Search results for: disease prediction

Authors: M.Younus, M. Athar Khan, Asif Adrees

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The present research endeavour was made to investigate the Public Health impact of Salmonellosis through consuming the meat and eggs of the carrier’s birds and to see the prevalence of Salmonella enteritidis and Salmonella typhimurium from poultry feed, poultry meat, and poultry eggs and their role in the chain of transmission of salmonellae to human beings and causing food poisoning. The ultimate objective was to generate data to improve the quality of poultry products and human health awareness. Salmonellosis is one of the most wide spread food borne zoonoses in all the continents of the world. The etiological agents Salmonella enteritidis and Salmonella typhimurium not only produce the disease but during the convalescent phase (after the recovery of disease) remain carriers for indefinite period of time. The carrier state was not only the source of spread of disease with in the poultry but also caused typhoid fever in humans. The chain of transmission started from poultry feed to poultry meat and ultimately to humans as dead end hosts. In this experiment a total number of 200 samples of human stool and blood were collected randomly (100 samples of human stool and 100 samples of human blood) of 100 patients suspected from food poisoning patients from different hospitals of Lahore area for the identification of Salmonella enteritidis and Salmonella typhimurium through PCR method in order to see the public health impact of Salmonellosis through consuming the meat and eggs of the carrier birds. On the average 14 and 10 stool samples were found positive against Salmonella enteritidis and Salmonella typhimurium from each of the 25 patients from each hospital respectively in case of suspected food poisoning patients. Similarly on an average 5% and 6% blood samples were found positive from 25 patients of each hospital respectively. There was a significant difference (P< 0.05) in the sero positivity of stool and blood samples of suspected food poisoning patients as far as Salmonella enteritidis and Salmonella typhimurium was concerned. However there was no significant difference (P<0.05) between the hospitals.

Keywords: salmonella, zoonosis, food, transmission, eggs

Procedia PDF Downloads 665

Authors: Sakeh N. Mohd, Bahari M. N. Abdul, Abdullah S. N. Akmar

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Oil palm production generates high export earnings to many countries especially in Southeast Asian region. Infection by necrotrophic fungus, Ganoderma boninense on oil palm results in basal stem rot which compromises oil palm production leading to significant economic loss. There are no reliable disease treatments nor promising resistant oil palm variety has been cultivated to eradicate the disease up to date. Thus, understanding molecular mechanisms underlying early interactions of oil palm with Ganoderma boninense may be vital to promote preventive or control measure of the disease. In the present study, four months old oil palm seedlings were infected via artificial inoculation of Ganoderma boninense on rubber wood blocks. Roots of six biological replicates of treated and untreated oil palm seedlings were harvested at 0, 3, 7 and 11 days post inoculation. Next-generation sequencing was performed to generate high-throughput RNA-Seq data and identify differentially expressed genes (DEGs) during early oil palm-Ganoderma boninense interaction. Based on de novo transcriptome assembly, a total of 427,122,605 paired-end clean reads were assembled into 30,654 unigenes. DEGs analysis revealed upregulation of 173 transcription factors on Ganoderma boninense-treated oil palm seedlings. Sixty-one transcription factors were categorized as DEGs according to stringent cut-off values of genes with log2 ratio [Number of treated oil palm seedlings/ Number of untreated oil palm seedlings] ≥ |1.0| (corresponding to 2-fold or more upregulation) and P-value ≤ 0.01. Transcription factors in response to biotic stress will be screened out from abiotic stress using reverse transcriptase polymerase chain reaction. Transcription factors unique to biotic stress will be verified using real-time polymerase chain reaction. The findings will help researchers to pinpoint defense response mechanism specific against Ganoderma boninense.

Keywords: Ganoderma boninense, necrotrophic, next-generation sequencing, transcription factors

Procedia PDF Downloads 266

Authors: Kohkan Shamsi

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Background: MRI and PET are now extensively utilized in Alzheimer's disease (AD) trials for patient eligibility, efficacy assessment, and safety evaluations but including imaging in AD trials impacts site selection process, patient recruitment, and patient retention. Methods: PET/MRI are performed at baseline and at multiple follow-up timepoints. This requires prospective site imaging qualification, evaluation of phantom data, training and continuous monitoring of machines for acquisition of standardized and consistent data. This also requires prospective patient/caregiver training as patients must go to multiple facilities for imaging examinations. We will share our experience form one of the largest AD programs. Lesson learned: Many neurological diseases have a similar presentation as AD or could confound the assessment of drug therapy. The inclusion of wrong patients has ethical and legal issues, and data could be excluded from the analysis. Centralized eligibility evaluation read process will be discussed. Amyloid related imaging abnormalities (ARIA) were observed in amyloid-β trials. FDA recommended regular monitoring of ARIA. Our experience in ARIA evaluations in large phase III study at > 350 sites will be presented. Efficacy evaluation: MRI is utilized to evaluate various volumes of the brain. FDG PET or amyloid PET agents has been used in AD trials. We will share our experience about site and central independent reads. Imaging logistic issues that need to be handled in the planning phase will also be discussed as it can impact patient compliance thereby increasing missing data and affecting study results. Conclusion: imaging must be prospectively planned to include standardizing imaging methodologies, site selection process and selecting assessment criteria. Training should be transparently conducted and documented. Prospective patient/caregiver awareness of imaging requirement is essential for patient compliance and reduction in missing imaging data.

