**1. Introduction**

Osseointegration [1] is a central event for oral implant function. This specific bone reaction has been described and studied at length for titanium and other materials. Technical innovations have led to improvements of bone reactions, such as material surface topographical changes [2–4] that have been vastly adopted by the oral implant industry, as well as different forms of chemical surface modulations [5,6]. Such surface related innovations have resulted in improved clinical results and widening of clinical indications [7,8]. However, the specific bone related control mechanisms that lead to osseointegration are still in need of scientific analyses, as are the reasons for marginal bone resorption. Generally speaking, the foreign body reaction (FBR) is accepted as the series of host events that follow the introduction of a material into tissues. The host–biomaterial interaction [9] depends on the type of material, clinical handling and on the tissue where the implant is placed (e.g., bone, skin, and blood vessel), as well as the host specific conditions. The immune system has a central role in the FBR [10–12] where the M1/M2-macrophage phenotype balance has been identified as one of the main controlling factors at the cellular level [13]. Macrophages are thus able to shift between an M1-phenotype (proinflammatory) and an M2-phenotype (reparative/anti-inflammatory), with obvious consequences for tissue reaction to biomaterials, and experimental modulation of this balance has been studied to direct a favorable pathway for bone regeneration [14]. The current authors have demonstrated an early M1/M2 shift around titanium, at 10 days of follow-up towards a dominant M2 macrophage phenotype [15], in contrast to other materials such as polyetheretherketone (PEEK) and Copper (Cu) that present mixed M1/M2 phenotypes at the same short term of follow-up. Osseointegration is thus seen as the result of an FBR which in the long run may achieve a foreign body equilibrium allowing for long term loading of implants [16]. However, the basis for the control of bone metabolism around implants in health and disease remains largely unclear [17]. Particularly the events taking place after the inflammatory period of initial healing and a possible immunological regulation of bone metabolism are examples of important fields for further studies. Our group has demonstrated that titanium activates the immune system when compared to a sham site at 10 and 28 days of follow-up [12]. In Part I of this series of studies (where the current work is Part II), the importance of the specific immune response around different materials when compared to a sham site was demonstrated at an early stage of 10 days [15]. The current study aims at comparing materials that do not osseointegrate, i.e., test materials copper (known to induce a pronounced FBR in soft tissues [18]) and PEEK (considered a bioinert material [19]), to a material that osseointegrates, cp titanium (control) at 10 and 28 days, in order to investigate and compare the respective immune modulation reactions between the inflammatory (10 days) and postinflammatory (28 days) stages of bone healing.

#### **2. Materials and Methods**

The current study consists of an experiment in the rabbit proximal tibia (metaphysis), comparing bone healing on sites where osteotomies were performed and one of three test materials were placed for comparison: titanium (Ti), copper (Cu), or polyether ether ketone (PEEK), where Ti was a control.

All implants were turned with a threaded 0.6 mm pitch height, 3.75 mm width, and Branemark MkIII design (Figure 1). The Ti implants were made of commercially pure titanium grade IV (98.55% Ti, with specified maximum traces of elements Fe, O, N, H, and C for the remaining 1.45%).

**Figure 1.** Implant design with 3.75mm width and 8mm length. Representative image of titanium (Ti) implant; copper (Cu) and polyetheretherketone (PEEK) implants with the same design.

#### *2.1. Surgical Procedure*

This study was performed on 12 mature, female New Zealand White Rabbits (*n* = 6 for each time point, 10 and 28 days, weight 3 to 4 Kg), with the ethical approval from the Ethics Committee for Animal Research (No. 13-011) of the École Nationale Vétérinaire D'Alfors, Maisons-Alfors, Val-de-Marne, France. The 6 animals at 10 days are the same used for Part I of this series of studies [12]. All care was taken to minimize animal pain or discomfort during and after the surgical procedures. For the surgical procedures, the rabbits were placed under general anesthesia using a mixture of medetomidine (Domitor; Zoetis, Florham Park, NJ, USA), ketamine (Imalgène 1000; Merial, Lyon, France), and diazepam (Valium; Roche, Basel, Switzerland) for induction, then applying subcutaneous buprenorphine (Buprecare; Animalcare, York, UK) and intramuscular meloxicam (Metacam; Boehringer Ingelheim Vetmedica, Inc., Ridgefield, CT, USA). A single incision was performed in the internal knee area on each side and the bone exposed for osteotomies and insertion of implants in the sites mentioned above. The surgical site was sutured with a resorbable suture (Vicryl 3/0; Ethicon, Cincinnati, OH, USA) and hemostasis achieved. Following surgery, Fentanyl patches (Duragesic; Janssen Pharmaceutica, Beerse, Belgium) were applied.

The osteotomies were produced with a sequence of increasing diameter twist drills, from 2 mm to 3.15 mm width, and a final countersink bur prepared the cortical part of the bone. The implants used were 3.75 mm in diameter, placed in an underprepared osteotomy to achieve primary (mechanical) stability.

The rabbits were housed in separate cages and were allowed to move and eat freely.

At 10 and 28 days, the rabbits were sacrificed with a lethal injection of sodium pentobarbital (Euthasol; Virbac, Fort Worth, TX, USA). The 6 animals at each time point had the implants removed through unscrewing. On 4 animals at 10 days and 5 animals at 28 days, bone was collected with a 2 mm twist drill from the periphery of the Ti, Cu, and PEEK sites on the most distal portion, and then processed through quantitative-polymerase chain reaction (qPCR). After this, at each time point, the implant sites were removed en bloc for histological processing on the 6 animals.

#### *2.2. Gene Expression Analysis—qPCR*

The bone samples for gene expression analysis at 10 or 28 days were collected from the distal side of the osteotomies of all three groups (following the removal of the implant from the implant sites), with a 2 mm twist drill that removed both cortical and marrow bone in the full length of the osteotomy, to enable the study of the 2 mm peri-implant bone area of each of the Ti, Cu, and PEEK sites. The samples were immediately transferred to separate sterile plastic recipients containing RNA*later* medium (AmbionInc, Austin, TX, USA) for preservation. The samples were then refrigerated first at 4 ◦C and then stored at –20 ◦C until processing.

#### 2.2.1. mRNA Isolation

Samples were homogenized using an ultrasound homogenizer (Sonoplus HD3100, Brandelin) in 1 ml PureZOL and total RNA was isolated via column fractionalization using the AurumTM Total RNA Fatty and Fibrous Tissue Kit (Bio-Rad Laboratories Inc.; Hercules, CA, USA) following the manufacturer's instructions. All the samples were DNAse treated using an on-column DNAse I contained in the kit to remove genomic DNA. The RNA quantity for each sample was analyzed in the NanoDrop 2000 Spectrophotometer (Thermo Scientific; Wilmington, DE, USA). BioRad iScript cDNA synthesis kit (Bio-Rad Laboratories Inc.; Hercules, CA, USA) was then used to convert mRNA into cDNA, following the manufacturer's instructions.

qPCR primers (Tataa Biocenter; Gothenburg, Sweden) were designed following the NCBI Sequence database, including the local factors chosen in order to characterize the immune, inflammatory, and bone metabolic pathways (Tables 1 and 2). All primers had efficiency between 90% and 110%.

#### **Table 1.** Gene sequences.

*NCF-1* (neutrophil cytosolic factor 1); *CD68* (macrosialin); *CD11b* (*MAC-1*, macrophage marker); *CD14* (monocyte differentiation antigen *CD14*); *ARG1* (Arginase 1); *IL-4* (Interleukin 4); *IL-13* (Interleukin 13); *M-CSF* (colony stimulating factor-macrophage); *OPG* (osteoprotegerin); *RANKL* (Receptor activator of nuclear factor kappa-B ligand); *TRAP* (tartrate resistant acid phosphatase); *CathK* (cathepsin K); *PPAR-*γ (peroxisome proliferator activated receptor gamma); *C3* (complement component 3); *C3aR1* (complement component 3a receptor 1); *CD46* (complement regulatory protein); *CD55* (decay accelerating factor for complement); *CD59* (complement regulatory protein); *C5* (complement component 5); *C5aR1* (complement component 5a receptor 1); *CD3* (T cell surface glycoprotein CD3); *CD4* (T cell surface glycoprotein CD4); *CD8* (T cell transmembrane glycoprotein CD8); *CD19* (B-lymphocyte surface protein CD19); *GAPDH* (glyceraldehyde-3-phosphate dehydrogenase); *ACT-*β (actin beta); *LDHA* (lactate dehydrogenase A).

**Table 2.** Correspondence between studied gene expression and biological entities.

*NCF-1* (neutrophil cytosolic factor 1); *CD68* (macrosialin); *CD11b* (*MAC-1*, macrophage marker); *CD14* (monocyte differentiation antigen *CD14*); *ARG1* (Arginase 1); *IL-4* (Interleukin 4); *IL-13* (Interleukin 13); *M-CSF* (colony stimulating factor-macrophage); *OPG* (osteoprotegerin); *RANKL* (Receptor activator of nuclear factor kappa-B ligand); *TRAP* (tartrate resistant acid phosphatase); *CathK* (cathepsin K); *PPAR-*γ (peroxisome proliferator activated receptor gamma); *C3* (complement component 3); *C3aR1* (complement component 3a receptor 1); *CD46* (complement regulatory protein); *CD55* (decay accelerating factor for complement); *CD59* (complement regulatory protein); *C5* (complement component 5); *C5aR1* (complement component 5a receptor 1); *CD3* (T cell surface glycoprotein CD3); *CD4* (T cell surface glycoprotein CD4); *CD8* (T cell transmembrane glycoprotein CD8); *CD19* (B-lymphocyte surface protein CD19); *GAPDH* (glyceraldehyde-3-phosphate dehydrogenase); *ACT-*β (actin beta); *LDHA* (lactate dehydrogenase A).

#### 2.2.2. Amplification Process

Five microliters of SsoAdvanced SYBR™ Green Supermix (Bio-Rad Laboratories Inc.; Hercules, CA, USA) and 1 μL of cDNA template together with 0.4 μM of forward and reverse primer were used

in the qPCR reaction. Each cDNA sample was performed on duplicates. The thermal cycles were performed on the CFX Connect Real-Time System (Bio-Rad Laboratories Inc.; Hercules, CA, USA). The CFX Manager Software 3.0 (Bio-Rad, Hercules, CA, USA) was used for the data analysis.

Three genes (*GAPDH*, *ACT-beta*, and *LDHA*) were selected as reference genes using the geNorm algorithm integrated in the CFX Manager Software. A quantification cycle (Cq) value of the chosen reference genes (Tables 1 and 2) was used as control; hence the mean Cq value of each target gene (Table 1) was normalized against the reference gene's Cq, giving the gene's relative expression. For calculation of fold-change, the ΔΔCq was used, comparing mRNA expressions from the different groups. Significance was set at *p* < 0.05 and the regulation threshold at ×2 fold-change.

#### *2.3. Decalcified Bone Histology*

After removal of the implants from the studied Ti, Cu, and PEEK sites on the 6 subjects of each time point, bone was removed en bloc and preserved in 10% formalin (4% buffered formaldehyde; VWR international, Leuven, Belgium) during 48 h for fixation. Samples were decalcified in Ethylene diamine tetra-acetic acid (10% unbuffered EDTA; Milestone srl, BG, Italy) for 4 weeks, with weekly substitution of the EDTA solution, dehydrated and embedded in paraffin (Tissue-Tek TEC; Sakura Finetek Europe BV, Leiden, NL, USA). Samples were sectioned (4-μm-thick) with a microtome (Microm HM355S; Microm International GmbH, Thermo Fischer Scientific, Walldorf, Germany) and stained with Haematoxylin-Eosin (HE) for histological analysis.


*Article* **Polycondensed Peptide Carriers Modified with Cyclic RGD Ligand for Targeted Suicide Gene Delivery to Uterine Fibroid Cells**

**Anna Egorova 1 , Sofia Shtykalova 1 , Marianna Maretina 1 , Alexander Selutin 2 , Natalia Shved 1 , Dmitriy Deviatkin 1 , Sergey Selkov 2 , Vladislav Baranov 1 and Anton Kiselev 1,\***

**Abstract:** Suicide gene therapy was suggested as a possible strategy for the treatment of uterine fibroids (UFs), which are the most common benign tumors inwomen of reproductive age. For successful suicide gene therapy, DNAtherapeutics should be specifically delivered to UF cells. Peptide carriers are promising non-viral gene delivery systems that can be easily modified with ligands and other biomolecules to overcome DNA transfer barriers. Here we designed polycondensed peptide carriers modified with a cyclic RGD moiety for targeted DNA delivery to UF cells. Molecular weights of the resultant polymers were determined, and inclusion of the ligand was confirmed by MALDI-TOF. The physicochemical properties of the polyplexes, as well as cellular DNA transport, toxicity, and transfection efficiency were studied, and the specificity of αvβ3 integrin-expressing cell transfection was proved. The modification with the ligand resulted in a three-fold increase of transfection efficiency. Modeling of the suicide gene therapy by transferring the HSV-TK suicide gene to primary cells obtained from myomatous nodes of uterine leiomyoma patients was carried out. We observed up to a 2.3-fold decrease in proliferative activity after ganciclovir treatment of the transfected cells. Pro- and anti-apoptotic gene expression analysis confirmed our findings that the developed polyplexes stimulate UF cell death in a suicide-specific manner.

**Keywords:** DNA delivery; peptide-based carriers; gene therapy; thymidine kinase; uterine fibroids; integrins; polycondensation


**3. Methods**

*Real Perspective of Fourier Transforms and Current Developments in Superconductivity*

buffalo diaphragms, goat skin, and native bovine cortical bone.

**2. Materials**

**2.1 Chemicals and reagents**

indicated otherwise).

**2.2 Equipments**

**2.3 Tissue samples**

hypotonic solutions [2, 3, 8, 10, 13, 15], and chelating agents (EDTA). These reagents at higher concentrations extensively disrupt the structural proteins of ECM scaffolds and make it impossible to analyze by routine techniques [17]. Fourier transform infrared (FTIR) spectroscopy is one of the preferred technique for identification of biomolecules through the study of their characteristic vibrational movements [11, 13, 14, 18]. This technique is simple, reproducible, nondestructive to the tissue, and only small amounts of tissue (micrograms to nanograms) with a minimum preparation are required. In addition, this technique also provides molecular-level information allowing investigation of functional groups, bonding types, and molecular conformations. The characteristic peaks in FTIR spectra are molecule specific and provide direct information about biochemical composition. This chapter highlights the application of FTIR spectroscopy for characterization of native and decellularized buffalo aortae,

Sodium dodecyl sulfate (SDS), Trypsin, Sodium chloride (NaCl), Phosphatebuffered saline (PBS), Ethylene diaminetetraacetic acid (EDTA), Sodium azide (NaN3), Gentamicin, Potassium bromide (KBr) powder. All the chemicals and reagents used were of the high purity and obtained from Sigma-Aldrich (St. Louis, MO, USA) unless mentioned otherwise. All solutions were prepared fresh using deionized water and analytical grade chemicals at room temperature (unless

Magnetic stirrer (C-MAG HS7, IKA, USA), Analytical digital lab balance (Citizen Enterprises, Delhi, India), Fourier transform infrared spectrophotometer (FTIR 8400 s Shimadzu Corporation, Tokyo, Japan), Bard Parker blade number 24, Autoclaved sterile dissecting scissors, Autoclaved sterile jar (Borosil, India), Sterile measuring cylinders (Borosil, India), Dishes (Borosil, India) and Protective equipment such as surgical gloves and surgical autoclaved instruments were used.

Fresh cadaver buffalo aorta (**Figure 1A**), buffalo diaphragm (**Figure 1B**) and goat skin (**Figure 1C**) collected in chilled (4°C) sterile 1X PBS (pH 7.4) containing 0.016% gentamicin (antibiotic), 0.0205% EDTA (proteolytic inhibitor) and 0.1% NaN3 (antimycotic) were our study materials. A cortical bone collected from the anterior diaphysis of the right femur of an adult cadaver Gir cow was also used.

