Cancer screening with multicancer detection tests: A translational science review.

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test.

Evaluating large language models on medical, lay language, and self-reported descriptions of genetic conditions.

Large language models (LLMs) are generating interest in medical settings. For example, LLMs can respond coherently to medical queries by providing plausible differential diagnoses based on clinical notes. However, there are many questions to explore, such as evaluating differences between open- and closed-source LLMs as well as LLM performance on queries from both medical and non-medical users. In this study, we assessed multiple LLMs, including Llama-2-chat, Vicuna, Medllama2, Bard/Gemini, Claude, ChatGPT3.5, and ChatGPT-4, as well as non-LLM approaches (Google search and Phenomizer) regarding their ability to identify genetic conditions from textbook-like clinician questions and their corresponding layperson translations related to 63 genetic conditions. For open-source LLMs, larger models were more accurate than smaller LLMs: 7b, 13b, and larger than 33b parameter models obtained accuracy ranges from 21%-49%, 41%-51%, and 54%-68%, respectively. Closed-source LLMs outperformed open-source LLMs, with ChatGPT-4 performing best (89%-90%). Three of 11 LLMs and Google search had significant performance gaps between clinician and layperson prompts. We also evaluated how in-context prompting and keyword removal affected open-source LLM performance. Models were provided with 2 types of in-context prompts: list-type prompts, which improved LLM performance, and definition-type prompts, which did not. We further analyzed removal of rare terms from descriptions, which decreased accuracy for 5 of 7 evaluated LLMs. Finally, we observed much lower performance with real individuals' descriptions; LLMs answered these questions with a maximum 21% accuracy.

Ensuring best practice in genomics education: A theory- and empirically informed evaluation framework.

Implementation of genomic medicine into healthcare requires a workforce educated through effective educational approaches. However, ascertaining the impact of genomics education activities or resources is limited by a lack of evaluation and inconsistent descriptions in the literature. We aim to support those developing genomics education to consider how best to capture evaluation data that demonstrate program outcomes and effectiveness within scope. Here, we present an evaluation framework that is adaptable to multiple settings for use by genomics educators with or without education or evaluation backgrounds. The framework was developed as part of a broader program supporting genomic research translation coordinated by the Australian Genomics consortium. We detail our mixed-methods approach involving an expert workshop, literature review and iterative expert input to reach consensus and synthesis of a new evaluation framework for genomics education. The resulting theory-informed and evidence-based framework encompasses evaluation across all stages of education program development, implementation and reporting, and acknowledges the critical role of stakeholders and the effects of external influences.

Ensuring best practice in genomics education: A scoping review of genomics education needs assessments and evaluations.

A health workforce capable of implementing genomic medicine requires effective genomics education. Genomics education interventions developed for health professions over the last two decades, and their impact, are variably described in the literature. To inform an evaluation framework for genomics education, we undertook an exploratory scoping review of published needs assessments for, and/or evaluations of, genomics education interventions for health professionals from 2000 to 2023. We retrieved and screened 4,659 records across the two searches with 363 being selected for full-text review and consideration by an interdisciplinary working group. 104 articles were selected for inclusion in the review-60 needs assessments, 52 genomics education evaluations, and eight describing both. Included articles spanned all years and described education interventions in over 30 countries. Target audiences included medical specialists, nurses/midwives, and/or allied health professionals. Evaluation questions, outcomes, and measures were extracted, categorized, and tabulated to iteratively compare measures across stages of genomics education evaluation: planning (pre-implementation), development and delivery (implementation), and impact (immediate, intermediate, or long-term outcomes). They are presented here along with descriptions of study designs. We document the wide variability in evaluation approaches and terminology used to define measures and note that few articles considered downstream (long-term) outcomes of genomics education interventions. Alongside the evaluation framework for genomics education, results from this scoping review form part of a toolkit to help educators to undertake rigorous genomics evaluation that is fit for purpose and can contribute to the growing evidence base of the contribution of genomics education in implementation strategies for genomic medicine.

Pressure-enhanced sensing of tissue oxygenation via endogenous porphyrin: Implications for dynamic visualization of cancer in surgery.

Fluorescence guidance is routinely used in surgery to enhance perfusion contrast in multiple types of diseases. Pressure-enhanced sensing of tissue oxygenation (PRESTO) via fluorescence is a technique extensively analyzed here, that uses an FDA-approved human precursor molecule, 5-aminolevulinic acid (ALA), to stimulate a unique delayed fluorescence signal that is representative of tissue hypoxia. The ALA precontrast agent is metabolized in most tissues into a red fluorescent molecule, protoporphyrin IX (PpIX), which has both prompt fluorescence, indicative of the concentration, and a delayed fluorescence, that is amplified in low tissue oxygen situations. Applied pressure from palpation induces transient capillary stasis and a resulting transient PRESTO contrast, dominant when there is near hypoxia. This study examined the kinetics and behavior of this effect in both normal and tumor tissues, with a prolonged high PRESTO contrast (contrast to background of 7.3) across 5 tumor models, due to sluggish capillaries and inhibited vasodynamics. This tissue function imaging approach is a fundamentally unique tool for real-time palpation-induced tissue response in vivo, relevant for chronic hypoxia, such as vascular diseases or oncologic surgery.

Practical Guidance for Clinical Microbiology Laboratories: Microbiologic diagnosis of implant-associated infections.

SUMMARYImplant-associated infections (IAIs) pose serious threats to patients and can be associated with significant morbidity and mortality. These infections may be difficult to diagnose due, in part, to biofilm formation on device surfaces, and because even when microbes are found, their clinical significance may be unclear. Despite recent advances in laboratory testing, IAIs remain a diagnostic challenge. From a therapeutic standpoint, many IAIs currently require device removal and prolonged courses of antimicrobial therapy to effect a cure. Therefore, making an accurate diagnosis, defining both the presence of infection and the involved microorganisms, is paramount. The sensitivity of standard microbial culture for IAI diagnosis varies depending on the type of IAI, the specimen analyzed, and the culture technique(s) used. Although IAI-specific culture-based diagnostics have been described, the challenge of culture-negative IAIs remains. Given this, molecular assays, including both nucleic acid amplification tests and next-generation sequencing-based assays, have been used. In this review, an overview of these challenging infections is presented, as well as an approach to their diagnosis from a microbiologic perspective.

Forecasting the Economic Burden of Cardiovascular Disease and Stroke in the United States Through 2050: A Presidential Advisory From the American Heart Association.

BACKGROUND: Quantifying the economic burden of cardiovascular disease and stroke over the coming decades may inform policy, health system, and community-level interventions for prevention and treatment. METHODS: We used nationally representative health, economic, and demographic data to project health care costs attributable to key cardiovascular risk factors (hypertension, diabetes, hypercholesterolemia) and conditions (coronary heart disease, stroke, heart failure, atrial fibrillation) through 2050. The human capital approach was used to estimate productivity losses from morbidity and premature mortality due to cardiovascular conditions. RESULTS: One in 3 US adults received care for a cardiovascular risk factor or condition in 2020. Annual inflation-adjusted (2022 US dollars) health care costs of cardiovascular risk factors are projected to triple between 2020 and 2050, from $400 billion to $1344 billion. For cardiovascular conditions, annual health care costs are projected to almost quadruple, from $393 billion to $1490 billion, and productivity losses are projected to increase by 54%, from $234 billion to $361 billion. Stroke is projected to account for the largest absolute increase in costs. Large relative increases among the Asian American population (497%) and Hispanic American population (489%) reflect the projected increases in the size of these populations. CONCLUSIONS: The economic burden of cardiovascular risk factors and overt cardiovascular disease in the United States is projected to increase substantially in the coming decades. Development and deployment of cost-effective programs and policies to promote cardiovascular health are urgently needed to rein in costs and to equitably enhance population health.

2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

AIM: The "2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease" provides recommendations to guide clinicians in the treatment of patients with lower extremity peripheral artery disease across its multiple clinical presentation subsets (ie, asymptomatic, chronic symptomatic, chronic limb-threatening ischemia, and acute limb ischemia). METHODS: A comprehensive literature search was conducted from October 2020 to June 2022, encompassing studies, reviews, and other evidence conducted on human subjects that was published in English from PubMed, EMBASE, the Cochrane Library, CINHL Complete, and other selected databases relevant to this guideline. Additional relevant studies, published through May 2023 during the peer review process, were also considered by the writing committee and added to the evidence tables where appropriate. STRUCTURE: Recommendations from the "2016 AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing comprehensive care for patients with peripheral artery disease have been developed.

Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator.

Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.

Medical Microrobots.

Scientists around the world have long aimed to produce miniature robots that can be controlled inside the human body to aid doctors in identifying and treating diseases. Such microrobots hold the potential to access hard-to-reach areas of the body through the natural lumina. Wireless access has the potential to overcome drawbacks of systemic therapy, as well as to enable completely new minimally invasive procedures. The aim of this review is fourfold: first, to provide a collection of valuable anatomical and physiological information on the target working environments together with engineering tools for the design of medical microrobots; second, to provide a comprehensive updated survey of the technological state of the art in relevant classes of medical microrobots; third, to analyze currently available tracking and closed-loop control strategies compatible with the in-body environment; and fourth, to explore the challenges still in place, to steer and inspire future research.

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data.

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Enhanced prostatic Esr1+ luminal epithelial cells in the absence of SRD5A2.

Steroid 5α reductase 2 (SRD5A2) converts testosterone to dihydrotestosterone and is crucial for prostatic development. 5α reductase inhibitors (5ARI) reduce prostate size in benign prostate hyperplasia (BPH) and ameliorate lower urinary tract symptoms secondary to BPH. However, the mechanisms of 5ARI functioning are still not fully understood. Here, we used a Srd5a2-/- mouse model and employed single-cell RNA sequencing to explore the impact of SRD5A2 absence on prostate cellular heterogeneity. Significant alterations in luminal epithelial cell (LE) populations were observed, alongside an increased proportion and proliferative phenotype of estrogen receptor 1 (ESR1)+ LE2 cells, following an SRD5A2-independent ESR1 differentiation trajectory. LE2 cells exhibited enhanced estrogen response gene signatures, suggesting an alternative pathway for prostate growth when SRD5A2 is absent. Human prostate biopsy analysis revealed an inverse correlation between the expressions of SRD5A2 and LE2 markers (ESR1/PKCα), and an inverse correlation between SRD5A2 and the clinical efficiency of 5ARI. These findings provide insights into 5ARI resistance mechanisms and potential alternative therapies for BPH-related lower urinary tract symptoms. © 2024 The Pathological Society of Great Britain and Ireland.

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. More specifically, both of the proposed frameworks utilize AlpaCare as base LLM which employs both few-shot in-context learning and instruction tuning techniques to extract PICO-related terms from the clinical trial reports. We applied these approaches to the widely used coarse-grained datasets such as EBM-NLP, EBM-COMET and fine-grained datasets such as EBM-NLPrev and EBM-NLPh. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at https://github.com/shrimonmuke0202/AlpaPICO.git.

Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model.

The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co-morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, ICD-10 coding is widely adopted worldwide. However, public hospitals in Pakistan have yet to implement it and automate the coding process. In this research, we implemented ICD-10-CM coding for a private database and named it Clinical Pool of Liver Transplant (CPLT). Additionally, we proposed a novel deep learning model called Deep Recurrent-Convolution Neural Network with a lambda-scaled Attention module (DRCNN-ATT) using the CPLT database to achieve automatic ICD-10-CM coding. DRCNN-ATT combines a bi-directional long short-term memory network (bi-LSTM), a multi-scale convolutional neural network (MS-CNN), and a lambda-scaled attention module. Experimental results demonstrate that deep recurrent convolutional neural network (DRCNN) faces attention score vanishing problem with a standard attention module for automatic ICD coding. However, adding a lambda-scaled attention module resolves this issue. We evaluated DRCNN-ATT model using two distinct datasets: a private CPLT dataset and a public MIMIC III top 50 dataset. The results indicate that the DRCNN-ATT model outperformed various baselines by generating 0.862 micro F1 and 0.25 macro F1 scores on CPLT dataset and 0.705 micro F1 and 0.655 macro F1 scores on MIMIC III top 50 dataset. Furthermore, we also deployed our model for automatic ICD-10-CM coding using ngrok and the Flask APIs, which receives input, processes it, and then returns the results.