Safe Supply Research Articles

Multi-criteria decision-making model for risk management in modular construction projects

The modular sector needs a precise guide to determine the most efficient risk management approaches. The main research objective of this study is to develop a multi-criteria decision-making model to find the most efficient risk management approach according to the relevant risk criteria. The risk criteria and risk management approaches for modular construction projects were also identified and classified within this scope. A systematic literature review, semi-structured interviews, and open-ended questionnaires were performed for identification and classification purposes. For ranking and quantifying the identified risks and risk approaches, as well as developing the decision-making model, the Delphi method and the Analytical Hierarchy Process (AHP) were conducted. A two-round Delphi method, with eleven experts, was conducted to achieve efficient performance scores of the identified risk management approaches. The percentage standard deviation decreased, Relative Importance Index (RII), Cronbach’s alpha, and Kendall’s coefficient of concordance (Kendall’s W) were calculated to ensure the outputs’ reliability, validity, and agreement level. The AHP method opted to quantify the Delphi method outputs, solve the multi-criteria decision-making process, and develop the multi-criteria decision-making model for risk management of modular construction projects. Triangulation results show that the critical risk categories are supply chain, health and safety, stakeholders, and governmental support. Lean principles such as the Last Planner System, Value Stream Mapping, Just in Time, and Kaizen are top-rated risk management approaches. This research’s novelty is identifying and analyzing crucial risk categories, providing the relevant risk management approaches ranked according to efficiency performance, and presenting a decision-making model as a guideline for risk management of modular construction projects.

Connectivity in transfusion medicine: Challenges, benefits, and goals

Transfusion medicine requires meticulous record keeping from the time a blood donation is made to the time a patient receives a transfusion. As such, blood collection establishments and processing laboratories generate large amounts of data. This data must be managed, analyzed, and visualized appropriately to ensure safety of the blood supply. Today, the use of information technology (IT) solutions for data management in transfusion medicine varies widely between institutions. In this report, blood center professionals describe how they currently use IT solutions to improve their blood processing methods, the challenges they have, and how they envision IT solutions improving transfusion medicine in the future.

Arsenic Removal and Iron Recovery from Arsenic-Bearing Iron Ores by Calcification-Magnetic Roasting and Magnetic Separation Process.

Extracting iron while minimizing the health and environmental risks associated with arsenic contamination necessitates the removal of arsenic from arsenic-bearing iron ores to ensure a safe and sustainable supply of this metal for industries. The beneficiation of iron minerals and arsenic-bearing minerals from arsenic-bearing iron ores with a calcification-magnetizing roasting and low-intensity magnetic separation (CMR-LMS) process is investigated in this work. The results show that the process is successful in extracting iron minerals and eliminating arsenic-containing minerals. The roasting involves two key steps: calcification and magnetizing, which change hematite and goethite into magnetite and arsenic-bearing minerals into calcium arsenates. The process's separation efficiency of the CMR-LMS is closely linked to the parameters such as roasting temperature, roasting time, coke, alkalinity, and the liberation of gangue minerals from iron minerals. Through grinding and secondary magnetic separation, the iron minerals and gangue components, as well as arsenic, in roasted sand can be further separated. The optimum procedure results in a high-grade iron concentrate with an iron assay of 65.65%, an Fe recovery rate of 80.07%, and an arsenic content of 0.085%, while achieving a 93.29% As removal rate from the original ore that has 45.32% Fe and 0.70% As.

