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PubMed2005

Automated Synthesis and Purification at Bayer CropScience Chemistry Frankfurt.

The Automated Synthesis and Purification team at Bayer CropScience in Frankfurt provides services and support in the areas of synthesis and post synthesis activities for chemists. This article describes our workflow and the special robotic systems used. We produce small to medium sized compound libraries using liquid phase techniques. An example of a compound library taken from the herbicide area is given.

Agrochemicals↗

PubMed2005

Datamining methodology for LC-MALDI-MS based peptide profiling.

This report will provide a brief overview of the application of data mining in proteomic peptide profiling used for medical biomarker research. Mass spectrometry based profiling of peptides and proteins is frequently used to distinguish disease from non-disease groups and to monitor and predict drug effects. It has the promising potential to enter clinical laboratories as a general purpose diagnostic tool. Data mining methodologies support biomedical science to manage the vast data sets obtained from these instrumentations. Here we will review the typical workflow of peptide profiling, together with typical data mining methodology. Mass spectrometric experiments in peptidomics raise numerous questions in the fields of signal processing, statistics, experimental design and discriminant analysis.

Animals↗

PubMed2006

eHiTS: an innovative approach to the docking and scoring function problems.

Virtual Ligand Screening (VLS) has become an integral part of the drug design process for many pharmaceutical companies. In protein structure based VLS the aim is to find a ligand that has a high binding affinity to the target receptor whose 3D structure is known. This review will describe the docking tool eHiTS. eHiTS is an exhaustive and systematic docking tool which contains many automated features that simplify the drug design workflow. A description of the unique docking algorithm and novel approach to scoring used within eHiTS is presented. In addition a validation study is presented that demonstrates the accuracy and wide applicability of eHiTS in re-docking bound ligands into their receptors.

Algorithms↗

PubMed2026

ChromID: A Protocol for Mapping Protein Chromatin Interactions in Living Cells.

Chromatin modifications regulate genome function by recruiting proteins that control transcription, genome organization, and DNA repair. Identifying the proteins associated with specific chromatin modifications is therefore essential for understanding how these regulatory processes operate. Traditional approaches, including chromatin immunoprecipitation and affinity purification coupled to mass spectrometry, have uncovered many chromatin-associated proteins. However, they often rely on crosslinking and chromatin fragmentation, which can disrupt native chromatin architecture and limit the detection of transient interactions. Here, we describe a proximity-labeling protocol for identifying the chromatin-dependent protein interactome associated with specific chromatin marks, termed ChromID. ChromID uses engineered chromatin readers (eCRs) fused to a promiscuous biotin ligase, which labels proteins in the immediate vicinity of the targeted chromatin mark. The protocol includes in vivo biotin labeling, nuclear extract preparation, streptavidin-based enrichment, and tryptic digestion for downstream LC-MS/MS analysis. The protocol has been validated across multiple cell types and chromatin contexts and can be extended to other chromatin-associated proteins, providing a versatile approach to profile chromatin-associated proteomes within their native cellular environment. Key features • Maps proteins associated with different chromatin modifications in living cells using engineered chromatin readers fused to TurboID, BASU, or other promiscuous biotin ligases. • Preserves native chromatin organization and captures transient chromatin-associated interactions that are often lost during conventional affinity purification workflows. • Validated across multiple chromatin contexts, including histone modifications, DNA methylation, transcription factors, RNA polymerase II, and DNA damage-associated chromatin states. • Applicable to diverse cell types and organisms and adaptable to other chromatin-associated proteins, including transcription factors and chromatin regulators.

Biotin proximity labeling↗

PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗

PubMed2026

Cost-Effectiveness of Electronic Patient-Reported Outcome Measure Interventions in Cancer: Systematic Review and Parameter Extraction for Economic Modeling.

