Biology
The Effectiveness of Nanomedicine in Relation to Traditional Therapies for Breast Carcinoma Treatment: Current Possibilities and Future Outlook
Breast cancer is one of the most prevalent cancers among women worldwide. In recent years a significant proportion of breast cancer research in Pakistan accounting for nearly two-thirds has focused on this disease. Nanotechnology has emerged as a promising tool in the detection diagnosis and treatment of breast cancer. This study presents an analysis of traditional breast cancer therapies and compares them with recent developments in nanomedicine.
The data were collected from online databases including Google Scholar PubMed and Web of Science to support the current study.
Various treatments face challenges including complications and drug resistance. A new approach has been developed to overcome chemoresistance in breast cancer patients. Nanotechnology utilizes both organic and inorganic methods to address breast cancer aiming to reduce tumor size and impede its development. The nanomedicine treatment involves active passive and stimuli-responsive targeting of nanocarriers to tumor cells. Although nanomedicine shows high effectiveness careful consideration must be given to the potential toxicity of nanomaterials particularly their impact on the immune system.
Nanomedicine offers a promising solution to overcome chemoresistance in breast cancer by targeted drug delivery through nanocarriers. While effective in reducing tumors concerns about nanomaterial toxicity especially its impact on the immune system must be addressed.
In summary nanomedicine proves to be an efficient method for treating breast cancer tumor growth. Further work is necessary to design safer and more effective medicines through nanomedicine.
Harnessing the Metaverse in Modern Medicine: Virtual, Augmented, and Extended Reality as Catalysts for Healthcare Innovation and Education
The metaverse a convergence of virtual augmented and physical realities is revolutionizing healthcare delivery education and patient engagement. Its backbone technologies include virtual reality (VR) augmented reality (AR) extended reality (XR) artificial intelligence (AI) blockchain and the internet of things (IoT).
A qualitative synthesis was conducted using peer-reviewed literature retrieved from electronic databases including PubMed Scopus IEEE Xplore Web of Science and Google Scholar. The search was restricted to studies published between 2010 and 2023 focusing on metaverse applications in healthcare such as surgery education diagnostics and telemedicine.
Findings revealed the metaverse’s integration into various domains such as XR-assisted surgeries (e.g. Johns Hopkins' AR spine surgery) immersive VR-based rehabilitation AR-enhanced diagnostics and AI-driven simulations. Platforms like Tetra Signum and WHO’s XR training programs have demonstrated clinical efficacy. Holographic modeling and digital twins have been found to be increasingly used in surgical planning and remote consultation.
The metaverse has been found to foster real-time multimodal interaction among clinicians and patients. However issues such as data privacy interoperability access disparities and legal ambiguity challenge full-scale adoption. Ethical implementation and infrastructure upgrades are crucial for equitable integration.
Metaverse technologies are transforming traditional medicine into a proactive personalized and data-driven system. By embedding immersive experiences into clinical and educational workflows they promise enhanced outcomes and democratized access to healthcare knowledge. Strategic policies and ethical safeguards are essential to unlock their full potential.
Exploring Toll-like Receptor-4 Associated Single Nucleotide Polymorphisms and Susceptibility to Microbial Infections
Toll-Like Receptor 4 (TLR4) is vital for the innate immune system as it recognizes a wide array of pathogens such as bacteria fungi and viruses. TLR4 activates downstream signaling pathways upon recognizing microbial components triggering and regulating the immune response. With improvements in genomic technologies it has become possible to identify Single-Nucleotide Polymorphisms (SNPs) in genes coding for immune receptors such as TLR4. These genetic differences may affect the way TLR4 reacts to various pathogens and thus the intensity and outcome of immune responses. It is essential to have a comprehensive understanding of SNPs associated with TLR4 to assess individual susceptibility to infection and inform personalized medicine strategies.
Multiple scientific literature review databases were utilized to examine TLR4 SNPs and their roles in pathogen recognition immune signaling and disease outcomes. These studies elaborated the role of TLR4 polymorphisms in various forms of infections involving bacteria fungi and viruses.
Some polymorphisms in the TLR4 gene including rs4986790 [Asp299Gly] and rs4986791 [Thr399Ile] have been associated with differing immune responses and increased susceptibility to septic shock candidiasis tuberculosis and viral infections. This inhibition of TLR4 signaling by the mutant alleles augments some arms of immune response while inhibiting others which in turn affects the severity of the infection and the response to treatment.
This review focuses on the identification and examination of SNPs in TLR4 and their association with infectious diseases caused by pathogens. The review also examines the impact of these SNPs on TLR4 signaling pathways and the immune response. Polymorphisms in the TLR4 gene including rs4986790 [Asp299Gly] and rs4986791 [Thr399Ile] have been associated with differing immune responses and increased susceptibility to septic shock candidiasis tuberculosis and viral infections.