Peer-led safer supply and opioid agonist treatment medication distribution: a case study from rural British Columbia

BackgroundBritish Columbia (BC) has been facing a public health emergency of overdose since 2016, with rural regions of the province facing the highest rates of death. Peers (in this case, people with lived experience of substance use) are known to be effective patient navigators in health systems and can play a role in connecting patients to care and reducing overdose risk.Case presentationWe outline a peer-led program focused on opioid agonist treatment and prescribed safe supply medication delivery that began in March 2020 at a clinic in rural BC. The peer takes an Indigenous harm reduction approach and is focused on meeting the needs of the whole person. The peer has regular contact with approximately 50 clients and navigates medication delivery and appointments for approximately 10–15 people each day. Clients have been retained on the medication, and experienced improvement in other outcomes, including securing housing, employment and managing acute and chronic health conditions. The peer has established contact with clients since March 2020 to support engagement with health care and continuity of medication access. This program highlights the importance and value of peer-led work and need for further investments in peer-led programs to respond to the unregulated drug poisoning crisis.ConclusionsThis peer-led intervention is a promising approach to engaging people who remain disconnected from health services in care in a rural community. This model could be adapted to other settings to support patient contact with the health system and medication access and continuity, with the ultimate goal of reducing overdose risk.

Facile Synthesis and the Thermal Properties of Al/Si Composites Prepared via Fast Hot-Pressing Sintering

In this paper, a novel power sintering technique, named fast hot-pressing sintering (FHP), which is able to achieve an ultrahigh heating rate similar to the spark plasma sintering (SPS) technique, but at a much lower cost, was applied to prepare a series of Al/Si composites with different Si volume ratios (12 vol.% to 70 vol.%) to meet the requirements of advanced packaging materials for electronic devices. In contrast to SPS, the FHP oven possesses a safe and budget-friendly current power supply, rather than a complex and expensive pulse power supply, for its heating power. The optimized sintering parameters (temperature, pressure and holding time) of FHP for preparing Al/Si composites were investigated and determined as 470 °C, 300 MPa and 5 min, respectively. In order to characterize the potential of Al/Si composites as packaging materials, thermal conductivities and coefficients of thermal expansion were studied. The thermal conductivity of the Al-40Si composite sintered by the FHP method is higher than that of the conventional SPS method (139 to 107 W m−1 K−1). With the increase in Si, the thermal conductivities and coefficients of thermal expansion on both decreases. Furthermore, the thermal conductivities obey the Agari model, whereas the coefficient of thermal expansion and Si volume ratios obey additivity. The numeric modeling would help develop required packaging materials based on the thermal performances of the substrate materials, like Si or GaAs semiconductor devices.

Transfusion Camp Rwanda: A prospective feasibility study evaluating the delivery of Transfusion Camp to a multidisciplinary group of postgraduate medical trainees in Rwanda.

Safe blood transfusion is an increasing priority in global health equity. The Global Health 2030 commission lists access to a safe blood supply as essential for all surgical and nonoperative patients. The objective of this study was to determine if Transfusion Camp, when modified through a collaborative partnership between experts in Canada and Rwanda, results in improved knowledge and confidence among trainees in a resource-limited setting in sub-Saharan Africa. This prospective study took place at The University Teaching Hospital of Kigali in Rwanda. Participants were postgraduate medical trainees from departments where blood transfusion is frequent. Participants watched five prerecorded lectures and then attended a 5-hour team-based learning seminar to consolidate learning. Pre- and post-data were analyzed on transfusion knowledge and trainee confidence. A Rasch analysis investigated the performance of individual questions in assessing respondent knowledge. Of 31 trainees from surgery, anesthesia, internal medicine, and pediatrics invited to the course, 27 trainees attended the in-person team-based learning and 24 trainees completed the pre- and post-course analysis. Trainee knowledge assessment improved from (mean ± SD) 7.7/20 ± 1.95 to 10.4/20 ± 2.4 (p < .0001) and this knowledge was maintained by 12 trainees on a 3-month follow-up with a mean score of 9.3/20 ± 2.3. Trainees reported increased confidence in managing transfusion medicine-related patient issues. This pilot study demonstrated that Transfusion Camp education content modified to the local context can result in increased knowledge and confidence in managing transfusion-related issues. These results will inform future planning of Transfusion Camp in resource-limited settings.

Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique.

With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies.

