Niosomes

Cervical cancer is a leading global health burden, especially in the context of low- and middle-income countries where screening and vaccination coverage are still low. Although prevention strategies have improved; however, the case for effective treatment modalities remain. Cervical cancer has undergone significant changes in its management since the introduction of multiple therapeutic options over the years. Management of early-stage disease can entail surgical options including conization, hysterectomy, trachelectomy, and pelvic exenteration. Radiotherapy with external beam radiation or brachytherapy remains fundamental to treatment and is frequently administered alongside chemotherapy aimed at improving sensitivity. For locally advanced and metastatic disease, chemotherapy (especially platinum-based regimens) is still the mainstay of treatment, and newer targeted therapies appear effective. In the last few years, immunotherapy has appeared as a revolutionary strategy, among the immune checkpoint inhibitors, therapeutic vaccines, and adoptive cell therapies showed promising results. Moreover, novel targeted therapeutics and combination approaches are being investigated in clinical trials, ushering in an era of personalized medicine for the cervical cancer patient population. Although these advancements lead to improved outcomes for patients, issues related to treatment selection, quality of life, fertility preservation, and access to care continue to be of utmost importance. This review summarizes the status of lock-in treatments in cervical cancer, illustrating both current use and future directions for established and emerging lock-in therapies, with an eye on their real-world clinical implementation and future directions.

The objective of the research was to develop and evaluate niosomal preparations of diclofenac sodium with the view of enhancing transdermal drug delivery in animals. Niosomes were prepared using thin-film hydration technique, which is based on non-ionic surfactants and vesicle, and they were characterized based on particle size, zeta potential, entrapment efficiency, and morphology. The performance of the formulations was calculated in comparison to conventional gel by undertaking ex vivo skin permeation test in rat skin and in vivo pharmacokinetic test on Wistar rats. Findings showed that niosomal preparations and specifically Niosome F3 had a smaller particle size, greater entrapment efficiency, and better stability which resulted in a significant enhancement of skin permeation and the sustained system levels of absorption. Ex vivo experiments revealed that optimized niosomes had almost twice the drug permeation rate versus conventional gel whereas in vivo experiments revealed improved peak plasma concentrations and increased drug retention. These data confirm the hypotheses suggested and indicate that niosomal carriers are an appropriate method of overcoming the barrier of stratum corneum, increasing drug delivery, and achieving controlled release. The research emphasizes the opportunities offered by niosomal formulations as a flexible and effective carrier of transdermal drugs delivery, as a method to enhance the therapeutic effect, the efficiency of the dosing schedule, and minimize the side effects experienced by the system.

The oral route is still the most desirable route of drug delivery because of its ease of use, patient compliance and cost, but there is a major limitation regarding poor solubility of many therapeutic compounds in aqueous medium, therefore, this limits the oral delivery of the drug and clinical outcome. Solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs), as well as self-emulsifying drug delivery systems (SEDDS), are the latest approaches to address these shortcomings. The nanoscales protect labile compounds against degraded by gastrointestinal system, increase solubility of drugs, and facilitate absorption via lymphatic system bypassing first-pass metabolism. Preclinical and animal experiments have demonstrated that SLNs give sustained release and gastrointestinal protection, NLCs give drug loading, stability and controlled release, and SEDDS gives dissolution, systemic exposure and lymphatic uptake. Another advantage of LNCs is better tissue distribution, extended circulation, ameliorated pharmacokinetic and pharmacodynamic activity. Regardless of their promise, such challenges as formulation stability, large scale production, interspecies translational differences, and excipient safety are critical factors. Altogether, LNCs provide a versatile and promising system of creation of effective patient-friendly oral preparations of drugs that are hardly soluble, and their possible clinical translation and specific treatment use have significant potential.

With an emphasis on formulation stability, safety, and clinical compatibility, the current study sought to create and assess stable parenteral emulsions for critical care medicine. Three batches of emulsions were made using pharmaceutical-grade lipids, emulsifiers, and isotonic agents, and the results were compared to control formulations. During a 90-day storage period at 4 °C, 25 °C, and 40 °C, physicochemical parameters such as droplet size, zeta potential, pH, and osmolarity were measured. While statistical analyses (ANOVA and Levene's Test) confirmed reproducibility and consistency across batches, sterility and endotoxin testing guaranteed microbial safety. According to the results, emulsions kept at 4 °C exhibited the best stability, whereas emulsions kept at higher temperatures experienced mild or rapid destabilization. Endotoxin levels were within pharmacopeial limits, and all batches remained sterile. The feasibility of creating stable and safe parenteral emulsions that can be administered intravenously to critically ill patients is highlighted in the study, along with the significance of appropriate storage conditions in maintaining formulation integrity.

Cancer continues to pose a significant global health challenge, affecting nearly one in five individuals throughout their lifetime. While chemotherapeutic agents like doxorubicin play a crucial role in cancer treatment, their use is frequently accompanied by serious side effects, particularly nephrotoxicity. This review critically examines the renoprotective effects of flavonoids in counteracting doxorubicin-induced kidney damage, with a focus on evidence from preclinical animal models. Doxorubicin mediates its cytotoxic effects by intercalating into DNA and inducing the formation of reactive oxygen species (ROS), which lead to oxidative stress, apoptosis, and subsequent renal injury. A comprehensive literature search was conducted using databases including Google Scholar, Scopus, pubmed, Springer, Wiley Online Library, and sciencedirect, targeting articles published between 2014 and 2024. Keywords used included “flavonoids,” “doxorubicin,” “nephrotoxicity,” “Renoprotective,” and “animal model.” Flavonoids, a broad class of plant-derived polyphenols, are well recognized for their antioxidant, anti-inflammatory, and anticancer activities. Certain flavonoids, similar to quercetin, rutin, kaempferol, morin, luteolin, apigenin, hesperidin, naringenin, diosmin, and anthocyanins, have shown significant effectiveness in reducing kidney damage caused by doxorubicin. This review highlights the promising role of flavonoids as potential adjuvants in reducing chemotherapy-associated renal side effects and enhancing the safety profile of anticancer regimens.

Increasing accuracy and efficiency together with diagnosis predictability makes artificial intelligence (AI) revolutionize healthcare sector operations. This paper explores how artificial intelligence (AI) detects early-stage diabetes mellitus as a metabolic chronic disease that shows increasing global prevalence. The objective of this research study evaluates how different artificial intelligence approaches including machine learning, deep learning, and natural language processing (NLP) function in terms of methods, accuracy level as well as data collection protocols and their practical clinical benefits. Research indicates that ensemble machine learning models alongside convolutional neural networks (CNNs) demonstrate superior identification abilities for detecting diabetes problems early on. The data quality issues along with generalization problems and limitations for clinical practice require further investigation.