Current Opinion in Neurology最新文献

Purpose of review: To review the landscape of chimeric antigen receptor T-cell (CAR T) therapy for gliomas as seen in recently published trials and discuss on-going challenges with new cancer immunotherapy treatments.

Recent findings: Given how CAR T therapy has revolutionized the treatment of several hematologic malignancies, there has been increasing interest in using immunotherapy, and particularly CAR T therapy for gliomas. Within the past decade, several first in human trials have published early patient experiences showing treatment is generally well tolerated but with limited efficacy, which may be improving with newer evolutions in CAR T design to overcome known resistance mechanisms in glioma treatment.

Summary: CAR T therapy is a promising avenue of treatment for high-grade gliomas, which have a universally poor prognosis as well as limited therapeutics. There are a growing number of CAR T clinical trials for CNS tumors and thus, an understanding of their treatment strategies, toxicity management, and overcoming resistance mechanisms will be important for both clinical practice and to identify areas for future research.

Purpose of review: The purpose of this review is to discuss the value of blood and CSF biomarkers in primary CNS tumors.

Recent findings: Several analytes can be assessed with liquid biopsy techniques, including circulating tumor cells, circulating cell-free tumor DNA, circulating cell-free RNA, circulating proteins and metabolites, extracellular vesicles and tumor-educated platelets. Among diffuse gliomas of the adult, ctDNA in blood or CSF has represented the most used analyte, with the detection of molecular alterations such as MGMT promoter, PTEN, EGFRVIII, TERT promoter mutation and IDH R132H mutation. In general, CSF is enriched for ctDNA as compared with plasma. The use of MRI-guided focused ultrasounds to disrupt the blood-brain barrier could enhance the level of biomarkers in both blood and CSF. The detection of MYD88 L265P mutation with digital droplet PCR and the detection of ctDNA with next generation sequencing represent the best tools to diagnose and monitoring CNS lymphomas under treatment. In meningiomas, the low concentration of ctDNA is a limiting factor for the detection of driver mutations, such as NF2, AKTs, SMO, KLF4, TRAF7, SMARCB1, SMARCE1, PTEN, and TERT; an alternative approach could be the isolation of ctDNA through circulating extracellular vesicles. Liquid biopsies are being used extensively for diagnosis and surveillance of diffuse midline gliomas, in particular with the detection of the driver mutation H3K27M. Last, specific methylome patterns in CSF may allow the distinction of glioblastomas from CNS lymphomas or meningiomas.

Summary: This review summarizes the current knowledge and future perspectives of liquid biopsy of blood and CSF for diagnosis and monitoring of primary CNS tumors.

Purpose of review: Brain tumor treatment presents challenges for patients and clinicians, with prognosis for many of the most common brain tumors being poor. Focused ultrasound (FUS) can be deployed in several ways to circumvent these challenges, including the need to penetrate the blood-brain barrier and spare healthy brain tissue. This article reviews current FUS applications within neuro-oncology, emphasizing ongoing or recently completed clinical trials.

Recent findings: Most clinical interest in FUS for neuro-oncology remains focused on exploring BBB disruption to enhance the delivery of standard-of-care therapeutics. More recently, the application of FUS for radiosensitization, liquid biopsy, and sonodynamic therapy is garnering increased clinical attention to assist in tumor ablation, early detection, and phenotypic diagnosis. Preclinical studies show encouraging data for the immunomodulatory effects of FUS, but these findings have yet to be tested clinically.

Summary: FUS is a burgeoning area of neuro-oncology research. Data from several forthcoming large clinical trials should help clarify its role in neuro-oncology care.

Purpose of review: This review highlights recent developments in noninvasive brain stimulation (NIBS) techniques and applications for improving motor outcomes after stroke. Two promising areas of development relate to deep brain neuromodulation and the use of single-pulse transcranial magnetic stimulation (TMS) within a prediction tool for predicting upper limb outcome for individual patients.

Recent findings: Systematic reviews highlight the inconsistent effect sizes of interventional NIBS for motor outcome after stroke, as well as limited evidence supporting the interhemispheric competition model. To improve the therapeutic efficacy of NIBS, studies have leveraged metaplasticity and priming approaches. Transcranial temporal interference stimulation (tTIS) and low-intensity focused ultrasound stimulation (LIFUS) are emerging NIBS techniques with potential for modulating deeper brain structures, which may hold promise for stroke neurorehabilitation. Additionally, motor evoked potential (MEP) status obtained with single-pulse TMS is a prognostic biomarker that could be used to tailor NIBS for individual patients.

Summary: Trials of interventional NIBS to improve stroke outcomes may be improved by applying NIBS in a more targeted manner. This could be achieved by taking advantage of NIBS techniques that can be targeted to deeper brain structures, using biomarkers of structural and functional reserve to stratify patients, and recruiting patients in more homogeneous time windows.

Purpose of review: As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions.

Recent findings: Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation).

Summary: Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques.

