ARSENIC IN DRINKING WATER AND URINE AND ITS RELATIONSHIP WITH MALIGNANT TUMORS OF URINARY TRACT IN OSIJEK-BARANJA COUNTY, CROATIA.

Increased values of arsenic in potable water in eastern Croatia has been a matter of scientific interest for the past two decades due to numerous health effects, including carcinogenic ones. This study investigated whether prolonged exposure to increased arsenic from water could be detectable through increased arsenic in urine, and whether it influenced the incidence of kidney and bladder cancer in Osijek-Baranja County. Inductively coupled plasma mass spectrometry (ICP-MS) was used for analysis of water samples from available water sources (wells, aqueducts). In addition, examinees from Osijek, Nasice, Vladislavci, Cepin and Dalj gave their urine samples for analysis. Data on cancer incidence were obtained from the Institute for Public Health Registry and cumulative incidence of kidney and bladder cancer was calculated for the period between January 1, 2000 and December 31, 2018. Elevated arsenic concentration in drinking water was recorded in Vladislavci, Cepin and Osijek area with values above the allowed maximum according to the EU standards (10 µg L-1) and as a result, arsenic levels in urine of the inhabitants were also elevated. Cumulative incidence for bladder cancer showed correlation between increased arsenic in water and urine in the areas affected by increased arsenic in water. Epidemiologic data suggest a conclusion that elevated arsenic could be considered at least as a cofounding factor for urinary tract cancer.

Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators' Psychological Status and Pain Syndromes.

Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators' psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks-concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators' poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).

Reflex Responses in Muscles of the Lower Extremities Elicited by Transcutaneous Stimulation of Cauda Equina: Part 1. Methodology and Normative Data.

INTRODUCTION: Transcutaneous electrical stimulation is used to stimulate the dorsal roots of the cauda equina. Multiple elicited responses recorded in the lower extremity muscles are called posterior root muscle reflexes (PRMRs). Normal PRMR values in the muscles of healthy lower extremities have yet to be determined. METHODS: Thirty subjects without known lumbosacral spinal root illness were included in this study. Subsequently, they were subjected to transcutaneous electrical stimulation of the cauda equina. Posterior root muscle reflex was recorded in the four muscle groups of both lower extremities. We elicited multiple PRMR and examined their characteristics in order to establish normal electrophysiological parameter values. RESULTS: Posterior root muscle reflex was successfully elicited in the tibialis anterior (96.7%), gastrocnemius (100%), quadriceps femoris (93.3%), and hamstring (96.7%). No statistically significant differences were found in the intensity of stimulation, latencies, or area under the PRMR between the right and left leg muscles. The area under PRMR varied significantly among the participants. Higher body weight and abdominal girth showed a significant positive correlation with stimulation intensity for eliciting PRMR, and a significant negative correlation with the area under PRMR. Older age showed a significant negative correlation with the success of eliciting PRMR and the area under the PRMR. CONCLUSIONS: Posterior root muscle reflex is a noninvasive and successful method for eliciting selective reflex responses of cauda equina posterior roots. Obtained values could be used in future studies to evaluate the utility of this methodology in clinical practice. This methodology could improve testing of the proximal lumbosacral nervous system functional integrity.

Assessment of cortical bone fracture resistance curves by fusing artificial neural networks and linear regression.

Bone injures (BI) represents one of the major health problems, together with cancer and cardiovascular diseases. Assessment of the risks associated with BI is nontrivial since fragility of human cortical bone is varying with age. Due to restrictions for performing experiments on humans, only a limited number of fracture resistance curves (R-curves) for particular ages have been reported in the literature. This study proposes a novel decision support system for the assessment of bone fracture resistance by fusing various artificial intelligence algorithms. The aim was to estimate the R-curve slope, toughness threshold and stress intensity factor using the two input parameters commonly available during a routine clinical examination: patients age and crack length. Using the data from the literature, the evolutionary assembled Artificial Neural Network was developed and used for the derivation of Linear regression (LR) models of R-curves for arbitrary age. Finally, by using the patient (age)-specific LR models and diagnosed crack size one could estimate the risk of bone fracture under given physiological conditions. Compared to the literature, we demonstrated improved performances for estimating nonlinear changes of R-curve slope (R2 = 0.82 vs. R2 = 0.76) and Toughness threshold with ageing (R2 = 0.73 vs. R2 = 0.66).