Background: Biochemistry is the science of the chemical composition of living things and of chemical changes in living things. Biochemical–laboratory diagnostics occupy a prominent place in medicine. Today's knowledge in the field of laboratory diagnostics enables reliable diagnostic verification of the physiological and pathological condition of the subject and monitoring of the patient's therapy. Objective: The aim of this article is to look at the economic and communication aspect of laboratory diagnostics in family medicine and present some statistically relevant data related to the already mentioned topic. Methods: Author used a few important sost analysis to assess every diagnostic and therapeutic procedure which should be analyzed from the aspect of its profitability, i.e. To determine their effectiveness and safety of application as stated in the Accreditation Standards for Health Centers. Results: A total of 5333 laboratory tests are represented in 1000 requests. The percentage representation of the most frequent individual laboratory tests in the requests of all teams of doctors involved in the health care system was in order; GUK (14%), BS (14%), urine (13.9%), SE (10.3%), total cholesterol (8.5%), triglycerides (8.4%), aminotransferases (6.7 %), creatinine (6.7%), urea (4.8%), bilirubin (0.9%), fibrinogen (0.9%), CRP (0.8%), AF (0.8%), HDL cholesterol (0.7%), calcium in serum (0.6%), phosphorus in serum (0.5%), acidum uricum (0.5%). Of the general practitioners, the largest number of patients referred to the biochemical and hematology laboratory were diagnosed with diabetes, followed by diseases of the urinary system and hypertension. The same is the case with family medicine doctors, while from specialist doctors, the largest number of patients are sent to the biochemical and hematology laboratory with diseases of the urinary tract, followed by diseases of the respiratory tract, endocrinological system and anemia. Conclusion: An economic analysis of the number of required laboratory tests by disease indicates a different number of points per required test and by disease. The highest costs are related to diabetes, followed by the costs of respiratory diseases, urinary diseases and finally hypertension.

Meat inspection is an important part of education for every veterinary student. However, traditional teaching methods require the sacrifice of living animals, and are thus considered expensive, inadequate and inhumane. Development of novel technologies has provided opportunities for new, improved ways of education. Smart 3D Meat Inspection (S3DMI) is an elearning tool that allows veterinary medicine students to acquire required skills using virtual 3D models of animal organs and carcasses. These models can be manipulated and “cut” just like real organs, allowing students to learn this essential skill without the need for animal carcasses. Students are allowed to practice any part of meat inspection as many times necessary, at their own pace, without time, place or resources limitations. This type of education is considered superior to traditional methods. There is no need for sacrification of animals for educational purposes and the cost of education is greatly reduced, while the educational quality is uninterrupted. Models developed for S3DMI can also be adjusted for courses like animal anatomy and pathology, which also require the use of real animal cadavers. S3DMI is still in its developmental stages, but it has a great potential to minimalize the need for animal sacrifice in the education of future veterinarians, while ensuring the quality improvement.

— Cause-effect graphing is a commonly used black-box technique with many applications in practice. It is important to be able to create accurate cause-effect graph specifications from system requirements before converting them to test case tables used for black-box testing. In this paper, a new graphical software tool for creating cause-effect graph specifications is presented. The tool uses standardized graphical notation for describing different types of nodes, logical relations and constraints, resulting in a visual representation of the desired cause-effect graph which can be exported for later usage and imported in the tool. The purpose of this work is to make the cause-effect graph specification process easier for users in order to solve some of the problems which arise due to the insufficient amount of understanding of cause-effect graph elements. The proposed tool was successfully used for creating cause-effect graph specifications for small, medium and large graphs. It was also successfully used for performing different types of tasks by users without any prior knowledge of the functionalities of the tool, indicating that the tool is easy to use, helpful and intuitive. The results indicate that the usage of standardized notation is easier to understand than non-standardized approaches from other tools.

Cause-effect graphs are a popular black-box testing technique. The most commonly used approach for generating test cases from cause-effect graph specifications uses backward-propagation of forced effect activations through the graph in order to get the values of causes for the desired test case. Many drawbacks have been identified when using this approach for different testing requirements. Several algorithms for automatically generating test case suites from cause-effect graph specifications have been proposed. However, many of these algorithms do not solve the main drawbacks of the initial back-propagation approach and offer only minor improvements for specific purposes. This work proposes two new algorithms for deriving test cases from cause-effect graph representations. Forward-propagation of cause values is used for generating the full feasible test case suite, whereas multiple effect activations are taken into account for reducing the feasible test case suite size. Evaluation of the test case suites generated by using the proposed algorithms was performed by using the newly introduced test effect coverage and fault detection rate effectiveness metrics. The evaluation shows that the proposed algorithms work in real time even for a very large number of cause nodes. The results also indicate that the proposed algorithm for generating all feasible test cases generates a larger test case suite, whereas the proposed algorithm for test case suite minimization generates a smaller test case subset than the originally proposed approaches while ensuring the maximum effect coverage, fault detection rate effectiveness and a better test effect coverage ratio.

This review mainly focuses on nanoparticle-based drug delivery systems fabricated from plants (starch, cellulose, pectin), animals (chitosan, gelatin) and microorganisms (dextran). Herein, the focus is on the physical-chemical properties of biopolymers and its derivatives and the mechanism of action in the treatments of cancer. Nanoparticle-based drug delivery systems improved efficacy by: increasing half-life of vulnerable drugs and proteins, improving the solubility of hydrophobic drugs, and allowing controlled and targeted release of drugs in diseased site. Of all the mentioned biopolymers, only dextran and pure pectin are problematic. Some clinical studies have shown unexpected side effects caused by dextran such as thrombocytopenia and hepatotoxicity and, pure pectin-based materials, undesirable swelling and corrosion properties. Doxorubicin has been used in combination with almost all of these biopolymers because it is widely used as an effective chemotherapeutic agent in the treatment of many types of solid tumors of the breast, lung, colon, ovary, prostate and bladder.

Recently, nanotechnology is widely used in agriculture with the aim of achieving high agricultural yields. Due to the unique surface and physicochemical properties, nanomaterials can be used to deliver nutrients to plants via nanoparticles, for the synthesis of nanopesticides, nanofungicides, and to design nanosensors for the detection of very low concentrations of pesticides and other contaminants. Excessive use of pesticides and fertilizers causes the loss of soil biodiversity and the development of resistance to pathogens. Nenoencapsulation of fertilizers, pesticides and herbicides is used for slow and specific dosed release of nutrients as well as agrochemicals. This paper discusses the applications of nanotechnology and their positive effect in agriculture in relation to the common methods used so far.

Aim: The chief aim of this study was monitoring of laboratory parameters of chronic kidney failure in elderly patients. Methods: All samples were taken and processed by standard methods and according to the principles of good laboratory practice. Data were collected in an organized and systematic manner in the form of a questionnaire with respect to ethical principles and as such were analyzed by statistical tests and analyses (Student's t-test, Analysis of variance-ANOVA, Pearson's and Spearman's correlation coefficients). The limit of statistical significance was set at p < 0.05. Results: Mean values of creatinine clearance and proteinuria for the total study population were: 41.30 ± 21.43 mL/min, 1.5 ± 2.3 g/L/24 h, respectively. Hematological parameters did not significantly differ from normal values. The highest frequency of comorbidities was observed in subjects aged ≥ 80 years with an average of 2.03 comorbidities per subject. Serum creatinine and urea values as well as creatinine clearance are good indicators of disease progression. Conclusion: The results of the presented research suggest that old age is a predisposing risk factor for the development of chronic kidney disease, and that in combination with comorbidities (hypertension and/or diabetes), it contributes to poor prognosis or disease progression.