PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS: We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS: Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .

Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

Unraveling the role of the circadian clock genes in cervical squamous cell carcinoma and endocervical adenocarcinoma: A prognostic indicator for prognostic, immunotherapy response, and chemotherapy sensitivity.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) account for a significant proportion of gynecological malignancies and represent a major global health concern. Globally, CESC is ranked as the fourth most common cancer among women. Conventional treatment of this disease has a less favorable prognosis for most patients. However, the discovery of early molecular biomarkers is therefore important for the diagnosis of CESC, as well as for slowing down their progression process. Methods: To identify differentially expressed genes strongly associated with prognosis, univariate Cox proportional hazard analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used. Using multiple Cox proportional hazard regression, a multifactorial model for prognostic risk assessment was then created. Results: The expression of biological clock-related genes, which varied considerably among distinct subtypes and were associated with significantly diverse prognoses, was used to categorize CESC patients. These findings demonstrate how the nomogram developed based on the 7-CRGs signature may assist physicians in creating more precise, accurate, and successful treatment plans that can aid CESC patients at 1, 3, and 5 years. Conclusions: By using machine learning techniques, we thoroughly investigated the impact of CRGs on the prognosis of CESC patients in this study. By creating a unique nomogram, we were able to accurately predict patient prognosis. At the same time, we showed new perspectives on the development of CESC and its treatment by analyzing the associations of the prognostic model with immunity, enrichment pathways, chemotherapy sensitivity, and so on. This research provides a new direction for clinical treatment.

Longitudinal multi-omics analysis uncovers the altered landscape of gut microbiota and plasma metabolome in response to high altitude.

BACKGROUND: Gut microbiota is significantly influenced by altitude. However, the dynamics of gut microbiota in relation to altitude remains undisclosed. METHODS: In this study, we investigated the microbiome profile of 610 healthy young men from three different places in China, grouped by altitude, duration of residence, and ethnicity. We conducted widely targeted metabolomic profiling and clinical testing to explore metabolic characteristics. RESULTS: Our findings revealed that as the Han individuals migrated from low altitude to high latitude, the gut microbiota gradually converged towards that of the Tibetan populations but reversed upon returning to lower altitude. Across different cohorts, we identified 51 species specifically enriched during acclimatization and 57 species enriched during deacclimatization to high altitude. Notably, Prevotella copri was found to be the most enriched taxon in both Tibetan and Han populations after ascending to high altitude. Furthermore, significant variations in host plasma metabolome and clinical indices at high altitude could be largely explained by changes in gut microbiota composition. Similar to Tibetans, 41 plasma metabolites, such as lactic acid, sphingosine-1-phosphate, taurine, and inositol, were significantly elevated in Han populations after ascending to high altitude. Germ-free animal experiments demonstrated that certain species, such as Escherichia coli and Klebsiella pneumoniae, which exhibited altitude-dependent variations in human populations, might play crucial roles in host purine metabolism. CONCLUSIONS: This study provides insights into the dynamics of gut microbiota and host plasma metabolome with respect to altitude changes, indicating that their dynamics may have implications for host health at high altitude and contribute to host adaptation. Video Abstract.

Relationships between blood chromium exposure and liver injury: Exploring the mediating role of systemic inflammation in a chromate-exposed population.

Hexavalent chromium and its compounds are prevalent pollutants, especially in the work environment, pose a significant risk for multisystem toxicity and cancers. While it is known that chromium accumulation in the liver can cause damage, the dose-response relationship between blood chromium (Cr) and liver injury, as well as the possible potential toxic mechanisms involved, remains poorly understood. To address this, we conducted a follow-up study of 590 visits from 305 participants to investigate the associations of blood Cr with biomarkers for liver injury, including serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and direct bilirubin (DBIL), and to evaluate the mediating effects of systemic inflammation. Platelet (PLT) and the platelet-to-lymphocyte ratio (PLR) were utilized as biomarkers of systemic inflammation. In the linear mixed-effects analyses, each 1-unit increase in blood Cr level was associated with estimated effect percentage increases of 0.82% (0.11%, 1.53%) in TBIL, 1.67% (0.06%, 3.28%) in DBIL, 0.73% (0.04%, 1.43%) in ALT and 2.08% (0.29%, 3.87%) in AST, respectively. Furthermore, PLT mediated 10.04%, 11.35%, and 10.77% increases in TBIL, DBIL, and ALT levels induced by chromate, respectively. In addition, PLR mediated 8.26% and 15.58% of the association between blood Cr and TBIL or ALT. These findings shed light on the mechanisms underlying blood Cr-induced liver injury, which is partly due to worsening systemic inflammation.

