The viability of utilizing biomass as a substitute for energy has been the subject of research recently due to the increasing focus on the circular economy. The complex and resistant structures of lignocellulosic waste materials, in particular, need efficient pretreatment and enzymatic saccharification in order to liberate the necessary saccharides, which can then be further fermented by pigment-producing microbe Dietzia sp. The isolated-strain-grown glucose was obtained from acid-treated rice straw residue. Acid treatment removed the hemicelluloses, and the lignin and cellulose remained intact within the rice straw. Hemicelluloses are used for the production of different products (Singh et al., 2021, Qaseem et al., 2021). Alkali treatment separates cellulose (Pal et al., 2022) from lignin, and further cellulose is saccaharified by cellulase enzymes to obtain free glucose for fermentation. The pigment was extracted in absolute ethanol. The colour values L*, a*, b*, and c* and the hue of the fermented pigment were also determined. Column chromatography was performed to purify the extracted pigment. The extracted pigment was identified by thin-layer chromatography. The pigment was characterized by a UV--Vis spectrophotometer and UPLC. The pH, temperature stability, and antimicrobial activity of canthaxanthin were determined. The isolated pigment has industrial potential, which can be used in food, pharma, and beverages as a colorant and also in nutraceuticals.

References

Singh, S., Kaur, D., Yadav, S.K. and Krishania, M., 2021. Process scale-up of an efficient acid-catalyzed steam pretreatment of rice straw for xylitol production by C. Tropicalis MTCC 6192. Bioresource Technology, 320, p.124422.

Qaseem, M.F., Shaheen, H. and Wu, A.M., 2021. Cell wall hemicellulose for sustainable industrial utilization. Renewable and Sustainable Energy Reviews, 144, p.110996.

Pal, P., Li, H. and Saravanamurugan, S., 2022. Removal of lignin and silica from rice straw for enhanced accessibility of holocellulose for the production of high-value chemicals. Bioresource Technology, 361, p.127661.

Background: The production of a small-diameter vascular graft (d<3 mm), utilizing state-of-the-art tissue engineering approaches, currently represents a major challenge in vessel microsurgery. Cardiovasular disease (CVD) affects more than 18 million people, worldwide. Therapeutic options in bypass surgery include the use of autologous grafts, such as the saphenous vein, or synthetic vascular grafts. However, both approaches present unfortunate events, limiting the lifespan of the implanted graft, thus requiring a new replacement surgery. Therefore, the production of vascular grafts utilizing decellularized human umbilical arteries may represent an alternative strategy in the treatment of CVD. Moreover, the efficient combination of the produced vascular graft with the host’s cells may result in the production of a fully histocompatible conduit, thus increasing the lifespan of the implant. For this purpose, we assessed the production of histocompatible vascular grafts obtained from decellularized human umbilical arteries (hUAs), which is the primary aim of this study. Methods: HUAs were decellularized using CHAPS and SDS detergents. The total hydroxyproline, sulphated glycosaminoglycans (sGAGs), and DNA content were quantified. Human endothelial cells (ECs) and smooth muscle cells (SMCs) were seeded in the decellularized hUAs. Typing of the Human Leucocyte Antigens (HLAs) was performed in hUAs both prior to and after the decellularization, as well as in seeded cellular populations. Results: Decellularized hUAs were characterized by the proper preservation of tissue architecture. Total hydroxyproline content was preserved, although sGAGs and DNA presented a statistically significant reduction. HLA typing only confirmed the presence of the seeded ECs and SMCs in the produced vascular grafts, further indicating the successful production of a histocompatible graft. Conclusion: The results of this study support the efficient production of histocompatible human vascular grafts. Based on the most frequent regional HLAs, a bank with histocompatible vessel grafts could be established, bringing personalized medicine a step closer to clinical utility.

