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In this study, the antibacterial activity of environmentally friendly synthesized 2D copper oxide nanoleaves (Cu 2 O NLs) on ceramic glazes and photocatalytic activity against methylene blue (MB) dye was investigated. The SPR band of biogenic Cu 2 O NLs was observed between 200–300 nm. Transmission electron microscopy (TEM) analysis revealed that the Cu 2 O NLs were in a nanoleaves structure and had a particle size of 42.04 nm. In order to determine the antibacterial activity of the synthesized Cu 2 O NLs in ceramic structures, NLs were added to the transparent glaze recipe used in tableware. The antibacterial properties of the test mixture were then examined against the gram ( +) and gram (-) bacteria. In addition, the photodegradation rate of Cu 2 O NLs against MB dye was found to be 68% after 90 min. Experimental results of Cu 2 O NLs synthesized with environmentally friendly have shown that they can have potential applications in the ceramic industry and water treatment systems.

The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called “gut-brain axis.” Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

PurposeIt is aimed to determine the Rubella and CMV prevalence in the pregnant women in Izmir and to research the effect of these infections on the course of pregnancy in the pregnant women exposed to infection during pregnancy.MethodsThe pregnant women applied to pregnancy outpatient department during 2001–2008 have been examined with enzyme-linked fluorescent assay (VIDAS; bioMérieux) method in terms of Rubella and CMV IgM and IgG antibodies and CMV IgG avidity test.ResultsTotally 5,959 pregnant women were included in the study. The seropositivity rates for Rubella and CMV were found as 97.8 and 98.3 %, IgM positivity rates were found as 0.37 and 0.18 %, respectively. Curettage was recommended to the pregnant women in which Rubella IgM positivity was detected in the first trimester of the pregnancy. Eight of the pregnant women in which IgM was found as positive after the 20th week of pregnancy were examined and three intrauterine growth retardation, one hypospadias and three normal deliveries were seen in these pregnant women. Any congenital anomaly finding was not detected in the pregnant women with positive CMV IgM.ConclusionsSeroprevalence values are high for Rubella and CMV in our region. It can be recommended not to check the pregnant women routinely for this purpose with the good implementation of Rubella vaccine programs.

This study aimed to determine the differences in terms of demographic characteristics and preferred stroke prevention strategies for patients with non-valvular atrial fibrillation living in seven geographical regions of Turkey. In total, 6273 patients were enrolled to this prospective, observational RAMSES study. The patients were divided into seven groups based on the geographical region of residence. In terms of the geographical distribution of the overall Turkish population, the highest number of patients were enrolled from Marmara (1677, 26.7%). All demographic characteristics were significantly different among regions. Preferred oral anticoagulants (OACs) also differed between geographical regions; non-vitamin K OACs were preceded by warfarin in East Anatolia, Aegean, Southeast Anatolia, and Black Sea. Nearly one-third of the patients (28%) did not receive any OAC therapy. However, the number of patients not receiving any OAC therapy was higher in Southeast Anatolia (51.1%) and East Anatolia (46.8%) compared with other geographical regions of Turkey. Inappropriate use of OACs was also more common in East and Southeast Anatolia. This study was the first to show that the demographic differences among the geographical regions may result in different preferences of stroke prevention strategies in Turkey. OACs are still under- or inappropriately utilized, particularly in the eastern provinces of Turkey.

Amaç: Çalışmamız Koronavirüs hastalığı-2019 (COVID-19) salgınının birinci ayında çocukların yeme düzenlerini, ebeveynlerinin koronavirüsü bulaşma riski ile ilgili kaygılarını ve bu kaygı ile ilişkili etkenlerin değerlendirilmesini ve salgın öncesi ile karşılaştırılmasını amaçlamıştır. Gereç ve Yöntem: Çalışma çok merkezli (n=8) ve kesitsel olarak planlanmıştır. Çalışma merkezlerine başvuran 6-18 yaş arası çocukların ailelerinden çalışmaya katılmayı kabul edenlere bilgilendirilmiş gönüllü olur formu ve anketleri içeren bilgiler, e-posta veya mesajlaşma yoluyla iletilmiştir. Bulgular: Çalışmaya ortalama 11,1 yaşında (standart sapma=3,4) 893 çocuk dahil edilmiştir. Çalışmaya dahil edilen çocukların %50,7’si (n=453) kızdır. Ailelerin çoğu, salgının birinci ayında çocuklarının yeme düzeninde değişiklik bildirmezken (%63,6, n=568), %21,4’ü çocuklarının yeme düzeninin daha iyi, %15,0’ı ise daha kötü olduğunu bildirmiştir. Ebeveynlerin %40.0’ı kendileri veya çocuklarına virüs bulaşmasından “çok kaygılı” olduğunu belirtmiştir. Kaygı düzeyi yüksek olan ebeveynler, düşük olanlara göre anlamlı derecede daha genç ve düşük eğitimlidir (sırasıyla; p<0,001, p=0,001). Sonuç: COVID-19 salgını ebeveynlerin kaygı düzeyini ve çocuklarının yeme düzenlerini etkilemiştir. Genç ve düşük eğitimli ebeveynlerin bulaşma riski açısından daha kaygılı oldukları saptanmıştır. Ebeveynlik yaşı ve eğitimi koruyucu ruh sağlığı girişimleri açısından önemli olabilir.

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder that spans over a continuum with multiple phases including preclinical, mild cognitive impairment, and dementia. Unlike most other chronic diseases there are limited number of human studies reporting on AD gut microbiota in the literature. These published studies suggest that the gut microbiota of AD continuum patients varies considerably throughout the disease stages, raising expectations for existence of multiple microbiota community types. However, the community types of AD gut microbiota were not systematically investigated before, leaving important research gap for diet-based intervention studies and recently initiated precision nutrition approaches aiming at stratifying patients into distinct dietary subgroups. Here, we comprehensively assessed the community types of gut microbiota across the AD continuum. We analyze 16S rRNA amplicon sequencing of stool samples from 27 mild cognitive patients, 47 AD, and 51 non-demented control subjects using tools compatible with compositional nature of microbiota. To characterize gut microbiota community types, we applied multiple machine learning techniques including partitioning around the medoid clustering, fitting probabilistic Dirichlet mixture model, Latent Dirichlet Allocation model, and performed topological data analysis for population scale microbiome stratification based on Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides partitioning of the gut microbiota across AD continuum while some methods provided fine scale resolution in partitioning the community landscape. The Signature taxa and neuropsychometric parameters together robustly classify the heterogenous groups within the cohort. Our results provide a framework for precision nutrition approaches and diet-based intervention studies targeting AD cohorts. The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease modifying treatments and drug trials have failed due partly to the heterogeneous and complex nature of the disease. Unlike other neurodegenerative diseases gut microbiota of AD patients is poorly studied. Recently initiated ambitious precision nutrition initiative or other diet-based interventions can potentially be more effective if the heterogeneous disease such as AD is deconstructed into multiple strata allowing for better identification of biomarkers across narrower patient population for improved results. Because gut microbiota is inherently integral part of the nutritional interventions there is unmet need for microbiota-informed stratification of AD clinical cohorts in nutritional studies. Our study fills in this gap and draws attention to the need for microbiota stratification as one of the essential steps for precision nutrition interventions. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions the newly developed probabilistic methods can provide fine scale resolution in partitioning the AD gut microbiome landscape.