Notes

Evaluation involving brucellar and tuberculous spondylodiscitis patients: outcomes of the multicenter "Backbone-1 Study".
Diagnosis of ocular graft-versus-host disease (oGVHD) is hampered by a lack of clinically-validated biomarkers. This study aims to predict disease severity on the basis of tear protein expression in mild oGVHD.

Forty-nine patients with and without chronic oGVHD after AHCT were recruited to a cross-sectional observational study. Patients were stratified using NIH guidelines for oGVHD severity NIH 0 (none; n = 14), NIH 1 (mild; n = 9), NIH 2 (moderate; n = 16), and NIH 3 (severe; n = 10). The proteomic profile of tears was analyzed using liquid chromatography-tandem mass spectrometry. Random forest and penalized logistic regression were used to generate classification and prediction models to stratify patients according to disease severity.

Mass spectrometry detected 785 proteins across all samples. check details A random forest model used to classify patients by disease grade achieved F1-measure values for correct classification of 0.95 (NIH 0), 0.8 (NIH 1), 0.74 (NIH 2), and 0.83 (NIH 3). A penalized logistic regression model was generated by comparing patients without oGVHD and those with mild oGVHD and applied to identify potential biomarkers present early in disease. A panel of 13 discriminant markers achieved significant diagnostic accuracy in identifying patients with moderate-to-severe disease.

Our work demonstrates the utility of tear protein biomarkers in classifying oGVHD severity and adds further evidence indicating ocular surface inflammation as a main driver of oGVHD clinical phenotype.

Expression levels of a 13-marker tear protein panel in AHCT patients with mild oGVHD may predict development of more severe oGVHD clinical phenotypes.
Expression levels of a 13-marker tear protein panel in AHCT patients with mild oGVHD may predict development of more severe oGVHD clinical phenotypes.
Fluorescence lifetime imaging ophthalmoscopy (FLIO) is a novel modality to investigate the human retina. This study aims to characterize the effects of age, pigmentation, and gender in FLIO.

A total of 97 eyes from 97 healthy subjects (mean age 37 ± 18 years, range 9-85 years) were investigated in this study. This study included 47 (49%) females and 50 males. The pigmentation analysis was a substudy including 64 subjects aged 18 to 40 years (mean age 29 ± 6 years). These were categorized in groups A (darkly pigmented, 8), B (medium pigmented, 20), and C (lightly pigmented, 36). Subjects received Heidelberg Engineering FLIO and optical coherence tomography imaging. Retinal autofluorescence lifetimes were detected in two spectral channels (short spectral channel [SSC] 498-560 nm; long spectral channel [LSC] 560-720 nm), and amplitude-weighted mean fluorescence lifetimes (τ
) were calculated. Additionally, autofluorescence lifetimes of melanin were measured in a cuvette.

Age significantly affected FLIO lifetimes, and age-related FLIO changes in the SSC start at approximately age 35 years, whereas the LSC shows a consistent prolongation with age from childhood. There were no gender- or pigmentation-specific significant differences of autofluorescence lifetimes.

This study confirms age-effects in FLIO but shows that the two channels are affected differently. The LSC appears to show the lifelong accumulation of lipofuscin. Furthermore, it is important to know that neither gender nor pigmentation significantly affect FLIO lifetimes.

This study helps to understand the FLIO technology better, which will aid in conducting future clinical studies.
This study helps to understand the FLIO technology better, which will aid in conducting future clinical studies.
Endothelin-1 (ET-1) is a potent vasoactive factor implicated in development of diabetic retinopathy, which is commonly associated with retinal edema and hyperglycemia. Although the vasomotor activity of venules contributes to the regulation of tissue fluid homeostasis, responses of human retinal venules to ET-1 under euglycemia and hyperglycemia remain unknown and the ET-1 receptor subtype corresponding to vasomotor function has not been determined. Herein, we addressed these issues by examining the reactivity of isolated human retinal venules to ET-1, and results from porcine retinal venules were compared.

Retinal tissues were obtained from patients undergoing enucleation. Human and porcine retinal venules were isolated and pressurized to assess diameter changes in response to ET-1 after exposure to 5 mM control glucose or 25 mM high glucose for 2 hours.

Both human and porcine retinal venules exposed to control glucose developed similar basal tone and constricted comparably to ET-1 in a concentration-dependent manner. ET-1-induced constrictions of human and porcine retinal venules were abolished by ET
receptor antagonist BQ123. During high glucose exposure, basal tone of human and porcine retinal venules was unaltered but ET-1-induced vasoconstrictions were enhanced.

ET-1 elicits comparable constriction of human and porcine retinal venules by activation of ET
receptors. In vitro hyperglycemia augments human and porcine retinal venular responses to ET-1.

Similarities in vasoconstriction to ET-1 between human and porcine retinal venules support the latter as an effective model of the human retinal microcirculation to help identify vascular targets for the treatment of retinal complications in patients with diabetes.
Similarities in vasoconstriction to ET-1 between human and porcine retinal venules support the latter as an effective model of the human retinal microcirculation to help identify vascular targets for the treatment of retinal complications in patients with diabetes.
To use machine learning in those with brain amyloid to predict thioflavin fluorescence (indicative of amyloid) of retinal deposits from their interactions with polarized light.

We imaged 933 retinal deposits in 28 subjects with post mortem evidence of brain amyloid using thioflavin fluorescence and polarization sensitive microscopy. Means and standard deviations of 14 polarimetric properties were input to machine learning algorithms. Two oversampling strategies were applied to overcome data imbalance. Three machine learning algorithms linear discriminant analysis, supporting vector machine, and random forest (RF) were trained to predict thioflavin positive deposits. For each method; accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were computed.

For the polarimetric positive deposits, using 1 oversampling method, RF had the highest area under the receiver operating characteristic curve (0.986), which was not different from that with the second oversampling method.
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