The conventional methods for the diagnosis and treatment of cancer, based on surgical, chemical, and radiation processes, are expensive, time-consuming, and painful. Therefore, research in this field has been devoted to developing precise, cost-effective, and rapid techniques for early cancer detection. In recent decades, optical biosensors have become powerful tools for identifying various biological and non-biological analytes. Optical biosensors possess features such as high sensitivity and accuracy, non-invasiveness, label-free detection, and compact size. The surface plasmon resonance (SPR) phenomenon is one of the optical phenomena created by the absorption of photons at the metal/dielectric interface. The key characteristic of SPR is its sensitivity to environmental changes, meaning that placing normal and cancerous tissue samples on the sensor will result in different sensor responses. Recently, studies in this field have progressed towards using the SPR phenomenon in metamaterials. In this design, a metamaterial structure with SPR properties is utilized to create a plasmonic biosensor capable of detecting samples of normal and human cancerous liver tissues. The main idea is to detect normal liver tissue from cancerous liver tissue with high sensitivity. The designed sensor is based on the metamaterial structure and surface plasmon resonance enhancement. The samples used in this sensor include several liver tissue samples from various patients, including normal and disease-free samples, metastatic liver samples, non-metastatic liver samples, carcinoma liver samples, and non-carcinoma liver samples. In fact, in the proposed sensor in this design, metastatic tissue can also be distinguished from carcinoma tissue.

Casein is a natural milk protein that has been investigated in drug delivery, cartilage tissue engineering, and other biotechnological applications. In particular, casein nanofibers are of high interest for tissue engineering. Electrospinning casein is usually performed by co-electrospinning it with a spinning agent, such as poly(vinyl alcohol), poly(caprolactone) (PCL), or poly(ethylene oxide) (PEO). Spinning casein solely has been found to be challenging due to its unsuitable viscoelasticity and extensive intermolecular interactions. Our study aims to optimize casein/PEO spinning solutions for needleless electrospinning in the wire-based electrospinning device Nanospider Lab. For this, PEO with different molecular weights (6 kDa-1 MDa) was mixed with different caseins in different ratios to prepare an aqueous spinning solution whose pH value was varied in the test series of experiments. Electrospinning duration (30 min) and nozzle size (0.9 mm) were kept constant, while the other spinning parameters, as well as the relative humidity in the spinning chamber, were modified. Our study reveals the impact of these parameters on the spinnability of the casein/PEO spinning solutions and the macroscopic homogeneity of the resulting nanofiber mats. Additionally, scanning electron microscopy (SEM) images show the impact of these parameters on the diameter distribution and morphology of the nanofibers. This poster gives the first overview of the optimum spinning parameters for the wire-based electrospinning of casein/PEO nanofiber mats.

Introduction: Professional dancers are particularly susceptible to the occurrence of pathologies in their lower limbs and especially in the hip joint due to biomechanical stress. These injuries are the result of a variety of factors and require a specific treatment method. The purpose of this review is to document these injuries, identify the factors that are responsible for their occurrence, and present the recommended treatment approaches.

Methodology: This research was conducted by searching the PubMed, ScienceDirect, and Google Scholar databases using keywords and key phrases such as "hip disorders", "dancer's hip", and "musculoskeletal loading". The resulting data were collected and evaluated according to the PRISMA guidelines.
Results and Discussion: The total number of studies included in the review was 44. Of these, 28 were related to hip pathologies in dancers, 7 were related to causative factors, 7 described types of proposed treatments, and 2 provided general information about dancing. Although dancers are quite prone to injuries, the studies conducted on this subject are limited. Hip pathologies occupy up to 50% of cases and may be due to hyperextension or a direct impact on the joint. The most prevalent conditions include snapping hip syndrome, femoroacetabular impingement syndrome, acetabular labral tears, bursitis, and fatigue fractures due to stress on the joint. The above injuries are likely to occur due to factors associated with the excessive range of motion of the joint and the forces it is exposed to, high training loads, hyperactivity, the laxity of a dancer's joints, gender, age, and BMI. When treating dancers' injuries, a conservative approach is the preferred strategy. Surgical treatment is frequently avoided due to the prolonged recovery period following surgery.
Conclusion: Dancer’s hip injuries, due to biomechanical loading, the factors that predispose individuals to their occurrence, and the proposed rehabilitation methods, require further research to improve the validity and reliability of the current findings.