Depletion Calculations for an Integral Small Molten Salt Reactor With Serpent

Abstract The molten salt reactor (MSR) concept is among the Generation IV designs considered feasible for providing clean, safe, sustainable, and economical energy supplies to the world's population. The depletion of fuel for a small modular fluoride molten salt reactor (sm-FMSR) with a closed fuel cycle based on the integral molten salt reactor concept has been investigated using Serpent. The Monte Carlo transport code Serpent has burnup capability and flow features that can be used to model fuel circulation and online fuel addition in an MSR. Three fueling schemes to control Serpent depletion cycles have been simulated and compared: step fueling (SF), continuous fueling with all fission products (FPs) accumulating in the reactor system (CFA), and continuous fueling with insoluble FPs separated from fuel (CFS). CFA and CFS require fewer depletion cycles that are longer in duration than the cycles required by SF, in order to maintain the effective multiplication factor (keff) within a working range over the seven years of the reactor fuel cycle. sm-FMSRs with SF and with CFA require similar quantities of “top-up” fuel, consume similar fuel (fissile) amounts, and result in similar fuel isotopic concentrations if keff is kept within a similar range. However, with separation of insoluble FPs from the circulating fuel, CFS gains a large reactivity worth due to the removal of FP poisons. This allows for reduction of fuel enrichment in both initial and total top-up fuel and leads to savings of a considerable fissile quantity in fueling MSR and in spent fuel. The Serpent depletion calculations require manual arithmetic calculations for adjustment of the Serpent built-in settings before the start of every calculation cycle for all three fueling schemes. Implementation of additional Serpent flow features in changing material volumes and flow constants would facilitate the simulation of the fuel depletion process and allow for more realistic simulations of fuel circulation.

Implementation of Indian National plasma policy at blood bank of a tertiary care hospital: A step towards strengthening of blood transfusion services

IntroductionThe World Health Organization has advocated that every country should make its own policy for ensuring safe and adequate supply of plasma derived medicinal products through mobilization and usage of locally collected plasma. The National Plasma Policy (NPP) of India was published in 2014 with a dual objective to achieve self sufficiency in the production of plasma derived medicinal products and at the same time to augment the component preparation facilities in India and overall upliftment of blood transfusion services in the country. Thus the present study was done to access the impact of implementation of NPP in our blood bank on the blood transfusion services in our hospital. Materials and methodsThe present study was a retrospective observational study conducted in the department of transfusion medicine of a tertiary care hospital in India involving analysis of data from 1st January 2019 till 31st December 2022. For the purpose of data analysis the time period was divided into 2 periods: (i) Pre-NPP implementation period from 1st January 2019 till 31st December 2020; (ii) Post-NPP period from 1st January 2021 till 31st December 2022. The following parameters were compared for the two periods: (i) component preparation rate; (ii) percentage of component therapy; (iii) total number of FFP transferred to plasma fractionation centers; (iv) total amount of exchange amount generated in lieu of transferred FFP to plasma fractionation centers. ResultsThe component preparation rate after NPP implementation was significantly higher as compared to the pre NPP implementation period (93.81% vs 56.70%; p = 0.007). The percentage of component therapy in the patients was also significantly higher as compared to the pre-NPP implementation period (97.9% vs 73.6%; p = 0.005). The total amount of exchange amount generation in Indian rupee (INR) after NPP implementation was INR 1419462 (15835€) while it was INR 636898 (7105€) in the pre NPP implementation period. This amount was utilized for procurement of various blood bank equipment, in addition 2 lab technicians were also hired for the blood bank. ConclusionsThe implementation of NPP resulted in upliftment of blood transfusion services in our hospital. Other low and middle income countries can benefit from implementation of similar plasma policy in their countries.

Integrated prediction of water pollution and risk assessment of water system connectivity based on dynamic model average and model selection criteria.

