Probability Index Research Articles

Performance of the systemic lupus erythematosus risk probability index (SLERPI) in the Egyptian college of rheumatology (ECR) study cohort.

This study aimed to evaluate the performance of systemic lupus erythematosus Risk Probability Index (SLERPI) in Egyptian patients with SLE using a national rheumatology database. The Egyptian College of Rheumatology (ECR) database comprised of 1,162 patients with SLE and 4,327 with miscellaneous rheumatological diseases who were recruited from the Rheumatology Departments across the country. The diagnosis of SLE was established by expert rheumatologists. Variables of the SLERPI were extracted and recorded as present or absent for each patient. The absolute value for the SLERPI score was calculated for each patient, and the diagnosis of SLE was accounted for if the score was greater than 7 points. Of 1,162 SLE patients evaluated, 1,031 (88.7%) patients were diagnosed with SLE according to the SLERPI, with an average score of 13.1 (3.8). Differences in the 14 SLERPI variables were significant between the SLE-SLERPI groups, except for the presence of leukopenia and positive ANA. As a score reduction item, the SLE-SLERPI > 7 group had lower interstitial lung diseases. Patients diagnosed with SLE according to SLERPI had significantly higher disease activity (p < 0.001), and this group more commonly received corticosteroids and mycophenolate mofetil. Compared to other miscellaneous rheumatological groups, all 14 SLERPI items are indeed more common in the SLE group. In terms of the overall performance of SLERPI in the diagnosis of SLE, the accuracy of SLERPI was 91.9% (95% CI 90.9%-92.9%), with a specificity of 96.95% and sensitivity of 86.9%. SLERPI showed that accuracy went up to 93.3% (95%CI 92.4%-94.2%), with a specificity of 94.9% and a sensitivity of 91.6% when patients with connective tissue diseases were taken out of the study. Using a large cohort of SLE, the SLERPI revealed excellent diagnostic efficacy and specificity. The use of SLERPI in clinical practice may contribute to improved patient diagnosis and prognosis. Key Points • SLERPI's performance has high diagnostic efficiency in Egyptian SLE patients. • SLERPI score can efficiently distinguish patients with SLE from other CTDs. • Within the SLERPI score, interstitial lung disease is the lowest predictor of SLE.

Is Idiopathic Granulomatous Mastitis a Subgroup of Systemic Lupus Erythematosus? A Preliminary Study

Objective: The study aimed to use the systemic lupus erythematosus risk probability index (SLERPI) to assess if patients with idiopathic granulomatous mastitis (IGM) meet the criteria for systemic lupus erythematosus (SLE). Methods: A total of 62 patients with IGM and 55 age- and sex-matched healthy controls (HC) were enrolled. The study included patients who were over 18 years old and had been diagnosed with IGM using a true-cut biopsy. The participants’ demographic, clinical, and laboratory data were recorded in detail. The presence of autoantibodies, such as RF, CCP, C3, C4, ANA, ENA profile, and Anti-dsDNA was documented. For the detection of SLE in IGM patients, we used the SLERPI (SLE risk probability index). Results: A total of 62 patients diagnosed with idiopathic granulomatous mastitis (age 35.22 ± 8.34, BMI 27.15 ± 3.41) were compared to 55 healthy controls (age 32.54 ± 8.67, BMI 26.97 ± 3.54). The present study assessed the performance of SLERPI in IGM, and SLERPI positivity was observed in 12 out of 62 (19.4%) IGM patients. There was a significant difference in arthritis and ANA levels in the SLERPI subgroups (p &lt; 001). Conclusions: The SLERPI index can be utilized to identify patients suspected of having systemic lupus erythematosus (SLE) in the IGM cohort.

