Analisis Metafora Konseptual "Rasa" Berasaskan Data Korpus dalam Kalangan Pesakit Kemurungan

Kajian ini meneliti fenomena kemurungan dalam kalangan pesakit MDD dari perspektif bahasa. Kajian kes yang menjadi reka bentuk kajian ini memfokuskan seorang pesakit MDD di Pusat Perubatan Universiti Malaya (PPUM). Kajian menggunakan Teori Metafora Konseptual (TMK) oleh Lakoff dan Johnson (1980), analisis metafora sistematik oleh Cameron et al. (2010) dan Prosedur Pengidentifikasian Metafora (PPM) oleh Kumpulan Pragglejaz (2007). Dengan membangunkan data korpus sendiri (DIY corpus), data lisan ditranskripsi untuk dianalisis secara mendalam. Kajian ini mendapati bahawa sebanyak 61% kata "rasa" yang ditemukan dalam data korpus menunjukkan rangkuman bahasa emosi dan perasaan seseorang. Kata "rasa" dalam data korpus dianalisis untuk melihat cara pesakit Major Depression Disorder (MDD) mengekspresikan emosi kesedihan, kekecewaan dan putus harapan yang disampaikan melalui ungkapan tersirat. Sebanyak 87% metafora linguistik mengkonseptualkan metafora kemurungan. Dapatan kajian juga menunjukkan bahawa bahasa Melayu mempunyai set metafora yang sama dengan kajian lepas apabila metafora kemurungan yang telah diidentifikasikan sangat relevan dengan pemetaan metafora konseptual yang sedia ada, iaitu KEMURUNGAN IALAH KEJATUHAN, KEMURUNGAN IALAH SIMPTOM FIZIKAL, KEMURUNGAN IALAH BENDA, KEMURUNGAN IALAH BEBAN, KEMURUNGAN IALAH ORGANISMA HIDUP, KEMURUNGAN IALAH PERJALANAN dan KEMURUNGAN IALAH PEPERANGAN. Gabungan metafora sistematik juga dapat membingkaikan pemikiran pesakit kemurungan dan masyarakat sekeliling sehingga membentuk satu set metafora sistematik, iaitu PERSEPSI PESAKIT MENTAL TERHADAP STIGMA MASYARAKAT IALAH PERSEPSI NEGATIF TERHADAP ISLAM.

Fat mass and obesity-associated protein regulates RNA methylation associated with depression-like behavior in mice

Post-transcriptional modifications of RNA, such as RNA methylation, can epigenetically regulate behavior, for instance learning and memory. However, it is unclear whether RNA methylation plays a critical role in the pathophysiology of major depression disorder (MDD). Here, we report that expression of the fat mass and obesity associated gene (FTO), an RNA demethylase, is downregulated in the hippocampus of patients with MDD and mouse models of depression. Suppressing Fto expression in the mouse hippocampus results in depression-like behaviors in adult mice, whereas overexpression of FTO expression leads to rescue of the depression-like phenotype. Epitranscriptomic profiling of N6-methyladenosine (m6A) RNA methylation in the hippocampus of Fto knockdown (KD), Fto knockout (cKO), and FTO-overexpressing (OE) mice allows us to identify adrenoceptor beta 2 (Adrb2) mRNA as a target of FTO. ADRB2 stimulation rescues the depression-like behaviors in mice and spine loss induced by hippocampal Fto deficiency, possibly via the modulation of hippocampal SIRT1 expression by c-MYC. Our findings suggest that FTO is a regulator of a mechanism underlying depression-like behavior in mice.

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.