Deep-learning model for evaluating histopathology of acute renal tubular injury.

Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute kidney injury and aid in treatment. To date, a limited number of study has used deep-learning models to assist in the histopathological diagnosis of acute kidney injury. This study aimed to perform histopathological segmentation to identify the four structures of acute renal tubular injury using deep-learning models. A segmentation model was used to classify tubule-specific injuries following cisplatin treatment. A total of 45 whole-slide images with 400 generated patches were used in the segmentation model, and 27,478 annotations were created for four classes: glomerulus, healthy tubules, necrotic tubules, and tubules with casts. A segmentation model was developed using the DeepLabV3 architecture with a MobileNetv3-Large backbone to accurately identify the four histopathological structures associated with acute renal tubular injury in PAS-stained mouse samples. In the segmentation model for four structures, the highest Intersection over Union and the Dice coefficient were obtained for the segmentation of the "glomerulus" class, followed by "necrotic tubules," "healthy tubules," and "tubules with cast" classes. The overall performance of the segmentation algorithm for all classes in the test set included an Intersection over Union of 0.7968 and a Dice coefficient of 0.8772. The Dice scores for the glomerulus, healthy tubules, necrotic tubules, and tubules with cast are 91.78 ± 11.09, 87.37 ± 4.02, 88.08 ± 6.83, and 83.64 ± 20.39%, respectively. The utilization of deep learning in a predictive model has demonstrated promising performance in accurately identifying the degree of injured renal tubules. These results may provide new opportunities for the application of the proposed methods to evaluate renal pathology more effectively.

Tailored synthesis of NdMnxFe1-xO3 perovskite nanoparticles with oxygen-vacancy defects for lithium-ion battery anodes.

In this study, we synthesize nanostructured NdMnxFe1-xO3 perovskites using a facile method to produce materials for the high-working-efficiency anodes of Li-ion batteries. A series of characterization assessments (e.g., X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and electron microscopy) were conducted, and the results confirmed the efficacious partial replacement of Fe ions with Mn ions in the NdFeO3 perovskite structure, occurrence of both amorphous and crystalline structures, presence of oxygen vacancies (VO), and interconnection between nanoparticles. The possibility of Mn ion replacement significantly affects the size, amount of VO, and ratio of amorphous phase in NdMnxFe1-xO3 perovskites. The NdMnxFe1-xO3 perovskite with x = 0.3 presents a notable electrochemical performance, including low charge transfer resistance, durable Coulombic efficiency, first-rate capacity reservation, high pseudo-behavior, and elongated 150-cycle service life, whereas no discernible capacity deterioration is observed. The reversible capacity of the anode after the 150th-cylcle was 713 mAh g-1, which represents a high-capacity value. The outstanding electrochemical efficiency resulted from the optimum presence of VO, interconnection between the nanoparticles, and distinctive properties of the NdFeO3 perovskite. The interconnection between nanoparticles was advantageous for forming a large electrolyte-electrode contact area, improving Li-ion diffusion rates, and enhancing pseudocapacitive effect. The attributes of perovskite crystals, coexistence of Mn and Fe throughout the charge/discharge process, and optimum VO precluded the electrode devastation that caused the Li2O-phase decomposition catalysis, enabling favorable reversible Li storage.

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography.

OBJECTIVE: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. MATERIALS AND METHODS: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. RESULTS: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. CONCLUSION: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging.

Objective: To investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision. Materials and methods: This retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set. Results: The model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619-0.829) and 0.742 (0.623-0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575-0.892) and 0.7 (0.558-0.842), 0.714 (0.608-0.820) and 0.767 (0.651-0.882), respectively. Conclusion: Our study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.

Highly sensitive and selective colorimetric detection of Pb(ii) ions using Michelia tonkinensis seed extract capped gold nanoparticles.

