Young Adults With Anterior Ischemic Optic Neuropathy: A Multicenter Optic Disc Drusen Study.

PURPOSE: Optic disc drusen (ODD), present in 2% of the general population, have occasionally been reported in patients with nonarteritic anterior ischemic optic neuropathy (NA-AION). The purpose of this study was to examine the prevalence of ODD in young patients with NA-AION. DESIGN: Retrospective, cross-sectional multicenter study. METHODS: All patients with NA-AION 50 years old or younger, seen in neuro-ophthalmology clinics of the international ODDS (Optic Disc Drusen Studies) Consortium between April 1, 2017, and March 31, 2019, were identified. Patients were included if ODD were diagnosed by any method, or if ODD were excluded by enhanced-depth imaging optical coherence tomography (EDI-OCT) using ODDS Consortium guidelines. NA-AION eyes with ODD were termed "ODD-AION"; those without were termed "NODD-AION". RESULTS: A total of 65 patients (127 eyes) with NA-AION were included (mean 41 years old). Of the 74 eyes with NA-AION, 51% had ODD-AION, whereas 43% of fellow eyes without NA-AION had ODD (P = .36). No significant differences were found between ODD-AION and NODD-AION eyes in terms of Snellen best-corrected VA or perimetric mean deviation. According to EDI-OCT results, 28% of eyes with NODD-AION had peripapillary hyperreflective ovoid mass-like structures (PHOMS); 7% had hyperreflective lines, whereas 54% with ODD-AION had PHOMS; and 66% had hyperreflective lines (P = .006 and P < .001, respectively). CONCLUSIONS: Most of these young NA-AION patients had ODD. This indicates that ODD may be an independent risk factor for the development of NA-AION, at least in younger patients. This study suggests ODD-AION be recognized as a novel diagnosis

On the analysis of the contact angle for impacting droplets using a polynomial fitting approach

ractical considerations on the measurement of the dynamic contact angle and the spreading diameter of impacting droplets are discussed in this paper. The contact angle of a liquid is commonly obtained either by a polynomial or a linear fitting to the droplet profile around the triple phase point. Previous works have focused on quasi-static or sessile droplets, or in cases where inertia does not play a major role on the contact angle dynamics. Here, we study the effect of droplet shape, the order of the fitting polynomial, and the fitting domain, on the measurement of the contact angle on various stages following droplet impact where the contact line is moving. Our results, presented in terms of the optical resolution and the droplet size, show that a quadratic fitting provides the most consistent results for a range of various droplet shapes. As expected, our results show that contact angle values are less sensitive to the fitting conditions for the cases where the droplet can be approximated to a spherical cap. Our experimental conditions include impact events with liquid droplets of different sizes and viscosities on various substrates. In addition, validating past works, our results show that the maximum spreading diameter can be parameterised by the Weber number and the rapidly advancing contact angle

Association of PGC-1alpha polymorphisms with age of onset and risk of Parkinson's disease

<p>Abstract</p> <p>Background</p> <p>Peroxisome proliferator-activated receptor-γ co-activator (PGC)-1α is a transcriptional co-activator of antioxidant genes and a master regulator of mitochondrial biogenesis. Parkinson's disease (PD) is associated with oxidative stress and mitochondrial dysfunction and recent work suggests a role for PGC-1α. We hypothesized that the rs8192678 <it>PGC-1α </it>single nucleotide polymorphism (SNP) may influence risk or age of onset of PD. The A10398G mitochondrial SNP has been inversely associated with risk of PD in some studies. In the current study we analyzed whether rs8192678 or other <it>PGC-1α </it>SNPs affect PD risk or age of onset, singularly or in association with the A10398G SNP.</p> <p>Methods</p> <p>Genomic DNA samples from 378 PD patients and 173 age-matched controls were analyzed by multiplexed probe sequencing, followed by statistical analyses of the association of each SNP, alone or in combination, with risk or age of onset of PD. Adjustments were made for age of onset being less than the age of sampling, and for the observed dependence between these two ages. The PD samples were obtained as two separate cohorts, therefore statistical methods accounted for different sampling methods between the two cohorts, and data were analyzed using Cox regression adjusted for sampling in the risk set definition and in the model.</p> <p>Results</p> <p>The rs8192678 PGC-1α SNP was not associated with the risk of PD. However, an association of the <it>PGC-1α </it>rs8192678 GG variant with longevity was seen in control subjects (p = 0.019). Exploratory studies indicated that the CC variant of rs6821591 was associated with risk of early onset PD (p = 0.029), with PD age of onset (p = 0.047), and with longevity (p = 0.022). The rs2970848 GG allele was associated with risk of late onset PD (p = 0.027).</p> <p>Conclusions</p> <p>These data reveal possible associations of the <it>PGC-1α </it>SNPs rs6821591 and rs2970848 with risk or age of onset of PD, and of the <it>PGC-1α </it>rs8192678 GG and the rs6821591 CC variants with longevity. If replicated in other datasets, these findings may have important implications regarding the role of <it>PGC-1α </it>in PD and longevity.</p

Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies

Substance dependence and mental health in northern Iran

Background: Today, substance dependence and illegal trading of narcotics is considered as a global issue. Since mental disorder has been reported in about 90% of the substance dependents, this study aimed at determining the rate of mental health in the substance dependents in Sari Township in 2011.Materials and Methods: In this study, 500 substance.dependent patients were selected using convenience sampling method. To collect data, SCL.90.R was used for the evaluation of their mental health and a demographic questionnaire was employed for identifying their personal information. The obtained data were analyzed by descriptive and inferential statistics using the SPSS software.Results: It was found that 90.4% of the participants were susceptible to mental disorder. Most of them suffered from depression, psychoticism, interpersonal sensitivity, anxiety, and paranoia. Also, there was significant relationship between the mental health of single, divorced and married addicts (P &lt; 0.21).Conclusion: Due to the presence of mental disorder in the  substance.dependent patients, it is recommended to help treat them by providing them with education, psychotherapy, and psychiatric medication.Keywords: Abuse and dependence, mental disorder, mental health, psychiatric researc

A molecular portrait of epithelial-mesenchymal plasticity in prostate cancer associated with clinical outcome.

The propensity of cancer cells to transition between epithelial and mesenchymal phenotypic states via the epithelial-mesenchymal transition (EMT) program can regulate metastatic processes, cancer progression, and treatment resistance. Transcriptional investigations using reversible models of EMT, revealed the mesenchymal-to-epithelial reverting transition (MErT) to be enriched in clinical samples of metastatic castrate resistant prostate cancer (mCRPC). From this enrichment, a metastasis-derived gene signature was identified that predicted more rapid cancer relapse and reduced survival across multiple human carcinoma types. Additionally, the transcriptional profile of MErT is not a simple mirror image of EMT as tumour cells retain a transcriptional "memory" following a reversible EMT. This memory was also enriched in mCRPC samples. Cumulatively, our studies reveal the transcriptional profile of epithelial-mesenchymal plasticity and highlight the unique transcriptional properties of MErT. Furthermore, our findings provide evidence to support the association of epithelial plasticity with poor clinical outcomes in multiple human carcinoma types