The intricate interplay between the human microbiota and the immune system has garnered significant attention in recent years, particularly concerning its implications in cancer biology. Macrophages, pivotal players in the tumor microenvironment (TME), exhibit diverse phenotypes that can either promote tumor progression or inhibit it. This review explores the multifaceted role of the microbiota in modulating macrophage polarization within the TME. We highlight recent findings that demonstrate how specific microbial communities influence macrophage behavior through metabolic pathways, immune signaling, and epigenetic modifications. Furthermore, we discuss the therapeutic potential of manipulating the microbiota to reprogram macrophage phenotypes, thereby enhancing antitumor immunity. By integrating insights from microbiology, immunology, and oncology, this article aims to provide a comprehensive overview of the microbiota’s impact on macrophage dynamics in cancer, paving the way for innovative therapeutic strategies that harness this relationship for improved clinical outcomes.

Introduction: The emergence of cancer immunotherapy has revolutionized cancer treatment, offering remarkable outcomes for patients across various malignancies. However, the heterogeneous response to immunotherapy underscores the necessity of understanding additional factors influencing treatment efficacy. Among these factors, the human microbiota has garnered significant attention for its potential role in modulating immune response. Body: This review explores the intricate relationship between the human microbiota and cancer immunotherapy, highlighting recent advances and potential mechanisms underlying microbial influence on treatment outcomes. Conclusion: Insights into the microbiome’s impact on immunotherapy response not only deepen our understanding of cancer pathogenesis but also hold promise for personalized therapeutic strategies aimed at optimizing patient outcomes.

Purpose: Cancer immunotherapy has transformed oncology, but underserved populations face persistent disparities in access and outcomes. This review explores how artificial intelligence (AI) can help mitigate these barriers. Methods: We conducted a narrative review based on peer-reviewed literature selected for relevance to artificial intelligence, cancer immunotherapy, and healthcare challenges, without restrictions on publication date. We searched three major electronic databases: PubMed, IEEE Xplore, and arXiv, covering both biomedical and computational literature. The search included publications from January 2015 through April 2024 to capture contemporary developments in AI and cancer immunotherapy. Results: AI tools such as machine learning, natural language processing, and predictive analytics can enhance early detection, personalize treatment, and improve clinical trial representation for historically underrepresented populations. Additionally, AI-driven solutions can aid in managing side effects, expanding telehealth, and addressing social determinants of health (SDOH). However, algorithmic bias, privacy concerns, and data diversity remain major challenges. Conclusions: With intentional design and implementation, AI holds the potential to reduce disparities in cancer immunotherapy and promote more inclusive oncology care. Future efforts must focus on ethical deployment, inclusive data collection, and interdisciplinary collaboration.

Background/Aim: Sleep disturbances are a prevalent but often overlooked issue among patients undergoing total knee arthroplasty (TKA). These disruptions significantly contribute to increased pain, delayed functional recovery, and reduced quality of life. This systematic review examines the association between sleep quality and postoperative recovery outcomes after TKA. Methods: The review utilized five databases: PubMed, Embase, Ovid/MEDLINE, CINAHL, and Scopus. We identified studies that evaluated sleep quality and recovery outcomes in adult TKA patients. Eligible studies used validated tools such as the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and actigraphy to examine recovery metrics, including pain, mobility, and overall recovery trajectories. A narrative synthesis was conducted to identify patterns and variations across the included studies. Results: Seven studies, involving a total of 902 patients, were included in this review. Poor sleep quality is consistently correlated with adverse recovery outcomes, such as higher pain levels, slower functional recovery, and diminished quality of life. Quantitative data underscored the association between elevated PSQI scores and delayed recovery metrics. Patients with higher PSQI scores reported elevated pain levels (VAS: 3.8 vs. 1.6) and poorer functional outcomes, as indicated by significantly higher WOMAC-Physical Function scores several weeks after TKA compared to groups with less prominent insomnia (P<0.05). Conclusion: Interventions targeting sleep disturbances, such as behavioral therapies, showed promising benefits. However, methodological variability limited the generalizability of findings. This review emphasizes the critical importance of sleep quality as a modifiable factor in optimizing recovery after TKA. Integrating sleep assessments and targeted interventions, such as cognitive-behavioral therapy for insomnia (CBT-I), into perioperative care can significantly enhance recovery trajectories and patient outcomes. Future research should prioritize standardizing methodologies and investigating the effectiveness of sleep-focused strategies across diverse patient populations.

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a high rate of recurrence and a dismal prognosis. Studies have shown that pancreatic cancer stem cells (PCSCs) are a subpopulation that contributes to tumor progression, resistance to therapeutics, and metastasis, making them a key subpopulation to target for treatment. Gedunin (GD), a natural compound derived from Azadirachta indica (neem), has shown anticancer properties in pancreatic cancer cells, but its effects on PCSCs remains unclear. This study evaluated the effects of GD in pancreatic cancer stem cells, highlighting its impacts on tumor growth and progression and focusing on its impact on the sonic hedgehog (Shh) signaling pathway. Methods: Functional assays were performed to assess the effect of GD on the sphere-forming ability, colony formation, and self-renewal of PCSCs. Athymic mice xenograft models were utilized to evaluate the tumor suppression effect of GD in vivo. Furthermore, the anticancer effect of GD on PCSCs was assessed using both in vitro and in vivo limiting dilution assay. GD-induced changes in Shh signaling and key stem cell marker expressions in PCSCs were evaluated. Results: GD effectively inhibited tumor growth in xenograft models and reduced the percentage of PCSCs. GD was effective in decreasing PCSCs’ proliferative, self-renewal, and colony-forming capacity. GD decreased the protein expression levels of key Shh signaling markers Gli1 and Shh, stem cell markers SOX2, Nanog, and Oct4, metastasis-related proteins MMP-2, MMP-3, and MMP-9, and EMT markers Tgf1, Slug, Snail, and Twist in both PDAC cells and PCSCs. We demonstrated a significant decrease in the spheroid formation and self-renewal capacity of the (ALDH+) PCSC population following GD treatment in HPAC cells, indicating its potential antagonistic effects on PCSCs. GD was highly effective in reducing tumor volume, stemness, and metastasis in both early and late chemotherapy. In vivo limiting dilution assay using CD133+/LGR5+ PCSC xenografts demonstrated that GD reduces tumor growth, metastasis, and stemness associated with PCSCs by downregulating the expression of Shh and Gli1. GD treatment also reduced micrometastatic lesions in the lung, liver, and brain, as identified using H&E staining. Conclusions: The findings highlight GD’s potential as a promising therapeutic candidate for PDAC, with the ability to target both bulk tumor cells and PCSCs. By simultaneously suppressing tumor growth, stemness, and metastatic spread, GD may contribute to more effective treatment strategies and improved patient outcomes.