Unexpected mutations occurred in CRISPR/Cas9 edited Drosophila analyzed by deeply whole genomic sequencing.

CRISPR/Cas9 possesses the most promising prospects as a gene-editing tool in post-genomic researches. It becomes an epoch-marking technique for the features of speed and convenience of genomic modification. However, it is still unclear whether CRISPR/Cas9 gene editing can cause irreversible damage to the genome. In this study, we successfully knocked out the WHITE gene in Drosophila, which governs eye color, utilizing CRISPR/Cas9 technology. Subsequently, we conducted high-throughput sequencing to assess the impact of this editing process on the stability of the entire genomic profile. The results revealed the presence of numerous unexpected mutations in the Drosophila genome, including 630 SNVs (Single Nucleotide Variants), 525 Indels (Insertion and Deletion) and 425 MSIs (microsatellite instability). Although the KO (knockout) specifically occurred on chromosome X, the majority of mutations were observed on chromosome 3, indicating that this effect is genome-wide and associated with the spatial structure between chromosomes, rather than being solely limited to the location of the KO gene. It is worth noting that most of the mutations occurred in the intergenic and intron regions, without exerting any significant on the function or healthy of the animal. In addition, the mutations downstream of the knockout gene well beyond the upstream. This study has found that gene editing can lead to unexpected mutations in the genome, but most of these mutations are harmless. This research has deepened our understanding of CRISPR/Cas9 and broadened its application prospects.

Synthesis and biological evaluation of triazolones/oxadiazolones as novel urease inhibitors.

Urease is the main virulence factor of infectious gastritis and gastric ulcers. Urease inhibitors are regarded as the first choice for the treatment of such diseases. Based on the triazolone/oxadiazolone skeleton, a urea-like fragment being able to specifically bind the urease activity pocket and prevent urea from hydrolysis, we designed and synthesized 45 triazolones/oxadiazolones as urease inhibitors. Eight compounds were proved to show excellent inhibitory activity against Helicobacter pylori urease, being more potency than the clinically used urease inhibitor acetohydroxamic acid. The most active inhibitor with IC50 value of 1.2 µM was over 20-fold higher potent than the positive control. Enzymatic kinetic assays showed that these novel inhibitors reversibly inhibited urease with a mixed competitive mechanism. Molecular dockings provided evidence for the observations in enzyme assays. Furthermore, these novel inhibitors were proved as drug-like compounds with very low cytotoxicity to mammalian cells and favorable water solubility. These results suggested that triazolone and oxadiazolone were promising scaffolds for the design and discovery of novel urease inhibitors, and were expected as good candidates for further drug development.

Correction: Zhang et al. Experimental and Numerical Studies on Hot Compressive Deformation Behavior of a Cu-Ni-Sn-Mn-Zn Alloy. Materials 2023, 16, 1445.

In the original publication [...].

Design and rationale of the ATTRACTIVE trial: a randomised trial of intrAThrombus Thrombolysis versus aspiRAtion thrombeCTomy during prImary percutaneous coronary interVEntion in ST-segment elevation myocardial infarction patients with high thrombus burden.

INTRODUCTION: ST-segment elevation myocardial infarction (STEMI) with high thrombus burden is associated with a poor prognosis. Manual aspiration thrombectomy reduces coronary vessel distal embolisation, improves microvascular perfusion and reduces cardiovascular deaths, but it promotes more strokes and transient ischaemic attacks in the subgroup with high thrombus burden. Intrathrombus thrombolysis (ie, the local delivery of thrombolytics into the coronary thrombus) is a recently proposed treatment approach that theoretically reduces thrombus volume and the risk of microvascular dysfunction. However, the safety and efficacy of intrathrombus thrombolysis lack sufficient clinical evidence. METHODS AND ANALYSIS: The intrAThrombus Thrombolysis versus aspiRAtion thrombeCTomy during prImary percutaneous coronary interVEntion trial is a multicentre, prospective, open-label, randomised controlled trial with the blinded assessment of outcomes. A total of 2500 STEMI patients with high thrombus burden who undergo primary percutaneous coronary intervention will be randomised 1:1 to intrathrombus thrombolysis with a pierced balloon or upfront routine manual aspiration thrombectomy. The primary outcome will be the composite of cardiovascular death, recurrent myocardial infarction, cardiogenic shock, heart failure readmission, stent thrombosis and target-vessel revascularisation up to 180 days. ETHICS AND DISSEMINATION: The trial was approved by Ethics Committees of China-Japan Friendship Hospital (2022-KY-013) and all other participating study centres. The results of this trial will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05554588.

