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Quantifying brain dynamics during anesthesia is an important challenge for understanding the neurophysiological mechanisms of anesthetic drug effect. Several single channel Electroencephalogram (EEG) indices have been proposed for monitoring anesthetic drug effect. The most commonly used single channel commercial index is the Bispectral index (BIS). However, this monitor has shown some drawbacks. In this study, a nonlinear functional connectivity measure named Standardized Permutation Mutual Information (SPMI) is proposed to describe communication between two-channel EEG signals at frontal and temporal brain regions during a controlled propofol-induced anesthesia and recovery design from eight subjects. The SPMI index has higher correlation with estimated propofol effect-site concentration and has better ability to distinguish three anesthetic states of patient than the other functional connectivity indexes (cross-correlation, coherence, phase analysis) and also the BIS index. Moreover, the SPMI index has a faster reaction to the effect of drug concentration, less variability at the consciousness state and better robustness to noise than BIS.

The high rate of hydrocarbon production in Ahvaz oilfield, is probably linked to environmental pollution, in particular in soils. In this study, 21 soil samples were collected and analyzed by Inductively Coupled Plasma Optical Emission Spectrometry to determine the degree of contamination and health risk by different computed indices. The results based on the geochemical index of an enrichment metal factor showed extremely high enrichment levels of Pb in the studied soils. The potential ecological risk index is reported in four regions with severe environmental issue, which included a sample of contaminated soil around the well and three samples of soil contaminated with petroleum-related drilling wastes. Health risk assessment of heavy metals in the region’s soil was studied. In the adults’ group, the risk of non-carcinogenic elements (risk of diseases besides cancer) due to high levels of Pb and the risk of carcinogenic ones, by As and Cr, threatened the health of exposed people. In the children’s group, the risk of non-carcinogenic factors was due to high levels of Pb and As. The risk of carcinogenic impact of Cr and Pb reaches levels threatening the health of exposed children. Due to the regional climate and the location of the petroleum field with respect to the dominant wind direction, it is likely that in the near future, the region will be a source of contaminated dusts and likely transferred contaminated soils to the residential and urban areas, which would be a health threat for residents of area.

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the

This research was conducted to determine the status of micronutrients (Fe, Zn, Cu, Mn, Co) and heavy metals (As, Pb) in the soil and rice crop in Ahvaz and Bawie counties, Iran. Twenty-eight composite soil and grain samples from selected rice farms were collected during the harvest time. The concentrations of elements in samples were determined by Inductively coupled plasma - optical emission spectrometry and in the grains by Inductively coupled plasma mass spectrometry device. Physicochemical properties of soil, bioconcentration factor, daily intakes and health risk assessment for elements were calculated. The results showed that the area is covered by alkaline saline soils, which is poor in micronutrients. The bioconcentration factor values of all elements were less than 1. Low levels of bioconcentration factor may be due to the low levels of nutrients in the soil as well as the physicochemical conditions of the soil. Furthermore, the results showed that the daily intake of Co (in the adults’ group) and Fe and Zn (in the children group) is very low. Health risk assessment showed that only adults are at the risk of non-cancerous and cancerous diseases caused by excess of As and Pb. Considering that Khuzestan province is the fourth largest producer of rice in Iran, the lack of micronutrients and presence of heavy metals in the rice produced in the study area can have adverse health effects on consumers. Investigation of this issue requires more detailed studies in the region.

In this work, an electrochemical aptasensor was described for the determination of prostate-specific antigen (PSA). Aptamer chains were decorated on the surface of a glassy carbon electrode (GCE) via carbon quantum dots/Au nanoparticles (Au/CQD). Structural analysis that was used to characterize the prepared materials shows that Au/CQD nanoparticles synthesized in a spherical shape with an average size of 70 nm. Furthermore, the combination of Au nanoparticles with CQD resulted in formation of crystalline the structure of the Au/CQD composite. To study the electrochemical performance of the prepared aptasensor, cyclic voltammetry, square wave voltammetry, and electrochemical impedance spectroscopy were used. The results show that the aptasensor has a good selectivity to PSA over other biomaterials with the time optimized about 30 min. K

