Relationship between clinical outcomes measures and personal and social performance functioning in a prospective,interventional study in schizophrenia

ObjectivesTo explore clinical and demographic characteristics impacting patient functioning by determining extent of overlap in factors driving change in Personal and Social Performance (PSP) and other clinical outcomes.MethodsPost‐hoc analysis from a single‐arm trial of paliperidone extended release in adult patients with nonacute symptomatic schizophrenia. Psychosocial functioning measures: PSP, Clinical Global Impression–Severity (CGI‐S), Positive and Negative Syndrome Scale (PANSS), Short‐Form 36 (SF‐36), treatment satisfaction, sleep quality/daytime drowsiness, and Extrapyramidal Symptoms Rating Scale.ResultsHighest correlations with PSP total score change included PANSS total score change (Spearman''s r = 0.607), PANSS general psychopathology change (r = 0.579), and CGI‐S change (r = 0.569). A PSP score change of −32 predicted 90% probability of deterioration in CGI‐S (score change of ≥1). The power of PSP change to predict PANSS total score change was lower. Linear stepwise regression demonstrated independent relationships for PSP change and: PANSS total change; CGI‐S change; SF‐36 Mental Component change; treatment satisfaction at endpoint; PSP at baseline; previous psychiatric hospitalizations. R ² = 0.55 meant that 45% of PSP variation could not be explained by other clinical outcome measures.ConclusionsPsychosocial functioning improvement is important in schizophrenia. PSP may be valuable for assessing functioning; it encompasses psychosocial and clinical factors not measured by other established assessments.     

Varicella zoster virus infection in neurological patients in Bulgaria

Journal of NeuroVirology - The clinical manifestations of neurological complications associated with varicella zoster virus (VZV) are non-specific and indistinguishable from those of other viral...     

Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks

The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters’ quantitative values for cutting-force calculation in cylindrical infeed grinding. The main stages of creating a method for conducting a virtual experiment to determine the cutting force depending on the array of defining parameters obtained from experimental studies are outlined. It will make it possible to get recommendations for the formation of a valid route for crankpin machining. The research’s scientific novelty lies in the developed scientific and methodological approach for determining the cutting force, based on the integrated application of an artificial neural network (ANN) and multi-parametric quasi-linear regression analysis. In particular, on production conditions, the proposed method allows the rapid and accurate assessment of the technological parameters’ influence on the power characteristics for the cutting process. A numerical experiment was conducted to study the cutting force and evaluate its value’s primary indicators based on the proposed method. The study’s practical value lies in studying how to improve the grinding performance of the main bearing and connecting rod journals by intensifying cutting modes and optimizing the structure of machining cycles.     

Chitosan-g-oligo/polylactide copolymer non-woven fibrous mats containing protein: from solid-state synthesis to electrospinning

Graft-copolymers based on bioresorbable synthetic (oligo-/polylactide) and natural (chitosan and collagen/gelatin) components were synthesized through solid-state reactive co-extrusion and used for fabrication of fibrous non-woven mats via the electrospinning technique. The effect of the macromolecular features of the initial components on the copolymer characteristics was evaluated using FTIR-spectroscopy, differential scanning calorimetry and elemental analysis. Dynamic light scattering analysis showed that the copolymers have a tendency to form stable ultra-fine dispersions with a mean size of macromolecular aggregates of 150 nm within chlorinated solvents. The copolymer-containing non-woven fibrous mats were fabricated via an electrospinning procedure using chloroform as a solvent. An effect of the copolymer composition on the casting solution''s viscosity, conductivity and surface tension was evaluated. Scanning electron microscopy showed that the obtained mats consist of randomly distributed fibers with a mean size of ∼5 μm and a more complex morphology than mats fabricated from neat polylactide. The proposed mechanochemical approach to obtain hybrid copolymeric compositions differs from typical liquid-phase methods in terms of high efficiency, simplicity and cleanness.

Amphiphilic chitosan-g-oligo/polylactide graft-copolymers were synthesized through solid-state reactive co-extrusion and used for fabrication of fibrous non-woven mats via the electrospinning technique using chloroform as a solvent.