Innovations in clinical neuroscience最新文献

Objective: The objective of this study was to review the characteristics, efficacy, and safety of suzetrigine in the treatment of acute pain.

Methods: Literature was identified using PubMed and clinicaltrials.gov. Search terms included suzetrigine, Journvax, and VX-548, with further limitation to those published in English. Articles selected for inclusion were trials evaluating the safety or efficacy of suzetrigine.

Results: Suzetrigine was evaluated for the treatment of moderate-to-severe acute pain in 2 published phase 2 trials and in 3 unpublished phase 3 trials. All studies report a statistically significant reduction in pain scores. High-dose suzetrigine showed statistically and clinically significant pain reduction vs placebo in abdominoplasty 48.4 (95% confidence interval [CI]: 33.6-63.1; P<0.0001), and bunionectomy 29.3 (95% CI: 14.0-44.6; P=0.0002). Additional unpublished data support potential future use in additional pain indications. Suzetrigine was well tolerated, with pruritus, muscle spasm, constipation, and headache reported as the most common side effects. Suzetrigine is a promising nonopioid alternative for acute pain and is the first of a potential new class of analgesics. However, its comparative efficacy to standard analgesics has not been established, nor has its efficacy when used beyond 14 days.

Conclusion: The recent United States Food and Drug Administration approval of suzetrigine introduces a novel class of analgesics; however, its clinical impact has yet to be defined.

Treatment-resistant depression (TRD) is a severe form of depression associated with high rates of disability and suicide, underscoring the need for effective therapeutic alternatives. In recent years, subanesthetic intravenous (IV) ketamine has emerged as a promising intervention due to its rapid antidepressant and antisuicidal effects. This report describes the case of a 41-year-old male patient with TRD treated with IV ketamine at a public hospital in Argentina-the first such reported case in this setting. We detail the diagnostic process, treatment protocol, outcomes, and practical considerations in implementing this novel therapy within a public health system.

To identify strokes, which rank among the leading global causes of death, doctors commonly employ computed tomography (CT) imaging. Researchers are increasingly applying deep learning (DL) and machine learning (ML) methods to improve the accuracy of stroke classification and detection. In particular, DL methods have demonstrated significant potential in segmenting stroke regions within brain medical images. However, training convolutional neural networks from scratch is challenging due to dataset size constraints. Achieving timely and precise stroke diagnosis remains a significant hurdle. The purpose of this research is to classify brain stroke cases using CT scan images through transfer learning strategies. Pretrained architectures including ResNet, VGG, Inception, and EfficientNet are utilized, with optimization carried out in the fastai framework to improve computational efficiency and reduce training time. We developed these artifical intelligence (AI) systems by training them with data, making adjustments throughout the process to ultimately categorize patients as having a stroke or not. We evaluated different models using measures such as accuracy, speed, and how well they differentiate normal and stroke-affected CT scans of the brain. This helped us choose the most effective model. In this research, ResNet50 outperformed other models in accuracy (88%), and DCNN was the most time-efficient model.

This ongoing column is dedicated to providing information to our readers on managing legal risks associated with medical practice. We invite questions from our readers. The answers are provided by PRMS (www.prms.com), a manager of medical professional liability insurance programs with services that include risk management consultation and other resources offered to health care providers to help improve patient outcomes and reduce professional liability risk. The answers published in this column represent those of only one risk management consulting company. Other risk management consulting companies or insurance carriers might provide different advice, and readers should take this into consideration. The information in this column does not constitute legal advice. For legal advice, contact your personal attorney. Note: The information and recommendations in this article are applicable to physicians and other healthcare professionals so "clinician" is used to indicate all treatment team members.

The management of schizophrenia, a complex and often debilitating mental illness, has traditionally centered on pharmacological interventions. However, the significant adverse effects of antipsychotic medications and the incomplete therapeutic response in many patients have spurred the investigation of adjunctive treatments. Nutritional psychiatry has emerged as a compelling area of interest, with a growing body of research suggesting that specific dietary and nutrient-based interventions could positively impact the underlying pathophysiology of schizophrenia. A narrative review of the scientific literature was conducted, focusing on systematic reviews, meta-analyses, and clinical trials examining nutritional interventions in individuals with schizophrenia. The evidence supporting the use of nutritional interventions in schizophrenia is, at present, largely preliminary and often inconclusive. The review also highlights significant methodological limitations across the existing literature, including small sample sizes, short intervention periods, lack of standardized dietary protocols, and failure to account for the vast individual differences in genetics, metabolism, and baseline nutritional status. Nutritional interventions hold theoretical promise as a safe and accessible adjunctive therapy for schizophrenia. However, the current evidence base is insufficient to form definitive clinical guidelines. The inherent complexity of nutritional science, with its focus on multitarget rather than single-target actions, necessitates a paradigm shift in research methodology. The path forward lies in the adoption of personalized and biomarker-guided approaches.

There are no approved drugs for autism core features; the heterogeneous biology, symptom presentation, and clinical outcomes complicate drug trials design, and have hampered therapeutic drug development. We synthesized expert viewpoints from industry and academia on recent diagnostics and biomarker advances as well as pharmacotherapy evidence, aligned to patient-focused drug development outcomes and key concepts. Key learnings include: (i) measure what matters to patients and caregivers; (ii) enrich populations where they can be rationally matched to mechanism; (iii) do not use diagnostic tools as efficacy endpoints; (iv) align biomarker science with a potential qualification pathway associated with relevant behavior and biology; (v) design trials that reduce placebo response, burden, and attrition; (vi) assess co-occurring conditions and treatments explicitly. Observations and recommendations serve as a practical roadmap for sponsors and clinicians to increase trial informativeness and chance of success.