Myasthenia Gravis (MG) is a rare neurological disorder affecting the neuromuscular junction. Clinical hallmarks are fatigability and weakness affecting the extraocular, axial, limb and/or respiratory muscles. Despite immunosuppressive treatment, mainly based on corticosteroids and nonsteroidal immunosuppressants, the burden of MG is still significant, both in terms of inadequate disease control and burdensome side effects. Driven by such limits, the past years have been characterized by an escalation of MG drug development, with novel molecules which now focuses on having a more targeted effect, with a higher safety and efficacy profile. As the pathogenic mechanism of MG are slowly being unravelled, new potential targets for treatments are being considered. This has led since 2017 to the Food and Drug Administration (FDA)-approval of three new drugs that either act by blocking the complement system (i.e., eculizumab and ravulizumab) or by blocking the neonatal Fc receptor thus preventing immunoglobulin recycling and reducing imunoglobulin G (IgG) antibodies (i.e., efgartigimod). Other drugs, with similar mechanism of action, are currently under review for approval. The repertoire of available and developmental therapies for MG is rapidly expanding, finally responding to the unmet need of a more targeted and effective therapeutic approach in MG.

Missense mutations in valosin-containing protein (VCP) can lead to a multisystem proteinopathy 1 (MSP1) with any combination of limb-girdle distribution inclusion body myopathy (IBM) (present in about 90% of cases), Paget's disease of bone, and frontotemporal dementia (IBMPFD). VCP mutations lead to gain of function activity with widespread disarray in cellular function, with enhanced ATPase activity, increased binding with its cofactors, and reduced mitofusin levels. This review highlights novel therapeutic approaches in VCP-MSP in in-vitro and in-vivo models. Furthermore, we also discuss therapies targeting mitochondrial dysfunction, autophagy, TDP-43 pathways, and gene therapies in other diseases with similar pathway involvement which can also be applicable in VCP-MSP. Being a rare disease, it is challenging to perform large-scale randomized control trials (RCTs) in VCP-MSP. However, it is important to recognize potential therapeutic targets, and assess their safety and efficacy in preclinical models, to initiate RCTs for potential therapies in this debilitating disease.

The purpose of this review is to summarise the recent developments in trial readiness, natural history studies, and interventional clinical trials for Becker muscular dystrophy (BMD). As several treatment concepts have claimed to convert patients with Duchenne muscular dystrophy (DMD) into a BMD phenotype, BMD itself has moved into the focus of clinical research. Natural history studies have helped to better characterize patients with BMD and the disease is now a target for interventional trials. In parallel, there have been advances in diagnostics and in the development of preclinical models. Despite increased collaborative efforts to improve trial readiness amongst patients with BMD, there is still a lack of long-term natural history data, and the broad spectrum of disease severity remains a challenge for well designed clinical trials.

An increasing number of peripheral neuro(no)pathies are identified as involving other components of the neurological system, particularly those that further impair balance. Here we aim to outline an evidence-based approach to the diagnosis of patients who present with a somatosensory disorder which also involves at least one other area of neurological impairment such as the vestibular, auditory, or cerebellar systems. Detailed objective investigation of patients who present with sensory impairment, particularly where the degree of imbalance is greater than would be expected, aids the accurate diagnosis of genetic, autoimmune, metabolic, and toxic neurological disease. Diagnosis and management of complex somatosensory disorders benefit from investigation which extends beyond the presenting sensory impairment.

The use of digital tools for remote cognitive measurement of older adults is generating increasing interest due to the numerous advantages offered for accessibility and scalability. However, these tools also pose distinctive challenges, necessitating a thorough analysis of their psychometric properties, feasibility and acceptability. In this narrative review, we present the recent literature on the use of web-based cognitive assessment to characterize cognition in older adults and to contribute to the diagnosis of age-related neurodegenerative diseases. We present and discuss three types of web-based cognitive assessments: conventional cognitive tests administered through videoconferencing; unsupervised web-based assessments conducted on a computer; and unsupervised web-based assessments performed on smartphones. There have been considerable progress documenting the properties, strengths and limitations of web-based cognitive assessments. For the three types of assessments reported here, the findings support their promising potential for older adults. However, certain aspects, such as the construct validity of these tools and the development of robust norms, remain less well documented. Nonetheless, the beneficial potential of these tools, and their current validation and feasibility data, justify their application [see Supplementary Digital Content (SDC), http://links.lww.com/CONR/A69 ].

The purpose is to review the results and clinical implications of recent studies of neuropathology in relation to neuropsychiatric symptoms (NPS) in Alzheimer's disease and related dementias, and discuss new therapeutic approaches based on evidence from clinical trials. In a large autopsy series from a national consortium, multiple neuropathologies of dementia subtypes were common and increased severity of specific NPS during life was associated with greater severity of neuropathology across diagnoses. Based on three clinical trials, brexpiprazole, which is an antipsychotic with dopamine and serotonin receptor partial agonism properties, was recently approved for the treatment of agitation in Alzheimer's dementia by the U.S. Food and Drug Administration (FDA). Its therapeutic profile indicates modest efficacy with high safety. Brexpiprazole has not been compared to other antipsychotics that are commonly prescribed to treat agitation in dementia, though none of them have been approved for this indication. Other drugs that showed positive results in Phase 2 trials are being tested in Phase 3 trials. These include cannabinoids and drug combinations that inhibit dextromethorphan metabolism peripherally, thereby increasing its bioavailability in the brain. Apathy is common in several types of dementia, and there is initial evidence that treatment with methylphenidate, a psychostimulant, may be efficacious with good tolerability. Greater understanding of the associations between NPS and dementia subtypes can improve clinical management of these disorders. In addition to the approval of brexpiprazole to treat agitation in Alzheimer's dementia, there is optimism about other medications based on ongoing clinical trials. Along with short-term improvement, altering the adverse impact on NPS on long-term prognosis remains an important challenge for the field.

The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD). Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities. Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.