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Claudin-5 (CLDN5) is an essential component of tight junctions (TJs) and is critical for the integrity of the blood-brain barrier (BBB), ensuring homeostasis and protection from damage to the central nervous system (CNS). Currently, many researchers have summarized the role and mechanisms of CLDN5 in CNS diseases. However, it is noteworthy that CLDN5 also plays a significant role in tumor growth and metastasis. In addition, abnormal CLDN5 expression is involved in the development of respiratory diseases, intestinal diseases, cardiac diseases, and diabetic ocular complications. This paper aims to review the structure, expression, and regulation of CLDN5, focusing on its role in tumors, including its expression and regulation, effects on malignant phenotypes, and clinical significance. Furthermore, this paper will provide an overview of the role and mechanisms of CLDN5 in respiratory diseases, intestinal diseases, cardiac diseases, and diabetic ocular complications.

The combination of all-inorganic perovskites (PVSKs) and polymers allows for free-standing flexible optoelectronic devices. However, solubility difference of the PVSK precursors and concerns over the compatibility between polymer carriers and PVSKs imply a great challenge to incorporate different kinds of PVSKs into polymer matrices by the same manufacturing process. In this work, PVSK precursors are introduced into poly(2-hydroxyethyl acrylate) (PHEA) hydrogels in sequence, in which the PVSK-gel composites are achieved with full-color emissions by simply varying the precursor species. Moreover, it is found that CsBr has a higher interaction energy with the (111) plane of CsPbBr3 than the (110) plane; thus, the CsPbBr3 crystals with a shape of truncated cube and tetragon are observed during the CsPbBr3–Cs4PbBr6 phase transition over time. The PVSK-gel composites feature excellent bendability, elasticity, and stretchable deformation (tensile strain > 500%), which allows for 3D printing emissive customized stereoscopic architectures with shape-memory features.

Peptides possessing antihypertensive attributes via inhibiting the angiotensin-converting enzyme (ACE) were derived through the enzymatic degradation of Trichiurus lepturus (ribbonfish) using alkaline protease. The resulting mixture underwent filtration using centrifugation, ultrafiltration tubes, and Sephadex G-25 gels. Peptides exhibiting ACE-inhibitory properties and DPPH free-radical-scavenging abilities were isolated and subsequently purified via LC/MS-MS, leading to the identification of over 100 peptide components. In silico screening yielded five ACE inhibitory peptides: FAGDDAPR, QGPIGPR, IFPRNPP, AGFAGDDAPR, and GPTGPAGPR. Among these, IFPRNPP and AGFAGDDAPR were found to be allergenic, while FAGDDAPRR, QGPIGPR, and GPTGPAGP showed good ACE-inhibitory effects. IC50 values for the latter peptides were obtained from HUVEC cells: FAGDDAPRR (IC50 = 262.98 μM), QGPIGPR (IC50 = 81.09 μM), and GPTGPAGP (IC50 = 168.11 μM). Peptide constituents derived from ribbonfish proteins effectively modulated ACE activity, thus underscoring their therapeutic potential. Molecular docking and modeling corroborated these findings, emphasizing the utility of functional foods as a promising avenue for the treatment and prevention of hypertension, with potential ancillary health benefits and applications as substitutes for synthetic drugs.

Enrofloxacin (ENR) is widely used as a synthetic fluoroquinolone antibiotic for disease control in aquatic animals. ENR aptamers were screened in this study using the magnetic bead-SELEX method, and a graphene oxide fluorescent sensor was developed to detect the ENR residues in aquatic products. Firstly, ENR was conjugated to amino magnetic beads by amidation reaction, and then the aptamer sequences showing high affinity to ENR were screened step by step by using the SELEX screening method. Finally, after 10 rounds of SELEX screening, six candidate aptamers with high affinity were obtained. Among these, ENR-Apt 6 was selected based on its secondary structure features, high affinity (Kd = 35.08 nM), and high specificity to ENR. Furthermore, a fluorescent sensor was prepared using graphene oxide and ENR-Apt 6. The results showed that the linear range of the sensor could reach 600 nM (R2 = 0.986), while its optimal linear range was 1–400 nM (R2 = 0.991), with the lowest detection limit of 14.72 nM. The prepared sensor was successfully used for the detection of ENR in real samples, with a recovery range of 83.676–114.992% and a relative standard deviation < 10% for most of the samples.

Ofloxacin (OFL) is widely used in animal husbandry and aquaculture due to its low price and broad spectrum of bacterial inhibition, etc. However, it is difficult to degrade and is retained in animal-derived food products, which are hazardous to human health. In this study, a simple and efficient method was developed for the detection of OFL residues in meat products. OFL coupled with amino magnetic beads by an amination reaction was used as a stationary phase. Aptamer AWO-06, which showed high affinity and specificity for OFL, was screened using the exponential enrichment (SELEX) technique. A fluorescent biosensor was developed by using AWO-06 as a probe and graphene oxide (GO) as a quencher. The OFL detection results could be obtained within 6 min. The linear range was observed in the range of 10–300 nM of the OFL concentration, and the limit of the detection of the sensor was 0.61 nM. Furthermore, the biosensor was stored at room temperature for more than 2 months, and its performance did not change. The developed biosensor in this study is easy to operate and rapid in response, and it is suitable for on-site detection. This study provided a novel method for the detection of OFL residues in meat products.

Dextranase (EC 3.2.1.11) is primarily applied in food, sugar, and pharmaceutical industries. This study focuses on using a cold shock Escherichia coli expression system to express marine dextranase SP5-Badex; enzyme activity increased about 2.2-fold compared to previous expression. This enzyme was employed to produce sweet potato porous starch, with special emphasis on the pore size of the starch. The water and oil adsorption rates of the porous starch increased by 1.43 and 1.51 times, respectively. Extensive Fourier transform infrared spectroscopy and X-ray diffraction revealed that the crystal structure of the sweet potato starch was unaltered by enzymatic hydrolysis. The adsorption capacities of the porous starch for curcumin and proanthocyanidins were 9.59 and 12.29 mg/g, respectively. Notably, the stability of proanthocyanidins was significantly enhanced through their encapsulation in porous starch. After 2.5 h of ultraviolet irradiation, the free radical scavenging rate of the encapsulated proanthocyanidins remained at 95.10%. Additionally, after 30 days of sunlight exposure, the free radical scavenging rate of the encapsulated proanthocyanidins (84.42%) was significantly higher than that (24.34%) observed in the control group. These research findings provide substantial experimental evidence for preparing sweet potato porous starch using marine dextranase.

In this study, delta-12 desaturase was overexpressed in Yarrowia lipolytica using the single-copy integrative vector pINA1312 and multicopy integrative vector pINA1292, resulting in the engineered yeast strains 1312-12 and 1292-12, respectively. The content of intracellular linoleic acid (LA) in the 1292-12 strain was much higher than in the 1312-12 strain and the control group. One interesting finding was that the 1292-12 strain showed obvious changes in surface morphology. The 1292-12 colonies were much smaller and smoother, whereas their single cells became much larger compared to the control strain. In addition, the dry cell weight (DCW) of the 1292-12 strain was obviously increased from 8.5 to 12.7 g/L, but the viable cell number sharply decreased from 107 to 105/mL. These results indicated that increased LA content in Yarrowia lipolytica could induce morphological changes or even oxidative stress-dependent cell death. The reactive oxygen species (ROS) and malondialdehyde (MDA) were accumulated in the 1292-12 strain, while the antioxidant activities of intracellular catalase (CAT) and superoxide dismutase (SOD) were significantly decreased by 27.6 and 32.0%, respectively. Furthermore, it was also revealed that these issues could be ameliorated by the exogenous supplementation of vitamin C, fish and colza oil.

In this study, an actinomycete was isolated from sea mud. The strain K1 was identified as Saccharomonospora sp. by 16S rDNA. The optimal enzyme production temperature, initial pH, time, and concentration of the inducer of this actinomycete strain K1 were 37 °C, pH 8.5, 72 h, and 2% dextran T20 of medium, respectively. Dextranase from strain K1 exhibited maximum activity at 8.5 pH and 50 °C. The molecular weight of the enzyme was 2+ and K+ enhanced its activity, whereas Fe3+ and Co2+ had an opposite effect. In addition, high-performance liquid chromatography showed that dextran was mainly hydrolyzed to isomaltoheptose and isomaltopentaose. Also, it could effectively remove biofilms of Streptococcus mutans. Furthermore, it could be used to prepare porous sweet potato starch. This is the first time a dextranase-producing actinomycete strain was screened from marine samples.

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network (CNN) reconstructed by deep learning (DL) framework in face image feature extraction (FE) and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent (SGD) and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild (LFW) face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1 (accuracy: 99.03%, loss: 0.0047, training time: 352 s, and overfitting rate: 1.006), and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time (206.44 s) and a higher correct matching rate (88.75%) than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference.