Dynamic interplay of nuclear receptors in tumor cell plasticity and drug resistance: Shifting gears in malignant transformations and applications in cancer therapeutics.

Recent advances have brought forth the complex interplay between tumor cell plasticity and its consequential impact on drug resistance and tumor recurrence, both of which are critical determinants of neoplastic progression and therapeutic efficacy. Various forms of tumor cell plasticity, instrumental in facilitating neoplastic cells to develop drug resistance, include epithelial-mesenchymal transition (EMT) alternatively termed epithelial-mesenchymal plasticity, the acquisition of cancer stem cell (CSC) attributes, and transdifferentiation into diverse cell lineages. Nuclear receptors (NRs) are a superfamily of transcription factors (TFs) that play an essential role in regulating a multitude of cellular processes, including cell proliferation, differentiation, and apoptosis. NRs have been implicated to play a critical role in modulating gene expression associated with tumor cell plasticity and drug resistance. This review aims to provide a comprehensive overview of the current understanding of how NRs regulate these key aspects of cancer biology. We discuss the diverse mechanisms through which NRs influence tumor cell plasticity, including EMT, stemness, and metastasis. Further, we explore the intricate relationship between NRs and drug resistance, highlighting the impact of NR signaling on chemotherapy, radiotherapy and targeted therapies. We also discuss the emerging therapeutic strategies targeting NRs to overcome tumor cell plasticity and drug resistance. This review also provides valuable insights into the current clinical trials that involve agonists or antagonists of NRs modulating various aspects of tumor cell plasticity, thereby delineating the potential of NRs as therapeutic targets for improved cancer treatment outcomes.

Lipid metabolism and its implications in tumor cell plasticity and drug resistance: what we learned thus far?

Metabolic reprogramming, a hallmark of cancer, allows cancer cells to adapt to their specific energy needs. The Warburg effect benefits cancer cells in both hypoxic and normoxic conditions and is a well-studied reprogramming of metabolism in cancer. Interestingly, the alteration of other metabolic pathways, especially lipid metabolism has also grabbed the attention of scientists worldwide. Lipids, primarily consisting of fatty acids, phospholipids and cholesterol, play essential roles as structural component of cell membrane, signalling molecule and energy reserves. This reprogramming primarily involves aberrations in the uptake, synthesis and breakdown of lipids, thereby contributing to the survival, proliferation, invasion, migration and metastasis of cancer cells. The development of resistance to the existing treatment modalities poses a major challenge in the field of cancer therapy. Also, the plasticity of tumor cells was reported to be a contributing factor for the development of resistance. A number of studies implicated that dysregulated lipid metabolism contributes to tumor cell plasticity and associated drug resistance. Therefore, it is important to understand the intricate reprogramming of lipid metabolism in cancer cells. In this review, we mainly focused on the implication of disturbed lipid metabolic events on inducing tumor cell plasticity-mediated drug resistance. In addition, we also discussed the concept of lipid peroxidation and its crucial role in phenotypic switching and resistance to ferroptosis in cancer cells. Elucidating the relationship between lipid metabolism, tumor cell plasticity and emergence of resistance will open new opportunities to develop innovative strategies and combinatorial approaches for the treatment of cancer.

Exploring the nexus of nuclear receptors in hematological malignancies.

Hematological malignancies (HM) represent a subset of neoplasms affecting the blood, bone marrow, and lymphatic systems, categorized primarily into leukemia, lymphoma, and multiple myeloma. Their prognosis varies considerably, with a frequent risk of relapse despite ongoing treatments. While contemporary therapeutic strategies have extended overall patient survival, they do not offer cures for advanced stages and often lead to challenges such as acquisition of drug resistance, recurrence, and severe side effects. The need for innovative therapeutic targets is vital to elevate both survival rates and patients' quality of life. Recent research has pivoted towards nuclear receptors (NRs) due to their role in modulating tumor cell characteristics including uncontrolled proliferation, differentiation, apoptosis evasion, invasion and migration. Existing evidence emphasizes NRs' critical role in HM. The regulation of NR expression through agonists, antagonists, or selective modulators, contingent upon their levels, offers promising clinical implications in HM management. Moreover, several anticancer agents targeting NRs have been approved by the Food and Drug Administration (FDA). This review highlights the integral function of NRs in HM's pathophysiology and the potential benefits of therapeutically targeting these receptors, suggesting a prospective avenue for more efficient therapeutic interventions against HM.

Natural compounds targeting nuclear receptors for effective cancer therapy.

Human nuclear receptors (NRs) are a family of forty-eight transcription factors that modulate gene expression both spatially and temporally. Numerous biochemical, physiological, and pathological processes including cell survival, proliferation, differentiation, metabolism, immune modulation, development, reproduction, and aging are extensively orchestrated by different NRs. The involvement of dysregulated NRs and NR-mediated signaling pathways in driving cancer cell hallmarks has been thoroughly investigated. Targeting NRs has been one of the major focuses of drug development strategies for cancer interventions. Interestingly, rapid progress in molecular biology and drug screening reveals that the naturally occurring compounds are promising modern oncology drugs which are free of potentially inevitable repercussions that are associated with synthetic compounds. Therefore, the purpose of this review is to draw our attention to the potential therapeutic effects of various classes of natural compounds that target NRs such as phytochemicals, dietary components, venom constituents, royal jelly-derived compounds, and microbial derivatives in the establishment of novel and safe medications for cancer treatment. This review also emphasizes molecular mechanisms and signaling pathways that are leveraged to promote the anti-cancer effects of these natural compounds. We have also critically reviewed and assessed the advantages and limitations of current preclinical and clinical studies on this subject for cancer prophylaxis. This might subsequently pave the way for new paradigms in the discovery of drugs that target specific cancer types.

Identifying novel aryl hydrocarbon receptor (AhR) modulators from clinically approved drugs: In silico screening and In vitro validation.

The aryl hydrocarbon receptor (AhR) functions as a vital ligand-activated transcription factor, governing both physiological and pathophysiological processes. Notably, it responds to xenobiotics, leading to a diverse array of outcomes. In the context of drug repurposing, we present here a combined approach of utilizing structure-based virtual screening and molecular dynamics simulations. This approach aims to identify potential AhR modulators from Drugbank repository of clinically approved drugs. By focusing on the AhR PAS-B binding pocket, our screening protocol included binding affinities calculations, complex stability, and interactions within the binding site as a filtering method. Comprehensive evaluations of all DrugBank small molecule database revealed ten promising hits. This included flibanserin, butoconazole, luliconazole, naftifine, triclabendazole, rosiglitazone, empagliflozin, benperidol, nebivolol, and zucapsaicin. Each exhibiting diverse binding behaviors and remarkably very low binding free energy. Experimental studies further illuminated their modulation of AhR signaling, and showing that they are consistently reducing AhR activity, except for luliconazole, which intriguingly enhances the AhR activity. This work demonstrates the possibility of using computational modelling as a quick screening tool to predict new AhR modulators from extensive drug libraries. Importantly, these findings hold immense therapeutic potential for addressing AhR-associated disorders. Consequently, it offers compelling prospects for innovative interventions through drug repurposing.

Natural compounds targeting YAP/TAZ axis in cancer: Current state of art and challenges.

Cancer has become a burgeoning global healthcare concern marked by its exponential growth and significant economic ramifications. Though advancements in the treatment modalities have increased the overall survival and quality of life, there are no definite treatments for the advanced stages of this malady. Hence, understanding the diseases etiologies and the underlying molecular complexities, will usher in the development of innovative therapeutics. Recently, YAP/TAZ transcriptional regulation has been of immense interest due to their role in development, tissue homeostasis and oncogenic transformations. YAP/TAZ axis functions as coactivators within the Hippo signaling cascade, exerting pivotal influence on processes such as proliferation, regeneration, development, and tissue renewal. In cancer, YAP is overexpressed in multiple tumor types and is associated with cancer stem cell attributes, chemoresistance, and metastasis. Activation of YAP/TAZ mirrors the cellular "social" behavior, encompassing factors such as cell adhesion and the mechanical signals transmitted to the cell from tissue structure and the surrounding extracellular matrix. Therefore, it presents a significant vulnerability in the clogs of tumors that could provide a wide window of therapeutic effectiveness. Natural compounds have been utilized extensively as successful interventions in the management of diverse chronic illnesses, including cancer. Owing to their capacity to influence multiple genes and pathways, natural compounds exhibit significant potential either as adjuvant therapy or in combination with conventional treatment options. In this review, we delineate the signaling nexus of YAP/TAZ axis, and present natural compounds as an alternate strategy to target cancer.

Pediatric en bloc kidney transplant from donors <15 kg: An excellent approach to expand the pediatric deceased donor pool.

INTRODUCTION: Most kidneys from small pediatric donors are transplanted to adult recipients because of the perceived risk of surgical complications and graft thrombosis. In this study, we aim to demonstrate our favorable outcomes in transplanting pediatric kidneys from donors <15 k into pediatric recipients. METHODS: This study retrospectively analyzes the outcomes of seven pediatric recipients of en block kidney transplants from pediatric donors weighing <15 kg performed at King Fahad Specialist Hospital-Dammam from December 2014 to January 2018. Baseline characteristics of donors and recipients were collected. The incidences of surgical complication, immediate, and intermediate graft function were the primary outcomes. RESULTS: The study included seven recipients monitored for a mean duration of 6.86 ± 1.35. Donors' and recipients' mean weights were 7.4 ± 3.2 kg and 20.7 ± 9.2 kg, respectively. Ureteric stricture occurred in one patient. There was a substantial improvement of 1-year estimated glomerular filtration rate (eGFR) compared to the 1-week mark (106.7 ± 26.38 mL/min. 1.73 m2 vs. 63.7 ± 22.92 mL/min/1.73 m2, p = .0069). The observed improvement in renal function persisted at the 5-year mark and during the last follow-up, with eGFR of 70.3 ± 40.7 mL/min/1.73 m2, and 79.8 ± 30.8 mL/min/1.73 m2, respectively. There was also increase of 27.9% in the size of the en bloc kidney observed at the 6 months. CONCLUSION: In a specialized transplant center with highly skilled surgeons, the utilization of en bloc kidney transplant from donors weighing less than 15 kg is an effective strategy for expanding the donor pool and ensuring favorable graft outcomes.

Exploring the therapeutic potential of layered double hydroxides and transition metal dichalcogenides through the convergence of rheumatology and nanotechnology using generative adversarial network.

Two-dimensional Layered double hydroxides (LDHs) are highly used in the biomedical domain due to their biocompatibility, biodegradability, controlled drug loading and release capabilities, and improved cellular permeability. The interaction of LDHs with biological systems could facilitate targeted drug delivery and make them an attractive option for various biomedical applications. Rheumatoid Arthritis (RA) requires targeted drug delivery for optimum therapeutic outcomes. In this study, stacked double hydroxide nanocomposites with dextran sulphate modification (LDH-DS) were developed while exhibiting both targeting and pH-sensitivity for rheumatological conditions. This research examines the loading, release kinetics, and efficiency of the therapeutics of interest in the LDH-based drug delivery system. The mean size of LDH-DS particles (300.1 ± 8.12 nm) is -12.11 ± 0.4 mV. The encapsulation efficiency was 48.52%, and the loading efficacy was 16.81%. In vitro release tests indicate that the drug's discharge is modified more rapidly in PBS at pH 5.4 compared to pH 5.6, which later reached 7.3, showing the case sensitivity to pH. A generative adversarial network (GAN) is used to analyze the drug delivery system in rheumatology. The GAN model achieved high accuracy and classification rates of 99.3% and 99.0%, respectively, and a validity of 99.5%. The second and third administrations resulted in a significant change with p-values of 0.001 and 0.05, respectively. This investigation unequivocally demonstrated that LDH functions as a biocompatible drug delivery matrix, significantly improving delivery effectiveness.

Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery.

