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Head and neck injuries are a heterogeneous group in terms of both clinical course and prognosis. For years, there have been attempts to create an ideal tool to predict the outcomes and severity of injuries. The aim of this study was evaluation of the use of selected artificial intelligence methods for outcome predictions of head and neck injuries. 6,824 consecutive cases of patients who sustained head and neck injuries, treated in hospitals in the Lublin Province between 2006-2018, whose data was provided by National Institute of Public Health / National Institute of Hygiene, were analyzed retrospectively. Patients were qualified using International Statistical Classification of Diseases and Related Health Problems (10th Revision). The multilayer perceptron (MLP) structure was utilized in numerical studies. Neural network training was achieved with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. In the designed network, the highest classification efficiency was obtained for the group of deaths (80.7%). The average value of correct classifications for all analyzed cases was 66%. The most important variable influencing the prognosis of an injured patient was diagnosis (weight 1.929). Gender and age were variables of less significance with weight 1.08 and 1.073, respectively. Designing a neural network was hindered due to the large amount of cases and linking of a large number of deaths with specific diagnosis (S06). With a predictive value of 80.7% for mortality, ANN can be a promising tool in the future; however, additional variables should be introduced into the algorithm to increase the predictive value of the network. Further studies, including other types of injuries and additional variables, are needed to introduce this method into clinical use.

The COVID-19 pandemic demanded changes in healthcare systems worldwide. The lockdown brought about difficulties in healthcare access. However, trauma still required further attention considering its modifications. The presented study aims to investigate the variances in epidemiological patterns of trauma during the lockdown and the previous year, with a view to better understand the modifications in healthcare provision. The authors analyzed data from the first lockdown in 2020 (12 March–30 May) and the same period in 2019 from 35 hospitals in Lublin Province. A total of 10,806 patients in 2019 and 5212 patients in 2020 were included in the research. The uncovered changes adhered to the total admissions and mortality rate, the frequency of injuries in particular body regions, and injury mechanisms. The lockdown period resulted in a reduction in trauma, requiring an altered approach to healthcare provision. Our research indicates that the altered approach facilitated during such periods is essential for delivering tailored help to trauma patients.

Introduction: Traumatic injuries are a significant global health concern, with profound medical and socioeconomic impacts. This study explores the patterns of trauma-related hospitalizations in the Lublin Province of Poland, with a particular focus on the periods before and during the COVID-19 pandemic. Aim of the Study: The primary aim of this research was to assess the trends in trauma admissions, the average length of hospital stays, and mortality rates associated with different types of injuries, comparing urban and rural settings over two distinct time periods: 2018–2019 and 2020–2021. Methods: This descriptive study analyzed trauma admission data from 35 hospitals in the Lublin Province, as recorded in the National General Hospital Morbidity Study (NGHMS). Patients were classified based on the International Classification of Diseases Revision 10 (ICD-10) codes. The data were compared for two periods: an 11-week span during the initial COVID-19 lockdown in 2020 and the equivalent period in 2019. Results: The study found a decrease in overall trauma admissions during the pandemic years (11,394 in 2020–2021 compared to 17,773 in 2018–2019). Notably, the average length of hospitalization increased during the pandemic, especially in rural areas (from 3.5 days in 2018–2019 to 5.5 days in 2020–2021 for head injuries). Male patients predominantly suffered from trauma, with a notable rise in female admissions for abdominal injuries during the pandemic. The maximal hospitalization days were higher in rural areas for head and neck injuries during the pandemic. Conclusions: The study highlights significant disparities in trauma care between urban and rural areas and between the pre-pandemic and pandemic periods. It underscores the need for healthcare systems to adapt to changing circumstances, particularly in rural settings, and calls for targeted strategies to address the specific challenges faced in trauma care during public health crises.

Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data. We collected a dataset comprising 144 corneal scans from adults aged 18–35, including an equal proportion of keratoconus and normal cases. Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. The Random Forest algorithm demonstrated the highest reliability, achieving an accuracy of 98% during training and 96% on the test set, while also identifying the most diagnostically relevant measurements. Unlike prior studies, our approach enables detailed comparison between model-selected features and clinically recognized diagnostic parameters. This interpretability provides a clinically meaningful bridge between AI-driven predictions and expert-based decision-making. The results suggest that machine learning models, particularly Random Forest, can effectively aid in the early detection of keratoconus in young individuals, potentially improving patient outcomes through timely intervention.