Introduction: Chronic nonspecific low back pain (CNLBP) is a multifactorial condition that affects a large percentage of the population worldwide. Therapeutic exercise is among the strategies used to treat the symptoms of the condition, and in recent years, the Clinical Pilates method has gained popularity as a treatment option (4). This study aims to investigate the effectiveness of the Clinical Pilates method in patients with CNLBP by analyzing quantitative data that evaluate the functionality of the lumbar spine (2,3). Methods: Twenty-two patients diagnosed with CNLBP were included in this research. The subjects were measured for their lumbar spine range of motion (ROM) and the time it took them to complete three clinical assessment tests. Two inertial measurement units (MMR+ and Mbientlab) were utilized to measure motion data throughout the execution of the prone plank bridge test (PBT), the side bridge test (SBT), and the supine bridge test (SUBT) (6). The data were obtained before and after the completion of a six-week Clinical Pilates therapeutic exercise rehabilitation program. Results and Discussion: Statistically significant differences were found in all three tests’ kinematic patterns and time pre- and post rehabilitation program. The subjects demonstrated improvement in all three assessments, as well as an increase in range of motion achieved throughout the execution of the program (1). Conclusions: These findings align with the existing literature that suggests possible enhancements in the clinical condition of patients following intervention with Clinical Pilates. Further research should be carried out using a wider range of clinical evaluation methods to confirm the efficacy of therapeutic exercise in treating symptoms of CNLBP (7,5).

Introduction: The real-time calculation of the centre of pressure (COP) during walking is of paramount importance for balance assessment and fall prevention. The aim of this study is to derive a balance index (BI) from the COP and its cyclogram and to test how well the BI works in distinguishing between diabetic and healthy volunteers.

Methods: This study involved 15 healthy volunteers and 14 people with diabetes. During walking, the COP was measured using Pedar-X insoles. The position of the COP was calculated at 10 locations: heel strike (E), toe-off (A), the beginning and end of the double support phase (D,B), and intersection points (C) on the COP cyclogram. The standard deviation of each COP cluster was calculated in the X and Y directions and multiplied (area of COP movement within ±1 standard deviation). The BI was calculated as the sum of the 10 areas. Furthermore, the ratio R=BC/AB was calculated from the average data of each cluster. The sensitivity and specificity of the logarithmically transformed BI and R were used to calculate the ROC, AUC, and the classification threshold.

Results and Discussion: LogBI was normally distributed in the diabetic (D) and healthy (H) volunteers, with significantly different (p<0.0001) averages of 1.686±0.228 and 2.070±0.282, respectively. The AUC was 0.861. At an optimal classification threshold of 1.9, the false positive H-data (identified as D) and the false negative D-data (identified as H) were approximately 20% each. The averages of logR were significantly different (p<0.0001): 1.054±0.312 and 0.143±0.306 for H and D, respectively. The AUC was 0.992. At an optimal threshold of 0.5, the false positive H-data and the false negative D-data were both about 4%.

Conclusions: The position of the COP within the cyclogram (logR) performed better than the standard deviation of the COP (logBI).

Introduction: Emotions influence human behavior and can be observed through changes in brain waves. The correlation of variables such as personality and decision-making with EEG patterns could contribute to new perspectivesf—for example, in job selection processes—aiming to find the most adequate profile for a given company role. The main aim of this work is to explore the interplay between brain responses and individual personality traits.

Methods: EEG signals were collected while participants completed DISC assessment questionnaires. The band power in the theta, alpha, beta, and delta bands was calculated and correlated with DISC profile (dominance, influence, steadiness, and conscientiousness) scores. A cohort of 12 participants, with an average age of 21.2 years, was used.

Results: Obtained results indicate that brain activity, especially in the alpha band, shows a correlation with emotional anxiety and was inversely correlated with dominance traits. Alpha wave variability was also observed in closed eye (CE) and open eye (OE) conditions among participants.

Conclusion: In this study, correlations of EEG band patterns were identified for specific DISC profile patterns. The supression of alpha waves was more correlated with the dominance profile, which showed the highest correlation values. These findings could have practical implications for job selection processes, especially for leadership roles. Future research will involve expanding the participant cohort to validate and refine these findings further.

Introduction: Autism Spectrum Disorder (ASD) is a common, inherited, heterogeneous, and lifelong neurodevelopmental disorder with underlying cognitive features that coexists with other conditions (1, 2). Walking is a fundamental human activity that is fully related to motor control. The identification of gait disorders may allow for early diagnosis and better treatment planning. The present study aims to systematically analyze and evaluate the spatiotemporal gait parameters in children with ASD and Typical Development (TD), correlate the results, and possibly identify motor patterns outside of physiological limits. Methods: A total of 12 children were divided into a TD group (5 males, 1 female; mean age: 4.3 years) and an ASD group (5 males, 1 female; mean age: 5 years). A motion capture system with six Vicon MCam optoelectronic cameras (Oxford Metrics Group Ltd.) and two Bertec force plates was used for gait analysis. The statistical software IBM SPSS Statistics 29 was used to conduct the analysis. Results and Discussion: No statistically significant differences were observed regarding the normalized values of the examined spatiotemporal parameters: gait velocity, stance time, gait cycle time, step length, step time, double support time, or step width. The findings of our study revealed that children with ASD had an increased gait velocity and a slight decrease in stance time, which aligns with the existing literature. Gait cycle time, step time, double support time, swing time, and step length and width reveal no significant differences between ASD and TD, which coincides with the findings from previous studies (3-6). Conversely, other studies have identified notable differences in spatiotemporal characteristics (7-9). Conclusions: These contradictory results explain the heterogeneity of the gait parameters observed in patients with ASD. Further research should be conducted in a non-laboratory setting, utilizing inertial measurement devices in conjunction with electromyographic data to thoroughly evaluate ASD gait patterns (10,11).

Smart textiles with embedded electronics can be used to monitor body functions, e.g., during sports or for medical reasons. One of the main problems of such embedded electronics is their washability. A recent study therefore investigated the washability of different inexpensive optical pulse sensors, as they could be integrated into clothing to enable pulse measurements. To evaluate the measured data, microcontrollers can also be integrated into the garment, which is why an ATtiny85 microcontroller additionally underwent washing tests. The main part of this study concentrated on code development to improve heartbeat detection using inexpensive optical pulse sensors. While all sensors and microcontrollers remained unaltered by 10 washing cycles, significant differences in the quality of the inexpensive optical pulse sensors were found, depending on the producer. As the optimum hardware, a combination was defined of an ESP8266 D1 mini as the textile-integrated transmitter and an ESP32 with a 1.8’’ display to receive and show the measured data outside the garment. Pulse detection criteria were defined, which could be used to calculate the heart beats per minute (BPM) with high reliability. The tests and developments can be used as the base for a complete textile-integrated optical pulse measurement with mobile data storage and depiction.

The integration of artificial intelligence (AI) systems with bioengineering technologies presents new ethical challenges that need to be carefully considered. One of the key ethical concerns is the potential for biases and errors in AI models as current large language models can sometimes provide incomplete or inaccurate information in specialized domains like bioengineering. There is a need for domain-specific AI models that are trained on the latest biomedical data and research to ensure the information provided is reliable and error-free. Other challenges lie in maintaining safety, transparency, privacy, accountability, and ethical governance when AI systems are used to aid bioengineering research or clinical decision-making. It is crucial that the reasoning and data behind AI-generated outputs are explainable and auditable, especially in high-stakes scenarios involving human health and safety. Robust governance frameworks are needed to ensure AI systems in bioengineering are used responsibly and ethically. Privacy and data governance are also critical ethical issues when it comes to using AI with biomedical and genomic data. There are also concerns surrounding the control and safeguarding of individuals' genetic information. The use of AI models that are trained on such sensitive data raises privacy risks that need to be carefully managed through strong data protection policies and safeguards. The use of AI systems to guide or automate aspects of such technologies could amplify some of the risks and concerns mentioned, such as off-target effects, ethical boundaries around human enhancement, and the need for robust informed consent processes. Ongoing multi-stakeholder dialogue and public engagement will be vital to ensure these powerful technologies are developed and deployed in a responsible, equitable, and socially conscious manner that prioritizes human wellbeing and environmental sustainability. A holistic and proactive approach that bridges bioengineering ethics and AI ethics will be key to navigating the opportunities and challenges that lie ahead.

Generally, cheap and environmentally friendly biosorbent materials attract the attention of researchers and become the focus of research. Cellulose (C₅H₁₀O₅) and hemicellulose (C₅H₁₀O₅) come to the fore among biosorbent materials in biosorption processes. However, lignin is also a very abundant and underutilized biopolymer resource that can be preferred for biosorbent production. Lignin (C₁₈H₁₃N₃Na₂O₈S₂) is an amorphous phenolic biopolymer with a structurally three-dimensional branched network structure. Three major cinnamyl alcohol (p-coumaryl alcohol, sinapyl alcohol, and coniferyl alcohol) monomers combine to form the lignin matrix. This biopolymer has advantages such as being available in large quantities, high selectivity and sorption capacity. However, one of the biggest disadvantages is that lignin exhibits a heterogeneous structure for the balanced production of biosorbents. The structural behavior of lignin depends largely on the source and the process conditions from which it is isolated. Today, although the use of lignin-based biosorbents is increasing, only 5% of the available lignin globally is used. Therefore, there are still significant opportunities for the development and evaluation of this material in various fields (water/wastewater treatment, industrial sectors, medicine and cosmetics, etc.). This review summarizes the world's current trends, perspectives, and recent developments in lignin-based biosorbents in terms of all properties of lignin.