Introduction: The increasing resistance of microorganisms to antibiotics has highlighted the urgent need for new antimicrobial agents. Filamentous fungi, commonly found in soil, are known producers of bioactive compounds, including antimicrobial agents. This study aimed to isolate and evaluate the antimicrobial activity of filamentous fungi found in soil samples, with the objective of identifying potential new sources of antimicrobial compounds that may offer alternatives to conventional antibiotics.

Methods: Soil samples were collected from a biological garden on the University Lusófona campus to isolate filamentous fungi using selective media. The isolated fungi were then subjected to antimicrobial activity tests using the agar well diffusion method against two pathogenic bacterial strains: Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive). The fungi that exhibited the most promising results were selected for DNA analysis. To accurately identify the fungal species, DNA extraction and polymerase chain reaction (PCR) amplification were performed. Sequencing data were analysed using the BLAST algorithm to confirm the identities of the isolated fungi.

Results: Several filamentous fungi were successfully isolated from the soil samples, including Penicillium pimiteouiense and Aspergillus niger. Both fungi exhibited significant antimicrobial activity, as demonstrated by the formation of inhibition halos in the presence of E. coli and S. aureus. These results indicate these fungi's potential to produce antimicrobial compounds effective against S. aureus and E. coli, two of the most representative pathogenic bacteria.

Conclusion: This study supports the potential of soil microbiota, particularly filamentous fungi, as a rich resource for discovering new antimicrobial compounds. The findings highlight the importance of further research to explore the mechanisms of action of these compounds and to develop them for clinical applications. The isolated fungi, namely P. pimiteouiense and A. niger, show promise as sources of new antimicrobial agents that could help combat antibiotic resistance and pathogenic bacteria.

Spheroids are three-dimensional models that play a crucial role in the study of tissues and tumors. Advances in technology have enabled the automated generation of spheroids with various experimental parameters, but the manual analysis of such data is time-consuming and prone to inaccuracies. Therefore, a robust and rapid solution for the morphological analysis of these models is required. This study presents a Python-based algorithm for the quantified analysis of 3D tumor spheroids (PANC1 cell line) produced through a robotic-enabled platform. The pipeline includes sharp image detection, instance segmentation, and contour analysis, using a YOLO (You Only Look Once) machine learning model to identify key morphological features of the tumor models, such as their shape, area, and circularity. The model is custom-trained on a dataset comprising 518 images of 3D tumor spheroids. Its accuracy is validated by comparing its results with manual annotations performed by experts on the test dataset. The model achieved an F1 score of 0.872 in training results, indicating a strong balance between precision and recall in its classification of morphological features. Furthermore, the algorithm facilitates the rapid and reproducible analysis of large datasets, reducing the workload and improving the overall quality of morphological assessment. This contributes to better insights into tumor behavior and the effects of drug treatments.

The implementation of digitized farming technologies along with site-specific precision management are possible responses to ever-increasing expectations from the agri-food industry. The field of robotics is demonstrating significant potentials and benefits when integrated into the modernized agriculture. Although still in the prototype stage, automation in agriculture has a bright future. Agri-robots are capable of performing various farming operations such as seeding, pruning, spraying, pest and disease detection, harvesting and weed control. The present study was designed to recognize the utility of multi-functional robots across vegetable fields in Baruipur, Sonarpur and Jaynagar blocks of the South-24 Parganas district, West Bengal, India. Seeding robots could offer precision in seeding functionary by augmenting plant densities to increase yield. Robotic application in disease and pest management (both detection and control) would probably contribute towards reductions in economic damage. Plant-detection robots utilizing high-quality sensors are highly reliable for estimations of crop volume and area, thereby determining the appropriate amount of fertilizer required. Such robots could also distinguish between crops and weeds on fields. The usage of such robots for crop estimation in the study area would also help in determining the accurate amount of weedicides required, thereby preventing damage caused by blanket spraying. The utilization of harvesting robots by farmers could assist them in localizing the most appropriate state of fruit and its careful handling without damaging the crop. Thus, the evolution and development of robotics could play a vital role in paving the path towards complete automation in agriculture sector with minimal human involvement.

