Atypical meningiomas (AM) comprise a heterogeneous conglomeration of meningiomas with higher local recurrence rates than their benign counterparts. Although adjuvant therapy following subtotal rese...

Background: Carpal tunnel syndrome (CTS) is the most common peripheral neuropathy. Moreover, carpal tunnel release (CTR) surgery generally has excellent results. The present study aimed to investigate the predictors of clinical outcomes and satisfaction in patients with CTR. Methods: In this observational prospective cohort study, 152 patients with open carpal tunnel release surgery were investigated. Complete clinical examinations were performed and recorded before the surgery, two weeks after the surgery and 6 months after the surgery. The Boston Carpal Tunnel Questionnaire (BCTQ) were assessed on admission and at last follow-up visits to evaluate clinical outcomes. Patients’ satisfaction was determined by a 10-point verbal descriptor nominal scale (1 = very poor, 5 = fair and 10 = excellent) and recorded during the last follow -up visits. Results: Among 152 patients who were investigated, there were 118 (77.6%) females and 34 (22.36%) males. Overall, surgery improved the outcomes based on Symptom Severity Scale (SSS) and Functional Status Scale (FSS) (P < 0.05). Most of the considered variables did not show significant effects on clinical outcomes and patients’ satisfaction. However, duration of symptoms and electrophysiological severity were the predictors of the change score in SSS(P < 0.05). As well as, age was the only predictor of the change score in FSS (P < 0.05). Finally, according to the linear regression model, the pre-operative grip strength and age were the independent predictors of post-operative satisfaction (P < 0.05). Conclusions: Results of the present study revealed that there was a significant improvement in clinical outcomes after CTS surgery. Stronger pre-operative grip strength and younger age were independent predictors of higher post-operative satisfaction. These results can be used in pre-operative counseling and management of post-operative expectations.

Introduction: The study of abnormal aggregation of proteins in different tissues of the body has recently earned great attention from researchers in various fields of science. Concerning neurological diseases, for instance, the accumulation of amyloid fibrils can contribute to Parkinson’s disease, a progressively severe neurodegenerative disorder. The most prominent features of this disease are the degeneration of neurons in the substantia nigra and accumulation of α-synuclein aggregates, especially in the brainstem, spinal cord, and cortical areas. Dopamine replacement therapies and other medications have reduced motor impairment and had positive consequences on patients’ quality of life. However, if these medications are stopped, symptoms of the disease will recur even more severely. Therefore, the improvement of therapies targeting more basic mechanisms like prevention of amyloid formation seems to be critical. It has been shown that the interactions between monolayers like graphene and amyloids could prevent their fibrillation. Methods: For the first time, the impact of four types of last-generation graphene-based nanostructures on the prevention of α-synuclein amyloid fibrillation was investigated in this study by using molecular dynamics simulation tools. Results: Although all monolayers were shown to prevent amyloid fibrillation, nitrogen-doped graphene (N-Graphene) caused the most instability in the secondary structure of α-synuclein amyloids. Moreover, among the four monolayers, N-Graphene was shown to present the highest absolute value of interaction energy, the lowest contact level of amyloid particles, the highest number of hydrogen bonds between water and amyloid molecules, the highest instability caused in α-synuclein particles, and the most significant decrease in the compactness of α-synuclein protein. Discussion: Ultimately, it was concluded that N-Graphene could be the most effective monolayer to disrupt amyloid fibrillation, and consequently, prevent the progression of Parkinson’s disease.

Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.

Background: There is a controversy about the management of patients with a thoracolumbar burst fracture. Despite the success of the conservative treatment in most of the cases, some patients failed the conservative treatment. The present study aimed to evaluate risk factors for the need for surgery during the follow-up period in these patients. Methods: We retrospectively evaluated 67 patients with a traumatic thoracolumbar burst fracture who managed conservatively at our center between May 2014 and May 2019. Suggested variables as potential risk factors for the failure of conservative treatment including age, gender, body mass index (BMI), smoking, diabetes, vertebral body compression rate (VBCR), percentage of anterior height compression (PAHC), Cobb angle, interpedicular distance (IPD), canal compromise, and pain intensity as visual analog scale (VAS) were compared between patients with successful conservative treatment and those with failure of non-operative management. Results: There were 41 males (61.2%) and 26 females (38.8%) with the mean follow-up time of 15.52 ± 5.30 months. Overall, 51 patients (76.1%) successfully completed conservative treatment. However, 16 cases (23.9%) failed the non-operative management. According to the binary logistic regression analysis, only age (risk ratio [RR], 2.21; 95% confidence interval [95%], 1.78–2.64; P = 0.019) and IPD (RR 1.97; 95% CI 1.61–2.33; P = 0.005) were the independent risk factors for the failure of the non-operative management. Conclusions: Our results showed that older patients and those with greater interpedicular distance are at a higher risk for failure of the conservative treatment. As a result, a closer follow-up should be considered for them.