Keywords: Alzheimer's disease, ARIA, MRI, PET, patient recruitment, retention

Procedia PDF Downloads 115

Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

Procedia PDF Downloads 238

Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

Procedia PDF Downloads 88

Authors: Yuexin Liu, Gordon B. Mills, Russell R. Broaddus, John N. Weinstein

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The lethality of endometrioid endometrial cancer (EEC) is primarily attributable to the high-stage diseases. However, there are no available biomarkers that predict EEC patient staging at the time of diagnosis. We aim to develop a predictive scheme to help in this regards. Using reverse-phase protein array expression profiles for 210 EEC cases from The Cancer Genome Atlas (TCGA), we constructed a Protein Scoring of EEC Staging (PSES) scheme for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MD Anderson Cancer Center (MDACC) using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used. PSES robustly distinguished high- from low-stage tumors in the TCGA cohort (area under the ROC curve [AUC]=0.74; 95% confidence interval [CI], 0.68 to 0.82) and in the validation cohort (AUC=0.67; 95% CI, 0.58 to 0.76). Even among grade 1 or 2 tumors, PSES was significantly higher in high- than in low-stage tumors in both the TCGA (P = 0.005) and MDACC (P = 0.006) cohorts. Patients with positive PSES score had significantly shorter progression-free survival than those with negative PSES in the TCGA (hazard ratio [HR], 2.033; 95% CI, 1.031 to 3.809; P = 0.04) and validation (HR, 3.306; 95% CI, 1.836 to 9.436; P = 0.0007) cohorts. The ErbB signaling pathway was most significantly enriched in the PSES proteins and downregulated in high-stage tumors. PSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.

Keywords: endometrial carcinoma, protein, protein scoring of EEC staging (PSES), stage

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Authors: Anuruddha Jayasuriya, Thanakorn Pheeraphan

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In this paper, a review of different mathematical models which can be used as prediction tools to assess the time to crack reinforced concrete (RC) due to corrosion is investigated. This investigation leads to an experimental study to validate a selected prediction model. Most of these mathematical models depend upon the mechanical behaviors, chemical behaviors, electrochemical behaviors or geometric aspects of the RC members during a corrosion process. The experimental program is designed to verify the accuracy of a well-selected mathematical model from a rigorous literature study. Fundamentally, the experimental program exemplifies both one-dimensional chloride diffusion using RC squared slab elements of 500 mm by 500 mm and two-dimensional chloride diffusion using RC squared column elements of 225 mm by 225 mm by 500 mm. Each set consists of three water-to-cement ratios (w/c); 0.4, 0.5, 0.6 and two cover depths; 25 mm and 50 mm. 12 mm bars are used for column elements and 16 mm bars are used for slab elements. All the samples are subjected to accelerated chloride corrosion in a chloride bath of 5% (w/w) sodium chloride (NaCl) solution. Based on a pre-screening of different models, it is clear that the well-selected mathematical model had included mechanical properties, chemical and electrochemical properties, nature of corrosion whether it is accelerated or natural, and the amount of porous area that rust products can accommodate before exerting expansive pressure on the surrounding concrete. The experimental results have shown that the selected model for both one-dimensional and two-dimensional chloride diffusion had ±20% and ±10% respective accuracies compared to the experimental output. The half-cell potential readings are also used to see the corrosion probability, and experimental results have shown that the mass loss is proportional to the negative half-cell potential readings that are obtained. Additionally, a statistical analysis is carried out in order to determine the most influential factor that affects the time to corrode the reinforcement in the concrete due to chloride diffusion. The factors considered for this analysis are w/c, bar diameter, and cover depth. The analysis is accomplished by using Minitab statistical software, and it showed that cover depth is the significant effect on the time to crack the concrete from chloride induced corrosion than other factors considered. Thus, the time predictions can be illustrated through the selected mathematical model as it covers a wide range of factors affecting the corrosion process, and it can be used to predetermine the durability concern of RC structures that are vulnerable to chloride exposure. And eventually, it is further concluded that cover thickness plays a vital role in durability in terms of chloride diffusion.