Introduction: This study presents the fabrication of a carbon nanotube-based temperature sensor and the development of a multiparameter patient monitoring system. The system integrates the temperature sensor with a galvanic skin response (GSR) sensor to monitor temperature, breath rate, and electrolyte profile, providing insights into the patient’s stress and physiological status.

Methods: The temperature sensor is fabricated using a stencil-printing method on a paper-based substrate, followed by encapsulation and calibration for temperature detection. The sensor is integrated into a system built around an Arduino Nano microcontroller, combined with a GSR module. The setup, designed as a chest band, includes an extended temperature sensor embedded in the patient’s mask for breath monitoring. Data on skin conductivity, temperature, and breath rate are wirelessly transmitted via a Bluetooth module.

Results: The carbon nanotube-based sensor demonstrated successful temperature detection, and the GSR sensor effectively monitored changes in skin resistance, indicating electrolyte levels. The system transmitted all collected data wirelessly, validating its functionality for real-time monitoring.

Conclusions: The developed system offers a simple yet effective solution for patient monitoring, particularly in settings lacking advanced equipment. By wirelessly tracking body temperature, breath rate, and electrolyte profile, it provides essential data for assessing patient stress and overall health, improving accessibility and patient comfort.

Alhagi maurorum, a plant species widely distributed in the arid regions of Uzbekistan, is known for its diverse therapeutic properties. Understanding its elemental composition is essential for assessing its nutritional value and potential medicinal applications. This study aims to quantify the macro and microelements present in various parts of Alhagi maurorum, specifically seeds and leaves, collected from the Qashqadaryo and Xorazm regions. The focus is on elements critical to human health, such as calcium, magnesium, sodium, and iron. Elemental analysis was conducted using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Samples of Alhagi maurorum seeds and leaves were collected, dried, and digested in a microwave system with nitric acid and hydrogen peroxide. The digested solutions were analyzed using ICP-MS to determine the concentrations of 61 elements. Standard calibration and control samples were used to ensure accuracy and precision during the analysis. The study found significant variations in the concentrations of macroelements between the seeds and leaves. Calcium was found in the highest concentration in the leaves (100,000 mg/kg), while magnesium and sodium also showed elevated levels, with concentrations up to 14,000 mg/kg and 4,200 mg/kg, respectively. Trace elements such as scandium, lithium, and cobalt were present in lower concentrations, generally below 0.5 mg/kg. Samples from the Xorazm region exhibited higher levels of iron, reaching up to 1,558 mg/kg. This study highlights the rich elemental composition of Alhagi maurorum, particularly its high calcium and magnesium content in the leaves, which may have implications for its use in nutritional supplements or pharmaceuticals. The regional differences in elemental concentrations suggest that environmental factors influence the uptake of these elements. ICP-MS proved to be an effective method for the precise quantification of both macro- and microelements in plant matrices.

Introduction and objectives:

Automatic classification systems have significantly advanced in hematology, enabling the identification of over 80% of hematological diseases through peripheral blood cell analysis. However, their black box nature complicates adaptation to new images with variability, affecting precision and reliability. This study proposes a methodology using explainability techniques, such as LIME and Saliency Map, to enhance model performance in the identification of leukocytes and other cell types.

Methods:

A dataset of 12298 leukocyte images, labeled by clinical pathologists and divided into five classes, basophils (1218), eosinophils (3117), lymphocytes (1214), monocytes (1420), and neutrophils (3329), was used to train a VGG19 convolutional neural network, achieving 98% accuracy on the test set. The model was then evaluated on a second dataset comprising neutrophils (416), lymphocytes (104), monocytes (43), and eosinophils (10), where accuracy dropped to 83%. Analysis of the 100 best- and 100 worst-classified images from both sets revealed that, in correctly classified images, Saliency Map showed high pixel activation across the entire cell except the nucleus, whereas misclassified images focused on the nucleus. LIME indicated a dependency on image borders.

Results:

To address this, zoom-based data augmentation was applied, reducing the model's reliance on superior and inferior borders. Progressive layer unfreezing revealed that adjusting the fourth convolutional block reduced focus on the nucleus and improved cell-wide activation. After re-training, performance significantly improved, achieving 99.4% accuracy, 99.8% precision, 99.6% sensitivity, 99.9% specificity, and a 99.6% F1-score on the second dataset.

Conclusion:

The proposed approach demonstrates that integrating LIME, Saliency Map, and layer unfreezing can effectively identify and adjust specific layers impacting model interpretability and accuracy. This integration enhances adaptability and interpretability in diverse clinical contexts, supporting improved model performance under varying data conditions.

Introduction: The venom of the coral snake species Micrurus corallinus is highly potent and exerts neurotoxic effects through presynaptic enzymes such as class IA phospholipases. However, due to the difficulty in obtaining the venom and the fact that most collected venom is used for antivenom production in Brazil, pharmacological studies of these toxins are scarce. Previous studies have already characterized the primary structure of M. corallinus PLA2. With the advent of tools like AlphaFold2 and recent improvements in tools like CHARMM-GUI, in silico studies of these molecules have become more accessible and accurate. This study proposes the in silico characterization of M. corallinus PLA2, comparing its structure with other characterized elapid PLA2s, evaluating both catalytic and presynaptic toxicity-related residues. Methods: The alignment of primary structures of PLA2s from Micrurus altirostris (F5CPF0), Micrurus nigrocinctus (P81166), Naja atra (P00598), and Pseudechis australis (P04056 and P04057) was performed using ClustalW. The three-dimensional structure of M. corallinus PLA2 was modeled using AlphaFold2. The interactions of the enzyme with its substrates (phospholipid or tridecanoic acid) were analyzed using the CHARMM-GUI web interface and the PPM 2.0 server. All molecular representations were created using the PyMOL molecular graphics software package. Results: Micrurus corallinus PLA2 presents all residues necessary for catalytic action: CCXXH48D49XC in the active site and GCY28CG30X32GXG in the Ca²⁺ binding loop. Both catalytic mechanisms, the single-water mechanism and the assisted-water mechanism, were evaluated, with the latter being more likely due to the large distance observed between His48 and the Ca²⁺ ion. Conclusion: The results indicate that M. corallinus PLA2 possesses all the necessary residues to exert its catalytic effects, supporting the possibility that this toxin is responsible for the presynaptic action observed in M. corallinus venom.

Muscle electrical control has been extensively documented in the pursuit of methodologies to extract pertinent information for the artificial reproduction of natural movements. Nevertheless, the physiological phenomena underlying this process are complex. The system comprises a finite set of actuators, each responsible for generating electrical impulses that propagate throughout the muscular tissue. The use of superficial electrodes for signal acquisition introduces an additional layer of complexity due to cross-talk phenomena. Consequently, the precise positioning of electrodes is imperative to enhance the quality of the extracted information. In this study, we evaluate the impact of electrode placement on movement recognition rates using a quadratic discriminant classifier, as well as the influence of unintended electrode displacement as a determining factor. This investigation utilizes a high-definition open-access database. Root Mean Square (RMS) values were computed from measurements obtained from 128 electrodes, and a sequential feature selection (SFS) algorithm was employed to identify the optimal subset of features. Recognition rates were calculated for each participant and for the overall sample of 18 participants to derive intersubject and intrasubject results. Furthermore, three displacement scenarios were developed: longitudinal displacement, transverse displacement, and diagonal displacement, aligned with muscle fiber orientation. The results encompass evaluations using the top four to ten most significant features identified via SFS, the feature subset, and all electrode measurements. This study shows that electrode positioning significantly impacts movement classification, with random displacement (7.5–12.54 mm) causing variations up to 17.16% within and 24% between subjects. RMS values per electrode were analyzed using the 4 to 10 most relevant features, revealing variations of 10.92% and 9.6% (4 features) and 6.4% and 7.6% (10 features). Cross-validation was employed to ensure that results were independent of data partitioning, and ANOVA was used to confirm statistically significant differences between group means.

