucent Cranial Implants: A Window into the Future of Management of Neurosurgical Patients? A Systematic Review and Cost Analysis.

BACKGROUND: Computeed tomography (CT) is a cornerstone of the identification and management of acute changes in neurosurgery patients. In addition to the monetary expense of CT scans, further costs are incurred due to the time of patient transport and radiation exposure. Ultrasounds (USs)offer a safe, inexpensive, and bedside alternative to CT but obstacles remain due to decreased penetrance in the adult skull. Sonolucent Cranial Implants (SCIs) offer a window for USs to view intracranial architectures. METHODS: The authors performed a PRISMA guidelines-based systematic review of the literature. Information was extracted from included articles in regards to illness pathology, US imaging feasibility, comparison to standard imaging, infections, and revisions. Costs were collected in regards to price of implant and follow-up imaging. RESULTS: A total of 226 articles resulted, of which 5 were included in the study. Ninety non-duplicate patients who received SCIs were analyzed. The pathologies of included patients is as follows: 51 patients were after extracranial-intracranial bypass, 37 after ventriculoperitoneal shunt placement for hydrocephalus, 1 after tumor resection, and 1 after cranioplasty following decompressive hemicraniectomy. All studies noted feasibility of US and comparability to standard imaging following SCI placement. Follow-up imaging with trans-sonolucent cranial implant ultrasound was estimated to save up to $4,000 per patient depending on the procedure. CONCLUSIONS: Initial studies suggest that US imaging through SCIs is a safe and efficacious alternative to CT imaging in neurosurgical patients. Cost analysis suggests that SCI and subsequent US can offer a cost savings compared with current treatment.

Impact of environmental pollutants on pediatric brain tumor incidence in New Jersey.

OBJECTIVE: The relationship between environmental contaminants and brain tumor incidence in adults has been thoroughly explored but research into how these contaminants affect pediatric brain tumor (PBT) incidence has not been explored. Children, typically having more limited geographical movement and thus more consistent environmental contaminant exposure, might offer more reliable insights into which environmental contaminants affect the incidence of brain tumors. The present study is the first to focus on exploring whether a possible association exists between the incidence of PBTs and exposure to environmental pollutants in New Jersey (NJ). METHODS: Linear regressions were run between PBT incidence and the concentration of air quality pollutants such as Ozone (O3), Particulate Matter 2.5 (PM2.5), Particulate Matter 10 (PM10), and Carbon Monoxide (CO). Similarly, linear regressions were run between PBT incidence and Elevated Blood Lead Levels (BLL). RESULTS: The study observed a significant positive relationship between O3 and PBT incidence (ß = 0.34, p = 0.028). However, the relationship between PBT incidence, and environmental pollutants such as CO (ß = 0.0047, p = 0.098), PM2.5 (ß = -0.2624, p = 0.74), and PM10 (ß = -0.7353, p = 0.073) were found to be nonsignificant. For elevated BLL, nonsignificant relationships with PBT incidence were observed at 10-14 µg/dL (ß = -39.38, p = 0.30), 15-19 µg/dL (ß = -67.00, p = 0.21), and 20-44 µg/dL (ß = -201.98, p = 0.12). CONCLUSIONS: The results indicate a possible impact of O3 on the incidence of PBTs in NJ. In contrast to the significant links found in prior studies of adult brain tumors, the associations between PBT occurrence and particulate matter were not significant. These findings highlight the importance of further investigating how environmental factors, especially O3, relate to PBTs.

Analysis of Concussions Reported in American Professional Football Injury Reports in the 2019 Through 2023 Regular Seasons.

