Iris Publishers-Open access Journal of Animal Husbandry & Dairy Science | Applications of Multivariate Multiple Linear Regression Models to Predict Body Weight of Local Chickens from Biometrical Traits in Ethiopia

 

Authored by Kefelegn Kebede*,

Abstract

The study aimed at assessing variability among biometrical traits, deducing components that describe these traits, and predicting body weight from both original and orthogonal traits using regression models. Body weight and nine biometrical traits namely, comb height, comb length, keel length, wattle length, body length, back length, breast circumference, wingspan, and shank length were measured on 720 (237 males and 483 females) randomly selected and extensively managed chickens. Phenotypic correlations among body weight and biometrical traits were positive and very highly significant (r = 0.51-0.93; P<0.0001). In the varimax rotation principal component (PC) factor analysis, two factors were extracted which accounted for 88.9% of the total variation. PC1 loaded heavily on comb length, wattle length, comb height, wingspan, shank length, and body length while PC2 loaded heavily on breast circumference and back length. When utilized as predictors in regression analyses, the interdependent biometrical traits had accounted for 82% variation in body weight. However, multicollinearity problem was existent in this estimation. Utilizing the extracted two factor scores as predictors, on the other hand, had positive significant effects on body weight, accounting for 78% variation in body weight. The use of factor scores provided better and reliable prediction of body weight as multicollinearity problem was handled and eliminated. The results found could be used as selection criteria for improving body weight of local chickens.

Keywords: Biometrical traits; Chicken; Principal Component Factor Analysis.

Introduction

Ethiopia is endowed with many livestock species with an estimated population of 62.6 million cattle, 31.7 million sheep, 33.0 million goats, and 61.5 million poultry FAOSTAT [1]. The country is owning a large number of poultry species which have lived, adapted and produced for many years in the country Tadelle [2], Halima [3] and Aberra and Tegene [4]. The local chicken genetic resources being the commonest to find, are in the hands of resource-poor farmers who rear these birds under the traditional husbandry system. This system in the rural areas plays a vital role in the rapidly growing economy of the country. It contributes in multiple ways to the livelihood and food security of the rural family Solomon [5]. Body weight had always been a trait of economic importance to livestock farmers. Knowledge about the relationship between body weight and biometrical traits is very important for poultry breeding, as biometrical traits have been found useful in contrasting size and shape of animals Mckracken [6], Ajayi [7].

Multiple linear regression (MLR) analysis has been used to interpret the relationship among body weight and biometrical traits in some animals Cankaya, Ogah [8]. Interpretation of results obtained from MLR analysis may be inaccurate due to the problem of multicollinearity Eyduran [9]. Consequently, PC factor analyses are used to reduce a large number of variables to a smaller number of factors for modelling purposes (Tabachnick and Fidell). This involves the use of PC factor scores as predictors of body weight in MLR models, thereby handling and eliminating multicollinearity problem (Grice, 2001).

Despite the rich genetic resource base of local chickens in Ethiopia, there is little information on its characterization using multivariate analysis approach. Therefore, the present study aimed at assessing variability among biometrical traits, deduce components that describe these traits and predicting body weight from both original and orthogonal traits using PC factor analysis.

Materials and Methods

Description of the study area

This study was conducted in the southern zone of Tigray which is located in the northern part of Ethiopia. It has an altitude ranging from 930 to 3925 m.a.s.l. The mean annual temperature varies from 9 0C to 28 0C; while the mean annual rainfall ranges from 400 to 912 mm. The southern zone of Tigray has eight districts from which this study focused on three districts: Raya-azebo, Endamehoni and Ofla (Figure 1).

