Linking genotoxicity and cytotoxicity with membrane fluidity: A comparative study in ovarian cancer cell lines following exposure to auranofin

publisher: Elsevier articletitle: Linking genotoxicity and cytotoxicity with membrane fluidity: A comparative study in ovarian cancer cell lines following exposure to auranofin journaltitle: Mutation Research/Genetic Toxicology and Environmental Mutagenesis articlelink: http://dx.doi.org/10.1016/j.mrgentox.2016.09.003 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

RiemannONets: Interpretable Neural Operators for Riemann Problems

Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis. Herein, we employ neural operators to solve Riemann problems encountered in compressible flows for extreme pressure jumps (up to $10^{10}$ pressure ratio). In particular, we first consider the DeepONet that we train in a two-stage process, following the recent work of \cite{lee2023training}, wherein the first stage, a basis is extracted from the trunk net, which is orthonormalized and subsequently is used in the second stage in training the branch net. This simple modification of DeepONet has a profound effect on its accuracy, efficiency, and robustness and leads to very accurate solutions to Riemann problems compared to the vanilla version. It also enables us to interpret the results physically as the hierarchical data-driven produced basis reflects all the flow features that would otherwise be introduced using ad hoc feature expansion layers. We also compare the results with another neural operator based on the U-Net for low, intermediate, and very high-pressure ratios that are very accurate for Riemann problems, especially for large pressure ratios, due to their multiscale nature but computationally more expensive. Overall, our study demonstrates that simple neural network architectures, if properly pre-trained, can achieve very accurate solutions of Riemann problems for real-time forecasting. The source code, along with its corresponding data, can be found at the following URL: https://github.com/apey236/RiemannONet/tree/mai

Glycosaminoglycans' for brain health: Harnessing glycosaminoglycan based biomaterials for treating central nervous system diseases and in-vitro modeling

Dysfunction of the central nervous system (CNS) following traumatic brain injuries (TBI), spinal cord injuries (SCI), or strokes remains challenging to address using existing medications and cell-based therapies. Although therapeutic cell administration, such as stem cells and neuronal progenitor cells (NPCs), have shown promise in regenerative properties, they have failed to provide substantial benefits. However, the development of living cortical tissue engineered grafts, created by encapsulating these cells within an extracellular matrix (ECM) mimetic hydrogel scaffold, presents a promising functional replacement for damaged cortex in cases of stroke, SCI, and TBI. These grafts facilitate neural network repair and regeneration following CNS injuries. Given that natural glycosaminoglycans (GAGs) are a major constituent of the CNS, GAG-based hydrogels hold potential for the next generation of CNS healing therapies and in vitro modeling of CNS diseases. Brain-specific GAGs not only offer structural and biochemical signaling support to encapsulated neural cells but also modulate the inflammatory response in lesioned brain tissue, facilitating host integration and regeneration. This review briefly discusses different roles of GAGs and their related proteoglycan counterparts in healthy and diseases brain and explores current trends and advancements in GAG-based biomaterials for treating CNS injuries and modeling diseases. Additionally, it examines injectable, 3D bioprintable, and conductive GAG-based scaffolds, highlighting their clinical potential for in vitro modeling of patient-specific neural dysfunction and their ability to enhance CNS regeneration and repair following CNS injury in vivo

Gold nanoparticles approach to detect chondroitin sulphate and hyaluronic acid urothelial coating

This study investigated the location of hyaluronic acid (HA)-and chondroitin sulphate (CS)-coated gold nanoparticles in rabbit bladder and evaluated gene expression of CD44, RHAMM and ICAM-1 receptors involved in HA and CS transport into the cell. Gold nanoparticles were synthesised by reduction of gold salts with HA or CS to form HA-AuNPs and CS-AuNPs. Bladder samples were incubated with CS-AuNPs and HA-AuNPs or without glycosaminoglycans. Transmission electron microscopy, optic microscopy and scanning electron microscopy were used to determine the location of the synthesised AuNPs. Real-time PCR was used to analyse expression of urothelial cell receptors CD44, RHAMM, ICAM-1, after ex vivo administration of CS-AuNPs and HA-AuNPs. We showed that HA-AuNPs and CS-AuNPs were located in the cytoplasm and tight junctions of urothelial umbrella cells; this appearance was absent in untreated bladders. There were no significant differences in gene expression levels for CD44, RHAMM and ICAM-1 receptors in treated versus control bladder tissues. In conclusion, we clearly showed the presence of exogenous GAGs in the bladder surface and the tight junctions between umbrella cells, which is important in the regeneration pathway of the urothelium. The GAGs-AuNPs offer a promising approach to understanding the biophysical properties and imaging of urothelial tissue

Structural Studies of Cu(II) & Ni(II) Complexes with a Chiral Schiff Base

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Subtypes of Relapsing-Remitting Multiple Sclerosis Identified by Network Analysis

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis

No evidence for association between SLC11A1 and visceral leishmaniasis in India.