A global analysis of nearshore and submarine springs: spatial distribution, controlling factors, and probability of presence

Coastal springs act as bi-directionally preferential flow paths between coastal aquifers and oceans. While these springs can supply coastal ecosystems with nutrients, they also present vulnerabilities such as contamination and seawater intrusion. Despite their significance, substantial knowledge gaps exist regarding coastal springs due to their complex hydrogeological nature. This study provides a comprehensive global assessment of coastal springs, focusing on their distribution, controlling factors, and likelihood of occurrence. A global dataset of known coastal springs was compiled and analyzed, revealing 66 % of identified springs in the Mediterranean region, mainly linked to karst systems. In contrast, fewer springs were noted in Africa, South America, and South Asia. Key factors influencing spring occurrence were examined using geostatistical methods and integrated into a multi-criteria decision-making framework to develop a Coastal Spring Probability Index (CSPI) along coastline. High-potential areas for coastal springs were identified in regions characterized by significant carbonate and volcanic rock formations, wetter climates, and active tectonic margins, such as southern Europe, the Caribbean, tropical islands, and eastern Asia. Conversely, regions with dry climates and high water demand, such as North Africa and South America, exhibited a lower likelihood of spring presence. These findings will serve as a baseline for local-scale studies and aimed at improving coastal spring inventories and establishing monitoring networks. It will contribute to vulnerability assessment studies, thereby enhancing the management of coastal groundwater resources. The study emphasizes the importance of multidisciplinary approaches to understand coastal spring dynamics and advocates for a strategic planning in groundwater management and conservation. Ongoing efforts to inventory and monitor coastal springs, coupled with targeted conservation measures, are essential for ensuring the long-term sustainability of coastal water resources and ecosystems.

A hybrid framework for Detection of Multivariate porphyry Cu Signatures and Anomaly Enhancement: Incorporation of SFA, GMPI, and Grey Wolf Optimization

Geochemical data from stream sediment are commonly employed for regional scale mineral exploration, as they can be utilized to detect geochemical anomalies associated with mineralization processes. However, recognizing efficient multielement geochemical signatures related to target mineralization using stream sediment geochemical data can be challenging due to the intricate nature of mineralization events. To achieve this goal, this article proposes a combination of statistics, logistic functions and machine learning techniques. Initially, the staged factor analysis (SFA) approach was applied to the data to determine the geochemical footprints associated with porphyry copper mineralization in the Varzaghan area. Subsequently, the geochemical mineralization probability index (GMPI) was utilized to facilitate appropriate separation, more precise identification, and the transfer of data into a fuzzy space. By using a combination of SFA and GMPI, it was feasible to enhance the detection of geochemical halos and concealed anomalies. Ultimately, the output of these two approaches was subjected to an improved clustering technique known as GWO-K-means to detect geochemical anomalies. The findings indicated that there is a strong relationship between known mineral occurrences (KMOs) and the identified anomalous areas. The success rate curve demonstrated that the optimized model outperformed the K-means model, proposing that optimization can boost the accuracy in identifying geochemical targets. Overall, the outcomes of this study can be beneficial for proper planning of future exploration activities.

EUROPE’S ENERGY SECURITY AMID RUSSO-UKRAINE WAR: NAVIGATING THE FUTURE BY EMPLOYING MODIFIED SCENARIO BUILDING TECHNIQUE

The Russo-Ukraine War has exposed the vulnerabilities of Western Europe’s energy security, which has traditionally relied heavily on Russian energy supplies. This excessive dependency has resulted in energy shortages, price volatility, and inflation due to continuing disruptions. By employing the scenario-building technique, this study offers insight into the implications of the War on energy security, strategic choices, and policy considerations for stakeholders as a result of scenario building. The scenario building technique used in this study is modified version that has been modelled by the author by totally overhauling the conventional model in practice. The study projects potential scenarios by leveraging the modified scenarios building and development technique. The findings signify the necessity of thoroughly reevaluating Europe’s stance towards the conflict, identifying looming threats, strategic adaptability regarding the diversification of energy resources and routes, and creating a collaborative European Union framework to articulate collective responses to mitigate the risks. Finally, the results are presented in four major probable future scenarios, each displaying unique challenges and strategic choices. Concluding with geopolitical, geostrategic and geoeconomic triggers and their graduation on impact and probability indices, offer a whole sum view to the stakeholders of the Western Europe to mitigate threats to their energy security amid ongoing Russo-Ukraine War. In a nutshell, while advancing the discourse on Western Europe’s energy security, the study offers a nuanced analysis and detailed strategic roadmap for the future of energy, where these scenarios underscore the critical need to pacify the conflict, anticipate the long-term risks and their mitigation, diversify the energy sources, enhance energy efficiency, formulate resilient energy policies, the collaborative framework within the European Union, collective energy purchase and storage mechanism.

