Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. PURPOSE: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). STUDY TYPE: Retrospective. POPULATION: 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. FIELD STRENGTH/SEQUENCE: 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. ASSESSMENT: Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. RESULTS: Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. DATA CONCLUSION: BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. TECHNICAL EFFICACY: Stage 2.

Indoxyl sulphate-TNFα axis mediates uremic encephalopathy in rodent acute kidney injury.

Acute kidney injury (AKI) is often accompanied by uremic encephalopathy resulting from accumulation of uremic toxins in brain possibly due to impaired blood-brain barrier (BBB) function. Anionic uremic toxins are substrates or inhibitors of organic anionic transporters (OATs). In this study we investigated the CNS behaviors and expression/function of BBB OAT3 in AKI rats and mice, which received intraperitoneal injection of cisplatin 8 and 20 mg/kg, respectively. We showed that cisplatin treatment significantly inhibited the expressions of OAT3, synaptophysin and microtubule-associated protein 2 (MAP2), impaired locomotor and exploration activities, and increased accumulation of uremic toxins in the brain of AKI rats and mice. In vitro studies showed that uremic toxins neither alter OAT3 expression in human cerebral microvascular endothelial cells, nor synaptophysin and MAP2 expressions in human neuroblastoma (SH-SY5Y) cells. In contrast, tumour necrosis factor alpha (TNFα) and the conditioned medium (CM) from RAW264.7 cells treated with indoxyl sulfate (IS) significantly impaired OAT3 expression. TNFα and CM from IS-treated BV-2 cells also inhibited synaptophysin and MAP2 expressions in SH-SY5Y cells. The alterations caused by TNFα and CMs in vitro, and by AKI and TNFα in vivo were abolished by infliximab, a monoclonal antibody designed to intercept and neutralize TNFα, suggesting that AKI impaired the expressions of OAT3, synaptophysin and MAP2 in the brain via IS-induced TNFα release from macrophages or microglia (termed as IS-TNFα axis). Treatment of mice with TNFα (0.5 mg·kg-1·d-1, i.p. for 3 days) significantly increased p-p65 expression and reduced the expressions of Nrf2 and HO-1. Inhibiting NF-κB pathway, silencing p65, or activating Nrf2 and HO-1 obviously attenuated TNFα-induced downregulation of OAT3, synaptophysin and MAP2 expressions. Significantly increased p-p65 and decreased Nrf2 and HO-1 protein levels were also detected in brain of AKI mice and rats. We conclude that AKI inhibits the expressions of OAT3, synaptophysin and MAP2 due to IS-induced TNFα release from macrophages or microglia. TNFα impairs the expressions of OAT3, synaptophysin and MAP2 partly via activating NF-κB pathway and inhibiting Nrf2-HO-1 pathway.

Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer.

PURPOSE: By radiomic analysis of the postcontrast CT images, this study aimed to predict locoregional recurrence (LR) of locally advanced oropharyngeal cancer (OPC) and hypopharyngeal cancer (HPC). METHODS: A total of 192 patients with stage III-IV OPC or HPC from two independent cohort were randomly split into a training cohort with 153 cases and a testing cohort with 39 cases. Only primary tumor mass was manually segmented. Radiomic features were extracted using PyRadiomics, and then the support vector machine was used to build the radiomic model with fivefold cross-validation process in the training data set. For each case, a radiomics score was generated to indicate the probability of LR. RESULTS: There were 94 patients with LR assigned in the progression group and 98 patients without LR assigned in the stable group. There was no significant difference of TNM staging, treatment strategies and common risk factors between these two groups. For the training data set, the radiomics model to predict LR showed 83.7% accuracy and 0.832 (95% CI 0.72, 0.87) area under the ROC curve (AUC). For the test data set, the accuracy and AUC slightly declined to 79.5% and 0.770 (95% CI 0.64, 0.80), respectively. The sensitivity/specificity of training and test data set for LR prediction were 77.6%/89.6%, and 66.7%/90.5%, respectively. CONCLUSIONS: The image-based radiomic approach could provide a reliable LR prediction model in locally advanced OPC and HPC. Early identification of those prone to post-treatment recurrence would be helpful for appropriate adjustments to treatment strategies and post-treatment surveillance.

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes.

A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p < 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p < 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p < 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p < 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p < 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009).

Fungal dynamic during apricot wine spontaneous fermentation and aromatic characteristics of Pichia kudriavzevii for potential as starter.

Microbial activity during spontaneous fermentation in alcoholic beverages have driven in developing the chemical and aromatic characteristic of products but not clear in apricot wines. We have characterised the composition of fungal communities and volatile metabolites in apricot wine spontaneous fermentation among two Shaanxi regions. Results showed that Aureobasidium, Alternaria, Pichia and Saccharomyces, were the dominant fungi in apricot wine fermentation. A total of 80 volatiles including esters, alcohols, acids and terpenes were detected from two apricot wines. Their correlations suggested that apricot wine aroma was mainly affected by Pichia kudriavzevii, rather than Saccharomyces cerevisiae we commonly considered. Furthermore, reinforced inoculation of P. kudriavzevii LQD20 has exhibited the commendable potential in enhancing sensory qualities. The results of this study provide fundamental information of the indigenous microbiota in microbial dynamic during apricot wine fermentation, which would be helpful in exploiting the strains with potential for industrial use as starter cultures.

Microbial diversity on grape epidermis and wine volatile aroma in spontaneous fermentation comprehensively driven by geography, subregion, and variety.

On their journey from the wine grape to the resulting wine, microbiota from grape surfaces controlled by multiple factors is transferred to wine spontaneous fermentation process with indisputable consequences for wine quality parameters. The associated microbiota was regionally distinct (defined to microbial terroir) but how these microbial patterns with significantly regional distinctiveness quantitatively drive the wine regional characteristics are not definite within a complete grape ecosystem at different geographical (> 300 km), subregional (< 10 km), and varietal scales. Here, we collected 24 samples (containing two grape varieties) from four subregions of two regions in Xinjiang wine production area to investigate fungal distribution patterns and the association with wine chemical composition at different evaluation scales. Meanwhile, the relationships were established between geographical, subregional, varietal community of fungi, and wine volatile aroma using partial least squares regression (PLSR) and structural equation modeling (SEM). Results show that microbial and volatile samples present the significantly regional difference inside the complete ecosystem. Microbiota showed a stronger heterogeneity at geography scales, which drove the distributions of subregional and varietal microbiota thereby influencing the volatile composition of finished wines. Moreover, geographical microbiota seems to weaken the effects of varietal community on wine aroma compounds. Microbial communities respond to environmental changes within a completely set grape-related ecosystem at different scales, and these responses resulted in the wine regional distinctiveness based on the volatile profiles. Our findings further confirmed the important role of microbial terroir in shaping wine styles and provided the new cerebration for the terroir drivers of microbiota.

Efficacy and safety of low-dose sacubitril/valsartan in heart failure patients: A systematic review and meta-analysis.

BACKGROUND: Controversy has persisted over the clinical benefits of low-dose sacubitril/valsartan in patients with heart failure (HF). HYPOTHESIS: Low-dose sacubitril/valsartan might also be effective and safe in HF patients. METHODS: Electronic databases including PubMed, Ovid, and Cochrane Library were systematically retrieved from inception to August 5, 2021. Review manager 5.4 and Stata 15.1 were employed in this systematic review and meta-analysis. Key efficacy outcomes of interest included HF hospitalization, all-cause mortality, left ventricular ejection fraction (LVEF), N-terminal pro-B-type natriuretic peptide (NT-proBNP), together with New York Heart Association (NYHA) functional class. The safety outcome was systolic blood pressure (SBP). The grading of recommendations assessment, development, and evaluation approach was conducted to evaluate the quality of evidence for each outcome. RESULTS: A total of 1269 studies were screened and 9 real-world studies met the inclusion criteria were included in the meta-analysis, with 1697 participants. Compared with low-dose sacubitril/valsartan, high-dose sacubitril/valsartan significantly reduced the risk of HF hospitalization (odds ratio [OR]: 0.4, 95% confidence interval [CI]: 0.27-0.61, p < .0001) and the risk of all-cause mortality (OR: 0.23, 95% CI: 0.11-0.47, p < .0001). However, there were no appreciable differences in improvements of NYHA (OR: 0.59, 95% CI: 0.15-2.35, p = .45), changes of LVEF (mean difference [MD]: 2.73%, 95% CI: -2.24% to 7.7%, p = .28), changes of NT-proBNP (MD: 43.09, 95% CI: -28.41 to 114.59, p = .24) and changes of SBP (MD: 3.01, 95% CI: -4.62 to 10.64, p = .44) between groups with low-dose and high-dose sacubitril/valsartan. CONCLUSIONS: Compared with high-dose sacubitril/valsartan, low-dose sacubitril/valsartan was associated with increased risks of HF hospitalization and all-cause mortality. However, no distinct between-group differences in improvements of NYHA, changes of LVEF, changes of NT-proBNP and changes of SBP were observed.

Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics.

Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted in radiologists' visual evaluation. More sophisticated computer algorithms are needed to extract the higher-order information. The purpose of this study was to apply a new deep learning algorithm, the bi-directional convolutional long short-term memory (CLSTM) network, and the radiomics analysis for differential diagnosis of PCa and benign prostatic hyperplasia (BPH). To systematically investigate the optimal amount of peritumoral tissue for improving diagnosis, a total of 9 ROIs were delineated by using 3 different methods. The results showed that bi-directional CLSTM with ± 20% region growing peritumoral ROI achieved the mean AUC of 0.89, better than the mean AUC of 0.84 by using the tumor alone without any peritumoral tissue (p = 0.25, not significant). For all 9 ROIs, deep learning had higher AUC than radiomics, but only reaching the significant difference for ± 20% region growing peritumoral ROI (0.89 vs. 0.79, p = 0.04). In conclusion, the kinetic information extracted from DCE-MRI using bi-directional CLSTM may provide helpful supplementary information for diagnosis of PCa.

Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification.

RATIONALE AND OBJECTIVES: Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability. MATERIALS AND METHODS: Two datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis. RESULTS: In the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant. CONCLUSION: ResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.

Prediction of poor outcome in stroke patients using radiomics analysis of intraparenchymal and intraventricular hemorrhage and clinical factors.

PURPOSE: To build three prognostic models using radiomics analysis of the hemorrhagic lesions, clinical variables, and their combination, to predict the outcome of stroke patients with spontaneous intracerebral hemorrhage (sICH). MATERIALS AND METHODS: Eighty-three sICH patients were included. Among them, 40 patients (48.2%) had poor prognosis with modified Rankin scale (mRS) of 5 and 6 at discharge, and the prognostic model was built to differentiate mRS ≤ 4 vs. 5 + 6. The region of interest (ROI) of intraparenchymal hemorrhage (IPH) and intraventricular hemorrhage (IVH) were separately segmented. Features were extracted using PyRadiomics, and the support vector machine was applied to select features and build radiomics models based on IPH and IPH + IVH. The clinical models were built using multivariate logistic regression, and then the radiomics scores were combined with clinical variables to build the combined model. RESULTS: When using IPH, the AUC for radiomics, clinical, and combined model was 0.78, 0.82, and 0.87, respectively. When using IPH + IVH, the AUC was increased to 0.80, 0.84, and 0.90, respectively. The combined model had a significantly improved AUC compared to the radiomics by DeLong test. A clinical prognostic model based on the ICH score of 0-1 only achieved AUC of 0.71. CONCLUSIONS: The combined model using the radiomics score derived from IPH + IVH and the clinical factors could achieve a high accuracy in prediction of sICH patients with poor outcome, which may be used to assist in making the decision about the optimal care.

Endometrial microstimulation effects on endometrial receptivity assessed by transvaginal color Doppler sonography.

OBJECTIVE: This study investigated the effect of endometrial microstimulation (EM) on endometrial receptivity using transvaginal color Doppler sonography (TVCDS). METHOD: Women of childbearing age who were preparing to conceive (n = 90) were randomly divided into the EM group (n = 30), who were examined by EM on days 3-5 of the menstrual cycle, and the control group (n = 60). TVCDS was conducted during the implantation window phase, and endometrial thickness, endometrial pattern, endometrial movement, blood flow type, and uterine and spiral arterial hemodynamic parameter measurements were made. The groups were compared to identify differences. RESULTS: Endometrial thickness (0.97 ± 0.18 cm and 0.95 ± 0.17 cm), endometrial movement (type 1: 46.7% and 51.7%; type 2: 30.0% and 28.3%; type 3: 6.7% and 5.0%; type 5: 16.7% and 15.0%), and hemodynamic parameters of the uterine (pulsatility index [PI]: 2.46 ± 0.50 and 2.41 ± 0.48; resistance index [RI]: 0.85 ± 0.05 and 0.84 ± 0.05) and spiral (PI: 1.11 ± 0.32 and 1.19 ± 0.33; RI: 0.48 ± 0.11 and 0.51 ± 0.08) arteries did not differ significantly between groups (P > 0.05). However, the endometrial pattern (a trilaminar pattern: 80.0% and 58.3%; P = 0.041) and blood flow type (type I: 16.7% and 43.3%; type II: 63.3% and 40.0%; type III 20.0% and 16.7%; P = 0.038) differed significantly between groups. CONCLUSION: Endometrial microstimulation did not alter endometrial pathological staging, endometrial thickness, or movement, nor did it affect uterine and spiral arterial blood flow parameters. However, it may be able to abrade abnormal endometrial tissue, optimizing the endometrial pattern. Endometrial microstimulation may support local spiral artery regeneration and increase endometrial blood supply in new cycles.

The Added Value of Intraventricular Hemorrhage on the Radiomics Analysis for the Prediction of Hematoma Expansion of Spontaneous Intracerebral Hemorrhage.

Background: Among patients undergoing head computed tomography (CT) scans within 3 h of spontaneous intracerebral hemorrhage (sICH), 28% to 38% have hematoma expansion (HE) on follow-up CT. This study aimed to predict HE using radiomics analysis and investigate the impact of intraventricular hemorrhage (IVH) compared with the conventional approach based on intraparenchymal hemorrhage (IPH) alone. Methods: This retrospective study enrolled 127 patients with baseline and follow-up non-contrast CT (NCCT) within 4~72 h of sICH. IPH and IVH were outlined separately for performing radiomics analysis. HE was defined as an absolute hematoma growth > 6 mL or percentage growth > 33% of either IPH (HEP) or a combination of IPH and IVH (HEP+V) at follow-up. Radiomic features were extracted using PyRadiomics, and then the support vector machine (SVM) was used to build the classification model. For each case, a radiomics score was generated to indicate the probability of HE. Results: There were 57 (44.9%) HEP and 70 (55.1%) non-HEP based on IPH alone, and 58 (45.7%) HEP+V and 69 (54.3%) non-HEP+V based on IPH + IVH. The majority (>94%) of HE patients had poor early outcomes (death or modified Rankin Scale > 3 at discharge). The radiomics model built using baseline IPH to predict HEP (RMP) showed 76.4% accuracy and 0.73 area under the ROC curve (AUC). The other model using IPH + IVH to predict HEP+V (RMP+V) had higher accuracy (81.9%) with AUC = 0.80, and this model could predict poor outcomes. The sensitivity/specificity of RMP and RMP+V for HE prediction were 71.9%/80.0% and 79.3%/84.1%, respectively. Conclusion: The proposed radiomics approach with additional IVH information can improve the accuracy in prediction of HE, which is associated with poor clinical outcomes. A reliable radiomics model may provide a robust tool to help manage ICH patients and to enroll high-risk ICH cases into anti-expansion or neuroprotection drug trials.

Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters.

(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.

Usage of image registration and three-dimensional visualization tools on serial computed tomography for the analysis of patients with traumatic intraparenchymal hemorrhages.

The aim of this study was to apply registration and three-dimensional (3D) display tools to assess the evolution of intraparenchymal hemorrhage (IPH) in patients with traumatic brain injury (TBI). We identified 109 TBI patients who had two computed tomography (CT) scans within 4 days retrospectively. The IPH was manually outlined. The registration was performed in 39 lesions from 29 patients with lesion volume < 1.5 cm on both baseline and follow-up CT. The center of mass (COM) of each lesion was calculated, and the distance between baseline and follow-up CT was used to evaluate the registration effect. The mean distances of COM before registration in the XYZ, XY, and YZ coordinates were 20.5 ± 10.2 mm, 17.8 ± 9.4 mm, and 15.9 ± 9.4 mm, respectively, which decreased significantly (p < 0.001) to 7.9 ± 4.9, 7.8 ± 5.0, and 6.1 ± 4.1 mm after registration. A 3D short video displaying the rendering view of all lesions in 34 randomly selected patients from baseline and follow-up scans were presented side-by-side for comparison. The detection rate of new IPH lesions increased in 3D videos (100%) as compared with axial CT slices (78.6-92.9%). A very high interrater agreement (k = 0.856) on perceiving IPH lesion progression upon viewing 3D video was noted, and the absolute volume increase was significantly higher (p < 0.001) for progressive lesions (median 7.36 cc) over non-progressive lesions (median 0.01 cc). Compared to patients with spontaneous hemorrhagic stroke, evaluation of multiple small traumatic hemorrhages in TBI is more challenging. The applied image analysis and visualization methods may provide helpful tools for comparing changes between serial CT scans.

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system. METHODS: A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation. RESULTS: The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p < = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001). CONCLUSION: Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.

[SUN's abdominal acupuncture for depression after methamphetamine withdrawal: a randomized controlled trial].

OBJECTIVE: To compare the clinical effect of SUN 's abdominal acupuncture and conventional acupuncture in the treatment of depression after methamphetamine withdrawal. METHODS: A total of 80 female patients with depression after methamphetamine withdrawal were randomly divided into an observation group (40 cases, 1 case dropped off) and a control group (40 cases, 2 cases dropped off). The control group was treated with conventional acupuncture at Baihui (GV 20), Yintang (GV 29), Taichong (LR 3), Shenmen (HT 7), Neiguan (PC 6), Danzhong (GV 17), and the observation group was treated with SUN 's abdominal acupuncture at area 1 of the abdomen and area 8 of the abdomen. Both groups were treated once a day, 30 min each time, 6 days as a course of treatment, 1 day rest between treatment courses, a total of 4 courses of treatment. The scores of withdrawal symptoms, Hamilton depression scale (HAMD), Pittsburgh sleep quality index (PSQI) scale and serum serotonin (5-HT) level were compared between the two groups before and after treatment. RESULTS: After treatment, the scores of withdrawal symptoms, HAMD and the various scores and total score of PSQI scale in the two groups were all lower than before treatment (P<0.01), and the scores of withdrawal symptoms, HAMD and the sleep quality, time to fall asleep, sleep time scores and total score of PSQI in the observation group were lower than the control group (P<0.05, P<0.01). After treatment, the serum 5-HT level of the two groups was increased (P<0.01), and that in the observation group was higher than the control group (P<0.05). CONCLUSION: SUN 's abdominal acupuncture can improve withdrawal symptom, depression and sleep quality, increase serum 5-HT content in treatment of depression after methamphetamine withdrawal, and has better effect than conventional acupuncture.

Directed evolution driving the generation of an efficient keratinase variant to facilitate the feather degradation.

Keratinases can specifically degrade keratins, which widely exist in hair, horns, claws and human skin. There is a great interest in developing keratinase to manage keratin waste generated by the poultry industry and reusing keratin products in agriculture, medical treatment and feed industries. Degradation of keratin waste by keratinase is more environmentally friendly and more sustainable compared with chemical and physical methods. However, the wild-type keratinase-producing strains usually cannot meet the requirements of industrial production, and some are pathogenic, limiting their development and utilization. The main purpose of this study is to improve the catalytic performance of keratinase via directed evolution technology for the degradation of feathers. We first constructed a mutant library through error-prone PCR and screened variants with enhanced enzyme activity. The keratinase activity was further improved through fermentation conditions optimization and fed-batch strategies in a 7-L bioreactor. As a result, nine mutants with enhanced activity were identified and the highest enzyme activity was improved from 1150 to 8448 U/mL finally. The mutant achieved efficient biodegradation of feathers, increasing the degradation rate from 49 to 88%. Moreover, a large number of amino acids and soluble peptides were obtained as degradation products, which were excellent protein resources to feed. Therefore, the study provided a keratinase mutant with application potential in the management of feather waste and preparation of protein feed additive.

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.

BACKGROUND: A wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions. MATERIALS AND METHODS: A total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing. RESULTS: The diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A-5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets. CONCLUSION: Diagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.

The complete mitochondrial genome of Taiwan slug-eating snake (Pareas formosensis) and phylogenetic analysis.

We report the complete mitochondrial genome (mtDNA) of Pareas formosensis (Squamata: Colubridae). This circular mtDNA is 17,703 bp in size and consists of 13 protein-coding genes, 22 transfer RNAs, 2 ribosomal RNAs, and 2 non-coding sequence (D-loop). The total of mtDNA was composition of 57.26% A + T and 42.74% G + C (T: 25.21%, C: 28.84%, A: 32.05%, G: 13.90%). The phylogenetic analysis revealed that P. formosensis formed a clade with other species of Pareas. This mtDNA sequence of P. formosensis provides useful data for studying the population genetics and phylogeography of Colubridae.

The role of microglia mediated pyroptosis in neonatal hypoxic-ischemic brain damage.

Neonatal hypoxic-ischemic encephalopathy (HIE) often leads to neonatal death or severe, irreversible neurological deficits. Pathologically, the occurrence of massive cell death and subsequent inflammation suggested that pyroptosis, an inflammation associated programed cell death, might play a role in HIE. Here, by measuring changes of key molecules in pyroptosis pathway in HIE patients, we discovered that their elevation levels tightly correlate with the severity of HIE. Next, we demonstrated that application of MCC950, a small molecule to inhibit NLRP3 inflammasome and thus pyroptosis, substantially alleviated pyroptosis and the injury severity in rats with neonatal hypoxic-ischemic brain damage (HIBD). Mechanistically, we showed that NLRP-3/caspase-1/GSDMD axis is required for microglia pyroptosis and activation. Our data demonstrated that microglia mediated pyroptosis played a crucial role in neonatal HIE, which shed lights into the development of intervention avenues targeting pyroptosis to treat HIE and traumatic brain injuries.