Realizing the gravity of the simulation: adaptation to simulated hypogravity leads to altered predictive control.

Accurate predictive abilities are important for a wide variety of animal behaviors. Inherent to many of these predictions is an understanding of the physics that underlie the behavior. Humans are specifically attuned to the physics on Earth but can learn to move in other environments (e.g., the surface of the Moon). However, the adjustments made to their physics-based predictions in the face of altered gravity are not fully understood. The current study aimed to characterize the locomotor adaptation to a novel paradigm for simulated reduced gravity. We hypothesized that exposure to simulated hypogravity would result in updated predictions of gravity-based movement. Twenty participants took part in a protocol that had them perform vertically targeted countermovement jumps before (PRE), during, and after (POST) a physical simulation of hypogravity. Jumping in simulated hypogravity had different neuromechanics from the PRE condition, with reduced ground impulses (p ≤ .009) and muscle activity prior to the time of landing (i.e., preactivation; p ≤ .016). In the 1 g POST condition, muscle preactivation remained reduced (p ≤ .033) and was delayed (p ≤ .008) by up to 33% for most muscles of the triceps surae, reflecting an expectation of hypogravity. The aftereffects in muscle preactivation, along with little-to-no change in muscle dynamics during ground contact, point to a neuromechanical adaptation that affects predictive, feed-forward systems over feedback systems. As such, we conclude that the neural representation, or internal model, of gravity is updated after exposure to simulated hypogravity.

Minimally invasive resection of intracranial lesions using tubular retractors: A single surgeon series.

OBJECTIVE: Tubular retractors are increasingly used due to their low complication rates, providing easier access to lesions while minimizing trauma from brain retraction. Our study presents the most extensive series of cases performed by a single surgeon aiming to assess the effectiveness and safety of a transcortical-transtubular approach for removing intracranial lesions. METHODS: We performed a retrospective review of patients who underwent resection of an intracranial lesion with the use of tubular retractors. Electronic medical records were reviewed for patient demographics, preoperative clinical deficits, diagnosis, preoperative and postoperative magnetic resonance imaging (MRI) scans, lesion characteristics including location, volume, extent of resection (EOR), postoperative complications, and postoperative deficits. RESULTS: 112 transtubular resections for intracranial lesions were performed. Patients presented with a diverse number of pathologies including metastasis (31.3 %), GBM (21.4 %), and colloid cysts (19.6 %) The mean pre-op lesion volume was 14.45 cm3. A gross total resection was achieved in 81 (71.7 %) cases. Seventeen (15.2 %) patients experienced early complications which included confusion, short-term memory difficulties, seizures, meningitis and motor and visual deficits. Four (3.6 %) patients had permanent complications, including one with aphasia and difficulty finding words, another with memory loss, a third with left-sided weakness, and one patient who developed new-onset long-term seizures. Mean post-operative hospitalization length was 3.8 days. CONCLUSION: Tubular retractors provide a minimally invasive approach for the extraction of intracranial lesions. They serve as an efficient tool in neurosurgery, facilitating the safe resection of deep-seated lesions with minimal complications.

Machine learning applications in craniosynostosis diagnosis and treatment prediction: a systematic review.

Craniosynostosis refers to the premature fusion of one or more of the fibrous cranial sutures connecting the bones of the skull. Machine learning (ML) is an emerging technology and its application to craniosynostosis detection and management is underexplored. This systematic review aims to evaluate the application of ML techniques in the diagnosis, severity assessment, and predictive modeling of craniosynostosis. A comprehensive search was conducted on the PubMed and Google Scholar databases using predefined keywords related to craniosynostosis and ML. Inclusion criteria encompassed peer-reviewed studies in English that investigated ML algorithms in craniosynostosis diagnosis, severity assessment, or treatment outcome prediction. Three independent reviewers screened the search results, performed full-text assessments, and extracted data from selected studies using a standardized form. Thirteen studies met the inclusion criteria and were included in the review. Of the thirteen papers examined on the application of ML to the identification and treatment of craniosynostosis, two papers were dedicated to sagittal craniosynostosis, five papers utilized several different types of craniosynostosis in the training and testing of their ML models, and six papers were dedicated to metopic craniosynostosis. ML models demonstrated high accuracy in identifying different types of craniosynostosis and objectively quantifying severity using innovative metrics such as metopic severity score and cranial morphology deviation. The findings highlight the significant strides made in utilizing ML techniques for craniosynostosis diagnosis, severity assessment, and predictive modeling. Predictive modeling of treatment outcomes following surgical interventions showed promising results, aiding in personalized treatment strategies. Despite methodological diversities among studies, the collective evidence underscores ML's transformative potential in revolutionizing craniosynostosis management.