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.

OBJECTIVE: 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-1β. 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.

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.

BACKGROUND: Multidrug-resistant HIV strains challenge treatment efficacy and increase mortality rates. Next-generation sequencing (NGS) technology swiftly detects variants, facilitating personalized antiretroviral therapy.
OBJECTIVE: 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.
METHODS: 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.
METHODS: 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.

OBJECTIVE: 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.

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.

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.
OBJECTIVE: 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.
CONCLUSIONS: 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.

UNASSIGNED: This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.