The development of multi-drug-resistant bacterial infections seriously threatens public health. Hence, efforts are needed to develop a new class of safe and effective antibacterial agents. Two-dimensional molybdenum disulfide (MoS₂)-based nanostructures have great potential as antibacterial agents, but their aggregation limits their further biomedical applications. Here, we report a bio-inspired synthesis of surface-modified MoS₂ nanoflowers (NFs) with a nature motif L-cysteine that show good colloidal stability in aqueous media. The formation of surface-modified MoS₂ NFs has been confirmed using XRD, SEM, TEM, XPS, FTIR, and TGA analysis. The antibacterial activity of as-prepared MoS₂ NFs examined over Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus) bacteria have shown excellent bactericidal activity. The Scanning Electron Microscopy (SEM) images of the bacteria confirm the membrane-directed antibacterial mechanisms where the nanosheets (NSs) in the NFs act as nanoblades and cause cell membrane damage. The antibacterial mechanisms of MoS₂-cys NFs are primarily attributed to membrane damage and the generation of oxidative stresses, which destroy both bacterial strains. The generation of oxidative stress can occur through reactive oxygen species (ROS)-dependent and -independent pathways, as confirmed using flow cytometry and fluorescence imaging and Ellman's assay, respectively. The excessive generation of ROS leads to the inactivation of the bacterial antioxidant defense mechanism. Moreover, the toxicity studies towards human foreskin fibroblast (HFF) cell lines suggested the good biocompatibility of these as-synthesized NFs. We report the intrinsic antibacterial efficiency of MoS₂-cys NFs without any external stimulus (light, H₂O₂, etc.), doping, or drug loading. Our study indicates that the appropriate surface modification of the flower-like morphology of MoS₂ can enhance their colloidal stability and intrinsic antibacterial potency for further applications such as antibacterial coatings, water disinfection, and wound healing.

Background:
Early detection of Parkinson’s disease (PD) is imperative for timely intervention. Olfactory dysfunction, a prominent non-motor symptom, alongside biomarkers derived from Cerebrospinal Fluid (CSF) analysis and dopamine transporter imaging, holds promise for early PD prediction. The burgeoning utilization of machine learning (ML) methodologies for prognosticating various pathological conditions has sparked interest in developing an enhanced ML model for PD prognosis, specifically targeting olfactory impairment symptoms.

Methods:
This study employed a systematic approach consisting of four stages: Data acquisition, Feature extraction, ML classifier development, and Results analysis. Initial data procurement involved accessing the Parkinson’s Progression Markers Initiative (PPMI) database, from which relevant non-motor features were extracted. Furthermore, features from the University of Pennsylvania Smell Identification Test, along with CSF markers such as Aβ1-42, α-synuclein, phosphorylated tau protein (P-tau181), total tau protein (Ttau), ratios of T-tau/Aβ1-42, P-tau181/Aβ1-42, and P-tau181/T tau, alongside striatal binding ratio (SBR) data, were incorporated. Subsequently, a comparative analysis of ML models was conducted based on their accuracy in predicting PD.

Results:
Automated diagnostic models leveraging ML techniques, including boosted logistic regression, classification trees, Bayes Net, and multilayer perceptron, were developed utilizing the significant features identified. The dataset was partitioned into training (80%) and testing (20%) subsets to assess model performance. Evaluation metrics such as accuracy and Area under the ROC Curve (AUC) were computed, with boosted logistic regression demonstrating the highest performance, achieving an accuracy of 98.29% and an AUC of 99.2%, surpassing existing models.

Conclusions:
Given the indirect nature of PD diagnosis and the substantial misdiagnosis rates attributed to the absence of definitive tests, the integration of ML models, particularly boosted logistic regression, presents a promising approach for enhancing diagnostic accuracy. The utility of ML algorithms in aiding clinical decision-making for PD diagnosis and emphasizes the potential for assisting healthcare professionals in more accurate disease prognosis and management.

Healing burns and wounds is a prevalent health issue. Metabolic and physiological problems such as hypertension, malignancies, kidney disorders, diabetes, and obesity disrupt the natural process of skin healing, leading to the development of ulcers, bedsores, and the need for amputations. These conditions contribute to mortality rates globally. Medical statistics indicate that approximately 1-2% of the global population experiences chronic wounds.

Polysaccharides are commonly employed in the production of superabsorbent polymers, which have the ability to absorb and retain aqueous solutions that are hundreds of times their own weight when dry. Chitosan is a widely recognized carbohydrate polymer with numerous potential clinical uses because of its antibacterial, anticoagulant, anticancer, and hemostatic properties. β-glucans often have a beneficial impact on the human immune system, offering antitumoral and antibacterial properties, which can expedite the process of healing

This work aimed to create innovative foams through the polymerization of curdlan–chitosan at a temperature of 90 ◦C, using a gradual addition method for incorporating bioactive compounds such as AgNO3 solution, aloe vera, gentamicin, and a mixture of all components. Evidence has shown that the existence of a medicinal substance has a notable impact on the rate at which edema occurs. Through in vivo testing, it was discovered that CUR/CS/MIX foams had a more pronounced impact on skin restoration when compared to pure CUR/CS and the untreated control. The investigation into antibacterial activity revealed the synergistic impact of the constituents against strains commonly found in hospitals. Therefore, the suggested straightforward approach for manufacturing biocompatible superabsorbent foams has exciting opportunities for developing novel functional platforms as temporary skin replacements for the treatment and regrowth of persistent wounds.

