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Environmental pollution and changing climatic conditions are likely to damage biodiversity not only on organismal level but on molecular level as well. The aim of the present study was to find the concentration of heavy metals in soil, water, feed, feathers, and blood and association of heavy metals with DNA damage of P. cristatus. The results showed that lead (Pb) was significantly (P 0.01) higher in soil and cadmium (Cd) was significantly (P 0.01) higher in soil and water. Chromium (Cr), zinc (Zn), nickel (Ni), and cobalt (Co) were significant (P 0.01) in feed. Manganese (Mn) was significantly (P 0.01) higher in feed and soil (surface). In addition, Pb and Cd concentrations were significant (P 0.01) in feathers while Cr and Zn concentrations were significantly (P 0.01) higher in feces. Nickel was significantly (P 0.01) higher in feathers and eggshell while Mn and Co concentrations were significantly (P 0.01) higher in blood and feces, respectively. Furthermore, significant positive correlation between Pb (r

Hypertension represent the commonest cause of death in 2017. Hypertension is classified into two types which are primary or essential hypertension and secondary hypertension. The perindopril-amlodipine combination showed a significant effect in reduction of the elevated BP and the cardiovascular complications.To evaluate the efficacy and safety of a fixed-dose single-pill combination of perindopril-amlodipine in hypertensive patients.We searched PubMed, Medline, SCOPUS, and Web of Science for relevant clinical trials. Quality appraisal was evaluated according to GRADE and we assessed the risk of bias using Cochrane's risk of bias tool. We included the following outcomes: systolic blood pressure, diastolic blood pressure, pulse pressure, mean blood pressure, heart rate, cough, dizziness, headache, and peripheral edema. We performed the analysis of homogeneous data under the fixed-effects model, while analysis of heterogeneous data was analyzed under the random-effects model. We conducted a meta-regression according to the dose.We included ten clinical trials. The pooled analysis showed that there was a significant reduction of the systolic blood pressure, diastolic blood pressure, pulse plessure, mean blood pressure, and heart rate after the the perindopril-amlodipine combination (MD = 18.96 [14.32, 23.60], P 0.0001), (MD = 11.90 [8.45, 15.35], P 0.0001), (MD = 8.44 [6.91, 9.97], P = 0.0001), (MD = 13.07 [5.86, 20.29], P = 0.0004), and (MD = 2.93 [0.89, 4.96], P = 0.005), respectively. The results of the meta-regression revealed that the efficacy is increased by increasing the dose (P 0.001) CONCLUSION: The use of the perindopril-amlodipine combination had a significant effect on the reduction of SBP, DBP, mean blood pressure, pulse pressure, and HR.

The Big Video Data generated in today's smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness. Violence Detection (VD), broadly plunging under Action and Activity recognition domain, is used to analyze Big Video data for anomalous actions incurred due to humans. The VD literature is traditionally based on manually engineered features, though advancements to deep learning based standalone models are developed for real-time VD analysis. This paper focuses on overview of deep sequence learning approaches along with localization strategies of the detected violence. This overview also dives into the initial image processing and machine learning-based VD literature and their possible advantages such as efficiency against the current complex models. Furthermore,the datasets are discussed, to provide an analysis of the current models, explaining their pros and cons with future directions in VD domain derived from an in-depth analysis of the previous methods., Artificial Intelligence Review