In recent years, with the rapid development of economy and society, river water environmental pollution incidents occur frequently, which seriously threaten the ecological health of the river and the safety of water supply. Water pollution prediction is an important basis for understanding development trends of the aquatic environment, preventing water pollution incidents and improving river water quality. However, due to the large uncertainty of hydrological, meteorological and water environment systems, it is challenging to accurately predict water environment quality using single model. In order to improve the accuracy and stability of water pollution prediction, this study proposed an integrated learning criterion that integrated dynamic model average and model selection (DMA-MS) and used this criterion to construct the integrated learning model for water pollution prediction. Finally, based on the prediction results of the integrated learning model, the connectivity risk of the connectivity project was evaluated. The results demonstrate that the integrated model based on the DMA-MS criterion effectively integrated the characteristics of a single model and could provide more accurate and stable predictions. The mean absolute percentage error (MAPE) of the integrated model was only 11.1%, which was 24.5%-45% lower than that of the single model. In addition, this study indicates that the nearest station was the most important factor affecting the performance of the prediction station, and managers should pay increased attention to the water environment of the control section that is close to their area. The results of the connectivity risk assessment indicate that although the water environment risks were not obvious, the connectivity project may still bring some risks to the crossed water system, especially in the non-flood season.

Enhancement of Power Systems Transient Stability with TCSC: A Case study of The Nigerian 330 kV, 48-Bus Network

A steady, safe, controllable, and high-quality power supply is necessary given the complexity of power systems, especially in the deregulated power sector seen in Nigeria. One of the main drawbacks of network expansion is the loss of the system's overall damping torque, which mitigates the system resulting to fluctuations and transient instability. Power system stability control difficulties have been addressed using flexible AC transmission systems (FACTS) controllers. This study examines the responses of the generator rotor angle, speed, Q-axis and D-axis voltage components behind transient reactance as well as the voltage magnitude profile under the influence of a three-phase fault for the purpose of enhancing transient stability. The Nigerian 48-bus power system network was modeled using commercial PSAT software in a MATLAB environment with a fault induced on Bus 33 (Geregu SubStation) and TCSC optimally positioned on line 21-28 using continuation power flow (CPF). According to simulation results with and without TCSC, a significant amount of oscillation in the power system was dampened, and the voltage profile was improved for power system transient stability.

Degree of blood safety of voluntary non-remunerated versus replacement blood donations: A multi-centre study of the large cohort of blood donors from two provinces of Pakistan.

Voluntary non-remunerated blood donors (VNRBDs) are recognized as being crucial for the safety and sustainability of national blood supplies. Systems based on replacement donors (RDs) pose high risks of transfusion transmissible infections (TTIs). Currently, only 10%-13% of blood donations are voluntary in Pakistan. No large-scale studies have been conducted to objectively evaluate the impact of the mode of donation on the frequency of TTIs, a gap this study aimed to fill. The study was conducted at the Indus Hospital, Karachi. Data from a total of 591,820 blood donations were included from 1 October 2017 to 30 May 2021 and evaluated for type of donations and results of TTI testing, primarily performed on Architect i2000SR (Abbott). The TTIs tested include hepatitis B virus, hepatitis C virus, human immunodeficiency virus, syphilis and malaria. A total of 477,938 (80.7%) RDs and 113,882 (19.3%) VNRBDs were screened. Among these, 53,590 (9.06%) were positive for TTIs. There were 10.2% positive RDs (10.08-10.25 95% confidence interval [CI]) while 4.4% in VNRBDs (4.29-4.53 95% CI). Co-infections were observed in 2367 (0.4%) RDs, while 159 (0.02%) in VNRBDs. Geographically, the highest frequency of TTIs was observed in semi-urban areas of Sindh (11.2%) and Punjab (9.6%). A site-wise comparison of TTIs in RD versus VNRBD showed significant differences (p-value 0.00). RDs are associated with higher frequencies of TTIs, compared with VNRBD. However, the study was unable to assess whether the significant difference was related to individual risk or repeat/first time status of the donors. Other important variables affecting frequency are the catchment area of the blood donors in Pakistan. Urban areas have less prevalence than semi-urban areas.

