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Biochar prepared from crop straw is an economical method for adsorbing bromocresol green (BCG) from textile industrial wastewater. However, there is limited research on the adsorption mechanism of biochar for the removal of BCG. This study utilized cucumber straw as raw material to prepare biochar with good adsorption potential and characterized its physicochemical properties. Through adsorption experiments, the effects of solution pH, biochar dosage, and initial dye concentration on adsorption performance were examined. The adsorption mechanism of cucumber straw biochar (CBC) for BCG was elucidated at the molecular level using adsorption kinetics, adsorption isotherm models, and density functional theory (DFT) calculations. Results show that the specific surface area of the CBC is 101.58 m2/g, and it has a high degree of carbonization, similar to the structure of graphite crystals. The presence of aromatic rings, –OH groups, and –COOH groups in CBC provides abundant adsorption sites for BCG. The adsorption process of CBC for BCG is influenced by both physical and chemical adsorption, and can be described by the Langmuir isotherm model, indicating a monolayer adsorption process. The theoretical maximum monolayer adsorption capacity (qm) of BCG at 298 K was calculated to be 99.18 mg/g. DFT calculations reveal interactions between BCG and CBC involving electrostatic interactions, van der Waals forces, halogen–π interactions, π–π interactions, and hydrogen bonds. Additionally, the interaction of hydrogen bonds between BCG and the –COOH group of biochar is stronger than that between BCG and the –OH group. These findings provide valuable insights into the preparation and application of efficient organic dye adsorbents. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). Materials and methods: A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test. Results: In predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807–0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1). Conclusions: The combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making. [ABSTRACT FROM AUTHOR]

Aqueous zinc−ion batteries (ZIBs) are widely recognized as highly promising energy storage devices because of their inherent characteristics, including superior safety, affordability, eco−friendliness, and various other benefits. However, the significant corrosion of the zinc metal anode, side reactions occurring between the anode and electrolyte, and the formation of zinc dendrites significantly hinder the practical utilization of ZIBs. Herein, we utilized an electrodeposition method to apply a unique hydrous molybdenum oxide (HMoOx) layer onto the surface of the zinc metal anode, aiming to mitigate its corrosion and side reactions during the process of zinc deposition and stripping. In addition, the HMoOx layer not only improved the hydrophilicity of the zinc anode, but also adjusted the migration of Zn2+, thus facilitating the uniform deposition of Zn2+ to reduce dendrite formation. A symmetrical cell with the HMoOx−Zn anode displayed reduced−voltage hysteresis (80 mV at 2.5 mA/cm2) and outstanding cycle stability after 3000 cycles, surpassing the performance of the uncoated Zn anode. Moreover, the HMoOx−Zn anode coupled with a γ−MnO2 cathode created a considerably more stable rechargeable full battery compared to the bare Zn anode. The HMoOx−Zn||γ−MnO2 full cell also displayed excellent cycling stability with a charge/discharge−specific capacity of 129/133 mAh g−1 after 300 cycles. In summary, this research offers a straightforward and advantageous approach that can significantly contribute to the future advancements in rechargeable ZIBs. [ABSTRACT FROM AUTHOR]

Background: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision‐making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. Purpose: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). Study Type: Retrospective. Population: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). Field Strength/Sequences: T2‐weighted imaging with a turbo spin echo sequence and diffusion‐weighted imaging with a single‐shot echo plane‐imaging sequence at 3.0 T. Assessment: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin‐Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate‐specific antigen, ADC value, and the lesion maximum cross‐sectional diameter. Statistical Tests: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. Results: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726–0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702–0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656–0.827]) and RI (0.664 [95% CI, 0.566–0.752]). Data Conclusion: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. Level of Evidence: 4 Technical Efficacy: Stage 4 [ABSTRACT FROM AUTHOR]

Background: Giardia lamblia (syn. G. intestinalis, G. duodenalis) is a primitive opportunistic protozoon, and one of the earliest differentiated eukaryotes. Despite its primitive nature, G. lamblia has a sophisticated cytoskeleton system, which is closely related to its proliferation and pathogenicity. Meanwhile, α giardin is a G. lamblia-specific cytoskeleton protein, which belongs to the annexin superfamily. Interestingly, G. lamblia has 21 annexin-like α giardins, i.e., more than higher eukaryotes. The functional differences among α giardin members are not fully understood. Methods: We took α-4 giardin, a member of α giardin family, as a research object. A morpholino-mediated knockdown experiment was performed to identify the effect of α-4 giardin on G. lamblia trophozoites biological traits. A yeast two-hybrid cDNA library of G. lamblia strain C2 trophozoites was screened for interaction partners of α-4 giardin. Co-immunoprecipitation and fluorescent colocalization confirmed the relationship between G. lamblia EB1 (gEB1) and α-4 giardin. Results: α-4 Giardin could inhibit the proliferation and adhesion of G. lamblia trophozoites. In addition, it interacted with G. lamblia EB1 (gEB1). Conclusions: α-4 Giardin was involved in proliferation and adhesion in G. lamblia trophozoites, and EB1, a crucial roles in mitosis, was an interacting partner of α-4 giardin. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. Material and methods: A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. Results: The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001). Conclusions: This radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists' PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further. Critical relevance statement: The radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans. Key points: • The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists. • The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool. • The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness. [ABSTRACT FROM AUTHOR]

In this study, an efficient visible light responsive MIL-125(Ti)/ZnIn2S4 hybrid structure (MIL-1.2/ZIS) was synthesized through two-step method with improved photocatalytic H2-evolution performance. The MIL-1.2/ZIS nanocomposites could retained the characteristic features including the highly surface area of metal organic frameworks and the hierarchical sheet-like structure of ZnIn2S4. The optimal sample could exhibit the H2 generation rate of 873.4 μmol g−1·h−1, and the improved photocatalytic activity could be ascribed to the well-matched band structure and the formation of hybrid interfaces, which could lead to the effective charge transfer and separation of photo-excited electron–hole pairs. We hope this paper could broad the potential utilization of MOF-based nanocomposites in photocatalytic energy conversion fields. [ABSTRACT FROM AUTHOR]

Background: Computed diffusion‐weighted images (cDWI) of random b value could be derived from acquired DWI (aDWI) with at least two different b values. However, its comparison between aDWI and cDWI images in locally advanced rectal cancer (LARC) patients after neoadjuvant therapy (NT) is needed. Purpose: To compare the cDWI and aDWI in image quality, restaging, and treatment response of LARC after NT. Study Type: Retrospective. Population: Eighty‐seven consecutive patients. Field Strength/Sequence: 3.0 T/DWI. Assessment: All patients underwent two DWI sequences, including conventional acquisition with b = 0 and 1000 s/mm2 (aDWIb1000) and another with b = 0 and 700 s/mm2 on a 3.0‐T MR scanner. The images of the latter were used to compute the diffusion images with b = 1000 s/mm2 (cDWIb1000). Four radiologists with 3, 4, 14, and 25 years of experience evaluated the images to compare the image quality, TN restaging performance, and treatment response between aDWIb1000 and cDWIb1000. Statistical Tests: Interclass correlation coefficients, weighted κ coefficient, paired Wilcoxon, and McNemar or Fisher test were used. A significance level of 0.05 was used. Results: The cDWIb1000 images were superior to the aDWIb1000 ones in both subjective and objective image quality. In T restaging, the overall diagnostic accuracy of cDWIb1000 images was higher than that of aDWIb1000 images (57.47% vs. 49.43%, P = 0.289 for the inexperienced radiologist; 77.01% vs. 63.22%, significant for the experienced radiologist), with better sensitivity in determining ypT0‐Tis tumors. Additionally, it increased the sensitivity in detecting ypT2 tumors for the inexperienced radiologist and ypT3 tumors for the experienced radiologist. N restaging and treatment response were found to be similar between two sequences for both radiologists. Data Conclusion: Compared to aDWIb1000 images, the computed ones might serve as a wise approach, providing comparable or better image quality, restaging performance, and treatment response assessment for LARC after NT. Level of Evidence: 3 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d′). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d′ for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice. [ABSTRACT FROM AUTHOR]

The purpose of this study is to test the feasibility for deep CNN–based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature–based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN–based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN–based AI methods, while radiomic feature–based machine learning methods achieved inferior classification results. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. Methods: The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. Results: DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). Conclusions: DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. Key Points: • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50. [ABSTRACT FROM AUTHOR]

Although advanced anode materials for the lithium‐ion battery have been investigated for decades, a reliable, high‐capacity, and durable material that can enable a fast charge remains elusive. Herein, we report that a metal phosphorous trichalcogenide of MnPS3 (manganese phosphorus trisulfide), endowed with a unique and layered van der Waals structure, is highly beneficial for the fast insertion/extraction of alkali metal ions and can facilitate changes in the buffer volume during cycles with robust structural stability. The few‐layered MnPS3 anodes displayed the desirable specific capacity and excellent rate chargeability owing to their good electronic and ionic conductivities. When assembled as a half‐cell lithium‐ion battery, a high reversible capacity of 380 mA h g−1 was maintained by the MnPS3 after 3000 cycles at a high current density of 4 A g−1, with a capacity retention of close to or above 100%. In full‐cell testing, a reversible capacity of 450 mA h g−1 after 200 cycles was maintained as well. The results of in‐situ TEM revealed that MnPS3 nanoflakes maintained a high structural integrity without exhibiting any pulverization after undergoing large volumetric expansion for the insertion of a large number of lithium ions. Their kinetics of lithium‐ion diffusion, stable structure, and high pseudocapacitance contributed to their comprehensive performance, for example, a high specific capacity, rapid charge–discharge, and long cyclability. MnPS3 is thus an efficient anode for the next generation of batteries with a fast charge/discharge capability. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). Methods: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. Results: In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). Conclusion: Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa. [ABSTRACT FROM AUTHOR]

Background: Endometriosis developing in a cesarean section (CS) scar is an unusual event. Malignant transformation arising on the background of scar endometriosis in the abdominal wall is extremely rare. Herein we report a case of clear cell carcinoma (CCC) arising in the abdominal wall from endometriosis tissues following CS and review previous literature. Case Presentation: A 48-year-old gravida 2 para 1 female presented with an abdominal wall mass at her CS scar, which increased in size and became painful in the last 2 years. Physical examination showed a multilocular solid mass of about 13 cm, at the previous CS scar. Computed tomography (CT) and magnetic resonance imaging (MRI) revealed a 12.8cm × 7.7cm multi-septate cystic lesion on the anterior abdominal wall, and histological examination showed that CCC was caused by the transformation of abdominal wall endometriosis (AWE). Conclusion: An endometriosis-associated malignancy should be considered in the differential with any enlarging mass in the abdominal wall scar. [ABSTRACT FROM AUTHOR]

Immune system dysregulation is associated with tumor incidence and growth. Here, we established an RNA-based individualized immune signature associated with prognosis for nonsmall cell lung cancer (NSCLC) to guide adjuvant therapy. We downloaded publicly accessible data on RNA expression and clinical characteristics of NSCLC from the Cancer Genome Atlas (TCGA). From immune-related genes (IRGs) retrieved from the immunology database and analysis portal (ImmPort) database, we then screened differentially expressed immune-related genes (DEIRGs). Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors. We also downloaded tumor immune infiltrate data and analyzed the correlations between lymphocytic infiltration with our risk scores, but found no significant association. Furthermore, we retrieved 86 differentially expressed transcription factors (TFs) to assess their regulatory relationships with the 26 prognostic DEIRGs. In summary, we developed a robust risk model to predict survival in patients with NSCLC, based on the expression of 26 IRGs. It provides novel predictive and therapeutic molecular targets. [ABSTRACT FROM AUTHOR]

