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The therapeutic efficacy of vaccines for treating cancers in clinics remains limited. Here, a rationally designed cancer vaccine by placing immunogenically differential and clinically approved aluminum (Al) or manganese (Mn) in a 2D nanosheet (NS) architecture together with antigens is reported. Structurally optimal NS with a high molar ratio of Mn to Al (MANS-H) features distinctive immune modulation, markedly promoting the influx of heterogeneous innate immune cells at the injection site. Stimulation of multiple subsets of dendritic cells (DCs) significantly increases the levels, subtypes, and functionalities of antigen-specific T cells. MANS-H demonstrates even greater effectiveness in the production of antigen-specific antibodies than the commercial adjuvant (Alhydrogel) by priming T helper (Th)2 cells rather than T follicular helper (Tfh) cells. Beyond humoral immunity, MANS-H evokes high frequencies of antigen-specific Th1 and CD8+ cell immunity, which are comparable with Quil-A that is widely used in veterinary vaccines. Immunized mice with MANS-H adjuvanted vaccines exert strong potency in tumor regression by promoting effector T cells infiltrating at tumor and overcoming tumor resistance in multiple highly aggressive tumor models. The engineered immunogen with an intriguing NS architecture and safe immunopotentiators offers the next clinical advance in cancer immunotherapy.

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Colon polyps have become a focal point of research due to their heightened potential to develop into appendiceal cancer, which has the highest mortality rate globally. Although numerous colon polyp segmentation methods have been developed using public polyp datasets, they tend to underperform on private datasets due to inconsistencies in data distribution and the difficulty of fine-tuning without annotations. In this paper, we propose a Self-Adaptive Teacher-Student (SATS) framework to segment colon polyps from unannotated private data by utilizing multiple publicly annotated datasets. The SATS trains multiple teacher networks on public datasets and then generates pseudo-labels on private data to assist in training a student network. To enhance the reliability of the pseudo-labels from the teacher networks, the SATS includes a newly proposed Uncertainty and Distance Fusion (UDFusion) strategy. UDFusion dynamically adjusts the pseudo-label weights based on a novel reconstruction similarity measure, innovatively bridging the gap between private and public data distributions. To ensure accurate identification and segmentation of colon polyps, the SATS also incorporates a Granular Attention Network (GANet) architecture for both teacher and student networks. GANet first identifies polyps roughly from a global perspective by encoding long-range anatomical dependencies and then refines this identification to remove false-positive areas through multi-scale background-foreground attention. The SATS framework was validated using three public datasets and one private dataset, achieving 76.30% on IoU, 86.00% on Recall, and 7.01 pixels on HD. These results outperform the existing five methods, indicating the effectiveness of this approach for colon polyp segmentation.

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Background: Metabolic reprogramming plays a crucial role in the development of colorectal cancer (CRC), influencing tumor heterogeneity, the tumor microenvironment, and metastasis. While the interaction between metabolism and CRC is critical for developing personalized treatments, gaps remain in understanding how tumor cell metabolism affects prognosis. Our study introduces novel insights by integrating single-cell and bulk transcriptome analyses to explore the metabolic landscape within CRC cells and its mechanisms influencing disease progression. This approach allows us to uncover metabolic heterogeneity and identify specific metabolic genes impacting metastasis, which have not been thoroughly examined in previous studies. Methods: We sourced microarray and single-cell RNA sequencing datasets from the Gene Expression Omnibus (GEO) and bulk sequencing data for CRC from The Cancer Genome Atlas (TCGA). We employed Gene Set Variation Analysis (GSVA) to assess metabolic pathway activity, consensus clustering to identify CRC-specific transcriptome subtypes in bulkseq, and rigorous quality controls, including the exclusion of cells with high mitochondrial gene expression in scRNA seq. Advanced analyses such as AUCcell, infercnvCNV, Non-negative Matrix Factorization (NMF), and CytoTRACE were utilized to dissect the cellular landscape and evaluate pathway activities and tumor cell stemness. The hdWGCNA algorithm helped identify prognosis-related hub genes, integrating these findings using a random forest machine learning model. Results: Kaplan-Meier survival curves identified 21 significant metabolic pathways linked to prognosis, with consensus clustering defining three CRC subtypes (C3, C2, C1) based on metabolic activity, which correlated with distinct clinical outcomes. The metabolic activity of the 13 cell subpopulations, particularly the epithelial cell subpopulation with active metabolic levels, was evaluated using AUCcell in scRNA seq. To further analyze tumor cells using infercnv, NMF disaggregated these cells into 10 cellular subpopulations. Among these, the C2 subpopulation exhibited higher stemness and tended to have a poorer prognosis compared to C6 and C0. Conversely, the C8, C3, and C1 subpopulations demonstrated a higher level of the five metabolic pathways, and the C3 and C8 subpopulations tended to have a more favorable prognosis. hdWGCNA identified 20 modules, from which we selected modules primarily expressed in high metabolic tumor subgroups and highly correlated with clinical information, including blue and cyan. By applying variable downscaling of RF to a total of 50 hub genes, seven gene signatures were obtained. Furthermore, molecules that were validated to be protective in GEO were screened alongside related molecules, resulting in the identification of prognostically relevant molecules such as UQCRFS1 and GRSF1. Additionally, the expression of GRSF1 was examined in colon cancer cell lines using qPCR and phenotypically verified by in vitro experiments. Conclusion: Our findings emphasize that high activity in specific metabolic pathways, including pyruvate metabolism and the tricarboxylic acid cycle, correlates with improved colon cancer outcomes, presenting new avenues for metabolic-based therapies. The identification of hub genes like GRSF1 and UQCRFS1 and their link to favorable metabolic profiles offers novel insights into tumor neovascularization and metastasis, with significant clinical implications for targeting metabolic pathways in CRC therapy.

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Innovation holds paramount importance for both nations and businesses. This article presents a panel regression model designed to assess the fixed effects of industry-university-research (IUR) cooperation projects on innovation performance. Furthermore, it examines the moderating impact of government innovation subsidies by utilizing data spanning from 2007 to 2021, encompassing 326 listed Chinese biopharmaceutical firms. Our findings reveal that industry-university-research-cooperation projects have the potential to significantly enhance innovation performance across three key metrics: input, output, and quality for firms. The presence of government innovation subsidies as a moderator is found to have a positive influence on IUR-cooperation projects and their innovative inputs. However, it can yield adverse effects on IUR-cooperation projects with respect to innovation outputs and quality. The insights presented in this paper introduce innovative recommendations for elevating corporate innovation quality and refining the policies governing IUR cooperation.

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Background: This network meta-analysis aimed to comprehensively compare the operative and postoperative outcomes of different parotidectomy incisions. Methods: Embase, PubMed, Web of Science, and Cochrane Central Register of Controlled Trials were searched up to April 2022. A complete Bayesian network meta-analysis was performed using the Markov Monte Carlo method in OpenBUGS. Results: Seventeen studies with 1609 patients were included. Thirteen were retrospective cohort studies, three were prospective cohort studies, and one was a randomized controlled study. The quality of evidence was rated as very low in most comparisons. The incision satisfaction score of the modified facelift incision (MFI), retroauricular hairline incision (RAHI), V-shaped incision (VI) were higher than that of the modified Blair incision (MBI) (MBI vs. MFI: mean difference [MD] -1.39; 95% credible interval [CrI] -2.23, -0.57) (MBI vs. RAHI: MD -2.25; 95% CrI -3.40, -1.12) (MBI vs. VI: MD -2.58; 95% CrI -3.71, -1.46); the tumor size treated by VI was smaller than that by MBI (MD 5.15; 95% CrI 0.76, 9.38) and MFI (MD 5.16; 95% CrI 0.34, 9.86); and the risk of transient facial palsy in the MFI was lower than that in the MBI (OR 2.13; 95% CrI 1.28, 3.64). There were no differences in operation time, drainage volume, wound infection, hematoma, salivary complications, Frey syndrome, or permanent facial palsy between incision types. Conclusion: The traditional MBI is frequently used for large tumor volumes, but the incision satisfaction score is low and postoperative complication control is poor. However, emerging incisions performed well in terms of incision satisfaction scores and control of complications. More randomized controlled trials are needed to compare the different parotidectomy incisions. Patients should be fully informed about the characteristics of each incision to make the most informed decision, along with the physician's advice. Systematic Review Registration: PROSPERO, identifier CRD42022331756.

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We developed a dual-wavelength-excitation aerosol fluorescence spectra detection device prototype. In our system, the 263 nm and 355 nm lasers are used to sequentially excite the fluorescence of aerosol stream, which is located spatially and temporally by two crossed infrared lasers; a bifurcated fiber bundle is applied to receive the fluorescence spectra of 274-463 nm and 374-565 nm. Besides, with a 32-channel photomultiplier tube as detector, a self-developed combined spectrometer with Czerny-Turner design is employed to detect the two band spectra in a preset timing sequence. Experiments show that the system can detect the fluorescence spectra, after dual-wavelength-excitation, of three intrinsic fluorophore samples and three bioaerosol samples.

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The government employs innovation subsidies as a key incentive strategy to promote companies to innovate more technically. This study analyses how innovation subsidies influences the quality of corporate innovation. We create an innovation quality index for pharmaceutical corporations using categorizing data from patent applications submitted by pharmaceutical companies. Using data from 180 listed Chinese pharmaceutical companies between 2010 and 2020, this study proposes a panel regression model to assess the influence of government innovation subsidies on innovation quality, as well as the moderating effect of CEOs' academic capital. How well innovations are subsidized is also affected by the heterogeneity of property rights. Innovation subsidy has a greater and more positive impact on non-SOEs. This article demonstrates that CEOs with academic credentials and executives with ties to the pharmaceutical industry have a variety of moderate effects. The research offers novel suggestions for enhancing business creativity and the innovation subsidy programme.

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The laser-induced fluorescence (LIF) technique, which has been widely used for food testing, can be combined with various algorithms to classify and recognize different kinds of honey. This paper proposes the Kolmogorov-Smirnov test-Gaussian mixture model (KS-GMM) algorithm, which is coupled with the LIF technique to realize accurate classification and recognition of different types of pure honey. The experiments are designed and carried out to obtain a set of LIF spectrum data from various honey and syrup samples. The proposed KS-GMM algorithm is applied for classification and recognition, with GMM, k-nearest neighbor (kNN), and decision tree algorithms as cross-validation methods. By comparing recognition results of training sets containing different amounts of data, it is found that the KS-GMM algorithm exhibits a maximum recognition accuracy of 96.52%. The research results prove that the KS-GMM algorithm outperforms, to the best of our knowledge, the other three algorithms in classifying and recognizing the honey types.

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Accurate and rapid spectrum fitting is very important for quantitatively analyzing laser-induced breakdown spectroscopy (LIBS). The Voigt function is often used to fit LIBS spectral lines. We propose a new approximate Voigt function formula. Based on the classic Lorentz-Gauss linear combination formula, a summation term was added that contained a specific convolution operation to improve the Voigt function's calculation and fitting accuracy. This formula can be used for the approximate calculation of the Voigt function with an overall accuracy of 0.31% and a full width at half-maximum internal accuracy of 0.25% when the ratio of Lorentzian linewidth to Gaussian linewidth is 1:1. The formula was then applied to LIBS data processing to fit four element spectral lines of calcium (Ca-393.37, 396.85, and 422.67 nm) and potassium (K-766.49 nm). The fitting results showed that this new approximate formula could fit at least seven data points, and compared with the complex plane partition method and the classic linear combination formula, the new formula had better fitting speed and accuracy.

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Increased glucose metabolism through the hexosamine pathway may result in insulin resistance, impaired insulin secretion, and diabetic nephropathy. We hypothesized that variants of GFPT1 encoding glutamine-fructose-6-phosphate amidotransferase, the rate limiting enzyme in this pathway, could increase GFPT1 gene expression and thus susceptibility to diabetes and diabetic nephropathy. To test this hypothesis, we screened for variation in the GFPT1 and flanking regions in Caucasian and African-American individuals. We tested each variant with over 5% allele frequency for an association with type 2 diabetes in Caucasian and African-American populations, and for an association with diabetic nephropathy in African-American subjects. We measured allele specific levels of GFPT1 mRNA and we compared mRNA levels across diagnostic categories for each ethnic group using RNA derived from transformed lymphocytes. None of the 8 variants detected altered the coding sequence or was present in a known regulatory region. We found a marginal association (p = 0.044) of 1/6 variants with diabetes in Caucasian subjects, and marginal associations of 2/7 variants with diabetic nephropathy among African-American subjects (p = 0.025, p = 0.041). Alleles marked by a variant in the 3' untranslated region were equally expressed, but in a small sample, GFPT1 mRNA levels were increased by 60% in Caucasians with diabetic nephropathy compared to diabetic individuals without nephropathy. Variants in the GFPT1 gene show suggestive evidence of an association with diabetic nephropathy among African-American individuals, and increased GFPT1 gene expression may characterize Caucasian subjects with diabetic nephropathy. Both findings need to be confirmed in other populations.

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Considerable data support adiponectin as an important adipose-derived insulin sensitizer that enhances fatty acid oxidation and alters hepatic gluconeogenesis. Adiponectin acts by way of two receptors, ADIPOR1 and ADIPOR2. ADIPOR1 is widely expressed in tissues, including muscle, liver, and pancreas, and binds the globular form of adiponectin with high affinity. To test the hypothesis that sequence variations in or near the ADIPOR1 gene contribute to the risk of developing type 2 diabetes and the metabolic syndrome, we screened the eight exons (including the untranslated exon 1) of the ADIPOR1 gene with flanking intronic sequences and the 5' and 3' flanking sequences. We identified 22 single nucleotide polymorphisms (SNPs) in Caucasian and African-American subjects, of which a single nonsynonymous SNP (N44K) in exon 2 was present only in African-American subjects. We typed 14 sequence variants that had minor allele frequencies >5%. No SNP was associated with type 2 diabetes in Caucasians or African Americans, and no SNP was a determinant of insulin sensitivity or insulin secretion among nondiabetic members of high-risk Caucasian families. However, the two alleles of a SNP in the 3' untranslated region were expressed unequally, and ADIPOR1 mRNA levels were significantly lower among transformed lymphocytes from diabetic African-American individuals than among control cell lines. This altered gene expression might suggest a role for ADIPOR1 in the metabolic syndrome.

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Increased flux of glucose through the hexosamine biosynthetic pathway has been implicated in insulin resistance, altered insulin secretion, and diabetic nephropathy. Glutamine:fructose-6-phosphate amidotransferase (GFPT), the rate limiting enzyme in hexosamine biosynthesis, is encoded by the unlinked but highly homologous genes GFPT1 and GFPT2. We tested the hypothesis that GFPT2 sequence variation contributed to the susceptibility to type 2 diabetes mellitus (T2DM) and diabetic nephropathy in Caucasian and African-American individuals. We identified 11 single nucleotide polymorphisms (SNPs), of which seven were common. A single variant in exon 14, I471V, altered the amino acid sequence, is conserved between human and mouse genes, and was associated with T2DM among Caucasians (P = 0.05). A trend to an association was noted with diabetic nephropathy among African-American individuals (P = 0.15). Several variants in the 3' untranslated region (UTR) and exon 18 were also associated with T2DM in Caucasian individuals (P < 0.05), and the SNP in the 3' UTR was associated with diabetic nephropathy in African-American subjects (P = 0.047). GFPT2 mRNA levels in transformed lymphocytes from study subjects were significantly increased among African-American subjects compared with Caucasian individuals, regardless of diagnosis. Furthermore, the associated allele of the 3' UTR SNP was approximately 2-fold overexpressed. We propose that the 3' UTR variant results in increased GFPT2 mRNA levels with resultant increased hexosamine flux. The I471V variant may contribute to altered protein function or may simply be in linkage disequilibrium with the 3' UTR.

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