Mediterranean diet diminishes the effects of Crohn's disease and improves its parameters: A systematic review.

BACKGROUND: The pathogenesis and clinical course of Crohn's disease (CD) is influenced by diet. Mediterranean Diet (MD) helps Crohn's patients through many mechanisms. AIMS: This study aimed to evaluate the effect of the MD on CD patients and to evaluate such effect on body parameters. METHODS: PubMed, Science Direct, Web of Science, MEDLINE and Cochrane central library were searched for MD and CD from 2010 to 2020. Included studies met the following criteria: (1) male and female adults (18-75 years) with a confirmed diagnosis of CD; (2) MD as an intervention; (3) original interventional Trial, Cross-Sectional Analysis, or Prospective Cohort Studies. RESULTS: Five studies were included, involving 83,564 participants. A small number of patients with CD fulfilled the P-MDS criteria, the overall scores were low, 4.7 and 4.5 for females and males respectively. Patients with an inactive disease whose adherence to MD was greater, the MD score was negatively correlated with disease activity (p <0.001) and positively with IBDQ (p = 0.008). Twenty-seven percent had a prevalence of impaired adherence to a MD (mMED score = 0-2), giving such a population a risk attributed to 12% for the later CD. Seventy-point reduction in CDAI + decreased fecal CRP / calprotectin, calprotectin <250 mcg/gm or >50% decrease from baseline and hsCRP < 5 mg/L or >50% from baseline. CONCLUSIONS: MD showed anti-inflammatory properties. Adherence to MD was associated with improvement in CD patients and negatively correlated with the disease activity, in addition to a lower risk of developing CD later in life.

Hydroethanolic Extract of Urtica dioica L. (Stinging Nettle) Leaves as Disaccharidase Inhibitor and Glucose Transport in Caco-2 Hinderer.

Herbal treatment for diabetes mellitus is widely used. The pharmacological activity is thought to be due to the phenolic compounds found in the plant leaves. The present study aims to investigate the phytochemical composition of Urtica dioica (UD) hydroethanolic extract and to screen its antidiabetic activity by disaccharidase hindering and glucose transport in Caco-2 cells. The results have shown that a total of 13 phenolic compounds in this work, viz. caffeic and coumaric acid esters (1, 2, 4-7, 10), ferulic derivative (3), and flavonoid glycosides (8, 9, 11-13), were identified using HPLC-DAD-ESI/MS2. The most abundant phenolic compounds were 8 (rutin) followed by 6 (caffeoylquinic acid III). Less predominant compounds were 4 (caffeoylquinic acid II) and 11 (kaempferol-O-rutinoside). The UD hydroethanolic extract showed 56%, 45%, and 28% (1.0 mg/mL) inhibition level for maltase, sucrase, and lactase, respectively. On the other hand, glucose transport was 1.48 times less at 1.0 mg/mL UD extract compared with the control containing no UD extract. The results confirmed that U. dioica is a potential antidiabetic herb having both anti-disaccharidase and glucose transport inhibitory properties, which explained the use of UD in traditional medicine.

A Genetic Folding Strategy Based Support Vector Machine to Optimize Lung Cancer Classification.

Cancer is defined as an abnormal growth of human cells classified into benign and malignant. The site makes further classification of cancers of initiation and genomic underpinnings. Lung cancer displays extreme heterogeneity, making genomic classification vital for future targeted therapies. Especially considering lung cancers account for 1.76 million deaths worldwide annually. However, tumors do not always correlate to cancer as they can be benign, severely dysplastic (pre-cancerous), or malignant (cancerous). Lung cancer presents with ambiguous symptoms, thus is difficult to diagnose and is detected later compared to other cancers. Diagnosis relies heavily on radiology and invasive procedures. Different models developed employing Artificial Intelligence (AI), and Machine Learning (ML) have been used to classify various cancers. In this study, the authors propose a Genetic Folding Strategy (GFS) based model to predict lung cancer from a lung cancer dataset. We developed and implemented GF to improve Support Vector Machines (SVM) classification kernel functions and used it to classify lung cancer. We developed and implemented GF to improve SVM classification kernel functions and used it to classify lung cancer. Classification performance evaluations and comparisons between the authors' GFS model and three SVM kernels, linear, polynomial and radial basis function, were conducted thoroughly on real lung cancer datasets. While using GFS in classifying lung cancer, the authors obtained an accuracy of 96.2%. This is the highest current accuracy compared to other kernels.

An enhanced Genetic Folding algorithm for prostate and breast cancer detection.

Cancer's genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model's area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.