Criteria Decision Research Articles

Thai Baht and Chinese Yuan Exchange Rate Forecasting Models: ARIMA and SMA-ARIMA Comparison

This study evaluates the effectiveness of classical Autoregressive Integrated Moving Average (ARIMA) models and kth Simple Moving Average - ARIMA (kth SMA-ARIMA) models in forecasting the exchange rate between the Thai Baht (THB) and the Chinese Yuan (CNY). The analysis uses a dataset of historical monthly exchange rates from January 2011 to November 2022, covering 143 months. The dataset is divided into two segments: the initial 127 months are used as the training dataset for model development, while the subsequent 16 months serve as the testing dataset to evaluate forecast accuracy. The Akaike Information Criterion (AIC) is the decision criterion for model selection during the development phase. The forecasting models' effectiveness is subsequently assessed on the testing dataset using two statistical measures: the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE). The findings indicate that the classical ARIMA (0,1,1) model outperforms the kth SMA-ARIMA models in this study, exhibiting the lowest RMSE and MAPE of 0.1702 and 2.6644, respectively. Additionally, a focused comparison of the kth SMA-ARIMA models for k = 2, 3, and 4 reveals that the 2nd SMA-ARIMA (0,1,2) model demonstrates superior performance compared to the 3rd and 4th SMA-ARIMA models. This superiority is reflected in their respective RMSE values of 0.3202, 0.5146, and 0.6339, and corresponding MAPE values of 5.3533, 8.7531, and 10.4949. These results provide valuable insights for decision-makers in the financial sector, enhancing investment strategy formulation based on anticipated currency movements.

Women entrepreneurs in rural Nigeria: formal versus informal credit schemes

PurposeThis study examines why low-wealth women entrepreneurs forgo mobile enabled money services and government supported micro finance for informal, community-based revolving loans in rural Nigeria.Design/methodology/approachThematic analysis of 25 interviews with women in rural, south-west Nigeria. Entrepreneurial ecosystem theory, in the gendered context of micro finance and community-based lending, is employed.FindingsThis study explains the paradox of forgoing seemingly accessible mobile enabled credit, and formal credit schemes (e.g. micro-finance programs) for informal, one-on-one borrowing. Convenience and trust-based relationships with respected community members ease the burden of time scarcity and vulnerability associated with formal capital. Flexible terms, autonomy, self-reliance and knowing who one is dealing with make Esusu a preferred source of finance. Findings are discussed in the context of gendered entrepreneurial ecosystems in which participants conduct business.Research limitations/implicationsThe sample is not representative of women entrepreneurs in rural Nigeria. Survivorship bias is acknowledged. Further research is needed on the psychological risks of informal capital and the benefits of community-based lending.Practical implicationsMeasures to scale mobile enabled credit, without commensurate interventions to address time management and other structural issues that confront women traders, limit their utility and impacts. Power differentials between women traders and lenders must also be considered in the design of lending products. Training of women traders and formal lenders should incorporate curricula about gender gaps in capital markets and systematic gender challenges to support entrepreneurs who seek to grow beyond subsistence enterprises.Originality/valueThis study documents decision criteria that motivate informal rural women traders to employ community-based revolving credit or Esusu. Findings inform measures to increase women entrepreneurs' access to capital in a rural sub-Saharan Africa contexts.

Career Guide Expert System for Nigerian Secondary Schools Based on Howard Gardner Categorisation of Intelligence

Career Guide Expert System for Nigerian secondary schools is an applied research targeted at developing a web-based career guidance system for students transitioning from the Universal Basic Education (UBE) Scheme to Senior secondary school (SSS). The transitioning process requires students to choose among groups of subjects (Science, Art, or Commercial) to study in SSS which ideally requires the services of counsellors. However, there is a shortage of counsellors in schools. This research will significantly address the problem of shortage of professional counsellors and inefficient associated manual career guidance, which can negatively affect the career trajectory of students. Three parameters (Howard Gardner Intelligence Categorization, Academic performance and student aspirations) were used in determining which of the three alternatives (Science, Art, and Commercial) suits a student the best. The inference engine of the Expert System was implemented using a weighted sum multiple decision criteria model to determine which alternative suits a student the most based on the data generated from the quiz attempted by a student. The system was validated by a professional counsellor who subjected the system to a test with twenty samples of student data and certified that the system was accurate. Thus, if this system is incorporated into the Nigerian educational system, it will revolutionize the career guidance process, leading to better performance of students and future career satisfaction.

