Robinia viscosa, whose natural habitat is localized in North America, is used mainly as an ornamental plant species and for the restoration of degraded areas. Among the metabolites found in the Clammy locust's glandular trichomes, there is a significant content of mucus and pectins [2]. Therefore, it should be considered also a potential medicinal plant with antioxidant and antibacterial activities.

The field experiment method was used. R. viscosa specimens were planted in semi-natural and cultivated phytocenoses in the north of Ukraine (plant resistance zone 5a: from -28.9°C to -26.1°C).

R. viscosa demonstrated a high degree of drought resistance in conditions of moisture deficit and high temperatures (up to +42°С), as well as a high degree of winter and frost resistance (tolerating temperatures as low as -28°С). It did not demonstrate the ability to reproduce through seeds. In the conditions of the cultivated phytocenosis, it had the ability to grow intensively due to root shoots: one 5–7-year-old R. viscosa plant produced 13±4 root sprouts per growing season at a distance of 1–8 m from the mother plant.

A. Konarska [1] notes that R. viscosa is a valuable melliferous species. Its nectar productivity is 1000 kg or more from 1 ha (according to oral reports from Ukrainian beekeepers). R. viscosa blooms after R. pseudoacacia L., which extends the period of a good honey harvest until September. In light of this, places where the Clammy locust is naturalized in the north of Ukraine should be considered potential sources of R. viscosa's raw materials.

References

1. Konarska, A. Microstructure of floral nectaries in Robinia viscosa var. hartwigii (Papilionoideae, Fabaceae) – valuable but little-known melliferous plant. Protoplasma 2020, 257 (2), 421. https://doi.org/10.1007/s00709-019-01453-4

2. Konarska, A.; Łotocka, B. Glandular trichomes of Robinia viscosa Vent. var. hartwigii (Koehne) Ashe (Faboideae, Fabaceae)-morphology, histochemistry and ultrastructure. Planta, 2020, 252(6), 102. https://doi.org/10.1007/s00425-020-03513-z

Human papillomavirus infection is the cause of cervical cancer. The majority of human papillomavirus (HPV) infections are benign and resolve on their own. However, there is a risk of chronic infection with high-risk strains of HPV. Human HPV turns on the E6AP ubiquitin-protein ligase (E3), which makes it easier for the p53 tumor suppressor protein to break down in cervical cancer. The p53 tumor suppressor protein is a governing protein that inhibits tumorigenesis by regulating cell division and facilitating DNA repair mechanisms. The p53 protein resides in the nucleus of cells across the organism. Human malignancies often display this mutation, which may confer resistance to oncological therapies. A third type of enzyme, called an E3 ubiquitin-protein ligase, takes ubiquitin from an E2 ubiquitin-conjugating enzyme as a thioester and moves it to its target proteins. In this study, we prepared the protein and potential ligand molecules for molecular docking studies. We also applied computational strategies to demonstrate the pharmacokinetic properties in five different stages of the selected ligands that describe the drug's journey through the body. The objective of this study is to identify nature-derived compounds that have the ability to inhibit the E6AP-UbcH7 protein complex. These compounds have the potential to inhibit the harmful effects of HPV on the p53 tumor suppressor in cervical cancer. After performing docking between the ligand molecules and the receptor protein molecule, we determined the molecular docking scores. We found that kaempferol required the least energy to interact with the protein (docking score of -5.896 kcal/mol). The molecular docking and ADMET test results indicate that kaempferol is the most promising therapeutic candidate compared to the other compounds.

Aim: The present study aimed to employ a VGG-16 convolutional neural network (CNN) system to determine alveolar bone loss and periodontal disease/health status from dental panoramic radiography images.

Materials and Methods: This study was conducted with a dataset of panoramic images obtained from an institution. The training dataset contained 1874 panoramic images, of which 953 were of bone loss cases and 921 were of periodontally healthy cases. The presence/absence of resorption at the bone crest was recorded in consideration of the distance between the enamel–cementum junctions of the teeth and the alveolar bone crest. Radiographs showing bone resorption with a horizontal/vertical shape or bone defects were included in the bone loss group. Images with artefacts, image distortion, and blur were excluded. All images in the dataset were resized to 1472 x 718 pixels, followed by preprocessing for arbitrary sequence formation using python language along with OpenCV, NumPy, Pandas, and Matplotlib libraries to generate an image dataset. The dataset was divided into a testing set and validation set. The validation dataset was used to validate the model. The Feature Extraction approach strategy with 10–20 epochs and learning rate 1e-4 (0.0001) for trainable layers were used. Training and validation datasets were used to predict and generate optimal weight factors for this CNN.

