Evaluation of cultivable aerobic bacterial flora from Russell's viper (Daboia russelii) oral cavity
Authors: Gunanidhi Sahoo
Laxmipriya Padhi
Sujogya Kumar Panda
Journal: Microbial Pathogenesis (2019)
Discipline: IMMUNOLOGY
Keywords: Antibiotic resistance; Daboia russelii; Indian venomous snakes; Oral microflora.
Affiliations: Department of Zoology, North Orissa University, Baripada, 757003, Odisha, India., Department of Zoology, Utkal University, Bhubaneswar, 751004, Odisha, India. Electronic address: .

Abstract:  Snake mouths contain a wide range of bacteria. Identifying these bacteria in snakes is very important to obtain an understanding of the etiological agents of secondary infections that may result from accidents during handling and/or snake bites. The present study aims to determine the pattern of oral bacterial flora of nine healthy Russell's vipers (Daboia russelii), and their susceptibility to common antibiotics. A total of 94 isolates were obtained in pure form, which demonstrated noticeable colony characteristics and which were further studied with several biochemical tests. The strains that showed distinctive colonies, morphology and biochemical parameters were additionally subjected to phylogenetic characterization using 16S rRNA gene sequences. Furthermore, all these isolates were studied for antibiotic susceptibility. The oral cavity of the Russell's viper harbors a wide range of pathogenic bacteria, including Gram-negative genera: Proteus sp., Pseudomonas sp., Salmonella sp., Providencia sp., Alcaligenes sp., Morganella sp., as well as E. coli, and Gram-positive genera: Bacillus and Enterococcus sp., Staphylococcus sp. and Lysinobacillus sp. Most of the isolates were resistant to antibiotics viz. penicillin, Amoxyclav, oxacillin, methicillin and streptomycin while sensitive towards imipenem, amikacin, norfloxacin, gatifloxacin, ciprofloxacin, gentamicin, tetracycline, chloramphenicol and azithromycin. The present study documents diverse bacteria predominant in the oral cavity of Daboia russelii and studied their antibiotic susceptibilities. 

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PubPeer comments:
 The author developed a theory of decision making based on incomplete **u201d linguistic preference relationsu201d**. The author introduced a set $$S={s_i | i=0, 1, ldots, t }$$ and defined an order on $S$ as $$s_{i} s_{j} quad text{if} quad i j$$ The author also claimed that each $s_i$ represents a possible value for a linguistic variable as illustrated in the context, for instance, $s_0 = text{extremely poor}$. However, with such a setting, it is no deference for a linguistic variable by denoting as $s_i$ or simply $i$ as in the case of widely used 5 or 7 point Likert scale. The author then introduced a notion $$bar{S} ={s_{alpha} ~|~ alpha in [-q, q]}$$ where $q t$ is a sufficiently large positive integer. Furthermore, the author defined arithmetic operations for terms in $bar{S}$, the enlarged set. However, these arithmetic operations cannot be closed over the set $bar{S}$ no matter how larger the value of $q$ is. Moreover, **the operations introduced are simply the arithmetic operations on the set of real values**, as long as the linguistic variables used are denoted by the integers $0, 1, ldots, t$ and the set $bar{S}$ is defined as the set of real values. As a consequence, the operation defined in Definition 2.1 is nothing but the conventional arithmetic averaging operation. The operation defined in Definition 2.2 is nothing but the conventional weighted arithmetic averaging operation. In other words, the only contribution of this paper is to write a real value (or an integer) $alpha$ as $s_{alpha}$ and write the $+$ and $-$ symbols as $oplus$ and $ominus$, respectively. ![file] , 
 Dear Rhipidura Albiventris, You have been taught many times previously that they have completely different practical implications and backgrounds. You are obviously unfamiliar with the field of linguistic decision making. But unfortunately you always intentionally ignore them, and thus, your such ridiculous comments are really meaningless and even malicious. Please see the links: [https: pubpeer.com publications B3CD7805A1D849BF3C5652817CE220]; [https: pubpeer.com publications E1D434E8728AFE3E1C975DF00A5E3B]; [https: pubpeer.com publications 224AA22A50D74F8AC27E24E2BD2748]. As many authors have pointed out that you just comment for comment, recently, you have commented thousands of papers and insisted on commenting dozens of different papers every day, especially blindly criticizing others in their fields of expertise. It is very strange and abnormal. Lastly, since you have so many puzzling u201cissuesu201d to be solved, I suggest you do some meaningful research to verify your ideas, develop and perfect the decision-making theory rather than indulge in making ignorant and questionable comments every day! Best Regards. , 
