A Fecal MicroRNA Signature by Small RNA Sequencing Accurately Distinguishes Colorectal Cancers: Results From a Multicenter Study.

BACKGROUND & AIMS: Fecal tests currently used for colorectal cancer (CRC) screening show limited accuracy in detecting early tumors or precancerous lesions. In this respect, we comprehensively evaluated stool microRNA (miRNA) profiles as biomarkers for noninvasive CRC diagnosis. METHODS: A total of 1273 small RNA sequencing experiments were performed in multiple biospecimens. In a cross-sectional study, miRNA profiles were investigated in fecal samples from an Italian and a Czech cohort (155 CRCs, 87 adenomas, 96 other intestinal diseases, 141 colonoscopy-negative controls). A predictive miRNA signature for cancer detection was defined by a machine learning strategy and tested in additional fecal samples from 141 CRC patients and 80 healthy volunteers. miRNA profiles were compared with those of 132 tumors/adenomas paired with adjacent mucosa, 210 plasma extracellular vesicle samples, and 185 fecal immunochemical test leftover samples. RESULTS: Twenty-five miRNAs showed altered levels in the stool of CRC patients in both cohorts (adjusted P < .05). A 5-miRNA signature, including miR-149-3p, miR-607-5p, miR-1246, miR-4488, and miR-6777-5p, distinguished patients from control individuals (area under the curve [AUC], 0.86; 95% confidence interval [CI], 0.79-0.94) and was validated in an independent cohort (AUC, 0.96; 95% CI, 0.92-1.00). The signature classified control individuals from patients with low-/high-stage tumors and advanced adenomas (AUC, 0.82; 95% CI, 0.71-0.97). Tissue miRNA profiles mirrored those of stool samples, and fecal profiles of different gastrointestinal diseases highlighted miRNAs specifically dysregulated in CRC. miRNA profiles in fecal immunochemical test leftover samples showed good correlation with those of stool collected in preservative buffer, and their alterations could be detected in adenoma or CRC patients. CONCLUSIONS: Our comprehensive fecal miRNome analysis identified a signature accurately discriminating cancer aimed at improving noninvasive diagnosis and screening strategies.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics.

INTRODUCTION: Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population. OBJECTIVES: This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs). METHODS: We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers. RESULTS: The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

A CAIX Dual-Targeting Small-Molecule Probe for Noninvasive Imaging of ccRCC.

Carbonic anhydrase IX (CAIX), a zinc metal transmembrane protein, is highly expressed in 95% of clear cell renal cell carcinomas (ccRCCs). A positron emission tomography (PET) probe designed to target CAIX in nuclear medicine imaging technology can achieve precise positioning, is noninvasive, and can be used to monitor CAIX expression in lesions in real time. In this study, we constructed a novel acetazolamide dual-targeted small-molecule probe [68Ga]Ga-LF-4, which targets CAIX by binding to a specific amino acid sequence. After attenuation correction, the radiolabeling yield reached 66.95 ± 0.57% (n = 5) after 15 min of reaction and the radiochemical purity reached 99% (n = 5). [68Ga]Ga-LF-4 has good in vitro and in vivo stability, and in vivo safety and high affinity for CAIX, with a Kd value of 6.62 nM. Moreover, [68Ga]Ga-LF-4 could be quickly cleared from the blood in vivo. The biodistribution study revealed that the [68Ga]Ga-LF-4 signal was concentrated in the heart, lung, and kidney after administration, which was the same as that observed in the micro-PET/CT study. In a ccRCC patient-derived xenograft (PDX) model, the signal significantly accumulated in the tumor after administration, where it was retained for up to 4 h. After competitive blockade with LF-4, uptake at the tumor site was significantly reduced. The SUVmax of the probe [68Ga]Ga-LF-4 at the ccRCC tumor site was three times greater than that in the PC3 group with low CAIX expression at 30 min (ccRCC vs PC3:1.86 ± 0.03 vs 0.62 ± 0.01, t = 48.2, P < 0.0001). These results indicate that [68Ga]Ga-LF-4 is a novel small-molecule probe that targets CAIX and can be used to image localized and metastatic ccRCC lesions.

Dermatoscopy and reflectance confocal microscopy for basal cell carcinoma diagnosis and diagnosis prediction score: A prospective and multicenter study on 1005 lesions.