Keratoconus is a progressive corneal disease that requires early and accurate detection to prevent severe visual impairment. This study presents a deep learning-based classification model for distinguishing between healthy and keratoconic eyes using dynamic corneal imaging data from the CORVIS system. A hybrid CNN-RNN architecture was developed, combining a fine-tuned InceptionV3 network for spatial feature extraction with a recurrent LSTM module to capture temporal patterns across image sequences. To ensure robust evaluation, a 10-fold stratified cross-validation strategy was employed, with data splits performed at the patient level to avoid data leakage. The model achieved an average accuracy, precision, recall, and F1-score of approximately 0.90 across folds, demonstrating strong generalization performance. Boxplot visualizations of metric distributions further confirmed model stability and revealed minimal performance variance. Class-wise analysis showed high effectiveness in detecting both healthy and keratoconic cases, although slightly greater variability was observed in the classification of healthy eyes. These results indicate that the proposed method is a promising tool for keratoconus screening and may complement existing diagnostic workflows. Further validation on external datasets is recommended prior to clinical deployment.

In recent years, a lot of attention has been given to searching for new additives which will effectively facilitate the process of immobilizing contaminants in the soil. This work considers the role of the enhanced nano zero valent iron (nZVI) strategy in the phytostabilization of soil contaminated with potentially toxic elements (PTEs). The experiment was carried out on soil that was highly contaminated with PTEs derived from areas in which metal waste had been stored for many years. The plants used comprised a mixture of grasses—Lolium perenne L. and Festuca rubra L. To determine the effect of the nZVI on the content of PTEs in soil and plants, the samples were analyzed using flame atomic absorption spectrometry (FAAS). The addition of nZVI significantly increased average plant biomass (38%), the contents of Cu (above 2-fold), Ni (44%), Cd (29%), Pb (68%), Zn (44%), and Cr (above 2-fold) in the roots as well as the soil pH. The addition of nZVI, on the other hand, was most effective in reducing the Zn content of soil when compared to the control series. Based on the investigations conducted, the application of nZVI to soil highly contaminated with PTEs is potentially beneficial for the restoration of polluted lands.

Recent theoretical and experimental studies have indicated the existence of a new stable phase of carbon with mixed sp 2 and sp 3 hybridized bonds—diaphite. Such a two-layered structure with sp 2 /sp 3 bonds may be observed after the photostimulation of highly oriented pyrolytic graphene with femtosecond laser pulses. This hidden multistability of graphene may be used to create a semiconducting phase immersed in the semimetallic continuum, resulting in bandgap opening. We demonstrate that bandgap opening and light emission from graphene is possible using continuous-wave laser beams with wavelengths from the visible (405 nm) to the near-infrared range (975 nm). We demonstrate that without the application of cooling, the effective temperature of the emitting sample remains lower than 900 K, which is far below the value predicted by the theory of black-body radiation. Moreover, light emission from a graphene sample may be observed at temperatures as low as 10 K. Graphene: white light emission Graphene ceramics can have their band gaps opened and emit white light when excited by visible or infrared light, report scientists in Poland. Wieslaw Strek and co-workers from the Polish Academy of Sciences and Wroclaw University of Technology say that the broadband emission is centred at 650 nm and has an intensity that is strongly influenced by the excitation laser power with a clear threshold. Their analysis suggests that the origin of the emission is entirely electronic in nature and is not related to incandescence or blackbody radiation. White-light emission is observed for cryogenically cooled samples with temperatures as low as 10 K. The graphene ceramic was fabricated by placing graphene flakes in a calcium carbonate toroid and subjecting them to a high temperature (550 °C) and a pressure of 8 GPa for 1 min.

Laser induced white light emission was observed from porous graphene foam irradiated with a focused continuous wave beam of the infrared laser diode. It was found that the intensity of the emission increases exponentially with increasing laser power density, having a saturation level at ca. 1.5 W and being characterized by stable emission conditions. It was also observed that the white light emission is spatially confined to the focal point dimensions of the illuminating laser light. Several other features of the laser induced white light emission were also discussed. It was observed that the white light emission is highly dependent on the electric field intensity, allowing one to modulate the emission intensity. The electric field intensity ca. 0.5 V/μm was able to decrease the white light intensity by half. Origins of the laser-induced white light emission along with its characteristic features were discussed in terms of avalanche multiphoton ionization, inter-valence charge transfer and possible plasma build-up processes. It is shown that the laser-induced white light emission may be well utilized in new types of white light sources.