Displaying publications 21 - 23 of 23 in total

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  1. Herr K, Berk M, Huang WL, Kato T, Lee JG, Ng CG, et al.
    Neuropsychiatr Dis Treat, 2024;20:2177-2191.
    PMID: 39588177 DOI: 10.2147/NDT.S487747
    PURPOSE: To explore the prevalence of anhedonia (ANH) in major depressive disorder (MDD) and treatment expectation and satisfaction among patients with MDD and physicians in the Asia-Pacific region.

    METHODS: This cross-sectional web-based survey was conducted in April-May 2023 among physicians and individuals aged ≥18 years with self-reported physician diagnosis of MDD (9-item Patient Health Questionnaire [PHQ-9] score ≥ 10) further stratified by anhedonia as measured by the Snaith-Hamilton Pleasure Scale (SHAPS): MDD-ANH (SHAPS score > 2) and MDD non-ANH (SHAPS score ≤ 2). The study assessed the prevalence of anhedonia in MDD as well as the perspectives on the treatment of anhedonia in MDD in terms of expectations and satisfaction among patients and physicians.

    RESULTS: The regional estimated prevalence of MDD was 16.1% where 52.5% of MDD respondents had ANH (SHAPS score ≥2). Depressed mood, mental changes, and changes in sleeping patterns prompted MDD-ANH (n = 1448) or MDD non-ANH (n = 836) respondents to seek medical consultation. Respondents with MDD-ANH (vs MDD non-ANH) reported significantly higher levels of depression and anhedonia, longer treatment duration, and preferred switching their existing medications over adding additional medications (all, p < 0.001). Over half of physicians (55.0%) were not treating anhedonia separately. Anhedonia-specific treatment goals seemed important to all respondents, while avoiding suicidal ideation was significantly important to physicians. MDD-ANH respondents reported in general the lowest level of satisfaction with treatment goals than MDD non-ANH and physician, with "improvements in sexual satisfaction" being the treatment goal with the lowest level of satisfaction.

    CONCLUSION: This first large-scale study conducted across the Asia-Pacific region provides a recent update on the prevalence of MDD and anhedonia in MDD and highlights unmet needs in the current therapeutic landscape for anhedonia in MDD, emphasizing the need for novel treatment.

  2. Thulasimani V, Shanmugavadivel K, Cho J, Veerappampalayam Easwaramoorthy S
    Neuropsychiatr Dis Treat, 2024;20:2203-2225.
    PMID: 39588176 DOI: 10.2147/NDT.S496307
    Alzheimer's Dementia (AD) is a progressive neurological disorder that affects memory and cognitive function, necessitating early detection for its effective management. This poses a significant challenge to global public health. The early and accurate detection of dementia is crucial for several reasons. First, timely detection facilitates early intervention and planning of treatment. Second, precise diagnostic methods are essential for distinguishing dementia from other cognitive disorders and medical conditions that may present with similar symptoms. Continuous analysis and improvements in detection methods have contributed to advancements in medical research. It helps to identify new biomarkers, refine existing diagnostic tools, and foster the development of innovative technologies, ultimately leading to more accurate and efficient diagnostic approaches for dementia. This paper presents a critical analysis of multimodal imaging datasets, learning algorithms, and optimisation techniques utilised in the context of Alzheimer's dementia detection. The focus is on understanding the advancements and challenges in employing diverse imaging modalities, such as MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and EEG (ElectroEncephaloGram). This study evaluated various machine learning algorithms, deep learning models, transfer learning techniques, and generative adversarial networks for the effective analysis of multi-modality imaging data for dementia detection. In addition, a critical examination of optimisation techniques encompassing optimisation algorithms and hyperparameter tuning strategies for processing and analysing images is presented in this study to discern their influence on model performance and generalisation. Thorough examination and enhancement of methods for dementia detection are fundamental for addressing the healthcare challenges posed by dementia, facilitating timely interventions, improving diagnostic accuracy, and advancing research in neurodegenerative diseases.
  3. Nedunjelian A, Ng CG, Lim PK, Sulaiman AH, Koh OH, Francis B
    Neuropsychiatr Dis Treat, 2025;21:465-475.
    PMID: 40070368 DOI: 10.2147/NDT.S494458
    BACKGROUND: Tardive dyskinesia (TD) is a movement disorder that is associated with the prolonged use of antipsychotics. The prevalence of TD varies widely from 20% to 50% but often undetected in schizophrenia patients treated with antipsychotics.

    AIM: This study is aimed at investigating the prevalence of TD among schizophrenia patients treated with antipsychotics and identifying the associated factors. This study also investigates the association of TD with personal and social functioning performance, and the severity of illness.

    METHODS: This was a cross-sectional study conducted at a teaching hospital in Malaysia. Patients were assessed using the Abnormal Involuntary Movement Scale (AIMS), Personal and Social Performance Scale (PSP) and the Clinical Global Impression Scale (CGI).

    RESULTS: Seventy-eight patients were recruited in this study. The prevalence of TD was 35.9%. Older age (OR 4.079, p = 0.006), Chinese ethnicity (OR 4.486, p = 0.020), longer duration of schizophrenia and antipsychotic treatment (OR 5.312, p = 0.001 and OR 5.500, p = 0.002 respectively) were also significantly associated with TD. TD patients notably demonstrated severe impairments in the self-care domain (71.4%). The presence of TD is associated with more severe overall clinical impairment (53.6%).

    CONCLUSION: TD remains a prevalent and concerning side effect of antipsychotic treatment in schizophrenia patients. There is a need for regular monitoring and the use of standardized assessment tools to detect TD.

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