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.

In this study, a marine microalga Spirulina sp.-derived protein was hydrolyzed using gastrointestinal enzymes to produce an angiotensin I (Ang I)-converting enzyme (ACE) inhibitory peptide. Following consecutive purification, the potent ACE inhibitory peptide was composed of 7 amino acids, Thr-Met‑Glu‑Pro‑Gly‑Lys-Pro (molecular weight, 759 Da). Analysis using the Lineweaver-Burk plot and molecular modeling suggested that the purified peptide acted as a mixed non-competitive inhibitor of ACE. The inhibitory effects of the peptide against the cellular production of vascular dysfunction-related factors induced by Ang II were also investigated. In human endothelial cells, the Ang II-induced production of nitric oxide and reactive oxygen species was inhibited, and the expression of inducible nitric oxide synthase (iNOS) and endothelin-1 (ET-1) was downregulated when the cells were cultured with the purified peptide. Moreover, the peptide blocked the activation of p38 mitogen‑activated protein kinase. These results indicated that this Spirulina sp.-derived peptide warrants further investigation as a potential pharmacological inhibitor of ACE and vascular dysfunction.

Significance: Despite the well-characterized structural symmetry of the dimeric transmembrane antibiotic gramicidin A, we show that the symmetry is broken by selective hydrogen bonding between eight waters comprising a transmembrane water wire and a specific subset of the 26 pore-lining carbonyl oxygens of the gramicidin A channel. The ¹⁷ O NMR spectroscopic resolution of the carbonyl resonances from the two subunits required the use of a world record high field magnet (35.2 T; 1,500 MHz for ¹ H). Uniquely, this result documented the millisecond timescale stability of the water wire orientation within the gramicidin A pore that had been reported to have only subnanosecond stability. These ¹⁷ O spectroscopic results portend wide applications in molecular biophysics and beyond.

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.

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.