Keywords: accelerated corrosion, chloride diffusion, corrosion cracks, passivation layer, reinforcement corrosion

Procedia PDF Downloads 218

Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

Procedia PDF Downloads 148

Authors: Nermin Isik, Ozlem Derinbay Ekici

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Bovine coccidiosis is a protozoan disease caused by various species of Eimeria and most signs of disease are chronic or subclinical. The aim of this study was to determine the prevalence of Eimeria spp. in calves in Konya, in Turkey. The study, conducted from January- February 2015, involved 240 faecal samples of calves in the age groups of <1 month, 1-3 months and >3 months in Konya city centre, in Turkey. In a retrospective study from these faecal samples of calves submitted to the University of Selcuk, Faculty of Veterinary Medicine, Laboratory of Parasitology were evaluated regarding the prevalence of Eimeria spp. Faecal samples were examined by Fulleborn saturated salt floatation technique. Eimeria oocysts were found in 8.33% of all samples. The positivity rates in each of the age groups were different. According to the age groups (<1 month, 1-3 months and >3 months), the Eimeria spp. were determined as 0.83, 22.73 and 7.41%, respectively. After examination of stool, detected oocysts were sporulated in 2.5% potassium dichromate at 22º C and species were identified as E. cylindrica, E. zuernii, E. ellipsoidalis, E. subspherica, E. bovis, E. auburnensis, E. canadensis, E. illinoisensis and E. brasiliensis in infected calves. In conclusion, the highest prevalence was observed in the age group of 1-3 months. The presence of Eimeria species in calves demonstrated for the first time in the Konya region in Turkey. Other etiologic agents should also be investigated in calves more seriously. Further molecular epidemiological studies should be performed in this community.

Keywords: Eimeria spp., calves, diarrhea, bovine coccidiosis

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

Procedia PDF Downloads 121

Authors: Afolakemi Helen Olaleye, Ogunjobi A. O

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Since coronavirus disease 2019 (COVID-19) was first reported in Nigeria, the virus has spread to virtually all sub-Saharan Africa (SSA) countries. In Nigeria, government agencies came together to create a goal-driven taskforce in improving our response against the virus. As COVID-19 international spread has been curtailed, community spread became rampant locally, leading to many health authorities raising concerns over the scarcity of medical consumables and supplies. Here at Lagos university teaching Hospital (LUTH), we present data analysis of COVID-19 infections offered at our Hospital (LUTH) and the surrounding communities. In addition, the adopted innovative solution to control the spread of infection, methods used in filling shortages of consumables, personal protective equipment (PPE), and use of mobile video technology in patient’s consultation. The management style and strategy adopted has led to a decline in infection rates in our community and among our front line staff. The current COVID -19 crisis has created an opportunity to test and demonstrate our pandemic response and control of infectious disease along with the revealed unknown potential in our community.

Keywords: COVID-19, preparedness, response, Lagos university teaching hospital

Procedia PDF Downloads 145

Authors: Manju Lata Sharma

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Damage to cells caused by free radicals is believed to play a central role in the ageing process and in disease progression. Withania somnifera is widely used in ayurvedic medicine, and it is one of the ingredients in many formulations to increase energy, improve overall health and longevity and prevent disease. Withania somnifera possesses antioxidative properties. The antioxdant activity of Withania somnifera consisting of an equimolar concentration of active principles of sitoindoside VII-X and withaferin A. The antioxidant effect of Withania somnifera extract was investigated on lipid peroxidation (LPO), superoxide dismutase (SOD) and catalase (CAT) activity in mice. Aim: To study the antioxidant activity of an extract of Withania somnifera leaf against a mice model of chronic stress. Healthy swiss albino mice (3-4 months old) selected from an inbred colony were divided in to 6 groups. Biochemical estimation revealed that stress induced a significant change in SOD, LPO, CAT AND GPX. These stress induced perturbations were attenuated Withania somnifera (50 and 100 mg/kg BW). Result: Withania somnifera tended to normalize the augmented SOD and LPO activities and enhanced the activities of CAT and GPX. The result indicates that treatment with an alcoholic extract of Withania somnifera produced a significant decrease in LPO ,and an increase in both SOD and CAT in brain mice. This indicates that Withania somnifera extract possesses free radical scavenging activity .

Keywords: Withania somnifera, antioxidant, lipid peroxidation, brain

Procedia PDF Downloads 366

Authors: Ghorbanali Mohammadi

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Background: Due to the widespread outbreak of the COVID-19 virus, a large number of people become infected with the disease every day and go to hospitals. The acute condition of this disease has caused the death of many people. Since all the stages of treatment for these people happen in the hospitals, nurses are at the forefront of the fight against this virus. This causes nurses to suffer from physical and mental health problems. Methods: Physical and mental problems in nurses were assessed using the Depression, Anxiety and Stress Scale (DASS-42) of Lovibond (1995) and the Nordic Questionnaire. Results: 90 nurses from emergency, intensive care, and coronary care units were examined, and a total of 180 questionnaires were collected and evaluated. It was found that 37.78%, 47.78%, and 21.11% of nurses have symptoms of depression, anxiety, and stress, respectively. 40% of the nurses had physical problems. In total, 65.17% of them were involved in one or more mental or physical illnesses. Conclusions: Of the three units surveyed, the nurses in intensive care, emergency room, and coronary care units worked more than ten hours a day. Examining the interaction of physical and mental health problems indicated that physical problems can aggravate mental problems.

Keywords: depression anxiety and stress scale of Lovibond, nordic questionnaire, mental health of nurses, physical health problems in nurses

Procedia PDF Downloads 122

Authors: Guy Rubin, Ravit Shay, Nimrod Rozen

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The accuracy of a diagnostic test used to classify a patient as having disease or being disease-free is a valuable piece of information to be used by the physician when making treatment decisions. Finger laceration, suspected to have nerve injury is a challenging decision for the treating surgeon. The purpose of this study was to evaluate the sensitivity, specificity and predictive values of six clinical tests in the diagnosis of digital nerve injury. The six clinical tests included light touch, pin prick, static and dynamic 2-point discrimination, Semmes Weinstein monofilament and wrinkle test. Data comparing pre-surgery examination with post-surgery results of 42 patients with 52 digital nerve injury was evaluated. The subjective examinations, light touch, pin prick, static and dynamic 2-point discrimination and Semmes-Weinstein monofilament were not sensitive (57.6, 69.7, 42.4, 40 and 66.8% respectively) and specific (36.8, 36.8, 47.4, 42.1 and 31.6% respectively). Wrinkle test, the only objective examination, was the most sensitive (78.1%) and specific (55.6%). This result gives no pre-operative examination the ability to predict the result of explorative surgery.

Keywords: digital nerve, injury, nerve examination, Semmes-Weinstein monofilamen, sensitivity, specificity, two point discrimination, wrinkle test

Procedia PDF Downloads 344

Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

Procedia PDF Downloads 258

Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis

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Authors: Suchithra V., Shreedhanya, Kavya Menon, Vidya Niranjan

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Skin metagenomics has a wide range of applications with direct relevance to the health of the organism. It gives us insight to the diverse community of microorganisms (the microbiome) harbored on the skin. In the recent years, it has become increasingly apparent that the interaction between skin microbiome and the human body plays a prominent role in immune system development, cancer development, disease pathology, and many other biological implications. Next Generation Sequencing has led to faster and better understanding of environmental organisms and their mutual interactions. This project is studying the human skin microbiome of different individuals having varied skin conditions. Bacterial 16S rRNA data of skin microbiome is downloaded from SRA toolkit provided by NCBI to perform metagenomics analysis. Twelve samples are selected with two controls, and 3 different categories, i.e., sex (male/female), skin type (moist/intermittently moist/sebaceous) and occlusion (occluded/intermittently occluded/exposed). Quality of the data is increased using Cutadapt, and its analysis is done using FastQC. USearch, a tool used to analyze an NGS data, provides a suitable platform to obtain taxonomy classification and abundance of bacteria from the metagenome data. The statistical tool used for analyzing the USearch result is METAGENassist. The results revealed that the top three abundant organisms found were: Prevotella, Corynebacterium, and Anaerococcus. Prevotella is known to be an infectious bacterium found on wound, tooth cavity, etc. Corynebacterium and Anaerococcus are opportunist bacteria responsible for skin odor. This result infers that Prevotella thrives easily in sebaceous skin conditions. Therefore it is better to undergo intermittently occluded treatment such as applying ointments, creams, etc. to treat wound for sebaceous skin type. Exposing the wound should be avoided as it leads to an increase in Prevotella abundance. Moist skin type individuals can opt for occluded or intermittently occluded treatment as they have shown to decrease the abundance of bacteria during treatment.

Keywords: bacterial 16S rRNA , next generation sequencing, skin metagenomics, skin microbiome, taxonomy

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Authors: Amardass Dhami, Clare Samuelson

