Çok boyutlu tümör modelleme.

Cancer’s complex behavior decreases success rates of the cancer therapies. The usual steps cancer therapy are, deciding phase of the cancer and planing the therapy according to medical guidelines and there is no room or chance for personalized medicine. Simulation systems that use patient specific data as input and up-to-date scientific evidence as business rules has chance to help clinicians for evidence based personalized medicine practice.In this study our aim is creating a basic model to guide researchers who are eager to start tumor modeling. Developed model tries to simulate adenocarcinoma which is a subtype of non-small cell lung carcinoma. Parameters of model gathered from literature is based on A549. In simulations effects of oxygen concentration and mutation rate are examined. Tumor cell number decreases and apoptosis frequency increases proportionally with oxygen concentration’s decrease. When mutation rate decreases tumors become more vulnerable and apoptosis rate increases. All these results proves that model is consistent with tumor biology rules.M.S. - Master of Scienc

TACIT KNOWLEDGE EXTRACTION FOR SOFTWARE REQUIREMENT SPECIFICATION (SRS): A PROPOSAL OF RESEARCH METHODOLOGY DESIGN AND EXECUTION FOR KNOWLEDGE VISUALIZATION

Knowledge extraction and visualization is becoming an important research area for the organizations in order to get and share the knowledge. Most important and useful part of the knowledge extraction and visualization is dedicated to tacit knowledge. There are already known methods to acquire the tacit knowledge. Yet, these methods are mostly general approaches applicable to all the areas in need of tacit knowledge extraction and become too abstract when applied to a specific domain. One such specific domain is the requirement specification process for the software project development. Our own experiences in the area as well as the scientific researches have shown that Software Requirement Specification (SRS) process has field-specific problems that need to be eliminated by using the suitable tacit knowledge extraction techniques. For example, the experts and/or users may not have a clear idea of their requirements. They may also be technically unsophisticated or have different vocabularies than the software developers. Benefiting from the existing body of academic literature in the related fields, as well as co-authors' experience from their domains of practice, this paper aims to find the concrete methods for extracting the tacit knowledge in the area of software project development with specific implications for these academic fields and practice domains, as well as more general suggestions for all related or concerned. To provide a base for future work, the paper also presents a proposal that aims to develop a tacit knowledge visualization framework to support know-where requirements of the organizational knowledge. With the implementation of our framework in a software application, it is aimed to create a virtual environment, where subject-based knowledge requirements will be answered by the visualized tacit knowledge of individuals and possibly the relations among individual members of the organization.&nbsp

Kişiselleştirilmiş tıp uygulamaları ve bir örnek

Kalp krizinden sonra ölüm sebeplerinde ikinci sırayı alan kanser modern çağın en önemli problemlerinden biridir. Buna rağmen kanser hastalarının sadece %25’nin aldığı tedavi beklenen etkiyi göstermektedir. Bu dramatik tablo kişiselleştirilmiş tıp uygulamalarının önemini ortaya koymaktadır. Bu çalışma kapsamında kişiselleştirilmiş tıp konusunda son çalışmalardan faydalanılarak genel bir bakış ortaya konulmuş ve geliştirdiğimiz kişiselleştirilebilir in-silico tümör modeli anlatılmıştır. Çalışmada öncelikle ideal in-silico tümör modeli tarif edilmiş ve kurulan modelin teorik altyapısı anlatılmıştır. Sonrasında modelin hangi yöntemlerle kişiselleştirilebileceği üzerinde durulmuş ve bu konudaki çalışmalar anlatılmıştır. Son olarak modelin nasıl doğrulandığı konusundaki bulgular irdelenmiş ve elde edilen sonuçlar grafik ve resimler ile sunulmuştur. Bu çalışma ile, kişiselleştirilmiş tıp uygulamalarında somut bir örnek ortaya koyarak, konu ile ilgilenen araştırmacılara bir yol açmayı hedefledik

Personalized Tumor Growth Prediction Using Multiscale Modeling

Purpose: Cancer is one of the most complex phenomena in biology and medicine. Extensive attempts have been made to work around this complexity. In this study, we try to take a selective approach; not modeling each particular facet in detail but rather only the pertinent and essential parts of the tumor system are simulated and followed by optimization, revealing specific traits. This leads us to a pellucid personalized model which is noteworthy as it closely approximates existing experimental results

A prospective study for gestational diabetes mellitus: Analysis of risk factors in Turkish women for early prediction

Objective: To define risk factors for the early prediction of gestational diabetes mellitus (GDM) because the risk of pre-eclampsia and preterm birth increases in mothers who are diagnosed with GDM. Materials and methods: A prospective study was designed and the data were collected by physicians prospectively from the patients who came to the clinic between the years 2019 and 2021; informed consent was obtained from the women. The prospective data comprised 489 patient records with 72 variables and the risk factors for early prediction of GDM were determined using logistic regression and random forest (RF), which is an advanced analysis method. Results: The obtained sensitivity and specificity values are 90% and 75% for logistic regression and 71% and 90% for the RF, respectively. Conclusion: In this prospective study of GDM in Turkish women; age, body mass index, level of hemoglobin A1c, level of fasting blood sugar, physical activity time in first trimester, gravidity, triglycerides, and high-density lipoprotein cholesterol were confirmed to be risk factors in analysis results

Finding underlying genetic mechanisms of two patients with autism spectrum disorder carrying familial apparently balanced chromosomal translocations

Background: Genetic etiologies of autism spectrum disorders (ASD) are complex, and the genetic factors identified so far are very diverse. In complex genetic diseases such as ASD, de novo or inherited chromosomal abnormalities are valuable findings for researchers with respect to identifying the underlying genetic risk factors. With gene mapping studies on these chromosomal abnormalities, dozens of genes have been associated with ASD and other neurodevelopmental genetic diseases. In the present study, we aimed to idenitfy the causative genetic factors in patients with ASD who have an apparently balanced chromosomal translocation in their karyotypes. Methods: For mapping the broken genes as a result of chromosomal translocations, we performed whole genome DNA sequencing. Chromosomal breakpoints and large DNA copy number variations (CNV) were determined after genome alignment. Identified CNVs and single nucleotide variations (SNV) were evaluated with VCF-BED intersect and Gemini tools, respectively. A targeted resequencing approach was performed on the JMJD1C gene in all of the ASD cohorts (220 patients). For molecular modeling, we used a homology modeling approach via the SWISS-MODEL. Results: We found that there was no contribution of the broken genes or regulator DNA sequences to ASD, whereas the SNVs on the JMJD1C, CNKSR2 and DDX11 genes were the most convincing genetic risk factors for underlying ASD phenotypes. Conclusions: Genetic etiologies of ASD should be analyzed comprehensively by taking into account of the all chromosomal structural abnormalities and de novo or inherited CNV/SNVs with all possible inheritance patterns