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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.

Breast milk is the optimal nutrition for infants, yet lactation insufficiency remains a common cause of early breastfeeding cessation. Moringa oleifera has been traditionally used as a galactagogue due to its rich micronutrient and phytosterol content. This systematic review assessed the effects of Moringa leaf supplementation on prolactin levels and breast milk volume in postpartum mothers with lactation insufficiency. A systematic search following PRISMA guidelines, was conducted for randomized controlled trials involving healthy postpartum women supplemented with Moringa oleifera. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. Eight studies met the inclusion criteria, with intervention durations ranged from 3 to 10 days. Moringa supplementation increased significantly breast milk volume by up to 400 mL/day compared to controls. Serum prolactin levels also rose significantly with a mean increase of 231.72 ng/mL Most studies exhibited low to moderate risk of bias, though one study exhibited high risk due to lack of binding and subjective outcome measurement. Moringa oleifera leaf supplementation appears to enhance lactation by increasing milk volume and prolactin levels in postpartum mothers. However, further longer-term studies are needed to establish optimal dosing, sustained effectiveness, and safety.

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.

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/MS[sup.2]. 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.