Antimicrobial modification of PLA scaffolds with ascorbic and fumaric acids via plasma treatment

An optimal medical scaffold should be biocompatible and biodegradable and should have adequate mechanical properties and scaffold architecture porosity, a precise three-dimensional shape, and a reasonable manufacturing method. Polylactic acid (PLA) is a natural biodegradable thermoplastic aliphatic polyester that can be fabricated into nanofiber structures through many techniques, and electrospinning is one of the most widely used methods. Medical fiber mat scaffolds have been associated with inflammation and infection and, in some cases, have resulted in tissue degradation. Therefore, surface modification with antimicrobial agents represents a suitable solution if the mechanical properties of the fiber mats are not affected. In this study, the surfaces of electrospun PLA fiber mats were modified with naturally occurring L-ascorbic acid (ASA) or fumaric acid (FA) via a plasma treatment method. It was found that 30 s of radio-frequency (RF) plasma treatment was effective enough for the wettability enhancement and hydroperoxide formation needed for subsequent grafting reactions with antimicrobial agents upon their decomposition. This modification led to changes in the surface properties of the PLA fiber mats, which were analyzed by various spectroscopic and microscopic techniques. FTIR-ATR confirmed the chemical composition changes after the modification process and the surface morphology/topography changes were proven by SEM and AFM. Moreover, nanomechanical changes of prepared PLA fiber mats were investigated by AFM using amplitude modulation-frequency modulation (AM-FM) technique. A significant enhancement in antimicrobial activity of such modified PLA fiber mats against gram-positive Staphylococcus aureus and gram-negative Escherichia coli are demonstrated herein. © 2020 The AuthorsQatar National Research Fund (a member of The Qatar Foundation) [22-076-1-011]; Qatar University Collaborative Grant [QUCG-CAM-20/21-3]; Czech Science FoundationGrant Agency of the Czech Republic [19-16861S

Urban stormwater retention capacity of nature-based solutions at different climatic conditions

Climate change and the continuing increase in human population creates a growing need to tackle urban stormwater problems. One promising mitigation option is by using nature-based solutions (NBS) – especially sustainable urban stormwater management technologies that are key elements of NBS action. We used a synthesis approach to compile available information about urban stormwater retention capacity of the most common sustainable urban drainage systems (SUDS) in different climatic conditions. Those SUDS targeting stormwater management through water retention and removal solutions (mainly by infiltration, overland flow and evapotranspiration), were addressed in this study. Selected SUDS were green roofs, bioretention systems (i.e. rain gardens), buffer and filter strips, vegetated swales, constructed wetlands, and water-pervious pavements. We found that despite a vast amount of data available from real-life applications and research results, there is a lack of decisive information about stormwater retention and removal capacity of selected SUDS. The available data show large variability in performance across different climatic conditions. It is therefore a challenge to set conclusive widely applicable guidelines for SUDS implementation based on available water retention data. Adequate data were available only to evaluate the water retention capacity of green roofs (average 56±20%) and we provide a comprehensive review on this function. However, as with other SUDS, still the same problem of high variability in the performance (min 11% and max 99% of retention) remains. This limits our ability to determine the capacity of green roofs to support better planning and wider implementation across climate zones. The further development of SUDS to support urban stormwater retention should be informed by and developed concurrently with the adaptation strategies to cope with climate change, especially with increasing frequency of extreme precipitation events that lead to high volumes of stormwater runoff

Assessment of predicted enzymatic activity of α‐N‐acetylglucosaminidase variants of unknown significance for CAGI 2016

The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase α‐N‐acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population‐scale analysis of disease epidemiology and rare variant association analysis

DNA isolation protocol effects on nuclear DNA analysis by microarrays, droplet digital PCR, and whole genome sequencing, and on mitochondrial DNA copy number estimation.

Potential bias introduced during DNA isolation is inadequately explored, although it could have significant impact on downstream analysis. To investigate this in human brain, we isolated DNA from cerebellum and frontal cortex using spin columns under different conditions, and salting-out. We first analysed DNA using array CGH, which revealed a striking wave pattern suggesting primarily GC-rich cerebellar losses, even against matched frontal cortex DNA, with a similar pattern on a SNP array. The aCGH changes varied with the isolation protocol. Droplet digital PCR of two genes also showed protocol-dependent losses. Whole genome sequencing showed GC-dependent variation in coverage with spin column isolation from cerebellum. We also extracted and sequenced DNA from substantia nigra using salting-out and phenol / chloroform. The mtDNA copy number, assessed by reads mapping to the mitochondrial genome, was higher in substantia nigra when using phenol / chloroform. We thus provide evidence for significant method-dependent bias in DNA isolation from human brain, as reported in rat tissues. This may contribute to array "waves", and could affect copy number determination, particularly if mosaicism is being sought, and sequencing coverage. Variations in isolation protocol may also affect apparent mtDNA abundance

In vitro fertilization does not increase the incidence of de novo copy number alterations in fetal and placental lineages

