Search Results (64)

Nanotheranostics combines therapeutic and diagnostic functions within multifunctional nanoparticle platforms to enable precision medicine. This review outlines a comprehensive framework for engineering nanotheranostic systems, focusing on core material composition, surface functionalization, and stimuli-responsive drug delivery. Targeting strategies—from ligand-based recognition to biomimetic interfaces—are examined alongside therapeutic modalities such as chemotherapy, photothermal and photodynamic therapies, gene silencing via RNA interference, and radio sensitization. We discuss advanced imaging techniques (fluorescence imaging FI), magnetic resonance imaging (MRI), positron emission tomography (PET), and photoacoustic imaging for real-time tracking and treatment guidance. Key considerations include physicochemical characterization (e.g., article size, surface charge, and morphology), biocompatibility, in-vitro efficacy, and in-vivo biodistribution. We also address challenges such as rapid biological clearance, tumor heterogeneity, and clinical translation, and propose future directions for developing safe, adaptable, and effective nanotheranostic platforms. This review serves as a roadmap for advancing next-generation nano systems in biomedical applications. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

Bacteriophages (phages), the most abundant biological entities on Earth, have long served as both model systems and therapeutic tools. Recent advances in synthetic biology and genetic engineering have revolutionized the capacity to tailor phages with enhanced functionality beyond their natural capabilities. This review outlines the current landscape of synthetic and functional engineering of phages, encompassing both in-vivo and in-vitro strategies. We describe in-vivo approaches such as phage recombineering systems, CRISPR-Cas-assisted editing, and bacterial retron-based methods, as well as synthetic assembly platforms including yeast-based artificial chromosomes, Gibson, Golden Gate, and iPac assemblies. In addition, we explore in-vitro rebooting using TXTL (transcription–translation) systems, which offer a flexible alternative to cell-based rebooting but are less effective for large genomes or structurally complex phages. Special focus is given to the design of customized phages for targeted applications, including host range expansion via receptor-binding protein modifications, delivery of antimicrobial proteins or CRISPR payloads, and the construction of biocontained, non-replicative capsid systems for safe clinical use. Through illustrative examples, we highlight how these technologies enable the transformation of phages into programmable bactericidal agents, precision diagnostic tools, and drug delivery vehicles. Together, these advances establish a powerful foundation for next-generation antimicrobial platforms and synthetic microbiology. Full article

Bacterial keratitis caused by Pseudomonas aeruginosa is indeed a serious concern due to its potential to cause blindness and its resistance to antibiotics, partly attributed to biofilm formation and cytotoxicity to the cornea. The present study uses a meta-analysis of a transcriptomics dataset to identify important genes and pathways in biofilm formation of P. aeruginosa induced keratitis. By combining data from several studies, meta-analysis can enhance statistical power and robustness, enabling the identification of 83 differentially expressed candidate genes, including fis that could serve as therapeutic targets. The approach of combining meta-analysis with virtual screening and in vitro methods provides a comprehensive strategy for identifying potential target genes and pathways crucial for bacterial biofilm formation and development anti-biofilm medications against P. aeruginosa infections. The study identified 83 candidate genes that exhibited differential expression in the biofilm state, with fis proposed as an ideal target for therapy for P. aeruginosa biofilm formation. These techniques, meta-analysis, virtual screening, and invitro methods were used in combination to diagnostically identify these genes, which play a significant role in biofilms. This finding has highlighted a hallmark target list for P. aeruginosa anti-biofilm potential treatments. Full article

Background: Advancements in genomic technologies are rapidly evolving, with the potential to transform laboratory diagnostics by enabling high-throughput analysis of complex biological data, such as microbiome data. Large Language Models (LLMs) have shown significant promise in extracting actionable insights from vast datasets, but their application in generating microbiome findings reports with clinical interpretations and lifestyle recommendations has not been explored yet. Methods: This article introduces an innovative framework that utilizes LLMs to automate the generation of findings reports in the context of microbiome diagnostics. The proposed model integrates LLMs within an event-driven, workflow-based architecture, designed to enhance scalability and adaptability in clinical laboratory environments. Special focus is given to aligning the model with clinical standards and regulatory guidelines such as the In-Vitro Diagnostic Regulation (IVDR) and the guidelines published by the High-Level Expert Group on Artificial Intelligence (HLEG AI). The implementation of this model was demonstrated through a prototype called “MicroFlow”. Results: The implementation of MicroFlow indicates the viability of automating findings report generation using LLMs. Initial evaluation by laboratory expert users indicated that the integration of LLMs is promising, with the generated reports being plausible and useful, although further testing on real-world data is necessary to assess the model’s accuracy and reliability. Conclusions: This work presents a potential approach for using LLMs to support the generation of findings reports in microbiome diagnostics. While the initial results seem promising, further evaluation and refinement are needed to ensure the model’s effectiveness and adherence to clinical standards. Future efforts will focus on improvements based on feedback from laboratory experts and comprehensive testing on real patient data. Full article

The vaginal microbiota can be classified into five major community state types (CSTs) based on the bacterial content. However, the link between different CST subtypes and vaginal infection remains unclear. Here, we analyzed 2017 vaginal microbiota samples from women of a reproductive age with vaginal infections that were published in the last decade. We found that L. iners was the most dominant in 34.8% of the vaginal samples, followed by L. crispatus (21.2%). CST I was common in healthy individuals, whereas CST III and IV were associated with dysbiosis and infection. CST III-B, IV-A, IV-B, and IV-C0 were prevalent in patients with bacterial vaginosis (BV). Based on the relative abundance of bacteria at the (sub)genus level, a random forest classifier was developed to predict vaginal infections with an area under the curve of 0.83. We further identified four modules of co-occurring bacterial taxa: L. crispatus, Gardnerella, Prevotella, and Bacteroides. The functional prediction revealed that nucleotide biosynthesis pathways were upregulated in patients with human papilloma virus, and carbohydrate degradation pathways were downregulated in patients with BV. Overall, our study identified the bacterial signatures of healthy and infected vaginal microbiota, providing unique insights into the clinical diagnosis and health status prediction of women of a reproductive age. Full article

Sepsis is a life-threatening syndrome triggered by infection and accompanied by high mortality, with antimicrobial resistances (AMRs) further escalating clinical challenges. The rapid and reliable detection of causative pathogens and AMRs are key factors for fast and appropriate treatment, in order to improve outcomes in septic patients. However, current sepsis diagnostics based on blood culture is limited by low sensitivity and specificity while current molecular approaches fail to enter clinical routine. Therefore, we developed a suppression PCR-based selective enrichment sequencing approach (SUPSETS), providing a molecular method combining multiplex suppression PCR with Nanopore sequencing to identify most common sepsis-causative pathogens and AMRs using plasma cell-free DNA. Applying only 1 mL of plasma, we targeted eight pathogens across three kingdoms and ten AMRs in a proof-of-concept study. SUPSETS was successfully tested in an experimental research study on the first ten clinical samples and revealed comparable results to clinical metagenomics while clearly outperforming blood culture. Several clinically relevant AMRs could be additionally detected. Furthermore, SUPSETS provided first pathogen and AMR-specific sequencing reads within minutes of starting sequencing, thereby potentially decreasing time-to-results to 11–13 h and suggesting diagnostic potential in sepsis. Full article

PDAC is one of the most common malignant tumors worldwide. The difficulty of early diagnosis and lack of effective treatment are the main reasons for its poor prognosis. Therefore, it is urgent to identify novel diagnostic and therapeutic targets for PDAC patients. The m⁷G methylation is a common type of RNA modification that plays a pivotal role in regulating tumor development. However, the correlation between m⁷G regulatory genes and PDAC progression remains unclear. By integrating gene expression and related clinical information of PDAC patients from TCGA and GEO cohorts, m⁷G binding protein NCBP2 was found to be highly expressed in PDAC patients. More importantly, PDAC patients with high NCBP2 expression had a worse prognosis. Stable NCBP2-knockdown and overexpression PDAC cell lines were constructed to further perform in-vitro and in-vivo experiments. NCBP2-knockdown significantly inhibited PDAC cell proliferation, while overexpression of NCBP2 dramatically promoted PDAC cell growth. Mechanistically, NCBP2 enhanced the translation of c-JUN, which in turn activated MEK/ERK signaling to promote PDAC progression. In conclusion, our study reveals that m⁷G reader NCBP2 promotes PDAC progression by activating MEK/ERK pathway, which could serve as a novel therapeutic target for PDAC patients. Full article

The detection of hepatitis E virus (HEV) RNA is the gold standard for HEV infection diagnosis. In order to address the quality control requirements for HEV RNA detection kits within China, we aimed to establish the first Chinese national standard for HEV RNA detection through a collaborative study. The candidate standard was quantified using digital PCR (dPCR). A total of five laboratories were invited to determine the estimated mean value of this national standard relative to the World Health Organization International Standard (WHO IS). Additionally, four commercial kits were used to assess the applicability of the candidate standard. The stability was determined by freeze–thaw cycles and storage at 37 °C, 25 °C and 4 °C. The estimated mean value of this national standard relative to the WHO IS was 5.67 log₁₀ IU/mL. Two out of the four commercial kits can detect as low as the estimated limit of detection (LOD). The degradation rates of samples in the stability study ranged from 4% to 19%. In conclusion, we have established the first Chinese national standard for HEV nucleic acid detection against WHO IS, which can be employed to evaluate the quality of HEV RNA detection kits. Full article

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm. Full article

Cell surface HLA-I molecules (Face-1) consist of a polypeptide heavy chain (HC) with two groove domains (G domain) and one constant domain (C-domain) as well as a light chain, B2-microglobulin (B2m). However, HCs can also independently emerge unfolded on the cell surface without peptides as B2m-free HC monomers (Face-2), B2m-free HC homodimers (Face 3), and B2m-free HC heterodimers (Face-4). The transport of these HLA variants from ER to the cell surface was confirmed by antiviral antibiotics that arrest the release of newly synthesized proteins from the ER. Face-2 occurs at low levels on the normal cell surface of the lung, bronchi, epidermis, esophagus, breast, stomach, ilium, colorectum, gall bladder, urinary bladder, seminal vesicles ovarian epithelia, endometrium, thymus, spleen, and lymphocytes. They are upregulated on immune cells upon activation by proinflammatory cytokines, anti-CD3 antibodies, antibiotics (e.g., ionomycin), phytohemagglutinin, retinoic acid, and phorbol myristate acetate. Their density on the cell surface remains high as long as the cells remain in an activated state. After activation-induced upregulation, the Face-2 molecules undergo homo- and hetero-dimerization (Face-3 and Face-4). Alterations in the redox environment promote dimerization. Heterodimerization can occur among and between the alleles of different haplotypes. The glycosylation of these variants differ from that of Face-1, and they may occur with bound exogenous peptides. Spontaneous arthritis occurs in HLA-B27+ mice lacking B2m (HLA-B27+ B2m−/−) but not in HLA-B27+ B2m+/− mice. The mice with HLA-B27 in Face-2 spontaneous configuration develop symptoms such as changes in nails and joints, hair loss, and swelling in paws, leading to ankyloses. Anti-HC-specific mAbs delay disease development. Some HLA-I polyreactive mAbs (MEM series) used for immunostaining confirm the existence of B2m-free variants in several cancer cells. The upregulation of Face-2 in human cancers occurs concomitantly with the downregulation of intact HLAs (Face-1). The HLA monomeric and dimeric variants interact with inhibitory and activating ligands (e.g., KIR), growth factors, cytokines, and neurotransmitters. Similarities in the amino acid sequences of the HLA-I variants and HLA-II β-chain suggest that Face-2 could be the progenitor of both HLA classes. These findings may support the recognition of these variants as a neo-HLA class and proto-HLA. Full article

Cardiovascular pathology is the leading cause of death and disability in the Western world, and current diagnostic testing usually evaluates the anatomy of the vessel to determine if the vessel contains blockages and plaques. However, there is a growing school of thought that other measures, such as wall shear stress, provide more useful information for earlier diagnosis and prediction of atherosclerotic related disease compared to pulsed-wave Doppler ultrasound, magnetic resonance angiography, or computed tomography angiography. A novel algorithm for quantifying wall shear stress (WSS) in atherosclerotic plaque using diagnostic ultrasound imaging, called Multifrequency ultrafast Doppler spectral analysis (MFUDSA), is presented. The development of this algorithm is presented, in addition to its optimisation using simulation studies and in-vitro experiments with flow phantoms approximating the early stages of cardiovascular disease. The presented algorithm is compared with commonly used WSS assessment methods, such as standard PW Doppler, Ultrafast Doppler, and Parabolic Doppler, as well as plane-wave Doppler. Compared to an equivalent processing architecture with one-dimensional Fourier analysis, the MFUDSA algorithm provided an increase in signal-to-noise ratio (SNR) by a factor of 4–8 and an increase in velocity resolution by a factor of 1.10–1.35. The results indicated that MFUDSA outperformed the others, with significant differences detected between the typical WSS values of moderate disease progression (p = 0.003) and severe disease progression (p = 0.001). The algorithm demonstrated an improved performance for the assessment of WSS and has potential to provide an earlier diagnosis of cardiovascular disease than current techniques allow. Full article

