Sarah Holper

INTRODUCTION: S‐adenosyl methionine (SAMe) is a pivotal metabolite in multiple neuronal pathways, including tau dephosphorylation. Reduced SAMe availability has been reported in the Alzheimer's disease (AD) brain, prompting interest in supplementation as a potential therapeutic strategy. METHODS: This multicenter, randomized, double‐blind, placebo‐controlled phase 2 study recruited people ( n = 63) with a clinical AD diagnosis. Participants received 180 days of SAMe (400 mg daily) or placebo. Primary outcome was change in plasma phosphorylated tau (p‐tau)217 concentration. Secondary endpoints included safety, tolerability, and cognitive outcomes. RESULTS: Mean percentage change in plasma p‐tau217 in the SAMe group was an increase of 53.22 (standard deviation [SD] 159.19) compared to 25.34 (SD 94.83) in the placebo group (standardized mean difference 37.58, 95% confidence interval −32.61, 107.76; p = 0.288). No significant differences were observed in safety or other secondary endpoints. DISCUSSION: SAMe did not demonstrate disease‐modifying efficacy at the dose and duration studied; however it was safe and well tolerated. TRIAL REGISTRATION: ACTRN12620000506998. Registered on the Australian New Zealand Clinical Trials Registry ( http://www.anzctr.org.au )

Epilepsy affects over 50 million people worldwide, with anti-seizure medications (ASMs) as the primary treatment for seizure control. However, ASM selection remains a “trial and error” process due to the lack of reliable predictors of effectiveness and tolerability. While machine learning approaches have been explored, existing models are limited to predicting outcomes only for ASMs encountered during training and have not leveraged recent biomedical foundation models for this task. This work investigates ASM outcome prediction using only patient MRI scans and reports. Specifically, we leverage biomedical vision-language foundation models and introduce a novel contextualized instruction-tuning framework that integrates expert-built knowledge trees of MRI entities to enhance their performance. Additionally, by training only on the four most commonly prescribed ASMs, our framework enables generalization to predicting outcomes and effectiveness for unseen ASMs not present during training. We evaluate our instruction-tuning framework on two retrospective epilepsy patient datasets, achieving an average AUC of 71.39 and 63.03 in predicting outcomes for four primary ASMs and three completely unseen ASMs, respectively. Our approach improves the AUC by 5.53 and 3.51 compared to standard report-based instruction tuning for seen and unseen ASMs, respectively. Our code, MRI knowledge tree, prompting templates, and TREE-TUNE generated instruction–answer tuning dataset are available at the link .

Information extraction (IE) from specialized clinical texts such as brain MRI reports is important for various clinical and population health contexts. However, this topic is under-explored due to privacy concerns limiting data availability and the inherent complexity and domain-specificity of clinical language. Common methods relying on substantial amounts of training data fail. The recent advances in large language model (LLM) research provide a promising solution to bridge the data scarcity gap, with improved ability to adapt to novel tasks with little supervision. We introduce a new, challenging dataset of 100 expert-annotated brain MRI reports, featuring 152 fine-grained entity types and 4 relation types, characterised by low inter-annotator agreement. This task reflects the inherent complexity and real-world ambiguity of medical text. We evaluate a small, open-weight LLM across span detection, named entity recognition, and relation extraction tasks. We compare few-shot prompting and parameter-efficient fine-tuning against specialized off-the-shelf biomedical IE systems. Our results demonstrate that both few-shot and fine-tuned LLM approaches substantially outperform off-the-shelf baselines. While LLMs show superiority, absolute performance, particularly for complex relations and fine-grained entities, remains modest, correlating with the dataset’s inherent difficulty and the extreme low-resource setting.

Background: We performed a systematic review and meta‐analysis of blood astrocyte biomarkers in Alzheimer's disease (AD).Method: MEDLINE and Web of Science were searched without any restrictions on language, time, or study design, for studies reporting blood levels of the astrocyte biomarkers GFAP, YKL‐40, and S100B in AD patients. Pooled effect sizes were determined using Hedge's g method with a random effects model. The review was prospectively registered on PROSPERO (registration number CRD42023458305).Result: The search identified 1186 studies; 36 met inclusion criteria (n=3366 AD patients, n=4115 cognitively unimpaired [CU]). Compared to CU individuals, AD patients had significantly higher GFAP and YKL‐40 levels (GFAP effect size 1.15, 95% CI 0.94‐1.36, P<.0001; YKL‐40 effect size 0.38, 95% CI 0.28‐0.49, P<.0001). Both biomarkers showed a trend towards a greater effect size in more advanced disease. No significant difference in blood S100B levels were identified.Conclusion: Our findings reveal significant elevations in blood GFAP and YKL‐40 levels in AD patients across the clinical spectrum, suggesting that both biomarkers accurately reflect AD‐related pathology.