Nanotechnology has emerged as a promising frontier in revolutionizing the early diagnosis and surgical management of gastric cancers. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high surgical and pharmacological therapy recurrence rates. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for detecting and treating cancer. Considering the limitations of GIC MRI and endoscopy and the complexity of gastric surgery, the early diagnosis and prompt treatment of gastric illnesses by nanotechnology has been a promising development. Nanoparticles directly target tumor cells, allowing their detection and removal. It also can be engineered to carry specific payloads, such as drugs or contrast agents, and enhance the efficacy and precision of cancer treatment. In this research, the boosting technique of machine learning was utilized to capture nonlinear interactions between a large number of input variables and outputs by using XGBoost and RNN-CNN as a classification method. The research sample included 350 patients, comprising 200 males and 150 females. The patients' mean ± SD was 50.34 ± 13.04 with a mean age of 50.34 ± 13.04. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.034), distant metastasis (P = 0.004), and tumor stage (P = 0.014) were shown to have a statistically significant link with GC patient survival. AUC was 93.54%, Accuracy 93.54%, F1-score 93.57%, Precision 93.65%, and Recall 93.87% when analyzing stomach pictures. Integrating nanotechnology with advanced machine learning techniques holds promise for improving the diagnosis and treatment of gastric cancer, providing new avenues for precision medicine and better patient outcomes.

Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics.

PURPOSE: To detect the Marchiafava Bignami Disease (MBD) using a distinct deep learning technique. BACKGROUND: Advanced deep learning methods are becoming more crucial in contemporary medical diagnostics, particularly for detecting intricate and uncommon neurological illnesses such as MBD. This rare neurodegenerative disorder, sometimes associated with persistent alcoholism, is characterized by the loss of myelin or tissue death in the corpus callosum. It poses significant diagnostic difficulties owing to its infrequency and the subtle signs it exhibits in its first stages, both clinically and on radiological scans. METHODS: The novel method of Variational Autoencoders (VAEs) in conjunction with attention mechanisms is used to identify MBD peculiar diseases accurately. VAEs are well-known for their proficiency in unsupervised learning and anomaly detection. They excel at analyzing extensive brain imaging datasets to uncover subtle patterns and abnormalities that traditional diagnostic approaches may overlook, especially those related to specific diseases. The use of attention mechanisms enhances this technique, enabling the model to concentrate on the most crucial elements of the imaging data, similar to the discerning observation of a skilled radiologist. Thus, we utilized the VAE with attention mechanisms in this study to detect MBD. Such a combination enables the prompt identification of MBD and assists in formulating more customized and efficient treatment strategies. RESULTS: A significant breakthrough in this field is the creation of a VAE equipped with attention mechanisms, which has shown outstanding performance by achieving accuracy rates of over 90% in accurately differentiating MBD from other neurodegenerative disorders. CONCLUSION: This model, which underwent training using a diverse range of MRI images, has shown a notable level of sensitivity and specificity, significantly minimizing the frequency of false positive results and strengthening the confidence and dependability of these sophisticated automated diagnostic tools.

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer.

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.

Therapeutic potential of tocotrienols as chemosensitizers in cancer therapy.

Chemoresistance is the adaptation of cancer cells against therapeutic agents. When exhibited by cancer cells, chemoresistance helps them to avoid apoptosis, cause relapse, and metastasize, making it challenging for chemotherapeutic agents to treat cancer. Various strategies like dosage modification of drugs, nanoparticle-based delivery of chemotherapeutics, antibody-drug conjugates, and so on are being used to target and reverse chemoresistance, one among such is combination therapy. It uses the combination of two or more therapeutic agents to reverse multidrug resistance and improve the effects of chemotherapy. Phytochemicals are known to exhibit chemosensitizing properties and are found to be effective against various cancers. Tocotrienols (T3) and tocopherols (T) are natural bioactive analogs of vitamin E, which exhibit important medicinal value and potential curative properties apart from serving as an antioxidant and nutrient supplement. Notably, T3 exhibits a variety of pharmacological activities like anticancer, anti-inflammatory, antiproliferative, and so on. The chemosensitizing property of tocotrienol is exhibited by modulating several signaling pathways and molecular targets involved in cancer cell survival, proliferation, invasion, migration, and metastasis like NF-κB, STATs, Akt/mTOR, Bax/Bcl-2, Wnt/ß-catenin, and many more. T3 sensitizes cancer cells to chemotherapeutic drugs including cisplatin, doxorubicin, and paclitaxel increasing drug concentration and cytotoxicity. Discussed herewith are the chemosensitizing properties of tocotrienols on various cancer cell types when combined with various drugs and biological molecules.

From incivility to outcomes: tracing the effects of nursing incivility on nurse well-being, patient engagement, and health outcomes.

BACKGROUND: Nursing incivility, defined as disrespectful behaviour toward nurses, is increasingly recognized as a pressing issue that affects nurses' well-being and quality of care. However, research on the pathways linking incivility to outcomes is limited, especially in Saudi hospitals. METHODS: This cross-sectional study examined relationships between perceived nursing incivility, nurse stress, patient engagement, and health outcomes in four Saudi hospitals. Using validated scales, 289 nurses and 512 patients completed surveys on exposure to incivility, stress levels, activation, and medication adherence. The outcomes included readmissions at 30 days and satisfaction. RESULTS: More than two-thirds of nurses reported experiencing moderate to severe workplace incivility. Correlation and regression analyzes revealed that nursing incivility was positively associated with nursing stress. An inverse relationship was found between stress and patient participation. Serial mediation analysis illuminated a detrimental cascade, incivility contributing to increased nurse stress, subsequently diminishing patient engagement, ultimately worsening care quality. Conclusions The findings present robust evidence that nursing incivility has adverse ripple effects, directly impacting nurse well-being while indirectly affecting patient outcomes through reduced care involvement. Practical implications advocate for systemic interventions focused on constructive nursing cultures and patient empowerment to improve both healthcare provider conditions and quality of care. This study provides compelling information to inform policies and strategies to mitigate workplace mistreatment and encourage participation among nurses and patients to improve health outcomes.

Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach.

Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.

Multimodal hybrid convolutional neural network based brain tumor grade classification.

An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes.

Differential modulation of cytochrome P450 enzymes by arsenicals in non-human experimental models.

Arsenic is a hazardous heavy metalloid that imposes threats to human health globally. It is widely spread throughout the environment in various forms. Arsenic-based compounds are either inorganic compounds (iAs) or organoarsenicals (oAs), where the latter are biotically generated from the former. Exposure to arsenic-based compounds results in varying biochemical derangements in living systems, leading eventually to toxic consequences. One important target for arsenic in biosystems is the network of metabolic enzymes, especially the superfamily of cytochrome P450 enzymes (CYPs) because of their prominent role in both endobiotic and xenobiotic metabolism. Therefore, the alteration of the CYPs by different arsenicals has been actively studied in the last few decades. We have previously summarized the findings of former studies investigating arsenic associated modulation of different CYPs in human experimental models. In this review, we focus on non-human models to get a complete picture about possible CYPs alterations in response to arsenic exposure.

Mercury and methylmercury differentially modulate hepatic cytochrome P450 1A1 and 1A2 in vivo and in vitro.

The cytochrome P450 1 A (CYP1A) subfamily enzymes are involved in the metabolic activation of several xenobiotics to toxic metabolites and reactive intermediates, resulting ultimately in carcinogenesis. Mercury and halogenated aromatic hydrocarbons (HAHs), typified by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), are persistent environmental pollutants involved in the modulation of aryl hydrocarbon receptor (AHR) gene battery, including cytochrome P450 (CYP) genes. We previously investigated the effect of coexposure to either inorganic or organic mercury (Hg+2 and MeHg) with TCDD on CYP1A1 in vitro. Thus, we examined the impact of coexposure to Hg+2 or MeHg and TCDD on AHR-regulated genes (Cyp1a1/1a2) in vivo and in vitro. Therefore, male C57BL/6 mice were injected intraperitoneally with MeHg or Hg+2 (2.5 mg/kg) in the absence and presence of TCDD (15 µg/kg) for 6 or 24 h. The concentration-dependent effect of MeHg was examined in murine hepatoma Hepa1c1c7 cells. In vivo, both MeHg and Hg2+ inhibited the TCDD-mediated induction of Cyp1a1/1a2 mRNA levels. However, Only Hg2+ was able to inhibit the TCDD-mediated induction at posttranscriptional levels of CYP1A1/1A2 protein and catalytic activity, suggesting differential modulation effects by Hg+2 and MeHg. In addition, the inhibitory role of HO-1 (Heme oxygenase-1) on CYP1A activity induced by TCDD was investigated using a HO-1 competitive inhibitor, tin-mesoporphyrin, that partially restored the MeHg-mediated decrease in CYP1A1 activity. This study demonstrates that MeHg, alongside Hg2+ , can differentially modulate the TCDD-induced AHR-regulated genes (Cyp1a1/1a2) at different expression levels in C57BL/6 mice liver and Hepa1c1c7 cells.

Outcome expectancies and resistance self-efficacy mediate the relationship between asthma diagnosis and e-cigarette use among youth and young adults.

INTRODUCTION: The use of electronic cigarettes (e-cigarettes) may exacerbate pulmonary complications in youth and young adults with asthma. We sought to identify the cognitive mechanisms that might explain e-cigarette use in this population. We hypothesized that e-cigarette outcome expectancies and e-cigarette resistance self-efficacy would mediate the relationship between asthma diagnosis and e-cigarette use in youth and young adults. METHODS: We enrolled youth and young adults (15-25 years old) in Alabama with a clinical diagnosis of asthma (n = 130) or without a diagnosis of any chronic pulmonary disease (n = 115; reference group). Author-constructed and validated questionnaires (young adult e-cigarette use outcome expectancies and modified Self-efficacy Scale for Adolescent Smoking) were administered to collect demographic data and assess susceptibility to e-cigarette use as well as current use of e-cigarettes, e-cigarette outcome expectancies, and e-cigarette resistance self-efficacy. We then conducted structural equation modeling to test whether e-cigarette expectancies and e-cigarette resistance self-efficacy mediate the relationship between asthma and susceptibility to e-cigarette use as well as current e-cigarette use. RESULTS: The frequency of the susceptibility to e-cigarette use and current e-cigarette use was lower among those with clinically diagnosed asthma than among those without asthma (35.8% vs. 59.8% for susceptibility and 6.0% vs. 18.2% for current use). Individuals with asthma reported weaker expectancies that e-cigarettes would make them feel relaxed which, in turn, was a significant predictor of lower susceptibility to e-cigarette use and current e-cigarette use, suggesting mediation. Finally, individuals with asthma demonstrated greater e-cigarette resistance self-efficacy in the context of social opportunities and friends' influence to use e-cigarettes. This self-efficacy was associated with lower susceptibility to e-cigarette use as well as current e-cigarette use. CONCLUSION: Although longitudinal studies are needed to determine relationships prospectively, targeted interventions that reduce outcome expectancies and increase resistance self-efficacy to e-cigarette use may further reduce e-cigarette use among youth and young adults with asthma.

Epidemiological associations of asthma status and tobacco use, substance use, and substance misuse among adults in the United States, 2015-2019.

Objective: Adults with asthma have a higher prevalence of substance use. However, knowledge is scarce regarding the associations between adults with asthma and tobacco use, substance use, and substance misuse. This study aimed to use national samples of United States adults to assess the comprehensive use and misuse of substances in adults with asthma.Method: This cross-sectional study comprised data drawn from the 2015 to 2019 National Survey on Drug Use and Health. Weighted logistic regressions were used to measure the associations of asthma status (lifetime and current) with last month's tobacco use; substance use (alcohol, marijuana, cocaine, crack, heroin, hallucinogens, inhalants); and substance misuse (pain relievers, tranquilizers, stimulants, sedatives). All regression models were controlled for sociodemographic characteristics, comorbidity, last-month serious psychological distress, and co-substance use and misuse. Results: Adults with lifetime asthma (Nweighted = 115,600,887) were less likely to use cigarettes, cigars, smokeless tobacco, inhalants, and polyuse of any substance. In contrast, adults with current asthma (Nweighted = 765,096,31) were more likely to use pipe tobacco, cocaine, non-prescribed tranquilizers, and less likely to use polytobacco products. Adults with lifetime asthma were associated with fewer last-month tobacco and inhalant use than those without lifetime asthma. However, adults with current asthma were associated with greater last month's pipe tobacco, cocaine, and non-prescribed tranquilizers. Conclusion: Thus, further longitudinal studies are recommended among adults with asthma to effectively design tailored treatment and prevention interventions.

A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment.

Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.