Insights into enhanced immobilization of uranyl carbonate from seawater by Fe-doped MXene.

Extracting uranium from seawater not only reduces radioactive contamination in seawater but also provides a source of uranium energy. However, due to the low concentration of uranium in seawater and the high salinity of seawater, extraction of uranium from seawater is challenging. In this work, we demonstrated a simple strategy to synthesize Fe-doped MXene (Fe@Ti3C2Tx) via a hydrothermal method and applied for uranium enrichment in seawater. The Fe@Ti3C2Tx exhibited excellent adsorption performance in high salinity environments. The removal capacity of Fe@Ti3C2Tx was determined to be 526.6 mg/g for UO2(CO3)22- at 328 K with quick reaction equilibrium (∼ 30 min). Kinetic and thermodynamic analyses of UO2(CO3)22- elimination process on Fe@Ti3C2Tx surface revealed it to be a spontaneous and endothermic single-phase elimination process. FT-IR and XPS analyses further indicated that the removal mechanism of UO2(CO3)22- by Fe@Ti3C2Tx was surface complexation. Our study suggests that Fe@Ti3C2Tx can provide a feasible solution for uranium enrichment in seawater.

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features.

BACKGROUND: Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity. OBJECTIVES: To develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high-risk (HR) and low-risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors. METHODS: The deep learning systems were trained on 161 H&E -stained sections which contained 51 sections of HR-BCC, 50 sections of LR-BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi-head self-attention (MSA) U-Net, nuclei segmentation by HoVer-Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers. RESULTS: MSA-U-Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977 ± 0.0159, 0.955 ± 0.0181, 0.885 ± 0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T-T_meanEdgeLength, T-T_Nsubgraph, S-T_HarmonicCentrality, S-S_Degrees. The risk stratification model can well predict HR-BCC and LR-BCC with 0.920 ± 0.0579, 0.839 ± 0.0176, 0.825 ± 0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR-BCC and LR-BCC comprised IntensityMin, Solidity, T-T_minEdgeLength, T-T_Coreness, T-T_Degrees, T-T_Betweenness, S-T_Degrees. CONCLUSIONS: This framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification.

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer.

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

Rational design of MOF@COF composites with multi-site functional groups for enhanced elimination of U(VI) from aqueous solution.

Both environment and human beings were menaced by the widespread application of radioactive uranium, high-performance and effective elimination of uranium from wastewater is of important meaning for development of environmental sustainability in the future. In this study, the water-stable MOF material and the highly crystalline COF were compounded by a mild hydrothermal strategy, which achieved efficient removal of U(VI) through the synergistic effect. The composites showed the characteristics of both COFs and MOFs, which will possess higher stability, larger surface area and faster adsorption efficiency that cannot be carried out by a single component. Batch experiments and characterizations (SEM, TEM, XRD, FT-IR, BET, XPS, etc.) indicated that UiO-66-NH2@LZU1 had more stable and multi-layer pore structure and rich active functional groups. The Langmuir model and the pseudo-second-order kinetics fitting was more suitable for the U(VI) elimination process. The greatest uranium adsorbing capacity of UiO-66-NH2@LZU1 (180.4 mg g-1) was observed to exceed the UiO-66-NH2 (108.8 mg g-1) and COF-LZU1 (65.8 mg g-1), which reached the excellent hybrid effects. Furthermore, FT-IR and XPS analyses confirmed that the most nitrogen-containing group from COF-LZU1 and oxygen-containing group of UiO-66-NH2 could be combined with U(VI). In addition, electrostatic interaction was also a mechanism during the removal process. This work displayed that UiO-66-NH2@LZU1 was a prospective hybrid material for radioactive waste remediation. The compound method and application mentioned in this work had provided a theoretical basis for designing and developing multi-functional composite adsorbents, which contributed to the development of new materials for radioactive wastewater treatment technologies.

Influence of humic acid on U(VI) elimination by ZIF-8: Synergistic chemical effect.

