Search

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]

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]

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]

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]

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]

We designed and fabricated a novel microdevice to facilitate continuous adsorption phenomena for biological sample preparation. Using the device, we also developed an online, highly integrated, multifunctional strategy, with a promise of accepting a large volume of crude tissue extracts with the end point generation of a reliable MS identification within 20 min. Under an external electric field, charged membranes can adsorb multiple layers of proteins, which exceed the capacity limit of common resins or membranes. It enlarges sample loading and trapping efficiency, thus bypasses the tradeoff between sample capacity and downstream detection sensitivity. This integrated approach, formed by synergistic utilization among electric field, membrane, and fluidic handling at the microscale, reduces the overall complexity of crude samples in one step for direct MS analysis. The sample preparation goals, including enrichment, desalting, removal of noncharged contaminants, and initial fractionation, can be rapidly performed in a single device. The strategy facilitates reproducible MS quantification by circumventing traditional laborious and time-consuming sample preparation steps. In addition, MEPD extended the ion trap linear dynamic range from 2 to at least 4 orders of magnitude by eliminating ion suppression effect, enriching target analyte(s), and decreasing sample loss during integrated sample preparation.

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]