Toward Equity and Transparency: A Content Analysis of Florida Elementary Acceleration Policies

In 2012, Florida adopted the Academically Challenging Curriculum to Enhance Learning (ACCEL) statute establishing minimum requirements for local education agencies to provide access to accelerated learning options for eligible K–12 public school students. This study examined Florida public school district acceleration policies for elementary students one decade after the enactment of the statute. We analyzed Student Progression Plans, school board policies, and district websites to explore the status of acceleration policy in the state, compare the accessibility of acceleration information across districts, and identify policy elements that support equity and those that create potential barriers for elementary students with advanced learning needs who could benefit from acceleration. The findings of this study indicated several areas for improvement, including the accessibility of acceleration information, the service options available to K–5 students, the protocols districts use, criteria for decision-making, and the implementation of acceleration. In addition, we discuss implications for school districts developing or revising acceleration policies and several avenues for future research. The preregistration and open-access materials for this study are available at https://osf.io/yc3xd/ .

Multi-objective optimization of an EDM process for Monel K-500 alloy using response surface methodology-multi-objective dragonfly algorithm

Monel K-500 is a high-performance superalloy composed of nickel and copper, renowned for its exceptional strength, hardness, and resistance to corrosion. To machine this material more precisely and accurately, Electrical Discharge Machining (EDM) is one of the best choices. In EDM, material removal rate (MRR) and electrode wear rate (EWR) are crucial performance parameters that are often conflicting in nature. These parameters depend on several input variables, including peak current (Ip), pulse on time (Ton), duty cycle (Tau), and servo voltage (SV). Optimizing the EDM process is essential for enhancing performance. In this research, a set of experiments were conducted using EDM on Monel K500 alloy to determine the optimal process parameters. The Box–Behnken design was used to prepare the experimental design matrix. Utilizing the experimental data, a second-order mathematical model was developed using Response Surface Methodology (RSM). R2 value is found to be 99.40% and 96.60% for MRR and EWR RSM-based prediction model, respectively. High value of R2 is indicated is indicated good adequacy for prediction. The mathematical model further used in multi-objective dragonfly algorithm (MODA): a new meta-heuristic optimization technique to solve multi-objective optimization problem of EDM. The MODA is a very useful technique to achieve optimal solutions from the multi decision criteria. Utilizing this technique, a set of non-dominated solutions was obtained. Further, the TOPSIS method was used to determine the most desirable optimal solution, which was found to be 0.0135 mm3/min for EWR and 6.968 mm3/min for MRR. These results were obtained when the optimal process parameters were selected as Ip = 6 A, Ton = 200 µs, Tau = 12, and SV = 41.6 V. Operators can machine Monel K500 by selecting the above-mentioned optimal parameters to achieve the best performance.

Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.

Investigating the Heterogeneity of "Study Twins".

Meta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favor of a common-effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of studies, for example, summarizing the results of two confirmatory clinical trials in phaseIII of a clinical development program. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible and include an empirical investigation of published ("twin") pairs of studies. A pair of estimates from two studies only provide very little evidence of homogeneity or heterogeneity of effects, and ad hoc decision criteria may often bemisleading.

Suitability Analysis of Solar Photovoltaic Farm Locations Using GIS: A Case Study of Nakuru County, Kenya

Renewable energy sources play a crucial role in reducing global reliance on fossil fuels. Advancements in technology has en- abled harnessing of renewable energy from solar, wind, and ocean tides to be viable. Solar energy, in particular, has gained significant global recognition as a renewable energy alternative. This study integrates Multi-Criteria Evaluation (MCE) and Geographic Information Systems (GIS) to assess Solar Photovoltaic Farms (SPVFs) suitability in Nakuru County, Kenya. The study considered seven criteria including; slope, solar radiation, aspect, land use land cover, proximity to roads, power transmission lines, and settlements. These were evaluated using the Analytical Hierarchy Process (AHP) to generate weights for each decision criterion. The weights were used to overlay independent criteria maps that were formed as a result of the re- classification of each criterion from which a composite rated map was developed. Similarly, a composite restriction map was created by leveraging on constraint criteria including; protected ecosystems, water bodies, settlement areas, slope over 20%, proximity to roads, proximity to the transmission line, and land use land cover. Results obtained from overlaying the composite rated and restricted maps reveal Nakuru County’s general suitability for SPVFs, except for Kuresoi North and Kuresoi South divisions which exhibit low solar radiation. Extremely-suitable areas ac- counted for 3.00% (224.14 km²), very-suitable areas 34.05% (2541.45 km2), moderately-suitable areas 7.76% (579.55 km2), marginally-suitable areas 1.47% (109.77 km2) while least-suitable areas covered 0.02% (1.39 km2). The study provides valuable data and information for government agencies and investors to identify potential Photovoltaic (PV) system sites. Furthermore, the government is encouraged to establish a favorable framework for solar PV exploration and provide incentives to the private sector to facilitate the establishment of SPVFs.

