Correction to: Siddika et al. Waste Glass in Cement and Geopolymer Concretes: A Review on Durability and Challenges. (Polymers (2021), 13, 2071

The authors wish to make the following corrections to this paper [1]: in the original version of our article, the unit of production rate of glass and waste glass was mistakenly written in “metric tons” but should be corrected to “million tons”. The corrected sentences are presented below: In Abstract: Every year, the world is producing around 100 million tons of waste glass (WG), the majority of them are going to landfills that create massive environmental problems. In Introduction: (first sentence of second paragraph) Globally, around 130 million tons of glass are being produced each year among which approximately 100 million tons are being discarded as waste. The authors apologize for this error and state that the scientific conclusions are unaf-fected. The original article has been updated

Assessment of Multi-year Hydrologic Performance of Bioretention Facility using Real-Time Sensors

Bioretention facilities are emerging as a popular way to deal with stormwater runoff in urban environments and address its concerns. Real-time sensors installed in bioretention facilities aid in understanding the performance and feasibility of such facilities over time. A bioretention facility in Corvallis, Oregon was monitored for a study period of 3 years using real-time sensors deployed to measure flows in and out of the facility along with soil moisture content in the bioretention cells. Flow was monitored using Steven’s SDX sensor, and Decagon’s CTD sensor. The soil moisture content was monitored using Steven’s Hydra Probes. Overall, results have indicated that performance of CTD sensors was significantly better than SDX sensors in both inlets and outlets. There was large variability in captured runoff ranging from high 94% water treatment to as low as 6% treatment. The bioretention cells were able to maintain peak flow reduction ratio of 0.87, 0.82, 0.85 and 0.76 for fall, winter, spring and summer respectively throughout the study period. Mean peak delays was 44, 19, 150 and 55mins for fall, winter, spring and summer respectively. Soil moisture reading were dependent on the longitudinal distance away from the inlet. The mean values increased from 2015 to 2017, however the variability and standard deviation decreased. Decay rates during drying periods had high variability in 2015 but showed stability after that year.

Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease

Disease subtyping, which helps to develop personalized treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if we can identify subclasses of disease, then it will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. This paper proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The new algorithm combines K-means with consensus clustering in order build cohort-specific decision trees that improve classification as well as aid the understanding of the underlying differences of the discovered groups. The methods are tested on a real-world freely available breast cancer dataset and data from a London hospital on systemic sclerosis, a rare potentially fatal condition. Results show that “nearest consensus clustering classification” improves the accuracy and the prediction significantly when this algorithm has been compared with competitive similar methods

Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease

Disease subtyping, which helps to develop personalized treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if we can identify subclasses of disease, then it will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. This paper proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The new algorithm combines K-means with consensus clustering in order build cohort-specific decision trees that improve classification as well as aid the understanding of the underlying differences of the discovered groups. The methods are tested on a real-world freely available breast cancer dataset and data from a London hospital on systemic sclerosis, a rare potentially fatal condition. Results show that "nearest consensus clustering classification" improves the accuracy and the prediction significantly when this algorithm has been compared with competitive similar methods

Unmasking of Brugada syndrome by lamotrigine in a patient with pre-existing epilepsy: A case report with review of the literature

Brugada syndrome is an inherited cardiac channelopathy arising from mutations in voltage-gated cardiac sodium channels. Idiopathic epilepsy portrays a coalescent underlying pathophysiological mechanism pertaining to the premature excitation of neuronal voltage-gated ion channels resulting in the disruption of presynaptic neurons and the unregulated release of excitatory neurotransmitters. The coexistence of epilepsy and Brugada syndrome may be explained by mutations in voltage-gated ion channels, which are coexpressed in cardiac and neural tissue. Moreover, the incidence of sudden unexpected death in epilepsy has been associated with malignant cardiac arrhythmias in the presence of mutations in voltage-gated ion channels. Lamotrigine is an antiepileptic drug that inhibits neuronal voltage-gated sodium channels, thus stabilizing neural impulse propagation and controlling seizure activity in the brain. However, lamotrigine has been shown to inhibit cardiac voltage-gated sodium channels resulting in a potential arrhythmogenic effect and the ability to unmask Brugada syndrome in genetically susceptible individuals. We are reporting a case of a 27-year-old male patient with a background of presumed idiopathic epilepsy who was initiated on lamotrigine therapy resulting in the unmasking of Brugada syndrome and the onset of syncopal episodes. This case provides further evidence for the arrhythmogenic capacity of lamotrigine and highlights the relationship between epilepsy and Brugada syndrome. In this report, we aim to review the current literature regarding the associations between epilepsy and Brugada syndrome and the impact of lamotrigine therapy on such patients

Purification and kinetics of the PHB depolymerase of Microbacterium paraoxydans RZS6 isolated from a dumping yard

Poly-β-hydroxybutyrate (PHB) depolymerase is known to decompose PHB, biodegradable polymers and therefore has great commercial significance in the bioplastic sector. However, reports on PHB depolymerases from isolates obtained from plastic-contaminated sites that reflect the potential of the source organism is scarce. In this study, we evaluated the production of extracellular PHB depolymerase from Microbacterium paraoxydans RZS6 isolated from the plastic-contaminated site in the municipal area of Shahada, Maharashtra, India, for the first time. The isolate was identified using 16S rRNA gene sequencing, gas chromatographic analysis of fatty acid methyl esters (GC-FAME), and BIOLOG method. Ithydro-lyzed PHB on minimal salt medium (MSM) containing PHB as the only source of carbon. The isolate produced PHB depolymerase at 45C during 48 h of incubation. The enzyme was purified most efficiently using octyl-sepharose CL-4B column, with the highest purification yield of 6.675 Umg-1mL-1. The activity of the enzyme was enhanced in the presence of Ca2+ and Mg2+ ions but inhibited by Fe2+ (1 mM) ions and mercaptoethanol (1000 rpm). the nzyme kinetic analysis revealed that the enzyme was a metalloenzyme; requiring Mg2+ ions, that showed optimum enzyme activity at 30C (mesophilic) and under neutrophilic (pH 7) conditions. Scale-up from the shake-flask level to a laboratory-scale bioreactor further enhanced the enzyme yield by 0.809 UmL-1. The molecular weight of the enzyme (40 kDa), as estimated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, closely resembled the PHB depolymerase of Aureobacterium saperdae. Our findings highlighted the applicability of M. paraoxydans as a producer of extracellular PHB depolymerase having potential of degrading PHB under diverse conditions

Vitamin D Receptor FokI, ApaI, and TaqI Polymorphisms in Lead Exposed Subjects From Saudi Arabia

Vitamin D receptor (VDR) gene polymorphisms were reported to influence blood lead levels (BLL) and the response of subjects to the symptoms of lead toxicity. However, no studies have been conducted in the Saudi Arabian population which has unique ethnicity and socio-demographic features. This study examined the polymorphisms in exon 2 (allele 1) and intron 8 (allele 2 and allele 3) of VDR gene and their relation to BLLs. As per the CDC guidelines, the recruited lead-exposed workers (N = 130) were categorized to two groups viz., low BLL group (<10 μg/dL) and high BLL group (>10 μg/dL). The low BLL group had a mean BLL of 4.37 μg/dL, while the high BLL group had levels of 18.12 μg/dL (p < 0.001). Overall, the genetic variants, TC and CC in the VDR FokI were significantly associated with a risk of lead toxicity and the allele “C” was a risk factor (p = 0.00026). Furthermore, the TT genotype of VDR ApaI significantly increased the risk of developing lead poisoning (p = 0.0006). The VDR TaqI SNP was not significantly associated with lead toxicity. The highest BLLs for VDR FokI-CC, VDR ApaI-GG, and VDR TaqI-TT genotypes from High BLL group were 18.42, 15.26, and 18.75 μg/dL, respectively. Older age (51–60 years) was found to be a significant confounding factor for BLLs (p = 0.012). Additional studies in larger sample sizes are needed to firmly establish the role of VDR genotypes and genetic susceptibility to lead poisoning

The effect of alpha-lipoic acid supplementation on anthropometric, glycemic, lipid, oxidative stress, and hormonal parameters in individuals with polycystic ovary syndrome: a systematic review and meta-analysis of randomized clinical trials

