Biology subjects

Martinez, M. E.

Publications and source records attributed to Martinez, M. E..

4 recordsLinked to original sources

bioRxiv2026

Leptospirosis in Southern Sea Otters from Central California: Detection of Fatal Infections and Pathologic Findings

Leptospira interrogans serovar Pomona infections cause periodic outbreaks among California sea lions (CSL; Zalophus californianus) and sporadic deaths in phocids; however, frequency of infection and associated health impacts are uncharacterized among sympatric threatened southern sea otters (SSO; Enhydra lutris nereis), which are important sentinels of coastal health. Given the broad impacts of L. interrogans on other marine mammals, our objective was to screen selected SSO for infection, determine whether leptospirosis contributes to SSO mortality, and describe leptospiral-associated lesions. A retrospective review (2005-2025) yielded 19 candidates that underwent case review, including Leptospira immunohistochemistry (IHC), serology, and polymerase chain reaction (PCR), with a special focus on renal and hepatic lesions. Kidney was PCR-positive for 74% (14/19) of suspected cases and for eight of these, DNA sequence-based serogroup typing detected L. interrogans serogroup Pomona. Seven of the 14 PCR-positive leptospirosis cases were classed as fatal based on positive renal IHC and moderate to severe tubulointerstitial nephritis. All fatal cases had anti-L. interrogans serovar Pomona antibody titers [≥]1:25,600. All remaining (n=7) PCR-positive cases were considered nonfatal leptospirosis cases due to minimal and/or unrelated renal lesions, and negative IHC. Nonfatal Leptospira-infected cases ranged from seronegative to low positive (1:400) for serovar Pomona. Antibody titers for Leptospira PCR-negative cases were negative. For fatal cases, gross renal changes were often inapparent or characterized by miliary white cortical foci. Renal histologic lesions included tubulointerstitial nephritis, acute tubular necrosis and suppurative tubulitis with intratubular bacteria in fatal cases, and positive IHC staining for leptospiral antigen in lesions. Gross hepatic changes were also inapparent in fatal cases and histologic lesions were rare, characterized in one animal by hepatocellular disassociation and in two sea otters by limited leptospiral antigen detection by IHC. Most Leptospira-infected sea otters (71%, 10/14) stranded during higher rainfall months in California, suggesting possible land-to-sea transmission from terrestrial hosts. Given these findings, and because L. interrogans serovar Pomona infections have been confirmed in sympatric CSLs and terrestrial mammals from adjacent watersheds, focused investigation of potential marine and terrestrial disease transmission dynamics could provide new information to reduce mortalities of SSO.

pathology↗

bioRxiv2025

A Convex Point Process Model of Heartbeat Dynamics for Inference, Prediction, and Information Quantification

The dynamics of heartbeat intervals provide important insights into cardiovascular and autonomic nervous system function. Conventional analytical approaches often use fixed-window averaging, which can obscure rapid changes and reduce temporal resolution. Point process models address this limitation by operating in continuous time, enabling more precise characterization of heartbeat variability. A landmark example is the history-dependent inverse Gaussian (IG) point process model of Barbieri et al. (2005), which captures temporal dependencies in heartbeat timing. However, the nonconvex likelihood of the IG model complicates parameter estimation, requiring careful initialization and adding computational burden. In this work, we introduce a convex alternative: a history-dependent gamma generalized linear model (GLM) for heartbeat dynamics. Applied to a tilt-table dataset, our approach yields accurate and robust heart rate estimation. We further extend the model to two more applications: (1) sequential prediction of interbeat intervals, outperforming common machine learning algorithms, and (2) computation of information-theoretic measures demonstrating its utility in quantifying the influence of cardiac medications on heartbeat dynamics.

bioengineering↗

bioRxiv2021

Cellular and transcriptional diversity over the course of human lactation

Human breast milk is a dynamic fluid that contains millions of cells, but their identities and phenotypic properties are poorly understood. We used single-cell RNA-seq (scRNA-seq) to characterize the transcriptomes of cells from human breast milk (hBM) across lactational time from 3 to 632 days postpartum in 15 donors. We find that the majority of cells in human breast milk are lactocytes, a specialized epithelial subset, and cell type frequencies shift over the course of lactation yielding greater epithelial diversity at later points. Analysis of lactocytes reveals a continuum of cell states characterized by transcriptional changes in hormone, growth factor, and milk production related pathways. Generalized additive models suggest that one sub-cluster, LALBAlow epithelial cells, increase as a function of time postpartum, daycare attendance, and the use of hormonal birth control. We identify several sub-clusters of macrophages in hBM that are enriched for tolerogenic functions, possibly playing a role in protecting the mammary gland during lactation. Our description of the cellular components of breast milk, their association with maternal-infant dyad metadata and quantification of alterations at the gene and pathways levels provides the first detailed longitudinal picture of human breast milk cells across lactational time. This work paves the way for future investigations of how a potential division of cellular labor and differential hormone regulation might be leveraged therapeutically to support healthy lactation and potentially aid in milk production.

genomics↗

bioRxiv2016

Creating a National Vector Surveillance System: Integrated mosquito trap data and digital epidemiology

According to the World Health Organization, every year more than a billion people are infected with vector-borne diseases worldwide. There are no vaccines for most vector-borne diseases. Vector control, therefore, is often the only way to prevent outbreaks. Despite the major impact of vectors on human health, knowledge gaps exist regarding their natural population dynamics. Even the most basic information--such as spatiotemporal abundance-- is not available. Mosquitoes transmit malaria and the viruses causing Yellow Fever, West Nile, Dengue, Chikungunya, and Zika in the Americas. The Americas have a long history of mosquito control efforts, including the unsustained but successful Aedes aegypti eradication initiative. In the US, municipalities have independently created agencies for mosquito control and monitoring. We propose that the ensemble of US mosquito control agencies can, and should, be used to develop a national--and potentially international--system for Cross-Scale Vector Monitoring and Control (CSVMaC), in which local level monitoring and control efforts are cross-linked by unified real-time data streaming to build the data capital needed to gain a mechanistic understanding of vector population dynamics. Vectors, and the pathogens they transmit, know no jurisdictions. The vision of CSVMaC is, therefore, to provide data for (i) the general study of mosquito ecology and (ii) to inform vector control during epidemics/outbreaks that impact multiple jurisdictions (i.e., counties, states, etc.). We reveal >1000 mosquito control agencies in the US with enormous troves of data that are hidden among many data silos. For CSVMaC, we propose the creation of a nationally-coordinated open-access database to collate mosquito data. The database would provide scientific and public health communities with highly resolved spatiotemporal data on arboviral disease vectors, empowering new interventions and insights while leveraging pre-existing human efforts, operational infrastructure, and investments already funded by taxpayers.

ecology↗