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Methicillin-resistant Staphylococcus aureus (MRSA) is responsible for skin and soft tissue infections with multi-resistance to many antibiotics. It is thus imperative to explore alternative antimicrobial treatments to ensure future treatment options. Nisin (NIS), an antibacterial peptide produced by Lactococcus lactis, was selected to combine with Oxacillin (OX), to evaluate the antimicrobial effect and potential mechanism against MRSA. The synergistic antimicrobial effect of OX and NIS was verified by Minimal Inhibitory Concentration (MIC) assays, checkerboard analysis, time-kill curve, biofilm producing ability, and mice skin infection model in vivo. For the potential synergistic antimicrobial mechanism, the microstructure and integrity change of MRSA cells were determined by Scanning and Transmission Electron Microscope (SEM and TEM), intracellular alkaline phosphatase activity and propidium iodide staining were assayed; And transcription of mecA, main gene of MRSA resistant to OX, were detected by qRT-PCR. The results showed NIS could restore the sensitivity of MRSA to OX and inhibit biofilm production; OX + NIS can make MRSA cell deform; NIS may recover OX sensitivity by inhibiting the transcription of mecA. In vivo, mice skin infection models indicate that OX + NIS can substantially alleviate MRSA infections. As a safe commercially available biological compound, NIS and the combination of antibiotics are worth developing as new anti-MRSA biomaterials.

There are multiple obstacles in the gastrointestinal tract (GIT) for oral administration of bioactive macromolecules. Here, we engineered an oral delivery vehicle (sporopollenin exine capsules with carboxymethylpachymaran (CMP)/metal ion modification) with targeted release based on food-grade ingredients and processing operations. Then, the interaction and binding mechanisms between CMP and metal ions in the vehicle were investigated. By using β-galactosidase (β-Gal) as a model protein, the systems were characterized for the surface morphology and monitored by the in vitro release profile of β-Gal. Notably, the CMP/metal ion systems not only markedly decreased the CMP dosage but also achieved a valid long-term release compared with the previously reported CMP system. Among all the systems, the CMP/3% AlCl3 system showed the best ability to control the release with the maximum residual activity of β-Gal at nearly 72% after 24 h of treatment. Subsequently, the interaction mechanism between CMP and metal ions within the system was characterized by the perspectives of microstructure, rheological properties, and spectroscopy characteristics. The results indicated that the low pH conditions are conducive to the further cross-linking of CMP and metal ions, resulting in a high gel strength and thus a dense structure, which can impact the controlled release of β-Gal in the GIT. Overall, the system may be utilized in the administration of medical and functional foods, specifically for the delivery of bioactive proteins via the oral route.

There are multiple obstacles for the storage and digestion of orally administered bioactive macromolecules. This study developed a low-cost and sustained-release delivery system (sporopollenin exine capsules with zein/tannic acid modification) of proteins with excellent storage stability, and at the same time provided insights into the sustained-release mechanism through exploring the interaction between zein and tannic acid (TA). β-Galactosidase (β-Gal) was utilized as a model protein and loaded into sporopollenin exine capsules (SECs), which were then coated with the zein/TA system. Under the optimized zein/TA conditions, the zein/TA system showed better performance than the zein alone system in the sustained release of β-Gal, with the residual activity of about 70.26% after 24 h of simulated digestion. Evaluation of the storage stability demonstrated a β-Gal residual activity of nearly 90% for 28 days at 25 °C. Additionally, FTIR analysis demonstrated that the stability of the zein/TA system depends on both hydrogen bonding and certain covalent bonding through the Schiff-base reaction, and the sustained release is regulated by the bonding strength.

Elemental selenium, a new type of selenium supplement, can be biosynthesized via microorganisms. This study is to characterize a patent probiotic bacteria Enterococcus durans A8-1, capable of reducing selenite (SeThe selenium nanoparticles synthesized from A8-1 were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), and X-ray photoelectron energy (XPS). The Caco2 cells were used to investigate the effects of Se-enriched A8-1 on the viability, membrane integrity, and the regulation of cellular inflammation through MTT and ELISA assays. The selenium-enriched metabolic function of A8-1 was analyzed by transcriptome sequencing.E. durans A8-1 has the ability to synthesize intracellular SeNPs that are incubated with 60 mg/L sodium selenite for 18 h at 37 °C with 7 % inoculum under aerobic conditions. The selenium-enriched transformation rate increased to 43.46 %. After selenium enrichment, there were no significant morphological changes in E. durans A8-1 cells. The cells also exhibited no cytotoxicity when incubated with Caco-2 cells, and increased cellular proliferation. Furthermore, Se-enriched A8-1 cells antagonize the adhesion of S. typhimurium ATCC14028 onto the surface of Caco-2 cells protecting cell membrane integrity and was assessed by measuring LDH and AKP activities (P 0.001, P 0.001). Moreover, Se-enriched A8-1 could protect Caco-2 cells from inflammation induced by lipopolysaccharide and help the cells alleviate the inflammation through the reduced expression of cytokine IL-8 (P = 0.0012, P 0.001) and TNF-α (P 0.001, P 0.001). Based on transcriptome sequencing in Se-enriched E. durans A8-1 cells, there were 485 up-regulated genes and 322 down-regulated genes (PE. durans A8-1 could convert extracellular selenite into intracellular biological SeNPs via redox pathway with strong selenium-rich metabolism, and its biological SeNPs have anti-inflammatory properties, which have the potential for the development of composite selenium nanomaterials and can be further studied for the function of SeNPs with potential applications.

Parkinson’s disease (PD) is a widely concerned neurodegenerative disease, due to its high prevalence in the elderly population. In this paper, a simple automated framework for PD detection is designed. The framework is proposed by extracting geometric and texture features based on facial visual information. Geometric features are defined via interrelationships of the facial landmarks, indicating the physical information of facial features. Texture features are extracted with a method fused with Local Binary Pattern (LBP) and Gray-level Co-occurrence Matrix (GLCM) that enhance the discriminative ability of the system, indicating the digital information of facial features. The proposed method performs well with a variety of classifiers, and the best result reaches 0.936 accuracy and 0.938 \({F}_{1}\) score. Results indicate this simple automated framework for PD detection could provide a valuable tool for the clinical assessment of PD, which could assist in PD screening effectively.

BACKGROUND Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible. OBJECTIVE The study aimed to develop a markerless 2D video, facial feature recognition–based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis. METHODS We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists. RESULTS The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86%. When these 2 features are combined, an F1 value of 88% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD. CONCLUSIONS PD patients commonly exhibit masked facial features. Videos of a facial feature recognition–based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient’s condition, especially during the COVID-19 pandemic.

Background Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible. Objective The study aimed to develop a markerless 2D video, facial feature recognition–based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis. Methods We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists. Results The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86%. When these 2 features are combined, an F1 value of 88% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD. Conclusions PD patients commonly exhibit masked facial features. Videos of a facial feature recognition–based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient’s condition, especially during the COVID-19 pandemic.

β-Galactosidase (β-Gal) as a dietary supplement can alleviate symptoms of lactose intolerance. However, β-Gal is deactivated due to the highly acidic conditions and proteases in the digestive tract. In this work, β-Gal was encapsulated into L. clavatum sporopollenin exine capsules (SECs) to fabricate an oral-controlled release system and increase the stability of β-Gal in the digestive tract. The SEC extraction process was optimized. A 3-hour vacuum loading was determined as the optimal loading time. Five different initial ratios of SECs : β-Gal were optimized with the maximum enzyme retention rate reaching 79.40 ± 1.96%. Furthermore, β-Gal-loaded SECs entrapped in carboxymethylpachymaran (CMP) could control the release of β-Gal under simulated gastrointestinal conditions (SGC). The optimal enzyme retention rate reached 65.33 ± 1.46% within 24 h under SGC. Collectively, these results indicated that the entrapped SECs could be used as an effective oral delivery vehicle of β-Gal to improve its performance as a dietary supplement in the digestion of lactose.