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This study assessed the relative clinical sensitivity and specificity, as well as reproducibility, for high-risk HPV types of the Roche cobas HPV test when processed using the Roche cobas 5800 system. The results from this study demonstrate that the cobas HPV test using the cobas 5800 system fulfils the Meijer criteria for use in population-based cervical screening. This clinical validation study also examines the clinical sensitivity and specificity based on partial genotyping, with separate detection of HPV16 and HPV18, compared with the Roche cobas 4800 HPV test, a second-generation standard comparator assay. The cobas HPV test has a relative clinical sensitivity of 1.000, when compared with the cobas 4800 HPV test to detect histologically confirmed CIN2+ lesions in woman aged 30 years or older, with a relative clinical specificity of 0.995. The general intra- and inter-laboratory agreement for the cobas HPV test on the cobas 5800 system for finding a HPV positive result were 99.1% and 99.6%, respectively.IMPORTANCEThis study demonstrates, for the first time, the clinical performance of the Roche cobas HPV test when processed using the new cobas 5800 system [cobas (5800)]. This study shows that the cobas (5800) demonstrates relative clinical sensitivity and specificity, when compared with a standard comparator HPV test, which meets the international HPV test validation requirements. Intra- and inter-laboratory reproducibility also fulfills these criteria. The current study demonstrates that the cobas (5800) can be used for primary HPV-based cervical screening on cervical specimens.

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BACKGROUND AND AIMS: Solar lentigo is a prevalent skin condition that affects a significant number of individuals. Fortunately, certain traditional Chinese medicines and monomers (TCMM) have proven effective in addressing these concerns. In this study, we evaluated the efficacy and underlying mechanism of TCMM, a combination of TCM and monomers in repairing solar lentigo. METHODS: We detected and identified the main compounds of TCM using liquid chromatography-mass spectrometry (LC-MS) and through the approach of network pharmacology, we screened drug and disease targets, visualized networks with Cytoscape software, analyzed targets via Gene ontology and KEGG, clinically validated predictions. In a mouse model, UVB-induced pigmentation was assessed, and the effects of TCMM were evaluated. A clinical trial on 30 patients validated the depigmenting agent. RESULTS: Active ingredients such as MSH, Butylated hydroxytoluen, Valerophenone, and Geranylacetone aid pigmentation treatment. One hundred and forty-three crore targets impact PI3K-Akt, MAPK signaling pathway, ect. pathways. TCMM reduced UVB-induced pigmentation, water loss, epidermal thickness, and melanin. It inhibited TYR, MITF, AKT1, VEGFA, PTGS2, TNF-α, IL-6, IL-1ß. Clinical and microscopic analysis showed significant pigmentation reduction. CONCLUSIONS: The treatment of solar lentigo can benefit from the TCMM. By targeting multiple factors and pathways, this approach offers a comprehensive and effective treatment strategy.

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In order for patients to gain the benefit of innovation in cardiac CT, it is necessary for coding, coverage, and payment to adapt to the novelty of algorithm-based healthcare procedures and services (ABHS). Appendix S to the CPT Code Set, the "AI Taxonomy", enables creation of discrete and differentiable codes for reimbursement of ABHS which has been clinically validated and FDA-labeled. Payment policy in OPPS and PFS is evolving to take account of the unique opportunities and issues arising from the clinical adoption of ABHS.

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BACKGROUND: Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss. OBJECTIVE: We evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center. METHODS: We prospectively recruited adult patients with diabetes at the Centre hospitalier de l'Université de Montréal (CHUM) in Montreal, Quebec, Canada. Patients underwent dual-pathway screening: first by the Computer Assisted Retinal Analysis (CARA) AI system (index test), then by standard ophthalmological examination (reference standard). We measured the AI system's sensitivity and specificity for detecting referable disease at the patient level, along with its performance for detecting any retinopathy and diabetic macular edema (DME) at the eye level, and potential cost savings. RESULTS: This study included 115 patients. CARA demonstrated a sensitivity of 87.5% (95% CI 71.9-95.0) and specificity of 66.2% (95% CI 54.3-76.3) for detecting referable disease at the patient level. For any retinopathy detection at the eye level, CARA showed 88.2% sensitivity (95% CI 76.6-94.5) and 71.4% specificity (95% CI 63.7-78.1). For DME detection, CARA had 100% sensitivity (95% CI 64.6-100) and 81.9% specificity (95% CI 75.6-86.8). Potential yearly savings from implementing CARA at the CHUM were estimated at CAD $245,635 (US $177,643.23, as of July 26, 2024) considering 5000 patients with diabetes. CONCLUSIONS: Our study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting. This system has the potential to improve screening efficiency and reduce costs at the CHUM, but more work is needed to validate it.

