The prompt and precise detection of breast cancer, a substantial worldwide health issue, is essential for enhancing patient results. Nevertheless, conventional techniques such as mammography encounter constraints in terms of sensitivity and specificity, resulting in overlooked diagnoses and unwarranted biopsies. Deep learning (DL) is a state-of-the-art technique that can improve breast cancer detection by effectively extracting complex characteristics from medical images and enabling precise categorization. This research introduces a novel framework designed for the nuanced categorization of breast cancer by analyzing histopathology images using DL techniques. The journey begins with the original histopathological images employing a substantial dataset comprising approximately 150,408 image patches of two separate categories based on the presence or absence of invasive ductal carcinoma (IDC), each with dimensions of 50x50 pixels and RGB colour representation. The system underwent rigorous pre-processing steps and employed data augmentation to reduce overfitting. These augmented images serve as the input for the fine-tuned DL models, a repertoire that includes Custom CNN, ResNet50, DenseNet201, and VGG16, all orchestrated for meticulous training, testing, and validation. After carefully analyzing the pre-trained models and custom CNN, we found that the fine-tuned VGG16 model had an exceptional performance, obtaining an accuracy rate of 96%. The proposed approach was subjected to thorough examination, confirming its efficacy and ability to reduce diagnostic errors caused by human factors.

Introduction: Layer-by-layer (LbL) assembly is characterized by controlled and prolonged drug release over 3 months compared to electrospinning or solvent casting methods, making it a promising approach for local chemotherapy platforms. The aim of this research was to develop local chemotherapy platforms using different layer-by-layer assembly methods to identify the most efficient drug encapsulation and to achieve the uniform and sustained drug release kinetics.

Methods: The platform developed in this study consists of a polycaprolactone substrate and a multilayer coating produced by LbL assembly. The coating contains the chemotherapeutic drug doxorubicin (DOX), which was stabilized for release by preforming an ionic complex between poly-γ-glutamic acid and DOX, which was then used as a polyanion during LbL assembly. Different LbL assembly methods (by spin or dip), various polycations and the addition of DOX-unloaded polyelectrolyte bilayers were examined. The drug release mechanism was studied in vitro in PBS, mimicking the natural environment of physiological fluids with pH 7.4 at 37 °С. The platforms were also tested for the in vitro antitumor activity of DOX using ovarian cancer cells (SKOV-3).

Results: DOX release from the obtained platforms was sustained for over 6 months with minimal burst kinetics and uniformity, but the drug encapsulation in LbL assembly by spin was tenfold higher. Dip-coated platforms had a coefficient of determination (R²) of 0.84, while spin-coated platforms had an R² of 0.94, when compared to a zero-order drug release model. The in vitro studies showed that the platform has no effect on the antitumor activity of DOX.

Conclusions: This work is encouraging for the development of drug delivery systems as it demonstrates the potential of spin-assisted assembly, offsetting the explosive nature of drug release, and showing its highest stability compared to similar studies.

Objectives/Introduction: Machine learning (ML) is a promising tool for assessing voice quality and dysphonia. Several public datasets containing recordings of both normal and pathological voices are available online. Since ML benefits from larger datasets, combining these available datasets could enhance ML performance. However, the varying environmental conditions under which these recordings were collected may impact ML accuracy, and the extent of this impact is unclear. This work aims to investigate how different data collection procedures affect ML efficacy in screening dysphonia.

Methods: Two datasets were considered. The first dataset included voice samples from 198 participants: 148 individuals with voice disorders and 50 vocally normal subjects. The second dataset, the publicly available PVQD database, included 276 subjects: 187 patients with voice problems and 89 without vocal issues. Various acoustic measurements (including perturbation, noise, cepstral, and spectral analyses) were estimated from the recordings using MATLAB scripts and Praat software. These measurements were derived from multiple types of speech productions: a sustained vowel /a:/ and running speech. Different ML models were trained on the extracted acoustic features from each recording and evaluated for accuracy, sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves to compare the impact of each dataset, collected under different procedures, on dysphonic voice classification.

Results and Conclusions: Accurate acoustic metrics were generated from the two datasets. Using these measurements, ML models were successfully trained and evaluated to classify dysphonic versus non-dysphonic speakers. The comparative analysis revealed discrepancies in classification accuracy among the models between the two datasets and when the datasets were combined. Identifying which ML models are robust or sensitive to changes in data collection environments helps in selecting appropriate models for tasks involving different datasets with varying data collection procedures. The outcome is an important step towards more reliable/effective ML tools in screening voice disorders.