Neuroinflammation, particularly early astrocyte reactivity, is a significant driver of Alzheimer disease (AD) pathogenesis. It is unclear how the levels of astrocyte biomarkers change in patients across the AD continuum and which best reflect AD-related change. We performed a systematic review and meta-analysis of 3 blood astrocyte biomarkers (glial fibrillary acidic protein [GFAP], chitinase-3-like protein 1 [YKL-40], and S100B) in patients clinically diagnosed with AD. MEDLINE and Web of Science were searched on March 23, 2023, without restrictions on language, time, or study design, for studies reporting blood levels of the astrocyte biomarkers GFAP, YKL-40, or S100B in patients on the AD continuum (including those with mild cognitive impairment [MCI] and dementia) and a cognitively unimpaired (CU) control population. AD diagnosis was based on established diagnostic criteria and/or comprehensive multidisciplinary clinical consensus. Studies reporting indirect biomarker measures (e.g., levels of biomarker autoantibodies) were excluded. Risk of bias assessment was performed using the revised Quality Assessment of Diagnostic Accuracy Studies tool. Pooled effect sizes were determined using the Hedge g method with a random-effects model. The review was prospectively registered on PROSPERO (registration number CRD42023458305). The search identified 1,186 studies; 36 met inclusion criteria (AD continuum n = 3,366, CU n = 4,115). No study was assessed to have a high risk of bias. Compared with CU individuals, patients on the AD continuum had higher GFAP and YKL-40 levels (GFAP effect size 1.15, 95% CI 0.94-1.36, p < 0.0001; YKL-40 effect size 0.38, 95% CI 0.28-0.49, p < 0.0001). Both biomarkers were elevated in more advanced clinical stages of the disease (i.e., in AD dementia compared with MCI due to AD: GFAP effect size 0.48, 95% CI 0.19-0.76, p = 0.0009; YKL-40 effect size 0.34, 95% CI 0.10-0.57, p = 0.0048). No significant differences in blood S100B levels were identified. We demonstrated significant elevations in blood GFAP and YKL-40 levels in patients on the AD continuum compared with CU individuals. Furthermore, within the AD clinical spectrum, significant elevation correlated with more advanced disease stage. Our findings suggest that both biomarkers reflect AD-related pathology. Our findings are limited by the lack of cultural and linguistic diversity in the study populations meta-analyzed. Future meta-analyses using a biomarker-defined AD population are warranted.

S-adenosyl methionine (SAMe) is a pivotal metabolite in multiple pathways required for neuronal homeostasis, several of which are compromised in Alzheimer&

Less than 50 years since tau was first isolated from a porcine brain, its detection in femtolitre concentrations in biological fluids is revolutionizing the diagnosis of neurodegenerative diseases. This review highlights the molecular and technological advances that have catapulted tau from obscurity to the forefront of biomarker diagnostics. Comprehensive updates are provided describing the burgeoning clinical applications of tau as a biomarker of neurodegeneration. For the clinician, tau not only enhances diagnostic accuracy, but holds promise as a predictor of clinical progression, phenotype, and response to drug therapy. For patients living with neurodegenerative disorders, characterization of tau dysregulation could provide much-needed clarity to a notoriously murky diagnostic landscape.

Background: A diagnosis of variant Creutzfeldt-Jakob disease (vCJD), the zoonotic prion disease related to transmission of bovine spongiform encephalopathy, can carry enormous public health ramifications. Until recently, all vCJD clinical cases were confined to patients displaying methionine homozygosity (MM) at codon 129 of the prion protein gene (PRNP). The recent diagnosis of vCJD in a patient heterozygous (MV) at codon 129 reignited concerns regarding a second wave of vCJD cases, with the possibility of phenotypic divergence from MM vCJD and greater overlap with sporadic CJD (sCJD) molecular subtypes. Method and results: We present a case of CJD with clinico-epidemiological and radiological characteristics creating initial concerns for vCJD. Thorough case evaluation, including data provided by genetic testing, autopsy and neuropathological histological analyses, provided a definitive diagnosis of the rare VV1 molecular subtype of sCJD. Conclusion: Distinguishing vCJD from sCJD is of vital public health importance and potentially more problematic with the development of non-MM vCJD cases. The patient described herein demonstrates that in addition to the clinico-epidemiological profile, combined supplementary pathological, biochemical and critical radiological analysis may be necessary for confident discrimination of sCJD, especially rare sub-types, from vCJD.