Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses are opposing factors in the human body and the balance between them is necessary to maintain a healthy body. An unhealthy routine or the degeneration of age can break the balance, leading to more ROS than antioxidants, causing damage to health. In general, the antioxidant mechanism is the combination of antioxidant molecules and ROS in a one-electron reaction. Creating computational models to promptly identify antioxidant candidates is essential in supporting antioxidant detection experiments in the laboratory. In this study, we proposed a machine learning-based model for this prediction purpose from a benchmark set of sequencing data. The experiments were conducted by using 10-fold cross-validation on the training process and validated by three different independent datasets. Different machine learning and deep learning algorithms have been evaluated on an optimal set of sequence features. Among them, Random Forest has been identified as the best model to identify antioxidant proteins with the highest performance. Our optimal model achieved high accuracy of 84.6%, as well as a balance in sensitivity (81.5%) and specificity (85.1%) for antioxidant protein identification on the training dataset. The performance results from different independent datasets also showed the significance in our model compared to previously published works on antioxidant protein identification.

The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.

Posterior eye diseases, such as age-related macular degeneration and diabetic retinopathy, are difficult to treat due to ineffective drug delivery to affected areas. Intravitreal injection is the primary method for posterior eye drug delivery; however, it is usually accompanied by complications. Therefore, an effective and non-invasive method is required. Self-assembling nanoparticles (NPs) made from gelatin-epigallocatechin gallate (EGCG) were synthesized (GE) and surface-decorated with hyaluronic acid (HA) for drug delivery to the retinal/choroidal area. Different HA concentrations were used to prepare NPs with negative (GEH−) or positive (GEH+) surface charges. The size/zeta potential and morphology of the NPs were characterized by a dynamic light scattering (DLS) system and transmission electron microscope (TEM). The size/zeta potential of GEH+ NPs was 253.4 nm and 9.2 mV. The GEH− NPs were 390.0 nm and −35.9 mV, respectively. The cytotoxicity was tested by adult human retinal pigment epithelial cells (ARPE-19), with the results revealing that variant NPs were non-toxicity at 0.2–50 µg/mL of EGCG, and that the highest amount of GEH+ NPs was accumulated in cells examined by flowcytometry. Topical delivery (eye drops) and subconjunctival injection (SCI) methods were used to evaluate the efficiency of NP delivery to the posterior eyes in a mouse model. Whole eyeball cryosections were used to trace the location of fluorescent NPs in the eyes. The area of fluorescent signal obtained in the posterior eyes treated with GEH+ NPs in both methods (eye drops: 6.89% and SCI: 14.55%) was the greatest when compared with other groups, especially higher than free dye solution (2.79%). In summary, GEH+ NPs can be transported to the retina by eye drops and SCI; in particular, eye drops are a noninvasive method. Furthermore, GEH+ NPs, characterized by a positive surface and HA decoration, could facilitate drug delivery to the posterior eye as a useful drug carrier.

Conductive cryogel is a new form of biomaterial that provides additional electrical properties, expanding its applications in biomedical engineering, particularly in tissue regeneration, such as neural and muscle tissues, and in drug-delivery systems. In this study, the gelatin cryogels (GCs) incorporating the conductive polymer PEDOT:PSS (PGC) at an optimal ratio (PGC2) could achieve suitable conductivity as an electrically responsive scaffold applied for controlled drug release. The characterization results demonstrated that PGC2 exhibited a well-defined porous structure (90-190 μm), a favorable water uptake capacity (2738.4%), degradability (26.1% degradation after 24 h), and reversible deformability upon compression, demonstrating its suitability for minimally invasive procedures via injection. The electron-transfer capability and higher conductivity of PGC2 were also confirmed by examining the electrochemical properties, which revealed a lower impedance (0.0174 MΩ) compared to nonconductive GC2 (0.0208 MΩ). Additionally, drug release tests revealed that drugs contained in PGC2 as electrically responsive scaffolds had a higher release response to electrical stimulation (ES) (23%) compared to the one without ES (7%). Cytocompatibility assays revealed that NIH-3T3 cells cocultured on PGC2 and those cultured with a PGC2 extraction solution exhibited cell viability above 70%, indicating nontoxicity, as confirmed by a WST-1 assay and a live/dead staining assay. Overall, PGC2 represents a promising biomaterial with injectability, conductivity, and cytocompatibility, offering potential applications in regenerative medicine, drug delivery, and bioelectronic scaffolding.

Accurate diagnosis of knee joint injuries from magnetic resonance (MR) images is critical for patient care. Background/Objectives: While deep learning has advanced 3D MR image analysis, its reliance on extensive labeled datasets is a major hurdle for diverse knee pathologies. Few-shot learning (FSL) addresses this by enabling models to classify new conditions from minimal annotated examples, often leveraging knowledge from related tasks. However, creating robust 3D FSL frameworks for varied knee injuries remains challenging. Methods: We introduce MedNet-FS, a 3D FSL framework that effectively classifies knee injuries by utilizing domain-specific pre-trained weights and generalized end-to-end (GE2E) loss for discriminative embeddings. Results: MedNet-FS, with knee-MRI-specific pre-training, significantly outperformed models using generic or other medical pre-trained weights and approached supervised learning performance on internal datasets with limited samples (e.g., achieving an area under the curve (AUC) of 0.76 for ACL tear classification with k = 40 support samples on the MRNet dataset). External validation on the KneeMRI dataset revealed challenges in classifying partially torn ACL (AUC up to 0.58) but demonstrated promising performance for distinguishing intact versus fully ruptured ACLs (AUC 0.62 with k = 40). Conclusions: These findings demonstrate that tailored FSL strategies can substantially reduce data dependency in developing specialized medical imaging tools. This approach fosters rapid AI tool development for knee injuries and offers a scalable solution for data scarcity in other medical imaging domains, potentially democratizing AI-assisted diagnostics, particularly for rare conditions or in resource-limited settings.