Ling Ling Chan

Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a role in hepatic cholesterol metabolism via low density lipoprotein receptor degradation. PCSK9 inhibitors have revolutionized lipid-lowering therapy, providing robust reduction of cardiovascular risk. Large-scale randomized controlled trials and long-term extensions (e.g., FOURIER and EBBINGHAUS trials) have shown no significant adverse effects of PCSK9 inhibitors on neuropsychological testing or patient-reported cognitive outcomes, even with prolonged and intensive lowering of low-density lipoproteins. Pre-clinical studies also suggest PCSK9 as a key regulator of neurobiological processes, including synaptic plasticity, amyloid-beta clearance, neuroinflammation, and blood–brain barrier integrity. Human genetic studies revealed complex, sometimes conflicting associations between PCSK9 variants and risk of Alzheimer’s disease, Parkinson’s disease, vascular dementia, and amyotrophic lateral sclerosis. In this review, we highlight the pathophysiologic mechanisms, emerging experimental therapeutics and clinical implications of PCSK9 inhibition in neurodegenerative disorders. Long-term adequately powered trials with robust neuropsychological, biomarker, and imaging endpoints, as well as mechanistic studies in human-derived models are needed to establish the cardiovascular and neurocognitive implications of PCSK9 inhibition and guide precision medicine strategies for those at elevated risk of neurodegeneration.

Background/Objectives: Rapid and accurate classification of ring-enhancing brain lesions (REBLs) into infection or neoplasm is key to clinical triaging for expedited diagnostics in the former to enhance treatment outcomes, especially in the immunocompromised patients. High-resolution three-dimensional (3D) T1 post-contrast (T1+C) MRI provides high-dimensional volumetric data for radiomics analysis. While radiomics is useful in brain neoplasm characterization, its utility in central nervous system infection remains under-explored. In this exploratory study, we aim to determine if a radiomics-machine learning model, based solely on a 3D T1+C MRI dataset, can distinguish infective from neoplastic REBLs. Methods: 92 patients (infection, n = 26; neoplasm, n = 66) with 402 REBLs, who fulfilled criteria for “definite” or “probable” infective or neoplastic REBLs, were identified from scans performed at our hospital over four years and formed the training/validation dataset. All REBLs were manually annotated on T1+C MRI images under radiological supervision. In total, 1197 radiomics features were extracted, feature selection performed using mutual information, and nine machine learning classifiers applied to assess patient-level infection vs. neoplasm classification performance. End-to-end 2D CNN baselines and hybrid radiomics–CNN configurations were additionally evaluated under the same protocol for comparative benchmarking. Model performance was tested on an external holdout dataset of 57 patients (infection, n = 25; neoplasm, n = 32) with 454 REBLs from another hospital. Results: The Multi-layer Perceptron (MLP) model using the Original + LoG + Wavelet feature group demonstrated superior performance. In the cross-validation cohort, it achieved a mean AUC of 0.80 ± 0.02, sensitivity of 0.83 ± 0.09, specificity of 0.77 ± 0.08, and balanced accuracy of 0.80 ± 0.02. On external holdout data, the same configuration showed stable and sustainable performance with an AUC of 0.84, sensitivity of 0.84, specificity of 0.75, and balanced accuracy of 0.80. Conclusions: Our radiomics-machine learning model, based solely on a high-resolution 3D T1+C dataset, shows potential for distinguishing infective REBLs from neoplastic REBLs. Further study, with additional MR sequences and clinical data in a multimodal MRI radiomics-machine learning model, is warranted.

To develop and evaluate orbital CT deep learning (DL) models in optic neuropathy (ON) prediction in patients diagnosed with thyroid eye disease (TED), using partial versus entire 2D versus 3D images for input. Patients with TED ±ON diagnosed at a quaternary-level practice and who underwent orbital CT between 2002 and 2017 were included. DL models were developed using annotated CT data. The DL models were used to evaluate the hold-out test set. ON classification performances were compared between models and medical specialists, and saliency maps applied to randomized cases. 36/252 orbits in 126 TED patients (mean age, 51 years; 81 women) had clinically confirmed ON. With 2D image input for ON prediction, our models achieved (a) sensitivity 89%, AUC 0.86 on entire coronal orbital apex including bony walls, and (b) specificity 92%, AUC 0.79 on partial axial lateral orbital wall only annotations. ON classification performance was similar (p = 0.58) between DL model and medical specialists. DL models trained on 2D CT annotations rival medical specialists in ON classification, with potential to objectively enhance clinical triage for sight-saving intervention and incorporate model variants in the workflow to harness differential performance metrics.