Quantitative method for the probability of structural damage based on moment theory

In the context of uncertainty in the inverse problem of damage identification, this study proposes a moment theory based probabilistic damage identification (MbPDI) method. Noise errors in the identification process are treated as uncertain parameters, while structural stiffness parameters are treated as functions of noise errors. A limit state function for damage identification is defined, and its probability is computed. Utilizing the concept of moment theory, the expansion series is performed for the limit state function around the most probable point of failure, with gradient information derived using sensitivity matrices. Based on the gradient information and uncertainty inputs, damage probabilities for various elements can be obtained. Additionally, this paper addresses the calculation of structural damage probabilities under the non-normal distribution of random variables by transforming non-normal variables into equivalent normal variables. The proposed probability index and expected index of structural damage in the probabilistic damage identification method has a clear physical meaning, and the calculation has high accuracy. The proposed method is validated through a mathematical example and a numerical simulation. Meanwhile, the influence of damage extent, noise level, and modal order on identification results are analyzed. Experimental validation of the method is also presented.

Validation of a machine learning algorithm for identifying infants at risk of hypoxic ischaemic encephalopathy in a large unseen data set

ObjectiveTo validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data.DesignSecondary review of electronic health record data...

Performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in a cohort of Colombian population

ObjectiveTo evaluate the performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in Colombian patients with systemic lupus erythematosus (SLE).MethodsThe Colombian cohort included 435 SLE patients and 430 controls with other autoimmune diseases (ADs). Clinical and serological data were collected, and SLE was indicated by SLERPI scores > 7. The American College of Rheumatology (ACR)-1997, Systemic Lupus International Collaborating Clinics (SLICC)-2012, and European League Against Rheumatism (EULAR)/ACR-2019 criteria were used as reference standards. The impact of overt polyautoimmunity (PolyA) on SLERPI performance was assessed. Additionally, multivariate lineal regression analysis was performed to evaluate the contribution of SLERPI features to the overall SLERPI score.ResultsSLE patients had higher SLERPI scores (P < 0.0001), with almost 90% meeting "definite" lupus criteria. Main factors influencing SLERPI included immunological disorder (β:44.75, P < 0.0001), malar/maculopapular rash (β:18.43, P < 0.0001), and anti-nuclear antibody positivity (β:15.65, P < 0.0001). In contrast, subacute cutaneous lupus erythematosus/discoid lupus erythematosus (β:2.40, P > 0.05) and interstitial lung disease (β:-21.58, P > 0.05) were not significant factors to the overall SLERPI score. SLERPI demonstrated high sensitivity for SLE, both for the overall SLE group and for those without overt PolyA (95.4% and 94.6%, respectively), but had relatively low specificity (92.8% and 93.7%, respectively). The model showed high sensitivity for hematological lupus (98.8%) and lupus nephritis (96.0%), but low sensitivity for neuropsychiatric lupus (93.2%). Compared to the ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria, SLERPI yielded the highest sensitivity and lowest specificity.ConclusionSLERPI efficiently identified SLE patients in a Colombian cohort, showing high sensitivity but low specificity. The model effectively distinguishes SLE patients, even in the presence of concurrent overt PolyA.Key Points•SLERPI has a high sensitivity, but low specificity compared to ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria in the Colombian population.•Within the SLERPI score, immunological disorder, malar/maculopapular rash, and anti-nuclear antibody positivity are the strongest predictors of SLE.•SLERPI model can efficiently distinguish patients with SLE, regardless of concomitant overt PolyA.•SLERPI demonstrates high sensitivity in identifying hematological and nephritic subphenotypes of SLE.

Statistical Study of the Mechanical Behavior of the New Fiber from the Strelitzia Juncea Plant Fibers: Application in Ecological Yarns

ABSTRACT To develop efficient, durable, and eco-friendly natural fiber composites, this study examines the physical properties of Strelitzia Juncea (SJ) fiber for potential industrial use. The focus is on how gauge length affects mechanical properties of SJ fibers, aiming to manufacture textile yarns. Tensile tests were performed on SJ yarns of varying diameters (0.95, 1.26, and 1.76 mm). In addition, 150 fibers across five-gauge lengths (GL) from 10 to 50 mm gave a strength of 731 to 270 MPa and a Young’s modulus of 28 to 31 GPa, with statistical analysis applied to evaluate the data’s variability. Statistical techniques were applied to assess and quantify this dispersion. Results were analyzed using Weibull distributions with different probability indices, along with least squares (LS) and maximum likelihood (ML) estimation techniques. Furthermore, for each tensile strength characteristic, a one-factor analysis of variance (ANOVA) was performed across the five groups.

