Search

Abstract The aim of this study was to explore the related factors linked to the development and infectivity of tuberculosis. This was achieved by comparing the clinical characteristics of patients with pulmonary tuberculosis (TB) who tested positive in smear Mycobacterium tuberculosis tests with this who tested negative in smear mycobacterium tests but positive in sputum Gene Xpert tests. We gathered clinical data of 1612 recently hospitalized patients diagnosed with pulmonary tuberculosis who tested positive either in sputum Gene-Xpert test or sputum smear Mycobacterium tuberculosis tests. The data was collected from January 1, 2018 to August 5, 2023, at Sichuan Provincial People's Hospital. We conducted separately analyzes and comparisons of the clinical characteristics between the two groups of patients, aiming to discussed the related factors influencing the development and infectivity of tuberculosis. In comparison to the GeneXpert positive group, the sputum smear positive group exhibited a higher proportion of elderly patients (aged 75–89) and individuals classified as underweight (BMI

IntroductionStress dynamic computed tomography myocardial perfusion imaging (CT-MPI) is an accurate quantitative method for diagnosing myocardial ischemia in coronary artery disease (CAD). However, its clinical application has been limited, partly due to the varied cutoff values for absolute myocardial blood flow (MBFa) and the uncertain value of the relative myocardial blood flow ratio (MBF-ratio). This study aimed to compare the diagnostic efficacy of and investigate the optimal cutoff values for MBFa and the MBF-ratio in CT-MPI for diagnosing myocardial ischemia in patients with hemodynamically significant CAD.MethodsPatients with suspected or known hemodynamically significant CAD who underwent CT-MPI + CT angiography and invasive coronary angiography (ICA)/fractional flow reserve (FFR) between October 2020 and December 2023 were retrospectively evaluated. ICA ≥80% or FFR ≤0.8 were set as the diagnostic standards for functional ischemia. The patients and vessels were categorized into ischemic and non-ischemic groups, and differences in MBFa and the MBF-ratio were compared between the groups. The area under the curve (AUC) and optimal cutoff values were calculated. Diagnostic efficacy parameters, such as sensitivity, specificity, and accuracy, were also compared. In addition, a consistency test was performed.ResultsA total of 46 patients (mean age: 65.37 ± 8.25 years; 120 vessels) were evaluated. Hemodynamically significant stenosis was detected in 30/46 patients (48%) and 81/120 vessels (67.5%). The MBFa and MBF-ratio values were significantly lower in the ischemic than in the non-ischemic group; in the per-vessel analysis, the MBFa values were 73 vs. 128 (P

Aster L. is an economically and phylogenetically important genus in the tribe Astereae. Here, the complete plastomes of the eight Aster species were assembled and characterized using next-generation sequencing datasets. The results indicated the complete plastomes of Aster had a quadripartite structure. These genomes were 152,045–152,729 bp in length and contained 132–133 genes, including 87 protein-coding genes, 37–38 tRNA genes, and eight rRNA genes. Expansion or contraction of inverted repeat regions and forward, palindromic, complement, and reverse repeats were detected in the eight Aster species. Additionally, our analyses showed the richest type of simple sequence repeats was A/T mononucleotides, and 14 highly variable regions were discovered by analyzing the border regions, sequence divergence, and hotspots. Phylogenetic analyses indicated that 27 species in Astereae were clustered into six clades, i.e., A to D, North American, and outgroup clades, and supported that the genera Heteropappus, Kalimeris, and Heteroplexis are nested within Aster. The results indicated the clades B to D might be considered as genera. Divergence time estimate showed the clades A, B, C, and D diverged at 23.15 Mya, 15.13 Mya, 24.29 Mya, and 21.66 Mya, respectively. These results shed light on the phylogenetic relationships of Aster and provided new information on species identification of Aster and its related genera.

Aster yaoshanensis sp. nov., a new species of the family Asteraceae is here described and illustrated. The species is presently known only from rock crevices of mountain valleys in Dayaoshan National Nature Reserve, Guangxi autonomous region, China. Phylogenetic analyses based on ITS sequences and complete plastome data have shown that this new species is a member of genus Aster with high support. Morphologically, it mostly resembles A. jishouensis, but it can be easily distinguished from the latter by bract indumentum (glabrous except margin ciliate vs. villous especially on veins abaxially, glabrous adaxially) and color (green vs. purple), shorter corolla (3.2–3.5 mm vs. 4.5–5.3 mm), bract stalk (obvious, ca.1.2 mm vs. sessile), and different distribution (Guangxi vs. Hunan). The detailed description, distribution map, and photos are provided. This study further elucidates the species identification, phylogeny and characteristic evolution of Aster.

Abstract Objective Preoperative prediction of meningioma venous sinus invasion would facilitate the selection of surgical approaches and predicting the prognosis. To predict venous sinus invasion in meningiomas, we used radiomic signatures to construct a model based on preoperative contrast‐enhanced T1‐weighted (T1C) and T2‐weighted (T2) magnetic resonance imaging. Methods In total, 599 patients with pathologically confirmed meningioma were retrospectively enrolled. For each patient enrolled in this study, 1595 radiomic signatures were extracted from T1C and T2 image sequences. Pearson correlation analysis and recursive feature elimination were used to select the most relevant signatures extracted from different image sequences, and logistic regression algorithms were used to build a radiomic model for risk prediction of meningioma sinus invasion. Furthermore, a nomogram was built by incorporating clinical characteristics and radiomic signatures, and a decision curve analysis was used to evaluate the clinical utility of the nomogram. Results Twenty radiomic signatures that were significantly related to venous sinus invasion were screened from 3190 radiomic signatures. Venous sinus invasion was associated with tumor position, and the clinicoradiomic model that incorporated the above characteristics (20 radiomic signatures and tumor position) had the best discriminating ability. The areas under the curve for the training and validation cohorts were 0.857 (95% confidence interval [CI], 0.824–0.890) and 0.824 (95% CI, 0.752–0.8976), respectively. Interpretation The clinicoradiomic model had good predictive performance for venous sinus invasion in meningioma, which can aid in devising surgical strategies and predicting prognosis.

Abstract Background This study aimed to investigate the relationship between active pulmonary tuberculosis (TB) and type 2 diabetes mellitus (T2DM) by analysing the clinical features and computed tomography (CT) findings of patients with active pulmonary TB and comorbid T2DM (TB-DM) in the LiangShan Yi regions. Methods We collected data from 154 hospitalised patients with TB-DM initially confirmed at an infectious disease hospital in the Liangshan Yi Autonomous Prefecture between 1 and 2019, and 31 December 2021. These were matched by sex and age ± 3 years to 145 hospitalised patients with initially confirmed pulmonary TB without comorbid T2DM (TB-NDM) over the same period. The clinical characteristics of the two groups were analysed separately. Three group-blinded radiologists independently analysed the CT findings and classified them into mild-to-moderate and severe groups. Severe chest CT lesion refers to a lesion that is less diffused or moderately dense and either exceeds the total volume of one lung, a high-density fused lesion greater than one-third of the volume of one lung, or a cavitary lesion with a maximum diameter ≥ 4 cm. Results No significant differences were observed in the presentation of clinical features. Regarding the severity of chest CT manifestation, patients with TB-DM had significantly more severe TB than those with TB-NDM (89.61% vs. 68.97%, P

BackgroundTo explore the value of dual-energy spectral CT in distinguishing solitary pulmonary tuberculosis (SP-TB) from solitary lung adenocarcinoma (S-LUAD).MethodsA total of 246 patients confirmed SP-TB (n = 86) or S-LUAD (n = 160) were retrospectively included. Spectral CT parameters include CT40keV value, CT70keV value, iodine concentration (IC), water concentration (WC), effective atomic number (Zeff), and spectral curve slope (λ70keV). Data were measured during the arterial phase (AP) and venous phase (VP). Chi-square test was used to compare categorical variables, Wilcoxon rank-sum test was used to compare continuous variables, and a two-sample t-test was used to compare spectral CT parameters. ROC curves were used to calculate diagnostic efficiency.ResultsThere were significant differences in spectral CT quantitative parameters (including CT40keV value [all P< 0.001] , CT70keV value [all P< 0.001], λ70keV [P< 0.001, and P = 0.027], Zeff [P =0.015, and P = 0.001], and IC [P =0.002, and P = 0.028]) between the two groups during the AP and VP. However, WC (P = 0.930, and P = 0.823) was not statistically different between the two groups. The ROC curve analysis showed that the AUC in the AP and VP was 90.9% (95% CI, 0.873-0.945) and 83.4% (95% CI, 0.780-0.887), respectively. The highest diagnostic performance (AUC, 97.6%; 95% CI, 0.961-0.991) was achieved when all spectral CT parameters were combined with clinical variables.ConclusionDual-energy spectral CT has a significant value in distinguishing SP-TB from S-LUAD.

BackgroundThis study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas.MethodsIn total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.ResultsSeveral classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.ConclusionOur ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.

PurposeThe aim was to investigate the association between microvascular invasion (MVI) and the peritumoral imaging features of gadolinium ethoxybenzyl DTPA-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) in hepatocellular carcinoma (HCC).MethodsUp until Feb 24, 2022, the PubMed, Embase, and Cochrane Library databases were carefully searched for relevant material. The software packages utilized for this meta-analysis were Review Manager 5.4.1, Meta-DiSc 1.4, and Stata16.0. Summary results are presented as sensitivity (SEN), specificity (SPE), diagnostic odds ratios (DORs), area under the receiver operating characteristic curve (AUC), and 95% confidence interval (CI). The sources of heterogeneity were investigated using subgroup analysis.ResultsAn aggregate of nineteen articles were remembered for this meta-analysis: peritumoral enhancement on the arterial phase (AP) was described in 13 of these studies and peritumoral hypointensity on the hepatobiliary phase (HBP) in all 19 studies. The SEN, SPE, DOR, and AUC of the 13 investigations on peritumoral enhancement on AP were 0.59 (95% CI, 0.41−0.58), 0.80 (95% CI, 0.75−0.85), 4 (95% CI, 3−6), and 0.73 (95% CI, 0.69−0.77), respectively. The SEN, SPE, DOR, and AUC of 19 studies on peritumoral hypointensity on HBP were 0.55 (95% CI, 0.45−0.64), 0.87 (95% CI, 0.81−0.91), 8 (95% CI, 5−12), and 0.80 (95% CI, 0.76−0.83), respectively. The subgroup analysis of two imaging features identified ten and seven potential factors for heterogeneity, respectively.ConclusionThe results of peritumoral enhancement on the AP and peritumoral hypointensity on HBP showed high SPE but low SEN. This indicates that the peritumoral imaging features on Gd-EOB-DTPA-enhanced MRI can be used as a noninvasive, excluded diagnosis for predicting hepatic MVI in HCC preoperatively. Moreover, the results of this analysis should be updated when additional data become available. Additionally, in the future, how to improve its SEN will be a new research direction.

This paper investigates the wideband channel characteristics obtained in a classroom and a hall indoor scenarios at 2-4, 9-11 and 27-29GHz. A virtual uniform circular array (UCA) based channel sounding system was utilized to capture wideband spatial channel characteristics. The propagation parameters of multipath components (MPCs) were estimated by using a high resolution parameter estimation (HRPE) algorithm. The estimated MPCs are further grouped into clusters via a novel clustering identification algorithm based on the KPowerMeans algorithm. The comparison of the composite and cluster-level characteristics at multiple frequency bands in both scenarios is investigated. Moreover, the impact of the indoor room environment, i.e. dimension and furniture, on the propagation channel is also analyzed. The statistics of channel parameters at multiple frequency bands extracted constitute a stochastic clustered spatial channel model.

Introduction and aim: It is difficult to distinguish between non-functioning pituitary macroadenomas (NFPMAs) and sellar meningiomas because of their overlapping imaging manifestations on routine MRI, especially in cases of meningiomas growing into the saddle. Here, we aimed to differentiate between these two tu mors using apparent diffusion coefficient (ADC) values and MRI characteristics. Methods: A total of 60 NFPMA and 52 sellar meningioma cases confirmed by the pathological analysis were retrospectively reviewed. All patients were examined via routine MRI and diffusion-weighted imaging (DWI) before undergoi ng surgery. The clinical characteristics, MRI characteristics, and max ADC (ADCmax), average ADC (ADCmean), and minimum ADC (ADCmin) values were compared between the two tumors via Chi-square test and two sample t-tests. Receiver ope rating characteristic (ROC) curve and binary logistic regression analyses were conducted to determine the discrimination ability. Results: The ADCmax, ADCmean, and ADCmin values were significantly higher in NFPMAs compared to sellar meningiomas (P < 0.001 for all). Among ADC values, ADCmax demonstrated good performance with an AUC of 0.896 (95% CI, 0.823–0.969) and accuracy of 88.7%. A cut-off value of 0.97 × 10 −3 mm2/s was used for ADCmax for differentiation between tumors. A combination of ADCmax values a nd clinicoradiological features showed the best discrimination ability for differential diagnosis between the two tumors, with an AUC of 0.981 (95% CI, 0.958–1.000) and accuracy of 96.9%. Conclusion: A combination of ADCmax and clinicoradiological features demonstrates good discrimination ability and high accuracy for differentiatio n between NFPMAs and sellar meningiomas, and is a potential quantitative tool to aid in the selection of surgical techniques.

BackgroundThis study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features.MethodsIn total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model.ResultsThere were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively].ConclusionOur combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma.

Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes.

ObjectiveTo investigate the spectral and perfusion computed tomography (CT) findings of peripheral lung cancer (PLC) and focal organizing pneumonia (FOP) and to compare the accuracy of spectral and perfusion CT imaging in distinguishing PLC from FOP.Materials and MethodsPatients who were suspected of having lung tumor and underwent “one-stop” chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. Patients who were suspected of having lung tumor and underwent “one-stop” chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. A total of 57 and 35 patients with PLC and FOP were included, respectively. Spectral parameters (CT40keV, CT70keV, CT100keV, iodine concentration [IC], water concentration [WC], and effective atomic number [Zeff]) of the lesions in the arterial and venous phases were measured in both groups. The slope of the spectral curve (K70keV) was calculated. The perfusion parameters, including blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface (PS), were measured simultaneously in both groups. The differences in the spectral and perfusion parameters between the groups were examined. Receiver operating characteristic (ROC) curves were generated to calculate and compare the area under the curve (AUC), sensitivity, specificity, and accuracy of both sets of parameters in both groups.ResultsThe patients’ demographic and clinical characteristics were similar in both groups (P > 0.05). In the arterial and venous phases, the values of spectral parameters (CT40keV, CT70keV, spectral curve K70keV, IC, and Zeff) were greater in the FOP group than in the PLC group (P < 0.05). In contrast, the values of the perfusion parameters (BV, BF, MTT, and PS) were smaller in the FOP group than in the PLC group (P < 0.05). The AUC of the combination of the spectral parameters was larger than that of the perfusion parameters. For the former imaging method, the AUC, sensitivity, and specificity were 0.89 (95% confidence interval [CI]: 0.82–0.96), 0.86, and 0.83, respectively. For the latter imaging method, the AUC, sensitivity, and specificity were 0.80 (95% CI: 0.70–0.90), 0.71, and 0.83, respectively. There was no significant difference in AUC between the two imaging methods (P > 0.05).ConclusionSpectral and perfusion CT both has the capability to differentiate PLC and FOP. However, compared to perfusion CT imaging, spectral CT imaging has higher diagnostic efficiency in distinguishing them.

ObjectiveThis study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC).Material and MethodsWe retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram.ResultsThe logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility.ConclusionsThis study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.

BackgroundThis study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT.MethodsIn total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis.ResultsThirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively.ConclusionsThe proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy.

Tyrosine kinase inhibitors (TKIs) provide clinical benefits to the lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, non-invasively determine EGFR mutation status in patients before targeted therapy remains a challenge. This study aimed to develop and validate a nomogram for preoperative prediction of EGFR mutation status in patients with lung adenocarcinoma. The medical records of 403 patients with lung adenocarcinoma confirmed by histology from January 2016 to June 2020 were retrospectively collected. We combined CT features and clinical risk factors and used them to build a prediction nomogram. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. The nomogram was further validated in an independent external cohort. Finally, a nomogram that contained CT features and clinical risk factors, which could conveniently and non-invasively predict EGFR mutation status in patients with lung adenocarcinoma before surgery.

Background: Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features. Methods: In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Findings: Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831–0•887) and 0•819 (95% CI, 0•775–0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively. Interpretation: Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas. Funding: This work was supported by the National Natural Science Foundation of China (81772006, 81922040); the Youth Innovation Promotion Association CAS (grant numbers 2019136); special fund project for doctoral training program of Lanzhou University Second Hospital (grant numbers YJS-BD-33).

The increasing requirement for the mobile data traffic accelerates the research of millimeterwave (mm-wave) for future wireless systems. Accurate characterization of the mm-wave propagation channel is fundamental and essential for the system design and performance evaluation. In this paper, we conducted measurement campaigns in various indoor scenarios, including classroom, office, and hall scenarios, at the frequency bands of 27-29 GHz. The spatial channel characteristics were recorded by using a large-scale uniform circular array. A high-resolution parameter estimation algorithm was applied to estimate the mm-wave spherical propagation parameters, i.e., the azimuth angle, elevation angle, delay, source distance, and complex amplitude of multipath components. With the same measurement system, the channel parameters including decay factor, delay spread, angular spread, and line of sight power ratio are investigated thoroughly in individual indoor scenarios and compared in different indoor scenarios. Furthermore, the impact of the furniture richness level and indoor geometry on the propagation parameters are also investigated.

Background: This study aimed to investigate the diagnostic value of machinelearning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas. Methods: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination. Results: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1. Conclusion: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool. [ABSTRACT FROM AUTHOR]

The fabrication of conducting polymer nanowires on thermally oxidized Si surfaces by use of DNA templates is described. The conductivity of the nanowires is very sensitive to the proton doping-undoping process, suggesting that the nanowires hold great promise for sensitive chemical sensor applications.

The internal ultrafine structure of human hair was explored with atomic force microscopy (AFM). Cross sections of hair were prepared by a proprietary technique that provided a smooth surface for effective imaging in contact-mode AFM. Investigations of virgin hair revealed structural details of cortical and cuticle cells consistent with previous transmission electron microscopy (TEM) studies, in addition to the identification of a boundary region surrounding macrofibrils of the cortex. The effects of bleaching and solvent extraction on the internal structure of hair were also investigated. In the cuticle cell, bleaching causes the most damage to the endocuticle and cell membrane complex, evident by erosion of these components. Similarly, bleaching results in crevices, cracks, and asperities in the cortex of hair. In addition, the cortical cell membrane complex appears compromised along with either lipid or protein structures at the outer boundaries of macrofibrils. In delipidated hair, most structural components of the fi ber appear intact with the exception of an overall swollen nature of the various morphological components. [ABSTRACT FROM AUTHOR]

Here, we report a visible-light-induced deboronative alkynylation reaction, which is redox-neutral and works with primary, secondary and tertiary alkyl trifluoroborates or boronic acids to generate aryl, alkyl and silyl substituted alkynes. This reaction is highly chemoselective and performs well on substrates containing alkenes, alkynes, aldehydes, ketones, esters, nitriles, azides, aryl halides, alkyl halides, alcohols, and indoles, with no detectable occurrence of side reactions. The mechanism of this novel C(sp3)-C(sp) bond coupling reaction was investigated by luminescence quenching, radical trapping, on-off light, and 13C-isotopic-labeling experiments. This reaction can be performed in neutral aqueous conditions, and it is compatible with amino acids, nucleosides, oligosaccharides, nucleic acids, proteins, and cell lysates. [ABSTRACT FROM AUTHOR]

The repair of cutaneous wounds in the adult body involves a complex series of spatially and temporally organized processes to prevent infection and restore homeostasis. Three characteristic phases of wound repair (inflammation, proliferation including re-epithelialization and remodelling) overlap in time and space. We have utilized a human skin wound-healing model to correlate changes in genotype and phenotype with infrared (IR) and confocal Raman spectroscopic images during the re-epithelialization of excisional wounds. The experimental protocols validated as IR images clearly delineate the keratin-rich migrating epithelial tongue from the collagen-rich wound bed. Multivariate statistical analysis of IR datasets acquired 6 days post-wounding reveal subtle spectral differences that map to distinct spatial distributions, which are correlated with immunofluorescent staining patterns of different keratin types. Images computed within collagen-rich regions expose complementary spatial patterns and identify elastin in the wound bed. The temporal sequence of events is explored through a comparison of gene array analysis with confocal Raman microscopy. Our approach demonstrates the feasibility of acquiring detailed molecular structure information from the various proteins and their subclasses involved in the wound-healing process. [ABSTRACT FROM AUTHOR]

The widespread adoption of transdermal drug delivery has been limited by the barrier properties of the outermost layer of the epidermis, the stratum corneum (SC). A variety of approaches have been developed to overcome the barrier, including the use of a prodrug form of an active therapeutic agent to enhance transdermal delivery. Once in the epidermis, the pro-molecule is converted to the active drug by endogenous enzymes or simple chemical hydrolysis. The prodrug selected for the current studies, 1-ethyloxycarbonyl-5-fluorouracil, is known to enhance transdermal delivery of 5-fluorouracil, an important systemic antitumor drug. Using confocal Raman microscopy on pigskin biopsies treated with prodrug, we are able to image the spatial distribution of both prodrug and drug in the SC and viable epidermis, thereby providing information about permeation and metabolism. This approach may readily be extended to a variety of dermatological processes.Journal of Investigative Dermatology (2007) 127, 1205–1209. doi:10.1038/sj.jid.5700690; published online 11 January 2007 [ABSTRACT FROM AUTHOR]

Vibrational microscopy and imaging offer several advantages for a variety of dermatological applications, ranging from studies of isolated single cells (corneocytes) to characterization of endogenous components in intact tissue. Two applications are described to illustrate the power of these techniques for skin research. First, the feasibility of tracking structural alterations in the components of individual corneocytes is demonstrated. Two solvents, DMSO and chloroform/methanol, commonly used in dermatological research, are shown to induce large reversible alterations (α-helix to β-sheet) in the secondary structure of keratin in isolated corneocytes. Second, factor analysis of image planes acquired with confocal Raman microscopy to a depth of 70 μm in intact pigskin, demonstrates the delineation of specific skin regions. Two particular components that are difficult to identify by other means were observed in the epidermis. One small region was formed from a conformationally ordered lipid phase containing cholesterol. In addition, the presence of nucleated cells in the tissue (most likely keratinocytes) was revealed by the spectral signatures of the phosphodiester and cytosine moieties of cellular DNA. [ABSTRACT FROM AUTHOR]

The outermost region of the epidermis, the stratum corneum (SC), provides an essential barrier to water loss and protects against exogenous substances. The functional integrity of the SC depends on a complex maturation and exfoliation process, which is often perturbed in skin diseases. The maturation of corneocytes isolated from different depths in healthy human SC was investigated using infrared (IR) spectroscopic imaging and Raman microscopy. Both IR and Raman spectral quality of individual corneocytes was high and revealed depth-dependent variations in molecular composition. Spectral changes were identified as arising from alterations in the concentration of the major constituents of natural moisturizing factor (NMF), important in maintaining SC hydration. A significant decrease in the concentration of NMF was observed for corneocytes isolated from superficial compared to deeper SC layers (layer 3 vs. layer 11, respectively). An IR parameter that measures the relative NMF concentration in corneocytes is introduced. The potential role of vibrational imaging to evaluate corneocyte composition and molecular structure in the treatment of NMF-related diseases is discussed.Journal of Investigative Dermatology (2006) 126, 1088–1094. doi:10.1038/sj.jid.5700225; published online 2 March 2006 [ABSTRACT FROM AUTHOR]