The implementation of infection prevention and control measures and health care utilisation in ACF-supported health facilities during the COVID-19 pandemic in Kinshasa, Democratic Republic of the Congo, 2020

ABSTRACT Background Infection prevention and control (IPC) was a central component of the Democratic Republic of the Congo’s COVID-19 response in 2020, aiming to prevent infections and ensure safe health service provision. Objectives We aimed to assess the evolution of IPC capacity in 65 health facilities supported by Action Contre la Faim in three health zones in Kinshasa (Binza Meteo (BM), Binza Ozone (BO), and Gombe), investigate how triage and alert validation were implemented, and estimate how health service utilisation changed in these facilities (April–December 2020). Methods We used three datasets: IPC Scorecard data assessing health facilities’ IPC capacity at baseline, monthly and weekly triage data, and monthly routine data on eight health services. We examined factors associated with triage and isolation capacity with a mixed-effects negative binomial model and estimated changes in health service utilisation with a mixed-model with random intercept and long-term trend for each health facility. We reported incidence rate ratios (IRRs) for level change when the pandemic began, for trend change, and for lockdown and post-lockdown periods (Gombe). We estimated cumulative and monthly percent differences with expected consultations. Results IPC capacity reached an average score of 90% by the end of the programme. A one-point increase in the IPC score was associated with +6% and +5% increases in triage capacity in BO and Gombe, respectively, and with +21% and +10% increases in isolation capacity in the same zones. When the pandemic began, decreases were seen in outpatient consultations (IRR: 0.67, 95% confidence interval (CI) [0.48–0.95] BM&BO-combined; IRR: 0.29, 95%CI [0.16–0.53] Gombe), consultations for respiratory tract infections (IRR: 0.48, 95%CI [0.28–0.87] BM&BO-combined), malaria (IRR: 0.60, 95%CI [0.43–0.84] BM&BO-combined, IRR: 0.33, 95%CI [0.18–0.58] Gombe), and vaccinations (IRR: 0.27, 95%CI [0.10–0.71] Gombe). Maternal health services decreased in Gombe (ANC1: IRR: 0.42, 95%CI [0.21–0.85]). Conclusions The effectiveness of the triage and alert validation process was affected by the complexity of implementing a broad clinical definition in limited-resource settings with a pre-pandemic epidemiological profile characterised by infectious diseases with symptoms like COVID-19. Readily available testing capacity remains key for future pandemic response to improve the disease understanding and maintain health services.

Adaptation of Renewable Energy Resources to Wireless EV Charging Using Deep Learning

When it comes to realizing a safe, efficient, and dependable power supply. More lately, there has been a rise in the use of data-driven methods for modeling electric vehicle (EV) charging. Since this problem involves a lot of unknowns, researchers are trying to implement model-free solutions. Reinforcement learning (RL) is one of several model-free methods now in use, and has seen extensive use in EV charging control. RL is a method to machine learning that prioritizes optimizing for cumulative reward rather than individual rewards. Solar photovoltaic and wind energy both have the potential to be used in the future to generate electricity. The CDDQN technique is developed, which incorporates an act constraint into the DDQN system in order to address the charging issue with such restrictions. The CDDQN optimizes charging procedures to better predict Q values and reduce charging choosing action errors. Wind and solar energy are great options since they do not harm the environment. The layout of the charging circuit is created and evaluated in MATLAB Simulink, taking into account the changing recharging requirements of EVs. Additionally, this work explores the use of RL in EV coordinating to study and design state-of-the-art optimized EMSs that may be used for EV recharging.

