Articles

Silver nanoparticles (Ag<SUP>+</SUP>NPs) contamination in the environment is a serious concern. This study investigated selected heavy metal (Ag<SUP>+</SUP>, Cd<SUP>2+,</SUP> Cr<SUP>2+</SUP> and Pb<SUP>2+</SUP>) concentrations at different sampling points to assess the risk to human health (infants, children, and adults). To do this, an enclosed area (laboratory) of 12.6 m X 8.5 m (107.1 m<SUP>2</SUP>) was clearly marked at different coded distances of S1, S2, S3, and S4 representing 2, 4, 6, and 8 m, while unpolluted atmosphere at 50 m away without Ag<SUP>+</SUP>NPs served as the control (S5). The silver fireworks were allowed to burn for an approximate 00h03m30s at each sampling points using a high-volume air sampler mounted at the Environmental Engineering Departmental Laboratory, Rivers State University, with windows and doors closed to simulate indoor conditions. Samples were digested using a mixture of analytical-grade nitric acid, analytical-grade hydrochloric acid and analyzed to evaluate the levels of heavy metals by atomic absorption spectrophotometry. The Ag<SUP>+</SUP> result at S1 shows 30,000 µg/cm<SUP>3</SUP>, S2 was 29,000 µg/cm<SUP>3</SUP>, while S3 was 28000 µg/cm<SUP>3</SUP> and then S4 was 13,000 µg/cm<SUP>3</SUP>. These results exceeded the permissible values of the United States National Ambient Air Concentration for rural, urban and industrial areas (0.0005, 0.004 and 0.6 µg/cm<SUP>3</SUP>, respectively). The result for the control (S5) (0.037 µg/cm<SUP>3</SUP>) was within the maximum allowable value. Results from other heavy metals such as Cd were 1000, 743, 401, 153, 0.001 µg/cm<SUP>3</SUP>, Cr was 5000, 4000, 3729, 2960, 0.002 µg/cm<SUP>3</SUP>, Pb was 0.048, 0.041, 0.035, 0.034 and 0.01, µg/cm<SUP>3</SUP>, respectively. However, higher values of Ag<SUP>+</SUP>, Cd, and Cr indicated a higher propensity for the metals to be toxic (bioavailable). In addition, the assessment of the potential health risk posed by these metals proved contaminated and harmful. Visitors recorded high values in exposure concentration<b> (</b>EC) and low values in average daily dose (ADD).

<b>Background</b>: There are currently numerous risk instruments available to aid in predicting the present or future chance of getting a cancer diagnosis. It aids in determining a person's likelihood of developing certain cancers by looking at various risk factors, including environmental, behavioral, and genetic. <b>Aim:</b> To analyze the effectiveness of cancer risk assessment techniques utilized in primary care settings. <b>Methods</b>: An integrative review of literature <b>Results</b>: Five (5) studies were met the criteria based on the inclusion and exclusion criteria. These tools demonstrated effectiveness in improving patient outcomes and serving as useful therapeutic tools in the primary care setting. <b>Conclusion:</b> Advantages that may aid clinicians in the primary care setting in validating the diagnosis and assisting patients in determining the early signs and symptoms in the diagnosis of cancer. The role of assessment tools can enhance the reliability and caliber of clinical judgment, which can enhance patient outcomes. <b>Implications</b>: The role of healthcare professionals, such as oncologists, nurses, and the healthcare team, on cancer risk assessment in the primary care setting across the lifespan is crucial to ensure a care plan tailored to each patient’s needs.

Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.

Essentiality of water sustain life, and a satisfactory supply must be readily available to promote health, prolong life expectancy and prevent diseases. This study assesses the sources and quality of drinking water in Piwoyi community of Federal Capital Territory, Abuja, Nigeria. Thirty-five (35) Boreholes and Two (2) Sachet water were identified sources of drinking water in Piwoyi Community. Six Samples (5 Boreholes and 1Sachet water) were selected at random and analyzed in the laboratory. The Physicochemical parameters examined include electrical conductivity (EC), pH, temperature, turbidity, dissolve oxygen (DO), chloride, total hardness, alkalinity, nitrate, calcium, magnesium, phosphate, phosphorous, sulphate, sodium, potassium, fluoride, bicarbonate, nitrate-nitrogen, nitrite, copper, iron and zinc; and Microbiological parameters include Coliforms, E-coli and Faecal Strep. The results of analysis shows significant concentration of physicochemical and microbiological parameters in the samples of water analyzed according to Nigerian Standard for Drinking Water Quality thereby makes the water unsafe for drinking. Drinking from these sources of water will pose serious health risk to the people of Piwoyi Community. Therefore, the study helps to identify the contaminated locations as well as assist to follow emerging remedial measures toward controlling the contamination source in the community. It also recommends continuous monitoring and enforcement of environmental violations, aggressive sensitization on water sanitation and hygiene; adequate purification of water at domestic level; and government support on potable water supply and establish reasonable management strategies for sustainable water quality protection toward protecting public health.