DATA MINING TECHNIQUES AND ALGORITHMS ON DATABASE OF PROCESS PARAMETERS

V diplomskem delu je predstavljen proces podatkovnega rudarjenja, njegovi algoritmi, tehnike in uporaba v praksi. V prvem delu se seznanimo s teorijo podatkovnega rudarjenja. Omenjene so tehnike podatkovnega rudarjenja in nekateri najbolj znani algoritmi. Podrobneje je predstavljen algoritem nevronskih mrež, ki se uporabi v praktičnem primeru. V drugem delu je po korakih splošne metode podatkovnega rudarjenja, predstavljene v prvem delu, predstavljen konkreten poslovni problem, ki ga rešujemo s podatkovnim rudarjenjem. Na bazah podatkov podjetja Impol sta zgrajena modela za iskanje povezav med kemijsko sestavo zlitine EN AW-7075 (interna oznaka PD30) in njenimi mehanskimi lastnostmi. Po združitvi različnih baz in agregiranju podatkov je bilo uporabljenih 675 množic zgodovinskih podatkov za zlitino PD30. Model je bil zgrajen z orodjem SPSS Modeler, s feed-forward nevronsko mrežo in vzvratnim širjenjem napake. Naučeni nevronski mreži napovedujeta mehanske lastnosti napetost tečenja (R0,2), natezna trdnost (Rm) in raztezek (A), kot funkcijo procesnih parametrov. Točnost napovedi modela nevronske mreže za napetost tečenja je 84,8%, točnost napovedi modela za natezno trdnost in raztezek pa 91,8%. S predstavljenima modeloma nevronskih mrež je pokazano, da lahko podjetje Impol razvije model za ocenjevanje končnih mehanskih lastnosti, kot funkcijo procesnih parametrov. S tem je omogočena optimizacija procesne poti glede na produktivnost in kvaliteto.In the graduation thesis we presented the data mining process, data mining techniques and algorithms on database of process parameters. The thesis begins with a short introduction of data mining process. We described the best know techniques and algorithms of data mining. In more details we presented algorithms of neural networks, wich we used to work on practical business problem. In the second part is described through the generic data mining method an alternative approach to the physical modeling, the artificial intelligence approach, based on the neural networks. Data for data mining process were collected in company Impol. After merge different database and aggregate data, 675 sets of complete history data were collected for alloy EN AW-7075 (internal use Impol as PD30). For building the neural network model we used SPSS tool, with one of most popular architecture Multilayer Feedforward with Backpropagation learning. This neural networks are capable of predicting yield strength (R0,2), tensile strength (Rm) and elongation (A) as function of process parameters. The accuracy of neural network model for yield strength is 84,8%, the accuracy of neural network model for tensile strength and elongation is 91,8%. With the represented models of neural network we show, that the company Impol can develop a model for estimation of the final product properties as a function of the process parameters. This allows optimizing the process path with respect to productivity and quality in the perspective

Verification and comparison of serological assays for quantification of specific anti-SARS-CoV-2 antibodies