Stephania dinklagei root is known in Nigeria for its medicinal properties, especially its role in the nervous system. N-methyl-D-aspartate receptor (NMDAR) has been linked to various neurological disorders. The potential of compounds obtained from S. dinklagei roots to inhibit the function of NMDAR is thus the focus of this in silico study. Liquid chromatography-mass spectrometry (LC-MS) was used to analyze compounds in S. dinklagei roots. Molecular docking against GluN2B NMDAR (PDB ID: 7SAD) was done using AutoDock Vina, while ADMET studies were carried out using SwissADME and ProTox 3.0 webservers. Memantine was used as the standard compound. Sixteen compounds were detected in the sub-fraction of S. dinklagei roots, with 75% having binding affinities > memantine (-5.3 kcal/mol). Geijerone, 7-(4,8-dimethylnona-3,7-dien-1-yl)-2,4',5,5',7',10-hexahydroxy-2,2'-dimethyl-1,2,3,4,9',10'-hexahydro-[1,1'-bianthracene]-4,9',10'-trione (DHBT) and alpha,beta-Dihydroxanthohumol (αβD), exhibited the highest binding affinities: -9.5, -9.3 and -7.0 respectively. The three compounds interacted with the same amino acid residues as memantine. Geijerone and αβD had 0 Lipinski, Ghose, Veber and Egan violations. They also have a bioavailability score of 0.55, are both soluble, and have a high gastrointestinal (GI) absorption profile. The three compounds are not Pgp substrates, and only geijerone was predicted to be a blood-brain barrier (BBB) permeant. DHBT and geijerone do not inhibit cytochrome P450 (CYP) enzymes; however, αβD inhibits all except CYP2C19. LogKp values of geijerone and αβD, -5.26 and -5.01 cm/s respectively, are comparable to memantine (-5.06 cm/s). The compounds belong to toxicity class 3 – 5. DHBT and αβD were predicted to be immunotoxic, respectively carcinogenic and nephrotoxic. Geijerone had a better binding affinity, similar drug-likeness, and ADME properties as memantine, but with a lower toxicity profile, and can therefore be further explored as an inhibitor of NMDAR.

Alzheimer’s disease (AD), an intense neurological illness, severely impacts memory, behaviour, and personality, posing a growing concern worldwide due to the aging population. Early and accurate detection is crucial as it enables preventive measures and personalized healthcare strategies that can significantly improve patient conditions. However, current diagnostic methods are often inaccurate in identifying the disease in its early stages, which is essential for effective treatment. Although deep learning-based bioimaging has shown promising results in medical image classification, challenges remain in achieving the highest accuracy for detecting AD. Existing approaches such as ResNet50, VGG19, InceptionV3, and AlexNet have shown potential, but they often lack reliability and accuracy due to several issues. To address these gaps, this paper proposes a new bioimaging technique by developing a custom Convolutional Neural Network (CNN) model for AD detection. This model is designed with optimized layers to enhance feature extraction from medical images, which are pivotal in identifying subtle biomarkers associated with AD. The experiment's first phase involves the construction of the custom CNN model with three convolutional layers, three max-pooling layers, one flatten layer, and two dense layers. The Adam optimizer and categorical cross-entropy are adopted to compile the model. The model’s training is carried out on 100 epochs with the patience set to 10 epochs. The second phase involves augmentation of the dataset images and adding a dropout layer to the custom CNN model. In addition, fine-tuned hyperparameters and advanced regularization methods were integrated to prevent overfitting. A comparative analysis of the proposed model with conventional models was performed on the dataset both before and after data augmentation. The experimental results demonstrate that the proposed custom CNN model significantly outperforms the pre-existing models, achieving a training accuracy of 100% and a testing accuracy of 99.79%, with a low training loss of 1.0148×10^-5 and a testing loss of 0.0205.