Loading [Contrib]/a11y/accessibility-menu.js
Research article Special Issues

Automatic recognition of giant panda vocalizations using wide spectrum features and deep neural network


  • Received: 06 May 2023 Revised: 19 June 2023 Accepted: 04 July 2023 Published: 24 July 2023
  • The goal of this study is to present an automatic vocalization recognition system of giant pandas (GPs). Over 12800 vocal samples of GPs were recorded at Chengdu Research Base of Giant Panda Breeding (CRBGPB) and labeled by CRBGPB animal husbandry staff. These vocal samples were divided into 16 categories, each with 800 samples. A novel deep neural network (DNN) named 3Fbank-GRU was proposed to automatically give labels to GP's vocalizations. Unlike existing human vocalization recognition frameworks based on Mel filter bank (Fbank) which used low-frequency features of voice only, we extracted the high, medium and low frequency features by Fbank and two self-deduced filter banks, named Medium Mel Filter bank (MFbank) and Reversed Mel Filter bank (RFbank). The three frequency features were sent into the 3Fbank-GRU to train and test. By training models using datasets labeled by CRBGPB animal husbandry staff and subsequent testing of trained models on recognizing tasks, the proposed method achieved recognition accuracy over 95%, which means that the automatic system can be used to accurately label large data sets of GP vocalizations collected by camera traps or other recording methods.

    Citation: Zhiwu Liao, Shaoxiang Hu, Rong Hou, Meiling Liu, Ping Xu, Zhihe Zhang, Peng Chen. Automatic recognition of giant panda vocalizations using wide spectrum features and deep neural network[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15456-15475. doi: 10.3934/mbe.2023690

    Related Papers:

    [1] Jean-Bernard Baillon, Guillaume Carlier . From discrete to continuous Wardrop equilibria. Networks and Heterogeneous Media, 2012, 7(2): 219-241. doi: 10.3934/nhm.2012.7.219
    [2] Edward S. Canepa, Alexandre M. Bayen, Christian G. Claudel . Spoofing cyber attack detection in probe-based traffic monitoring systems using mixed integer linear programming. Networks and Heterogeneous Media, 2013, 8(3): 783-802. doi: 10.3934/nhm.2013.8.783
    [3] Matthieu Canaud, Lyudmila Mihaylova, Jacques Sau, Nour-Eddin El Faouzi . Probability hypothesis density filtering for real-time traffic state estimation and prediction. Networks and Heterogeneous Media, 2013, 8(3): 825-842. doi: 10.3934/nhm.2013.8.825
    [4] Raúl M. Falcón, Venkitachalam Aparna, Nagaraj Mohanapriya . Optimal secret share distribution in degree splitting communication networks. Networks and Heterogeneous Media, 2023, 18(4): 1713-1746. doi: 10.3934/nhm.2023075
    [5] Samitha Samaranayake, Axel Parmentier, Ethan Xuan, Alexandre Bayen . A mathematical framework for delay analysis in single source networks. Networks and Heterogeneous Media, 2017, 12(1): 113-145. doi: 10.3934/nhm.2017005
    [6] Carlos F. Daganzo . On the variational theory of traffic flow: well-posedness, duality and applications. Networks and Heterogeneous Media, 2006, 1(4): 601-619. doi: 10.3934/nhm.2006.1.601
    [7] Leah Anderson, Thomas Pumir, Dimitrios Triantafyllos, Alexandre M. Bayen . Stability and implementation of a cycle-based max pressure controller for signalized traffic networks. Networks and Heterogeneous Media, 2018, 13(2): 241-260. doi: 10.3934/nhm.2018011
    [8] Félicien BOURDIN . Splitting scheme for a macroscopic crowd motion model with congestion for a two-typed population. Networks and Heterogeneous Media, 2022, 17(5): 783-801. doi: 10.3934/nhm.2022026
    [9] Anya Désilles . Viability approach to Hamilton-Jacobi-Moskowitz problem involving variable regulation parameters. Networks and Heterogeneous Media, 2013, 8(3): 707-726. doi: 10.3934/nhm.2013.8.707
    [10] Fethallah Benmansour, Guillaume Carlier, Gabriel Peyré, Filippo Santambrogio . Numerical approximation of continuous traffic congestion equilibria. Networks and Heterogeneous Media, 2009, 4(3): 605-623. doi: 10.3934/nhm.2009.4.605
  • The goal of this study is to present an automatic vocalization recognition system of giant pandas (GPs). Over 12800 vocal samples of GPs were recorded at Chengdu Research Base of Giant Panda Breeding (CRBGPB) and labeled by CRBGPB animal husbandry staff. These vocal samples were divided into 16 categories, each with 800 samples. A novel deep neural network (DNN) named 3Fbank-GRU was proposed to automatically give labels to GP's vocalizations. Unlike existing human vocalization recognition frameworks based on Mel filter bank (Fbank) which used low-frequency features of voice only, we extracted the high, medium and low frequency features by Fbank and two self-deduced filter banks, named Medium Mel Filter bank (MFbank) and Reversed Mel Filter bank (RFbank). The three frequency features were sent into the 3Fbank-GRU to train and test. By training models using datasets labeled by CRBGPB animal husbandry staff and subsequent testing of trained models on recognizing tasks, the proposed method achieved recognition accuracy over 95%, which means that the automatic system can be used to accurately label large data sets of GP vocalizations collected by camera traps or other recording methods.



    Multidrug-resistant bacteria (MDRB) are microorganisms that are resistant to one or more antimicrobial agents. They are usually resistant to all but one or two commercially available antimicrobial agents. This definition includes microbes that have acquired resistance to at least one agent in three or more antimicrobial categories. The MDRB of clinical interest include: Methicillin-resistant Staphylococcus aureus (MRSA), Staphylococcus aureus with resistance to vancomycin [these are Vancomycin-intermediate Staphylococcus aureus (VISA) and Vancomycin-resistant Staphylococcus aureus (VRSA)],Vancomycin-resistant enterococci (VRE), Extended spectrum beta-lactamases (ESBLs) producing gram-negative bacilli, Multidrug-resistant Streptococcus pneumoniae (MDRSP), Carbapenem-resistant Enterobacteriaceae (CRE) and Multidrug-resistant Acinetobacter baumannii [1][3] .

    Infectious diseases caused by MDRB are an important burden globally. They have for centuries been among the leading causes of death, disability, growing challenges to health security and human progress, especially in developing countries [4].

    Although, many new antibacterial drugs have been produced, bacteria exhibiting resistance to them have increased and is becoming a global concern as we are fast running out of therapeutic options [5],[6]. The challenges of antimicrobial resistance are faced in both the health care and community settings, necessitating a broad approach with multiple partners across the continuum of care. For example, 18–33% of MRSA colonized patients subsequently developed MRSA infections. Community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) strains also constitute an increasing proportion of hospital-onset MRSA infections. The Centre for Disease Control and Prevention (CDC) estimated that over 2 million illnesses and 23,000 deaths per year are attributable to antibiotic resistance in the United States [3].

    Vancomycin is widely prescribed for the treatment of infections caused by MRSA; but the emergence of VISA and VRSA has been reported by many authors. Really, teicoplanin, daptomycin, linezolid, etc are expensive drugs which are currently prescribed when faced with MRSA with low sensitivity to vancomycin. However, development of resistance to these drugs has been identified worldwide [7][11].

    Usage of plants in fighting against illnesses and diseases has deep roots in man's history. Researchers are interested in plant extracts as medicines because there are several reports regarding the antimicrobial activity of their crude extracts which might be better substitutes for conventional antibiotics. Recent published reports opined that medicinal plants with anti-MRSA activity can be considered for treatment of MRSA infections [8],[12]. This present work is a brief review on MRSA, VISA, VRSA and some medicinal plants with anti-MRSA activities.

    Staphylococcus aureus is a Gram-positive coccoid bacterium. The cells are arranged in irregular grape-like appearance and they are usually found as normal flora in humans and animals. It is ubiquitous in the human population and 30–40% of adults are asymptomatic carriers. It is also a major pathogen of human and can cause a range of infections from mild skin infections and food poisoning, to life threatening infections [13][17].

    Resistance to methicillin by S. aureus was initially observed in 1961 shortly after the antibacterial agent was introduced clinically and since then, there has been a global epidemic of Methicillin-resistant Staphylococcus aureus (MRSA) in both healthcare and community settings [18][20]. MRSA isolates from the UK and Denmark in the early 1960s constituted the very first epidemic MRSA clone soon after methicillin was introduced and it has since emerged as an important pathogen in human medicine [21][23]. Although, methicillin is no longer prescribed for patients and has been replaced by isoxazolyl penicillins, particularly flucloxacillin in the UK, the acronym MRSA has stayed [24]. It is characterized by antibiotic resistance to penicillins, cephalosporins, carbapenems and has tendency of developing resistance to quinolones, aminoglycosides, and macrolides [10],[25],[26].

    The origination of MRSA was as a result of Staphylococcal Cassette Chromosome mec (SCCmec) genes acquired by methicillin-susceptible S. aureus (MSSA). The SCCmec harbours the mecA gene which encodes the penicillin-binding protein (PBP2a) that confers resistance to all β-lactam antibiotics [10],[27][29]. SCCmec also contains the cassette chromosome recombinases (ccr) gene complex. The ccr genes (composed of ccrC or a pair of ccrA and ccrB) encode recombinases mediating integration and excision of SCCmec into or from the chromosome. The ccr genes and surrounding genes form the ccr gene complex. In addition to ccr and mec gene complexes, SCCmec contains a few other genes and various other mobile genetic elements such as: insertion sequences, transposons and plasmids [30],[31].

    Eleven different types of SCCmec (I-XI) and five allotypes of the ccr gene complexes (ccrAB1, ccrAB2, ccrAB3, ccrAB4 and ccrC) have been reported. Generally, SCCmec types I, II, III, VI and VIII are called hospital-acquired MRSA or (HA-MRSA). Types IV, V and VII as community-acquired (CA-MRSA) while types IX, X and XI as livestock-associated MRSA (LA-MRSA) [31],[32]. Expression of methicillin resistance in S. aureus is commonly under regulatory control by mecI or blaI gene. The mecI and blaI repressors are controlled by the mecRI and blaRI transducers [20].

    MRSA remains a major public health concern worldwide and a therapeutic challenge as the antibacterial drugs effective for treatment are scanty and costly. The changing epidemiology of MRSA infections, varying resistance to commonly used antibiotics and involvement in hospital and community infections are influencing the use and clinical outcomes of currently available anti-infective agents [33].

    Vancomycin is an antibacterial agent that inhibits cell wall production by binding with the D-alanyl-D-alanine C terminus of the bacterial cell wall precursors, and subsequently preventing cross-linking by transpeptidation. Vancomycin acts extracellularly and inhibits late-stage peptidoglycan biosynthesis which results in the intracellular accumulation of UDP-linked MurNAc-pentapeptide precursors. The vancomycin complex involves a number of hydrogen bonds between the peptide component of vancomycin and the D-Ala-D-Ala residue. Any process that interferes with vancomycin binding to D-Ala-D-Ala residues in the cell wall will decrease the potency of the drug [13],[36].

    Vancomycin was widely utilized for the treatment of MRSA infections and has led to the emergence of vancomycin-intermediate and vancomycin-resistant S. aureus (VISA and VRSA) [37]. This also triggered off alarms in the medical community as S. aureus causes life-threatening infections in hospitalized and non-hospitalized patients [38]. Vancomycin-intermediate S. aureus (VISA), heterogeneous vancomycin-intermediate S. aureus (hVISA) and vancomycin-resistant S. aureus (VRSA) are the three classes of S. aureus that are resistant to vancomycin which have emerged in different locations of the world [39].

    Vancomycin-intermediate S. aureus (VISA) was first reported from Japan in 1996 with reduced susceptibility to vancomycin (having a Minimum Inhibitory Concentration (MIC) of 8 mg/L). It has now spread to other hospitals in Asia, France, Brazil, USA, United Kingdom, etc [40]. S. aureus vancomycin breakpoints were redefined by the Clinical and Laboratory Standards Institute (CLSI) in 2006 as follows: resistant at MIC ≥ 16 µg/ml, intermediate at 4–8 µg/ml and susceptible at ≤ 2 µg/ml [34][36].

    VISA isolates emerged as a result of mutations (not their acquisition of foreign genetic elements) in MRSA isolates during treatment of patients with vancomycin. The comparison of vancomycin-susceptible and -resistant isolates to the VISA isolates showed that the mutations often occurred in the walkR, vraSR, rpoB (ribosomal) genes and the yvqF/vraSR system. Usually, the relevant mutated genes seemed to be directly or indirectly involved with the biosynthesis/metabolism of the staphylococcal cell wall [41].

    Often, there were treatment failures when VISA infections were treated with vancomycin [41]. It was observed that under vancomycin selective pressure usually during treatment, the VISA strains with a vancomycin MIC of 8 µg/ml have emerged and led to therapy failure. However, the nature of this resistance phenotype (VISA) was unstable especially when vancomycin selective pressure is removed as some strains reverted back to vancomycin-susceptible strains with MIC at 2 µg/ml [36].

    In 1997, the first case of hVISA was reported in Japan. The cultures of hVISA strains contain both low-frequency subpopulations of bacteria with increased vancomycin MIC value and high frequency of bacteria with low vancomycin MIC values (close to those of susceptible strains) [41]. The MIC for hVISA strains was defined by the presence of subpopulations of VISA at a rate of one organism per 105 to 106 organisms [42],[43]. The hVISA strains were detected using vancomycin population analysis profile (PAP) which was proposed as the most accurate method for hVISA detection; however, it is relatively time-consuming and requires the use of a spiral plater. The hVISA strain has generally required formal population analysis using the serial passage of screened isolates of S. aureus on selective agar containing increasing concentrations of vancomycin for its detection [13]. Results are generally not ready until at least 3 to 5 days [36].

    VISA and hVISA strains have thickened cell wall with reduced glycopeptide cross-linking as a result of the complex reorganization of cell wall metabolism. It has been proposed that the thickened cell wall may trap and sequester vancomycin and consequently, interferes with its mode of action [13]. This could be due to alteration in peptidoglycan production leading to increased residues of D alanyl-D-alanine, which bind vancomycin molecules and prevent them from reaching the target sites [18][20].

    In 2002, the first hospital strain of Vancomycin-resistant S. aureus (VRSA) was reported in the United States [44]. The acquisition of vanA gene from vancomycin-resistant enterococci resulted in the emergence of vancomycin-resistant strains of S. aureus (VRSA) with vancomycin MIC value greater than 16 µg/ml [36],[41],[45].

    MRSA has spread worldwide, and its prevalence has increased in both health-care and community environments. The proportion of MRSA varied among countries such as for instance: 0.4% in Sweden [24]; 25% in western part to 50% in southern India [10]; 33%–43% in Nigeria [46]; 37–56% in Greece, Portugal and Romania in 2014 [47]. High prevalence of MRSA with rates greater than 50% has also been reported in hospitals worldwide including in Asia, Malta, North and South America [29],[48]. Variation in the prevalence rates of MRSA was due to different epidemiological factors such as geographical and health system capability in running infection control program [49].

    Akanbi and Mbe [50] reported a prevalence range of 0% to 6% VRSA in southern parts of Nigeria among clinical isolates and also 57.7% in Zaria, northern Nigeria. Goud, et al., [51] reported a vancomycin resistance in 1.4% of S. aureus isolates in southern India. Other countries such as: Australia, Korea, Hong Kong, Scotland, Israel, Thailand, South Africa, etc have also reported S. aureus with vancomycin sensitivity reduction with prevalence ranges from 0–74% [20],[36],[52].

    Currently, there are seven common antibiotics used against MRSA, which are: vancomycin, daptomycin, linezolid, Sulfamethoxazole and trimethoprim (TMP-SMZ), quinupristin-dalfopristin, clindamycin and tigecycline. These antibiotics are gradually losing their efficiency as MRSA strains are developing resistance against them [8],[20],[53]. Presently, the therapeutic alternatives available for treatment of infections caused by MRSA and S. aureus with reduced vancomycin susceptibility are limited. Therefore, there is a global urgency for the development of novel drugs that will be effective in the treatment of S. aureus exhibiting multidrug resistance so as to combat the scourge caused by the microorganism in the globe [52].

    Natural products including medicinal plants have contributed immensely to human health, well-being and development of novel drugs. They are useful natural blueprints for the development of new drugs (especially in western countries) or/and phytomedicines purified to be used for the treatment of disease (commonly in developing countries and Europe) [54]. Medicinal plants can be valuable therapeutic resources. In numerous developing countries, including Nigeria, 80% of patients use home-made phytomedicines to treat infectious diseases. Despite the availability of modern medicine in some communities, the use of medicinal plants has remained high due to their efficacy, popularity and low cost. They also represent sources of potentially important new pharmaceutical substances since all the plants parts are utilized in traditional treatment and can therefore, act as lead compounds (Table 1).

    The applications of phytomedicines for human well being and as blueprints for developing novel useful drugs have drastically increased worldwide in recent years [77].

    The emergence of multidrug-resistant infectious agents associated with over- and inappropriate use of antibiotics has necessitated the World Health Organization (WHO) to acknowledge and pronounce the urgent need to develop novel antimicrobials and/or new approaches to tackle the menace caused by them in the globe; these have subsequently led to the resuscitation of the interest in medicinal plants [78]. The most common bacteria that have been used in susceptibility tests with numerous medicinal plants include: Staphylococcus aureus, methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), Pseudomonas aeruginosa Helicobacter pylori, etc [54]. Presently, numerous studies have reported the antibacterial activity of many plant extracts against MRSA. In this study, only fifty-one (51) plants with anti-MRSA activities from thirty-five (35) families were mentioned (Table 1). The minimum inhibitory concentrations (MIC) values of the plants on the tested MRSA strains were between 1.25 µg/ml to 6.30 mg/ml. Twenty-nine of the plants had MIC values < 1.0 mg/ml while the remaining twenty-two MIC values were > 1.0 mg/ml but < 8.0 mg/ml. Extracts exhibiting activities with MIC values below 8 mg/ml are widely accepted to possess some antimicrobial activity while those with values below 1 mg/ml are considered noteworthy [77],[79]. However, most of the plants in this review were not tested on S. aureus strains with reduced vancomycin susceptibility.

    The solvents used for the medicinal plants extraction in this review were ethanol and methanol (Table 1). This is probably because alcoholic extracts have higher antimicrobial activity than aqueous extracts. It has been reported that ethanolic extracts have higher antimicrobial activity than aqueous extracts because of the presence of higher amounts of polyphenols. They are more efficient in cell walls and seeds degradation causing polyphenols to be released from cells. Also, the enzyme polyphenol oxidase, degrades polyphenols in water extracts but is inactive in methanol and ethanol. Moreover, water is a better medium for the growth of microorganisms than ethanol [80].

    Although, methanol is more polar than ethanol but it is not frequently used for plant extraction due to its cytotoxic nature that may give incorrect results [81].

    Extracts of medicinal plants are rich in phytochemicals. Phytochemicals or secondary metabolites are natural protective agents biosynthesized by plants against external stress and pathogenic attack. They are crucial for plant defences and survival. They have been divided into several categories: phenolics, alkaloids, steroids, terpenes, saponins, etc. They exhibit other bioactivities such as antimutagenic, anticarcinogenic, antioxidant, antimicrobial, and anti-inflammatory properties and are therefore responsible for the medicinal potential of plants (Table 2). Hence, from this review, anti-MRSA plants have antibacterial effect on MRSA strains and other medicinal/therapeutic uses as depicted in Table 2.

