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The source for reactive dye transfer of cotton fabric varies in different studies, and there is limited information on the washing factors. This study explored the source of Reactive Red 195 colored cotton fabric dye transfer and reported the effects of three key washing factors and their interactions with two types of dye transfer inhibitors. First, the high-performance liquid chromatography results show that the peak of completely hydrolyzed Reactive Red 195 matches the peak of the washing residue of the fabric colored by Reactive Red 195. The dye transfer process is positively related to the hydrolyzed dyes. Based on this, response surface method models for the three factors, water consumption, water hardness, and dye transfer inhibitors concentration for polyvinylpyrrolidone and lauryl betaine were built. Both response surface method models show that due to the presence of Ca2+ and Mg2+, the increase in water hardness reduces the zeta potential on the fabric surface, and causes a decrease of electrostatic repulsion between the dye and the fabric. The dye transfer inhibiting effect is reduced. The increase in dye transfer inhibitor concentration strengthened the effect of inhibiting the transfer of hydrolyzed Reactive Red 195. In addition, there is a cross effect between water hardness and dye transfer inhibitor concentration. There is optimal water hardness and dye transfer inhibitor concentration to maximize the inhibition of hydrolyzed Reactive Red 195 dye transfer. The water consumption shows a limited effect because the washing time is long enough to reach a balance in the fabric and washing fluids. Polyvinylpyrrolidone with many hydrophilic groups is easy to combine with hydrolyzed dyes. While lauryl betaine is a small molecule surfactant, the structural differences make the response surface method model show that polyvinylpyrrolidone has a better effect than lauryl betaine. The discoveries can be used to guide dye transfer inhibitor development. Further investigations, including dye transfer inhibitor synthesis and modification, are needed.

You Only Look Once version 5 (YOLOv5) is the latest YOLO series, which may be a useful tool for detecting and classifying breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) and help clinicians make a rapid, accurate diagnosis and provide treatment. Data were retrospectively collected from a single-center study. The performances of the four submodels (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) were compared. The diagnostic performances of YOLOv5s were comparable with some convolutional neural network models for breast lesion identification in breast ultrasonography and mammography. This study may provide novel insights into the detection and classification of breast lesions on DCE-MRI. Thus, a sufficiently large series of data and high-quality DCE-MRIs are warranted. Owing to its applications in artificial intelligence-assisted imaging diagnosis, this method has promising prospects.

Background To develop and validate a radiomics nomogram to determine the primary site of liver metastases from gastric and colorectal cancer based on texture analysis. Methods We enrolled 555 patients with liver metastases, comprising 277 with gastric cancer (GC) and 278 with colorectal cancer (CRC), and randomly divided them into the training and validation cohorts at a ratio of 7:3. Radiomics features were extracted from venous phase computed tomography (CT) scans. Univariate analysis revealed three texture features potentially correlated with the identification model (p

Background Dispensing Error occurs frequently in outpatient pharmacies. The aim of this article is to explore the value of the YOLO-V5 deep learning algorithm for object detection in packaging drugs from outpatient pharmacies and to develop an artificial intelligence assist pharmacist drug dispensation system (AI-APDDS) capable of completely reducing or eliminating dispensing errors. Methods A total of 1784 images from 136 different packaging drugs were collected and labeled to form a deep learning dataset. The dataset was split into training and validation sets at a ratio of 3:1. The YOLO-V5 deep-learning algorithm was trained using images from our dataset (training epochs:1000, batch size:4, learning rate:0.01). The values of precision (Pr) and mean average precision(mAP) were used as measures for model performance evaluation. Results Pr in the training set was, for all models, equal to 1.00. The mAP_0.5 of YOLO-V5x was 0.992, which was higher than that of YOLO-V5n (0.975), YOLO-V5s (0.976), YOLO-V5m (0.978), and YOLO-V5l (0.983). The mAP_0.5:0.95 of YOLO-V5x was 0.955, which was higher than those of YOLO-V5n (0.916), YOLO-V5s (0.920), YOLO-V5m (0.921) and YOLO-V5l (0.935). The training time and model size were 92.67 hours and 465MB, respectively, for YOLO-V5x, which were the highest among the four models. The speed of detection for one image was 6.0ms for YOLO-V5n, which was the fastest among the four models. Conclusions It can identify the packaging drugs accurately for five submodels of YOLO-V5, it is feasible to implement the artificial-intelligence assisted drug dispensation system for pharmacist to achieve “zero” dispensing error.

Background In clinical practice, reducing unnecessary biopsies of BI-RADS 4 lesions of the mammogram is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies of Inception V3 to reduce the number of unnecessary biopsies of mammographic BI-RADS 4 lesions for residents. Methods A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions were collected and confirmed by clinical pathology or biopsy. The dataset of breast mammography images was randomly divided into three subsets: a training set, testing set, and validation set at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on InceptionV3, and 11 fine-tuning strategies were attempted to improve the performance of the InceptionV3 model. Mammography images from 362 patients with BI-RADS 4 breast lesions were employed as validation set 2. All breast lesions in validation set 2 were pathologically confirmed. Two images from each lesion were tested and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), f1 score (f1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model on validation set 2. Results The S5 model was the best fit for the data. The Pr, Rc, \(f1\) and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of downgraded lesions by S5 were 90.73%, 84.76%, and 80.19% for category 4A,4B,4C, respectively. The overall proportion of lesions downgraded by S5 was 85.91% in Category 4. There was no statistical difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). Conclusion The S5 model we propose here can be used as an effective approach to reduce the number of unnecessary biopsies of mammographic BI-RADS 4 lesions for residents and have important clinical uses.

Background: Early and rapid diagnosis of breast cancer is very important. Traditional method for detecting and diagnosing breast cancer may lead to a false positive or negative result. Presently, most of the deep learning models used in breast cancer detection prevents their use on mobile phones or low-configuration devices. This study intends to evaluate the capability of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Methods: The diagnostic efficiencies of MobileNetV1_False(V1_False), MobileNetV1_True (V1_True), MobileNetV2_False(V2_False) and MobileNetV2_True(V2_True) were tested and evaluated under the same program architecture and dataset (2124 images in benign group and 2226 images in malignant group). A relatively optimal model was selected (with the highest accuracy in the training set and test set), and two fine-tuning strategies (S0 and S1) were used to modify it. The accuracy (Ac) of different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), f1 score(f1) and area under the receiver operating characteristic curve (AUC), were taken as the performance indicators. Results: The Ac of V1_True (0.9815) was higher than those of V1_False (0.9749), V2_False (0.9672) and V2_True (0.9699). Overfitting were observed in all models. Pr, Rc, f1 and AUC of V1_True were 0.79,0.73,0.74 and 0.74, respectively, they were higher than those of V1_False (0.77,0.73,0.73 and 0.73), V2_False (0.74,0.65,0.66 and 0.65) and V2_True (0.76,0.67,0.68 and 0.67).Conclusion: The MobileNetV1_True model can differentiate between benign and malignant breast lesions on breast DCE-MRI. Future work is necessary to improve the generalization capability of the proposed method.

Background Intravoxel incoherent motion (IVIM) can provide quantitative information about water diffusion and perfusion that can be used to evaluate hepatic injury, but it has not been studied in hepatic injury induced by intestinal ischemia–reperfusion (IIR). Dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) can provide perfusion data, but it is unclear whether it can provide useful information for assessing hepatic injury induced by IIR. Purpose To examine whether IVIM and DCE‐MRI can detect early IIR‐induced hepatic changes, and to evaluate the relationship between IVIM and DCE‐derived parameters and biochemical indicators and histological scores. Study Type Prospective pre‐clinical study. Population Forty‐two male Sprague–Dawley rats. Field Strength/Sequence IVIM‐diffusion‐weighted imaging (DWI) using diffusion‐weighted echo‐planar imaging sequence and DCE‐MRI using fast spoiled gradient recalled‐based sequence at 3.0 T. Assessment All rats were randomly divided into the control group (Sham), the simple ischemia group, the ischemia–reperfusion (IR) group (IR1h, IR2h, IR3h, and IR4h) in a model of secondary hepatic injury caused by IIR, and IIR was induced by clamping the superior mesenteric artery for 60 minutes and then removing the vascular clamp. Advanced Workstation (AW) 4.6 was used to calculate the imaging parameters (apparent diffusion coefficient [ADC], true diffusion coefficient [D], perfusion‐related diffusion [D *] and volume fraction [f]) of IVIM. OmniKinetics (OK) software was used to calculate the DCE imaging parameters (K trans, K ep, and V e). Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were analyzed with an automatic biochemical analyzer. Superoxide dismutase (SOD) activity was assessed using the nitro‐blue tetrazolium method. Malondialdehyde (MDA) was determined by thiobarbituric acid colorimetry. Histopathology was performed with hematoxylin and eosin staining. Statistical Tests One‐way analysis of variance (ANOVA) and Bonferroni post‐hoc tests were used to analyze the imaging parameters and biochemical indicators among the different groups. Pearson correlation analysis was applied to determine the correlation between imaging parameters and biochemical indicators or histological score. Results ALT and MDA reached peak levels at IR4h, while SOD reached the minimum level at IR4h (all P

In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differentiate malignant from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We collected 4260 images of benign lesions and 4140 images of malignant lesions of the breast pertaining to pathologically confirmed cases. The benign and malignant groups was randomly divided into a training set and a testing set at a ratio of 9:1. A DTL model based on the DenseNet201 model was established, and the effectiveness of 4 fine-tuning strategies (S0: strategy 0, S1: strategy; S2: strategy; and S3: strategy) was compared. Additionally, DCE-MRI images of 48 breast lesions were selected to verify the robustness of the model. Ten images were obtained for each lesion. The classification was considered correct if more than 5 images were correctly classified. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, precision (Pr), recall rate (Rc), f1 score (f1), and area under the receiver operating characteristic curve (AUROC) in the validation set. The Ac of the 4 fine-tuning strategies reached 100.00% in the training set. The S2 strategy exhibited good convergence in the testing set. The Ac of S2 was 98.01% in the testing set, which was higher than those of S0 (93.10%), S1 (90.45%), and S3 (93.90%). The average classification Pr, Rc, f1, and AUROC of S2 in the validation set were (89.00%, 80.00%, 0.81, and 0.79, respectively) higher than those of S0 (76.00%, 67.00%, 0.69, and 0.65, respectively), S1 (60.00%, 60.00%, 0.60, 0.66, and respectively), and S3 (77.00%, 73.00%, 0.74, 0.72, respectively). The degree of coincidence between S2 and the histopathological method for differentiating between benign and malignant breast lesions was high (κ = 0.749). The S2 strategy can improve the robustness of the DenseNet201 model in relatively small breast DCE-MRI datasets, and this is a reliable method to increase the Ac of discriminating benign from malignant breast lesions on DCE-MRI.