Results: Of 100 bone loss cases, the CNN system evaluated 92 correctly and 8 incorrectly. Further, of 100 periodontally healthy cases, it evaluated 89 correctly and 11incorrectly. The sensitivity, specificity, precision, accuracy, and F1 score were 0.8317, 0.8683, 0.8918, 0.8927, and 0.8615, respectively.

Conclusion: The CNN model was successful in assessing the alveolar bone loss and periodontal disease status using panoramic radiographs. This study resulted in effective image segmentation which can more accurately predict grading of periodontal bone loss, avoiding user interpretation for overlapping on periodontal classification.

Introduction and objectives:

Myelodysplastic syndrome (MDS) is a blood disorder marked by abnormal blood cell production and a high risk of leukemia. Neutrophils, a type of leukocyte, can exhibit hypogranulation, an early indicator of MDS. While automated analyzers can identify leukocyte types, they cannot distinguish between normal and hypogranulated neutrophils, a challenge that deep learning techniques can address.

The objective of this work is to develop a two-stage identification system using convolutional neural networks (CNNs). The first stage classifies five types of leukocytes, including normal and hypogranulated neutrophils, while the second stage distinguishes between normal and hypogranulated neutrophils.

Methods

Images were collected at the Hospital Clínic de Barcelona using the CellaVision DM96 analyzer, resulting in a dataset of 500 images of basophils, lymphocytes, eosinophils and monocytes and 5089 images of neutrophils (4595 normal and 494 hypogranulated). The dataset was split into training sets (320 images per leukocyte type, with 160 normal and 160 hypogranulated neutrophils), validation and test sets, and the rest were used for the test set. Two CNN-based models were developed: the first one with VGG19 architecture to differentiate between leukocyte types and the second one with ConvNeXt architecture to distinguish normal from hypogranulated neutrophils. A final proof of concept was performed with 1000 images of neutrophils from MDS patients to simulate real clinical conditions.

Results

The test set results showed 99.57% accuracy for the first model and 99.32% for the second, identifying 4415 normal and 282 hypogranulated neutrophils. In the proof of concept, the accuracies were 96.7% for the first model and 80% for the second, with sensitivities of 87.9% for hypogranulated neutrophils.

Conclusion

The proposed system showed high accuracy in testing and acceptable performance in the proof of concept for detecting neutrophil hypogranulation in MDS patients. However, its clinical performance was lower, indicating a need for more diverse data and refinement for improved accuracy in real-world applications.

A monitoring study conducted on young adults (20-24-years old) from the Madrid region (Spain) revealed a lower iron (Fe) status in this demographic, particularly in women. Given the potential adverse effects of suboptimal iron status on academic performance, we monitored iron status in adolescents through analyzing it in hair. This approach could be advantageous because it is not affected by rapid fluctuations due to dietary intake, which can complicate the interpretation of iron status assessments based on other biological samples. Scalp hair was collected from 97 adolescents (13- to 16-years-old; 68 girls) living in Alcalá de Henares (Madrid). Fe was monitored by ICP-MS (LoD=1.148 µg/g). Contrarily to the results observed in young adults, Fe hair concentrations did show sex dependency, being significantly higher in female adolescents [p-value=0.000057; median and ranges; all in µg/g: 5.524 (3.167-13.262) vs. 4.464 (2.666-6.173)]. The observed effect of sex might be reflective of the endocrine system, which usually becomes active earlier in females with the onset of adolescence. Thus, the levels of Fe did not show statistical differences according to the four areas of residency (p-value=0.370) distributed in relation to Alcalá’s environmental characteristics: 5.230 (green spaces), 5.197 (urban), 5.522 (traffic), and 6.225 (industrial). The levels of Fe were similar to those detected in young adults (5.157 vs. 5.054) but were much lower than those detected in individuals 11-15-years-old (14.8; all in µg/g) living in the Madrid region. Previous studies have documented a decline in iron in hair with age, which could explain our observations. Although our results have shown a similar trend in relation to the effects of age and sex reported in the literature, they might highlight a lower Fe status in Alcalá’s adolescent population, which should be further investigated to increase public health interventions to enhance the status of iron in the population monitored.

