Functional Characterization of the Epidermal Cholinergic System In Vitro

The aim of this study was to analyze the influence of cholinergic and anticholinergic drugs on epidermal physiology using organotypic cocultures (OTCs). Blocking of all acetylcholine receptors (AChRs) by combined treatment with mecamylamine and atropine or treatment with strychnine (blocking α9nAChR) for 7–14 days resulted in a complete inhibition of epidermal differentiation and proliferation. Blockage of nicotinic (n)AChR with mecamylamine led to a less pronounced delay in epidermal differentiation and proliferation than blockage of muscarinic (m)AChR with atropine, evidenced by reduced epithelial thickness and expression of terminal differentiation markers like cytokeratin 2e or filaggrin. In OTCs treated with atropine, mecamylamine, or strychnine, we could demonstrate intracellular lipid accumulation in the lower epidermal layers, indicating a severely disturbed epidermal barrier. In addition, we observed prominent acantholysis in the basal and lower suprabasal layers in mecamylamine-, atropine-, and strychnine-treated cultures, accompanied by a decreased expression of cell adhesion proteins. This globally reduced cell adhesion led to cell death via intrinsic activation of apoptosis. In contrast, stimulation of nAChR and mAChR with cholinergic drugs resulted in a significantly thickened epithelium, accompanied by an improved epithelial maturation. In summary, we show that epidermal AChR are crucially involved in the regulation of epidermal homeostasis

Конструкторская модернизация средств измерения предельной частоты СВЧ умножительных полупроводниковых диодов

Целью работы является конструкторская модернизация установки измерения предельной частоты СВЧ умножительных диодов с целью повышения точности средств измерений и автоматической обработки результатов. В результате исследований был проведен анализ средств измерений метрологических характеристик диодов. На основе ГОСТ 19656.9-79 была разработана методика измерения предельной частоты СВЧ умножительных полупроводниковых диодов с применением современного измерительного оборудования. Было разработано программное обеспечение, которое позволило значительно сократить время измерения параметров диодов, увеличить функционал и повысить производительность средства измерений.The purpose of work is design modernization of installation of measurement of the cut-off frequency of the microwave of multiplier diodes for the purpose of increase in accuracy of measuring instruments and automatic processing of results. As a result of researches the analysis of measuring instruments of metrological characteristics of diodes was carried out. On the basis of GOST 19656.9-79 the measuring technique of the cut-off frequency of the microwave of multiplier crystal diodes with use of the modern measuring equipment was developed. The software which allowed to reduce considerably time of measurement of parameters of diodes, to increase a functional and to increase efficiency of a measuring instrument was developed

Nicotinic receptors on rat alveolar macrophages dampen ATP-induced increase in cytosolic calcium concentration

Background: Nicotinic acetylcholine receptors (nAChR) have been identified on a variety of cells of the immune system and are generally considered to trigger anti-inflammatory events. In the present study, we determine the nAChR inventory of rat alveolar macrophages (AM), and investigate the cellular events evoked by stimulation with nicotine. Methods: Rat AM were isolated freshly by bronchoalveolar lavage. The expression of nAChR subunits was analyzed by RT-PCR, immunohistochemistry, and Western blotting. To evaluate function of nAChR subunits, electrophysiological recordings and measurements of intracellular calcium concentration ([Ca2+]i) were conducted. Results: Positive RT-PCR results were obtained for nAChR subunits α3, α5, α9, α10, β1, and β2, with most stable expression being noted for subunits α9, α10, β1, and β2. Notably, mRNA coding for subunit α7 which is proposed to convey the nicotinic anti-inflammatory response of macrophages from other sources than the lung was not detected. RT-PCR data were supported by immunohistochemistry on AM isolated by lavage, as well as in lung tissue sections and by Western blotting. Neither whole-cell patch clamp recordings nor measurements of [Ca2+]i revealed changes in membrane current in response to ACh and in [Ca2+]i in response to nicotine, respectively. However, nicotine (100 μM), given 2 min prior to ATP, significantly reduced the ATP-induced rise in [Ca2+]i by 30%. This effect was blocked by α-bungarotoxin and did not depend on the presence of extracellular calcium. Conclusions: Rat AM are equipped with modulatory nAChR with properties distinct from ionotropic nAChR mediating synaptic transmission in the nervous system. Their stimulation with nicotine dampens ATP-induced Ca2+-release from intracellular stores. Thus, the present study identifies the first acute receptor-mediated nicotinic effect on AM with anti-inflammatory potential

Preponderance of the oncogenic V599E and V599K mutations in B-raf kinase domain is enhanced in melanoma cutaneous/subcutaneous metastases

BACKGROUND: Downstream of Ras, the serine/threonine kinase B-raf has been reported to be mutated, among other carcinomas, in a substantial subset of primary melanomas with a preponderance of mutations within the kinase domain including the activating V599E and V599K transitions. METHODS: We here investigated a representative series of 60 resection specimens of cutaneous and subcutaneous melanoma metastases for the presence of mutations within the activation segment (exon 15) of the B-raf kinase domain by polymerase chain reaction (PCR) and single-strand conformation polymorphism (SSCP) gel electrophoresis. RESULTS: Sequencing of cloned PCR-SSCP amplicons resulted in 24 (40%) samples harbouring somatic mutations which is not exceeding the mutation frequency in recently investigated primary melanomas. The activating mutation T1796A was present in 24/60 (40%) resection specimens, followed in frequency by the oncogenic g1795A mutation in 8/60 (13%) cases. As to the B-raf protein sequence, the acidic amino acid transitions V599E and V599K were predicted in 19/60 (32%) and 6/60 (10%) cases, resepectively, but were not associated with enhanced risk for subsequent metastasis in patients' follow up. In comparison to the primary melanomas that we recently investigated, the spectrum of predicted B-raf protein mutations narrowed significantly in the cutaneous/subcutaneous metastases. Unexpectedly, V599 and V599E mutations were absent in cutaneous/subcutaneous metastases derived from acrolentiginous melanomas as preceding primary tumours. CONCLUSION: During transition from primary melanomas towards cutaneous/subcutaneous metastases, the spectrum of predicted B-raf mutations narrows significantly. Focusing on the V599E and V599K, these oncogenic mutations are likely to affect melanocyte-specific pathways controlling proliferation and differentiation

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd