Secondary loss of initially spared white and grey matter is a major driver of morbidity after spinal cord injury (SCI). Current treatments have not substantially changed in decades and are limited to surgical decompression and blood pressure management. White matter atrophy after SCI is primarily caused by secondary axonal degeneration (SAD), which is triggered by maladaptive axonal uptake of sodium and calcium through a multitude of ion channels and transporters. While specific inhibitors have been studied, none have been translated into clinical use, in part due to the diverse array of involved channels. Here, we studied whether amiodarone, an FDA-approved antiarrhythmic drug that exerts pleotropic inhibition of multiple sodium and calcium channels, might be neuro- and axonoprotective after SCI precisely because of its broad inhibitory profile. Mice were submitted to off-midline thoracic SCI versus sham surgery and treated with amiodarone versus vehicle control within 15 min and after 4 h of injury. We found that amiodarone treatment after SCI improved locomotor function, which was longitudinally measured over 28 days with the Basso mouse scale, accelerating rotarod, and inclined plane tests. Amiodarone treatment reduced spinal cord atrophy and white matter loss at 28 days after injury, assessed by spinal cord wet weights and by volumetric measurements of grey and white matter in serial coronal sections of spinal cords stained with luxol fast blue and cresyl violet. Amiodarone was directly axonoprotective after SCI, with reduced losses of neurofilament heavy positive axons at 28 days. Interestingly, long-term amiodarone-mediated axonoprotection was accompanied by a reduction of SAD at early time points, measured by counting axonal spheroids 24 h after SCI in fluorescently labeled corticospinal tract axons imaged with light sheet imaging. Overall, these data identify amiodarone as a potentially axonoprotective agent that could be repurposed to treat secondary injury after SCI.
After a traumatic brain injury, around 12% of patients require surgical interventions during their index hospitalization due to delayed or progressive intracranial hemorrhage or complications such as elevated intracranial pressure (ICP)1. Compiling data from four harmonized studies with 288 patients that have high-frequency physiological measurements, including ICP, we aimed to determine factors associated with those surgeries and whether longitudinal physiological measurements could be used to predict the need for craniectomy or craniotomy at least 1 h before the surgery occurred. The outcome was the occurrence of the first cranial surgery 6-120 h post-injury with 2:1 matched controls for those without surgery. Covariates included baseline characteristics and dynamic physiological measurements. Univariate associations were assessed, and the area under the receiving operating characteristic curve (AUC) was used to compare various machine learning and multivariable statistical models for the prediction of surgery. It was found that means, medians, and transgressions of both ICP and mean arterial pressure, as well as the linear regression slope of ICP by time in the 6 h prior to surgery, were significantly and independently related to whether a patient had cranial surgery or not. The best-performing model was found using random forests supervised learning algorithm (AUC = 0.75, 95% confidence interval 0.61-0.88). This model may assist clinicians in predicting when they may need to perform an emergent neurosurgical procedure, thus preventing more damage from elevated ICPs.
In 2023, the American Congress of Rehabilitation Medicine Brain Injury Interdisciplinary Special Interest Group (ACRM BI-ISIG) Mild Traumatic Brain Injury (TBI) Task Force published updated diagnostic criteria for mild TBI. These criteria were developed in collaboration with a panel of 32 subject matter experts in mild TBI using the Delphi method. The 2023 ACRM diagnostic criteria marked the first update since 1993, incorporating three decades of research advancements in our understanding of mild TBI. To facilitate the consistent use of the new diagnostic criteria, the ACRM BI-ISIG Mild TBI Task Force initiated a special project in September 2023 to develop a structured interview to apply the ACRM diagnostic criteria for mild TBI in clinical and research settings. The purpose of this article is to describe the development of the ACRM Structured TBI Interview and the accompanying documents. The ACRM Structured TBI Interview was developed in four phases: (1) initial development of a draft interview by two project leads, (2) review and revision over three rounds by 17 members of the ACRM BI-ISIG Mild TBI Task Force, (3) external review by 19 subject matter experts in mild TBI, and (4) field testing of the ACRM Structured TBI Interview by 11 interviewers who completed 25 diagnostic interviews. In addition to the ACRM Structured TBI Interview, three other documents were developed to help facilitate the administration of the interview (Administration Guide) and to apply the diagnostic criteria (Diagnostic Coding Form and Diagnostic Flow Diagram). A Short Form was also developed for use in contexts where administering the full structured interview is not feasible due to time constraints.
