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"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients' expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients' narratives data, by linking the patients' expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30]. [ABSTRACT FROM AUTHOR]

Glaucoma is characterized by pathological elevation of intraocular pressure (IOP) due to dysfunctional trabecular meshwork (TM), which is the primary cause of irreversible vision loss. There are currently no effective treatment strategies for glaucoma. Mitochondrial function plays a crucial role in regulating IOP within the TM. In this study, primary TM cells treated with dexamethasone were used to simulate glaucomatous changes, showing abnormal cellular cytoskeleton, increased expression of extracellular matrix, and disrupted mitochondrial fusion and fission dynamics. Furthermore, glaucomatous TM cell line GTM3 exhibited impaired mitochondrial membrane potential and phagocytic function, accompanied by decreased oxidative respiratory levels as compared to normal TM cells iHTM. Mechanistically, lower NAD + levels in GTM3, possibly associated with increased expression of key enzymes CD38 and PARP1 related to NAD + consumption, were observed. Supplementation of NAD + restored mitochondrial function and cellular viability in GTM3 cells. Therefore, we propose that the aberrant mitochondrial function in glaucomatous TM cells may be attributed to increased NAD + consumption dependent on CD38 and PARP1, and NAD + supplementation could effectively ameliorate mitochondrial function and improve TM function, providing a novel alternative approach for glaucoma treatment. • Human glaucomatous TM cells GTM 3 exhibited mitochondrial dysfunction and NAD + deficiency. • The excessive NAD+ consumption of PARP1 and CD38 was the main cause of NAD+ deficiency. • NAD+ supplementation improved the mitochondrial and cellular function of glaucomatous TM cells. [ABSTRACT FROM AUTHOR]