DNA methylation markers for risk of metastasis in a cohort of men with localized prostate cancer

Accurately identifying life-threatening prostate cancer (PCa) at time of diagnosis remains an unsolved problem. We evaluated whether DNA methylation status of selected candidate genes can predict the risk of metastasis beyond clinical risk factors in men with untreated PCa. A nested case-control study was conducted among men diagnosed with localized PCa at Kaiser Permanente California between 01/01/1997-12/31/2006 who did not receive curative treatments. Cases were those who developed metastasis within 10 years from diagnosis. Controls were selected using density sampling. Ninety-eight candidate genes were selected from functional categories of cell cycle control, metastasis/tumour suppressors, cell signalling, cell adhesion/motility/invasion, angiogenesis, and immune function, and 41 from pluripotency genes. Cancer DNA from diagnostic biopsy blocks were extracted and analysed. Associations of methylation status were assessed using CpG site level and principal components-based analysis in conditional logistic regressions. In 215 cases and 404 controls, 27 candidate genes were found to be statistically significant in at least one of the two analytical approaches. The agreement between the methods was 25.9% (7 candidate genes, including 2 pluripotency markers). The DNA methylation status of several candidate genes was significantly associated with risk of metastasis in untreated localized PCa patients. These findings may inform future risk prediction models for PCa metastasis beyond clinical characteristics.

Excitation functions for fast neutron induced reactions on iron and lead

Cross sections of the 54Fe(n,p)54Mn, 54Fe(n,α)51Cr, 56Fe(n,p)56Mn and 204Pb (n,2n)203Pb reactions induced by D-T neutrons were obtained with activation method and γ-ray spectrometry technique. Experimental values measured in this work are consistent with most of the previous literature data. These reactions cross sections were theoretically calculated by using the TALYS-1.96 and EMPIRE-3.2.3 codes from threshold up to 20 MeV, and significant discrepancies were found between calculated results and experiment data. In addition, experimental values are compared with evaluated nuclear data of the CENDL-3.2, ENDF/B-VIII.0, JENDL-5, BROND-3.1 and JEFF-3.3 libraries, and significant difference was found for the 54Fe(n,α)51Cr reaction in ENDF/B-VIII.0 library but not for other reactions.

Rapid and sensitive visual detection of avian leukosis virus by reverse transcription loop-mediated isothermal amplification combined with a lateral flow immunochromatographic strip assay

Considering that avian leukosis virus (ALV) infection has inflicted massive economic losses on the poultry breeding industry in most countries, its early diagnosis remains an important measure for timely treatment and control of the disease, for which a rapid and sensitive point-of-care test is required. We established a user-friendly, economical, and rapid visualization method for ALV amplification products based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) combined with an immunochromatographic strip in a lateral flow device (LFD). Using the ALVp27 gene as the target, five RT-LAMP primers and one fluorescein-isothiocyanate-labeled probe were designed. After 60 min of RT-LAMP amplification at 64 °C, the products could be visualized directly using the LFD. The detection limit of this assay for ALV detection was 102 RNA copies/μL, and the sensitivity was 100 times that of reverse transcription polymerase chain reaction (RT-PCR), showing high specificity and sensitivity. To verify the clinical practicality of this assay for detecting ALV, the gold standard RT-PCR method was used for comparison, and consistent results were obtained with both assays. Thus, the assay described here can be used for rapid detection of ALV in resource-limited environments.

Metabolic engineering Corynebacterium glutamicum for D-chiro-inositol production

D-chiro-inositol (DCI) is a potential drug for the treatment of type II diabetes and polycystic ovary syndrome. In order to effectively synthesize DCI in Corynebacterium glutamicum, the genes related to inositol catabolism in clusters iol1 and iol2 were knocked out in C. glutamicum SN01 to generate the chassis strain DCI-1. DCI-1 did not grow in and catabolize myo-inositol (MI). Subsequently, different exogenous and endogenous inosose isomerases were expressed in DCI-1 and their conversion ability of DCI from MI were compared. After fermentation, the strain DCI-7 co-expressing inosose isomerase IolI2 and inositol dehydrogenase IolG was identified as the optimal strain. Its DCI titer reached 3.21 g/L in the presence of 20 g/L MI. On this basis, the pH, temperature and MI concentration during whole-cell conversion of DCI by strain DCI-7 were optimized. Finally, the optimal condition that achieved the highest DCI titer of 6.96 g/L were obtained at pH 8.0, 37 °C and addition of 40 g/L MI. To our knowledge, it is the highest DCI titer ever reported.

Isoliquiritigenin Containing PH Sensitive Micelles for Enhanced Anti-Colitis Activity

Isoliquiritigenin (ISL) is known to have a variety of pharmacological activities, but its poor water solubility limits its application. In order to improve the bioavailability of ISL and its anti-colitis activity, this study aims to develop an effective drug delivery system loaded with ISL. In this study, ISL pH-sensitive micelles (ISL-M) were prepared by thin film hydration method. The micellar size (PS), polydispersity index (PDI), electrokinetic potential (ζ-potential), drug loading (DL), encapsulation rate (EE) and other physical parameters were characterized. The storage stability of ISL-M was tested, release in vitro and pharmacokinetic studies in rats were performed, and the anti-inflammatory effect of ISL-M on ulcerative colitis induced by dextran sulfate sodium (DSS) was evaluated. The results showed that PS, PDI, ZP, EE% and DL% of ISL-M were 151.15±1.04 nm, 0.092±0.014, -31.32±0.721 mV, 93.97±1.53 % and 8.42±0.34 %, respectively. Compared with unformulated ISL (F-ISL), the cumulative release rate of ISL-M in the three different media was significantly increased and showed a certain pH sensitivity. The area under drug curve (AUC0-t) and peak concentration (Cmax) of ISL-M group were 2.94 and 4.06 times higher than those of ISL group. In addition, ISL-M is expected to develop new methods for increasing the bioavailability and anti-inflammatory activity of ISL.

Rational design of triazine-based conjugated polymers with enhanced charge separation ability for photocatalytic hydrogen evolution

Triazine-based conjugated polymers (TCPs) are promising organic catalysts for green H2 production, since their photocatalytic performance can be easily regulated via appropriate molecular design. However, apart from weak absorption of visible light, weak charge separation and transport abilities also considerably restrict the photocatalytic performance of TCPs. Herein, we report two novel TCP photocatalysts with donor-acceptor (D-A) and donor-π-acceptor (D-π-A) structures using dibenzo[g,p]chrysene (Dc), thiophene (T), and 2,4,6-triphenyl-1,3,5-triazine (Tz) as the donor, π-spacer, and acceptor, respectively. Compared to Dc-Tz with a D-A structure, Dc-T-Tz exhibits a broader light absorption edge and more efficient charge separation and transmission due to its D-π-A structure and strong dipole effect. These properties enable Dc-T-Tz to display a prominent H2 production rate of 45.13 mmol h-1 g-1 under ultraviolet-visible (UV-Vis) light (λ > 300 nm). Therefore, Dc-T-Tz represents state-of-the-art TCP photocatalysts to date.

Concentrations of per- and polyfluoroalkyl substances in vegetables from Sydney and Newcastle, Australia

BACKGROUND

This study investigated per- and polyfluoroalkyl substances (PFASs) in 53 fruit and vegetable samples collected from a local wholesale and retail market in Sydney and a local supermarket in Newcastle. As there is limited information about PFAS levels in vegetables on the market, this study aimed to fill this gap and assess potential risks for humans through consumption of these vegetables.

METHODS

QuEChERS extraction - a solid-phase extraction method, a portmanteau word formed from 'quick, easy, cheap, effective, rugged and safe' - followed by enhanced matrix removal-lipid cleaning and liquid chromatography-tandem mass spectrometry analysis were used to detect 30 PFASs in vegetables.

RESULTS

PFOA was detected in 7 out of the 53 samples, with concentrations of 0.038-1.996 ng g-1 fresh weight; PFOS was detected in 2 samples only, with concentrations ranging from 0.132 to 0.911 ng g-1 fresh weight. PFHxS was not detected in any sample in this study. PFOA and PFOS concentrations measured in vegetables in this study constituted daily intake of 2.03 ng kg-1 body weight (BW) and 1.98 ng kg-1 BW, respectively, according to recommended daily vegetable intake and BW data from the Australian Bureau of Statistics. The most sensitive population group is girls of 4-8 years of age. These estimated exposure levels represent up to 1.3% of the tolerable daily intake for PFOA (160 ng kg-1 BW) and 9.9% for PFOS (20 ng kg-1 BW) according to Food Standards Australia New Zealand. Consumption of the vegetables from the study locations poses a marginal risk to human health. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2

OBJECTIVES

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious, fast-spreading, and insidious. Most patients present with normal findings on lung computed tomography (CT). The current study aimed to develop and validate a tracheal CT radiomics model to predict Omicron variant infection.

MATERIALS AND METHODS

In this retrospective study, a radiomics model was developed based on a training set consisting of 157 patients with an Omicron variant infection and 239 healthy controls between 1 January and 30 April 2022. A set of morphological expansions, with dilations of 1, 3, 5, 7, and 9 voxels, was applied to the trachea, and radiomic features were extracted from different dilation voxels of the trachea. Logistic regression (LR), support vector machines (SVM), and random forests (RF) were developed and evaluated; the models were validated on 67 patients with the Omicron variant and on 103 healthy controls between 1 May and 30 July 2022.

RESULTS

Logistic regression with 12 radiomic features extracted from the tracheal wall with dilation of 5 voxels achieved the highest classification performance compared with the other models. The LR model achieved an area under the curve of 0.993 (95% confidence interval [CI]: 0.987-0.998) in the training set and 0.989 (95% CI: 0.979-0.999) in the validation set. Sensitivity, specificity, and accuracy of the model for the training set were 0.994, 0.946, and 0.965, respectively, whereas those for the validation set were 0.970, 0.952, and 0.959, respectively.

CONCLUSION

The tracheal CT radiomics model reliably identified the Omicron variant of SARS-CoV‑2, and may help in clinical decision-making in future, especially in cases of normal lung CT findings.

Identification of a potent palladium-aryldiphosphine catalytic system for high-performance carbonylation of alkenes

The development of stable and efficient ligands is of vital significance to enhance the catalytic performance of carbonylation reactions of alkenes. Herein, an aryldiphosphine ligand (L11) bearing the [Ph2P(ortho-C6H4)]2CH2 skeleton is reported for palladium-catalyzed regioselective carbonylation of alkenes. Compared with the industrially successful Pd/1,2-bis(di-tert-butylphosphinomethyl)benzene catalyst, catalytic efficiency catalyzed by Pd/L11 on methoxycarbonylation of ethylene is obtained, exhibiting better catalytic performance (TON: >2,390,000; TOF: 100,000 h-1; selectivity: >99%) and stronger oxygen-resistance stability. Moreover, a substrate compatibility (122 examples) including chiral and bioactive alkenes or alcohols is achieved with up to 99% yield and 99% regioselectivity. Experimental and computational investigations show that the appropriate bite angle of aryldiphosphine ligand and the favorable interaction of 1,4-dioxane with Pd/L11 synergistically contribute to high activity and selectivity while the electron deficient phosphines originated from electron delocalization endow L11 with excellent oxygen-resistance stability.

Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning

Background

Cerebral small vessel disease lacks specific clinical manifestations, and extraction of valuable features from multimodal images is expected to improve its diagnostic accuracy. In this study, we used deep learning techniques to segment cerebral small vessel disease imaging markers in multimodal magnetic resonance images and analyze them with clinical risk factors.

Methods and results

We recruited 211 lacunar stroke patients and 83 control patients. The patients' cerebral small vessel disease markers were automatically segmented using a V-shaped bottleneck network, and the number and volume were calculated after manual correction. The segmentation results of the V-shaped bottleneck network for white matter hyperintensity and recent small subcortical infarction were in high agreement with the ground truth (DSC>0.90). In small lesion segmentation, cerebral microbleed (average recall=0.778; average precision=0.758) and perivascular spaces (average recall=0.953; average precision=0.923) were superior to lacunar infarct (average recall=0.339; average precision=0.432) in recall and precision. Binary logistic regression analysis showed that age, systolic blood pressure, and total cerebral small vessel disease load score were independent risk factors for lacunar stroke (P<0.05). Ordered logistic regression analysis showed age was positively correlated with cerebral small vessel disease load score and total cholesterol was negatively correlated with cerebral small vessel disease score (P<0.05).

Conclusion

Lacunar stroke patients exhibited higher cerebral small vessel disease imaging markers, and age, systolic blood pressure, and total cerebral small vessel disease score were independent risk factors for lacunar stroke patients. V-shaped bottleneck network segmentation network based on multimodal deep learning can segment and quantify various cerebral small vessel disease lesions to some extent.

Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging

BACKGROUND

The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging.

METHODS

The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

RESULTS

The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857).

CONCLUSION

The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.

Design and Catalytic Asymmetric Synthesis of Furan-Indole Compounds Bearing both Axial and Central Chirality

In the chemistry community, catalytic asymmetric synthesis of furan-based compounds bearing both axial and central chirality has proven to be a significant but challenging issue owing to the importance and difficulty in constructing such frameworks. In this work, we have realized the first catalytic asymmetric synthesis of five-five-membered furan-based compounds bearing both axial and central chirality via organocatalytic asymmetric (2+4) annulation of achiral furan-indoles with 2,3-indolyldimethanols with uncommon regioselectivity. By this strategy, furan-indole compounds bearing both axial and central chirality were synthesized in high yields with excellent regio-, diastereo-, and enantioselectivities. Moreover, theoretical calculations were conducted to provide an in-depth understanding of the reaction pathway, activation mode, and the origin of the selectivity.

Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI

RATIONALE AND OBJECTIVES

Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI.

MATERIALS AND METHODS

First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression.

RESULTS

The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression.

CONCLUSION

The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.

Fast neutron induced reaction cross sections on natural manganese and tantalum

The cross sections for the 55Mn(n,2n)54Mn, 181Ta(n,2n)180gTa, and 181Ta(n,p)181Hf reactions were measured to be 705.1 ± 26.1 mb at 14.0 MeV, 1362.7 ± 87.2 mb at 13.6 MeV, and 2.31 ± 0.09 mb at 13.6 MeV, respectively, by using an off-line γ-ray spectroscopic technique. The neutrons were produced via the 3H(d,n)4He reaction. The monitor reactions 27Al(n,α)24Na and 93Nb(n,2n)92mNb were used for neutron flux determination. The results from the present work were compared with those of the literature and the evaluated data from ENDF/B-VIII.0, JEFF-3.3, JENDL-5, CENDL-3.2, and BROND-3.1 libraries. Besides, the cross sections were also estimated with the TALYS-1.96 nuclear model code using different level density models for a better description of the present work and literature data. The present experimental results were found to be in good agreement with most of the available literature data and with the evaluated data.

Atmospheric deposition and river runoff stimulate the utilization of dissolved organic phosphorus in coastal seas

In coastal seas, the role of atmospheric deposition and river runoff in dissolved organic phosphorus (DOP) utilization is not well understood. Here, we address this knowledge gap by combining microcosm experiments with a global approach considering the relationship between the activity of alkaline phosphatases and changes in phytoplankton biomass in relation to the concentration of dissolved inorganic phosphorus (DIP). Our results suggest that the addition of aerosols and riverine water stimulate the biological utilization of DOP in coastal seas primarily by depleting DIP due to increasing nitrogen concentrations, which enhances phytoplankton growth. This "Anthropogenic Nitrogen Pump" was therefore identified to make DOP an important source of phosphorus for phytoplankton in coastal seas but only when the ratio of chlorophyll a to DIP [Log10 (Chl a / DIP)] is larger than 1.20. Our study therefore suggests that anthropogenic nitrogen input might contribute to the phosphorus cycle in coastal seas.

Carbon dots@noble metal nanoparticle composites: research progress report

Carbon dots@noble metal nanoparticle composites are formed by combining carbon dots and metal nanoparticles using various strategies. Carbon dots exhibit a reducing ability and function as stabilisers; consequently, metal-ion solutions can be directly reduced by them to synthesise gold, silver, and gold-silver alloy particles. Carbon dots@gold/silver/gold-silver particle composites have demonstrated the potential for several practical applications owing to their superior properties and simple preparation process. Until now, several review articles have been published to summarise fluorescent carbon dots or noble metal nanomaterials. Compared with metal-free carbon dots, carbon dots@noble metal nanoparticles have a unique morphology and structure, resulting in new physicochemical properties, which allow for sensing, bioimaging, and bacteriostasis applications. Therefore, to promote the effective development of carbon dots@noble metal nanoparticle composites, this paper primarily reviews carbon dots@gold/silver/gold-silver alloy nanoparticle composites for the first time in terms of the following aspects. (1) The synthesis strategies of carbon dots@noble metal nanoparticle composites are outlined. The principle and function of carbon dots in the synthesis strategies are examined. The advantages and disadvantages of these methods and composites are analysed. (2) The characteristics and properties of such composites are described. (3) The applications of these composite materials are summarised. Finally, the potentials and limitations of carbon dots@noble metal nanoparticle composites are discussed, thus laying the foundation for their further development.

CYLD induces high oxidative stress and DNA damage through class I HDACs to promote radiosensitivity in nasopharyngeal carcinoma

Abnormal expression of Cylindromatosis (CYLD), a tumor suppressor molecule, plays an important role in tumor development and treatment. In this work, we found that CYLD binds to class I histone deacetylases (HDAC1 and HDAC2) through its N-terminal domain and inhibits HDAC1 activity. RNA sequencing showed that CYLD-HDAC axis regulates cellular antioxidant response via Nrf2 and its target genes. Then we revealed a mechanism that class I HDACs mediate redox abnormalities in CYLD low-expressing tumors. HDACs are central players in the DNA damage signaling. We further confirmed that CYLD regulates radiation-induced DNA damage and repair response through inhibiting class I HDACs. Furthermore, CYLD mediates nasopharyngeal carcinoma cell radiosensitivity through class I HDACs. Thus, we identified the function of the CYLD-HDAC axis in radiotherapy and blocking HDACs by Chidamide can increase the sensitivity of cancer cells and tumors to radiation therapy both in vitro and in vivo. In addition, ChIP and luciferase reporter assays revealed that CYLD could be transcriptionally regulated by zinc finger protein 202 (ZNF202). Our findings offer novel insight into the function of CYLD in tumor and uncover important roles for CYLD-HDAC axis in radiosensitivity, which provide new molecular target and therapeutic strategy for tumor radiotherapy.

A Chromosome-Level Genome Assembly of the Non-Hematophagous Leech Whitmania pigra (Whitman 1884): Identification and Expression Analysis of Antithrombotic Genes

Despite being a non-hematophagous leech, Whitmania pigra is widely used in traditional Chinese medicine for the treatment of antithrombotic diseases. In this study, we provide a high quality genome of W. pigra and based on which, we performed a systematic identification of the potential antithrombotic genes and their corresponding proteins. We identified twenty antithrombotic gene families including thirteen coagulation inhibitors, three platelet aggregation inhibitors, three fibrinolysis enhancers, and one tissue penetration enhancer. Unexpectedly, a total of 79 antithrombotic genes were identified, more than a typical blood-feeding Hirudinaria manillensis, which had only 72 antithrombotic genes. In addition, combining with the RNA-seq data of W. pigra and H. manillensis, we calculated the expression levels of antithrombotic genes of the two species. Five and four gene families had significantly higher and lower expression levels in W. pigra than in H. manillensis, respectively. These results showed that the number and expression level of antithrombotic genes of a non-hematophagous leech are not always less than those of a hematophagous leech. Our study provides the most comprehensive collection of antithrombotic biomacromolecules from a non-hematophagous leech to date and will significantly enhance the investigation and utilization of leech derivatives in thrombosis therapy research and pharmaceutical applications.

In Situ Electrospinning MOF-Derived Highly Dispersed α-Cobalt Confined in Nitrogen-Doped Carbon Nanofibers Nanozyme for Biomolecule Monitoring

Designing a metal-organic framework (MOF)-derived nanozyme with highly dispersed active sites and high catalytic activity as well as robust structure for colorimetric biosensing of diverse biomolecules remains a substantial challenge. Here, an MOF-derived highly dispersed and pure α-cobalt confined in a nitrogen-doped carbon nanofiber (α-Co@NCNF) nanozyme with superior glucose oxidase (GOD)- and peroxidase (POD)-like activities was constructed for colorimetric assay of multiple biomolecules. Specifically, the α-Co@NCNF nanozyme was synthesized, utilizing in situ electrospinning Co-MOFs into polyacrylonitrile nanofiber (PAN) followed by a pyrolysis process. Taking advantage of the in situ electrospinning strategy, the α-Co nanoparticles were confined in continuous porous NCNF to restrict the growth and prevent the aggregation and oxidation during the pyrolysis process. The resulting special structure considerably improved the enzyme-like performance. A series of experiments validate that the enzyme-like activity of the α-Co@NCNF nanozyme was superior to that of Co@CoO@NCNF (derivatives from Co-MOFs grown on the surface of PAN nanofiber) and nature enzymes. Furthermore, α-Co@NCNF nanozyme-based colorimetric biosensing was developed for monitoring glucose, hydrogen peroxide (H2O2), and glutathione (GSH) and the corresponding linear ranges are 0.1-50 and 50-900 μM and 5-55 and 0.1-20 μM accompanied by the corresponding low detection of 0.03, 1.66, and 0.03 μM. The proposed method for the construction of α-Co@NCNF nanozyme with dual enzyme-like properties provides a new insight for designing novel nanozymes and has prospects for application in colorimetric biosensing.

A MOF nanozyme-mediated acetylcholinesterase-free colorimetric strategy for direct detection of organophosphorus pesticides

Although some simple and rapid colorimetric methods have been developed to detect organophosphorus pesticides (OPs), the difficult extraction and easy denaturation of acetylcholinesterase (AChE) are still drawbacks needing to be overcome. Here, we propose a MOF nanozyme-mediated AChE-free colorimetric strategy for the direct detection of OPs. In the presence of OPs (pirimiphos-methyl as a model), the intense blue of oxidized 3,3',5,5'-tetramethylbenzidine (TMB) becomes light due to the quenching effect of OPs towards hydroxyl radicals (˙OH) that are generated by the decomposition of H2O2 catalyzed by the Cu4Co6 ZIF nanozyme with excellent peroxidase (POD)-like activity. The developed colorimetric sensor exhibits assay performance and offers a universal and promising analysis strategy for detecting OPs in practical samples.

Key technology of vector removal decoupling in a slope-based figuring model and application in continuous phase plate fabrication

For the high-precision fabrication of a continuous phase plate (CPP), a combined decoupling algorithm of single-step decoupling based on the Clairaut-Schwarz theorem and global decoupling by stagewise iteration is proposed. It attempts to address the problem of the low accuracy and limitation of the existing slope-based figuring (SF) model in two-dimensional applications caused by the vector removal coupling between the tool slope influence function and the material removal slope due to the inherent convolution effect in the SF model. The shortcomings of CPP interferometry and the application bottleneck of the Hartmann test in traditional height-based figuring model are studied. The generation mechanism of vector removal coupling is analyzed and compensated. A CPP of 85m m×85m m was successfully machined by the decoupled slope-based figuring model, and the root mean square (RMS) of the surface height error accounted for 6.01% of the RMS of the design value. The research results can effectively improve the convergence and certainty of CPP fabrication using the slope-based figuring model.

Catalytic Asymmetric Synthesis of Atropisomers Bearing Multiple Chiral Elements: An Emerging Field

With the rapid development of asymmetric catalysis, the demand for the enantioselective synthesis of complex and diverse molecules with different chiral elements is increasing. Owing to the unique features of atropisomerism, the catalytic asymmetric synthesis of atropisomers has attracted a considerable interest from the chemical science community. In particular, introducing additional chiral elements, such as carbon centered chirality, heteroatomic chirality, planar chirality, and helical chirality, into atropisomers provides an opportunity to incorporate new properties into axially chiral compounds, thus expanding the potential applications of atropisomers. Thus, it is important to perform catalytic asymmetric transformations to synthesize atropisomers bearing multiple chiral elements. In spite of challenges in such transformations, in recent years, chemists have devised powerful strategies under asymmetric organocatalysis or metal catalysis, synthesizing a wide range of enantioenriched atropisomers bearing multiple chiral elements. Therefore, the catalytic asymmetric synthesis of atropisomers bearing multiple chiral elements has become an emerging field. This review summarizes the rapid progress in this field and indicates challenges, thereby promoting this field to a new horizon.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging

Purpose

Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs).

Method

This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models.

Result

Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

Conclusion

The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI

Background

Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).

Methods

155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.

Results

Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

Conclusion

The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Survival analysis of patients with extrahepatic cholangiocarcinoma: a nomogram for clinical and MRI features

BACKGROUND

This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features.

METHODS

A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined, and the effects of clinical and imaging characteristics on overall survival (OS) were analyzed by constructing Cox proportional hazard regression models. A nomogram was constructed to predict OS, and calibration curves and time-dependent receiver operating characteristic (ROC) curves were employed to assess OS accuracy.

RESULTS

Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion's position, the signal intensity ratio of liver to paraspinal muscle (SIRLiver/Muscle), and the signal intensity ratio of spleen to paraspinal muscle (SIRSpleen/Muscle) on T2WI sequences were significantly associated with OS, and these variables were included in a nomogram. The concordance index of nomogram for predicting OS was 0.766, and the AUC values of the nomogram predicting 1-year and 2-year OS rates were 0.838 and 0.863, respectively. The calibration curve demonstrated good agreement between predicted and observed OS. 5-fold and 10-fold cross-validation show good stability of nomogram predictions.

CONCLUSIONS

Our nomogram based on clinical, laboratory, and MRI features well predicted OS of ECC patients, and could be considered as a convenient and personalized prediction tool for clinicians to make decisions.

Measurement and analysis of leakage neutron spectra from Lead slab samples with D-T neutrons

The leakage neutron spectra from three different sizes of Lead samples were measured by a TOF technique at 60° and 120°. The essential characteristic properties of the experimental measurement spectra can be reproduced well by MCNP code simulations with the ENDF/B-VIII.0, CENDL-3.2, JENDL-5.0, JEFF-3.3 and TENDL-2021 evaluated nuclear data libraries. The calculated results of JENDL-5.0 and JEFF-3.3 libraries agree better with the experimental data in the whole energy range. The results from ENDF/B-VIII.0 and CENDL-3.2 are overestimated in the 4-9 MeV range at 60° and in the 4-12.5 MeV range at 120°. The differences of the leakage neutron spectra by MCNP simulations using five evaluated nuclear data libraries mainly originate from the differences of the spectrum distributions of neutron reaction channels in these libraries. And the secondary neutron energy distribution and angular distribution from the five libraries have been present to explain it.

S-adenosylmethionine attenuates angiotensin II-induced aortic dissection formation by inhibiting vascular smooth muscle cell phenotypic switch and autophagy

It is well known that aortic dissection (AD) is a very aggressive class of vascular diseases. S-adenosylmethionine (SAM) is an autophagy inhibitor with anti-inflammatory and anti-oxidative stress effects; however, the role of SAM in AD is unknown. In this study, we constructed an animal model of AD using subcutaneous minipump continuous infusion of AngII-induced ApoE-/-mice and a cytopathic model using AngII-induced primary vascular smooth muscle cells (VSMCs) to investigate the possible role of SAM in AD. The results showed that mice in the AngII + SAM group had significantly lower AD incidence, significantly prolonged survival, and reduced vascular elastic fiber disruption compared with mice in the AngII group. In addition, SAM significantly inhibited autophagy in vivo and in vitro. Meanwhile, SAM also inhibited the cellular phenotypic switch, mainly by up regulating the expression levels of contractile marker proteins [α-smooth muscle actin (α-SMA) and smooth muscle 22α (SM22α)] and down regulating the expression levels of synthetic marker proteins [osteoblast protein (OPN), matrix metalloproteinase-2 (MMP2), and matrix metalloproteinase-9 (MMP9)]. Molecularly, SAM inhibited AD formation mainly by activating the PI3K/AKT/mTOR signaling pathway. Using a PI3K inhibitor (LY294002) significantly reversed the protective effect of SAM in AngII-induced mice and VSMCs.Our study demonstrates the protective effect of SAM on mice under AngII-induced AD for the first time. SAM prevented AD formation mainly by inhibiting cellular phenotypic switch and autophagy, and activation of the PI3K/AKT/mTOR signaling pathway is a possible molecular mechanism. Thus, SAM may be a novel strategy for the treatment of AD.

Coarse-to-fine registration and time-intensity curves constraint for liver DCE-MRI synthesis

Image registration plays a crucial role in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), used as a fundamental step for the subsequent diagnosis of benign and malignant tumors. However, the registration process encounters significant challenges due to the substantial intensity changes observed among different time points, resulting from the injection of contrast agents. Furthermore, previous studies have often overlooked the alignment of small structures, such as tumors and vessels. In this work, we propose a novel DCE-MRI registration framework that can effectively align the DCE-MRI time series. Specifically, our DCE-MRI registration framework consists of two steps, i.e., a de-enhancement synthesis step and a coarse-to-fine registration step. In the de-enhancement synthesis step, a disentanglement network separates DCE-MRI images into a content component representing the anatomical structures and a style component indicating the presence or absence of contrast agents. This step generates synthetic images where the contrast agents are removed from the original images, alleviating the negative effects of intensity changes on the subsequent registration process. In the registration step, we utilize a coarse registration network followed by a refined registration network. These two networks facilitate the estimation of both the coarse and refined displacement vector fields (DVFs) in a pairwise and groupwise registration manner, respectively. In addition, to enhance the alignment accuracy for small structures, a voxel-wise constraint is further conducted by assessing the smoothness of the time-intensity curves (TICs). Experimental results on liver DCE-MRI demonstrate that our proposed method outperforms state-of-the-art approaches, offering more robust and accurate alignment results.

Ultra-Smooth Polishing of Single-Crystal Silicon Carbide by Pulsed-Ion-Beam Sputtering of Quantum-Dot Sacrificial Layers

Single-crystal silicon carbide has excellent electrical, mechanical, and chemical properties. However, due to its high hardness material properties, achieving high-precision manufacturing of single-crystal silicon carbide with an ultra-smooth surface is difficult. In this work, quantum dots were introduced as a sacrificial layer in polishing for pulsed-ion-beam sputtering of single-crystal SiC. The surface of single-crystal silicon carbide with a quantum-dot sacrificial layer was sputtered using a pulsed-ion beam and compared with the surface of single-crystal silicon carbide sputtered directly. The surface roughness evolution of single-crystal silicon carbide etched using a pulsed ion beam was studied, and the mechanism of sacrificial layer sputtering was analyzed theoretically. The results show that direct sputtering of single-crystal silicon carbide will deteriorate the surface quality. On the contrary, the surface roughness of single-crystal silicon carbide with a quantum-dot sacrificial layer added using pulsed-ion-beam sputtering was effectively suppressed, the surface shape accuracy of the Ø120 mm sample was converged to 7.63 nm RMS, and the roughness was reduced to 0.21 nm RMS. Therefore, the single-crystal silicon carbide with the quantum-dot sacrificial layer added via pulsed-ion-beam sputtering can effectively reduce the micro-morphology roughness phenomenon caused by ion-beam sputtering, and it is expected to realize the manufacture of a high-precision ultra-smooth surface of single-crystal silicon carbide.

Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images

OBJECTIVES

To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.

MATERIALS AND METHODS

Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians.

RESULTS

There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician.

CONCLUSION

The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules.

CLINICAL RELEVANCE STATEMENT

The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis.

KEY POINTS

• According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.

Root damage of street trees in urban environments: An overview of its hazards, causes, and prevention and control measures

Root damage from urban street trees represents a substantial concern arising from the conflict between root growth and limited growth spaces. Nonetheless, the phenomenon of root damage, which threatens the safety of urban facilities, appears to have received little scholarly attention. Moreover, the effectiveness of some proposed measures for root damage prevention and control has not yet received consistent evaluation. Accordingly, this review aims to examine root damage, including its causes and available prevention and control measures. Urban trees are found to have a high potential to exert root damage on infrastructures when the following factors exist. These include large and mature tree, fast-growing trees, trees planted in limited soil volumes, shallow-rooted tree with buttress roots, trees whose diameter at breast height exceeds 10 cm, old and cracked road paving, high soil surface moisture content, short distances between trees and sidewalks (<2 to 3 m), and underground pipes that are already broken and made of metals or stones. The phenotypic traits of trees may be the primary factor causing root damage when there is a mismatch between the root-soil requirements of urban street trees and the actual soil environment. The poor effectiveness of root damage prevention and control measures may be attributed to the lack of connection between the development of control measures and the mechanism of root damage.

Multimodal transformer network for incomplete image generation and diagnosis of Alzheimer's disease

Multimodal images such as magnetic resonance imaging (MRI) and positron emission tomography (PET) could provide complementary information about the brain and have been widely investigated for the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD). However, multimodal brain images are often incomplete in clinical practice. It is still challenging to make use of multimodality for disease diagnosis with missing data. In this paper, we propose a deep learning framework with the multi-level guided generative adversarial network (MLG-GAN) and multimodal transformer (Mul-T) for incomplete image generation and disease classification, respectively. First, MLG-GAN is proposed to generate the missing data, guided by multi-level information from voxels, features, and tasks. In addition to voxel-level supervision and task-level constraint, a feature-level auto-regression branch is proposed to embed the features of target images for an accurate generation. With the complete multimodal images, we propose a Mul-T network for disease diagnosis, which can not only combine the global and local features but also model the latent interactions and correlations from one modality to another with the cross-modal attention mechanism. Comprehensive experiments on three independent datasets (i.e., ADNI-1, ADNI-2, and OASIS-3) show that the proposed method achieves superior performance in the tasks of image generation and disease diagnosis compared to state-of-the-art methods.

Organocatalytic Diastereoselective (4 + 1) Cycloaddition of o-Hydroxyphenyl-Substituted Secondary Phosphine Oxides

The first organocatalytic diastereoselective (4 + 1) cycloaddition of o-hydroxyphenyl-substituted secondary phosphine oxides (SPOs) has been established, which makes use of o-hydroxyphenyl substituted SPOs as suitable four-atom phosphorus-containing 1,4-dinucleophiles and 3-indolylformaldehydes as competent 1,1-dielectrophiles under Bro̷nsted acid catalysis. The reaction mechanism was suggested to involve the formation of 3-indolylmethanol intermediates and vinyliminium intermediates, which played an important role in controlling the reactivity and diastereoselectivity of the (4 + 1) cycloaddition under Bro̷nsted acid catalysis. By this approach, a series of benzo oxaphospholes bearing P- and C-stereocenters were synthesized in moderate to good yields (50%-95% yields) with excellent diastereoselectivities (all >95:5 dr). This reaction not only represents the first organocatalytic diastereoselective (4 + 1) cycloaddition of o-hydroxyphenyl-substituted SPOs but also provides an efficient and diastereoselective method for the construction of phosphorus-containing benzo five-membered heterocyclic skeletons bearing both P-stereocenter and C-stereocenter.

New insights into the binding of PF4 to long heparin oligosaccharides in ultralarge complexes using mass spectrometry

BACKGROUND

Heparin-induced thrombocytopenia (HIT) is a serious complication caused by heparin drugs. The ultralarge complexes formed by platelet factor 4 (PF4) with heparin or low molecular weight heparins (LMWHs) are important participants in inducing the immune response and HIT.

OBJECTIVES

We aim at characterizing the interaction between PF4 and long-chain heparin oligosaccharides and providing robust analytical methods for the analysis of PF4-heparin complexes.

METHODS

In this work, the characteristics of PF4-enoxaparin complexes after incubation in different molar ratios and concentrations were analyzed by multiple analytical methods, especially liquid chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry with multiple reaction monitoring were developed to qualitatively and quantitatively monitor heparin oligosaccharides and PF4 in HIT-inducing complexes.

RESULTS

The results showed that the largest proportion of ultralarge complexes formed by PF4 and enoxaparin was at a specific molar ratio, ie, a PF4/enoxaparin ratio of 2:1, while the ultralarge complexes contained PF4 tetramer and enoxaparin at a molar ratio of approximately 2:1.

CONCLUSION

A binding model of PF4 and enoxaparin in ultralarge complexes is proposed with one heparin oligosaccharide chain (∼ dp18) bound to 2 PF4 tetramers in different morphologies to form ultralarge complexes, while PF4 tetramer is surrounded by multiple heparin chains in smaller complexes. Our study provides new insights into the structural mechanism of PF4-LMWH interaction, which help to further understand the mechanism of LMWH immunogenicity and develop safer heparin products.

Determination of bioactive flavonoids using β-cyclodextrin combined with chitosan-modified magnetic nanoparticles

To accurately determine flavonoids (rutin, quercetin or kaempferol), it is necessary to extract them from complex matrices. The ultrasound-assisted magnetic dispersion microsolid phase extraction technique has been predominantly used for separation and enrichment of the target analytes. The combination of magnetic chitosan nanoparticles and a deep eutectic supramolecular solvent (DESP) is likely to enhance the efficiency of flavonoid extraction from food. In this study, adsorbents were prepared by modifying chitosan with magnetic nanoparticles, and the eluent was a DESP derived from β-cyclodextrin and an organic acid. The successful preparation of these materials was confirmed by FTIR, XRD, FE-SEM and 1H NMR. The extraction recovery rates exceeded 93 %, with limits of detection and quantitation ranging from 0.9 to 2.4 μg/L and 2.7 to 7.2 μg/L, respectively, and the flavonoid clearance rates for ABTS and DPPH radicals reached 100 %. Therefore, the integration of magnetic chitosan nanoparticles with the DESP provides a new and efficient method for the extraction of flavonoids while also presenting a potential application of the DESP in separations.

Dose-dependent action of cordycepin on the microbiome-gut-brain-adipose axis in mice exposed to stress

The high risk for anxiety and depression among individuals with stress has become a growing concern globally. Stress-related mental disorders are often accompanied by symptoms of metabolic dysfunction. Cordycepin is a Chinese herbal medicine commonly used for its metabolism-enhancing effects. We aimed to investigate the dose-dependent effects of cordycepin on psycho-metabolic disorders induced by stress. Our behavioral tests revealed that 12.5 mg/kg cordycepin by oral gavage significantly attenuated the anxiety- and depression-like behaviors induced by stress in mice. At 25 mg/kg, cordycepin restored the reduced weight and cell size of adipose tissues caused by stress. Besides ameliorating the metabolic dysbiosis of gut microbiota due to stress, cordycepin significantly reduced the elevated contents of 5-hydroxyindoleacetic acid in the serum and prefrontal cortex at 12.5 mg/kg and reversed the decrease in adipose induced by stress at 25 mg/kg. Correlation analyses further revealed that 12.5 mg/kg cordycepin reversed stress-induced changes in the intestinal microbiome of NK4A214_group and decreased serum Myristic acid and PC(15:0/18:1(11Z)) and cytokines, such as IFN-γ and IL-1β. 25 mg/kg cordycepin reversed stress-induced changes in the abundances of Prevoteaceae_UCG-001 and Desulfovibrio, increased serum L-alanine level, and decreased serum Inosine-5'-monophosphate level. Cordycepin thereby ameliorated the anxiety- and depression-like behaviors as well as disturbances in the adipose metabolism of mice exposed to stress. Overall, these findings offer evidence indicating that the prominent effects of cordycepin in the brain and adipose tissues are dose dependent, thus highlight the importance of evaluating the precise therapeutic effects of different cordycepin doses on psycho-metabolic diseases.

A Novel circ_0104345/miR-876-3p/ZBTB20 Axis Regulates the Proliferation, Migration, Invasion, and Apoptosis of Breast Cancer Cells

Breast cancer (BC) is one of the most common malignant tumors in women. CircRNA/miRNA/mRNA regulatory axes have been shown to be involved in the pathogenesis of BC. Here, we sought to analyze the functional mechanism of circ_0104345 in BC. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the levels of circ_0104345, miR-876-3p and zinc finger and BTB domain containing 20 (ZBTB20) mRNA. Cell Counting Kit-8 (CCK8) and 5-ethynyl-2'-deoxyuridine (EdU) assays were used to test cell viability and proliferation, respectively. Cell migration was tested by wound healing assay, and cell invasion was examined by transwell assay. Tube formation ability was tested by angiogenesis assay. Flow cytometry was applied for cell apoptosis. Western blot assay was utilized to measure the protein expression. The relationship between miR-876-3p and circ_0104345 or ZBTB20 was identified by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Xenografts in mice were conducted to analyze the effect of sh-circ_0104345 on tumor growth in vivo. Circ_0104345 and ZBTB20 were upregulated and miR-876-3p expression was decreased in BC. Circ_0104345 knockdown inhibited cell proliferation, migration, invasion, and enhanced cell apoptosis. MiR-876-3p was targeted by circ_0104345. MiR-876-3p depletion reversed the effects of circ_0104345 downregulation on the progression of BC cells. ZBTB20 was regulated by circ_0104345 through miR-876-3p. The effects of miR-876-3p on BC cell behaviors were restored by ZBTB20 increase. The results of in vivo experiments indicated that silencing of circ_0104345 blocked the growth of xenograft tumors. In this study, we demonstrated, for the first time, the crucial regulation of the new circ_0104345/miR-876-3p/ZBTB20 axis in the biological phenotypes of BC cells.

Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram

OBJECTIVES

Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for advanced hepatocellular carcinoma (Ad-HCC). As identifying patients with Ad-HCC who would obtain objective response (OR) to HAIC preoperatively remains a challenge, we aimed to develop an automatic and non-invasive model for predicting HAIC response.

METHODS

A total of 458 patients with Ad-HCC who underwent HAIC were retrospectively included from three hospitals (310 for training, 77 for internal validation, and 71 for external validation). The deep learning and radiomic features were extracted from the automatically segmented liver region on contrast-enhanced computed tomography images. Then, a deep learning radiomic nomogram (DLRN) was constructed by integrating deep learning scores, radiomic scores, and significant clinical variables with multivariate logistic regression. Model performance was assessed by AUC and Kaplan-Meier estimator.

RESULTS

After automatic segmentation, only a few modifications were needed (less than 30 min for 458 patients). The DLRN achieved an AUC of 0.988 in the training cohort, 0.915 in the internal validation cohort, and 0.896 in the external validation cohort, respectively, outperforming other models in HAIC response prediction. Moreover, survival risk stratification was also successfully performed by the DLRN. The overall survival (OS) of the predictive OR group was significantly longer than that of the predictive non-OR group (median OS: 26.0 vs. 12.3 months, p < 0.001).

CONCLUSIONS

The DLRN provided a satisfactory performance for predicting HAIC response, which is essential to identify Ad-HCC patients for HAIC and may potentially benefit personalized pre-treatment decision-making.

CLINICAL RELEVANCE STATEMENT

This study presents an accurate and automatic method for predicting response to hepatic arterial infusion chemotherapy in patients with advanced hepatocellular carcinoma, and therefore help in defining the best candidates for this treatment.

KEY POINTS

• Deep learning radiomic nomogram (DLRN) based on automatic segmentation of CECT can accurately predict hepatic arterial infusion chemotherapy (HAIC) response of advanced HCC patients. • The proposed prediction model can perform survival risk stratification and is an easy-to-use tool for personalized pre-treatment decision-making for advanced HCC patients.

Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas

OBJECTIVES

To assess the value of coordinatized lesion location analysis (CLLA), in empowering ROI-based imaging diagnosis of gliomas by improving accuracy and generalization performances.

METHODS

In this retrospective study, pre-operative contrasted T1-weighted and T2-weighted MR images were obtained from patients with gliomas from three centers: Jinling Hospital, Tiantan Hospital, and the Cancer Genome Atlas Program. Based on CLLA and ROI-based radiomic analyses, a fusion location-radiomics model was constructed to predict tumor grades, isocitrate dehydrogenase (IDH) status, and overall survival (OS). An inter-site cross-validation strategy was used for assessing the performances of the fusion model on accuracy and generalization with the value of area under the curve (AUC) and delta accuracy (ACC) (ACCtesting-ACCtraining). Comparisons of diagnostic performances were performed between the fusion model and the other two models constructed with location and radiomics analysis using DeLong's test and Wilcoxon signed ranks test.

RESULTS

A total of 679 patients (mean age, 50 years ± 14 [standard deviation]; 388 men) were enrolled. Based on tumor location probabilistic maps, fusion location-radiomics models (averaged AUC values of grade/IDH/OS: 0.756/0.748/0.768) showed the highest accuracy in contrast to radiomics models (0.731/0.686/0.716) and location models (0.706/0.712/0.740). Notably, fusion models ([median Delta ACC: - 0.125, interquartile range: 0.130]) demonstrated improved generalization than that of radiomics model ([- 0.200, 0.195], p = 0.018).

CONCLUSIONS

CLLA could empower ROI-based radiomics diagnosis of gliomas by improving the accuracy and generalization of the models.

CLINICAL RELEVANCE STATEMENT

This study proposed a coordinatized lesion location analysis for glioma diagnosis, which could improve the performances of the conventional ROI-based radiomics model in accuracy and generalization.

KEY POINTS

• Using coordinatized lesion location analysis, we mapped anatomic distribution patterns of gliomas with specific pathological and clinical features and constructed glioma prediction models. • We integrated coordinatized lesion location analysis into ROI-based analysis of radiomics to propose new fusion location-radiomics models. • Fusion location-radiomics models, with the advantages of being less influenced by variabilities, improved accuracy, and generalization performances of ROI-based radiomics models on predicting the diagnosis of gliomas.

Polyelectrolyte chain conformation matters in macroscopic supramolecular self-assembly

We demonstrate molecular-conformation-dependent macroscopic supramolecular self-assembly (MSA) driven by electrostatic interactions. Evidence from single molecular force spectroscopy reveals that polyelectrolytes modified on MSA component surfaces make MSA possible with a loop conformation, while those with a flat conformation lead to no assembly, which is attributed to distinct molecular mobility. We believe that this finding is also applicable in fundamental phenomena such as surface adsorption and adhesion regarding polymers.

A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images

Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. The application of deep learning techniques holds promise for automated histological pathology diagnosis. In this study, we collected 733 whole slide images from four medical centers, of which 456 were used for model training, 150 for internal validation, and 127 for multi-center testing. The study includes 5 types of common gliomas. A subtask-guided multi-instance learning image-to-label training pipeline was employed. The pipeline leveraged "patch prompting" for the model to converge with reasonable computational cost. Experiments showed that an overall accuracy of 0.79 in the internal validation dataset. The performance on the multi-center testing dataset showed an overall accuracy to 0.73. The findings suggest a minor yet acceptable performance decrease in multi-center data, demonstrating the model's strong generalizability and establishing a robust foundation for future clinical applications.

A common spectrum underlying brain disorders across lifespan revealed by deep learning on brain networks

Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. However, the key neuroimaging evidence remains unrevealed for elucidating such commonness and the relationships among these disorders. To explore this puzzle, we build a restricted single-branch deep learning model, using multi-site functional magnetic resonance imaging data (N = 4,410, 6 sites), for classifying 5 different early- and late-life brain disorders from healthy controls (cognitively unimpaired). Our model achieves 62.6 ± 1.9% overall classification accuracy and thus supports us in detecting a set of commonly affected functional subnetworks, including default mode, executive control, visual, and limbic networks. In the deep-layer representation of data, we observe young and aging patients with disorders are continuously distributed, which is in line with the clinical concept of the "spectrum of disorders." The relationships among brain disorders from the revealed spectrum promote the understanding of disorder comorbidities and time associations in the lifespan.

MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network

BACKGROUND

Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI.

METHODS

We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated.

RESULTS

Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively.

CONCLUSION

The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Enhanced adsorption of phenol from aqueous solution by KOH combined Fe-Zn bimetallic oxide co-pyrolysis biochar: Fabrication, performance, and mechanism

In this study, impregnation combined with KOH activation with different mixing methods was used to prepare magnetic biochar. The effects of synthetic method on biochar physicochemical properties and adsorption performance were explored. The results showed that treatment of a Fe-Zn oxide with KOH activation provided excellent adsorption properties with adsorption capacity of 458.90 mg/g due to well-developed microporous structure and rich-in O-containing functional groups as well as exposed oxidizing functional groups (Fe2O3 and FeOOH). Langmuir-Freundlich and pseudo-second-order models accurately fit phenol adsorption. Neutral conditions (pH = 6) and lower ionic strengths were beneficial to phenol removal. Additionally, the predominant adsorption processes were physisorption and chemisorption. Correlation analyses and characterization data confirmed that pore filling, π-π interactions and surface complexation were the dominant driving forces for phenol adsorption. This research provides an environmentally friendly method for utilizing agricultural wastes for the removal of a variety of pollutions from aquatic environment.

Chitosan nanoparticles encapsulated with BEZ235 prevent acute rejection in mouse heart transplantation

Acute rejection may manifest following heart transplantation, despite the implementation of relatively well-established immunosuppression protocols. The significance of the mTOR signaling pathway in rejection is widely acknowledged. BEZ235, a second-generation mTOR inhibitor with dual inhibitory effects on PI3K and mTOR, holds promise for clinical applications. This study developed a nanodelivery system, BEZ235@NP, to facilitate the intracellular delivery of BEZ235, which enhances efficacy and reduces adverse effects by improving the poor solubility of BEZ235. In the complete MHCII-mismatched model, BEZ235@NP significantly prolonged cardiac allografts survival compared to free BEZ235, which was attributed to more effective suppression of effector T cell activation and promotion of greater expansion of Tregs. These nanoparticles demonstrated excellent biosafety and exhibited no short-term biotoxicity upon investigation. To elucidate the mechanism, primary T cells were isolated from the spleen and it was observed that BEZ235@NP treatment resulted in the arrest of these cells in the G0/G1 phase. As indicated by Western blot analysis, BEZ235@NP substantially reduced mTOR phosphorylation. This, in turn, suppressed downstream pathways and ultimately exerted an anti-proliferative and anti-activating effect on cells. Furthermore, it was observed that inhibition of the mTOR pathway stimulated T-cell autophagy. In conclusion, the strategy of intracellular delivery of BEZ235 presents promising applications for the treatment of acute rejection.

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images

BACKGROUND

Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear.

PURPOSE

To distinguish primary site of BM and identify the best DL models.

STUDY TYPE

Retrospective.

POPULATION

A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included.

FIELD STRENGTH/SEQUENCE

A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE).

ASSESSMENT

Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps.

STATISTICAL TESTS

The area under the receiver operating characteristics curve (AUC) assess each classification performance.

RESULTS

3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions.

DATA CONCLUSION

DL models may help to distinguish the origins of BM based on MRI data.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT

OBJECTIVE

To study the value of radiomics models based on plain and multiphase contrast-enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors (GISTs).

METHODS

A total of 215 patients with GISTs were retrospectively analyzed, including 150 patients in one hospital as the training set and 65 patients in another hospital as the external verification set. The tumor at the largest level of CT images was delineated as the region of interest (ROI). The maximum diameter of the ROI was defined as the tumor size. A total of 851 radiomics features were extracted from each ROI by 3D Slicer Radiomics. After dimensionality reduction, three machine learning classification algorithms including logistic regression (LR), random forest (RF), and support vector machine (SVM) were used for Ki-67 expression prediction. Using a multivariable logistic model, a nomogram was established to predict the expression of Ki-67 individually.

RESULTS

Delong tests showed that the SVM models had the highest accuracy in the arterial phase (Z value 0.217-1.139) and venous phase (Z value 0.022-1.396). For the plain phase, LR and SVM models had the highest accuracy (Z value 0.874-1.824, 1.139-1.763). For the delayed phase, LR models had the highest accuracy (Z value 0.056-1.824). For the combined phase, RF models had the highest accuracy (Z value 0.232-1.978). There was no significant difference among the above models for KI-67 expression prediction (Z value 0.022-1.978). A nomogram was developed with a C-index of 0.913 (95% CI, 0.878 to 0.956).

CONCLUSIONS

Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. For patients who were not suitable to use contrast agents, plain scan could be used as an alternative.

CLINICAL RELEVANCE STATEMENT

CT radiomics could accurately predict the expression of Ki-67 in GIST, which has a great clinical value in reflecting the proliferative activity of tumor cells and helping determine whether a patient is suitable for adjuvant therapy with imatinib.

KEY POINTS

• Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. • For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. • A radiomics nomogram was developed to allow personalized preoperative evaluation with high accuracy.

TaG-Net: Topology-Aware Graph Network for Centerline-Based Vessel Labeling

Anatomical labeling of head and neck vessels is a vital step for cerebrovascular disease diagnosis. However, it remains challenging to automatically and accurately label vessels in computed tomography angiography (CTA) since head and neck vessels are tortuous, branched, and often spatially close to nearby vasculature. To address these challenges, we propose a novel topology-aware graph network (TaG-Net) for vessel labeling. It combines the advantages of volumetric image segmentation in the voxel space and centerline labeling in the line space, wherein the voxel space provides detailed local appearance information, and line space offers high-level anatomical and topological information of vessels through the vascular graph constructed from centerlines. First, we extract centerlines from the initial vessel segmentation and construct a vascular graph from them. Then, we conduct vascular graph labeling using TaG-Net, in which techniques of topology-preserving sampling, topology-aware feature grouping, and multi-scale vascular graph are designed. After that, the labeled vascular graph is utilized to improve volumetric segmentation via vessel completion. Finally, the head and neck vessels of 18 segments are labeled by assigning centerline labels to the refined segmentation. We have conducted experiments on CTA images of 401 subjects, and experimental results show superior vessel segmentation and labeling of our method compared to other state-of-the-art methods.