BACKGROUND: Basal cell carcinoma (BCC) is usually diagnosed by clinical and dermatoscopy examination, but diagnostic accuracy may be suboptimal. Reflectance confocal microscopy (RCM) imaging increases skin cancer diagnostic accuracy. OBJECTIVE: To evaluate additional benefit in diagnostic accuracy of handheld RCM in a prospective controlled clinical setting. METHODS: A prospective, multicenter study in 3 skin cancer reference centers in Italy enrolling consecutive lesions with clinical-dermatoscopic suspicion of BCC (ClinicalTrials.gov: NCT04789421). RESULTS: A total of 1005 lesions were included, of which 474 histopathologically confirmed versus 531 diagnosed by clinical-dermatoscopic-RCM correlation, confirmed with 2 years of follow-up. Specifically, 740 were confirmed BCCs. Sensitivity and specificity for dermatoscopy alone was 93.2% (95% CI, 91.2-94.9) and 51.7% (95% CI, 45.5-57.9); positive predictive value was 84.4 (95% CI, 81.7-86.8) and negative predictive value 73.3 (95% CI, 66.3-79.5). Adjunctive RCM reported higher rates: 97.8 (95% CI, 96.5-98.8) sensitivity and 86.8 (95% CI, 82.1-90.6) specificity, with positive predictive value of 95.4 (95% CI, 93.6-96.8) and negative predictive value 93.5 (95% CI, 89.7-96.2). LIMITATIONS: Study conducted in a single country. CONCLUSIONS: Adjunctive handheld RCM assessment of lesions clinically suspicious for BCC permits higher diagnostic accuracy with minimal false negative lesions.

Performance of MAST, FAST, and MEFIB in predicting metabolic dysfunction-associated steatohepatitis.

BACKGROUND AND AIM: To identify individuals with metabolic dysfunction-associated steatohepatitis (MASH) or "at-risk" MASH among patients with metabolic dysfunction-associated steatotic liver disease (MASLD), three noninvasive models are available with satisfactory efficiency, which include magnetic resonance imaging [MRI]- AST (MAST), FibroScan-AST (FAST score), and magnetic resonance elastography [MRE] plus FIB-4 (MEFIB). We aimed to evaluate the most accurate approach for diagnosing MASH or "at-risk" MASH. METHODS: We included 108 biopsy-proven MASLD patients who underwent simultaneous assessment of MRE, MRI proton density fat fraction (MRI-PDFF), and FibroScan scans. Compared with the histological diagnosis, we analyzed the AUC of each model and assessed the accuracy. RESULTS: Our study cohort consisted of 64.8% of MASH and 25.9% of "at-risk" MASH. When analyzing the performance of each model for the diagnostic accuracy of MASH, we found that the AUC [95% CI] of MAST was comparable to FAST (0.803 [0.719-0.886] vs 0.799 [0.707-0.891], P = 0.930) and better than MEFIB (0.671 [0.571-0.772], P = 0.005). Similar findings were observed in the "at-risk" MASH patients. The AUCs [95% CI] for MAST, FAST, and MEFIB were 0.810 [0.719-0.900], 0.782 [0.689-0.874], and 0.729 [0.619-0.838], respectively. The models of MAST and FAST had comparable AUCs (P = 0.347), which were statistically significantly higher than that of MEFIB (P = 0.041). Additionally, the cutoffs for diagnosis of MASH were lower than "at-risk" MASH. CONCLUSION: MAST and FAST performed better than MEFIB in diagnosing "at-risk" MASH and MASH using lower cutoff values. Our findings provided evidence for selecting the most accurate noninvasive model to identify patients with MASH or at-risk MASH.

Developing a Predictive Model for Minimal or Mild Endometriosis as a Clinical Screening Tool in Infertile Women: Uterosacral Tenderness as a Key Predictor.

STUDY OBJECTIVE: To develop a noninvasive predictive model based on patients with infertility for identifying minimal or mild endometriosis. DESIGN: A retrospective cohort study. SETTING: This study was conducted at a tertiary referral center. PATIENTS: A total of consecutive 1365 patients with infertility who underwent laparoscopy between January 2013 and August 2020 were divided into a training set (n = 910) for developing the predictive model and a validation set (n = 455) to confirm the model's prediction efficiency. The patients were randomly assigned in a 2:1 ratio. INTERVENTIONS: Sensitivities, specificities, area under the curve, the Hosmer-Lemeshow goodness of fit test, Net Reclassification Improvement index, and Integrated Discrimination Improvement index were evaluated in the training set to select the optimum model. In the validation set, the model's discriminations, calibrations, and clinical use were tested for validation. MEASUREMENTS AND MAIN RESULTS: In the training set, there were 587 patients with minimal or mild endometriosis and 323 patients without endometriosis. The combination of clinical parameters in the model was evaluated for both statistical and clinical significance. The best-performing model ultimately included body mass index, dysmenorrhea, dyspareunia, uterosacral tenderness, and serum cancer antigen 125 (CA-125). The nomogram based on this model demonstrated sensitivities of 87.7% and 93.3%, specificities of 68.6% and 66.4%, and area under the curve of 0.84 (95% confidence interval 0.81-0.87) and 0.85 (95% confidence interval 0.80-0.89) for the training and validation sets, respectively. Calibration curves and decision curve analyses also indicated that the model had good calibration and clinical value. Uterosacral tenderness emerged as the most valuable predictor. CONCLUSION: This study successfully developed a predictive model with high accuracy in identifying infertile women with minimal or mild endometriosis based on clinical characteristics, signs, and cost-effective blood tests. This model would assist clinicians in screening infertile women for minimal or mild endometriosis, thereby facilitating early diagnosis and treatment.

Noninvasive diagnosis of interstitial fibrosis in chronic kidney disease: a systematic review and meta-analysis.

RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Identifying the tissues-of-origin of circulating cell-free DNAs is a promising way in noninvasive diagnostics.

Advances in sequencing technologies facilitate personalized disease-risk profiling and clinical diagnosis. In recent years, some great progress has been made in noninvasive diagnoses based on cell-free DNAs (cfDNAs). It exploits the fact that dead cells release DNA fragments into the circulation, and some DNA fragments carry information that indicates their tissues-of-origin (TOOs). Based on the signals used for identifying the TOOs of cfDNAs, the existing methods can be classified into three categories: cfDNA mutation-based methods, methylation pattern-based methods and cfDNA fragmentation pattern-based methods. In cfDNA mutation-based methods, the SNP information or the detected mutations in driven genes of certain diseases are employed to identify the TOOs of cfDNAs. Methylation pattern-based methods are developed to identify the TOOs of cfDNAs based on the tissue-specific methylation patterns. In cfDNA fragmentation pattern-based methods, cfDNA fragmentation patterns, such as nucleosome positioning or preferred end coordinates of cfDNAs, are used to predict the TOOs of cfDNAs. In this paper, the strategies and challenges in each category are reviewed. Furthermore, the representative applications based on the TOOs of cfDNAs, including noninvasive prenatal testing, noninvasive cancer screening, transplantation rejection monitoring and parasitic infection detection, are also reviewed. Moreover, the challenges and future work in identifying the TOOs of cfDNAs are discussed. Our research provides a comprehensive picture of the development and challenges in identifying the TOOs of cfDNAs, which may benefit bioinformatics researchers to develop new methods to improve the identification of the TOOs of cfDNAs.

VEGFR2 targeted microbubble-based ultrasound molecular imaging improving the diagnostic sensitivity of microinvasive cervical cancer.

BACKGROUND: The current diagnostic methods of microinvasive cervical cancer lesions are imaging diagnosis and pathological evaluation. Pathological evaluation is invasive and imaging approaches are of extremely low diagnostic performance. There is a paucity of effective and noninvasive imaging approaches for these extremely early cervical cancer during clinical practice. In recent years, ultrasound molecular imaging (USMI) with vascular endothelial growth factor receptor type 2 (VEGFR2) targeted microbubble (MBVEGFR2) has been reported to improve the early diagnosis rates of breast cancer (including ductal carcinoma in situ), pancreatic cancer and hepatic micrometastases. Herein, we aimed to assess the feasibility of MBVEGFR2-based USMI in extremely early cervical cancer detection to provide an accurate imaging modality for microinvasive cervical cancer (International Federation of Gynecology and Obstetrics (FIGO) Stage IA1 and IA2). RESULTS: We found MBVEGFR2-based USMI could successfully distinguish extremely early lesions in diameter < 3 mm from surrounding normal tissues (all P < 0.05), and the sensitivity gradually decreased along with increasing tumor diameter. Moreover, normalized intensity difference (NID) values showed a good linear correlation with microvessel density (MVD) (R2 = 0.75). In addition, all tumors could not be identified from surrounding muscles in subtracted ultrasound images when mice were administered MBCon. CONCLUSIONS: Overall, MBVEGFR2-based USMI has huge potential for clinical application for the early detection of microinvasive cervical cancer (FIGO Stage IA1 and IA2), providing the foothold for future studies on the imaging screening of this patient population.

Noninvasive diagnosis of oral squamous cell carcinoma by multi-level deep residual learning on optical coherence tomography images.

BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) is one of the most severe cancers in the world, and its early detection is crucial for saving patients. There is an inevitable necessity to develop the automatic noninvasive OSCC diagnosis approach to identify the malignant tissues on Optical Coherence Tomography (OCT) images. METHODS: This study presents a novel Multi-Level Deep Residual Learning (MDRL) network to identify malignant and benign(normal) tissues from OCT images and trains the network in 460 OCT images captured from 37 patients. The diagnostic performances are compared with different methods in the image-level and the resected patch-level. RESULTS: The MDRL system achieves the excellent diagnostic performance, with 91.2% sensitivity, 83.6% specificity, 87.5% accuracy, 85.3% PPV, and 90.2% NPV in image-level, with 0.92 AUC value. Besides, it also implements 100% sensitivity, 86.7% specificity, 93.1% accuracy, 87.5% PPV, and 100% NPV in the resected patch-level. CONCLUSION: The developed deep learning system expresses superior performance in noninvasive oral squamous cell carcinoma diagnosis, compared with traditional CNNs and a specialist.

Extracellular status of thrombospondin-2 in type 2 diabetes mellitus and utility as a biomarker in the determination of early diabetic kidney disease.

OBJECTIVE: Thrombospondin-2 (TSP-2) is a multifunctional matricellular glycoprotein correlated with glucose homeostasis, insulin sensitivity, and estimated glomerular filtration rate. Investigation of the association of TSP-2 with type 2 diabetes mellitus (T2DM) and the potential diagnostic value of serum TSP-2 for detecting early diabetic kidney disease (DKD) is needed. RESEARCH DESIGN AND METHODS: An enzyme-linked immunosorbent assay was used for detection serum TSP-2 levels in 494 Chinese T2DM subjects. The protein expression of TSP-2 in the kidney and other tissues were tested by western blotting. RESULTS: Serum TSP-2 levels in T2DM subjects were significantly higher than in healthy individuals. Serum TSP-2 correlated positively with triglycerides, serum uric acid, creatinine, platelets, and urinary albumin-to-creatinine ratio (UACR), but negatively with estimated glomerular filtration rate, after adjusting for age, sex, and T2DM duration. Logistic regression analysis demonstrated an independent association between serum TSP-2 and early DKD. Furthermore, the high UACR identified at risk of early DKD increased significantly from 0.78 (95%CI 0.73-0.83) to 0.82 (95%CI 0.77-0.86, p < 0.001) when added to a clinical model consisting of TSP-2 and age. In db/db mice, serum TSP-2 levels were elevated. TSP-2 expression was markedly increased in the kidney tissue compared with that in db/m and m/m mice. Furthermore, serum TSP-2 expression correlated well with UACR in mice. CONCLUSIONS: TSP-2 is a novel glycoprotein associated with early DKD in patients with T2DM. The paradoxical increase of serum TSP-2 in T2DM individuals may be due to a compensatory response to chronic inflammatory and renal vascular endothelial growth, warranting further investigation.

Oral squamous cell carcinoma diagnosed from saliva metabolic profiling.

Saliva is a noninvasive biofluid that can contain metabolite signatures of oral squamous cell carcinoma (OSCC). Conductive polymer spray ionization mass spectrometry (CPSI-MS) is employed to record a wide range of metabolite species within a few seconds, making this technique appealing as a point-of-care method for the early detection of OSCC. Saliva samples from 373 volunteers, 124 who are healthy, 124 who have premalignant lesions, and 125 who are OSCC patients, were collected for discovering and validating dysregulated metabolites and determining altered metabolic pathways. Metabolite markers were reconfirmed at the primary tissue level by desorption electrospray ionization MS imaging (DESI-MSI), demonstrating the reliability of diagnoses based on saliva metabolomics. With the aid of machine learning (ML), OSCC and premalignant lesions can be distinguished from the normal physical condition in real time with an accuracy of 86.7%, on a person by person basis. These results suggest that the combination of CPSI-MS and ML is a feasible tool for accurate, automated diagnosis of OSCC in clinical practice.

Diagnostic efficacy of FibroScan for liver inflammation in patients with chronic hepatitis B: a single-center study with 1185 liver biopsies as controls.

BACKGROUND: Noninvasive diagnostic technologies that can dynamically monitor changes in liver inflammation are highly important for the management of chronic hepatitis B (CHB) patients and thus warrant further exploration. This study assessed the diagnostic efficacy of FibroScan for liver inflammation in CHB patients. METHODS: A total of 1185 patients were selected, and ultrasound-guided liver biopsy was performed within 1 month after the FibroScan test. The liver stiffness measurement (LSM), the reliability criteria (IQR/M) of LSM, the quality of liver biopsy (complete portal area, PA), and the liver inflammation grades were the main observation items of this study. With liver biopsy as the control, the diagnostic efficacy of FibroScan for liver inflammation in CHB patients was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: The grade of liver inflammation was positively correlated with the stage of fibrosis (rho = 0.829, P < 0.001). Different grades of inflammation will have significant rise in LSM values within the same fibrosis stage, and LSM values were positively correlated with liver inflammation grade and fibrosis stage, and the rho is 0.579 and 0.593 respectively (P < 0.001). Significant differences in the LSM of FibroScan were observed among different grades of liver inflammation (P < 0.0001). Liver biopsy (PA > 10) served as the control, and the cutoff point and the area under ROC curves (AUCs) of the LSMs for different inflammation grades were as follows: G2, 8.6 kPa, 0.775; G3 9.8 kPa, 0.818; and G4, 11.0 kPa; 0.832. With LSM cutoff values of 8.6 kPa, 9.8 kPa and 11.0 kPa, FibroScan showed certain diagnostic value for CHB patients with G2, G3 and G4 liver inflammation, especially those with G4 inflammation. CONCLUSIONS: The grade of liver inflammation was positively correlated with the stage of fibrosis, different grades of inflammation will have significant rise in LSM values within the same fibrosis stage. In addition to liver fibrosis, FibroScan could evaluate liver inflammation in CHB patients in a noninvasive manner.

Examination of Subungual Hematomas and Subungual Melanocytic Lesions by Using Optical Coherence Tomography and Dermoscopy.

INTRODUCTION: Examination of subungual pigmented lesions is sometimes a diagnostic challenge for clinicians. OBJECTIVES: The study was aimed to investigate characteristic patterns in optical coherence tomography (OCT) of subungual hematomas and determine distinctive features that can differentiate them from subungual melanocytic lesions. METHODS: VivoSight® (Michelson Diagnostics, Maidstone, UK) was used to examine 71 subungual hematomas and 11 subungual melanocytic lesions in 69 patients (18 female and 51 male patients). RESULTS: On OCT, bleeding was related to sharply defined black sickle-shaped (p < 0.001) or globular regions (not significant [ns]) with a hyperreflective margin (0.002), a grey center (0.013), hyperreflective lines in the area (ns) or periphery (p = 0.031), peripheral fading (p = 0.029), and red dots in the area (p = 0.001). In the 1 case of melanoma in situ examined, we found curved vessels with irregular sizes and distribution on the dermis of the nailbed, while subungual hematomas and subungual benign nevi presented as clustered red dots and/or regularly distributed curved vessels. CONCLUSION: Our findings indicate that the use of OCT in addition to dermoscopy provides high-resolution optical imaging information for the diagnosis of subungual hematoma and facilitates the differential diagnosis of subungual hematomas and subungual melanocytic lesions.

Dermoscopy, reflectance confocal microscopy, and fluorescence staining for the noninvasive diagnosis of crusted scabies.

A 91-year-old woman presented with a 3-month history of [extensive](javascript:;) cutaneous lesions with intense pruritus. She lived in a nursing home for a long time. Physical examination revealed a generalized erythematous and scaly rash with intense hyperkeratotic lesions on the neck, trunk, and limbs. Dermoscopy showed a sinuous burrow filled with white dot eggs and feces on the hand with a mite at the end of the burrow. Reflectance confocal microscopy (RCM) manifested a sinuous burrow and a mite. The presence of mites was confirmed with fluorescence staining. The patient was diagnosed with crusted scabies and started treatment with 10% sulfur ointment. Her lesions and pruritus were resolved after 2 weeks.

The role of cytokeratin 19 levels in the determination of endometriosis stages.

OBJECTIVE/AIM: Endometrisosis, one of the most common gynecological disease, is characterized by the presence of endometriotic tissue outside of uterine cavity. The development and the validation of a simple blood biomarker specific and sensitive for endometriosis may facilitate the rapid and the accurate diagnosis of the disease and thus early treatment. Cytokeratin expression changes during epithelial differentiation and this expression is important for the modulation and the control of cell cycle regulation, tumor cell motility and apoptosis. Cytokeratin 19 (CK-19) is expressed in most simple epithelial cells and their malignant counterparts. The aim of this study is to investigate serum CK-19 expression levels in patients with endometriosis and to determine the diagnostic role of CK-19 levels in differentiating various stage of endometriosis. METHODS: Ctytokeratin-19 expression and level were studied in 70 endometriosis patients and 50 volunteers by ELISA and RT-PCR. ROC analysis was performed by comparing all stages with each other and with the control group. RESULTS: The CK-19 levels were significantly higher in the endometriosis groups than that of the control group by ELISA and RT-PCR. A significant (p < .05) difference was observed in endometriosis patients according to the stages. CONCLUSION: Based on our data, it suggests that Cytokeratin-19 may have a potential role in the development of endometriosis.

Diagnosis of liver cirrhosis with two-dimensional shear wave elastography in biliary atresia before Kasai portoenterostomy.

PURPOSE: To evaluate the application value of two-dimensional shear wave elastography (2D-SWE) for non-invasive diagnosis of liver cirrhosis (LC) in patients with biliary atresia (BA) before Kasai portoenterostomy (KP), and the cutoff value of liver stiffness measurement (LSM) for diagnosing LC. METHODS: The clinical data of 51 patients with BA who were diagnosed via surgery and pathological results from May 2017 to December 2018 in the department of general surgery, Beijing Children's Hospital, Capital Medical University, were retrospectively analyzed. The liver tissue specimens obtained during KP were evaluated according to the METAVIR criteria. The LSM was obtained using the 2D-SWE technique before KP. RESULTS: There was a grade positive correlation between LSM and METAVIR staging, and the Spearman correlation coefficient was 0.432 (P = 0.002). The AUC for 2D-SWE diagnosing LC (METAVIR score S = 4) in patients with BA before KP was 0.843 (95% confidence interval 0.736 ~ 0.950). The best cutoff value was 16.05 kPa, and the corresponding sensitivity was 75.0%, specificity was 83.7%, positive predictive value (PPV) was 46.1%, negative predictive value (NPV) was 94.7%, and the accuracy was 82.4%. CONCLUSION: 2D-SWE can be used to noninvasively diagnose LC in patients with BA before KP, and the cutoff value is 16.05 kPa.

High-Throughput Salivary Metabolite Profiling on an Ultralow Noise Tip-Enhanced Laser Desorption Ionization Mass Spectrometry Platform for Noninvasive Diagnosis of Early Lung Cancer.

Lung cancer (LC) is a widespread cancer that is the cause of the highest mortality rate accounting for 25% of all cancer deaths. To date, most LC patients are diagnosed at the advanced stage owing to the lack of obvious symptoms in the early stage and the limitations of current clinical diagnostic techniques. Therefore, developing a high throughput technique for early screening is of great importance. In this work, we established an effective and rapid salivary metabolic analysis platform for early LC diagnosis and combined metabolomics and transcriptomics to reveal the metabolic fluctuations correlated to LC. Saliva samples were collected from a total of 150 volunteers including 89 patients with early LC, 11 patients with advanced LC, and 50 healthy controls. The metabolic profiling of noninvasive samples was investigated on an ultralow noise TELDI-MS platform. In addition, data normalization methods were screened and assessed to overcome the MS signal variation caused by individual difference for biomarker mining. For untargeted metabolic profiling of saliva samples, around 264 peaks could be reliably detected in each sample. After multivariate analysis, 23 metabolites were sorted out and verified to be related to the dysfunction of the amino acid and nucleotide metabolism in early LC. Notably, transcriptomic data from online TCGA repository were utilized to support findings from the salivary metabolomics experiment, including the disorder of amino acid biosynthesis and amino acid metabolism. Based on the verified differential metabolites, early LC patients could be clearly distinguished from healthy controls with a sensitivity of 97.2% and a specificity of 92%. The ultralow noise TELDI-MS platform displayed satisfactory ability to explore salivary metabolite information and discover potential biomarkers that may help develop a noninvasive screening tool for early LC.

Including Ratio of Platelets to Liver Stiffness Improves Accuracy of Screening for Esophageal Varices That Require Treatment.

BACKGROUND & AIMS: Based on platelets and liver stiffness measurements, the Baveno VI criteria (B6C), the expanded B6C (EB6C), and the ANTICIPATE score can be used to rule out varices needing treatment (VNT) in patients with compensated chronic liver disease. We aimed to improve these tests by including data on the ratio of platelets to liver stiffness. METHODS: In a retrospective analysis of data from 10 study populations, collected from 2004 through 2018, we randomly assigned data from 2368 patients with chronic liver disease of different etiologies to a derivation population (n = 1579; 15.1% with VNT, 50.2% with viral hepatitis, 28.9% with nonalcoholic fatty liver disease, 20.8% with alcohol-associated liver disease, with model for end-stage liver disease scores of 9.5 ± 3.0, and 93.0% with liver stiffness measurements ≥10 kPa) or a validation population (n = 789). Test results were compared with results from a sequential algorithm (VariScreen). VariScreen incorporated data on platelets or liver stiffness measurements and then the ratio of platelets to liver stiffness measurement, adjusted for etiology, patient sex, and international normalized ratio. RESULTS: In the derivation population, endoscopies were spared for 23.9% of patients using the B6C (VNT missed in 2.9%), 24.3% of patients using the ANTICIPATE score (VNT missed in 4.6%), 34.5% of patients using VariScreen (VNT missed in 2.9%), and 41.9% of patients using the EB6C (VNT missed in 10.9%). Differences in spared endoscopy rates were significant (P ≤ .001), except for B6C vs ANTICIPATE and in missed VNT only for EB6C vs the others (P ≤ .009). VariScreen was the only safe test regardless of sex or etiology (missed VNT ≤5%). Moreover, VariScreen secured screening without missed VNT in patients with model for end-stage liver disease scores higher than 10. This overall strategy performed better than a selective strategy restricted to patients with compensated liver disease. Test performance and safety did not differ significantly among populations. CONCLUSIONS: In a retrospective study of data from 2368 patients with chronic liver disease, we found that the B6C are safe whereas the EB6C are unsafe, based on missed VNT. The VariScreen algorithm performed well in patients with chronic liver disease of any etiology or severity. It is the only test that safely rules out VNT and can be used in clinical practice.

Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies.

BACKGROUND: Lower extremity arterial Doppler (LEAD) and duplex carotid ultrasound studies are used for the initial evaluation of peripheral arterial disease and carotid stenosis. However, intra- and inter-laboratory variability exists between interpreters, and other interpreter responsibilities can delay the timeliness of the report. To address these deficits, we examined whether machine learning algorithms could be used to classify these Doppler ultrasound studies. METHODS: We developed a hierarchical deep learning model to classify aortoiliac, femoropopliteal, and trifurcation disease in LEAD ultrasound studies and a random forest machine learning algorithm to classify the amount of carotid stenosis from duplex carotid ultrasound studies using experienced physician interpretation in an active, credentialed vascular laboratory as the reference standard. Waveforms, pressures, flow velocities, and the presence of plaque were input into a hierarchal neural network. Artificial intelligence was developed to automate the interpretation of these LEAD and carotid duplex ultrasound studies. Statistical analysis was performed using the confusion matrix. RESULTS: We extracted 5761 LEAD ultrasound studies from 2015 to 2017 and 18,650 duplex carotid ultrasound studies from 2016 to 2018 from the Indiana University Health system. The results showed the ability of artificial intelligence algorithms and method, with 97.0% accuracy for predicting normal cases, 88.2% accuracy for aortoiliac disease, 90.1% accuracy for femoropopliteal disease, and 90.5% accuracy for trifurcation disease. For internal carotid artery stenosis, the accuracy was 99.2% for predicting 0% to 49% stenosis, 100% for predicting 50% to 69% stenosis, 100% for predicting >70% stenosis, and 100% for predicting occlusion. For common carotid artery stenosis, the accuracy was 99.9% for predicting 0% to 49% stenosis, 100% for predicting 50% to 99% stenosis, and 100% for predicting occlusion. CONCLUSIONS: The machine learning models using LEAD data, with the collected blood pressure and waveform data, and duplex carotid ultrasound data with the flow velocities and the presence of plaque, showed that novel machine learning models are reliable in differentiating normal from diseased arterial systems and accurate in classifying the extent of vascular disease.