In this study, gold nanoparticles (AuNPs) were synthesized via a green and environmentally-friendly approach and applied as a colorimetric probe for detecting Pb2+ ions in aqueous solution. Instead of toxic chemicals, Michelia tonkinensis (MT) seed extract was used for reducing Au3+ and stabilizing the formed AuNPs. The synthesis conditions, including temperature, reaction time, and Au3+ ion concentration, were optimized at 90 °C, 40 min, and 1.25 mM, respectively. The physicochemical properties of the produced MT-AuNPs were assessed by means of transmission electron microscopy, X-ray diffraction, field emission scanning electron microscopy, dynamic light scattering, and Fourier-transform infrared spectroscopy. The characterization results revealed that the MT-AuNPs exhibited a spherical shape with a size of about 15 nm capped by an organic layer. The colorimetric assay based on MT-AuNPs showed excellent sensitivity and selectivity toward Pb2+ ions with the limit of detection value of 0.03 µM and the limit of quantification of 0.09 µM in the linear range of 50-500 µM. The recoveries of inter-day and intra-day tests were 97.84-102.08% and 98.78-102.34%, respectively. The MT-AuNPs probe also demonstrated good and reproducible recoveries (98.71-101.01%) in analyzing Pb2+ in drinking water samples, indicating satisfactory practicability and operability of the proposed method.

Novel biogenic gold nanoparticles stabilized on poly(styrene-co-maleic anhydride) as an effective material for reduction of nitrophenols and colorimetric detection of Pb(II).

Biogenic gold nanoparticles (AuNPs) have been extensively studied for the catalytic conversion of nitrophenols (NP) into aminophenols and the colorimetric quantification of heavy metal ions in aqueous solutions. However, the high self-agglomeration ability of colloidal nanoparticles is one of the major obstacles hindering their application. In the present study, we offered novel biogenic AuNPs synthesized by a green approach using Cistanche deserticola (CD) extract as a bioreducing agent and stabilized on poly(styrene-co-maleic anhydride) (PSMA). The prepared Au@PSMA nanoparticles were characterized by various techniques (HR-TEM, SEAD, FE-SEM, DLS, TGA, XRD, and FTIR) and studied for two applications: the catalytic reduction of 3-NP by NaBH4 and the sensing detection of Pb2+ ions. The optimal conditions for the synthesis of AuNPs were investigated and established at 60 °C, 20 min, pH of 9, and 0.5 mM Au3+. Morphological studies showed that AuNPs synthesized by CD extract were mostly spherical with a mean diameter of 25 nm, while the size of polymer-integrated AuNPs was more than two-fold larger. Since PSMA acted as a matrix keeping the nanoparticles from coagulation and maintaining the optimal surface area, AuNPs integrated with PSMA showed higher catalytic efficiency with a faster reaction rate and lower activation energy than conventional nanoparticles. Au@PSMA could completely reduce 3-NP within 10 min with a rate constant of 0.127 min-1 and activation energy of 9.96 kJ/mol. The presence of PSMA also improved the stability and recyclability of AuNPs. Used as a sensor, Au@PSMA exhibited excellent sensitivity and selectivity for Pb2+ ions with a limit of detection of 0.03 µM in the linear range of 0-100 µM. The study results suggested that Au@PSMA could be used as a promising catalyst for the reduction of NP and the colorimetric sensor for detection of Pb2+ ions in aqueous environmental samples.

Novel biogenic silver and gold nanoparticles for multifunctional applications: Green synthesis, catalytic and antibacterial activity, and colorimetric detection of Fe(III) ions.

In this study, novel biogenic silver (AgNPs) and gold nanoparticles (AuNPs) were developed using a green approach with Ganoderma lucidum (GL) extract. The optimization of synthesis conditions for the best outcomes was conducted. The prepared materials were characterized and their applicability in catalysis, antibacterial and chemical sensing was comprehensively evaluated. The GL-AgNPs crystals were formed in a spherical shape with an average diameter of 50 nm, while GL-AuNPs exhibited multi-shaped structures with sizes ranging from 15 to 40 nm. As a catalyst, the synthesized nanoparticles showed excellent catalytic activity (>98% in 9 min) and reusability (>95% after five recycles) in converting 4-nitrophenol to 4-aminophenol. As an antimicrobial agent, GL-AuNPs were low effective in inhibiting the growth of bacteria, while GL-AgNPs expressed strong antibacterial activity against all the tested strains. The highest growth inhibition activity of GL-AgNPs was observed against B. subtilis (14.58 ± 0.35 mm), followed by B. cereus (13.8 ± 0.52 mm), P. aeruginosa (12.38 ± 0.64 mm), E. coli (11.3 ± 0.72 mm), and S. aureus (10.41 ± 0.31 mm). Besides, GL-AgNPs also demonstrated high selectivity and sensitivity in the colorimetric detection of Fe3+ in aqueous solution with a detection limit of 1.85 nM. Due to the suitable thickness of the protective organic layer and the appropriate particle size, GL-AgNPs validated the triple role as a high-performance catalyst, antimicrobial agent, and nanosensor for environmental monitoring and remediation.

Highly sensitive and low-cost colourimetric detection of glucose and ascorbic acid based on silver nanozyme biosynthesized by Gleditsia australis fruit.

In this study, a simple, eco-friendly and low-cost approach was used to fabricate silver nanoparticles (AgNPs) from an aqueous extract of Gleditsia australis (GA) fruit. The nanoparticles synthesized in the optimal condition have an average size of 14 nm. The peroxidase-like activity of GA-AgNP in the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) in combination with hydrogen peroxide (H2O2) was investigated. Further, optimal conditions for the use of peroxidase-like catalytic activity in sensing applications were identified. The colourimetric detection of H2O2 showed a linear range of 1-8 mM with a limit of detection (LOD) of 0.34 mM. The oxidation of TMB (red-TMB) enables the detection of glucose, which is converted into H2O2 and gluconic acid in the presence of the enzyme glucose oxidase. The observations showed linearity from 0.05 to 1.5 mM with a LOD of 0.038 mM. Moreover, the blue colour of oxidized TMB (ox-TMB) was reduced according to ascorbic acid (AA) concentration, with a linear range of 0.03-0.14 mM and a LOD of 3.0 µM. The practical use of the sensing system for the detection of AA was studied using real fruit juice and showed good sensitivity. Hence, the easy-to-use peroxidase-like sensor provides a new platform for the detection of bioactive compounds in biological systems.

Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.

OBJECTIVE: This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer. MATERIALS AND METHODS: One hundred seventy-four MR-detected additional lesions (benign, n = 86; malignancy, n = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data were randomly split to training (80%) and independent test sets (20%). In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. A decision tree method was utilized to build a classifier model using clinical imaging interpretation (CII) features assessed by radiologists. Area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity were used to compare the diagnostic performance. RESULTS: The RA models trained using radiomics features from the intratumor-ROI showed comparable performance to the CII model (accuracy, AUROC: 73.3%, 69.6% for the SUB RA model; 70.0%, 75.1% for the T2 RA model; 73.3%, 72.0% for the CII model). The diagnostic performance increased when the radiomics and CII features were combined to build a fusion model. The fusion model that combines the CII features and radiomics features from multiparametric MRI data demonstrated the highest performance with an accuracy of 86.7% and an AUROC of 91.1%. The external test showed a similar pattern where the fusion models demonstrated higher levels of performance compared with the RA- or CII-only models. The accuracy and AUROC of the SUB+T2 RA+CII model in the external test were 80.6% and 91.4%, respectively. CONCLUSION: Our study demonstrated the feasibility of using RA with machine learning approach based on multiparametric MRI for quantitatively characterizing MR-detected additional lesions. The fusion model demonstrated an improved diagnostic performance over the models trained with either RA or CII alone.