Swedish public procurement and the defence industry: obstacles and opportunities

PurposeA deteriorating security situation and an increased need for defence equipment calls for new forms of collaboration between Armed Forces and the defence industry. This paper aims to investigate the ways in which the accelerating demand for increased security of supply of equipment and supplies to the Armed Forces requires adaptability in the procurement process that is governed by laws on public procurement (PP).Design/methodology/approachThis paper is based on a review of current literature as well as empirical data obtained through interviews with representatives from the Swedish Defence Materiel Administration and the Swedish defence industry.FindingsCollaboration with the globalized defence industry requires new approaches, where the PP rules make procurement of a safe supply of defence equipment difficult.Research limitations/implicationsThe study's empirical data and findings are based on the Swedish context. In order to draw more general conclusions in a defence context, the study should be expanded to cover more nations.Practical implicationsThe findings will enable the defence industry and the procurement authorizations to better understand the requirements of Armed Forces, and how to cooperate under applicable legal and regulatory requirements.Originality/valueThe paper extends the extant body of academic knowledge of the security of supply into the defence sector. It serves as a first step towards articulating a call for new approaches to collaboration in defence supply chains.

Algae in a Drinking Water Reservoir: Development of an Air–Water–Algal Growth Model (AWAM) for Long-Term Prediction

The condition of the water in drinking water source environments is critical for public health. However, in recent years, the rapid growth of algae has become a significant threat to the safety of the drinking water supply. This study investigated the temporal trends and spatial differences in algae in the Nanwan Reservoir during 2022. Regression analysis using the least-squares method demonstrated that water temperature and initial biomass concentration were critical parameters that influenced the rate of algal growth. An air–water–algal growth model (AWAM) for algal growth prediction was developed using a 30-day forecast of air temperature, an air-to-water temperature extrapolating equation, a water temperature–algal growth relationship, and only four monthly measurements of algal concentration. The results demonstrated that the model accurately predicted algal growth in the next 30 days, with an R2 of 0.738, which aligned with the monitored results. Compared to the upstream Wudaohe River inflow point, algal growth in the drinking water intake area near the downstream dam was delayed by at least 30 days. By using the upstream inflow area as a reference point, the prediction period was extended to provide a 60-day early warning. The extended prediction period and the reduced need for monitoring data make the model more convenient for guiding the prevention and control of algal blooms in drinking water reservoirs.

Public Health Risks and External Food Safety in the Canadian Marketplace

The expansion of the food industry has increased the demand for diverse food products, which poses new challenges to food safety. The production, transportation, and handling of food products from various sources and countries can increase the likelihood of foodborne illness outbreaks and product contamination. Regulatory developments, such as the Hazard Analysis and Critical Control Points (HACCP) systems, have helped mitigate trade barriers and ensure a safe food supply chain. However, different food safety regulations and standards in importing countries can make it challenging to determine if imported goods are as safe as domestically produced ones. Total coliform testing is a commonly used indicator of potential fecal contamination in food products. Thus, this study aims to provide a comparative analysis between a domestic and an international pasteurized dairy product falling under category 3 to provide literature and the means to mitigate safety risks.

Airway topicalization in pediatric anesthesia: An international cross-sectional study.

There is no national or international consensus or guideline on recommended dosing of lidocaine for airway topicalization in children. Doses quoted in the literature vary substantially. The primary aim of the study was to ascertain current international dosing practices (mg.kg-1 and concentration of solution) for lidocaine airway topicalization in children. The secondary aims included examining aftercare instructions for those receiving lidocaine airway topicalization and instances of local anesthetic systemic toxicity secondary to the use of lidocaine for airway topicalization in pediatric patients. This cross-sectional study consisted of 11-20 questions across three domains-population demographics, clinical practice, and local anesthetic systemic toxicity. It adhered to the consensus-based checklist for reporting of survey studies. Responses were collected over 14 weeks using a combination of probability (cluster and simple random) and nonprobability (purposive, convenience and snowball) sampling. Data were analyzed based on the response rate per question with proportions expressed as percentages and nonparametric data expressed as median (interquartile range [range]) in an effort to minimize nonresponse error. No weighting of items or propensity scoring was applied. After initial exclusions, 1501 participants from 69 countries, across six continents, were included. Consultant anesthetists or those with an equivalent level of experience accounted for 1262/1501 (84.1%) of responses. Results showed heterogeneity in dosing and timing regimens and evidence that dosing may contribute to adverse outcomes. The maximum dose reported by participants who use lidocaine for airway topicalization as part of their normal practice was 5 mg.kg-1 (4-6 mg.kg-1 [0.5-50]) median (interquartile range [range]) over 2 h (1-4 h [0-30]). The results support the need for further research and consensus in this area, in order to provide safe provision of lidocaine airway topicalization in children. It is hoped the results of this study can support future collaborative work in this area.