Objectives/Introduction: In acoustic voice assessment, recordings are typically collected from diverse environments with varying levels of noise and reverberation. These room acoustics are known to affect the quality of recordings and acoustic analysis, but their impact on advanced tools like machine learning remains little understood. This paper investigates how different room acoustics, particularly reverberation, influence machine learning performance in assessing voice quality and dysphonia.

Methods: This retrospective study utilized voice recordings of sustained /a:/ samples from 193 subjects (145 with voice disorders and 48 without vocal problems). The recordings were modified to add on different levels of reverberation and noise using Audacity software, simulating various room acoustic environments. Using a MATLAB script and Praat software, we extracted different acoustic measurements (temporal- and spectral-based metrics) from the original and corrupted recordings. Various machine learning models were then trained on the generated acoustic features. The models were evaluated for accuracy, sensitivity, and specificity to compare the impact of the recordings, both before and after adding reverberation and noise effects, on machine learning performance in detecting voice disorders.

Results and Conclusions: The recordings were successfully mixed with varying levels of reverberation and noise, creating a diverse set of datasets. Machine learning models were trained and evaluated on these datasets to classify normal and pathological voices under different noise and reverberation conditions. A comparison of the models demonstrated that higher levels of reverberation and noise degrade classification performance. Identifying the acceptable room acoustic conditions where machine learning models produce reliable results helps in optimizing and standardizing environmental conditions for data collection, ensuring accurate voice assessment outcomes.

The transition into the naïve state in human pluripotent stem cells (hPSCs) represents a crucial advancement in stem cell biology, as it captures the cells in a more primitive/neutral and epigenetically unrestricted state, therefore offering more plasticity compared to the primed state. Achieving this naïve state is essential for maximizing the potential of hPSCs in regenerative medicine and developmental studies.

Traditionally, Matrigel has been employed as a plate coating for growing hPSCs; however, Matrigel contains animal-derived components that may present transgenic significant limitations for clinical applications. In this study, we explore the efficacy of using a xeno-free plate coating, PMEDSAH, for inducing hPSCs to a naïve state using FINE (Szczerbinska,et.al) and NHSM (Weinberger, et.al, Romayor et.al.) cell culture media. Our findings indicate that PMEDSAH supports robust cell growth and pluripotency with NHSM media, offering a viable alternative to Matrigel. Both media demonstrated effective reversion to the naïve state, characterized by the expression of key naïve pluripotency markers. Our data suggest that NHSM media in PMEDSAH plates seemed to enhance naïve cell induction. This work underscores the importance of adopting xeno-free culture systems and optimized media formulations to advance the clinical and research applications of naïve hPSCs.

Food contamination with Listeria monocytogenes can cause health problems of increasing global concern. The resistance of L. monocytogenes to antibiotics requires finding alternative solutions to protect human health. This mini study was designed to evaluate the effects of antibiotics and some particularly essential oils on L. monocytogenes isolates from food of an animal origin and isolates from food-producing surfaces.

This aim was to investigate the effects of seven antibiotics (amikacin, gentamicin, penicillin, cephalexin, ceftriaxone, tetracycline, and nalidixic acid) on L. monocytogenes isolated from food and surface samples. Also, the antibacterial activity of four essential oils (thyme, oregano, peppermint, and rosemary) against the growth of L. monocytogenes isolates was investigated. Listeria monocytogenes isolates were from food of an animal origin and food-producing surfaces as a potential source of food cross-contamination and subsequently a causative agent of listeriosis.

The isolation and determination of L. monocytogenes from food samples followed ISO 11290-1, including the API Listeria. Surface samplings were performed following ISO 18593 followed by the isolation and determination of L. monocytogenes. The disc diffusion method was used and the tests were performed in triplicate.

The results of this study confirmed more pronounced antibacterial activity of essential oils compared to antimicrobial drugs. The essential oil of Thymus vulgaris showed a bactericidal effect against all tested L. monocytogenes isolates.

Volatile fatty acid (VFA) is a crucial intermediate molecule for numerous industrial applications and can be synthesized by Clostridium strains through optimized fermentation pathways. However, the industrial-scale biotechnological production of VFAs remains a significant challenge, requiring highly efficient and cost-effective methodologies. The contemporary shift towards environmentally and economically sustainable production techniques, such as the conversion of lignocellulosic materials (LMs) into high-value chemicals and fuels, is exemplified by the consolidated bioprocessing (CBP) strategy. This innovative approach amalgamates enzyme production, saccharification, and fermentation into a single, streamlined process. Despite its potential, the realization of the high yield and productivity of VFAs from LMs through CBP remains challenging. There exists a critical need for research that establishes robust and economically viable strategies for the application of Clostridium strains and the utilization of LMs within CBP frameworks. This study aims to critically evaluate the current advancements in employing LMs as substrates for Clostridium strains and the development of integrated fermentation processes for VFA production via CBP. The outcomes of this research hold the potential to significantly advance the sustainable production of high-value chemical products and biofuels, thereby contributing to the development of more efficient and cost-effective industrial processes. By enhancing the understanding and implementation of these CBP techniques, can move closer to achieving industrial-scale VFA production while advancing the concepts of bioeconomy and circularity.