Leta 2020 je Svetovna zdravstvena organizacija razglasila pandemijo bolezni COVID 19 in pozvala države k sprejemanju ukrepov za preprečevanje širjenja virusa SARS CoV 2. Hitra in učinkovita diagnostika je bila na začetku pandemije ključnega pomena pri preprečevanju širjenja bolezni, zato so se na tržišču pojavili številni testi. Kot zlati standard določanja virusa SARS-CoV¬ 2 je bila izbrana metoda RT-PCR, ki daje najbolj zanesljive rezultate, saj omogoča detekcijo nukleinskih kislin. Žal pa so slabosti metode čas izvedbe, cena testa in zahteva po izobraženem kadru. To je vodilo do razvoja hitrih antigenskih testov, ki omogočijo detekcijo virusa SARS CoV 2 v zgolj petnajstih minutah, so preprosti za uporabo in jih lahko izvaja vsak posameznik. Kljub temu pa detekcija antigena ni dovolj za spremljanje pandemije. Pomembni so tudi serološki testi, ki omogočajo detekcijo protiteles. Namen magistrske naloge je bil overjanje nove serološke metode za kvantifikacijo protiteles proti virusu SARS CoV 2 na Kliničnem inštitutu za klinično kemijo in biokemijo Univerzitetnega kliničnega centra Ljubljana. Uporabili smo vzorce 63 anonimnih polnoletnih preiskovancev, izmed katerih je bilo 36 RT PCR pozitivnih in 27 RT PCR negativnih. Metoda, ki smo jo overili za nadaljnjo uporabo v klinični praksi, je Elecsys® Anti SARS CoV 2 S proizvajalca Roche Diagnostics, ki omogoča merjenje celokupne koncentracije protiteles proti proteinu S virusa SARS CoV 2. Za overjanje metode smo izračunali natančnost in pravilnost metode, ki sta ustrezali zahtevam laboratorija. Izračunali smo tudi diagnostično specifičnost, katere vrednost je bila 96,30 % (95-% CI: 81,03–99,91 %), in diagnostično občutljivost metode, katere vrednost je bila 97,22 % (95-% CI: 85,47–99,93 %). Metodo smo primerjali z metodama COVID 19 S1 RBD protein Human IgM ELISA Kit in COVID 19 S1 RBD protein Human IgG ELISA Kit proizvajalca Ray Bio. Diagnostični specifičnosti obeh metod sta imeli vrednost 100 % (95-% CI: 87,23–100 %). Diagnostična občutljivost COVID 19 S1 RBD protein Human IgM ELISA Kit je imela vrednost 8,33 % (95-% CI: 1,75–22,47 %), za metodo COVID 19 S1 RBD protein Human IgG ELISA kit pa 33,33 % (95-% CI: 18,56–50,97 %). Vse tri metode smo na koncu primerjali še s pomočjo Cohenovega koeficienta kappa, s katerim smo opazili komaj zaznavno ujemanje med metodami. Če povzamemo, je metoda Elecsys® Anti SARS CoV 2 S optimalna izbira za kvantifikacijo protiteles v klinični praksi, medtem ko metodi proizvajalca Ray Bio ne dajeta dovolj zanesljivih rezultatov.In the beginning of 2020, the World Health Organization announced a pandemic of COVID 19. They called on states to take on measures to prevent the spread of SARS CoV 2 virus. A rapid and effective diagnostic was a key for having a virus spread under control, so many new tests became available on the market. The RT PCR method was chosen as a golden standard for diagnosis of COVID 19. It gives the most reliable results, because it is based on detection of nucleic acid of the virus. This method also has a few cons, such as performance time, price of the test and a need for educated personnel. That has led to the development of rapid antigen tests that can detect the SARS CoV 2 virus in only fifteen minutes, are easy to use and can be performed by anyone. However, antigen detection is not sufficient for a pandemic monitoring. Serological tests are also of a big importance. The purpose of the master’s thesis was a verification of a new serological method for quantification of antibodies against SARS CoV 2 at the Institute of Clinical Chemistry and Biochemistry University Medical Centre Ljubljana. We used 65 anonymous samples, collected form adults – 36 of them were RT-PCR positive and 27 RT PCR were negative. The method chosen for the implementation in clinical practice was Elecsys® Anti SARS CoV 2 S, manufactured by Roche Diagnostics, which allowed us to measure a total concentration of antibodies against S protein of SARS CoV 2. For the verification, we calculated precision and accuracy of this method, and both of them met the laboratory requirements. The next step was to calculate the diagnostic specificity that was 96.30% (95% CI: 81.03–99.91%) and sensitivity that was 97.22% (95% CI: 85.47–99.93%). This method was then compared to COVID 19 S1 RBD protein Human IgM ELISA Kit and COVID 19 S1 RBD protein Human IgG ELISA Kit, manufactured by Ray Bio that were developed for research use only. Diagnostic specificity of both methods was 100% (95% CI: 87.23–100%). Diagnostic sensitivity for COVID 19 S1 RBD protein Human IgM ELISA Kit was only 8.33% (95% CI: 1.75–22.47%), and 33.33% (95% CI: 18.56–50.97%) for COVID 19 S1 RBD protein Human IgG ELISA Kit. The last step was to compare all three methods with the Cohen’s coefficient Kappa, which showed only slight agreement between verified methods. In conclusion, the Elecsys® Anti SARS CoV 2 S method is the optimal choice for quantification of antibodies against SARS CoV 2 in clinical practice, while products manufactured by Ray Bio are not reliable enough for clinical use