    Table 1.  Medicinal plants with activities on methicillin-resistant S. aureus (MRSA).
    Botanical name Family Local/Common name Place of collection Plant part used Extracting Solvent MIC /MBC (mg/ml) MRSA MIC /MBC (mg/ml) VRSA References
    Acacia catechu (L. f.) Willd Fabaceae Cutch tree, black catechu Thailand Wood Ethanol 1.6–3.2/25 55,56
    Garcinia mangostana L. Clusiaceae Mangosteen Thailand Fruit shell Ethanol 0.05–0.4/0.1–0.4 55,57
    Impatiens balsamina Balsaminaceae Garden balsam Thailand Leaf Ethanol 6.3/25 55,58
    Peltophorum ptercarpum (DC.) Fabaceae Yellow flame tree Thailand Bark Ethanol 0.1–0.8/6.3 55,59
    Psidium guajava L. Myrtaceae Guava Thailand Leaf Ethanol 0.2–1.6/6.3 55,60
    Punica granatum L. Punicaceae Pomegranate Thailand Fruit shell Ethanol 0.2–0.4/1.6–3.2 55,61
    Uncaria gambir (Hunter) Roxb. Rubiaceae Gambier, White cutch Thailand Leaf, stem Ethanol 0.4–0.8/3.2 55,62
    Walsura robusta Meliaceae Bonlichu Thailand Wood Ethanol 1.6–3.2/25 55,63
    Swietenia mahagoni Meliaceae Mahagoni Malaysia Seed Ethanol 0.2–0.78/0.78–1.56 64
    Tinospora crispa Menispermaceae Patawali Malaysia Stem Ethanol 0.4–0.78/0.78–1.56 64
    Butea monosperma Lam. Fabaceae Flame-of-the-forest India Leaf Ethanol 5.91/13.30 1.16/2.62 65
    Callistemon rigidus R.Br. Myrtaceae Stiff bottlebrush India Leaf Methanol 0.00125–0.08 66
    Acacia albida Del. Fabaceae Gawo Nigeria Stem bark Methanol 3.0/4.0 67
    Anchomanes difformis Engl. Araceae Chakara Nigeria Roots Methanol 4.0/5.0 67
    Boscia senegalensis Del. Capparidaceae Anza Nigeria Roots Methanol 5.0/6.0 67
    Moringa oleifera Lam. Moringacceae Zogale Nigeria Leaf Ethanol 4.0/5.0 67
    Mormodica basalmina Linn. Cucurbitaceae Garahuni Nigeria Whole plant Methanol 4.0/5.0 67
    Nymphaea lotus Linn. Nymphaeaceae White lotus Nigeria Leaf Ethanol 5.0–10.0/10.0–30.0 5.0–10.0/10.0–30.0 68
    Pavetta crassipes K. Schum. Rubiaceae Gadau Nigeria Leaf Methanol 4.0/5.0 67
    Phyllanthus amarus Schum. Thonn. Euphorbiaceae Geron tsuntsaye Nigeria Whole plant Methanol 4.0/5.0 67
    Vernonia blumeoides Hook. f. Asteraceae Bagashi Nigeria Aerial part Ethanol 4.0/5.0 67
    Curcuma xanthorrhiza Zingiberaceae Java ginger Indonesia Rhizome Ethanol 0.5/ND 69
    Kaempferia pandurata Roxb. Zingiberaceae Temu kunci, fingerroot Indonesia Rhizome Ethanol 0.3/ND 69
    Senna alata Fabaceae Candle bush Indonesia Leaf Ethanol 0.5/ND 69
    Mallotus yunnanensis Pax et. Hoffm. Euphorbiaceae - China Tender Branches & leaves (TBL) Ethanol 0.008–0.032/0.064–0.26 70
    Skimmia arborescens Anders. Rutaceae Japanese skimmia China TBL Ethanol 0.016–0.064/0.13–0.26 70
    Cyclobalanopsis austroglauca Y.T. Chang Fagaceae Oak China TBL Ethanol 0.016–0.064/0.13–0.51 70
    Manglietia hongheensis Y.m Shui et. W.H. Chen. Magnoliaceae Magnolia China TBL Ethanol 0.008–0.13/0.032–0.51 70
    Brandisia hancei Hook.f. Scrophulariaceae - China Whole plant Ethanol 0.032–0.064/0.13–0.26 70
    Evodia daneillii (Benn) Hemsl. Rutaceae Bebe tree China TBL Ethanol 0.032–0.064/0.064–0.26 70
    Schima sinensis (Hemsl. et. Wils) Airy-shaw. Theaceae Schima China TBL Ethanol 0.016–0.064/0.064–0.26 70
    Garcinia morella Desr. Clusiaceae Gamboge China Whole plant Ethanol 0.016–0.064/0.064–0.26 70
    Meliosma squamulata Hance. Lauraceae - China TBL Ethanol 0.032–0.064/0.13–0.26 70
    Curculigo orchioides Gaertn. Hypoxidaceae Golden eye-grass China Whole plant Ethanol 0.26–0.51/0.51–>2.05 70
    Euonymus fortunei (Turcz.); Hand. Mazz. Celastraceae Spindle, Winter creeper China Vane Ethanol 0.51/1.02–>2.05 70
    Alnus nepalensis D. Don. Betulaceae Nepalese alder China TBL Ethanol 0.26–1.02/1.02–>2.05 70
    Illicium simonsii Maxim. Illiciaceae - China TBL Ethanol 0.51–1.02/1.02–>2.05 70
    Blumea balsamifer (Linn.) D.C. Asteraceae Sambong China Whole plant Ethanol 0.064–0.26/0.26–1.02 70
    Machilus salicina Hance. Lauraceae Liu ye run nan China TBL Ethanol 0.51–1.02/1.02–>2.05 70
    Schisandra viridis A.c.Smith. Schisandraceae Magnolia vine China Vane Ethanol 0.064–0.26/0.26–1.02 70
    Selaginella tamariscina (Seauv.) Spring. Selaginellaceae Little club moss China Whole plant Ethanol 0.51–1.02/1.02–>2.05 70
    Celastrus orbiculatus Thunb. Celastraceae Chinese bittersweet China Vane Ethanol 0.51–1.02/1.02–>2.05 70
    Polygonum molleD. Don. Polygonaceae Knotweed China Whole plant Ethanol 0.26–0.51/1.02–>2.05 70
    Carex prainii C.B. Clarke Cyperaceae Sedges China Whole plant Ethanol 1.02–2.05/2.05–>2.05 70
    Embelia burmf. Myrsinaceae Baberung, Vidanga China Leaves Ethanol 0.51–1.02/1.02–>2.05 70
    Melianthus major L. Melianthaceae Giant honey flower South Africa Leaves Ethanol 0.78/3.12 71
    Melianthus comosusVahl Melianthaceae Honey flower South Africa Leaves Ethanol 0.39/1.56 71
    Dodonaea angustifolia (L.f.) Benth Sapindaceae Sticky hopbush, sand olive South Africa Leaves Ethanol 0.59/1.17 71
    Withania somnifera L. Solanaceae Ashwagandha, Winter cherry South Africa Roots & leaves Ethanol 1.56/>6.25 71,72,73
    Quercus infectoria Olivier Fagaceae “Machika or Oak galls South Africa Nutgalls Ethanol 0.4–3.2/3.2–6.3 74
    Thymus vulgaris L. Lamiaceae Thyme Peru Leaves Essential oil 0.057/ND 75,76

    Key: ND- Not done; MIC- Minimum inhibitory concentration; MBC- Minimum bactericidal concentration; VRSA- Vancomycin-resistant S. aureus

