There is now evidence that schizophrenia and deficit schizophrenia are neuro-immune conditions and that oxidative stress toxicity (OSTOX) may play a pathophysiological role. Aims of the study to compare OSTOX biomarkers and antioxidant (ANTIOX) defenses in deficit versus non-deficit schizophrenia. We examined lipid hydroperoxides (LOOH), malondialdehyde (MDA), advanced oxidation protein products (AOPP), sulfhydryl (-SH) groups, paraoxonase 1 (PON1) activity and PON1 Q192R genotypes, and total radical-trapping antioxidant parameter (TRAP) as well as immune biomarkers in patients with deficit (n = 40) and non-deficit (n = 40) schizophrenia and healthy controls (n = 40). Deficit schizophrenia is characterized by significantly increased levels of AOPP and lowered -SH, and PON1 activity, while no changes in the OSTOX/ANTIOX biomarkers were found in non-deficit schizophrenia. An increased OSTOX/ANTIOX ratio was significantly associated with deficit versus non-deficit schizophrenia (odds ratio = 3.15, p  less then  0.001). Partial least squares analysis showed that 47.6% of the variance in a latent vector extracted from psychosis, excitation, hostility, mannerism, negative symptoms, psychomotor retardation, formal thought disorders, and neurocognitive test scores was explained by LOOH+AOPP, PON1 genotype + activity, CCL11, tumor necrosis factor (TNF)-α, and IgA responses to neurotoxic tryptophan catabolites (TRYCATs), whereas -SH groups and IgM responses to MDA showed indirect effects mediated by OSTOX and neuro-immune biomarkers. When overall severity of schizophrenia increases, multiple immune and oxidative (especially protein oxidation indicating chlorinative stress) neurotoxicities and impairments in immune-protective resilience become more prominent and shape a distinct nosological entity, namely deficit schizophrenia. The nomothetic network psychiatry approach allows building causal-pathway-phenotype models using machine learning techniques.The cytolytic protein perforin has a crucial role in infections and tumor surveillance. Recently, it has also been associated with many brain diseases, such as neurodegenerative diseases and stroke. Therefore, inhibitors of perforin have attracted interest as novel drug candidates. We have previously reported that converting a perforin inhibitor into an L-type amino acid transporter 1 (LAT1)-utilizing prodrug can improve the compound's brain drug delivery not only across the blood-brain barrier (BBB) but also into the brain parenchymal cells neurons, astrocytes, and microglia. The present study evaluated whether the increased uptake into mouse primary cortical astrocytes and subsequently improvements in the cellular bioavailability of this brain-targeted perforin inhibitor prodrug could enhance its pharmacological effects, such as inhibition of production of caspase-3/-7, lipid peroxidation products and prostaglandin E2 (PGE2) in the lipopolysaccharide (LPS)-induced neuroinflammation mouse model. It was demonstrated that increased brain and cellular drug delivery could improve the ability of perforin inhibitors to elicit their pharmacological effects in the brain at nano- to picomolar levels. Furthermore, the prodrug displayed multifunctional properties since it also inhibited the activity of several key enzymes related to Alzheimer's disease (AD), such as the β-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1), acetylcholinesterase (AChE), and most probably also cyclooxygenases (COX) at micromolar concentrations. Therefore, this prodrug is a potential drug candidate for preventing Aβ-accumulation and ACh-depletion in addition to combatting neuroinflammation, oxidative stress, and neural apoptosis within the brain. Graphical abstract.
  Assessment of quality of life in patients with stable angina and normal gated single-photon emission computed tomography myocardial perfusion imaging (MPI) remains undefined. Symptom evolution in response to imaging findings has important implications on further diagnostic testing and therapeutic interventions.
 
  Prospective cohort study was conducted at the University of Alabama at Birmingham enrolling 87 adult participants with stable chest pain from the emergency room, hospital setting, and outpatient clinics. Patients underwent MPI with technetium-99m Sestamibi and had a normal study. Participants filled out Seattle Angina Questionnaires initially and at 3-month follow-up.
 
  Among the 87 participants (60 ± 12years; 40% African American, 70% women, 29% diabetes), the mean score increased by an absolute value of 14.2 [95% CI 10.4-18.7, P < .001] in physical limitation, 23.2 [95% CI 17.1-29.4, P < .001] in angina stability, 10.9 [95% CI 7.6-14.1, P < .001] in angina frequency, and 20.6 [95% CI 16.5-24.7, P < .001] in disease perception.  https://www.selleckchem.com/products/peficitinb-asp015k-jnj-54781532.html  There was no significant change in the mean score of treatment satisfaction [- 1.4, 95% CI - 4.7 to 1.8, P = .38]. At 3-month follow-up, 28 of 87 participants (32%) were angina free.
 
  Patients with stable chest pain and normal MPI experience significant improvement in functional status, quality of life, and disease perception in the short term.
 Patients with stable chest pain and normal MPI experience significant improvement in functional status, quality of life, and disease perception in the short term.
  Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image.
 
  The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results.