Natalia Marek-Trzonkowska and Piotr Trzonkowski of the Medical University of Gdańsk talk about trust, coordination, and creative conflicts – in the first of a series of interviews with scientists who are partners both at work and in life.

In this work, a real-time label-free microwave sensing mechanism for glucose concentration monitoring using a planar biosensor configured with an inset fed microstrip patch antenna has been demonstrated. A microstrip patch antenna with the resonating frequency of 1.45 GHz has been designed and is fabricated on the Flame Retardant (FR-4) substrate. Due to the intense electromagnetic field at the edges of the patch antenna, edge length has been used as the detecting area where the sample under test (SUT) interacts with the electromagnetic field. The Poly-Dimethyl-Siloxane (PDMS) with the trench in the centre has been employed as the sample holder. Here, the SUT is the glucose dissolved in DI (de-ionized) water with the concentration range of 0.2 to 0.6 g/mL. The dielectric constant dependency on the glucose concentration has been used as the distinguishing factor which results in a shift in the S-parameter. The experimentally measured RF parameters were observed closely which showed the shift in S11 magnitude from –40 to –15 dB and resonant frequency from 1.27 to 1.3 GHz w.r.t the SUT solution of 0.2 to 0.6 g/mL with linear regression coefficient of 0.881, and 0.983 respectively.

Glucose concentration measurement is essential for diagnosis, monitoring and treatment of various medical conditions like diabetes mellitus, hypoglycemia, etc. This paper presents a novel image-processing and machine learning based approach for glucose concentration measurement. Experimentation based on Glucose oxidase - peroxidase (GOD/POD) method has been performed to create the database. Glucose in the sample reacts with the reagent wherein the concentration of glucose is detected using colorimetric principle. Colour intensity thus produced, is proportional to the glucose concentration and varies at different levels. Existing clinical chemistry analyzers use spectrophotometry to estimate the glucose level of the sample. Instead, this developed system uses simplified hardware arrangement and estimates glucose concentration by capturing the image of the sample. After further processing, its Saturation (S) and Luminance (Y) values are extracted from the captured image. Linear regression based machine learning algorithm is used for training the dataset consists of saturation and luminance values of images at different concentration levels. Integration of machine learning provides the benefit of improved accuracy and predictability in determining glucose level. The detection of glucose concentrations in the range of 10–400 mg/dl has been evaluated. The results of the developed system were verified with the currently used spectrophotometry based Trace40 clinical chemistry analyzer. The deviation of the estimated values from the actual values was found to be around 2- 3%.

This study aimed to determine the levels of milk cell total protein (TP), reduced nicotinamide adenine dinucleotide phosphate (NADPH), total glutathione (tGSH), activities of glucose-6-phos- phate dehydrogenase (G6PD) and glutathione peroxidase (GPx) in subclinical mastitic cows. Milk from each udder was collected and grouped by the California Mastitis Test. Then, a somatic cell count (SCC) was performed, and the groups were re-scored as control (5–87 × 103 cells), 1st group (154–381 × 103 cells), 2nd group (418–851 × 103 cells), 3rd group (914–1958 × 103 cells), and 4th group (2275–8528 × 103 cells). Milk cell TP, NADPH, tGSH levels, G6PD, and GPx ac- tivities were assessed. Microbiological diagnosis and aerobic mesophyle general organism (AMG, cfu/g) were also conducted. In mastitic milk, TP, NADPH, and tGSH levels, and G6PD and GPx activities were significantly reduced per cell (in samples of 106 cells). In addition, milk SCC was positively correlated with AMG (r=0.561, p<0.001), NADPH (r=0.380, p<0.01), TP (r=0.347, p<0.01) and G6PD (r=0.540, p<0.001). There was also positive correlation between NADPH (r=0.428, p<0.01), TP (r=0.638, p<0.001) and AMG. NADPH was positively correlated with TP (r=0.239, p<0.05), GPx (r=0.265, p<0.05) and G6PD (r=0.248, p=0.056). Total protein was positively correlated with tGSH (r=0.354, p<0.01) and G6PD (r=0.643, p<0.001). There was a negative correlation between tGSH and GPx activity (r=-0.306, p<0.05). The microbiological analysis showed the following ratio of pathogens: Coagulase-Negative Staphylococci 66.6%, Streptococcus spp 9.5%, Bacillus spp 9.5%, yeast 4.8%, and mixed infections 9.5%.