     | Show Table
    DownLoad: CSV
    Table 2.  Anti-MRSA plants with their phytochemical contents and medicinal uses.
    Medicinal Plant Phytochemical content Medicinal uses References
    Acacia catechu tannins, flavonoids, amino acids , saponins, triterpenoids Cold, cough, diarrhea, piles,fever. ulcers, boils,etc 82,83
    Garcinia mangostana Xanthones and phenolics (tannins) skin infections, wounds, dysentery, urinary disorders, cystitis and gonorrhoea 84,85
    Impatiens balsamina flavanoids, triterpenoids, glycosides, fatty acids & alkaloids diuretic, emetic, laxative, demulcent and tonic 86
    Peltophorum ptercarpum fatty acids, amino acids, terpenoids, phenolics, flavonoids, alkaloids, steroids etc. stomatitis, insomnia, skin troubles, constipation, ringworm, insomnia, dysentery, muscular pains, sores, and skin disorders 87
    Psidium guajava Tannins, Steroids, Alkaloids, glycosides, vitamins, carbohydrates diarrhea, sore throat, vomiting, stomach upset, vertigo etc. 88
    Punica granatum Tannins, Alkaloids, glycosides, vitamins, carbohydrates, flavanoids, saponins, triterpenoids sore throats, coughs, urinary infections, digestive disorders, skin disorders, arthritis, expel worms 89
    Uncaria gambir tannins, catechin, gambiriins wounds and ulcers, fevers, headaches, gastrointestinal illnesses, bacterial/fungal infections, diarrhoea, sore throat 90,91
    Walsura robusta Sesquiterpenoid 10-nitro-isodauc-3-en-15-al, 10-oxo-isodauc-3-en-15-al Antibacterial, antimicrobial, astringent, diarrhea 92,93
    Swietenia mahagoni Alkaloids, terpenoids, anthraquinone, cardiac glycosides, saponins, phenols, flavonoids, etc Hypertension, diabetes, malaria, amoebiasis, cough, chest pain, tuberculosis, antibacterial 64,94
    Tinospora crispa Triterpenes, flavones o-glycosides (apigenine), picroretoside, berberine, palmatine, picroretine & resin Fever, jaundice, hyperglycemia, wounds, intestinal worms, skin infections, antibacterial activity 64
    Butea monosperma Tannins, Saponins, Alkaloids, Glycosides, Carbohydrates hepatoprotective, antidiabetic, antihelmintic, antimicrobial, antitumour, antiulcer, inflammatory diseases, wound healing, etc 65
    Callistemon rigidus Tannins & phenolic compounds, Lipids & fats, Steroids, Alkaloids, Saponins, Terpenoids Treatment of cough, bronchitis and respiratory tract infections 66,95
    Acacia albida Alkaloids, tannins, saponins, phenols, flavonoids respiratory infections, skin infections, digestive disorders, malaria and other fevers, toothache in humans and eye infections in livestock. 96
    Anchomanes difformis Alkaloids, tannins, saponins cough, respiratory diseases, dysentery 97,98
    Boscia senegalensis Alkaloids, anthraquinone, cardiac glycosides, saponins, phenols, tannins, etc Anticancer and ulcer swellings 98
    Moringa oleifera anthraquinone, cardiac glycosides, saponins, phenols, tannins, flavonoids Asthma, eye infections, migraine, headache, febrifuge, abortifacient 98
    Mormodica basalmina resins, alkaloids, flavonoids, glycosides, steroids, terpenes, cardiac glycoside, saponins anti-HIV, anti-plasmodial,anti-diarrheal, anti-septic, anti-bacterial, anti-viral, anti-inflammatory, anti-microbial, etc 99
    Nymphaea lotus phenols, tannins, saponins, alkaloids and steroids aphrodisiac, anodyne, astringent, cardiotonic, sedative, analgesic and as anti-inflammatory agent. 68,100
    Pavetta crassipes flavonoids, sugars, tannins, saponins, glycosides, alkaloids and polyphenols respiratory infections and abdominal disorders,gonorrhoeae,cough remedy 101,102
    Phyllanthus amarus lignans, flavonoids, hydrolysable tannins (ellagitannins), polyphenols, triterpenes, sterols and alkaloids. used in the problems of stomach, genitourinary system, liver, kidney and spleen. It is bitter, astringent, stomachic, diuretic, febrifuge and antiseptic 103
    Vernonia blumeoides glycosides, saponins, alkaloids, tannins, flavonoids, steroids/terpenes treatment of various human ailments including parasitic (malaria) and infectious diseases 104
    Curcuma xanthorrhiza Alkaloids, terpenoids, cardiac glycosides, saponins, phenols, flavonoids, coumarin Treatment of liver damage, hypertension, diabetes, and cancer. 105,106
    Kaempferia pandurata Flavonoids, such as pinostrobin, pinocembrin, alpinetin, cardamonin, etc Treatment of cough, stomach distended, diuretic, anti-anthelminthic, uterus inflammation, vaginal infection 107,108
    Senna alata flavonoids, tannic acid, anthocyanin, alkaloids, quercetin and coumarins Antimicrobial, antifungal, ringworm, asthma, aphthous ulcers 109
    Mallotus yunnanensis Polyphenols, tannins, flavonoids, coumarins, various terpenoids hepatitis, sore, otitis media, stomach and duodenal ulcer, enlarged spleen and boils swelling, hematuria leucorrhea and traumatic bleeding 70
    Skimmia arborescens alkaloids, coumarins, triterpenoids, phenols HBV (skimmianine), rheumatoid, paralysisa, beriberi, and containing toxic substances 70
    Cyclobalanopsis austroglauca None astringing sores, carbuncles, dysentery, hemostasis and vaginal discharge 70
    Manglietia hongheensis Alkaloids vomiting, diarrhea, dysentery, constipation and geriatric hacking cough 70
    Brandisia hancei hydroxytyrosol derivatives and glycosides jaundice, boils, swelling, tuberculosis injury, hematemesis, osteomyelitis, periostitis, rheumatism and pain 70
    Evodia daneillii alkaloids, flavonoid glycosides, flavaprin, limonoids diarrhea, abdominal pain and vomiting 70
    Schima sinensis benzoquinone, tannins, phenols, lignans, flavonoids, triterpenoids furuncle and swelling 70
    Garcinia Morella phenols (gambogic acid), flavonoids (xanthones),triterpenoids wound rot, carbuncle, tinea, ulcer and sore, anthelminthic and containing toxic substances 70
    Meliosma squamulata Triterpenoids scabies, carbuncle boils swollen poison, hemorrhoids, enterobiasis, beriberi, rheumatoid, and snake bite 70
    Curculigo orchioides triterpenoids, lignans, flavonoids, alkaloids, stereoids diarrhea, ulcer, pus and muscles atrophy 70
    Euonymus fortune alkaloids, triterpenoids, flavonoids chronic diarrhea, dysentery, dispersing blood stasis and traumatic bleeding 70
    Alnus nepalensis tannins, triterpenoids, flavonoids, phenols bleeding of the nose, enteritis and dysentery 70
    Illicium simonsii terpenoids, lignans, flavonoids, phenols scabies, bladder hernias, mixed cropping of edible spices and containing toxic substances 70
    Blumea balsamifera flavonoids, simple terpenoids anti-rheumatism, ringworm and sores, dysentery, detoxification and snake bite 70
    Machilus salicina alkaloids, lignans carbuncle, furunculosis and sore pain 70
    Schisandra viridis lignans, triterpenoids, organic acids urticaria, herpes zoster, rheumatism and analgesia 70
    Selaginella tamariscina flavonoids, phenol glycosides, trehalose inflammation, pharyngolaryngitis and bacteriostasis 70
    Celastrus orbiculatus sesquiterpene, flavonoids dysentery, multiple abscess, Herpes zoster, detoxification, inflammatory, cellulites and snake bite 70
    Polygonum molle tannins, flavonoids, alkaloids carbuncle, swollen abscess, fistula and scrofula 70
    Carex prainii alkaloids, polyphenols, flavonoids antipyresis, diuretic and chyluria 70
    Embelia burm quinones, triterpenoids, flavonoids heat clearing and detoxicating, pharyngitis, dysentery, diarrhea, furuncle ulcer, skin itching, swelling and pain of hemorrhoids, etc 70
    Melianthus major quercetin 3-O-β-galactoside-6-gallate, kaempferol 3-O-α-arabinopyranoside wound healing and sores 71,110
    Melianthus comosus Triterpenoids wound healing, sores, skin inflammation, snakebite 110,111,112
    Dodonaea angustifolia diterpenoids, flavonoids skin infections and irritations, inflammation, tuberculosis and pneumonia 110,113
    Withania somnifera withanolides, alkaloids, chlorogenic acid, glycosides, glucose, tannins, and flavonoids anti-inflammatory, antimicrobial, antitumour, anti-convulsant, sedative 72,73
    Quercus infectoria tannin, saponin, gallic acid and ellagic acid hemorrhages, chronic diarrhea, dysentery, Skin disease, sore throat 114,115,116
    Thymus vulgaris alkaloids, carbohydrates and glycosides, flavonoid, resins, saponins, tannins, sterols and triterpenes headache, fevers, ulcers, arthritis, microbial infections even cancers 117