Accurate and noninvasive monitoring of the depth of anesthesia (DoA) is highly desirable. Since the anesthetic drugs act mainly on the central nervous system, the analysis of brain activity using electroencephalogram (EEG) is very useful. This paper proposes a novel automated method for assessing the DoA using EEG. First, 11 features including spectral, fractal, and entropy are extracted from EEG signal and then, by applying an algorithm according to exhaustive search of all subsets of features, a combination of the best features (Beta-index, sample entropy, shannon permutation entropy, and detrended fluctuation analysis) is selected. Accordingly, we feed these extracted features to a new neurofuzzy classification algorithm, adaptive neurofuzzy inference system with linguistic hedges (ANFIS-LH). This structure can successfully model systems with nonlinear relationships between input and output, and also classify overlapped classes accurately. ANFIS-LH, which is based on modified classical fuzzy rules, reduces the effects of the insignificant features in input space, which causes overlapping and modifies the output layer structure. The presented method classifies EEG data into awake, light, general, and deep states during anesthesia with sevoflurane in 17 patients. Its accuracy is 92% compared to a commercial monitoring system (response entropy index) successfully. Moreover, this method reaches the classification accuracy of 93% to categorize EEG signal to awake and general anesthesia states by another database of propofol and volatile anesthesia in 50 patients. To sum up, this method is potentially applicable to a new real-time monitoring system to help the anesthesiologist with continuous assessment of DoA quickly and accurately.

Transcranial direct current stimulation (tDCS) is a non-invasive neuro-stimulation technique that can modulate cortical excitability. Similarly, yoga is shown to affect the brain's neural activity and networks. Here, we aimed to investigate the effect of combined yoga and tDCS on brain oscillations and networks using resting-state electroencephalography recordings. In a randomized, cross-over, double-blind design, twenty-two healthy subjects participated in a yoga/active tDCS session (2 mA; 20 min; anode-F3, cathode F4) or yoga/sham tDCS on 2 separate days. Resting-state EEG data were collected before and after each intervention. Power spectral density (PSD) and functional connectivity, measured by a synchronization measure, phase-locking value, were computed for each condition. There were no significant differences in PSD values among the two interventions. The network-based statistic method was employed for detecting functional connectivity differences between yoga/active and yoga/sham tDCS interventions. Results show that the addition of active tDCS to yoga is associated with increased functional connectivity of the scalp and source EEG data in the frontal area. The changes were widespread, intra-hemispheric, and inter-hemispheric connections, which were mainly between the frontal area to other regions. At the source level, most of the connectivity changes were found in the fronto-parietal network. These findings suggest that combining yoga with tDCS might lead to brain network changes related to the executive and attentional functions.

The Trachyspermum copticum essential oil (TCEO) has been incorporated into niosomes (NIO-TCEO). The chemical composition of the TCEO has been evaluated using Gas chromatography mass spectrometry (GC-MS) and the antioxidant activity of the TCEO has been characterized by radical scavenging capacity (RSC). The niosomes have been characterized with respect to their size, zeta potential, morphology, encapsulation efficiency, in vitro release, and cell toxicity against hepatocellular carcinoma cell line (HepG2). The interaction of the TCEO with niosomes has been studied using Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Our study has shown an excellent antioxidant activity of the essential oil with IC50 of 18.32 ± 3.6 μg/ml. The size of the niosomes has been around 151.06 ± 5.3 nm and the TCEO entrapment efficiency has been 87.41 ± 5.97%. Our results have demonstrated that niosomes loaded with TCEO have a potential application for cancer therapy.

Cerebellar acute ischemic stroke (AIS) can be a complication of minor head trauma, vertebral artery dissection, vasospasm or systemic hypoperfusion. CT scan usually is negative few hours after acute infarction. Magnetic resonance imaging (MRI) is superior to CT scan for posterior fossa lesions and also in acute phase of cerebellar stroke especially in children. Here we report a 9 yr old girl referred to the Pediatric Emergency Room, Moosavi Hospital, Zanjan, Iran in January 2017 presenting with sudden onset of headache and recurrent vomiting, ataxia, and history of 3 consecutive days of fever and malaise. In the report of MRI, there were abnormal low T1 and high T2 signal intensity in left cerebellar hemisphere involving superior and middle cerebellar peduncles. After 4 days of admission, the patient became drowsy, symptoms progressed and transferred to the pediatric intensive care unit (PICU). The patient underwent hemispherectomy surgery of the left cerebellar hemisphere because of acute obstructive hydrocephaly. After 5 months of occupational therapy, the force of her extremities was normal and the ataxia completely disappeared. Childhood acute ischemic stroke although rare can happen with cerebellar involvement. Because in our patient the first brain CT scan was nearly normal and a false negative rate for initial computed tomography (CT) scanning of 60%-80% also contributes to missed and delayed diagnosis of childhood AIS, for every child presenting with acute ataxia without identified cause in addition to CT scan, MRI also being ordered and from the beginning besides other causes, stroke be contemplated as a cause of ataxia.