As a conclusion, when evaluating the enzyme and oxidative stress parameters in milk, it is more suitable to assign values based on cell count rather than ml of milk. The linear correlation between the SCC and AMG, milk cell NADPH, TP and G6PD suggests that these parameters could be used as markers of mastitis.

Blood glucose level monitoring and control is of utmost importance to millions of people who have been diagnosed with diabetes or similar illnesses. One of the conventional tests for measuring how the human body breaks down glucose is IVGTT, the Intravenous Glucose Tolerance Test. The difficulty of computing the models of glucose-insulin interaction presents an issue when attempting to implement them in embedded hardware. The Metabolic P (MP), contrary to other models, does not require solving differential equations to compute, thus it could be an effective modelling approach for real-time applications. The present paper proves that MP system methodology-based IVGTT implementation in the Field Programmable Gate Arrays (FPGA) technology is reasonably precise and sufficiently flexible to be used effectively in multi-user scenarios. Presentation of the state-of-the-art focuses on glucose-insulin interaction models, glucose monitoring systems and MP system implementation techniques. Methods for MP system computations and techniques for their implementation on FPGA, together with the original unified MP system implementation technique, have been presented in this paper. The results of an elaborate investigation into the IVGTT MP systems, as well as their single and unified MP implementation techniques have also been considered. It is shown that the techniques developed are applicable to all known IVGTT MP systems, and can achieve RMSE not higher than 15% using a word length of at least 32 bits. The novel MP system combined quality metrics and its pictorial representation allow the analysis of various implementation characteristics. Compared to the unified pipelined IVGTT MP system implementation technique, the developed unified combinational technique ensures a 2‒3 times higher speed.

Prevalence of metabolic syndrome (MetS) and its components is a growing issue, including pediatric populations. However, because of many definitions used, it is difficult to assess the true fre-quency of these problems.
The aim of this study was to assess the prevalence of MetS and its components as well as the frequency of problems with inadequate nutritional status among adolescents.
One hundred ninety-six teenagers aged 15–18 years, living in Krakow and its vicinity were examined including measurement of blood pressure, anthropometric parameters and blood levels of cholesterol and glucose.
The prevalence of MetS was low and varied from 0.5% to 2.0% depending on the definition. Based on Cook’s definition of MetS, the most common components were hypertension (12.8%) and hypertriglycer-idemia (12.8%). Improper body weight (based on BMI) was found in 23.5% of adolescents, including 5.1% underweight, and 18.4% overweight or obese. According to the body fat percentage (BF%), 45.8% of the boys were underfat and 6.3% had too much body fat, while only 4% of the girls were below the BF% reference values and 15% above. All girls and 86.5% of boys had too low total body water. In conclusion, the prevalence of metabolic syndrome in population of Krakow adolescents was relatively low, but more than 12% of the adolescents had a hypertension or hypertriglyceridemia. Based on BMI most of adolescents were found to have adequate body weight, but examination of fat content in the body high prevalence of underfat was observed, especially among boys.

Intensive hypoglycemic treatment is the strongest preventive strategy against the development of microvascular complications of type 2 diabetes (T2DM), including diabetic nephropathy. However, some antidiabetic drugs, i.e. sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA) have an additional renoprotective effect beyond glucose control by itself. Similar, both SGLT-2i and GLP1-RA have been demonstrated to decrease the risk of adverse cardiovascular (CV) events in CV outcome trials. Nevertheless, there are relevant differences in CV and renal effects of SGLT-2i and GLP1-RA. First, SGLT2i reduced the incidence and progression of albuminuria and prevented loss of kidney function, while predominant renal benefits of GLP1-RA were driven by albuminuria outcomes. Second, the risk of heart failure (HF) hospitalizations decreased on SGLT2i but not on GLP1-RA, which gives priority to SGLT2i in T2DM and HF, especially with depressed EF. Third, either GLP1-RA (reducing predominantly atherosclerosis-dependent events) or SGLT-2i, should be used in T2DM and established atherosclerotic CV disease (ASCVD) or other indicators of high CV risk. In this review, we have briefly compared clinical practice guidelines of the American Diabetes Association (2020 and 2021 versions), Polish Diabetes Association (2020) and the European Society of Cardiology/European Association for the Study of Diabetes (2019), with a focus on the choice between SGLT-2i and GLP1-RA in patients with diabetic kidney disease.