Bacterial Identification and Susceptibility Testing
Abstract
Background. Standard antimicrobial susceptibility testing (AST) in Mueller–Hinton broth samples a single metabolic state, yet bacterial susceptibility in vivo depends on nutrient availability, host-derived metabolites, and spatial structure. The extent to which this mismatch produces clinically relevant errors in susceptibility classification has not been systematically mapped. Objective. To map the evidence linking metabolic context to conditional antibiotic susceptibility and to identify gaps between mechanistic insight, physiologically relevant models, and clinical validation. Methods. We conducted a scoping review following PRISMA-ScR. The search combined PubMed, Semantic Scholar, Web of Science, and Embase with AI-assisted discovery and screening using Consensus AI and large language models, with full human verification at every inclusion decision. AI use is reported per PRISMA-trAIce. Records were classified by mechanism (porin regulation, efflux, stringent response, metabolic rewiring, biofilm structure) and by infection-relevant context (gut-mimicking media, bicarbonate and host factors, single-cell and heterogeneity, in vivo models, clinical validation). Results. Of 1,543,377 records identified, 100 studies met inclusion criteria. Mechanistic evidence was dense across all seven mechanistic foci. In contrast, evidence in physiologically relevant contexts was sparse: gut-mimicking AST appeared in only one mechanism category, and clinical validation was absent in five of seven. The most prominent gap was the absence of prospective clinical validation linking host-mimicking AST results to patient outcomes. Conclusions. Metabolic state is a primary determinant of antibiotic susceptibility, and standard Mueller–Hinton AST samples only one such state. The field has established mechanisms but not clinical translation. Prospective validation of host-mimicking AST across pathogen and drug classes is the highest-priority next step.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The findings show that speed related agile practices are used to a greater extent in comparison to quality practices, and that software startups who adopt the Lean Startup approach do not sacrifice quality for speed more than other startups do.
Jevgenija Pantiuchina, Marco Mondini, Dron Khanna et al.· International Conference on...· 84 citations· ⚡4
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.