C6orf15 promotes liver metastasis via WNT/ß-catenin signalling in colorectal cancer.

BACKGROUND: Colon cancer ranks third among global tumours and second in cancer-related mortality, prompting an urgent need to explore new therapeutic targets. C6orf15 is a novel gene that has been reported only in Sjogren's syndrome and systemic lupus erythematosus patients. We found a close correlation between increased C6orf15 expression and the occurrence of colon cancer. The aim of this study was to explore the potential of C6orf15 as a therapeutic target for colorectal cancer. METHOD: RNA-seq differential expression analysis of the TCGA database was performed using the R package 'limma.' The correlation between target genes and survival as well as tumour analysis was analysed using GEPIA. Western blot and PCR were used to assess C6orf15 expression in colorectal cancer tissue samples. Immunofluorescence and immunohistochemistry were used to assess C6orf15 subcellular localization and tissue expression. The role of C6orf15 in liver metastasis progression was investigated via a mouse spleen infection liver metastasis model. The association of C6orf15 with signalling pathways was assessed using the GSEA-Hallmark database. Immunohistochemistry (IHC), qPCR and western blotting were performed to assess the expression of related mRNAs or proteins. Biological characteristics were evaluated through cell migration assays, MTT assays, and Seahorse XF96 analysis to monitor fatty acid metabolism. RESULTS: C6orf15 was significantly associated with liver metastasis and survival in CRC patients as determined by the bioinformatic analysis and further verified by immunohistochemistry (IHC), qPCR and western blot results. The upregulation of C6orf15 expression in CRC cells can promote the nuclear translocation of ß-catenin and cause an increase in downstream transcription. This leads to changes in the epithelial-mesenchymal transition (EMT) and alterations in fatty acid metabolism, which together promote liver metastasis of CRC. CONCLUSION: Our study identified C6orf15 as a marker of liver metastasis in CRC. C6orf15 can activate the WNT/ß-catenin signalling pathway to promote EMT and fatty acid metabolism in CRC.

Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

BERT-siRNA: siRNA target prediction based on BERT pre-trained interpretable model.

Silencing mRNA through siRNA is vital for RNA interference (RNAi), necessitating accurate computational methods for siRNA selection. Current approaches, relying on machine learning, often face challenges with large data requirements and intricate data preprocessing, leading to reduced accuracy. To address this challenge, we propose a BERT model-based siRNA target gene knockdown efficiency prediction method called BERT-siRNA, which consists of a pre-trained DNA-BERT module and Multilayer Perceptron module. It applies the concept of transfer learning to avoid the limitation of a small sample size and the need for extensive preprocessing processes. By fine-tuning on various siRNA datasets after pretraining on extensive genomic data using DNA-BERT to enhance predictive capabilities. Our model clearly outperforms all existing siRNA prediction models through testing on the independent public siRNA dataset. Furthermore, the model's consistent predictions of high-efficiency siRNA knockdown for SARS-CoV-2, as well as its alignment with experimental results for PDCD1, CD38, and IL6, demonstrate the reliability and stability of the model. In addition, the attention scores for all 19-nt positions in the dataset indicate that the model's attention is predominantly focused on the 5' end of the siRNA. The step-by-step visualization of the hidden layer's classification progressively clarified and explained the effective feature extraction of the MLP layer. The explainability of model by analysis the attention scores and hidden layers is also our main purpose in this work, making it more explainable and reliable for biological researchers.

