A deep learning algorithm to identify carotid plaques and assess their stability.

Background: Carotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning. Methods: A total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People's Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices. Results: Modeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%. Conclusion: Deep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.

Radiomics combined with clinical and MRI features may provide preoperative evaluation of suboptimal debulking surgery for serous ovarian carcinoma.

PURPOSE: To develop and validate a model for predicting suboptimal debulking surgery (SDS) of serous ovarian carcinoma (SOC) using radiomics method, clinical and MRI features. METHODS: 228 patients eligible from institution A (randomly divided into the training and internal validation cohorts) and 45 patients from institution B (external validation cohort) were collected and retrospectively analyzed. All patients underwent abdominal pelvic enhanced MRI scan, including T2-weighted imaging fat-suppressed fast spin-echo (T2FSE), T1-weighted dual-echo magnetic resonance imaging (T1DEI), diffusion weighted imaging (DWI), and T1 with contrast enhancement (T1CE). We extracted, selected and eliminated highly correlated radiomic features for each sequence. Then, Radiomic models were made by each single sequence, dual-sequence (T1CE + T2FSE), and all-sequence, respectively. Univariate and multivariate analyses were performed to screen the clinical and MRI independent predictors. The radiomic model with the highest area under the curve (AUC) was used to combine the independent predictors as a combined model. RESULTS: The optimal radiomic model was based on dual sequences (T2FSE + T1CE) among the five radiomic models (AUC = 0.720, P < 0.05). Serum carbohydrate antigen 125, the relationship between sigmoid colon/rectum and ovarian mass or mass implanted in Douglas' pouch, diaphragm nodules, and peritoneum/mesentery nodules were considered independent predictors. The AUC of the radiomic-clinical-radiological model was higher than either the optimal radiomic model or the clinical-radiological model in the training cohort (AUC = 0.908 vs. 0.720/0.854). CONCLUSIONS: The radiomic-clinical-radiological model has an overall algorithm reproducibility and may help create individualized treatment programs and improve the prognosis of patients with SOC.

Quantifying Microstructure Features for High-Performance Solid Oxide Cells.

The drive for sustainable energy solutions has spurred interest in solid oxide fuel cells (SOFCs). This study investigates the impact of sintering temperature on SOFC anode microstructures using advanced 3D focused ion beam-scanning electron microscopy (FIB-SEM). The anode's ceramic-metal composition significantly influences electrochemical performance, making optimization crucial. Comparing cells sintered at different temperatures reveals that a lower sintering temperature enhances yttria-stabilized zirconia (YSZ) and nickel distribution, volume, and particle size, along with the triple-phase boundary (TPB) interface. Three-dimensional reconstructions illustrate that the cell sintered at a lower temperature exhibits a well-defined pore network, leading to increased TPB density. Hydrogen flow simulations demonstrate comparable permeability for both cells. Electrochemical characterization confirms the superior performance of the cell sintered at the lower temperature, displaying higher power density and lower total cell resistance. This FIB-SEM methodology provides precise insights into the microstructure-performance relationship, eliminating the need for hypothetical structures and enhancing our understanding of SOFC behavior under different fabrication conditions.

cAMP-independent DNA binding of the CRP family protein DdrI from Deinococcus radiodurans.

The cAMP receptor proteins (CRPs) play a critical role in bacterial environmental adaptation by regulating global gene expression levels via cAMP binding. Here, we report the structure of DdrI, a CRP family protein from Deinococcus radiodurans. Combined with biochemical, kinetic, and molecular dynamics simulations analyses, our results indicate that DdrI adopts a DNA-binding conformation in the absence of cAMP and can form stable complexes with the target DNA sequence of classical CRPs. Further analysis revealed that the high-affinity cAMP binding pocket of DdrI is partially filled with Tyr113-Arg55-Glu65 sidechains, mimicking the anti-cAMP-mediated allosteric transition. Moreover, the second syn-cAMP binding site of DdrI at the protein-DNA interface is more negatively charged compared to that of classical CRPs, and manganese ions can enhance its DNA binding affinity. DdrI can also bind to a target sequence that mimics another transcription factor, DdrO, suggesting potential cross-talk between these two transcription factors. These findings reveal a class of CRPs that are independent of cAMP activation and provide valuable insights into the environmental adaptation mechanisms of D. radiodurans.IMPORTANCEBacteria need to respond to environmental changes at the gene transcriptional level, which is critical for their evolution, virulence, and industrial applications. The cAMP receptor protein (CRP) of Escherichia coli (ecCRP) senses changes in intracellular cAMP levels and is a classic example of allosteric effects in textbooks. However, the structures and biochemical activities of CRPs are not generally conserved and there exist different mechanisms. In this study, we found that the proposed CRP from Deinococcus radiodurans, DdrI, exhibited DNA binding ability independent of cAMP binding and adopted an apo structure resembling the activated CRP. Manganese can enhance the DNA binding of DdrI while allowing some degree of freedom for its target sequence. These results suggest that CRPs can evolve to become a class of cAMP-independent global regulators, enabling bacteria to adapt to different environments according to their characteristics. The first-discovered CRP family member, ecCRP (or CAP) may well not be typical of the family and be very different to the ancestral CRP-family transcription factor.

Single-walled carbon nanotube-protein complex: A strategy to improve the immune response to protein in mice.

Vaccines represent an effective tool for controlling disease infection. As a key component of vaccines, many types of adjuvants have been developed and used today. This study is designed to investigate the efficacy of single-walled carbon nanotubes (SWCNTs) as a new adjuvant. The results showed that SWCNT could adsorb the antigen by intermolecular action, and the adsorption rate was significantly higher after dispersion of the SWCNTs in a sonic bath. The titer of specific antibody of mice in the SWCNTs group was higher than that of the mice in the antigen control group, confirming the adjuvant efficacy of SWCNTs. During immunisation, the specific antibody was detected earlier in the mice of the SWCNTs group, especially when the amount of antigen was reduced. And it was proved that the titer of antibodies was higher after subcutaneous and intraperitoneal injection compared to intramuscular injection. Most importantly, the mice immunised with SWCNTs showed almost the same level of immunity as the mice in the FCA (Freund's complete adjuvant) group, indicating that the SWCNTs were an effective adjuvant. In addition, the mice in the SWCNT group maintained antibody levels for 90 days after the last booster vaccination and showed a good state of health during the observed period. We also found that the SWCNTs were able to induce macrophages activation and enhance antigen uptake by mouse peritoneal macrophages.

An emerging antibacterial nanovaccine for enhanced chemotherapy by selectively eliminating tumor-colonizing bacteria.

Fusobacterium nucleatum (F. nucleatum), an oral anaerobe, is prevalent in colorectal cancer and is closely related to increased cancer cell growth, metastasis, and poor treatment outcomes. Bacterial vaccines capable of selectively eliminating bacteria present a promising approach to targeting intratumor F. nucleatum, thereby enhancing cancer treatment. Although adjuvants have been employed to enhance the immune response, the vaccine's effectiveness is constrained by inadequate T-cell activation necessary for eradicating intracellular pathogens. In this study, we developed a minimalistic, biomimetic nanovaccine by integrating highly immunostimulatory adjuvant cholesterol-modified CpG oligonucleotides into the autologously derived F. nucleatum membranes. Compared to the traditional vaccines consisting of inactivated bacteria and Alum adjuvant, the nanovaccine coupled with bacterial membranes and adjuvants could remarkably improve multiple antigens and adjuvant co-delivery to dendritic cells, maximizing their ability to achieve effective antigen presentation and strong downstream immune progress. Notably, the nanovaccine exhibits outstanding selective prophylactic and therapeutic effects, eliminating F. nucleatum without affecting intratumoral and gut microbiota. It significantly enhances chemotherapy efficacy and reduces cancer metastasis in F. nucleatum-infected colorectal cancer. Overall, this work represents the rational application of bacterial nanovaccine and provides a blueprint for future development in enhancing the antitumor effect against bacterial-infected cancer.

A causal multiomics study discriminates the early immune features of Ad5-vectored Ebola vaccine recipients.

The vaccine-induced innate immune response is essential for the generation of an antibody response. To date, how Ad5-vectored vaccines are influenced by preexisting anti-Ad5 antibodies during activation of the early immune response remains unclear. Here, we investigated the specific alterations in GP1,2-specific IgG-related elements of the early immune response at the genetic, molecular, and cellular levels on days 0, 1, 3, and 7 after Ad5-EBOV vaccination. In a causal multiomics analysis, distinct early immune responses associated with GP1,2-specific IgG were observed in Ad5-EBOV recipients with a low level of preexisting anti-Ad5 antibodies. This study revealed the correlates of the Ad5-EBOV-induced IgG response and provided mechanistic evidence for overcoming preexisting Ad5 immunity during the administration of Ad5-vectored vaccines.

Knowledge level and health information-seeking behavior of people with diabetes in rural areas: a multicenter cross-sectional study.

Introduction: There is a lack of research on the current level of diabetes knowledge and health information-seeking behaviors among patients with diabetes in rural areas of China's economically underdeveloped regions during COVID-19, as well as a lack of up-to-date evidence on glycemic control and the incidence of complications among rural patients with diabetes. Objectives: To investigate the prevalence of glycemic control and complications among patients with diabetes in rural areas, to explore the current status and correlation of diabetes knowledge level and health information-seeking behavior, and to analyze the factors affecting diabetes knowledge level. Methods: From January 2022 to July 2022, we conducted a screening on diabetic complications and a questionnaire survey among 2,178 patients with diabetes in 15 county hospitals in rural areas of Guangxi Zhuang Autonomous Region. The patients' knowledge level and health information-seeking behavior were investigated. Spearman correlation analysis was used to assess the correlation between diabetes knowledge and health information-seeking behavior. Multiple linear regression analysis was used to test how demographic information and health information-seeking behavior influenced the level of diabetes knowledge. Results: Of 2,178 patients with diabetes in rural areas, 1,684 (77.32%) had poor glycemic control, and the prevalence of diabetic complications was estimated to be 72.13%. Patients with diabetes had poor diabetes knowledge and health information-seeking behavior, and there is a strong positive correlation between them. Diabetes knowledge level was influenced by per capita household disposable income, occupational status, gender, age, ethnicity, family history of diabetes, insulin use, glycated hemoglobin, education level, number of complications and health information-seeking behavior. Conclusion: Patients with diabetes in rural areas have poor glycemic control and a high incidence of diabetic complications. Patients with diabetes in rural areas have poor knowledge and inadequate health information-seeking behavior. Systematic and standardized education should be provided to improve patients' diabetes knowledge and thus improve their self-management ability.

Moderately reducing N input to mitigate heat stress in maize.

In a warming climate, high temperature stress greatly threatens crop yields. Maize is critical to food security, but frequent extreme heat events coincide temporally and spatially with the period of kernel number determination (e.g., flowering stage), greatly limiting maize yields. In this context, how to increase or at least maintain maize yield has become more important. Nitrogen fertilizer (N) is widely used to improve maize yields, but its effect in heat stress is unclear. For this, we collected 1536 pairs of comparisons from 113 studies concerning N conducted in the past 20 years over China. We classified the data into two groups - without high temperature stress (NHT) and with high temperature stress during the critical period for maize kernel number determination (HT) - based on the national meteorological data. We comprehensively evaluated N effects on grain yield under HT and NHT using meta-analysis. The effect of N on maize yield became significantly smaller in HT than that in NHT. In NHT, soil characteristics, crop management practices, and climatic conditions all significantly affected N effects on maize yield, but in HT, only a few factors such as soil organic matter and mean annual precipitation significantly affected N effects. Hence, it is difficult to improve N effect by improving soil characteristics and crop management when meeting with high temperature stress during flowering. On average, N effect increased with increased N input, but there were respective N input thresholds in NHT and HT, beyond which N effects on maize yield remained stable. According to the thresholds, it is speculated that moderately reducing N input (~20 %) likely increased high temperature tolerance of maize during flowering. These findings have important implications for the optimization of N management under a warming climate.

A Wooden Carbon-Based Photocatalyst for Water Treatment.

Due to a large number of harmful chemicals flowing into the water source in production and life, the water quality deteriorates, and the use value of water is reduced or lost. Biochar has a strong physical adsorption effect, but it can only separate pollutants from water and cannot eliminate pollutants fundamentally. Photocatalytic degradation technology using photocatalysts uses chemical methods to degrade or mineralize organic pollutants, but it is difficult to recover and reuse. Woody biomass has the advantages of huge reserves, convenient access and a low price. Processing woody biomass into biochar and then combining it with photocatalysts has played a complementary role. In this paper, the shortcomings of a photocatalyst and biochar in water treatment are introduced, respectively, and the advantages of a woody biochar-based photocatalyst made by combining them are summarized. The preparation and assembly methods of the woody biochar-based photocatalyst starting from the preparation of biochar are listed, and the water treatment efficiency of the woody biochar-based photocatalyst using different photocatalysts is listed. Finally, the future development of the woody biochar-based photocatalyst is summarized and prospected.

Identification of Novel Regulators of Leaf Senescence Using a Deep Learning Model.

Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN's predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.

A Comparative Study of Removal of Acid Red 27 by Adsorption on Four Different Chitosan Morphologies.

To investigate the relationship between structures and adsorption properties, four different morphologies of chitosan, with hydrogel (CSH), aerogel (CSA), powder (CSP), and electrospinning nanofiber (CSEN) characteristics, were employed as adsorbents for the removal of Acid Red 27. The structures and morphologies of the four chitosan adsorbents were characterized with SEM, XRD, ATR-FTIR, and BET methods. The adsorption behaviors and mechanisms of the four chitosan adsorbents were comparatively studied. All adsorption behaviors exhibited a good fit with the pseudo-second-order kinetic model (R2 > 0.99) and Langmuir isotherm model (R2 > 0.99). Comparing the adsorption rates and the maximum adsorption capacities, the order was CSH > CSA > CSP > CSEN. The maximum adsorption capacities of CSH, CSA, CSP, and CSEN were 2732.2 (4.523), 676.7 (1.119), 534.8 (0.885), and 215.5 (0.357) mg/g (mmol/g) at 20 °C, respectively. The crystallinities of CSH, CSA, CSP, and CSEN were calculated as 0.41%, 6.97%, 8.76%, and 39.77%, respectively. The crystallinity of the four chitosan adsorbents was the main factor impacting the adsorption rates and adsorption capacities, compared with the specific surface area. With the decrease in crystallinity, the adsorption rates and capacities of the four chitosan adsorbents increased gradually under the same experimental conditions. CSH with a low crystallinity and large specific surface area resulted in the highest adsorption rate and capacity.

Effect of cation valence on the retrogradation, gelatinization and gel characteristics of maize starch.

This study aimed to examine the impact of trivalent, divalent, or monovalent cations dissolving into water and being mixed with maize starch to influence its retrogradation, gelatinization, and gel characteristics. The result of the analysis using a differential scanning calorimeter showed that all cations raised the peak of gelatinization temperature of maize starch, especially Al3+ or Fe3+, while trivalent cations reduced the enthalpy. The result of the analysis using a rapid viscosity analyzer showed that trivalent cation caused lower trough viscosity, final viscosity, and pasting temperature but higher breakdown viscosity of maize starch than monovalent or divalent cations. Confocal laser scanning microscopy showed that the cation promoted the destruction of gelatinized maize starch granules, especially Zn2+, Fe3+, or Al3+. Additionally, trivalent Fe3+ or Al3+ caused higher gel strength of maize starch. Generally, the cation with higher valence changed more retrogradation, gelatinization, and gel characteristics of maize starch.

Industrially Scalable Textile Sensing Interfaces for Extended Artificial Tactile and Human Motion Monitoring without Compromising Comfort.

Smart wearables with the capability for continuous monitoring, perceiving, and understanding human tactile and motion signals, while ensuring comfort, are highly sought after for intelligent healthcare and smart life systems. However, concurrently achieving high-performance tactile sensing, long-lasting wearing comfort, and industrialized fabrication by a low-cost strategy remains a great challenge. This is primarily due to critical research gaps in novel textile structure design for seamless integration with sensing elements. Here, an all-in-one biaxial insertion knit architecture is reported to topologically integrate sensing units within double-knit loops for the fabrication of a large-scale tactile sensing textile by using low-cost industrial manufacturing routes. High sensitivity, stability, and low hysteresis of arrayed sensing units are achieved through engineering of fractal structures of hierarchically patterned piezoresistive yarns via blistering and twisting processing. The as-prepared tactile sensing textiles show desirable sensing performance and robust mechanical property, while ensuring excellent conformability, tailorability, breathability (288 mm s-1), and moisture permeability (3591 g m-2 per day) for minimizing the effect on wearing comfort. The multifunctional applications of tactile sensing textiles are demonstrated in continuously monitoring human motions, tactile interactions with the environment, and recognizing biometric gait. Moreover, we also demonstrate that machine learning-assisted sensing textiles can accurately predict body postures, which holds great promise in advancing the development of personalized healthcare robotics, prosthetics, and intelligent interaction devices.

Enhanced Detection of Novel Low-Frequency Gene Fusions via High-Yield Ligation and Multiplexed Enrichment Sequencing.

Panel-based methods are commonly employed for the analysis of novel gene fusions in precision diagnostics and new drug development in cancer. However, these methods are constrained by limitations in ligation yield and the enrichment of novel gene fusions with low variant allele frequencies. In this study, we conducted a pioneering investigation into the stability of double-stranded adapter DNA, resulting in improved ligation yield and enhanced conversion efficiency. Additionally, we implemented blocker displacement amplification, achieving a remarkable 7-fold enrichment of novel gene fusions. Leveraging the pre-enrichment achieved with this approach, we successfully applied it to Nanopore sequencing, enabling ultra-fast analysis of novel gene fusions within one hour with high sensitivity. This method offers a robust and remarkably sensitive mean of analyzing novel gene fusions, promising the discovery of pivotal biomarkers that can significantly improve cancer diagnostics and the development of new therapeutic strategies.

Adsorption and desorption of Hg(II) by four aged microplastics and its effects on gaseous elemental mercury production in seawater.

Microplastics (MPs) weather after entering the environment gradually, and the interaction with metal ions in the aqueous environment has received extensive attention. However, there are few studies on Hg(Ⅱ), especially the effect of MPs on the release of Hg0(DEM) in water after entering the aqueous environment. In this study, four types of MPs (PP, PE, PET, PVC) were selected to study the adsorption and desorption behavior of Hg(Ⅱ) after photoaging and to explore the influence of MPs on the release of DEM in seawater under different lighting conditions. The results showed that the specific surface area, negative charges, and oxygen-containing functional group of MPs increased after aging. The adsorption capacity of aged MPs for Hg(Ⅱ) was significantly improved, which was consistent with the pseudo-first-order and pseudo-second-order model, indicating that the adsorption process was a chemical and physical adsorption. The fitting results of the in-particle diffusion model indicated that the adsorption was controlled by multiple steps. Hg(Ⅱ) was easier to desorb in the simulated gastric fluid environment. Because the aged MPs had the stronger binding force to Hg(Ⅱ), their desorption rate is lower than new MPs. Under visible light and UVA irradiation, MPs inhibited the release of Hg0. Under UVA, the mass of DEM produced in seawater with aged PE and PVC was higher than that of new PE and PVC. The aged PE and PVC could produce more ·O2-, which was conducive to the reduction of mercury. However, in UVB irradiation, the addition of MPs promoted the release of DEM, and ·O2- also played an important contribution in affecting the photochemical reaction of mercury. Therefore, the presence of aged MPs will significantly affect the water-air exchange of Hg in water. Compared with new MPs, aged MPs improved the contribution of free radicals in Hg transformation by releasing reactive oxygen species. This study extends the understanding of the effects of MPs on the geochemical cycle of Hg(Ⅱ) in seawater, better assesses the potential combined ecological risks of MPs and Hg(Ⅱ), and provides certain guidance for the pollution prevention and control of MPs.

Dean vortex-enhanced blood plasma separation in self-driven spiral microchannel flow with cross-flow microfilters.

Point-of-care (POC) diagnostic devices have been developing rapidly in recent years, but they are mainly using saliva instead of blood as a test sample. A highly efficient self-separation during the self-driven flow without power systems is desired for expanding the point-of-care diagnostic devices. Microfiltration stands out as a promising technique for blood plasma separation but faces limitations due to blood cell clogging, resulting in reduced separation speed and efficiency. These limitations are mainly caused by the high viscosity and hematocrit in the blood flow. A small increment in the hematocrit of the blood significantly increases the pressure needed for the blood plasma separation in the micro-filters and decreases the separation speed and efficiency. Addressing this challenge, this study explores the feasibility of diluting whole blood within a microfluidic device without external power systems. This study implemented a spiral microchannel utilizing the inertial focusing and Dean vortex effects to focus the red blood cells and extract the blood with lower hematocrit. The inertial migration of the particles during the capillary flow was first investigated experimentally; a maximum of 88% of the particles migrated to the bottom and top equilibrium positions in the optimized 350 × 60 µm (cross-sectional area, 5.8 aspect ratio) microchannel. With the optimized dimension of the microchannel, the whole blood samples within the physiological hematocrit range were tested in the experiments, and more than 10% of the hematocrit reduction was compared between the outer branch outlet and inner branch outlet in the 350 × 60 µm microchannel.

[Design and Fabrication of Porous Implants Manufactured by 3D Printing].

Different porous structures were studied through finite element analysis and then optimal porous structure was selected for the orthopedic applications. The optimal Voronoi structure was designed and fabricated using 3D printing. The mechanical properties and osseointegration ability were both investigated. The mechanical tests showed that the tensile strength, compressive strength and bending strength of Voronoi structures were obviously higher than that of the human bone, and the modulus of Voronoi structures were similar to human bone. In addition, the animal experimental results exhibited that obvious bone ingrowth was found from Month 1 to Month 6. This study provides some theoretical references for the orthopedic application of porous structures.

Prediction of microvascular invasion and pathological differentiation of hepatocellular carcinoma based on a deep learning model.

PURPOSE: To develop a deep learning (DL) model based on preoperative contrast-enhanced computed tomography (CECT) images to predict microvascular invasion (MVI) and pathological differentiation of hepatocellular carcinoma (HCC). METHODS: This retrospective study included 640 consecutive patients who underwent surgical resection and were pathologically diagnosed with HCC at two medical institutions from April 2017 to May 2022. CECT images and relevant clinical parameters were collected. All the data were divided into 368 training sets, 138 test sets and 134 validation sets. Through DL, a segmentation model was used to obtain a region of interest (ROI) of the liver, and a classification model was established to predict the pathological status of HCC. RESULTS: The liver segmentation model based on the 3D U-Network had a mean intersection over union (mIoU) score of 0.9120 and a Dice score of 0.9473. Among all the classification prediction models based on the Swin transformer, the fusion models combining image information and clinical parameters exhibited the best performance. The area under the curve (AUC) of the fusion model for predicting the MVI status was 0.941, its accuracy was 0.917, and its specificity was 0.908. The AUC values of the fusion model for predicting poorly differentiated, moderately differentiated and highly differentiated HCC based on the test set were 0.962, 0.957 and 0.996, respectively. CONCLUSION: The established DL models established can be used to noninvasively and effectively predict the MVI status and the degree of pathological differentiation of HCC, and aid in clinical diagnosis and treatment.