Comparison of Structural and Physicochemical Characteristics of Skin Collagen from Chum Salmon (Cold-Water Fish) and Nile Tilapia (Warm-Water Fish).

This study compared collagens from cold-water and warm-water fish for their structural, rheological, and functional properties, and explored their potential applications, aiming to realize the high-value utilization of marine biological resources. To this end, chum salmon skin collagen (CSSC) and Nile tilapia skin collagen (NTSC) were both successfully extracted. Collagens from the two species had different primary and secondary structures, with NTSC having a higher molecular weight, imino acid content, and α-helices and ß-turns content. The denaturation temperatures were 12.01 °C for CSSC and 31.31 °C for NTSC. CSSC was dominated by viscous behavior and its structure varied with temperature, while NTSC was dominated by elastic behavior and its structure remained stable with temperature. Both collagens had good oil holding capacity, foaming capacity, and emulsifying activity, but NTSC had better water holding capacity and foaming and emulsifying stability. Their different properties make CSSC more suitable for the preservation of frozen and chilled foods and the production of sparkling beverages, and give NTSC greater potential in biofunctional materials and solid food processing.

Fabrication and characterisation of collagen/pullulan ultra-thin fibers by electrospinning.

Collagen electrospun fibers are promising materials for food packaging and tissue engineering. The conventional electrospinning of collagen, however, is usually carried out by dissolving it in organic reagents, which are toxic. In this study, collagen/pullulan (COL/PUL) ultra-thin fibers were prepared by electrospinning using acetic acid as a solvent. Compared to the conventional preparation method, the proposed method is safe and does not produce toxic solvent residues. The introduction of PUL increased the degree of molecular entanglement in the solution, so the viscosity of the COL/PUL electrospun solution increased from 0.50 ± 0.01 Pa∙s to 4.40 ± 0.08 Pa∙s, and the electrical conductivity decreased from 1954.00 ± 1.00 mS/cm to 1372.33 ± 0.58 mS/cm. Scanning electron microscopy analysis confirmed that PUL improved the spinnability of COL, and smooth, defect-free COL/PUL ultra-thin fibers with diameters of 215.32 ± 40.56 nm and 240.97 ± 53.93 nm were successfully prepared at a viscosity of greater than 1.18 Pa∙s. As the proportion of PUL increased, intramolecular hydrogen bonds became the dominant interaction between COL and PUL. The intermolecular hydrogen bonding content decreased from 52.05 % to 36.45 %, and the intramolecular hydrogen bonding content increased from 46.11 % to 62.95 %. The COL was gradually unfolded, the content of α-helices decreased from 33.57 % to 25.91 % and the random coils increased from 34.22 % to 40.09 %. More than 36 % of the triple helix fraction of COL was retained by the COL/PUL ultra-thin fibers, whereas only 16 % of the triple helix fraction of COL was retained by the COL nanofibers prepared with 2.2.2-trifluoroethanol. These results could serve as a reference for the development of green food COL-based fibers.

Multi-modal learning for inpatient length of stay prediction.

Predicting inpatient length of stay (LoS) is important for hospitals aiming to improve service efficiency and enhance management capabilities. Patient medical records are strongly associated with LoS. However, due to diverse modalities, heterogeneity, and complexity of data, it becomes challenging to effectively leverage these heterogeneous data to put forth a predictive model that can accurately predict LoS. To address the challenge, this study aims to establish a novel data-fusion model, termed as DF-Mdl, to integrate heterogeneous clinical data for predicting the LoS of inpatients between hospital discharge and admission. Multi-modal data such as demographic data, clinical notes, laboratory test results, and medical images are utilized in our proposed methodology with individual "basic" sub-models separately applied to each different data modality. Specifically, a convolutional neural network (CNN) model, which we termed CRXMDL, is designed for chest X-ray (CXR) image data, two long short-term memory networks are used to extract features from long text data, and a novel attention-embedded 1D convolutional neural network is developed to extract useful information from numerical data. Finally, these basic models are integrated to form a new data-fusion model (DF-Mdl) for inpatient LoS prediction. The proposed method attains the best R2 and EVAR values of 0.6039 and 0.6042 among competitors for the LoS prediction on the Medical Information Mart for Intensive Care (MIMIC)-IV test dataset. Empirical evidence suggests better performance compared with other state-of-the-art (SOTA) methods, which demonstrates the effectiveness and feasibility of the proposed approach.

Effect of pH on the Structure, Functional Properties and Rheological Properties of Collagen from Greenfin Horse-Faced Filefish (Thamnaconus septentrionalis) Skin.

Collagen is an important biopolymer widely used in food, cosmetics and biomedical applications. Understanding the effect of pH on the structure and properties of collagen is beneficial for its further processing and exploitation. In this study, greenfin horse-faced filefish skin collagen (GHSC) was prepared and identified as a type I collagen. We systematically investigated the effect of pH on the structural, functional and rheological properties of GHSC. Scanning electron microscopy showed that the collagen morphology changed from an ordered stacked sheet structure to a rough silk-like structure as pH increased. Gaussian-fitted Fourier infrared spectroscopy results of the collagen revealed that it unfolded with increasing pH. Moreover, the ordered structure was reduced, and random coils became the dominant conformation. Its ß-sheet and random coil contents increased from 18.43 ± 0.08 and 33.62 ± 0.17 to 19.72 ± 0.02 and 39.53 ± 1.03%, respectively, with increasing pH. α-helices and ß-turns decreased from 35.00 ± 0.26 and 12.95 ± 0.01 to 29.39 ± 0.92 and 11.36 ± 0.10%, respectively. The increase in ß-sheets and random coils allowed the pI-treated collagen to exhibit maximum water contact angle. The emulsification and foaming properties decreased and then increased with increasing pH in a V-shape. The increased net surface charge and ß-sheets in collagen benefited its emulsification and foaming properties. The rheological results showed that the protoprotein exhibited shear-thinning properties in all pH ranges. The collagen solutions showed liquid-like behaviour in low-pH (2, 4) solutions and solid-like behaviour in high-pH (6, 7.83 and 10) solutions. Moreover, the frequency-dependent properties of the storage modulus (G') and loss modulus (G″) of the collagen solutions weakened with increasing pH. Collagen has considerable frequency-dependent properties of G' and G″ at low pH (2, 4). Thus, the importance of collagen raw material preparation for subsequent processing was emphasised, which may provide new insights into applying collagen-based materials in food, biomaterials and tissue engineering.

A deep learning approach for inpatient length of stay and mortality prediction.

PURPOSE: Accurate prediction of the Length of Stay (LoS) and mortality in the Intensive Care Unit (ICU) is crucial for effective hospital management, and it can assist clinicians for real-time demand capacity (RTDC) administration, thereby improving healthcare quality and service levels. METHODS: This paper proposes a novel one-dimensional (1D) multi-scale convolutional neural network architecture, namely 1D-MSNet, to predict inpatients' LoS and mortality in ICU. First, a 1D multi-scale convolution framework is proposed to enlarge the convolutional receptive fields and enhance the richness of the convolutional features. Following the convolutional layers, an atrous causal spatial pyramid pooling (SPP) module is incorporated into the networks to extract high-level features. The optimized Focal Loss (FL) function is combined with the synthetic minority over-sampling technique (SMOTE) to mitigate the imbalanced-class issue. RESULTS: On the MIMIC-IV v1.0 benchmark dataset, the proposed approach achieves the optimum R-Square and RMSE values of 0.57 and 3.61 for the LoS prediction, and the highest test accuracy of 97.73% for the mortality prediction. CONCLUSION: The proposed approach presents a superior performance in comparison with other state-of-the-art, and it can effectively perform the LoS and mortality prediction tasks.

Dense & Attention Convolutional Neural Networks for Toe Walking Recognition.

Idiopathic toe walking (ITW) is a gait disorder where children's initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are incorporated into the network to highlight useful features while suppressing unwanted noises. Also, the Focal Loss function is enhanced to alleviate the imbalance sample issue. The proposed approach outperforms other methods and obtains a superior performance. It achieves a test recall of 88.91% for recognizing idiopathic toe walking on the local dataset collected from real-world experimental scenarios. To ensure the scalability and generalizability of the proposed approach, the algorithm is further validated through the publicly available datasets, and the proposed approach achieves an average precision, recall, and F1-Score of 89.34%, 91.50%, and 92.04%, respectively. Experimental results present a competitive performance and demonstrate the validity and feasibility of the proposed approach.

The predictive model for COVID-19 pandemic plastic pollution by using deep learning method.

Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others.

Weakly-supervised learning method for the recognition of potato leaf diseases.

As a crucial food crop, potatoes are highly consumed worldwide, while they are also susceptible to being infected by diverse diseases. Early detection and diagnosis can prevent the epidemic of plant diseases and raise crop yields. To this end, this study proposed a weakly-supervised learning approach for the identification of potato plant diseases. The foundation network was applied with the lightweight MobileNet V2, and to enhance the learning ability for minute lesion features, we modified the existing MobileNet-V2 architecture using the fine-tuning approach conducted by transfer learning. Then, the atrous convolution along with the SPP module was embedded into the pre-trained networks, which was followed by a hybrid attention mechanism containing channel attention and spatial attention submodules to efficiently extract high-dimensional features of plant disease images. The proposed approach outperformed other compared methods and achieved a superior performance gain. It realized an average recall rate of 91.99% for recognizing potato disease types on the publicly accessible dataset. In practical field scenarios, the proposed approach separately attained an average accuracy and specificity of 97.33% and 98.39% on the locally collected image dataset. Experimental results present a competitive performance and demonstrate the validity and feasibility of the proposed approach.

Valorization of Fish Processing By-Products: Microstructural, Rheological, Functional, and Properties of Silver Carp Skin Type I Collagen.

The objective of this study was to develop aquatic collagen production from fish processing by-product skin as a possible alternative to terrestrial sources. Silver carp skin collagen (SCSC) was isolated and identified as type I collagen, and LC-MS/MS analysis confirmed the SCSC as Hypophthalmichthys molitrix type I collagen, where the yield of SCSC was 40.35 ± 0.63% (dry basis weight). The thermal denaturation temperature (Td) value of SCSC was 30.37 °C, which was superior to the collagen of deep-sea fish and freshwater fish. Notably, SCSC had higher thermal stability than human placental collagen, and the rheological experiments showed that the SCSC was a shear-thinning pseudoplastic fluid. Moreover, SCSC was functionally superior to some other collagens from terrestrial sources, such as sheep, chicken cartilage, and pig skin collagen. Additionally, SCSC could provide a suitable environment for MC3T3-E1 cell growth and maintain normal cellular morphology. These results indicated that SCSC could be used for further applications in food, cosmetics, and biomedical fields.

Preparation and Characterization of Tilapia Collagen-Thermoplastic Polyurethane Composite Nanofiber Membranes.

Marine collagen is an ideal material for tissue engineering due to its excellent biological properties. However, the limited mechanical properties and poor stability of marine collagen limit its application in tissue engineering. Here, collagen was extracted from the skin of tilapia (Oreochromis nilotica). Collagen-thermoplastic polyurethane (Col-TPU) fibrous membranes were prepared using tilapia collagen as a foundational material, and their physicochemical and biocompatibility were investigated. Fourier transform infrared spectroscopy results showed that thermoplastic polyurethane was successfully combined with collagen, and the triple helix structure of collagen was retained. X-ray diffraction and differential scanning calorimetry results showed relatively good compatibility between collagen and TPU.SEM results showed that the average diameter of the composite nanofiber membrane decreased with increasing thermoplastic polyurethane proportion. The mechanical evaluation and thermogravimetric analysis showed that the thermal stability and tensile properties of Col-TPU fibrous membranes were significantly improved with increasing TPU. Cytotoxicity experiments confirmed that fibrous membranes with different ratios of thermoplastic polyurethane content showed no significant toxicity to fibroblasts; Col-TPU fibrous membranes were conducive to the migration and adhesion of cells. Thus, these Col-TPU composite nanofiber membranes might be used as a potential biomaterial in tissue regeneration.

Identification and release kinetics of peptides from tilapia skin collagen during alcalase hydrolysis.

Collagen from tilapia skin was extracted and confirmed as type I collagen. Collagen was then hydrolyzed with alcalase for 4 h and the released peptides were identified. The structure-activity relationship of collagen-released peptides showed that proline at position C3 played a key role in improving ACE inhibitory activity, while proline at position C2 had a negative effect. Collagen peptide release kinetics showed that with the extension of time, the number of peptides increased dramatically at first, decreased, and then tended to be stable. This indicated that collagen peptides mainly originated from primary enzymolysis at the first stage and began to undergo secondary hydrolysis in the second stage. Afterwards, secondary enzymolysis was dominant at the third stage and finally remained stable at final two stages. Understanding the pattern of collagen peptide release kinetics might offer a powerful approach in the collagen-peptide food processing industry to better control food safety and quality.

Preparation and Characterization of Thermally Stable Collagens from the Scales of Lizardfish (Synodus macrops).

Marine collagen is gaining vast interest because of its high biocompatibility and lack of religious and social restrictions compared with collagen from terrestrial sources. In this study, lizardfish (Synodus macrops) scales were used to isolate acid-soluble collagen (ASC) and pepsin-soluble collagen (PSC). Both ASC and PSC were identified as type I collagen with intact triple-helix structures by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and spectroscopy. The ASC and PSC had high amino acids of 237 residues/1000 residues and 236 residues/1000 residues, respectively. Thus, the maximum transition temperature (Tmax) of ASC (43.2 °C) was higher than that of PSC (42.5 °C). Interestingly, the Tmax of both ASC and PSC was higher than that of rat tail collagen (39.4 °C) and calf skin collagen (35.0 °C), the terrestrial collagen. Solubility tests showed that both ASC and PSC exhibited high solubility in the acidic pH ranges. ASC was less susceptible to the "salting out" effect compared with PSC. Both collagen types were nontoxic to HaCaT and MC3T3-E1 cells, and ASC was associated with a higher cell viability than PSC. These results indicated that ASC from lizardfish scales could be an alternative to terrestrial sources of collagen, with potential for biomedical applications.

A turning point prediction method of stock price based on RVFL-GMDH and chaotic time series analysis.

Stock market prediction is extremely important for investors because knowing the future trend of stock prices will reduce the risk of investing capital for profit. Therefore, seeking an accurate, fast, and effective approach to identify the stock market movement is of great practical significance. This study proposes a novel turning point prediction method for the time series analysis of stock price. Through the chaos theory analysis and application, we put forward a new modeling approach for the nonlinear dynamic system. The turning indicator of time series is computed firstly; then, by applying the RVFL-GMDH model, we perform the turning point prediction of the stock price, which is based on the fractal characteristic of a strange attractor with an infinite self-similar structure. The experimental findings confirm the efficacy of the proposed procedure and have become successful for the intelligent decision support of the stock trading strategy. Supplementary Information: The online version contains supplementary material available at 10.1007/s10115-021-01602-3.

Detection of rice plant diseases based on deep transfer learning.

BACKGROUND: As the primary food for nearly half of the world's population, rice is cultivated almost all over the world, especially in Asian countries. However, the farmers and planting experts have been facing many persistent agricultural challenges for centuries, such as different diseases of rice. The severe rice diseases may lead to no harvest of grains; therefore, a fast, automatic, less expensive and accurate method to detect rice diseases is highly desired in the field of agricultural information. RESULTS: In this article, we study the deep learning approach for solving the task since it has shown outstanding performance in image processing and classification problem. Combining the advantages of both, the DenseNet pre-trained on ImageNet and Inception module were selected to be used in the network, and this approach presents a superior performance with respect to other state-of-the-art methods. It achieves an average predicting accuracy of no less than 94.07% in the public dataset. Even when multiple diseases were considered, the average accuracy reaches 98.63% for the class prediction of rice disease images. CONCLUSIONS: The experimental results prove the validity of the proposed approach, and it is accomplished efficiently for rice disease detection. © 2020 Society of Chemical Industry.

Physicochemical and Functional Properties of Type I Collagens in Red Stingray (Dasyatis akajei) Skin.

Collagen is widely used in the pharmaceutical, tissue engineering, nutraceutical, and cosmetic industries. In this study, acid-soluble collagen (ASC) and pepsin-soluble collagen (PSC) were extracted from the skin of red stingray, and its physicochemical and functional properties were investigated. The yields of ASC and PSC were 33.95 ± 0.7% and 37.18 ± 0.71% (on a dry weight basis), respectively. ASC and PSC were identified as type I collagen by Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) analysis, possessing a complete triple helix structure as determined by UV absorption, Fourier transform infrared, circular dichroism, and X-ray diffraction spectroscopy. Contact angle experiments indicated that PSC was more hydrophobic than ASC. Thermal stability tests revealed that the melting temperature of PSC from red stingray skin was higher than that of PSC from duck skin, and the difference in the melting temperature between these two PSCs was 9.24 °C. Additionally, both ASC and PSC were functionally superior to some other proteins from terrestrial sources, such as scallop gonad protein, whey protein, and goose liver protein. These results suggest that PSC from red stingray skin could be used instead of terrestrial animal collagen in drugs, foods, cosmetics, and biological functional materials, and as scaffolds for bone regeneration.

Docosahexaenoic acid inhibits hepatic stellate cell activation to attenuate liver fibrosis in a PPARγ-dependent manner.

Docosahexaenoic acid (DHA) has been found to have a hepatoprotective effect. In this study, we investigated the role of peroxisome proliferator-activated receptor γ (PPARγ) in DHA regulation of liver fibrosis. DHA was found to inhibit hepatic stellate cell (HSC)-LX2 cell viability and downregulate marker proteins of HSC activation. Furthermore, DHA induced cell cycle arrest at G1 phase in HSCs. Antagonism of PPARγ by GW9662 abrogated the effects of DHA on HSCs. Computer-aided molecular docking predicted that DHA bound to PPARγ via hydrogen bonding with residues Ser289, His323, Tyr473, and His499. We overexpressed Ser289 mutant PPARγ in HSC-LX2 cells and investigated fibrotic marker modulation, and found that DHA effects on HSCs were diminished. Thus, bonding with the Ser289 residue might be indispensable for DHA to activate PPARγ to exert its inhibiting effect on activated HSCs. Last, data from a CCl4-treated mouse model confirmed that PPARγ activation was required for DHA to attenuate liver fibrosis.

Electrodialysis Extraction of Pufferfish Skin (Takifugu flavidus): A Promising Source of Collagen.

Collagen is widely used in drugs, biomaterials, foods, and cosmetics. By-products of the fishing industry are rich sources of collagen, which can be used as an alternative to collagen traditionally harvested from land mammals. However, commercial applications of fish-based collagen are limited by the low efficiency, low productivity, and low sustainability of the extraction process. This study applied a new technique (electrodialysis) for the extraction of Takifugu flavidus skin collagen. We found electrodialysis to have better economic and environmental outcomes than traditional dialysis as it significantly reduced the purification time and wastewater (~95%) while maintaining high extraction yield (67.3 ± 1.3 g/100 g dry weight, p < 0.05). SDS-PAGE, amino acid composition analysis, and spectrophotometric characterization indicated that electrodialysis treatment retained the physicochemical properties of T. flavidus collagen. Heavy metals and tetrodotoxin analyses indicated the safety of T. flavidus collagen. Notably, the collagen had similar thermal stability to calf skin collagen, with the maximum transition temperature and denaturation temperature of 41.8 ± 0.35 and 28.4 ± 2.5 °C, respectively. All evidence suggests that electrodialysis is a promising technique for extracting collagen in the fishing industry and that T. flavidus skin collagen could serve as an alternative source of collagen to meet the increasing demand from consumers.

The protective effects of Nile tilapia (Oreochromis niloticus) scale collagen hydrolysate against oxidative stress induced by tributyltin in HepG2 cells.

Oxidative stress is regarded as one of the most important factors associated with many diseases, such as atherosclerosis, cancer, and diabetes. Various chemicals are released into the environment, causing environmental pollution. Importantly, many of them may cause damage to organisms through oxidative stress. In this work, we investigated the possible protective effects of Nile tilapia (Oreochromis niloticus) scale collagen hydrolysate (TSCH) (molecular weight approximately 4 kDa) against tributyltin (TBT)-induced oxidative stress in vitro. The results showed that pretreatment with TSCH protected against decreases in cell viability and changes in cell morphology in HepG2 cells exposed to TBT. Treatment with TSCH reduced the TBT-induced elevation in malondialdehyde (MDA) levels in HepG2 cells in a dose-dependent manner. Pretreatment with TSCH increased glutathione reductase (GR) and superoxide dismutase (SOD) activity. Moreover, TSCH decreased the expression of the proapoptotic protein Bax, reducing apoptosis. These results suggest that the protective mechanism of TSCH may be associated with its ability to scavenge MDA, increase antioxidant enzyme activity and downregulate the expression of Bax.

[Comparative Analysis of Different Soil Amendment Treatments on Rice Heavy Metal Accumulation and Yield Effect in Pb and Cd Contaminated Farmland].

Using selected sepiolite (SEP) and biochar (BC) as contrasts, we investigated the effects of a new cross-linked modified chitin (CC) on the bioavailability of Pb and Cd in soils, the yield of rice, and the absorption and accumulation of Pb and Cd in different parts of rice plants in a field environment. We hope this study provides the basis for the application of this material to improve soil fertility, and a direction for further soil improvement studies. A field experiment was carried out in 2015-2016 on selected Pb- and Cd-contaminated rice fields in Linghai, Liaoning. The changes in soil pH and available Pb and Cd in the soil were analyzed after the rice was harvested(October 2016). The effects of different treatments on the growth traits and yield of rice, the absorption of Pb and Cd by rice roots, stems and leaves, and grains were compared. The results showed that adding 167-333 kg·hm-2 CC could increase the soil pH value by 0.36-0.45 units, decreasing the contents of available Pb and Cd in the soil by 46.39%-64.01% and 29.73%-43.24% respectively (P<0.05). This treatment significantly reduced the Pb and Cd contents in all parts of rice (P<0.05) compared to conventional fertilization; Pb and Cd contents in different parts of rice were significantly reduced (P<0.05) by 16.09%-38.14% and 21.22%-31.38% in the root, 19.17%-46.92% and 25.66%-45.34% in the stem and leaf, and 29.47%-58.25% and 44.75%-64.02% in the grain, respectively. The treatment of adding 333 kg·hm-2 CC (CC-2) reduced the contents of Pb and Cd in rice grains to 0.2041±0.011 mg·kg-1 and 0.1922±0.021 mg·kg-1, respectively, which were lower than or close to the limit values of Pb and Cd in rice (0.20 mg·kg-1) as per GB 2762-2005. Compared to conventional fertilization, SEP treatment, and BC treatment, without adding any amendments, the yield per mu of rice under CC treatment increased by 33.6-47, 27.6-44, and 8.67-34.77 kg, respectively. The effect of CC-2 treatment on yield was the most obvious; the yield of rice per mu increased by 47 kg, and the yield increase rate was 8.59%. The ability of CC to repair soil contaminated by Pb and Cd and to reduce the contents of Pb and Cd in rice was not weaker than that of SEP and BC. The CC treatment also controlled the migration and redistribution of Pb and Cd in soil-rice systems, and significantly increased the yield of rice. It has good potential to ensure the safe production of rice.

Processing Optimization and Characterization of Angiotensin-Ι-Converting Enzyme Inhibitory Peptides from Lizardfish (Synodus macrops) Scale Gelatin.

Hypertension can cause coronary heart disease. Synthetic angiotensin-converting enzyme (ACE) inhibitors are effective antihypertensive drugs but often cause side effects. The aim of this study was to prepare potential ACE inhibitors from scales. Gelatin was extracted from lizardfish scales. Then, scale gelatin was enzymolyzed to prepare ACE inhibitory peptides using response surface methodology. Proteolytic conditions after optimization were as follows: pH 7.0, enzyme substrate ratio 3.2%, temperature 47 °C, and proteolysis lasting 2 h and 50 min. The experimental ACE inhibitory activity under optimal conditions was 86.0 ± 0.4%. Among the 118 peptides identified from gelatin hydrolysates, 87.3% were hydrophilic and 93.22% had a molecular weight <2000 Da. Gelatin peptides had high stability upon exposure to high temperature and pH as well as gastrointestinal tract enzymes. Gelatin peptides showed an antihypertensive effect in spontaneously hypertensive rats at a dosage of 2 g/kg in the long-term experiments. A new ACE inhibitory peptide was isolated from gelatin hydrolysates, and was identified as AGPPGSDGQPGAK with an IC50 value of 420 ± 20 μM. In this way, ACE inhibitory peptides derived from scale gelatin have the potential to be used as healthy ACE-inhibiting drug raw materials.