Deciphering a Prognostic Signature Based on Soluble Mediators Defines the Immune Landscape and Predicts Prognosis in HNSCC.

BACKGROUND: The study on Head and Neck Squamous Cell Carcinoma (HNSCC), a prevalent and aggressive form of head and neck cancer, focuses on the often-overlooked role of soluble mediators. The objective is to leverage a transcriptome-based risk analysis utilizing soluble mediator-related genes (SMRGs) to provide novel insights into prognosis and immunotherapy efficacy in HNSCC patients. METHODS: We analyzed the expression and prognostic significance of 10,859 SMRGs using 502 HNSCC and 44 normal samples from the TCGA-HNSC cohort in The Cancer Genome Atlas (TCGA). The samples were divided into training and test sets in a 7:3 ratio, with an additional external validation using 40 tumor samples from the International Cancer Genome Consortium (ICGC). Key differentially expressed genes (DEGs) with prognostic significance were identified through univariate and Lasso-Cox regression analyses. A prognostic model based on 20 SMRGs was developed using Lasso and multivariate Cox regression. We assessed the clinical outcomes and immune status in high-risk (HR) and low-risk (LR) HNSCC patients utilizing the BEST databases and single-sample Gene Set Enrichment Analysis (ssGSEA). RESULTS: The 20 SMRGs were crucial in predicting the prognosis of HNSCC, with the SMRG signature emerging as an independent prognostic indicator. Patients classified in the HR group exhibited poorer outcomes compared to those in the LR group. A nomogram, integrating clinical characteristics and risk scores, demonstrated substantial prognostic value. Immunotherapy appeared to be more effective in the LR group, possibly attributed to enhanced immune infiltration and expression of immune checkpoints. CONCLUSIONS: The model based on soluble mediator-associated genes offers a fresh perspective for assessing the pre-immune efficacy and showcases robust predictive capabilities. This innovative approach holds significant promise in advancing the field of precision immuno-oncology research, providing valuable insights for personalized treatment strategies in HNSCC.

[Tacrolimus Induces Pain by Upregulating the Synaptic Expression of AMPA Receptors in the Spinal Cord Dorsal Horn]. / ä»–å ‹èŽ«å¸é€šè¿‡ä¸Šè°ƒè„Šé«“èƒŒè§’AMPA受体的突触表达诱发疼痛.

Objective: To investigate the effects of long-term administration of tacrolimus (also known as FK506) on the pain-related behaviors in mice and to study the underlying mechanism of pain induced by FK506 via measuring the effect of FK506 on the synaptic expression and phosphorylation of alpha-amino-3-hyroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor in the spinal cord dorsal horn of mice. Methods: 1) A total of 24 mice were evenly and randomly assigned to two groups, a FK506 group and a Saline group. The FK506 group was given daily intraperitoneal injection of FK506 and the Saline group received normal saline. Both groups received injection once a day for 7 days in a row. Some of the mice ( n=6 in each group) were monitored for the changes in the paw withdrawal threshold (PWT), the paw withdrawal latency (PWL), and the spontaneous pain behaviors to establish the pain model. The other mice ( n=6 in each group) of each group underwent isolation of the dorsal horn when obvious pain symptoms were induced on day 7 of injection. Then, immunoblotting was performed to determine the synaptic expression and phosphorylation levels of GluA1 and GluA2 subunits of AMPA receptors. 2) The mice were randomly divided into two groups, FK506+calcineurin (CaN) group and FK506+Saline group ( n=6 in each group). After the pain model was constructed, the mice were given intrathecal injection of recombinant CaN (also know as 33 U) or normal saline. Then, 60 minutes later, the PWT and the PWL of the mice were measured to investigate the role of CaN in FK506-induced pain. 3) Another18 mice were selected. The mice were randomly and evenly assigned to three groups, a control group (receiving intraperitoneal injection of normal saline followed by intrathecal injection of normal saline), FK506+Saline group (receiving intraperitoneal injection of FK506 followed by intrathecal injection of normal saline) and FK506+CaN group (receiving intraperitoneal injection of FK506 followed by intrathecal injection of CaN). Then, 60 minutes later, the spinal cords were isolated and subjected to immunoblotting assay to determine the role of CaN in FK506-induced AMPA receptor modification. Results: 1) After 7 consecutive days of intraperitoneal injection of FK506, the PWT and PWL of mice dropped significantly, reaching on day 7 as low as 22.3%±0.05% and 66.6%±0.05% of the control group, respectively ( P<0.01). The FK506-treated mice displayed evident spontaneous pain behavior, presenting significantly increased licking activities ( P<0.01). These results indicated that FK506-induced pain model was successfully established. Immunoblotting assay showed that the total expressions of GluA1 and GluA2 subunits in the spinal dorsal horn of the FK506 group remained unchanged in comparison with those of the Saline group. However, FK506 specifically induced an increase in the synaptic expression of GluA1. In addition, the phosphorylation levels of GluA1 at Ser845 and Ser831 in FK506-treated mice were significantly increased in comparison with those of the control group ( P<0.05). 2) Compared with those of the mice in the FK506+Saline group, the PWT and the PWL of mice in the FK506+CaN group were significantly increased ( P<0.05). 3) Compared with those of the FK506+Saline group, the synaptic expression of GluA1 were decreased in FK506+CaN group ( P<0.01) and the phosphorylation levels of GluA1 at Ser845 and Ser831 were significantly downregulated ( P<0.001). Conclusion: The hyper-expression and hyperphosphorylation of GluA1 subunit in the spinal cord dorsal horn resulting from CaN inhibition contributes to the FK506-induced pain syndrome. FK506 induces the synaptic hyper-expression and hyperphosphorylation of GluA1 in the dorsal horn of the spinal cord through CaN inhibition, thereby inducing pain.

Green manufacturing for achieving carbon neutrality goal requires innovative technologies: A bibliometric analysis from 1991 to 2022.

Recent years have seen a significant increase in interest in green manufacturing as a key driver of global carbon-neutral efforts and sustainable development. To find the research hotspots of green manufacturing and reveal future research trends, this study reviewed and analyzed research articles from the Web of Science database on green manufacturing from 1991 to 2022 using a bibliometric method. The findings indicate a significant rise in the number of articles related to green manufacturing since the 2010s. Moreover, there has been an increase in the involvement of scholars from developing countries such as China and India in this field. Based on the literature review and bibliometric cluster analysis on green manufacturing, we believed that future research may continue following the lines of intelligent technology integration, adoption of frontier engineering techniques, and industry development in line with carbon reduction targets. A framework for future green manufacturing development is proposed, with a focus on Chinese policies. The framework could provide policy implications for developing countries looking to pursue opportunities for development in green manufacturing.

Corrigendum: Prediction of severe CRS and determination of biomarkers in B cell-acute lymphoblastic leukemia treated with CAR-T cells.

[This corrects the article DOI: 10.3389/fimmu.2023.1273507.].

GPR39 regulated spinal glycinergic inhibition and mechanical inflammatory pain.

G protein-coupled receptor 39 (GPR39) senses the change of extracellular divalent zinc ion and signals through multiple G proteins to a broad spectrum of downstream effectors. Here, we found that GPR39 was prevalent at inhibitory synapses of spinal cord somatostatin-positive (SOM+) interneurons, a mechanosensitive subpopulation that is critical for the conveyance of mechanical pain. GPR39 complexed specifically with inhibitory glycine receptors (GlyRs) and helped maintain glycinergic transmission in a manner independent of G protein signalings. Targeted knockdown of GPR39 in SOM+ interneurons reduced the glycinergic inhibition and facilitated the excitatory output from SOM+ interneurons to spinoparabrachial neurons that engaged superspinal neural circuits encoding both the sensory discriminative and affective motivational domains of pain experience. Our data showed that pharmacological activation of GPR39 or augmenting GPR39 interaction with GlyRs at the spinal level effectively alleviated the sensory and affective pain induced by complete Freund's adjuvant and implicated GPR39 as a promising therapeutic target for the treatment of inflammatory mechanical pain.

KARR-seq reveals cellular higher-order RNA structures and RNA-RNA interactions.

RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order structures and how RNA molecules may interact with each other to facilitate functions remain largely unknown. Here we present KARR-seq, which uses N3-kethoxal labeling and multifunctional chemical crosslinkers to covalently trap and determine RNA-RNA interactions and higher-order RNA structures inside cells, independent of local protein binding to RNA. KARR-seq depicts higher-order RNA structure and detects widespread intermolecular RNA-RNA interactions with high sensitivity and accuracy. Using KARR-seq, we show that translation represses mRNA compaction under native and stress conditions. We determined the higher-order RNA structures of respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) and identified RNA-RNA interactions between the viruses and the host RNAs that potentially regulate viral replication.

Managing urban development could halve nitrogen pollution in China.

Halving nitrogen pollution is crucial for achieving Sustainable Development Goals (SDGs). However, how to reduce nitrogen pollution from multiple sources remains challenging. Here we show that reactive nitrogen (Nr) pollution could be roughly halved by managed urban development in China by 2050, with NH3, NOx and N2O atmospheric emissions declining by 44%, 30% and 33%, respectively, and Nr to water bodies by 53%. While rural-urban migration increases point-source nitrogen emissions in metropolitan areas, it promotes large-scale farming, reducing rural sewage and agricultural non-point-source pollution, potentially improving national air and water quality. An investment of approximately US$ 61 billion in waste treatment, land consolidation, and livestock relocation yields an overall benefit of US$ 245 billion. This underscores the feasibility and cost-effectiveness of halving Nr pollution through urbanization, contributing significantly to SDG1 (No poverty), SDG2 (Zero hunger), SDG6 (Clean water), SDG12 (Responsible consumption and production), SDG14 (Climate Action), and so on.

Spatial and temporal variations of surface background ozone in China analyzed with the grid-stretching capability of GEOS-Chem High Performance.

Surface background ozone, defined as the ozone in the absence of domestic anthropogenic emissions, is important for developing emission reduction strategies. Here we apply the recently developed GEOS-Chem High Performance (GCHP) global atmospheric chemistry model with ∼0.5° stretched resolution over China to understand the sources of Chinese background ozone (CNB) in the metric of daily maximum 8 h average (MDA8) and to identify the drivers of its interannual variability (IAV) from 2015 to 2019. The GCHP ozone simulations over China are evaluated with an ensemble of surface and aircraft measurements. The five-year national-mean CNB ozone is estimated as 37.9 ppbv, with a spatially west-to-southeast downward gradient (55 to 25 ppbv) and a summer peak (42.5 ppbv). High background levels in western China are due to abundant transport from the free troposphere and adjacent foreign regions, while in eastern China, domestic formation from surface natural precursors is also important. We find greater importance of soil nitric oxides (NOx) than biogenic volatile organic compound emissions to CNB ozone in summer (6.4 vs. 3.9 ppbv), as ozone formation becomes increasingly NOx-sensitive when suppressing anthropogenic emissions. The percentage of daily CNB ozone to total surface ozone generally decreases with increasing daily total ozone, indicating an increased contribution of domestic anthropogenic emissions on polluted days. CNB ozone shows the largest IAV in summer, with standard deviations (seasonal means) of ∼5 ppbv over Qinghai-Tibet Plateau (QTP) and >3.5 ppbv in eastern China. CNB values in QTP are strongly correlated with horizontal circulation anomalies in the middle troposphere, while soil NOx emissions largely drive the IAV in the east. El Nino can inhibit CNB ozone formation in Southeast China by increased precipitation and lower temperature locally in spring, but enhance CNB in Southwest China through increased biomass burning emissions in Southeast Asia.

Computational design towards a boiling-resistant single-chain sweet protein monellin.

