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Rapid population growth and industrialization have led to the increased use of low-quality water sources for irrigation, such as drainage, saline water, and wastewater. In recent years, treated wastewater (TWW) has become an alternative irrigation resource to address water shortages and meet urban demand. TWW irrigation offers a sustainable solution to water scarcity challenges and can potentially enhance the growth of ornamental plants. Although the benefits of TWW irrigation for field and agricultural plants have been extensively reviewed, there is a notable lack of comprehensive reviews focusing exclusively on TWW's effects on ornamental plants. This review paper provides a comprehensive systematic review of the utilization of TWW for irrigating ornamental plants. It explores the key factors influencing the effectiveness of TWW irrigation, the methods employed, and the environmental benefits associated with this practice. The objective is to highlight how TWW irrigation with ornamental plants can be a perfect solution that benefits both the environment and the aesthetics of the surroundings. [ABSTRACT FROM AUTHOR]

Polygenic prediction of complex trait phenotypes has become important in human genetics, especially in the context of precision medicine. Recently, mr.mash, a flexible and computationally efficient method that models multiple phenotypes jointly and leverages sharing of effects across such phenotypes to improve prediction accuracy, was introduced. However, a drawback of mr.mash is that it requires individual-level data, which are often not publicly available. In this work, we introduce mr.mash-rss, an extension of the mr.mash model that requires only summary statistics from Genome-Wide Association Studies (GWAS) and linkage disequilibrium (LD) estimates from a reference panel. By using summary data, we achieve the twin goal of increasing the applicability of the mr.mash model to data sets that are not publicly available and making it scalable to biobank-size data. Through simulations, we show that mr.mash-rss is competitive with, and often outperforms, current state-of-the-art methods for single- and multi-phenotype polygenic prediction in a variety of scenarios that differ in the pattern of effect sharing across phenotypes, the number of phenotypes, the number of causal variants, and the genomic heritability. We also present a real data analysis of 16 blood cell phenotypes in the UK Biobank, showing that mr.mash-rss achieves higher prediction accuracy than competing methods for the majority of traits, especially when the data set has smaller sample size. Author summary: Polygenic prediction refers to the use of an individual's genetic information (i.e., genotypes) to predict traits (i.e., phenotypes), which are often of medical relevance. It is known that some phenotypes are related and are affected by the same genotypes. When this is the case, it is possible to improve the accuracy of predictions by using methods that model multiple phenotypes jointly and account for shared effects. mr.mash is a recently developed multi-phenotype method that can learn which effects are shared and has been shown to improve prediction. However, mr.mash requires large data sets of genetic and phenotypic information collected at the individual level. Such data are often unavailable due to privacy concerns, or are difficult to work with due to the computational resources needed to analyze data of this size. Our work extends mr.mash to require only summary statistics from Genome-Wide Association Studies, which are usually publicly available, instead of individual-level data. In addition, the computations using summary statistics do not depend on sample size, making the newly developed mr.mash-rss scalable to extremely large data sets. Using simulations and real data analysis, we show that our method is competitive with other methods for polygenic prediction. [ABSTRACT FROM AUTHOR]

Opioid use disorder (OUD) affects millions of people worldwide. While it is known that OUD originates from many factors, including social and environmental factors, the role of genetic variants in developing the disease has also been reported. This study aims to investigate the genetic variants associated with the risk of developing OUD upon exposure. Twenty-three subjects who had previously been given opioid-based painkillers to undergo minor surgical treatment were recruited at Prisma Health Upstate clinic and elsewhere. Eleven were considered nonpersistent opioid users (controls), and 12 were persistent opioid users (cases) at the time of sample collection after an initial surgery. The subjects were asked to provide saliva samples, which were subjected to DNA sequencing at Clemson University Center for Human Genetics, and variant calling was performed. The genome-wide association studies (GWASs) for genes known to be associated with OUD resulted in 13 variants (intronic or SNV) with genome-wide significance (raw p-value < 0.01) and two missense variants, rs6265 (p.Val66Met in BNDF isoform a) and rs1799971 (p.Asn40Asp) in OPRM1, previously reported in the literature. Furthermore, extending the GWASs to find all genomic variants and filtering the variants to include only variants found in cases (persistent opioid users) but not in controls (nonpersistent opioid users) resulted in 11 new variants (p-value < 0.005). Considering that OUD is a complex disease and the effect might come from different variants in the same genes, we performed a co-occurrence analysis of variants on the genes. We identified eight additional genes that harbor multiple variants, including four genes: LRFN3, ZMIZ1, RYR3, and OR1L6, with three or more variants in the case subjects but not in the control individuals. The performed PPI network construction, along with functional enrichment, indicated that the variants occur in calcium signaling, circadian entrainment, morphine addiction, alcoholism, and opioid signaling pathways, which are closely related to OUD or addiction in general. [ABSTRACT FROM AUTHOR]

