Lymphoid organ-targeted nanomaterials for immunomodulation of cancer, inflammation, and beyond.

Nanomaterials exhibit significant potential for stimulating immune responses, offering both local and systemic modulation across a variety of diseases. The lymphoid organs, such as the spleen and lymph nodes, are home to various immune cells, including monocytes and dendritic cells, which contribute to both the progression and prevention/treatment of diseases. Consequently, many nanomaterial formulations are being rationally designed to target these organs and engage with specific cell types, thereby inducing therapeutic and protective effects. In this review, we explore crucial cellular interactions and processes involved in immune regulation and highlight innovative nano-based immunomodulatory approaches. We outline essential considerations in nanomaterial design with an emphasis on their impact on biological interactions, targeting capabilities, and treatment efficacy. Through selected examples, we illustrate the strategic targeting of therapeutically active nanomaterials to lymphoid organs and the subsequent immunomodulation for infection resistance, inflammation suppression, self-antigen tolerance, and cancer immunotherapy. Additionally, we address current challenges, discuss emerging topics, and share our outlook on future developments in the field.

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.

Background: Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. Objective: This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. Methods: The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient's OUD risk level, which was then compared to the ML application's risk assignments. Results: A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. Conclusions: A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.

Case report: Exploring the utility of whole-body bone scintigraphy for pediatric Langerhans cell histiocytosis: insights from clinical practice.

Purpose: This mini-review delves into the realm of Langerhans cell histiocytosis (LCH) in children, focusing on its skeletal involvement. By synthesizing pertinent literature, we sought to provide a comprehensive understanding of LCH's clinical and radiographic spectrum. Our study then demonstrates the diagnostic prowess of whole-body 99mTc-methyl diphosphonate (MDP) scintigraphy in LCH cases, underscoring its value in tandem with existing knowledge. Methods: Our approach involved an extensive literature review that contextualized LCH within the current medical landscape. Subsequently, we presented a case series featuring five pediatric instances of skeletal LCH, one accompanied by soft tissue infiltration. The principal aim was to illuminate the diagnostic and staging potential of whole-body 99mTc-MDP scintigraphy, augmenting existing insights. Results: Through meticulous literature synthesis, we highlighted pediatric LCH's protean clinical manifestations and radiological variability. Aligning with this spectrum, our case series underscored the role of 99mTc-MDP scintigraphy in diagnosing and staging LCH. Among the five pediatric cases, one demonstrated concurrent soft tissue involvement. This aligns with the multifaceted nature of LCH presentations. Conclusion: Pediatric LCH can present with a wide range of clinical and radiologic features. By amalgamating our cases with extant literature, we stress the necessity of a multimodal strategy. 99mTc-MDP scintigraphy emerged as an indispensable tool for accurate staging and soft tissue detection. Our findings collectively advocate for a holistic approach to managing LCH, ensuring informed therapeutic decisions for optimal patient outcomes.