Citations

Cells expressing features of senescence, including upregulation of p21 and p16, appear transiently following tissue injury, yet the properties of these cells or how they contrast with age-induced senescent cells remains unclear. Here, we used skeletal injury as a model and identified the rapid appearance following fracture of p21 + cells expressing senescence markers, mainly as osteochondroprogenitors (OCHs) and neutrophils. Targeted genetic clearance of p21 + cells suppressed senescence-associated signatures within the fracture callus and accelerated fracture healing. By contrast, p21 + cell clearance did not alter bone loss due to aging; conversely, p16 + cell clearance, known to alleviate skeletal aging, did not affect fracture healing. Following fracture, p21 + neutrophils were enriched in signaling pathways known to induce paracrine stromal senescence, while p21 + OCHs were highly enriched in senescence-associated secretory phenotype factors known to impair bone formation. Further analysis revealed an injury-specific stem cell–like OCH subset that was p21 + and highly inflammatory, with a similar inflammatory mesenchymal population (fibro-adipogenic progenitors) evident following muscle injury. Thus, intercommunicating senescent-like neutrophils and mesenchymal progenitor cells were key regulators of tissue repair in bone and potentially across tissues. Moreover, our findings established contextual roles of p21 + versus p16 + senescent/senescent-like cells that may be leveraged for therapeutic opportunities.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully.

Abstract Purpose: We previously reported that postmenopausal women with estrogen receptor-α–positive breast cancer receiving adjuvant anastrozole 1 mg/day (ANA1) with estrone (E1) ≥1.3 pg/mL and estradiol (E2) ≥0.5 pg/mL [inadequate estrogen suppression (IES)] had a threefold increased risk of a breast cancer event. The objective of this study was to determine if increasing anastrozole to 10 mg/day (ANA10) could result in adequate estrogen suppression (AES: E1 <1.3 pg/mL and/or E2 <0.5 pg/mL) among those with IES on ANA1. Patients and Methods: Postmenopausal women with estrogen receptor-α–positive breast cancer planning to receive adjuvant ANA1 were eligible. E1 and E2 were assessed pre- and post-8 to 10 weeks of ANA1. Those with IES were switched to 8- to 10-week cycles of ANA10 followed by letrozole 2.5 mg/day. E1 and E2 were assessed after each cycle. Anastrozole concentrations were measured post-ANA1 and post-ANA10. Primary analyses included patients who documented taking at least 80% of the planned treatment (adherent cohort). Results: In total, 132 (84.6%) of 156 eligible patients were ANA1 adherent. IES occurred in 40 (30.3%) adherent patients. Twenty-five (78.1%) of 32 patients who began ANA10 were adherent, and AES was achieved in 19 (76.0%; 90% confidence interval, 58.1%–89.0%) patients. Anastrozole concentrations post-ANA1 and post-ANA10 did not differ by estrogen suppression status among adherent patients. AES was maintained/attained in 21 (91.3%) of 23 letrozole-adherent patients. Conclusions: Approximately 30% of ANA1-adherent patients had IES. Among those who switched to ANA10 and were adherent, 76% had AES. Further studies are required to validate emerging data that ANA1 results in IES for some patients and to determine the clinical benefit of switching to ANA10 or an alternative aromatase inhibitor.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non–Small Cell Lung Cancer (NSCLC) provide recommendations for the treatment of patients with NSCLC, including diagnosis, primary disease management, surveillance for relapse, and subsequent treatment. The panel has updated the list of recommended targeted therapies based on recent FDA approvals and clinical data. This selection from the NCCN Guidelines for NSCLC focuses on treatment recommendations for advanced or metastatic NSCLC with actionable molecular biomarkers.

Bladder cancer, the sixth most common cancer in the United States, is most commonly of the urothelial carcinoma histologic subtype. The clinical spectrum of bladder cancer is divided into 3 categories that differ in prognosis, management, and therapeutic aims: (1) non–muscle-invasive bladder cancer (NMIBC); (2) muscle invasive, nonmetastatic disease; and (3) metastatic bladder cancer. These NCCN Guidelines Insights detail recent updates to the NCCN Guidelines for Bladder Cancer, including changes in the fifth edition of the WHO Classification of Tumours: Urinary and Male Genital Tumours and how the NCCN Guidelines aligned with these updates; new and emerging treatment options for bacillus Calmette-Guérin (BCG)–unresponsive NMIBC; and updates to systemic therapy recommendations for advanced or metastatic disease.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Abstract Background Perioperative morbidity and mortality remain a point of concern for the Associating Liver Partition and Portal Vein Ligation for Staged hepatectomy (ALPPS) procedure. Post-hepatectomy liver failure (PHLF) after the second step poses the most significant risk factor for futile outcome. Recovery of liver function after the first step of ALPPS is critically important to allow for sufficient liver function after this surgery. The APRI+ALBI score has been proposed as an easily assessable score to monitor liver function and its dynamic recovery. Aims We explored if the APRI+ALBI score was able to assess liver function recovery after the first step of ALPPS and allow for optimal timing of the 2nd step. Methods Based on the ALPPS registry, patients from 2012 to 2020 with available APRI+ALBI score were included. Postoperative outcomes (PHLF A – C, PHLF B + C, 90-day mortality and severe morbidity) were defined as per standard definitions. The APRI/ALBI score was monitored perioperatively. Results Overall, 464 patients from 16 international participating centers in the ALPPS registry were included. Clinically relevant PHLF (B + C) was observed in 7.5 % and of these 63 % of patients ultimately died due to liver failure. The APRI+ALBI score immediately increased after each surgical intervention and continuously decrease thereafter. Failure of the APRI+ALBI score to decrease until the 2nd step of ALPPS predicted PHLF B+C (p = 0.001; AUC 0.78). Particularly patients with high APRI+ALBI scores and short interstage intervals were at risk to develop PHLF B+C. Conclusion The APRI+ALBI score objectifies liver function recovery after the 1st step of ALPPS and failure to sufficiently decreased is associated with a higher risk for PHLF B+C. This might allow for optimized planning of the 2nd step of ALPPS to allow for sufficient liver function recovery to minimize the risk for PHLF B+C and associated futile outcome.

Gastroparesis presents with nausea, early satiety, vomiting, bloating, distention, and abdominal pain and is diagnosed by documenting delayed passage of contents from the stomach to the intestines in the absence of mechanical obstruction. Several factors contribute to gastroparesis pathophysiology. Pylorospasm, or increased pyloric contractility, is present in many patients and may be a target for therapy of cases of refractory gastroparesis when medical management is insufficient. Pylorus-directed interventions including botulinum toxin injections, pneumatic balloon dilation, stenting, surgical pyloroplasty, and gastric per-oral endoscopic myotomy are increasingly employed to treat selected patients with gastroparesis who are unresponsive to medications.