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Individuals entering incarceration are at high risk for infectious diseases, other ill conditions, and risky behavior. Typically, the status of active pulmonary tuberculosis (PTB) is not known at the time of admission. Early detection and treatment are essential for effective TB control. So far, no study has compared the diagnostic accuracy of various TB screening tools in detention using a network meta-analysis (NMA). We aimed to investigate the diagnostic accuracy of active PTB screening tests upon detention admission. We searched PubMed, Global Index Medicus, the Cochrane Library electronic databases, and grey literature for publications reporting detention TB entry screening in March 2022 and January 2024. Inclusion was non-restrictive regarding time, language, location, reference standards, or screening tests. Eligible study designs comprised comparative, observational, and diagnostic studies. Publications had to report TB screening of individuals entering confinement and provide data for diagnostic accuracy calculations. The QUADAS-2 tool was designed to assess the quality of primary diagnostic accuracy studies. This systematic review was registered with PROSPERO (CRD42022307863) and conducted without external funding. We screened a total of 2,455 records. Despite extensive searching, no studies met our inclusion criteria. However, we identified evidence revealing key differences in screening algorithm application. In conclusion, more diagnostic accuracy data on TB screening algorithms for detention admission worldwide needs to be collected. We recommend that global TB initiatives set up multi-site studies to investigate the diagnostic accuracy of TB screening on admission in low- and high-prevalence criminal justice systems. Further network meta-analyses of these studies could inform policymakers and public health experts to establish or fine-tune TB control in detention settings., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s).)

β-thalassemia is a quantitative hemoglobin (Hb) disorder resulting in reduced production of Hb A and increased levels of Hb A 2 . Diagnosis of β-thalassemia can be problematic when combined with other structural Hb variants, so that the separation approaches in routine clinical centers are not sufficiently decisive to obtain accurate results. Here, we separate the intact Hb subunits by high-performance liquid chromatography, followed by top-down tandem mass spectrometry of intact subunits to distinguish Hb variants. Proton transfer reaction-parallel ion parking (PTR-PIP), in which a radical anion removes protons from multiply charged precursor ions and produces charge-reduced ions spanning a limited m / z range, was used to increase the signal-to-noise ratio of the subunits of interest. We demonstrate that the δ/β ratio can act as a biomarker to identify β-thalassemia in normal electrospray ionization MS1 and PTR-PIP MS1. The application of PTR-PIP significantly increases the sensitivity and specificity of the HPLC-MS method to identify δ/β ratio as a thalassemia biomarker., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 THE AUTHORS. Publishing services by ELSEVIER B.V. on behalf of MSACL.)

Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors.)

Objective: Prolonged air leak is the most common complication of thoracic surgery. Intraoperative leak site detection is the first step in decreasing the risk of leak-related postoperative complications., Methods: We retrospectively reviewed the surgical videos of patients who underwent lung resection at our institution. In the training phase, deep learning-based air leak detection software was developed using leak-positive endoscopic images. In the testing phase, a different data set was used to evaluate our proposed application for each predicted box., Results: A total of 110 originally captured and labeled images obtained from 70 surgeries were preprocessed for the training data set. The testing data set contained 64 leak-positive and 45 leak-negative sites. The testing data set was obtained from 93 operations, including 58 patients in whom an air leak was present and 35 patients in whom an air leak was absent. In the testing phase, our software detected leak sites with a sensitivity and specificity of 81.3% and 68.9%, respectively., Conclusions: We have successfully developed a deep learning-based leak site detection application, which can be used in deflated lungs. Although the current version is still a prototype with a limited training data set, it is a novel concept of leak detection based entirely on visual information., (© 2022 The Author(s).)

Purpose: To develop and validate an automated deep learning (DL)-based artificial intelligence (AI) platform for diagnosing and grading cataracts using slit-lamp and retroillumination lens photographs based on the Lens Opacities Classification System (LOCS) III., Design: Cross-sectional study in which a convolutional neural network was trained and tested using photographs of slit-lamp and retroillumination lens photographs., Participants: One thousand three hundred thirty-five slit-lamp images and 637 retroillumination lens images from 596 patients., Methods: Slit-lamp and retroillumination lens photographs were graded by 2 trained graders using LOCS III. Image datasets were labeled and divided into training, validation, and test datasets. We trained and validated AI platforms with 4 key strategies in the AI domain: (1) region detection network for redundant information inside data, (2) data augmentation and transfer learning for the small dataset size problem, (3) generalized cross-entropy loss for dataset bias, and (4) class balanced loss for class imbalance problems. The performance of the AI platform was reinforced with an ensemble of 3 AI algorithms: ResNet18, WideResNet50-2, and ResNext50., Main Outcome Measures: Diagnostic and LOCS III-based grading prediction performance of AI platforms., Results: The AI platform showed robust diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.9992 [95% confidence interval (CI), 0.9986-0.9998] and 0.9994 [95% CI, 0.9989-0.9998]; accuracy, 98.82% [95% CI, 97.7%-99.9%] and 98.51% [95% CI, 97.4%-99.6%]) and LOCS III-based grading prediction performance (AUC, 0.9567 [95% CI, 0.9501-0.9633] and 0.9650 [95% CI, 0.9509-0.9792]; accuracy, 91.22% [95% CI, 89.4%-93.0%] and 90.26% [95% CI, 88.6%-91.9%]) for nuclear opalescence (NO) and nuclear color (NC) using slit-lamp photographs, respectively. For cortical opacity (CO) and posterior subcapsular opacity (PSC), the system achieved high diagnostic performance (AUC, 0.9680 [95% CI, 0.9579-0.9781] and 0.9465 [95% CI, 0.9348-0.9582]; accuracy, 96.21% [95% CI, 94.4%-98.0%] and 92.17% [95% CI, 88.6%-95.8%]) and good LOCS III-based grading prediction performance (AUC, 0.9044 [95% CI, 0.8958-0.9129] and 0.9174 [95% CI, 0.9055-0.9295]; accuracy, 91.33% [95% CI, 89.7%-93.0%] and 87.89% [95% CI, 85.6%-90.2%]) using retroillumination images., Conclusions: Our DL-based AI platform successfully yielded accurate and precise detection and grading of NO and NC in 7-level classification and CO and PSC in 6-level classification, overcoming the limitations of medical databases such as few training data or biased label distribution., (© 2022 by the American Academy of Ophthalmology.)

Purpose: This study aims to determine if the presence of specific clinical and computed tomography (CT) patterns are associated with epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer., Methods: A systematic literature review and meta-analysis was carried out in 6 databases between January 2002 and July 2021. The relationship between clinical and CT patterns to detect EGFR mutation was measured and pooled using odds ratios (OR). These results were used to build several mathematical models to predict EGFR mutation., Results: 34 retrospective diagnostic accuracy studies met the inclusion and exclusion criteria. The results showed that ground-glass opacities (GGO) have an OR of 1.86 (95%CI 1.34 -2.57), air bronchogram OR 1.60 (95%CI 1.38 - 1.85), vascular convergence OR 1.39 (95%CI 1.12 - 1.74), pleural retraction OR 1.99 (95%CI 1.72 - 2.31), spiculation OR 1.42 (95%CI 1.19 - 1.70), cavitation OR 0.70 (95%CI 0.57 - 0.86), early disease stage OR 1.58 (95%CI 1.14 - 2.18), non-smoker status OR 2.79 (95%CI 2.34 - 3.31), female gender OR 2.33 (95%CI 1.97 - 2.75). A mathematical model was built, including all clinical and CT patterns assessed, showing an area under the curve (AUC) of 0.81., Conclusions: GGO, air bronchogram, vascular convergence, pleural retraction, spiculated margins, early disease stage, female gender, and non-smoking status are significant risk factors for EGFR mutation. At the same time, cavitation is a protective factor for EGFR mutation. The mathematical model built acts as a good predictor for EGFR mutation in patients with lung adenocarcinoma., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Purpose: This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC)., Methods: Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation., Results: The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment., Conclusion: QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684., Competing Interests: Gregory Czarnota received funding from Terry Fox Foundation Program Project Grant from the Hecht Foundation (grant number 1083). None of the other authors have any other conflict of interest to declare., (© 2021 The Authors.)

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.

Objectives: High sensitivity cardiac troponin T and I (hs-cTnT and hs-cTnI) assays show analytical, diagnostic and prognostic improvement over contemporary sensitive cTn assays. However, given the importance of troponin in the diagnosis of myocardial infarction, implementing this test requires rigorous analytical and clinical verification across the total testing pathway. This was the aim of this study., Design and Methods: Analytical verification included assessment of critical outlier frequency, for hs-cTnI and cTnI assays. Concordance for paired cTnI and hs-cTnI measurements ( n =1096) was verified using 99th percentiles for both genders (cTnI: 30 ng/L, hs-cTnI: 25 ng/L) and for men and women separately (hs-cTnI: M: 34;F: 16 ng/L). Discordant data was correlated with clinical and laboratory information. Diagnosis of Acute Coronary Syndrome (ACS) or Non-ACS was adjudicated by two cardiologists independently., Results: The hs-cTnI assay showed a lower (10-fold) critical outlier rate (0.091%) and more detectable results above the limit of detection (LOD) (23.4%) and 99th percentile (2.4%), compared to cTnI. Analytical concordance between the two assays was high (94.5%) but decreased (91.7%) when gender-specific hs-cTnI cut-offs were used. The hs-cTnI assay gave fewer false negatives (up to 1.0%) but disproportionately more false positives (up to 6.7%) overall, which improved (3.9%) for serial measurements., Conclusions: Laboratories should analytically and clinically verify hs-cTn assays before use, with attention to performance and the clinical and diagnostic algorithms that support appropriate testing and result interpretation. Work in the pre- and post-analytical phases is necessary to augment the analytical improvement in the new era of troponin testing.

A first analysis of a database of shared preclinical safety data for 1214 small molecule drugs and drug candidates extracted from 3970 reports donated by thirteen pharmaceutical companies for the eTOX project (www.etoxproject.eu) is presented. Species, duration of exposure and administration route data were analysed to assess if large enough subsets of homogenous data are available for building in silico predictive models. Prevalence of treatment related effects for the different types of findings recorded were analysed. The eTOX ontology was used to determine the most common treatment-related clinical chemistry and histopathology findings reported in the database. The data were then mined to evaluate sensitivity of established in vivo biomarkers for liver toxicity risk assessment. The value of the database to inform other drug development projects during early drug development is illustrated by a case study.