MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions.

Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug-target interaction predictions.

Effects of ultrashort wave diathermy on skin wounds in rabbit ears.

PURPOSE: Ultrashort wave diathermy (USWD) is commonly used in diseases associated with osteoarticular and soft tissue injuries. However, while accelerating wound healing and preventing joint stiffness, there have been few reports on whether it leads to excessive hypertrophic scarring. The aim was to investigate the effects of different doses of USWD on hypertrophic scars. MATERIALS AND METHODS: A rabbit model of hypertrophic scars was used to determine which dose of USWD reduced scar hyperplasia. The scar thickness was calculated using Sirius red staining. All protein expression levels were determined by western blotting, including fibrosis, collagen deposition, and neoangiogenesis related proteins. Subsequently, flow cytometry and ELISAs were used to determine the proportions of macrophage and inflammatory levels. RESULTS: The wounds with USWD in histopathology showed the dermis was more markedly thickened in the 120 mA group, whereas the wounds with the 60 mA were less raised, comparing with the 0 mA; all detected protein levels were increased significantly, the 120 mA group comparing with the others, including heat shock, fibrosis, and neoangiogenesis, whereas the collagen deposition relative protein levels were decreased, the 60 mA group comparing with Sham group; Finally, in the proportion of macrophages and inflammatory levels the 120 mA group were the highest, and the group Sham was lower than group 60 mA. CONCLUSIONS: In hypertrophic scars, the 60 mA USWD could relieve scar formation and inflammatory reactions; however, higher doses could result in opposite consequences.

Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.

OBJECTIVES: To determine whether a CT-based machine learning (ML) can differentiate benign renal tumors from renal cell carcinomas (RCCs) and improve radiologists' diagnostic performance, and evaluate the impact of variable CT imaging phases, slices, tumor sizes, and region of interest (ROI) segmentation strategies. METHODS: Patients with pathologically proven RCCs and benign renal tumors from our institution between 2008 and 2020 were included as the training dataset for ML model development and internal validation (including 418 RCCs and 78 benign tumors), and patients from two independent institutions and a public database (TCIA) were included as the external dataset for individual testing (including 262 RCCs and 47 benign tumors). Features were extracted from three-phase CT images. CatBoost was used for feature selection and ML model establishment. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML model. RESULTS: The ML model based on 3D images performed better than that based on 2D images, with the highest AUC of 0.81 and accuracy (ACC) of 0.86. All three radiologists achieved better performance by referring to the classifier's decision, with accuracies increasing from 0.82 to 0.87, 0.82 to 0.88, and 0.76 to 0.87. The ML model achieved higher negative predictive values (NPV, 0.82-0.99), and the radiologists achieved higher positive predictive values (PPV, 0.91-0.95). CONCLUSIONS: A ML classifier based on whole-tumor three-phase CT images can be a useful and promising tool for differentiating RCCs from benign renal tumors. The ML model also perfectly complements radiologist interpretations. KEY POINTS: • A machine learning classifier based on CT images could be a reliable way to differentiate RCCs from benign renal tumors. • The machine learning model perfectly complemented the radiologists' interpretations. • Subtle variances in ROI delineation had little effect on the performance of the ML classifier.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Case series of ovarian neuroendocrine carcinoma: overview of clinicopathological features.

BACKGROUND: Ovarian neuroendocrine carcinoma (O-NEC) is a relatively uncommon neoplasm, and the current knowledge regarding its diagnosis and management is limited. In this series, our objective was to provide an overview of the clinicopathological characteristics of the disease by analyzing clinical case data to establish a theoretical foundation for the diagnosis and management of O-NEC. CASE PRESENTATION: We included three patients in the present case series, all of whom were diagnosed with primary O-NEC based on pathomorphological observation and immunohistochemistry. Patient 1 was a 62-year-old patient diagnosed with small cell carcinoma (SCC) of the pulmonary type. Post-surgery, the patient was diagnosed with stage II SCC of the ovary and underwent standardized chemotherapy; however, imaging examinations conducted at the 16-month follow-up revealed the existence of lymph node metastasis. Unfortunately, she passed away 21 months after the surgery. The other two patients were diagnosed with carcinoid tumors, one at age 39 and the other at age 71. Post-surgery, patient 2 was diagnosed with a carcinoid in the left ovary, whereas patient 3 was diagnosed with a carcinoid in her right ovary based on clinical evaluation. Neither of the cases received adjuvant therapy following surgery; however, they have both survived for 9 and 10 years, respectively, as of date. CONCLUSION: Primary O-NECs are rare and of diverse histological types, each of which has its own unique biological features and prognosis. SCC is a neoplasm characterized by high malignancy and a poor prognosis, whereas carcinoid tumors are of lesser malignancy and have a more favorable prognosis.

