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PubMed2005

Latest advances in antiaging medicine.

Rapid progress is being made in our ability to modify the aging process. Rather than serving as a period of debility and decreasing health, for many people, the later years of life are becoming a period of continued productivity, independence and good health. Progress is also being made in increasing average lifespan. The leading causes of death (cardiovascular disease, cancer, lung disease, diabetes) are the end result of decades-long processes. With current knowledge, it is possible to delay the onset of these diseases. This can be assisted by lifestyle choices incorporating healthful diet, exercise, stress management, and nutritional supplementation. Emerging genomics technology will allow individuals to establish personalized programs, while early detection of heart disease and cancer will contribute to longevity. Biotechnological therapies involving stem cells, recombinant DNA, proteomics, therapeutic cloning and gene-based therapies are expected to play major roles in promoting successful aging. We are at the threshold of artificial intelligence (AI) and nanotechnology (NT). AI will allow for a merging of our biological thinking with advanced forms of non-biological intelligence to vastly expand our ability to think, create and experience. NT will ultimately allow us to build devices able to build molecules much like our current cellular machinery does, one atom at a time. It is the goal of today's antiaging medicine to forestall disease and aging long enough for people to utilize the powerful biotechnology and nanotechnology therapies that will be developed over the decades ahead. These future therapies have the potential to greatly extend longevity.

Aging↗

PubMed2026

Cyclin-dependent kinase 4 and 6 inhibitors and the breast cancer immune ecosystem: immune remodeling, resistance, and therapeutic reprogramming.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6 inhibitors) combined with endocrine therapy have become a therapeutic backbone for hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer, yet durable disease control is frequently limited by intrinsic and acquired resistance. Canonical tumor-cell mechanisms, including retinoblastoma-pathway escape, cyclin E-cyclin-dependent kinase 2 (CDK2) activation, endocrine adaptation, and phosphoinositide 3-kinase (PI3K)-AKT-mechanistic target of rapamycin (mTOR) signaling, explain only part of this failure because they do not fully capture dynamic immune and stromal remodeling. Preclinical and translational studies indicate that early CDK4/6 inhibition can enhance antigen presentation, activate interferon-related programs, restrain regulatory T cells, and promote a T-cell-inflamed state. These effects are conditional and may not persist during prolonged treatment. Sustained therapy can instead drive heterogeneous resistant niches characterized by stromal remodeling, myeloid recruitment, checkpoint adaptation, and T-cell dysfunction. This immune-state dependence provides a rationale for immune checkpoint blockade, although clinical combinations have shown mixed efficacy and clinically relevant hepatic, pulmonary, and hematologic toxicities. Sequential or lead-in strategies therefore warrant prospective evaluation. Oxidative phosphorylation (OXPHOS) and redox adaptation may sustain selected resistant states and expose context-dependent ferroptotic vulnerabilities. Ferroptosis may connect tumor-cell killing with immune regulation, whereas nanomedicine may improve tumor-selective delivery. Both strategies remain largely preclinical and require further evaluation of pharmacokinetics, biodistribution, toxicity, manufacturability, and immune-cell safety. This Review distinguishes intrinsic from acquired resistance across interpatient, intratumoral, spatial, and temporal dimensions. It integrates tumor-cell escape with cytokine, immune, stromal, vascular, and metabolic remodeling and summarizes emerging therapeutic strategies. We further propose a candidate biomarker-informed framework that integrates genomic profiling, spatial immune architecture, circulating biomarkers, T-cell receptor (TCR) dynamics, transcriptomic and single-cell analyses, artificial intelligence (AI)-assisted multimodal integration, and longitudinal sampling. This framework is intended to support biomarker development and prospective trial design rather than current clinical decision-making, providing a translational basis for testing state-informed and sequence-aware therapeutic strategies.

Humans↗

PubMed2026

How advances in machine learning drive early detection and risk prediction of early-onset colorectal cancer.

Early-onset colorectal cancer (EOCRC), defined as colorectal cancer diagnosed before age 50, is rising across high- and middle-income settings whilst organised screening stays anchored to older age thresholds. Blood-based liquid biopsy, combined with machine learning, is the most plausible route to early detection in this group because it does not depend on bowel preparation, endoscopy capacity, or adherence to stool-based testing. The gap is structural: incidence climbs fastest in the population below the age at which any guideline-endorsed modality is offered. The analytical challenge is that early-stage tumour-derived signals in plasma are low in abundance and distributed across heterogeneous molecular layers: circulating tumour DNA mutations, aberrant methylation, cfDNA fragmentomics, and small non-coding RNA. Machine learning converts these into a single calibrated probability. This review examines where artificial intelligence (AI)-driven liquid biopsy genuinely adds diagnostic value in EOCRC, distinguishes components in which learned models are decorative from those in which they are mechanistically necessary, and identifies the validation deficit separating research cohorts from deployable clinical tools. It summarises the first-generation tools used clinically for early detection and post-treatment monitoring, then considers analytes from exosome-bound microRNAs to long-read whole-genome sequencing of circulating plasma DNA, which reads cytosine modification natively, resolves methylation and fragmentation on single molecules, and characterises structural events short reads cannot anchor. Any analyte can feed a learned model, but more diverse input yields better discrimination. The central argument is that approved, guideline-included blood tests were validated in populations aged 45 and above, and their performance in younger patients cannot be assumed.

cfDNA fragmentomics↗

PubMed2026

Exposome influences: a multi-omics perspective on the combined toxic effects of pharmaceuticals and personal care products in Alzheimer's disease.

According to WHO data, approximately 57 million people worldwide were affected by dementia in 2021, with prevalence projected to rise. Alzheimer's disease (AD), responsible for 60%-80% of dementia cases, continues to be a leading cause of mortality, with current treatments offering limited efficacy and disease-modifying therapies lacking widespread adoption or conclusive safety evidence, shifting the focus toward prevention and risk modification. Risk factors for AD include both non-modifiable elements, such as age, genetics, and gender, and modifiable factors, like environmental pollution, health status, and diet. While age remains the primary non-modifiable risk factor, early-onset dementia represents only up to 9% of cases. Addressing modifiable factors is essential, as it could prevent or delay almost half of dementia cases, with interventions-such as increased physical activity, smoking cessation, alcohol limitation, and overall health management-being significantly associated with a reduced risk. In this context, the exposome approach offers a comprehensive, integrative framework in which both modifiable and non-modifiable risk factors interact to influence individual susceptibility. Within the neural exposome, chronic low-dose exposure to xenobiotics-such as industrial chemicals, pesticides, metals, pharmaceuticals and personal care products (PPCPs), and air pollutants-may induce neurodegeneration via mechanisms including oxidative stress, neuroinflammation, proteinopathies, and epigenetic modifications, although establishing causality remains challenging. Integration of genomics, transcriptomics, proteomics, metabolomics, and lipidomics, combined with artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL), provides promising avenues for biomarker discovery, enhanced preventive strategies, early non-invasive diagnosis, and therapeutic target identification by integrating multi-layered biological data with exposure profiles. This review highlights emerging AD risk factors-including PPCPs-underscoring complex, multifactorial nature of AD and exposome, and the requirement for an interdisciplinary research approach, while also addressing several critical research gaps and methodological limitations.

Alzheimer’s disease↗

PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗

PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMed2001

Utterance-based proposed spot diagnostic system of vocal tract malfunction.