A situational analysis of human resource and non-communicable diseases management for community health workers in Chengdu, China: a cross-sectional study.

BACKGROUND: Non-communicable diseases (NCDs) pose a major challenge to health economic cost and residents' health status. Community health workers (CHWs) are the gatekeeper of primary health care. OBJECTIVE: This study aimed to conduct a situational analysis of current human resource and requirements of NCDs-related training among CHWs in Chengdu with regard to address to understand the suggestions for improvement of challenges and barriers. METHODS: A descriptive online cross-sectional survey was conducted among CHWs (doctors and nurses) from 23 districts and counties in Chengdu. Sociodemographic and NCDs-related variables were collected. Univariate analysis and multiple response analysis were used to describe the characteristics of these variables. RESULTS: 711 doctors and 637 nurses completely responded. There were significant differences among gender, age, educational levels, professional title, working year, type of institution, urban circle and registration in general practice between doctors and nurses (P < 0.001). 60.6% of doctors were female, compared to 98.0% for nurses. 58.2% of doctors held a bachelor's degree compared with 45.4% of nurses, while 48.3% of nurses held a junior college degree compared with 25.7% of doctors. Higher levels of professional title and registration in general practice were found in doctors compared with nurses. The proportions of NCDs' category, NCDs-related roles and tasks, NCDs-related training contents and forms that CHWs have attend and hoped to gain more were significantly different between doctors and nurses (P < 0.001). In general, the proportions in nurses were much lower than those of doctors (P < 0.05). The top five diseases managed by CHWs were hypertension, diabetes, cerebrovascular disease, chronic respiratory diseases and mental diseases. The five most reported roles performed among doctors included the distribution of health education (91.4%), following up (85.9%), establishing archives (71.3%), medicine adjustment (64.7%) and treatment implementation (52.0%). The top three diseases managed by nurses were same with doctors. The top four and five tasks were contact with patients or health services (39.6%) and referral (16.6%) in nurses. Most CHWs had received primary and common diseases-related trainings, but they had few opportunities to study in a tertiary hospital (40.4% in doctors and 20.9% in nurses, respectively), attend domestic academic conferences (26.9% in doctors vs. 9.7% in nurses), and take part in training courses (44.9% in nurses). CHWs hoped that the above-discussed training contents and forms could be provided more in the future. Besides basic skills related trainings, some specific skills related trainings should be strengthened. CONCLUSION: The qualifications in doctors were much better than those of nurses. The roles performed by CHWs in NCDs management are varied form common and frequent disease management to subsequent follow up and supervision. CHWs hope to receive more desired and oriented trainings. There is a need for building capacity of CHWs, optimizing and defining CHWs' role, facilitating postgraduate medical education support and strengthening multidisciplinary collaboration would be effective in NCDs management.

Localizing From Classification: Self-Directed Weakly Supervised Object Localization for Remote Sensing Images.

In recent years, object localization and detection methods in remote sensing images (RSIs) have received increasing attention due to their broad applications. However, most previous fully supervised methods require a large number of time-consuming and labor-intensive instance-level annotations. Compared with those fully supervised methods, weakly supervised object localization (WSOL) aims to recognize object instances using only image-level labels, which greatly saves the labeling costs of RSIs. In this article, we propose a self-directed weakly supervised strategy (SD-WSS) to perform WSOL in RSIs. To specify, we fully exploit and enhance the spatial feature extraction capability of the RSIs' classification model to accurately localize the objects of interest. To alleviate the serious discriminative region problem exhibited by previous WSOL methods, the spatial location information implicit in the classification model is carefully extracted by GradCAM ++ to guide the learning procedure. Furthermore, to eliminate the interference from complex backgrounds of RSIs, we design a novel self-directed loss to make the model optimize itself and explicitly tell it where to look. Finally, we review and annotate the existing remote sensing scene classification dataset and create two new WSOL benchmarks in RSIs, named C45V2 and PN2. We conduct extensive experiments to evaluate the proposed method and six mainstream WSOL methods with three backbones on C45V2 and PN2. The results demonstrate that our proposed method achieves better performance when compared with state-of-the-arts.

Association of magnesium sulfate use with mortality in critically ill patients with sepsis: a retrospective propensity score-matched cohort study.

BACKGROUND: Trials have demonstrated lower rates of acute kidney injury in critically ill patients receiving magnesium supplementation, but they have yielded conflicting results regarding mortality. METHODS: This is a retrospective cohort study based on the MIMIC-IV (Medical Information Mart in Intensive Care-IV) database. Adult critically ill patients with sepsis were included in the analysis. The exposure was magnesium sulfate use during ICU stay. The primary outcome was 28-day all-cause mortality. Propensity score matching (PSM) was conducted at a 1:1 ratio. Multivariable analyses were used to adjust for confounders. RESULTS: The pre-matched and propensity score-matched cohorts included 10 999 and 6052 patients, respectively. In the PSM analysis, 28-day all-cause mortality rate was 20.2% (611/3026) in the magnesium sulfate use group and 25.0% (757/3026) in the no use group. Magnesium sulfate use was associated with lower 28-day all-cause mortality (hazard ratio [HR], 0.70; 95% CI, 0.61-0.79; P<0.001). Lower mortality was observed regardless of baseline serum magnesium status: for hypomagnesaemia, HR, 0.64; 95% confidence interval (CI), 0.45-0.93; P=0.020; for normomagnesaemia, HR, 0.70; 95% CI, 0.61-0.80; P<0.001. Magnesium sulfate use was also associated with lower ICU mortality (odds ratio [OR], 0.52; 95% CI, 0.42-0.64; P<0.001), lower in-hospital mortality (OR, 0.65; 95% CI, 0.55-0.77; P<0.001), and renal replacement therapy (OR, 0.67; 95% CI, 0.52-0.87; P=0.002). A sensitivity analysis using the entire cohort also demonstrated lower 28-day all-cause mortality (HR, 0.62; 95% CI, 0.56-0.69; P<0.001). CONCLUSIONS: Magnesium sulfate use was associated with lower mortality in critically ill patients with sepsis. Prospective studies are needed to verify this finding.

Photothermal and Catalytic Performance of Multifunctional Cu-Fe Bimetallic Prussian Blue Nanocubes with the Assistance of Near-Infrared Radiation.

Copper and iron are the basic metal elements that have attracted much attention in industry. Prussian blue (PB) is a significant class of metal-organic frameworks (MOFs); however, the lack of such linkages between the structure and properties, as well as properties differences, limits their potential applications. In this paper, the Cu-based Prussian blue nanocubes with and without Fe doping were synthesized. With the increasing reaction time, the morphology of the Cu-based Prussian blue nanocubes without Fe doping (PB:Cu NCs) changes from cuboidal to circular, and finally grows back to cuboidal. However, Cu-based Prussian blue nanocubes with Fe doping (PB: CuFe NCs) grow directly from the cube and eventually collapse. The nanocubes show a notable red shift with the tunable spectra from 400 nm to 700 nm. Compared with PB: Cu NCs, the PB: CuFe NCs have higher temperature rise under 808 nm irradiation and better photothermal efficacy. The catalytic efficiency of PB: CuFe NCs changes with the pH and reaches its maximum value of 1.021 mM with a pH of 5.5. The enhanced catalytic reaction by the near-infrared radiation plasmonic photothermal effect is also confirmed. This work highlights the potential of the developed PB: Cu and PB: CuFe NCs for photothermal-enhanced co-catalysis nanomaterials.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study.

Objective: Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms. Methods: Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings. Results: DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C). Conclusion: A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.

Achieving Better Category Separability for Hyperspectral Image Classification: A Spatial-Spectral Approach.

The task of hyperspectral image (HSI) classification has attracted extensive attention. The rich spectral information in HSIs not only provides more detailed information but also brings a lot of redundant information. Redundant information makes spectral curves of different categories have similar trends, which leads to poor category separability. In this article, we achieve better category separability from the perspective of increasing the difference between categories and reducing the variation within category, thus improving the classification accuracy. Specifically, we propose the template spectrum-based processing module from spectral perspective, which can effectively expose the unique characteristics of different categories and reduce the difficulty of model mining key features. Second, we design an adaptive dual attention network from spatial perspective, where the target pixel can adaptively aggregate high-level features by evaluating the confidence of effective information in different receptive fields. Compared with the single adjacency scheme, the adaptive dual attention mechanism makes the ability of target pixel to combine spatial information to reduce variation more stable. Finally, we designed a dispersion loss from the classifier's perspective. By supervising the learnable parameters of the final classification layer, the loss makes the category standard eigenvectors learned by the model more dispersed, which improves the category separability and reduces the rate of misclassification. Experiments on three common datasets show that our proposed method is superior to the comparison method.

Experimental and Numerical Studies on Hot Compressive Deformation Behavior of a Cu-Ni-Sn-Mn-Zn Alloy.

Cu-9Ni-6Sn alloys have received widespread attention due to their good mechanical properties and resistance to stress relaxation in the electronic and electrical industries. The hot compression deformation behaviors of the Cu-9Ni-6Sn-0.3Mn-0.2Zn alloy were investigated using the Gleeble-3500 thermal simulator at a temperature range of 700-900 °C and a strain rate range of 0.001-1 s-1. The microstructural evolution of the Cu-9Ni-6Sn alloy during hot compression was studied by means of an optical microscope and a scanning electron microscope. The constitutive equation of hot compression of the alloy was constructed by peak flow stress, and the corresponding 3D hot processing maps were plotted. The results showed that the peak flow stress decreased with the increase in the compression temperature and the decrease in the strain rate. The hot deformation activation energy was calculated as 243.67 kJ/mol by the Arrhenius equation, and the optimum deformation parameters for the alloy were 740-760 °C and 840-900 °C with a strain rate of 0.001~0.01 s-1. According to Deform-3D finite element simulation results, the distribution of the equivalent strain field in the hot deformation samples was inhomogeneous. The alloy was more sensitive to the deformation rate than to the temperature. The simulation results can provide a guideline for the optimization of the microstructure and hot deformation parameters of the Cu-9Ni-6Sn-0.3Mn-0.2Zn alloy.

Inhibition of Notch 1 signaling in the subacute stage after stroke promotes striatal astrocyte-derived neurogenesis.

Inhibition of Notch1 signaling has been shown to promote astrocyte-derived neurogenesis after stroke. To investigate the regulatory role of Notch1 signaling in this process, in this study, we used a rat model of stroke based on middle cerebral artery occlusion and assessed the behavior of reactive astrocytes post-stroke. We used the γ-secretase inhibitor N-[N-(3,5-diuorophenacetyl)-1-alanyl]-S-phenylglycine t-butylester (DAPT) to block Notch1 signaling at 1, 4, and 7 days after injury. Our results showed that only administration of DAPT at 4 days after stroke promoted astrocyte-derived neurogenesis, as manifested by recovery of white matter fiber bundle integrity on magnetic resonance imaging, which is consistent with recovery of neurologic function. These findings suggest that inhibition of Notch1 signaling at the subacute stage post-stroke mediates neural repair by promoting astrocyte-derived neurogenesis.

Work Hardening Behavior and Microstructure Evolution of a Cu-Ti-Cr-Mg Alloy during Room Temperature and Cryogenic Rolling.

A Cu-1.79Ti-0.39Cr-0.1Mg (wt.%) alloy was prepared by a vacuum induction melting furnace in a high-purity argon atmosphere. The effects of room temperature rolling and cryogenic rolling on the microstructure, textures, and mechanical properties of the alloy were investigated by means of electron backscatter diffraction, transmission electron microscopy, and X-ray diffraction. The results show that the hardness of the cryogenically rolled alloy is 18-30 HV higher than that of the room temperature rolled alloy at any tested rolling reduction. The yield strength and tensile strength of the alloy cryogenically rolled by 90% reduction are 723 MPa and 796 MPa, respectively. With the increase of rolling reduction, the orientation density of the Cube texture decreases, while the Brass texture increases. The Brass texture is preferred especially during the cryogenic rolling, suggesting that the cross-slip is inhibited at the cryogenic temperature. The dislocation densities of Cu-Ti-Cr-Mg alloy increase significantly during the deformation, finally reaching 23.03 × 10-14 m-2 and 29.98 × 10-14 m-2 after a 90% reduction for the room temperature rolled and cryogenically rolled alloys, respectively. This difference could be attributed to the impediment effect of cryogenic temperature on dynamic recovery and dynamic recrystallization. The cryogenic temperature promotes the formation of the dislocation and the nano-twins, leading to the improvement of the mechanical properties of the alloy.

Identification of (N-aryl-N-arylsulfonyl)aminoacetohydroxamic acids as novel urease inhibitors and the mechanism exploration.

Thirty-three (N-aryl-N-arylsulfonyl)aminoacetohydroxamic acids were synthesized in an effort to develop novel urease inhibitors. Among these compounds, 2-(N-(3-nitrophenyl)-N-(4-tert-butylphenylsulfonyl))aminoacetohydroxamic acid (e2) exhibited excellent inhibitory activity against Helicobacter pylori urease with no perceptible cytotoxicity to mammalian cells. Compound e2 showed over 690-fold higher potency than the clinical used urease inhibitor acetohydroxamic acid, reversibly inhibiting urease with a mixed mechanism. Molecular modeling revealed that (N-aryl-N-arylsulfonyl)aminoacetohydroxamic acids may possibly bind Ni ions and two hydrophobic regions with a 'Y'-like shape.

The thoracic anatomy of Pselaphus heisei (Pselaphinae, Staphylinidae, Coleoptera).

We document external and internal thoracic structures of the free-living pselaphine beetle Pselaphus heisei (Pselaphitae) using a set of traditional and modern techniques. Like in the specialized myrmecophile Claviger testaceus (Clavigeritae), the skeletal elements of the pro- and pterothorax are highly compact, with largely reduced inter- and intrasegmental sutures. Features previously listed as synapomorphies of staphylinid subgroups, to which Pselaphinae belong, are confirmed for P. heisei. The only previously proposed thoracic synapomorphy of Pselaphinae, the mesoventral foveae, is likely transformed in P. heisei: we assume that the concavities are directed mesad and internally fused, thus forming a broad channel ("perforation") extending through the keel-like median region of the mesoventrite. The prothoracic foveal system is strongly reduced, with only one pair of pits present in front of the procoxae. Their internal invaginations form a transverse ventral endoskeletal bar that stabilizes the prothorax. The condition observed in the free-living P. heisei is in contrast with previous hypotheses linking the reduction of the foveal system with myrmecophily. Moreover, traces of the mesoventral foveae are even preserved in the highly specialized inquiline C. testaceus. Gland cells are associated with areas of hyaline squamose setae on different body regions, suggesting release of secretions on the ventral side of the head, pro- and mesothorax, and abdominal base. Similar specialized setal patches are common in Pselaphini and related groups within Pselaphitae. The prothoracic musculature in P. heisei is more complex than that in the myrmecophilous C. testaceus and the free-living, unspecialized Creophilus maxillosus (Staphylininae). The metathoracic muscle system is strongly simplified, demonstrating that P. heisei cannot fly, even though wings, some skeletal elements of the flight apparatus, and some small direct flight muscles are preserved. It cannot be fully excluded that indirect flight muscles and thus a functional flight apparatus is preserved in a certain percentage of individuals.

Understanding Private Car Aggregation Effect via Spatio-Temporal Analysis of Trajectory Data.

Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management to urban planning. However, this work is challenging, especially on weekends, due to the inefficient representations of spatiotemporal features for such aggregation effect and the considerable randomness of private car mobility on weekends. In this article, we propose a deep learning framework for a spatiotemporal attention network (STANet) with a neural algorithm logic unit (NALU), the so-called STANet-NALU, to understand the dynamic aggregation effect of private cars on weekends. Specifically: 1) we design an improved kernel density estimator (KDE) by defining a log-cosh loss function to calculate the spatial distribution of the aggregation effect with guaranteed robustness and 2) we utilize the stay time of private cars as a temporal feature to represent the nonlinear temporal correlation of the aggregation effect. Next, we propose a spatiotemporal attention module that separately captures the dynamic spatial correlation and nonlinear temporal correlation of the private car aggregation effect, and then we design a gate control unit to fuse spatiotemporal features adaptively. Further, we establish the STANet-NALU structure, which provides the model with numerical extrapolation ability to generate promising prediction results of the private car aggregation effect on weekends. We conduct extensive experiments based on real-world private car trajectories data. The results reveal that the proposed STANet-NALU outperforms the well-known existing methods in terms of various metrics, including the mean absolute error (MAE), root mean square error (RMSE), Kullback-Leibler divergence (KL), and R2.

Achieving Reliable Intervehicle Positioning Based on Redheffer Weighted Least Squares Model Under Multi-GNSS Outages.

Achieving reliable intervehicle positioning is one of the most fundamental elements for many vehicular applications, including collision avoidance and autonomous driving. Vehicle position is generally provided by a global navigation satellite system (GNSS), which unfortunately suffers from inaccuracy to varying degrees in challenging environments, for example, GNSS outages. In this article, a reliable fusion technique, called non-Gaussian Redheffer weighted least squares ( n GRWLSs), is proposed. This new approach highlights the intervehicle positioning estimation in multi-GNSS outage environments, such as complete, partial, and free GNSS pseudorange outages. The proposed method combines, on the one hand, the benefits of the Gaussian dynamical matrix principle and the Redheffer distribution function for the sparse property in complete GNSS pseudorange outages and, on the other hand, the use of the optimal window size to regulate the data flow generated by both the inertial navigation systems (INSs) and GNSS during a partial GNSS pseudorange outage. During the free GNSS pseudorange outage, the process ignores data from the INS, and instead, GNSS pseudorange information alone will be considered to compute the intervehicle positioning information. Consequently, weighted least squares is used as an intervehicle positioning estimator. To address the pseudorange uncommon and INS measurement noises, the generalized error distribution (GED) is used to estimate the non-Gaussian densities. Finally, road-test experiments are implemented to evaluate the consistency of the proposed approach. The experimental results show that the proposed n GRWLS can accurately estimate the intervehicle positioning under various conditions (free, partial, and complete GNSS pseudorange outages).

Removal of methylene blue by porous biochar obtained by KOH activation from bamboo biochar.

A series of activated biochar (KBBC-700, KBBC-800 and KBBC-900) which were modified by KOH and pyrolysis at various temperatures from ball-milling bamboo powder were obtained. The physicochemical properties and pore structures of activated biochar were investigated by scanning electron microscopy (SEM), fourier transform infrared spectoscopy (FT-IR), X-ray diffraction (XRD) and N2 adsorption/desorption. The adsorption performance for the removal of methylene blue (MB) was deeply studied. The results showed that KBBC-900 obtained at activation temperature of 900 °C exhibited a great surface area which reached 562 m2/g with 0.460 cm3/g of total pore volume. The enhancement of adsorption capacity could be ascribed to the increase of surface oxygen-containing functional groups, aromatization and mesoporous channels. The adsorption capacity was up to 67.46 mg/g under the optimum adsorption parameters with 2 g/L of adsorbent dose, 11 of initial solution pH and 298 K of the reactive temperature. The adsorption capacity was 70.63% of the first time after the material was recycled for three cycles. The kinetics indicated that the adsorption equilibrium time for MB on KBBC-900 was of about 20 min with the data fitted better to the pseudo-second-order kinetics model. The adsorption process was mainly dominated by chemical adsorption. Meanwhile, the adsorption isotherm showed that the Langmuir model fitted the best, and thermodynamic parameters revealed that the adsorption reaction was the endothermic nature and the spontaneous process. Adsorption of MB mainly attributed to electrostatic interactions, cation-π electron interaction and redox reaction. This study suggested that the activated biochar obtained by KOH activation from bamboo biochar has great potentials in the practical application to remove MB from wastewater.

A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study.

Objective: Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. Methods: We retrospectively reviewed the clinicopathology characteristics of patients with geriatric TC in the Surveillance, Epidemiology, and End Results database (SEER) from 2004 to 2018. The risk predictors used to build the nomograms were derived from the Cox proportional risk regression. These nomograms were used to predict 1-, 3-, and 5-year overall survival and cancer-specific survival in elderly patients with TC. The accuracy and discriminability of the new model were evaluated by the consistency index (C-index) and calibration curve. The clinical applicability value of the model was assessed using the decision curve analysis. Results: We used the SEER database to include 16475 patients with geriatric TC diagnosed from 2004 to 2018. The patients from 2004 to 2015 were randomly sorted out on a scale of 7:3. They were classified into a training group (n = 8623) and a validation group (n = 3669). Patients with TC diagnosed in 2016-2018 were classified into external validation groups (n = 4183). The overall survival nomogram consisted of 10 variables (age, gender, marital status, histologic type, grade, TNM stage, surgery status, and tumor size). A cancer-specific survival nomogram consisted of eight factors (age, tumor size, grade, histologic type, surgery, and TNM stage). The C-index values for the training, validation, and external validation groups were 0.775 (95% confidence interval [CI] 0.785-0.765), 0.776 (95% CI 0.792-0.760), and 0.895(95% CI 0.873-0.917), respectively. The overall survival was consistent with a nomogram based on the calibration curve. Besides, the decision curve analysis showed excellent clinical application value of the nomogram. Additionally, we found that surgery could improve the prognosis of patients with geriatric at high-risk (P < 0.001) but not those at low-risk (P = 0.069). Conclusion: This was the first study to construct predictive survival nomograms for patients with geriatric TC. The well-established nomograms and the actual results could guide follow-up management strategies.