A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI.

BACKGROUND: Deep learning has been used to detect or characterize prostate cancer (PCa) on medical images. The present study was designed to develop an integrated transfer learning nomogram (TLN) for the prediction of PCa and benign conditions (BCs) on magnetic resonance imaging (MRI). METHODS: In this retrospective study, a total of 709 patients with pathologically confirmed PCa and BCs from two institutions were included and divided into training (n = 309), internal validation (n = 200), and external validation (n = 200) cohorts. A transfer learning signature (TLS) that was pretrained with the whole slide images of PCa and fine-tuned on prebiopsy MRI images was constructed. A TLN that integrated the TLS, the Prostate Imaging-Reporting and Data System (PI-RADS) score, and the clinical factor was developed by multivariate logistic regression. The performance of the TLS, clinical model (CM), and TLN were evaluated in the validation cohorts using the receiver operating characteristic (ROC) curve, the Delong test, the integrated discrimination improvement (IDI), and decision curve analysis. RESULTS: TLS, PI-RADS score, and age were selected for TLN construction. The TLN yielded areas under the curve of 0.9757 (95% CI, 0.9613-0.9902), 0.9255 (95% CI, 0.8873-0.9638), and 0.8766 (95% CI, 0.8267-0.9264) in the training, internal validation, and external validation cohorts, respectively, for the discrimination of PCa and BCs. The TLN outperformed the TLS and the CM in both the internal and external validation cohorts. The decision curve showed that the TLN added more net benefit than the CM. CONCLUSIONS: The proposed TLN has the potential to be used as a noninvasive tool for PCa and BCs differentiation.

Construction, Characterization, and Application of an Ammonium Transporter (AmtB) Deletion Mutant of the Nitrogen-Fixing Bacterium Kosakonia radicincitans GXGL-4A in Cucumis sativus L. Seedlings.

Nitrogen is an important factor affecting crop yield, but excessive use of chemical nitrogen fertilizer has caused decline in nitrogen utilization and soil and water pollution. Reducing the utilization of chemical nitrogen fertilizers by biological nitrogen fixation (BNF) is feasible for green production of crops. However, there are few reports on how to have more ammonium produced by nitrogen-fixing bacteria (NFB) flow outside the cell. In the present study, the amtB gene encoding an ammonium transporter (AmtB) in the genome of NFB strain Kosakonia radicincitans GXGL-4A was deleted and the △amtB mutant was characterized. The results showed that deletion of the amtB gene had no influence on the growth of bacterial cells. The extracellular ammonium nitrogen (NH4+) content of the △amtB mutant under nitrogen-free culture conditions was significantly higher than that of the wild-type strain GXGL-4A (WT-GXGL-4A), suggesting disruption of NH4+ transport. Meanwhile, the plant growth-promoting effect in cucumber seedlings was visualized after fertilization using cells of the △amtB mutant. NFB fertilization continuously increased the cucumber rhizosphere soil pH. The nitrate nitrogen (NO3-) content in soil in the △amtB treatment group was significantly higher than that in the WT-GXGL-4A treatment group in the short term but there was no difference in soil NH4+ contents between groups. Soil enzymatic activities varied during a 45-day assessment period, indicating that △amtB fertilization influenced soil nitrogen cycling in the cucumber rhizosphere. The results will provide a solid foundation for developing the NFB GXGL-4A into an efficient biofertilizer agent.

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer.

BACKGROUND: Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. PURPOSE: To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. MATERIAL AND METHODS: In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. RESULTS: Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. CONCLUSION: The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

Neutrophil to high-density lipoprotein cholesterol ratio predicts adverse cardiovascular outcomes in subjects with pre-diabetes: a large cohort study from China.

BACKGROUND: This study aimed to examine whether the neutrophil to high-density lipoprotein cholesterol ratio (NHR) can predict cardiovascular outcomes in normoglycemic individuals with elevated fasting glucose levels. METHODS: A total of 130,801 participants with normal blood glucose levels were enrolled in the Kailuan study. Participants were categorized according to NHR quartiles and further divided into normal glucose regulation (NGR) and pre-diabetes (pre-DM) subgroups. The follow-up endpoint was major adverse cardiovascular events (CVE), including stroke and myocardial infarction. RESULTS: Over a median of 12.53 (8.95-13.08) years of follow-up, subjects with NHR levels in the highest quartile experienced more CVE than those with NHR levels in the lowest quartile. Multivariate Cox analyses showed that continuous changes in NHR (hazard ratio, 1.21; 95% confidence interval [CI], 1.15-1.28) and the highest quartile of NHR (hazard ratio, 1.30; 95% CI, 1.21-1.39) were independent predictors of CVE (all P < 0.001). Furthermore, when participants were categorized by both NHR quartile and glucose metabolism status, the NHR level in the highest quartile plus pre-DM group was associated with a 1.60-fold (95% CI, 1.38-1.86; P < 0.001] higher risk of CVE than that in the lowest quartile plus normoglycemic group. Significantly, the addition of NHR only, presence of pre-DM only, or combination of NHR and pre-DM to the prediction algorithm, including traditional risk factors, improved the C-statistic by 0.19, 0.05, and 0.23 (all P < 0.001). CONCLUSIONS: Elevated NHR or fasting blood glucose level were independently associated with a higher risk of CVE among normoglycemic individuals. Moreover, pre-DM participants with high NHR levels tended to have worse prognosis, suggesting that NHR could provide greater risk stratification value than traditional risk factors for subjects with pre-DM.

Siderophore Synthesis Ability of the Nitrogen-Fixing Bacterium (NFB) GXGL-4A is Regulated at the Transcriptional Level by a Transcriptional Factor (trX) and an Aminomethyltransferase-Encoding Gene (amt).

Kosakonia radicincitans GXGL-4A, a gram-negative nitrogen-fixing (NF) bacterial strain is coated with a thick capsulatus on the surface of cell wall, which becomes a physical barrier for exogenous DNA to enter the cell, so the operation of genetic transformation is difficult. In this study, an optimized Tn5 transposon mutagenesis system was established by using a high osmotic HO-1 medium combined with the electroporation transformation. Eventually, a mutant library containing a total of 1633 Tn5 insertional mutants were established. Of these mutants, the mutants M81 and M107 were found to have an enhanced capability to synthesize siderophore through the CAS agar plate assay and the spectrophotometric determination. The bacterial cells of two mutants were applied in cucumber growth-promoting experiment. Cucumber seedlings treated with M81 and M107 cells had a significant increase in biomass including seedling height, seedling fresh weight, root fresh weight, and root length. The whole genome sequencing of the mutants M81 and M107 showed that the integration sites of Tn5 transposon element were located in MmyB-like helix-turn-helix transcription regulator (locus tag: A3780_19720, trX) and aminomethyltransferase-encoding genes (locus tag: A3780_01680, amt) in the genome of GXGL-4A, respectively. The ability of siderophore synthesis of the target mutants was improved by Tn5 insertion mutagenesis, and the mutants obtained showed a good plant growth-promoting effect when applied to the cucumber seedlings. The results suggest that the identified functional genes regulates the biosynthesis of siderophore in azotobacter GXGL-4A, and the specific mechanism needs to be further investigated.

MIP image derived from abbreviated breast MRI: potential to reduce unnecessary sub-nipple biopsies during nipple-sparing mastectomy for breast cancer.

OBJECTIVE: To determine the value of a maximum-intensity projection (MIP) image derived from abbreviated breast MRI for excluding occult nipple-areolar complex (NAC) involvement in patients with breast cancer. METHODS: This prospective study included breast cancer patients with clinically normal NACs between April 2016 and May 2019. Abbreviated breast MRI was performed, and an MIP image was generated for each patient. MIP images were examined for the following features: asymmetric nipple enhancement, tumor-nipple distance (TND), tumor diameter, lesion type, location, and multifocality. Independent predictive MIP features for occult NAC involvement were identified by univariable and multivariable logistic regression analyses. Models based on independent predictive MIP features were developed, and their diagnostic performances were evaluated using ROC analysis. The utility of an MIP image for excluding occult NAC involvement was assessed by considering NPVs across patient subgroups. RESULTS: Eight hundred forty-three patients (67 NAC-positive and 776 NAC-negative) were enrolled. On MIP images, asymmetric nipple enhancement (odds ratio, 6.098; p < 0.001) and TND (odds ratio, 0.564; p = 0.003) were independent predictors of occult NAC involvement. A parallel test model of "asymmetric nipple enhancement or TND ≤ 15 mm" yielded the highest AUC value (0.838) among prediction models. The NPV of MIP images for excluding occult NAC involvement was 99.5%, which was applicable across various patient subgroups. CONCLUSIONS: A single MIP image derived from abbreviated breast MRI has utility for excluding occult NAC involvement in breast cancer patients and reducing the number of unnecessary sub-nipple biopsies in nipple-sparing mastectomy. KEY POINTS: • On MIP images derived from abbreviated breast MRI, asymmetric nipple enhancement and tumor-nipple distance were independent predictors for occult nipple involvement in patients with breast cancer. • Negative findings on MIP image can help select patients at minimal risk of occult nipple involvement, for whom unnecessary intraoperative sub-nipple biopsies in nipple-sparing mastectomy can be omitted.

Polyketone metabolites isolated from Rhodiola tibetica endohytic fungus Alternaria sp. HJT-Y7 and their SARS-CoV-2 virus inhibitory activitives.

Six new polyketone metabolites, compounds (1-6) and seven known polyketone compounds (7-13) were isolated from Rhodiola tibetica endophytic fungus Alternaria sp. The structural elucidation of five new polyketone metabolites were elucidated on the basis of spectroscopic including 2D NMR and HRMS and spectrometric analysis. Inhibition rate evaluation revealed that compounds 1(EC50 = 0.02 mM), 3(EC50 = 0.3 mM), 6(EC50 = 0.07 mM), 8(EC50 = 0.1 mM) and 9(EC50 = 0.04 mM) had inhibitory effect on the SARS-CoV-2 virus.

A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer.

OBJECTIVE: This study aimed to preoperatively differentiate primary gastric lymphoma from Borrmann type IV gastric cancer by heterogeneity nomogram based on routine contrast-enhanced computed tomographic images. METHODS: We enrolled 189 patients from 2 hospitals (90 in the training cohort and 99 in the validation cohort). Subjective findings, including high-enhanced mucosal sign, high-enhanced serosa sign, nodular or an irregular outer layer of the gastric wall, and perigastric fat infiltration, were assessed to construct a subjective finding model. A deep learning model was developed to segment tumor areas, from which 1680 three-dimensional heterogeneity radiomic parameters, including first-order entropy, second-order entropy, and texture complexity, were extracted to build a heterogeneity signature by least absolute shrinkage and selection operator logistic regression. A nomogram that integrates heterogeneity signature and subjective findings was developed by multivariate logistic regression. The diagnostic performance of the nomogram was assessed by discrimination and clinical usefulness. RESULTS: High-enhanced serosa sign and nodular or an irregular outer layer of the gastric wall were identified as independent predictors for building the subjective finding model. High-enhanced serosa sign and heterogeneity signature were significant predictors for differentiating the 2 groups (all, P < 0.05). The area under the curve with heterogeneity nomogram was 0.932 (95% confidence interval, 0.863-0.973) in the validation cohort. Decision curve analysis and stratified analysis confirmed the clinical utility of the heterogeneity nomogram. CONCLUSIONS: The proposed heterogeneity radiomic nomogram on contrast-enhanced computed tomographic images may help differentiate primary gastric lymphoma from Borrmann type IV gastric cancer preoperatively.

Two isopentenyl resorcinols from Peperomia tetraphylla.

Two isopentenyl resorcinols, peperobtusin B and peperobtusin C, have been isolated from Peperomia tetraphylla. Their structures were determined on the basis of spectroscopic methods, especially 1H NMR, 13C NMR, 2D NMR, and HR-TOF-MS. Two compounds were evaluated for cytostatic activity against G2, A 549, Hela and HCT 116 cells, but cytostatic activity of both compounds is weak.

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.

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference.

BACKGROUND: Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper. METHODS: First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed. RESULTS: To verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively. CONCLUSIONS: The experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging.

Fully endoscopic microvascular decompression of the hemifacial spasm: our experience.

PURPOSE: Microvascular decompression (MVD) surgery has been accepted as a potentially curative method for hemifacial spasm (HFS). The primary cause of failure of MVD is incomplete decompression of the offending vessel due to inadequate visualization. This study is aimed at evaluating the benefit of endoscopic visualization and the value of fully endoscopic MVD. METHODS: From March 2016 to March 2018, 45 HFS patients underwent fully endoscopic MVD in our department. From opening the dura to preparing to close, the assistant held the endoscope and the surgeon operated. Abnormal muscle response (AMR) and brainstem auditory evoked potentials (BAEP) were monitored. For every patient, the offending vessel was transposed or interposed and achieved complete decompression. AMR was used to evaluate the adequacy of decompression at the end of the surgery. The intra-operative findings and postoperative outcomes and complications were analyzed. RESULTS: Immediately after surgeries, 39 patients (86.7%) achieved excellent result; 2 cases (4.4%) had good result. So the postoperative effective rate was 91.1% (41/45). During 12-36 month follow-up, the outcomes were excellent in 42 cases (93.3%) and good in 2 cases (4.4%), and the effective rate reached to 97.8% (44/45). No recurrence was noted. The postoperative complications were found in 2 patients (4.4%). One patient (2.2%) showed delayed facial palsy on the tenth day but was fully recovered 1 month later. Intracranial infection was noticed in 1 patient (2.2%) and was cured by using intravenous antibiotics for 2 weeks. There was no hearing impairment, hoarseness, or other complications. CONCLUSIONS: Fully endoscopic MVD is both safe and effective in the treatment of HFS. Electrophysiological monitoring is helpful to gain a good result and reduce hearing impairment.

The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification.

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1-4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

[Preliminary study of dihydroartemisin in inhibiting invasion and metastasis of hepatoma cells].

It is reported that dihydroartemisinin could reduce the expression of phosphorylated adhesion kinase and matrix metalloproteinase-2, inhibit the growth, migration and invasion of ovarian cancer cells, promote the formation of Treg cells through TGF-beta/Smad signaling pathway, and play an immunosuppressive role; dihydroartemisinin could also inhibit the growth of lung cancer cells by inhibiting the expression of vascular endothelial growth factor(VEGF) receptor KDR. However, there are few studies on dihydroartemisinin in hepatocellular carcinoma cells. In order to preliminarily explore the effect of dihydroartemisinin on invasion and metastasis of hepatocellular carcinoma cells, CCK-8 method and crystal violet staining were used to detect the effect of dihydroartemisinin on the growth of hepatocellular carcinoma cell 7402 and highly metastatic hepatocellular carcinoma cell MHCC97 H. The effects of dihydroartemisinin on the invasion and metastasis of hepatocellular carcinoma cell 7402 and highly metastatic hepatocellular carcinoma cell MHCC97 H were studied by using cell wound healing and Transwell. Western blot was used to detect the protein expression of epidermal growth factor receptor(EGFR) and its downstream signaling pathway in cells treated with dihydroartemisinin for 48 hours. The results showed that dihydroartemisinin could inhibit the growth of hepatocellular carcinoma cell 7402 and highly metastatic hepatocellular carcinoma cell MHCC97 H at 25 µmol·L~(-1). As compared with the control group, the number of cell clones was significantly reduced, and the ability of cell migration and invasion was weakened. Western blot results showed that as compared with the control group, dihydroartemisinin group could down-regulate the protein expression of EGFR and its downstream signaling pathways p-AKT, p-ERK, N-cadherin, Snail and Slug, and up-regulate the expression of E-cadherin protein, thus affecting the migration, invasion and metastasis of hepatocellular carcinoma cells 7402 and MHCC97 H.

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.

BACKGROUND: Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection. PURPOSE: To explore the use of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)-based radiomics for preoperative prediction of LVI in invasive breast cancer. STUDY TYPE: Prospective. POPULATION: Ninety training cohort patients (22 LVI-positive and 68 LVI-negative) and 59 validation cohort patients (22 LVI-positive and 37 LVI-negative) were enrolled. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, T1 -weighted DCE-MRI. ASSESSMENT: Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE-MRI. A radiomics signature was constructed in the training cohort with 10-fold cross-validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort. STATISTICAL TESTS: Mann-Whitney U-test, chi-square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA). RESULTS: Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone. DATA CONCLUSION: The DCE-MRI-based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847-857.