LongeCityNews Last Updated: 21 February 2026 - 11:20 PM

Cytomegalovirus is a form of herpesvirus that is prevalent in the human population. As is the case for other herpesviruses, the immune system struggles to clear cytomegalovirus from the body. It becomes a persistent infection. Few people make it to late life without being infected, at least judging by those regions of the world where there is good data on cytomegalovirus prevalence. Cytomegalovirus infection typically goes unnoticed and produces no evident symptoms, at least in the vast majority of individuals who have a normally functioning immune system. But evidence suggests that the presence of cytomegalovirus infection has a corrosive effect on the immune system in late life. Ever more cells become specialized to focus on cytomegalovirus at the expense of populations needed to conduct other activities.

Researchers have correlated the presence of cytomegalovirus with risk of various age-related diseases, but it is unclear as to whether (a) cytomegalovirus infection selects for individuals with more dysfunctional immune systems and thus a higher burden of inflammation to drive the onset and progression of age-related diseases, or (b) cytomegalovirus is actively contributing to disease progression in some way, whether via promoting immune dysfunction and inflammation, or some other mechanism or mechanisms. It is also unclear as to how great a contribution is provided to disease progression by cytomegalovirus, if it is indeed providing a meaningful contribution. These sorts of questions are hard to definitively answer in human medicine. The most feasible approach is probably to develop the means to clear cytomegalovirus from the body, and see how the uninfected fare versus the infected over the long term.

Human cytomegalovirus infection and cognitive decline: insights from population and experimental studies

Human cytomegalovirus (HCMV), a ubiquitous DNA betaherpesvirus, is capable of persistent infection and immunomodulation, particularly in immunocompromised and elderly hosts. Emerging evidence links HCMV to neurodegenerative diseases through its multifaceted immunomodulatory effects. This review summarizes key viral architectures and mechanisms, epidemiological trends, and experimental data supporting HCMV's role in cognitive decline.

The association between HCMV infection and cognitive impairment has been explored across multiple large-scale studies, though findings remain heterogeneous. In the Sacramento Area Latino Study on Aging (SALSA), a prospective cohort of 1,204 older Mexican Americans (mean age 70.3 ± 6.8), higher HCMV IgG levels - but not HSV-1 - were significantly associated with accelerated cognitive decline over four years, independent of age, sex, education, income, and comorbidities. Postmortem and in vitro studies further implicate HCMV in neurodegenerative processes. In a PCR-based analysis, HCMV DNA was detected in 93% of brain specimens from patients with vascular dementia, compared to 34% of age-matched controls. In AD patients, HCMV seropositivity has been associated with increased neurofibrillary tangle (NFT) burden and elevated interferon-γ levels in cerebrospinal fluid (CSF) - a cytokine detected only in seropositive individuals .

Animal studies have also provided mechanistic insights into how cytomegalovirus infection may contribute to neurodegeneration. In vitro, murine CMV (MCMV) infection induces tau pathology in mouse fibroblasts and rat neuronal cells, dependent on late viral gene expression but independent of glycogen synthase kinase 3β (GSK3β) activity - suggesting an alternative pathway for tau phosphorylation. In vivo, repeated systemic MCMV infection in mice has been shown to elevate neuroinflammatory markers, disrupt mitochondrial function, increase oxidative stress, and impair cognitive performance.

While a causal role for HCMV in neurodegeneration remains unproven, future studies - particularly those leveraging antiviral therapies or vaccines aimed at preventing AD and vascular dementia - may clarify whether the virus functions as an etiological contributor. Additional approaches, including probiotics or fecal microbiota transplantation that influence HCMV latency and reactivation, also warrant close investigation as potential strategies to mitigate cognitive decline in susceptible populations.

A new system, which consists of a large LLM and a network of agentic tools, outperformed several other models and human physicians [1].

Too rare to easily diagnose

Rare diseases can be notoriously hard to diagnose. Patients average over 5 years to receive a correct diagnosis, enduring repeated referrals, misdiagnoses, and unnecessary interventions in what is known in rare disease medicine as ‘the diagnostic odyssey’ [2]. These rare diseases, defined as conditions affecting fewer than 1 in 2,000 people, collectively impact over 300 million people worldwide. About 7,000 distinct disorders of this type have been identified, with 80% of them being genetic in origin [3].

While AI assistants have shown great promise in diagnostics, diagnosing rare diseases remains a daunting task even for them. Rare diseases are often multisystemic and require cross-disciplinary knowledge; individual diseases have very few cases, making supervised learning hard; and hundreds of new rare genetic diseases are discovered per year, so knowledge is constantly shifting. On top of that, clinical deployment of such models demands transparent reasoning rather than black-box predictions.

In a new study published in Nature, an international team of scientists has presented DeepRare, a multi-agent system for differential diagnosis of rare diseases. While based on the large language model DeepSeek-V3, the system is different from a basic LLM in that it integrates more than 40 specialized agentic tools for various tasks.

Not your usual LLM

DeepRare uses a three-tier design. Tier 1 is the Central Host, a large LLM with a memory bank. It orchestrates the entire workflow: decomposes the diagnostic task, decides which agents to invoke, synthesizes evidence, makes tentative diagnoses, and runs self-reflection loops. Tier 2 is the Agent Servers layer. It consists of six specialized modules, each managing its own tools, such as the Phenotype Extractor, which converts free-text clinical narratives into standardized terms, and the Knowledge Searcher, which retrieves data in real time from web search engines and medical-specific sources. Retrieved documents are then summarized and relevance-filtered by a lightweight LLM (GPT-4o-mini). The external data sources the agents use, such as Google, PubMed, and Wikipedia, constitute Tier 3.

The system operates in two stages. The first one is information collection, where phenotype and genotype branches run in parallel. The phenotype branch standardizes HPO (Human Phenotype Ontology) terms, retrieves relevant literature and cases, and runs phenotype analysis tools. The genotype branch annotates variants and ranks them by clinical significance. The central host then performs synthetic analysis and generates a tentative diagnosis list.

The second stage is self-reflective, where the central host critically re-evaluates each hypothesis against all collected evidence. If all candidates are ruled out during self-reflection, the system goes back, increases the search depth, collects more evidence, and repeats as needed. Once candidates survive self-reflection, the system generates a final ranked list of diseases with reasoning chains (free-text rationales with clickable reference links).

DeepRare’s crucial advantage is that it does not have to be pre-trained on cases of rare diseases, as training LLMs requires a lot of data which simply does not exist for rare diseases, some of which are only known from a handful of cases. Instead, a generally trained LLM orchestrates specialized tools for data retrieval and analysis, synthesizes their outputs through reasoning, and iteratively validates its own conclusions.

Best in class

DeepRare was evaluated across nine datasets, spanning 6,401 total cases, 2,919 distinct rare diseases, and 14 medical specialties. The metrics used were Recall@1, @3, and @5 (whether the correct diagnosis appears in the top 1, 3, or 5 predictions).

The first evaluation was against 15 other digital tools, including general LLMs, reasoning LLMs, medical LLMs, and agentic systems. All the models received standardized Human Phenotype Ontology (HPO) descriptions as input.

DeepRare achieved an average Recall@1 of 57.18% and Recall@3 of 65.25% across all benchmarks, outperforming the second-best method (Claude-3.7-Sonnet-thinking) by 23.79% and 18.65% margins, respectively. However, given the pace of LLM development, several models released after the study’s design period were not included in the comparison; for instance, the top ChatGPT version the study used was 4o.

The model then went head-to-head with human expert physicians. 163 clinical cases were presented identically to DeepRare and five rare disease physicians with at least 10 years of experience, who were allowed to use search engines but not AI tools. DeepRare achieved Recall@1 of 64.4% vs. physicians’ 54.6% and Recall@5 of 78.5% vs. 65.6%. According to the authors, this is one of the first demonstrations of a computational model surpassing expert physicians on rare disease phenotype-based diagnosis.

To validate DeepRare’s reasoning, the researchers then turned to ten associate chief physicians, who evaluated 180 randomly sampled cases. They assessed whether each cited reference was both reliable and directly relevant to the diagnostic conclusion and found reference accuracy to be 95.4%.

Literature

[1] Zhao, W., Wu, C., Fan, Y., Qiu, P., Zhang, X., Sun, Y., Zhou, X., Zhang, S., Peng, Y., Wang, Y., Sun, X., Zhang, Y., Yu, Y., Sun, K., & Xie, W. (2026). An agentic system for rare disease diagnosis with traceable reasoning. Nature, 10.1038/s41586-025-10097-9. Advance online publication.

[2] Glaubitz, R., Heinrich, L., Tesch, F., Seifert, M., Reber, K. C., Marschall, U., … & Müller, G. (2025). The cost of the diagnostic odyssey of patients with suspected rare diseases. Orphanet Journal of Rare Diseases, 20(1), 222.

[3] Nguengang Wakap, S., Lambert, D. M., Olry, A., Rodwell, C., Gueydan, C., Lanneau, V., … & Rath, A. (2020). Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. European journal of human genetics, 28(2), 165-173.

The failure of anti-amyloid-β immunotherapies to more than slightly slow the progression of Alzheimer's disease has not much dented the amyloid cascade hypothesis, just clarified that amyloid-β becomes unimportant to disease progression once at the stage of sizable tau aggregration, neuroinflammation, and loss of cognitive function. The consensus continues to be that amyloid aggregation is the originating cause of Alzheimer's disease, the pathology that sets the stage for what comes later. That hypothesis will be confirmed or disproven in the years ahead as anti-amyloid-β immunotherapies are deployed in ever earlier stages of the condition. There may be other approaches to obtaining useful data, however. Here, researchers note that an existing drug, levetiracetam, reduces amyloid-β production in the brain, which will in turn reduce misfolding and aggregation of amyloid-β. This suggests the potential for a trial to directly assess its ability to delay or prevent Alzheimer's disease.

Amyloid-β (Aβ) peptides are a defining feature of Alzheimer's disease (AD). These peptides are produced by the proteolytic processing of the amyloid precursor protein (APP), which can occur through the synaptic vesicle (SV) cycle. However, how amyloidogenic APP processing alters SV composition and presynaptic function is poorly understood. Using App knock-in mouse models of amyloid pathology, we found that proteins with impaired degradation accumulate at presynaptic sites together with Aβ42 in the SV lumen.

Levetiracetam (Lev) is a US Food and Drug Administration-approved antiepileptic that targets SVs and has shown therapeutic potential to reduce AD phenotypes through an undefined mechanism. We found that Lev lowers Aβ42 levels by reducing amyloidogenic APP processing in an SV-dependent manner. Lev modified SV cycling and increased APP cell surface expression, which promoted its preferential processing through the nonamyloidogenic pathway.

Stable isotope labeling combined with mass spectrometry confirmed that Lev prevents Aβ42 production in vivo. In transgenic mice with aggressive amyloid pathology, electrophysiology and immunofluorescence confirmed that Lev restores SV cycling abnormalities and reduces synapse loss. Brains from patients with Down syndrome also displayed presynaptic protein accumulation before the occurrence of substantial Aβ pathology, supporting the hypothesis that protein accumulation is a relevant pathogenic event in amyloid pathology. Together, these findings highlight the potential to prevent Aβ pathology before irreversible damage occurs.

Link: https://doi.org/10.1126/scitranslmed.adp3984

Academic research is, as a rule, always short of funding. Researchers are consistently strongly motivated to find less costly approaches to animal studies. One aspect of this pressure is that the standard, most widely used animal models of disease tend to be the ones that can be created as rapidly as possible, using various toxic, damaging strategies to reproduce aspects of aging in relatively young mice. Time has its own cost, and budgets don't stretch to waiting around for mice to get old. Thus in this modern era of enthusiasm for targeting the mechanisms of aging, the research community finds itself in the position of knowing too little about how aging interacts with disease processes.

Mouse models of Parkinson's disease (PD) are invaluable for advancing our understanding of the disease, and there is much hope that their use will help develop new therapeutic interventions. PD is a complex multisystem disorder characterized by a spectrum of motor and non-motor symptoms, and numerous mouse models have been developed to study its various aspects. While age is the primary risk factor for PD, the role of biological aging in PD is still unclear, and it is often overlooked in the design and application of these models. This omission risks missing critical insights into disease mechanisms and opportunities for the development and translation of novel interventions, in particular as aging biology is emerging as a therapeutic target.

The International Network for Parkinson's Disease Modelling and AGEing (PD-AGE), funded by the Michael J. Fox Foundation for Parkinson's Research, was established to address critical gaps in our understanding of the role of aging in PD. Its creation was prompted by a workshop that brought together leading experts in PD modeling and aging who collectively highlighted the need for a systematic investigation into how aging contributes to PD.

To achieve its goals, PD-AGE was divided into four working groups, each focusing on different models. Here, we report on the working group that focused on approaches using mouse models and conducted a series of workshops to build consensus on prioritizing models of aging and PD, experimental approaches, and the standardization of protocols for their characterization. The result is a comprehensive roadmap for selecting optimal models, defining relevant measurements, and harmonizing protocols.

Link: https://doi.org/10.1038/s41531-025-01239-x