A Hybrid Decision Support System for Partition Walls

Partition walls play a crucial role in buildings, influencing their aesthetics, functionality, and integration with other architectural elements. However, the selection process for partition wall types is often challenging due to the multitude of options available, varying decision criteria, and inadequate decision-making practices in the Architecture, Engineering, and Construction (AEC) industry. To address these challenges and improve decision-making, a hybrid Decision Support System (DSS) named PartitionWall Pro is proposed. This tool combines both document-driven and model-driven approaches to assist in the selection and design of partition walls. The document-driven aspect utilizes a choosing-by-advantages (CBA) model to compare the advantages of different partition wall options, while the model-driven component employs computational design models to analyze the structural integrity of unreinforced masonry partition walls. Validation procedures ensure the reliability and accuracy of the DSS in practical applications. Through case studies involving a warehouse and a school, the study demonstrates how the DSS simplifies decision-making processes and encourages the adoption of cost-effective partition wall solutions. The results underline the potential of the DSS to enhance efficiency, foster stakeholder discussions, and improve communication in building design projects, thereby offering valuable insights for researchers and industry professionals alike, ultimately transforming partition wall design practice.

New insights in the management of pseudomyxoma peritonei.

While a rare entity, peritoneal pseudomyxoma treatment evolves. Decision-making criteria improve with imaging development and exploratory laparoscopy. Surgery remains at the core of the therapeutic strategy whatever disease progression. Complete cytoreduction plus hyperthermic intraperitoneal chemotherapy (HIPEC) is standard of care. Iterative cytoreduction or debulking is sometimes justified. Intraperitoneal chemotherapy modalities change with early postoperative HIPEC or pressurized intraperitoneal aerosol chemotherapy. Systemic or local treatment such as new chemo/immuno-therapies or BromAc should improve outcomes. Expertise and multicentric cooperation are more than ever needed.

Advanced Decision- Making Framework for Sustainable Energy Retrofit of Existing Commercial Office Buildings

In the background of rising global initiatives to combat climate change and promote better energy management, retrofitting of current structures has become a major strategy. This abstract and literature review aims at presenting a full review of a decision-making approach that can be used in choosing sustainable options for energy retrofitting. This particular framework is designed to help guide retrofit decision-making due to the many issues that surround the process by providing an economic as well as an environmental and social context. Energy retrofitting means improving the existing structure or complex to decrease its energy intensity and have a negative impact on the environment. As stated earlier, it is a critical process for attaining sustainability goals and advancing the performance of buildings. But the decision on which retrofit measures should be implemented can be rather difficult because of the availability of a vast number of technologies and because more factors must be considered, such as cost and energy savings. The decision-making framework provided in this article aims at categorizing retrofit selection into this list of features and views the current article as a way of making the process more efficient. It encompasses the life-cycle cost analysis (LCCA), the economic valuation, and the multi-criteria decision analysis tools and criteria. These tools facilitate the evaluation of financial viability, the advantages of climatically retrofitting, and the environmental effects of distinct retrofitting choices. The above-mentioned framework is exemplified with a case study of an office building retrofit project. The following case includes an example of the application of the mentioned framework and illustrates the explanation of how it helps to make decisions. Due to the consideration of costs, energy, and environmental performance, the framework enables the stakeholders to choose appropriate retrofit measures. Other than the case study, the framework also focuses on the need to establish a sustainable energy retrofit DSS. Thus, the DSS supplements collected data on building performance, retrofit technologies, and economic aspects, which can be accessed by the stakeholders in real-time and with built-in facilities for scenario analysis. This system improves decision-making since retrofit results can be monitored and evaluated frequently. Both tools and strategies for the decision-making of sustainable energy retrofits bring a logical and systematic approach aimed at neutralizing the decision-making challenges related to retrofit selection. By applying economic, environmental, and social factors in the decision-making process, the framework assists the stakeholders in reaching energy efficiency and sustainability goals. The development of another strong decision-support system also improves the working of the framework and checks that retrofit projects are done efficiently and are strategic to the general goal of sustainable development.