OBJECTIVE: The National Football League (NFL) has seen increasing scrutiny regarding its management of concussions, especially following an on-field incident involving the Miami Dolphins' quarterback Tua Tagovailoa in the 2022 season. We hope to elucidate the recent trends in the diagnosis and management of concussions during the course of 5 NFL seasons from 2019 to 2023. METHODS: We queried the NFL injury reports from the 2019 through 2023 database recording players listed with concussions. The weeks missed were calculated using the NFL game logs. Players' concussions that did not occur in the games, those complicated by other injuries, and those affected by roster status were excluded. RESULTS: Searches of the NFL injury reports resulted in the identification of 664 of 692 concussions (96%) that occurred in regular season games across the 2019-2023 seasons. During the course of these 5 seasons, 31% of the players returned without missing a game, 39% of the players missed 1 game, and 30% of the players missed ≥2 games. No significant difference in the number of concussions per game or weeks missed was observed across the seasons observed. Players with concussions on teams that made the playoffs saw fewer weeks missed than those on non-playoff teams (0.86 vs. 1.37; P = 0.002). CONCLUSIONS: Since the start of the 2021 NFL season, an increasing incidence of concussions has been noted; however, there was no change observed in the number of weeks missed after the concussions. Trends in the rates of concussions across the seasons remain largely stable, despite increased scrutiny over concussions in the sport.

Can Publicly Available Artificial Intelligence Successfully Identify Current Procedural Terminology Codes for Common Procedures in Neurosurgery?

OBJECTIVE: Coding for neurosurgical procedures is a complex process that is dynamically changing year to year, through the annual introduction and removal of codes and modifiers. The authors hoped to elucidate if publicly available artificial intelligence (AI) could offer solutions for neurosurgeons with regard to coding. METHODS: Multiple publicly available AI platforms were asked to provide Current Procedural Terminology (CPT) codes and Revenue Value Units (RVU) values for common neurosurgical procedures of the brain and spine with a given indication for the procedure. The responses of platforms were recorded and compared to the currently valid CPT codes used for the procedure and the amount of RVUs that would be gained. RESULTS: Six platforms and Google were asked for the appropriate CPT codes for 10 endovascular, spinal, and cranial procedures each. The highest performing platforms were as follows: Perplexity.AI identified 70% of endovascular, BingAI identified 55% of spinal, and ChatGPT 4.0 with Bing identified 75% of cranial CPT codes. With regard to RVUs, the top performer gained 78% of endovascular, 42% of spinal, and 70% of cranial possible RVUs. With regard to accuracy, AI platforms on average outperformed Google (45% vs. 25%, P = 0.04236). CONCLUSIONS: The ability of publicly available AIs to successfully code for neurosurgical procedures holds great promise in the future. Future development of AI should focus on improving accuracy with regard to CPT codes and providing supporting documentation for its decisions. Improvement on the existing capabilities of AI platforms can allow for increased operational efficiency and cost savings for practices.

Can Artificial Intelligence Mitigate Missed Diagnoses by Generating Differential Diagnoses for Neurosurgeons?

BACKGROUND/OBJECTIVE: Neurosurgery emphasizes the criticality of accurate differential diagnoses, with diagnostic delays posing significant health and economic challenges. As large language models (LLMs) emerge as transformative tools in healthcare, this study seeks to elucidate their role in assisting neurosurgeons with the differential diagnosis process, especially during preliminary consultations. METHODS: This study employed 3 chat-based LLMs, ChatGPT (versions 3.5 and 4.0), Perplexity AI, and Bard AI, to evaluate their diagnostic accuracy. Each LLM was prompted using clinical vignettes, and their responses were recorded to generate differential diagnoses for 20 common and uncommon neurosurgical disorders. Disease-specific prompts were crafted using Dynamed, a clinical reference tool. The accuracy of the LLMs was determined based on their ability to identify the target disease within their top differential diagnoses correctly. RESULTS: For the initial differential, ChatGPT 3.5 achieved an accuracy of 52.63%, while ChatGPT 4.0 performed slightly better at 53.68%. Perplexity AI and Bard AI demonstrated 40.00% and 29.47% accuracy, respectively. As the number of considered differentials increased from 2 to 5, ChatGPT 3.5 reached its peak accuracy of 77.89% for the top 5 differentials. Bard AI and Perplexity AI had varied performances, with Bard AI improving in the top 5 differentials at 62.11%. On a disease-specific note, the LLMs excelled in diagnosing conditions like epilepsy and cervical spine stenosis but faced challenges with more complex diseases such as Moyamoya disease and amyotrophic lateral sclerosis. CONCLUSIONS: LLMs showcase the potential to enhance diagnostic accuracy and decrease the incidence of missed diagnoses in neurosurgery.