Background: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. Objective: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. Methods: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. Results: Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. Conclusion: Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance. [ABSTRACT FROM AUTHOR]

Objective: Ischemic stroke seriously threatens human health, characterized by the high rates of incidence, disability, and death. Developing a reliable animal model that mimics most of the features of stroke is critical for pathological studies and clinical research. In this study, we aimed to establish and examine a model of middle cerebral artery occlusion (MCAO) guided by digital subtraction angiography (DSA) in cynomolgus monkeys. Materials and Methods: In this study, 15 adult male cynomolgus monkeys were enrolled. Under the guidance of DSA, a MCAO model was established by injecting an autologous venous clot into the middle cerebral artery (MCA) via femoral artery catheter. Thrombolytic therapy with alteplase (rt-PA) was given to eight of these monkeys at 3 h after the occlusion. Blood test and imaging examination, such as computed tomography angiography (CTA), CT perfusion (CTP), brain magnetic resonance imaging (MRI), and brain magnetic resonance angiography (MRA), were performed after the operation to identify the post-infarction changes. The behavioral performance of cynomolgus monkeys was continuously observed for 7 days after operation. The animals were eunthanized on the 8th day after operation, and then the brain tissues of monkeys were taken for triphenyltetrazolium chloride (TTC) staining. Results: Among the 15 cynomolgus monkeys, 12 of them were successfully modeled, as confirmed by the imaging findings and staining assessment. One monkey died of brain hernia resulted from intracranial hemorrhage confirmed by necropsy. DSA, CTA, and MRA indicated the presence of an arterial occlusion. CTP and MRI showed acute focal cerebral ischemia. TTC staining revealed infarct lesions formed in the brain tissues. Conclusion: Our study may provide an optimal non-human primate model for an in-depth study of the pathogenesis and treatment of focal cerebral ischemia. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study was to evaluate the computed diffusion-weighted images (DWI) in image quality and diagnostic performance of rectal cancer by comparing with the acquired DWI. Methods: A total of 103 consecutive patients with primary rectal cancer were enrolled in this study. All patients underwent two DWI sequences, namely, conventional acquisition with b = 0 and 1,000 s/mm2 (aDWIb1,000) and another with b = 0 and 700 s/mm2 on a 3.0T MR scanner (MAGNETOM Prisma; Siemens Healthcare, Germany). The images (b = 0 and 700 s/mm2) were used to compute the diffusion images with b value of 1,000 s/mm2 (cDWIb1,000). Qualitative and quantitative analysis of both computed and acquired DWI images was performed, namely, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal intensity ratio (SIR), and also diagnostic staging performance. Interclass correlation coefficients, weighted κ coefficient, Friedman test, Wilcoxon paired test, and McNemar or Fisher test were used for repeatability and comparison assessment. Results: Compared with the aDWIb1,000 images, the cDWIb1,000 ones exhibited significant higher scores of subjective image quality (all P <0.050). SNR, SIR, and CNR of the cDWIb1,000 images were superior to those of the aDWIb1,000 ones (P <0.001). The overall diagnostic accuracy of computed images was higher than that of the aDWIb1,000 images in T stage (P <0.001), with markedly better sensitivity and specificity in distinguishing T1–2 tumors from the T3–4 ones (P <0.050). Conclusion: cDWIb1,000 images from lower b values might be a useful alternative option and comparable to the acquired DWI, providing better image quality and diagnostic performance in preoperative rectal cancer staging. [ABSTRACT FROM AUTHOR]

Background: To evaluate the preoperative predictive value of radiomics in the diagnosis of breast cancer (BC). Methods: By searching PubMed and Embase libraries, our study identified 19 eligible studies. We conducted a meta-analysis to assess the differential value in the preoperative assessment of BC using radiomics methods. Results: Nineteen radiomics studies focusing on the diagnostic efficacy of BC and involving 5865 patients were enrolled. The integrated sensitivity and specificity were 0.84 (95% CI: 0.80–0.87, I 2 = 76.44%) and 0.83 (95% CI: 0.78–0.87, I 2 = 81.79%), respectively. The AUC based on the SROC curve was 0.91, indicating a high diagnostic value. Conclusion: Radiomics has shown excellent diagnostic performance in the preoperative prediction of BC and is expected to be a promising method in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: To explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer. Methods: The clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n =123) and validation set (n = 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated. Results: There is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set. Conclusions: We revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients. [ABSTRACT FROM AUTHOR]

A novel negative-sense, single-stranded (ss) RNA virus was identified in a "Shennong Jinhuanghou" (SJ) grapevine showing severe chlorotic mottling symptoms by integrating high-throughput sequencing (HTS) and conventional Sanger sequencing of reverse transcription polymerase chain reaction (RT-PCR) products. The virus was provisionally named as "grapevine emaravirus A" (GEVA). GEVA had a genome comprising five genomic RNA segments, each containing a single open reading frame on the viral complementary strand and two untranslated regions with complementary 13- nt stretches at the 5′ and 3′ terminal ends. RNA1 (7,090 nt), RNA2 (2,097 nt), RNA3 (1,615 nt), and RNA4 (1,640 nt) encoded putative proteins P1–P4 that, based on their conserved motifs, were identified as the RNA-dependent RNA polymerase, glycoprotein, nucleocapsid protein, and movement protein, respectively. However, the functional role of protein P5 encoded by RNA5 (1,308 nt) could not be determined. Phylogenetic trees constructed based on amino acids of P1 to P4, allocated GEVA in clade I, together with other species-related emaraviruses. These data support the proposal that GEVA is a representative member of a novel species in the genus Emaravirus of the family Fimoviridae. Moreover, when GEVA was graft-transmitted to SJ and "Beta" grapevines, all grafted plants showed the same symptoms, similar to those observed in the source of the inoculum. This is the first report to our knowledge of an emaravirus infecting grapevine and its possible association with chlorotic mottling symptoms. [ABSTRACT FROM AUTHOR]

Summary: Purpose Immune-related adverse events (IrAEs) are auto-immune reactions associated with immune checkpoint inhibitor-based therapy (ICI). To date, little is known about immunotherapy-induced pneumonitis (IIP). In this study, we investigated the clinical and CT features of IIP in non-small cell lung cancer (NSCLC) patients treated with ICI. Methods CT images and clinical data of 98 NSCLC patients in our hospital were retrospectively analyzed after ICI therapy, and the incidence, onset time, CT findings, grade, treatment and prognosis of IIP were recorded. Results Nineteen patients developed IIP, which occurred 42∼210 days after ICI therapy, and the median time was 97 days. The CT findings for IIP showed multifocal ground-glass opacity (GGO) in 5 cases, patchy shadows in 6 cases, mixed distribution of patchy and strip-like shadows in 4 cases, and patchy shadows with honeycomb lung in 4 cases. The mean age and proportions of smokers, CD3+ and CD4+ of T lymphocyte subset in patients with IIP were significantly higher than those in patients without IIP (all p < 0.05). Among 19 patients with IIP, there were 10 patients with grade 1 ~ 2 and 9 patients with grade 3 ~ 4; 13 patients received hormone therapy, 12 of them were improved or stable, and 1 patient got worse after hormone therapy. No deaths from IIP were found. Conclusion IIP is a relatively rare but serious adverse event, and it is sensitive to hormone therapy. Its CT manifestations are diverse, and timely detection and treatment are the keys to reduce IIP. [ABSTRACT FROM AUTHOR]

Purpose: The relationship between parameters of brain functional networks and clinical indexes is unclear so far in patients with diabetic retinopathy (DR). This paper is to investigate this. Methods: Twenty-one patients with different grades of DR and 21 age- and sex-matched healthy controls were enrolled from August 2012 to September 2014. The clinical indexes recorded included DR grade, duration of diabetes, HbA1c, diabetic foot screen, fasting plasma glucose, insulin, Homa-β, Homa-IR, insulin sensitive index (ISI), Mini-Mental State Examination (MMSE), and patient sex and age. Subjects were scanned using 3-T MR with blood-oxygen-level-dependent and 3D-FSPGR sequences. MR data was analyzed via preprocessing and functional network construction, and quantified indexes of network (clustering coefficient, characteristic path length, global efficiency, degree distribution, and small worldness) were evaluated. Statistics consisted of ANOVA and correlation. Results: There were significant differences between patients and controls among clustering coefficient, characteristic path length, degree distribution, and small worldness parameters ( P < 0.05). MMSE scores negatively correlated with characteristic path length, and Hb1Ac negatively correlated with small worldness. MMSE, duration of diabetes, diabetic foot screen, fasting plasma glucose, insulin, Homa-β, Homa-IR, ISI, DR grade, and patient age, except from Hb1Ac, correlated with degree distribution in certain brain areas. Conclusion: Brain functional networks are altered, specifically in the areas of visual function and cognition, and these alterations may reflect the severity of visual weakness and cognitive decline in DR patients. Moreover, the brain networks may be affected both by long-standing and instant clinical factors. [ABSTRACT FROM AUTHOR]

Background: Ureter peristalsis is a basic physiological function regulated by myogenic and neurogenic factors. The distribution and function of β-adrenergic receptors (β-AR) in the human ureter remain unknown. The aim of this study was to investigate the expression of β-AR subtypes in the normal and dilated human ureter. Methods: The upper, middle, and lower segments of normal and dilated ureters were collected from patients undergoing surgery for carcinoma of the kidney and upper urinary tract and ureteral stenosis. The mucosa and muscular layers were separated. Expression of β1-AR, β2-AR, and β3-AR mRNA and protein levels were detected by real-time PCR, western blot, and immunohistochemistry. Results: In both mucosa and muscular layers, the mRNA and protein expressions of β1-AR, β2-AR, and β3-AR were lower in the dilated ureter compared with the normal ureter. β1-AR mRNA was significantly decreased (by 76.64%; P < 0.01) in the mucosa layer of the middle segment of the dilated ureter. β1-AR and β3-AR mRNA were significantly decreased (by 75.53 and 53.62%, respectively; P < 0.01) in the muscular layer of the lower segment of the dilated ureter. Similar findings were observed for protein expression. Conclusions: The downregulation of β-ARs after ureter dilation, particularly for β1-AR and β3-AR in the muscular layer, suggests a potential compensatory mechanism involving increased contraction of the ureter to push urine through the obstruction. Thus, β-ARs may be a potential target for treatment of ureter obstruction. [ABSTRACT FROM AUTHOR]

A successful qualitative and quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in air-borne particulates in Hong Kong City has been carried out by the heuristic evolving latent projection (HELP) method recently developed in chemometrics. The resolution result showed that 47 compounds were involved in this sample. Eighteen of them were interesting PAHs and/or their derivatives. The result agreed very well with the one obtained by capillary column GGMS. The resolved UV-visible spectra match well with the standards. With the help of HELP method, accurate quantitative results were also obtained for all the overlapping peaks. The separation ability of chromatography is greatly enhanced by chemometric methods. The HELP method provides a completely new way for quick and accurate analysis of the two-dimensional data produced by hyphenated chromatographic instruments. [ABSTRACT FROM AUTHOR]