2D Catalytic Nanozyme Enables Cascade Enzyodynamic Effect-Boosted and Ca2+ Overload-Induced Synergistic Ferroptosis/Apoptosis in Tumor.

The introduction of glucose oxidase, exhibiting characteristics of glucose consumption and H2O2 production, represents an emerging antineoplastic therapeutic approach that disrupts nutrient supply and promotes efficient generation of reactive oxygen species (ROS). However, the instability of natural enzymes and their low therapeutic efficacy significantly impede their broader application. In this context, 2D Ca2Mn8O16 nanosheets (CMO NSs) designed and engineered to serve as a high-performance nanozyme, enhancing the enzyodynamic effect for a ferroptosis-apoptosis synergistic tumor therapy, are presented. In addition to mimicking activities of glutathione peroxidase, catalase, oxidase, and peroxidase, the engineered CMO NSs exhibit glucose oxidase-mimicking activities. This feature contributes to their antitumor performance through cascade catalytic reactions, involving the disruption of glucose supply, self-supply of H2O2, and subsequent efficient ROS generation. The exogenous Ca2+ released from CMO NSs, along with the endogenous Ca2+ enrichment induced by ROS from the peroxidase- and oxidase-mimicking activities of CMO NSs, collectively mediate Ca2+ overload, leading to apoptosis. Importantly, the ferroptosis process is triggered synchronously through ROS output and glutathione consumption. The application of exogenous ultrasound stimulation further enhances the efficiency of ferroptosis-apoptosis synergistic tumor treatment. This work underscores the crucial role of enzyodynamic performance in ferroptosis-apoptosis synergistic therapy against tumors.

Bionanoengineered 2D monoelemental selenene for piezothrombolysis.

Thrombosis-related diseases represent the leading causes of disability or death worldwide. However, conventional thrombolytic therapies are subjected to narrow therapeutic window, short circulation half-life and bleeding. Herein, we rationally design and develop a safe and efficient nonpharmaceutical thrombolysis strategy based on a specific piezocatalytic effect arising from platelet membrane (PM)-conjugated two-dimensional (2D) piezoelectric selenene, Se-PM nanosheets (NSs). The 2D selenene is fabricated from nonlayered bulk selenium powder by a facile liquid-phase exfoliation method, and the PM conjugation confers selenene with the distinct thrombus-homing feature. Under ultrasonic activation, the piezoelectric characteristic of selenene triggers electrons and holes separation, resulting in generation of reactive oxygen species (ROS) by reacting with surrounding H2O and O2 in the thrombosis microenvironment for thrombolysis. Both systematic in vitro and in vivo assessments demonstrate that the biocompatible Se-PM NSs efficiently degrade erythrocytes, fibrin and artificial blood clots under ultrasound irradiation. Compared to the clinical thrombolytic drug urokinase plasminogen activator, the engineered Se-PM NSs possess excellent thrombolytic efficacy by single treatment in the tail thrombosis animal model without bleeding risk. The engineered Se-PM nanoplatform marks an exciting jumping-off point for research into the application of piezocatalysis in clinical treatment of thrombosis.

In vivo structural and functional imaging of human nailbed microvasculature using photoacoustic microscopy.

Monitoring microvascular structure and function is of great significance for the diagnosis of many diseases. In this study, we demonstrate the feasibility of OR-PAM to nailbed microcirculation detection as a new, to the best of our knowledge, application scenario in humans. We propose a dual-wavelength optical-resolution photoacoustic microscopy (OR-PAM) with improved local-flexible coupling to image human nailbed microvasculature. Microchip lasers with 532 nm wavelength are employed as the pump sources. The 558 nm laser is generated from the 532 nm laser through the stimulated Raman scattering effect. The flowing water, circulated by a peristaltic pump, maintains the acoustic coupling between the ultrasonic transducer and the sample. These designs improve the sensitivity, practicality, and stability of the OR-PAM system for human in vivo experiments. The imaging of the mouse ear demonstrates the ability of our system to acquire structural and functional information. Then, the system is applied to image human nailbed microvasculature. The imaging results reveal that the superficial capillaries are arranged in a straight sagittal pattern, approximately parallel to the long axis of the finger. The arterial and venular limbs are distinguished according to their oxygen saturation differences. Additionally, the images successfully discover the capillary loops with single or multiple twists, the oxygen release at the end of the capillary loop, and the changes when the nailbed is abnormal.

Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE: This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS: In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier. RESULTS: Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only. CONCLUSIONS: The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.

Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography.

The most common BRAF mutation is thymine (T) to adenine (A) missense mutation in nucleotide 1796 (T1796A, V600E). The BRAFV600E gene encodes a protein-dependent kinase (PDK), which is a key component of the mitogen-activated protein kinase pathway and essential for controlling cell proliferation, differentiation, and death. The BRAFV600E mutation causes PDK to be activated improperly and continuously, resulting in abnormal proliferation and differentiation in PTC. Based on elastography ultrasound (US) radiomic features, this study seeks to create and validate six distinct machine learning algorithms to predict BRAFV6OOE mutation in PTC patients prior to surgery. This study employed routine US strain elastography image data from 138 PTC patients. The patients were separated into two groups: those who did not have the BRAFV600E mutation (n = 75) and those who did have the mutation (n = 63). The patients were randomly assigned to one of two data sets: training (70%), or validation (30%). From strain elastography US images, a total of 479 radiomic features were retrieved. Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified tenfold cross-validation were used to decrease the features. Based on selected radiomic features, six machine learning algorithms including support vector machine with the linear kernel (SVM_L), support vector machine with radial basis function kernel (SVM_RBF), logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were compared to predict the possibility of BRAFV600E. The accuracy (ACC), the area under the curve (AUC), sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), and calibration curves of the machine learning algorithms were used to evaluate their performance. ① The machine learning algorithms' diagnostic performance depended on 27 radiomic features. ② AUCs for NB, KNN, LDA, LR, SVM_L, and SVM_RBF were 0.80 (95% confidence interval [CI]: 0.65-0.91), 0.87 (95% CI 0.73-0.95), 0.91(95% CI 0.79-0.98), 0.92 (95% CI 0.80-0.98), 0.93 (95% CI 0.80-0.98), and 0.98 (95% CI 0.88-1.00), respectively. ③ There was a significant difference in echogenicity,vertical and horizontal diameter ratios, and elasticity between PTC patients with BRAFV600E and PTC patients without BRAFV600E. Machine learning algorithms based on US elastography radiomic features are capable of predicting the likelihood of BRAFV600E in PTC patients, which can assist physicians in identifying the risk of BRAFV600E in PTC patients. Among the six machine learning algorithms, the support vector machine with radial basis function (SVM_RBF) achieved the best ACC (0.93), AUC (0.98), SEN (0.95), SPEC (0.90), PPV (0.91), and NPV (0.95).

Lung cancer with breast metastasis: a case report and review of the literature.

In this article, we present a rare case of breast metastasis of lung cancer. Chest computed tomography (CT) for a woman in her early 50s indicated right lung malignancy, multiple bone metastases, and an irregular mass in her right breast. Further inquiry into the case history revealed that the patient had been aware of the breast mass for 3 years, without respiratory symptoms. Biopsy of the breast mass suggested estrogen receptor (ER) (+), progesterone receptor (PR) (-), and human epidermal growth factor receptor 2 (HER2) (+ +) breast cancer. The patient was initially diagnosed with breast cancer with lung and bone metastasis. However, comprehensive breast cancer treatment was ineffective, and thyroid transcription factor-1 (TTF-1), napsin A, and cytokeratin 7 (CK7) were evaluated to better understand the origin of the cancer. To the best of our knowledge, this patient had the longest reported disease course from presentation with a breast lump as the first symptom to the final diagnosis of breast metastasis of lung cancer. To provide a better reference for differential diagnosis of ambiguous tumors, we also performed a systematic literature review.

Radiomics features from whole thyroid gland tissue for prediction of cervical lymph node metastasis in the patients with papillary thyroid carcinoma.

OBJECTIVE: We aimed to develop a clinical-radiomics nomogram that could predict the cervical lymph node metastasis (CLNM) of patients with papillary thyroid carcinoma (PTC) using clinical characteristics as well as radiomics features of dual energy computed tomography (DECT). METHOD: Patients from our hospital with suspected PTC who underwent DECT for preoperative assessment between January 2021 and February 2022 were retrospectively recruited. Clinical characteristics were obtained from the medical record system. Clinical characteristics and rad-scores were examined by univariate and multivariate logistic regression. All features were incorporated into the LASSO regression model, with penalty parameter tuning performed using tenfold cross-validation, to screen risk factors for CLNM. An easily accessible radiomics nomogram was constructed. Receiver Operating Characteristic (ROC) curve together with Area Under the Curve (AUC) analysis was conducted to evaluate the discrimination performance of the model. Calibration curves were employed to assess the calibration performance of the clinical-radiomics nomogram, followed by goodness-of-fit testing. Decision curve analysis (DCA) was performed to determine the clinical utility of the established models by estimating net benefits at varying threshold probabilities for training and testing groups. RESULTS: A total of 461 patients were retrospectively recruited. The rates of CLNM were 49.3% (70 /142) in the training cohort and 53.3% (32/60) in the testing cohort. Out of the 960 extracted radiomics features, 192 were significantly different in positive and negative groups (p < 0.05). On the basis of the training cohort, 12 stable features with nonzero coefficients were selected using LASSO regression. LASSO regression identified 7 risk factors for CLNM, including male gender, maximum tumor size > 10 mm, multifocality, CT-reported central CLN status, US-reported central CLN status, rad-score, and TGAb. A nomogram was developed using these factors to predict the risk of CLNM. The AUC values in each cohort were 0.850 and 0.797, respectively. The calibration curve together with the Hosmer-Lemeshow test for the nomogram indicated good agreement between predicted and pathological CLN statuses in the training and testing cohorts. Results of DCA proved that the nomogram offers a superior net benefit for predicting CLNM compared to the "treat all or none" strategy across the majority of risk thresholds. CONCLUSION: A nomogram comprising the clinical characteristics as well as radiomics features of DECT and US was constructed for the prediction of CLNM for patients with PTC, which in determining whether lateral compartment neck dissection is warranted.

High-resolution in vivo imaging of human nailbed microvasculature by using photoacoustic microscopy.

Microcirculation imaging has significantly clinical value in early diagnosis and curative effect judgment of various diseases. The most superficial layer of the nailbed is rich in capillaries, which is suitable as a window on the microcirculation. However, few techniques can noninvasively observe the blood supply distribution of the nailbed, especially for high-resolution imaging of capillaries. In this study, we adapted an optical-resolution photoacoustic microscopy (OR-PAM) to image the nailbed microvasculature. The imaging sensitivity was significantly improved by hydration pretreatment of the nail. In vitro phantom experiments demonstrate that the sensitivity was improved about 3.5 times after hydration. In vivo imaging experiments of the nailbed microvasculature were conducted to further examine the enhanced sensitivity and practicability of OR-PAM. Moreover, the quantitative analysis of capillary loops showed that OR-PAM can extract the detection indicators including vascular morphology, diameter, and length, which provides a basis for clinical microcirculation detection using OR-PAM.

Development of a multiplex panel with 31 multi-allelic InDels for forensic DNA typing.

Insertion/Deletion (InDel) polymorphic genetic markers are abundant in human genomes. Diallelic InDel markers have been widely studied for forensic purposes, yet the low polymorphic information content limits their application and current InDel panels remain to be improved. In this study, multi-allelic InDels located out of low complexity sequence regions were selected in the datasets from East Asian populations, and a multiplex amplification system containing 31 multi-allelic InDel markers and the Amelogenin marker (FA-HID32plex) was constructed and optimized. The preliminary study on sensitivity, species specificity, inhibitor tolerance, mixture resolution, and the detection of degraded samples demonstrates that the FA-HID32plex is highly sensitive, specific, and robust for traces and degraded samples. The combined power of discrimination (CPD) of 31 multi-allelic InDel markers was 0.999 999 999 999 999 999 85, and the cumulative probability of exclusion (CPE) was 0.999 920 in a Chinese Han population, which indicates a high discrimination power. Altogether, the FA-HID32plex panel could provide reliable supplements or stand-alone information in individual identification and paternity testing, especially for challenging samples.

Improving photoacoustic imaging in low signal-to-noise ratio by using spatial and polarity coherence.

To suppress the noise and sidelobe of photoacoustic images, a method is proposed combined with spatial coherence and polarity coherence. In this method, PA signals are delayed, multiplied, then performed polarity coherence, and finally summed. The polarity of delayed-and-multiplied signals rather than the amplitude is considered in polarity coherence operation. The polarity coherence factor is calculated based on the standard deviation of the polarity. Then, the factor as weights is applied to the coherent sum output after spatial autocorrelation to finally obtain the image. The simulated and experimental results prove that the noise level can be effectively suppressed due to its relatively low polarity coherence factor. Compared with the delay-and-sum method, the quantitative results in simulations show that the image contrast and full-width at half-maximum of the proposed method increase by about 227.0 % and 56.5 % when the signal-to-noise ratio of the raw signal is 0 dB, respectively. Besides achieving a better image contrast, this method obtains improvements in sidelobe attenuation and has a narrow main lobe.

Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model.

We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 PTC patients. According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group. All patients were randomly divided into a training cohort (n = 143) and a validation cohort (n = 62). A total of 1046 USR features of lesion areas were extracted. The features were reduced using Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified 15-fold cross-validation. Several machine learning classifiers were employed to build a Clinical model based on clinical variables, a USR model based solely on extracted USR features, and a Clinical-USR model based on the combination of clinical variables and USR features. The Clinical-USR model could discriminate between PTC patients with CLNM and PTC patients without CLNM in the training (AUC, 0.78) and validation cohorts (AUC, 0.71). When compared to the Clinical model, the USR model had higher AUCs in the validation (0.74 vs. 0.63) cohorts. The Clinical-USR model demonstrated higher AUC values in the validation cohort (0.71 vs. 0.63) compared to the Clinical model. The newly developed Clinical-USR model is feasible for predicting CLNM in patients with PTC.

Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS.

The aim of the present study was to develop a radiomics nomogram to assess whether thyroid nodules (TNs) < 1 cm are benign or malignant. From March 2021 to March 2022, 156 patients were admitted to the Affiliated Hospital of Nantong University, and from September 2017 to March 2022, 116 patients were retrospectively collected from the Jiangsu Provincial Hospital of Integrated Traditional Chinese and Western Medicine. These patients were divided into a training group and an external test group. A radiomics nomogram was established using multivariate logistics regression analysis using the radiomics score and clinical data, including the ultrasound feature scoring terms from the thyroid imaging reporting and data system (TI-RADS). The radiomics nomogram incorporated the correlated predictors, and compared with the clinical model (training set AUC: 0.795; test set AUC: 0.783) and radiomics model (training set AUC: 0.774; test set AUC: 0.740), had better discrimination performance and correction effects in both the training set (AUC: 0.866) and the test set (AUC: 0.866). Both the decision curve analysis and clinical impact curve showed that the nomogram had a high clinical application value. The nomogram constructed based on TI-RADS and radiomics features had good results in predicting and distinguishing benign and malignant TNs < 1 cm.

Investigation of Linear Amplification Using Abasic Site-Containing Primers Coupled to Routine STR Typing for LT-DNA Analysis.

Obtaining a full short tandem repeat (STR) profile from a low template DNA (LT-DNA) still presents a challenge for conventional methods due to significant stochastic effects and polymerase slippage. A novel amplification method with a lower cost and higher accuracy is required to improve the DNA amount. Previous studies suggested that DNA polymerases without bypass activity could not perform processive DNA synthesis beyond abasic sites in vitro and our results showed a lack of bypass activity for Phusion, Pfu and KAPA DNA polymerases in this study. Based on this feature, we developed a novel linear amplification method, termed Linear Aamplification for double-stranded DNA using primers with abasic sites near 3' end (abLAFD), to limit the replication error. The amplification efficiency was evaluated by qPCR analysis with a result of approximately a 130-fold increase in target DNA. In a LT-DNA analysis, the abLAFD method can be employed as a pre-PCR. Similar to nested PCRs, primer sets used for the abLAFD method were designed as external primers suitable for commercial multiplex STR amplification assays. The practical performance of the abLAFD method was evaluated by coupling it to a routine PP21 STR analysis using 50 pg and 25 pg DNA. Compared to reference profiles, all abLAFD profiles showed significantly recovered alleles, increased average peak height and heterozygote balance with a comparable stutter ratio. Altogether, our results support the theory that the abLAFD method is a promising strategy coupled to STR typing for forensic LT-DNA analysis.

A comparison of DP-TOF Mass Spectroscopy (MS) and Next Generation Sequencing (NGS) methods for detecting molecular mutations in thyroid nodules fine needle aspiration biopsies.

Mutations in the B-Raf proto-oncogene, serine/threonine kinase (BRAF), have been linked to a variety of solid tumors such as papillary thyroid carcinoma. The purpose of this study was to compare the DP-TOF, a DNA mass spectroscopy (MS) platform, and next-generation sequencing (NGS) methods for detecting multiple-gene mutations (including BRAFV600E) in thyroid nodule fine-needle aspiration fluid. In this study, we collected samples from 93 patients who had previously undergone NGS detection and had sufficient DNA samples remaining. The MS method was used to detect multiple-gene mutations (including BRAFV600E) in DNA remaining samples. NGS detection method was used as the standard. The MS method's overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 95.8%, 100%, 100%, and 88%, respectively in BRAFV600E gene mutation detection. With a kappa-value of 0.92 (95%CI 0.82-0.99), the level of agreement between these methods was incredibly high. Furthermore, when compared to NGS in multiple-gene detection, the MS method demonstrated higher sensitivity and specificity, 82.9% and 100%, respectively. In addition, we collected the postoperative pathological findings of 50 patients. When the postoperative pathological findings were used as the standard, the MS method demonstrated higher sensitivity and specificity, at 80% and 80%, respectively. Our findings show that the MS method can be used as an inexpensive, accurate, and dependable initial screening method to detect genes mutations and as an adjunct to clinical diagnosis.

Inference of population structure and admixture proportion from Y chromosomal data of Chinese population.

In the past two decades, Y chromosome data has been generated for human population genetic studies. These Y chromosome datasets were produced with various testing methods and markers, thus difficult to combine them for a comprehensive analysis. In this study, we combine four human Y chromosomal datasets of Han, Tibetan, Hui, and Li ethnic groups. The dataset contains 27 microsatellites and 137 single nucleotide polymorphisms these populations share in common. We assembled a single dataset containing 2439 individuals from 25 nationwide populations in China. A systematic analysis of genetic distance and clustering was performed. To determine the gene flow of the studied population with worldwide populations, we modeled the ancestry informative markers. The reference panel was regarded as a mixture of South Asian (SAS), East Asian (EAS), European (EUR), African (AFR), and American (AMR) populations from 1000 Genomes data of Y chromosome using nonlinear data-fitting. We then calculated the admixture proportion of these four studied populations with 26 worldwide populations. The results showed that the Han and Hui have great genetic affinity, and Hui is the most admixed ethnic group, with 61.53% EAS, 34.65% SAS, 1.91% AFR, 1.56% AMR, and 0.04% EUR ancestry component (the AMR is highly admixed and thus should be ignored). All the other three ethnic groups contained more than 97% EAS ancestry component. The Li is the least admixed population in this study. The combined dataset in this study is the largest of this kind reported to date and proposes reference population data for use in future paternal genetic studies and forensic genealogical identification.

Differentiation Between Granulomatous Lobular Mastitis and Breast Cancer Using Quantitative Parameters on Contrast-Enhanced Ultrasound.

Objective: To investigate the Contrast-enhanced ultrasound (CEUS) imaging characteristics of granulomatous lobular mastitis (GLM) and the value of differentiating GLM from breast cancer. Materials and methods: The study included 30 women with GLM (mean age 36.7 ± 5 years [SD]) and 58 women with breast cancer (mean age 48. ± 8 years [SD]) who were scheduled for ultrasound-guided tissue biopsy. All patients were evaluated with conventional US and CEUS prior to the biopsy. In both groups, the parameters of the quantitative and qualitative analysis of the CEUS were recorded and compared. The receiver-operating-characteristics curves (ROC) were created. Sensitivity, specificity, cut-off, and area under the curve (AUC) values were calculated. Results: TTP values in GLM were statistically higher than in breast cancer (mean, 27.63 ± 7.29 vs. 20.10 ± 6.11), but WIS values were lower (mean, 0.16 ± 0.05 vs. 0.28 ± 0.17). Rich vascularity was discovered in 54.45% of breast cancer patients, but only 30.00% of GLM patients had rich vascularity. The AUC for the ROC test was 0.791 and 0.807, respectively. The optimal cut-off value for TTP was 24.5s, and the WIS cut-off value was 0.185dB/s, yielding 73.33% sensitivity, 84.48% specificity, and 86.21% sensitivity, 70% specificity respectively in the diagnosis of GLM. The lesion scores reduced from 4 to 3 with the addition of CEUS for the patients with GLM. However, the scores did not change for the patients with breast cancer. Conclusion: CEUS could help distinguish GLM from breast cancer by detecting higher TTP and WIS values, potentially influencing clinical decision-making for additional biopsies.

Differential diagnosis of diffuse sclerotic thyroid papillary carcinoma and Hashimoto's thyroiditis using fine-needle aspiration cytology, BRAFV600E , and ultrasound elastography.

BACKGROUND: The diffuse sclerosing variant of papillary thyroid carcinoma (DSV-PTC) has ultrasound findings that are similar to Hashimoto's thyroiditis (HT), resulting in under-diagnosis. DSV-PTC combined with HT is also common, so early and accurate diagnosis of DSV-PTC using a variety of diagnostic techniques, including FNAC, BRAFV600E mutation detection, and ultrasound elastography, is critical. OBJECTIVE: To assess the diagnostic value of fine-needle aspiration cytology (FNAC) and BRAFV600E detection in combination with ultrasound elastography in the diagnosis of DSV-PTC. METHODS: We performed a retrospective analysis of 40 patients with pathologically confirmed DSV-PTC and 43 patients with HT admitted to our hospital's ultrasound department between January 2015 and December 2020. Preoperative FNAC, BRAFV600E mutation detection, and ultrasound elastography imaging were all performed on all patients. For a definitive diagnosis, the results of these tests were compared to postoperative pathological findings. The diagnostic value of FNAC, BRAFV600E mutation detection, ultrasound elasticity imaging, and their combination for DSV-PTC diagnosis was assessed. RESULTS: The mean elastic strain rate ratio (E1/E2) of the 40 DSV-PTC cases was 5.75 ± 2.14, while that of the 43 HT cases was 2.81 ± 1.20. The receiver operating characteristic (ROC) curve was generated using the average value of E2/E1. The area under the ROC curve was 0.910, and the optimal E2/E1 cut-off value was 4.500. When FNAC, BRAFV600E mutation detection, and ultrasound elasticity imaging detection were combined, the diagnostic sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of DSV-PTC diagnosis were 92.5%, 95.3%, 93.2%, 94.9%, and 94.0%, respectively, which were significantly higher than the single technique (p < 0.05). CONCLUSIONS: The use of FNAC, BRAFV600E mutation detection, and ultrasound elastography in combination is more helpful in establishing an accurate diagnosis of DSV-PTC than using a single diagnostic technique alone.

Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAFV600E Mutations in Papillary Thyroid Carcinoma.

BRAFV600E is the most common mutated gene in thyroid cancer and is most closely related to papillary thyroid carcinoma(PTC). We investigated the value of elasticity and grayscale ultrasonography for predicting BRAFV600E mutations in PTC. Methods: 138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined. Patients were divided into BRAFV600E mutation-free group (n=75) and BRAFV600E mutation group (n=63). Patients were randomly divided into training (n=96) and test (n=42) groups. A total of 479 radiomic features were extracted from the grayscale and elasticity ultra-sonograms. Regression analysis was done to select the features that provided the most information. Then, 10-fold cross-validation was used to compare the performance of different classification algorithms. Logistic regression was used to predict BRAFV600E mutations. Results: Eight radiomics features were extracted from the grayscale ultrasonogram, and five radiomics features were extracted from the elasticity ultrasonogram. Three models were developed using these radiomic features. The models were derived from elasticity ultrasound, grayscale ultrasound, and a combination of grayscale and elasticity ultrasound, with areas under the curve (AUC) 0.952 [95% confidence interval (CI), 0.914-0.990], AUC 0.792 [95% CI, 0.703-0.882], and AUC 0.985 [95% CI, 0.965-1.000] in the training dataset, AUC 0.931 [95% CI, 0.841-1.000], AUC 0. 725 [95% CI, 0.569-0.880], and AUC 0.938 [95% CI, 0.851-1.000] in the test dataset, respectively. Conclusion: The radiomic model based on grayscale and elasticity ultrasound had a good predictive value for BRAFV600E gene mutations in patients with PTC.

Application of machine learning for ancestry inference using multi-InDel markers.

Ancestry inference through population stratification plays an important role in forensic applications. Specifically, ancestry information inferred from forensic DNA evidence can provide vital clues for criminal investigations. Current advances in ancestry inference mostly focus on ancestry informative markers. Hereinto, multi-InDel was proposed as one of the compound markers performing well in complex ancestral classification in the subpopulation of Asia. However, research on analytical methods necessary to make reliable predictions is lacking. The newly proposed compound markers could be assessed with alternative methods. In this study, promising discriminant methods were explored using multi-InDel markers for forensic ancestry inference. As a prerequisite, the adopted multi-InDel markers were assessed by classical methods for population genetics, such as FST analysis, MDS and STRUCTURE. In addition, dimensionality reduction methods and serial reduction strategies were applied for data visualization. Subsequently, machine learning methods, including logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), were evaluated by diverse approaches. As the result of multifarious analyses through comparisons and estimations, XGBoost with one-hot encoding was shown to be more effective in population stratification and ancestry inference for challenging cases with admixed populations.

Assessing Diagnostic Value of Combining Ultrasound and MRI in Extrathyroidal Extension of Papillary Thyroid Carcinoma.

Purpose: To explore the separate diagnostic value of preoperative ultrasound (US), magnetic resonance imaging (MRI), and the combination of US and MRI in extrathyroidal extension (ETE) of papillary thyroid carcinoma (PTC). Materials and Methods: This retrospective study was approved by the Affiliated People's Hospital of Jiangsu University review board. A total of 158 PTC patients with ETE received preoperative US and MRI examination and underwent surgery between May 2014 and December 2018 in Affiliated People's Hospital of Jiangsu University. For each case, the US and MRI features of ETE were retrospectively and independently investigated by two radiologists. The clinical assessment for each case was implemented, respectively, using US imaging only, MRI only, and a combination of both modalities at three different time points with one-month intervals. Results: The diagnostic accuracies of US, MRI, and the combined set for T3 (minimal ETE) were 91.7% (88/96), 74.0% (71/96), and 97.9% (94/96), respectively, indicating a significantly different performance (P < 0.001). The diagnostic accuracies for T4 (extensive ETE) were 62.9% (39/62), 87.1% (54/62), and 93.5% (58/62), respectively. The difference between the three methods for T4 was statistically significant (P = 0.000). The diagnostic accuracies for overall ETE were 80.4% (127/158), 79.1% (125/158), and 96.2% (152/158), respectively. The difference between the three methods for ETE was statistically significant (P = 0.001). Conclusion: This study suggests that ETE can be predicted most accurately by the combination of preoperative US and MRI.