Sweet proteins offer a promising solution as sugar substitutes by providing a sugar-like sweetness without the negative health impacts linked to sugar or artificial sweeteners. However, the low thermal stability of sweet proteins has hindered their applications. In this study, we took a computational approach utilizing ΔΔG calculations in PyRosetta to enhance the thermostability of single-chain monellin (MNEI). By generating and characterizing 21 variants with single mutation, we identified 11 variants with higher melting temperature (Tm) than that of MNEI. To further enhance the thermal stability, we conducted structural analysis and designed an additional set of 14 variants with multiple mutations. Among these variants, four exhibited a significant improvement in thermal stability, with an increase of at least 20 °C (Tm > 96 °C) compared to MNEI, while maintaining their sweetness. Remarkably, these variants remained soluble even after being heated in boiling water for one hour. Moreover, they displayed exceptional stability across alkaline, acidic and neutral environments. These findings highlight the tremendous potential of these variants for applications in the food and beverage industry. Additionally, this study provides valuable strategies for protein engineering to enhance the thermal stability of sweet proteins.

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts.

BACKGROUND: With the COVID-19 outbreak, an increasing number of individuals are concerned about their health, particularly their immune status. However, as of now, there is no available algorithm that effectively assesses the immune status of normal, healthy individuals. In response to this, a new score-based method is proposed that utilizes complete blood cell counts (CBC) to provide early warning of disease risks, such as COVID-19. METHODS: First, data on immune-related CBC measurements from 16,715 healthy individuals were collected. Then, a three-platform model was developed to normalize the data, and a Gaussian mixture model was optimized with expectation maximization (EM-GMM) to cluster the immune status of healthy individuals. Based on the results, Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) were used to determine the correlation of each CBC index with the immune status. Consequently, a weighted sum model was constructed to calculate a continuous immunity score, enabling the evaluation of immune status. RESULTS: The results demonstrated a significant negative correlation between the immunity score and the age of healthy individuals, thereby validating the effectiveness of the proposed method. In addition, a nonlinear polynomial regression model was developed to depict this trend. By comparing an individual's immune status with the reference value corresponding to their age, their immune status can be evaluated. CONCLUSION: In summary, this study has established a novel model for evaluating the immune status of healthy individuals, providing a good approach for early detection of abnormal immune status in healthy individuals. It is helpful in early warning of the risk of infectious diseases and of significant importance.

Uncovering the Impact of Aggrephagy in the Development of Alzheimer's Disease: Insights Into Diagnostic and Therapeutic Approaches from Machine Learning Analysis.

BACKGROUND: Alzheimer's disease (AD) stands as a widespread neurodegenerative disorder marked by the gradual onset of memory impairment, predominantly impacting the elderly. With projections indicating a substantial surge in AD diagnoses, exceeding 13.8 million individuals by 2050, there arises an urgent imperative to discern novel biomarkers for AD. METHODS: To accomplish these objectives, we explored immune cell infiltration and the expression patterns of immune cells and immune function-related genes of AD patients. Furthermore, we utilized the consensus clustering method combined with aggrephagy-related genes (ARGs) for typing AD patients and categorized AD specimens into distinct clusters (C1, C2). A total of 272 candidate genes were meticulously identified through a combination of differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Subsequently, we applied three machine learning algorithms-namely random forest (RF), support vector machine (SVM), and generalized linear model (GLM)-to pinpoint a pathogenic signature comprising five genes associated with AD. To validate the predictive accuracy of these identified genes in discerning AD progression, we constructed nomograms. RESULTS: Our analyses uncovered that cluster C2 exhibits a higher immune expression than C1. Based on the ROC(0.956). We identified five characteristic genes (PFKFB4, PDK3, KIAA0319L, CEBPD, and PHC2T) associated with AD immune cells and function. The nomograms constructed on the basis of these five diagnostic genes demonstrated effectiveness. In the validation group, the ROC values were found to be 0.760 and 0.838, respectively. These results validate the robustness and reliability of the diagnostic model, affirming its potential for accurate identification of AD. CONCLUSION: Our findings not only contribute to a deeper understanding of the molecular mechanisms underlying AD but also offer valuable insights for drug development and clinical analysis. The limitation of our study is the limited sample size, and although AD-related genes were identified and some of the mechanisms elucidated, further experiments are needed to elucidate the more in-depth mechanisms of these characterized genes in the disease.