Physiological and Psychological Factors Contribute to Real-Time Esophageal Symptom Reporting in Patients With Refractory Reflux.

Real-time symptom reporting during ambulatory reflux monitoring plays a key role in the evaluation of esophageal symptoms, although the underlying processes are poorly understood. We aim to identify the psychological and physiological factors associated with real-time reflux symptom reporting and symptom-reflux association parameters. Adult patients with refractory reflux symptoms completed psychosocial questionnaires and standard 24-hour pH-impedance monitoring. A hurdle-Poisson model evaluated the association between psychological and physiological (proton pump inhibitor [PPI] use, total number of reflux episodes) variables on real-time symptom frequency, assessed through a button press within 2 minutes of experiencing a symptom. Logistic regression assessed the variables associated with symptom association probability (SAP) and symptom index classification (positive/negative). Complementary machine learning analyses with 8-fold cross-validation further identified variables associated with symptom frequency and sought to optimize SAP classification performance. Both psychological (pain-related anxiety, depressive symptoms, trait anxiety) and physiological (total number of reflux episodes, off PPI during testing) variables were associated with symptom frequency. The total number of reflux episodes and being studied off PPI were significantly associated with a higher likelihood of being classified as SAP or symptom index positive. The best-performing model in the machine learning analysis demonstrated a poor job of correctly classifying patients as SAP positive/negative (misclassification rate = 41.4%). Real-time reflux symptom reporting is a multifactorial process, with both psychological and physiological processes contributing to different aspects of the reflux disease experience. Findings build on questionnaire-based research to underscore the importance of including psychological processes in our understanding of esophageal symptom reporting.

Probability-oriented disturbance estimation-triggered control via collaborative and adaptive Bayesian optimization for reentry vehicles

The paper investigates the performance improvement issue for reentry vehicles under uncertainties from the perspective of probability. The disturbance estimation-triggered control (DETC) proves to achieve transient performance increase compared with the standard disturbance-observer control methods, and the presented approach further exploits the probability-oriented transient performance improvement based on the collaborative and adaptive Bayesian optimization (CABO) technique, which constructs the main contribution of the paper. Based on the attitude dynamics of reentry vehicles, the DETC method is first introduced to guarantee the tracking stability and robustness against the uncertainties including the aerodynamic perturbation and wind effects. Meanwhile, the performance improvement is analyzed theoretically. Then, by virtue of the CABO algorithm, the CABO-based DETC is presented by combining the performance and probability indexes. Finally, the simulation results verify the effectiveness of the proposed control scheme and parameters influence is also discussed.

Purification of Potential Antimicrobial Metabolites from Endophytic Fusarium oxysporum Isolated from Myrtus communis.