The increasing use of artificial intelligence-driven chatbots for medical queries requires a systematic evaluation of their accuracy, reliability, and potential role in patient education. This study assesses the performance of three widely used chatbots-ChatGPT, Google Gemini, and Microsoft CoPilot-in answering patient-oriented questions related to traumatic brain injury (TBI). A standardized set of questions related to TBI was developed, divided into eight subtopics, and presented to each chatbot using unified prompts. The responses were evaluated together with reference answers prepared by experts from a group of specialists in the fields of neurology, neurosurgery, and neurorehabilitation, and subsequently assessed in a survey of patients undergoing rehabilitation for TBI. Performance was evaluated using a modified scoring framework in five key dimensions of quality. Statistical analysis included multivariate analysis of variance to compare chatbot performance and logistic regression analysis to determine the likelihood of chatbot responses being considered an adequate substitute for expert advice. Significant differences between the chatbots were found in several quality dimensions, with ChatGPT scoring higher than Gemini and CoPilot on reliability, responsiveness, and perceived trustworthiness (p < 0.05). No chatbot consistently demonstrated an advantage in conveying empathy. Logistic regression analysis revealed that responses from ChatGPT were significantly more likely to be rated as an adequate substitute for expert input (p < 0.0001, OR = 4.3, 95% CI: 2.4-7.6). AI-driven chatbots vary in their ability to provide high-quality medical information, with significant differences in reliability and responsiveness. While ChatGPT outperformed other models in providing structured information, further improvements in context awareness and empathy are needed before broader clinical integration can be considered.
Neurogenic lower urinary tract dysfunction (NLUTD) is a major cause of morbidity and reduced quality of life after spinal cord injury (SCI). In pre-clinical research, small and large animal models such as rats, dogs, and minipigs have been used to investigate NLUTD through urodynamic studies (UDS) such as conventional filling cystometry. Although filling cystometry is currently considered the gold standard for bladder monitoring in pre-clinical research, this approach has several well-recognized limitations. The aim of this study was to develop and evaluate the feasibility of an implantable, radiotelemetric system for monitoring bladder pressure in a Yucatan minipig model of SCI. The transmitter was surgically implanted in the dome of the bladder and several UDS experiments were conducted to evaluate the system's effectiveness at measuring pressure compared to conventional UDS equipment. We observed a strong correlation and agreement between the transmural telemetry sensor and the UDS system. There was no significant difference between bladder compliance and baseline bladder pressure between the two sensor systems. However, the telemetry system recorded significantly lower voiding and non-voiding contraction pressure amplitudes as well as lower voiding threshold pressures and detrusor after-contraction measured with the telemetry system. The telemetry system appeared to be a reliable and accurate method for assessing bladder pressure and allowed for an evaluation of urodynamics in a pig model of SCI for several months. The application of this method could enable a more detailed in vivo evaluation of NLUTD after SCI and a better understanding of micturition behavior during natural-filling, ambulatory urodynamics.