Background: Currently, no tests can definitively diagnose and distinguish neuromyelitis optica spectrum disorder (NMOSD) from multiple sclerosis (MS). Methods: Initially, cerebrospinal fluid (CSF) proteomics were employed to uncover the novel biomarkers that differentiate NMOSD from MS into cohorts of 10 MS and 10 NMOSD patients. Subsequently, screening biomarkers were validated using an enzyme-linked immunosorbent assay method and CSF and serum samples from 20 MS patients, 20 NMOSD patients, 20 non-inflammatory neurological controls, and 20 healthy controls. Results: In study cohort, insulin-like growth factor-binding protein 7 (IGFBP7) and lysosome-associated membrane glycoprotein 2 (LAMP2) were screened. In validation cohort, serum and CSF IGFBP7 not only exhibited higher levels in MS and NMOSD patients than controls, but also had greatest area under the curve (AUC, above or equal to 0.8) in MS and NMOSD diagnoses. Serum IGFBP7 (0.945) and CSF IGFBP7 (0.890) also had the greatest AUCs for predicting MS progression, while serum LAMP2 had a moderate curve (0.720). Conclusions: IGFBP7 was superior in diagnosing MS and NMOSD, and IGFBP7 and serum LAMP2 performed exceptionally well in predicting the MS progression. These results offered reasons for further investigations into the functions of IGFBP7 and LAMP2 in MS and NMOSD. Full article

Rheumatoid arthritis (RA) is a progressive, inflammatory, autoimmune, symmetrical polyarticular arthritis. It is characterized by synovial infiltration and activation of several types of immune cells, culminating in their apoptosis and antibody generation against “altered” autoantigens. β2-microglobulin (β2m)-associated heavy chains (HCs) of HLA antigens, also known as closed conformers (Face-1), undergo “alteration” during activation of immune cells, resulting in β2m-free structural variants, including monomeric open conformers (Face-2) that are capable of dimerizing as either homodimers (Face-3) or as heterodimers (Face-4). β2m-free HCs uncover the cryptic epitopes that can elicit antibodies (Abs). We report here the levels of IgM and IgG Abs against both β2m and HCs of HLA-E, HLA-F, and HLA-G in 74 RA patients receiving immunosuppressive drugs. Anti-β2m IgM was present in 20 of 74 patients, whereas anti-β2m IgG was found in only 8 patients. Abs against β2m would be expected if Abs were generated against β2m-associated HLA HCs. The majority of patients were devoid of either anti-β2m IgM or IgG but had Abs against HCs of different HLA-Ib molecules. The paucity of anti-β2m Abs in this cohort of patients suggests that Abs were developed against β2m-free HLA HCs, such as Face-2, Face-3, and Face-4. While 63 of 68 patients had IgG Abs against anti-HLA-F HCs, 36 and 50 patients showed IgG Ab reactivity against HLA-E and anti-HLA-G HCs, respectively. Evidently, anti-HLA-F HC Abs are the most predominant anti-HLA-Ib HC IgG Abs in RA patients. The incidence and intensity of Abs against HLA-E, HLA-F, and HLA-G in the normal control group were much higher than those observed in RA patients. Evidently, the lower level of Abs in RA patients points to the impact of the immunosuppressive drugs on these patients. These results underscore the need for further studies to unravel the nature of HLA-F variants on activated immune cells and synoviocytes of RA patients. Full article

Abdominal sepsis triggers the transition of microorganisms from the gut to the peritoneum and bloodstream. Unfortunately, there is a limitation of methods and biomarkers to reliably study the emergence of pathobiomes and to monitor their respective dynamics. Three-month-old CD-1 female mice underwent cecal ligation and puncture (CLP) to induce abdominal sepsis. Serial and terminal endpoint specimens were collected for fecal, peritoneal lavage, and blood samples within 72 h. Microbial species compositions were determined by NGS of (cell-free) DNA and confirmed by microbiological cultivation. As a result, CLP induced rapid and early changes of gut microbial communities, with a transition of pathogenic species into the peritoneum and blood detected at 24 h post-CLP. NGS was able to identify pathogenic species in a time course-dependent manner in individual mice using cfDNA from as few as 30 microliters of blood. Absolute levels of cfDNA from pathogens changed rapidly during acute sepsis, demonstrating its short half-life. Pathogenic species and genera in CLP mice significantly overlapped with pathobiomes from septic patients. The study demonstrated that pathobiomes serve as reservoirs following CLP for the transition of pathogens into the bloodstream. Due to its short half-life, cfDNA can serve as a precise biomarker for pathogen identification in blood. Full article