The rise of microbial resistance and emerging infections pose significant health threats. Natural products from endophytic fungi offer a promising source of novel compounds with the potential as major drug leads. This research aims to screen Myrtus communis and Moringa oleifera for endophytic fungi and screen their metabolites for antibacterial and antifungal potential. Six endophytic fungal strains were isolated using a potato dextrose agar (PDA) medium. The M. communis isolates were designated MC1, MC2, and MC3, and the M. oleifera isolates were named MO1, MO2, and MO3. Preliminary bioactivity testing revealed that the MC3 isolate exhibited significant growth inhibition against multidrug-resistant bacterial and fungal pathogens, including Staphylococcus aureus, Enterococcus faecalis, Bacillus subtilis, Pseudomonas aeruginosa, Escherichia coli, Candida albicans, and Candida glabrata. The MC3 isolate was identified as Fusarium oxysporum through morphological and microscopic methods. For metabolite production, the fungal strain was cultured in potato dextrose broth (PDB) medium at 28°C for 14days in a shaking incubator. The metabolites were purified using various chromatographic techniques, HPLC and GC-MS. The GC-MS analysis of the bioactive compound containing fungal strain (F. oxysporum) revealed multiple compounds at different retention times using the NIST-20 Library. Based on RSI values and probability indices, two compounds were targeted for further purification. Structure elucidation was performed using 1D and 2D nuclear magnetic resonance (NMR) experiments on a Varian 500 NMR machine. The compounds identified were ethyl iso-allocholate (C26H44O5, exact mass 436.32) and 1-monolinoleoyl glycerol trimethylsilyl ether (C27H56O4Si2, exact mass 500.37). The MS (NIST-20) library facilitated the investigation of the in silico antimicrobial activity of these compounds against the elastase virulence protein of P. aeruginosa and protease Sapp1p from C. parapsilosis. Both the compounds were docked with druggable proteins using the Glide induced fit docking (IFD) algorithm. The ethyl iso-allocholate and 1-monolinoleoyl glycerol trimethylsilyl ether compounds showed binding scores - 10.07kcalmol-1 and - 7.47kcalmol-1 against elastase, and - 8.16kcalmol-1 and - 6.89kcalmol-1 against aspartic protease, respectively. In vitro studies confirmed the inhibitory activity of these compounds against multidrug-resistant P. aeruginosa and E. faecalis. Ethyl iso-allocholate exhibited higher bioactivity against P. aeruginosa with inhibition rates of 41%, 27%, and 35% at concentrations of 1000, 500, and 250µgmL-1, respectively. These results suggest that bioactive compounds from F. oxysporum have the potential as antimicrobial agents, warranting further research.

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.

MINERALOGICAL AND GEOCHEMICAL PARAMETERS OF SNOW COVER WITHIN THE ANTHROPOGENICALLY DISTURBED SITES OF THE NADYM-PUR INTERFLUVE (NORTHERN PART OF WESTERN SIBERIA)

The article analyzes the content of metals in solid aerosols and snow water sampled in the anthropogeni-cally disturbed and background sites of the Nadym-Pur interfluve. The results of microscopic studies of solid aerosols showed the presence of secondary minerals, technogenic formations, as well as a large amount of pyrite in samples from the eastern part of the study area. The maximum concentrations of metals in solid aero-sols were found in snow of the sanitary protection zones of waste disposal facilities, with the exception of Ca, which prevails in snow cover of Novy Urengoy (up to 10% of dry matter). The urbanized area is characterized by the highest values of dust load (up to 111 mg/m2 a day). The local contamination with Ba within the waste landfills in Novy Urengoy is associated with the sites of barite and drilling waste processing. We applied the factor analysis to identify the main metal associations characteristic of solid aerosols from different functional zones: Ca-Co-V-Mn (city), Ba-Sr-K-Na and Cd-Cu-Pb (waste disposal sites), Cu-Ni-Cd (the entire studied area). Based on the study of the content of dissolved forms in snow water, a high mobility of metals in the northern landscapes and its decrease in urban environment were revealed. The mineralogical and geochemical analysis of snow suggests the influence of the Norilsk industrial region on the eastern part of the Nadym-Pur interfluve, Zn, Cu, Ni, Pb and the mineral pyrite being the indicators. The risks of environmental pollution under the aerotechnogenic transport are associated with high content of Zn, Cu, Ni, Pb and Ba in solid aero-sols. The calculation of the Toxicity Probability Index (MERMQ) showed that some aerosol samples from the landfill sites have a high probability of biological effects. Using data on the chemical composition of soils and soil water, no visible effect of snow pollution on the growth of metal concentrations in soil and soil water was revealed, with the exception of local contamination with Ba, which led to a twofold increase in the content of the metal in the upper soil horizon.