It is not surprising that speech recognition by machine, has received a great deal of attention through the techniques of artificial intelligence (AI), like expert systems to support decisions in various intended fields. One proposal that is based on the expert system paradigm is to diagnose a malfunction of the vocal tract during uttering recommended utterances for this purpose. The choice of these utterances is achieved according to the position and the manner of articulation. Four important features of acoustic analysis of speech are, fundamental frequency, F0, Formants, (F1-F5), amplitude, and the harmonic structure (tone vs. noise). The most Candidate features in the proposed diagnostic system are both fundamental frequency and/or the formants. These are considered to be the Core of the intended work. The throat, mouth and nose as the resonating champers will support this attitude and will affect, negatively, the range of the mentioned frequencies when they are out of the anatomical and/or physical functions. The discrete speech (isolated words) as the most recognizable utterances. Will be considered to put aside the difficulties of both connected and continuous word-based recognition. In a diagnostic systems, the generality is an essential issue, that is to consider "Speaker independent" recognizer which needs more efforts during the system training phase. The paper presents a rough (initial) spotting diagnostic system to be the base for a future detailed system for specific defects of precised organs belonging to the vocal tract. Arabic Vowels as well as some Consonants would be the target, taking into account the age range, that is (20-25) years-aged matures. Different recommended utterances, Arabic segmented alphabetic, focused on various points through the vocal apparatus. Speech related waveforms, as well as the associated fundamental frequencies and formants have been considered in the normal and the Corresponding abnormal Cases. The deviations that appeared in the frequency pattern have indicated the defected articulator that is dominant in the intended utterance production. The results illustrated, would be the base for designing a dedicated hardware unit, which may be reliable for the physicians interesting in this field. Human beings communicate with one another primarily by speech, and speech brings human beings closer together speech sounds travel through the air at the rate of about 330 meter per second, whereas impulses travel a long nerve pathways in the body at a rate of about 60 meter per second. The time it takes for a spoken word to be heard and understood by a listener may be shorter than the time it takes as a neural message to travel to the brain, [1]. Speech not only for human Communication, but it also has many applications in different fields. Some of these applications are machine control commands base systems, speech-to-text, and Text-to-speech systems, natural language-based systems, and medical diagnosis systems for vocal tract malfunction, the issue of this paper. One difficulty of speech based-systems is the fact that not everyone speaks the same way, even those who supposedly speak the same language at the same way. The term dialect is used to refer to this variability and is emphasized in case of uttering with different languages. The above discussion is concerning with the social and emotional variability which can be modified with reasonable efforts. The great variabilities, which are difficulty to be manipulated, are belonging to the inheritance and anatomical aspects. So it is worthy and to propose a methodology that can be used globally inspite of different social communities.

Adult↗

PubMed2002

Supporting physicians in taking decisions in clinical guidelines: the GLARE "what if" facility.

GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. GLARE is unique in its approach to supporting the decision-making process of users/physicians faced with various alternatives in the guidelines. In many cases, the best alternative cannot be determined on the basis of "local information" alone (i.e., by considering just the selection criteria associated with the decision at hand), but must also take into account information stemming from relevant alternative pathways. Exploitation of "global information" available in the various pathways is made possible by GLARE through the "what if" facility, a form of hypothetical reasoning which allows users to gather relevant decision parameters (e.g., costs, resources, times) from selected parts of the guideline in a semi-automatic fashion. In particular, the extremely complex task of coping with temporal information involves the extension and adaptation of various techniques developed by the Artificial Intelligence (AI) community.

Decision Making, Computer-Assisted↗

PubMed1992

[Medical informatics as a complementary method in medical education].

The practice of the decision making at the bed side especially highlights the place to be devoted to medical informatics both at the pre- and post-graduate levels. Still in a relatively recent past, say the 50s-60s, most of the medical educational efforts were delivered when watching and then imitating the medical behaviour of an older physician. The medical educators were aware that besides the formal lessons related to selected chapters of medical textbooks, there were an obvious need for better training in the ability to make sound clinical judgements. If this ability has been considered only as an artful and intuitive process neither subjected to theoretical analysis nor to be captured in a formal quantitative model, now things have changed to such an extent that it becomes broadly shared that a science of medical decision making can be reasonably founded and this threefold: 1) Upon a formulated logic, 2) The probability theory, and 3) A value theory. The first gives the hand to artificial intelligence (AI) technics, the third to medical information data bases dealing either with patients (like in hospital information systems) or with literature like MEDLINE or electronic "cookbooks". Basically the probabilistic theory is based here upon a priori probabilities related to patients informations and data and opens the way to bayesian decision making. After this little summary it is stressed that educational informatics in medicine would appear either very central or very marginal, if not optional.

Computer-Assisted Instruction↗

PubMed2004

The GLARE approach to clinical guidelines: main features.

In this paper, we present GLARE, a domain-independent prototypical system for acquiring, representing and executing clinical guidelines. GLARE has been built within a 7-year project with Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy) and has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure. GLARE is characterized by the adoption of advanced Artificial Intelligence (AI) techniques, to support medical decision making and to manage temporal knowledge.

Decision Making, Computer-Assisted↗

PubMed1989

Expert systems in medicine.

The emergence of the artificial intelligence (AI) in computer technology and its application in the medical field enables the researchers to carry out such intelligent activities like image processing, medical reasoning systems, clinical decision supporting and natural language understanding, etc. A gastroenterological expert system application is briefly demonstrated in this paper. Similar expert systems can be seen to be useful in the research of gastrointestinal cytoprotection, including the plan of different compounds with cytoprotective effect, experimental and clinical medical research.

Decision Making, Computer-Assisted↗

PubMed1989

Protein databases and software on BIONET.

BIONET provides databases, software, and networking/communications tools to over 2500 molecular biologists worldwide. Software for the analysis of nucleic acid and protein sequence data is provided by both IntelliGenetics and academic contributors. BIONET is currently implementing dedicated high speed servers for searching protein databases, as well as providing more flexible tools for protein structure recognition and prediction. In this review, protein databases and analysis software available on the BIONET resource are described, and progress in providing new tools for structure prediction, comparative sequence analysis, and pattern recognition using Artificial Intelligence (AI) techniques are summarized.

Amino Acid Sequence↗

PubMed1989

Computer-assisted interpretation in forensic toxicology: morphine-involved deaths.

Case data from 200 morphine-involved deaths (Spiehler, V. and Brown, R., Journal of Forensic Sciences, Vol. 32, No. 4, July 1987, pp. 906-916) were analyzed for patterns and relationships using artificial intelligence (AI) computer software. Case parameters were blood unconjugated morphine, blood, brain, and liver total morphine, sex, age, frequency of use, time of death after injection, cause of death, and presence of other drugs. The programs used were Expert 4 (Biosoft-Cambridge), BEAGLE (Warm Boot, Ltd.), and KnowledgeMaker (Knowledge Garden Inc.). Interpretation was defined as estimating the dose, response, and time after drug dosing. The AI programs were used to advise on time and response outcomes for cases, to calculate the probability of the estimate being true, to develop rules for interpretation of morphine-involved cases, and to diagram a decision tree. On known cases the AI programs were successful 70 to 90% of the time in classifying the cases as to response and time. No data on dose were available in this database. The success rate in individual cases was proportional to the program-estimated probability. All three programs found the case parameters of most value in predicting response to be blood unconjugated morphine, blood total morphine, and liver total morphine. The case data most useful in estimating time of death since drug injection were blood unconjugated morphine, percent unconjugated morphine in blood, and brain total morphine. The rule induction programs found that morphine overdoses were characterized by blood unconjugated morphine greater than 0.24 micrograms/mL, liver morphine greater than 0.50 to 0.75 micrograms/g, brain morphine greater than 0.08 micrograms/g or greater than blood unconjugated morphine, and percent blood unconjugated morphine greater than 37%. Rapid deaths were characterized by percent unconjugated morphine greater than 44 to 50%; blood unconjugated morphine, as a function of other drugs present, greater than 0.09 to 0.21 micrograms/mL; and brain total morphine greater than 0.16 to 0.22 micrograms/g. This work demonstrates that inexpensive AI programs commercially available for personal computers can be useful in interpretation in forensic toxicology.

Cause of Death↗

PubMed1994

An intelligent system for comparing protein structures.

An approach to protein structure comparison is presented which uses techniques of artificial intelligence (AI) to generate a mapping between two protein structures. The approach proceeds by first identifying the seed of a possible mapping, and then searching for ways to extend the seed by incorporating corresponding elements from the two proteins. Correspondence is judged using heuristic functions which assess the similarity of the structural environments of the elements. The search can be guided by separately encoded knowledge. A prototype has been implemented which is able to rapidly create mappings with a high degree of accuracy in test cases.

Animals↗

PubMed2026

Experimental validation of an AI-driven digital healthcare platform for oral health behavior and plaque assessment among vietnamese children.

BACKGROUND: Oral health among children in developing countries, including Vietnam, remains a significant public health concern. Innovative approaches leveraging artificial intelligence AI-based digital health platforms may offer effective strategies for managing dental plaque and promoting better oral hygiene behaviors among school-aged children. This study aimed to evaluate the effectiveness of an AI-driven oral healthcare platform (Denti-i Vietnam) in improving oral hygiene and behavioral outcomes among Vietnamese primary school students. METHODS: A total of 204 primary school students aged 8-10 years in Hanoi, Vietnam, participated in this experimental study. Participants were randomly assigned to an intervention group (n = 107), which used the AI-driven oral healthcare platform, and a comparison group (n = 97), which received traditional oral health education via pamphlets. Oral health behaviors, dental plaque levels (Simplified Oral Hygiene Index; OHI-S), and caries indices (dft/DMFT) were assessed at baseline and after the intervention period. RESULTS: The intervention group demonstrated a significant reduction in the OHI-S score compared to baseline (2.49 ± 0.60 to 1.70 ± 0.76, p < 0.001), particularly in the debris component, indicating enhanced plaque control. Notable improvements were also observed in oral hygiene behaviors, including increased frequency of toothbrushing before and after breakfast (p < 0.01) and more frequent parental assistance during brushing (p = 0.03). Furthermore, parental awareness of dental caries significantly increased in the intervention group (p = 0.001). CONCLUSIONS: The AI-driven oral healthcare platform significantly improved both oral hygiene behaviors and plaque control among Vietnamese primary school children. These findings suggest that AI-driven digital health tools can serve as practical and scalable solutions for promoting oral health in developing countries.

Humans↗

PubMed2023

The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.

INTRODUCTION: The purpose of this study was to evaluate the capabilities of large language models such as Chat Generative Pretrained Transformer (ChatGPT) to diagnose glaucoma based on specific clinical case descriptions with comparison to the performance of senior ophthalmology resident trainees. METHODS: We selected 11 cases with primary and secondary glaucoma from a publicly accessible online database of case reports. A total of four cases had primary glaucoma including open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, and inflammatory glaucoma. We input the text of each case detail into ChatGPT and asked for provisional and differential diagnoses. We then presented the details of 11 cases to three senior ophthalmology residents and recorded their provisional and differential diagnoses. We finally evaluated the responses based on the correct diagnoses and evaluated agreements. RESULTS: The provisional diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, and third ophthalmology residents were 9, 7, and 7, respectively. CONCLUSIONS: The accuracy of ChatGPT in diagnosing patients with primary and secondary glaucoma, using specific case examples, was similar or better than senior ophthalmology residents. With further development, ChatGPT may have the potential to be used in clinical care settings, such as primary care offices, for triaging and in eye care clinical practices to provide objective and quick diagnoses of patients with glaucoma.

Artificial intelligence (AI)↗

PubMed2026

De-extinction technology and its application to conservation.

De-extinction, once the realm of science fiction, has evolved into a tangible scientific endeavor thanks to breakthroughs in genome sequencing, engineering, advanced assisted reproductive technologies, and stem cell biology. Alongside this work are innovations in reintroduction science and artificial intelligence, which are refining strategies for species translocations, rewilding, and long-term ecosystem monitoring of de-extinct species and populations. While the primary motivation for de-extinction is restoring lost ecological functions to eroded ecosystems, each of these technologies can also be applied to conservation biology for de-endangerment, offering new solutions for biodiversity preservation. This review synthesizes the technological advancements emerging from de-extinction science and explores their broad applications in conservation, demonstrating how de-extinction is both about resurrecting lost species and about expanding the conservation toolkit to sustain and rebuild biodiversity in the face of accelerating environmental change.

Conservation of Natural Resources↗

PubMed2026

Leptospira-host interactions: advancing next-generation vaccines and diagnostics.

SUMMARYLeptospirosis, a widespread zoonotic disease caused by pathogenic Leptospira species, remains a major public health challenge, particularly in tropical and subtropical regions. Despite advances in understanding Leptospira biology and pathogenesis, effective disease control continues to be limited by the lack of rapid, early diagnostics, and broadly protective vaccines. This review comprehensively examines recent progress in deciphering Leptospira-host interactions, with emphasis on key virulence factors, immune-evasion mechanisms, and host immune responses that influence disease outcomes. Particular focus is placed on the molecular and cellular basis of adhesion, invasion, immune modulation, and persistent colonization. We further discuss the limitations of current vaccines and diagnostic approaches, and highlight how emerging technologies, including pan-genomics, proteomics, reverse vaccinology, immunoinformatics, and omics-based antigen discovery, are facilitating the development of next-generation vaccines and diagnostics. Finally, we outline major translational challenges and future perspectives for improving clinical management, surveillance, and prevention of leptospirosis. The concepts discussed in this review may also provide broader insights into vaccine and diagnostic development for other zoonotic bacterial infections.

Humans↗