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Background: Sickle cell disease (SCD) is a complex, multisystem condition that significantly impacts patients' quality of life, characterized by acute illness episodes, progressive organ damage, and reduced life expectancy. In the UK, over 13,000 individuals are affected, with South Yorkshire having the fifth highest prevalence, including approximately 800 patients. Retinal complications in SCD can manifest as either proliferative or non-proliferative disease, with proliferative changes being more prevalent. These retinal issues can cause significant morbidity, including visual loss and increased care requirements, underscoring the need for regular monitoring. An integrated approach was applied to ensure timely interventions, ultimately enhancing patient outcomes and reduce ‘did not attend’ rates. Aim: To assess the factors which may influence attendance to Haematology and Ophthalmology Clinics with attention towards levels of deprivation towards non-attendance. Method : A retrospective study on 84 eligible patients, from the regional tertiary Centre for Sickle Cell Care (Sheffield Teaching Hospital) from 2017 to 2023. The study focused on the incidence of sickle cell eye disease, specifically examining the outcomes of patients who attended the combined haematology and ophthalmology clinics. Patients who did not attend either clinic were excluded from the analysis to ensure a clear understanding of the combined clinic's impact. This data was then compared with the United Kingdom’s Index of Multiple Deprivation (IMD) datasets to assess if inequalities of care affected this population. Results: The study concluded that the effectiveness of combining haematology and ophthalmology clinics was reduced following the intervention. The DNA rates increased to 40% for the haematology clinic. Additionally, a significant proportion of the cohort was classified as residing in areas of deprivation, suggesting a possible link between socioeconomic factors and non-attendance rates Conclusion: These findings underscore the challenges of integrating care for SCD patients, particularly in relation to socioeconomic barriers. Despite the intent to streamline care and improve patient outcomes, the increase in DNA rates points to the need for further investigation into the underlying causes of non-attendance. Addressing these issues, especially in deprived areas, could enhance the effectiveness of combined clinics and ensure that patients receive the necessary monitoring and interventions for their eye health and overall well-being. Future strategies may need to focus on improving accessibility, outreach, and support for patients to mitigate the impact of socioeconomic factors on healthcare attendance.

Keywords: south yorkshire, sickle cell anemia, deprivation, factors, haematology

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: N. Chikhi-Chorfi, L. Arbia, S. Zenia, H.Lounici

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Opuntia ficus-indica belongs to the Cactaceae family. The cactus is mainly cultivated for its fruit (prickly pear) that, eaten after pealing, is sweet and juicy, and rich in nutritional compounds, such as ascorbic acid and polyphenols. Different parts of O. ficus-indica are used in the traditional medicine of several countries: the cladodes are utilized to reduce serum cholesterol level and blood pressure, for treatment of ulcers, rheumatic pain, wounds, fatigue, capillary fragility, and liver conditions. This original study, investigate the effect of polyphenols of O. ficus indica (cactus) cladodes against periodontal bacteria collected from patients with periodontitis. The quantitative analysis of total polyphenols (TPP) was determined with Follin-Ciocalteu method. Different concentrations of extracts of O. ficus indica were tested by the disk method on two bacterial strains: Porphyromonas gingivalis and Prevotella intermedia responsible for periodontal disease. The results showed a good correlation between the concentration of total polyphenols and the antibacterial activity of the extracts of Opuntia ficus indica against P. gingivalis and P. intermedia with R² = 0.94 and R² = 0.90 respectively. This observation suggests that these extracts could be used in the treatment and prevention of periodontitis.

Keywords: periodontal disease, P. gingivalis, P. intermedia, polyphenols, Opuntia ficus indica

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Authors: Kalbiye Konanc, Ergin Ozturk

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To examine the effects of an ethanol liquid extract obtained from raw bee propolis (PE) on fattening performance and physiology such as vaccine-antibody relationship, microbial profile, immune status and some blood parameters of broiler chickens were used a total of 600 broiler (Ross 308) chicks, obtained from eggs of 288, 38-weeks-old broiler breeding. There were 6 groups: CC (Parent-Control and Offspring-Control, CP (Parent-Control and Offspring-propolis extract, Cip (Parent-Control and Offspring-in-ovo propolis extract), Cis (Parent-Control and Chickens-in-ovo saline), PeC (Parent-propolis extract and Offspring-Control), PeP (Parent-Propolis extract and Offspring-Propolis extract). Each group was consisted of 10 replications with 10 broiler offspring, and the experiment was lasted for 6 weeks with ethanol-extracted propolis concentration is 400 ppm/kg diet. While the highest feed consumptions at 0-21 days and 0-42 days were found in PeC, the best feed conversion ratio at 0-42 days was found in CP group. The live weight gains were found not to be different among the groups. The highest alanine aminotransferase activities were found in CC and CP and aspartate aminotransferase activities in PeP and PeC groups. The highest triglyceride and total antioxidant levels were found highest in CC and the highest total oxidant level in Cip group. IgA level in hatched eggs and IgM value after slaughtering were highest in Cip group. The best immune response was obtained for 21st day Newcastle Disease vaccine in CC and Cis groups and for 28th day Infectious Bursal Disease vaccine in CP group. The highest total aerobic microorganism and the lowest total fungi count were found in PeP group. In conclusion, it was determined that in-ovo propolis ethanol extract (Cip) increased the maternal antibody levels, that had not consistent effects on blood biochemical parameters except for triglyceride, that led to decrease in E. coli counts and that it can provide strong immune response against Infectious Bursal Disease.