*Gross images of buffalo aorta (a), buffalo diaphragm (B), and goat skin (C).*

**40**

**Figure 1.**


*2.1. Sample*

Intentional sample made up of 27 internal women in the Women's Prison of Bucaramanga (Colombia), who voluntarily decided to participate in this study and have been part of the "BONDINGS" program since 2018 (BONDINGS—Affective Bonds NGO 2020). It was essential that all the participants reported having a partner before and/or during the process of entering the prison. Women's personal data are reserved and, therefore, the identification code created for this study is used: from S-1 to S-27 (women registered on lines 1 to 27 in the database).


**1. Introduction**

Mineral resources play a significant role in China's economic development. At present, China is relatively limited in terms of mineral resource storage, and will especially be reliant on the importation for copper for a period in the future. This has affected China's industrial development to a large extent. Therefore, scientific predictions and evaluations of potential mineral resources are essential and serve as a key guarantee for the sustainable development of China's national economy. Currently, resource prediction and evaluation has become a research focus in the field of mineral exploration [1].

Geostatistics is a branch of statistics established by French statistician G. Matheorn. Based on the theory of regionalized variables and with variation functions as a tool, it is a science that explores natural phenomena which occur both at random and with a certain structure in spatial distribution [2]. It initially targeted the single spatial parameter statistical model as a focus, which gave rise to linear geostatistics [2–4]. With the development of

**Citation:** Lv, X.; Yang, W.; Liu, X.; Wang, G. Applications of Radial Basis Functional Link Networks in the Exploration for Lala Copper Deposits in Sichuan Province, China. *Minerals* **2022**, *12*, 352. https://doi.org/ 10.3390/min12030352

Academic Editor: Amin Beiranvand Pour

Received: 20 January 2022 Accepted: 10 March 2022 Published: 15 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

spatial parameters that tend to be more complex and diverse, nonlinear geostatistics [5,6] have emerged when linear geostatistics failed to provide solutions.

Since the 1970s, the development of methodological systems of regional metallogenic prediction has accelerated. It mainly covers three directions, two of which are regional metallogenic predictions based on the research of geological metallogenic theory and one which is based on the research of metallogenic dynamics theory [7]. The regional metallogenic prediction method adopted in this study relies on comprehensive geological research as the foundation, deposit models or geological anomalies as the bases, the computer as a means of research, and comprehensive quantitative analysis of multivariate information, such as geology, geophysics, geochemistry, and remote sensing data, as key approaches [8–10].

GIS technology, a computer system which integrates collection, storage, management, analysis, display, and application, is a general technology for analyzing and processing multivariate geographic data [11]. In metallogenic prediction, its application has advantages such as the integrated management of multivariate geoscience data, data space simulation analysis, and data quantification [12]. In recent years, the methodology system of metallogenic predictions based on GIS technology has been continuously deepened and developed. In the late 1980s, the Geological Survey of Canada used GIS technology for the potential mapping of mineral resources, mineral exploration, and mineral resource evaluation [13]. In 1988, Wyborn et al., from Australia, adopted GIS technology to evaluate mineral resource potentials [14]. In 2012, Silva et al. applied the ArcGIS-SDM fuzzy logic method to generate mineral exploration maps and evaluate potential exploration areas [15,16]. Additionally, Li et al. successfully developed the GeoCube software by integrating mathematical modeling methods [17] and quantitatively extracted and integrated 3D geoscience prospecting information of the Luanchuan mining area in Henan Province with the help of the software in question [18]. Combining GIS resource evaluation technology and 3D modeling technology, Wang et al. extracted and integrated comprehensive metallogenic information from the zones at different depths in the mining area [19].

Artificial neural networks (ANNs) have been widely applied in regional metallogenic prediction as a nonlinear classification method [20]. It classifies and recognizes data by imitating human biological neurons with the capacity to process complex nonlinear spatial datasets. To date, a wide range of ANNs has been developed, such as RBFLN, generalized regression neural networks (GRNNs), and probabilistic neural networks (PNNs) [21–23]. Previous studies have revealed that RBFLN is superior in metallogenic prediction.

Based on the metallogenic geological conditions and the metallogenic model of the Lala copper concentration area in Sichuan (Figure 1), this paper describes how multivariate geoscience ore-forming information was integrated with the help of GIS technology and the use of the RBFLN model. Predictive prospective outcomes and superimposed verification results of ore body models indicated that this method is capable of rendering theoretical bases and practical suggestions for mineral prospecting effort at deep zones in this area.

**Figure 1.** Geological model for prospecting predictions of the Lala copper deposit (modified from [24]). **Figure 1.** Geological model for prospecting predictions of the Lala copper deposit (modified from [24]).

#### **2. Geological Setting 2. Geological Setting**

#### *2.1. Regional Geology 2.1. Regional Geology*

The Lala region is located on the eastern edge of the midsection of the Kangding– Yunnan Axis of the Yangtze Platform which extends from north to south, and north of the Huili–Eastern Sichuan Aulacogen, which stretches from east to west (Figure 2). Spreading across the border between China and Vietnam, the Kangding–Yunnan copper belt lies on the western fringe of the Yangtze Plate, with a length of about 300 km. A famous ironoxide copper gold (IOCG) metallogenic area [25,26], the province has more than 50 IOCG deposits, the most representative of which are the Luodang Copper Mine and Hongnipo Copper Mine (Figure 3). The Lala region is located on the eastern edge of the midsection of the Kangding– Yunnan Axis of the Yangtze Platform which extends from north to south, and north of the Huili–Eastern Sichuan Aulacogen, which stretches from east to west (Figure 2). Spreading across the border between China and Vietnam, the Kangding–Yunnan copper belt lies on the western fringe of the Yangtze Plate, with a length of about 300 km. A famous ironoxide copper gold (IOCG) metallogenic area [25,26], the province has more than 50 IOCG deposits, the most representative of which are the Luodang Copper Mine and Hongnipo Copper Mine (Figure 3).

The strata in this region developed from the Early Proterozoic Erathem to the Cenozoic Erathem. Except for the Ordovician–Carboniferous strata, all other strata can be found here, with those of the Proterozoic Erathem and the Mesozoic Erathem most developed. Stratigraphic combinations in this region mainly cover the Pre-Sinian System, the Sinian to Silurian Systems, the Permian System, the Triassic to Cretaceous Systems, and the Cenozoic Erathem [24]. The Hekou Group and the Huili Group are the major orebearing strata, whereas the industrial ore bodies are mainly hosted in the middle and The strata in this region developed from the Early Proterozoic Erathem to the Cenozoic Erathem. Except for the Ordovician–Carboniferous strata, all other strata can be found here, with those of the Proterozoic Erathem and the Mesozoic Erathem most developed. Stratigraphic combinations in this region mainly cover the Pre-Sinian System, the Sinian to Silurian Systems, the Permian System, the Triassic to Cretaceous Systems, and the Cenozoic Erathem [24]. The Hekou Group and the Huili Group are the major ore-bearing strata, whereas the industrial ore bodies are mainly hosted in the middle and lower parts of the Luodang Subgroup of the Hekou Group.

lower parts of the Luodang Subgroup of the Hekou Group. This region is typical of frequent and intense magmatic activities, which feature multiple cycles and multiple stages—the major stages are Jinning, Chengjiang, Variscan, and Indosinian Periods. Due to its unique tectonic–magmatic conditions and relatively developed fluid activities, this region is abundant in metal minerals [24]. This region is typical of frequent and intense magmatic activities, which feature multiple cycles and multiple stages—the major stages are Jinning, Chengjiang, Variscan, and Indosinian Periods. Due to its unique tectonic–magmatic conditions and relatively developed fluid activities, this region is abundant in metal minerals [24].

*Minerals* **2022**, *12*, x 4 of 16

**Figure 2.** Geotectonic location of the Lala area (modified from [24]). **Figure 2.** Geotectonic location of the Lala area (modified from [24]). **Figure 2.** Geotectonic location of the Lala area (modified from [24]).

