An estimation of future county-level cement production and associated air pollutant emissions in China through artificial neural networks.

Cement production and its air pollutant and carbon dioxides (CO2) emissions in China will be relocated greatly as a joint effect of diverse development of industrial economy and implementation of environmental policies for different regions. The future pathway and spatial pattern of emissions are important for policy making of air quality improvement and CO2 emission abatement, as well as coordinating regional development. In this study, we developed an artificial neural network (ANN) model to predict cement production at the county level and to calculate the associated emissions of air pollutants and CO2 at the county level till 2060. Results show that the cement production will decline from 2327 million metric tons (Mt) in 2015 to 704 Mt. in 2060 under the Shared Socioeconomic Pathways 1 (SSP1). Counties closer to provincial capital will experience greater retirement of cement industry. Likewise, the emissions of air pollutants and CO2 will experience a steady downward trend driven by the declining cement production and the improvement of pollution control technologies. There will be a more significant regional heterogeneity in the reduction of production and emissions at city level compared to the province level. With the clearance for nearly two-thirds of counties, future cement production and emissions will be more intensively distributed in a few cities. The shares of emissions in southwestern regions will grow from 2015 to 2060 while those of eastern regions will continue decreasing. The comparison between the changing spatial distributions of emissions and gross domestic product (GDP) indicates a positive effect of existing policies in reconciling regional economic development and air pollution controls. The outcome could support the analyses on the impact of industrial development on air quality and public health, and the method can be applied widely for other industrial sectors for a more comprehensive understanding of future emission relocation.

CoF2 coupled MXene with facile active phase reconstruction for oxygen evolution reaction.

The combined CoF2/MXene system was effective in the facile cobalt active species formation induced by surface reconstruction and charge redistribution to promote oxygen evolution reaction.

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

Innovative Synthesis of Au Nanoparticle-Trapped Flexible Macrocrystals: Achieving Stable Black Crystal Wires with Broadband Absorption.

In optical materials, the development of absorbers for a wide spectrum is a focal point of research. A pivotal challenge lies in ensuring the stability and durability of optical absorbers, particularly at elevated temperatures. This study introduces a novel approach to creating absorbers with diverse colors, focusing on the synthesis and properties of black crystal wires. In contrast to black gold nanoparticle (Au NP) precipitates, which change color within hours under similar conditions, the method involves strategically trapping Au NPs within defects during the growth of single crystals. This results in black crystal wires that not only exhibit broadband absorption but also maintain exceptional stability even under prolonged exposure to high temperatures. The method also involves the controlled synthesis of colorless and red crystal wires. As a proof of concept, these stable black Au crystal wires demonstrate superior performance in photothermal conversion applications. The methodology, derived from the crystal growth process, presents a defect template that offers a novel approach to material design. Furthermore, these unique crystals, available in various colors, hold significant promise for a range of unexplored applications.

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study.

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Role of AMPK-regulated autophagy in retinal pigment epithelial cell homeostasis: A review.

The retinal pigment epithelium (RPE) is a regularly arranged monolayer of cells in the outermost layer of the retina. It is crucial for transporting nutrients and metabolic substances in the retina and maintaining the retinal barrier. RPE dysfunction causes diseases related to vision loss. Thus, understanding the mechanisms involved in normal RPE function is vital. Adenosine monophosphate-activated protein kinase (AMPK) is an RPE energy sensor regulating various signaling and metabolic pathways to maintain cellular energetic homeostasis. AMPK activation is involved in multiple signaling pathways regulated by autophagy in the RPE, thereby protecting the cells from oxidative stress and slowing RPE degeneration. In this review, we attempt to broaden the understanding of the pathogenesis of RPE dysfunction by focusing on the role and mechanism of AMPK regulation of autophagy in the RPE. The correlation between RPE cellular homeostasis and role of AMPK was determined by analyzing the structure and mechanism of AMPK and its signaling pathway in autophagy. The protective effect of AMPK-regulated autophagy on the RPE for gaining insights into the regulatory pathways of RPE dysfunction has been discussed.

[Effects of PM2.5 and O3 sub-chronic combined exposure on ATP amount and ATPase activities in rat nasal mucosa].

OBJECTIVE: To evaluate the effects of fine particle matter (PM2.5) and ozone (O3) combined exposure on adenosine triphosphate (ATP) amount and ATPase activities in nasal mucosa of Sprague Dawley (SD) rats. METHODS: Twenty male SD rats were divided into control group (n=10) and exposure group (n=10) by random number table method. The rats were fed in the conventional clean environment and the air pollutant exposure system established by our team, respectively, and exposed for 208 d. During the exposure period, the concentrations of PM2.5 and O3 in the exposure system were monitored, and a comprehensive assessment of PM2.5 and O3 in the exposure system was conducted by combining self-measurement and site data. On the 208 d of exposure, the core, liver, spleen, kidney, testis and other major organs and nasal mucosal tissues of the rats were harvested. Each organ was weighed and the organ coefficient calculated. The total amount of ATP was measured by bioluminescence, and the activities of Na+-K+ -ATPase and Ca2+ -ATPase were detected by spectrophotometry. The t test of two independent samples was used to compare the differences among the indicator groups. RESULTS: From the 3rd week to the end of exposure duration, the body weight of the rats in the exposure group was higher than that in the control group (P < 0.05), and there was no significant difference in organ coefficients between the two groups. The average daily PM2.5 concentration in the exposure group was (30.68±19.23) µg/m3, and the maximum 8 h ozone concentration (O3-8 h) was (82.45±35.81) µg/m3. The chemiluminescence value (792.4±274.1) IU/L of ATP in nasal mucosa of the rats in the exposure group was lower than that in the control group (1 126.8±218.1) IU/L. The Na+-K+-ATPase activity (1.53±0.85) U/mg in nasal mucosa of the rats in the exposure group was lower than that in the control group (4.31±1.60) U/mg (P < 0.05). The protein content of nasal mucosa in the control group and the exposure group were (302.14±52.51) mg/L and (234.58±53.49) mg/L, respectively, and the activity of Ca2+-ATPase was (0.81±0.27) U/mg and (0.99±0.73) U/mg, respectively. There was no significant difference between the groups. CONCLUSION: The ability of power capacity decreased in the rat nasal mucossa under the sub-chronic low-concentration exposure of PM2.5 and O3.

Three SARS-CoV-2 spike protein variants delivered intranasally by measles and mumps vaccines are broadly protective.

As the new SARS-CoV-2 Omicron variants and subvariants emerge, there is an urgency to develop intranasal, broadly protective vaccines. Here, we developed highly efficacious, intranasal trivalent SARS-CoV-2 vaccine candidates (TVC) based on three components of the MMR vaccine: measles virus (MeV), mumps virus (MuV) Jeryl Lynn (JL1) strain, and MuV JL2 strain. Specifically, MeV, MuV-JL1, and MuV-JL2 vaccine strains, each expressing prefusion spike (preS-6P) from a different variant of concern (VoC), were combined to generate TVCs. Intranasal immunization of IFNAR1-/- mice and female hamsters with TVCs generated high levels of S-specific serum IgG antibodies, broad neutralizing antibodies, and mucosal IgA antibodies as well as tissue-resident memory T cells in the lungs. The immunized female hamsters were protected from challenge with SARS-CoV-2 original WA1, B.1.617.2, and B.1.1.529 strains. The preexisting MeV and MuV immunity does not significantly interfere with the efficacy of TVC. Thus, the trivalent platform is a promising next-generation SARS-CoV-2 vaccine candidate.

Diagnostic Value of Superb Microvascular Imaging of the Rotator Cuff Interval for the Early Diagnosis of Frozen Shoulder.

Purpose: To explore the early diagnostic value of superb microvascular imaging (SMI) features within the rotator cuff gap for frozen shoulder. Patients and Methods: This prospective study enrolled patients with acute early-stage frozen shoulder seeking treatment at Zhabei Central Hospital in Jing'an District, Shanghai, between July 2021 and December 2022 were enrolled in this study. Healthy controls were collected in a 1:1 ratio from the same hospital's physical examination center. All participants underwent SMI and power Doppler ultrasound (PDUS) of the rotator cuff gap. Results: The study included 79 patients with frozen shoulder and 77 healthy controls. Compared with the healthy control group, the patient group had a higher proportion of hypoechoic rotator cuff gap (81.0% vs 48.1%, P<0.001), a thicker coracohumeral ligament (2.60±1.01 vs 2.03±0.97, P<0.001), a thicker glenohumeral joint capsule (3.10±0.99 vs 2.46±1.17, P<0.001), and elevated blood grading using SMI (P<0.001) and PDUS (P=0.014). The highest area under the curve (AUC) was observed for SMI blood flow grading (AUC=0.824, 95% CI: 0.755-0.880, P<0.001), resulting in 82% sensitivity and 77% specificity when using a cutoff of 1. SMI blood flow grading was associated with external rotation <30° (P=0.007) and abduction <30° (P=0.013) but not with internal rotation <30° (P=0.630) or flexion <30° (P=0.562). Conclusion: The grading of SMI blood flow may emerge as a valuable predictive indicator for the early stages of frozen shoulder. This simple ultrasound technique holds the potential to enhance the diagnostic process, enabling early initiation of treatment and potentially improving patient outcomes.