 #2 This is an interesting response repeatedly posted by a same user who is not the author of the paper. It is clear that we donu2019t need a theory like $$1 text{ apple} oplus 2 text{ apples} = 3 text{ apples}$$ It is simply $$1+2=3$$ As for the widely used Likert scale in survey research, we donu2019t need define the arithmetic operation like $$text{neutral} oplus text{satisfied} = text{very satisfied}$$ and justify it from the perspective of linguistic meaning. If the 5-point Likert scale is used with $$text{very unsatisfied} =1quad text{unsatisfied} =2quad text{neural} =3quad text{satisfied} =4 quad text{very satisfied} =5$$ we may obtain a sample of ordinal data valued by $${1, 2, 3, 4, 5}$$ An ordinal value (variable) is a categorical value for which ranking is possible between different such values. We may consider ordinal data partial categorical and partial quantitative and hence **extend them to interval type of values so that the arithmetic operations like addition and subtraction can be defined on them.** In this sense, we perform the transformation like $$1, 2, 3, 4, 5 text{ in the ordinal sense} quad to quad 1, 2, 3, 4, 5 text{ in the interval sense} $$ and then we can perform the operation like $$0.5(3) + 0.5(4) = 3.5$$ It is cleat that we donu2019t need an operation like $$0.5(text{satisfied}) oplus 0.5(text{very satisfied}) = text{something called a virtual linguistic term} $$ These are all **very fundamental** procedures in data processing. Writing the numbers $3$ and $4$ as $s_3$ and $s_4$ then defining $$0.5s_{3}oplus 0.5s_{4} = s_{3.5}$$ is mathematically nonsense. , 
 Dear Rhipidura Albiventris, The essential feature of linguistic computation is not the form of a linguistic term. We usually write $s_alpha$. Of course, one may write $alpha$ like the manner of AHP or the manner of the Likert Scale. There is a one-one mapping which map $s_alpha$ to $alpha$. For example: for a linguistic term $$s_3: good$$ itu2019s OK if one prefers $$3: good$$ But we have to always keep in mind that, in linguistic decision making, $3$ (or $s_3$) is restricted by its semantics which is usually represented by a fuzzy set. We can (and should) forget that it is an integer. Considering two terms $3: good$ and $4: very~good$, the average (by a specific manner) of them may be not $3.5$. It depends on the rule of how the semantics are aggregated. Even if the result is $3.5$, its restriction is a fuzzy set which implies an expression like u201clocated between good and very goodu201d. This is different from the Likert scale in survey research. In the case of Likert scale, the operation (addition) of integers is employed. (Please let me know if Iu2019m wrong.) Considering the 5-point Likert scale: $$1: very~unsatisfied, 2: unsatisfied, 3: neural, 4: satisfied, 5: very~satisfied$$ A sample of data could be obtained from a questionnaire. The sum of the data is computed directly to represent the general idea, attitude or preference of the focused concept or topic. This is rational only if a large amount (saying, 50 or 100) of statements are included in the questionnaire. In linguistic decision making, the operation of $s_alpha$ and $s_eta$ could be denoted as $s_alpha oplus s_beta$ formally (or $s_alpha oplus_w s_beta$ to highlight the involved parameter). From a historical view, the rule of $s_alpha oplus s_beta$ is specified by different strategies (or saying, different linguistic computational models): In semantic model: ![file] In symbolic model: ![file] Generally, a translation $ $ re-translation process is included. In the 2-tuple linguistic model and the virtual linguistic model, the approximation procedure is not necessary. Operations could be defined by specific one of the strategies. Roughly, I think an operation rule is acceptable if it satisfies some necessary properties such as monotonicity. For example, one could specify $oplus$ by $$s_alpha oplus_1 s_beta = s_alpha vee s_beta = max {s_alpha, s_beta }$$ $$s_alpha oplus_2 s_beta = s_alpha wedge s_beta = min {s_alpha, s_beta }$$ $$s_alpha oplus_w s_beta = s_{w alpha + (1-w) _beta} (or~ s_{round(w alpha + (1-w) _beta)} )$$ They all have application implications. We could find their applications in literature. Best Regards. , 
 #4 The last three operations defined are simply $$max{ alpha, beta}$$ $$min{ alpha, beta}$$ $$walpha + (1-w)beta$$ They have been used for thousands of years. What is the point for the argument? , 
 Dear Rhipidura Albiventris, Please check the last paragraph carefully. The three operations which implement the classical operations are just simple examples. Best Regards. ,