Recognition of TLR4 SNPs would provide information on susceptibility to various infections and immune modulation. Current knowledge of genetic variations will lead to the identification of biomarkers for infectious diseases and consequently patient-specific treatment and vaccine generation targeted toward specific genotypes in precision medicine particularly in immunology.
Molecular Docking to Explore the Interaction of Emodin and Piplartine with Biological Receptors of the TGF-β Signaling Pathway in Head and Neck Squamous Cell Carcinoma
Inflammation and cytokine expression play key roles in HNSCC development. TGF-β regulates multiple cancer-related processes and natural compounds such as emodin and piplartine are known for their anti-cancer and anti-inflammatory effects. These effects are related to the ability to modulate molecular targets linked to the TGF-β signaling pathway.
This study conducted an in silico analysis of natural compounds interacting with TGF-β pathway receptors. Six candidate receptors (LTBP1 TGF-β1 TGFβR1 SMAD2 E2F4 and EMP3) were selected based on database and literature searches. Protein and ligand models were obtained from specific databases and molecular docking was performed using CB-Dock2.
Emodin showed docking scores of -7.4 -7.6 -9.0 -7.5 -6.9 and -7.0 kcal/mol with receptors LTBP1 TGF-β1 TGFβR1 SMAD2 E2F4 and EMP3 respectively. Piplartine showed lower docking scores of -6.6 -6.4 -8.2 -6.7 -6.0 and -6.0 kcal/mol respectively. The molecular docking protocol was validated by redocking the known inhibitor to TGFβR1 resulting in a low RMSD value confirming the reliability of CB-Dock2.
According to these in silico results emodin exhibits higher binding affinity to TGF-β receptors than piplartine. New candidate receptors (EMP3 LTBP1 and E2F4) were identified as potential therapeutic targets in HNSCC. High EMP3 expression significantly correlates with worse patient survival indicating its prognostic potential.
These findings provide a promising preliminary indication of emodin as a potential modulator of targets in the TGF-β signaling pathway in HNSCC supporting its further investigation in in vitro and in vivo models.
Computational Evaluation of ADMET Properties and Molecular Docking Studies of 1,2,4-oxadiazole Analogs as Potential Inhibitors of Prostate Cancer
Prostate cancer remains a significant cause of cancer-related mortality among men worldwide. While non-steroidal anti-androgens (NSAAs) provide therapeutic advantages over steroidal agents their clinical use is hindered by adverse effects like gynecomastia and hepatotoxicity. This study aims to design novel 124-oxadiazole derivatives with improved bioavailability efficacy and safety profiles.
Molecular docking studies were conducted using Molegro Virtual Docker (MVD) 6.0 with the androgen receptor (PDB ID: 1Z95) as the target. ADMET profiling was performed using SwissADME and pkCSM to assess pharmacokinetic and toxicity parameters.
Docking analysis revealed 10 potent inhibitors with significant binding affinities among which PS04 PS05 PS07 PS08 PS09 PS10 and PS12 exhibited optimal ADMET properties. These compounds demonstrated high gastrointestinal absorption low toxicity and favorable bioavailability.
The identified 124-oxadiazole derivatives show promise as safer and more effective NSAAs by addressing limitations of current therapies. Their strong receptor interactions and favorable pharmacokinetics suggest potential for clinical development.
This study provides valuable insights for developing next-generation NSAAs laying the groundwork for further in vitro and in vivo validation.
Significance of Artificial Intelligence in Animal Disease Recognition
Artificial intelligence (AI) is a rapidly expanding field of innovative technology that has great potential to transform many different scientific and technological fields. AI can be used in veterinary treatment and animal disease management to produce better outcomes for people and animals. AI can help in many disciplines including genetics cancer research epidemiology disease surveillance therapy and vaccine development studies on antimicrobial resistance (AMR) and is presented as an essential tool to address worldwide health issues in many fields. Most investigational AI-driven animal care research focuses on data collection processing evaluation and analysis for animal behaviour detection disease surveillance growth estimation and environmental monitoring. This paper describes and investigates the potential consequences of different elements of AI on animal disease and how AI is developing across many disciplines; the most prominent are deep learning and machine learning. Machine learning (ML) can be used to create models capable of predicting the future through algorithms that discover patterns in data. The development of AI technologies has sped up the process of drug discovery by locating possible therapeutic targets and improving candidate medications. This paper discusses these advancements while also analyzing the opportunities that lie ahead for artificial intelligence in the field of animal disease control. We also highlight the potential of AI to preserve the wellness of humans and animals across nations highlighting the role AI plays in advancing the management of animal illnesses.