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The existence of latent cellular reservoirs is recognized as the major barrier to an HIV cure. Reactivating and eliminating "shock and kill" or permanently silencing "block and lock" the latent HIV reservoir, as well as gene editing, remain promising approaches, but so far have proven to be only partially successful. Moreover, using latency reversing agents or "block and lock" drugs pose additional considerations, including the ability to cause cellular toxicity, a potential lack of specificity for HIV, or low potency when each agent is used alone. RNA molecules, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are becoming increasingly recognized as important regulators of gene expression. RNA-based approaches for combatting HIV latency represent a promising strategy since both miRNAs and lncRNAs are more cell-type and tissue specific than protein coding genes. Thus, a higher specificity of targeting the latent HIV reservoir with less overall cellular toxicity can likely be achieved. In this review, we summarize current knowledge about HIV gene expression regulation by miRNAs and lncRNAs encoded in the human genome, as well as regulatory molecules encoded in the HIV g

HIV epidemiological data is increasingly complex, requiring advanced computation for accurate cluster detection and forecasting. We employed quantum-accelerated machine learning to analyze HIV prevalence at the ZIP-code level using AIDSVu and synthetic SDoH data for 2022. Our approach compared classical clustering (DBSCAN, HDBSCAN) with a quantum approximate optimization algorithm (QAOA), developed a hybrid quantum-classical neural network for HIV prevalence forecasting, and used quantum Bayesian networks to explore causal links between SDoH factors and HIV incidence. The QAOA-based method achieved 92% accuracy in cluster detection within 1.6 seconds, outperforming classical algorithms. Meanwhile, the hybrid quantum-classical neural network predicted HIV prevalence with 94% accuracy, surpassing a purely classical counterpart. Quantum Bayesian analysis identified housing instability as a key driver of HIV cluster emergence and expansion, with stigma exerting a geographically variable influence. These quantum-enhanced methods deliver greater precision and efficiency in HIV surveillance while illuminating critical causal pathways. This work can guide targeted interventions, optimize r

Estimating new HIV infections is significant yet challenging due to the difficulty in distinguishing between recent and long-term infections. We demonstrate that HIV recency status (recent v.s. long-term) could be determined from the combination of self-report testing history and biomarkers, which are increasingly available in bio-behavioral surveys. HIV recency status is partially observed, given the self-report testing history. For example, people who tested positive for HIV over one year ago should have a long-term infection. Based on the nationally representative samples collected by the Population-based HIV Impact Assessment (PHIA) Project, we propose a likelihood-based probabilistic model for HIV recency classification. The model incorporates both labeled and unlabeled data and integrates the mechanism of how HIV recency status depends on biomarkers and the mechanism of how HIV recency status, together with the self-report time of the most recent HIV test, impacts the test results, via a set of logistic regression models. We compare our method to logistic regression and the binary classification tree (current practice) on Malawi, Zimbabwe, and Zambia PHIA data, as well as on

The OraQuick In-Home HIV self-test represents a fast, inexpensive, and convenient method for users to assess their HIV status. If integrated thoughtfully into existing testing practices, accompanied by efficient pathways to formal diagnosis, self-testing could both enhance HIV awareness and reduce HIV incidence. However, currently available self-tests are less sensitive, particularly for recent infection, than gold-standard laboratory tests. It is important to understand the impact if some portion of standard testing is replaced by self-tests. We introduced a novel compartmental model to evaluate the effects of self-testing among gay, bisexual and other men who have sex with men (MSM) in the United States for the period 2020 to 2030. We varied the model for different screening rates, self-test proportions, and delays to diagnosis for those identified through self-tests to determine the potential impact on HIV incidence and awareness of status. When HIV self-tests are strictly supplemental, self-testing can decrease HIV incidence among MSM in the US by up to 10% and increase awareness of status among MSM from 85% to 91% over a 10-year period, provided linkage to care and formal diag

Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, compari

The government's effort to alleviate HIV stigma has been justified by the suppression effect of stigma on the HIV testing rate. Nevertheless, the deterrence effect of stigma on undesirable sexual behaviours has long been overlooked. This study adapts the existing framework on HIV stigma with an additional stage that formally models people's choices on whether to take preventive measures in sex. The model shows that, when sex is explicitly modelled, the suppression and deterrence effects coexist, which makes the net societal impact of HIV stigma ambiguous. A utilitarian welfare analysis concludes that the welfare-maximizing stigma level can be higher than its natural level, implying that the government's effort to reduce stigma is not always welfare-improving. Instead, the study provides a rationale for maintaining a certain level of HIV stigma to maximize social welfare.

