Integration of high-resolution nuclear magnetic resonance (NMR) spectroscopy with microfluidic lab-on-a-chip devices is challenging due to limited sensitivity and line broadening caused by magnetic susceptibility inhomogeneities. We present a novel double-stripline NMR probe head that accommodates planar microfluidic devices, and obtains the NMR spectrum from a rectangular sample chamber on the chip with a volume of 2 l. Finite element analysis is used to jointly optimise the detector and sample volume geometry for sensitivity and RF homogeneity. A prototype of the optimised design has been built, and its properties have been characterised experimentally. The performance in terms of sensitivity and RF homogeneity closely agrees with the numerical predictions. The system reaches a mass limit of detection of 1.57 nMol p s, comparing very favourably with other micro-NMR systems. The spectral resolution of this chipGprobe system is better than 1.75 Hz at a magnetic field of 7 T, with excellent line shape.

Using 1H NMR metabolomics, we biochemically profiled saliva samples collected from healthy-controls (n = 12), mild cognitive impairment (MCI) sufferers (n = 8), and Alzheimer's disease (AD) patients (n = 9). We accurately identified significant concentration changes in 22 metabolites in the saliva of MCI and AD patients compared to controls. This pilot study demonstrates the potential for using metabolomics and saliva for the early diagnosis of AD. Given the ease and convenience of collecting saliva, the development of accurate and sensitive salivary biomarkers would be ideal for screening those at greatest risk of developing AD.

Pandemics leave significant marks on the memories of societies with their permanent impacts. Going beyond a cause of disease or death, they can have consequences in many aspects, psychological, social and economic ones being in the first place. The Covid-19 outbreak, which first emerged in China and has spread to the whole world as of the first months of 2020, has the potential to constitute a breaking the course of history, as well. Turkey is located on the transit point between Asia and Europe with its geographical position, and thus, received its share from the outbreak of Covid-19, which spreads through social contact. The first official case was recorded on 11 March 2020, and then the virus spread rapidly. This study aims to assess the attitude of the public towards Covid-19 at times when the impact of the disease reached maximum. To this end, data were collected from 1586 people with different socio-demographic features through Covid-19 Pandemic Community Scale. The impact of the pandemic on the society was measured in three dimensions as Sensitivity to Pandemic, Protection against Pandemic and Social Trust. The research results showed that the people had high levels of sensitivity to the pandemic, exerted the maximum effort for protection and social trust was above the average although it fell behind the other dimensions. As a consequence, it can be concluded that Covid-19 has had a significant impact on the Turkish people.

Parkinson's disease is the second most common neurodegenerative disease. In the vast majority of cases the origin is not genetic and the cause is not well understood, although progressive accumulation of α-synuclein aggregates appears central to the pathogenesis. Currently, treatments that slow disease progression are lacking, and there are no robust biomarkers that can facilitate the development of such treatments or act as aids in early diagnosis. Therefore, we have defined metabolomic changes in the brain and serum in an animal model of prodromal Parkinson's disease. We biochemically profiled the brain tissue and serum in a mouse model with progressive synucleinopathy propagation in the brain triggered by unilateral injection of preformed α-synuclein fibrils in the olfactory bulb. In total, we accurately identified and quantified 71 metabolites in the brain and 182 in serum using H NMR and targeted mass spectrometry, respectively. Using multivariate analysis, we accurately identified which metabolites explain the most variation between cases and controls. Using pathway enrichment analysis, we highlight significantly perturbed biochemical pathways in the brain and correlate these with the progression of the disease. Furthermore, we identified the top six discriminatory metabolites and were able to develop a model capable of identifying animals with the pathology from healthy controls with high accuracy (AUC (95% CI) = 0.861 (0.755-0.968)). Our study highlights the utility of metabolomics in identifying elements of Parkinson's disease pathogenesis and for the development of early diagnostic biomarkers of the disease.

The gut microbiome can impact brain health and is altered in Parkinson’s disease (PD). The vermiform appendix is a lymphoid tissue in the cecum implicated in the storage and regulation of the gut microbiota. We sought to determine whether the appendix microbiome is altered in PD and to analyze the biological consequences of the microbial alterations. We investigated the changes in the functional microbiota in the appendix of PD patients relative to controls (n = 12 PD, 16 C) by metatranscriptomic analysis. We found microbial dysbiosis affecting lipid metabolism, including an upregulation of bacteria responsible for secondary bile acid synthesis. We then quantitatively measure changes in bile acid abundance in PD relative to the controls in the appendix (n = 15 PD, 12 C) and ileum (n = 20 PD, 20 C). Bile acid analysis in the PD appendix reveals an increase in hydrophobic and secondary bile acids, deoxycholic acid (DCA) and lithocholic acid (LCA). Further proteomic and transcriptomic analysis in the appendix and ileum corroborated these findings, highlighting changes in the PD gut that are consistent with a disruption in bile acid control, including alterations in mediators of cholesterol homeostasis and lipid metabolism. Microbially derived toxic bile acids are heightened in PD, which suggests biliary abnormalities may play a role in PD pathogenesis.

Abbreviations:1 H NMR-Proton nuclear magnetic resonance; AD -Alzheimer's disease; AUC -area under the curve; AUROC -area under the receiver operating curve; BBB -blood brain barrier; BCAA -branched-chain amino acid; CNS -central nervous system; CSF -cerebral spinal fluid; DSS -Sodium 2,2-dimethyl-2-silapentane-5-sulfonate; FDR -false discovery rate; fMRI -functional magnetic resonance imaging; GC-Tof-MS -gas chromatography time of flight mass spectrometry; HD -Huntington's disease; IGF-1 -insulin like growth factor; MRImagnetic resonance imaging; MAS-NMR -magic angle spinning NMR; MRS -magnetic resonance spectroscopy; ROC -Receiver operating characteristic.

The aim of this study was to explore feasibility of 1 H NMR metabolic fingerprinting for discrimination of authenticity of saffron using principal component analysis (PCA) modeling. Authentic reference Iranian saffron (n = 31) and commercial samples (n = 32) were used. Cross-validated PCA models based on 1 H NMR spectra of solutions prepared by direct extraction of grinded saffron with methanol-d 4 distinguished reference Iranian saffron samples from commercial samples that formed several distinct clusters, some of which represent falsified samples as confirmed by microscopic analysis. The production sites and drying conditions of the authentic reference Iranian samples were not reflected in the current dataset. Picrocrocin and glycosyl esters of crocetin emerged as the most important 1 H NMR markers of authentic saffron by using statistical correlation spectroscopy. In conclusion, 1 H NMR spectra of saffron extracts combined with pattern recognition by PCA provide immediate means of unsupervised classification of saffron samples.

Objective To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine‐learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL). Methods AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF ‘sludge’ were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy‐based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine‐learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver–operating characteristics curve (AUC) with 95% CI, sensitivity and specificity. Results Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810–0.970) for delivery < 34 weeks' gestation, 0.890 (0.790–0.990) for delivery < 28 days post‐amniocentesis and 0.792 (0.689–0.894) for NICU admission. These values were higher overall than for the other five machine‐learning methods, although each individual machine‐learning technique yielded statistically significant prediction of the different perinatal outcomes. Conclusions This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.