**Figure 3.** Distribution map of copper deposits in the Lala area (modified from [24,27]). *2.2. Deposit Geology* **Figure 3.** Distribution map of copper deposits in the Lala area (modified from [24,27]).

*2.2. Deposit Geology*

#### *2.2. Deposit Geology*

#### 2.2.1. Luodang Copper Deposit

Comprising 32 ore bodies, the Luodang copper deposit is 1960 miles long from east to west and 900 miles wide from north to south, covering an area of 1.76 km2 . The upper section of the volcanic sedimentary cycle—Luodang formation complex, in the middle of the Hekou Group—is the major ore-bearing stratum. The ore body is stratiform, stratiformlike, and lentoid, and is generated in an overlapping-imbricate shape [28]. The attitude of the ore body, basically resembling that of the surrounding rocks, is controlled by lithology and stratum position and fluctuates with the folds of the surrounding rocks. The ore body presents an overall trend from east to west and tilts southward with an angle of 15◦–40◦ [29].

#### 2.2.2. Hongnipo Copper Deposit

The Hongnipo deposit sits about 5 km south of the Luodang deposit, with the ore body hosted in the Hekou Group strata in the Early Proterozoic metamorphic–volcanic sedimentary sequence and controlled by stratum position and lithology to a large extent [30]. The Tianshengba Group is the primary ore-bearing stratum, followed by the Luodang Group. The ore body is stratiform, stratiform-like, and podiform, and is generated in a bedding mode. The ore-bearing surrounding rocks mainly comprise carbonaceous quartz albitite, dolomitic quartz albitite, and quartz albitite at the bottom of the lower section of the Tianshengba Group.

#### **3. Radial Basis Functional Link Networks**

RBFLN, proposed by Broomhead in 1988 [31], is a kind of ANN that can process complex nonlinear spatial datasets. RBFLN is of great importance in dealing with the nonlinear relationship between known deposits and evidence factor layers. Taking the known deposits and non-deposits as training samples [32–34], RBFLN identifies the RBF network relationship between these samples and evidence factors as a nonlinear neural network algorithm (NNA), comprising a three-layer feedforward structure: input layer, hidden layer, and output layer [35,36] (Figure 4). The RBFLN structure includes an input layer composed of *N* nodes which receive the input feature vector *x* and a hidden layer comprising *M* neurons, each of which is represented by a Gaussian RBF. Each neuron in the hidden layer receives the input feature vector *x* and outputs the value *y*. If *x q* is inputted into the mth neuron, the output value *y q m* can be expressed as [37]:

$$y\_m^q = e^{\left[-\|\mathbf{x}^q - \mathbf{v}^m\|^2 / 2\sigma\_m^2\right]}, \ 0 < y \le 1\tag{1}$$

where the value *q* ranges from 1 to *N* (*N* is the number of training samples), and the value *M* ranges from 1 to *M* (*M* is the number of neurons in the hidden layer). *x q* represents the nth input feature vector, *v m* refers to the center of the mth RBF in the hidden layer, which corresponds to the maximum likelihood point of the RBF, and *σ m* represents the width or spread function of the mth neuron [34,38].

The overall output feature vector *z* contributes to the linear combination of output weights. If the abovementioned *y q m* is output to the *j*th output neuron, the formula can be expressed as [37]:

$$z\_j^q = \left[\frac{1}{M+N}\right] \left\{ \left[\sum u\_{mj} \times y\_m^q\right] + \sum [w\_{nj} \times x + b\_j] \leftarrow t \right\} \tag{2}$$

where *umj* represents the synaptic weight between the hidden layer and the input layer, whereas *wnj* represents the synaptic weight between the input layer and the output layer. Additionally, the constant *bj* is added to the formula. The two synaptic weights were repeatedly modified through iteration processes until the output layer *Z* approached the target *T*.

**Figure 4.** Architecture diagram of RBFLN (modified from [34]). **Figure 4.** Architecture diagram of RBFLN (modified from [34]).

#### **4. Extraction and Integration of Geoscience Datasets 4. Extraction and Integration of Geoscience Datasets**

GIS technology was used to extract and process information on ore-forming anomalies based on the collected data of mineral geology (1:50,000), aeromagnetic anomalies (1:50,000), copper geochemistry in the Huili–Huidong area, comprehensive anomalies of chemical elements, and the remote sensing images presented in this paper. GIS technology was used to extract and process information on ore-forming anomalies based on the collected data of mineral geology (1:50,000), aeromagnetic anomalies (1:50,000), copper geochemistry in the Huili–Huidong area, comprehensive anomalies of chemical elements, and the remote sensing images presented in this paper.

The Beijing 54 projection coordinate system (PCS) was adopted in datasets, and the storage format was a grid (unit: m). The Beijing 54 projection coordinate system (PCS) was adopted in datasets, and the storage format was a grid (unit: m).

The information extraction of multivariate geoscience datasets in the study area is shown below: The information extraction of multivariate geoscience datasets in the study area is shown below:

#### (1) Geological Information Extraction and Interpretation (1) Geological Information Extraction and Interpretation

Using the mineral geological map (1:50,000) as a reference, we used MorPAS and ArcGIS software to extract four kinds of information on ore-forming anomalies: strata configuration entropy (Figure 5a), tectonic fracture isodensity (Figure 5b), tectonic fractal dimension (Figure 5c), and rock buffering (Figure 5d). Specifically, entropy can reflect the complexity of the structure. For instance, strata configuration entropy reveals the complexity of strata. There is a positive correlation between strata complexity and strata configuration entropy, and the latter changes with changes in the former [39]. From the perspective of space, tectonic fracture isodensity can be used to construct the development degree of information on anomalies in certain areas, which represents the cumulative sum of the tectonic length in a specific unit grid. Tectonic fractal dimension, an indicator to evaluate the complexity of fault structure, is positively correlated with the metallogenetic probability and has advantages over tectonic fracture isodensity. Magma intrusion is intense in this study area, and some magmatic rocks are occurrence ores. In addition, ores can be produced through magmatic rocks' contact metasomatism with the surrounding rocks; thus, the peripheral parts of magmatic rocks are also favorable metallogenic sites. Using the mineral geological map (1:50,000) as a reference, we used MorPAS and ArcGIS software to extract four kinds of information on ore-forming anomalies: strata configuration entropy (Figure 5a), tectonic fracture isodensity (Figure 5b), tectonic fractal dimension (Figure 5c), and rock buffering (Figure 5d). Specifically, entropy can reflect the complexity of the structure. For instance, strata configuration entropy reveals the complexity of strata. There is a positive correlation between strata complexity and strata configuration entropy, and the latter changes with changes in the former [39]. From the perspective of space, tectonic fracture isodensity can be used to construct the development degree of information on anomalies in certain areas, which represents the cumulative sum of the tectonic length in a specific unit grid. Tectonic fractal dimension, an indicator to evaluate the complexity of fault structure, is positively correlated with the metallogenetic probability and has advantages over tectonic fracture isodensity. Magma intrusion is intense in this study area, and some magmatic rocks are occurrence ores. In addition, ores can be produced through magmatic rocks' contact metasomatism with the surrounding rocks; thus, the peripheral parts of magmatic rocks are also favorable metallogenic sites.

**Figure 5.** (**a**) Formation combination entropy; (**b**) isodensity map of structural faults; (**c**) dimension value map of structural faults; (**d**) rock mass buffer distribution map. **Figure 5.** (**a**) Formation combination entropy; (**b**) isodensity map of structural faults; (**c**) dimension value map of structural faults; (**d**) rock mass buffer distribution map.