Constructing Ecological Networks for Mountainous Urban Areas Based on Morphological Spatial Pattern Analysis and Minimum Cumulative Resistance Models: A Case Study of Yongtai County

In order to alleviate the increased habitat fragmentation caused by the accelerating urbanization and ecological deterioration, constructing ecological networks is an effective way to improve ecological connectivity, facilitate regional energy flow, and promote biodiversity enhancement. In this study, Yongtai County was taken as the research object, and the morphological spatial pattern analysis (MSPA) method was used to analyze the landscape pattern, identify the ecological source sites, classify the ecological source sites according to the importance degree by possible connectivity index (PC) and the Delta values for probability index of connectivity (dPC), and then construct the potential ecological corridors with the help of the minimum cumulative resistance (MCR) model to generate the ecological network, and then put forward the optimization strategy according to the current situation. The results show that (1) the core area of Yongtai County is 1071.06 km2, the largest among all landscape types, with a fragmented distribution, high degree of fragmentation, and poor connectivity, mainly in the east and southwest, and sparser in the middle. (2) The area of highest resistance value is mainly located in the built-up areas of towns and rural settlements in the central and northwestern parts of the country; the lowest value is distributed in the southwest and southeast, and the land use mode is mainly expressed as woodland. (3) The ecological network consists of 13 ecological sources and 78 potential ecological corridors. The ecological sources are mainly located in the east and southwest, with high connectivity; the potential ecological corridors are distributed in the form of a network, with fewer in the center, resulting in the phenomenon of ecological disconnection. (4) Lack of ecological sources and corridors, serious landscape fragmentation, and optimization of ecological network by adding and protecting ecological sources, repairing ecological breakpoints and building stepping stones. This study is of guiding significance for urban green space system planning, biodiversity protection, and ecosystem function enhancement in Yongtai County, and also provides reference for ecological protection and optimization in other mountainous cities.

Study on mapping method of irrigated cultivated land–taking Nebraska as an example

The distribution information of irrigated cultivated land is important to many studies, including grain yield estimation, water resources management, drought risk assessment, and so on. This study developed a feature variable, called the irrigation probability index (IPI), based on the principle that irrigation can slow down or inhibit the evolution from meteorological drought to agricultural drought. Nebraska, which has a good irrigation information database, was selected as the study area. Combining IPI with the irrigation statistical data and other selected feature variables, irrigated cultivated land in Nebraska was mapped in 2017 using both the spatialization method and remote sensing classification methods (random forest and support vector machine). We verified the effectiveness of IPI in identifying irrigated cultivated land, analyzed the importance of different feature variables for irrigated cultivated land identification, and compared the accuracy of different irrigation mapping methods. The results show that the IPI is significantly correlated with the actual irrigation area, the area of irrigation facilities, and the number of active irrigation wells. It has a better indicative ability of irrigation in areas with a suitable climate or arid areas, than in areas with a humid climate. The crop water stress index (CWSI), IPI, vegetation index (EVI and NDVI), and land surface temperature difference between day and night are the most important feature variables that are used to distinguish irrigated cultivated land from rain-fed cultivated land. There are differences between the recognition accuracies of the three mapping methods in different climatic regions. Overall, the spatialization method had the highest accuracy (overall accuracy = 86.92 %, kappa = 0.74) and the highest similarity with the existing high-resolution irrigation maps. This study provides a direct and useful reference for other researchers to select feature variables and mapping methods in irrigated cultivated land mapping research.

Maximum surgical blood ordering schedule for elective surgical procedures in Omdurman teaching hospital, Sudan

BackgroundThe need for blood during a surgical procedure is greater than what blood banks are able to provide. There is an excessive amount of blood being ordered for elective surgeries, surpassing the actual requirements. Only 30% of the cross matched blood is actually used in these surgeries. The accuracy of estimating the transfusion needs before a surgical procedure can be determined by looking at the cross match to transfusion ratio and the transfusion index. “These indicators play a crucial role in developing the maximum surgical blood ordering schedule; in this study, these indicators were tested.”Aim of studyIs to determine the efficiency of blood ordering and transfusion practices for patients undergoing elective surgeries.MethodsThis study is a prospective cross-sectional hospital-based study done at Omdurman Teaching Hospital-Sudan. Conducted for the duration of 6 months period from July to December 2019.The study participants were patients who underwent elective surgical procedures in general surgery and Urology departments as total coverage sample over a period of study duration. Ethical clearance obtained from ethical committee of Sudan Medical Specialization Board.ResultsTwo hundreds seven patients included in this study, the amount of blood units requested were 443-unit, cross matching for 98.6% (n 437) of units were done. Only 100 unit were Transfused (22,8%). The calculated CT ratio was 4.4, transfusion index was 1.6 and transfusion probability was 29.9%.ConclusionTransfusion probability and transfusion index of the present study were optimal but comparatively higher than the standard guidelines as most of the cross matched blood was not utilized.