Renewable Energy Technology Selection for Hotel Buildings: A Systematic Approach Based on AHP and VIKOR Methods

This study provides an assessment of renewable energy technology utilization in hotel buildings, which are significant structures in terms of energy consumption. The aim of the study is to determine suitable renewable energy technologies (RETs) for hotel buildings by defining criteria for evaluating RETs, assessing the relative importance of these criteria, and proposing a multi-criteria decision-making framework to solve the problem of selecting the most appropriate RETs during the design stage. The alternatives for RETs and the criteria for their evaluation are gathered through a literature review and expert consultations. Eight fundamental RETs used in hotel buildings (such as heat pumps, solar panels, biomass boilers, etc.) are examined, and nine selection criteria are analyzed. According to the weights determined by the Analytic Hierarchy Process (AHP) method, the initial investment cost is the most influential decision criterion, with a weight of 0.314. As a result of applying the AHP and VIKOR (Multi-Criteria Optimization and Compromise Solution) methods for technology selection, photovoltaic panels emerge as the top-ranked choice. This comprehensive evaluation provides stakeholders in the building production process of hotel buildings with detailed analyses and multi-criteria decision-making methods for selecting RETs.

The value of the reflective discussion in decision-making using multi-criteria decision analysis (MCDA): an example of determining the value contribution of tabelecleucel for the treatment of the Epstein Barr virus-positive post-transplant lymphoproliferative disease (EBV+ PTLD)

BackgroundThe aim of this study was to assess the contribution of the reflective multidisciplinary discussion in determining the value contribution of innovative drugs through the multi-criteria decision analysis (MCDA). This methodology considers all relevant criteria for healthcare decision-making in a global, transparent, and systematic manner and from the perspective of relevant stakeholders. The determination of value contribution of tabelecleucel for the treatment of Epstein-Barr virus-positive post-transplant lymphoproliferative disease (EBV+ PTLD) compared to salvage therapy was used as an example.ResultsTabelecleucel obtained a value contribution score of 0.63 and increased to 0.75 after the reflective discussion. EBV+ PTLD was considered a life-threatening disease (5.0 ± 0.0), with a significant unmet need for an approved treatment (5.0 ± 0.0). Tabelecleucel was perceived as bringing improvements in terms of efficacy (4.2 ± 0.8) and safety (3.8 ± 0.8) compared to the salvage therapy. Most experts considered that the high efficacy and safety results could represent an improvement in the quality of life of patients (2.3 ± 1.2) along with savings in medical costs (2.3 ± 2.0) and non-medical costs (2.7 ± 1.6) compared to the salvage therapy. However, others emphasized the need of more evidence to confirm these improvements and savings over time. Tabelecleucel was regarded as potentially modifying the clinical course of the disease (4.3 ± 0.8) and supported by high-quality evidence (3.2 ± 0.4). All contextual criteria were valued highly positively for tabelecleucel. "Safety/Tolerability" and "Other medical costs" were the criteria that experienced the highest change in the re-test conducted after the reflective discussion. The reflective discussion allowed resolving doubts or misinterpretations of the experts, so the re-test obtained more accurate and consistent results of the value contribution of tabelecleucel.ConclusionsThe study shows that the MCDA methodology is a useful tool for decision-making on innovative treatments for the management of rare diseases. It also highlights the importance of reflective multidisciplinary discussion for its ability to resolve doubts or misinterpretations of experts, subsequently allowing to obtain more consistent and reliable results on the value contribution of the drug, being potentially more positive.