(2) Geophysical Information Extraction and Interpretation (2) Geophysical Information Extraction and Interpretation

According to the aeromagnetic anomaly data (1:50,000), information on the aeromagnetic ore-forming anomalies of the study (Figure 6) area was extracted. In our research, we collected physical property data measured by our predecessors in this study [26], providing an important reference for interpreting magnetic anomalies. From the aeromagnetic isoline map, most deposits were located in the zero-line area at the junction of positive and negative anomalies. In terms of the magnetic field characteristics, we found that According to the aeromagnetic anomaly data (1:50,000), information on the aeromagnetic ore-forming anomalies of the study (Figure 6) area was extracted. In our research, we collected physical property data measured by our predecessors in this study [26], providing an important reference for interpreting magnetic anomalies. From the aeromagnetic isoline map, most deposits were located in the zero-line area at the junction of positive and negative

anomalies. In terms of the magnetic field characteristics, we found that the gentle negative magnetic fields in the study area were a comprehensive reflection of a weak magnetic basement and sedimentary cover; the strong magnetic arched, beaded, and disordered magnetic anomalies were a reflection of basic volcanic rocks; and some relatively regular strong magnetic anomalies were a reflection of basic and intermediate-basic intrusive rocks. the gentle negative magnetic fields in the study area were a comprehensive reflection of a weak magnetic basement and sedimentary cover; the strong magnetic arched, beaded, and disordered magnetic anomalies were a reflection of basic volcanic rocks; and some relatively regular strong magnetic anomalies were a reflection of basic and intermediatebasic intrusive rocks.

**Figure 6.** Aeromagnetic anomaly map. **Figure 6.** Aeromagnetic anomaly map.

(3) Geochemical Information Extraction and Interpretation (3) Geochemical Information Extraction and Interpretation

The Cu element anomaly map (Figure 7a) and the comprehensive anomaly map (Figure 7b) of Cu–Co–Mo elements in this study area were provided by the Development and Research Center of the National Geological Archives of China. The Cu anomaly directly indicated the existence of Cu deposits or mineralization, and the comprehensive Cu–Co– Mo anomaly indirectly indicated the existence of Cu deposits or mineralized zones. The Cu element anomaly map (Figure 7a) and the comprehensive anomaly map (Figure 7b) of Cu–Co–Mo elements in this study area were provided by the Development and Research Center of the National Geological Archives of China. The Cu anomaly directly indicated the existence of Cu deposits or mineralization, and the comprehensive Cu–Co–Mo anomaly indirectly indicated the existence of Cu deposits or mineralized zones.

(4) Remote Sensing Information Extraction and Interpretation

Different surface features of the earth display various spectral characteristics, and the altered surrounding rocks characterizing the existence of deposits and mineralized zones are no exception. Remote sensing technology can detect the altered surrounding rocks (if any) exposed on the earth's surface. The abnormal data of remote sensing alteration information are also an important basis for indicating mineralization. The distribution map of iron-stained alterations (Figure 8a) and remote sensing tectonic information in this study area were provided by the Development and Research Center of the National Geological Archives of China. In ArcGIS, we first divided the study area into grids, in accordance with a certain grid size. After that, we counted the total degree of linear fracture in each grid, and we assigned this value to the center point of the grid. Finally, we spatially interpolated the point data, and we obtained the isodensity map of the line–ring structure (Figure 8b).

**Figure 7.** (**a**) Map of Cu anomalies; (**b**) map of comprehensive anomalies. **Figure 7.** (**a**) Map of Cu anomalies; (**b**) map of comprehensive anomalies.

*Minerals* **2022**, *12*, x 10 of 16

**Figure 8.** (**a**) Distribution map of iron-stained alteration information; (**b**) iso-density map of line– loop structure. **Figure 8.** (**a**) Distribution map of iron-stained alteration information; (**b**) iso-density map of line– loop structure.

#### **5. Application of RBFLN in the Lala Copper Deposit 5. Application of RBFLN in the Lala Copper Deposit**

*5.1. Training and Classification 5.1. Training and Classification*

area.

According to the aforementioned geology–geophysics–geochemistry–remote sensing ore-forming information, we used GeoXplore 5.1 software, the SDM module of ArcGIS According to the aforementioned geology–geophysics–geochemistry–remote sensing ore-forming information, we used GeoXplore 5.1 software, the SDM module of ArcGIS 10.5

10.5 software, which were both developed by Environmental Systems Research Institute (ESRI), and the RBFLN model to perform metallogenic prospect predictions in the study

Twenty-five deposit sites and twenty-four non-deposit sites were involved in this research. A common way to evaluate the trained RBFLN was to use one-third of the training sites for verification and use the rest of the training sites to evaluate the network performance. Therefore, seven deposits and eight non-deposits were selected for verification, and eighteen deposits and sixteen non-deposits were chosen for training (Figure 9).

The nine abovementioned geoscience layers were combined by rasterization and reclassification, thus forming a unique grid. We divided the study area into a 100 m × 100 m grid scale, and the cumulative number of grids was 33,864. Each pixel in the grid data comprised feature vectors X (X1, X2, X3, X4, X5, X6, X7, X8, and X9) in the nine-dimensional

In the training stage, the number of RBFs and the number of iterations were changed to obtain the minimum sum of squared error (SSE) between the output result and the target vector [34]. SSE represented the mismatch degree between the output result and the target vector, and a smaller value contributed to a higher matching degree. With the gradual increase in the number of iterations, SSE showed a downward trend and tended to be stable (Figure 10). When the number of iterations increased by more than 160, the network

space, which served as the input layer of the RBFLN model.

seemed to be affected by over-learning, which led to overfitting [32,34].

software, which were both developed by Environmental Systems Research Institute (ESRI), and the RBFLN model to perform metallogenic prospect predictions in the study area.

Twenty-five deposit sites and twenty-four non-deposit sites were involved in this research. A common way to evaluate the trained RBFLN was to use one-third of the training sites for verification and use the rest of the training sites to evaluate the network performance. Therefore, seven deposits and eight non-deposits were selected for verification, and eighteen deposits and sixteen non-deposits were chosen for training (Figure 9).

The nine abovementioned geoscience layers were combined by rasterization and reclassification, thus forming a unique grid. We divided the study area into a 100 m × 100 m grid scale, and the cumulative number of grids was 33,864. Each pixel in the grid data comprised feature vectors X (X1, X2, X3, X4, X5, X6, X7, X8, and X9) in the nine-dimensional space, which served as the input layer of the RBFLN model.

In the training stage, the number of RBFs and the number of iterations were changed to obtain the minimum sum of squared error (SSE) between the output result and the target vector [34]. SSE represented the mismatch degree between the output result and the target vector, and a smaller value contributed to a higher matching degree. With the gradual increase in the number of iterations, SSE showed a downward trend and tended to be stable (Figure 10). When the number of iterations increased by more than 160, the network seemed to be affected by over-learning, which led to overfitting [32,34]. *Minerals* **2022**, *12*, x 11 of 16

**Figure 9.** Location map of deposits and non-deposit training and validation sites used for training the neural networks. **Figure 9.** Location map of deposits and non-deposit training and validation sites used for training the neural networks.

**Figure 10.** Number of iterations versus SSE for selected number of RBFs.

Accordingly, the results obtained through the RBFLN model with 40 RBFs and 120 iterations were selected to generate a probability output table with a value range of 0– 1.577, which was normalized between 0 and 1. A relation graph of probability values and cumulative percentages of the study area was drawn (Figure 11), and the inflection points on the graph were calibrated to define the category intervals. Specifically, the posterior probability interval of the higher metallogenic target area was *p* ≥ 0.65, whereas that of the

*5.2. Validation*

**Figure 9.** Location map of deposits and non-deposit training and validation sites used for training

**Figure 10.** Number of iterations versus SSE for selected number of RBFs. **Figure 10.** Number of iterations versus SSE for selected number of RBFs.

#### *5.2. Validation*

the neural networks.

*5.2. Validation*  Accordingly, the results obtained through the RBFLN model with 40 RBFs and 120 iterations were selected to generate a probability output table with a value range of 0– 1.577, which was normalized between 0 and 1. A relation graph of probability values and cumulative percentages of the study area was drawn (Figure 11), and the inflection points on the graph were calibrated to define the category intervals. Specifically, the posterior probability interval of the higher metallogenic target area was *p* ≥ 0.65, whereas that of the Accordingly, the results obtained through the RBFLN model with 40 RBFs and 120 iterations were selected to generate a probability output table with a value range of 0–1.577, which was normalized between 0 and 1. A relation graph of probability values and cumulative percentages of the study area was drawn (Figure 11), and the inflection points on the graph were calibrated to define the category intervals. Specifically, the posterior probability interval of the higher metallogenic target area was *p* ≥ 0.65, whereas that of the medium metallogenic target area was 0.49 < *p* < 0.65. Based on the features of the abovementioned intervals and the geological characteristics of the Lala concentration area, eight ore-prospecting prospective areas were delineated in this paper (Figure 12).

The validation data in the previous section were used to evaluate the performance of the RBFLN. The results demonstrated that five of the seven verification deposits were delineated in high prospective areas, indicating that the classification accuracy of unknown features was 71%. Additionally, one verification deposit and all non-verification deposits were delineated in low prospective areas, and one verification deposit was delineated in a medium prospective area. The comprehensive verification manifested that 86% of the verification deposits were in high and medium prospective areas (Figure 12).

Moreover, 3D visualization technology can be used to verify the prediction results and the existing ore bodies in the Luodang mining area, further proving the performance of the RBFLN model (Figure 13). The results showed that the distribution range of ore bodies was consistent with the prediction results of the RBFLN model.

medium metallogenic target area was 0.49 < *p* < 0.65. Based on the features of the abovementioned intervals and the geological characteristics of the Lala concentration area, eight

The validation data in the previous section were used to evaluate the performance of the RBFLN. The results demonstrated that five of the seven verification deposits were delineated in high prospective areas, indicating that the classification accuracy of unknown features was 71%. Additionally, one verification deposit and all non-verification deposits were delineated in low prospective areas, and one verification deposit was delineated in a medium prospective area. The comprehensive verification manifested that 86% of the

Moreover, 3D visualization technology can be used to verify the prediction results and the existing ore bodies in the Luodang mining area, further proving the performance of the RBFLN model (Figure 13). The results showed that the distribution range of ore

ore-prospecting prospective areas were delineated in this paper (Figure 12).

verification deposits were in high and medium prospective areas (Figure 12).

bodies was consistent with the prediction results of the RBFLN model.

**Figure 11. Figure 11.**  The probability values and the cumulative percentages for the study area. The probability values and the cumulative percentages for the study area.

**Figure 12.** Prospective map of mineralization. **Figure 12.** Prospective map of mineralization.

View; (**b**) Left view; (**c**) Right view.

**6. Conclusions**

**Figure 13.** Superposition map of the ore body model and metallogenic prospect area. (**a**) Top

(1) We established a multivariate comprehensive ore-prospecting information model of geology–geophysics–geochemistry–remote sensing in the study area based on GIS technology, and we determined the existence of several ore-prospecting indicators

**Figure 12.** Prospective map of mineralization.

**Figure 13.** Superposition map of the ore body model and metallogenic prospect area. (**a**) Top View; (**b**) Left view; (**c**) Right view. **Figure 13.** Superposition map of the ore body model and metallogenic prospect area. (**a**) Top View; (**b**) Left view; (**c**) Right view.

#### **6. Conclusions 6. Conclusions**

**Author Contributions:** Conceptualization, X.L. (Xiumei Lv); methodology, X.L. (Xiumei Lv); software, X.L. (Xiumei Lv) and X.L. (Xiaoning Liu); validation, W.Y. and G.W.; writing—original draft preparation, X.L. (Xiumei Lv); writing—review and editing, G.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Data sharing is not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.


**2.4 Antiemetic effect**

Emesis is one of the common side effects of numerous drugs. Emesis is also a common problem of other associated diseases. Natural products used for this purpose are well developed traditionally. The copper sulfate and ipecac-induced emesis has been blocked by the *P. vera* leaves and nut extract [22]. Copper sulfate induces emesis through GIT irritation, and ipecac induces emesis through GIT irritation and chemically stimulated the CTZ. The chemical constitutes of ipecac get readily absorbed and interact with 5HT3 and dopamine receptors. A mechanistic study is needed to confirm the extract mode of action.


**4. Discussion**

*Global Warming and Climate Change*

**84**

**Table 2.**

**Figure 5.**

*TGA analysis of LFCO-5.*

**Table 1.**

the LFO towards favorable Cu doping.

*Calculations obtained from BET analysis about structural properties.*

*Lattice parameter and cell volume calculations inferred from XRD analysis.*

velocity was used) (**Figure 5**).

The information can be observed from **Table 2**, the prepared samples exhibited

TGA analysis results shown below proves that the catalyst is acting on the soot to reduce the carbon black particles in the controlled environment (here fixed air

similar morphology, relating to the pore size consisting of nano and spherical particles. That the Cu-doping did not significantly affect the morphology and particle size of sample. The decrease in the pore size signifies that the Cu ions have been inserted into the lattice. On the other hand the XRD shoes no distortion into the lattice, which in turn explains the possible structural acceptance capability of

The focus of the research is to dissect the problem of global warming by understanding scope doping of copper on the LFO structure, then to analyze the effect on the properties of the LFO and correlate them with the performance characteristics of the catalyst with the reaction of soot oxidation. Following the results, we are attempting to circumnavigate the altered properties of LFCO to the catalytical performance.

Firstly, no crystalline Cu peaks were observed and the congruent, perovskite phase formed in all cases (LFO-10Cu–LFO-30Cu). However, the amount of Cu doping in the inverse proportion with the intensity of diffraction peaks because it was broadening them.

> *Cell volume can be calculated for orthorhombic structure as V = abc. For the LFCO-10- (since a = c), V = (5.554^2)\*7.845 A°m^3 = 242.16 A°m^3 and so on Which gives the below inverse relation*

The cell volume of LaFe1-xCuxO3 is marginally smaller than that of LFO; this value decreased with increasing Cu doping.

We can observe the trend in the BET analysis when synthesized at maximum Cu doping concentration; the particles may have fused to form large convolutions, This may also explain the more significant pore volumes and sizes for the samples of LFO-10Cu and LFO-20Cu when compared with others [8]. Increasing pore volume is the indication of more room for oxygen ions to combine with carbon, following reaction will spread light on the intricacies of the steps involved. Now as we see in the culmination of discussion, we are relating how the catalyst could have acted on the UHC (soot) and where rate-limiting step where the most unstable species carbon trioxide is formed and exactly in that step the LFO-Cu will facilitate the conversion in the diesel particulate filter (DPF) It will lower the temperature required for reaction to attain 50% conversion. From the TGA, we could infer the temperature to be around 350°C which is significantly lower than 600°C observed in reality and 500°C observed in the 0.

The controlled environment of the TGA equipment.

The chemical aspects of soot oxidation as discussed earlier indicated a need for catalyst. Here we can infer from TGA data that indeed the 50% conversion temperature of the catalyzed reaction is near to the exhaust temperature of the diesel engine, which in turn follows the possibility of soot oxidation reaction in the DPFs. Once installed the catalyst will reduce the amount of soot exiting the exhaust. The decrease in soot

**Figure 6.** *Possible reaction mechanism and role of catalyst—soot oxidation.* emissions from diesel engines will reduce the amount of carbon in the atmosphere, quantification of which can be extrapolated as 6 g hold per liter of diesel burnt.

As stated earlier, the formation of reactive carbon and oxidation of that species the basis of soot oxidation first order recombination reaction takes place,

$$\text{H} \ast \text{+OH} \ast \text{+C}\_{\text{(free)}} - \text{H}\_2\text{O} + \text{C}\_{\text{(reactive)}}$$

And concerning **Figure 6**, soot oxidation mainly involves the reaction of formation of CO2; soot oxidation is a slow process at high temperatures with relatively high activation energy (143 kJ/mol at average of 600°C) However due to increased surface activity of LFCO-20% it would be effective at the exhaust temperature itself (350°C). This would mean the catalyst is effective in converting soot to its subsequent oxides.


**4. Physiological effects of other legume proteins**

Soy protein has excellent health benefits, but many soybeans grown in the world are genetically modified organisms [GMOs]. There is no problem with the safety of GMO soybeans. However, from the perspective of security, the use of soy protein in foods tends to be withheld. Recently, the use of pea and lupin proteins instead of soy protein has increased worldwide. Initially, pea protein was a substitute for soybean protein as an ingredient with physical characteristics functions, after that, its beneficial health function has been reported mainly in sports nutrition. Mung bean protein has a structure very similar to that of β-conglycinin. Mung bean protein has been reported to be responsible for the beneficial physiological functions reported for β-conglycinin.

#### **4.1 Pea protein**

Field pea [*Pisum sativum* L.] is grown in 84 different countries and constitutes the largest percentage [36%] of total pulse production worldwide [79]. Global pea production has continuously increased over the last 30 years. In 2008, field pea was cultivated on over 10 million hectares worldwide with a total world production of

12.13 million tons [80]. The top 5 countries for pea production are Canada, Russia, China, India and the USA. The global market for pea protein is expected to reach 34.8 million US dollars by 2020 [81]. The physical and chemical properties of pea protein can significantly influence its behaviors in food processing, storage and consumption [82, 83].

Life expectancy continues to increase worldwide. In the United States, adults 65 years of age and older are projected to more than double from 600 million to 1.6 billion worldwide between 2015 and 2050 [84]. Proper body composition, physical fitness, and a healthy appetite have been reported to lead to successful aging with higher performance [85, 86]. Skeletal muscle mass and strength begin to decline at age 30, and the rate of these losses accelerates at age 60 [87]. Protein ingestion strongly increases muscle protein synthesis rates [88]. Amino acids serve as precursors for de novo muscle protein synthesis and can act as strong signaling molecules activating translation initiation via the mechanistic/mammalian target of rapamycin complex-1 (mTORC1) pathway [89]. It was shown that BCAA ingestion increases myofibrillar protein synthesis rates during recovery from exercise only in young males [90]. Whey protein isolate [WPI] was used as the animal protein source because of its high concentration of BCAAs and its ability to increase satiety in response to a mixed meal [91]. While whey protein supplementation is known to enhance adaptations to resistance training, not all athletes are able or willing to consume whey or animal proteins. Vegetarian athletes who want to stick to a plantbased diet or those with restrictions on other animal foods often rely on other plant proteins as an equivalent alternative to whey protein [92]. Self-identify as vegetarian in just over 5% of U.S. adults aged 18–34 years and self-awareness as vegan in more than half of these respondents are reported in a 2016 Harris Poll conducted by the Vegetarian Resource Group [93]. Meat Free Mondays' movement and an upsurge of plant-based protein food products in the marketplace strongly reflect the recent acceptance of these lifestyles [94].

Field pea contains a well-balanced amino acid profile [95]. Because of its availability, low cost, nutritional value and health benefits, pea protein has been widely used as a substitute for soybean or animal proteins in various functional applications [96–99]. Pea protein can also be used as a nutritional supplement for sports and exercises. Pea protein is an excellent source of BCAAs and has high and balanced contents of leucine, isoleucine and valine. Indeed, there are reports that pea protein is as useful as whey protein in sports nutrition (**Table 4**) [100–103].

In the future, pea protein is expected to be widely used as a sports nutritional supplement as well as a physical and functional ingredient in place of soybean protein.

#### **4.2 Lupin protein**

Lupin (*Lupinus* L.) is an ancient pulse "bean" crop, and in the new genus of modern agriculture, the lupin seeds have great potential for high-protein food, animal feed, food potential, soil fertility improvement, plants as cover crops, crop residues as stable feed, and soil improvement [104, 105]. Lupin is well known for its ability to fix nitrogen and grow on infertile soils, and is further known to be valuable in terms of cropping rotations during the growing season in agriculture with cereals, hay, oilseeds, beans of other legumes, and disease break crops for pasture [104, 106]. Wild indigenous lupins have bitter alkaloids. All modern species of *L. angustifolius* have total alkaloid levels in seeds of up to 200 mg/kg [0.02%] or less, which is 100 times lower than the seed alkaloid levels of nearly wild types. Compared to almost all food crops, lupins have only recently become of interest in modern crop breeding.

*Soybean and Other Legume Proteins Exhibit Beneficial Physiological Effects on Metabolic… DOI: http://dx.doi.org/10.5772/intechopen.99955*

*Note:* ↓ *and* ↑ *signs represent decrease and increase, respectively, after supplement of active compounds [pea protein only or, pea and whey proteins].*

#### **Table 4.**

*Clinical studies of pea protein for sports nutrition.*

There has been considerable interest in lupin seeds recently, and as a human health food, the seeds are very high in dietary fiber, gluten-free, and virtually starch-free, and therefore have a very low glycemic impact [107]. What makes lupins even more valuable is that there are no genetically modified (GM) bean varieties under commercial cultivation. World production of lupin seed increased quickly in the 1970s and is dominated by Australian production.

Lupin seeds are high in protein, with levels similar to soybeans. Their grains are also known to be high in total dietary fiber, ~40 g/100 g dry matter, making lupins unique among ancient grains and beans. The main category of protein in lupin grains is globulin, with albumin making up the remainder. The major globulin categories are α-conglutin [35–37 g/100 g total protein], β-conglutin [44–45 g/100 g total protein], γ-conglutin [4–5 g/100 g total protein], and δ-conglutin [10–12 g/100 g total protein] [108–111]. Nutritionally, the limiting amino acids in lupin protein are the sulfur-containing amino acids methionine and cysteine [112]. Compared to soy protein, which have a more complete essential amino acid profile, the lupin protein was reported to be slightly below the required

level of sulfur-containing amino acids needed by adults [113]. However, Singla et al. reported that the sulfur-containing amino acid levels of lupin protein were similar to those of soy [114]. This discrepancy is probably due to differences in lupin protein varieties and production environments. Carvajal-Larenas et al. reviewed in detail the amino acid composition of whole lupin seeds and concluded that it varies slightly among species. In vitro digestibility is ~98% high for uncooked lupin protein and is similar to soybean [115].

In vitro models of Lupinus albus γ-conglutin have shown the biological activity that enhances insulin and metformin activity on intracellular glucose consumption, indicating the potential for regulation on blood glucose by γ-conglutin [116]. As a possible improvement of lipid metabolism, an increase in LDL receptor activity has been demonstrated by HepG2 cells [117]. Furthermore, isolated lupin proteins of have been reported to have hyperlipidemic, anti-atherogenic, and hypocholesterolemic effects in rabbits, rats, and chickens [118, 119]. Several clinical human studies have shown that lupin protein decreases total and LDL cholesterol, as well as triglyceride and reduce the glycaemic response (**Table 5**) [120–127].

In general, the anti-nutrient factor of lupins is considered to be low compared to other legumes such as soybeans. Specifically, protease inhibitors are present at very low levels and are of minor importance in lupin crops. Trypsin inhibitor activity is described as "negligible" in Lupinus species, "very strong" at 43–84 trypsin inhibitor units [TIU/mg] in soybeans, and high [17–51 TIU/mg] in common beans [128]. Bitter lupin seed varieties contain quinolizine alkaloids, which may be toxic to humans. These toxic effects were recently reviewed by Carvajal-Larenes et al. [115]. Therefore, its maximum legal level of 0.02 g/100 g lupine powder and food has been legislated in several countries. There were no differences in alkaloids in grains among commercial *L. angustifolius* cultivars from western Australia in the same region and season, and all samples were below the levels permitted for maximum human food use.

Lupin protein, a legume, is a plant protein with similar attributes to soybean protein [129] and can be a substitute for soybean in the food industry [130, 131]. Further extensive research is expected due to the need for alternatives to animal proteins.

#### **4.3 Mung bean protein**

The mung bean (*Vigna radiata* L.) is one of the most important edible legume crops, grown on more than 6 million ha worldwide (approximately 8.5% of the global pulse area) and consumed by most households in Asia [132]. For individuals who cannot afford animal proteins or those who are vegetarian, mung bean is comparatively low cost and is a good source of protein. Furthermore, mung bean protein is more easily digestible than protein in other legumes [133]. In addition to the nutritional properties of mung bean, it has been known that mung beans have various physical regulation functions from ancient times. In the Compendium of Materia Medica (the "*Bencao Gangmu*"), a well-known Chinese pharmacopeia, mung beans have recorded to be utilized as a traditional Chinese medicine for its detoxification activities, recuperation of mentality, ability to alleviate heat stroke, and regulation of gastrointestinal upset.

Mung bean protein isolate (MuPI) dose-dependently reduced plasma lipid levels, such as total cholesterol, triglycerides, and non-high-density lipoprotein cholesterol [non-HDL-C] in hamsters [134, 135]. The mechanism underlying the cholesterol-lowering activity of mung bean protein was speculated to increase fecal bile acid and sterol excretion and decrease cholesterol absorption and synthesis. This mechanism is the same as that reported for SPI [68, 69]. In another study,

*Soybean and Other Legume Proteins Exhibit Beneficial Physiological Effects on Metabolic… DOI: http://dx.doi.org/10.5772/intechopen.99955*

*Note:* ↓ *and* ↑ *signs represent decrease and increase, respectively, after supplement of active compounds. Totalcholesterol (Total-C); low-density lipoprotein cholesterol (LDL-C); triglyceride (TG); high-density lipoprotein cholesterol (HDL-C).*

#### **Table 5.**

*Clinical studies of lupin protein on improving lipid and glucose metabolisms.*

MuPI was found to lower blood triglyceride levels in normal rats by inducing adiponectin and reducing triglyceride synthesis via insulin signaling [136]. This mechanism is the same as that reported for β-conglycinin [23]. From these findings, MuPI can be expected to be more effective in improving lipid metabolism. The main component of MuPI, accounting for over 80% of the protein, is 8S globulin. 8S globulin exhibited the highest degree of sequence identity [68%] and structural similarity with β-conglycinin [137, 138]. MuPI is expected to exhibit a four times stronger beneficial function on human health than SPI, in which β-conglycinin accounts for only 20% of the total protein.

The positive effects of MuPI on glucose metabolism in pre-diabetes patients was confirmed. In recent double-blind, placebo-controlled clinical trial, the test group subjects were instructed to consume a total of 2.5 g of MuPI twice daily for 12 weeks, with pre-diabetes [fasting plasma glucose level of 110–125 mg/dL or 2-h plasma glucose level of 140–200 mg/dL by the 75-g glucose tolerance test]. In this study, MuPI was shown to suppress to increase fasting plasma glucose and insulin levels compared to the placebo group. Triglyceride levels significantly decreased in subjects with hyperlipidaemia [139]. Another double-blind, placebo-controlled clinical trial of 44 healthy subjects showed that after consumption of 3.0 g/d MuPI for 8 weeks, insulin levels and homeostatic model assessment of insulin resistance values significantly decreased, and plasma glucose levels showed a downtrend, although it was not significant [140]. The lack of a beneficial effect of MuPI on blood glucose concentrations may be attributed to the exclusion of volunteers with abnormal blood glucose concentrations in this study. In this study, the body compositions of subjects were measured by dual-energy X-ray absorptiometry. As a result, a decrease in body fat mass and an increase in lean body mass in the test group were revealed. Conversely, in the control group, body fat mass increased and lean body mass decreased. The differences in body fat mass and lean body mass within each group and between the test and control groups were not statistically significant. However, the adiponectin level in the test group significantly increased, and it decreased in the control group. There was a significant difference between the net changes in the test and control groups [140]. These findings indicate that MuPI might improve insulin sensitivity by decreasing the accumulation of visceral fat.

Non-alcoholic fatty liver disease [NAFLD] represents a spectrum of liver diseases involving hepatocyte dysfunction caused by hepatic triglyceride accumulation in these cells. The prevalence of NAFLD has increased with the increased prevalence of obesity and metabolic syndrome. NAFLD is now a common disease, affecting 30% of the US population and 20% of Asian and European populations [141]. Rodent studies have shown that SPI intake reduces hepatic triglyceride accumulation [142, 143]. The detailed mechanism underlying the hepatic triglyceride-reducing effect of SPI remains to be elucidated, but β-conglycinin is likely to play an important role [135]. Indeed, the administration of purified β-conglycinin results in an even stronger reduction in hepatic triglycerides than SPI administration [18, 144]. From these results, it is expected that MuPI also has a preventive effect on NAFLD by preventing hepatic triglyceride accumulation. The effect of MuPI on hepatic triglyceride accumulation elucidated the potential ability of MuPI to prevent NAFLD onset and progression in experiments using an atherogenic diet-induced NASH mouse model in mice fed a normal-fat or high-fat diet [145]. In the abovementioned clinical trial [140], Alanine aminotransferase [ALT] levels increased slightly in the control group, whereas significantly decreased in the test group. Of the blood test items, ALT is one of important indicators of the degree of liver dysfunction.

The released peptides obtained from mung bean protein hydrolysate may exhibit bioactivity as angiotensin I-converting enzyme (ACE) inhibitors,

*Soybean and Other Legume Proteins Exhibit Beneficial Physiological Effects on Metabolic… DOI: http://dx.doi.org/10.5772/intechopen.99955*

antioxidants, and anti-cancer Asiatic acid carriers due to their sequence characteristics [146, 147]. A peptide [<3 kDa], with a small molecular weight isolated from MuPI hydrolysates, was reported to show high ACE inhibitory and antioxidant activities, including DPPH radical scavenging activity, hydroxyl radical scavenging ability, and metal-chelating activity [146]. Three kinds of novel peptides exerting high ACE inhibitory activity were isolated from the alcalase hydrolysate of MuPI, and the amino acid sequences of these peptides were identified to be Lys-Asp-Tyr-Arg-Leu, Val-Thr-Pro-Ala-Leu-Arg, and Lys-Leu-Pro-Ala-Gly-Thr-Leu-Phe [148].

The relationships between MuPI intake, strength, and lean body mass (LBM) in underactive vegetarians were examined, and the impact of MuPI supplementation on these indices was recorded utilizing an eight-week, randomized, controlled feeding trial. LBM significantly correlated with grams of protein consumed daily and was also significantly correlated with grip strength and lower body strength [149]. Mung beans are inadequate in threonine, tryptophan, and the sulfur-containing amino acids cysteine and methionine, but they contain high levels of essential amino acids, notably leucine, lysine, and phenylalanine [150]. Although it is necessary to consider the amino acid balance, it is expected that MuPI will be widely used in the field of sports nutrition in the future.

## **5. Conclusion**

If the current pace of population growth continues, the global population is expected to surpass 9 billion by 2050. In addition to this increase in population, the change of dietary habits of emerging countries due to their increased GDP will require, in 2050, we will need twice as much protein as we had in 2005.

So far, we have been able to meet the increasing demand for protein by improving the productivity of agriculture. However, in the future, this growth alone will not be enough to absorb the increase, and the balance between supply and demand will begin to collapse as early as 2030. This prediction is called the "protein crisis," and has recently begun to attract attention, especially in Europe and the United States. To solve this protein crisis, it is essential to use highly productive plant proteins as food ingredients instead of animal proteins, which are less efficient in production.

WHO has called for the need to address the double burden of malnutrition. This means that we need to look not only at nutrient deficiencies, but also at nutrient excesses. Obesity caused by over-nutrition and the resulting lifestyle-related diseases are spreading around the world. In this regard, consumer demand for plant protein-based products is high and expected to grow considerably in the next decade. A variety of soy and other plant-based functional foods have been recommended by many health organizations worldwide.

Currently, contributions to the SDGs (Sustainable Developing Goals) are being appealed around the world. There is widespread recognition that the replacement of animal protein with vegetable protein not only contributes to human health, but also to the earth health. Wider and prudent use of plant proteins in the diet can help to supply adequate high-quality protein for the population and may reduce the potential for adverse environmental consequences.

This chapter focused on the recently reported physiological functions of legumes-derived plant proteins, including soybeans. Further research is expected to lead to more widely use of the legumes introduced in this chapter and to the discovery and use of legumes with new functionalities.