Voice disorder in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a chronic disease characterized by progressive tissue damage. In recent decades, novel treatments have greatly extended the life span of SLE patients. This creates a high demand for identifying the overarching symptoms associated with SLE and developing therapies that improve their life quality under chronic care. We hypothesized that SLE patients would present dysphonic symptoms. Given that voice disorders can reduce life quality, identifying a potential SLE-related dysphonia could be relevant for the appraisal and management of this disease. We measured objective vocal parameters and perceived vocal quality with the GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain) scale in SLE patients and compared them to matched healthy controls. SLE patients also filled a questionnaire reporting perceived vocal deficits. SLE patients had significantly lower vocal intensity and harmonics to noise ratio, as well as increased jitter and shimmer. All subjective parameters of the GRBAS scale were significantly abnormal in SLE patients. Additionally, the vast majority of SLE patients (29/36) reported at least one perceived vocal deficit, with the most prevalent deficits being vocal fatigue (19/36) and hoarseness (17/36). Self-reported voice deficits were highly correlated with altered GRBAS scores. Additionally, tissue damage scores in different organ systems correlated with dysphonic symptoms, suggesting that some features of SLE-related dysphonia are due to tissue damage. Our results show that a large fraction of SLE patients suffers from perceivable dysphonia and may benefit from voice therapy in order to improve quality of life

Linear regressions between objective and subjective vocal parameters with potential determinants of dysphonia and self-reported vocal deficits.

<p><b>A:</b> Matrix representing the R² values of linear regressions between selected variables. Note the high correlations (R² > 0.15) between HNR, G, B, R and S with the number of self-reported vocal deficits. <b>B:</b> Matrix representing the <i>P</i> values of linear regressions between selected variables. Note the significant correlations (<i>P</i> < 0.05) between HNR, G, B, R and S with the number of self-reported vocal deficits. <b>C</b>: Scatter-plot representations of the significant correlations identified in the correlation matrices in A and B.</p

Linear regressions between objective and subjective vocal parameters with tissue damage as measured by the SLICC/ACR damage index.

<p><b>A:</b> Matrix representing the R² values of linear regressions between selected variables. Note the high correlations (R² > 0.15) between intensity with summed damage scores and renal, cardiovascular, musculoskeletal and skin scores, as well as between pulmonary damage scores with jitter, shimmer and HNR. <b>B:</b> Matrix representing the <i>P</i> values of linear regressions between selected variables. Note the significant correlations (<i>P</i> < 0.05) between intensity with summed damage scores and renal, cardiovascular, musculoskeletal and skin scores, as well as between pulmonary damage scores with jitter, shimmer and HNR. <b>C</b>: Scatter-plot representations of selected significant correlations identified in the correlation matrices in A and B.</p

Objective and subjective (GRBAS) vocal parameters of control subjects (n = 32) and SLE patients (n = 36).

<p>Horizontal lines represent the population median. Each graph shows values for healthy controls and SLE patients of <b>A:</b> F₀; <b>B:</b> vocal intensity; <b>C:</b> jitter (main formant frequency variability); <b>D:</b> shimmer (intensity variability); <b>E:</b> HNR; <b>F:</b> G (general grade of dysphonia); <b>G:</b> R (roughness); <b>H:</b> B (breathiness); <b>I:</b> A (asthenia); <b>J:</b> S (strain); * = <i>P</i> < 0.05; ** = <i>P</i> < 0.001. *** = <i>P</i> < 0.0001.</p

Assessment of the PETase conformational changes induced bypoly(ethylene terephthalate) binding

Recently, a bacterium strain of Ideonella sakaiensis was identified with the uncommon ability to degrade the poly(ethylene terephthalate) (PET). The PETase from I. sakaiensis strain 201-F6 (IsPETase) catalyzes the hydrolysis of PET converting it to mono(2-hydroxyethyl) terephthalic acid (MHET), bis(2-hydroxyethyl)-TPA (BHET), and terephthalic acid (TPA). Despite the potential of this enzyme for mitigation or elimination of environmental contaminants, one of the limitations of the use of IsPETase for PET degradation is the fact that it acts only at moderate temperature due to its low thermal stability. Besides, molecular details of the main interactions of PET in the active site of IsPETase remain unclear. Herein, molecular docking and molecular dynamics (MD) simulations were applied to analyze structural changes of IsPETase induced by PET binding. Results from the essential dynamics revealed that the β1-β2 connecting loop is very flexible. This loop is located far from the active site of IsPETase and we suggest that it can be considered for mutagenesis to increase the thermal stability of IsPETase. The free energy landscape (FEL) demonstrates that the main change in the transition between the unbound to the bound state is associated with the β7-α5 connecting loop, where the catalytic residue Asp206 is located. Overall, the present study provides insights into the molecular binding mechanism of PET into the IsPETase structure and a computational strategy for mapping flexible regions of this enzyme, which can be useful for the engineering of more efficient enzymes for recycling plastic polymers using biological systems