BACKGROUND: Raised intracranial pressure (ICP) contributes to approximately 20% of the admissions in the paediatric intensive care unit (PICU) in our setting. Timely identification and treatment of raised ICP is important to prevent brain herniation and death in such cases. The objective of this study was to examine the role of optic nerve sheath diameter (ONSD) in detecting clinically relevant raised ICP in children.
METHODS: A hospital-based observational analytical study in a PICU of a tertiary care institute in India on children aged 2-14 years. ONSD was measured in all children on three time points that is, day 1, day 2 and between day 4 and 7 of admission. ONSD values were compared between children with and without clinical signs of raised ICP.
RESULTS: Out of 137 paediatric patients recruited, 34 had signs of raised ICP. Mean ONSD on day 1 was higher in children with signs of raised ICP (4.99±0.57 vs 4.06±0.40; p<0.01). Mean ONSD on day 2 also was higher in raised ICP patients (4.94±0.55 vs 4.04±0.40; p<0.01). The third reading between days 4 and 7 of admission was less than the first 2 values but still higher in raised ICP patients (4.48±1.26 vs 3.99±0.57; p<0.001). The cut-off ONSD value for detecting raised ICP was 4.46 mm on the ROC curve with an area under curve 0.906 (95% CI 0.844 to 0.968), 85.3% sensitivity and 86.4% specificity. There was no difference in ONSD between the right and the left eyes at any time point irrespective of signs of raised ICP.
CONCLUSIONS: We found that measurement of ONSD by transorbital ultrasound was able to detect clinically relevant raised ICP with an excellent discriminatory performance at the cut-off value of 4.46 mm.

Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.

We hypothesized that the triglyceride-glucose (TyG)-alanine aminotransferase (ALT) index, which combines the TyG index with ALT, may enhance sensitivity and specificity in detecting the severity of nonalcoholic fatty liver disease (NAFLD). A total of 131 NAFLD patients with a mean age of 11.5 ± 2.29 years were enrolled, and severity was assessed by ultrasound fatty liver index (US-FLI) scoring. The TyG-ALT index was defined as ln(fasting triglyceride [mg/dL] × fasting glucose [mg/dL] × ALT [IU/L]/2). Multiple linear regression analysis revealed a significant association between the TyG-ALT index and US-FLI (β = 0.317, P < .001) after controlling for sex, age, and body mass index. The TyG-ALT index showed a more stable and superior ability to detect the severity of NAFLD compared to both ALT and the TyG index. The area under the curve values, listed in the order of ALT, TyG index, and TyG-ALT index, were as follows: 0.737 (P < .001), 0.599 (P = .055), and 0.704 (P < .001) at US-FLI ≥ 4 points; 0.717 (P < .001), 0.720 (P < .001), and 0.775 (P < .001) at US-FLI ≥ 5 points; and 0.689 (P < .05), 0.748 (P < .01), and 0.775 (P < .001) at US-FLI ≥ 6 points. The TyG-ALT index is associated with US-FLI score and superior to both ALT and the TyG index in predicting NAFLD severity. These findings indicate the potential of the TyG-ALT index in the management of pediatric NAFLD progression.

UNASSIGNED: The rapid detection of Mycobacterium tuberculosis (MTB) is essential for controlling tuberculosis. Methods We designed a portable thermocycler-based real-time fluorescence loop-mediated isothermal amplification assay (cyp141-RealAmp) using six oligonucleotide primers derived from cyp141 to detect MTB. A combined number of 213 sputum samples (169 obtained from clinically diagnosed cases of pulmonary TB and 44 from a control group without tuberculosis) underwent Acid-fast bacillus (AFB) smear, culture, Xpert MTB/RIF assays, and cyp141-RealAmp assay.
UNASSIGNED: By targeting MTB cyp141, this technique could detect as low as 10 copies/reaction within 30 min, and it was successfully rejected by other mycobacteria and other bacterial species tested. Of the 169 patients, there was no statistical difference between the detection rate of cyp141-RealAmp (92.90%, 95% CI: 89.03-96.07) and that of Xpert MTB/RIF (94.67%, 95% CI: 91.28-98.06) (P > 0.05), but both were statistically higher than that of culture (65.68%, 95% CI: 58.52-72.84) (P< 0.05) and AFB (57.40%, 95% CI: 49.94-64.86) (P< 0.05). Both cyp141-RealAmp and Xpert MTB/RIF had a specificity of 100%. Furthermore, a high concordance between cyp141-RealAmp and Xpert MTB/RIF was found (Kappa = 0.89).
UNASSIGNED: The cyp141-RealAmp assay was shown to be effective, responsive, and accurate in this study. This method offers a prospective strategy for the speedy and precise detection of MTB.

UNASSIGNED: Researchers have delved into noninvasive diagnostic methods of renal fibrosis (RF) in chronic kidney disease, including ultrasound (US), magnetic resonance imaging (MRI), and radiomics. However, the value of these diagnostic methods in the noninvasive diagnosis of RF remains contentious. Consequently, the present study aimed to systematically delineate the accuracy of the noninvasive diagnosis of RF.
UNASSIGNED: A systematic search covering PubMed, Embase, Cochrane Library, and Web of Science databases for all data available up to 28 July 2023 was conducted for eligible studies.
UNASSIGNED: We included 21 studies covering 4885 participants. Among them, nine studies utilized US as a noninvasive diagnostic method, eight studies used MRI, and four articles employed radiomics. The sensitivity and specificity of US for detecting RF were 0.81 (95% CI: 0.76-0.86) and 0.79 (95% CI: 0.72-0.84). The sensitivity and specificity of MRI were 0.77 (95% CI: 0.70-0.83) and 0.92 (95% CI: 0.85-0.96). The sensitivity and specificity of radiomics were 0.69 (95% CI: 0.59-0.77) and 0.78 (95% CI: 0.68-0.85).
UNASSIGNED: The current early noninvasive diagnostic methods for RF include US, MRI, and radiomics. However, this study demonstrates that US has a higher sensitivity for the detection of RF compared to MRI. Compared to US, radiomics studies based on US did not show superior advantages. Therefore, challenges still exist in the current radiomics approaches for diagnosing RF, and further exploration of optimized artificial intelligence (AI) algorithms and technologies is needed.

BACKGROUND: The steep rise in substance use and substance use disorder (SUD) shows an urgency to assess its prevalence using valid measures. This systematic review summarizes the validity of measures to assess the prevalence of substance use and SUD in the US estimated in population and sub-population-based surveys.
METHODS: A literature search was performed using nine online databases. Studies were included in the review if they were published in English and tested the validity of substance use and SUD measures among US adults at the general or sub-population level. Independent reviews were conducted by the authors to complete data synthesis and assess the risk of bias.
RESULTS: Overall, 46 studies validating substance use/SUD (n = 46) measures were included in this review, in which 63% were conducted in clinical settings and 89% assessed the validity of SUD measures. Among the studies that assessed SUD screening measures, 78% examined a generic SUD measure, and the rest screened for specific disorders. Almost every study used a different survey measure. Overall, sensitivity and specificity tests were conducted in over a third of the studies for validation, and 10 studies used receiver operating characteristics curve.
CONCLUSIONS: Findings suggest a lack of standardized methods in surveys measuring and reporting prevalence of substance use/SUD among US adults. It highlights a critical need to develop short measures for assessing SUD that do not require lengthy, time-consuming data collection that would be difficult to incorporate into population-based surveys assessing a multitude of health dimensions.
BACKGROUND: PROSPERO CRD42022298280.

BACKGROUND: Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.
METHODS: Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies\' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.
RESULTS: This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.
CONCLUSIONS: This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

BACKGROUND: Primary cervical cancer screening and treating precancerous lesions are effective ways to prevent cervical cancer. However, the coverage rates of human papillomavirus (HPV) vaccines and routine screening are low in most developing countries and even some developed countries. This study aimed to explore the benefit of an artificial intelligence-assisted cytology (AI) system in a screening program for a cervical cancer high-risk population in China.
METHODS: A total of 1231 liquid-based cytology (LBC) slides from women who underwent colposcopy at the Chinese PLA General Hospital from 2018 to 2020 were collected. All women had received a histological diagnosis based on the results of colposcopy and biopsy. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), false-positive rate (FPR), false-negative rate (FNR), overall accuracy (OA), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and Youden index (YI) of the AI, LBC, HPV, LBC + HPV, AI + LBC, AI + HPV and HPV Seq LBC screening strategies at low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) thresholds were calculated to assess their effectiveness. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic values of the different screening strategies.
RESULTS: The Se and Sp of the primary AI-alone strategy at the LSIL and HSIL thresholds were superior to those of the LBC + HPV cotesting strategy. Among the screening strategies, the YIs of the AI strategy at the LSIL + threshold and HSIL + threshold were the highest. At the HSIL + threshold, the AI strategy achieved the best result, with an AUC value of 0.621 (95% CI, 0.587-0.654), whereas HPV testing achieved the worst result, with an AUC value of 0.521 (95% CI, 0.484-0.559). Similarly, at the LSIL + threshold, the LBC-based strategy achieved the best result, with an AUC of 0.637 (95% CI, 0.606-0.668), whereas HPV testing achieved the worst result, with an AUC of 0.524 (95% CI, 0.491-0.557). Moreover, the AUCs of the AI and LBC strategies at this threshold were similar (0.631 and 0.637, respectively).
CONCLUSIONS: These results confirmed that AI-only screening was the most authoritative method for diagnosing HSILs and LSILs, improving the accuracy of colposcopy diagnosis, and was more beneficial for patients than traditional LBC + HPV cotesting.

Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.

Introduction. The absence of a gold-standard methodology for the microbiological diagnosis of urinary tract infections (UTI) has led to insufficient standardization of criteria for the interpretation of results and processing methods, particularly incubation time and culture media.Hypothesis. 48-hour incubation time period and use of blood agar enhances the sensitivity of microorganisms isolated significantly.Aim. To determine the sensitivity of blood agar and Brilliance UTI chromogenic agar, incubating for different periods (24-48 hours), for the detection of positive urine cultures.Methodoloy. Comparisons were made between all possible combinations of media and incubation times. As the gold-standard reference, we used the routine methodology of our laboratory, which involves prior screening with available clinical data, flow cytometry, sediment analysis and/or Gram staining. Screened samples were then cultured on blood agar and chromogenic agar and incubated for 48 hours. Also, based on the results of Gram staining, additional media were added in selected cases.Results. The most significant difference was found between chromogenic agar incubated for 24 hours and blood agar incubated for 48 hours, with the latter method allowing the recovery of 10.14 % more microorganisms (P < 0.0001). Furthermore, the value of performing Gram staining to guide processing was demonstrated, as it avoided the loss of at least 5.14 % of isolates.Conclusions. At least in urological and nephrological patients it is essential to include enriched culture media (blood agar) or to extend the incubation times due to the improvement of the diagnostic sensitivity of urine cultures. Gram staining also can help detect the presence of fastidious microorganisms or mixed infections, indicating whether rich and/or selective media should be included to enhance the diagnostic sensitivity of cultures. If this methodology is not followed, it should be noted that besides fastidious species, fastidious strains of Escherichia coli, Proteus mirabilis, Pseudomonas aerugniosa and Stenotrophomonas maltophilia will also be missed.