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BACKGROUND: Multidrug-resistant HIV strains challenge treatment efficacy and increase mortality rates. Next-generation sequencing (NGS) technology swiftly detects variants, facilitating personalized antiretroviral therapy. AIM: This study aimed to validate the Vela Diagnostics NGS platform for HIV drug resistance mutation analysis, rigorously assessed with clinical samples and CAP proficiency testing controls previously analyzed by Sanger sequencing. METHOD: The experimental approach involved the following: RNA extraction from clinical specimens, reverse transcription polymerase chain reaction (RT-PCR) utilizing the Sentosa SX 101 platform, library preparation with the Sentosa SQ HIV Genotyping Assay, template preparation, sequencing using the Sentosa SQ301 instrument, and subsequent data analysis employing the Sentosa SQ Suite and SQ Reporter software. Drug resistance profiles were interpreted using the Stanford HIV Drug Resistance Database (HIVdb) with the HXB2 reference sequence. RESULTS: The Vela NGS system successfully identified a comprehensive array of drug resistance mutations across the tested samples: 28 nucleoside reverse transcriptase inhibitors (NRTI), 25 non-nucleoside reverse transcriptase inhibitors (NNRTI), 25 protease inhibitors (PI), and 10 integrase gene-specific variants. Dilution experiments further validated the system's sensitivity, detecting drug resistance mutations even at viral loads lower than the recommended threshold (1000 copies/mL) set by Vela Diagnostics. SCOPE: This study underscores the validation and clinical applicability of the Vela NGS system, and its implementation may offer clinicians enhanced precision in therapeutic decision-making for individuals living with HIV.

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AIMS: Dried blood volumetric absorptive microsamples (VAMS) may facilitate home-based sampling to enhance therapeutic drug monitoring after transplantation. This study aimed to clinically validate a liquid chromatography-tandem mass spectrometry assay using 2 VAMS devices with different sampling locations (Tasso-M20 for the upper arm and Mitra for the finger). Patient preferences were also evaluated. METHODS: Clinical validation was performed for tacrolimus and mycophenolic acid by comparison of paired VAMS and venipuncture samples using Passing-Bablok regression and Bland-Altman analysis. Conversion of mycophenolic acid VAMS to serum concentrations was evaluated using haematocrit-dependent formulas and fixed correction factors defined a priori. Patients' perspectives, including useability, acceptability and feasibility, were also investigated using established questionnaires. RESULTS: Paired samples (n = 50) were collected from 25 kidney transplant recipients. Differences for tacrolimus whole-blood concentration were within ±20% for 86 and 88% of samples from the upper arm and fingerstick, respectively. Using correction factors of 1.3 for the upper-arm and 1.47 for finger-prick samples, 84 and 76% of the paired samples, respectively, were within ±20% for mycophenolic acid serum concentration. Patient experience surveys demonstrated limited pain and acceptable useability of the upper-arm device. CONCLUSIONS: Tacrolimus and mycophenolic acid can be measured using 2 common VAMS devices with similar analytical performance. Patients are supportive of home-based monitoring with a preference for the Tasso-M20 device.

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Artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing health care by offering unprecedented opportunities to enhance patient care, optimize clinical workflows, and advance medical research. However, the integration of AI and ML into healthcare systems raises significant ethical considerations that must be carefully addressed to ensure responsible and equitable deployment. This comprehensive review explored the multifaceted ethical considerations surrounding the use of AI and ML in health care, including privacy and data security, algorithmic bias, transparency, clinical validation, and professional responsibility. By critically examining these ethical dimensions, stakeholders can navigate the ethical complexities of AI and ML integration in health care, while safeguarding patient welfare and upholding ethical principles. By embracing ethical best practices and fostering collaboration across interdisciplinary teams, the healthcare community can harness the full potential of AI and ML technologies to usher in a new era of personalized data-driven health care that prioritizes patient well-being and equity.

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BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data. AIM: To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients. METHODS: Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model. RESULTS: More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility. CONCLUSION: This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.

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Objectives: This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease. Methods: The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics. Results: Correlation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average. Conclusions: Data-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal-external validations.

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Background: Rapid and accurate diagnosis of the causative agents is essential for clinical management of bloodstream infections (BSIs) that might induce sepsis/septic shock. A considerable number of suspected sepsis patients initially enter the health-care system through an emergency department (ED), hence it is vital to establish an early strategy to recognize sepsis and initiate prompt care in ED. This study aimed to evaluate the diagnostic performance and clinical value of droplet digital PCR (ddPCR) assay in suspected sepsis patients in the ED. Methods: This was a prospective single-centered observational study including patients admitted to the ED from 25 October 2022 to 3 June 2023 with suspected BSIs screened by Modified Shapiro Score (MSS) score. The comparison between ddPCR and blood culture (BC) was performed to evaluate the diagnostic performance of ddPCR for BSIs. Meanwhile, correlative analysis between ddPCR and the inflammatory and prognostic-related biomarkers were conducted to explore the relevance. Further, the health economic evaluation of the ddPCR was analyzed. Results: 258 samples from 228 patients, with BC and ddPCR performed simultaneously, were included in this study. We found that ddPCR results were positive in 48.13% (103 of 214) of episodes, with identification of 132 pathogens. In contrast, BC only detected 18 positives, 88.89% of which were identified by ddPCR. When considering culture-proven BSIs, ddPCR shows an overall sensitivity of 88.89% and specificity of 55.61%, the optimal diagnostic power for quantifying BSI through ddPCR is achieved with a copy cutoff of 155.5. We further found that ddPCR exhibited a high accuracy especially in liver abscess patients. Among all the identified virus by ddPCR, EBV has a substantially higher positive rate with a link to immunosuppression. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity as well as prognosis. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs. Conclusions: The multiplexed ddPCR delivers precise and quantitative load data on the causal pathogen, offers the ability to monitor the patient's condition and may serve as early warning of sepsis in time-urgent clinical situations as ED. Importance: Early detection and effective administration of antibiotics are essential to improve clinical outcomes for those with life-threatening infection in the emergency department. ddPCR, an emerging tool for rapid and sensitive pathogen identification used as a precise bedside test, has developed to address the current challenges of BSI diagnosis and precise treatment. It characterizes sensitivity, specificity, reproducibility, and absolute quantifications without a standard curve. ddPCR can detect causative pathogens and related resistance genes in patients with suspected BSIs within a span of three hours. In addition, it can identify polymicrobial BSIs and dynamically monitor changes in pathogenic microorganisms in the blood and can be used to evaluate antibiotic efficacy and survival prognosis. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs.

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Since its first appearance, severe acute respiratory syndrome coronavirus 2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein. A highly selective multiple reaction monitoring-cubed (MRM3) strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral nucleocapsid protein peptides from nasopharyngeal swabs samples presenting a cycle threshold value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immunopurification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with cycle threshold values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted MS.

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OBJECTIVE: Psoriatic disease remains underdiagnosed and undertreated. We developed and validated a suite of novel, sensor-based smartphone assessments (Psorcast app) that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease. METHODS: Participants with psoriasis (PsO) or psoriatic arthritis (PsA) and healthy controls were recruited between June 5, 2019, and November 10, 2021, at 2 academic medical centers. Concordance and accuracy of digital measures and image-based machine learning models were compared to their analogous clinical measures from trained rheumatologists and dermatologists. RESULTS: Of 104 study participants, 51 (49%) were female and 53 (51%) were male, with a mean age of 42.3 years (SD 12.6). Seventy-nine (76%) participants had PsA, 16 (15.4%) had PsO, and 9 (8.7%) were healthy controls. Digital patient assessment of percent body surface area (BSA) affected with PsO demonstrated very strong concordance (Lin concordance correlation coefficient [CCC] 0.94 [95% CI 0.91-0.96]) with physician-assessed BSA. The in-clinic and remote target plaque physician global assessments showed fair-to-moderate concordance (CCCerythema 0.72 [0.59-0.85]; CCCinduration 0.72 [0.62-0.82]; CCCscaling 0.60 [0.48-0.72]). Machine learning models of hand photos taken by patients accurately identified clinically diagnosed nail PsO with an accuracy of 0.76. The Digital Jar Open assessment categorized physician-assessed upper extremity involvement, considering joint tenderness or enthesitis (AUROC 0.68 [0.47-0.85]). CONCLUSION: The Psorcast digital assessments achieved significant clinical validity, although they require further validation in larger cohorts before use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and freely available.

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OBJECTIVES: Myeloid neoplasms require comprehensive characterization of genetic abnormalities, including single-nucleotide variants, small insertions and deletions, and fusions and translocations for management. The Oncomine Myeloid Assay GX v2 (Thermo Fisher Scientific) analyzes 17 full genes, 28 hotspot genes, 30 fusion driver genes, and 5 expression genes. METHODS: The validation set included 192 DNA samples, 28 RNA samples, and 9 cell lines and contrived controls. The DNA and RNA were extracted from both peripheral blood and bone marrow. Library preparation, templating, and sequencing was performed on the fully automated Genexus Integrated Sequencer (Thermo Fisher Scientific). The sequencing data were analyzed by manual curation, default Oncomine filters and the Oncomine Reporter (Thermo Fisher Scientific). RESULTS: Of the 600 reference pathogenic DNA variants targeted by the assay, concordance was seen in 98.3% of unfiltered variant call format files. Precision and reproducibility were 100%, and the lower limit of detection was 2% variant allele frequency for DNA. Inability to detect variants in long homopolymer regions intrinsic to the Ion Torrent chemistry led to 7 missed variants; 100% concordance was seen with reference RNA samples. CONCLUSIONS: This extensive clinical validation of the Oncomine Myeloid Assay GX v2 on the Genexus Integrated Sequencer with its built-in bioinformatics pipeline and Ion Torrent Oncomine Reporter shows robust performance in terms of variant calling accuracy, precision, and reproducibility, with the advantage of a rapid turnaround time of 2 days. The greatest limitation is the inability to detect variants in long homopolymer regions.

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Digital health technologies (DHTs) at the intersection of health, medical informatics, and business aim to enhance patient care through personalised digital approaches. Ensuring the efficacy and reliability of these innovations demands rigorous clinical validation. A PubMed literature review (January 2006 to July 2023) identified 1250 papers, highlighting growing academic interest. A focused narrative review (January 2018 to July 2023) delved into challenges, highlighting issues such as diverse regulatory landscapes, adoption issues in complex healthcare systems, and a plethora of evaluation frameworks lacking pragmatic guidance. Existing frameworks often omit crucial criteria, neglect empirical evidence, and clinical effectiveness is rarely included as a criterion for DHT quality. The paper underscores the urgency of addressing challenges in accreditation, adoption, business models, and integration to safeguard the quality, efficacy, and safety of DHTs. A pivotal illustration of collaborative efforts to address these challenges is exemplified by the Digital Health Validation Center, dedicated to generating clinical evidence of innovative healthcare technologies and facilitating seamless technology transfer. In conclusion, it is necessary to harmonise evaluation approaches and frameworks, improve regulatory clarity, and commit to collaboration to integrate rigorous clinical validation and empirical evidence throughout the DHT life cycle.

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INTRODUCTION: Over the last decade increasing examples indicate opportunities to measure patient functioning and its relevance for clinical and regulatory decision making via endpoints collected through digital health technologies. More recently, we have seen such measures support primary study endpoints and enable smaller trials. The field is advancing fast: validation requirements have been proposed in the literature and regulators are releasing new guidances to review these endpoints. Pharmaceutical companies are embracing collaborations to develop them and working with academia and patient organizations in their development. However, the road to validation and regulatory acceptance is lengthy. The full value of digital endpoints cannot be unlocked until better collaboration and modular evidence frameworks are developed enabling re-use of evidence and repurposing of digital endpoints. AREAS COVERED: This paper proposes a solution by presenting a novel modular evidence framework -the Digital Evidence Ecosystem and Protocols (DEEP)- enabling repurposing of measurement solutions, re-use of evidence, application of standards and also facilitates collaboration with health technology assessment bodies. EXPERT OPINION: The integration of digital endpoints in healthcare, essential for personalized and remote care, requires harmonization and transparency. The proposed novel stack model offers a modular approach, fostering collaboration and expediting the adoption in patient care.

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The practical application of photodynamic therapy (PDT) demands targeted and activatable photosensitizers to mitigate off-target phototoxicity common in "always on" photosensitizers during light exposure. Herein, a cyclometalated iridium complex-based activatable photodynamic molecular hybrid, Cy-Ir-7-nitrobenzofurazan (NBD), is demonstrated as a biomedicine for molecular precision. This design integrates a hydrogen sulfide (H2S)-responsive NBD unit with a hydroxy-appended iridium complex, Cy-Ir-OH. In normal physiological conditions, the electron-rich Ir metal center exerts electron transfer to the NBD unit, quenches the excited state dynamics, and establishes a PDT-off state. Upon exposure to H2S, Cy-Ir-NBD activates into the potent photosensitizer Cy-Ir-OH through nucleophilic substitution. This mechanism ensures exceptional specificity, enabling targeted phototherapy in H2S-rich cancer cells. Additionally, we observed that Cy-Ir-NBD-induced H2S depletion disrupts S-sulfhydration of the glyceraldehyde-3-phosphate dehydrogenase enzyme, impairing glycolysis and ATP production in the cellular milieu. This sequential therapeutic process of Cy-Ir-NBD is governed by the positively charged central iridium ion that ensures mitochondria-mediated apoptosis in cancer cells. Dual-modality SERS and fluorescence imaging validate apoptotic events, highlighting Cy-Ir-NBD as an advanced theranostic molecular entity for activatable PDT. Finally, as a proof of concept, clinical assessment is evaluated with the blood samples of breast cancer patients and healthy volunteers, based on their H2S overexpression capability through SERS and fluorescence, revealing Cy-Ir-NBD to be a promising predictor for PDT activation in advanced cancer phototherapy.

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BACKGROUND: BD Barricor™ tubes have been proposed to decrease laboratory turnaround time (TAT). We analytically validated and then clinically verified these tubes for use with Abbott Alinity™ and Siemens Atellica® highly sensitive cardiac troponin I (hs-cTnI) assays. METHODS: hs-cTnI measurements were undertaken in paired Barricor™ and in-use PSTII™ tubes on both systems. 359 matched samples with hs-cTnI levels between 3 and 15,000 ng/L (Atellica® values) were used to assess the hemolysis rate and make method comparisons. 599 paired patient samples were collected on emergency department (ED) admission to compare the performance of the rapid acute myocardial infarction (AMI) rule-out strategy based on hs-cTnI concentrations lower than recommended thresholds (<4 ng/L Alinity™; <5 ng/L Atellica®) when different tubes and systems were employed. RESULTS: No between-tube differences in hemolysis rate were seen when free hemoglobin concentrations in plasma samples were ≥0.25 g/L, even if PSTII™ showed a significant increase of hemolysis rate vs. Barricor™ (31% vs. 22%, p=0.007) when a lower cut-off for hemolysis (≥0.11 g/L) was employed on the Atellica® detection system. The alternate use of these tubes did not influence the hs-cTnI results obtained from either of the two assays, which remained markedly biased (~40%) irrespective of the tube used. The expected optimal ability of very low hs-cTnI values on ED admission for ruling out AMI was confirmed by using both systems regardless of the tube type. CONCLUSIONS: Barricor™ and PSTII™ tubes can provide analytically equivalent hs-cTnI results when used on either Alinity™ or Atellica® hs-cTnI assays.

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OBJECTIVE: To evaluate the sensitivity of human papillomavirus (HPV) tested urine to detect high-grade cervical precancer (cervical intraepithelial neoplasia grade 2+ [CIN2+]) using two urine collection devices. DESIGN: Randomised controlled trial. SETTING: St Mary's Hospital, Manchester, UK. POPULATION: Colposcopy attendees with abnormal cervical screening; a total of 480 participants were randomised. Matched urine and cervical samples were available for 235 and 230 participants using a first-void urine (FVU)-collection device and standard pot, respectively. METHODS: Urine was self-collected and mixed with preservative - randomised 1:1 to FVU-collection device (Novosanis Colli-pee® 10 mL with urine conservation medium [UCM]) or standard pot. Matched clinician-collected cervical samples were taken before colposcopy. HPV testing used Roche cobas® 8800. A questionnaire evaluated urine self-sampling acceptability. MAIN OUTCOME MEASURES: The primary outcome measured sensitivity of HPV-tested urine (FVU-collection device and standard pot) for CIN2+ detection. Secondary outcomes compared HPV-tested cervical and urine samples for CIN2+ and evaluated the acceptability of urine self-sampling. RESULTS: Urine HPV test sensitivity for CIN2+ was higher with the FVU-collection device (90.3%, 95% CI 83.7%-94.9%, 112/124) than the standard pot (73.4%, 95% CI 64.7%-80.9%, 91/124, p = 0.0005). The relative sensitivity of FVU-device-collected urine was 0.92 (95% CI 0.87-0.97, pMcN = 0.004) compared with cervical, considering that all women were referred after a positive cervical HPV test. Urine-based sampling was acceptable to colposcopy attendees. CONCLUSIONS: Testing of FVU-device-collected urine for HPV was superior to standard-pot-collected urine in colposcopy attendees and has promising sensitivity for CIN2+ detection. General population HPV testing of FVU-device-collected urine will establish its clinical performance and acceptability as an alternative to routine cervical screening.

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BACKGROUND AND AIMS: Advances in health, especially in prevention, diagnosis, and treatment, have significantly impacted the way of facing emerging infectious diseases. Yet, events such as the COVID-19 pandemic have shown that there is still a long way to go. Therefore, an urgent need exists for portable and easily deployable point-of-care (POC) detection tools. Biosensors at the POC remain in the laboratory in an analytical characterization step and are not yet mature enough to reach the market massively. In this context, it is necessary to progress in validating these devices to demonstrate their relevance in detecting different disease biomarkers. This work reports on the clinical validation of an electrochemical immunosensor for detecting SARS-CoV-2. METHODS: A monocentric retrospective cohort study was conducted with 150 random nasopharyngeal swabs or tracheal aspiration samples tested by RT-PCR. The immunosensor based on magnetic beads and chronoamperometry detected SARS-CoV-2 through the spike-angiotensin-converting protein (ACE2) immunocomplex. RESULTS: This biosensor demonstrated 96.04 % clinical sensitivity and 87.75 % clinical specificity in detecting SARS-CoV-2 in the samples, highly correlated with the RT-PCR gold standard. CONCLUSIONS: It demonstrates the potential of electrochemical biosensors to be implemented as highly sensitive and easily deployable detection strategies even in remote locations.

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BACKGROUND: The antiepileptic drug lamotrigine (LTG) shows high pharmacokinetic variability due to genotype influence and concomitant use of glucuronidation inducers and inhibitors, both of which may be frequently taken by elderly patients. Our goal was to develop a reliable quantification method for lamotrigine and its main glucuronide metabolite lamotrigine-N2-glucuronide (LTG-N2-GLU) in dried blood spots (DBS) to enable routine therapeutic drug monitoring and to identify altered metabolic activity for early detection of drug interactions possibly leading to suboptimal drug response. RESULTS: The analytical method was validated in terms of selectivity, accuracy, precision, matrix effects, haematocrit, blood spot volume influence, and stability. It was applied to a clinical study, and the DBS results were compared to the concentrations determined in plasma samples. A good correlation was established for both analytes in DBS and plasma samples, taking into account the haematocrit and blood cell-to-plasma partition coefficients. It was demonstrated that the method is suitable for the determination of the metabolite-to-parent ratio to reveal the metabolic status of individual patients. CONCLUSIONS: The clinical validation performed confirmed that the DBS technique is a reliable alternative for plasma lamotrigine and its glucuronide determination.