Keywords: bee propolis, in-ovo feeding, immune parameters, poultry, maternal antibody, microorganisms

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Authors: Hsiang-Yu Yuan, Marc Baguelin, Kin O. Kwok, Nimalan Arinaminpathy, Edwin Leeuwen, Steven Riley

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Traditional syndromic surveillance for influenza has substantial public health value in characterizing epidemics. Because the relationship between syndromic incidence and the true infection events can vary from one population to another and from one year to another, recent studies rely on combining serological test results with syndromic data from traditional surveillance into epidemic models to make inference on epidemiological processes of influenza. However, despite the widespread availability of serological data, epidemic models have thus far not explicitly represented antibody titre levels and their correspondence with immunity. Most studies use dichotomized data with a threshold (Typically, a titre of 1:40 was used) to define individuals as likely recently infected and likely immune and further estimate the cumulative incidence. Underestimation of Influenza attack rate could be resulted from the dichotomized data. In order to improve the use of serosurveillance data, here, a refinement of the concept of the stratified immunity within an epidemic model for influenza transmission was proposed, such that all individual antibody titre levels were enumerated explicitly and mapped onto a variable scale of susceptibility in different age groups. Haemagglutination inhibition titres from 523 individuals and 465 individuals during pre- and post-pandemic phase of the 2009 pandemic in Hong Kong were collected. The model was fitted to serological data in age-structured population using Bayesian framework and was able to reproduce key features of the epidemics. The effects of age-specific antibody boosting and protection were explored in greater detail. RB was defined to be the effective reproductive number in the presence of stratified immunity and its temporal dynamics was compared to the traditional epidemic model using use dichotomized seropositivity data. Deviance Information Criterion (DIC) was used to measure the fitness of the model to serological data with different mechanisms of the serological response. The results demonstrated that the differential antibody response with age was present (ΔDIC = -7.0). The age-specific mixing patterns with children specific transmissibility, rather than pre-existing immunity, was most likely to explain the high serological attack rates in children and low serological attack rates in elderly (ΔDIC = -38.5). Our results suggested that the disease dynamics and herd immunity of a population could be described more accurately for influenza when the distribution of immunity was explicitly represented, rather than relying only on the dichotomous states 'susceptible' and 'immune' defined by the threshold titre (1:40) (ΔDIC = -11.5). During the outbreak, RB declined slowly from 1.22[1.16-1.28] in the first four months after 1st May. RB dropped rapidly below to 1 during September and October, which was consistent to the observed epidemic peak time in the late September. One of the most important challenges for infectious disease control is to monitor disease transmissibility in real time with statistics such as the effective reproduction number. Once early estimates of antibody boosting and protection are obtained, disease dynamics can be reconstructed, which are valuable for infectious disease prevention and control.

Keywords: effective reproductive number, epidemic model, influenza epidemic dynamics, stratified immunity

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Authors: S.K. Bhandari, E. Veneklaas, L. McCaw, R. Mazanec, K. Whitford, M. Renton

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Eucalyptus marginata, E. diversicolor and Corymbia calophylla form widespread forests in south-west Western Australia (SWWA). These forests have economic and ecological importance, and therefore, tree growth and sustainable management are of high priority. This paper aimed to analyse and model the growth of these species at both stand and individual levels, but this presentation will focus on predicting the growth of E. Marginata at the individual tree level. More specifically, the study wanted to investigate how well individual E. marginata tree growth could be predicted by considering the diameter and height of the tree at the start of the growth period, and whether this prediction could be improved by also accounting for the competition from neighbouring trees in different ways. The study also wanted to investigate how many neighbouring trees or what neighbourhood distance needed to be considered when accounting for competition. To achieve this aim, the Pearson correlation coefficient was examined among competition indices (CIs), between CIs and dbh growth, and selected the competition index that can best predict the diameter growth of individual trees of E. marginata forest managed under different thinning regimes at Inglehope in SWWA. Furthermore, individual tree growth models were developed using simple linear regression, multiple linear regression, and linear mixed effect modelling approaches. Individual tree growth models were developed for thinned and unthinned stand separately. The developed models were validated using two approaches. In the first approach, models were validated using a subset of data that was not used in model fitting. In the second approach, the model of the one growth period was validated with the data of another growth period. Tree size (diameter and height) was a significant predictor of growth. This prediction was improved when the competition was included in the model. The fit statistic (coefficient of determination) of the model ranged from 0.31 to 0.68. The model with spatial competition indices validated as being more accurate than with non-spatial indices. The model prediction can be optimized if 10 to 15 competitors (by number) or competitors within ~10 m (by distance) from the base of the subject tree are included in the model, which can reduce the time and cost of collecting the information about the competitors. As competition from neighbours was a significant predictor with a negative effect on growth, it is recommended including neighbourhood competition when predicting growth and considering thinning treatments to minimize the effect of competition on growth. These model approaches are likely to be useful tools for the conservations and sustainable management of forests of E. marginata in SWWA. As a next step in optimizing the number and distance of competitors, further studies in larger size plots and with a larger number of plots than those used in the present study are recommended.