Obstructive sleep apnea (OSA) and Parkinson’s disease (PD) are highly prevalent conditions with significant global health impacts. OSA affects 17–34% of middle-aged adults, while more than 10 million worldwide have PD. Clinical studies demonstrate a bidirectional relationship, with the OSA prevalence being higher among PD patients suggesting that hypoxia and sleep fragmentation contribute to worsening motor and cognitive symptoms. Conversely, PD-associated neurodegeneration impairs respiratory control, exacerbating OSA. Diagnostic differentiation is particularly challenging due to overlapping symptoms, such as sleep disturbances, cognitive decline, and autonomic dysfunction. Emerging neuromodulation therapies, including deep brain stimulation and hypoglossal nerve stimulation, show dual therapeutic potential. The interplay between OSA and PD draws attention to the need for integrated diagnostic and therapeutic approaches. Additional longitudinal studies to evaluate their cause–effect relationship and identify neuroimaging and biochemical biomarkers to elucidate novel pathophysiologic clues can potentially identify novel therapeutic targets.

Objectives: The splenial angle (SA), measured on axial DTI colour fractional anisotropy MRI, outperformed the callosal angle (CA) in predicting idiopathic normal pressure hydrocephalus (NPH) patients from those with Alzheimer’s dementia, Parkinson’s disease (PD), and healthy controls (HC). We investigated its reliability and classification performance on more commonly acquired T1 magnetization-prepared rapid gradient-echo (MPRAGE) and fluid-attenuated inversion-recovery (FLAIR) images. Methods: Splenial angle was measured on axial MPRAGE and FLAIR images in 57 subjects (19 NPH, PD and HC each) by 2 raters and compared across groups. Receiver operating characteristics (ROC) analysis was used to assess its classification performance, differentiating NPH from non-NPH groups, in comparison to the CA. Results: Inter-rater reliability for SA was excellent (intraclass correlation coefficients ≥ 0.91). Splenial angle was effective in differentiating NPH from non-NPH patients on MPRAGE and FLAIR images (P < .001). Its ROC curves showed excellent performance classifying NPH from HC (Area under the curve [AUC] 1) and PD (AUC > 0.93) groups and were highly comparable to those for CA (1; 0.947). Angles wider than 60° and narrower than 45° robustly (100%) excluded and predicted NPH from HC, respectively. The narrower 45° cutoff yielded better sensitivity (84.2%-89.5%) in differentiating NPH from PD patients. Conclusions: The SA on MPRAGE/FLAIR images showed excellent inter-rater reliability and classification performance predicting NPH from non-NPH groups, rivalling those of the CA. Advances in knowledge: The SA on MPRAGE and FLAIR images is reproducible and shows excellent diagnostic performance, differentiating NPH from non-NPH groups, with potential to replace the CA in NPH screening given its accessibility on routine axial neuroimaging.

Background: The diagnostic evaluation of ring-enhancing brain lesions (REBLs) is challenging, especially in immunocompromised patients. We conducted a retrospective study to describe the clinicodemographic and radiological features among patients presenting with REBLs to a tertiary referral center. Methods: Radiological reports of all patients who underwent brain computed tomography or magnetic resonance (MR) imaging between 1 November 2013 and 31 October 2017 were filtered for terms indicative of REBLs. Infectious diseases physicians reviewed the medical records to confirm the diagnosis. Results: Over the 4-year study period, there were 42 patients with infective REBLs and 249 with neoplastic REBLs. Pyogenic brain abscesses (PBAs) (20 of 42 [47.6%]) were the most common cause of infective REBLs, followed by tuberculous brain abscesses (TBAs) (9 of 42 [21.4%]) and Nocardia brain abscesses (NBAs) (6 of 42 [14.3%]). The patients were predominantly male, with a mean age of 55.2 years. Fewer than half were febrile at presentation. Cerebrospinal fluid investigations established the microbiological diagnosis in fewer than half of those who underwent lumbar puncture or extraventricular drain insertion. Conversely, brain biopsy yielded the microbiological diagnosis in almost all patients (16 of 17) who underwent the operation. Median white blood cell counts and C-reactive protein were higher in those with PBAs or NBAs than in those with TBAs. All with PBAs and NBAs who underwent MR imaging had diffusion-weighted imaging–hyperintense lesions, compared with only about half of those with TBAs. Conclusions: Our study has revealed important distinguishing features between infective REBLs and neoplastic REBLs and between PBAs, TBAs, and NBAs.