Optical coherence tomography angiography evaluation of retinal and optic disc microvascular morphological characteristics in retinal vein occlusion.

BACKGROUND: To evaluate microvascular morphological characteristics of the retina and optic disc (OD) in retinal vein occlusion (RVO) patients using optical coherence tomography angiography (OCTA), compare the results to age- and gender-matched healthy subjects, and determine correlations between OCTA parameters and best-corrected visual acuity (BCVA) and age. METHODS: In this retrospective study, right eyes of 53 RVO patients and 51 healthy subjects were compared regarding BCVA, as well as superficial and deep capillary plexus (SCP and DCP) vessel densities (VDs), foveal avascular zone (FAZ) parameters, outer retinal and choriocapillaris flow areas, OD whole and peripapillary VDs, and retinal nerve fiber layer thickness (RNFLT). Retinal vein occlusion patients were further divided into subgroups based on therapy and risk factors, and OCTA parameters were compared. RESULTS: Retinal vein occlusion rate or OCTA parameters did not differ significantly by gender (p > 0.05). Retinal vein occlusion patients had significantly decreased BCVA, whole, parafoveal and perifoveal SCP and DCP VDs, as well as VDs 300 µm area around FAZ (FD-300) than healthy subjects (p < 0.001). Their choriocapillaris flow area, RNFLT, whole and peripapillary VDs were also affected. However, FAZ area did not differ significantly between groups. Superior RNFLT (p = 0.016) and whole peripapillary VD (p < 0.001) differed significantly between laser photocoagulation-treated and non-treated patients. The remaining OCTA parameters revealed no significant differences CONCLUSIONS: The RVO and its therapeutic alternatives may affect both OD and retinal VDs. Given its numerous benefits, it seems that OCTA will be used more frequently in clinics for RVO diagnosis, monitoring, and therapeutic response evaluation.

Role of affective commitment in the relationship between emotional labor and life satisfaction in nurses.

PURPOSE: This study investigated relationships between hospice nurses' emotional labor, life satisfaction, and affective commitment (moderator). We started with the assumption that displaying real emotions rather than faking them may increase life satisfaction. DESIGN AND METHODS: The study utilized a cross-sectional survey data analysis. A total of 322 nurses participated in the study. Hayes' process examined the moderation relationship predicting emotional labor and life satisfaction. FINDINGS: The results showed that emotional labor partially affected life satisfaction. However, those effects varied at different levels of affective commitment and interestingly diminished at a high level. PRACTICE IMPLICATIONS: Nurse leaders should understand and transform the surface emotional setting of nurses to more profound acting emotions and then to natural emotional responses, which otherwise can generate emotional conflict causing unsatisfactory life.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

L-Dopa synthesis catalyzed by tyrosinase immobilized in poly(ethyleneoxide) conducting polymers.

1-3,4-Dihydroxy phenylalanine called as l-Dopa is a precursor of dopamine and an important neural message transmitter and it has been a preferred drug for the treatment of Parkinson's disease. In this study, with regards to the synthesis of L-Dopa two types of biosensors were designed by immobilizing tyrosinase on conducting polymers: thiophene capped poly(ethyleneoxide)/polypyrrole (PEO-co-PPy) and 3-methylthienyl methacrylate-co-p-vinylbenzyloxy poly(ethyleneoxide)/polypyrrole (CP-co-PPy). PEO-co-PPy and CP-co-PPy were synthesized electrochemically and tyrosinase immobilized by entrapment during electropolymerization. L-Tyrosine was used as the substrate for L-Dopa synthesis. The kinetic parameters of the designed biosensors, maximum reaction rate of the enzyme (Vmax) and Michaelis Menten constant (Km) were determined. Vmax were found as 0.007 µmol/(minelectrode) for PEO-co-PPy matrix and 0.012 µmol/(minelectrode) for CP-co-PPy matrix. Km values were determined as 3.4 and 9.2 mM for PEO-co-PPy and CP-co-PPy matrices, respectively. Optimum temperature and pH, operational and shelf life stabilities of immobilized enzyme were also examined.