Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imaging.

OBJECTIVE: The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences. METHODS: 71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI. The qualitative evaluation indicators, including overall image quality (OIQ), sharpness, noise, artifacts, lesion detectability (LC), lesion characterization (LD), cardiacmotion-related signal loss (CSL), and diagnostic confidence (DC), were evaluated in two readers, and further statistically compared using paired Wilcoxon rank-sum test among three sequences. RESULTS: 176 lesions were detected in DL-RT-T2W and Arms-RT-T2WI, and 175 were detected in FSE-RT-T2WI. The acquisition time of DL-RT-T2WI was improved by 4.8-7.9 folds compared to the other two sequences. The OIQ was scored highest for DL-RT-T2WI (R1, 4.61 ± 0.52 and R2, 4.62 ± 0.49), was significantly superior to Arms-RT-T2WI (R1, 4.30 ± 0.66 and R2, 4.34 ± 0.69) and FSE-RT-T2WI (R1, 3.65 ± 1.08 and R2, 3.75 ± 1.01). Artifacts and sharpness scored highest for DL-RT-T2WI, followed by Arms-RT-T2WI, and were lowest for FSE-RT-T2WI in both two readers. Noise and CSL for DL-RT-T2WI scored similar to Arms-RT-T2WI (P > 0.05) and were significantly superior to FSE-RT-T2WI (P < 0.001). Both LD and LC for DL-RT-T2WI were significantly superior to Arms-RT-T2WI and FSE-RT-T2WI in two readers (P < 0.001). DC for DL-RT-T2WI scored best, significantly superior to Arms-RT-T2WI (P < 0.010) and FSE-RT-T2WI (P < 0.001). CONCLUSIONS: The novel ultra-fast DL-RT-T2WI is feasible for liver imaging and lesion characterization and diagnosis, not only offers a significant improvement in acquisition time but also outperforms Arms-RT-T2WI and FSE-RT-T2WI concerning image quality and DC.

Sacubitril/valsartan ameliorates tubulointerstitial fibrosis by restoring mitochondrial homeostasis in diabetic kidney disease.

BACKGROUND: Tubulointerstitial fibrosis plays an important role in the progression of diabetic kidney disease (DKD). Sacubitril/valsartan (Sac/Val) exerts a robust beneficial effect in DKD. However, the potential functional effect of Sac/Val on tubulointerstitial fibrosis in DKD is still largely unclear. METHODS: Streptozotocin-induced diabetic mice were given Sac/Val or Val by intragastric administration once a day for 12 weeks. The renal function, the pathological changes of tubule injury and tubulointerstitial fibrosis, as well as mitochondrial morphology of renal tubules in mice, were evaluated. Genome-wide gene expression analysis was performed to identify the potential mechanisms. Meanwhile, human tubular epithelial cells (HK-2) were cultured in high glucose condition containing LBQ657/valsartan (LBQ/Val). Further, mitochondrial functions and Sirt1/PGC1α pathway of tubular epithelial cells were assessed by Western blot, Real-time-PCR, JC-1, MitoSOX or MitoTracker. Finally, the Sirt1 specific inhibitor, EX527, was used to explore the potential effects of Sirt1 signaling in vivo and in vitro. RESULTS: We found that Sac/Val significantly ameliorated the decline of renal function and tubulointerstitial fibrosis in DKD mice. The enrichment analysis of gene expression indicated metabolism as an important modulator in DKD mice with Sac/Val administration, in which mitochondrial homeostasis plays a pivotal role. Then, the decreased expression of Tfam and Cox IV;, as well as changes of mitochondrial function and morphology, demonstrated the disruption of mitochondrial homeostasis under DKD conditions. Interestingly, Sac/Val administration was found to restore mitochondrial homeostasis in DKD mice and in vitro model of HK-2 cells. Further, we demonstrated that Sirt1/PGC1α, a crucial pathway in mitochondrial homeostasis, was activated by Sac/Val both in vivo and in vitro. Finally, the beneficial effects of Sac/Val on mitochondrial homeostasis and tubulointerstitial fibrosis was partially abolished in the presence of Sirt1 specific inhibitor. CONCLUSIONS: Taken together, we demonstrate that Sac/Val ameliorates tubulointerstitial fibrosis by restoring Sirt1/PGC1α pathway-mediated mitochondrial homeostasis in DKD, providing a theoretical basis for delaying the progression of DKD in clinical practice.

Structural Heredity in Catalysis: CO2 Self-Selective CeO2 Nanocrystals for Efficient Photothermal CO2 Hydrogenation to Methane.

The chemical inertness of CO2 molecules makes their adsorption and activation on a catalyst surface one of the key challenges in recycling CO2 into chemical fuels. However, the traditional template synthesis and chemical modification strategies used to tackle this problem face severe structural collapse and modifier deactivation issues during the often-needed post-processing procedure. Herein, a CO2 self-selective hydrothermal growth strategy is proposed for the synthesis of CeO2 octahedral nanocrystals that participate in strong physicochemical interactions with CO2 molecules. The intense affinity for CO2 molecules persists during successive high-temperature treatments required for Ni deposition. This demonstrates the excellent structural heredity of the CO2 self-selective CeO2 nanocrystals, which leads to an outstanding photothermal CH4 productivity exceeding 9 mmol h-1 mcat -2 and an impressive selectivity of >99%. The excellent performance is correlated with the abundant oxygen vacancies and hydroxyl species on the CeO2 surface, which create many frustrated Lewis-pair active sites, and the strong interaction between Ni and CeO2 that promotes the dissociation of H2 molecules and the spillover of H atoms, thereby greatly benefitting the photothermal CO2 methanation reaction. This self-selective hydrothermal growth strategy represents a new pathway for the development of effective catalysts for targeted chemical reactions.

Device-independent quantum randomness-enhanced zero-knowledge proof.

Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, noninteractive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning, blockchain, and social networks. However, the heuristic is typically built upon the random oracle model that makes ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol. Here, we present a quantum solution to the problem. Instead of resorting to a random oracle model, we implement a quantum randomness service. This service generates random numbers certified by the loophole-free Bell test and delivers them with postquantum cryptography (PQC) authentication. By employing this service, we conceive and implement NIZKP of the three-coloring problem. By bridging together three prominent research themes, quantum nonlocality, PQC, and ZKP, we anticipate this work to inspire more innovative applications that combine quantum information science and the cryptography field.

Volumetric Compression Shifts Rho GTPase Balance and Induces Mechanobiological Cell State Transition.

During development and disease progression, cells are subject to osmotic and mechanical stresses that modulate cell volume, which fundamentally influences cell homeostasis and has been linked to a variety of cellular functions. It is not well understood how the mechanobiological state of cells is programmed by the interplay of intracellular organization and complex extracellular mechanics when stimulated by cell volume modulation. Here, by controlling cell volume via osmotic pressure, we evaluate physical phenotypes (including cell shape, morphodynamics, traction force, and extracellular matrix (ECM) remodeling) and molecular signaling (YAP), and we uncover fundamental transitions in active biophysical states. We demonstrate that volumetric compression shifts the ratiometric balance of Rho GTPase activities, thereby altering mechanosensing and cytoskeletal organization in a reversible manner. Specifically, volumetric compression controls cell spreading, adhesion formation, and YAP nuclear translocation, while maintaining cell contractile activity. Furthermore, we show that on physiologically relevant fibrillar collagen I matrices, which are highly non-elastic, cells exhibit additional modes of cell volume-dependent mechanosensing that are not observable on elastic substrates. Notably, volumetric compression regulates the dynamics of cell-ECM interactions and irreversible ECM remodeling via Rac-directed protrusion dynamics, at both the single-cell level and the multicellular level. Our findings support that cell volume is a master biophysical regulator and reveal its roles in cell mechanical state transition, cell-ECM interactions, and biophysical tissue programming.

Which news topics drive economic prosperity in China?

Precise and real-time measurements of economic prosperity are vital to a country's economic system. This study aims to identify news topics that promoted economic prosperity in China from 2011-2021. By extracting economic topics from news text data, we construct a news coincidence index with comprehensive information and strong timeliness and reveal the trend of topic contribution. The Latent Dirichlet Allocation (LDA) topic model is applied to extract economic topics from the news. We use a mixed-frequency dynamic factor model to track rapid economic development without using high-frequency weekly and daily data. We identify the six most influential topics and investigate their evolution, which may serve as a reference for economic construction and regulation.

Quantum Complementarity Approach to Device-Independent Security.

Complementarity is an essential feature of quantum mechanics. The preparation of an eigenstate of one observable implies complete randomness in its complementary observable. In quantum cryptography, complementarity allows us to formulate security analyses in terms of phase-error correction. However, the concept becomes much subtler in the device-independent regime that offers security without device characterization. Security proofs of device-independent quantum cryptography tasks are often complex and quite different from those of their more standard device-dependent cousins. The existing proofs pose huge challenges to experiments, among which large data-size requirement is a crux. Here, we show the complementarity security origin of the device-independent tasks. By linking complementarity with quantum nonlocality, we recast the device-independent scheme into a quantum error correction protocol. Going beyond the identical-and-independent-distribution case, we consider the most general attack. We generalize the sample entropy in classical Shannon theory for the finite-size analysis. Our method exhibits good finite-size performance and brings the device-independent scheme to a more practical regime. Applying it to the data in a recent ion-trap-based device-independent quantum key distribution experiment, one could reduce the requirement on data size to less than a third. Furthermore, the operational meaning of complementarity naturally extends device-independent scenarios to advantage key distillation, easing experiments by tolerating higher loss and lower transmittance.

Lipid-inspired biomimicking morphosynthesis of a series of complex concave silica architectures.

The lipid-inspired biosilicification process enables the creation of a series of concave silica nanoarchitectures in the complex shapes of nanobowls, nanodishes, nanoboats, and nanoloops. The reaction at a pH of 8 initially allows the formation of thin and elastic circular gel nanosheets that can undergo inducible stretching and folding, which subsequently evolves into nanodish and nanobowl through a potential global buckling process. The adjustment of the pH to 9 and 4 enables the production of more complex morphogens of nanoboat and nanoloop, respectively. These unique silica nanoarchitectures may have a wide scope of potential application from nanoreactors in heterogenous catalysis to drug delivery systems and optical materials.

Development and validation of a prognostic nomogram for the renal relapse of lupus nephritis / Desarrollo y validación de un nomograma pronóstico para la recaída renal de la nefritis lúpica

Objectives This study aims to assess the risk of relapse after complete remission (CR) and partial remission (PR), and to develop a prognostic nomogram predicting the probability in lupus nephritis (LN) patients. Methods Data from patients with LN who had been in remission were collected as a training cohort. The prognostic factors were analyzed using the univariable and multivariable Cox model for the training group. A nomogram was then developed using significant predictors in multivariable analysis. Both discrimination and calibration were assessed by bootstrapping with 100 resamples. Results A total of 247 participants were enrolled, including 108 in the relapse group and 139 in the no relapse group. In multivariate Cox analysis, Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), erythrocyte sedimentation rate (ESR), complement 1q (C1q), and antiphospholipid (aPL), anti-Sm antibody were found to be significant for predicting relapse rates. The prognostic nomogram including the aforementioned factors effectively predicted 1- and 3-year probability of flare-free. Moreover, a favorable consistency between the predicted and actual survival probabilities was demonstrated using calibration curves. Conclusions High SLEDAI, ESR, and positive aPL, anti-Sm antibody are potential risk factors for LN flare, while high C1q can reduce its recurrence. The visualized model we established can help predict the relapse risk of LN and aid clinical decision-making for individual patients (AU)

Objetivos Este estudio pretende evaluar el riesgo de recaída tras la remisión completa y la remisión parcial, y desarrollar un nomograma pronóstico que prediga la probabilidad en pacientes con nefritis lúpica (NL). Métodos Se recogieron datos de pacientes con NL que habían estado en remisión como cohorte de entrenamiento. Se analizaron los factores pronósticos utilizando el modelo COX univariable y multivariable para el grupo de entrenamiento. A continuación se desarrolló un nomograma utilizando los predictores significativos en el análisis multivariable. Tanto la discriminación como la calibración se evaluaron mediante bootstrapping con 100 remuestreos. Resultados Se inscribió a un total de 247 participantes, incluidos 108 en el grupo de recaída y 139 en el grupo sin recaída. En el análisis multivariante de Cox, el índice de actividad de la enfermedad lúpica eritematosa sistémica (SLEDAI), la velocidad de sedimentación globular (VSG), el complemento 1q (C1q) y los anticuerpos antifosfolípidos (aPL) y anti-Sm resultaron significativos para predecir las tasas de recaída. El nomograma pronóstico que incluía los factores mencionados predijo eficazmente la probabilidad a 1 y a 3 años de estar libre de reagudizaciones. Además, se demostró una coherencia favorable entre las probabilidades de supervivencia previstas y las reales mediante curvas de calibración. Conclusiones SLEDAI alto, VSG y aPL positivo, anticuerpos anti-Sm son factores de riesgo potenciales de reagudización de la NL, mientras que C1q alto puede reducir su recurrencia. El modelo visualizado que establecimos puede ayudar a predecir el riesgo de recidiva de la NL y ayudar a la toma de decisiones clínicas para pacientes individuales (AU)

Compression drives diverse transcriptomic and phenotypic adaptations in melanoma.

Physical forces are prominent during tumor progression. However, it is still unclear how they impact and drive the diverse phenotypes found in cancer. Here, we apply an integrative approach to investigate the impact of compression on melanoma cells. We apply bioinformatics to screen for the most significant compression-induced transcriptomic changes and investigate phenotypic responses. We show that compression-induced transcriptomic changes are associated with both improvement and worsening of patient prognoses. Phenotypically, volumetric compression inhibits cell proliferation and cell migration. It also induces organelle stress and intracellular oxidative stress and increases pigmentation in malignant melanoma cells and normal human melanocytes. Finally, cells that have undergone compression become more resistant to cisplatin treatment. Our findings indicate that volumetric compression is a double-edged sword for melanoma progression and drives tumor evolution.

Metformin-Loaded Chitosan Hydrogels Suppress Bladder Tumor Growth in an Orthotopic Mouse Model via Intravesical Administration.

Our previous study found that the intravesical perfusion of metformin has excellent inhibitory effects against bladder cancer (BC). However, this administration route allows the drug to be diluted and excreted in urine. Therefore, increasing the adhesion of metformin to the bladder mucosal layer may prolong the retention time and increase the pharmacological activity. It is well known that chitosan (Cs) has a strong adhesion to the bladder mucosal layer. Thus, this study established a novel formulation of metformin to enhance its antitumor activity by extending its retention time. In this research, we prepared Cs freeze-dried powder and investigated the effect of metformin-loaded chitosan hydrogels (MLCH) in vitro and in vivo. The results showed that MLCH had a strong inhibitory effect against proliferation and colony formation in vitro. The reduction in BC weight and the expression of tumor biomarkers in orthotopic mice showed the robust antitumor activity of MLCH via intravesical administration in vivo. The non-toxic profile of MLCH was observed as well, using histological examinations. Mechanistically, MLCH showed stronger functional activation of the AMPKα/mTOR signaling pathway compared with metformin alone. These findings aim to make this novel formulation an efficient candidate for managing BC via intravesical administration.

Virtual reality: a promising instrument to promote sail education.

Sailing has gained an increasing attention among children and adolescents in China, which raised a strong need for sail courses through physical education (PE). However, challenges in teaching practice arise with rapid development of the sport. In the current study, we proposed a perspective that virtual reality (VR) technology makes high-quality sail education accessible for students. Critical analysis summarized the prominent features that enhance sail education, including immersive experience, interactive learning, the first-person view, and practice under well-controlled conditions. Further, research on VR sport training indicated successful transfer from virtual environment to real situation. Specifically, significant improvement in skill performance and tactical behaviors were identified, which was attributed to the enhanced perception-action coupling after VR training. Additionally, VR-based coding programs provide students with affordances of designing the virtual environment. The content design education promotes comprehension and application of knowledge and theories when students develop the simulated environment with a high level of presence. Therefore, VR technology is a promising instrument to meet the increasing demand on sail education. While VR enriches educational resources for a large class size, the interdisciplinary feature of VR-based sail course can attract students with different study interests and backgrounds to the class.

Trends in cardiovascular burden and mortality among adults with prediabetes in the US, 1999-2018.

BACKGROUND: Trends in cardiovascular disease (CVD) risk factors and mortality have been reported among US adults with diabetes, but not among those with prediabetes. This study aimed to examine and compare the trends in CVD risk factors and mortality in US adults with diabetes and prediabetes. METHODS: In this serial, cross-sectional study, medical records of prediabetic patients from the National Health and Nutrition Examination Survey (NHANES) among adults aged 18 years or older were retrospectively reviewed. RESULTS: Data on 17,193 individuals including 7803 with diabetes and 9390 with prediabetes were analyzed from 1999-2000 to 2017-2018. A similar non-linear trend in the mean blood pressure (p = .991) and plasma fasting glucose (p = .068) was observed among the population with diabetes and prediabetes. The mean hemoglobin A1c decreased from 7.5% to 7.1% in diabetes and the trend was different from that in prediabetes (p = .004). Among both groups, a significant decline in the mean total cholesterol was identified while there was a difference in the trend (p = .003). The prevalence of hypertension remained largely the same for diabetes and it declined from 59.8% to 48.8% for prediabetes (p = .044). The prevalence of dyslipidemia increased from 40.4% to 53.5% in diabetes and it remained stable for prediabetes. The all-cause mortality decreased from 148.2 to 93.6 per 10,000 person-years between 1999-2006 and 2007-2014 in prediabetes and heart diseases mortality remained unchanged. CONCLUSION: In the US from 1999 to 2018, the adults with prediabetes had different secular trends in the mean hemoglobin A1c and the prevalence of hypertension and dyslipidemia compared to those with diabetes. There was a significant reduction in all-cause mortality from 1999 to 2014 for the population with prediabetes. However, heart diseases mortality remained stable among them.

Brain-Computer Interfaces in Visualized Medicine.

The brain-computer interface (BCI), also known as a brain-machine interface (BMI), has attracted extensive attention in biomedical applications. More importantly, BCI technologies have substantially revolutionized early predictions, diagnostic techniques, and rehabilitation strategies addressing acute diseases because of BCI's innovations and clinical translations. Therefore, in this chapter, a comprehensive description of the basic concepts of BCI will be exhibited, and various visualization techniques employed in BCI's medical applications will be discussed.

Development and validation of a prognostic nomogram for the renal relapse of lupus nephritis.

OBJECTIVES: This study aims to assess the risk of relapse after complete remission (CR) and partial remission (PR), and to develop a prognostic nomogram predicting the probability in lupus nephritis (LN) patients. METHODS: Data from patients with LN who had been in remission were collected as a training cohort. The prognostic factors were analyzed using the univariable and multivariable Cox model for the training group. A nomogram was then developed using significant predictors in multivariable analysis. Both discrimination and calibration were assessed by bootstrapping with 100 resamples. RESULTS: A total of 247 participants were enrolled, including 108 in the relapse group and 139 in the no relapse group. In multivariate Cox analysis, Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), erythrocyte sedimentation rate (ESR), complement 1q (C1q), and antiphospholipid (aPL), anti-Sm antibody were found to be significant for predicting relapse rates. The prognostic nomogram including the aforementioned factors effectively predicted 1- and 3-year probability of flare-free. Moreover, a favorable consistency between the predicted and actual survival probabilities was demonstrated using calibration curves. CONCLUSIONS: High SLEDAI, ESR, and positive aPL, anti-Sm antibody are potential risk factors for LN flare, while high C1q can reduce its recurrence. The visualized model we established can help predict the relapse risk of LN and aid clinical decision-making for individual patients.

Loss of Sirt1 promotes exosome secretion from podocytes by inhibiting lysosomal acidification in diabetic nephropathy.

Podocyte injury is a characteristic feature of diabetic nephropathy (DN). The secretion of exosomes in podocytes increases significantly in DN; however, the precise mechanisms remain poorly understood. Here, we demonstrated that Sirtuin1 (Sirt1) was significantly downregulated in podocytes in DN, which correlated negatively with increased exosome secretion. Similar results were observed in vitro. We found that lysosomal acidification in podocytes following high glucose administration was markedly inhibited, resulting in the decreased lysosomal degradation of multivesicular bodies. Mechanistically, we indicated that loss of Sirt1 contributed to the inhibited lysosomal acidification by decreasing the expression of the A subunit of the lysosomal vacuolar-type H+ ATPase proton pump (ATP6V1A) in podocytes. Overexpression of Sirt1 significantly improved lysosomal acidification with increased expression of ATP6V1A and inhibited exosome secretion. These findings suggest that dysfunctional Sirt1-mediated lysosomal acidification is the exact mechanism of increased secretion of exosomes in podocytes in DN, providing insights into potential therapeutic strategies for preventing DN progression.

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.

MOTIVATION: In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior. RESULTS: We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quantifying 2D or 3D phenotype, our analytical method requires less time, needs no specialized equipment and is capable of much higher throughput, making it ideal for applications such as high-throughput drug screening and clinical diagnosis. AVAILABILITY AND IMPLEMENTATION: https://github.com/trevor-chan/Melanoma_NetworkMorphology. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN.

Objective.Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics. However, they still have shortcomings of large memory footprints and slow inference speed.Approach.To solve the above problems of the current study, we propose a novel lightweight convolutional neural network model combining the Convolutional Block Attention Module (CBAM). Its performance for patient-independent seizure detection is evaluated on two long-term continuous iEEG datasets: SWEC-ETHZ and TJU-HH. Finally, we reproduce four other patient-independent methods to compare with our method and calculate the memory footprints and inference speed for all methods.Main results.Our method achieves 83.81% sensitivity (SEN) and 85.4% specificity (SPE) on the SWEC-ETHZ dataset and 86.63% SEN and 92.21% SPE on the TJU-HH dataset. In particular, it takes only 11 ms to infer 10 min iEEG (128 channels), and its memory footprint is only 22 kB. Compared to baseline methods, our method not only achieves better patient-independent seizure detection performance but also has a smaller memory footprint and faster inference speed.Significance.To our knowledge, this is the first iEEG-based patient-independent seizure detection study. This facilitates the application of seizure detection algorithms to the future clinic.

Self-assembly of mesoscale collagen architectures and applications in 3D cell migration.

3D in vitro tumor models have recently been investigated as they can recapitulate key features in the tumor microenvironment. Reconstruction of a biomimetic scaffold is critical in these models. However, most current methods focus on modulating local properties, e.g. micro- and nano-scaled topographies, without capturing the global millimeter or intermediate mesoscale features. Here we introduced a method for modulating the collagen I-based extracellular matrix structure by disruption of fibrillogenesis and the gelation process through mechanical agitation. With this method, we generated collagen scaffolds that are thickened and wavy at a larger scale while featuring global softness. Thickened collagen patches were interconnected with loose collagen networks, highly resembling collagen architecture in the tumor stroma. This thickened collagen network promoted tumor cell dissemination. In addition, this novel modified scaffold triggered differences in morphology and migratory behaviors of tumor cells. Altogether, our method for altered collagen architecture paves new ways for studying in detail cell behavior in physiologically relevant biological processes. STATEMENT OF SIGNIFICANCE: Tumor progression usually involves chronic tissue damage and repair processes. Hallmarks of tumors are highly overlapped with those of wound healing. To mimic the tumor milieu, collagen-based scaffolds are widely used. These scaffolds focus on modulating microscale topographies and mechanics, lacking global architecture similarity compared with in vivo architecture. Here we introduced one type of thick collagen bundles that mimics ECM architecture in human skin scars. These thickened collagen bundles are long and wavy while featuring global softness. This collagen architecture imposes fewer steric restraints and promotes tumor cell dissemination. Our findings demonstrate a distinct picture of cell behaviors and intercellular interactions, highlighting the importance of collagen architecture and spatial heterogeneity of the tumor microenvironment.

A Multiparametric Nomogram for Predicting Delayed Graft Function in Adult Recipients of Pediatric Donor Kidneys.

BACKGROUND: Delayed graft function (DGF) is one of the most common postoperative complications after kidney transplantation. The ability to predict DGF after transplantation can greatly aid clinical decision-making. Several models have been proposed to predict DGF in adult recipients of adult donor kidneys, but there is currently no model to predict DGF in adult recipients of pediatric donor transplants. Therefore, based on our medical records, we retrospectively investigated the pretransplant risk factors of DGF in transplants from pediatric donors to adult recipients. METHODS: Our center is in compliance with national laws, the Declaration of Istanbul, and the Helsinki Congress. The donors used by us were organs donated after the death of citizens and the participants were neither paid nor coerced. A retrospective review of 84 adult patients who received pediatric donor kidneys at a single center from April 4, 2015 to November 17, 2021 was conducted to investigate the pretransplant risk factors for the development of DGF. RESULTS: DGF was observed in 45 of 68 patients (66.17%) in the training group and 9 of 16 patients (56.25%) in the validation group. Multivariate logistic analysis showed that kidney donor profile index, cold ischemia time, number of human leukocyte antigen mismatches, and pretransplant dialysis duration were significant independent risk factors for DGF. By integrating these 4 factors, we constructed a nomogram model to predict DGF. According to the prediction model, the area under the curve of DGF of the training group and validation group was 0.899 and 0.905, respectively. CONCLUSION: We have constructed a novel, reliable, and accurate visual nomogram that provides a practical tool for predicting DGF in adult recipients of pediatric donor kidneys.