Plasma proteomic analysis on neuropathic pain in idiopathic peripheral neuropathy patients

Background and Aims: Why only half of the idiopathic peripheral neuropathy (IPN) patients develop neuropathic pain remains unknown. By conducting a proteomics analysis on IPN patients, we aimed to discover proteins and new pathways that are associated with neuropathic pain. Methods: We conducted unbiased mass-spectrometry proteomics analysis on blood plasma from 31 IPN patients with severe neuropathic pain and 29 IPN patients with no pain, to investigate protein biomarkers and protein–protein interactions associated with neuropathic pain. Univariate modeling was done with linear mixed modeling (LMM) and corrected for multiple testing. Multivariate modeling was performed using elastic net analysis and validated with internal cross-validation and bootstrapping. Results: In the univariate analysis, 73 proteins showed a p-value <.05 and 12 proteins showed a p-value <.01. None were significant after Benjamini–Hochberg adjustment for multiple testing. Elastic net analysis created a model containing 12 proteins with reasonable discriminatory power to differentiate between painful and painless IPN (false-negative rate 0.10, false-positive rate 0.18, and an area under the curve 0.75). Eight of these 12 proteins were clustered into one interaction network, significantly enriched for the complement and coagulation pathway (Benjamini–Hochberg adjusted p-value =.0057), with complement component 3 (C3) as the central node. Bootstrap validation identified insulin-like growth factor-binding protein 2 (IGFBP2), complement factor H-related protein 4 (CFHR4), and ferritin light chain (FTL), as the most discriminatory proteins of the original 12 identified. Interpretation: This proteomics analysis suggests a role for the complement system in neuropathic pain in IPN

Effect of various treatments on the production of first grade grafts of Thompson Seedless grapevine on Vitis solonis x V. riparia 1616 rootstock

Soaking Solonis X Riparia 1616 vine rootstock cuttings in 50 ppm NAA for 24 hours before grafting gave the highest rate of Thompson Seedless graftage success. Cold stratification of cuttings gave superior results than other treatments. However, IAA, IBA, GA, thiamin and sucrose reduced the percentage of success. Steeping the basal portion of rootstock cuttings in water at 14-16 °C for 6 or 12, and 12 hours for cold-stored and freshly prepared cuttings respectively improved the grafting success. Basal soaking for 24 hours and then removing the lower 3 cm or 24 hours' soaking upside-down were advantageous for precooled rootstock cuttings. Yet, waxing the grafting zone increased only the success rate of directly grafted cuttings.Einfluß verschiedener Behandlungsverfahren auf die Erzeugung pflanzfähiger Pfropfreben aus Thompson Seedless auf der Unterlage Vitis solonis X Vitis riparia 1616Durch 24stündiges Eintauchen des Unterlagenschnittholzes von Vitis solonis X V. riparia 1616 in 50 ppm NES vor der Veredlung mit Thompson Seedless wurde der höchste Prozentsatz pflanzfähiger Pfropfreben erzielt. Kältestratifikation des Schnittholzes erbrachte bessere Ergebnisse als die anderen Behandlungen. IES, IBS, GS, Thiamin und Saccharose verringerten die Anwuchsprozente. Wurden die Unterlagen mit dem basalen Ende in 14-16 °C warmes Wasser eingetaucht, und zwar kalt gelagertes Schnittholz 6 oder 12 Stunden, frisch geschnittenes Holz 12 Stunden lang, so war der Pfropferfolg beträchtlich verbessert. Bei vorgekühltem Unterlagenschnittholz waren auch 24stündiges Quellen des basalen Endes mit anschließendem Entfernen der unteren 3 cm oder 24stündiges Eintauchen des oberen Endes von Vorteil. überziehen der Pfropfstelle mit Paraffin erhöhte die Anwuchsrate nur im Falle des unmittelbar vor der Pfropfung geernteten Holzes

Multiple instance learning based classification of diabetic retinopathy in weakly-labeled widefield OCTA en face images

Abstract Diabetic retinopathy (DR), a pathologic change of the human retinal vasculature, is the leading cause of blindness in working-age adults with diabetes mellitus. Optical coherence tomography angiography (OCTA), a functional extension of optical coherence tomography, has shown potential as a tool for early diagnosis of DR through its ability to visualize the retinal vasculature in all spatial dimensions. Previously introduced deep learning-based classifiers were able to support the detection of DR in OCTA images, but require expert labeling at the pixel level, a labor-intensive and expensive process. We present a multiple instance learning-based network, MIL-ResNet,14 that is capable of detecting biomarkers in an OCTA dataset with high accuracy, without the need for annotations other than the information whether a scan is from a diabetic patient or not. The dataset we used for this study was acquired with a diagnostic ultra-widefield swept-source OCT device with a MHz A-scan rate. We were able to show that our proposed method outperforms previous state-of-the-art networks for this classification task, ResNet14 and VGG16. In addition, our network pays special attention to clinically relevant biomarkers and is robust against adversarial attacks. Therefore, we believe that it could serve as a powerful diagnostic decision support tool for clinical ophthalmic screening