ZIF-8, a sort of zeolitic imidazolate frameworks (ZIFs), had showed superior adsorptive property of typical radionuclide U(VI), but it reminded uncertain how the performance of ZIF-8 would be affected by adding humic acid (HA). HA could significantly change the surface charge of ZIFs and the transport of U(VI) in natural settings, which affected the eradication of U(VI) in aquatic ecology. Thus the impact of HA for the U(VI) removal by ZIF-8 as well as its mechanism had been analyzed by batch experiments and spectral analyses. It was demonstrated that the addition of HA increased the maximum removal capacity towards U(VI) from 781.2 mg g-1 to 1398.5 mg g-1. Moreover, removal property in acidic solution was improved, and the influence of background ions on ZIF-8 was reduced. The detailed mechanism was further explored by microscopic spectral analysis. The zeta potential showed that HA enhanced the electronegativity of ZIF-8 thus enhancing the electrostatic interaction with positive ions. Moreover, FT-IR and XPS further indicated that HA enhanced the removal capacity by affecting the surface complexation phenomena and strong chemical interactions between U(VI) and ZIF-8. Also, investigations indicated that the incorporation of HA improved the removal efficiency for U(VI), which had far-reaching significance for the application of ZIF-8 in practical environment.

CT radiomic signature predicts survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma.

Chemoradiation is a common therapeutic regimen for human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). However, not all patients benefit from chemotherapy, especially patients with low-risk characteristics. We aim to develop and validate a prognostic and predictive radiomic image signature (pRiS) to inform survival and chemotherapy benefit using computed tomography (CT) scans from 491 stage I and II HPV-associated OPSCC, which were divided into three cohorts D1-D3. The prognostic performance of pRiS was evaluated on two test sets (D2, n = 162; D3, n = 269) using concordance index. Patients from D2 and D3 who received either radiotherapy alone or chemoradiation were used to validate pRiS as predictive of added benefit of chemotherapy. Seven features were selected to construct pRiS, which was found to be prognostic of overall survival (OS) on univariate analysis in D2 (hazard ratio [HR] = 2.14, 95% confidence interval [CI], 1.1-4.16, p = 0.02) and D3 (HR = 2.74, 95% CI, 1.34-5.62, p = 0.006). Chemotherapy was associated with improved OS for high-pRiS patients in D2 (radiation vs chemoradiation, HR = 4.47, 95% CI, 1.73-11.6, p = 0.002) and D3 (radiation vs chemoradiation, HR = 2.99, 95% CI, 1.04-8.63, p = 0.04). In contrast, chemotherapy did not improve OS for low-pRiS patients, which indicates these patients did not derive additional benefit from chemotherapy and could be considered for treatment de-escalation. The proposed radiomic signature was prognostic of patient survival and informed benefit from chemotherapy for stage I and II HPV-associated OPSCC patients.

A review of iodine in plants with biofortification: Uptake, accumulation, transportation, function, and toxicity.

Iodine deficiency can cause thyroid disease, a serious health problem that has been affecting humans since several years. The biofortification of plants with iodine is an effective strategy for regulating iodine content in humans. In addition, radioiodine released into the atmosphere may contaminate terrestrial ecosystem along with dry or wet deposition and its accumulation in plants may cause exposure risks to humans via food chain. Recent progress in understanding the mechanisms related to iodine uptake, elementary speciation, dynamic transportation, nutritional role, and toxicity in plants is reviewed here. First, we introduced the iodine cycle in a marine-atmosphere-land system. The content and speciation of iodine in plants under natural conditions and biofortification backgrounds were also analyzed. We then discussed the mechanisms of iodine uptake and efflux by plants. The promotion or inhibition effects of iodine on plant growth were also investigated. Finally, the participation of radioiodine in plant growth and its safety risks along the food chain were evaluated. Furthermore, future challenges and opportunities for understanding the participation of iodine in plants have been outlined.

[Multi-tissue segmentation model of whole slide image of pancreatic cancer based on multi task and attention mechanism].

Accurate segmentation of whole slide images is of great significance for the diagnosis of pancreatic cancer. However, developing an automatic model is challenging due to the complex content, limited samples, and high sample heterogeneity of pathological images. This paper presented a multi-tissue segmentation model for whole slide images of pancreatic cancer. We introduced an attention mechanism in building blocks, and designed a multi-task learning framework as well as proper auxiliary tasks to enhance model performance. The model was trained and tested with the pancreatic cancer pathological image dataset from Shanghai Changhai Hospital. And the data of TCGA, as an external independent validation cohort, was used for external validation. The F1 scores of the model exceeded 0.97 and 0.92 in the internal dataset and external dataset, respectively. Moreover, the generalization performance was also better than the baseline method significantly. These results demonstrate that the proposed model can accurately segment eight kinds of tissue regions in whole slide images of pancreatic cancer, which can provide reliable basis for clinical diagnosis.

Highly efficient extraction of uranium from seawater by polyamide and amidoxime co-functionalized MXene.

Uranium mainly exists in the form of uranyl carbonate in seawater. [UO2(CO3)3]4- has strong stability, which increases the difficulty of uranium extraction from seawater. Meanwhile, the complex marine environment, a large number of coexisting competing ions and biological pollution are all non-negligible disturbing factors. Herein, we introduced amidoxime (AO) groups into the surface of Ti3C2 and grafted polyamides (PA) by a simple one-step hydrothermal method to produce an efficient seawater uranium extraction adsorbent Ti3C2-AO-PA. Owing to the amidoxime groups, the material was highly selective for uranium. And the large number of amino groups in the polyamides gave it ideal resistance to biofouling. The possibility of Ti3C2-AO-PA as an adsorbent for uranium extraction from seawater was confirmed by various characterization techniques, numerous adsorption batch experiments, simulated seawater experiments and antibacterial performance tests. It was demonstrated that the uptake of [UO2(CO3)3]4- by Ti3C2-AO-PA showed fast reaction kinetics (about 120 min), brilliant absorption capacity (81.1 mg·g-1 at pH 8.3), significant high selectivity (32.8 mg-U/g-Ads) and outstanding anti-biological contamination performance (92.9% antibacterial rate). XPS and DFT further indicated that the high extraction ability of Ti3C2-AO-PA for uranium was mainly attributed to the strong complexation of AO and -NH2 with [UO2(CO3)3]4-. These conclusions showed that Ti3C2-AO-PA not only had an ideal application prospect for uranium extraction from seawater, but also provided an available strategy for rapid and selective uranium adsorption from real seawater.

A novel pipeline for computerized mouse spermatogenesis staging.

MOTIVATION: Differentiating 12 stages of the mouse seminiferous epithelial cycle is vital towards understanding the dynamic spermatogenesis process. However, it is challenging since two adjacent spermatogenic stages are morphologically similar. Distinguishing Stages I-III from Stages IV-V is important for histologists to understand sperm development in wildtype mice and spermatogenic defects in infertile mice. To achieve this, we propose a novel pipeline for computerized spermatogenesis staging (CSS). RESULTS: The CSS pipeline comprises four parts: (i) A seminiferous tubule segmentation model is developed to extract every single tubule; (ii) A multi-scale learning (MSL) model is developed to integrate local and global information of a seminiferous tubule to distinguish Stages I-V from Stages VI-XII; (iii) a multi-task learning (MTL) model is developed to segment the multiple testicular cells for Stages I-V without an exhaustive requirement for manual annotation; (iv) A set of 204D image-derived features is developed to discriminate Stages I-III from Stages IV-V by capturing cell-level and image-level representation. Experimental results suggest that the proposed MSL and MTL models outperform classic single-scale and single-task models when manual annotation is limited. In addition, the proposed image-derived features are discriminative between Stages I-III and Stages IV-V. In conclusion, the CSS pipeline can not only provide histologists with a solution to facilitate quantitative analysis for spermatogenesis stage identification but also help them to uncover novel computerized image-derived biomarkers. AVAILABILITY AND IMPLEMENTATION: https://github.com/jydada/CSS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.

Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images.

Intrahepatic cholangiocarcinoma (ICC) is cancer that originates from the liver's secondary ductal epithelium or branch. Due to the lack of early-stage clinical symptoms and very high mortality, the 5-year postoperative survival rate is only about 35%. A critical step to improve patients' survival is accurately predicting their survival status and giving appropriate treatment. The tumor microenvironment of ICC is the immediate environment on which the tumor cell growth depends. The differentiation of tumor glands, the stroma status, and the tumor-infiltrating lymphocytes in such environments are strictly related to the tumor progress. It is crucial to develop a computerized system for characterizing the tumor environment. This work aims to develop the quantitative histomorphological features that describe lymphocyte density distribution at the cell level and the different components at the tumor's tissue level in H&E-stained whole slide images (WSIs). The goal is to explore whether these features could stratify patients' survival. This study comprised of 127 patients diagnosed with ICC after surgery, where 78 cases were randomly chosen as the modeling set, and the rest of the 49 cases were testing set. Deep learning-based models were developed for tissue segmentation and lymphocyte detection in the WSIs. A total of 107-dimensional features, including different type of graph features on the WSIs were extracted by exploring the histomorphological patterns of these identified tumor tissue and lymphocytes. The top 3 discriminative features were chosen with the mRMR algorithm via 5-fold cross-validation to predict the patient's survival. The model's performance was evaluated on the independent testing set, which achieved an AUC of 0.6818 and the log-rank test p-value of 0.03. The Cox multivariable test was used to control the TNM staging, γ-Glutamytransferase, and the Peritumoral Glisson's Sheath Invasion. It showed that our model could independently predict survival risk with a p-value of 0.048 and HR (95% confidence interval) of 2.90 (1.01-8.32). These results indicated that the composition in tissue-level and global arrangement of lymphocytes in the cell-level could distinguish ICC patients' survival risk.

Deep SED-Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules.

The development of mouse spermatozoa is a continuous process from spermatogonia, spermatocytes, spermatids to mature sperm. Those developing germ cells (spermatogonia, spermatocyte, and spermatids) together with supporting sertoli cells are all enclosed inside seminiferous tubules of the testis, their identification is key to testis histology and pathology analysis. Automated segmentation of all these cells is a challenging task because of their dynamical changes in different stages. The accurate segmentation of testicular cells is critical in developing computerized spermatogenesis staging. In this paper, we present a novel segmentation model, SED-Net, which incorporates a squeeze-and-excitation (SE) module and a dense unit. The SE module optimizes and obtains features from different channels, whereas the dense unit uses fewer parameters to enhance the use of features. A human-in-the-loop strategy, named deep interactive learning, is developed to achieve better segmentation performance while reducing the workload of manual annotation and time consumption. Across a cohort of 274 seminiferous tubules from stages VI to VIII, the SED-Net achieved a pixel accuracy of 0.930, a mean pixel accuracy of 0.866, a mean intersection over union of 0.710, and a frequency weighted intersection over union of 0.878, respectively, in terms of four types of testicular cell segmentation. There is no significant difference between manual annotated tubules and segmentation results by SED-Net in cell composition analysis for tubules from stages VI to VIII. In addition, we performed cell composition analysis on 2346 segmented seminiferous tubule images from 12 segmented testicular section results. The results provided quantitation of cells of various testicular cell types across 12 stages. The rule reflects the cell variation tendency across 12 stages during development of mouse spermatozoa. The method could enable us to not only analyze cell morphology and staging during the development of mouse spermatozoa but also potentially could be applied to the study of reproductive diseases such as infertility.

Effect of Shewanella oneidensis MR-1 on U(VI) sequestration by montmorillonite.

Bacteria may change the physicochemical properties of montmorillonite and further effect the disposal of high-level radioactive waste. Therefore, we explored the influence of Shewanella oneidensis MR-1 on the elimination of representative radionuclide U(VI) by montmorillonite (MMT). The batch experiments showed that MR-1 significantly enhanced the removal efficiency of U(VI), the adsorption capacity of MMT improved from 8.4 to 16.1 mg/g after addition of MR-1, and the adsorption type changed from Langmuir to Freundlich. FTIR and XPS analysis revealed that hydroxyl, phosphate, carbonyl and amine in MMT + MR-1 were primary actors in the elimination of U(VI). The U 4f high-resolution XPS spectrum of MMT + MR-1 showed U(VI) and U(IV) peaks at the same time, indicating that the adsorption process was accompanied by the reduction reaction, which may be due to the extracellular respiration of MR-1. These investigations are significant to insight the potential significance of microbial processes for the transport and elimination of U(VI) in repositories, which in return will contribute to their safe disposal.

Applications of water-stable metal-organic frameworks in the removal of water pollutants: A review.

Because the pollutants produced by human activities have destroyed the ecological balance of natural water environment, and caused severe impact on human life safety and environmental security. Hence the task of water environment restoration is imminent. Metal-organic frameworks (MOFs), structured from organic ligands and inorganic metal ions, are notable for their outstanding crystallinity, diverse structures, large surface areas, adsorption performance, and excellent component tunability. The water stability of MOFs is a key requisite for their possible actual applications in separation, catalysis, adsorption, and other water environment remediation areas because it is necessary to safeguard the integrity of the material structure during utilization. In this article, we comprehensively review state-of-the-art research progress on the promising potential of MOFs as excellent nanomaterials to remove contaminants from the water environment. Firstly, the fundamental characteristics and preparation methods of several typical water-stable MOFs include UiO, MIL, and ZIF are introduced. Then, the removal property and mechanism of heavy metal ions, radionuclide contaminants, drugs, and organic dyes by different MOFs were compared. Finally, the application prospect of MOFs in pollutant remediation prospected. In this review, the synthesis methods and application in water pollutant removal are explored, which provide ways toward the effective use of water-stable MOFs in materials design and environmental remediation.

Recent advances in metal-organic framework membranes for water treatment: A review.

Among many separation membranes reported to date, the favorable polymer affinity and unique physio-chemical performances of metal-organic frameworks (MOFs) including ultra-high surface area, regular and highly controlled porosity have drawn widespread attention in industrial and academic communities. In this comprehensive review, the developmental timeline of MOF containing membranes for water treatment were clarified. The removal efficiencies, elimination mechanisms, as well as possible influencing factors of various MOF containing membranes that applied to water treatment were systematically summarized. The excellent removal performances of MOF containing membranes for various pollutants were determined by the size-exclusion, π-π stacking interaction, electrostatic interaction, hydrogen bonding and so on. Since the progress of engineered MOF containing membranes for practical wastewater treatment applications lags, we further analyzed the potential environmental application of MOF containing membranes from four aspects (stability of MOFs, antifouling performance of membranes, compatibility between MOF fillers and polymer matrix, dispersity of MOF nanoparticles in matrix), hoping to provide some meaningful insights.

A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer.

BACKGROUND: We developed and validated a prognostic and predictive computational pathology risk score (CoRiS) using H&E stained tissue images from patients with early-stage non-small cell lung cancer (ES-NSCLC). METHODS: 1330 patients with ES-NSCLC were acquired from 3 independent sources and divided into four cohorts D1-4. D1 comprised 100 surgery treated patients and was used to identify prognostic features via an elastic-net Cox model to predict overall and disease-free survival. CoRiS was constructed using the Cox model coefficients for the top features. The prognostic performance of CoRiS was evaluated on D2 (N=331), D3 (N=657) and D4 (N=242). Patients from D2 and D3 which comprised surgery + chemotherapy were used to validate CoRiS as predictive of added benefit to adjuvant chemotherapy (ACT) by comparing survival between different CoRiS defined risk groups. FINDINGS: CoRiS was found to be prognostic on univariable analysis, D2 (hazard ratio (HR) = 1.41, adjusted (adj.) P = .01) and D3 (HR = 1.35, adj. P < .001). Multivariable analysis showed CoRiS was independently prognostic, D2 (HR = 1.41, adj. P < .001) and D3 (HR = 1.35, adj. P < .001), after adjusting for clinico-pathologic factors. CoRiS was also able to identify high-risk patients who derived survival benefit from ACT D2 (HR = 0.42, adj. P = .006) and D3 (HR = 0.46, adj. P = .08). INTERPRETATION: CoRiS is a tissue non-destructive, quantitative and low-cost tool that could potentially help guide management of ES-NSCLC patients. FUNDING: Data collection, anlaysis, and computation resources of the research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers: 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01. National Center for Research Resources under award number 1 C06 RR12463-01. VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.

Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis.

Spermatogenesis in mammals is a cyclic process of spermatogenic cell development in the seminiferous epithelium that can be subdivided into 12 subsequent stages. Histological staging analysis of testis sections, specifically of seminiferous tubule cross-sections, is the only effective method to evaluate the quality of the spermatogenic process and to determine developmental defects leading to infertility. Such staging analysis, however, is tedious and time-consuming, and it may take a long time to become proficient. We now have developed a Computerized Staging system of Spermatogenesis (CSS) for mouse testis sections through learning of an expert with decades of experience in mouse testis staging. The development of the CSS system comprised three major parts: 1) Developing computational image analysis models for mouse testis sections; 2) Automated classification of each seminiferous tubule cross-section into three stage groups: Early Stages (ES: stages I-V), Middle Stages (MS: stages VI-VIII), and Late Stages (LS: stages IV-XII); 3) Automated classification of MS into distinct stages VI, VII-mVIII, and late VIII based on newly developed histomorphological features. A cohort of 40 H&E stained normal mouse testis sections was built according to three modules where 28 cross-sections were leveraged for developing tubule region segmentation, spermatogenic cells types and multi-concentric-layers segmentation models. The rest of 12 testis cross-sections, approximately 2314 tubules whose stages were manually annotated by two expert testis histologists, served as the basis for developing the CSS system. The CSS system's accuracy of mean and standard deviation (MSD) in identifying ES, MS, and LS were 0.93 ± 0.03, 0.94 ± 0.11, and 0.89 ± 0.05 and 0.85 ± 0.12, 0.88 ± 0.07, and 0.96 ± 0.04 for one with 5 years of experience, respectively. The CSS system's accuracy of MSD in identifying stages VI, VII-mVIII, and late VIII are 0.74 ± 0.03, 0.85 ± 0.04, and 0.78 ± 0.06 and 0.34 ± 0.18, 0.78 ± 0.16, and 0.44 ± 0.25 for one with 5 years of experience, respectively. In terms of time it takes to collect these data, it takes on average 3 hours for a histologist and 1.87 hours for the CSS system to finish evaluating an entire testis section (computed with a PC (I7-6800k 4.0 GHzwith 32GB of RAM & 256G SSD) and a Titan 1080Ti GPU). Therefore, the CSS system is more accurate and faster compared to a human histologist in staging, and further optimization and development will not only lead to a complete staging of all 12 stages of mouse spermatogenesis but also could aid in the future diagnosis of human infertility. Moreover, the top-ranking histomorphological features identified by the CSS classifier are consistent with the primary features used by histologists in discriminating stages VI, VII-mVIII, and late VIII.

Designed Core-Shell Fe3O4@Polydopamine for Effectively Removing Uranium(VI) from Aqueous Solution.

Adsorbents with the combination of magnetic separation and removal performance are expected for reducing the adverse impact of nuclear pollution. In this study, the core-shell Fe3O4@polydopamine (Fe3O4@PDA) was successfully synthesized and used for removal of uranium (U(VI)) ion from aqueous solution. The abundant N-containing groups derived from PDA exist as the chelate sites for U(VI) and contribute greatly for U(VI) removal. Experimental results show that Fe3O4@PDA (56.39 mg g-1) exhibits greater sorption capacity for U(VI) removal compared with the pure Fe3O4 (9.17 mg g-1). The sorption isotherm can be well fitted with Freundlich model and the sorption process is endothermic and spontaneous. The removal of U(VI) can be explained by the complexation of U(VI) with -NH-, -NH2 and C-O in the surface of Fe3O4@PDA by X-ray photoelectron spectroscopy (XPS) analysis.