Isolation and Characterization of Contemporary Bovine Coronavirus Strains.

Bovine coronavirus (BCoV) poses a threat to cattle health worldwide, contributing to both respiratory and enteric diseases. However, few contemporary strains have been isolated. In this study, 71 samples (10 nasal and 61 fecal) were collected from one farm in Ohio in 2021 and three farms in Georgia in 2023. They were screened by BCoV-specific real-time reverse transcription-PCR, and 15 BCoV-positive samples were identified. Among them, five BCoV strains from fecal samples were isolated using human rectal tumor-18 (HRT-18) cells. The genomic sequences of five strains were obtained. The phylogenetic analysis illustrated that these new strains clustered with US BCoVs that have been detected since the 1990s. Sequence analyses of the spike proteins of four pairs of BCoVs, with each pair originally collected from the respiratory and enteric sites of one animal, revealed the potential amino acid residue patterns, such as D1180 for all four enteric BCoVs and G1180 for three of four respiratory BCoVs. This project provides new BCoV isolates and sequences and underscores the genetic diversity of BcoVs, the unknown mechanisms of disease types, and the necessity of sustained surveillance and research for BCoVs.

Functional near-infrared spectroscopy-based neurofeedback training regulates time-on-task effects and enhances sustained cognitive performance.

Time-on-task effect is a common consequence of long-term cognitive demand work, which reflects reduced behavioral performance and increases the risk of accidents. Neurofeedback is a neuromodulation method that can guide individuals to regulate their brain activity and manifest as changes in related symptoms and cognitive behaviors. This study aimed to examine the effects of functional near-infrared spectroscopy-based neurofeedback training on time-on-task effects and sustained cognitive performance. A randomized, single-blind, sham-controlled study was performed: 17 participants received feedback signals of their own dorsolateral prefrontal cortex activity (neurofeedback group), and 16 participants received feedback signals of dorsolateral prefrontal cortex activity from the neurofeedback group (sham-neurofeedback group). All participants received 5 neurofeedback training sessions and completed 2 sustained cognitive tasks, including a 2-back task and a psychomotor vigilance task, to evaluate behavioral performance changes following neurofeedback training. Results showed that neurofeedback relative to the sham-neurofeedback group exhibited increased dorsolateral prefrontal cortex activation, increased accuracy in the 2-back task, and decreased mean response time in the psychomotor vigilance task after neurofeedback training. In addition, the neurofeedback group showed slower decline performance during the sustained 2-back task after neurofeedback training compared with sham-neurofeedback group. These findings demonstrate that neurofeedback training could regulate time-on-task effects on difficult task and enhance performance on sustained cognitive tasks by increasing dorsolateral prefrontal cortex activity.

Measuring Human Auditory Evoked Fields with a Flexible Multi-Channel OPM-Based MEG System.

BACKGROUND: Magnetoencephalography (MEG) is a non-invasive imaging technique for directly measuring the external magnetic field generated from synchronously activated pyramidal neurons in the brain. The optically pumped magnetometer (OPM) is known for its less expensive, non-cryogenic, movable and user-friendly custom-design provides the potential for a change in functional neuroimaging based on MEG. METHODS: An array of OPMs covering the opposite sides of a subject's head is placed inside a magnetically shielded room (MSR) and responses evoked from the auditory cortices are measured. RESULTS: High signal-to-noise ratio auditory evoked response fields (AEFs) were detected by a wearable OPM-MEG system in a MSR, for which a flexible helmet was specially designed to minimize the sensor-to-head distance, along with a set of bi-planar coils developed for background field and gradient nulling. Neuronal current sources activated in AEF experiments were localized and the auditory cortices showed the highest activities. Performance of the hybrid optically pumped magnetometer-magnetoencephalography/electroencephalography (OPM-MEG/EEG) system was also assessed. CONCLUSIONS: The multi-channel OPM-MEG system performs well in a custom built MSR equipped with bi-planar coils and detects human AEFs with a flexible helmet. Moreover, the similarities and differences of auditory evoked potentials (AEPs) and AEFs are discussed, while the operation of OPM-MEG sensors in conjunction with EEG electrodes provides an encouraging combination for the exploration of hybrid OPM-MEG/EEG systems.

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.

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 .

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.

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.

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.

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.

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.

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.