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

Background: Surfactin is a cyclic heptapeptide that is closed by a β-hydroxy fatty acid chain. This potent biosurfactant is produced by different Bacillus subtilis strains. This lipopeptide has numerous attractive biological activities, including antibacterial, antiviral, antimycoplasma, hemolytic, and other different and powerful surface and interface activities. Methods: Understanding how surfactin binds to different membranes and its mechanism of action can help us modify and optimize its structure to improve the potential efficacy of this lipopeptide in the future. For this purpose, we studied the interaction of this lipopeptide with two types of lipid bilayer models, including palmitoyl-oleoyl-glycero- phosphocholine (POPC) and palmitoyl-oleoyl-phosphtidylglycerol (POPG) as the prokaryotic and eukaryotic membrane models, respectively. Results: The obtained data have shown that the tendency of surfactin for these membranes is different. According to the analysis, this molecule binds to both membranes peripherally, and its interaction with the negative membrane is also more potent compared to the zwitterionic membrane. Moreover, we found that surfactin destabilized POPG more than POPC. This suggests that surfactin may act by modifying the membrane’s bulk physical properties. Conclusions: As a final point, this study has shown that surfactin has different behaviors in the eukaryotic and prokaryotic cell membranes and can modify and amplify its action in order to use it for antibacterial drugs.

Purpose- This thesis aims to identify the role of the Balanced Scorecard (BSC) for managing strategy risks as well as the types of strategy risks that can be managed using four perspectives of the BSC in the Iranian Petroleum Industry Equipment Manufacturers (IPIEM). Design/ approach/ methodology- In this thesis cross-sectional design and the deduction approach are used. For collecting data for quantitative analysis, a questionnaire was conducted by the research team. Then the data collected from respondents were then analyzed through running simple linear regression analysis in the SPSS software. Findings- The first research question (RQ) is about BSC’s roles in managing strategy risks in IPIEM. These roles are risk assessment, risk controlling and collecting data for decision making of strategy risks. It was proved by the research team that BSC can play a role of the assessment of strategy risks in IPIEM. This means by using BSC as an RM tool in IPIEM, companies can assess strategy risks through identifying, analysing and evaluating strategy risks. However, the results indicate risk controlling and collecting data for decision making cannot be managed by using BSC. The second Research question is about the types of strategy risks that four perspectives of BSC can manage. The results show that from the 8 strategy risks chosen for this thesis, 6 of them which are “liquidity risk” from the financial perspective; “risk of clients’ opposition to pilot testing of the product” from the customer perspective; “risk of improper design of the product at development stages”, and “risk of improper selection of international partners” from the internal perspective; “risk of incorrect evaluation & selection of technology options” and “the risk of not enough operational experience in similar previous projects” from the learning and growth perspective can be managed through using BSC as an RM tool in IPIEM. Based on the conclusion of RQ1, the effect can now be adjusted into RQ2 findings. This study concludes that IPIEM can use BSC for risk assessment of the above-mentioned six different strategy risks. It can also be concluded that the BSC cannot be a full RM tool for managing strategy risks in the companies, since it only can apply for one of the three processes of RM; risk assessment.

