High-dose salicylate induces temporary moderate hearing loss and the perception of a high-pitched tinnitus in humans and animals. Previous studies demonstrated that high doses of salicylate increase N-methyl-d-aspartate (NMDA) receptor levels, resulting in a rise in Ca²⁺ influx and induction of excitotoxicity. Glutamate excitotoxicity is associated with failure in the maintenance of calcium homeostasis, mitochondrial dysfunction, and production of reactive oxygen species (ROS). Valproic acid (VPA) is widely used for the management of bipolar disorder, epilepsy, and migraine headaches, and is known to regulate NMDA receptor activity. In this study, we examined the beneficial effects of VPA in a salicylate-induced tinnitus model in vitro and in vivo. Cells were pretreated with VPA followed by salicylate treatment. The expression levels of NMDA receptor subunit NR2B, phosphorylated cAMP response element-binding protein—an apoptosis marker, and intracellular levels of ROS were measured using several biochemical techniques. We observed increased expression of NR2B and its related genes TNFα and ARC, increased intracellular ROS levels, and induced expression of cleaved caspase-3. These salicylate-induced changes were attenuated in the neuronal cell line SH-SY5Y and rat cortical neurons after VPA pretreatment. Together, these results provide evidence of the beneficial effects of VPA in a salicylate-induced temporary hearing loss and tinnitus model.

Background/Aims: Disease-modifying therapy for Alzheimer’s disease (AD) has led to a need for biomarkers to identify prodromal AD and very early stage of AD dementia. We aimed to identify the cutoff values of cerebrospinal fluid (CSF) biomarkers for detecting prodromal AD. Methods: We assessed 56 patients with amnestic mild cognitive impairment (aMCI) who underwent lumbar puncture. Additionally, 87 healthy elderly individuals and 34 patients with AD dementia served as controls. Positron emission tomography was performed using florbetaben as a probe. We analyzed the concentration of Aβ_1–42, total tau protein (t-Tau), and tau protein phosphorylated at threonine 181 (p-Tau¹⁸¹) in CSF with INNOTEST enzyme-linked immunosorbent assay. Results: For the detection of prodromal AD in patients with aMCI, the cutoff values of CSF Aβ_1–42, t-Tau, and p-Tau¹⁸¹ were 749.5 pg/mL, 225.6 pg/mL, and 43.5 pg/mL, respectively. To discriminate prodromal AD in patients with aMCI, the t-Tau/Aβ_1–42 and ­p-Tau¹⁸¹/Aβ_1–42 ratios defined cutoff values at 0.298 and 0.059, respectively. Conclusions: CSF biomarkers are very useful tools for the differential diagnosis of prodromal AD in aMCI patients. The concentration of CSF biomarkers is well correlated with the stages of the AD spectrum.

Background Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied. Methods A total of 8590 patients with AIS admitted within 5 days of symptom onset were enrolled. The primary outcome was the occurrence of MACEs (a composite of stroke, acute myocardial infarction or death) over 12 months. The performance of deep learning models (DeepSurv and Deep-Survival-Machines (DeepSM)) and traditional survival models (Cox proportional hazards (CoxPH) and random survival forest (RSF)) were compared using the time-dependent concordance index ( index). Results Given the top 1 to all 60 clinical factors according to feature importance, CoxPH and RSF yielded index of 0.7236–0.8222 and 0.7279–0.8335, respectively. Adding image features improved the performance of deep learning models and traditional models assisted by deep learning models. DeepSurv and DeepSM yielded the best index of 0.8496 and 0.8531 when images were added to all 39 relevant clinical factors, respectively. In feature importance, brain image was consistently ranked highly. Deep learning models automatically extracted the image features directly from personalised brain images and predicted the risk and date of future MACEs at the individual level. Conclusions Deep learning models using clinical data and brain images could improve the prediction of MACEs and provide personalised outcome prediction for patients with AIS. Deep learning models will allow us to develop more accurate and tailored prognostic prediction systems that outperform traditional models.

Alzheimer's disease (AD) is a global neurodegenerative disorder that affects millions of individuals worldwide. Actual AD imaging datasets challenge the construction of reliable longitudinal models owing to imaging modality uncertainty. In addition, they are still unable to retain or obtain important information during disease progression from previous to followup time points. For example, the output values of current gates in recurrent models should be close to a specific value that indicates the model is uncertain about retaining or forgetting information. In this study, we propose a model which can extract and constrain each modality into a common representation space to capture intermodality interactions among different modalities associated with modality uncertainty to predict AD progression. In addition, we provide an auxiliary function to enhance the ability of recurrent gate robustly and effectively in controlling the flow of information over time using longitudinal data. We conducted comparative analysis on data from the Alzheimer's Disease Neuroimaging Initiative database. Our model outperformed other methods across all evaluation metrics. Therefore, the proposed model provides a promising solution for addressing modality uncertainty challenges in multimodal longitudinal AD progression prediction.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The increasing demand for high-accuracy forecasts of disease progression has led to a surge in research employing multi-modality variables for prediction. In this review, we selected articles published from 2016 through June 2024, adhering strictly to our exclusion-inclusion criteria. These articles employed a minimum of two types of variables, including clinical, genetic, biomarker, and neuroimaging modalities. We conducted a comprehensive review and discussion on the application of multi-modality approaches in predicting PD progression. The predictive mechanisms, advantages, and shortcomings of relevant key modalities in predicting PD progression are discussed in the paper. The findings suggest that integrating multiple modalities resulted in more accurate predictions compared to those of fewer modalities in similar conditions. Furthermore, we identified some limitations in the existing field. Future studies that harness advancements in multi-modality variables and machine learning algorithms can mitigate these limitations and enhance predictive accuracy in PD progression.

Increasing evidence indicates that sarcopenia and obesity can be risk factors for incident dementia. We investigated the association of body composition including muscle and visceral adipose tissue (VAT) with the probability of Alzheimer’s disease (AD) in healthy middle-aged and elderly subjects using ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT). This study included 110 healthy subjects with available whole-body FDG PET/CT scans and medical records. Muscle and VAT tissues were measured on the abdominal CT slice, and the PMOD Alzheimer’s discrimination FDG PET analysis tool (PALZ) score was evaluated on the brain PET of the same subject using software PALZ. Skeletal muscle index (r: −0.306; P = 0.031) was significantly negatively associated with the PALZ score in the elderly patients. Muscle area (β: −0.640; P = 0.043) and skeletal muscle index (β: −0.557; P = 0.043) were independently associated with the PALZ score in elderly subjects after adjustments for sex, duration of education, hypertension, diabetes mellitus, and smoking and drinking status. Increased muscle tissue was associated with a lower probability of AD in elderly subjects, but VAT was not associated with a lower probability of AD in middle-or older-aged adults.