Fabrication of cinnamon essential oil nanoemulsions with high antibacterial activities via microfluidization.

The high volatility and hydrophobicity of cinnamon essential oils (CiEO) limited their practical application. To enhance their stability and antibacterial activity, nanoemulsions encapsulating CiEO were prepared using hydroxypropyl-ß-cyclodextrin/lauroyl arginate (HPCD/LAE) inclusion complexes through high-pressure microfluidization (HPM). Effects of HPM parameters on the stability and antibacterial properties of nanoemulsion were investigated. Results revealed that increased processing pressure and cycle numbers were associated with reduced droplet size and greater homogeneity in CiEO distribution. Storage and thermal stability were optimized at 100 MPa and seven cycles. Moreover, the nanoemulsions showed strong synergistic antibacterial against E. coli (19.79 mm) and S. aureus (23.61 mm) compared with LAE (11.52 mm and 12.82 mm, respectively) and CiEO alone (13.26 mm and 17.68 mm, respectively). This study provided new information for constructing CiEO nanoemulsion, which is suitable for use in the food industry.

Application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system for undergraduate medical students.

BACKGROUND: Process-based teaching is a new education model. SPARK case database is a free medical imaging case database. This manuscript aimed to explore the application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system. METHODS: 117 third year medical students were included. They were divided into Group A, B, C and D according to the curriculum arrangement. Group A and B attended the experimental class at the same time, A was the experimental group, B was the control group. Group C and D attended experimental classes at the same time, C was the experimental group, D was the control group. The experimental group used SPARK case database, while the control group used traditional teaching model for learning. The four groups of students were respectively tested after the theoretical class, before the experimental class, after the experimental class, and one week after the experimental class to compare the results. Finally, all students used SPARK case database to study, and were tested one month after the experimental class to compare their differences. RESULTS: The scores after the theoretical class of Group A and B were (100.0 ± 25.4), (101.0 ± 23.8)(t=-0.160, P > 0.05), Group C and D were (94.7 ± 23.7), (92.1 ± 18.6)(t = 0.467, P > 0.05). The scores of Group A and B before and after the experimental class and one week after the experimental class were respectively (84.1 ± 17.4), (72.1 ± 21.3)(t = 2.363, P < 0.05), (107.6 ± 14.3), (102.1 ± 18.0)(t = 1.292, P > 0.05), (89.7 ± 24.3), (66.6 ± 23.2)(t = 3.706, P < 0.05). The scores of Group C and D were (94.0 ± 17.3), (72.8 ± 25.5)(t = 3.755, P < 0.05), (107.3 ± 20.3), (93.1 ± 20.9)(t = 2.652, P < 0.05), (100.3 ± 19.7), (77.2 ± 24.0)(t = 4.039, P < 0.05). The scores of Group A and B for one month after the experimental class were (86.6 ± 28.8), (84.5 ± 24.0)(t = 0.297, P > 0.05), and Group C and D were (95.7 ± 20.3), (91.7 ± 23.0)(t = 0.699, P > 0.05). CONCLUSIONS: The process-based teaching based on SPARK case database could improve the radiology practice ability of the musculoskeletal system of students.

Artificial intelligence model system for bone age assessment of preschool children.

BACKGROUD: Our study aimed to assess the impact of inter- and intra-observer variations when utilizing an artificial intelligence (AI) system for bone age assessment (BAA) of preschool children. METHODS: A retrospective study was conducted involving a total sample of 53 female individuals and 41 male individuals aged 3-6 years in China. Radiographs were assessed by four mid-level radiology reviewers using the TW3 and RUS-CHN methods. Bone age (BA) was analyzed in two separate situations, with/without the assistance of AI. Following a 4-week wash-out period, radiographs were reevaluated in the same manner. Accuracy metrics, the correlation coefficient (ICC)and Bland-Altman plots were employed. RESULTS: The accuracy of BAA by the reviewers was significantly improved with AI. The results of RMSE and MAE decreased in both methods (p < 0.001). When comparing inter-observer agreement in both methods and intra-observer reproducibility in two interpretations, the ICC results were improved with AI. The ICC values increased in both two interpretations for both methods and exceeded 0.99 with AI. CONCLUSION: In the assessment of BA for preschool children, AI was found to be capable of reducing inter-observer variability and enhancing intra-observer reproducibility, which can be considered an important tool for clinical work by radiologists. IMPACT: The RUS-CHN method is a special bone age method devised to be suitable for Chinese children. The preschool stage is a critical phase for children, marked by a high degree of variability that renders BA prediction challenging. The accuracy of BAA by the reviewers can be significantly improved with the aid of an AI model system. This study is the first to assess the impact of inter- and intra-observer variations when utilizing an AI model system for BAA of preschool children using both the TW3 and RUS-CHN methods.

From phenotype to mechanism: Prenatal spectrum of NKAP mutation-related disorder and its pathogenesis inducing congenital heart disease.

NKAP mutations are associated with Hackmann-Di Donato-type X-linked syndromic intellectual developmental disorder (MRXSHD, MIM: #301039). Here, we elucidate the potential prenatal manifestation of NKAP mutation-associated disorder for the first time, alongside revealing the relationship between NKAP mutations and congenital heart defect (CHD) in the Chinese population. An NKAP mutation (NM_024528.4: c.988C>T, p.Arg330Cys) was identified in two foetuses presenting with CHD. Subsequent mechanistic exploration revealed a marked downregulation of NKAP transcription within HEK293T cells transfected with NKAP p.R330C. However, no significant change was observed at the protein level. Moreover, the mutation led to a dysregulation in the transcription of genes associated with cardiac morphogenesis, such as DHRS3, DNAH11 and JAG1. Additionally, our research determined that NKAP p.R330C affected Nkap protein intra-nuclear distribution, and binding with Hdac3. Summarily, our study strengthens NKAP mutations as a cause of CHD and prompts the reclassification of NKAP p.R330C as likely pathogenic, thereby establishing a prospective prenatal phenotypic spectrum that provides new insight into the prenatal diagnosis of CHD. Our findings also provide evidence of NKAP p.R330C pathogenicity and demonstrate the potential mechanism by which p.R330C dysregulates cardiac developmental gene transcription by altering Nkap intra-nuclear distribution and obstructing the interaction between Nkap and Hdac3, thereby leading to CHD.

A novel GLI3 frameshift mutation in a Chinese pedigree with polydactyly: A case report.

Background: GLI3 gene mutations can result in various forms of polysyndactyly, such as Greig cephalopolysyndactyly syndrome (GCPS, MIM: #175700), Pallister-Hall syndrome (PHS, MIM: #146510), and isolated polydactyly (IPD, MIM: #174200, #174700). Reports on IPD-associated GLI3 mutations are rare. In this study, a novel GLI3 mutation was identified in a Chinese family with IPD. Results: We report a family with six members affected by IPD. The family members demonstrated several special phenotypes, including sex differences, abnormal finger joint development, and different polydactyly types. We identified a novel frameshift variant in the GLI3 gene (NM_000168.6: c.1820_1821del, NP_000159.3: p.Tyr607Cysfs*9) by whole-exome sequencing. Further analysis suggested that this mutation was the cause of polydactyly in this family. Conclusions: The discovery of this novel frameshift variant in our study further solidifies the relationship between IPD and GLI3 and expands the previously established spectrum of GLI3 mutations and associated phenotypes.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates.

BACKGROUND: Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. RESULTS: The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. CONCLUSIONS: We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

Preparation and characterization of starch/PBAT film containing hydroxypropyl-ß-cyclodextrin/ethyl lauroyl arginate/cinnamon essential oil microcapsules and its application in the preservation of strawberry.

Cinnamon essential oil (CEO) was emulsified by hydroxypropyl-ß-cyclodextrin/ ethyl lauroyl arginate (HPCD/LAE) complex to make nanoemulsions, which were then incorporated into maltodextrin (MD) to prepare HPCD/LAE/CEO/MD microcapsules by spray drying. The starch/polybutylene adipate terephthalate (starch/PBAT, SP) based extrusion-blowing films containing above microcapsules were developed and used as packaging materials for strawberry preservation. The morphology, encapsulation efficiency, thermal and antibacterial properties of microcapsules with different formulations were investigated. The effects of microcapsules on the physicochemical and antimicrobial properties of SP films were evaluated. When the formula was 4 % HPCD/LAE-3% CEO-10% MD (HL-3C-MD), the microcapsule had the smallest particle size (3.3 µm), the highest encapsulation efficiency (84.51 %) of CEO and the best antibacterial effect. The mechanical and antimicrobial properties of the SP film were enhanced while the water vapor transmittance and oxygen permeability decreased with the incorporation of HL-3C-MD microcapsules. The films effectively reduced the weight loss rate (49.03 %), decay rate (40.59 %) and the total number of colonies (2.474 log CFU/g) and molds (2.936 log CFU/g), thus extending the shelf life of strawberries. This study revealed that the developed SP films containing HPCD/LAE/CEO microcapsules had potential applications in degradable bioactive food packaging materials.

Structure, rheological properties, and biocompatibility of Laponite® cross-linked starch/polyvinyl alcohol hydrogels.

Hydrogels, owing to their unique porous structures, hydrophilic properties, and biocompatibility, are being developed as scaffolds for bone grafts. However, the use of toxic initiators or cross-linking agents is a drawback. To overcome this, we developed Laponite®/cross-linked starch/polyvinyl alcohol (PVA) hydrogels prepared by one-step solution mixing. The structure, rheological properties, and biocompatibility of the hydrogels were investigated. Zeta potential, Fourier transform infrared, and X-ray diffraction analyses showed that hydrogen bonding and electrostatic interactions jointly maintained the structure of the cross-linked hydrogel systems. At a Laponite® concentration of 10 %, the hydrogel with a starch/PVA ratio of 2:2 exhibited a uniform porous structure, the highest storage modulus (G'), and the lowest degradation rate. At a starch/PVA ratio of 2:2, the G' increased; however, the degradation rate decreased with the increase in Laponite® content from 5 % to 20 %. These results indicate that the mechanical strength and degradation rate of the hydrogels could be adjusted by altering the starch/PVA ratio and the amount of Laponite®. In vitro cytotoxicity experiments showed that the Laponite®/starch/PVA (LSP) hydrogels were non-toxic to MC3T3-E1 cells. The starch/PVA ratio had no obvious effect on the proliferation of MC3T3-E1 cells, but an increase in Laponite® content significantly promoted cell proliferation. In summary, the results suggest that these LSP hydrogels have great potential for applications in bone tissue engineering.

[Analysis of the Environmental Risk of Livestock Manure Pollution and Resource Treatment Technology].

Rapid development of the livestock and poultry industry has greatly promoted the rural economic prosperity of China. However, the problems resulting from the livestock manure, such as large emissions, low utilization rate, and environmental pollution are also becoming increasingly serious. Based on the current situation of livestock manure discharge in China, the typical contaminants in livestock manure and their pollution characteristics in soil, water, and air were systematically analyzed in this study. Taking heavy metals and antibiotics as the characteristic pollutants, the common risk assessment methods for livestock manure pollution were described. Moreover, the main harmless disposal and recycling treatment technologies of livestock and poultry manure at home and abroad were compared and analyzed. The application prospect and value of these technologies such as the thermochemical conversion method and the biological method in energization or fertilization were evaluated. Furthermore, the prominent problems in the pollution control of livestock manure are discussed, and the development trends in the resource treatment technology of livestock manure were also prospected.

Preparation and characterization of carnauba wax-based particle with hierarchical structure and its use as hydrophobic coating for chitosan films.

To improve the hydrophobicity of polysaccharide-based films, hydrophobic carnauba wax-based particles were prepared by Pickering emulsion. The influence of the different size of the particles on the structure and hydrophobicity of the chitosan coating films were investigated. The results showed that micro-scale particles (average particle size 25.04 µm) with nano-scale (5-10 nm) TiO2 uniformly distributed on the surface of the particles were formed by Pickering emulsion. The chitosan coating films showed higher contact angle and lower sliding angle compared to the control due to the hierarchical structure, hydrophobicity and arrangement of the particles. In addition, the small particle (23-48 µm) coating film showed higher hydrophobicity than the large particle coating film (48-70 µm) due to the small particle size and the formation of more small gaps. The gaps were conducive to form "air cushion" which reduced the contact area between water and the coating films and thus increased contact angle and decreased sliding angle. The coating films showed high chemical stability and low residual rates of liquid food. The results suggest that Pickering emulsion is an effective method to create wax-based particles with hierarchical structure and the particles have potential to be used as hydrophobic coating materials.

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.

Background: Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods: The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results: Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion: The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China.

OBJECTIVE: Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). METHODS: Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. RESULTS: The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). CONCLUSION: This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

Effect of low temperature extrusion-modified potato starch addition on properties of whole wheat dough and texture of whole wheat youtiao.

In this study, low temperature extrusion-modified potato starch (MPS) was added to improve properties of whole wheat dough and textural quality of resulted youtiao. Extrusion temperature (60, 90 ℃) and barrel moisture content (30, 42 and 54%) were set as test variables. The results suggested that the low temperature extrusion processing caused moderate gelatinization and improved gel-forming properties of potato starch. MPS addition decreased the setback by up to 46%, and enhanced the viscoelasticity of whole wheat dough significantly. Compared to the whole wheat-based youtiao, the addition of 10% MPS decreased the hardness by up to 72%, and increased the springiness and specific volume by 32% and 22%, respectively. The addition of MPS prepared at lower extrusion temperature (60 ℃) and moderate moisture content (42%) resulted in the optimum textural qualities of whole wheat youtiao. This study will help better understand the role of MPS in whole wheat-based food product.

Effect of nano-TiO2 particle size on the bonding performance and film-forming properties of starch-based wood adhesives.

The effect of nano-TiO2 particle size on the properties of starch-based wood adhesives was studied in this work. Our findings indicate that a smaller size of nano-TiO2 particles corresponds with a larger specific surface area and more hydroxyl sites on the particle surface that interact with latex molecules, forming a more compact network structure. Therefore, the bonding performance and water resistance of the adhesive were enhanced. In addition, rheology results showed that the adhesive behaves as a pseudoplastic fluid. Small-angle X-ray scattering and energy dispersive spectroscopy confirmed the good compatibility and dispersion of nano-TiO2 in the adhesive films. Diffusing wave spectroscopy and scanning electron microscopy showed that smaller TiO2 particles were more favorable for the formation of smoother and denser films. These results are of great significance for improving the structure and properties of starch-based wood adhesives and preparing high-performance environmentally friendly biobased adhesives.

Quinoa protein Pickering emulsion: A promising cryoprotectant to enhance the freeze-thaw stability of fish myofibril gels.

In this work, the effects of different QPE addition on the freeze-thaw (F-T) stability of fish myofibrillar protein (MP) gels were revealed. During freezing process, QPE decreased the freezing point of MP gels and shortened the time to pass through the maximum-ice-crystal-formation zone. The occurrence of thermal hysteresis effect led to the formation of small ice crystals and alleviated the damage to MP gel network. The incorporation of 7.5% QPE also reduced the free water amount to 19.23% and improved the water holding capacity of MP gels. Furthermore, the incorporation of QPE decreased the carbonyl content of MP gels after F-T cycles and delayed the protein oxidation. Meanwhile, QPE addition maintained the stability of the tertiary structure of MP gels via stabilizing the microenvironment of tyrosine and tryptophan. Overall, QPE shows the potential as a new cryoprotectant to improve the F-T stability of MP gel products.

Application of SPARK teaching in acute abdomen radiography teaching for undergraduate medical students.

BACKGROUND: Acute abdomen is a series of acute and severe abdominal diseases commonly encountered in clinic. It is important to strengthen the image teaching of acute abdomen for undergraduates. AIM: This study aimed to explore the application effect of SPARK[sub-speciality (S), problem-based learning (P), assessment (A), report (R) and reading skill (K)] teaching mode in the experimental teaching of acute abdomen for undergraduate medical students. METHODS: We selected a total of 58 third year medical students for observation. The students were divided into experimental group and control group. Among them, 29 students in the experimental group studied in SPARK teaching mode, 29 students in the control group studied in traditional teaching mode. The two groups of students were tested after the theory class, before and after the experimental class, and one week after the experimental class, to compare the application effects of the two teaching modes. After the test one week after the experimental class, the two groups of students jointly adopted SPARK mode to learn, and were tested again one month after the experimental class to compare whether the two groups of students achieved the same results. The total score of all tests was 150. RESULTS: The average scores of the experimental group and the control group after theory class were (69.0 ± 26.4) and (72.1 ± 24.1) respectively, with no statistical difference (t = 0.468, P = 0.642). The average scores of the experimental group before, after and one week after the experimental class were higher than those of the control group. The experimental group was (84.5 ± 23.1), (109.7 ± 23.8), (105.5 ± 31.0) respectively, and the control group was (52.8 ± 15.1), (93.8 ± 17.0), (80.0 ± 22.8) respectively. The differences were statistically significant (t = -6.195, P = 0.00; t = -2.919, P = 0.05; t = -3.569, P = 0.01). The average scores of the experimental group and the control group after one month were (99.0 ± 31.0) and (95.5 ± 25.6) respectively, and there was no significant difference between the two groups (t = -0.462, P = 0.646). CONCLUSIONS: The SPARK teaching mode was helpful for undergraduate medical students to consolidate image foundation, improve image reading skills.

E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation.

BACKGROUND: U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors. OBJECTIVE: We use spatial enhancement filtering technology to compensate for the semantic gap and propose an enhanced dense U-Net (E-DU), aiming to apply it to multimodal medical image segmentation to improve the segmentation performance and efficiency. METHODS: Before combining encoder and decoder features, we replace the traditional skip connection with a multiscale denoise enhancement (MDE) module. The encoder features need to be deeply convolved by the spatial enhancement filter and then combined with the decoder features. We propose a simple and efficient deep full convolution network structure E-DU, which can not only fuse semantically various features but also denoise and enhance the feature map. RESULTS: We performed experiments on medical image segmentation datasets with seven image modalities and combined MDE with various baseline networks to perform ablation studies. E-DU achieved the best segmentation results on evaluation indicators such as DSC on the U-Net family, with DSC values of 97.78, 97.64, 95.31, 94.42, 94.93, 98.85, and 98.38 (%), respectively. The addition of the MDE module to the attention mechanism network improves segmentation performance and efficiency, reflecting its generalization performance. In comparison to advanced methods, our method is also competitive. CONCLUSION: Our proposed MDE module has a good segmentation effect and operating efficiency, and it can be easily extended to multiple modal medical segmentation datasets. Our idea and method can achieve clinical multimodal medical image segmentation and make full use of image information to provide clinical decision support. It has great application value and promotion prospects.

Identification and analysis of a CD8+ T cell-related prognostic signature for colorectal cancer based on bulk RNA sequencing and scRNA sequencing data: A STROBE-compliant retrospective study.

Colorectal cancer (CRC) is one of the most common malignancies worldwide, leading to a large number of cancer-related mortalities. Aberrant CD8+ T cell infiltration plays a critical role in tumor progression and patient prognosis. This study aimed to identify a prognostic model for CRC based on CD8+ T cell-related genes. The infiltration levels of immune cells in CRC tissues were accessed by the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) analysis was used to select CD8+ T cell-related genes. Prognostic genes were identified using Cox regression analysis and Kaplan-Meier curves. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct prognostic models. Gene set enrichment analysis (GSEA) was performed to annotate enriched gene sets. Single-cell RNA (scRNA) sequencing analysis was used to examine gene expression in different cell types. We found that the downregulated infiltration level of CD8+ T cells was an independent prognostic factor for CRC and selected a cluster of differentially expressed genes correlated with CD8+ T cell infiltration (CD8TDEGs). Subsequently, we identified 18 prognostic CD8TDEGs, according to which patients were reclassified into two clusters with distinct overall survival. Seven prognostic CD8TDEGs were selected to calculate the constructed prognostic model's risk scores. Interestingly, although CRC tissues with higher risk scores had higher infiltration levels of CD8+ T cells, the level of immune checkpoint genes was also high. Moreover, the scRNA-sequencing analysis showed that the expression levels of CD8TDEGs in the prognostic model varied among different types of cells. This study constructed a novel prognostic model for CRC and provided a foundation for targeting CD8+ T cell infiltration to improve the survival of CRC patients.

Immunotherapy-based novel nanoparticles in the treatment of gastrointestinal cancer: Trends and challenges.

Gastrointestinal cancer (GIC) is the most common cancer with a poor prognosis. Currently, surgery is the main treatment for GIC. However, the high rate of postoperative recurrence leads to a low five-year survival rate. In recent years, immunotherapy has received much attention. As the only immunotherapy drugs approved by the Food and Drug Administration (FDA), immune checkpoint blockade (ICB) drugs have great potential in cancer therapy. Nevertheless, the efficacy of ICB treatment is greatly limited by the low immunogenicity and immunosuppressive microenvironment of GIC. Therefore, the targets of immunotherapy have expanded from ICB to increasing tumor immunogenicity, increasing the recruitment and maturation of immune cells and reducing the proportion of inhibitory immune cells, such as M2-like macrophages, regulatory T cells and myeloid-derived suppressor cells. Moreover, with the development of nanotechnology, a variety of nanoparticles have been approved by the FDA for clinical therapy, so novel nanodrug delivery systems have become a research focus for anticancer therapy. In this review, we summarize recent advances in the application of immunotherapy-based nanoparticles in GICs, such as gastric cancer, hepatocellular carcinoma, colorectal cancer and pancreatic cancer, and described the existing challenges and future trends.

Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a "sampling-feature-hyperparameter-weight" synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R2 of BSEE2 fused by the "nonlinear + linear" method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R2 of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.