A comparative configuration and decision of main restrictions of a renewable energy powered campus micro grid: A case study

Higher education institutes are representative of small cities. Availability of building level data enables categorizing the models according to load profile instead of developing one model for whole communities, which is a gap in literature.In this study, hourly electricity consumption data of 34 buildings measured in Selcuk University is utilized to develop optimum Zero energy campus (ZEC) model. ZEC models developed until now used data of whole campus. In this study, campus and building level ZEC models are compared using PV/Wind/Biomass/Hydropower with/without battery.Biomass is found as a good option for high annual electricity consumption (AEC) buildings whereas using more than two type of renewable systems was better for low AEC. Required system size was found to be smaller at campus level design. However, renewable factor (RF) was higher at building level design which decreased levelized cost of electricity (LCOE). Therefore, the main limitation of ZEC is found as electricity selling price. Campus level design will be cost effective in case of electricity selling price to be the same or more then purchasing price. As a result of this study hourly load profile and electricity selling/purchasing prices are found to be the main decision criteria’s on optimum HRES design.

Problems Encountered in Humanitarian Aid Logistics and Solution Suggestions by Integrated QFD Method: The Case of Hatay

In recent years, extraordinary situations and humanitarian crises have increased the number of studies in the field of humanitarian aid logistics. Although various solutions have been tried to be produced for the problems experienced in humanitarian aid logistics in disasters occurring worldwide and, in our country, these solutions have generally not been suitable for regional needs. The aim of this study is to identify the problems encountered in humanitarian aid activities carried out in Hatay province and to develop solutions to these problems. Humanitarian logistics is a complex process involving many actors. This complexity requires a combination of both requirements and decision criteria to be evaluated. In this study, a model that can provide flexible yet effective solutions to the problems encountered in the humanitarian logistics planning process is proposed. This model involves combining qualitative and quantitative data using the Quality Function Deployment (QFD) method. In this study, a model that integrates Delphi and Analytical Network Process (ANP) methods with Quality Function Deployment (QFD) technique has been developed to identify the problems encountered in humanitarian aid logistics in Hatay province and to develop solutions to these problems. In line with the developed model, the study generally consisted of three stages. In the first stage, the Delphi method was used to identify the problems in humanitarian aid logistics by taking into account the relevant literature and the opinions of the representatives of the institutions/organizations providing humanitarian aid services in Hatay. Then, the priority values (weights) of these problems were determined by Analytic Network Process (ANP) method. In the last stage, solution proposals were developed through the Quality Function Deployment (QFD) relationship matrices. In humanitarian aid logistics, 14 problems were identified by Delphi technique. With the prioritisation of the results obtained with ANP, ‘Problems Arising from Lack of Continuity-Sustainability’ emerged as the most important problem. Then, among the 13 solution suggestions determined by expert opinions, ‘Establishing a system to monitor humanitarian aid’ was determined as the most effective solution suggestion in eliminating the problems in humanitarian aid logistics. In the future, it would be useful to conduct similar research with the participation of more experts in different regions or countries. In addition, the findings of the study can be compared with different multi-criteria decision-making methods to provide a broader perspective. In this context, the use of integrated methodologies in the field of humanitarian aid logistics will allow for a more comprehensive approach to the problems.

A Robust Wind Turbine Component Health Status Indicator

Wind turbine components’ failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine’s component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection of the more relevant variables and the generation of a health status indicator (Failure Index) to be considered as a decision criterion in Operation and Maintenance activities. The proposed methodology is based on Gaussian Mixture Copula Models (GMCMs) combined with a smoothing method (Cubic spline smoothing) to define a component’s health index based on the previous behavior and relationships between the considered variables. The GMCM allows for determining the component’s status in a multivariate environment, providing the selected variables’ joint probability and obtaining an easy-to-track univariate health status indicator. When the health of a component is degrading, anomalous behavior becomes apparent in certain Supervisory Control and Data Acquisition (SCADA) signals. By monitoring these SCADA signals using this indicator, the proposed anomaly detection method could capture the deviations from the healthy working state. The resulting indicator shows whether any failure is likely to occur in a wind turbine component and would aid in a preventive intervention scheduling.

Taphonomic signatures of early scavenging by black and turkey vultures.

