Value In Differential Diagnosis Research Articles

The value of ultrasound viscosity imaging in preoperative differential diagnosis between malignant and benign breast lesions: Preliminary clinical applications.

To investigate the value of ultrasound viscosity imaging in preoperative differential diagnosis between malignant and benign breast lesions. This prospective study was approved by our institutional review board and informed consents were signed by all patients. Patients diagnosed with focal solid breast lesions who plan to undergo surgical or ultrasound guided core needle biopsy were included. A Mindray Resona R9 Elite diagnostic ultrasound system equipped with a linear array transducer (L15-3 MHz) was used. First BMUS was used to identify the lesions, afterwards, ultrasound viscosity imaging and shear wave elastography measurements were performed for each lesion. Viscosity (Pa*s) coefficient and shear-wave speed (m/s) of each breast lesion were measured. Taking the final histopathological results as the gold standard, the optimal cut-off value of viscosity and elastography values in differential diagnosis between malignant and benign breast lesions were evaluated. Diagnostic performance was assessed by using the area under the receiver operating characteristic curve (ROC). From July 2023 to March 2024, 159 consecutive patients with a total of 184 solid breast lesions were included. Among which 74 (40.2%) malignant lesions and 110 (59.8%) benign ones were diagnosed according to the final histopathological results. The mean maximal viscosity coefficient (7.56±3.63 Pa*s) of malignant lesions was significantly higher than that of benign ones (4.52±2.33 Pa*s), whereas the mean maximal SWE value (6.53±1.87 m/s) of malignant lesions was also obviously higher than that of benign ones (4.80±1.62 m/s) (P < 0.05). The cut-off value for diagnosis of malignant breast tumors was 5.39 Pa*s for viscosity, with 75.68% sensitivity, 68.18% specificity and 0.763 AUROC (P < 0.05). When combined viscosity with SWE values, the sensitivity and specificity was 75.68% and 75.45%, with an-area under the receiver operating characteristic curve (AUC) 0.787. The value of viscosity measurement may provide additional value for preoperative non-invasive differential diagnosis between malignant and benign breast lesions.

The value of differential diagnosis of post-reflux swallow-induced peristaltic wave index between esophageal diseases of gut-brain interaction and gastroesophageal reflux disease

Objective: To analyze the value of differential diagnosis of post-reflux swallow-induced peristaltic wave index (PSPWI) between esophageal diseases of gut-brain interaction (E-DGBI) and gastroesophageal reflux disease (GERD). Methods: The patients diagnosed as E-DGBI and GERD in the Department of Gastroenterology, Beijing Tsinghua Changguang Hospital from June 2016 to June 2018 were retrospectively included. The medical records, gastroscopy and 24-hour pH-intraluminal impedance monitoring reports were collected. According to clinical diagnosis, the patients were divided into E-DGBI group and GERD group. PSPWI, acid exposure time (AET), DeMeester score, total reflux times and mean nocturnal baseline impedance (MNBI) were compared between the both group. The receiver operating characteristic curve (ROC) of PSPWI, AET, total reflux times and MNBI was drawn to identify the sensitivity and specificity of E-DGBI and GERD. The cut-off values were calculated with Youden index and the area under the curve (AUC) was compared. Results: A total of 73 patients were included,including 36 males and 37 females, whose age [M (Q1, Q3)] was 54.0 (42.6, 63.0) years. There were 39 cases in E-DGBI group and 34 cases in GERD group. The 24-h AET [0.5 (0.1, 1.6) vs 6.0 (3.6, 11.9)], DeMeester score [2.8 (1.1, 5.7) vs 20.4 (14.7, 47.4) score] and total reflux times [32.0 (19.0, 53.0) vs 66.0 (34.3, 96.0) times] were significantly higher in E-DGBI group than those in GERD group (all P<0.001);MNBI in E-DGBI group was higher than that in GERD group[2 375.0 (1 726.0, 2 978.0)Ω vs 1 361.0 (814.0, 2 273.5)Ω, P<0.001]. The PSPWI in E-DGBI group is higher than that in GERD (0.56±0.19 vs 0.25±0.15, P<0.001). According to ROC, with PSPWI>0.46 as the cut-off value, the AUC was 0.895 (95%CI: 0.827-0.963) in differentiating E-DGBI and GERD. The sensitivity was 94.1% and the specificity was 69.2%. There were no significant differences of AUC between PSPWI and AET or total reflux events (all P>0.05). But the AUC of PSPWI was higher than that of MNBI (AUC=0.749,95%CI:0.632-0.866,P=0.031). Conclusion: PSPWI can better distinguish E-DGBI from GERD.

The combined detection of hematological indicators is used for the differential diagnosis of colorectal cancer and benign-colorectal lesions.

