The transformation of clinical laboratories towards digitalization requires processes that improve digital maturity. This requires establishing connectivity, end-to-end workflow, and advanced analytical technologies and techniques. Digital technologies have the key role here, directing laboratory personnel and scientists to move their focus from routine to more complex and meaningful work. This requires their empowerment in working with new instruments and software. Strategies leading clinical laboratories through this transformation are not without challenges, but different models are being developed to overcome them. The essential is the role of interoperability.

Background: Many of the mobile applications (apps) used for delivering health interventions involve laboratory medicine data. This survey was conducted to search the online market for health apps that manage laboratory medicine data. The aim was to review them and perform a quality evaluation. Methods: Apps search criteria were “Lab results blood work”, “Lab results”, and “Health apps”. After the stepwise exclusion process, 52 selected apps were downloaded and analyzed. For review and content analysis of the apps, a multidimensional tool for classifying and rating the quality of mobile health apps – Mobile App Rating Scale (MARS), was used. Results: Selected apps were classified into five categories according to their intended use by patients or physicians, and the type of data engaged. Spearman’s correlation analysis found significant correlations between MARS individual scoring items, as with the subjective quality and number of technical aspects. Kruskal-Wallis analysis showed a significant difference in the number of technical aspects employed, MARS engagement and informational quality score items, total score, and subjective quality. The lowest values for all of these items were in the category of apps designed for patients, and the post hoc test showed that the difference was statistically significant between this and the values in all other categories. Conclusions: Apps designed for patients, are of the poorest quality, considering the total quality of the content and information they provide, estimated using the MARS tool. This estimation needs to be validated for laboratory medicine apps, and eventually modified after consideration of specific quality benchmarks.

Objective: The measurement of pancreatic elastase (PE) in feces is used widely to screen for pancreatic exocrine insufficiency. The aim of our study was to evaluate the relationship of PE with residual beta cell secretion and metabolic control in patients with diabetes mellitus. Method: We determined the presence of PE in specimens via enzyme-linked immunosorbent assay (ELISA), whereas serum fasting glucose, C-peptide, amylase, lipase, triglycerides, total 25(OH)-vitamin D, C-reactive protein (CRP), and hemoglobin A_1c (HbA_1c) concentrations were assayed using routine laboratory tests. Results: PE values in 48 patients with diabetes were significantly lower than in 24 healthy volunteers (P = .001). In one-third of participants with diabetes mellitus, PE were less than 200 µg per g, indicating pancreatic functional insufficiency. Among the patients in the cohort, PE correlated positively with C-peptide levels (P = .04), lipase (P = .009), CRP (P = .04), sex (P = .03), and BMI (P = .02) but not significantly with duration of diabetes (P = .81) or levels of HbA_1c (P = .87), amylase (P = .06), total 25(OH)-vitamin D (P = .16), or triglycerides (P = .52). Conclusion: Our results demonstrated a strong association of diabetes with low PE levels.

Background: Surgical stress and pain result in activation of hypothalamus-pituitary-adrenal axis. The aim of this study was to establish the effects of postoperative pain and various modalities of analgesic administration on salivary and serum cortisol levels, as well as to establish the validity of salivary cortisol as a stress indicator in surgical patients. Methods: A randomized controlled trial involved 60 patients scheduled for elective abdominal aortic aneurysm surgery. Patients were randomly divided into two groups depending on the model of postoperative analgesia. The first group (MI - morphine intermittently) included patients given morphine doses 0.1 mg/kg/6h s.c. intermittently. The second group (MPCA - morphine patient-controlled analgesia) included patients who received morphine via the PCA system - intravenous administration of morphine adjusted to a dose of 1 mg per shot and a lockout interval of 6 minutes. Results: The intensity of pain did not significantly vary until the hour 10 post-surgery. However, in the period from hour 10 to hour 18 post-surgery, higher intensity of pain was reported in group MPCA (P < 0.05). Hemodynamic instability was more prevalent in the MI group (40.0% vs 6.7%, P = 0.0048). Serum cortisol levels were almost identical in both groups (MI 509.4 nmol/L vs MPCA 511.0 nmol/L, P = 0.1473). Higher values of salivary cortisol were recorded in group MPCA; however, the difference was not statistically significant (47.1 nmol/L vs 116.3 nmol/L, P = 0.0970). Conclusion: Our study confirmed that salivary cortisol is a more sensitive stress biomarker in surgical patients as compared to blood cortisol.

