The purpose of this study is to shed some light on structural changes in the manufacturing industry of Russia and eight other transition countries during the last decade. The export performance of countries in one and the same market can under certain conditions be used to compare the state of the art in national industries. On the basis of full and comparable trade data from Eurostat, we perform comparative analysis of the development of manufacturing exports to the European Union by Russia and eight other transition countries over the period 1993–2000. The transition countries selected for comparison are Hungary, the Czech Republic, Poland, Romania, Bulgaria, Estonia, Latvia, and Ukraine. For all these countries, as well as for Russia, the European Union is the most important trade partner. The paper provides a comparative analysis of the breakdown of manufacturing exports by factor intensities, export concentration and specialisation, and relative price positions in the European market. All these approaches give a rather unfavourable picture of Russia’s performance. We observe a relative shrinking in exports of labour–intensive, specialised–supplier and science–based sectors. At the same time the role of the sector based on natural resources, which was traditionally predominant in Russia’s exports, is strengthening further. We also observe an alarmingly high level of concentration in Russia’s manufacturing exports and their growing specialisation in base metals, together with an intense de–specialisation in engineering products.
Purpose: To evaluate the clinical efficacy and safety of modified SMILE surgery for mild myopia. Methods: The study involved 68patients (135eyes), operated by SMILE methods for mild myopia. The first group included patients operated by standard technology with a 15μm thickness of neural optical lenticular layer, the second group – patients, operated by a modified SMILE technology monolayer with thickness of 30μm. A comprehensive ophthalmology examination was performed before the surgery and after the surgical treatment – on the following day, in one month and in 12 months. Results: In all patients of both groups the monocular uncorrected visual acuity after the operation after the period of one day, one month, and a year did not differ and made 0.88±0.15, 0.92±0.1 and 0.95±0.08 respectively, for the second group – 0.87±0.18, 0.92±0.15 and 0.96±0.10 (p≥0.05). Follow-up in the early and late postoperative period convincingly demonstrated that visual acuity, refraction, spatial sensitivity, corneal hysteresis indices and even a subjective assessment of visual quality have no statistically significant differences among patients in both groups. During the operation and late postoperative period the complications were not noted. Conclusion: the study found that the modified technology provides high visual acuity, while minimally changing the biomechanical properties of the cornea. Thus, a modified SMILE operation is highly predictable and safe method in correction of slight degree myopia
Background: Biomarker gene expression is becoming more commonly utilized for clinical decision-making in oncology clinical practice. However, complex tumor tissue comprises a population of cancer cells (CC) and the tumor microenvironment (TME), causing expression signals belonging to the CC and TME calculated from bulk RNA-seq of the tumor tissue to be indistinguishable. To circumvent this, Helenus, a gene expression deconvolution tool, was developed to estimate TME-specific gene expression, consequently, providing precise CC-specific gene expression. Methods: Helenus performs the “subtraction” of TME gene expression from the total expression calculated from bulk RNA-seq of the tumor tissue. To accurately reconstruct the CC expression profile, LightGBM gene models were trained on artificial transcriptomes created from > 1,000 different solid tumor cancer cell lines and > 3,000 samples of various TME cellular proportions. The LighGBM gene models included genes expressed predominantly in the TME (e.g., CD3E), both the TME and the CC (e.g., BCL6), or in the CC (e.g., HER2). The input features included: 1) RNA percentages of TME cell types predicted by the cell deconvolution tool Kassandra (Zaitsev et al., 2022); 2) evaluation of TME target gene expression via the estimation of its weighted average expression profile in TME cell populations; and 3) a set of TME- and CC-specific genes. The resulting predictions were adjusted based on the CC cell fraction. To evaluate Helenus’ performance, CC and TME RNA were mixed at different ratios using various cancer cell lines and peripheral blood-derived TME cell populations and suspensions of tumor cells prepared from cancer tissue across multiple tumor purity dilutions. Results: Helenus deconvolution resulted in an increased concordance correlation value from 0.73 to 0.98 between the real gene expression profile of pure CC and the reconstructed CC expression from bulk RNA-seq. Helenus showed high concordance between the gene expression profile of sorted cancer cell lines and the deconvolved gene expression across a wide range of CC RNA concentrations (20-90%) mixed with imitated TME RNA at varying concentrations. Helenus demonstrated high performance calculating gene expression of multiple clinically relevant biomarkers in the TME:cancer cell line mixes: CD274 (PD-L1) (mean absolute error [MAE] ~3.5-fold reduction); HLA-A (~2.8-fold MAE reduction); MKI67 (Ki-67, ~2.2-fold MAE reduction), ERBB2 (HER2, ~1.7-fold MAE reduction). Helenus deconvolved CC expression and found significant correlations with CC gene amplifications and deletions (e.g., BCL-2, VNN3) independent of tumor purity (p < 0.003). Conclusion: Helenus, the CC gene expression deconvolution tool, was developed with high accuracy to contribute to tumor diagnosis, disease monitoring, treatment decisions, and clinically relevant biomarker identification. Citation Format: Valentina Beliaeva, Ekaterina Ivleva, Boris Shpak, Daniil Litvinov, Anastasia Zotova, Krystle Nomie, Daniiar Dyikanov, Alexander Kuznetsov, Maria Savchenko, Aleksandr Zaitsev, Nathan Fowler, Alexander Bagaev. Computational cancer cell gene expression deconvolution from tumor bulk RNA-seq via the machine learning algorithm Helenus. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5401.
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