Methods of sampling and data collection

A total of 720 local chickens of age six months and above (240 chickens (72 males and 168 females) from Raya-Azebo, 240 chickens (78 males and 162 females) from Endamehoni and 240 chickens (87 males and 153 females) from Ofla) were randomly selected. Body weight and biometrical traits (comb height, comb length, keel length, wattle length, body length, back length, breast circumference, wingspan, and shank length) were recorded following the recommended FAO descriptors for chicken genetic resources FAO [10]. Measuring tapes and a digital balance of 1g precision were used to measure the respective biometrical traits and body weight of sampled chickens.

Statistical data analysis

All statistical analyses were performed using SAS 9.4 software (version 9.4; SAS Institute Inc., NC). In livestock, such studies are mainly carried out on females due to their larger numbers Bene [11] Ndumu [12] and Traore’ A [13]. Thus, in this study, in order to avoid potential sampling bias due to the low number of males, only females were considered in the analysis.

Exploratory data analysis

The body weight and biometrical traits were subjected to exploratory data analysis to get results of descriptive statistics and correlation matrices using the PROC UNIVARIATE and PROC CORR procedures of SAS (version 9.4; SAS Institute Inc., NC).

Principal component (PC) factor analysis

Estimating the number of PCs

Several criteria are available for determining the number of PCs to be extracted. In this study, the criteria Kaiser–Guttman rule, the screen test, and parallel analysis plot were used.

PC loadings

These are correlation coefficients between the PC scores and the original traits. A high positive correlation between PC1 and a trait indicates that the trait is associated with the direction of the maximum amount of variation in the dataset. A strong correlation between a trait and PC2 indicates that the trait is responsible for the next largest variation in the data perpendicular to PC1, and so on.

Multiple linear regression (MLR) models

MLR procedure was used to obtain models for predicting body weight from interdependent biometrical traits (i), and from independent PC factor scores (ii)

BW = b0 + b1X1 + … + bkXk (i)

BW = b0 + b1PC1 + … + bkPCk (ii)

where, BW is the body weight, „bo‟ is the intercept, b1 is the with partial regression coefficient of the with biometrical trait, Xi or the with PC.

Results and Discussion

Exploratory data analysis

(Table 1) shows the mean ± standard error, standard deviation, coefficient of variation, minimum, and maximum estimates of body weight and biometrical traits of the chickens.

Table 1: Descriptive statistic results of body weight and biometrical traits.

The mean body weight was 1112 g while the biometrical traits were 1.05 cm (CH), 3.11 cm (CL), 12.53 cm (KL), 1.46 cm (WL), 31.18 cm (BL), 18.05 cm (BaL), 25.81 cm (BC), 67.07 cm (WS), and 7.60 cm (SL), respectively. Wattle length varied most (CV = 80.8 %) while breast circumference (CV = 6.9 %) varied the least. The descriptive statistics results found in this study agree with earlier reports by Eskindir [14] and Getachew [15].

The degree of linear association among the biometrical traits measured by the Pearson correlation coefficient (r) and their statistical significance are presented in Table 2. The correlation coefficients varied from 0.51 (between BC and CH) to 0.93 (between WL and CL). Among 45 possible pairs of correlations, all pairs of correlations were found positive and significant, indicating that the data is suitable for performing PC factor analysis. Such positive and very highly significant correlation coefficient values have also been reported in chickens by the studies of Eskindir [14] and Getachew [15].

Table 2: Phenotypic correlations and their statistical significance levels among body weight and biometrical traits of chickens***.

PC factor analysis

Anti-image correlations computed showed that partial correlations were low, indicating that true factors existed in the data. This was further supported by Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, which was found to be sufficiently high with a value of 0.782. Eyduran [9] reported that a KMO measure of 0.60 and above is considered adequate. Bartlett’s sphericity test for testing the null hypothesis that the correlation matrix is an identity matrix was used to verifying the applicability of PCA. The value of Bartlett’s sphericity test was significant (p-value = 0.001), implying applicability of PC factor analysis used to the data set. Eigenvalues, percentage of total variance with rotated component matrix and communalities Table 3 shows the eigenvalue of the total variance, the rotated component matrix and communalities of the biometrical traits investigated. The table shows how much of the total variance of the observed traits is explained by each of the PCs after varimax rotation of the component matrix. Two PC common factors were identified with eigenvalues of 7.97 (PC1) and 0.91 (PC2). PC1 explained 79.7 % of the total variance while PC2 explained only 9.1 %. Accordingly, the first two PC factors combined accounted for 88.9 % of the total variability present in the parameters measured. The communalities are the proportion of variance that each variable has in common with other variables. Thus if the communality of a trait is high, it means that the extracted factors explained a big proportion of the variance the trait. The communality values ranged from 0.81 (BaL) to 0.94 (CL) indicating that the data are conformable to PC factor analysis.

Table 3: Eigenvalues and shares of total variance along with factor loadings after varimax rotation and communalities.

PC loadings presented in Table 3 are the correlation coefficient between the first two PC scores and the original traits. They measure the importance of each biometrical trait in accounting for the variability in the PC. That is, the larger the loadings in absolute terms the more influential the variables are in forming the new PC and vice versa. The first factor (PC1) loaded heavily on comb length (0.91), wattle length (0.90), comb height (0.88), wingspan (0.82), shank length (0.74), and body length (0.71) while the second factor (PC2) loaded heavily on breast circumference (0.93), and back length (0.71). The loading classification found in this study is somewhat similar to those reported by Uda Yakubu [16] and immature Uda Salako [17]. A scree-parallel analysis plot of eigenvalues against their PCs is shown in Figure 2 below. The plot demonstrates the distribution of variance among the components graphically. For each PC, the corresponding eigenvalue is plotted on the y-axis. By definition, the variance of each component is less than the preceding one. Here there appears to be a marked decrease in downward slope after the second PC implying that one can summarize the nine biometrical traits by the first two PCs.

Body weight prediction of chickens from morphometric traits and their independent PC factor scores

The interdependent original biometrical traits and their independent PC factor scores were used to the predict body weight of chickens. Table 4 presents the regression coefficient, their standard errors, t-value, p-values, variance inflation factor (VIF) values, and R2 obtained from MLR analysis. The regression of body weight on KL, WL, BL, BaL, BC, WS, and SL was significant, while it was not significant for CH, and CL. When utilized as predictors in regression analyses, the interdependent biometrical traits had accounted for 82% variation in body weight. These findings are consistent with the findings of Eskindir [14] and Getachew [15] in chickens. The genetic improvement of body weight is necessary in order to increase meat yield and egg yields of chickens, and this requires adequate knowledge of correlated traits that can be considered when selection is to be applied. However, the use of interdependent predictors should be treated with caution, since multicollinearity is associated with unstable estimates of regression coefficients Ibe body length were having VIF values greater than 10. Rook [21] stated that VIF values above 10 indicate severe collinearity which leads to unstable estimation of the regression coefficient. To overcome this limitation, the use of PC factor scores for prediction of body weight as predictors is used Keskin [22], Ogah [8], Yakubu [16]. These PCs are orthogonal to each other and are more reliable in body weight prediction. In the present study, the use of PC1 as a single predictor explained 12 % of the total variability in body weight. However, PC1 and PC2 together accounted for 78 % of the variation in body weight of the chickens. The two factors selected were found to have significant (p<0.0001) positive linear relationship with body weight (Table 4). In other words, body weight will be expected to increase as the values of factor 1 and 2 scores increase.

Table 4: MLR of body weight on original morphometric traits and their PC factor scores.

Conclusions

PC factor analysis was used in this study to assess variability among biometrical traits, deduce components that describe these traits, and predict body weight from both original and orthogonal traits using MLR models. The technique extracted two PCs that could aid in selection and breeding programmes. The traits accounted for PC1 are comb length, wattle length, comb height, wingspan, shank length, and body length; while for PC2 the corresponding traits were breast circumference, and back length [23-26].

The use of independent orthogonal indices (PC1 and PC2) derived from factors’ score was more appropriate than the use of original interrelated biometrical traits for predicting the body weight of chickens, as multicollinearity problems were eliminated. The two factors extracted from the present investigation could be used to select animals based on a group of variables rather than isolated traits.

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Iris Publishers-Open access Journal of Robotics & Automation Technology | Corrected Internal Torques of Gyroscopic Effects

 

Authored by Ryspek Usubamatov*,

Abstract

The dynamics of the rotating objects is the complex piece of the classical mechanics which example presented by gyroscopic effects. Researchers study gyroscope problems during the centuries and obtained not comforting analytical results. The physics of gyroscopic effects does not have an adequate description. The recent investigation finally solved the gyroscope problems in principle but some mathematical models for gyroscopic inertial torques contained mechanical errors. The corrected gyroscope theory opened breakthrough directions in the solutions to dynamics of rotating objects. This publication presents a summation of the gyroscope theory that gives a general presentation of its principal parts.

Keywords: Gyroscopic devices; Gyroscope theory

Introduction

The gyroscopic effects are the most compound and attractive part of the dynamics of rotating objects in engineering [1-4]. Beginning with the Industrial Revolution, scientists of the entire world tried to find a mathematical model for gyroscopic effects and describe their physics. Researchers of the past centuries who did not know the several principles of the physical laws of matter could not solve this problem in principle. They developed only partially and simplified analytical models. The gyroscopic effects are based on the principles of classical mechanics that were discovered for the time of two hundred years from the middle of the 17 century. The researchers of the following centuries could not combine the principles of mechanics for the solution of the gyroscopic effects. For the practical applications of gyroscopic devices, they developed numerical modeling and simplified theory for the educational processes [5-7]. These unresolved problems present a challenge to new researchers, which today publish many works related to the dynamic of rotating objects [8-10].

The latest investigations discovered and mathematically described the physics of gyroscope effects, which are a manifestation of the action of the system of several interrelated inertial torques generated by the rotating mass. The new segments of knowledge formulated the fundamental principles of gyroscope theory that presented by the centrifugal, Coriolis forces, the change in the angular momentum, and the ratio of the angular velocities of the gyroscope about their axes of motions. The new method for deriving mathematical models for inertial torques produced by masses of rotating objects can be applied to any rotating objects of different designs [11,12]. It can be a spinning disc, cylinder, ring, cone spheres, paraboloid, propeller, etc., and nonsymmetrical objects.

The new gyroscope theory contains complex mathematical models that have some incorrect processing. The errors are mechanical and not principle [13,14]. The any innovations are accompanied by successes and failures, but after corrections are finally accepted. This summary presents the fundamental properties of the spinning disc mathematically formulated and validated by practical tests. The worked-out result brings a significant impact to the gyroscope theory and opens a new direction in the chapter of dynamics of classical mechanics.

Methodology

The physics of gyroscopic effects shows the external torque acting on the spinning objects, yields the system of inertial torques generated by the one rotating mass. The system contains the eight inertial torques of interrelated action, about two axes of the spinning objects and generated by the following inertial forces:

• Centrifugal forces of the rotating masses that produce resistance and precessed inertial torques

• Coriolis forces are a product of the tangential velocity of rotating masses and the precessed angular velocity of the turn of the disc that produces the resistance inertial torque.

• The change in the angular momentum that is the precessed inertial torque

The two resistance and two precession torques are acting about each axis. All inertial torques are interrelated by the ratio of the angular velocities of the spinning objects about axes of rotation.

The action of the external and inertial torques on the spinning disc is shown in Figure 1.

Where T is the external torque, Tct.i,Tcr.i,Tam.i,are the inertial torques generated by the centrifugal, Coriolis force and the change in the angular momentum acting about axis i respectively; ω, wx,- wyare the angular velocity of the dis about axis oz, ox and oy, respectively; R is the radius of the disc, and γ is the angle of the inclination of the disc axle.

The action of the inertial torques, generated by the distributed rotating mass elements presented in Figure 2.

Where m is the mass element of the disc; ω is the constant angular velocity of the disc; α is the angle of the mass element’s disposition; Δyis the angle of turn for the disc’s plane sinΔy = Δyfor the small values of the angle); ym = (2.3)Rsin m α and xm = (2.3)Rcosα is the distance of the mass element’s location relative to axes ymand xm, respectively, fct.i, fct.i, fam.i.are the components of the centrifugal, Coriolis force and the change in the angular momentum acing about axis i, respectively; T, Tct, TcrTamis the external and inertial torques respectively; V is the tangential velocity of the mass element; A, B, C is the centroids.

The mathematical models for inertial torques and the dependency of the angular velocities for the precessions of the spinning disc about axes are presented in Table 1.

Table 1: Inertial torques of the spinning disc and the dependency of the angular velocities of the precessions.

Where ωi is the angular velocity of gyroscope rotation about axis i; ω is the angular velocity of the spinning disc about axis oz; J is the mass moment of inertia of the spinning disc; γ is the angle of disposition. The turn of the spinning disc of horizontal disposition about axis ox is active by the dependency until its turn about axis oy on the angle. It is demonstrated on the gimbal motions of the laboratory gyroscope. The following turn of the outer gimbal does not lead to the turn of the inner gimbal with the spinning disc.

Figure 3 shows the change in the angular disposition φ about axis oy of the precessed motion versus the change in the angular disposition γ about axis ox.

The diagram demonstrates the gyroscope precessions validated by the practical tests.

Conclusion

The gyroscopic effects were quite complex physical and mathematical problems for a long time. The known publications related to gyroscopic effects contain many simplifications and assumptions and do not respond to the engineering practice. This is an unusual phenomenon in science of the classical mechanics, which methods can solve more complex problems. A recent study of the gyroscopic effects finally solved this old task in principle. Some mathematical models for the gyroscopic effects were published with errors in the complex analytical processing of inertial torques that are now corrected. The new methods of gyroscope theory open a new chapter in the dynamics of the rotating object of classical mechanics. Engineering receives a new analytical approach for solving gyroscopic effects. The corrected inertial torques of gyroscope theory can be useful in practice and present a good example for education processes.

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Iris Publishers-Open access Journal of Oceanography & Marine Biology | Avian Frugivory and seed dispersal of Batangi in District Haripur, KPk, Pakistan

 

Authored by Saira Bibi*,

Abstract

Study was conducted in village chhajjian. A total of 200 birds were recorded to visit the Batangi fruites during the survey belonging to eight species in different localities of district Haripur, as most of these trees were found in village chhajjian so the highest amount of the visitors were recorded in this area. These included three species of Brahminy Starling Sturnus pagodarum, bulbuls, Pycnonotus sp., Common Myna Acridotheres tristis, Asian Koel Eudynamys scolopacea, White-headed Babbler Turdoides affnis and Small Green-Billed Malkoha Phaenicophaeus viridirostris. Highest proportion of feeding visits was contributed by Red whiskered Bulbul, Pycnonotus jocosus (21.3%) also we recorded the yellow and red vented bulbul to forage on these trees.

Keywords: Frugivores; Dispersal; Pyrus pashia

Introduction

Pyrus pashia belong to the family Rosaceae [1] based on geographic distribution it is divided into Occidental and Oriental pears [2]. Including China, Japan, and the Korean Peninsula, The majority of Oriental pears are native to East Asia, while Occidental pears are to Europe, North Africa, Asia Minor, Iran, Central Asia, and Afghanistan. Besides displaying high resistance to stress, they are important germ plasm resources for genetic breeding of pears. However, due to the ecological damage caused by global warming and urbanization many of these wild populations are in danger. Grazing, Fire and exploitation of the wood for furniture, carving and oil are threatening the species [3]. The pulp of fleshy fruits, with the edible, soft, nutritious tissues surrounding the seeds is a primary food source for many animals, notably birds and mammals [4]. Away from the parent plants these animals regurgitate, defecate, spit out or otherwise drop the undamaged seeds; in natural communities they are the seed dispersers that establish a dynamic link between the fruiting plant and the seed-seedling bank [5]. In most ecosystems Avian frugivores are considered as the most important seed dispersers [6]. Seed predator’s animals are Parrots, some pigeons and finches [7]. For identifying the roles of individual disperser species play in plant recruitment dynamics, the study of interactions between avian frugivores and plant including conservation and restoration is important thus having implications for both theoretical understanding of mutualisms, species interactions and for applied work, [8]. So, the keeping in view the importance of these plants we conducted this study to assess the role of different frugivores in the seed spreading in district Haripur.

Materials and Methods

Study area

Study area was conducted in the district Haripur KPk Pakistan. Haripur is the main city of the Haripur District in Hazara, Khyber Pakhtunkhwa in Pakistan, with Swabi and Buner to the west, some 65 km north of Islamabad and 35 km south of Abbottabad. It is in a hilly plain area at an altitude of 520 m. Having the 33.9946° N, 72.9106° E. With the pleasant weather and hilly areas with grasses and pine trees (Figure 1).

Methodology

Study was conducted as a field survey during the complete year 2018. Trees with ripe fruits were selected. By the observer a pair of binoculars was used, usually 10–15 m away who sat near the tree and watched the canopy for recording animal visits. During the prolonged watches, the observer noted the visitor (bird/mammal), name, fruit-feeding frequency visits by different species, and fruit handling behaviour (whether fruit ingested whole or only partly seeds dropped and eaten). Bird’s names were known by literature.

Results and Discussion

A total of 200 birds were recorded to visit the Batangi fruites during the survey belonging to eight species in different localities of district Haripur, as most of these trees were found in village chhajjian so the highest amount of the visitors were recorded in this area. These included three species of Brahminy Starling Sturnus pagodarum, bulbuls, Pycnonotus sp., Common Myna Acridotheres tristis, Asian Koel Eudynamys scolopacea, White-headed Babbler Turdoides affnis and Small Green-Billed Malkoha Phaenicophaeus viridirostris. Highest proportion of feeding visits was contributed by Red whiskered Bulbul, Pycnonotus jocosus (21.3%) also we recorded the yellow and red vented bulbul to forage on these trees followed by White-headed Babbler (17.7%) and Asian Koel (19.3%). Among the various avian families, Pycnonotidae (bulbuls) made the majority of the visits (45.6%), even though when these trees were flowering these species were frequently found, followed by Sturnidae (mynas) (22.6%). Brown-headed Barbet Megalaima zeylanica was also found in different localities of district Haripur (Table 1).

Table 1:Different visitor of Batangi fruites in different areas of district Haripur.

The whole fruit was swallowed by the birds, Asian Koel, Brownheaded Barbet, Mynas, Starlings and Indian Grey Hornbill. Most often, in piecemeal bulbuls ate the fruit and dropped the seeds under the canopy itself. Occasionally, small fruits were swallowed by them. During the study period in chhajjian the 350 birds belonging to three species visited the focal tree. Among the frugivorous birds, the highest proportion of visits was made by the Asian Koel (55.4%) followed by Common Myna (40.6%), and Rose-ringed Parakeet Psttacula krameri (10%). To eat fruit In addition to birds, grey squirrels also visited the plant. While myna and koel used up the whole fruit, parakeets ate the seeds. Birds constituted the principal seed dispersers. Except squirrels o other mammals were foraging. Most of the natural seedlings of sandal were foundgrowing in the middle of thorny bushes, where the birds seem to have dropped the seeds. Birds that are benefcial to sandalwood dispersal and regeneration were Koel, Common Myna, Brahminy Starling, Brownheaded Barbet, and White-headed Babbler. These species visited the fruit crop more frequently and swallowed the fruit wholly. Hence, these species could be considered as major seed dispersers. Asian Koel seems to have preference to sandal fruits. Due to their smaller beak and narrow gape Bulbuls could not swallow the whole fruit. They appear to play a minor role only. In seed dispersal Parakeets did not play any role. They consumed the fruits mainly to digest the seeds and hence considered as seed predators. Greenbilled Malkoha made very few visits, thus contributing only a minor role. Sustaining the Asian Koel population will ensure the renewal of trees in the forests. Seed dispersing bird species such as koel Efforts need to be undertaken to provide a healthy habitat, as the population of tree is drastically dwindling in the wild.