Although chromosomal instability (CIN) is a common phenomenon in cleavage-stage embryogenesis following in vitro fertilization (IVF)1,2,3, its rate in naturally conceived human embryos is unknown. CIN leads to mosaic embryos that contain a combination of genetically normal and abnormal cells, and is significantly higher in in vitro-produced preimplantation embryos as compared to in vivo-conceived preimplantation embryos4. Even though embryos with CIN-derived complex aneuploidies may arrest between the cleavage and blastocyst stages of embryogenesis5,6, a high number of embryos containing abnormal cells can pass this strong selection barrier7,8. However, neither the prevalence nor extent of CIN during prenatal development and at birth, following IVF treatment, is well understood. Here we profiled the genomic landscape of fetal and placental tissues postpartum from both IVF and naturally conceived children, to investigate the prevalence and persistence of large genetic aberrations that probably arose from IVF-related CIN. We demonstrate that CIN is not preserved at later stages of prenatal development, and that de novo numerical aberrations or large structural DNA imbalances occur at similar rates in IVF and naturally conceived live-born neonates. Our findings affirm that human IVF treatment has no detrimental effect on the chromosomal constitution of fetal and placental lineages

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Factors Influencing the Antenatal Care Attendance of Pregnant Women During the First COVID-19 Wave Lockdown in Thailand

Thiwarphorn Chalermpichai, Kultida Subsomboon, Rungtip Kasak, Orrawan Pinitlertsakun, Saowaros Pangzup Department of Obstetrics and Gynecological Nursing, Faculty of Nursing, Mahidol University, Bangkok, ThailandCorrespondence: Thiwarphorn Chalermpichai, 2 Faculty of Nursing, Mahidol University, Wang Lang Road, Bangkoknoi, Bangkok, 10700, Thailand, Tel +662-419-7466-80 Ext 1810, Fax +662-412-8415, Email [email protected]: The coronavirus disease 2019 (COVID-19) outbreak impacted healthcare service management worldwide. Thailand had limited healthcare resources. During the pandemic, several medical supplies were in high demand and expensive. The Thai government needed to declare a lockdown to reduce the unnecessary use of medical supplies. Antenatal care (ANC) services have adapted to the outbreak situation. However, information about the potential impact of COVID-19 lockdown on pregnant women and the reduction of disease exposure risk in this population remains unclear. Thus, this study aimed to assess the percentage of ANC attendance and factors affecting the scheduled ANC attendance of pregnant women during the first COVID-19 wave lockdown in Thailand.Methods: This retrospective cross-sectional study included Thai women who were pregnant between 1 March and 31 May 2020. An online survey was conducted among pregnant women who had first ever ANC attendance before 1 March 2020. A total of 266 completed responses were returned and analysed. Statistically, the sample size was representative of the population. The predictors of scheduled ANC attendance during the lockdown were identified through logistic regression analysis.Results: Overall, 223 (83.8%) pregnant women had scheduled ANC attendance during the lockdown. The predictive factors of ANC attendance were non-relocation (adjusted odds ratio [AOR] = 2.91, 95% confidence interval [CI]: 1.009– 8.381) and access to health services (AOR = 2.234, 95% CI: 1.125– 4.436).Conclusion: During the lockdown, ANC attendance slightly declined, and the extended duration of each ANC or reduced face-to-face interactions with healthcare professionals. For pregnant women with non-relocation, healthcare providers must provide opportunities to contact them directly if they had doubts. The limited number of pregnant women who access health services allowed the clinic to be less crowded and therefore easy to ANC attendance.Keywords: access care, ANC, COVID-19, pandemic, pregnant women, Thailan

Bentonite-decorated calix [4] arene: A new, promising hybrid material for heavy-metal removal

There is global concern about the contamination of ground, river, and tap waters as well as soil contamination with heavy metal ions; these chemical species are known to not degrade and to cause severe health problems if ingested by humans and animals. Such environmental and health concerns necessitate the development of ultrasensitive sensors and high-capacity adsorbents. This study demonstrates for the first time the potential of organophilic bentonite combined with tetra(2-pyridylmethyl)amide calix [4] arene as a high-performance hybrid material for the removal of toxic heavy metals. After consecutive synthesis steps, the modified bentonites were thoroughly characterized by FT-IR, XRD, UV spectroscopy, and TEM. In particular, the XRD analysis showed strong supporting evidence for intercalation in the clay following each modification step. The salient feature of the newly prepared hybrid material is its high extraction capacity for Cd(II) and Zn(II) metals, as determined by atomic absorption spectrometry and UV spectrometry. Different preparation methods, with respect to the quantity of the added cationic surfactant, were investigated to determine the optimal conditions for synthesis. The extraction percentage for the as-prepared hybrid material was measured to be as high as 97.4% and 94.2% for Cd(II) and Zn(II), respectively.This paper was made possible by the NPRP award [ 8-878-1-172 ] from Qatar National Research Fund (a member of the Qatar foundation ). The statements made herein are solely the responsibility of the Authors