Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling.

BACKGROUND: The integration of artificial intelligence (AI) and machine learning (ML) in peritoneal dialysis (PD) presents transformative opportunities for optimizing treatment outcomes and informing clinical decision-making. This study aims to provide a comprehensive overview of the applications of AI/ML techniques in PD, focusing on their potential to predict clinical outcomes and enhance patient care. MATERIALS AND METHODS: This systematic review was conducted according to PRISMA guidelines (2020), searching key databases for articles on AI and ML applications in PD. The inclusion criteria were stringent, ensuring the selection of high-quality studies. The search strategy comprised MeSH terms and keywords related to PD, AI, and ML. 793 articles were identified, with nine ultimately meeting the inclusion criteria. The review utilized a narrative synthesis approach to summarize findings due to anticipated study heterogeneity. RESULTS: Nine studies met the inclusion criteria. The studies varied in sample size and employed diverse AI and ML techniques, reflecting the breadth of data considered. Mortality prediction emerged as a recurrent theme, demonstrating the significance of AI and ML in prognostic accuracy. Predictive modeling extended to technique failure, hospital stay prediction, and pathogen-specific immune responses, showcasing the versatility of AI and ML applications in PD. CONCLUSIONS: This systematic review highlights the diverse applications of AI/ML in peritoneal dialysis, demonstrating their potential to enhance predictive accuracy, risk stratification, and decision support. However, limitations such as small sample sizes, single-center studies, and potential biases warrant further research and external validation. Future perspectives include integrating these AI/ML models into routine clinical practice and exploring additional use cases to improve patient outcomes and healthcare decision-making in PD.

Gender Matters: A Multidimensional Approach to Optimizing Cardiovascular Health in Women.

Cardiovascular diseases remain a leading cause of mortality among women, yet they are often underestimated and insufficiently addressed. This narrative review delves into the gender disparities in cardiovascular health, underscoring the critical importance of recognizing and addressing the unique challenges women face. The article explores the pathophysiological differences between men and women, highlighting the role of hormonal factors, such as estrogen and menopause, in conferring cardioprotection or increasing risk. It examines the complexities of diagnosis and assessment, including differences in symptom presentation, diagnostic accuracy, and the challenges of interpreting non-invasive testing in women. The review also highlights the need for tailored risk assessment and prevention strategies, incorporating sex-specific conditions and pregnancy-related factors. It emphasizes the importance of lifestyle modifications and interventions, as well as the potential benefits of personalized treatment approaches, considering gender-specific variations in medication responses and cardiac interventions. Furthermore, the article sheds light on the impact of psychosocial and sociocultural factors, such as gender norms, mental health considerations, and access to healthcare, on women's cardiovascular health. It also addresses the significant gaps and challenges in research, including the historical underrepresentation of women in clinical trials and the lack of sex- and gender-sensitive studies. Finally, the review advocates for a multidisciplinary approach, involving patient-centered care, shared decision-making, and collaboration among policymakers, stakeholders, and healthcare systems. This comprehensive strategy aims to enhance awareness, prevention, diagnosis, and treatment of cardiovascular disease in women, ultimately improving health outcomes and reducing the burden of this often overlooked epidemic.

Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches.

Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.

Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review.

Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.

Unraveling the Enigma of Aortic Dissection: From Genetics to Innovative Therapies.

Aortic dissection (AD) presents a critical medical emergency characterized by a tear in the aortic wall, necessitating prompt recognition and management to mitigate catastrophic complications. Despite advancements in medical technology and therapeutic interventions, AD remains a formidable challenge, often resulting in severe morbidity and mortality. This narrative review provides a comprehensive overview of AD, encompassing its clinical presentation, diagnostic modalities, and management strategies, while also exploring emerging trends and innovations in its management. Genetic predispositions significantly influence AD pathogenesis, with over 30 contributory genes identified, emphasizing the importance of genetic screening and counseling. Classification systems such as Stanford and DeBakey, alongside their revised counterparts, aid in categorizing AD and guiding treatment decisions. Advancements in diagnostic imaging, including transesophageal echocardiography and computed tomography angiography, have enhanced diagnostic precision, augmented by artificial intelligence and machine learning algorithms. Pharmacological innovations focus on optimizing medical therapy, while surgical and endovascular approaches offer minimally invasive treatment options. Hybrid procedures and aortic valve-sparing techniques broaden treatment avenues, while bioresorbable stent grafts hold promise for tissue regeneration. Collaborative efforts and ongoing research are essential to address remaining challenges and improve outcomes in managing AD. This review contributes to the understanding of AD's complexity and facilitates informed decision-making in clinical practice, underscoring the imperative for continued innovation and research in AD management.