Antimicrobial resistance represents one of the main threats to global health due to some pathogens being extremely resistant to existing antibiotics. In this scenario, it is necessary for new drugs to become promising. To this end, in vivo nonclinical trials are an important step in the development of new drugs due to their relatively low cost, the possibility of mimicking pathological conditions in living organisms, and the fact that they provide relevant data on toxicity and antibacterial activity. However, there is no homogeneity in the studies regarding the techniques and protocols used to achieve the desired clinical conditions. This is a narrative review, the objective of which is to evaluate which models are used to induce sepsis in mice. The research was carried out using the PubMed and Virtual Health Library databases, with the descriptors “models of bacterial infection”, “mus musculus”, “sepsis”, and “in vivo”, using the AND and OR connectors. Twenty-seven articles were selected, of which 40% used the intraperitoneal technique—with the species Escherichia coli and Staphylococcus aureus being the most commonly used—33% were performed with cecal puncture ligation (CPL), and in the other 26%, the administration route was intravenous, preferably via the caudal route. In most studies, the final disease induced was sepsis, differing in relation to the focus, which varied between urinary, abdominal, and pulmonary. The main organs removed were the spleen, lungs, liver, and kidneys, which were subjected to histopathological examination and bacterial count. Thus, it is concluded that the most commonly used models are obtained via the intraperitoneal administration route and CPL and that there is a wide range of protocols used for confirmation.

The widespread use of organophosphorus pesticides, such as chlorpyrifos, in agriculture has raised significant environmental and health concerns due to their persistence and toxic effects. To address these challenges, we present the development of a portable, solar-powered, and energy-autonomous system for the detection of chlorpyrifos in water using Electrochemical Impedance Spectroscopy (EIS). The system integrates an EmstatPico card for electrochemical measurements, a Raspberry Pi Zero 2W as a wireless data server, and Python-based software to control and execute the EIS tests. Powered by a lithium-ion battery charged via a solar panel, this setup is suitable for field applications without the need for external power sources.

The detection of chlorpyrifos is achieved through a commercial amperometric electrochemical biosensor based on acetylcholinesterase (AChE), which is sensitive to neurotoxic inhibitors such as chlorpyrifos. Nine water samples containing different concentrations of chlorpyrifos were prepared to evaluate the system’s performance. EIS measurements were conducted with a frequency sweep ranging from 200 kHz to 20 Hz and an alternating signal amplitude of 15 mV. The results showed an inverse relationship between the chlorpyrifos concentration and impedance, along with a decrease in the phase angle as the analyte concentration increased.

By utilizing Python and a Raspberry Pi, this system opens possibilities for integrating machine learning and artificial intelligence algorithms, enabling real-time data analysis, pattern recognition, and predictive modeling to further enhance the accuracy and adaptability of pesticide detection.

The findings suggest that this portable, energy-autonomous system is a simple, efficient, and sensitive tool for detecting chlorpyrifos in liquid samples. It has potential applications in environmental monitoring and public health, offering an alternative to traditional analytical techniques that are often costly and complex and require extensive sample preparation.

Hops (Humulus lupulus L.) have been extensively investigated for their multifunctional properties across various industries, including food, cosmetics, and pharmaceuticals. This study collected samples from several hop cultivars (Nugget, Cascade, Chinook) and wild-type hops from the Bragança region, Portugal. The plant materials studied comprised cones, stems, and leaves, while in the case of the Nugget byproduct, additional plant parts such as seeds, bracts, and vegetative tissue were also analyzed.

The main goal was to use UV-VIS and HPLC spectrophotometric techniques to assess the chemical composition and pharmacological features of hydromethanolic hop extracts. The presence of several phytochemicals, including flavonoids, phenolic acids, alkaloids, and terpenes was associated with the antibacterial, antioxidant, and anti-inflammatory actions of the key bioactive characteristics evaluated. Using enzymatic inhibition tests that target the enzymes tyrosinase, elastase, collagenase, and hyaluronidase, the extracts' sun protection factor (SPF) and dermatological bioactivity were further assessed. Human fibroblast (HFF-1) and keratinocyte (HaCaT) cell lines were used to test in vitro cytotoxicity. The hydromethanolic extracts were found to contain bioactive phenolic compounds, including isoquercetin, kaempferol, rutin, and apigenin. Outstandingly, the extracts exhibited potent antibacterial activity against Cutibacterium acnes and demonstrated significant elastase-inhibitory effects.