Sonication is a suitable method for loading nanobody into glioblastoma small extracellular vesicles

Glioblastoma is one of the deadliest cancers, therefore novel efficient therapeutic approaches are urgently required. One of such are nanobodies, prospective nano-sized bio-drugs with advantageous characteristics. Nanobodies can target intracellular proteins, but to increase their efficiency, the delivery system should be applied. Here, we examined small extracellular vesicles as a delivery system for anti-vimentin nanobody Nb79. Nb79 was loaded in small extracellular vesicles either by incubation with glioblastoma cells, by passive loading into isolated small extracellular vesicles or by sonication of isolated small extracellular vesicles. Small extracellular vesicles secreted by glioblastoma cells were isolated by ultracentrifugation on sucrose cushion. The size distribution and average size of sonicated and non-sonicated small extracellular vesicles were determined by nanoparticle tracking analysis method. The loading of Nb79 into small extracellular vesicles by incubation with cells, passive loading or sonication was confirmed by Western blot and electron microscopy. The effect of small extracellular vesicles on cell survival was determined by WST-1 reagent. Loading of small extracellular vesicles by incubation of cells with Nb79 was unsuccessful and resulted in substantial cell death. On the other hand, as confirmed by Western blot and electron microscopy, sonication is a successful method for obtaining Nb79-loaded small extracellular vesicles. Small extracellular vesicles also had an effect on cell viability. Small extracellular vesicles without Nb79 increased survival of U251 and NCH644 cells for 20–25%, while the Nb79-loaded small extracellular vesicles decreased survival of NCH421k by 11%. We demonstrated that sonication is a suitable method to load nanobodies into exosome, and these small extracellular vesicles could in turn reduce cell survival. The method could be translated also to other applications, such as targeted delivery system of other protein-based drugs

Anti-Vimentin Nanobody Decreases Glioblastoma Cell Invasion In Vitro and In Vivo

Purpose: Glioblastoma (GBM) is the most common primary brain tumour and one of the deadliest cancers. In addition to late diagnosis and inadequate treatment, the extremely low survival rate is also due to the lack of appropriate therapeutic biomarkers and corresponding therapeutic agents. One of the potential therapeutic biomarkers is the intermediate filament vimentin, which is associated with epithelial-mesenchymal transition (EMT). The purpose of this study was to analyse the effect of the anti-vimentin nanobody Nb79 on cell invasion in vitro and in vivo. To further our understanding of the mechanism of action, we investigated the association between Nb79 and EMT in GBM and GBM stem cells by analysing the expression levels of key EMT-related proteins. Methods: The expression of vimentin in glioma tissues and cells was determined by RT-qPCR. An invasion assay was performed on differentiated glioblastoma cell line U-87 MG and stem cell line NCH421k in vitro as well as in vivo in zebrafish embryos. The effect of Nb79 on expression of EMT biomarkers beta-catenin, vimentin, ZEB-1 and ZO1 was determined by Western blot and immunocytochemistry. Results: Our study shows that vimentin is upregulated in glioblastoma tissue compared to lower grade glioma and non-tumour brain tissue. We demonstrated that treatment with Nb79 reduced glioblastoma cell invasion by up to 64% in vitro and up to 21% in vivo. In addition, we found that the tight junction protein ZO-1 had higher expression on the cell membrane, when treated with inhibitory anti-vimentin Nb79 compared to control. Conclusion: In conclusion, our results suggest that anti-vimentin nanobody Nb79 is a promising tool to target glioblastoma cell invasion