Keywords: competition, growth, model, thinning

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Authors: Sandra Soca Lozano, Teresa Isabel Lozano Pérez, Germain Weber

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Cancer is a chronic disease of high prevalence in children and adolescents. Medical care in Cuba is carried out by a multidisciplinary team and family is the mediator between this team and the patient. Around this disease, there are interwoven many stereotypes and taboos by its relation to death. In this research report, we describe the work paradigm of psychological care to patients suffering from these diseases in the University Pediatric Hospital Juan Manuel Márquez of Havana, Cuba. We present the psychosocial factors that must be taken into account to provide comprehensive care and ensuring the quality of life of patients and their families. We also present the factors related to the health team and the management of information done with the patient. This is a descriptive proposal from the working experience accumulated in the named institution and in the review of the literature. As a result of this report we make a proposal of teamwork and the aspects in which psychological intervention should be continue performing in terms of increasing the quality of the care made by the health team. We conclude that it is necessary to continue improving the information management of children and adolescents with theses health problems and took into account their right to autonomy.

Keywords: comprehensive care, management of information, psychosocial factors, right to autonomy

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Ali Montazar

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This study aims to assess the viability of sub-surface drip irrigation (SDI) using several ongoing and conducted researches in the low desert region of California. The experiments were carried out in the University of California Desert Research and Extension Center (UC DREC) and ten commercial fields at alfalfa, sugar beets, dehydrated onions, and spinach crops. The results demonstrated greater yields, actual crop water consumption, and water productivity of SDI as compared with conventional irrigation practices (border, furrow, and sprinkler irrigation) with an average increase of 21%, 7%, and 15%, respectively. The severity of plant disease, particularly root rot in sugar beet, and downy mildew in onions and spinach, were significantly lower in SDI than furrow and sprinkler irrigation (an average of 3-5 times). While utilizing this irrigation technology may have ability to achieve higher yields, conserve water, improve the efficiency of water and nutrient use, and manage food safety risks and plant disease, further work is required to better understand the impact of management practices and strategies on the viability of SDI application, and maintain its profitability in various agricultural production systems as water, labor costs, and environmental concerns increase.

Keywords: alfalfa, onions, spinach, sugar beets, subsurface drip irrigation

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Authors: Fatima Al-Humaid

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This research work was done to investigate the cause of paralysis in Najdi lambs born in certain farms where the drinking water and diet contained high concentrations of sulphur. The drinking water in these farms was obtained from deep bore wells drilled in the farm. The lambs developed paralysis of the hind limbs at the age of 4-6 weeks and their condition deteriorated continuously until they finally died. The appetite and suckling ability remained good throughout the course of the disease but when the lambs were completely unable to move and reach for the udder, feed and water they died. Postmortem examination of the brain of paralyzed lambs showed that it was liquefied. When the brain was examined histologically, a liquefactive necrosis was seen in the form of cavities in the nervous tissue. Similar histologic picture was seen in the spinal cord of the affected lambs. Analysis for the mineral content of the fodder showed that the concentration of sulphur was 21.6 3.4 g/kg DM which is considered very high for the nutrition of sheep. Analysis for the concentration of copper and selenium in the feed showed that the concentrations of both were normal. This excluded diseases such as swayback which is caused by copper deficiency and white muscle disease, which caused by selenium deficiency. Both of these two last diseases are characterized by paralysis of lambs.

Keywords: brain histology, sulphur poisoning, Najdi sheep, veterinary medicine

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Authors: Milca F. Coelho, K. Bousson, Kawser Ahmed

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To accurately track an aerospace vehicle in a time-critical situation and in a highly nonlinear environment, is one of the strongest interests within the aerospace community. The tracking is achieved by estimating accurately the state of a moving target, which is composed of a set of variables that can provide a complete status of the system at a given time. One of the main ingredients for a good estimation performance is the use of efficient estimation algorithms. A well-known framework is the Kalman filtering methods, designed for prediction and estimation problems. The success of the Kalman Filter (KF) in engineering applications is mostly due to the Extended Kalman Filter (EKF), which is based on local linearization. Besides its popularity, the EKF presents several limitations. To address these limitations and as a possible solution to tracking problems, this paper proposes the use of the Ensemble Kalman Filter (EnKF). Although the EnKF is being extensively used in the context of weather forecasting and it is being recognized for producing accurate and computationally effective estimation on systems with a very high dimension, it is almost unknown by the tracking community. The EnKF was initially proposed as an attempt to improve the error covariance calculation, which on the classic Kalman Filter is difficult to implement. Also, in the EnKF method the prediction and analysis error covariances have ensemble representations. These ensembles have sizes which limit the number of degrees of freedom, in a way that the filter error covariance calculations are a lot more practical for modest ensemble sizes. In this paper, a realistic simulation of a radar tracking was performed, where the EnKF was applied and compared with the Extended Kalman Filter. The results suggested that the EnKF is a promising tool for tracking applications, offering more advantages in terms of performance.