Gazing Beyond the Horizon: A Systematic Review Unveiling the Theranostic Potential of Quantum Dots in Alzheimer's Disease.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by cognitive decline, poses a significant healthcare challenge worldwide. The accumulation of amyloid-beta (Aß) plaques and hyperphosphorylated tau protein drives neuronal degeneration and neuroinflammation, perpetuating disease progression. Despite advancements in understanding the cellular and molecular mechanisms, treatment hurdles persist, emphasizing the need for innovative intervention strategies. Quantum dots (QDs) emerge as promising nanotechnological tools with unique photo-physical properties, offering advantages over conventional imaging modalities. This systematic review endeavors to elucidate the theranostic potential of QDs in AD by synthesizing preclinical and clinical evidence. A comprehensive search across electronic databases yielded 20 eligible studies investigating the diagnostic, therapeutic, or combined theranostic applications of various QDs in AD. The findings unveil the diverse roles of QDs, including inhibiting Aß and tau aggregation, modulating amyloidogenesis pathways, restoring membrane fluidity, and enabling simultaneous detection of AD biomarkers. The review highlights the potential of QDs in targeting multiple pathological hallmarks, delivering therapeutic payloads across the blood-brain barrier, and facilitating real-time imaging and high-throughput screening. While promising, challenges such as biocompatibility, surface modifications, and clinical translation warrant further investigation. This systematic review provides a comprehensive synthesis of the theranostic potential of QDs in AD, paving the way for translational research and clinical implementation.

An Emerging Threat: A Systematic Review of Endocarditis Caused by Gemella Species.

Infective endocarditis caused by Gemella species is increasingly recognized as an emerging clinical entity. Gemella species are fastidious gram-positive cocci that are typically commensal organisms but can become opportunistic pathogens. This systematic review aimed to provide a comprehensive overview of endocarditis due to Gemella species by synthesizing existing evidence. A total of 52 case reports were identified through a rigorous search and selection process. The most prevalent causative species were G. morbillorum (46.3%) and G. haemolysans (25.9%), with a striking male predominance (79.6%). The clinical presentation was largely nonspecific, mirroring typical infective endocarditis. However, the indolent nature of the illness and fastidious growth requirements of Gemella species often led to diagnostic delays. Echocardiography, particularly transesophageal echocardiography, played a crucial role in the diagnosis, enabling the detection of valvular vegetation and the assessment of complications. Management posed significant challenges, including the need for broad-spectrum empirical antibiotic therapy and increasing antimicrobial resistance among Gemella isolates. Surgical intervention was frequently required for severe valvular dysfunction, persistent infection, or embolic complications. Despite advances in diagnosis and treatment, endocarditis due to Gemella species remains associated with significant morbidity and mortality, underscoring the importance of early recognition and multidisciplinary management. This review highlights the emerging clinical significance of Gemella species as causative agents of infective endocarditis and identifies areas for further research.

Leveraging Artificial Intelligence and Machine Learning to Optimize Enhanced Recovery After Surgery (ERAS) Protocols.

Enhanced recovery after surgery (ERAS) protocols have transformed perioperative care by implementing evidence-based strategies to hasten patient recovery, decrease complications, and shorten hospital stays. However, challenges such as inconsistent adherence and the need for personalized adjustments persist, prompting exploration into innovative solutions. The emergence of artificial intelligence (AI) and machine learning (ML) offers a promising avenue for optimizing ERAS protocols. While ERAS emphasizes preoperative optimization, minimally invasive surgery (MIS), and standardized postoperative care, challenges such as adherence variability and resource constraints impede its effectiveness. AI/ML technologies offer opportunities to overcome these challenges by enabling real-time risk prediction, personalized interventions, and efficient resource allocation. AI/ML applications in ERAS extend to patient risk stratification, personalized care plans, and outcome prediction. By analyzing extensive patient datasets, AI/ML algorithms can predict individual patient risks and tailor interventions accordingly. Moreover, AI/ML facilitates proactive interventions through predictive modeling of postoperative outcomes, optimizing resource allocation, and enhancing patient care. Despite the potential benefits, integrating AI and ML into ERAS protocols faces obstacles such as data access, ethical considerations, and healthcare professional training. Overcoming these challenges requires a human-centered approach, fostering collaboration among clinicians, data scientists, and patients. Transparent communication, robust cybersecurity measures, and ethical model validation are crucial for successful integration. It is essential to ensure that AI and ML complement rather than replace human expertise, with clinicians maintaining oversight and accountability.

Navigating the Gut-Cardiac Axis: Understanding Cardiovascular Complications in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) presents a complex interplay of chronic inflammation in the gastrointestinal tract and is associated with various extraintestinal manifestations, including cardiovascular complications (CVCs). IBD patients face an elevated risk of CVCs, including coronary artery disease, heart failure, arrhythmias, stroke, peripheral artery disease, venous thromboembolism, and mesenteric ischemia, necessitating comprehensive cardiovascular risk assessment and management. The intricate interplay between chronic inflammation, genetic predisposition, environmental factors, and immune dysregulation likely contributes to the development of CVCs in IBD patients. While the exact mechanisms linking IBD and CVCs remain speculative, potential pathways may involve shared inflammatory pathways, endothelial dysfunction, dysbiosis of the gut microbiome, and traditional cardiovascular risk factors exacerbated by the chronic inflammatory state. Moreover, IBD medications, particularly corticosteroids, may impact cardiovascular health by inducing hypertension, insulin resistance, and dyslipidemia, further amplifying the overall CVC risk. Lifestyle factors such as smoking, obesity, and dietary habits may also exacerbate cardiovascular risks in individuals with IBD. Lifestyle modifications, including smoking cessation, adoption of a heart-healthy diet, regular exercise, and optimization of traditional cardiovascular risk factors, play a fundamental role in mitigating CVC risk. Emerging preventive strategies targeting inflammation modulation and gut microbiome interventions hold promise for future interventions, although further research is warranted to elucidate their efficacy and safety profiles in the context of IBD. Continued interdisciplinary collaboration, advanced research methodologies, and innovative interventions are essential to address the growing burden of CVCs in individuals living with IBD and to improve their long-term cardiovascular outcomes.

Partnering With Technology: Advancing Laparoscopy With Artificial Intelligence and Machine Learning.

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in optimizing laparoscopic surgery, offering innovative solutions to enhance surgical precision, efficiency, and safety. This editorial explores the potential role of AI/ML across the surgical continuum, including preoperative optimization, intraoperative assistance, and postoperative care. It outlines the benefits of laparoscopic surgery compared to traditional open procedures and identifies current challenges such as technical difficulty and human error. The editorial discusses how AI and ML technologies can address these challenges, including patient selection and risk stratification, surgical planning and simulation, and personalized medicine approaches. Moreover, it examines the role of AI/ML in intraoperative assistance, such as instrument tracking and guidance, real-time tissue analysis, and the detection of potential complications. Postoperative care and follow-up are also explored, highlighting the potential of AI/ML in monitoring patient recovery, predicting and preventing complications, and tailoring rehabilitation plans. Ethical concerns surrounding data privacy and security, the lack of transparency in decision-making, potential job displacement, and regulatory frameworks are discussed as challenges to the widespread adoption of AI/ML in laparoscopic surgery. Finally, potential areas for further research and exploration are outlined, emphasizing interdisciplinary collaboration and the need for transparent and accountable AI systems. Overall, this editorial provides insights into the challenges and opportunities in harnessing AI/ML technologies to optimize laparoscopic surgery and improve patient outcomes.

Navigating the Surgical Landscape: A Comprehensive Analysis of Endoscopic vs. Microscopic Transsphenoidal Pituitary Surgery Outcomes.

Pituitary surgery, a critical intervention for various pituitary disorders, has sparked ongoing debates regarding the preference between endoscopic and microscopic transsphenoidal approaches. This systematic review delves into the outcomes associated with these techniques, taking into account the recent advancements in neurosurgery. The minimally invasive nature of endoscopy, providing improved visualization and reduced morbidity, stands in contrast to the well-established track record of the conventional microscopic method. Examining outcomes for disorders such as Cushing's disease and acromegaly, the review synthesizes evidence from Denmark, Bulgaria, and China. Noteworthy advantages of endoscopy encompass higher resection rates, shorter surgery durations, and fewer complications, endorsing its effectiveness in pituitary surgery. While emphasizing the necessity for prospective trials, the review concludes that endoscopic approaches consistently showcase favorable outcomes, influencing the ongoing discourse on the optimal surgical strategies for pituitary disorders.

A Comprehensive Analysis of Single-Incision Laparoscopic Cholecystectomy: Trends, Challenges, and Future Directions.

Single-incision laparoscopic cholecystectomy (SILC) is a minimally invasive surgical technique introduced as an advancement to laparoscopic cholecystectomy (LC). This narrative review delves into the emergence of SILC, emphasizing its distinct advantages such as improved cosmesis, reduced postoperative pain, and potentially faster recovery compared to traditional LC. The study meticulously examines current trends and challenges in SILC, including variations in techniques and their impact on patient outcomes. Furthermore, the article sheds light on the technical intricacies and longer operative times associated with SILC. It aims to contribute valuable insights to the medical community by synthesizing existing literature and recent research findings, fostering a deeper understanding of SILC, and guiding future advancements in minimally invasive surgical approaches. The discussion extends to the learning curve, complications, and a comparative analysis between SILC and traditional LC, offering a nuanced understanding of their respective strengths and limitations. The article concludes with a forward-looking perspective, exploring future directions and innovations in SILC, including advancements in surgical techniques and the integration of innovative technologies, such as robotic assistance and in vivo robots, to enhance precision and efficacy. The call for continued research into the long-term outcomes, safety, and refined patient selection criteria emphasizes the evolving landscape of SILC and its potential to shape the future of minimally invasive abdominal surgeries.

The Potential of Salivary Biomarkers in Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review.

Pancreatic ductal adenocarcinoma (PDAC) is a formidable global health concern with a dire prognosis, highlighting the critical need for early detection strategies. This systematic review delves into the potential of salivary biomarkers as a non-invasive means for identifying PDAC at its incipient stages. Saliva's proximity to the circulatory system enables the detection of tumor-derived biomolecules, making it an ideal candidate for mass screening. The analysis of three selected studies reveals promising candidates such as Neisseria mucosa, Fusobacterium periodonticum, polyamines, and specific long non-coding RNAs (lncRNAs). Notably, polyamines like spermine show potential in distinguishing PDAC, while lncRNAs HOX transcript antisense RNA (HOTAIR) and plasmacytoma variant translocation 1 (PVT1) exhibit superior sensitivity and specificity compared to traditional serum markers. However, challenges, including small sample sizes and a lack of validation, underscore the need for standardized diagnostic panels and large-scale collaborative studies. Advancements in nanotechnology, machine learning, and ethical considerations are crucial for harnessing the diagnostic potential of saliva. The review emphasizes the imperative for extensive clinical trials to validate salivary biomarkers, ensuring not only diagnostic accuracy but also cost-effectiveness, patient compliance, and long-term benefits in the realm of PDAC screening. Longitudinal studies are recommended to unravel temporal changes in salivary biomarkers, shedding light on disease progression and treatment response.

From Antibodies to Artificial Intelligence: A Comprehensive Review of Diagnostic Challenges in Hashimoto's Thyroiditis.

Hashimoto's thyroiditis (HT) poses diagnostic challenges due to its diverse clinical presentation and the intricacies of autoimmune thyroid diseases. This comprehensive narrative review explores the evolving landscape of diagnostic challenges in HT, aiming to provide a thorough understanding of the complexities involved in its diagnosis. The diagnostic criteria for HT involve a multifaceted approach, including clinical features, laboratory findings, and imaging studies. Serum antibodies against thyroid antigens, primarily thyroperoxidase (TPO) and thyroglobulin, play a crucial role in confirming the autoimmune nature of the disease. However, seronegative HT adds complexity by presenting without detectable antibodies. The significance of addressing diagnostic challenges lies in potential delays and misdiagnoses, emphasizing the need for accurate and timely intervention. The review explores future directions, emphasizing molecular and cellular aspects, genetic factors, and the emerging field of thyroid regeneration. Standardized diagnostic criteria are essential, considering the subjective nature of the current process. The heterogeneity of disease manifestations complicates targeted treatments, necessitating a deeper understanding of clinical presentations and underlying pathophysiology. Future research directions and challenges outlined in this review contribute to advancing our understanding and improving diagnostic precision in HT.

Navigating the Future of Cardiac Diagnostics: Insights From Artificial Neural Networks.

Cardiovascular diseases remain a leading cause of mortality globally, necessitating innovative approaches for early detection and precise diagnostic methodologies. Artificial neural networks (ANNs), inspired by the complexity of the human brain's neural networks, have emerged as powerful tools for transforming the landscape of cardiac diagnostics. ANNs are capable of learning complex patterns from data. In cardiac diagnostics, these networks are employed to analyze intricate cardiovascular data, providing insights into diseases such as coronary artery disease and arrhythmias. From personalized medicine approaches to predictive analytics, ANNs can revolutionize the identification of cardiovascular risks, enabling timely interventions and preventive measures. The integration of ANNs with wearable devices and telemedicine is poised to establish a connected healthcare ecosystem, providing holistic and continuous cardiac monitoring. However, challenges persist, including ethical considerations surrounding patient data and uncertainties in diagnostic outcomes. Looking forward, the prospects of ANNs in cardiac diagnostics are promising. Anticipated technological advancements and collaborative efforts between medical and technological communities are expected to drive innovation, address current challenges, and foster a new era of precision cardiac care.

Emerging Horizons in the Diagnosis of Pancreatic Cancer: The Role of Circulating microRNAs as Early Detection Biomarkers for Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a poor prognosis, primarily due to a late diagnosis. Recent studies have focused on identifying non-invasive biomarkers for early detection, with microRNAs (miRNAs) emerging as promising candidates. This systematic review aims to evaluate the potential of circulating miRNAs as biomarkers for the early detection of PDAC, analyzing their diagnostic accuracy, specificity, and sensitivity. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search across PubMed, Embase, and the Cochrane Library was conducted. Studies published from January 2013 to October 2023 focusing on miRNA biomarkers for early PDAC detection were included. Data synthesis was performed through a narrative approach due to the heterogeneity of the studies. Nine studies met the inclusion criteria. Key findings include the elevated levels of specific miRNAs, such as miR-18a, miR-106a, and miR-25, in early-stage PDAC patients compared to controls. The integration of miRNA profiles with traditional biomarkers like CA19-9 showed improved diagnostic performance. However, challenges in the standardization of miRNA evaluation methodologies were noted. Circulating miRNAs demonstrate significant potential as non-invasive biomarkers for early PDAC detection. Despite promising results, further research and standardization are necessary for clinical application.

Maxillofacial challenge: Rare presentation of central giant cell tumor involving both maxilla and mandible.

INTRODUCTION AND IMPORTANCE: Central giant cell tumor (CGCT) of bone is an uncommon yet locally aggressive neoplasm originating from undifferentiated mesenchymal cells in bone marrow. This case report explores a rare presentation in the maxilla extending to the mandible, emphasizing the complexity of CGCT management and the need for a multidisciplinary approach. CASE PRESENTATION: A 35-year-old female presented with a progressively enlarging non-tender, firm swelling on the left maxilla and a similar mandibular swelling. Paraesthesia of the left lower lip and chin accompanied the mandibular swelling. CT scans and 3D reconstructions revealed expansive osteolytic defects affecting the maxilla and mandible. Biochemical tests supported a central giant cell tumor diagnosis. Histopathology confirmed spindle cell proliferation and multinucleated giant cells in both lesions. Surgical intervention involved excision and reconstruction. A five-month follow-up showed no recurrence, affirming the treatment's success. CLINICAL DISCUSSION: Central giant cell tumors (CGCTs) of bone are primarily benign, arising from undifferentiated mesenchymal cells. While mostly benign, they carry a rare potential for malignancy. Diagnosis involves imaging (CT, MRI, bone scintigraphy) and confirmation through biopsy. Surgical resection is the standard treatment, with radiotherapy considered in challenging cases. Recurrence rates vary with the extent of surgical intervention. Alternative treatments like cryotherapy and chemotherapy show varying success. CONCLUSION: This case emphasizes the necessity of precise histopathological diagnosis for CGCT management. The intricate nature of maxillary involvement, coupled with mandibular association, mandates a multidisciplinary approach. Surgery, while the primary treatment, should be judiciously determined based on tumor characteristics and recurrence.

Ferroptosis and Triple-Negative Breast Cancer: A Systematic Overview of Prognostic Insights and Therapeutic Potential.

In the realm of oncology, the prognosis and treatment of triple-negative breast cancer (TNBC) have long been challenges for researchers and clinicians. Characterized by its aggressive nature and limited therapeutic options, TNBC demands innovative approaches to understanding its underlying mechanisms and improving patient outcomes. One such avenue of exploration that has emerged in recent years is the study of ferroptosis, a form of regulated cell death driven by iron-dependent lipid peroxidation. Ferroptosis has garnered increasing attention due to its potential relevance in the context of TNBC. This systematic review aims to shed light on the intricate interplay between ferroptosis and the prognosis of TNBC. The article delves into a comprehensive examination of the existing literature to provide a holistic understanding of the subject. By investigating ferroptosis as both an intervention and a prognostic factor in TNBC, this article seeks to unravel its potential as a therapeutic target and prognostic marker. The emerging evidence and heterogeneity of ferroptosis in TNBC underscore the need for a systematic approach to assess its impact on patient outcomes. This review will serve as a valuable resource for researchers, clinicians, and healthcare professionals striving to enhance our knowledge of TNBC and explore novel avenues for prognosis and treatment.

From Microbes to Memories: Challenges and Future Perspectives Regarding the Gut-Brain Axis for Improved Cognitive Health in Alzheimer's.

The gut-brain axis, a bidirectional communication network between the gastrointestinal tract and the central nervous system, regulates various physiological processes crucial for health, including immune response, metabolism, and neurotransmitter production. In the context of neurodegenerative diseases, especially Alzheimer's disease (AD), understanding the intricate connection of the gut-brain axis has gained significance. Disturbances along this axis have been implicated in neurodegenerative diseases, emphasizing its role in AD pathogenesis. Microbiota dysbiosis, influenced by diet, lifestyle, and genetics, contributes to altered gut permeability, leading to protein dyshomeostasis, astroglial activation, neuroinflammation, and cognitive decline. Understanding these mechanisms is crucial for developing interventions to restore a healthy gut microbiota and potentially mitigate AD-related cognitive decline. The bidirectional communication along the gut-brain axis involves microbial metabolites, influencing oxidative stress, protein aggregation, and other pathways linked to neuroprotection. Modulating the gut microbiota through dietary changes, prebiotics, probiotics, or fecal microbiota transplantation emerges as a promising approach to target cognitive decline in AD. Despite progress, challenges persist, including the correlational nature of studies, the complexity of the gut microbiome, and variations in methodologies. Standardization is essential for reliable findings and the identification of biomarkers associated with AD. Unanswered questions warrant further exploration, particularly in understanding specific mechanisms, the temporal dynamics of microbiota changes, and the influence of diet and lifestyle on the gut-brain axis in AD. Future perspectives involve promising therapeutic interventions targeting the gut-brain axis, emphasizing personalized medicine to optimize outcomes based on individual variations in the gut-brain axis characteristics.

Beyond Diet and Exercise: The Impact of Gut Microbiota on Control of Obesity.

Obesity, a widespread health concern characterized by the excessive accumulation of body fat, is a complex condition influenced by genetics, environment, and social determinants. Recent research has increasingly focused on the role of gut microbiota in obesity, highlighting its pivotal involvement in various metabolic processes. The gut microbiota, a diverse community of microorganisms residing in the gastrointestinal tract, interacts with the host in a myriad of ways, impacting energy metabolism, appetite regulation, inflammation, and the gut-brain axis. Dietary choices significantly shape the gut microbiota, with diets high in fat and carbohydrates promoting the growth of harmful bacteria while reducing beneficial microbes. Lifestyle factors, like physical activity and smoking, also influence gut microbiota composition. Antibiotics and medications can disrupt microbial diversity, potentially contributing to obesity. Early-life experiences, including maternal obesity during pregnancy, play a vital role in the developmental origins of obesity. Therapeutic interventions targeting the gut microbiota, including prebiotics, probiotics, fecal microbiota transplantation, bacterial consortium therapy, and precision nutrition, offer promising avenues for reshaping the gut microbiota and positively influencing weight regulation and metabolic health. Clinical applications of microbiota-based therapies are on the horizon, with potential implications for personalized treatments and condition-based interventions. Emerging technologies, such as next-generation sequencing and advanced bioinformatics, empower researchers to identify specific target species for microbiota-based therapeutics, opening new possibilities in healthcare. Despite the promising outlook, microbiota-based therapies face challenges related to microbial selection, safety, and regulatory issues. However, with ongoing research and advances in the field, these challenges can be addressed to unlock the full potential of microbiota-based interventions.