Residential care is primarily considered most appropriate for young people with complex needs, often due to multi-type traumas. Children in care are disproportionately disadvantaged, with children in residential care most vulnerable, which is why it is so important to understand mechanisms that support resilience and post-traumatic growth for this group of young people. This review aimed to advance our understanding of how interventions, reflections upon experience, and constructs of resilience can enhance developing resilience in children’s homes for young people in care. International quantitative and qualitative studies were sought to identify features and mechanisms of care that underpin developing resilience. Following a systematic search of six databases, 25 papers were selected for review, with a total sample of 3198 individuals up to the age of 30 years old who were either receiving residential care (N = 3037) or who were care leavers (N = 161). Themes from the quantitative studies and a narrative synthesis of qualitative studies were developed. Therapeutic mechanisms and processes to support the development of resilience included experiencing love and trust with staff in homes through therapeutic relationships, nurturing self-compassion, promoting self-value and self-belief, positive future thinking, problem-focused coping, school engagement, constructing a positive origin story, and positive visualisations of a stable future. Measures of resilience could more accurately reflect post-traumatic growth and potential for resilience development for this unique group of young people, which in turn could inform intervention design and evaluation. Measures appreciative of intrapersonal, relational, community and environmental factors could be particularly useful for intervention design.

The emergence of various diseases during the COVID-19 pandemic made health workers more attentive, and one of the new pathogens is the black fungus (mucormycosis). As a result, millions of lives have already been lost. As a result of the mutation, the virus is constantly changing its traits, including the rate of disease transmission, virulence, pathogenesis, and clinical signs. A recent analysis revealed that some COVID-19 patients were also coinfected with a fungal disease called mucormycosis (black fungus). India has already categorized the COVID-19 patient black fungus outbreak as an epidemic. Only a few reports are observed in other countries. The immune system is weakened by COVID-19 medication, rendering it more prone to illnesses like black fungus (mucormycosis). COVID-19, which is caused by a B.1.617 strain of the SARS-CoV-2 virus, has been circulating in India since April 2021. Mucormycosis is a rare fungal infection induced by exposure to a fungus called mucormycete. The most typically implicated genera are Mucor rhyzuprhizopusdia and Cunninghamella. Mucormycosis is also known as zygomycosis. The main causes of infection are soil, dumping sites, ancient building walls, and other sources of infection (reservoir words "mucormycosis" and "zygomycosis" are occasionally interchanged). Zygomycota, on the other hand, has been identified as polyphyletic and is not currently included in fungal classification systems; also, zygomycosis includes Entomophthorales, but mucormycosis does not. This current review will be focused on the etiology and virulence factors of COVID-19/mucormycosis coinfections in COVID-19-associated mucormycosis patients, as well as their prevalence, diagnosis, and treatment.

Antibiotic resistance genes (ARGs) and antimicrobial resistance elements (AMR) are novel environmental contaminants that pose a significant risk to human health globally. Freshwater contains a variety of microorganisms that might affect human health; its quality must be assessed before use. However, the dynamics of mobile genetic elements (MGEs) and ARG propagation in freshwater have rarely been studied in Singapore. Therefore, this study used metagenomics to compare diversity, virulence factor composition, and ARG and MGE co-occurrence with bacterial communities in paired (n = 8) environmental freshwater samples. KneadData, FMAP, and Kraken2 were used for bioinformatics analysis and R (v4.1.1) for statistical analysis. Sequence reads with a total of 9043 species were taxonomically classified into 66 phyla, 130 classes, 261 orders, 584 families, and 2477 genera. Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes were found the Phyla in all samples. Analysis of QIIME output by PICRUSt and ß-diversity showed unique clusters and functional microbial community structures. A total of 2961 ARGs were found that conferred resistance to multidrug, aminoglycosides, tetracyclines, elfamycins, and more. The classified ARG mechanism revealed significant distribution of virulence factors in bacterial cells. Transposes and transposon were highly correlated to ARG gene transfer. Co-occurrence network analysis showed several MGEs appear to use the same ARGs (intI and rho) and were dominant in all samples. Furthermore, ARGs are also highly correlated with bacteria like Campylobacter and Escherichia. This study enhances the understanding of antibiotic risk assessment and provides a new perspective on bacterial assembly contamination and the functional prevalence of ARGs and MGEs with antibiotic resistance bacteria. Moreover, it raises public awareness because these contaminants put people's lives at risk of acquiring bacterial infections. In addition, it can also help propose hybrid water treatment approaches.