This article aims to investigate the clinical value of hemoglobin/red cell distribution width ratio (Hb/RDW), C-reactive protein/albumin ratio (CAR) and plateletcrit (PCT) combined with carcinoembryonic antigen (CEA) in colorectal cancer (CRC) auxiliary diagnosis. We retrospectively analyzed in 718 subjects (212 with CRC, 209 with benign colorectal lesions (BCL), 111 with other cancers, and 186 healthy controls). The CAR, PCT, and CEA in the CRC group were higher than those in the BCL, other cancers, and the healthy control group. However, Hb/RDW in the CRC group was lower than the other three groups. Moreover, there were significant differences in Hb/RDW and CEA among different T-N-M stages (all P< 0.05). Multivariate logistic regression showed that low level of Hb/RDW and high level of CAR, CEA, PCT were risk factors for CRC, and are correlated with CRC stage. Additionally, the area under the receiver operating characteristic curve (AUC) of Hb/RDW+CEA (AUC: 0.735), CAR+CEA (AUC: 0.748), PCT+CEA (AUC: 0.807) was larger than that of Hb/RDW (AUC: 0.503), CAR (AUC: 0.614), or PCT (AUC: 0.713) alone (all P< 0.001) in distinguishing CRC from BCL. Hb/RDW, CAR, PCT, and CEA are independent risk factors for CRC. Hb/RDW, CAR, and PCT combined with CEA have significant value for auxiliary differential diagnosis of CRC and BCL.

Differential diagnosis value of sympathetic skin response and cutaneous silent period on early-stage multiple system atrophy and Parkinson disease

Early differentiation between Parkinson's disease (PD) and Multiple system atrophy (MSA), particularly the parkinsonian subtypes (MSA-P), is challenging due to similar clinical symptoms. We aimed to evaluate Sympathetic skin response (SSR) and Cutaneous silent period (CSP) parameters in patients with MSA-P and PD to identify possible biomarkers that could distinguish the two groups of patients in early stage. 22 individuals with early-stage MSA-P, 29 with early-stage PD, and 28 healthy controls were recruited from Guangdong Provincial People's Hospital. Demographic data was collected for all participants. Their SSR and CSP were evaluated using clinical electromyography equipment. Data were compared between different groups. The diagnostic accuracy of SSR and CSP parameters was calculated using the ROC curve. Logistic regression was used to produce an integration model to enhance diagnostic utility. Foot amplitude, CSP end latency and duration distinguished MSA-P from PD with the area under the curve (AUC) 0.770, 0.806, and 0.776, respectively. Foot and hand SSR amplitude distinguished PD from HC with the AUC 0.871 and 0.768, respectively. Foot SSR amplitude, hand SSR amplitude, and CSP end latency distinguished MSA-P from HC with the AUC 0.964, 0.872, and 0.812, respectively. The combination of SSR and CSP parameters differentiation between MSA-P and PD, PD and HC with the AUC 0.829 and 0.879, respectively. Analysis of SSR and CSP parameters showed excellent diagnostic accuracy in discriminating patients with early-stage MSA-P from HC and good diagnostic accuracy in discriminating patients with MSA-P from PD with early stages.

Expression of Fascin-1 and its diagnostic value in liver cancer

Although some studies have reported on the expression and clinical significance of Fascin-1 (FSCN1) in liver cancer, the clinical application and differential diagnosis value of FSCN1 in liver cancer are still unclear. The aim of this study was to analyze the expression level of FSCN1 protein in liver cancer tissues and explore its diagnostic and application value in differentiating between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The immunehistochemical analysis was used to detect the expression of FSCN1 in 108 cases of HCC, 26 cases of ICC, 23 cases of liver cirrhosis, and 11 cases of normal liver tissues. The differences in the positive expression rate and strong positive expression rate of FSCN1 among different groups were analyzed. The positive rate of FSCN1 in normal liver tissues, liver cirrhosis, HCC, and ICC tissues was 0.0% (0/11), 0.0% (0/23), 13.9% (15/108), and 92.3% (24/26), respectively, while the strong positive rate was 0.0% (0/11), 0.0% (0/23), 0.9% (1/108), and 69.2% (18/26), respectively. Both the positive rate and strong positive rate of FSCN1 in ICC tissues were significantly higher than those in HCC, liver cirrhosis, and normal liver tissues. Additionally, the positive rate of FSCN1 in moderately to poorly differentiated HCC tissues was 18.8% (15/80), significantly higher than in well-differentiated HCC (0.0%, 0/28) (P = 0.031). In liver cancer, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FSCN1 positive prediction for ICC were 92.3%, 86.1%, 61.5%, and 97.9%, respectively, whereas the sensitivity, specificity, PPV, and NPV of FSCN1 strong positive prediction for ICC were 69.2%, 99.1%, 94.7%, and 93.0%, respectively. These results suggest that FSCN1 may play an important role in the occurrence and progression of liver cancer, and it can be used as a novel diagnostic marker for ICC.

MNDA expression and its value in differential diagnosis of B-cell non-Hodgkin lymphomas: a comprehensive analysis of a large series of 1293 cases.

MNDA (myeloid nuclear differentiation antigen) has been considered as a potential diagnostic marker for marginal zone lymphoma (MZL), but its utility in distinguishing MZL from other B-cell non-Hodgkin lymphomas (B-NHLs) and its clinicopathologic relevance in diffuse large B-cell lymphoma (DLBCL) are ambiguous. We comprehensively investigated MNDA expression in a large series of B-NHLs and evaluated its diagnostic value. MNDA expression in a cohort of 1293 cases of B-NHLs and 338 cases of reactive lymphoid hyperplasia (RLH) was determined using immunohistochemistry and compared among different types of B-NHL. The clinicopathologic relevance of MNDA in DLBCL was investigated. MNDA was highly expressed in MZLs (437/663, 65.9%), compared with the confined staining in marginal zone B-cells in RLH; whereas neoplastic cells with plasmacytic differentiation lost MNDA expression. MNDA expression was significantly higher in mantle cell lymphoma (MCL, 79.6%, p = 0.006), whereas lower in chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL, 44.8%, p = 0.001) and lymphoplasmacytic lymphoma (LPL, 25%, p = 0.016), and dramatically lower in follicular lymphoma (FL, 5.2%, p < 0.001), compared with MZL. 29.6% (63/213) of DLBCLs were positive for MNDA. The cases in non-GCB group exhibited a higher rate of MNDA positivity (39.8%) compared to those in GCB group (16.3%) (p < 0.001), and MNDA staining was more frequently observed in DLBCLs with BCL2/MYC double-expression (50%) than those without BCL2/MYC double-expression (24.8%) (p = 0.001). Furthermore, there was a significant correlation between MNDA and CD5 expression in DLBCL (p = 0.036). MNDA was highly expressed in MZL with a potential utility in differential diagnosis between MZL and RLH as well as FL, whereas its value in distinguishing MZL from MCL, CLL/SLL is limited. In addition, MNDA expression in DLBCL was more frequently seen in the non-GCB group and the BCL2/MYC double-expression group, and demonstrated a correlation with CD5, which deserves further investigation. The clinical relevance of MNDA and its correlation with the prognosis of these lymphomas also warrant to be fully elucidated.

Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma

ObjectiveTo investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) based on CT and MR multiphase radiomics combined with different machine learning models and compare the diagnostic efficacy between different radiomics models.BackgroundPrimary liver cancer is one of the most common clinical malignancies, hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, accounting for approximately 90% of cases. A clear diagnosis of HCC is important for the individualized treatment of patients with HCC. However, more sophisticated diagnostic modalities need to be explored.MethodsThis retrospective study included 211 patients with liver lesions: 97 HCC and 124 non-hepatocellular carcinoma (non-HCC) who underwent CT and MRI. Imaging data were used to obtain imaging features of lesions and radiomics regions of interest (ROI). The extracted imaging features were combined to construct different radiomics models. The clinical data and imaging features were then combined with radiomics features to construct the combined models. Support Vector Machine (SVM), K-nearest Neighbor (KNN), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP) six machine learning models were used for training. Five-fold cross-validation was used to train the models, and ROC curves were used to analyze the diagnostic efficacy of each model and calculate the accuracy rate. Model training and efficacy test were performed as before.ResultsStatistical analysis showed that some clinical data (gender and concomitant cirrhosis) and imaging features (presence of envelope, marked enhancement in the arterial phase, rapid contouring in the portal phase, uniform density/signal and concomitant steatosis) were statistical differences (P < 0.001). The results of machine learning models showed that KNN had the best diagnostic efficacy. The results of the combined model showed that SVM had the best diagnostic efficacy, indicating that the combined model (accuracy 0.824) had better diagnostic efficacy than the radiomics-only model.ConclusionsOur results demonstrate that the radiomic features of CT and MRI combined with machine learning models enable differential diagnosis of HCC and non-HCC (malignant, benign). The diagnostic model with dual radiomic had better diagnostic efficacy. The combined model was superior to the radiomic model alone.

Clinical feasibility study of early 30-minute dynamic FDG-PET scanning protocol for patients with lung lesions

PurposeThis study aimed to evaluate the clinical feasibility of early 30-minute dynamic 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) scanning protocol for patients with lung lesions in comparison to the standard 65-minute dynamic FDG-PET scanning as a reference.MethodsDynamic 18F-FDG PET images of 146 patients with 181 lung lesions (including 146 lesions confirmed by histology) were analyzed in this prospective study. Dynamic images were reconstructed into 28 frames with a specific temporal division protocol for the scan data acquired 65 min post-injection. Ki images and quantitative parameters Ki based on two different acquisition durations [the first 30 min (Ki-30 min) and 65 min (Ki-65 min)] were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. The two acquisition durations were compared for Ki image quality (including visual score analysis and number of lesions detected) and Ki value (including accuracy of Ki, the value of differential diagnosis of lung lesions and prediction of PD-L1 status) by Wilcoxon’s rank sum test, Spearman’s rank correlation analysis, receiver operating characteristic (ROC) curve, and the DeLong test. The significant testing level (alpha) was set to 0.05.ResultsThe quality of the Ki-30 min images was not significantly different from the Ki-65 min images based on visual score analysis (P > 0.05). In terms of Ki value, among 181 lesions, Ki-65 min was statistically higher than Ki-30 min (0.027 ± 0.017 ml/g/min vs. 0.026 ± 0.018 ml/g/min, P < 0.05), while a very high correlation was obtained between Ki-65 min and Ki-30 min (r = 0.977, P < 0.05). In the differential diagnosis of lung lesions, ROC analysis was performed on 146 histologically confirmed lesions, the area under the curve (AUC) of Ki-65 min, Ki-30 min, and SUVmax was 0.816, 0.816, and 0.709, respectively. According to the Delong test, no significant differences in the diagnostic accuracies were found between Ki-65 min and Ki-30 min (P > 0.05), while the diagnostic accuracies of Ki-65 min and Ki-30 min were both significantly higher than that of SUVmax (P < 0.05). In 73 (NSCLC) lesions with definite PD-L1 expression results, the Ki-65 min, Ki-30 min, and SUVmax in PD-L1 positivity were significantly higher than that in PD-L1 negativity (P < 0.05). And no significant differences in predicting PD-L1 positivity were found among Ki-65 min, Ki-30 min, and SUVmax (AUC = 0.704, 0.695, and 0.737, respectively, P > 0.05), according to the results of ROC analysis and Delong test.ConclusionsThis study indicates that an early 30-minute dynamic FDG-PET acquisition appears to be sufficient to provide quantitative images with good-quality and accurate Ki values for the assessment of lung lesions and prediction of PD-L1 expression. Protocols with a shortened early 30-minute acquisition time may be considered for patients who have difficulty with prolonged acquisitions to improve the efficiency of clinical acquisitions.

Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.

Differential Diagnosis in the Management of Acute Respiratory Infections through Point-of-Care Rapid Testing in a Post-Pandemic Scenario in Latin America: Special Focus on COVID-19, Influenza, and Respiratory Syncytial Virus

This review provides a comprehensive summary of evidence to explore the role and value of differential diagnosis in the management of Acute Respiratory Infections (ARIs) through point-of-care (POC) rapid testing in a post-pandemic scenario, paying particular attention to coronavirus disease 2019 (COVID-19), influenza, and respiratory syncytial virus (RSV). The document builds on a review of literature and policies and a process of validation and feedback by a group of seven experts from Latin America (LATAM). Evidence was collected to understand scientific and policy perspectives on the differential diagnosis of ARIs and POC rapid testing, with a focus on seven countries: Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, and Peru. The evidence indicates that POC rapid testing can serve to improve ARI case management, epidemiological surveillance, research and innovation, and evidence-based decision-making. With multiple types of rapid tests available for POC, decisions regarding which tests to use require the consideration of the testing purpose, available resources, and test characteristics regarding accuracy, accessibility, affordability, and results turnaround time. Based on the understanding of the current situation, this document provides a set of recommendations for the implementation of POC rapid testing in LATAM, supporting decision-making and guiding efforts by a broad range of stakeholders.

Histograms of computed tomography values in differential diagnosis of benign and malignant osteogenic lesions.