Keywords: Kalman filter, nonlinear state estimation, optimal tracking, stochastic environment

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Authors: Fergal Fouhy, Alan O’Keeffe, Sean Costelloe, Michael Clarkson

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Background: Sarcoidosis is a systemic, non-infectious disease of unknown aetiology, characterized by non-caseating granulomatous inflammation. The lung is most often affected (90%); however, the condition can affect all organs, including the kidneys. There is limited evidence describing the incidence and characteristics of renal involvement in sarcoidosis. Serum angiotensin-converting enzyme (ACE) is a recognised biomarker used in the diagnosis and monitoring of sarcoidosis. Methods: A single-centre, retrospective cohort study of patients presenting to Cork University Hospital (CUH) in 2015 with first-time elevations of serum ACE was performed. This included an initial database review of ACE and other biochemistry results, followed by a medical chart review to confirm the presence or absence of sarcoidosis and management thereof. Acute kidney injury (AKI) was staged using the AKIN criteria, and chronic kidney disease (CKD) was staged using the KDIGO criteria. Follow-up was assessed over five years tracking serum creatinine, serum calcium, and estimated glomerular filtration rates (eGFR). Results: 119 patients were identified as having a first raised serum ACE in 2015. Seventy-nine male patients and forty female patients were identified. The mean age of patients identified was 47 years old. 11% had CKD at baseline. 18% developed an AKI at least once within the next five years. A further 6% developed CKD during this time period. 13% developed hypercalcemia. The patients within the lowest quartile of serums ACE had an incidence of sarcoidosis of 5%. None of this group developed hypercalcemia, 23% developed AKI, and 7% developed CKD. Of the patients with a serum ACE in the highest quartile, almost all had documented diagnoses of sarcoidosis with an incidence of 96%. 3% of this group developed hypercalcemia, 13% AKI and 3% developed CKD. Conclusions: There was an unexpectedly high incidence of AKI in patients who had a raised serum ACE. Not all patients with a raised serum ACE had a confirmed diagnosis of sarcoidosis. There does not appear to be a relationship between increased serum ACE levels and increased incidence of hypercalcaemia, AKI, and CKD. Ideally, all patients should have biopsy-proven sarcoidosis. This is an initial study that should be replicated with larger numbers and including multiple centres.

Keywords: sarcoidosis, acute kidney injury, chronic kidney disease, hypercalcemia

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Authors: Vasudha Devi, Arul Amuthan, K. Narayanan, Praveen KS, Venkata Rao J

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Background: Siddha Medicine is one of the Indian traditional medical systems, in which the cancer disease is mentioned as 'putrunoi' which literally means the disease of growth like termite mound. There are number of formulations available for the treatment of cancer disease. Neeradi muthu vallathymezugu (NMV) and thamira kattu chendooram (TKC) are two drugs commonly prescribed by Siddha physicians. These drugs have been clinically reported to be safe and effective when given orally. Though these formulations are in practice for centuries, no efforts have been made to standardize them and explore their anti-cancer potential systematically. Objective: To compare the cytotoxic activity of NMV and TKC with doxorubicin using cancer cell lines. Materials and methods: For this study, ethanol extract of NMV was taken, whereas TKC was used as such. In vitro cytotoxic activity was evaluated by sulphorhodamine (SRB) assay against human hepatic cancer cells (HepG2), human breast cancer cells (MCF-7) and human cervical cancer cells [KeLa]. Doxorubicin was used as the standard. The SRB assay is based on the ability of cellular proteins to bind with sulphorhodamine-B. The number of live cells in drug treated cell lines directly affects the color formation in the assay, which is estimated calorimetrically by measuring the absorbance at 540 nm to calculate the cytotoxicity (inhibitory concentration - IC50 value) of the drug. Results: The IC50values of NMV, TKC and doxorubicin against HepG2 were 3.08 µg/ml, 20.21 µg/ml and 1.21µg/ml respectively. In MCF-7, it was 11.75 µg/ml, 17.67 µg/ml and 2.8µg/ml. In HeLa, the values were 24.76 µg/ml, 73.35 µg/ml and 1.12µg/ml. Conclusions: The study proves the possible anti-cancer potential of these two formulations. Compared to TKC, NMV showed good cytotoxic effect even at low dose. Human hepatic cancer cells responded well even at very low dose, when compared to other cancer cells. Though, cytotoxic potential of these compounds was found to be less compared to doxorubicin, the isolated lead compound may have the potential to be used as an anticancer drug clinically.

Keywords: Neeradi muthu vallathymezugu (Hydnocarpus laurifolia), thamira kattu chendooram, cytotoxicity, in-vitro, Siddha Medicine

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