This study investigated the longitudinal trajectory of changes in antioxidative and anti-inflammatory high-density lipoprotein (HDL) components during healthy pregnancy and pregnancy with cardiometabolic complications. We recruited and longitudinally followed 84 women with healthy pregnancies and 46 pregnant women who developed cardiometabolic pregnancy complications (gestational diabetes mellitus and hypertensive disorders of pregnancy). Their general lipid profiles, oxidative stress status, inflammatory status, and antioxidative and anti-inflammatory HDL components were analyzed. The results of our study confirmed the expected trajectory for the routine lipid parameters. Our study results indicate more intensive oxidative stress and a higher level of inflammation in the group with complications compared with the control group. Sphingosine-1-phosphate (S1P) was significantly lower in the first trimester in the group with complications compared with the control group (p < 0.05). We did not find significant differences in the apolipoprotein A1 (Apo A1) concentrations in the first trimester between the control group and the group with complications, but in the second and third trimesters, the group with complications had significantly higher concentrations (p < 0.001, p < 0.05, respectively). The S1P, paraoxonase 1 (PON1), and serum amyloid A (SAA) concentrations were significantly lower in the group with complications in the first trimester. During the second trimester, only the SAA concentrations were identified as significantly lower in the group with complications compared with the control group, while in the third trimester, the PON1, apolipoprotein M (Apo M), and SAA concentrations were all significantly lower in the group with complications. Through a multivariate binary logistic regression analysis, the S1P concentration in the first trimester was distinguished as an HDL-associated marker independently associated with cardiometabolic pregnancy complications. In conclusion, our study results showed that HDL remodeling differs between healthy pregnancies and pregnancies with maternal cardiometabolic complications, with changed HDL composition and functionality consequently impacting its biological functionality in the latter case.

Medical laboratories require a great amount of energy, ranging from three to six times more than a standard office building. Also, they consume massive amounts of water and gases and produce diverse waste products, some of which are toxic. Their energy requirements are complex and when estimating the total carbon footprint of a medical laboratory, we need to keep in mind the different contributors present. First, laboratory instrumentation varies for various laboratory tests, requiring different amounts of electricity, gas, water, and consumables. Furthermore, according to some estimates, laboratory infrastructure of heating, ventilation, and air-conditioning (HVAC) system may account for 40-60% of energy consumption, depending on its complexity and different requirements for different buildings. A lighting/shading system may consume up to 15% of the total energy. Also, the computers and their supporting systems needed for laboratory data processing use electrical power and contribute to the carbon footprint of medical laboratories. The systematic approach to mitigate the environmental impact of the healthcare sector in general, and medical laboratories in particular, is the voluntary adoption of an Environmental Management System (EMS) according to the International Standards ISO 14001 or the European EMAS (Eco-Management and Audit Scheme). Focusing on managing energy efficiency, the ISO 50001 Energy Management Systems - Requirements with guidance for use, can help medical laboratories develop and implement an energy-saving strategy by establishing achievable energy usage goals, defining action plans to achieve them, and quantifying improvements. To prevent and manage the risks of uncontrolled energy consumption in medical laboratories, it is important to bear in mind the energy-intensive laboratory features, thus facilitating the implementation of strategies for energy conservation and sustainability. Additionally, laboratory buildings have a substantial embodied carbon that significantly affects their functional environmental impact. In recent years, many initiatives, tools, and methods that address the environmental sustainability of health research have been developed. Many of them are applicable and are used in clinical laboratory settings even though they are not developed specifically for it. Recently, the European Federation of Clinical Chemistry and Laboratory Medicine generated educational material that aims to both raise awareness of medical laboratories carbon footprint, and to guide them on how to decrease it. This review aims to give an overview of the current efforts to improve the sustainability of medical laboratories in the sense of energy conservation. We will present the extent of the environmental impact of healthcare structures and medical laboratories, identify the major contributors to their carbon footprint, focus on energy consumption, and finally, offer strategies that could mitigate it.