Introduction: Acute ischemic stroke (AIS) is one of the most prevalent neurological disorders globally and is a significant contributor to long-term disability and mortality. Objective: The current study aims to investigate the serum level of fibrinogen and its prognostic significance in patients with acute ischemic stroke. Methodology: This prospective observational analysis was conducted at Department of Medicine, Venkateshwara Institute of Medical Sciences Gajraula U.P. India during 1st Sept 2022 to 31 March 2024. A total of 225 patients were enrolled in the study. Upon admission, baseline demographic and clinical data were collected, including age, sex, medical history (e.g., hypertension, diabetes, smoking status), and prior history of stroke or cardiovascular disease. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS) at the time of hospital admission. Results: A total of 225 patients with acute ischemic stroke (AIS) were included in the study. The mean age of the patients was 65.3 ± 12.8 years, with 125 (55.6%) male and 100 (44.4%) female patients. Hypertension was the most common comorbidity, present in 150 patients (66.7%), followed by diabetes mellitus in 90 patients (40.0%), and a history of smoking in 80 patients (35.6%). Patients with poor outcomes (mRS = 3) had a higher mean fibrinogen level (490.2 ± 105.9 mg/dL) compared to those with good outcomes (mRS < 3), who had a mean fibrinogen level of 405.7 ± 85.3 mg/dL. A higher percentage of patients (53.3%) experienced poor outcomes, and the difference in fibrinogen levels between the two groups was statistically significant (p = 0.001). Conclusion: It is concluded that elevated serum fibrinogen levels are significantly associated with poor functional outcomes and higher mortality in patients with acute ischemic stroke. [ABSTRACT FROM AUTHOR]

Background: There has already been much evidence linking diabetes to public health threats globally, and the disease has been established to pose an increased risk of severe actions in COVID-19 cases. Objective: The basic aim of this study is to find random blood glucose and its correlation with HbA1c levels in COVID-19-positive patients. Methodology: This crosssectional study was conducted at Department of Medicine, Venkateshwara Institute of Medical Sciences Gajraula U.P. India during 1st July 2020 to 31 Dec 2022. The study included 185 patients who met the inclusion criteria of the study. Data were collected through systematically designed questionnaires which included all information related to demographics, gender, history, and symptoms of disease. Upon admission, random blood glucose levels were measured using a glucometer. Results: Data were collected from 185 patients with a mean age of 58.4±12.3 years and a mean BMI of 28.6±4.5 kg/m². Males constituted 59.5% (n=110) of the sample, while females made up 40.5% (n=75). The mean random blood glucose (RBG) level was 194.3±68.5 mg/dL, and the mean HbA1c was 7.8±1.5%, indicating a notable degree of acute and chronic hyperglycemia among the patients. In the overall patient group (n=185), the correlation coefficient was 0.63 (p < 0.001), indicating a moderate correlation. In diabetic patients (n=85), the correlation was stronger, with an r-value of 0.71 (p < 0.001). Among non-diabetic patients (n=100), the correlation was weaker but still significant, with an r-value of 0.52 (p < 0.01). Conclusion: It is concluded that both random blood glucose (RBG) and HbA1c levels are important markers in predicting the severity and outcomes of COVID-19 in infected patients, particularly those with preexisting diabetes. [ABSTRACT FROM AUTHOR]