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Evaluation of three Aspergillus antibody assays for screening of chronic pulmonary aspergillosis: prospective diagnostic accuracy study.

OBJECTIVES: Chronic pulmonary aspergillosis (CPA) is a frequent complication of pulmonary tuberculosis (PTB), particularly in high-burden settings where access to reliable serological diagnostics remains limited. We evaluated the diagnostic performance of two immunochromatographic technology (ICT) lateral flow assays (LFAs) and an ELISA for CPA screening among patients with active or previously treated PTB. METHODS: In this two-year prospective multicentre diagnostic evaluation, serum from adults with prior or active PTB was tested using the Era Biology Aspergillus IgG ICT LFA, LDBio Aspergillus IgG/IgM ICT LFA, and Bordier Aspergillus fumigatus IgG ELISA. CPA diagnosis was established using a consensus composite reference standard incorporating clinical, immunological, radiological, and microbiological criteria. The Bordier ELISA was used as part of the immunological component of the consensus CPA diagnosis, with a cutoff optical density of ≥1.0. Diagnostic accuracy, agreement statistics, receiver operating characteristic analysis, and latent class analysis (LCA) were performed. RESULTS: Among 340 participants, 24 (7.06%) had CPA. Proportion of participants with positive antibody tests among all tested individuals were 6.76% for LDBio ICT LFA, 20.0% for Era Biology ICT LFA, and 11.47% for Bordier ELISA. Against consensus CPA diagnosis, Bordier ELISA showed 87.50% sensitivity and 94.30% specificity, LDBio ICT LFA 58.33% sensitivity and 97.15% specificity, and Era Biology LFA 66.67% sensitivity and 83.54% specificity. LCA estimated CPA prevalence at 7.72%. LCA-derived sensitivities and specificities were 86.58% and 99.92% for LDBio ICT LFA, 83.39% and 85.31% for Era Biology LFA, and 79.10% and 94.19% for Bordier ELISA. CONCLUSIONS: The Bordier ELISA showed high sensitivity and specificity, while the LDBio ICT LFA demonstrated very high specificity with strong LCA-derived performance. These findings support the use of ELISA for laboratory diagnosis and ICT as a point-of-care screening tool for CPA in resource-limited settings. Era Biology Aspergillus IgG LFA demonstrated moderate sensitivity and acceptable diagnostic performance, indicating its potential utility as a supplementary screening assay for CPA in settings where rapid, point-of-care testing is required.

Humans

Risk Factors and Predictive Model for Postoperative High Myopia in Children Undergoing Congenital Cataract Surgery With Intraocular Lens Implantation.

PURPOSE: To identify risk factors associated with the development of high myopia following congenital cataract surgery and to establish a robust predictive model. DESIGN: Retrospective clinical cohort study. SUBJECTS: This retrospective study included 106 pediatric patients who underwent congenital cataract surgery with primary IOL implantation (mean follow-up 8.19 years). The model was externally validated in an independent cohort of 72 patients with a mean follow-up of 7.83 years. METHODS: Preoperative and postoperative ocular biometric parameters were collected. Risk factors for postoperative high myopia were analyzed using Cox proportional hazards regression, which served as the basis for model construction. The predictive performance of the model was rigorously evaluated for discrimination and calibration. Discriminative ability was quantified using Harrell's C-index and the area under the receiver operating characteristic curve (AUC). Model calibration was assessed via calibration plots by comparing predicted probabilities with actual observed outcomes. Internal validation was performed using a bootstrapping method (500 iterations) to ensure model stability and adjust for potential overfitting. RESULTS: An initial postoperative refraction of <+0.75D, and a higher IOL Power to Axial length Ratio (IOL/AL ratio) were identified as significant risk factors for the development of postoperative high myopia. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. The predictive model demonstrated robust performance, achieving a C-index of 0.711 (internal validation C-index: 0.713). The area under the receiver operating characteristic curve (AUC) values for predicting high myopia at 5 and 10 years were 0.858 and 0.745, respectively. Furthermore, calibration curves demonstrated excellent agreement between the predicted and observed outcomes throughout the follow-up period. In external validation, the model achieved a C-index of 0.825, 5-year AUC of 0.833, and 10-year AUC of 0.713. CONCLUSIONS: Our analysis established that initial postoperative refraction <+0.75D, and an elevated IOL/AL ratio are key determinants of high myopia risk following surgery. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. This predictive framework provides clinicians with a practical tool to optimize preoperative IOL selection and identify high-risk infants who require vigilant myopia prevention and balanced amblyopia management.

Humans

Comparison of black carbon measurements using filter-specific reference transmittance to those using lab blanks or an average of unloaded filters.

Filter-based optical techniques compare light transmission intensities between loaded (I) and unloaded (I0) filters as a measure of light-absorbing mass for subsequent estimations of equivalent black carbon (eBC). We analyzed 5,379 15&#x2009;mm Teflon filters from the Household Air Pollution Intervention Network (HAPIN) trial to assess the influence that different methods of I0 estimations have on eBC measures. We compared eBC measurements using filter-specific I0 values (Method 1) to those using three other methods of I0 estimation: the lab blank scan from a given session (Method 2), the average of all pre-sample filter scans (Method 3), and the average of all lab blank filter scans (Method 4). We assessed the agreement between Method 1 and the alternative methods using Bland-Altman analysis. We also assessed the relationship between Method 1 and the alternative methods across the complete measurement range and after stratifying exposure data into quartiles according to Method 1 eBC exposures. The mean (SD) personal eBC exposure for Method 1 was 7.8&#x2009;&#x3bc;g/m3 (5.9), and exposures ranged from 1.3 to 46.8&#x2009;&#x3bc;g/m3. Compared to Method 1, eBC using Methods 2, 3, and 4 were higher by 0.7&#x2009;&#x3bc;g/m3, 0.1&#x2009;&#x3bc;g/m3, and 0.7&#x2009;&#x3bc;g/m3, respectively. The performances of linear regression models between Method 1 and all other methods were moderate to strong (R2 range: 0.42-0.93) in the second, third, and fourth quartiles; however, the models in the first quartile (eBC range: 1.3-2.9&#x2009;&#x3bc;g/m3) performed poorly (R2&#x2009;=&#x2009;0.25-0.26), with error approximately 25% of the mean. Our findings suggest that, in most instances, conventional methods for obtaining I0 values can be used to sufficiently characterize eBC; however, analyzing filters before sampling adds appreciably to the accuracy of eBC estimations in lower concentration settings.Implications: Filter-based optical techniques compare light transmission intensities between loaded (I) and unloaded (I0) filters as a measure of light-absorbing mass for subsequent estimations of equivalent black carbon (eBC). To assess the influence that different methods of I0 estimations have on eBC measures, we analyzed 5,379 15 mm Teflon filters from the Household Air Pollution Intervention Network (HAPIN) trial. Our findings suggest that, in most instances, conventional methods for obtaining I0 values can be used to sufficiently characterize eBC; however, analyzing filters before sampling adds appreciably to the accuracy of eBC estimations in lower concentration settings.

Soot

Smartphone Apps for Preventing Adolescent Health Problems Among Health Care Professionals: Systematic Search and Quality Assessment.

BACKGROUND: Health care professionals must consider multiple dimensions of prevention when consulting with adolescents. Identifying risky behaviors early in adolescence is crucial for reducing both morbidity and mortality. General practitioners are increasingly eager to incorporate digital tools for prevention into their consultations with adolescents; however, the relevance and clinical validity of these digital tools are not always established or well-known. Consequently, primary care professionals require guidance and support in selecting relevant mobile health (mHealth) tools. OBJECTIVE: The aim of this study is to identify relevant and useful digital apps to help primary care professionals detect at-risk adolescents across all recommended areas of prevention: orthopedics, mental health, substance abuse, risk behaviors, sexual health, vaccinations, social relationships, and nutrition. METHODS: A systematic review of smartphone apps, with an analysis of content quality, was carried out by 4 researchers using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. The App Store and Google Play Store platforms were surveyed. The inclusion criteria were as follows: free of charge, date of last update, availability in French or English, relevance of the preventive approach to adolescents, and scientific validation. Four health care professionals assessed the apps: 2 selected the apps relevant to health care professionals, then 3 analyzed these apps using the French version of the Mobile App Rating Scale (MARS-F). Intraclass correlation coefficient, model (2,1) (2-way random effects, absolute agreement, single measures); standard error of measurement; and mean absolute error were also calculated. RESULTS: A total of 976 apps were identified, 49 of which had disappeared from the platforms prior to analysis. Nine apps were retained. Seven (0.72%) were included after evaluation using the MARS-F: 2 on mental health and 5 on sexual health (including 3 on contraception only). The mean MARS-F interrater score ranged from 2.5/5 to 3.8/5. The global MARS-F score demonstrated a pooled SD of 0.60 and an intraclass correlation coefficient (2,1) of 0.0003, resulting in a calculated standard error of measurement of 0.60. The average discrepancy between raters was a mean absolute error of 0.53. CONCLUSIONS: No similar studies have been identified in the literature that specifically focus on mobile apps designed to support health care professionals in delivering preventive care to adolescents. Of the 8 areas of prevention identified as relevant for adolescents, only 3 are addressed by the apps validated through our methodology (5 focus on sexual health). Consequently, current apps are insufficient to support health care professionals in their overall preventive work with adolescents. Such a review should be conducted systematically prior to the development of any new tool to prevent duplication and channel creative efforts toward truly innovative digital solutions. Furthermore, a thorough analysis of relevant, recommended websites is essential, as these resources complement the use of mobile apps designed for health care professionals.

Humans