     | Show Table
    DownLoad: CSV

    The therapeutic properties of these medicinal plants obtained from their phytochemicals could be employed for drug development [118]. The antibacterial (anti-MRSA) activity of these plants is attributed to their phytochemical contents. For instance, flavonoids complex with bacterial cell wall, extracellular and soluble protein while tannins inactivate microbial adhesions, enzymes and cell envelop proteins [55],[67][69],[119].

    Although, these anti-MRSA plants are likely promising candidates for drug development for MRSA infections, it has been reported that most plants contain potentially toxic, mutagenic, and/or carcinogenic substances. Therefore, it is highly recommended that medicinal plants undergo a critical sequential antimicrobial, pharmacological, and toxicology screening to ascertain their safety and selection as good candidates for novel drug development [77],[79],[120].

    S. aureus is a common microorganism that is widely spread in the human population with many being asymptomatic carriers. It can also cause life-threatening infections and its strains have evolved into MRSA and strains with reduced vancomycin susceptibility (VISA, hVISA and VRSA). These strains cause infections and diseases that are either difficult to treat or resistant to the empiric antibiotics usually prescribed for treatment. The globe is running short of drugs/antibiotics available for therapy as a result of infections associated with this organism.

    Many research studies have reported that some medicinal plants in different countries have anti-MRSA activities due to their phytochemical contents. These plants can be employed as alternative candidates for drug development to halt or/and control the infections of multi-drug resistant S. aureus. However, there is a need for further studies to adequately determine the safety and clinical efficacy of anti-MRSA plants to man.



