Dietary Probiotic Effect of Lactococcus lactis WFLU12 on Low-Molecular-Weight Metabolites and Growth of Olive Flounder (Paralichythys olivaceus)

The use of probiotics is considered an attractive biocontrol method. It is effective in growth promotion in aquaculture. However, the mode of action of probiotics in fish in terms of growth promotion remains unclear. The objective of the present study was to investigate growth promotion effect of dietary administration of host-derived probiotics, Lactococcus lactis WFLU12, on olive flounder compared to control group fed with basal diet by analyzing their intestinal and serum metabolome using capillary electrophoresis mass spectrometry with time-of flight (CE-TOFMS). Results of CE-TOFMS revealed that 53 out of 200 metabolites from intestinal luminal metabolome and 5 out of 171 metabolites from serum metabolome, respectively, were present in significantly higher concentrations in the probiotic-fed group than those in the control group. Concentrations of metabolites such as citrulline, tricarboxylic acid cycle (TCA) intermediates, short chain fatty acids, vitamins, and taurine were significantly higher in the probiotic-fed group than those in the control group. The probiotic strain WFLU12 also possesses genes encoding enzymes to help produce these metabolites. Therefore, it is highly likely that these increased metabolites linked to growth promotion in olive flounder are due to supplementation of the probiotic strain. To the best of our knowledge, this is the first study to show that dietary probiotics can greatly influence metabolome in fish. Findings of the present study may reveal important implications for maximizing the efficiency of using dietary additives to optimize fish health and growth

Marine-derived biopolymers as potential bioplastics, an eco-friendly alternative

Summary: The manufacturing and consumption of plastic products have steadily increased over the past decades due to rising global demand, resulting not only in the depletion of petroleum resources but also increased environmental pollution due to the non-biodegradable nature of conventional plastics. Moreover, despite being introduced into the market as an alternative to conventional petroleum-based plastics, biobased plastics are mainly manufactured from agricultural crop-based sources, which has negative impacts on the environment and the livelihoods of people. Marine-derived bioplastics are becoming a promising and cost-effective solution to the rising demand for plastic products. The physicochemical, biological, and degradation properties of marine-derived bioplastics have made them promising substances for many applications. However, more research is required for their large-scale implementation. Therefore, this review summarizes the raw materials of marine-derived bioplastics such as algae, animals, and microorganisms, as well as their extraction processes and properties. These insights could thus accelerate the production of marine-derived bioplastics as a novel alternative to prevailing bioplastics by taking advantage of marine biomass

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification

A 3D-Printed Polycaprolactone/Marine Collagen Scaffold Reinforced with Carbonated Hydroxyapatite from Fish Bones for Bone Regeneration

In bone tissue regeneration, extracellular matrix (ECM) and bioceramics are important factors, because of their osteogenic potential and cell–matrix interactions. Surface modifications with hydrophilic material including proteins show significant potential in tissue engineering applications, because scaffolds are generally fabricated using synthetic polymers and bioceramics. In the present study, carbonated hydroxyapatite (CHA) and marine atelocollagen (MC) were extracted from the bones and skins, respectively, of Paralichthys olivaceus. The extracted CHA was characterized using Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) analysis, while MC was characterized using FTIR spectroscopy and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The scaffolds consisting of polycaprolactone (PCL), and different compositions of CHA (2.5%, 5%, and 10%) were fabricated using a three-axis plotting system and coated with 2% MC. Then, the MC3T3-E1 cells were seeded on the scaffolds to evaluate the osteogenic differentiation in vitro, and in vivo calvarial implantation of the scaffolds was performed to study bone tissue regeneration. The results of mineralization confirmed that the MC/PCL, 2.5% CHA/MC/PCL, 5% CHA/MC/PCL, and 10% CHA/MC/PCL scaffolds increased osteogenic differentiation by 302%, 858%, 970%, and 1044%, respectively, compared with pure PCL scaffolds. Consequently, these results suggest that CHA and MC obtained from byproducts of P. olivaceus are superior alternatives for land animal-derived substances

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan