CHRNA6 RNA In Situ Hybridization Is a Useful Tool for the Diagnosis of Extraskeletal Myxoid Chondrosarcoma.

Extraskeletal myxoid chondrosarcoma (EMC) is an uncommon mesenchymal neoplasm characteristically composed of uniform-appearing round to spindle-shaped cells with eosinophilic cytoplasm and abundant myxoid extracellular matrix. Although the majority of cases harbor a pathognomonic t(9;22) translocation that fuses EWSR1 with the orphan nuclear receptor NR4A3, there are less common variants that partner NR4A3 with TAF15, TCF12, or TFG. By immunohistochemistry, EMC has features of both cartilaginous and neuroendocrine differentiation, as evidenced by inconsistent expression of S100 protein and synaptophysin or INSM1, respectively, in a subset of cases. Given the limitations of available immunohistochemical stains for the diagnosis of EMC, we analyzed genome-wide gene expression microarray data to identify candidate biomarkers based on differential expression in EMC in comparison with other mesenchymal neoplasms. This analysis pointed to CHRNA6 as the gene with the highest relative expression in EMC (96-fold; P = 8.2 × 10-26) and the only gene with >50-fold increased expression in EMC compared with other tumors. Using RNA chromogenic in situ hybridization, we observed strong and diffuse expression of CHRNA6 in 25 cases of EMC, including both EWSR1-rearranged and TAF15-rearranged variants. All examined cases of histologic mimics were negative for CHRNA6 overexpression; however, limited CHRNA6 expression, not reaching a threshold of >5 puncta or 1 aggregate of chromogen in >25% of cells, was observed in 69 of 685 mimics (10.1%), spanning an array of mesenchymal tumors. Taken together, these findings suggest that, with careful interpretation and the use of appropriate thresholds, CHRNA6 RNA chromogenic in situ hybridization is a potentially useful ancillary histologic tool for the diagnosis of EMC.

Pitfalls in soft tissue cytopathology.

Fine needle aspiration biopsy (FNAB) is a diagnostic modality for the evaluation of suspicious soft tissue masses. Despite its reasonable sensitivity, specificity and positive predictive value in differentiating benign from malignant neoplasms, the exact subtyping of the primary soft tissue tumours can be challenging. Certain tumours constitute "pitfalls" and add to the diagnostic challenge. This review provides a detailed account of the diagnostic challenges in soft tissue cytopathology, including pitfalls and, more importantly, the ways to overcome these challenges by integrating clinical details, key cytomorphological features and judicious application of ancillary techniques.

Genomic catastrophe, the peritoneal cavity and ovarian cancer prevention.

The current theory of carcinogenesis for the deadliest of 'ovarian' cancers-high-grade serous carcinoma (HGSC)-holds that the malignancy develops first in the fallopian tube and spreads to the ovaries, peritoneum, and/or regional lymph nodes. This is based primarily on the observation of early forms of serous neoplasia (serous tubal intraepithelial lesions [STILs], and serous tubal intraepithelial carcinomas [STICS]) in the fimbria of women undergoing risk reduction surgery. However, these lesions are uncommon in the general population, confer a low risk (5%) of HGSC following their removal in at-risk women with germ-line BRCA1/2 mutations, and require 4 or more years to recur as intraperitoneal HGSC. These features suggest that isolated STILs and STICs behave as precursors, with uncertain cancer risk rather than carcinomas. Their evolution to HGSC within, or after, escape from the tube could proceed stepwise with multiple biologic events; however, it is unclear whether tubal or ovarian HGSCs encountered in the setting of advanced disease evolved in the same fashion. The latter scenario could also be explained by a 'catastrophic' model in which STICs suddenly develop with invasive and metastatic potential, overwhelming or obscuring the site of origin. Moreover, a similar model might explain the sudden emergence of HGSC in the peritoneal cavity following escape of precursor cells years before. Long-term follow-up data from opportunistic or prophylactic salpingectomy should shed light on where malignant transformation occurs, as well as the timeline from precursor to metastatic HGSC. © 2022 The Pathological Society of Great Britain and Ireland.

Multiplex digital PCR with digital melting curve analysis on a self-partitioning SlipChip.

Digital polymerase chain reaction (digital PCR) can provide absolute quantification of target nucleic acids with high sensitivity, excellent precision, and superior resolution. Digital PCR has broad applications in both life science research and clinical molecular diagnostics. However, limited by current fluorescence imaging methods, parallel quantification of multiple target molecules in a single digital PCR remains challenging. Here, we present a multiplex digital PCR method using digital melting curve analysis (digital MCA) with a SlipChip microfluidic system. The self-partitioning SlipChip (sp-SlipChip) can generate an array of nanoliter microdroplets with trackable physical positions using a simple loading-and-slipping operation. A fluorescence imaging adaptor and an in situ thermal cycler can be used to perform digital PCR and digital MCA on the sp-SlipChip. The unique signature melting temperature (Tm) designed for amplification products can be used as a fingerprint to further classify the positive amplification partitions into different subgroups. Amplicons with Tm differences as low as 1.5 degrees celsius were clearly separated, and multiple amplicons in the same partition could also be distinguished by digital MCA. We further demonstrated this digital MCA method with simultaneous digital quantification of five common respiratory pathogens, including Staphylococcus aureus, Acinetobacter baumannii, Streptococcus pneumoniae, Hemophilus influenzae, and Klebsiella pneumoniae. Since digital MCA only requires an intercalation dye instead of sequence-specific hydrolysis probes to perform multiplex digital PCR analysis, it can be less expensive and not limited to the number of fluorescence channels.

A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma.

OBJECTIVES: This study aims to develop a clinically practical model to predict EGFR mutation in lung adenocarcinoma patients according to radiomics signatures based on PET/CT and clinical risk factors. METHODS: This retrospective study included 583 lung adenocarcinoma patients, including 295 (50.60%) patients with EGFR mutation and 288 (49.40%) patients without EGFR mutation. The clinical risk factors associated with lung adenocarcinoma were collected at the same time. We developed PET/CT, CT, and PET radiomics models for the prediction of EGFR mutation using multivariate logistic regression analysis, respectively. We also constructed a combined PET/CT radiomics-clinical model by nomogram analysis. The diagnostic performance and clinical net benefit of this risk-scoring model were examined via receiver operating characteristic (ROC) curve analysis while the clinical usefulness of this model was evaluated by decision curve analysis (DCA). RESULTS: The ROC analysis showed predictive performance for the PET/CT radiomics model (AUC = 0.76), better than the PET model (AUC = 0.71, Delong test: Z = 3.03, p value = 0.002) and the CT model (AUC = 0.74, Delong test: Z = 1.66, p value = 0.098). Also, the PET/CT radiomics-clinical combined model has a better performance (AUC = 0.84) to predict EGFR mutation than the PET/CT radiomics model (AUC = 0.76, Delong test: D = 2.70, df = 790.81, p value < 0.001) or the clinical model (AUC = 0.81, Delong test: Z = 3.46, p value < 0.001). CONCLUSIONS: We demonstrated that the combined PET/CT radiomics-clinical model has an advantage to predict EGFR mutation in lung adenocarcinoma. KEY POINTS: • Radiomics from lung tumor increase the efficiency of the prediction for EGFR mutation in clinical lung adenocarcinoma on PET/CT. • A radiomic nomogram was developed to predict EGFR mutation. • Combining PET/CT radiomics-clinical model has an advantage to predict EGFR mutation.

MYC expression has limited utility in the distinction of undifferentiated radiation-associated sarcomas from sporadic sarcomas and sarcomatoid carcinoma.

AIMS: MYC is a proto-oncogene that is frequently dysregulated in various malignancies, through translocation or amplification. Radiation-associated angiosarcoma frequently shows MYC amplification, and immunohistochemical expression has been shown to be a reliable surrogate marker for amplification, but less is known about MYC expression in other sarcoma types, despite reports of MYC amplification in some undifferentiated/unclassified radiation-associated sarcomas (RASs). Distinguishing putative RAS from non-radiation-associated sarcoma or sarcomatoid carcinoma can be difficult. The aim of this study was to determine the prevalence and potential diagnostic utility of MYC in this context, by evaluating MYC expression in a cohort of RASs, non-radiation-associated sarcomas, and sarcomatoid carcinomas. METHODS AND RESULTS: Three hundred and eighty-five neoplasms were evaluated, including 81 RASs (18 angiosarcomas; 57 undifferentiated sarcomas; three leiomyosarcomas; and three malignant peripheral nerve sheath tumours), 267 non-radiation-associated sarcomas, and 37 sarcomatoid carcinomas. Immunohistochemistry was performed with a monoclonal anti-MYC antibody. Staining in tumour cells was scored on the basis of extent (focal, 1-4%; multifocal, 5-49%; and diffuse, ≥50%) and intensity (strong, moderate, and weak). One hundred percent of radiation-associated angiosarcomas expressed MYC diffusely. Expression was infrequent among other types of RAS (9.5%), and the frequency was similar to that in non-radiation-associated sarcomas (9.7%). MYC expression was more common in sarcomatoid carcinomas, occurring in 43%. The extent and intensity of staining were variable in all groups. CONCLUSION: MYC expression is infrequent among RASs other than angiosarcoma, and has a similar prevalence in sporadic sarcomas. Given the frequency of expression in sarcomatoid carcinomas, MYC expression outside the context of radiation-associated angiosarcoma is of limited diagnostic utility, and should be interpreted with caution after exclusion of sarcomatoid carcinoma where relevant.

Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (≤ 3 cm): PET/CT findings and correlations with lymph node metastasis.

OBJECTIVE: To investigate the PET/CT findings in lung invasive adenocarcinoma with minor components of micropapillary or solid contents and its association with lymph node metastasis. MATERIALS AND METHODS: A total of 506 lung invasive adenocarcinoma (≤ 3 cm) patients who underwent a PET/CT examination and resection surgery were included. According to the proportion of solid/micropapillary components, the patients were classified into three groups: solid/micropapillary-negative (SMPN) (n = 258), solid/micropapillary-minor (SMPM; > 5% not predominant) (n = 158) and solid/micropapillary-predominant (SMPP; > 5% most dominant) (n = 90). The patients' PET/CT findings, including SUVmax, MTV, TLG and CT characteristics, and other clinical factors were compared by one-way ANOVA test. Logistic regression analysis was done to identify the most predictive findings for lymph node metastasis. RESULTS: The value of SUVmax, MTV, TLG and tumor size was highest in SMPP group, followed by SMPM and SMPN group (P < 0.001).The areas under the curve for SUVmax, MTV and TLG for node metastasis were 0.822, 0.843 and 0.835, respectively. Univariate analysis found that the SMPP and SMPM group had more lymph node metastasis than the SMPN group (P < 0.001). Furthermore, the lymph node metastasis group had higher CEA, SUVmax, MTV, TLG, tumor size and more pleural invasion (P < 0.001). Logistic regression analysis found that SMPP pathological type, SMPM pathological type, higher CEA and male patients were risk factors for lymph node metastasis (P < 0.01). CONCLUSIONS: Lung invasive adenocarcinoma with micropapillary or solid contents had higher SUVmax, MTV, TLG and tumor size and was associated with lymph node metastasis, even if they were not predominant.

23-valent pneumococcal polysaccharide vaccine elicits hierarchical antibody and cellular responses in healthy and tuberculosis-cured elderly, and HIV-1-infected subjects.

The PPV23 immunizes healthy elderly and other high-risk populations against pneumococcal disease. Immune mechanisms whereby these populations differently mount antibody(Ab) and cellular responses to PPV23 vaccination remain unknown. Here, healthy elderly, those elderly with prior tuberculosis-cured history (TB-cured), and HIV-infected humans were vaccinated with PPV23, and assessed for opsonophagocytic Ab responses and potential cellular mechanisms. PPV23 vaccination elicited hierarchical responses of opsonophagocytic Ab. PPV23-elicited Ab titers were highest in healthy elderly, significantly lower in TB-cured elderly and lowest in HIV-infected subjects. Mechanistically, high PPV23-elicited Ab titers in healthy elderly were associated with increases in CD19 + CD69+ cells and CD19 + CD138 + plasma cells. Surprisingly, TB-cured elderly failed to show PPV23-induced increases in these cells. While HIV-infected subjects showed a depressed CD19 + CD69+ cellular response, PPV23 vaccination uncovered HIV-related over-reactive increases in CD19 + CD138 + cells. For the first time, we demonstrate that PPV23-elicted opsonophagocytic Ab titers correlate with different cellular responses in healthy, TB-cured and HIV statuses.

A Systematic Approach to Predicting Spring Force for Sagittal Craniosynostosis Surgery.

Spring-assisted surgery (SAS) can effectively treat scaphocephaly by reshaping crania with the appropriate spring force. However, it is difficult to accurately estimate spring force without considering biomechanical properties of tissues. This study presents and validates a reliable system to accurately predict the spring force for sagittal craniosynostosis surgery. The authors randomly chose 23 patients who underwent SAS and had been followed for at least 2 years. An elastic model was designed to characterize the biomechanical behavior of calvarial bone tissue for each individual. After simulating the contact force on accurate position of the skull strip with the springs, the finite element method was applied to calculating the stress of each tissue node based on the elastic model. A support vector regression approach was then used to model the relationships between biomechanical properties generated from spring force, bone thickness, and the change of cephalic index after surgery. Therefore, for a new patient, the optimal spring force can be predicted based on the learned model with virtual spring simulation and dynamic programming approach prior to SAS. Leave-one-out cross-validation was implemented to assess the accuracy of our prediction. As a result, the mean prediction accuracy of this model was 93.35%, demonstrating the great potential of this model as a useful adjunct for preoperative planning tool.

A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians.

The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-based clinical assessment methods are highly subjective and dependent on the neurologist's expertise. Alternate automated methods for arising-from-chair assessment can be established based on quantitative susceptibility mapping (QSM) images with multiple-instance learning. However, performance stability for such methods can be typically undermined by the presence of irrelevant or spuriously-relevant features that mask the intrinsic causal features. Therefore, we propose a QSM-based arising-from-chair assessment method using a causal graph-neural-network framework, where counterfactual and debiasing strategies are developed and integrated into this framework for capturing causal features. Specifically, the counterfactual strategy is proposed to suppress irrelevant features caused by background noise, by producing incorrect predictions when dropping causal parts. The debiasing strategy is proposed to suppress spuriously relevant features caused by the sampling bias and it comprises a resampling guidance scheme for selecting stable instances and a causal invariance constraint for improving stability under various interferences. The results of extensive experiments demonstrated the superiority of the proposed method in detecting arising-from-chair abnormalities. Its clinical feasibility was further confirmed by the coincidence between the selected causal features and those reported in earlier medical studies. Additionally, the proposed method was extensible for another motion task of leg agility. Overall, this study provides a potential tool for automated arising-from-chair assessment in PD patients, and also introduces causal counterfactual thinking in medical image analysis. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CFGNN-PDarising.

Causality-Enhanced Multiple Instance Learning With Graph Convolutional Networks for Parkinsonian Freezing-of-Gait Assessment.

Freezing of gait (FoG) is a common disabling symptom of Parkinson's disease (PD). It is clinically characterized by sudden and transient walking interruptions for specific human body parts, and it presents the localization in time and space. Due to the difficulty in extracting global fine-grained features from lengthy videos, developing an automated five-point FoG scoring system is quite challenging. Therefore, we propose a novel video-based automated five-classification FoG assessment method with a causality-enhanced multiple-instance-learning graph convolutional network (GCN). This method involves developing a temporal segmentation GCN to segment each video into three motion stages for stage-level feature modeling, followed by a multiple-instance-learning framework to divide each stage into short clips for instance-level feature extraction. Subsequently, an uncertainty-driven multiple-instance-learning GCN is developed to capture spatial and temporal fine-grained features through GCN scheme and uncertainty learning, respectively, for acquiring global representations. Finally, a causality-enhanced graph generation strategy is proposed to exploit causal inference for mining and enhancing human structures causally related to clinical assessment, thereby extracting spatial causal features. Extensive experimental results demonstrate the excellent performance of the proposed method on five-classification FoG assessment with an accuracy of 62.72% and an acceptable accuracy of 91.32%, which is confirmed by independent testing. Additionally, it enables temporal and spatial localization of FoG events to a certain extent, facilitating reasonable clinical interpretations. In conclusion, our method provides a valuable tool for automated FoG assessment in PD, and the proposed causality-related component exhibits promising potential for extension to other general and medical fine-grained action recognition tasks.

A causality-inspired generalized model for automated pancreatic cancer diagnosis.

Pancreatic cancer (PC) is a severely malignant cancer variant with high mortality. Since PC has no obvious symptoms, most PC patients are belatedly diagnosed at advanced disease stages. Recently, artificial intelligence (AI) approaches have demonstrated promising prospects for early diagnosis of pancreatic cancer. However, certain non-causal factors (such as intensity and texture appearance variations, also called confounders) tend to induce spurious correlation with PC diagnosis. This undermines the generalization performance and the clinical applicability of the AI-based PC diagnosis approaches. Therefore, we propose a causal intervention based automated method for pancreatic cancer diagnosis with contrast-enhanced computerized tomography (CT) images, where a confounding effects reduction scheme is developed for alleviating spurious correlations to achieve unbiased learning, thereby improving the generalization performance. Specifically, a continuous image generation strategy was developed to simulate wide variations of intensity differences caused by imaging heterogeneities, where Monte Carlo sampling is added to further enhance the continuity of simulated images. Then, to enhance the pancreatic texture variability, a texture diversification method was introduced in conjunction with gradient-based data augmentation. Finally, a causal intervention strategy was proposed to alleviate the adverse confounding effects by decoupling the causal and non-causal factors and combining them randomly. Extensive experiments showed remarkable diagnosis performance on a cross-validation dataset. Also, promising generalization performance with an average accuracy of 0.87 was attained on three independent test sets of a total of 782 subjects. Therefore, the proposed method shows high clinical feasibility and applicability for pancreatic cancer diagnosis.

A Causality-Aware Graph Convolutional Network Framework for Rigidity Assessment in Parkinsonians.

Rigidity is one of the common motor disorders in Parkinson's disease (PD), which lead to life quality deterioration. The widely-used rating-scale-based approach for rigidity assessment still depends on the availability of experienced neurologists and is limited by rating subjectivity. Given the recent successful applications of quantitative susceptibility mapping (QSM) in auxiliary PD diagnosis, automated assessment of PD rigidity can be essentially achieved through QSM analysis. However, a major challenge is the performance instability due to the confounding factors (e.g., noise and distribution shift) which conceal the truly-causal features. Therefore, we propose a causality-aware graph convolutional network (GCN) framework, where causal feature selection is combined with causal invariance to ensure that causality-informed model decisions are reached. Firstly, a GCN model that integrates causal feature selection is systematically constructed at three graph levels: node, structure, and representation. In this model, a causal diagram is learned to extract a subgraph with truly-causal information. Secondly, a non-causal perturbation strategy is developed along with an invariance constraint to ensure the stability of the assessment results under different distributions, and thus avoid spurious correlations caused by distribution shifts. The superiority of the proposed method is shown by extensive experiments and the clinical value is revealed by the direct relevance of selected brain regions to rigidity in PD. Besides, its extensibility is verified on other two tasks: PD bradykinesia and mental state for Alzheimer's disease. Overall, we provide a clinically-potential tool for automated and stable assessment of PD rigidity. Our source code will be available at https://github.com/SJTUBME-QianLab/Causality-Aware-Rigidity.

A Generalizable Causal-Invariance-Driven Segmentation Model for Peripancreatic Vessels.

Segmenting peripancreatic vessels in CT, including the superior mesenteric artery (SMA), the coeliac artery (CA), and the partial portal venous system (PPVS), is crucial for preoperative resectability analysis in pancreatic cancer. However, the clinical applicability of vessel segmentation methods is impeded by the low generalizability on multi-center data, mainly attributed to the wide variations in image appearance, namely the spurious correlation factor. Therefore, we propose a causal-invariance-driven generalizable segmentation model for peripancreatic vessels. It incorporates interventions at both image and feature levels to guide the model to capture causal information by enforcing consistency across datasets, thus enhancing the generalization performance. Specifically, firstly, a contrast-driven image intervention strategy is proposed to construct image-level interventions by generating images with various contrast-related appearances and seeking invariant causal features. Secondly, the feature intervention strategy is designed, where various patterns of feature bias across different centers are simulated to pursue invariant prediction. The proposed model achieved high DSC scores (79.69%, 82.62%, and 83.10%) for the three vessels on a cross-validation set containing 134 cases. Its generalizability was further confirmed on three independent test sets of 233 cases. Overall, the proposed method provides an accurate and generalizable segmentation model for peripancreatic vessels and offers a promising paradigm for increasing the generalizability of segmentation models from a causality perspective. Our source codes will be released at https://github.com/SJTUBME-QianLab/PC_VesselSeg.

Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance.

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.

When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. RESULTS: Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. SIGNIFICANCE: The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

Prediction of intravenous immunoglobulin retreatment in children with Kawasaki disease using models combining lymphocyte subset and cytokine profile in an East Asian cohort.

Objectives: For children with Kawasaki disease (KD) at high risk of developing coronary artery lesions and requiring retreatment with intravenous immunoglobulin (IVIG), the availability of accurate prediction models remains limited because of inconsistent variables and unsatisfactory prediction results. We aimed to construct models to predict patient's probability of IVIG retreatment combining children's individual inflammatory characteristics. Methods: Clinical manifestations and laboratory examinations of 266 children with KD were retrospectively analysed to build a development cohort data set (DC) and a validation cohort data set (VC). In the DC, binary logistic regression analyses were performed using R language. Nomograms and receiver operating curves were plotted. The concordance index (C index), net reclassification index, integrated discrimination improvement index and confusion matrix were applied to evaluate and validate the models. Results: Models_5V and _9V were established. Both contained variables including the percentages of CD8+ T cells, CD4+ T cells, CD3+ T cells, levels of interleukin (IL)-2R and CRP. Model_9V additionally included variables for IL-6, TNF-α, NT-proBNP and sex, with a C index of 0.86 (95% CI 0.79-0.92). When model_9V was compared with model_5V, the NRI and IDI were 0.15 (95% CI 0.01-0.30, P < 0.01) and 0.07 (95% CI 0.02-0.12, P < 0.01). In the VC, the sensitivity, specificity and precision of model_9V were 1, 0.875 and 0.667, while those of model_5V were 0.833, 0.875 and 0.625. Conclusion: Model_9V combined cytokine profiles and lymphocyte subsets with clinical characteristics and was superior to model_5V achieving satisfactory predictive power and providing a novel strategy early to identify patients who needed IVIG retreatment.

Rapid Prototyping of Thermoplastic Microfluidic 3D Cell Culture Devices by Creating Regional Hydrophilicity Discrepancy.

Microfluidic 3D cell culture devices that enable the recapitulation of key aspects of organ structures and functions in vivo represent a promising preclinical platform to improve translational success during drug discovery. Essential to these engineered devices is the spatial patterning of cells from different tissue types within a confined microenvironment. Traditional fabrication strategies lack the scalability, cost-effectiveness, and rapid prototyping capabilities required for industrial applications, especially for processes involving thermoplastic materials. Here, an approach to pattern fluid guides inside microchannels is introduced by establishing differential hydrophilicity using pressure-sensitive adhesives as masks and a subsequent selective coating with a biocompatible polymer. Optimal coating conditions are identified using polyvinylpyrrolidone, which resulted in rapid and consistent hydrogel flow in both the open-chip prototype and the fully bonded device containing additional features for medium perfusion. The suitability of the device for dynamic 3D cell culture is tested by growing human hepatocytes in the device under controlled fluid flow for a 14-day period. Additionally, the study demonstrated the potential of using the device for pharmaceutical high-throughput screening applications, such as predicting drug-induced liver injury. The approach offers a facile strategy of rapid prototyping thermoplastic microfluidic organ chips with varying geometries, microstructures, and substrate materials.

Early Diagnosis of Colorectal Cancer Based on Bisulfite-free Site-specific Methylation Identification PCR Strategy: High-Sensitivity, Accuracy, and Primary Medical Accessibility.

Due to its decade-long progression, colorectal cancer (CRC) is most suitable for population screening to achieve a significant reduction in its incidence and mortality. DNA methylation has emerged as a potential marker for the early detection of CRC. However, the current mainstream methylation detection method represented by bisulfite conversion has issues such as tedious operation, DNA damage, and unsatisfactory sensitivity. Herein, a new high-performance CRC screening tool based on the promising specific terminal-mediated polymerase chain reaction (STEM-PCR) strategy is developed. CRC-related methylation-specific candidate CpG sites are first prescreened through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using self-developed bioinformatics. Next, 9 homebrew colorectal cancer DNA methylated STEM‒PCR assays (ColoC-mSTEM) with high sensitivity (0.1%) and high specificity are established to identify candidate sites. The clinical diagnostic performance of these selected methylation sites is confirmed and validated by a case-control study. The optimized diagnostic model has an overall sensitivity of 94.8% and a specificity of 95.0% for detecting early-stage CRC. Taken together, ColoC-mSTEM, based on a single methylation-specific site, is a promising diagnostic approach for the early detection of CRC which is perfectly suitable for the screening needs of CRC in primary healthcare institutions.