Registration and finding anatomical correspondent landmarks amongst the volumetric images in both inter and intra-subject studies are prevalent challenges, especially in the field of cardiac image analysis. On the other hand, among other functional criteria, the strain is promising to identify differences in the early stages of cardiac diseases. This study aims to develop a transformation model on the existing FFD non-rigid registration algorithm using combining the shape-based and the intensity-based approaches. A novel two-step multi-resolution non-rigid FFD-based registration algorithm was proposed to measure the radial strain of the left ventricle during the cardiac cycle using cine-MRI images. The endocardial wall shape information was introduced to improve the registration accuracy by combining the original intensity-based registration with the shape-based registration approach. The proposed algorithm was evaluated on ten sequences of cine-MR images: First, the proposed algorithm was validated by two identified 3D-landmarks and then applied to a healthy subject and an LVH patient to calculate the left ventricle regional radial strain during a cardiac cycle. Regarding two anatomical landmarks, their estimated displacements from our proposed two-step registration algorithm showed a better match with the reference values compared to the classical registration method. Moreover, a comparison of normal and abnormal radial segmental strain values calculated by our proposed algorithm showed a clear difference in their functional properties. The promising results showed that the proposed registration algorithm outperformed the conventional one in terms of the accuracies of the point-tracking.

Purpose COVID-19 has infected millions of people worldwide. One of the most important hurdles in controlling the spread of this disease is the inefficiency and lack of medical tests. Computed tomography (CT) scans are promising in providing accurate and fast detection of COVID-19. However, determining COVID-19 requires highly trained radiologists and suffers from inter-observer variability. To remedy these limitations, this paper introduces an automatic methodology based on an ensemble of deep transfer learning for the detection of COVID-19. Methods A total of 15 pre-trained convolutional neural networks (CNNs) architectures: EfficientNets(B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50 and Inception_resnet_v2 are used and then fine-tuned on the target task. After that, we built an ensemble method based on majority voting of the best combination of deep transfer learning outputs to further improve the recognition performance. We have used a publicly available dataset of CT scans, which consists of 349 CT scans labeled as being positive for COVID-19 and 397 negative COVID-19 CT scans that are normal or contain other types of lung diseases. Results The experimental results indicate that the majority voting of 5 deep transfer learning architecture with EfficientNetB0, EfficientNetB3, EfficientNetB5, Inception_resnet_v2, and Xception has the higher results than the individual transfer learning structure and among the other models based on precision (0.857), recall (0.854) and accuracy (0.85) metrics in diagnosing COVID-19 from CT scans. Conclusion Our study based on an ensemble deep transfer learning system with different pre-trained CNNs architectures can work well on a publicly available dataset of CT images for the diagnosis of COVID-19 based on CT scans.

Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.

Progressive external ophthalmoplegia (PEO) is a slowly progressive myopathy characterized by extraocular muscles involvement, leading to frozen eyes without diplopia. The pattern of inheritance may be mitochondrial, autosomal dominant or, rarely, autosomal recessive. Sporadic forms were also reported. Muscular involvement other than extraocular muscles may occur with varying degrees of weakness, but this mostly happens many years after the disease begins. There are also scattered data about systemic signs besides ophthalmoplegia. This article aims to review non-ophthalmic findings of PEO from a clinicogenetical point of view.

Acute lymphoblastic leukemia (ALL) is the most frequently leukemia and categorized into three morphological subtypes named L1, L2 and L3. Early diagnosis of ALL plays a key role in treatment procedure especially in the case of children. Several similarities between morphology of three subtypes ALL (L1, L2, L3) and lymphocyte subtypes (normal, reactive and atypical) as noncancerous cells have remained a high challenge. Diagnosis of ALL and lymphocyte subtypes are done by microscopic viewing examination of cells in the peripheral blood samples by hematologists. Since this exam is time-consuming, boring and dependent on the skill of the hematologists, automatic systems are desired to overcome these limitations. In this study, 312 microscopic images including 958 cells are obtained from blood samples of 7 normal subjects and 14 patients. The first step of proposed system is image enhancement to decreases the effects of various luminosity situations with transformation from RGB to HSV color space and then applying histogram equalization on V channel for equalizing the grey level of image lightness. Nuclei segmentation from the blood cell images is the second step and performed using fuzzy c-means (FCM) clustering. After identify cluster of nuclei, we performed opening and closing process in morphological operation binary in order to remove extra noises and fill some minor holes in the nuclei. Moreover, to discrete the link between nuclei, watershed transform was applied. Then, a set of quantitative features (five geometric features about the size and figure of a cell and 36 statistical features about the spatial arrangement of intensities of nuclei image) are extracted to characterize the properties of these nuclei. In the next step, due to high number of features, the best features are selected by exhaustive search of all of the subsets of features and 13 features are selected. The final step is the classification of L1, L2, L3, normal, reactive and atypical cells by applying Random Forest (RF) classifier and result in 98% accuracy. We compared RF classifier with two other commonly classifiers named: MultiLayer Perceptron (MLP), and multi-SVM classifier with more success especially for recognition of L1, normal and reactive cells. So, this system can be used as an assistant diagnostic tool for hematologists to recognize subtypes of ALL and lymphocyte.

