Search results for: haemorrhoids

Authors: Priyanka Sharma, Tarkewshwar Dubey, Hemalatha Siva

Abstract:

Hemorrhoids are one of the most common anorectal diseases around the world. Severalfactors are involved in causing hemorrhoids including irregularbowel function (constipation, diarrhea), exercise, gravity, low fiberdiet, pregnancy, obesity, high abdominal pressure, prolongedsitting, genetic factors, and aging. Pain, bleeding, itching,swelling and anal discharge are the symptoms of the disease. Due to limitedmodern pharmacotherapeutic options available for treatment, theherbal medicines remain the choice of therapy. Blumea lacera (Burm f.) DC. belonging to the Asteraceae family is a common plain land weed of Bangladesh. Traditionally it has been used for treatment of hemorrhoids.Considering the above fact, present study was aimed to validate the ethnomedicinal use of B. lacera leaves on experimental hemorrhoids in rats. The anti-hemorrhoid activity was performed by using croton oil induced rat models. The parameters studied were assessment of TNF-α and IL-6, Evans blue exudation, macroscopic severity score, recto-anal coefficient, histomorphological scores. Also, in vivo antioxidant parameters and histopathological studies were also performed. All paramaters exhibited significant anti-hemorrhoid activity. Moreover ethanolic extract of B. lacera (EBL) leaves 400mg/kg showed ameliorative effect oncroton oil induced hemorrhoids.In conclusion, EBL exhibitedbeneficial effect on croton oil- induced hemorrhoids and validates its ethnomedicinal use in treatment of piles.

Keywords: haemorrhoids, IL-6, piles, TNF-α

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Authors: Abubakar Ahmed, Zainab Mohammed, Hadiza D. Nuhu, Hamisu Ibrahim

Abstract:

Introduction: The plant Tacazzea apiculata Oliv (Periplocaceae) is widely distributed in tropical West Africa. It is claimed to have multiple uses in traditional medicine among which are its use to treat hemorrhoids, inflammations and cancers. Methods: Ethno-botanical survey through interview and using show-and-tell method of data collection were conducted among Hausa and Fulani tribes of northern Nigeria with the view to document useful information on the numerous claims by the local people on the plant. Results: The results revealed that the plant T. apiculata has relative popularity among the herbalist (38.2 %), nomads (14.8 %) and fishermen (16.0%). The most important uses of the plant in traditional medicine are inflammation (Fedelity level: 25.7 %) and Haemorrhoids (Fedelity level: 17.1 %) Conclusion: These results suggest the relevance of T. apiculata in traditional medicine and as a good candidate for drug Development.

Keywords: ethno-botany, periplocaceae, Tacazzea apiculata, traditional medicine

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Authors: Khadijat Ayanpeju Abdulsalam, Bolanle Mary Olawoye, Paul Babatunde Ayoola

Abstract:

The potential of Baobab leaves is understudied and not yet fully documented. The purpose of this work is to highlight the important nutritional value and practical qualities of baobab leaves. In this research, proximate analysis was studied to determine the macronutrient quantitative analysis in baobab leaves. Studies were also conducted on other characteristics, such as moisture content, which is significant to the food business since it affects food quality, preservation, and resistance to deterioration. Dietary fiber, which was also studied, has important health benefits, such as lowering blood cholesterol levels by lowering low-density lipoprotein or "bad" cholesterol. It functions as an anti-obesity and anti-diabetic agent, lowering the likelihood of haemorrhoids developing. Additionally, increasing face bulk and short-chain fatty acid synthesis improves gastrointestinal health and overall wellness. Baobab leaves had a moisture content of 6.4%, fat of 16.1%, ash of 3.2%, protein of 18.7%, carbohydrate 57.2% and crude fiber of 4.1%. The minerals determined in the sample of baobab leaves are Ca, Fe, Mg, K, Na, P, and Zn with Potassium (347.6±0.70) as the most abundant mineral while Zn (9.31±0.60) is the least abundant. The functional properties studied include pH, gelation temperature, bulk density, water absorption capacity, oil absorption capacity, foaming property, emulsifying property, and stability and swelling capacity, which are 8.72, 29, 0.39, 138, 98.20, 0.80, 72.80, and 73.50 respectively. The Fourier Transform InfraRed absorption spectra show bands like C=O, C-Cl and N-H. Baobab leaves are edible, nutritious, and non-toxic, as the mineral contents are within the required range.

Keywords: dietary fibre, proximate analysis, macronutrients, minerals, baobab leaves, frequency range

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

Abstract:

Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

Procedia PDF Downloads 85