BACKGROUND: Complex digital interventions that integrate electronic patient-reported outcome measures (ePROM) into clinical practice in cancer have the potential to improve quality of life, increase survival, and reduce health resource use and costs. Such systems can help patients with cancer self-manage chemotherapy symptoms, reduce clinicians' workloads through automated decision support, and resolve problems earlier. However, more research on the cost-effectiveness of ePROM monitoring is needed. OBJECTIVE: This paper comprises two complementary components: (1) a systematic literature review summarizing and evaluating the quantitative and qualitative evidence related to the cost-effectiveness of ePROM monitoring and (2) a health economic model parameter extraction. We also conducted supplementary targeted searches and scoping to provide context to our findings. METHODS: We searched Ovid (including MEDLINE and Embase), Scopus, and the International Health Technology Assessment Database for original English-language papers published on or before March 2025 using search strings that combined terms related to ePROMs, health economics, and cancer/oncology. We included papers reporting health economic-related outcomes for ePROM interventions designed for adult cancer populations and excluded screening tools and conference abstracts. RESULTS: We included 34 publications from 27 unique studies and identified and analyzed 26 ePROM-integrated interventions within these. Most (23/26) of the included interventions explicitly described some form of alert handling and automated decision support based on remote ePROM monitoring. Of the 34 publications, 5 presented full cost-effectiveness analysis results, of which 3 were highly uncertain and lacked clear differences in costs and health outcomes between ePROMs and standard care; conversely, 2 presented strong evidence of cost-effectiveness due to quality-of-life improvements, reduced hospitalizations, and potentially more autonomy in health-related travel (eg, ePROM-monitored patients can drive or walk to the hospital instead of using taxis or ambulances). A further 5 publications reported partial health economic results (eg, cost-consequence and budget impact), of which 1 detected no difference in strategies; in contrast, 4 reported lower health resource use and costs of ePROMs, mainly due to hospitalization reductions. Overall, 12 of the 27 studies included a qualitative component but mostly focused on user experience and design-related themes; only 2 of these addressed economic-specific themes (eg, changes in workflow and resource use due to ePROM implementation and integration), indicating some potential for time saving due to ePROM monitoring. CONCLUSIONS: Some ePROM-integrated interventions demonstrated cost-effectiveness in cancer care, but the evidence base remains limited. Where evidence does exist, cost-effectiveness appears driven by reduced hospitalization and improved quality of life. Qualitative research within the included studies rarely addressed economic questions. We provide a detailed parameter extraction for use in future economic modeling and recommend research priorities, including quantitative mapping of ePROM symptom data onto health resource use patterns, and qualitative work exploring how ePROM implementation affects clinical workloads and patient-perspective costs.

Humans↗

PubMed2025

Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study.

BACKGROUND: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. OBJECTIVE: We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health's (NIH) All of Us Research Program (AOU). AOU is an ongoing national, multiyear study aimed to build a research cohort of 1 million participants that reflects the diversity of the United States, including minority, health-disparate, and other populations underrepresented in biomedical research (UBR). METHODS: We collaborated with community members, health care provider organizations (HPOs), and NIH leadership to design, build, and validate a secure, feature-rich digital platform to facilitate multisite, hybrid, and remote study participation and multimodal data collection in AOU. Participants were recruited by in-person, print, and online digital campaigns. Participants securely accessed the DHRP via web and mobile apps, either independently or with research staff support. The participant-facing tool facilitated electronic informed consent (eConsent), multisource data collection (eg, surveys, genomic results, wearables, and electronic health records [EHRs]), and ongoing participant engagement. We also built tools for research staff to conduct remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management. RESULTS: We built a secure, participant-centric DHRP with engaging functionality used to recruit, engage, and collect data from 705,719 diverse participants throughout the United States. As of April 2024, 87% (n=613,976) of the participants enrolled via the platform were from UBR groups, including racial and ethnic minorities (n=282,429, 46%), rural dwelling individuals (n=49,118, 8%), those over the age of 65 years (n=190,333, 31%), and individuals with low socioeconomic status (n=122,795, 20%). CONCLUSIONS: We built a participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, and socioeconomic backgrounds and other UBR groups. This DHRP demonstrated successful use among diverse participants. These findings could be used as best practices for the effective use of digital platforms to build and sustain cohorts of various study designs and increase engagement with diverse populations in health research.