This systematic review and meta-analysis aimed to examine the effect of the antioxidant alpha-lipoic acid (ALA) on various cardiometabolic risk factors and hormonal parameters in patients with polycystic ovary syndrome (PCOS). We searched PubMed, EMBASE, SCOPUS, Cochrane Library, and Web of Science databases without language restrictions until May 2023 to find randomized controlled trials (RCTs) that assessed the impact of ALA supplementation on anthropometric, glycemic, lipid, oxidative stress, and hormonal parameters in women with PCOS. Outcomes were summarized using the standardized mean difference (SMD) and 95% confidence interval (CI) in a random-effects model. An I2 statistic of >60% established significant between-study heterogeneity. The overall certainty of the evidence for each outcome was determined using the grading of recommendations, assessment, development, and evaluations system. Seven RCTs met the inclusion criteria. The ALA group had significant reductions in fasting blood sugar (fasting blood sugar (FBS), n=7 RCTs, SMD, −0.60; 95% CI, −1.10 to −0.10; I2=63.54%, moderate certainty of evidence) and homeostatic model assessment for insulin resistance (homeostatic model assessment of insulin resistance (HOMA-IR), n=4 RCTs, SMD, −2.03; 95% CI, −3.85 to −0.20; I2=96.32%, low certainty of evidence) compared with the control group. However, significant differences were observed between the groups in body mass index, insulin, estrogen, follicle-stimulating hormone, luteinizing hormone, testosterone, low-density lipoprotein, high-density lipoprotein, triglyceride, total cholesterol, malondialdehyde, or total antioxidant capacity profiles. ALA supplementation improves FBS and HOMA-IR levels in women with PCOS. ALA consumption is an effective complementary therapy for the management of women with PCOS

Clinico-radiological features, molecular spectrum, and identification of prognostic factors in developmental and epileptic encephalopathy due to inosine triphosphate pyrophosphatase (ITPase) deficiency

Developmental and epileptic encephalopathy 35 (DEE 35) is a severe neurological condition caused by biallelic variants in ITPA, encoding inosine triphosphate pyrophosphatase, an essential enzyme in purine metabolism. We delineate the genotypic and phenotypic spectrum of DEE 35, analyzing possible predictors for adverse clinical outcomes. We investigated a cohort of 28 new patients and reviewed previously described cases, providing a comprehensive characterization of 40 subjects. Exome sequencing was performed to identify underlying ITPA pathogenic variants. Brain MRI (magnetic resonance imaging) scans were systematically analyzed to delineate the neuroradiological spectrum. Survival curves according to the Kaplan–Meier method and log-rank test were used to investigate outcome predictors in different subgroups of patients. We identified 18 distinct ITPA pathogenic variants, including 14 novel variants, and two deletions. All subjects showed profound developmental delay, microcephaly, and refractory epilepsy followed by neurodevelopmental regression. Brain MRI revision revealed a recurrent pattern of delayed myelination and restricted diffusion of early myelinating structures. Congenital microcephaly and cardiac involvement were statistically significant novel clinical predictors of adverse outcomes. We refined the molecular, clinical, and neuroradiological characterization of ITPase deficiency, and identified new clinical predictors which may have a potentially important impact on diagnosis, counseling, and follow-up of affected individuals

Low Temperature Synthesis of Superparamagnetic Iron Oxide (Fe3O4) Nanoparticles and Their ROS Mediated Inhibition of Biofilm Formed by Food-Associated Bacteria

In the present study, a facile environmentally friendly approach was described to prepare monodisperse iron oxide (Fe3O4) nanoparticles (IONPs) by low temperature solution route. The synthesized nanoparticles were characterized using x-ray diffraction spectroscopy (XRD), Raman spectroscopy, field emission scanning electron microscopy (FESEM) measurements, Fourier-Transform Infrared Spectroscopy (FTIR), and Thermogravimetric analysis (TGA) analyses. XRD patterns revealed high crystalline quality of the nanoparticles. SEM micrographs showed the monodispersed IONPs with size ranging from 6 to 9 nm. Synthesized nanoparticles demonstrated MICs of 32, 64, and 128 μg/ml against Gram negative bacteria i.e., Serratia marcescens, Escherichia coli, and Pseudomonas aeruginosa, respectively, and 32 μg/ml against Gram positive bacteria Listeria monocytogenes. IOPNs at its respective sub-MICs demonstrated significant reduction of alginate and exopolysaccharide production and subsequently demonstrated broad-spectrum inhibition of biofilm ranging from 16 to 88% in the test bacteria. Biofilm reduction was also examined using SEM and Confocal Laser Scanning Microscopy (CLSM). Interaction of IONPs with bacterial cells generated ROS contributing to reduced biofilm formation. The present study for the first time report that these IONPs were effective in obliterating pre-formed biofilms. Thus, it is envisaged that these nanoparticles with broad-spectrum biofilm inhibitory property could be exploited in the food industry as well as in medical settings to curtail biofilm based infections and losses