Assessment of the Impact of a Daily Rehabilitation Program on Anxiety and Depression Symptoms and the Quality of Life of People with Mental Disorders during the COVID-19 Pandemic

Community psychiatry is a modern and effective form of care for patients with mental disorders. The aim of the study was to assess the impact of a rehabilitation program at the Mental Health Support Centre in Tarnowskie Góry (Poland) on reducing severity of anxiety and depression symptoms, as well as improving overall quality of life during the COVID-19 pandemic. The study involved 35 patients, examined with an authors’ questionnaire on sociodemographic data, the Hospital Scale of Anxiety and Depression (HADS) and the Short Form Health Survey (SF-36). Data was obtained during the first national lockdown and compared to data gathered before the pandemic on the same study group. Imposed restrictions, negative emotional state during lockdown, subjectively assessed higher health risk and a low level of knowledge about the COVID-19 pandemic did not significantly correlate with a severity of depression and anxiety, as well as general quality of life. However, the comparison of the results obtained in HADS and SF-36 scales show a significant improvement in both categories. Rehabilitation activities, including physical training, cognitive exercise and social therapy, reduce the severity of the symptoms and have a positive effect on the overall quality of life in patients suffering from schizophrenia and affective disorders. Therefore, holistic mental health support services may positively affect building an individual resilience. The severity of anxiety symptoms during the COVID-19 pandemic shows a negative correlation with the patient’s age

Cerebrospinal Fluid Correlates of Depression in Huntington's Disease

• Patients with Huntington's disease (HD) commonly have concomitant depressive disorders. Prompted by reports of elevated corticotropin releasing factor (CRF) and reduced 5-hydroxyindoleacetic acid (5-HIAA) concentrations in lumbar cerebrospinal fluid (CSF) of patients with major depression, these CSF constituents were examined in 56 nonmedicated patients who were in the early stages of HD. Elevated CRF concentrations were found in patients with HD in comparison with a control group of 21 subjects without neurologic illness. The CSF 5-HIAA concentrations in patients with HD did not differ from that in four normal volunteers. Patients with HD who had depressive disorders (major depression or dysthymia) did not differ from those without depression with respect to CSF 5-HIAA or CRF concentration. However, a positive correlation was observed between severity of major depression and CRF concentration. These findings suggest that the depression associated with HD may differ neurochemically from that seen in other major depressive disorders, and support the notion that clinically significant depressive symptoms reflect heterogeneous pathophysiologic conditions with different neurochemical correlates

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available

Nicotinic alpha 7 receptor agonists EVP-6124 and BMS-933043, attenuate scopolamine-induced deficits in visuo-spatial paired associates learning - Fig 1

<p>Chemical structures of A) BMS-933043, B) RG3847, C) EVP-6124 and D) donepezil.</p