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This work presents the results of using tree-based models, including Gradient Boosting, Extra Trees, and Random Forest, to model the solubility of hyoscine drug and solvent density based on pressure and temperature as inputs. The models were trained on a dataset of hyoscine drug with known solubility and density values, optimized with WCA algorithm, and their accuracy was evaluated using R 2 , MSE, MAPE, and Max Error metrics. The results showed that Gradient Boosting and Extra Trees models had high accuracy, with R 2 values above 0.96 and low MAPE and Max Error values for both solubility and density output. The Random Forest model was less accurate than the other two models. These findings demonstrate the effectiveness of tree-based models for predicting the solubility and density of chemical compounds and have potential applications in determination of drug solubility prior to process design by correlation of solubility and density to input parameters including pressure and temperature.

Despite recent advances in the diagnosis and treatment of breast cancer (BC), it remains a global health issue affecting millions of women annually. Poor prognosis in BC patients is often linked to drug resistance as well as the lack of effective therapeutic options for metastatic and triple-negative BC. In response to these unmet needs, extensive research efforts have been devoted to exploring the anti-BC potentials of natural products owing to their multi-target mechanisms of action and good safety profiles. Various medicinal plant extracts/essential oils and natural bioactive compounds have demonstrated anti-cancer activities in preclinical BC models. Despite the promising preclinical results, however, the clinical translation of natural products has often been hindered by their poor stability, aqueous solubility and bioavailability. There have been attempts to overcome these limitations, particularly via the use of nano-based drug delivery systems (NDDSs). This review highlights the tumour targeting mechanisms of NDDSs, the advantages and disadvantages of the major classes of NDDSs and their current clinical status in BC treatment. Besides, it also discusses the proposed anti-BC mechanisms and nanoformulations of nine medicinal plants' extracts/essential oils and nine natural bioactive compounds; selected via the screening of various scientific databases, including PubMed, Scopus and Google Scholar, based on the following keywords: \"Natural Product AND Nanoparticle AND Breast Cancer\". Overall, these nanoformulations exhibit improved anti-cancer efficacy against preclinical BC models, with some demonstrating biocompatibility with normal cell lines and mouse models. Further clinical studies are, however, warranted to ascertain their efficacy and biocompatibility in humans. Keywords: breast cancer, drug delivery, molecular mechanisms, nanoparticles, natural products, phytomedicine

The skin is the largest organ in the human body, composed of the epidermis and the dermis. It provides protection and acts as a barrier against external menaces like allergens, chemicals, systemic toxicity, and infectious organisms. Skin disorders like cancer, dermatitis, psoriasis, wounds, skin aging, acne, and skin infection occur frequently and can impact human life. According to a growing body of evidence, several studies have reported that natural products have the potential for treating skin disorders. Building on this information, this review provides brief information about the action of the most important in vitro and in vivo research on the use of ten selected natural products in inflammatory, neoplastic, and infectious skin disorders and their mechanisms that have been reported to date. The related studies and articles were searched from several databases, including PubMed, Google, Google Scholar, and ScienceDirect. Ten natural products that have been reported widely on skin disorders were reviewed in this study, with most showing anti-inflammatory, antioxidant, anti-microbial, and anti-cancer effects as the main therapeutic actions. Overall, most of the natural products reported in this review can reduce and suppress inflammatory markers, like tumor necrosis factor-alpha (TNF-α), scavenge reactive oxygen species (ROS), induce cancer cell death through apoptosis, and prevent bacteria, fungal, and virus infections indicating their potentials. This review also highlighted the challenges and opportunities of natural products in transdermal/topical delivery systems and their safety considerations for skin disorders. Our findings indicated that natural products might be a low-cost, well-tolerated, and safe treatment for skin diseases. However, a larger number of clinical trials are required to validate these findings. Natural products in combination with modern drugs, as well as the development of novel delivery mechanisms, represent a very promising area for future drug discovery of these natural leads against skin disorders.

The detection, segmentation, and differentiation of benign and malignant nuclei from the histopathology images is a challenging task for the early diagnosis of breast cancer. Misinterpretation of True Negative (TN) and False Positive (FP) can generate incorrect results. The proposed Cognitive Computing Process (CCP) detects and segments the nuclei using Deep U-Net with Spatial Attention Mechanisms (SAM) and microns-per-pixel measurements to accurately locate and assess nuclei density. To separate the nuclei of benign and malignant, the patches are introduced to leverage the model’s learning process. The proposed Smart Neural Network (SNN) models contain Smart Convolutional Neural Network (SCNN) and Deep Convolutional Neural Network (DCNN) to reduce incorrect results. Proposed CCP and SNN were evaluated using the BreakHis dataset, which contains 5547 images of benign and malignant samples at various magnifications (40×, 100×, 200×, 400×). These images were processed into patches, totaling 11,642, 9282, 9102, and 9678 patches, each 224 × 224 pixels. The CCP model outperformed state-of-the-art models UNet, Residual UNet (ResUNet), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) with a Dice coefficient of 99.90%, an F1-score of 99.04%, a precision of 99.80%, and a recall of 99.76%. The learning process began with a learning rate of 0.01 and a decay rate of 0.8, and the SCNN achieved false negative and false positive rates of 0.04 and 0.05 for low-density nuclei at 400× and 40× magnification, respectively. In contrast, the Deep Convolutional Neural Network (DCNN) recorded rates of 0.02 and 0.01. For high-density patches, the SCNN model FN and FP rates of 0.0 and 0.08, while the DCNN reported 0.09 and 0.0. The proposed learning process with Smart Neural Networks (SNN) achieved high precision (77–99%), recall (75–99%), F1-score (75–99%), and an AUC of 86–100%. The combination of CCP and SNN improved accuracy over existing CNN models like ResNet50, VGG19, DenseNet109, DenseNet201, and VGG16. An ablation study showed a p-value of 0.00003 based on the AUC, highlighting the model’s potential to enhance automated breast cancer diagnosis and support clinical decision-making.

Parkinson’s disease (PD) and Alzheimer’s disease (AD) are neurodegenerative disorders that have emerged as among the serious health problems of the 21st century. The medications currently available to treat AD and PD have limited efficacy and are associated with side effects. Natural products are one of the most vital and conservative sources of medicines for treating neurological problems. Karanjin is a furanoflavonoid, isolated mainly from Pongamia pinnata with several medicinal plants, and has been reported for numerous health benefits. However, the effect of karanjin on AD and PD has not yet been systematically investigated. To evaluate the neuroprotective effect of karanjin, extensive in silico studies starting with molecular docking against five putative targets for AD and four targets for PD were conducted. The findings were compared with three standard drugs using Auto Dock 4.1 and Molegro Virtual Docker software. Additionally, the physiochemical properties (Lipinski rule of five), drug-likeness and parameters including absorption, distribution, metabolism, elimination and toxicity (ADMET) profiles of karanjin were also studied. The molecular dynamics (MD) simulations were performed with two selective karanjin docking complexes to analyze the dynamic behaviors and binding free energy at 100 ns time scale. In addition, frontier molecular orbitals (FMOs) and density-functional theory (DFT) were also investigated from computational quantum mechanism perspectives using the Avogadro-ORCA 1.2.0 platform. Karanjin complies with all five of Lipinski’s drug-likeness rules with suitable ADMET profiles for therapeutic use. The docking scores (kcal/mol) showed comparatively higher potency against AD and PD associated targets than currently used standard drugs. Overall, the potential binding affinity from molecular docking, static thermodynamics feature from MD-simulation and other multiparametric drug-ability profiles suggest that karanjin could be considered as a suitable therapeutic lead for AD and PD treatment. Furthermore, the present results were strongly correlated with the earlier study on karanjin in an Alzheimer’s animal model. However, necessary in vivo studies, clinical trials, bioavailability, permeability and safe dose administration, etc. must be required to use karanjin as a potential drug against AD and PD treatment, where the in silico results are more helpful to accelerate the drug development.

Curcuma longa Linn. ( C. longa ), popularly known as turmeric, belongs to the Zingiberaceae family and has a long historical background of having healing properties against many diseases. In Unani and Ayurveda medicine, C. longa has been used for liver obstruction and jaundice, and has been applied externally for ulcers and inflammation. Additionally, it is employed in several other ailments such as cough, cold, dental issues, indigestion, skin infections, blood purification, asthma, piles, bronchitis, tumor, wounds, and hepatic disorders, and is used as an antiseptic. Curcumin, a major constituent of C. longa , is well known for its therapeutic potential in numerous disorders. However, there is a lack of literature on the therapeutic potential of C. longa in contrast to curcumin. Hence, the present review aimed to provide in-depth information by highlighting knowledge gaps in traditional and scientific evidence about C. longa in relation to curcumin. The relationship to one another in terms of biological action includes their antioxidant, anti-inflammatory, neuroprotective, anticancer, hepatoprotective, cardioprotective, immunomodulatory, antifertility, antimicrobial, antiallergic, antidermatophytic, and antidepressant properties. Furthermore, in-depth discussion of C. longa on its taxonomic categorization, traditional uses, botanical description, phytochemical ingredients, pharmacology, toxicity, and safety aspects in relation to its major compound curcumin is needed to explore the trends and perspectives for future research. Considering all of the promising evidence to date, there is still a lack of supportive evidence especially from clinical trials on the adjunct use of C. longa and curcumin. This prompts further preclinical and clinical investigations on curcumin.

