Template-guided Silicon Micromotor Assembly for Enhanced Cell Manipulation.

Light-driven micro/nanorobots (LMNRs) are tiny, untethered machines with great potential in fields like precision medicine, nano manufacturing, and various other domains. However, their practicality hinges on developing light-manipulation strategies that combine versatile functionalities, flexible design options, and precise controllability. Our study introduces an innovative approach to construct micro/nanorobots (MNRs) by utilizing micro/nanomotors as fundamental building blocks. Inspired by silicon Metal-Insulator-Semiconductor solar cell principles, we design a new type of optomagnetic hybrid micromotors (OHMs). These OHMs have been skillfully optimized with integrated magnetic constituent, resulting in efficient light propulsion, precise magnetic navigation, and the potential for controlled assembly. One of the key features of the OHMs is their ability to exhibit diverse motion modes influenced by fracture surfaces and interactions with the environment, streamlining cargo conveyance along "micro expressway" - the predesigned microchannels. Further enhancing their versatility, a template-guided assembly strategy facilitates the assembly of these micromotors into functional microrobots, encompassing various configurations such as "V-shaped", "N-shaped", and 3D structured microrobots. The heightened capabilities of these microrobots, underscore the innovative potential inherent in hybrid micromotor design and assembly, which provides a foundational platform for the realization of multi-component microrobots. Our work moves a step toward forthcoming microrobotic entities boasting advanced functionalities.

Development and validation of a deep learning signature for predicting lymphovascular invasion and survival outcomes in clinical stage IA lung adenocarcinoma: A multicenter retrospective cohort study.

PURPOSE: The presence of lymphovascular invasion (LVI) influences the management and outcomes of patients with clinical stage IA lung adenocarcinoma. The objective was the development of a deep learning (DL) signature for the prediction of LVI and stratification of prognosis. METHODS: A total of 2077 patients from three centers were retrospectively enrolled and divided into a training set (n = 1515), an internal validation set (n = 381), and an external set (n = 181). A -three-dimensional residual neural network was used to extract the DL signature and three models, namely, the clinical, DL, and combined models, were developed. Diagnostic efficiency was assessed by ROC curves and AUC values. Kaplan-Meier curves and Cox proportional hazards regression analyses were conducted to evaluate links between various factors and disease-free survival. RESULTS: The DL model could effectively predict LVI, shown by AUC values of 0.72 (95 %CI: 0.68-0.76) and 0.63 (0.54-0.73) in the internal and external validation sets, respectively. The incorporation of DL signature and clinical-radiological factors increased the AUC to 0.74 (0.71-0.78) and 0.77 (0.70-0.84) in comparison with the DL and clinical models (AUC of 0.71 [0.68-0.75], 0.71 [0.61-0.81]) in the internal and external validation sets, respectively. Pathologic LVI, LVI predicted by both DL and combined models were associated with unfavorable prognosis (all p < 0.05). CONCLUSION: The effectiveness of the DL signature in the diagnosis of LVI and prognosis prediction in patients with clinical stage IA lung adenocarcinoma was demonstrated. These findings suggest the potential of the model in clinical decision-making.

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma.

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.

Carbon dots in breadcrumbs: Effect of frying on them and interaction with human serum albumin.

This research confirmed the existence of carbon dots (CDs) in breadcrumbs before frying, and CDs could be significantly affected by frying. The content of CDs increased from 0.013 ± 0.002% to 1.029 ± 0.002%, and the fluorescence quantum yield increased from 1.82 ± 0.01% to 3.16 ± 0.002% after frying at 180℃ for 5 min. The size reduced from 3.32 ± 0.71 nm to 2.67 ± 0.48 nm, and the content of N increased from 1.58% to 2.53%. In addition, the interaction of the CDs and human serum albumin (HSA) through electrostatic and hydrophobic induces the increase of α-helix structure and the change of the amino acid microenvironment of HSA. CDs corona, which may have physiological significance, was found through the transmission electron microscope.

Sulfhydryl-functionalized carbon dots as effective probes for fluorescence enhancement detection of patulin.

In this study, sulfydryl-functionalized nitrogen-doped carbon dots (SH-NCDs) was synthesized by amide reaction of hydrothermally synthesized carbon dots with l-cysteine and used to detect patulin selectively. The SH-NCDs exhibited excitation wavelength-independent fluorescence in the range 300-360 nm. The modified sulfhydryl group (-SH) on the surface of NCDs served as a specific recognition site to capture patulin. The addition reaction between patulin and the -SH on the SH-NCDs surface resulted in enhanced fluorescence. SH-NCDs was used as a fluorescent probe for label-free detection of patulin, showing excellent sensitivity in the linear range of 0.1-400 ng mL-1, with detection limits as low as 0.053 ng mL-1. The fluorescent probe has specific selectivity for patulin. The recoveries of patulin in apple juice and grape juice were 88.9 %-99.2 % and 92.5 %-101.8 %, respectively. These results showed that the sensor designed in this experiment selectively detected the target patulin from complex food systems.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

PURPOSE: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). METHODS: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. RESULTS: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228-0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074-0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. CONCLUSIONS: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.

Polyethylenimine-functionalized nitrogen and sulfur co-doped carbon dots as effective fluorescent probes for detection of Hg2+ ions.

Surface modification of nitrogen and sulfur co-doped carbon quantum dots (N, S-CDs) were performed using cysteine and polyethylenimine as raw materials. The prepared N, S-CDs exhibited excitation-independent in the range of 300-380 nm. Furthermore, mercury(II) ions (Hg2+) can effectively quench the fluorescence intensity of the N, S-CDs. Based on this, we developed a fluorescence sensor with high sensitivity and selectivity to detect Hg2+. Under optimized conditions, the sensor showed good linearity in the range of 0-500 nM, and the limit of detection is 9.2 nM. Further, the sensor showed high sensitivity to Hg2+ in lake water and rice samples. The recovery of the Hg2+ in lake water and rice samples ranged between 98.2 % and 109.5 % with a relative standard deviation below 5.8 %. With outstanding sensitivity and selectivity, the fluorescence sensor provides a promising platform for monitoring Hg2+ in real samples.

Preparation and characterization of chitosan-based antimicrobial films containing encapsulated lemon essential oil by ionic gelation and cranberry juice.

Research about biodegradable antimicrobial films continues to receive a lot of attention due to the plastic pollution crisis and the need for environment-friendly and safe food products. In this study, we developed chitosan-based antimicrobial films using a combination of encapsulated lemon essential oil (LEO) by ionic gelation and cranberry juice and evaluated the performance of the films. Our results indicated that the incorporation of LEO microspheres and cranberry juice into the chitosan films improved the UV barrier and thermal properties as well as antioxidant activity of the films. The increase in antioxidants was consistent with the chemical components in LEO and cranberry juice as determined by GC-MS; some of which possess antioxidant properties. Furthermore, following antimicrobial activity test, considerable inhibition halo of 11 and 20 mm were observed respectively against fungi Candida albicans and Penicillium roqueforti, particularly in presence of the film containing both LEO microspheres and cranberry juice.

Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.

OBJECTIVES: To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs). METHODS: This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]0~+3), 6 mm crossing tumor border (PTV-3~+3), and 6 mm external to the tumor border (PTV0~+6). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. RESULTS: A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV-3~+3 (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV0~+3 (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV0~+6 (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05. CONCLUSIONS: A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS: • Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]-3~+3) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs). MATERIALS AND METHODS: In total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network. Deep learning signature (DLS) was developed via the least absolute shrinkage and selection operator (LASSO). New DLN integrating clinical variables, subjective CT findings, and DLS was constructed. The diagnostic efficiency and discriminative capability were analyzed using the receiver operating characteristic method and decision curve analysis (DCA). RESULTS: In total, 18 deep learning features with non-zero coefficients were enrolled to develop the DLS, which was statistically different between the MIA and IAC groups. Independent predictors of DLS and lobulated sharp were used to build the DLN. The areas under the curves of the DLN were 0.889 (95% confidence interval (CI): 0.824-0.936), 0.915 (95% CI: 0.846-0.959), and 0.914 (95% CI: 0.848-0.958) in the training, internal validation, and external validation cohorts, respectively. After stratification analysis and DCA, the DLN showed potential generalization ability. CONCLUSION: The DLN incorporating the DLS and subjective CT findings have strong potential to distinguish MIA from IAC in patients with SSPNs, and will facilitate the suitable treatment method selection for the management of SSPNs.

Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system.

BACKGROUND: Artificial intelligence (AI) products have been widely used for the clinical detection of primary lung tumors. However, their performance and accuracy in risk prediction for metastases or benign lesions remain underexplored. This study evaluated the accuracy of an AI-driven commercial computer-aided detection (CAD) product (InferRead CT Lung Research, ICLR) in malignancy risk prediction using a real-world database. METHODS: This retrospective study assessed 486 consecutive resected lung lesions, including 320 adenocarcinomas, 40 other malignancies, 55 metastases, and 71 benign lesions, from September 2015 to November 2018. The malignancy risk probability of each lesion was obtained using the ICLR software based on a 3D convolutional neural network (CNN) with DenseNet architecture as a backbone (without clinical data). Two resident doctors independently graded each lesion using patient clinical history. One doctor (R1) has 3 years of chest radiology experience, and the other doctor (R2) has 3 years of general radiology experience. Cochran's Q test was used to assess the performances of the AI compared to the radiologists. RESULTS: The accuracy of malignancy-risk prediction using the ICLR for adenocarcinomas, other malignancies, metastases, and benign lesions was 93.4% (299/320), 95.0% (38/40), 50.9% (28/55), and 40.8% (29/71), respectively. The accuracy was significantly higher in adenocarcinomas and other malignancies compared to metastases and benign lesions (all P<0.05). The overall accuracy of risk prediction for R1 was 93.6% (455/486) and 87.4% for R2 (425/486), both of which were higher than the 81.1% accuracy obtained with the ICLR (394/486) (R1 vs. ICLR: P<0.001; R2 vs. ICLR: P=0.001), especially in assessing the risk of metastases (P<0.05). R1 performed better than R2 at risk prediction (P=0.001). CONCLUSIONS: The accuracy of the ICLR for risk prediction is very high for primary lung cancers but poor for metastases and benign lesions.

Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging.

PURPOSE: To evaluate whether the extent of COVID-19 pneumonia on CT scans using quantitative CT imaging obtained early in the illness can predict its future severity. METHODS: We conducted a retrospective single-center study on confirmed COVID-19 patients between January 18, 2020 and March 5, 2020. A quantitative AI algorithm was used to evaluate each patient's CT scan to determine the proportion of the lungs with pneumonia (VR) and the rate of change (RAR) in VR from scan to scan. Patients were classified as being in the severe or non-severe group based on their final symptoms. Penalized B-splines regression modeling was used to examine the relationship between mean VR and days from onset of symptoms in the two groups, with 95% and 99% confidence intervals. RESULTS: Median VR max was 18.6% (IQR 9.1-32.7%) in 21 patients in the severe group, significantly higher (P < 0.0001) than in the 53 patients in non-severe group (1.8% (IQR 0.4-5.7%)). RAR was increasing with a median RAR of 2.1% (IQR 0.4-5.5%) in severe and 0.4% (IQR 0.1-0.9%) in non-severe group, which was significantly different (P < 0.0001). Penalized B-spline analyses showed positive relationships between VR and days from onset of symptom. The 95% confidence limits of the predicted means for the two groups diverged 5 days after the onset of initial symptoms with a threshold of 11.9%. CONCLUSION: Five days after the initial onset of symptoms, CT could predict the patients who later developed severe symptoms with 95% confidence.

Edible Bird's Nest Ameliorates Dextran Sulfate Sodium-Induced Ulcerative Colitis in C57BL/6J Mice by Restoring the Th17/Treg Cell Balance.

Ulcerative colitis (UC) is a type of inflammatory bowel disease (IBD) with a complex aetiology that commonly recurs. Most drugs for UC treatment interfere with metabolism and immune responses, often causing some serious adverse reactions. Therefore, the development of alternative treatments, including nutritional supplements and probiotics, have been one of the main areas of current research due to fewer side effect. As both a Chinese medicine and a food, edible bird's nest (EBN) has high nutritional value. Modern pharmacological studies have shown that it has anti-inflammatory, immunoregulatory, antiviral and neuroprotective effects. In this study, UC was induced with dextran sulfate sodium (DSS) to investigate the protective effect of EBN on colitis mice and the related mechanism. The body weight, faecal morphology and faecal occult blood results of mice were recorded every day from the beginning of the modelling period. After the end of the experiment, the length of the colon was measured, and the colon was collected for histopathological detection, inflammatory factor detection and immunohistochemical detection. Mouse spleens were dissected for flow cytometry. The results showed that in mice with colitis, EBN improved symptoms of colitis, reduced colonic injury, and inhibited the increases in the levels of the pro-inflammatory cytokines IL-1ß and TNF-α. The T helper 17 (Th17)/regulatory T (Treg) cell balance was restored by decreasing the expression of IL-17A and IL-6 in intestinal tissues, increasing the expression of TGF-ß, and decreasing the number of Th17 cells in each EBN dose group. These findings suggest that EBN has a protective effect on DSS-mediated colitis in mice, mainly by restoring the Th17/Treg cell balance.

Genetic Identification of Edible Bird's Nest in Thailand Based on ARMS-PCR.

Edible bird's nest (EBN) is a popular delicacy in the Asian Pacific region originating from Indonesia, Malaysia, Thailand and Vietnam, which consist of various potential medicine value in Traditional Chinese Medicine (TCM). Thailand is one of the main exporters of EBN. However, the genetic information of EBN, a key part of molecular biology, has yet to be reported in Thailand. It is necessary to explore the genetic information of EBN in Thailand based on a quick and simple method to help protect the rights and interests of consumers. This research aimed to systematically evaluate different methods of extracting EBN DNA to improve the efficiency of the analysis of cytochrome b (Cytb) and NADH dehydrogenase subunit 2 (ND2) gene sequences, the establishment of phylogenetic trees, and the genetic information of EBN in Thailand. Additionally, we aimed to develop a quick and simple method for identifying EBN from different species based on the genetic information and amplification-refractory mutation system PCR (ARMS-PCR). By comparing the four methods [cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), kit and guanidinium isothiocyanate methods] for EBN extraction, we found that the guanidinium isothiocyanate method was the optimal extraction method. Phylogenetic trees generated on the basis of Cytb and ND2 gene analyses showed that 26 samples of house EBN and 4 samples of cave EBN came from Aerodramus fuciphagus and Aerodramus maximus, respectively. In addition, to distinguish different samples from different species of Apodiformes, we designed 4 polymerase chain reaction (PCR) amplification primers based on the ND2 gene sequences of A. fuciphagus and A. maximus. The ARMS-PCR results showed band lengths for A. fuciphagus EBN of 533, 402, and 201 bp, while those for A. maximus EBN were 463, 317, and 201 bp. Collectively, the results showed that ARMS-PCR is a fast and simple method for the genetic identification of EBN based on designing specific original identification primers.

Molecular analysis of edible bird's nest and rapid authentication of Aerodramus fuciphagus from its subspecies by PCR-RFLP based on the cytb gene.

Edible bird's nest (EBN), for its great nutritional value, is widely used around the world, especially in China and Singapore. EBNs of different origins and types may vary in price and quality. Nowadays, birds' nests are difficult to identify morphologically, except for some whole bird's nests of which origins can be roughly identified. In this study, forty-two samples were collected from different regions for sequencing analysis and phylogenetic classification to initially determine their origins. Two stable enzyme digestion sites were found in the analysis of restriction maps of the species. Then, a quick and specific PCR-RFLP method was established to identify the EBN samples' origins. The genetic identification results indicated that the forty-two samples were from five origins. With the Af/g-486bp-F/R primer and restriction enzyme Taq I, Aerodramus fuciphagus (A. fuciphagus) was efficiently differentiated from the other species. Furthermore, the cytb-592bp-F/R primer and the BamH I enzyme were found to be useful in distinguishing Aerodramus fuciphagus (A. fuciphagus) from its subspecies (Aerodramus germani, A. germani). The PCR-RFLP method provides a potential tool for the rapid discrimination of A. fuciphagus at the species and even the subspecies levels to ensure the quality of the EBN products.

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.

PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). MATERIALS AND METHODS: The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). RESULTS: Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs). METHODS: Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192). RESULTS: DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839-0.927), 0.879 (95% CI, 0.813-0.928), and 0.809 (95% CI, 0.746-0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability. CONCLUSIONS: The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs. KEY POINTS: • The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs. • The performance of the deep learning feature was superior to that of the radiomics feature. • The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.

Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule.

PURPOSE: To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs). METHOD: We retrospectively recruited 426 patients with SPSNs from two centers and assigned them to training (n = 123), internal validation (n = 121), and external validation cohorts (n = 182). A model of deep learning (DL) was built for tumor segmentation from routine computed tomography (CT) images and extraction of 3D radiomics features. We used the least absolute shrinkage and selection operator (LASSO) logistic regression to build a radiomics signature. A clinical model was developed with clinical factors, including age, gender, and CT-based subjective findings (eg, lesion size, lesion location, lesion margin, lobulated sharp, and spiculation sign). We constructed individualized radiomics nomograms incorporating the radiomics signature and clinical factors to validate the diagnostic ability. RESULTS: Three factors - radiomics signature, age, and spiculation sign - were found to be independent predictors and were used to build the radiomics nomogram, which showed better diagnostic accuracy than any single model (all net reclassification improvement p < 0.05). The area under curve yielded was 0.9660 (95% confidence interval [CI], 0.9390-0.9931), 0.9342 (95% CI, 0.8944-0.9739), and 0.9064 (95% CI, 0.8639-0.9490) for the training, internal validation, and external validation cohorts, respectively. Decision curve analysis (DCA) and stratification analysis showed the nomogram has potential for generalizability. CONCLUSION: The radiomics nomogram we developed can preoperatively distinguish between LAC and TBG in patient with a SPSN.

Behavior of imidazolinone herbicide enantiomers in earthworm-soil microcosms: Degradation and bioaccumulation.

Imidazolinone herbicides are a group of chiral herbicides that are widely used to control weeds in crops. Despite their wide use, few studies on the behavior of enantiomers in terrestrial systems have been reported. In this study, the bioaccumulation of imazamox, imazapic, and imazethapyr enantiomers in earthworm and their degradation in soils were assessed using earthworm-soil microcosms. The bioaccumulation of the three herbicides in earthworm was not significantly enantioselective. Imazamox and imazethapyr did not significant stereoselective degradation in soil (p > 0.05), while the enantioselectivity of the degradation of imazapic was significant (p < 0.05). Furthermore, biota to soil accumulation factor (BSAF) values were also calculated for three herbicides. Relationships between BSAF values and organic matter content of soil and log KOW of herbicides were investigated. The BSAFs values were negatively correlated with the log KOW of herbicides, and were positively correlated with organic matter content of soil in earthworm-soil microcosms. These relationships indicated that chemical hydrophobicity (Kow) and organic matter content of soil were good predictors to estimate the bioavailability of imidazolinone herbicides to earthworm.

Effect of initial support particle size of MnO x /TiO2 catalysts in the selective catalytic reduction of NO with NH3.

A series of manganese-based catalysts supported by 5-10 nm, 10-25 nm, 40 nm and 60 nm anatase TiO2 particles was synthesized via an impregnation method to investigate the effect of the initial support particle size on the selective catalytic reduction (SCR) of NO with NH3. All catalysts were characterized by transmission electron microscopy (TEM), N2 physisorption/desorption, X-ray diffraction (XRD), temperature programmed techniques, X-ray photoelectron spectroscopy (XPS) and in situ diffuse reflectance infrared transform spectroscopy (DRIFTS). TEM results indicated that the particle sizes of the MnO x /TiO2 catalysts were similar after the calcination process, although the initial TiO2 support particle sizes were different. However, the initial TiO2 support particle sizes were found to have a significant influence on the SCR catalytic performance. XPS and NH3-TPD results of the MnO x /TiO2 catalysts illustrated that the surface Mn4+/Mn molar ratio and acid amount could be influenced by the initial TiO2 support particle sizes. The order of surface Mn4+/Mn molar ratio and acid amount over the MnO x /TiO2 catalysts was as follows: MnO x /TiO2(10-25) > MnO x /TiO2(40) > MnO x /TiO2(60) > MnO x /TiO2(5-10), which agreed well with the order of SCR performance. In situ DRIFTS results revealed that the NH3-SCR reactions over MnO x /TiO2 at low temperature occurred via a Langmuir-Hinshelwood mechanism. More importantly, it was found that the bridge and bidentate nitrates were the main active substances for the low-temperature SCR reaction, and bridge nitrate adsorbed on Mn4+ showed superior SCR activity among all the adsorbed NO x species. The variation of the initial TiO2 support particle size over MnO x /TiO2 could change the surface Mn4+/Mn molar ratio, which could influence the adsorption of NO x species, thus bringing about the diversity of the SCR catalytic performance.