Complex Laboratory Tests Research Articles

Use of machine learning models to predict the water penetration depth in concrete

Machine learning (ML) is a robust tool within the artificial intelligence domain that offers unique solutions for predictive modeling. Prediction of water penetration depth (Wpen) is crucial for assessing the durability and service life of concrete while reducing reliance on complex and time-consuming laboratory tests. This study investigates the impact of concrete composition, age, and compressive strength on Wpen using a dataset of 311 concrete specimens. Multiple supervised ML models were employed in predicting Wpen, including linear regression (LR), Gaussian process regression (GPR), support vector machine (SVM), random forest (RF), regression tree (RT), and hybrid RF-SVM models. Results revealed that hybrid RF-SVM model and regression tree accurately predicted Wpen. The models’ performance improved by including concrete age and compressive strength. The models were validated using data from relevant literature. This research provides valuable insights into predicting water penetration depth in concrete, offers practical tools for assessing concrete durability, and offers a more sustainable approach than laboratory testing.

Toward Sound Localization Testing in Virtual Reality to Aid in the Screening of Auditory Processing Disorders.

Sound localization testing is key for comprehensive hearing evaluations, particularly in cases of suspected auditory processing disorders. However, sound localization is not commonly assessed in clinical practice, likely due to the complexity and size of conventional measurement systems, which require semicircular loudspeaker arrays in large and acoustically treated rooms. To address this issue, we investigated the feasibility of testing sound localization in virtual reality (VR). Previous research has shown that virtualization can lead to an increase in localization blur. To measure these effects, we conducted a study with a group of normal-hearing adults, comparing sound localization performance in different augmented reality and VR scenarios. We started with a conventional loudspeaker-based measurement setup and gradually moved to a virtual audiovisual environment, testing sound localization in each scenario using a within-participant design. The loudspeaker-based experiment yielded results comparable to those reported in the literature, and the results of the virtual localization test provided new insights into localization performance in state-of-the-art VR environments. By comparing localization performance between the loudspeaker-based and virtual conditions, we were able to estimate the increase in localization blur induced by virtualization relative to a conventional test setup. Notably, our study provides the first proxy normative cutoff values for sound localization testing in VR. As an outlook, we discuss the potential of a VR-based sound localization test as a suitable, accessible, and portable alternative to conventional setups and how it could serve as a time- and resource-saving prescreening tool to avoid unnecessarily extensive and complex laboratory testing.

Evaluation of the Correlation Between Nasal Secretion ECP-MPO Test Papers and Immune Markers in Subcutaneous Immunotherapy of Dust Mites.

Up to now, there is no generally accepted biomarker to indicate the clinical response of immunotherapy. This study mainly analyzed the correlation between eosinophil cationic protein-myeloperoxidase (ECP-MPO) test papers and other immunotherapy indices in subcutaneous immunotherapy of dust mites and to explore whether the test paper can be used as an auxiliary index to quickly evaluate the efficacy of immunotherapy. This study included 53 participants who received subcutaneous immunotherapy at the allergy clinic of Renmin Hospital of Wuhan University and 28 control participants. Six visits were conducted during a prospective study over one year. The results of the ECP-MPO test paper, nasal secretion eosinophil smear and count, nasal secretion ECP concentration, and clinical symptom scores were collected during five follow-up visits after the start of subcutaneous immunotherapy. Th1/Th2/Th17 cytokines, chemokines, IgE, IgG4 against dust mite components, and ECP concentrations were detected in the serum of participants at baseline, six months, and one year after subcutaneous immunotherapy. The ECP test paper is not only easy to operate, but also can effectively and quickly detect the concentrations of ECP in the nasal secretion and diagnose allergic rhinitis. Symptom score is an important index for evaluating clinical immune efficacy, during subcutaneous immunotherapy, the ECP test paper showed a positive correlation with the symptom score. Simultaneously, during immunotherapy, the changes in the chromogenic grading of the test paper were synchronized with the changes in inflammatory cytokines and eosinophilic chemokines in Th2 cells of serum dust mite IgE. The sIgG4 against dust mites weakly negatively correlated with the concentration of ECP in nasal secretions and the color classification of the ECP test paper. The ECP-MPO test paper has a certain correlation with subcutaneous immunotherapy markers of allergic rhinitis, indicating that the ECP test paper may become an auxiliary biomarker to replace other complex laboratory tests.

Relevance of a Customized Version of ChatGPT Explaining Laboratory Test Results in Patient Education.

ChatGPT is an artificial intelligence (AI) chatbot application. In this study, we explore the creation and use of a customized version of ChatGPT designed specifically for patient education, called "Lab Explainer." Lab Explainer aims to simplify and clarify the results of complex laboratory tests for patients, using the sophisticated capabilities of AI in natural language processing; it analyses various laboratory test data and provides clear explanations and contextual information. The approach involved adapting OpenAI's ChatGPT model specifically to analyze laboratory test data. The results suggest that Lab Explainer has the potential to improve understanding by providing an interpretation of laboratory tests to the patient. In conclusion, the Lab Explainer can assist patient education by providing intelligible interpretations of laboratory tests.

Estimating the initial fracture energy of concrete using various machine learning techniques

The assessment of the energy required for crack propagation in concrete structures has been fascinating since fracture mechanics was applied to concrete. In the case of concrete, considered a quasi-brittle material, the fracture energy has proven to be a crucial factor in the reliable design of structures and modeling failure behavior. However, due to the complex, time-consuming, and expensive laboratory tests, there has been ongoing and intense debate regarding the methods to estimate the fracture energy of concrete. The advent of machine learning (ML) methods in this domain can hold great promise for resolving such issues once and for all. This study used a comprehensive analysis of twelve ML algorithms for estimating the initial fracture energy of concrete (IFEC), utilizing a more extensive and diverse database (500 data points) than previous studies. The performance of the ML models was evaluated using several metrics, such as coefficient of determination (R2) and variance accounted for (VAF). The findings revealed that all the ML models employed in this study demonstrate remarkable accuracy in estimating the IFEC value, with R2 and VAF values of more than 0.86 and 93.10 %, respectively. A ranking of the models based on their estimation accuracy was provided, facilitating the selection of the support vector regression (R2 = 0.9897; VAF = 99.50 %) and long-short-term memory (R2 = 0.9804; VAF = 99.00 %) methods as the most reliable models for IFEC estimation. Both the laboratory test and ML models presented the highest IFEC value for a water-to-cement ratio of 0.35. Additionally, by increasing the values of each of the parameter's maximum size of aggregates (from 7 mm to 35 mm) and the specimen’s age (from 3 days to 180 days), the IFEC value was increased by about 100 %. Notably, a user-friendly software based on the ML models was developed, enabling fast and highly accurate estimation of IFEC, thereby eliminating the need for time-consuming and expensive laboratory tests.

Plasma proteomic profiles predict individual future health risk.

Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.

Calculation Method for Traffic Load-Induced Permanent Deformation in Soils under Flexible Pavements

Rutting is one of the most common types of distress in flexible pavement structures. There are two fundamental methods of designing pavement structures: conventional empirical methods and analytical approaches. Many analytical and empirical design procedures assume that rutting is mostly of asphalt origin and can be reduced by limiting the vertical deformation or stress at the top of the subgrade, but they do not quantify the rutting depth itself. Mechanistic–empirical models to predict the permanent deformations of unbound pavement layers have been well investigated and are rather common in North America; however, they are not widely utilized in the rest of the world. To date, there is no generally accepted, widely recognized, and documented procedure for calculating permanent deformations and thus for determining the rutting depth in flexible pavement courses originating from the unbound granular layers. This paper presents a layered calculation method with which the deformation of soil layers (base, subbase, and subgrade courses) under flexible pavements due to repeated traffic load can be determined. In the first step, the cyclic strain amplitude is calculated using a nonlinear material model that is based on particle size distribution parameters (d50 and CU) and dependent on the mean normal stress, relative density, and actual strain level. In the second step, the HCA (High Cycle Accumulation) model is used to calculate the residual settlement of each sublayer as a function of the number of cycles. It is shown that the developed model is suitable for describing different types of subgrades and pavement cross-sections. It is also demonstrated with finite element calculations that the developed model describes both the elastic and plastic strains sufficiently accurately. The developed model can predict the settlement and rutting of pavement structures with sufficient accuracy based on easily available particle size distribution parameters without the need for complex laboratory and finite element tests.

Gel-Forming Soil Conditioners of Combined Action: Laboratory Tests for Functionality and Stability.