Introduction: The rising prevalence of cardiovascular disease, especially hypertension, necessitates continuous and non-invasive blood pressure (BP) monitoring. While previous studies have investigated the potential for estimating BP from electrocardiograms (ECGs) , these claims require further verification. This study presents a preliminary investigation into the feasibility of estimating BP from ECG pulse morphology using a novel deep compositional Bayesian neural network (auto-BNN).

Methods: Our model leverages a deep learning architecture to capture the variations in ECG morphology associated with BP. It incorporates convolutional neural network (CNN) layers for ECG waveform feature extraction, a long short-term memory (LSTM) unit to capture temporal dependencies in ECG sequences, and variational layers based on auto-BNN to enable uncertainty estimation.

Results and Discussion: An initial evaluation of data from 130 individuals sourced from Physionet yielded mean errors of 3.38 mmHg (systolic) and 2.40 mmHg (diastolic) with standard deviations of 13.20 mmHg and 11.88 mmHg, respectively. These results suggest that our model could potentially capture correlations between BP variations and ECG signals, such as changes in R wave amplitude, ST-segment depression, T-wave inversions, and widened P waves associated with high BP, as well as sinus tachycardia and ST-segment/T-wave changes associated with low BP. However, it is important to note that these correlations may have captured the relationship between heart rate (HR) and BP. Further research should explore methods to exclude HR information from ECGs to ensure the validity of BP estimation findings.

Conclusions: This preliminary study demonstrates the potential feasibility of using ECG pulse morphology for BP estimation. However, further validation on larger and more diverse datasets is crucial to assess the generalizability of our approach. While our initial results are encouraging, it is important to note that achieving very high accuracy in BP estimation solely from ECGs may be inherently challenging due to the complex and multifaceted factors influencing BP.

Introduction: The COVID-19 pandemic highlighted the critical need for accurate forecasting models to inform public health decision-making. This study compares the performance of three time series techniques—Long Short-Term Memory (LSTM) networks, Transformer models, and Autoregressive Integrated Moving Average (ARIMA)—in predicting the spread of COVID-19.

Methods: We trained and evaluated LSTM, Transformer (Temporal Fusion Transformer), and ARIMA models using the publicly available Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) COVID-19 Data Repository, encompassing confirmed cases, deaths, vaccination rates, and relevant socio-economic factors. Model performance was assessed using mean absolute error (MAE) and root mean squared error (RMSE) for 7-day and 14-day forecasting horizons.

Results and Discussion: The Transformer-based model consistently outperformed both the LSTM and ARIMA models in terms of forecasting accuracy. For 7-day forecasts, the Transformer achieved an MAE of 85 cases per 100,000 population and an RMSE of 120, while LSTM had an MAE of 90 and RMSE of 125 and ARIMA had an MAE of 105 and RMSE of 155. For 14-day forecasts, the Transformer maintained its superior performance with an MAE of 110 and RMSE of 150, compared to LSTM (MAE of 115 and RMSE of 155) and ARIMA (MAE of 138 and RMSE of 178). The Transformer's ability to capture long-range dependencies and incorporate diverse data sources contributed to its improved performance. Notably, all models were able to capture sudden shifts in the spread of the virus, enabling timely alerts for potential outbreaks.

Conclusion: This study demonstrates the superior performance of Transformer-based models in forecasting the COVID-19 pandemic compared to LSTM and ARIMA models. The findings underscore the potential of Transformers in epidemiological modelling and highlight the importance of leveraging advanced deep learning techniques for accurate and timely predictions in public health crises. Further research will explore the integration of additional data sources and model refinements to enhance forecasting capabilities for future outbreaks.