The use of histogram analysis of computed tomography (CT) values is a potential method for differentiating between benign osteoblastic lesions (BOLs) and malignant osteoblastic lesions (MOLs). To explore the diagnostic efficacy of histogram analysis in accurately distinguishing between BOLs and MOLs based on CT values. A total of 25 BOLs and 25 MOLs, which were confirmed through pathology or imaging follow-up, were included in this study. FireVoxel software was used to process the lesions and obtain various histogram parameters, including mean value, standard deviation, variance, coefficient of variation, skewness, kurtosis, entropy value, and percentiles ranging from 1st to 99th. Statistical tests, such as two independent-sample t-tests and the Mann-Whitney U test with Bonferroni correction, were employed to compare the differences in histogram parameters between BOLs and MOLs. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy of each parameter. Significant differences were observed in several histogram parameters between BOLs and MOLs, including the mean value, coefficient of variation, skewness, and various percentiles. Notably, the 25th percentile demonstrated the highest diagnostic efficacy, as indicated by the largest area under the curve in the ROC curve analysis. Histogram analysis of CT values provides valuable diagnostic information for accurately differentiating between BOLs and MOLs. Among the different parameters, the 25th percentile parameter proves to be the most effective in this discrimination process.

Applying Machine Learning to Support Early Diagnosis of Light-Chain Amyloidosis: A Combination of Knowledge-Based Approach with Data-Driven Approach

Introduction Immunoglobulin light chain (AL) amyloidosis is a rare disease involving the clonal proliferation of bone-marrow-residing plasma cell and resulting in overproduction of serum immunoglobulin free light chains that affects multiple organs. There are several available effective treatments including autologous stem cell transplant, bortezomib, anti-CD38 antibodies, and immunomodulatory drugs. However, because of the atypical symptoms and signs of this disease, diagnostic delays are still the major challenge resulting in poor prognosis. In recent years, machine learning (ML) models have been used to assist in early diagnosis. Therefore, to address this clinical unmet need, we aim to build ML algorithms from clinical data and assess their performance in differentiating AL amyloidosis from similar conditions. Methods Monocenter medical records data were collected from 49 patients with AL amyloidosis and 198 non-AL amyloidosis patients on a ratio of 1:4 in Peking University People's Hospital between January 1, 2013, and December 31, 2021. The non-AL amyloidosis group were patients with diseases of similar symptoms including autoimmune liver disease, myocarditis, and hypertrophic cardiomyopathy. Variables for model development were selected from 30 demographic characteristics and clinical features from routine clinical examination based on the results of recursive feature elimination and hematologists' knowledge. We proposed a four-step approach to develop and evaluate the diagnostic models. In the first step, all patients were randomly allocated into a training set and a testing set with a ratio of 4:1. Second, we derived five separate ML models including logistic regression, support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), and CatBoost algorithms to differentiate AL amyloidosis from other diseases with similar symptoms and validated the models using five-fold cross validation methods. Third, parameters of model with the highest areas under the receiver operating characteristic curves (AUROC) were updated in the full training set. Finally, the performances of the selected model were evaluated by AUROC, sensitivity, specificity and F1-score in the testing set. Results Twelve features including alanine aminotransferase, troponin, albumin, aspartate aminotransferase, activated partial thromboplastin time, albumin and globulin (A/G) ratio, direct bilirubin, platelet, fibrinogen, blood urea nitrogen, body weight and age were selected to construct ML models. The AUROC values for AL amyloidosis differential diagnosis were 0.55 with logistic regression, 0.63 with SVM, 0.84 with XGBoost, 0.89 with LightGBM, and 0.88 with CatBoost. The LightGBM model, which achieved the highest AUROC, also achieved the best performance with a sensitivity of 0.92, a specificity of 0.60, a F1-score of 0.73, a negative predictive value of 0.97, a positive predictive value of 0.60, and an accuracy of 0.82. Conclusion Our results show that the LightGBM model has the best performance to identify patients with AL amyloidosis from patients with similar symptoms. This novel ML-based diagnostic model has potential to assist in the earlier diagnosis of AL amyloidosis in clinical settings. Further studies are needed to confirm these findings in different study populations.

Diagnostic Value of CD25, CD69, and CD134 on Tuberculosis-Specific Antigen-Stimulated CD4+ T Cells for Tuberculous Pleurisy.

Rapid and accurate methods for the diagnosis of tuberculous pleurisy (TP) are urgently needed. Activation markers of tuberculosis (TB)-reactive T cells are considered promising for the diagnosis of active TB (ATB). Different activation indexes may play different roles in the progression of TB, but there are few reports on T cell activation indicators, except for HLA-DR. Hence, we evaluated the expression of early (CD25 and CD69) and late (CD134) activation markers on TB antigen-stimulated CD4+ T cells in populations with different TB infection status and investigated their diagnostic value for ATB, particularly, for TP. Moreover, we compared the differences in the diagnostic efficacy among the indexes from peripheral blood (PB) and pleural fluid (PF) for TP. The expression of each activation marker was significantly increased in TB-infected populations (patients with ATB and latent TB infection vs. healthy individuals; patients with TP vs. non-TP) and was significantly higher in the PF than in the PB of patients with TP. The diagnostic performance of the coexpressed activation markers was superior to that of single expression markers in the differential diagnosis of ATB and non-TB, with CD25+CD134+ showing the best diagnostic efficiency (AUC: 0.93, 95% CI, 0.87-0.99; sensitivity: 86.7%, 95% CI, 72.5%-94.5%; and specificity: 94.0%, 95% CI, 82.5%-98.4%). Except for TB-IGRA, the activation indexes were more accurate than conventional laboratory methods for ATB diagnosis. In addition, the expression of CD25+CD134+ in PB and PF was the best values for differential diagnosis of TP and NTP, with AUCs of 0.87 (95% CI, 0.77-0.96) and 0.95 (95% CI, 0.90-1.00), respectively. Our study provides information on the diagnostic value of different activation markers for TB and shows that the expression of CD25+CD134+ on CD4+ T cells in PF can serve as a potential marker for TP diagnosis.

Gd-EOB-DTPA-Enhanced Magnetic Resonance Imaging Combined with T1 Mapping Identifies Dysplastic Module and Hepatocellular Carcinoma: A Retrospective Study.

Hepatocellular carcinoma (HCC) is the leading type of liver cancer in adults, often resulting in fatal outcomes for those with cirrhosis. Dysplastic nodule (DN) is a liver nodule that is substantial in size, ranging from 1-2 cm. However, accurately distinguishing between DN and HCC on imaging has posed a challenge. The aim of this study is to assess the usefulness of Gd-EOB-DTPA-enhanced MRI T1 mapping in distinguishing between DN and HCC. This study analyzed 66 patients with confirmed HCC or DN who underwent Gd-EOB-DTPA-enhanced MRI T1 mapping before surgery or puncture at the Army Medical Center in China. The T1 values of each lesion were measured before and after Gd-EOB-DTPA administration, and various calculations were made, including absolute and percentage reduction in T1 value and coefficient of variation. The t-test was used to compare these values between the two groups, and the efficacy of T1 mapping values for differential diagnosis of HCC and DN was evaluated using the receiver operating characteristic curve (ROC). The study found that T1pre, T1hp, ΔT1, ΔT1%, and CV in the HCC group were significantly higher than in the DN group (p < 0.01). The accuracy of T1hp, ΔT1, and CVT1-hp in identifying HCC from DN was high, with AUCs of 0.955, 0.910, and 0.932, respectively. ΔT1% also had some accuracy, with an AUC of 0.818. Our results provide preliminary evidence that Gd-EOB-DTPA-enhanced MRI T1, CVT1-hp mapping, can be a valuable tool in diagnosing and differentiating between HCC and DN.

Clinical value of vestibulo-ocular reflex in the differentiation of spinocerebellar ataxias

The diagnosis of spinocerebellar ataxia (SCA) currently depends upon genetic testing. Although genetic testing for SCA is highly specific, clinical parameters for the differentiation of SCAs are still insufficient. We aimed to assess the vestibulo-ocular reflex (VOR) parameters of various SCA subtypes to determine whether they have substantial value in differential diagnosis. We consecutively enrolled 33 genetically confirmed SCA patients (SCA2 = 8, SCA3 = 6, SCA6 = 10, SCA7 = 9). Normative data were obtained from 36 age- and gender-matched healthy controls. Quantitative indicators of VOR were measured using video head impulse test (HIT) and combined ocular motor dysfunctions were investigated using video-oculography. Compared with the control group, the VOR gains in SCA2 were relatively spared, but were markedly decreased for all six canals in SCA3. The VOR gains for the posterior canals (PCs) were significantly decreased in SCA6, and for both vertical canals were decreased in SCA7. The VOR gains for the horizontal canals in SCA3 were negatively correlated with disease severity (R = -0.900, p = 0.037). Abnormal catch-up saccades were common in SCA3 and SCA6, rare in SCA7 and absent in SCA2. Spontaneous, headshaking-induced, and positional nystagmus were only documented in SCA6. SCA3 and SCA6 commonly showed horizontal gaze-evoked nystagmus, but SCA2 and SCA7 had characteristic saccadic slowing without gaze-evoked nystagmus. VOR impairments are common in SCAs, but their patterns vary depending on subtype. In addition to ocular motor characteristics, distinctive VOR performance for each subtype using video HIT may aid the differential diagnosis of the SCA genotypes.

Value of [18F]AlF-NOTA-FAPI-04 PET/CT for differential diagnosis of malignant and various inflammatory lung lesions: comparison with [18F]FDG PET/CT.

Uptake of the imaging tracers [18F]AlF-NOTA-FAPI-04 and [18F]FDG varies in some inflammatory lesions, which may result in false-positive findings for malignancy on PET/CT. Our aim was to compare the [18F]AlF-NOTA-FAPI-04 and [18F]FDG PET/CT imaging features of malignant and various inflammatory lung lesions and to analyze their value for differential diagnosis. We retrospectively analyzed [18F]AlF-NOTA-FAPI-04 PET/CT scans from 67 cancer patients taken between December 2020 and January 2022, as well as the scans of 32 patients who also underwent [18F]FDG PET/CT imaging. The maximum and mean standardized uptake values (SUVmax and SUVmean, respectively) and lesion-to-background ratio (LBR) were calculated. The predictive capabilities of semiquantitative PET/CT parameters were analyzed by receiver operating characteristic curve analysis. A total of 70 inflammatory and 37 malignant lung lesions were evaluated by [18F]AlF‑NOTA‑FAPI‑04 PET/CT, and 33 inflammatory and 26 malignant lung lesions also were evaluated by [18F]FDG PET/CT. Inflammatory lesions exhibited lower [18F]AlF-NOTA-FAPI-04 and [18F]FDG uptake compared to malignant lesions, with statistically significant differences in SUVmax, SUVmean, and LBR (all p < 0.001). [18F]AlF-NOTA-FAPI-04 uptake also varied among different types of inflammatory lesions (SUVmax, p = 0.005; SUVmean, p = 0.008; LBR, p < 0.001), with the highest uptake observed in bronchiectasis with infection, followed by postobstructive pneumonia, and the lowest in pneumonia. [18F]FDG uptake was higher in postobstructive pneumonia than in pneumonia (SUVmax, p = 0.009; SUVmean, p = 0.016; LBR, p = 0.004). [18F]AlF-NOTA-FAPI-04/[18F]FDG PET/CT showed significantly lower uptake in inflammatory lesions than malignancies as well as variation in different types of inflammatory lesions, and thus, may be valuable for distinguishing malignant and various inflammatory findings. Our study confirmed that the uptake of [18F]AlF-NOTA-FAPI-04/[18F]FDG PET/CT in inflammatory and malignant lung lesions is different, which is beneficial to distinguish inflammatory and malignant lung lesions in clinic. • Malignant and different inflammatory lung lesions showed varying degrees of uptake of [18F]AlF-NOTA-FAPI-04 and [18F]FDG. • Inflammatory lung lesions showed significantly less uptake than malignancies, and uptake varied among different types of inflammatory lesions. • Both types of PET/CT could differentiate malignant and various inflammatory lung findings.

Differences in serum cytokine levels distinguish between clinically noninvasive lung adenocarcinoma and invasive lung adenocarcinoma: A cross-sectional study.

Lung cancer incidence and mortality remain high and are now the leading cause of cancer-related death. Lung adenocarcinoma (LUAD) is one of the main histological subtypes of lung cancer. Previous studies have shown the role of inflammation in the development of lung cancer, but the relationship between cytokines and LUAD is still unclear. To further differentiate and explore the association of cytokines with the risk of non-invasive and invasive LUAD, we studied and assessed serum cytokine levels in patients with two types of LUAD. A cohort study of 90 non-invasive LUAD and 90 invasive LUAD was retrospectively included, and the clinical characteristics were recorded in detail. The differences in the levels of 12 serum cytokines (IFN-α, IFN-γ, IL-10, IL-12P70, IL-17A, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, and TNF-α) between the two groups of patients with LUAD were analyzed and evaluated. And we evaluated the clinical value of cytokine differential diagnosis of invasive LUAD based on receiver operating characteristics (ROC) curves. The mean age of the patients was 56.6 years, and the proportions of males and females were 38.9% and 61.1%, respectively. IFN-α, IL-1β, IL-2, IL-6, TNF-α, IL-4, and IL-8 were significantly increased in patients with invasive LUAD compared with the non-invasive LUAD group. Further research found that smoking is an important factor, with changes in the four cytokines IL-1β, IL-6, IL-8, and TNF-α being significantly higher in the smoking group of patients with invasive LUAD. It can be seen from the area under the curve that IL-1β and IL-2 have a significant differential diagnosis. We observed differences in preoperative serum cytokine levels between patients with invasive and non-invasive LUAD, which may serve as potential serum biomarkers for clinical differential diagnosis and disease progression assessment.

Analysis of clinicopathological features of 68 cases of malignant pleural mesothelioma

Objective: To analyze the clinicopathological characteristics of diffuse malignant pleural mesothelioma (MPM), and explore the diagnostic methods in order to improve the early diagnosis rate. Methods: In January 2019 to January 2022, the clinical features, auxiliary examination and immunohistochemical results of 68 cases of MPM were analyzed retrospectively. The pathogenic features, histopathological morphology and the expression of related antibodies including Calretinin (CR), D2-40 and WT-1 were summarized. Results: Among the 68 patients, 40 male (58.82%), 28 female (41.18%), male to female ratio was 1.43%, median age was 58 years old; 50% of patients in Dayao County, epithelial mesothelioma 59 cases (86.76%), occurred in right chest in 39 cases (57.35%), left chest in 25 cases (36.76%), and 4 cases in both sides (5.89%). The most common initial clinical manifestations were pleural effusion (95.59%), chest pain (36.75%), chest tightness and shortness of breath (30.88%). The main imaging findings were pleural effusion in 49 cases (98.00%) and pleural thickening in 46 cases (92.00%). MPM tumor cells often expressed Calretinin, CK5/6, WT1 and D2-40, while TTF-1, NapsinA and CEA, the main markers differentiated from lung adenocarcinoma were negative. Serum CYFRA21-1 and CEA have high value in differential diagnosis of benign and malignant pleural effusions. Conclusion: Diffuse MPM has diverse histological and cytological morphology, which needs to be differentiated from a variety of diseases. Correct diagnosis of diffuse MPM through immunohistochemistry requires the combined application of a group of Mesothelium related antibodies.

Population-based affective-disorder-related biomedical/biophysical multi-hyper-morbidity across the lifespan: A 16-year population study.

There are few if any life-span population-based studies of psychiatric disorder-associated biomedical and biophysical disorders and diseases (morbidity). To scope the present state of research regarding the biomedical and biophysical morbidity associated with affective and mental disorder in epidemiological samples, and to examine the life-span relationship between affective disorders and biomedical/biophysical disorders to illustrate a novel approach employing the odds ratio to represent the intensity of biomedical and biophysical morbidity associated in time in a population. A repeatable systematic literature search of PubMed was represented in summary. Additionally, a regional population-based dataset was constructed and analyzed to represent the age- and sex-specific diagnoses (International Classification of Diseases Version 9, ICD-9) for those with and without affective disorder. The analysis presents a novel index of the relative age-specific frequency of life-span biomedical and biophysical diagnoses associated with affective disorder. The volume of biomedical and biophysical morbidity associated with mental disorder literature has increased, yet few studies measure comprehensive temporal hyper-morbidity (over-representation of diseases over time, either before or after the index diagnostic event) in populations. Further, there have been only a few population-based studies examining the morbidity associated with affective disorder and only one that examines the full diagnostic range of lifespan morbidity. Substantial differences arose between males and females with more females than males having greater frequencies of diagnoses. The age-specific distributions of the maximum proportional diagnosis frequency ratios for each sex illustrate the greatest diagnosis-specific differences when comparing the biomedical and biophysical diagnoses of those with and without affective disorder when the same diagnosis was represented in each grouping at the same age. Clinical research needs to focus on more than one or two comorbid biomedical or biophysical disorders at a time. Comprehensive population-based examination of the lifespan biomedical and biophysical multi-morbidity associated with affective disorder has the potential to directly inform clinical practice. Representing the proportional ratios of age-specific frequency of diagnoses for the full range of ICD-9 diagnoses is a novel analytical model. Diagnostic frequency appears a viable representation of a given disease state, such as affective disorder. Fortunately, the WPA has developed a global education section to better understand the biomedical and biophysical morbidity associated with all psychiatric disorders. This has been identified by the WPA as the psychiatric practice challenge of the 21st century.

Delivering substance use prevention interventions for adolescents in educational settings: A scoping review.

Currently, a proportion of adolescents use alcohol, tobacco, and illicit drugs, which inevitably harms their health and academic progress. Adolescence is a peak period for substance use initiation and a critical time for preventing substance use problems. Various entities, such as families, schools, and communities, have implemented a variety of interventions to alleviate adolescent substance use problems, and schools play a unique role. To explore the types, characteristics, and effectiveness of substance use interventions in educational settings for adole-scents, we conducted a scoping review and identified 32 studies after screening. We divided the 32 studies according to intervention type, including curriculum interventions focusing on cognitive-behavioral skill enhancement, exercise interventions, peer interventions and family-school cooperation, and electronic interventions. Except for the mixed results on electronic interventions, the results showed that the other interventions were beneficial to different extents in alleviating adolescent substance use problems. In addition, we analyzed and summarized the advantages and challenges of intervening in adolescent substance use in educational settings. Schools can use equipment and human resources to provide adolescents with various types of intervention measures, but they also face challenges such as stigmatization, ineffective coordination among multiple resources, and poor implementation effects. In the future, school-based intervention measures can fully utilize big data and artificial intelligence technology and collaborate with families and communities to intervene appro-priately while paying attention to the comorbidity risks of substance use disorders and psychological health issues.