The research analyzes technological properties and stability of innovative gel-forming polymeric materials for complex soil conditioning. These materials combine improvements in the water retention, dispersity, hydraulic properties, anti-erosion and anti-pathogenic protection of the soil along with a high resistance to negative environmental factors (osmotic stress, compression in the pores, microbial biodegradation). Laboratory analysis was based on an original system of instrumental methods, new mathematical models, and the criteria and gradations of the quality of gels and their compositions with mineral soil substrates. The new materials have a technologically optimal degree of swelling (200-600 kg/kg in pure water and saline solutions with 1-3 g/L TDS), high values of surface energy (>130 kJ/kg), specific surface area (>600 m2/g), threshold of gel collapse (>80 mmol/L), half-life (>5 years), and a powerful fungicidal effect (EC50 biocides doses of 10-60 ppm). Due to these properties, the new gel-forming materials, in small doses of 0.1-0.3% increased the water retention and dispersity of sandy substrates to the level of loams, reduced the saturated hydraulic conductivity 20-140 times, suppressed the evaporation 2-4 times, and formed a windproof soil crust (strength up to 100 kPa). These new methodological developments and recommendations are useful for the complex laboratory testing of hydrogels in small (5-10 g) soil samples.

The role of anthropometric measurements in identifying cardiometabolic diseases in obstructive sleep apnea syndrome.

Anthropometric indices continue to be important measurements especially in predicting obesity and various diseases it causes. The fact that these measurements are very simple to apply and provide clinicians with important information especially in identifying cardiometabolic diseases cannot be overlooked. The use of these indices without the need for complex measurements or laboratory tests gives rapid results on the risks of chronic diseases. Obesity is an important risk factor in obstructive sleep apnea syndrome, and anthropometric measurements showing body fat distribution are of great importance. In addition to the diagnosis of the disease, it is extremely important that the measurements reflecting obesity which is directly related to weight should not be ignored in predicting additional cardiometabolic diseases in this patient group. An ever-increasing variety of anthropometric measurements that reflect abdominal obesity with objective data enables the diagnosis of the disease as well as the determination of cardiometabolic risks of patients with asymptomatic sleep apnea syndrome.

Seroprevalence of Bovine Leptospirosis using Indirect ELISA

Background: Leptospirosis is a zoonotic bacterial disease causing abortion, stillbirths and loss of milk production in livestock resulting in huge economic losses. Leptospirosis in cattle has been under-reported and under-diagnosed in India due to non-specific symptoms, complex laboratory tests, fastidious nature of bacterium and lack of proper diagnostic facilities. Scanty data pertaining to epidemiological status of leptospirosis is available in land locked states of India including Madhya Pradesh, thus to fulfil the gap, the current work was conducted to investigate seroprevalence of leptospirosis in cattle. Methods: In the present study, cattle with a history of repeat breeding, abortion, haemogalactia and mastitis, were screened from various organized and unorganized farms of Jabalpur. A total of 300 blood samples were collected from suspected cattle and serum was separated. Enzyme linked Immune-sorbent assay was done as per the guidelines. Result: The seroprevalence of leptospirosis among suspected cases was 48.67 per cent, it was significantly higher in organized sector in cattle with the history of abortion followed by repeat breeding. High seropositivity was reported in older cattle.

Clinical Decision Support for Laboratory Testing.

As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging. We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation. CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.

Chagas Immunochromatographic Rapid Test in the Serological Diagnosis of Trypanosoma cruzi Infection in Wild and Domestic Canids.

