Comparative Study of Abiotic Stress Factors on GC-MS-Detected Phytoconstituents of Aloe greatheadii var: davyana Using Heat Map and Hierarchical Clustering Dendrogram

AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment

Background

Several pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9-68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent.

Objective

This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery.

Methods

A YouTube search for "Incontinence after cancer surgery" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results.

Results

The quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram.

Conclusions

YouTube videos on "Incontinence after Cancer Surgery" present a "High" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.

Chemotaxonomy, antibacterial and antioxidant activities of selected aromatic plants from Tabuk region-KSA

Chemotaxonomy is a valuable tool for obtaining taxonomic insights, which are most effectively employed in combination with other forms of data to establish a system of classification that closely reflects natural connections. The utilization of plant secondary metabolites possessing diverse therapeutic qualities signifies the growing exploitation of natural products in the medical discipline. The objectives of the current study encompassed the identification of phytochemicals in the extracts of nine species of medicinal plants, the examination of their chemotaxonomic properties, and the assessment of the antibacterial and antioxidant capabilities exhibited by the extracts. GC-MS technology was employed for the identification of phytochemical compounds. The study utilized ClassyFire, an automated chemical classification system that incorporates an extensive and computable classification, to categorize chemicals. The chemical classification of plants was examined by the application of principal component analysis (PCA) and cluster analysis (CA). The bactericidal properties of plants were assessed against four harmful bacterial species using the disc diffusion technique. The antioxidant properties of plant extracts were assessed employing the DPPH free radical scavenging methodology, and the half maximal effective concentration (EC50) was determined using dose response models. The calculator being referred to is the Quest Graph™ EC50 Calculator. In the plant extracts, the analysis disclosed the occurrence of 160 phytochemicals, classified into 36 phytochemical classes. The results of CA and PCA demonstrated the proximity and associations among Asteraceae species, while indicating the divergence of the two Lamiaceae species. Achillea fragrantissima and Ducrosia flabellifolia demonstrated the most diversity in phytochemical classes, while Thymus vulgaris displayed the highest level of dominance. Pulicaria undulata and T. vulgaris had the most notable antibacterial activity. D. flabellifolia and P. incisa demonstrated the highest levels of antioxidant activity. Ethanol exhibited superior antibacterial efficacy compared to other solvents. The remarkable biological activities exhibited by these plant extracts can be ascribed to the copious presence of certain chemicals, predominantly sesquiterpenoids, monoterpenoids, benzene and its derivatives, naphthalenes, fatty acyls, and phenols. The susceptibility of Gram-positive bacterial species to plant extracts was shown to be higher in comparison to Gram-negative bacterial species.

A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis

BACKGROUND

Skin cancers (SCs) arise due to the proliferation of atypical cells that have the potential to infiltrate or metastasize to different areas of the body. There is a lack of understanding regarding the country-based collaborations among authors (CBCA) and article themes on SCs. A clustering algorithm capable of categorizing CBCA and article themes on skin cancer is required. This study aimed to apply a follower-leading clustering algorithm to classify CBCA and article themes and present articles that deserve reading in recent ten years.

METHODS

Between 2013 and 2022, a total of 6526 articles focusing on SC were extracted from the Web of Science core collection. The descriptive, diagnostic, predictive, and prescriptive analytics model was employed to visualize the study results. Various visualizations, including 4-quadrant radar plots, line charts, scatter plots, network charts, chord diagrams, and impact beam plots, were utilized. The category, journal, authorship, and L-index score were employed to assess individual research achievements. Diagnostic analytics were used to cluster the CBCA and identify common article themes. Keyword weights were utilized to predict article citations, and noteworthy articles were highlighted in prescriptive analytics based on the 100 most highly cited articles on SC (T100SC).

RESULTS

The primary entities contributing to SC research include the United States, the University of California, San Francisco in US, dermatology department, and the author Andreas Stang from Germany, who possess higher category, journal, authorship, and L-index scores. The Journal of the American Academy of Dermatology has published the highest number of articles (n = 336, accounting for 5.16% of the total). From the T100SC, 7 distinct themes were identified, with melanoma being the predominant theme (92% representation). A strong correlation was observed between the number of article citations and the keyword weights (F = 81.63; P < .0001). Two articles with the highest citation counts were recommended for reading.

CONCLUSION

By applying the descriptive, diagnostic, predictive, and prescriptive analytics model, 2 noteworthy articles were identified and highlighted on an impact beam plot. These articles are considered deserving of attention and could potentially inspire further research in the field of bibliometrics, focusing on relevant topics related to melanoma.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

BACKGROUND

There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality.

OBJECTIVE

The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency.

METHODS

Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map.

RESULTS

Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics.

CONCLUSION

Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

Datura stramonium Flowers as a Potential Natural Resource of Bioactive Molecules: Identification of Anti-Inflammatory Agents and Molecular Docking Analysis

The present study investigated the antioxidant, antibacterial, antiviral and anti-inflammatory activities of different aerial parts (flowers, leaves and seeds) of Datura stramonium. The plant material was extracted with 80% methanol for about 24 h. The sensitivity to microorganisms analysis was performed by the microdilution technique. Antioxidant tests were performed by scavenging the DPPH and ABTS radicals, and by FRAP assay. Anti-inflammatory activity was evaluated through the inhibition of nitric oxide production in activated macrophage RAW 264.7 cells. Cell viability was assessed with an MTT assay. Results show that the flower extract revealed a powerful antimicrobial capacity against Gram-positive bacteria and strong antioxidant and anti-inflammatory activities. No significant cytotoxicity to activated macrophages was recorded. High resolution electrospray ionization mass spectrometry and nuclear magnetic resonance analysis identified two molecules with important anti-inflammatory effects: 12α-hydroxydaturametelin B and daturametelin B. Molecular docking analysis with both pro-inflammatory agents tumor necrosis factor alpha and interleukin-6 revealed that both compounds showed good binding features with the selected target proteins. Our results suggest that D. stramonium flower is a promising source of compounds with potential antioxidant, antibacterial, and anti-inflammatory activities. Isolated withanolide steroidal lactones from D. stramonium flower extract with promising anti-inflammatory activity have therapeutic potential against inflammatory disorders.

Identification of Daphne genkwa and Its Vinegar-Processed Products by Ultraperformance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry and Chemometrics

Crude herbs of Daphne genkwa (CHDG) are often used in traditional Chinese medicine to treat scabies baldness, carbuncles, and chilblain owing to their significant purgation and curative effects. The most common technique for processing DG involves the use of vinegar to reduce the toxicity of CHDG and enhance its clinical efficacy. Vinegar-processed DG (VPDG) is used as an internal medicine to treat chest and abdominal water accumulation, phlegm accumulation, asthma, and constipation, among other diseases. In this study, the changes in the chemical composition of CHDG after vinegar processing and the inner components of the changed curative effects were elucidated using optimized ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Untargeted metabolomics, based on multivariate statistical analyses, was also used to profile differences between CHDG and VPDG. Eight marker compounds were identified using orthogonal partial least-squares discrimination analysis, which indicated significant differences between CHDG and VPDG. The concentrations of apigenin-7-O-β-d-methylglucuronate and hydroxygenkwanin were considerably higher in VPDG than those in CHDG, whereas the amounts of caffeic acid, quercetin, tiliroside, naringenin, genkwanines O, and orthobenzoate 2 were significantly lower. The obtained results can indicate the transformation mechanisms of certain changed compounds. To the best of our knowledge, this study is the first to employ mass spectrometry to detect the marker components of CHDG and VPDG.