Background: High HIV transmission persists in many U.S. jurisdictions despite prevention efforts. HIV self-testing offers a means to overcome barriers associated with routine laboratory-based testing but carries a risk of increasing incidence if replacement effects reduce overall test sensitivity. Methods: A linearized four-compartment HIV transmission model was applied to 38 Ending the HIV Epidemic (EHE) priority jurisdictions. A threshold testing level was defined to counterbalance potential negative effects from reduced self-test sensitivity. Both the percentage of self-tests and the overall testing rate were varied to quantify 10-year changes in HIV incidence. Results: Substantial heterogeneity emerged across districts. Incidence reductions exceeded 5 percent in some areas, while others saw only minor effects. Jurisdictions with higher baseline testing displayed an elevated risk of increased incidence from substitution of laboratory-based testing with self-tests. In contrast, a derived Awareness Reproduction Number, capturing transmissions attributable to undiagnosed infection, strongly correlated with the magnitude of possible incidence declines. Conclusions: Local epidemiolog

eHealth has strong potential to advance HIV care in low- and middle-income countries. Given the sensitivity of HIV-related information and the risks associated with unintended HIV status disclosure, clients' privacy perceptions towards eHealth applications should be examined to develop client-centered technologies. Through focus group discussions with antiretroviral therapy (ART) clients from Lighthouse Trust, Malawi's public HIV care program, we explored perceptions of data security and privacy, including their understanding of data flow and their concerns about data confidentiality across several layers of data use. Our findings highlight the broad privacy concerns that affect ART clients' day-to-day choices, clients' trust in Malawi's health system, and their acceptance of, and familiarity with, point-of-care technologies used in HIV care. Based on our findings, we provide recommendations for building robust digital health systems in low- and middle-income countries with limited resources, nascent privacy regulations, and political will to take action to protect client data.

Introduction: The value of integrating federal HIV services data with HIV surveillance is currently unknown. Upstream and complete case capture is essential in preventing future HIV transmission. Methods: This study integrated Ryan White, Social Security Disability Insurance, Medicare, Children Health Insurance Programs and Medicaid demographic aggregates from 2005 to 2018 for people living with HIV and compared them with Centers for Disease Control and Prevention HIV surveillance by demographic aggregate. Surveillance Unknown, Service Known (SUSK) candidate aggregates were identified from aggregates where services aggregate volumes exceeded surveillance aggregate volumes. A distribution approach and a deep learning model series were used to identify SUSK candidate aggregates where surveillance cases exceeded services cases in aggregate. Results: Medicare had the most candidate SUSK aggregates. Medicaid may have candidate SUSK aggregates where cases approach parity with surveillance. Deep learning was able to detect candidate SUSK aggregates even where surveillance cases exceed service cases. Conclusions: Integration of CMS case level records with HIV surveillance records can incre

With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

Incidence estimation of HIV infection can be performed using recent infection testing algorithm (RITA) results from a cross-sectional sample. This allows practitioners to understand population trends in the HIV epidemic without having to perform longitudinal follow-up on a cohort of individuals. The utility of the approach is limited by its precision, driven by the (low) sensitivity of the RITA at identifying recent infection. By utilizing results of previous HIV tests that individuals may have taken, we consider an enhanced RITA with increased sensitivity (and specificity). We use it to propose an enhanced estimator for incidence estimation. We prove the theoretical properties of the enhanced estimator and illustrate its numerical performance in simulation studies. We apply the estimator to data from a cluster-randomized trial to study the effect of community-level HIV interventions on HIV incidence. We demonstrate that the enhanced estimator provides a more precise estimate of HIV incidence compared to the standard estimator.

Accurate HIV incidence estimation based on individual recent infection status (recent vs long-term infection) is important for monitoring the epidemic, targeting interventions to those at greatest risk of new infection, and evaluating existing programs of prevention and treatment. Starting from 2015, the Population-based HIV Impact Assessment (PHIA) individual-level surveys are implemented in the most-affected countries in sub-Saharan Africa. PHIA is a nationally-representative HIV-focused survey that combines household visits with key questions and cutting-edge technologies such as biomarker tests for HIV antibody and HIV viral load which offer the unique opportunity of distinguishing between recent infection and long-term infection, and providing relevant HIV information by age, gender, and location. In this article, we propose a semi-supervised logistic regression model for estimating individual level HIV recency status. It incorporates information from multiple data sources -- the PHIA survey where the true HIV recency status is unknown, and the cohort studies provided in the literature where the relationship between HIV recency status and the covariates are presented in the fo

During an infection, HIV experiences strong selection by immune system T cells. Recent experimental work has shown that MHC escape mutations form an important pathway for HIV to avoid such selection. In this paper, we study a model of MHC escape mutation. The model is a predator-prey model with two prey, composed of two HIV variants, and one predator, the immune system CD8 cells. We assume that one HIV variant is visible to CD8 cells and one is not. The model takes the form of a system of stochastic differential equations. Motivated by well-known results concerning the short life-cycle of HIV intrahost, we assume that HIV population dynamics occur on a faster time scale then CD8 population dynamics. This separation of time scales allows us to analyze our model using an asymptotic approach. Using this model we study the impact of an MHC escape mutation on the population dynamics and genetic evolution of the intrahost HIV population. From the perspective of population dynamics, we show that the competition between the visible and invisible HIV variants can reach steady states in which either a single variant exists or in which coexistence occurs depending on the parameter regime. We

What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It's also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare ins

Cytotoxic T lymphocytes (CTLs) are immune system cells that are thought to play an important role in controlling HIV infection. We develop a stochastic ODE model of HIV-CTL interaction that extends current deterministic ODE models. Based on this stochastic model, we consider the effect of CTL attack on intrahost HIV lineages assuming CTLs attack several epitopes with equal strength. In this setting, we introduce a limiting version of our stochastic ODE under which we show that the coalescence of HIV lineages can be described by a simple paintbox construction. Through numerical experiments, we show that our results under the limiting stochastic ODE accurately reflect HIV lineages under CTL attack when the HIV population size is on the low end of its hypothesized range. Current techniques of HIV lineage construction depend on the Kingman coalescent. Our results give an explicit connection between CTL attack and HIV lineages.

The last few years have seen rapid progress in transitioning quantum computing from lab to industry. In healthcare and life sciences, more than 40 proof-of-concept experiments and studies have been conducted; an increasing number of these are even run on real quantum hardware. Major investments have been made with hundreds of millions of dollars already allocated towards quantum applications and hardware in medicine. In addition to pharmaceutical and life sciences uses, clinical and medical applications are now increasingly coming into the picture. This chapter focuses on three key use case areas associated with (precision) medicine, including genomics and clinical research, diagnostics, and treatments and interventions. Examples of organizations and the use cases they have been researching are given; ideas how the development of practical quantum computing applications can be further accelerated are described.

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario d

We analyze 14,651 HIV1 reverse transcriptase (HIV RT) sequences from the Stanford HIV Drug Resistance Database labeled with treatment regimen in order to study the evolution this enzyme under drug selection in the clinic. Our goal is to identify distinct sectors of HIV RT's sequence space that are undergoing evolution as a way to identify individual selections and/or evolutionary solutions. We utilize Uniform Manifold Approximation and Projection (UMAP), a graph-based dimensionality reduction technique uniquely suited for the detection of non-linear dependencies and visualize the results using an unsupervised clustering algorithm based on density analysis. Our analysis produced 21 distinct clusters of sequences. Supporting the biological significance of these clusters, they tend to represent phylogenetically related sequences with strong correspondence to distinct treatment regimens. Thus, this method for visualization of areas of HIV RT undergoing evolution can help infer information about selective pressures, although it is correlative. The mutation signatures associated with each cluster may represent the higher-order epistatic context facilitating these evolutionary pathways, i

Sexual contacts are the main spreading route of HIV. This puts sex workers at higher risk of infection even in populations where HIV prevalence is moderate or low. Alongside condom use, Pre-Exposure Prophylaxis (PrEP) is an effective tool for sex workers to reduce their risk of HIV acquisition. However, PrEP provides no direct protection against sexually transmitted infections (STIs) other than HIV, unlike condoms. We use an empirical network of sexual contacts among female sex workers (FSWs) and clients to simulate the spread of HIV and gonorrhea. We then investigate the effect of PrEP adoption and adherence, on both HIV and gonorrhea prevalence. We also study the effect of a potential increase in condomless acts due to lowered risk perception with respect of the no-PrEP scenario (risk compensation). We find that when HIV is the only disease circulating, PrEP is effective in reducing HIV prevalence, even with high risk compensation. Instead, the complex interplay between the two diseases shows that different levels of risk compensation require different intervention strategies. Finally, we find that providing PrEP only to the most active FSWs is less effective than uniform PrEP ad