These bioactivities highlight the potential of hops as a valuable source of bioactive compounds for future applications in pharmaceutical, cosmetic, and nutraceutical development.

Introduction

Epidermal integrin α3β1 has emerged as a promising therapeutic target in impaired wound healing as it has been shown to be highly expressed during wound healing and has been linked to keratinocyte migration in vitro. However, its role in re-epithelialization in vivo has remained unclear. This study aims to clarify this role by using a novel inducible epidermis-specific α3 knockout (iα3eKO) murine model.

Methods

Young (8-week-old) and aged (22-month-old) iα3eKO mice were treated topically with tamoxifen (4OHT) to induce α3 knockout, or with vehicle control (acetone) 5 and 3 days prior to wounding. The backs of the mice were shaved, disinfected, then wounded with 4mm full-thickness biopsy punches. Frozen sections were prepared and immunostained with anti-integrin α3, anti-K14, anti-Ki67, and DAPI. Keratinocyte proliferation and wound re-epithelialization were assessed in each age group.

Results

Young 4OHT-treated mice exhibited markedly reduced wound re-epithelialization compared to their vehicle-treated counterparts (p<0.0001). Keratinocyte proliferation was also decreased in wound-distal hair follicles in young animals (p = 0.0235), suggesting a lack of proliferating keratinocytes contributes to the reduced wound re-epithelialization. Keratinocyte proliferation was similarly decreased in aged mice, and 4OHT treatment did not further reduce healing parameters. Additionally, our findings indicate that epidermal α3β1 levels naturally decline with advanced murine age, and preliminary results indicate that this may also occur during human aging. Therefore, we hypothesize that reduced levels of α3β1 contribute to reduced capacity for wound healing observed in the aged population.

Conclusions

Overall, our work indicates that integrin α3β1 promotes wound re-epithelialization and declines with chronological age, suggesting that wound healing mechanisms may be impaired in the elderly due to a natural decrease in α3β1 levels. Future studies will reveal whether therapeutic promotion of integrin α3β1 function will aid in wound closure in the elderly and/or in hard-to-heal wounds, in general.

Introduction: Microbial contamination in the cosmetic industry represents a significant risk to product safety, potentially causing health issues for consumers and leading to costly recalls. Common contaminants, such as Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, can be introduced during manufacturing or consumer use, resulting in contaminations and product spoilage. Conventional detection methods commonly used, like plate counting, are slow, often taking up to a week to yield results, which delays quality control processes and increases analysis costs. The Fluorescence In Situ Hybridization (FISH) technique offers a faster, specific, and sensitive alternative for directly detecting microorganisms in cosmetic products. This study aims to design and validate new DNA-FISH probes, optimized to function without formamide, for the rapid identification of cosmetic microbial contaminants.

Methods: In silico probes targeting E. coli, P. aeruginosa, and S. aureus were designed using the DECIPHER program, which included sequence alignment, as well as assessments for specificity and efficiency. The 23S ribosomal RNA gene was selected due to its high variability and functional relevance, making it an ideal target for specific identification. Probes were evaluated both in silico and experimentally with target and non-target microorganisms. The FISH procedure was optimized to exclude formamide, a commonly used but toxic solvent in FISH protocols, thereby enhancing safety and usability.

Results: The designed probes demonstrated high specificity and efficiency for detecting the target microorganisms in both in silico and experimental conditions. Experimental validation confirmed that the probes could reliably identify E. coli, P. aeruginosa, and S. aureus in controlled cultures, producing fluorescent signals without the need for formamide.

Conclusions: This study successfully developed formamide-free DNA-FISH probes for the rapid identification of key microbial contaminants in cosmetic products. The proposed method addresses the limitations of traditional microbiological testing by providing quicker, safer, and more reliable results, thereby improving quality control processes and product safety in the cosmetic industry.