Assessment of Liquefaction Potential by Comparing Semi-Empirical Methods Based on the CPT Test

Abstract Liquefaction is a dangerous and temporary phenomenon whereby water-saturated soil loses all or part of its strength. Undrained conditions associated with cyclic loading increase water pressure in soil pores, thereby reducing effective stress. The aim of this study is, on the one hand, to report on the phenomenon of liquefaction of sand, clay and silt deposits in more or less water-saturated zones in the section located at the heart of the central alluvial plain of the Oued Sebou in the mio-plioquaternary Gharb basin and, on the other hand, to study the ability of semi-empirical methods to correctly assess liquefaction potential, while specifying the most appropriate method for the area studied. The study is based on data from experimental results of static penetrometer tests between the Mnasra - Ouelad Salama zone in the Oued Sebou alluvial plain of Morocco's mio-plio-quaternary Gharb basin, made up of sandy, sandy-clay, sandy-silt and silty-sandy formations, which are more sensitive to liquefaction due to their saturation and grain size. We present and discuss the results of Olsen's method, Juang's method and Robertson's method, which are based on the CPT static penetrometer test, as well as looking at the impact of dynamic loading and soil structure on liquefaction probability index values.

Clinical validation of a blood-based liquid biopsy test integrating cell-free DNA quantification and next-generation sequencing for cancer screening in dogs.

To validate the performance of a novel, integrated test for canine cancer screening that combines cell-free DNA quantification with next-generation sequencing (NGS) analysis. Retrospective data from a total of 1,947 cancer-diagnosed and presumably cancer-free dogs were used to validate test performance for the detection of 7 predefined cancer types (lymphoma, hemangiosarcoma, osteosarcoma, leukemia, histiocytic sarcoma, primary lung tumors, and urothelial carcinoma), using independent training and testing sets. Cell-free DNA quantification data from all samples were analyzed using a proprietary machine learning algorithm to determine a Cancer Probability Index (High, Moderate, or Low). High and Low Probability of Cancer were final result classifications. Moderate cases were additionally analyzed by NGS to arrive at a final classification of High Probability of Cancer (Cancer Signal Detected) or Low Probability of Cancer (Cancer Signal Not Detected). Of the 595 dogs in the testing set, 89% (n = 530) received a High or Low Probability result based on the machine learning algorithm; 11% (65) were Moderate Probability, and NGS results were used to assign a final classification. Overall, 87 of 122 dogs with the 7 predefined cancer types were classified as High Probability and 467 of 473 presumably cancer-free dogs were classified as Low Probability, corresponding to a sensitivity of 71.3% for the predefined cancer types at a specificity of 98.7%. This integrated test offers a novel option to screen for cancer types that may be difficult to detect by physical examination at a dog's wellness visit.

Environmental Suppression Mediates the Relationship between Posttraumatic Stress and Cannabis Use Among Trauma-Exposed College Students.

Trauma exposure and posttraumatic stress (PTS) are prominent risk factors for problematic substance use, but little research has evaluated the mechanisms that link PTS and substance use. Emerging research supports the utility of reward probability (i.e., access to and pleasure experienced in response to environmental reward) as a mediator of the relationship between trauma and alcohol use problems. However, no existing studies have examined whether reward probability mediates the link between posttraumatic stress (PTS) and cannabis use in trauma exposed individuals. In a cross-sectional design, undergraduate students (N = 404) anonymously completed online measures, the Reward Probability Index (RPI; Carvalho et al., 2011), the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL5; Blevins et al., 2015), and the Cannabis Use Disorder Identification Test - Revised (CUDIT-R; Adamson et al., 2010), to satisfy course requirements. A parallel mediation path analysis model was used to test the hypothesis that RPI scales (Reward Probability, RP; Environmental Suppression, ES) would mediate the relationship between PTS and cannabis misuse. Findings revealed that RPI-ES fully mediated the relationship between PTS and cannabis misuse, whereas RPI-RP did not. Results support the utility of the RPI-ES scale as a transdiagnostic risk factor for both PTS and cannabis use and suggest further investigation into treatment approaches that optimize opportunities for (non-substance) rewarding experiences in the treatment of co-occurring PTS and substance misuse.