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Iris Publishers-Open access Journal of Urology & Nephrology | Situs Inversus & Adult Polycystic Kidney Disease

 

Authored by M Nauman Hashmi*,

Abstract

Situs Inversus totalis is rare disorder in which there is transposition of organs position. Polycystic kidney disease is a disorder in which water containing cysts are formed in kidneys. Though named as polycystic kidney disease but cysts are formed in other organs such as liver, pancreas, seminal vesicles & arachnoid membrane. Association of these two conditions is rare.

Introduction

Situs Inversus & polycystic kidney disease combination is a rare entity. Recent research suggests ciliary abnormalities is root cause of both conditions.

Case

A 45-year-old male presented to emergency with complaints of backache, left flank pain & blood in urine for 1 week. Patient is the third child of consanguineous parents and has 6 brothers and 3 sisters. One sister is known to have renal disease secondary to NSAIDS abuse. He is married and father of six children with eldest daughter of 14 years of age. He is an occasional smoker. He had no history of sinusitis or repeated chest infections. On physical examination, apex beat was palpable in the right 5th intercostal space in mid clavicular line. The cardiac dullness was on the right side and the heart sounds were similarly disposed. Abdomen was soft and the liver was palpable in left hypochondrium. Patient had bilateral palpable kidneys on bimanual palpation. On admission, his blood reports Hb 12.1 gm/dl, Corrected Ca 6.2 mg/dl, Serum Creatinine 14.1 mg/dl. Chest x-ray obtained during admission (Figure 1), revealed dextrocardia and raised suspicion of situs inversus totalis. CT abdomen (Figure 2) showed situs inversus totalis of all the abdominal organs and bilateral enlarged kidneys with multiple renal cysts of variable sizes and densities. Hepatic cysts were also seen. No detectable abnormality was seen in lung fields. All of these findings were compatible with those of end stage renal disease due to autosomal polycystic kidney disease (ADPKD). Patient required emergent hemodialysis total five dialysis session were done during stay in hospital & was discharged on maintenance hemodialysis.

Discussion

Human organs orientation in 99.99% individuals is with left sided heart, liver in right hypochondrium & spleen in left. Rarely this pattern is disturbed which can range from single organ disarray called situs solitus to situs inversus totalis where there is complete reversal of normal organ orientation [1]. Hieronymus Fabricius, known also by Latin & Italian names, Fabricius ab Aquapendente or Girolamo Fabrizi d’Acquapendente, first described disorders of laterality. A historical survey shows a case of reversed liver and spleen reported by Fabricius in 1600 [2]. After discovery of laterality in 1600, in 1903 primary ciliary defect was reported by A.K Zivert and later by Kartagener in 1933.This founded the root for further research to look for connection between situs inversus & cystic kidney disease [3, 4]. At that time nobody was aware that two types of cilia would be involved in laterality development and that genes causing cystic kidney disease would play a role [5]. In last 4 decades a lot of groundbreaking work has been done in this regard. First theory of lack of dynein arm was put down as cause of immotile cilia, then two cilia model was purposed and later few genes have been labelled for heterotaxy such as GALNT 11 [6].

Conclusion

We are reporting a patient with situs inversus totalis & polycystic kidney disease which is a rare combination though hypothesis purpose a common underlying defect.

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