An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers

Association of Lymphopenia with Short Term Outcomes of Sepsis Patients; a Brief Report

Introduction: Studies have claimed that low lymphocyte count is independently correlated with 28-day survival of sepsis patients. Therefore, this study aimed to evaluate the value of lymphopenia in predicting the short-term outcome of sepsis patients. Methods: This cross-sectional study was performed on sepsis patients referred to the emergency department during an 8-month period and relationship of lymphopenia with 28-day mortality and probability of septic shock and readmission due to sepsis was assessed. Results: 124 cases with the mean age of 66.12 ± 15.82 (21-90) years were studied (54.8% male). 81 (65.3%) cases had lymphopenia (59.3% male). Lymphopenic patients had a significantly higher mean age (p = 0.003), higher need for ICU admission (p < 0.001), higher prevalence of 28-day septic shock (p < 0.001), higher 28-day mortality (p < 0.001), higher probability of readmission due to sepsis (p = 0.048), and higher SOFA score (p < 0.001). During 28 days of follow up, 57 (46%) patients were expired. They had a higher prevalence of septic shock (p < 0.001) and higher SOFA score (p < 0.001). Multivariate analysis showed that septic shock (OR=364.6; 95% CI: 26.3 to 5051.7; p = 0.001) and lymphopenia (OR=19.2; 95% CI: 1.7 to 211.3; p = 0.016) were the independent predictors of 28-day mortality. Conclusions: Based on the findings, lymphopenia was independently associated with higher 28-day mortality and lymphopenic patients were older than the control group and had a significantly higher need for ICU admission, higher probability of 28-day septic shock and readmission due to sepsis, and higher SOFA score

An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers

The association between metformin administration and non-Hodgkin lymphoma; a systematic review and meta-analysis of cohort and case-control studies

Introduction: Metformin, a blood sugar-lowering agent, has the potential to be an anti-cancer agent. However, its role in lymphoma remains uncertain. Objectives: This study sought to examine the correlation between the utilization of metformin and non-Hodgkin lymphoma through the application of a systematic review and meta-analysis methodology. Materials and Methods: This investigation was carried out in the form of a methodical examination and meta-analysis in accordance with the PRISMA guidelines. Databases such as Scopus, PubMed, Web of Science, Cochrane, and the Google Scholar search engine were thoroughly explored without any temporal limitations until September 20, 2023. The data was analyzed utilizing the STATA 14 software, and the level of significance for the tests was established at P<0.05. Results: The results, obtained by combining six observational studies (five cohort studies and one case-control study) with a total sample size of 2 330 787 individuals, showed that the odds ratio (OR) for the association between metformin use and non-Hodgkin lymphoma in all studies was 0.91 (95% CI: 0.78, 1.07). In cohort studies, the OR was 0.91 (95% CI: 0.74, 1.11), and in the case-control study, it was 0.93 (95% CI: 0.79, 1.10). None of these relationships were statistically significant. The odds ratio between metformin uses and chronic lymphocytic leukemia/small lymphocytic leukemia was 0.93 (95% CI: 0.71, 1.21), and the odds ratio between metformin use and diffuse large B-cell lymphoma was 1.06 (95% CI: 0.61, 1.83), both of which were not statistically significant. Conclusion: This investigation’s findings indicated no statistically noteworthy correlation exists between the utilization of metformin and the probability of contracting non-Hodgkin lymphoma, chronic lymphocytic leukemia/small lymphocytic leukemia, and diffuse large B-cell lymphoma. Registration: This study was conducted following the PRISMA checklist. Its protocol was registered on the PROSPERO (CRD42023469100) and Research Registry (UIN: reviewregistry1721) websites

Evaluation of the energy performance of glass construction materials in different degree day regions

Yapı sektöründe enerji verimliliğine katkı sağlamak amacıyla mevcut binalarda enerji tüketimi azaltılmalı ve yeni binalar enerji etkin tasarlanmalıdır. Binaların opak ve saydam yüzeyleri, enerji kazanç ve kayıpları açısından büyük önem taşımaktadır. Cam tipi, binalarda saydam yüzeylerin sebep olduğu enerji kazanç ve kayıp miktarlarını etkileyen parametrelerden biri olup binanın ısıtma amaçlı enerji tüketimini etkileyebilir. Camların sebep olabileceği enerji tüketim miktarının ön tasarım evresinde iklimsel özelliklere göre analiz edilmesi ve çözüm önerilerinin geliştirilmesi enerji etkin tasarımlar açısından önemlidir. Bu bağlamda tez çalışmasında, Türkiye'de sayıları hızla artan konut binalarında enerji etkin cam tiplerinin seçimi konusunda farkındalık yaratılması ve bu konuda yapı sektöründeki ilgili aktörlere ışık tutulması hedeflenmektedir. Bu çerçevede, TS 825 Binalarda Isı Yalıtım Kuralları Standardı kapsamında, Türkiye'nin üç farklı derece gün bölgesinde yer aldığı varsayılan Toplu Konut İdaresi (TOKİ)'nin tip bir konut projesi dikkate alınarak bir değerlendirme yapılmıştır. Tek ısıcam, çift ısıcam, tek low-e cam ve çift low-e cam seçeneklerinden oniki senaryo oluşturulmuştur. Bu senaryolar, mekan ısıtması için doğalgaz kullanımından kaynaklanan enerji tüketimi açısından enerji analiz programı eQUEST ile analiz edilmiştir. Elde edilen analiz sonuçlarına göre cam tipinin enerji tüketimi üzerinde etkili olduğu görülmüştür.Type of glass has become one of the parameters affecting the amount of energy gains and losses caused by transparent surfaces and can affect energy consumption used for heating buildings. The analysis of glasses according to climatic conditions and development of solutions for the amount of energy consumption caused by glasses is very important from energy-efficient design perspective. In this regard, this thesis aims to raise awareness in the increasing number of residential buildings in Turkey on the selection of energy-efficient glass types and provide guidelines for the actors involved in the construction sector. Accordingly, in TS 825 for buildings, under Standard for Thermal Insulation Regulations, a typical housing project from default Mass Housing Administration (TOKI) located in three regions with different degrees of sunlight was evaluated. Twelve scenarios were created from single glazing, double glazing, single low-e glass, and double low-e glass options. These scenarios were analyzed by eQuest energy analysis program in terms of natural gas use for space heating and energy consumption. The obtained results indicated that the type of glass was found to affect energy consumption

Influence of spherical anisotropy on optical mass sensing in a molecular-plasmonic optomechanical system

We use an all-optical pump-probe method to develop a mass sensing mechanism in a molecular plasmonic system at room temperature. The system consists of a double-clamped graphene nanoribbon that parametrically interacts with two types of isotropic and anisotropic spherical plasmonic cavities in the presence of a strong pump field and a weak probe pulse. Based on the mode-selective quantization scheme and analogy with the canonical model of the cavity optomechanics, we formulate the Hamiltonian of the system in terms of the electromagnetic Greens tensor. In this manner, we derive an explicit form of size-dependent optomechanical coupling function and plasmonic damping rate, which include the modal, geometrical, and material features of the plasmonic structure. Engineering material features of the plasmonic nanostructure, we find that the intensity of the probe field transmission spectrum for radially anisotropic spherical nanocavity enhances significantly compared to the silver sphere nanocavity due to the mode volume reduction. This scheme can provide to achieve the minimum measurable mass at room temperature