Canis lupus familiaris (domestic dog) represents a reliable sentinel for the occurrence of a well-established transmission cycle of Trypanosoma cruzi among wild mammals in the surroundings and, consequently, where the risk of human infection exists. Serological diagnosis is the chosen method to identify T. cruzi infection in dogs that, in Brazil, rarely present positive parasitological tests. The use of recombinant chimeric parasitic antigens results in a sensitive and specific serological diagnostic test in contrast to the use of crude T. cruzi antigens. Our objective was to evaluate the Chagas/Bio-Manguinhos Lateral Flow Immunochromatographic Rapid Test (Chagas-LFRT) for the diagnosis of T. cruzi infection in domestic dogs and the potential of application of this diagnostic platform to wild canid species. Two recombinant proteins (IBMP-8.1 and IBMP-8.4) that displayed the best performance in the enzyme immunoassay (ELISA) in previous studies were tested in a platform with two diagnostic bands. A panel of 281 dog serum samples was evaluated: 133 positive for T. cruzi by serological diagnosis, including 20 samples with positive blood cultures belonging to different discrete typing units (DTUs); 129 negative samples; and 19 samples from dogs infected by other trypanosomatids: Leishmania infantum, Trypanosoma rangeli, Trypanosoma caninum and Crithidia mellificae, in addition to samples infected by Anaplasma platys, Dirofilaria immitis and Erlichia sp. that were employed to evaluate eventual cross-reactions. We also evaluated the Chagas-LFRT to detect T. cruzi infection in 9 serum samples from six wild canid species. We observed that the intensity pattern of the bands was directly proportional to the serological titer observed in IFAT. The sensitivity was 94%, the specificity was 91% according to the ROC curve, and the defined cutoff was an optical density of 4.8. The agreement obtained was considered substantial by the kappa analysis (84%). From T. cruzi positive hemoculture samples, 88.9% were positive by Chagas-LFRT. The test was efficient in recognizing infections by five of the six T. cruzi DTUs. Cross-reactions were not observed in infections by L. infantum, T. rangeli, T. caninum and D. immitis; however, they were observed in sera of dogs infected by Crithidia mellificae, Anaplasma sp. and Erlichia sp. A strong reaction was observed when serum samples from wild canids were submitted to the Protein A affinity test, confirming its applicability for these species. This test will allow rapid preventive actions in areas with high risk to the emergence of Chagas disease in a safer, reliable, low-cost and immediate manner, without the need for more complex laboratory tests.

Error evaluation in the laboratory testing process and laboratory information systems.

BackgroundThe laboratory testing process consist of five analysis phases featuring the total testing process framework. Activities in laboratory process, including those of testing are error-prone and affect the use of laboratory information systems. This study seeks to identify error factors related to system use and the first and last phases of the laboratory testing process using a proposed framework known as total testing process-laboratory information systems.MethodsWe conducted a qualitative case study evaluation in two private hospitals and a medical laboratory. We collected data using interviews, observations, and document analysis methods involving physicians, nurses, an information technology officer, and the laboratory staff. We employed the proposed framework and Lean problem solving tools namely Value Stream Mapping and A3 for data analysis.ResultsErrors in laboratory information systems and the laboratory testing process were attributed to failure to fulfill user requirements, poor cooperation between the information technology unit and laboratory, inconsistency of software design in system integration, errors during inter-system data transmission, and lack of motivation in system use. The error factors are related to system development elements, namely, latent failures that considerably affected the information quality and system use. Errors in system development were also attributed to poor service quality.ConclusionsComplex laboratory testing process and laboratory information systems require rigorous evaluation in minimizing errors and ensuring patient safety. The proposed framework and Lean approach are applicable for evaluating the laboratory testing process and laboratory information systems in a rigorous, comprehensive, and structured manner.

A voltage dynamic-based state of charge estimation method for batteries storage systems

In recent years, the use of Lithium-ion batteries in smart power systems and hybrid/electric vehicles has become increasingly popular since they provide a flexible and cost-effective way to store and deliver power. Their full integration into more complex systems requires an accurate estimate of the energy a battery is currently storing, a.k.a. State of Charge (SoC). However, the standard techniques present in the literature provide an accurate estimation of the SoC only having a priori knowledge about the battery. Moreover, their accuracy degrades if the battery working conditions (e.g., external temperature) are variable over time, or battery measurements necessary for the SoC estimation are affected by offset or gain biases. To overcome these limitations, this paper proposes a novel data-driven optimization based methodology for battery SoC estimation, namely VDB-SE. The proposed methodology provides accurate SoC estimations without knowing battery model parameters, such as capacity and internal resistance, whose characterization would require complex and long laboratory tests. Experimental verification and comparisons demonstrate that VDB-SE performance are comparable to the state-of-the-art algorithms over a wide range of working conditions. Indeed, the difference in terms of performance is smaller than 0.2%. Moreover, experimental results showed that on a real energy storage system the proposed method provides a SoC estimation with an error of less than 2.1%.

PROFESSIONAL ADVANCED EDUCATION - THE BASIS OF QUALITY HIGHER EDUCATION

Education is a process on which the future of a nation depends. In addition, a quality educational process is a necessary condition for higher education, without which education will not be competitive. Teachers, educational technologies, methods, tools (visual aids, textbooks, technical devices, etc.), educational management are involved in the process.
 Education provides general cultural and professionally oriented training of a graduate of the degree of junior specialist, determines the amount of special knowledge, skills and abilities sufficient to solve typical tasks of the specialist in the respective position, including the ability to use theoretical knowledge and practical skills, to perform the technological stages of manufacturing orthopedic prostheses, to provide medical care in emergencies, to assess the impact of dangerous factors on the development of occupational diseases.
 Education provides the activities of a nurse, which includes organizational functions, determining the needs of patients, the ability to prepare patients for complex laboratory tests and instrumental methods of examination, special care and care for patients, participation in treatment and prevention, conduct an educational work in accordance with the principles of nursing ethics and deontology, constant improvement of professional level, practice of practical skills in the departments of hospitals, hospices and family medicine clinics.
 Education provides students with the ability to form a worldview, understanding the principles of society, the ability to think abstractly, analyze and synthesize on the basis of logical arguments and verified facts, consider social phenomena in development and specific historical conditions, lead a discussion, use appropriate terminology and thoughts expression in oral and written forms in the native language.
 Students are taught to use knowledge in the field of socio-economic training, communicate in a foreign language, be ready to work in a foreign language environment using information technology, search for processing and analysis of information from various sources, communicate professionally, provide harmonious and constructive relationships in the performance of professional tasks. At IFNMU Medical College, students learn the ability to interpret and use theoretical knowledge and practical skills on diseases questions, methods of organizing the safety of human life, their protection from the possible consequences of accidents and disasters.
 At the same time, education, and hence its quality, is not limited to learning outcomes. It is also the physical, intellectual and spiritual development of the individual, the level of his upbringing.
 That is why well-known scientists do not have a single opinion on the concept of quality education.
 The quality of education depends on many factors: teaching staff, the level of their skills; resource provision of the educational process; management of the educational process; curricula of educational content and educational and methodological support; effective educational technologies; psychological support of the educational process, as well as from the subjects of education, concerted actions with parents, etc.
 Of course, education will be of good quality if all components interact effectively. The article highlights the features of providing quality professional before higher education, its interrelation with efficiency of preparation of experts in higher educational institutions is defined.

Validation of local p16 testing for determination of human papilloma virus status eligibility on a low risk oropharyngeal cancer trial - A Trans-Tasman Radiation Oncology Group study.

Accurate determination of human papilloma virus (HPV) status is critical when identifying patients with oropharyngeal squamous cell carcinoma (OPSCC) who may be candidates for de-escalation trials. In this study we investigated whether local p16 screening, by immunohistochemistry (IHC), has high positive predictive value (PPV) for HPV status in a good prognosis HPV positive OPSCC (HPVOPSCC) population treated on a clinical trial. Patients enrolled on the TROG 12.01 randomised trial for good prognosis HPVOPSCC were randomised based on local p16 IHC testing but subsequently had central p16 IHC and HPV RNA in situ hybridisation (HPV RNA ISH) testing. Correlations between the local and central p16 and central HPV RNA ISH were studied. The main outcome was the positive predictive value (PPV) of local pathology laboratory testing of p16. 176/182 patients had samples available for central testing. 172/176 were evaluable for central testing of p16, and all were confirmed to be p16 positive (172/172, 100%, 95% CI=[97.9%, 100%]). Similarly, 100% of those evaluable for HPV RNA ISH (155/155, 100%, 95% CI=[97.6%, 100%]) were confirmed HPV positive, indicating p16 overexpression driven by transcriptionally active HPV and a PPV of 100% for local p16 testing. Our results validate the suitability of local pathology laboratory p16 testing alone, in populations with a high attributable fraction of OPSCC due to HPV, to screen and enrol low risk HPVOPSCC patients onto de-intensification trials. This obviates the need for upfront more complex and expensive HPV assays and/or central laboratory testing.

Optimal target in septic shock resuscitation.

Septic shock presents a high risk of morbidity and mortality. Through therapeutic strategies, such as fluid administration and vasoactive agents, clinicians intend to rapidly restore tissue perfusion. Nonetheless, these interventions have narrow therapeutic margins. Adequate perfusion monitoring is paramount to avoid progressive hypoperfusion or detrimental over-resuscitation. During early stages of septic shock, macrohemodynamic derangements, such as hypovolemia and decreased cardiac output (CO) tend to predominate. However, during late septic shock, endothelial and coagulation dysfunction induce severe alterations of the microcirculation, making it more difficult to achieve tissue reperfusion. Multiple perfusion variables have been described in the literature, from bedside clinical examination to complex laboratory tests. Moreover, all of them present inherent flaws and limitations. After the ANDROMEDA-SHOCK trial, there is evidence that capillary refill time (CRT) is an interesting resuscitation target, due to its rapid kinetics and correlation with deep hypoperfusion markers. New concepts such as hemodynamic coherence and flow responsiveness may be used at the bedside to select the best treatment strategies at any time-point. A multimodal perfusion monitoring and an integrated analysis with macrohemodynamic parameters is mandatory to optimize the resuscitation of septic shock patients.

Investigating mechanical characteristics of aggregate structure for road materials

ABSTRACT Aggregate structure plays a significant role in the pavement performance of road materials. However, there is a lack of fundamental understanding of mechanical characteristics and evolution characteristics of the aggregate structure in the existing research. To understand this complex structure, laboratory tests and numerical simulations using the discrete element method were used to investigate macroscopic mechanical properties of aggregate structure and microscopic aggregate contact forces, thus revealing the mechanical role of each size aggregate and evolution characteristics of the aggregate structure. Based on this, the specification gradations of road materials were optimised to build a strong aggregate structure. Laboratory tests and engineering application show that road materials using the proposed gradation can exhibit improved pavement performance and engineering effects.

Estimating the viscoelastic properties of local hot-mix asphalt

The mechanistic empirical method for designing flexible pavement requires determination of the strain that results from applying wheel load. To achieve this, the viscoelastic properties of asphalt concrete is required to be characterised numerically in order to permit simulating such asphalt concrete in finite element modelling for layered systems of flexible pavement. Accordingly, the estimation of the viscoelastic properties of local hot mix asphalt (HMA) was the target of this study. This was achieved using the interconversion process, with the master curve of shear modulus estimated from the master curves of dynamic modulus and phase angle developed within the Hirsch model by substituting the binder dynamic and volumetric properties of local Hot-Mix Asphalt (HMA) into the model. The master curve of shear modulus was constructed with aid of the interconversion process from the creep compliance resulting from Indirect Tensile Tests (ITT) under different testing conditions. The calculated master curve of shear modulus was then used to validate the estimated results. The validation revealed that the estimation method offered good ability to determine the properties required. Numeric values, known as the Prony series, used to characterise asphaltic pavement layers numerically, were established by fitting the normalised master curve of shear modulus. The research results showed that this estimation method is an appropriate way to determine the viscoelastic properties of HMA as opposed to using costly and complex laboratory tests.

Mean platelet volume as a predictor of mortality in patients with severe hospital pneumonia

Background. Hospital-Acquired Pneumonia is one of the leading cause of death among the ICU patients. The most important questions related to treatment of patients with Pneumonia are determination of prognostic factors, the severity criteria and diagnostic markers of disease duration. Platelets, as basic cells of hemostasis play a signi­ficant role in inflammation and immunity as a trigger in pathogenesis of many diseases. Mean platelet volume (MPV) reflects the severity of illness’s outcome and might have and prognostic value. The aim of the study — to determinate the dynamic of MPV assessment in patients with Hospital-Acquired Pneumonia and its prognostic value. Materials and methods. A 45 patients with confirmed diagnosis of Hospital-Acquired Pneumonia with average hospital duration of 16 days (7–32) were divided in two groups according to the outcome of illness. Group 1 included 27 of survived patients, while group 2 consisted of 18 deceased patients. The complex laboratory tests were performed in order to assess the coagulation system. The Complete Blood Count panel and determination of morphological characteristics of platelets (MPV, platelet large cell ratio (P-LCR), platelet distribution width (PDW)). Results. The increase of MPV registered in first days of disease has been noted in both groups. Although in repeated measurements shown that MPV was more increased in group of deceased patients and it has been high throughout all period of hospitalisation. Conclusions. MPV reflects the activity of platelets and most probably it has a prognostic value. The increase of MPV (as the Δ in MPV level at admission and discharge from hospital) might been related to worsen of outcome in patients with Hospital-Acquired pneumonia.