Schizophrenia (SZ) is a severe disorder of the human brain which disturbs behavioral characteristics such as interruption in thinking, memory, perception, speech and other living activities. If the patient suffering from SZ is not diagnosed and treated in the early stages, damage to human behavioral abilities in its later stages could become more severe. Therefore, early discovery of SZ may help to cure or limit the effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as SZ due to having high temporal resolution information, and being a noninvasive and inexpensive method. This paper introduces an automatic methodology based on transfer learning with deep convolutional neural networks (CNNs) for the diagnosis of SZ patients from healthy controls. First, EEG signals are converted into images by applying a time–frequency approach called continuous wavelet transform (CWT) method. Then, the images of EEG signals are applied to the four popular pre-trained CNNs: AlexNet, ResNet-18, VGG-19 and Inception-v3. The output of convolutional and pooling layers of these models are used as deep features and are fed into the support vector machine (SVM) classifier. We have tuned the parameters of SVM to classify SZ patients and healthy subjects. The efficiency of the proposed method is evaluated on EEG signals from 14 healthy subjects and 14 SZ patients. The experiments showed that the combination of frontal, central, parietal, and occipital regions applied to the ResNet-18-SVM achieved best results with accuracy, sensitivity and specificity of 98.60% ± 2.29, 99.65% ± 2.35 and 96.92% ± 2.25, respectively. Therefore, the proposed method as a diagnostic tool can help clinicians in detection of the SZ patients for early diagnosis and treatment.

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN-2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.

In this paper we have explored a new discrete probability mass function that has been generated through compounding mechanism. This newly proposed probability mass function is essentially a mixture of Poisson and Ailamujia distribution. Furthermore the parameter estimation has also been discussed by using Maximum likelihood estimation (MLE) technique. Moreover, we have also studied some important properties of the proposed model that include factorial moments, raw moments, mean, variance, and coefficient of variation. In the end, the application and potentiality of the proposed model have been tested statistically and it has been shown that the proposed model can be employed to model a real life data set to get an adequate fit that has also corroborated through graphically.

ObjectiveTo evaluate clinical, interferon and imaging predictors of progression from ‘At Risk’ to autoimmune connective tissue diseases (AI-CTDs).MethodsA prospective observational study was conducted in At-Risk of AI-CTD (defined as antinuclear antibody (ANA) positive; ≤1 clinical systemic lupus erythematosus (SLE) criterion; symptom duration Results118 individuals with 12-month follow-up were included. Of these, 19/118 (16%) progressed to AI-CTD (SLE=14, primary Sjogren’s=5). At baseline, both IFN scores differed among At-Risk, HCs and SLE groups (pConclusionA two-factor interferon score and family history predict progression from ANA positivity to AI-CTD. These interferon scores may allow stratification of individuals At-Risk of AI-CTD permitting early intervention for disease prevention and avoid irreversible organ damage.

Background Craniofacial clefts are one of the rarest congenital malformations. When it comes to genetic inheritance patterns of craniofacial clefts, there are few studies on this subject due to the rarity and sporadic nature. In this study an extended family whose members have multiple craniofacial clefts has been introduced. Methods The authors haphazardly found a family whose members have craniofacial clefts, either Tessier no. 0 or both Tessier nos. 0 and 14, and, consequently, they were categorized into 3 groups based on the severity of clefts. Results Forty-two craniofacial clefts were noticed within the family. Twenty-two (52.3%) of patients were females and 20 (47.6%) were males. Twenty-three (54.8%) members had isolated Tessier no. 0 cleft, whereas 19 (45.2%) members had both Tessier nos. 0 and 14. Of all 42 patients, 18 (42.8%), 3 (7.1%), and 21 (50%) patients had severe, intermediate, and mild clefts, respectively. Conclusion The present study reveals that in some patients genetic background is likely to be the cause of clefts.