In this study, we explore a three-species ecological model incorporating the concept of refuge alongside fear, focusing on a non-delay scenario in the intricate dynamics of the food web within the biological system. The prey population is modeled to exhibit logistic growth until it reaches a predefined carrying capacity, mirroring the typical pattern of population dynamics in the absence of predators. Within this framework, diseased prey is postulated to consume healthy prey utilizing the Holling's type II functional response, elucidating how predators interact with their prey based on specific parameters. Furthermore, the predators are assumed to engage in consumption using the Beddington–DeAngelis and Crowley–Martin response functions. Our analysis aims to ascertain the non-negativity of the solutions, ensuring that they remain within biologically realistic boundaries and exhibit stability over time. By exploring every biologically feasible fixed point of the system, we seek to unravel the stable states of the ecosystem. Local stability is evaluated through the distribution of eigenvalues, providing insights into the system's equilibrium behavior. Additionally, we conduct a thorough examination of Hopf bifurcation concerning the fear factor $b$, shedding light on potential dynamic transitions. To validate our theoretical findings, numerical solutions are meticulously scrutinized using the MATLAB software package. Through this comprehensive approach, we offer a practical understanding of the model's behavior under specific conditions, bridging the gap between theoretical analysis and a real-world biological system. By validating the theoretical findings through numerical solutions, researchers can develop predictive models to forecast the behavior of ecological systems under different scenarios. This predictive capability can be valuable for assessing the impact of environmental changes or human interventions on ecosystems. Overall, this study will provide a solid foundation for future research endeavors in ecological modeling and biological system and ecosystem management.

Chronic wounds present a significant challenge for modern medicine, often leading to a substantial reduction in patients' quality of life and high treatment costs. In response to these challenges, this project explores the application of biohydrogels containing adaptogens in the therapy of chronic wounds. Due to their unique physicochemical properties, biohydrogels can provide an optimal healing environment, including appropriate moisture, protection against infections, and controlled release of bioactive substances. Adaptogens, known for their antioxidant, anti-inflammatory, and immunomodulatory properties, can further support tissue regeneration processes.

The aim of this project is to develop and evaluate biohydrogels containing selected adaptogens such as Ashwagandha, Rhodiola Rosea, and Ginseng. This research will include the optimization of hydrogel composition and in vitro studies on adaptogen release and their effects on skin cells. It is anticipated that the use of adaptogens in biohydrogels will contribute to shortening wound healing time, reducing inflammation, and improving the overall health status of patients with chronic wounds. The results of this project could lead to the development of novel chronic wound therapies, offering effective and safe solutions for patients and the healthcare system.

This project is financed with funds from the state budget granted by the Minister of Science within the framework of the "Student Scientific Clubs Create Innovations" (SKN/SP/601893/2024) "Application of Biohydrogels Containing Adaptogens in Innovative Chronic Wound Therapy" .The research was carried out within the SMART-MAT Functional Materials Science Club (section Smart-Mat) at the Faculty of Materials Engineering and Physics of the Cracow University of Technology.

Plants are considered prospective sources of a wide range of pharmaceutical substances. Plant biomass can be applied to the secretion of desired secondary metabolites with biological activity. In nature, the amount of bioactive compounds received from plants is relatively low, and their extraction is problematic. To overcome all of these problems and increase biomass proliferation, as well the production of secondary metabolites, in vitro techniques could be applied. Furthermore, elicitation is a well-known method to stimulate the secretion of bioactive substances. Studies show that immobilization could also be beneficial for increasing plant growth and productivity. Elicitor-coated polymeric-based scaffolds are a combination of both of these methods. Such platforms could be used as easy and cheap bioengineering tools for increasing the production of secondary metabolites.

The scope of this study was to examinate the influence of the fungal chitosan's viscosity on the biomass growth and the secondary metabolite production in Rindera graeca hairy root cultures. The transgenic roots were immobilized on hybrid PLA–chitosan scaffolds. The surfaces of the scaffolds were modified using different chitosan viscosities, i.e., 10-120 cps, 100-300 cps, or 2000-3500 cps. The average concentration of elicitors in the platforms was 25% m/m. As a control culture, transgenic roots cultured on unmodified PLA scaffolds have been applied. The increase in the fresh biomass and the amount of naphthoquinones produced in the Rindera graeca hairy root cultures were determined quantitatively.

Increasing the viscosity of the fungal chitosan had a great impact on the plant biomass proliferation, as well as on the secretion of secondary metabolites. Increasing growth of the hairy roots was observed with increasing chitosan viscosity, while the effect on the production of the naphthoquinone derivatives was quite the opposite.

This research was funded by the National Science Centre (NCN), Poland, grant no. 2021/41/N/ST8/00958.