The Blast Exposure Threshold Survey (BETS) is a recently developed measure of lifetime blast exposure. Although promising, it is considered a fundamental tenet to establish that the BETS (and other measures like it) have good psychometric properties before it can be recommended for clinical use. The purpose of this study was to examine the test-retest reliability of the BETS in a military sample. Participants were 83 United States service members and veterans prospectively recruited from three military medical treatment facilities and from the community. Participants were classified into two broad groups as part of a larger study: traumatic brain injury (TBI; n = 41; mild-severe TBI) and controls (n = 42; injured and non-injured controls). Participants completed the BETS, Neurobehavioral Symptom Inventory, and a brief structured interview to gather basic demographic, military, and injury-related information (e.g., age, education, deployments, etc.). In addition, participants completed the BETS on a second occasion (T2) 3 weeks following the first administration (T1). Using Spearman rho correlation analyses, the test-retest reliability of the BETS Generalized Blast Exposure Value (GBEV) was classified as "acceptable" (r = 0.76). However, when comparing individual responses across T1 and T2, 33% of the sample reported significant inconsistencies in the endorsement of the five weapons categories. The most problematic inconsistency (∼10% of the sample) related to the failure of some participants to consistently endorse, or not endorse, exposure to a weapons category at T1 and T2 (e.g., T1 = exposure present; T2 = exposure absent). Less problematic, but also of concern, was the failure of some participants (∼23%) to consistently report the same number of years in which they were exposed to a weapons category from T1 and T2 (e.g., T1 = 10 years; T2 = 5 years). Factors associated with inconsistent reporting from T1 to T2 included higher GBEV scores, older age, higher number of years in the military, higher number of deployments, and higher blast exposure. This is one of the first studies to comprehensively examine the test-retest reliability of the BETS GBEV. Overall, the test-retest reliability of the GBEV was considered statistically acceptable and provides support for the use of the GBEV in both clinical and research settings. Concerningly, however, substantial inconsistencies were found in the basic reporting of weapons exposure in 33% of the sample that need to be addressed. Future researchers should identify ways to improve the BETS to increase response consistency over time.
Acute brain injury (ABI) is a severe neurological disorder in which inflammation and immune responses play a key role, with the Triggering Receptor Expressed on Myeloid Cells-1 (TREM1) being involved. Inhibition of TREM1 can alleviate neuroinflammation and damage, but the evidence from these pre-clinical studies remains unclear. This study summarizes and evaluates the results of animal experiments on the treatment of ABI with the TREM1 inhibitor LP17, exploring the effects of using LP17 to treat ABI animal models on neurological function, inflammatory indicators, and brain barrier function. As of April 30, 2025, this review conducted a detailed search of eight databases for studies on LP17 in ABI animal models. It performed a systematic review and meta-analysis of the included studies. The literature was independently screened, and data were extracted and assessed. RevMan 5.4 software was used for the meta-analysis. Compared with controls, the TREM1 inhibitor LP17 significantly reduced brain water content (standardized mean difference [SMD]: -1.36; 95% confidence interval [CI]: -1.77, -0.94; p < 0.00001) and neurological deficit scores (SMD: -1.37; 95% CI: -1.76, -0.97; p < 0.00001). It also decreased the expression of pro-inflammatory cytokines, including IL-1β (SMD: -1.88; 95% CI: -2.63, -1.13; p < 0.00001) and TNF-α (SMD: -2.91; 95% CI: -3.89, -1.92; p < 0.00001). LP17 mitigated blood-brain barrier (BBB) disruption (SMD: -1.58; 95% CI: -2.47, -0.68; p = 0.0005) and enhanced ZO-1 expression (SMD: 2.77; 95% CI: 1.73, 3.80; p < 0.00001). It also inhibited the activation of nuclear factor-κB (SMD: -1.70; 95% CI: -2.58, -0.83; p = 0.0001), NLRP3 (SMD: -2.33; 95% CI: -3.27, -1.39; p < 0.00001), and Caspase-1 (SMD: -2.03; 95% CI: -2.92, -1.14; p < 0.00001). LP17 has neuroprotective effects in ABI animal models, likely through reducing neuroinflammation, preserving BBB integrity, and inhibiting apoptotic pathways. Further studies are needed to explore its mechanisms to better guide clinical use.
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