BACKGROUND: SLC11A1 has pleiotropic effects on macrophage function and remains a strong candidate for infectious disease susceptibility. 5' and/or 3' polymorphisms have been associated with tuberculosis, leprosy, and visceral leishmaniasis (VL). Most studies undertaken to date were under-powered, and none has been replicated within a population. Association with tuberculosis has replicated variably across populations. Here we investigate SLC11A1 and VL in India. METHODS: Nine polymorphisms (rs34448891, rs7573065, rs2276631, rs3731865, rs17221959, rs2279015, rs17235409, rs17235416, rs17229009) that tag linkage disequilibrium blocks across SLC11A1 were genotyped in primary family-based (313 cases; 176 families) and replication (941 cases; 992 controls) samples. Family- and population-based analyses were performed to look for association between SLC11A1 variants and VL. Quantitative RT/PCR was used to compare SLC11A1 expression in mRNA from paired splenic aspirates taken before and after treatment from 24 VL patients carrying different genotypes at the functional promoter GTn polymorphism (rs34448891). RESULTS: No associations were observed between VL and polymorphisms at SLC11A1 that were either robust to correction for multiple testing or replicated across primary and replication samples. No differences in expression of SLC11A1 were observed when comparing pre- and post-treatment samples, or between individuals carrying different genotypes at the GTn repeat. CONCLUSIONS: This is the first well-powered study of SLC11A1 as a candidate for VL, which we conclude does not have a major role in regulating VL susceptibility in India.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes

High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a Large Language Model (LLM) incorporating generative AI, a Natural Language Processing (NLP) approach utilizing deep learning for span categorization, and a hybrid approach combining word vectors with machine learning. The approach that implemented GPT-4 (a Large Language Model) demonstrated superior performance, suggesting that Large Language Models are poised to be the preferred method for high-throughput phenotyping of physician notes.Submitted to AMIA Annual Symposium 2024, San Francisco C

Site-Specific Online Fertilizer Recommendations in Small Cardamom (Elettaria cardamomum (L.) Maton through CardSApp offers fertilizer savings and higher economic returns

The cultivation of small cardamom in the Indian Cardamom Hill (ICH) region of the Western Ghats, southern India, has become highly intensive, with heavy use of fertilizers and pesticides. The cultivation of small cardamom in the Indian Cardamom Hill (ICH) region of the Western Ghats, Southern India, has become highly intensive, with heavy use of fertilizers and pesticides. The ICH region is one of the world's biodiversity hotspots. Numerous studies have reported that the excessive use of chemical fertilizers has caused soil acidification (lowering of pH) and nutrient imbalances. This has resulted in unsustainable production of cardamom, a high-value spice crop that contributes significantly to India's export earnings. This study aimed to conduct a geo-tagged soil fertility survey in selected cardamom-growing areas within the ICH, assess soil fertility and provide sitespecific fertilizer recommendations to farmers via a mobile- or web-based application. A digital application, CardSApp, designed for the small cardamom sector, offers tailored fertilizer recommendations based on local soil and crop requirements. A representative geo-tagged soil survey was conducted in Udumbanchola taluk, located in Idukki district of Kerala, within the ICH. The surveyed area spanned from 76°59'01.19'' to 77°16'11.55'' East longitude and 9° 38'21.54'' to 10°04'46.60'' North latitude. Spatial interpolation techniques were used to generate maps showing the distribution of primary, secondary and micronutrients in the soil. The study revealed a high availability of NPK nutrients in cardamom soils. The study also revealed deficiencies in secondary nutrients like magnesium and sulfur, as well as micronutrients such as boron. Most of the soils showed signs of acidification. These findings highlight opportunities to optimize fertilizer usage and improve soil health. Specifically, NPK usage can be reduced or phosphatic fertilizers omitted in 58% of the area, potentially savings of ?12.27 crores annually in the surveyed taluk alone. Additionally, addressing deficiencies in secondary and micronutrients can further enhance soil health and crop productivity. To aid farmers in decision-making, the Android- and web-based application CardSApp leverages interpolated soil fertility data. CardSApp offers site-specific recommendations tailored to the soil nutrient profiles identified in the survey. CardSApp enables farmers to optimize fertilizer use, correct nutrient imbalances and apply amendments for pH correction. These practices can improve both farm productivity and income. The adoption of this online fertilizer recommendation system by farmers in the ICH is expected to rationalize fertilizer usage in cardamom cultivation, improve both soil and plant health and support sustainable production with higher economic returns

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk outcome pairs, and new data on risk exposure levels and risk outcome associations. Methods: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings: In 2017,34.1 million (95% uncertainty interval [UI] 33.3-35.0) deaths and 121 billion (144-1.28) DALYs were attributable to GBD risk factors. Globally, 61.0% (59.6-62.4) of deaths and 48.3% (46.3-50.2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10.4 million (9.39-11.5) deaths and 218 million (198-237) DALYs, followed by smoking (7.10 million [6.83-7.37] deaths and 182 million [173-193] DALYs), high fasting plasma glucose (6.53 million [5.23-8.23] deaths and 171 million [144-201] DALYs), high body-mass index (BMI; 4.72 million [2.99-6.70] deaths and 148 million [98.6-202] DALYs), and short gestation for birthweight (1.43 million [1.36-1.51] deaths and 139 million [131-147] DALYs). In total, risk-attributable DALYs declined by 4.9% (3.3-6.5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23.5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18.6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning