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.

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Importance: Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs. Objective: To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS. Design: One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups. Setting: Two large U.S. health systems. Participants: Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. Intervention: The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Main Outcomes and Measures: Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission. Results: A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. Conclusions and Relevance: A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. Trial Registration: ClinicalTrials.gov Identifier: NCT03911687.

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.

The Potential of Fecal and Urinary Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer often diagnosed at advanced stages, highlighting the urgent need for early detection strategies. This systematic review explores the potential of fecal and urinary biomarkers for early PDAC detection. A comprehensive search identified eight relevant studies investigating various biomarkers, including proteins, metabolites, microbial profiles, DNA mutations, and non-coding RNAs. Promising findings suggest that urinary biomarkers related to metabolic alterations, inflammatory processes, fecal microbiome profiles, and fecal miRNAs hold diagnostic potential even at early stages of PDAC. Combining biomarkers into panels may enhance diagnostic accuracy. Challenges such as validation in larger cohorts, standardization of protocols, and regulatory approval must be addressed for clinical translation. Despite these hurdles, non-invasive urinary and fecal biomarkers represent a promising avenue for improving PDAC outcomes through early detection.

The Evaluation of Artificial Intelligence Technology for the Differentiation of Fresh Human Blood Cells From Other Species' Blood in the Investigation of Crime Scenes.

OBJECTIVES: The current study used the deep machine learning approach to differentiate human blood specimens from cow, goat, and chicken blood stains based on cell morphology. METHODS: A total of 1,955 known Giemsa-stained digitized images were acquired from the blood of humans, cows, goats, and chickens. To train the deep learning models, the well-known VGG16, Resnet18, and Resnet34 algorithms were used. Based on the image analysis, confusion matrices were generated. RESULTS: Findings showed that the F1 score for the chicken, cow, goat, and human classes were all equal to 1.0 for each of the three algorithms. The Matthews correlation coefficient (MCC) was 1 for chickens, cows, and humans in all three algorithms, while the MCC score was 0.989 for goats by ResNet18, and it was 0.994 for both ResNet34 and VGG16 algorithms. The three algorithms showed 100% sensitivity, specificity, and positive and negative predictive values for the human, cow, and chicken cells. For the goat cells, the data showed 100% sensitivity and negative predictive values with specificity and positive predictive values ranging from 98.5% to 99.6%. CONCLUSION: These data showed the importance of deep learning as a potential tool for the differentiation of the species of origin of fresh crime scene blood stains.

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.

Symplasmic and transmembrane zinc transport is modulated by cadmium in the Cd/Zn hyperaccumulator Sedum alfredii.

This study investigated the influence of Cd (25 µM) on Zn accumulation in a hyperaccumulating (HE) and a non-hyperaccumulating (NHE) ecotype of Sedum alfredii Hance at short-term supply of replete (Zn5, 5 µM) and excess (Zn400, 400 µM) Zn. Cd inhibited Zn accumulation in both ecotypes, especially under Zn400, in organs with active metal sequestration, i.e. roots of NHE and shoots of HE. Direct biochemical Cd/Zn competition at the metal-protein interaction and changes in transporter gene expression contributed to the observed accumulation patterns in the roots. Specifically, in HE, Cd stimulated SaZIP4 and SaPCR2 under Zn5, but downregulated SaIRT1 and SaZIP4 under Zn400. However, Cd downregulated related transporter genes, except for SaNRAMP1, in NHE, irrespective of Zn. Cadmium stimulated casparian strip (CSs) development in NHE, as part of the defense response, while it had a subtle effect on the (CS) in HE. Moreover, Cd delayed the initiation of the suberin lamellae (SL) in HE, but stimulated SL deposition in NHE under both Zn5 or Zn400. Changes in suberization were mainly ascribed to suberin-biosynthesis-related genes and hormonal signaling. Altogether, Cd regulated Zn accumulation mainly via symplasmic and transmembrane transport in HE, while Cd inhibited both symplasmic and apoplasmic Zn transport in NHE.

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.

A Deeper Depth of Response After Salvage Therapy Improves Outcomes of Autologous Stem Cell Transplantation in Relapsed Lymphoma and the Feasibility of Non-controlled Rate Freezing of Peripheral Blood Stem Cells.

Background High-dose chemotherapy followed by autologous stem cell transplantation is considered a standard treatment approach for patients with relapsed Hodgkin's lymphoma (HL) and non-Hodgkin lymphoma (NHL). The goal of autologous stem cell transplant in relapsed lymphoma is to achieve long-term disease control, i.e., cure, in contrast to disorders like multiple myeloma, where it only prolongs the duration of remission, progression-free survival, and improves the quality of life. Published outcomes of high-dose therapy and ASCT and the impact of different factors affecting survival in low- to middle-income countries are very limited. Our study analyzed all the autologous stem cell transplants performed in our center over a six-year period to ascertain engraftment, responses, outcomes, and variables that may have impacted transplant outcomes. Methods We conducted a retrospective study including 76 patients from January 2015 to December 2020. Data were retrieved from electronic medical records at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan. Results Out of a total of 82 autologous transplant patients, 76 were eligible for the study, out of which 50 (66%) had HL and 26 (34%) had NHL. The median age was 29 years (range 18-53) and 29 years (range 20-45) for HL and NHL, respectively. The male-to-female ratio was 5:2 and 4:1 for HL and NHL, respectively. The majority had advanced-stage disease, 85% in HL and 75% in NHL. The minimum cell dose infused was 2.5 million CD34+ cells/kg. Median days to platelets and ANC engraftment were 14 and 11 days, respectively. The 30-day transplant-related mortality was 8.9% and 7.4% in HL and NHL, respectively. The 100-day mortality was 15.2% and 11% in HL and NHL, respectively. The two-year disease-free survival (DFS) and overall survival (OS) were 83% and 83%, respectively, in HL patients. The two-year DFS and OS were 78% and 85%, respectively, in NHL patients. Conclusion High-dose therapy and autologous stem cell transplantation in low- to middle-income countries are limited to relatively younger patients, potentially curative conditions such as lymphoma, and predominantly after achieving a complete response to salvage therapy due to limited resources. Due to these factors, our study shows excellent response rates and survival outcomes compared to internationally published data. Engraftment was also excellent and comparable to published data despite the non-controlled rate freezing of peripheral blood stem cells.

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.

Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.

BACKGROUND: Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied. OBJECTIVES: The study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema. METHODS: Two pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes. RESULTS: RoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes. CONCLUSION: The study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.

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.