Respiratory tract infections caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses are persistent and critical. The Cobas Liat SARS-CoV-2 & influenza A/B assay (Multiplex Liat), the FDA-authorized point-of-care reverse transcriptase polymerase chain reaction (RT-PCR) assay, has a turnaround time of 20 min and high accuracy. This study evaluates the pooled performance of this assay to provide practical information. This meta-analysis was registered in PROSPERO (registration number: CRD42023467579). A systematic literature search was conducted within PubMed, Ovid-EMBASE, and the Cochrane Library for articles evaluating the accuracy of the Multiplex Liat assay through September 2023. A random-effects model was used to calculate the pooled diagnostic values with real-time RT-PCR (rRT-PCR) as a reference test. A total of 4,705 samples from eight studies were included in the primary meta-analysis. The overall pooled sensitivity and specificity of Multiplex Liat were 100.0 % (95 % confidence interval [CI] = 96.7 %-100.0 %) and 99.7 % (95 % CI = 98.7 %-99.9 %), respectively. The presence of variants of concern or in-house rRT-PCR assays as reference standards did not significantly affect the pooled diagnostic performance of the Multiplex Liat. When 5,333 samples from nine studies were assessed for sensitivity, the pooled sensitivity was 100.0 % (95 % CI = 85.8 %-100.0 %) without a significant difference. This meta-analysis demonstrates the usefulness of Multiplex Liat for the detection of SARS-CoV-2 based on pooled diagnostic values. These practical findings may facilitate appropriate settings for the diagnosis and management of patients with respiratory tract infections.

UNASSIGNED: Cerebral radiation necrosis (RN) is often a delayed phenomenon occurring several months to years after the completion of radiation treatment. Differentiating RN from tumor recurrence presents a diagnostic challenge on standard MRI. To date, no evidence-based guidelines exist regarding imaging modalities best suited for this purpose. We aim to review the current literature and perform a diagnostic meta-analysis comparing various imaging modalities that have been studied to differentiate tumor recurrence and RN.
METHODS: A systematic search adherent to PRISMA guidelines was performed using Scopus, PubMed/MEDLINE, and Embase. Pooled sensitivities and specificities were determined using a random-effects or fixed-effects proportional meta-analysis based on heterogeneity. Using diagnostic odds ratios, a diagnostic frequentist random-effects network meta-analysis was performed, and studies were ranked using P-score hierarchical ranking.
RESULTS: The analysis included 127 studies with a total of 220 imaging datasets, including the following imaging modalities: MRI (n = 10), MR Spectroscopy (MRS) (n = 28), dynamic contrast-enhanced MRI (n = 7), dynamic susceptibility contrast MRI (n = 36), MR arterial spin labeling (n = 5), diffusion-weighted imaging (n = 13), diffusion tensor imaging (DTI) (n = 2), PET (n = 89), and single photon emission computed tomography (SPECT) (n = 30). MRS had the highest pooled sensitivity (90.7%). DTI had the highest pooled specificity (90.5%). Our hierarchical ranking ranked SPECT and MRS as most preferable, and MRI was ranked as least preferable.
CONCLUSIONS: These findings suggest SPECT and MRS carry greater utility than standard MRI in distinguishing RN from tumor recurrence.

OBJECTIVE: Previous studies have shown inconsistent results regarding the usefulness of prolactin adjustment in bilateral inferior petrosal sinus sampling (BIPSS) in the differential diagnosis of adrenocorticotropic hormone (ACTH)-dependent Cushing syndrome (CS). This meta-analysis compared the diagnostic accuracy of prolactin adjustment versus no adjustment in BIPSS.
METHODS: This study searched the PubMed, Embase, Web of Science, Cochrane library, and WanFang databases for published data as of March 2022 on the use of prolactin adjustment in BIPSS to differentially diagnose ACTH-dependent CS. A Bayesian joint bivariate model was used in the head-to-head comparison of the diagnostic accuracy.
RESULTS: This meta-analysis included a total of 10 studies with 300 patients. The combined sensitivity and specificity for BIPSS without prolactin adjustment were 94.47% (95% CI, 88.67%-98.44%) and 91.14% (95% CI, 57.17%-99.80%), respectively. The combined sensitivity and specificity after prolactin adjustment were 99.97% (95% CI, 99.03%-100.00%) and 80.69% (95% CI, 50.27%-97.82%), respectively. After the prolactin adjustment, the sensitivity of BIPSS to diagnose Cushing disease was improved with a statistically significant difference (difference in sensitivity, 5.39%; 95% CI, 1.37%-11.26%), whereas the decrease in specificity was without a statistically significant difference (difference in specificity, -9.20%; 95% CI, -42.02% to 26.61%).
CONCLUSIONS: This meta-analysis indicated that prolactin adjustment in BIPSS might help to recognize Cushing disease among patient with ACTH-dependent CS. However, prolactin adjustment may not help identify ectopic ACTH syndrome in BIPSS.

BACKGROUND: Early and accurate detection of periprosthetic joint infection (PJI) after hip and/or knee arthroplasty remains challenging. This systematic review and meta-analysis of diagnostic test accuracy studies aimed to evaluate the diagnostic accuracy of serum and synovial fluid interleukin (IL)-6 in detecting PJI.
METHODS: We searched 3 databases for studies through December 31, 2021, using medical sub-headings terms and keywords. Studies reported sensitivity and specificity of serum and synovial fluid IL-6 in detecting PJI were considered. We calculated the pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic curve (AUC) to evaluate the diagnostic accuracy of serum and synovial fluid IL-6.
RESULTS: Thirty studies were included. The pooled sensitivity, specificity, positive and negative likelihood ratio, DOR, and AUC of serum IL-6 in detecting PJI were 0.76 (0.69-0.81), 0.88 (0.82-0.92), 6.2 (4.3-9.0), 0.28 (0.22-0.35), 22 (14-36), and 0.88 (0.85-0.91), respectively. However, synovial fluid IL-6 achieved a pooled sensitivity of 0.87 (0.75-0.93), specificity of 0.90 (0.85-0.93), positive and negative likelihood ratio of 8.5 (5.3-13.6) and 0.15 (0.08-0.29), DOR of 57 (21-156), and AUC of 0.94 (0.92-0.96), which were higher than serum IL-6.
CONCLUSIONS: Synovial fluid IL-6 test may be a promising test for PJI after hip and/or knee arthroplasty. However, considering the limited volume of synovial fluid and invasive acquisition of synovial fluid IL-6, serum IL-6 test may be also considered.

BACKGROUND: This study\'s objective was to assess the diagnostic properties of the Whooley questions in identifying depression among perinatal women according to previously published studies. We conducted a systematic review and meta-analysis of studies investigating the diagnostic accuracy of the Whooley questions in perinatal women.
METHODS: Nine databases were searched in October 2021. All primary studies evaluated the diagnostic properties of the Whooley questions compared with a diagnostic gold standard in women during the perinatal period or, if not identified as being in the perinatal stage within the study, women between 20 weeks\' gestation to 4 weeks post-delivery. A bivariate mixed-effects meta-analysis was performed to estimate pooled diagnostic properties and measure heterogeneity. Meta-regression was conducted to evaluate factors contributing to heterogeneity.
RESULTS: Six studies were included in the review; five were used in the meta-analysis. The pooled sensitivity (95 % confidence interval) was 0.95 (0.81-0.99), pooled specificity was 0.60 (0.44-0.74), pooled positive likelihood ratio was 2.4 (1.6-3.4), pooled negative likelihood ratio was 0.09 (0.02-0.32), and pooled diagnostic odds ratio was 27 (7-106); heterogeneity was substantial (I2 = 0.90, 0.81-1.00). Participant age and setting (community vs. hospital) significantly contributed to heterogeneity.
CONCLUSIONS: The Whooley questions have high sensitivity but moderate specificity for perinatal women. The Whooley questions are a short and acceptable tool for identifying depression in perinatal women. However, a potential risk exists of incorrectly identifying a high proportion of women as positive. Using the Whooley questions followed by a secondary case-finding tool could reduce the misdiagnosis risk.

Active SARS-CoV-2 infection is confirmed mainly through the detection of viral nucleic acid via the reverse transcriptase polymerase chain reaction (RT-PCR) technique. Methods to assess humoral responses contribute to the monitoring of the disease and confirmation of exposure to the virus.
To evaluate the accuracy of tests for IgM and IgG antibodies for SARS-CoV-2 infection confirmed by RT-PCR and utility as complementary data for immunosurveillance.
Literature research was performed by searching the terms \"COVID-19\", \"COVID-19 diagnostic testing\" and \"test\" in the databases MEDLINE, EMBASE, Cochrane Library, Web of Science and Cumulative Index to Nursing and Allied Health Literature to search for potentially eligible observational studies without language restrictions published up to September 2020.
The pooled sensitivity and specificity, regardless of collection moment, was 80.0% (CI 95% 72.0-86.0) and 97.0% (CI 95% 94.0-98.0) for \"IgM and/or IgG\", respectively. Serology considering immunoglobulins M and G together had a high accuracy performance on \"fifteenth day and after\": sensitivity and specificity was 91.0% (CI 95% 85.0-94.0) and 98.0% (CI 95% 95.0-99.0) respectively, DOR 461 and AUC 0.98.
This study shows that serology is a group of tests with high accuracy, mainly following the second week after infection.

BACKGROUND: Periprosthetic joint infection (PJI) is a devastating complication after total hip arthroplasty (THA) or total knee arthroplasty (TKA). It is scarce and contradicting evidence supporting the use of serum D-dimer to diagnose PJI in revision THA and TKA. This systematic review and meta-analysis aimed to investigate the accuracy of D-dimer in the diagnosis of periprosthetic infections.
METHODS: The PubMed, Embase, Web of Science were systematically searched from the inception dates to August 15, 2020. We included all diagnostic studies of D-dimer in the diagnosis of periprosthetic infections. The literature\'s quality was evaluated by the QUADAS-2 tool, and Stata16 and Revman5.3 software carried out the meta-analysis.
RESULTS: Of 115 citations identified by the search strategy, 10 studies (comprising 1756 participants) met the inclusion criteria. The literature quality assessment results show that most of the literature is low-risk bias literature. The combined sensitivity of D-dimer in diagnosing periprosthetic infections was 0.81 (95% confidence interval [CI] 0.71-0.88), combined specificity was 0.74 (95% CI 0.61-0.84), combined positive likelihood ratio was 3.1 (95% CI 2.0-5.0), combined negative likelihood ratio 0.26 (95% CI 0.16-0.41), combined diagnosis odds ratio 12 (95% CI 5-27), area under the Summary Receiver Operator Characteristic Curve (SROC) is 0.85 (95% CI 0.81-0.88). The data are statistically significant.
CONCLUSIONS: D-dimer has a high diagnostic value in diagnosing PJI and has clinical significance in diagnosing periprosthetic infection. In addition, there are relatively few studies on the threshold of D-dimer, different sampling types, different laboratory detection methods, and different races, so more prospective trials with large samples, multi-centers, and scientific design should be carried out in the future.

BACKGROUND: We designed a meta-analysis to evaluate the clinical significance and efficacy of circulating noncoding RNAs (ncRNAs) in the early prediction of preeclampsia.
METHODS: PubMed, Embase and the Cochrane Library were used to search for literature. The combined prediction performance was evaluated by calculating the area under the summary receiver operator characteristic (SROC) curve. The potential sources of heterogeneity were analysed by meta-regression analysis and subgroup analysis. All statistical analyses and mapping were performed by RevMan 5.3 and Stata 12.0.
RESULTS: A total of 41 studies from 14 articles, including 557 preeclampsia patients and 842 controls, were included in our meta-analysis. All studies collected blood before onset. NcRNAs in blood performed relatively well in predicting preeclampsia. The combined sensitivity was 0.71, the specificity was 0.84, and the area under the SROC curve (AUC) was 0.86. Peripheral blood mononuclear cell (PBMC) samples showed the best diagnostic accuracy. The combined AUC was 0.93. Combined detection was better than single detection, and miRNA was better than circRNA. The heterogeneity of the study was determined by sample size, lncRNA characteristics, lncRNA source and race.
CONCLUSIONS: Circulating ncRNAs can be valuable biomarkers used as candidates for noninvasive early predictive biomarkers of preeclampsia and have great clinical application prospects. The clinical value of ncRNAs needs to be tested by further multicentre, comprehensive and prospective studies, and the test criteria should be established.

UNASSIGNED: Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices.
UNASSIGNED: We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words.
UNASSIGNED: Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented.
UNASSIGNED: Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.

BACKGROUND: Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging.
METHODS: We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis.
RESULTS: We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79-84%), specificity of 84% (82-87%) and AUC of 0·90 (0·87-0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83-90%) for machine learning and 86% (82-89%) for deep learning, and a pooled specificity of 89% (82-93%) for machine learning, and 87% (82-91%) for deep learning.
CONCLUSIONS: AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field.
BACKGROUND: College students\' innovative entrepreneurial training plan program .