Cardiovascular diseases (CVDs) are one of the leading causes of morbidity and mortality in both developed and developing countries, affecting millions of individuals each year. Despite the fact that successful therapeutic drugs for the management and treatment of CVDs are available on the market, nutritional fruits appear to offer the greatest benefits to the heart and have been proved to alleviate CVDs. Experimental studies have also demonstrated that nutritional fruits have potential protective effects against CVDs. The aim of the review was to provide a comprehensive summary of scientific evidence on the effect of 10 of the most commonly available nutritional fruits reported against CVDs and describe the associated mechanisms of action. Relevant literatures were searched and collected from several scientific databases including PubMed, ScienceDirect, Google Scholar and Scopus. In the context of CVDs, 10 commonly consumed nutritious fruits including apple, avocado, grapes, mango, orange, kiwi, pomegranate, papaya, pineapple, and watermelon were analysed and addressed. The cardioprotective mechanisms of the 10 nutritional fruits were also compiled and highlighted. Overall, the present review found that the nutritious fruits and their constituents have significant benefits for the management and treatment of CVDs such as myocardial infarction, hypertension, peripheral artery disease, coronary artery disease, cardiomyopathies, dyslipidemias, ischemic stroke, aortic aneurysm, atherosclerosis, cardiac hypertrophy and heart failure, diabetic cardiovascular complications, drug-induced cardiotoxicity and cardiomyopathy. Among the 10 nutritional fruits, pomegranate and grapes have been well explored, and the mechanisms of action are well documented against CVDs. All of the nutritional fruits mentioned are edible and readily accessible on the market. Consuming these fruits, which may contain varying amounts of active constituents depending on the food source and season, the development of nutritious fruits-based health supplements would be more realistic for consistent CVD protection.

In recent years, hormone therapy has been shown to be a remarkable treatment option for cancer. Hormone treatment for gynecological cancers involves the use of medications that reduce the level of hormones or inhibit their biological activity, thereby stopping or slowing cancer growth. Hormone treatment works by preventing hormones from causing cancer cells to multiply. Aromatase inhibitors, anti-estrogens, progestin, estrogen receptor (ER) antagonists, GnRH agonists, and progestogen are effectively used as therapeutics for vulvar cancer, cervical cancer, vaginal cancer, uterine cancer, and ovarian cancer. Hormone replacement therapy has a high success rate. In particular, progestogen and estrogen replacement are associated with a decreased incidence of gynecological cancers in women infected with human papillomavirus (HPV). The activation of estrogen via the transcriptional functionality of ERα may either be promoted or decreased by gene products of HPV. Hormonal treatment is frequently administered to patients with hormone-sensitive recurring or metastatic gynecologic malignancies, although response rates and therapeutic outcomes are inconsistent. Therefore, this review outlines the use of hormonal therapy for gynecological cancers and identifies the current knowledge gaps.

Purpose of Review Comparative research on sex and/or gender differences in occupational hazard exposures is necessary for effective work injury and illness prevention strategies. This scoping review summarizes the peer-reviewed literature from 2009 to 2019 on exposure differences to occupational hazards between men and women, across occupations, and within the same occupation. Recent Findings Fifty-eight studies retrieved from eight databases met our inclusion criteria. Of these, 30 studies were found on physical hazards, 38 studies on psychological/psychosocial hazards, 5 studies on biological hazards, and 17 studies on chemical hazards. The majority of studies reported that men were exposed to noise, vibration, medical radiation, physically demanding work, solar radiation, falls, biomechanical risks, chemical hazards, and blood contamination; while women were exposed to wet work, bullying and discrimination, work stress, and biological agents. Within the same occupations, men were more likely to be exposed to physical hazards, with the exception of women in health care occupations and exposure to prolonged standing. Women compared to men in the same occupations were more likely to experience harassment, while men compared to women in the same occupations reported higher work stress. Men reported more exposure to hazardous chemicals in the same occupations as women. Summary The review suggests that men and women have different exposures to occupational hazards and that these differences are not solely due to a gendered distribution of the labor force by occupation. Findings may inform prevention efforts seeking to reduce gender inequalities in occupational health. Future research is needed to explain the reasons for sex/gender inequality differences in exposures within the same occupation.

Controlled release of a desired drug from porous polymeric biomaterials was analyzed via computational method. The method is based on simulation of mass transfer and utilization of artificial intelligence (AI). This study explores the efficacy of three regression models, i.e., Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Gradient Boosting (GB) in determining the concentration of a chemical substance ( C ) based on coordinates ( r , z ). Leveraging Firefly Optimization (FFA) for hyperparameter optimization, the models are fine-tuned to maximize their predictive performance. The findings unveil notable disparities in the performance metrics of the models, with GB showcasing the most impressive R 2 score of 0.9977, indicative of a remarkable alignment with the data. GPR closely trails with an R 2 score of 0.88754, while KRR falls short with an R 2 score of 0.76134. Additionally, GB manifests the most modest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) among the trio of models, further cementing its supremacy in predictive precision. These outcomes accentuate the significance of judiciously selecting regression methodologies and optimization approaches for adeptly modeling intricate spatial datasets.