Purpose of review: Neuroimaging has been instrumental in shaping current understanding of the pathoanatomical signature of amyotrophic lateral sclerosis (ALS) across clinically well defined patient cohorts. The potential utility of imaging as an objective disease marker, however, remains poorly defined.

Recent findings: Increasingly advanced quantitative and computational imaging studies have highlighted emerging clinical applications for neuroimaging as a complementary clinical modality for diagnosis, monitoring, and modelling disease propagation. Multimodal neuroimaging has demonstrated novel approaches for capturing primary motor disease. Extra-motor subcortical dysfunction is increasingly recognized as key modulators of disease propagation.

Summary: The neural signature of cortical and subcortical dysfunction in ALS has been well defined at the population level. Objective metrics of focal primary motor dysfunction are increasingly sensitive and translatable to the individual patient level. Integrity of extra-motor subcortical abnormalities are recognized to represent critical pathways of the ALS disease 'connectome', predicting pathological spread. Neuroimaging plays a pivotal role in capturing upper motor neuron pathology in ALS. Their potential clinical role as objective disease markers for disease classification, longitudinal monitoring, and prognosis in ALS have become increasingly well defined.

Purpose of review: Alongside motor and cognitive symptoms, amyotrophic lateral sclerosis (ALS) and ALS with frontotemporal dementia (ALSFTD) present with behavioural symptoms, which can be challenging for all affected by the disease. A scoping review of studies published between 2011 and 2024 was conducted to present the breadth of behavioural symptoms in ALS and ALSFTD, explore how they are described and assessed, and identify patterns in the literature.

Findings: This scoping review identified 3939 articles, with 111/3939 meeting eligibility criteria. Most studies were from Australia (23.22%), Italy (16.94%) and the UK (14.29%); 75.67% were cross-sectional. Sample size ranged from 1 to 1013, as case studies were included. Overall mean age (100/111 studies) was 61.32 (SD = 4.15). Proportion of male patients (reported 102/111 studies) was 61.49%; mean disease duration (reported in 86/111 records) was 32.63 months (SD = 24.72). Papers described a broad range of behavioural symptoms (465 examples), which were thematically collated into seven categories: disinhibition (27.74%), apathy (25.16%), perseverative/compulsive behaviours (17.42%), hyperorality (10.53%), loss of sympathy or empathy (8.6%), psychotic symptoms (7.74%), and loss of insight about disease and changes (2.8%). Most studies (78.37%) used validated behavioural assessments that elicited carer's perspectives.

Summary: Despite extensive evidence of behavioural symptoms in ALS, implementation of assessments and management of behavioural symptoms in clinical care remain limited. Clinicians must assess behavioural symptoms, as these can negatively affect disease prognosis, patient treatment engagement and increase family distress. Measures capturing carers' perspectives through interviews are ideal as they can reveal anosognosia, lack of sympathy and lack of empathy.

Purpose of review: In the last 30 years, there have many publications describing the pattern of muscle involvement of different neuromuscular diseases leading to an increase in the information available for diagnosis. A high degree of expertise is needed to remember all the patterns described. Some attempts to use artificial intelligence or analysing muscle MRIs have been developed. We review the main patterns of involvement in limb girdle muscular dystrophies (LGMDs) and summarize the strategies for using artificial intelligence tools in this field.

Recent findings: The most frequent LGMDs have a widely described pattern of muscle involvement; however, for those rarer diseases, there is still not too much information available. patients. Most of the articles still include only pelvic and lower limbs muscles, which provide an incomplete picture of the diseases. AI tools have efficiently demonstrated to predict diagnosis of a limited number of disease with high accuracy.

Summary: Muscle MRI continues being a useful tool supporting the diagnosis of patients with LGMD and other neuromuscular diseases. However, the huge variety of patterns described makes their use in clinics a complicated task. Artificial intelligence tools are helping in that regard and there are already some accessible machine learning algorithms that can be used by the global medical community.

Purpose of review: There is no diagnostic biomarker that can reliably detect Guillain-Barré syndrome (GBS) or chronic inflammatory demyelinating polyneuropathy (CIDP). Diagnosis relies upon integrating key clinical characteristics and relevant supportive data. Consequently, misdiagnosis and delayed diagnosis are common. Diagnostic criteria have proven valuable resources to improve diagnosis, but are underutilized during routine clinical care.

Recent findings: In 2021, the EAN/PNS CIDP criteria was published, and were followed by the EAN/PNS GBS criteria in 2023. Both guidelines utilized GRADE methodology to formulate evidence-based recommendations that are intended to be used by adult and paediatric clinicians across diverse care settings to optimize diagnostic accuracy and improve patient outcomes during routine clinical care.

Summary: The EAN/PNS GBS and CIDP criteria detail specific clinical, electrophysiological, and laboratory features that raise diagnostic confidence, and call attention to diagnostic mimics. The sensitivity of EAN/PNS and other modern criteria to detect GBS and CIDP is high, but utilization during clinical practice is low. Complexity is one factor limiting widespread application. Strategies are needed to optimize criteria adoption during routine clinical care such that GBS and CIDP diagnosis can be achieved with greater speed and accuracy.