Introduction: Eye detection is critical in a variety of applications, ranging from facial recognition in human-computer interfaces to the analysis of human behavior and disease diagnosis. Scientific literature highlights the eyes as the most significant feature of the face, prompting extensive research in eye detection. Given the iris region's circular nature, the Hough Transform for Circles (HTC) emerges as a promising technique for identifying eyes. Utilizing the parametric equation of a circle, the HTC facilitates eye location through the template matching method. Moreover, HTC offers a non-invasive alternative to active approaches such as infrared eye detection and can reconstruct image shapes even with information loss due to digital processing. This study aims to apply HTC for detecting eyes on human faces.

Methods: Digital processing was conducted on 30 resized images (200x233) sourced from a public database. During the detection stage, code was implemented to derive the Hough space and recognize circles. An eye pair detector was then developed using the coordinates of the centers and radii of the circles identified by the Hough Transform. Finally, pairs of eyes were detected on various male and female faces.

Results and Discussion: Experiments on diverse faces revealed that applying HTC alone was insufficient for accurate eye identification, as circles other than the eyes were frequently detected. This led to the hypothesis that accurate identification could be achieved by focusing solely on the eye region, leveraging the symmetry of the face. This hypothesis was confirmed, demonstrating that the region corresponding to the eyes could be accurately identified by analyzing facial symmetry.

Conclusions: The findings indicate that it is feasible to non-invasively detect the eye region on human faces using the Hough Circles Transform. By incorporating the analysis of facial symmetry, specifically the interocular distance, HTC can reliably identify the eye region.

Introduction

Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging modality that is continuously growing, both in the clinical and scientific fields. The analysis of these images requires a very complex and varied post-processing of the obtained images. This causes the results of different studies to be non-comparable or difficult to characterize. In order to simplify the processing and obtain objective results with analyzable metrics, this work proposes the development of an analysis methodology to obtain statistical values on brain activation areas segmented by region.

Methods

Several specialized tools were used. First, the FreeSurfer scientific package was used for brain segmentation. Then, widely used software for the analysis of fMRI data, FSL, was used for activation areas. The last processing and analysis steps were performed with the 3D Slicer medical image visualization tool. Based on these tools, a method was defined to obtain fMRI activation metrics for each of the 148 brain structures obtained by FreeSurfer.

This method was applied to the database proposed by [Masterson TD et al., 2016], where studies of neuronal response to visual food stimuli were carried out at two different times of the day. From this, it is possible to perform a comparative analysis of different metrics for each functional area and to define the levels of significance.

Results

A method was obtained for generating metrics that characterize functional studies based on the use of open-source scientific tools. This method was then applied to a database of fMRI images. Finally, a non-parametric statistical study was used based on the characterization of each area for all subjects, obtaining the most significant features.

Conclusion

This work applied a methodology for automatic fMRI image processing to obtain metrics and perform the most convenient statistical analyses. This method can be extended to different intra- and inter-patient comparisons.

Mosses are one of the simplest autotrophic cryptogams invading land and are characterized by erect habits, lacking true leaves, roots and stems within the plant body. Due to their versatile tolerance and resistance capability, they have become an indispensable component of vegetation and constitute one of the most important parts of the biodiversity in mountains, wetlands, moist forests and tundra ecosystems. These tiny plants also act as a "remarkable reservoir" of natural products and can also synthesize various primary and secondary metabolites which are comparable to those in vascular plants. These metabolites have high biological properties and could become a potential source for treating various diseases. The present investigation was carried out to analyze the phytochemical compounds present in the moss Barbula constricta, with the use of a network pharmacology approach to detect its anti-inflammatory potential. The moss species was collected from its natural habitat for preparation of the plant extract, which was carried out using a solvent extraction method. The analysis was carried out using a Shimadzu QP 2010 Ultra gas chromatograph interfaced with a quadrupole mass spectrometer. From the obtained results, it was observed that these plants contain aliphatic fatty acids, terpenes, steroids and other primary and secondary metabolites. Some of the observed compounds have shown anti-inflammatory activity, which increases their phytochemical potential and their importance in drug and pharmaceutical industries.