Humans↗

PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗

PubMed2026

Development of a Blockchain-Based Platform to Enable Indigenous Data Sovereignty and Shared Research Participation With Indigenous Communities: Technology Prototyping and Community Engagement Study.

BACKGROUND: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. OBJECTIVE: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. METHODS: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. RESULTS: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. CONCLUSIONS: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.

Blockchain↗

PubMed2026

Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.

BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent but remains substantially underdiagnosed. Polysomnography (PSG) is the reference standard, but its cost and limited availability constrain large-scale case identification. AI-based screening tools may support risk stratification and referral prioritization, but their diagnostic accuracy across apnea-hypopnea index (AHI) thresholds remains uncertain. OBJECTIVE: This review aimed to systematically evaluate the diagnostic accuracy of AI-based OSA screening tools at AHI thresholds of ≥5, ≥15, and ≥30 events/hour, with emphasis on models using non-PSG-derived inputs. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies published from January 1, 2016, to May 3, 2026. Eligible studies included adults evaluated for suspected OSA or recruited from population-based cohorts, assessed AI-based models intended or interpretable for OSA screening, risk prediction, or screening-oriented severity classification, used PSG as the reference standard, and reported sufficient data to construct or reconstruct 2×2 contingency tables. Diagnostic accuracy was synthesized separately by AHI threshold and input source using bivariate random-effects models, with 95% CIs and prediction intervals (PIs). Risk of bias and certainty of evidence were assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. RESULTS: A total of 60 studies were included, of which 47 contributed data to the meta-analysis. At AHI thresholds of ≥5, ≥15, and ≥30 events/hour, pooled sensitivities were 0.94 (95% CI 0.92-0.96; 95% PI 0.71-0.99), 0.87 (95% CI 0.84-0.89; 95% PI 0.66-0.96), and 0.83 (95% CI 0.79-0.87; 95% PI 0.61-0.94), respectively; the corresponding specificities were 0.77 (95% CI 0.69-0.84; 95% PI 0.30-0.96), 0.81 (95% CI 0.75-0.85; 95% PI 0.39-0.96), and 0.91 (95% CI 0.87-0.94; 95% PI 0.55-0.99), respectively. The corresponding areas under the summary receiver operating characteristic curves were 0.943, 0.907, and 0.920. For non-PSG-derived tools, sensitivities were 0.92, 0.85, and 0.81, and specificities were 0.70, 0.74, and 0.85 at the 3 thresholds, respectively. For PSG-derived models, sensitivities were 0.96, 0.90, and 0.85, and specificities were 0.82, 0.88, and 0.96, respectively. Exploratory subgroup analyses suggested performance variation across selected study and model characteristics, including region, algorithmic framework, data source, and validation method. CONCLUSIONS: AI-based tools showed generally favorable screening performance for OSA across clinically relevant AHI thresholds, although wide PIs suggest variable performance across future comparable populations and settings. By synthesizing diagnostic accuracy across 3 AHI thresholds and distinguishing non-PSG-derived from PSG-derived models, this review extends previous broad or modality-specific reviews and offers a clinically interpretable, pathway-specific basis for linking model performance to intended use. The findings may clarify potential roles for non-PSG-derived tools in front-end screening and referral prioritization and for PSG-derived models in reduced-channel assessment and sleep-laboratory workflow support. Given substantial heterogeneity, limited external validation, and low or very low certainty of evidence, prospective validation is needed before routine implementation.

Humans↗

PubMedDate not supplied

Doctors who are using e-mail with their patients: a qualitative exploration.

BACKGROUND: Despite the potential for rapid, asynchronous, documentable communication, the use of e-mail for physician-patient communication has not been widely adopted. OBJECTIVE: To survey physicians currently using e-mail with their patients daily to understand their experiences. METHODS: In-depth phone interviews of 45 physicians currently using e-mail with patients were audio taped and transcribed verbatim. Two investigators independently qualitatively coded comments. Differences were adjudicated by group consensus. RESULTS: Almost all of the 642 comments from these physicians who currently use e-mail with patients daily could be grouped into 1 of 4 broad domains: (1) e-mail access and content, (2) effects of e-mail on the doctor-patient relationship, (3) managing clinical issues by e-mail, and (4) integrating e-mail into office processes. The most consistent theme was that e-mail communication enhances chronic-disease management. Many physicians also reported improved continuity of care and increased flexibility in responding to nonurgent issues. Integration of e-mail into daily workflow, such as utilization of office personnel, appears to be a significant area of concern for many of the physicians. For other issues, such as content, efficiency of e-mail, and confidentiality, there were diverging experiences and opinions. Physicians appear to be selective in choosing which patients they will communicate with via e-mail, but the criteria for selection is unclear. CONCLUSION: These physician respondents did perceive benefits to e-mail with a select group of patients. Several areas, such as identifying clinical situations where e-mail communication is effective, incorporating e-mail into office flow, and being reimbursed for online medical care/communication, need to be addressed before this mode of communication diffuses into most practices.

Adult↗

PubMed2004

Peer-review and publication of research protocols and proposals: a role for open access journals.

Peer-review and publication of research protocols offer several advantages to all parties involved. Among these are the following opportunities for authors: external expert opinion on the methods, demonstration to funding agencies of prior expert review of the protocol, proof of priority of ideas and methods, and solicitation of potential collaborators. We think that review and publication of protocols is an important role for Open Access journals. Because of their electronic form, openness for readers, and author-pays business model, they are better suited than traditional journals to ensure the sustainability and quality of protocol reviews and publications. In this editorial, we describe the workflow for investigators in eHealth research, from protocol submission to a funding agency, to protocol review and (optionally) publication at JMIR, to registration of trials at the International eHealth Study Registry (IESR), and to publication of the report. One innovation at JMIR is that protocol peer reviewers will be paid a honorarium, which will be drawn partly from a new submission fee for protocol reviews. Separating the article processing fee into a submission and a publishing fee will allow authors to opt for "peer-review only" (without subsequent publication) at reduced costs, if they wish to await a funding decision or for other reasons decide not to make the protocol public.

Access to Information↗

PubMed2002

A simple method of capturing PACS and other radiographic images for digital teaching files or other image repositories.

OBJECTIVE: We sought to develop an easy-to-use method of capturing and storing radiographic images. CONCLUSION: The method that we developed can capture any digital image-including an image from a picture archiving and communication system (PACS)-using widely available, inexpensive software. Our method is easy to learn, simple to use, and inexpensive to implement. It is adaptable in a wide range of networking environments and can capture and store images rapidly for a variety of uses. It can be used without interfering with clinical workflow at the PACS workstation.

Humans↗

PubMed2026

Quantum Threats to Bitcoin, Cryptocurrency and Blockchain.

OBJECTIVE: The authors evaluated how quantum computing threatens the cryptographic primitives used in Bitcoin and other blockchain systems. These findings were translated into a standards-aligned post-quantum migration profile for healthcare ledgers, including consent, identity, provenance, audit, and encrypted off-chain data exchange. METHODOLOGY: Narrative analysis, theoretical security analysis, and healthcare-oriented deployment mapping of classical public-key and hash primitives used in blockchain protocols were paired with an implementation-oriented migration profile based on finalized National Institute of Standards and Technology (NIST) post-quantum standards. We summarize the mathematical assumptions underlying RSA, Elliptic Curve Cryptography/Elliptic Curve Digital Signature Algorithm (ECC/ECDSA), and Secure Hash Algorithm (SHA-2/SHA-3-family) hash functions; analyze their susceptibility to Shor's and Grover's quantum algorithms; compare Federal Information Processing Standards (FIPS) 203 Module-Lattice-Based Key-Encapsulation Mechanism Standard (ML-KEM), FIPS 204 Module-Lattice-Based Digital Signature Standard (ML-DSA), and FIPS 205 Stateless Hash-Based Digital Signature Standard (SLH-DSA); and map their distinct roles to healthcare-ledger authorization, auditability, identity, and encrypted off-chain exchange. The term Advanced Hybrid Module-LWE & Code-Based (AHMC) is used only as shorthand for a standards-aligned hybrid post-quantum cryptography (PQC) migration profile and does not denote a proprietary product, a novel algorithm, or a new cryptographic primitive. Proposed adoption of hybrid, quantum-resistant cryptographic primitives for cryptocurrency wallets, transaction signatures, and ledger security during an interim migration period (hybrid classical + PQC, followed by PQC-only). Qualitative and implementation-oriented assessment of (1) break feasibility of RSA/ECC under Shor's algorithm, (2) effective security reduction for hash functions under Grover's algorithm, (3) security assumptions and composition requirements of a standards-aligned hybrid migration profile, (4) transaction-size and verification-cost impact, and (5) healthcare-specific implications for long-retention consent, identity, provenance, and audit records. RESULTS: Shor's algorithm reduces integer factorization and discrete logarithms to polynomial time, directly compromising Rivest-Shamir-Adleman (RSA) and ECC/ECDSA once fault-tolerant, large-scale quantum computers exist. Grover's algorithm yields a quadratic speedup for brute-force search, effectively halving the security margin of symmetric keys and hash functions at fixed output sizes. The AHMC-L1/L3/L5 profiles use ML-KEM-512/768/1024 for key establishment and ML-DSA-44/65/87 for transaction authentication, with SLH-DSA as a hash-based fallback. During a hybrid ECDSA+PQC migration, verification requires one classical and one PQC verification per authorization; transaction-size overhead is dominated by PQC signatures, approximately 2.4 KB for ML-DSA-44, 3.3 KB for ML-DSA-65, and 4.6 KB for ML-DSA-87 before script and encoding overhead. For healthcare ledgers, these findings support selective use of post-quantum signatures for:high-value state transitions,one-time public-key registration where possible, andcontinued off-chain storage of protected health information.Deployment suitability remains contingent on workflow-specific latency, storage, availability, key lifecycle, and side-channel testing. CONCLUSIONS: Quantum risk to blockchain signatures has direct implications for healthcare systems that depend on long-lived consent, identity, provenance, and audit records. A staged, standards-aligned migration profile can preserve authorization and ledger verifiability while keeping protected health information off-chain. The AHMC label refers only to this migration profile, not to a new cryptographic primitive; healthcare adoption requires open implementations, empirical benchmarking, crypto-agile key governance, and side-channel-resistant engineering.

ML-DSA↗

PubMed1988

Surrogate testing for non-A, non-B hepatitis in Queensland, Australia: an ALT microtitre tray method for screening blood donors.

The estimation of plasma alanine aminotransferase (ALT) has been proposed as a surrogate test to identify potential non-A, non-B hepatitis carriers in blood donor populations. This report describes an ALT screening procedure which uses wells of microtitration trays as reactant vessels. The method utilizes a rate reading photometer, is economical and conveniently fits into the routine workflow. Within-batch and between-batch precision was 4.1% and 6.3% at enzyme concentrations of 49 IU/I. Results of testing 29,675 healthy blood donors gave values which ranged between 1.0 IU/I and 214 IU/I. A study of 762 donations showed a significant difference in mean ALT values between males and females (p less than 0.01). When a cut-off value of 46 IU/I was used, 2.5 percent of donations were considered unsuitable for transfusion. The medico-legal implications that may arise from the introduction of this screening test into the routine work flow are discussed.

Alanine Transaminase↗

PubMed2006

Computer assistance in total knee replacement - a critical assessment of current health care technology.

The last five years have seen the rapid development of computer assisted surgery (CAS) in total knee replacement (TKR). Many surgeons perform TKR using navigation systems, which offer user-friendly workflows and reproducible results. A number of level 1 and 2 studies, according to evidence-based medicine criteria, have demonstrated that navigation in TKR allows a more precise implantation of the prosthesis, though CAS still requires an experienced surgeon and is time consuming. Nevertheless, 30% of surgeons in Germany who perform TKR have used navigation. It is still not known whether this new technique improves the longevity of TKR, however, and we therefore performed a meta-analysis to assess the results of this new health care technology.

Arthroplasty, Replacement, Knee↗

PubMedDate not supplied

A framework for the integration of distributed autonomous healthcare information systems.

This paper presents a domain-specific framework for the integration of geographically distributed heterogeneous autonomous healthcare information systems. Integration is seen and understood in terms of access to information resources, and in terms of content, structure, and visualization of patient record segments, with the goal of creating a virtual patient record at the medical workstations. The proposed approach covers integration aspects related to presentation, control, data, and function. The architecture and the components of the framework are based on the newly introduced Patient Meta-Record (PMR) concept, but also make use of existing or emerging standards for distributed object-oriented computing and information presentation. At the application and middle-ware layers of the presented healthcare information systems architecture, the PMR concept enhances or enables services for data mediation, distributed directory access, and workflow management. At the bitways layer, high performance networks provide a fast medium, which is indispensable for medical image distribution, collaborative work, hypermedia library applications, and teleconsultation.

Computer Communication Networks↗

PubMed2026

Beyond antibiotics: artificial intelligence-enabled anti-infective ecosystems for next-generation precision therapeutics against antimicrobial resistance.

The rapid global expansion of antimicrobial resistance (AMR) threatens to undermine decades of progress in infectious disease management and highlights the limitations of conventional antibiotic-centered therapeutic strategies. Although emerging technologies-including antimicrobial peptides, bacteriophage therapy, CRISPR-based antimicrobials, microbiome therapeutics, anti-virulence approaches, nanotechnology-enabled drug delivery, and artificial intelligence (AI)-have individually demonstrated considerable promise, they are predominantly being developed as independent interventions rather than as coordinated components of an integrated therapeutic strategy. This Perspective proposes the Intelligent Anti-Infective Ecosystem (IAIE) as a conceptual systems-level framework that computationally integrates multimodal diagnostics, pathogen genomics, microbiome profiling, AI-assisted decision support, programmable precision therapeutics, ecological monitoring, and longitudinal clinical feedback within a continuously learning dynamically optimized workflow. Unlike existing paradigms that primarily optimize individual technologies or therapeutic decisions, IAIE emphasizes closed-loop coordination among complementary antimicrobial approaches to support precision-guided infection management while preserving microbiome integrity and mitigating resistance selection pressure. We further outline the core components, operational principles, translational challenges, and technology readiness of the major therapeutic platforms that could contribute to such an ecosystem, while distinguishing clinically established interventions from emerging experimental strategies. Importantly, IAIE should be interpreted as a prospective conceptual architecture rather than an existing clinical platform. Its proposed clinical value remains to be established through sequential computational, preclinical, and prospective clinical investigations using standardized microbiological, ecological, and patient-centered outcome measures. By framing antimicrobial innovation within an responsive systems perspective, IAIE provides a roadmap for future multidisciplinary research aimed at integrating artificial intelligence and systems microbiology to enable sustainable management of antimicrobial resistance.

Humans↗