    [1] G. Peters, A note on the vocal behaviour of the giant panda, Ailuropoda melanoleuca (David, 1869), Z. Saeugetierkd., 47 (1982), 236–246.
    [2] D. G. Kleiman, Ethology and reproduction of captive giant pandas (Ailuropoda melanoleuca), Z. Tierpsychol., 62 (1983), 1–46.
    [3] G. B. Schaller, J. Hu, W. Pan, J. Zhu, The Giant Pandas of Wolong, University of Chicago Press in Chicago, 1985.
    [4] B. Charlton, Z. H. Zhang, R. Snyder, The information content of giant panda, Ailuropoda melanoleuca, bleats: acoustic cues to sex, age and size, Anim. Behav., 78 (2009), 893–898. https://doi.org/10.1016/j.anbehav.2009.06.029 doi: 10.1016/j.anbehav.2009.06.029
    [5] B. Charlton, Y. Huang, R. Swaisgood, Vocal discrimination of potential mates by female giant pandas (Ailuropoda melanoleuca), Biol. Lett., 5 (2009), 597–599. https://doi.org/10.1098/rsbl.2009.0331 doi: 10.1098/rsbl.2009.0331
    [6] M. Xu, Z. P. Wang, D. Z. Liu, Cross-modal signaling in giant pandas, Chin. Sci. Bull., 57 (2012), 344–348. https://doi.org/10.1007/s11434-011-4843-y doi: 10.1007/s11434-011-4843-y
    [7] A. S. Stoeger, A. Baotic, D. Li, B. D. Charlton, Acoustic features indicate arousal in infant giant panda vocalisations, Ethology, 118 (2012), 896–905. https://doi.org/10.1111/j.1439-0310.2012.02080.x doi: 10.1111/j.1439-0310.2012.02080.x
    [8] B. Anton, A. S. Stoeger, D. S. Li, C. X. Tang, B. D. Charlton, The vocal repertoire of infant giant pandas (Ailuropoda melanoleuca), Bioacoustics, 23 (2014), 15–28, http://doi.org/10.1080/09524622.2013.798744 doi: 10.1080/09524622.2013.798744
    [9] B. D. Charlton, M. S. Martin-Wintle, M. A. Owen, H. Zhang, R. R. Swaisgood, Vocal behaviour predicts mating success in giant pandas, R. Soc. Open Sci., 10 (2018), 181323. https://doi.org/10.1098/rsos.181323 doi: 10.1098/rsos.181323
    [10] B. D. Charlton, M. A. Owen, X. Zhou, H. Zhang, R. R. Swaisgood, Influence of season and social context on male giant panda (Ailuropoda melanoleuca) vocal behaviour, PloS One, 14 (2019), e0225772. https://doi.org/10.1371/journal.pone.0225772 doi: 10.1371/journal.pone.0225772
    [11] K. F. Lee, H. W. Hon, R. Reddy, An overview of the SPHINX speech recognition system, IEEE Trans. Acoust. Speech Signal Process., 38 (1990), 35–45. http://doi.org/10.1109/29.45616 doi: 10.1109/29.45616
    [12] L. R. Bahl, P. F. Brown, P. V. D. Souza, R. L. Mercer, Maximum mutual information estimation of hidden Markov model parameters for speech recognition, in ICASSP'86. IEEE International Conference on Acoustics, Speech, and Signal Processing, 11 (1986), 49–52. http://doi.org/10.1109/ICASSP.1986.1169179
    [13] D. A. Reynolds, R. C. Rose, Robust text-independent identification using Gaussian mixture speaker models, IEEE Trans. Speech Audio Process., 3 (1995), 72–83. http://doi.org/10.1109/89.365379 doi: 10.1109/89.365379
    [14] W. B. Cavnar, J. M. Trenkle, N-gram-based text categorization, in Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, (1994), 14. http://doi.org/161175.10.1.1.21.3248 & rep = rep1 & type = pdf
    [15] J. Colonna, T. Peet, C. A. Ferreira, A. M. Jorge, E. F. Gomes, J. Gama, Automatic classification of anuran sounds using convolutional neural networks, in Proceedings of the Ninth International c* Conference on Computer Science & Software Engineering, ACM, (2016), 73–78. http://doi.org/10.1145/2948992.2949016
    [16] H. Goëau, H. Glotin, W. P. Vellinga, R. Planqué, A. Joly, LifeCLEF bird identification task 2016: the arrival of deep learning, in CLEF: Conference and Labs of the Evaluation Forum, Évora, Portugal, (2016), 440–449.
    [17] D. Stowell, Computational bioacoustics with deep learning: a review and roadmap, PeerJ, 10 (2021), e13152. http://doi.org/10.7717/peerj.13152 doi: 10.7717/peerj.13152
    [18] A. Graves, A. R. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, (2013), 6645–6649. http://doi.org/10.1109/ICASSP.2013.6638947
    [19] F. A. Gers, J. Schmidhuber, F. Cummins, Learning to forget: Continual prediction with LSTM, Neural Comput., 12 (2000), 2451–2471. http://doi.org/10.1049/cp:19991218 doi: 10.1049/cp:19991218
    [20] F. A. Gers, N. N. Schraudolph, J. Schmidhuber, Learning precise timing with LSTM recurrent networks, J. Mach. Learn. Res., 3 (2002), 115–143. http://doi.org/10.1162/153244303768966139 doi: 10.1162/153244303768966139
    [21] J. Xie, S. Zhao, X. Li, D. Ni, J. Zhang, KD-CLDNN: Lightweight automatic recognition model based on bird vocalization, Appl. Acoust., 188 (2022), 108550. http://doi.org/10.1016/j.apacoust.2021.108550 doi: 10.1016/j.apacoust.2021.108550
    [22] C. Bergler, M. Schmitt, R. X. Cheng, H. Schröter, A. Maier, V. Barth, et al., Deep representation learning for orca call type classification, in Text, Speech, and Dialogue: 22nd International Conference, TSD 2019, Ljubljana, Slovenia, September 11–13, 2019, Proceedings 22, Springer, 11697 (2019), 274–286. http://doi.org/10.1007/978-3-030-27947-9_23
    [23] E. E. Waddell, J. H. Rasmussen, A. Širović, Applying artificial intelligence methods to detect and classify fish calls from the northern gulf of Mexico, J. Mar. Sci. Eng., 9 (2021), 1128. http://doi.org/10.3390/jmse9101128. doi: 10.3390/jmse9101128
    [24] J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, preprint, arXiv: 1412.3555.
    [25] W. Yan, M. Tang, Z. Chen, P. Chen, Q. Zhao, P. Que, et al., Automatically predicting giant panda mating success based on acoustic features, Global Ecol. Conserv., 24 (2020), e01301. https://doi.org/10.1016/j.gecco.2020.e01301 doi: 10.1016/j.gecco.2020.e01301
  • This article has been cited by:

    1. Nguyen Thi Thu Thuy, Tran Thanh Tung, Strong convergence of one-step inertial algorithm for a class of bilevel variational inequalities, 2025, 0233-1934, 1, 10.1080/02331934.2024.2448737
    2. Nguyen Thi Thu Thuy, Tran Thanh Tung, Le Xuan Ly, Strongly convergent two-step inertial algorithm for a class of bilevel variational inequalities, 2025, 44, 2238-3603, 10.1007/s40314-024-03078-7
    3. Le Xuan Ly, Nguyen Thi Thu Thuy, Nguyen Quoc Anh, Relaxed two-step inertial method for solving a class of split variational inequality problems with application to traffic analysis, 2025, 0233-1934, 1, 10.1080/02331934.2025.2459191
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2658) PDF downloads(104) Cited by(4)

Figures and Tables

Figures(9)  /  Tables(8)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog