Clinical performance of zirconia-based tooth-supported fixed dental prostheses: a systematic review and meta-analysis.

OBJECTIVES: This study aimed to investigate the clinical performance of zirconia-based fixed dental prostheses (FDPs) in comparison to metal-ceramic (MC) FDPs. METHODS: A comprehensive search on MEDLINE (PubMed), Web of Science (Core Collection), Scopus up to June 2024 was conducted. Studies that compared the success, survival and complication rates between zirconia based FDPs and MC FDPs were eligible for inclusion. RESULTS: Thirty-one articles were identified, of which 22 were included for systematic review and 7 RCTs were included for meta-analysis. 10, 9 and 3 studies were classified to mean follow-up ≤ 5 years, 5 years < mean Follow-up ≤ 10 years, mean Follow-up >10 years, respectively. In the pooled analysis, 180 bilaminar zirconia (ZC) FDPs and 206 MC FDPs were included. ZC FDPs were significantly associated with more failures (RR=3.64, p=0.009) and more Ceramic Chipping (RR=2.92, p<0.0001) when compared to MC FDPs. Higher risks of Framework Fracture (RR=4.57, p=0.18), Loss of Retention (RR=4.79, p=0.17), Secondary Caries (RR=1.25, p=0.68), Endodontic complications (RR=1.30, p=0.74) and Marginal Integrity (RR=1.07, p=0.88) were also found in ZC FDPs when compared to those of MC FDPs, but with no statistical difference. CONCLUSION: The current evidence continues to support the preference for traditional MC FDPs over ZC FDPs. Studies indicate that ZC FDPs have higher failure rates and more complications compared to MC FDPs, with ceramic chipping being a significant concern. There is lack of long term (>10 years follow-up) evidence of the clinical performance of ZC FDPs and monolithic zirconia FDPs. CLINICAL SIGNIFICANCE: The study suggests that despite the growing popularity of zirconia, evidence shows MC FDPs may still be considered preferable to ZC FDPs.

Mitigating Capacity and Voltage Decay in Li-Rich Cathode Via Dual-Phase Design.

High-capacity O3-type lithium-rich manganese-based (LRM) materials exhibit significant structural instability and severe voltage decay, which limit their practical applications. In contrast, the O2-type LRM materials demonstrate remarkable structural stability despite offering lower capacity. In this study, a composite material, O3@O2-LRM is designed, by coating the main structure of O3-type LRM with a minor amount of O2-type LRM to combine the high capacity of the O3 phase with the superior stability of the O2 phase. Electrochemical tests demonstrate that O3@O2-LRM exhibits both high specific capacity and reduced voltage decay. Furthermore, a series of characterizations after different cycles confirm its enhanced structure stability compared to O3-LRM. This novel structure holds great promise for developing advanced cathode materials capable of meeting the demanding requirements of next-generation Li-ion batteries.

The Parameter Calibration of Social Force Model for Pedestrian Flow Simulation Based on YOLOv5.

With the increasing importance of subways in urban public transportation systems, pedestrian flow simulation for supporting station management and risk analysis becomes more necessary. There is a need to calibrate the simulation model parameters with real-world pedestrian flow data to achieve a simulation closer to the real situation. This study presents a calibration approach based on YOLOv5 for calibrating the simulation model parameters in the social force model inserted in Anylogic. This study compared the simulation results after model calibration with real data. The results show that (1) the parameters calibrated in this paper can reproduce the characteristics of pedestrian flow in the station; (2) the calibration model not only decreases global errors but also overcomes the common phenomenon of large differences between simulation and reality.

[Characteristics of Soil Organic Carbon and Its Influencing Factors of Different Plant Communities in the Yellow River Delta].

Changes in soil organic carbon （SOC） are of great importance to the evolution of soil quality. The distribution characteristics of soil organic carbon （SOC）, easily oxidizable organic carbon （EOC）, dissolved organic carbon （DOC）, and particulate organic carbon （POC） were investigated in the 0-50 cm soil layer of the Phragmites australis, Suaeda salsa, and Tamarix chinensis communities of the supratidal zone in the Yellow River Delta as the research subjects. Then, the composition and sources of soil dissolved organic matter （DOM） were analyzed based on the UV-vis spectroscopy, three-dimensional excitation emission matrix spectroscopy, and parallel factor analysis （PARAFAC）. Finally, the key factors affecting the characteristics of soil organic carbon and DOM fractions of different plant communities were finally revealed in combination with the physicochemical properties of the soil. The results showed that： ① Comparing different communities, the S. salsa community had the highest ω（SOC） at 7.53 g·kg-1, the T. chinensis community had the highest ω（DOC） at 0.98 g·kg-1, and the P. australis community had significantly higher ω（EOC） and ω（POC） than those of the S. salsa and T. chinensis communities at 1.47 g·kg-1 and 0.65 g·kg-1, respectively. The vertical distribution showed a tendency to decrease with deeper soil layers, except for POC concentration. ② The main components of soil DOM of the P. australis, S. salsa, and T. chinensis communities were humus, protein-like substances, and fulvic acid-like substances, of which exogenous components accounted for 55.80%, 56.41%, and 52.81% in the above communities, respectively. ③ Comparing different communities, the humification degree of the P. australis community was significantly higher than that of the S. salsa and T. chinensi communities, but its aromaticity and proportion of biological sources were significantly lower than those of the T. chinensi community. On the vertical profile of the soil, DOM aromaticity and humification degree gradually increased with the deepening of the soil layer, and the deeper soils were mainly dominated by small molecular weight DOM with a lower proportion of hydrophobic fraction. ④ Redundant analysis showed that N （P<0.01）, NO2--N （P<0.01）, and NH4+-N （P<0.05） were the key factors affecting the changes in soil organic carbon and DOM fractions.

Multifunctional Cross-Linking Composite Binder Enables the Stable Performance of Si-Based Anodes for High-Energy-Density Lithium-Ion Batteries.

The intrinsic volumetric stress during cycling is the main obstacle for developing Si-based materials as high-energy-density lithium-ion battery anodes. Elastic binders have been demonstrated as an efficient approach to alleviate the stress of Si. Herein, we design a tough 3D hard/soft polymeric network (LPTS) using lithiated poly(acrylic acid), silk sericin, and highly branched tannic acid. Covalent cross-linking provides a robust mechanical strength to endure the large stress. The formed multiple hydrogen bonds with bonding energies between 3.46 and 25 kcal mol-1 can effectively dissipate the stress through sequential hydrogen bond disassociation. The multifunctional LPTS binder maintains the integrity of the Si-based electrodes during repeated discharging/charging. Additionally, Li+ can be transferred via a Li-conducting group (-COOLi), thereby enhancing the ionic conductivity of electrodes. Consequently, the Si/LPTS electrode exhibits an improved initial Coulombic efficiency and excellent durability over 400 cycles. Meanwhile, this binder is also suitable for Si-C anodes, enabling stable cycling at a high areal capacity >3.6 mAh cm-2 and delivering 72.2% capacity retention for the LFP||Si-C/LPTS full cell after 200 cycles. This study provides insight into developing efficient Si-based binders that are facile and low-cost for next-generation high-energy-density systems.

Exploring the coupling mode of water and fertilizer for improving growth, fruit quality, and yield of the pear in the arid region.

Considering the pear in the arid region as the research object, single-factor testing and water-fertilizer coupling testing were conducted. The response of pear tree growth to water, nitrogen, and phosphorus was explored and provided a theoretical basis for efficient water and fertilizer management. Among them, the single-factor test set water, nitrogen, and phosphorus as the three factors, and five levels were set. Screening out W3, W4, N3, N4, P3, and P4 promoted plant nutrient uptake and fruit quality. Eight treatments were set up in the water and fertilizer coupling test: Treatment 1 (T1, W3N3P3), Treatment 2 (T2, W3N3P4), Treatment 3 (T3, W3N4P3), Treatment 4 (T4, W3N4P4), Treatment 5 (T5, W4N3P3), Treatment 6 (T6, W4N3P4), Treatment 7 (T7, W4N4P3), and Treatment 8 (T8, W4N4P4). The results showed that the leaf area index of the T1, T2, T3, and T4 treatments was significantly higher than that of the other treatments at maturity. The yield, single fruit weight, and primary fruit rate were the highest under T3 treatment. The gray correlation degree analysis of fruit quality showed that the T3 treatment had the highest degree of correlation and ranking of each fruit quality index, indicating that the T3 treatment had the highest fruit quality. The yield model showed that irrigation with 6510.06 m3 hm-2, nitrogen fertilizer with 337.5 kg N hm-2, and phosphate fertilizer with 262.5 kg P hm-2 had the best yield. A detailed investigation of pear tree growth and fruit quality showed that the T3 treatment had the best fruit growth and development performance, and the pear fruit quality was the best.

Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view.

Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal in vivo experimental data. The results demonstrate that PAT-ADN exhibits excellent performance in effectively removing artifacts. In particular, under extremely sparse view (e.g., 16 projections), structural similarity index and peak signal-to-noise ratio are improved by ∼188 % and ∼85 % in in vivo experimental data using the proposed method compared to traditional reconstruction methods. PAT-ADN improves the imaging performance of PAT, opening up possibilities for its application in multiple domains.

Temporal Dynamics and Contribution of Phage Community to the Prevalence of Antibiotic Resistance Genes in a Full-Scale Sludge Anaerobic Digestion Plant.

Anaerobic digestion (AD) is an important biological resource recovery process, where microorganisms play key roles for material transformation. There has been some knowledge about the prokaryotic community and antibiotic resistance genes (ARGs) in AD, but there has been very limited knowledge of phages. In this study, samples from a full-scale AD plant were collected over 13 months, sequenced, and analyzed for viral and prokaryotic metagenomes. Totally, 3015 viral operational taxonomic units (vOTUs) were detected, mostly assigned to Caudoviricetes. The phage community had faster temporal variation than the prokaryotic community. Warm seasons harbored a higher abundance of both temperate phages and broad host-range phages. Seven ARGs of 6 subtypes were carried by 20 vOTUs, a representative ermT gene was synthesized and expressed, and the resistance activity in the host was examined, confirming the real activity of virus-carried ARGs in the AD process. Some of the ARGs were horizontally transferred between the phage and prokaryotic genomes. However, phage infection was not found to contribute to ARG transfer. This study provided an insight into the ecological patterns of the phage community, confirmed the antibiotic resistance activity of virus-carried ARGs, evaluated the contribution of phages on the ARG prevalence, and laid the foundation for the control strategies of the community and antibiotic resistance in the AD process.

Fast and sensitive CRISPR detection by minimized interference of target amplification.

Despite the great potential of CRISPR-based detection, it has not been competitive with other market diagnostics for on-site and in-home testing. Here we dissect the rate-limiting factors that undermine the performance of Cas12b- and Cas13a-mediated detection. In one-pot testing, Cas12b interferes with loop-mediated isothermal amplification by binding to and cleaving the amplicon, while Cas13a directly degrades the viral RNA, reducing its amplification. We found that the protospacer-adjacent motif-interacting domain engineered Cas12b accelerated one-pot testing with 10-10,000-fold improved sensitivity, and detected 85 out of 85 SARS-CoV-2 clinical samples with a sensitivity of 0.5 cp µl-1, making it superior to wild-type Cas12b. In parallel, by diminishing the interference of Cas13a with viral RNA, the optimized Cas13a-based assay detected 86 out of 87 SARS-CoV-2 clinical samples at room temperature in 30 min with a sensitivity of 0.5 cp µl-1. The relaxed reaction conditions and improved performance of CRISPR-based assays make them competitive for widespread use in pathogen detection.

Effects of exercise on muscle fiber conversion, muscle development and meat quality of Sunit sheep.

This study aimed to investigate the effects of exercise on muscle fiber conversion, muscle development and meat quality in the biceps femoris (BF) muscle of Sunit sheep. Twelve Sunit sheep with similar body weight were divided into two groups: control group (C group) and exercise group (E group), E group lambs underwent 6 km of exercise training per day for 90 d. The findings revealed that compared with the C group, exercise training enhanced the expression of MyHC IIa mRNA, decreased the number ratio of type IIB muscle fibers and the expression of MyHC IIb mRNA (P < 0.05). Furthermore, the E group lamb displayed higher creatine kinase (CK) activity, and lactic acid levels (P < 0.05), while glycogen content and lactic dehydrogenase (LDH) activity showed opposite trends (P < 0.05). Exercise significantly up-regulated the mRNA expression of AMP-activated protein kinase α1 (AMPKα1), sirtuin1 (SIRT1), peroxisome proliferator-activated receptor gamma coactivator 1-α (PGC-1α), cytochrome c oxidase IV (COX IV), protein kinase B (Akt), mammalian target of rapamycin (mTOR) and p70 Ribosomal S6 Kinase 1 (p70s6k1) (P < 0.05), suggesting exercise promoted muscle fiber conversion by mediating AMPK/PGC-1α pathway, and improved skeletal muscle development via Akt/mTOR pathway. Besides, backfat thickness and pH45min value in the E group decreased significantly, while the pH24, a*, and shear force value increased significantly (P < 0.05). To conclude, this study suggested that exercise training can be used to alter muscle fiber characteristics and muscle development in lamb production.

An explainable unsupervised risk early warning framework based on the empirical cumulative distribution function: Application to dairy safety.

Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, lack of interpretability of risk classification, and insufficient risk traceability. This study aims to explore an efficient food safety risk assessment model that takes into account robustness, interpretability and traceability. Therefore, the Explainable unsupervised risk Warning Framework based on the Empirical cumulative Distribution function (EWFED) was proposed. Firstly, the detection data's underlying distribution is estimated as non-parametric by calculating each testing indicator's empirical cumulative distribution. Next, the tail probabilities of each testing indicator are estimated based on these distributions and summarized to obtain the sample risk value. Finally, the "3σ Rule" is used to achieve explainable risk classification of qualified samples, and the reasons for unqualified samples are tracked according to the risk score of each testing indicator. The experiments of the EWFED model on two types of dairy product detection data in actual application scenarios have verified its effectiveness, achieving interpretable risk division and risk tracing of unqualified samples. Therefore, this study provides a more robust and systematic food safety risk assessment method to promote precise management and control of food safety risks effectively.

Big data-driven spatio-temporal heterogeneity analysis of Beijing's catering service industry during the COVID-19 pandemic.

The Catering Service Industry (CSI) experienced profound impacts due to the COVID-19 pandemic. However, the long-term and multi-timepoint analysis using big data remained limited, influencing governmental decision-making. We applied Kernel Density Estimation, Shannon Diversity Index, and the Geographic detector to explore the spatial heterogeneity and determinants of the CSI in Beijing during the pandemic, with monthly granularity. The temporal-spatial dynamics of the CSI presented a "W"-shaped trend from 2018 to 2023, with pivotal shifts aligning with key pandemic stages. Spatial characteristics exhibited heterogeneity, with greater stability in the city center and more pronounced shifts in peripheral urban zones. Districts facing intricate outbreaks showed lower catering income, and Chinese eateries exhibited heightened resilience compared to others. The CSI displayed strong interconnections with living service sectors. Development in each district was influenced by economic level, population distribution, service facilities convenience, and the risk of the COVID-19 pandemic. Dominant factors included total retail sales of consumer goods, permanent population, average Baidu Heat Index, density of transportation and catering service facilities, infection cases and the consecutive days with confirmed cases existing. Consequently, we suggested seizing post-pandemic recovery as an avenue to unlock the CSI's substantial potential, ushering a fresh phase of growth.

Long-term occurrence, resistance risk and chaotic characteristics of antibiotic resistance genes in sludge anaerobic digestion system.

The long-term occurrence, dynamics and risk of antibiotic resistance genes (ARGs) in anaerobic digestion (AD) of excess sludge (ES) are not fully understood. Therefore, 13-month metagenomic monitoring was carried out in a full-scale AD plant. The highest ARG abundance and risk scores were observed in spring. AD achieved a 35 % removal rate for the total ARG abundance, but the risk score of AD sludge was not always lower than ES samples, because of the higher proportion of Rank I ARGs in AD sludge. ARGs showed less obvious patterns under linear models compared with microbial community, implying their chaotic dynamics, which was further confirmed by nonlinearity tests. Empirical dynamic modeling performed better than the autoregressive integrated moving average model for ARG dynamics, especially for those with simple and nonlinear dynamics. This study highlighted spring for its higher ARG abundance and risk, and recommended nonlinear models for revealing the dynamics of ARGs.

The crucial regulatory role of type I interferon in inflammatory diseases.

Type I interferon (IFN-I) plays crucial roles in the regulation of inflammation and it is associated with various inflammatory diseases including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and periodontitis, impacting people's health and quality of life. It is well-established that IFN-Is affect immune responses and inflammatory factors by regulating some signaling. However, currently, there is no comprehensive overview of the crucial regulatory role of IFN-I in distinctive pathways as well as associated inflammatory diseases. This review aims to provide a narrative of the involvement of IFN-I in different signaling pathways, mainly mediating the related key factors with specific targets in the pathways and signaling cascades to influence the progression of inflammatory diseases. As such, we suggested that IFN-Is induce inflammatory regulation through the stimulation of certain factors in signaling pathways, which displays possible efficient treatment methods and provides a reference for the precise control of inflammatory diseases.

Proteomic and single-cell analysis shed new light on the anti-inflammatory role of interferonß in chronic periodontitis.

Periodontitis, a condition that results in periodontal attachment loss and alveolar bone resorption, contributes to the global burden of oral disease. The underlying mechanism of periodontitis involves the dysbiosis and dyshomeostasis between host and oral microbes, among which the macrophage is one of the major innate immune cell players, producing interferon ß (IFNß) in response to bacterial infection. The objective of this research was to examine the interaction of macrophages with periodontitis and the role and mechanism of IFNß on macrophages. IFNß has been shown to have the potential to induce the differentiation of M1 to M2 macrophages, which are stimulated by low levels of lipopolysaccharide (LPS). Additionally, IFNß has been demonstrated to promote the production of ISG15 by macrophages, which leads to the inhibition of the innate immune response. Moreover, our investigation revealed that IFNß has the potential to augment the secretion of ISG15 and its downstream cytokine, IL10, in LPS-stimulated macrophages. Single-cell analysis was conducted on the gingival tissues of patients with periodontitis, which revealed a higher proportion of macrophages in the periodontitis-diseased tissue and increased expression of IFNß, ISG15, and IL10. Gene Set Enrichment Analysis indicated that bacterial infection was associated with upregulation of IFNß, ISG15, and IL10. Notably, only IL10 has been linked to immunosuppression, indicating that the IFNß-ISG15-IL10 axis might promote an anti-inflammatory response in periodontitis through IL10 expression. It is also found that macrophage phenotype transitions in periodontitis involve the release of higher levels of IFNß, ISG15, and IL10 by the anti-inflammatory M2 macrophage phenotype compared to the pro-inflammatory M1 phenotype and myeloid-derived suppressor cells (MDSCs). This implies that the IFNß-induced production of IL10 might be linked to the M2 macrophage phenotype. Furthermore, cell communication analysis demonstrated that IL10 can promote fibroblast proliferation in periodontal tissues via STAT3 signaling.

Whole genome sequence-based characterization of virulence and antimicrobial resistance gene profiles of Staphylococcus aureus isolated from food poisoning incidents in eastern China.

Staphylococcus aureus is an opportunistic foodborne pathogen occasionally isolated from diarrhea patients. In recent years, increasing studies have reported the detection of S. aureus in food poisoning incidents due to food contamination in the North and South of China. However, the epidemiology and genetic characteristics of S. aureus from food poisoning incidents in Eastern China remain unknown. The present study examined the genetic characteristics, antimicrobial resistance, and virulent factors of multidrug-resistant S. aureus isolated from 22 food poisoning incidents reported by the hospitals and health centers in Eastern China from 2011 to 2021. A total of 117 resistant and enterotoxigenic S. aureus isolates were collected and sequenced, among which 20 isolates were identified as methicillin resistant. Genetic analysis revealed 19 distinct CC/ST types, with CC6, CC22, CC59, CC88, and CC398 being the most frequent variants in methicillin-resistant S. aureus (MRSA). A considerable shift in CC types from CC1 to CC398 between 2011 and 2021 was observed in this study, indicating that CC398 may be the main epidemic strain circulating in the current food poisoning incidents. Additionally, genes for enterotoxins were detected in 55 isolates, with a prevalence of 27.8% (27/97) for methicillin-sensitive variants and 35.0% (7/20) for MRSA. The scn gene was detected in 59.0% of the isolates, demonstrating diverse contaminations of S. aureus among livestock-to-human transmission. Of the 117 isolates, only ten isolates displayed multi-drug resistance (MDR) to penicillin, tetracycline, and macrolides. None of the 117 foodborne S. aureus isolates tested positive for vanA in this study. Together, the present study provided phylogenetic characteristics of S. aureus from food poisoning incidents that emerged in Eastern China from 2011 to 2021. Our results suggested that these diarrhea episodes were hypotonic and merely transient low-MDR infections, however, further research for continued surveillance given the detection of virulence and antimicrobial resistance determinants is required to elucidate the genomic characteristics of pathogenic S. aureus in food poisoning incidents in the context of public health.

Facile Synthesis of a SiOx-Graphite Composite toward Practically Accessible High-Energy-Density Lithium-Ion Battery Anodes.

SiOx-based material is a promising candidate for lithium-ion batteries (LIBs) owing to its high theoretical capacity. The inherent disadvantages of poor electronic conductivity and large volume variation can be solved by constructing the outermost carbon layer and reserving internal voids. However, the practical application of SiOx/C composites remains a great challenge due to the unsatisfactory energy density. Herein, we propose a facile synthetic approach for fabricating SNG/H-SiOx@C composites, which are constructed by amorphous carbon, hollow SiOx (H-SiOx), and spherical natural graphite (SNG). H-SiOx alleviates volume expansion, while amorphous carbon promotes Li+ migration and stable solid electrolyte interphase (SEI) formation. The as-prepared SNG/H-SiOx@C demonstrates a high reversible capacity (465 mAh g-1), excellent durability (93% capacity retention at 0.5C after 500 cycles), lower average delithiation potential than SNG (0.143 V after 500 cycles), and a 14% gravimetric energy density improvement at a loading level of 4.5 mg cm-2. Even at a compacted density of 1.5 g cm-3, the SNG/H-SiOx@C anode presents a modest volume deformation of 14.3% after 100 cycles at 0.1C.

Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach.

Background: Abuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data. Methods: In this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods. Results: The best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R2 value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%. Conclusions: Our study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances.

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network.

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

Single-cell analysis reveals that cancer-associated fibroblasts stimulate oral squamous cell carcinoma invasion via the TGF-ß/Smad pathway.

Although substantial progress has been made in cancer biology and treatment, the prognosis of oral squamous cell carcinoma (OSCC) is still not satisfactory because of local tumor invasion and frequent lymph node metastasis. The tumor microenvironment (TME) is a potential target in which cancer-associated fibroblasts (CAFs) are of great significance due to their interactions with cancer cells. However, the exact mechanism is still unclear. Therefore, we focus on the crosstalk between cancer cells and CAFs and discover that CAFs are the main source of TGF-ß1. Transwell assays and western blot analysis further prove that CAFs activate the TGF-ß1/Smad pathway to promote OSCC invasion. Through survival analysis, we confirm that CAF overexpression is correlated with poor overall survival in OSCC. To further elucidate the origin and role of CAFs in OSCC, we analyze single-cell RNA sequencing (scRNA-seq) data from 14 OSCC tumor samples and identify four distinct cell types, including CAFs, in the TME, indicating high intratumoral heterogeneity. Then, two subtypes of CAFs, namely, myofibroblasts (mCAFs) and inflammatory CAFs (iCAFs), are further distinguished. Based on the differentially upregulated genes of mCAFs and iCAFs, GO enrichment analysis reveals their different roles in OSCC progression. Furthermore, the gene expression pattern is dynamically altered across pseudotime, potentially taking part in the transformation from epithelial to mCAFs or iCAFs through the epithelial to mesenchymal transition.