Scavenging is critical for nutrient cycling and maintenance of healthy ecosystems. While there is substantial research into the identification of taphonomic signatures from facultative mammalian scavengers, early stage scavenging signatures by vultures remain unknown. Further, some vulture species are opportunistic predators, highlighting the need to define signatures observed in the course of normal scavenging behavior. We placed stillborn neonatal calves in an unoccupied pasture and used motion-trigger camera traps to quantify scavenging effort, then conducted necropsies to evaluate the effect of black vulture (Coragyps atratus) and turkey vulture (Cathartes aura) scavenging effort on carcass consumption. We measured the order of consumption of different tissue types to delineate which anatomic structures vultures consume first. Scavenging trials with higher numbers of vultures feeding on the carcass for longer were associated with decreased remaining tongue and abdominal viscera, and a larger umbilical wound. Greater maximum flock sizes were associated with decreased remaining tongue and abdominal viscera, a larger umbilical wound, and greater biomass consumption. Black vultures targeted the perineum and tongue earlier, while turkey vultures targeted the eyes, perineum, and tongue. These results are consistent with the idea that vultures prefer tissues that are easy to access and contain high nutrient content. These patterns form a distinctive taphonomic signature that can be used to identify early scavenging by black and turkey vultures. Our results demonstrate that criteria commonly used to identify livestock depredation by black vultures only document vulture presence and not predation. This distinction implies that new and more definitive criteria need to be developed and put into practice for more accurate decision criteria in livestock depredation compensation programs.

Spatial integration for firm and load-following wind generation

Abstract Wind power has considerable potential to decarbonise electricity systems due to its low cost and wide availability. However, its variability is one factor limiting uptake. We propose a simple analytical framework to optimise the distribution of wind capacity across regions to achieve a maximally firm or load-following profile. We develop a novel dataset of simulated hourly wind capacity factors (CFs) with bias correction for 111 Chinese provinces, European countries and US states spanning ten years (∼10 million observations). This flexible framework allows for near-optimal analysis, integration of demand, and consideration of additional decision criteria without additional modelling. We find that spatial integration of wind resources optimising the distribution of capacities provides significant benefits in terms of higher CF or lower residual load and lower variability at sub-, quasi- and inter-continental levels. We employ the concept of firmness as achieving a reliable and certain generation profile and show that, in the best case, the intercontinental interconnection between China, Europe and the US could restrict wind CFs to within the range of 15%–40% for 99% of the time. Smaller configurations corresponding to existing electricity markets also provide more certain and reliable generation profiles than isolated individual regions.

Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging—Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

Microbiological and Molecular Investigation of Antimicrobial Resistance in Staphylococcus aureus Isolates from Western Romanian Dairy Farms: An Epidemiological Approach.

Antimicrobial therapy is the most frequently used medical intervention for bovine mastitis in the dairy industry. This study aims to monitor the extent of the antimicrobial resistance (AMR) problem in Staphylococcus aureus in the dairy industry in Western Romania. Twenty farms were selected by random sampling in a transverse epidemiological study conducted across four counties in Western Romania and divided into livestock units. This study assessed the association between the resistance genes to phenotypic expression of resistance and susceptibility. Isolates of S. aureus were identified and q-PCR reactions were used to detect antibiotic resistance genes. One hundred and fifty bovine and 20 human samples were positive for S. aureus. Twenty five percent of bovine isolates (30/120) and none(0/30) of the human isolates were methicillin-resistant S. aureus (MRSA). All isolates were susceptible to fosfomycin, ciprofloxacin, netilmicin, and resistant to ampicillin and penicillin. S. aureus isolates regarded as phenotypically resistant (R) were influenced by the origin of the samples (human versus bovine, χ2 = 36.510, p = 0.013), whether they were methicillin-resistant S. aureus (χ2 = 108.891, p < 0.000), the county (χ2 = 103.282, p < 0.000) and farm of isolation (χ2 = 740.841, p < 0.000), but not by the size of the farm (χ2 = 65.036, p = 0.306). The multiple antibiotic resistance index was calculated for each sample as the number regarded as phenotypically resistant (R)/total antibiotics tested (MARI = 0.590 ± 0.023) was significantly higher (p < 0.000) inmethicillin-resistant S. aureus (0.898 ± 0.019) than non-methicillin-resistant S. aureus (0.524 ± 0.024) isolates. For the antibiotics tested, the total penetrance (P%) of the resistance genes was 59%, 83% for blaZ, 56% for cfr, 50% for erm(B), 53% for erm(C), 57% for mecA and 32% for tet(K). Penetrance can be used as a parameter for guidance towards a more accurate targeting of chemotherapy. P% in S. aureus was strongly positively correlated with the multiple antibiotic resistance index (r = +0.878, p < 0.000) with the potential to use the same limit value as an antibiotic management decision criterion. Considering cow mastitis, the penetrance value combined with the multiple antibiotic resistance index suggests that penetrance could serve as a useful parameter for more precise targeting of chemotherapy for S. aureus.