Modified electrode decorated with silver as a novel non-enzymatic sensor for the determination of ammonium in water

Ammonium is an essential component of the nitrogen cycle, which is essential for nitrogen cycling in ecosystems. On the other hand, ammonium pollution in water poses a great threat to the ecosystem and human health. Accurate and timely determination of ammonium content is of great importance for environmental management and ensuring the safety of water supply. Here we report a highly sensitive electrochemical sensor for ammonium in water samples. The modified electrode is based on the incorporation of silver nitrate (AgNO3) into a carbon paste embedded with 1-aminoanthraquinone and supported by multi-walled carbon nanotubes, which are commercially available. A potential of 0.75 V is applied to the modified electrode, followed by activation in hydrochloric acid. The modified electrode was used for square wave voltammetry of ammonium in water in the potential range of − 0.4–0.2 V. The performance of ammonium analysis was determined in terms of square wave frequency, square wave amplitude and concentration of electrolyte solution (sodium sulphate). The calculation of the surface area according to the Randles–Sevcik equation resulted in the largest surface area for the Ag/pAAQ/MWCNTs/CPE. The modified electrode exhibited a linear range of 5–100 µM NH4+ in 0.1 M Na2SO4 with a detection limit of 0.03 µM NH4+ (3σ). In addition, the modified electrode showed high precision with an RSD value of 9.93% for 10 repeated measurements. No interfering effect was observed at twofold and tenfold additive concentrations of foreign ions. Good recoveries were obtained in the analysis of tap and mineral water after spiking with a concentration of ammonium ions.

A cross sectional study on seroprevalence of transfusion-transmitted infections among blood donors at tertiary care hospital

Background: Transfusion transmitted infections (TTIs) are the major problem associated with blood transfusions, and accurate projections of TTI risk are essential for tracking the safety of the blood supply. This research recognized the seroprevalence trends and shifting patterns of TTIs, in a multispecialty hospital. The aim of the study was to assess the prevalence of TTI infections among blood donors and to compare the changing seroprevalence trends in blood donors. Materials and methods: A cross sectional study was conducted from January 2017 to December 2022 at KIMS Blood Centre, Secunderabad, India. All donors reporting to the blood centre during the period were screened for human immunodeficiency virus (HIV) 1 &amp; 2, hepatitis C viruses, hepatitis B surface antigen (HBsAg), total hepatitis B core antibody (aHBC) and hepatitis B surface antibody (aHBs), malaria and syphilis. Screening of HIV, hepatitis B and hepatitis C viruses were done by chemilumiencies, while syphilis and malaria were screened by RPR method. Results: A total of 69741 voluntary blood donors were screened, of which 68857 were males and 1083 were females. Seropositivity of HIV, hepatitis B, hepatitis C viruses, malaria, syphilis were 0.36 %, 0.59%, 0.48%,0.01% and 0.07% respectively. Addition tests were aHBC and aHBs which showed among all the potential donors, the most common TTI prevalence at our centre was hepatitis B followed by HCV, HIV, syphilis and malaria. Conclusion: Public awareness, careful donor selection, vigilance, and adoption of newer techniques for early detection are needed to reduce the incidence of TTIs. Keywords: human immunodeficiency virus; HIV; hepatitis B; hepatitis C; seroprevalence; transfusion transmitted infections