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.

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.

MOTIVATION: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. RESULTS: This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. AVAILABILITY AND IMPLEMENTATION: HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.

CSCA U-Net: A channel and space compound attention CNN for medical image segmentation.

Image segmentation is one of the vital steps in medical image analysis. A large number of methods based on convolutional neural networks have emerged, which can extract abstract features from multiple-modality medical images, learn valuable information that is difficult to recognize by humans, and obtain more reliable results than traditional image segmentation approaches. U-Net, due to its simple structure and excellent performance, is widely used in medical image segmentation. In this paper, to further improve the performance of U-Net, we propose a channel and space compound attention (CSCA) convolutional neural network, CSCA U-Net in abbreviation, which increases the network depth and employs a double squeeze-and-excitation (DSE) block in the bottleneck layer to enhance feature extraction and obtain more high-level semantic features. Moreover, the characteristics of the proposed method are three-fold: (1) channel and space compound attention (CSCA) block, (2) cross-layer feature fusion (CLFF), and (3) deep supervision (DS). Extensive experiments on several available medical image datasets, including Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS, CVC-T, 2018 Data Science Bowl (2018 DSB), ISIC 2018, and JSUAH-Cerebellum, show that CSCA U-Net achieves competitive results and significantly improves generalization performance. The codes and trained models are available at https://github.com/xiaolanshu/CSCA-U-Net.

Retraction: An MSN-PEG-IP drug delivery system and IL13Rα2 as targeted therapy for glioma.

Retraction of 'An MSN-PEG-IP drug delivery system and IL13Rα2 as targeted therapy for glioma' by Jinlong Shi et al., Nanoscale, 2017, 9, 8970-8981, https://doi.org/10.1039/C6NR08786H.

Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing.

Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.

CircIQGAP1 regulates RCAN1 and RCAN2 through the mechanism of ceRNA and promotes the growth of malignant glioma.

Circular RNA (circRNA) is one of non-coding RNA with specific circular structure, which has been found to be involved in regulating a series of malignant biological behaviors in many malignant tumors. In this study, based on the IDH1 molecular typing of gliomas, we identified a significant downregulation of circRNA (circIQGAP1) expression in IDH1 mutant gliomas by high-throughput sequencing. In 79 tissue samples, we confirmed that circIQGAP1 expression was significantly downregulated in IDH1 mutant gliomas, and that low circIQGAP1 expression was positively associated with better prognosis. Knockdown of circIQGAP1 in glioma cell lines inhibited glioma cell malignancy and conversely overexpression of circIQGAP1 promoted glioma malignancy. circIQGAP1 regulated glioma cell migration, proliferation, invasion and apoptosis through miR-1256/RCAN1/Bax/Bcl-2/Caspase3 and miR-622/RCAN2/Bax/Bcl-2/Caspase3 axes. These results suggest that circIQGAP1 plays an important role in glioma development, promotes tumor growth, and is a potential therapeutic target for glioma.

LncRNA RARA-AS1 could serve as a novel prognostic biomarker in pan-cancer and promote proliferation and migration in glioblastoma.

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cancer progression and are potential biomarkers for diagnosis and treatment. This study investigates the role of RARA Antisense RNA 1 (RARA-AS1) in cancer and its implications for diagnosis and treatment. Various bioinformatics tools were conducted to analyze the expression patterns, immune-related functions, methylation, and gene expression correlations of RARA-AS1, mainly including the comparisons of different subgroups and correlation analyses between RARA-AS1 expression and other factors. Furthermore, we used short hairpin RNA to perform knockdown experiments, investigating the effects of RARA-AS1 on cell proliferation, invasion, and migration in glioblastoma. Our results revealed that RARA-AS1 has distinct expression patterns in different cancers and exhibits notable correlation with prognosis. Additionally, RARA-AS1 is highly correlated with certain immune checkpoints and mismatch repair genes, indicating its potential role in immune infiltration and related immunotherapy. Further analysis identified potential effective drugs for RARA-AS1 and demonstrated its potential RNA binding protein (RBP) mechanism in glioblastoma. Besides, a series of functional experiments indicated inhibiting RARA-AS1 could decrease cell proliferation, invasion, and migration of glioblastoma cell lines. Finally, RARA-AS1 could act as an independent prognostic factor for glioblastoma patients and may serve as a promising therapeutic target. All in all, Our study provides a comprehensive understanding of the functions and implications of RARA-AS1 in pan-cancer, highlighting it as a promising biomarker for survival. It is also an independent risk factor affecting prognosis in glioblastoma and an important factor affecting proliferation and migration in glioblastoma, setting the stage for further mechanistic investigations.

Single-cell RNA-seq reveals a microenvironment and an exhaustion state of T/NK cells in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous blood cancer. Effective immunotherapies for AML are hindered by a lack of understanding of the tumor microenvironment (TME). Here, we retrieved published single-cell RNA sequencing data for 128,688 cells derived from 29 bone marrow aspirates, including 21 AML patients and eight healthy donors. We established a global tumor ecosystem including nine main cell types. Myeloid, T, and NK cells were further re-clustered and annotated. Developmental trajectory analysis indicated that exhausted CD8+ T cells might develop via tissue residual memory T cells (TRM) in the AML TME. Significantly higher expression levels of exhaustion molecules in AML TRM cells suggested that these cells were influenced by the TME and entered an exhausted state. Meanwhile, the upregulation of checkpoint molecules and downregulation of granzyme were also observed in AML NK cells, suggesting an exhaustion state. In conclusion, our comprehensive profiling of T/NK subpopulations provides deeper insights into the AML immunosuppressive ecosystem, which is critical for immunotherapies.

In Situ Attached Photothermal Immunomodulation-Enhanced Nanozyme for the Inhibition of Postoperative Malignant Glioma Recurrence.

Glioblastoma (GBM) is one of the most challenging malignant brain tumors to treat. Herein, we describe a nanoenzyme hemostatic matrix strategy with the tumor cavity in situ application that simultaneously serves as photothermal agent and induces immunogenic cell death after GBM surgical resection to enhance the antitumor immunity and delay tumor recurrence. The hemostatic matrix system (Surgiflo@PCN) contains Surgiflo, a multispace structure that can be used to penetrate different shapes of tumor cavities to prevent postoperative tumor cavity hemorrhage. As well, porous palladium-copper nanoclusters (PCNs) have adjustable enzyme-like activities (oxidase, peroxidase, and catalase) responsible for formation of reactive oxygen species (ROS) under near-infrared (808 nm) laser irradiation. When the Surgiflo@PCN entered the resected tumor cavity, the first action was the direct killing of glioma cells via ROS and photothermal therapy (PTT). The second action was the induction of immunogenic cell death by PCN-enhanced oxidative stress and PTT, which reversed the immunosuppressive tumor microenvironment and enhanced the antitumor immune response. This eradicated residual glioma cells and prevented recurrence. The collective findings demonstrate that Surgiflo@PCN kills glioma cells directly through ROS and PTT and enhances antiglioma immunity and kills glioma cells indirectly. The "one-stone, two-birds" strategy could become an effective photothermal immunotherapy in GBM patients.

Synthesis, Crystal Structure, and Nonlinear-Optical Properties of a Diamond-Like Chalcogenide Cu2GeS3.

Metal sulfides with diamond-like (DL) structures generally exhibit excellent mid-IR nonlinear-optical (NLO) properties. Here, Cu2GeS3 (CGS) as a member of the DL chalcogenides was synthesized by a high-temperature solid-state method, and the optical properties were carefully studied experimentally and theoretically. The results revealed that CGS has a large second harmonic generation (0.8 × AgGaSe2) and a moderate birefringence of 0.067 at 1064 nm. In addition, the linear and NLO properties of the A2MS3 (A = Cu, Li; M = Ge, Si) series of compounds were evaluated and compared with the help of first-principles calculations.

Antioxidant Cascade Nanoenzyme Antagonize Inflammatory Pain by Modulating MAPK/p-65 Signaling Pathway.

Chronic pain has attracted wide interest because it is a major obstacle affecting the quality of life. Consequently, safe, efficient, and low-addictive drugs are highly desirable. Nanoparticles (NPs) with robust anti-oxidative stress and anti-inflammatory properties possess therapeutic possibilities for inflammatory pain. Herein, a bioactive zeolitic imidazolate framework (ZIF)-8-capped superoxide dismutase (SOD) and Fe3 O4 NPs (SOD&Fe3 O4 @ZIF-8, SFZ) is developed to achieve enhanced catalytic, antioxidative activities, and inflammatory environment selectivity, ultimately improving analgesic efficacy. SFZ NPs reduce tert-butyl hydroperoxide (t-BOOH)-induced reactive oxygen species (ROS) overproduction, thereby depressing the oxidative stress and inhibiting the lipopolysaccharide (LPS)-induced inflammatory response in microglia. After intrathecal injection, SFZ NPs efficiently accumulate at the lumbar enlargement of the spinal cord and significantly relieve complete Freund's adjuvant (CFA)-induced inflammatory pain in mice. Moreover, the detailed mechanism of inflammatory pain therapy via SFZ NPs is further studied, where SFZ NPs inhibit the activation of the mitogen-activated protein kinase (MAPK)/p-65 signaling pathway, leading to reductions in phosphorylated protein levels (p-65, p-ERK, p-JNK, and p-p38) and inflammatory factors (tumor necrosis factor [TNF]-α, interleukin [IL]-6, and IL-1ß), thereby preventing microglia and astrocyte activation for acesodyne. This study provides a new cascade nanoenzyme for antioxidant treatments and explores its potential applications as non-opioid analgesics.

From zero to hero: the spatio-temporal patterns and migration influence mechanism of world-class athletes in China.

At present, China is in an important stage of transition into a global sports power where "outstanding competitive sports talents" are required to play an important role. Therefore, it is of great theoretical and practical significance to conduct in-depth research on domestic "outstanding competitive sports talents" to promote the sustainable development of China's competitive sports and enhance its comprehensive strength. In this study, WCA refers to "world-class athletes," indicating a group of talents who won medals in international sports events such as the Olympic Games during 2009-2019. In this regard, this study uses statistical and spatial analysis methods to reveal the spatial and temporal characteristics, evolutionary process, and migration mechanism of Chinese WCA. The conclusion shows that: In terms of temporal characteristics, the population numbers in general and among different genders are characterized by a "three peaks and two troughs" pattern. In contrast, the individual temporal pattern is characterized by an "inverted U″ and "inverted V″, with an average age of 23.18 years. In terms of space, a positive correlation is shown on the whole (Moran's I > 0), however, characteristics of geographical proximity and spatial heterogeneity are not prominent, illustrating the spatial form of random distribution with low aggregation which is primarily concentrated in the southeast of China and demonstrates a "northeast-southwest" trend. There are apparent differences between areas of origin and immigration areas: Liaoning and Shandong are the main areas of origin while destination areas are frequently located in the southeast and "People's Liberation Army of China" (PLA for short). Lastly, this paper discusses the causes and influences of the migration groups from three aspects: the migrating talents, the areas of origin and immigration areas, and Chinese sports, revealing the formation and influence mechanisms.

SPTSSA Is a Prognostic Marker for Glioblastoma Associated with Tumor-Infiltrating Immune Cells and Oxidative Stress.

Background: SPTSSA encodes the small subunit A of serine palmitoyltransferase. It catalyzes the formation of sphingoid long-chain base backbone of sphingolipids. Its role in glioma prognosis and tumor-infiltrating immune cells remains unclear. Methods: We analyzed SPTSSA expression and association with clinical prognosis using GEPIA and CGGA database. Then, GSEA was performed to identify relevant biological functions of SPTSSA. The correlations between SPTSSA expression and tumor immune infiltrates were investigated using CIBERSORT and TIMER. Finally, IHC and IF were performed to confirm the value of prognosis and the correlation with immune infiltration. Results: SPTSSA expression was significantly upregulated in diffuse glioma compared to normal tissues and associated with poor survival in GEPIA and CGGA database. Then, we identified biological processes and signaling pathways associated with SPTSSA expression. The result showed that SPTSSA enriched in the GO term like oxidative stress. Finally, we showed that SPTSSA expression was significantly associated with tumor-infiltrating immune cells and overall survival via IHC. Conclusion: These findings suggest that SPTSSA expression might be used as a prognostic biomarker for glioma and potential target for novel glioma therapy.

HAUS Augmin-Like Complex Subunit 1 Influences Tumour Microenvironment and Prognostic Outcomes in Glioma.

Background: The expression of HAUS Augmin-like complex subunit 1 (HAUS1), a protein-coding gene, is low in normal samples among various cancers with pan-cancer analysis. The depletion of HAUS1 in cells decreases the G2/M cell compartment and induces apoptosis. However, the detailed expression pattern of HAUS1 and its correlation with immune infiltration in glioma (LGG and GBM) (LGG: low-grade glioma; GBM: glioblastoma) remain unknown. Therefore, in this study, we examined the role and prognostic value of HAUS1 in glioma. Methods: Transcriptional expression data of HAUS1 were collected from the CGGA and TCGA databases. The Kaplan-Meier analysis, univariate and multivariate Cox analyses, and receiver operating characteristic (ROC) curves were used to analyse the clinical significance of HAUS1 in glioma. The STRING database was used to analyse protein-protein interactions (PPI), and the "ClusterProfiler" package was used for functional enrichment analysis to examine the possible biological roles of HAUS1. In addition, the HAUS1 promoter methylation modification was analysed using MEXPRESS, and the association between HAUS1 expression and tumour-infiltrating immune cells was investigated using CIBERSORT. Results: Based on the data retrieved from TCGA (703 samples) and CGGA (1018 samples), an elevated expression of HAUS1 was observed in glioma samples, which was associated with poorer survival of patients, unfavourable clinical characteristics, 1p/19q codeletion status, WHO grade, and IDH mutation status. Furthermore, multivariate and univariate Cox analyses revealed that HAUS1 was an independent predictor of glioma. HAUS1 expression level was associated with several tumour-infiltrating immune cells, such as Th2 cells, macrophages, and activated dendritic cells. The outcomes of ROC curve analysis showed that HAUS1 was good to prognosticate immune infiltrating levels in glioma with a higher area under the curve (AUC) value (AUC = 0.974). Conclusions: HAUS1 was upregulated and served as a biomarker for poor prognosis in patients with glioma. High HAUS1 expression was associated with several tumour-infiltrating immune cells such as Th2 cells, macrophages, and activated dendritic cells, which had high infiltration levels. Therefore, these findings suggest that HAUS1 is a potential biomarker for predicting the prognosis of patients with glioma and plays a pivotal role in immune infiltration in glioma.

Proteomic and clinical biomarkers for acute mountain sickness in a longitudinal cohort.

Ascending to high-altitude by non-high-altitude natives is a well-suited model for studying acclimatization to extreme environments. Acute mountain sickness (AMS) is frequently experienced by visitors. The diagnosis of AMS mainly depends on a self-questionnaire, revealing the need for reliable biomarkers for AMS. Here, we profiled 22 AMS symptom phenotypes, 65 clinical indexes, and plasma proteomic profiles of AMS via a combination of proximity extension assay and multiple reaction monitoring of a longitudinal cohort of 53 individuals. We quantified 1069 proteins and validated 102 proteins. Via differential analysis, machine learning, and functional association analyses. We found and validated that RET played an important role in the pathogenesis of AMS. With high-accuracies (AUCs > 0.9) of XGBoost-based models, we prioritized ADAM15, PHGDH, and TRAF2 as protective, predictive, and diagnostic biomarkers, respectively. Our findings shed light on the precision medicine for AMS and the understanding of acclimatization to high-altitude environments.

The active lung microbiota landscape of COVID-19 patients through the metatranscriptome data analysis.

Introduction: With the outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the interaction between the host and SARS-CoV-2 was widely studied. However, it is unclear whether and how SARS-CoV-2 infection affects lung microflora, which contribute to COVID-19 complications. Methods: Here, we analyzed the metatranscriptomic data of bronchoalveolar lavage fluid (BALF) of 19 COVID-19 patients and 23 healthy controls from 6 independent projects and detailed the active microbiota landscape in both healthy individuals and COVID-19 patients. Results: The infection of SARS-CoV-2 could deeply change the lung microbiota, evidenced by the α-diversity, ß-diversity, and species composition analysis based on bacterial microbiota and virome. Pathogens (e.g., Klebsiella oxytoca causing pneumonia as well), immunomodulatory probiotics (e.g., lactic acid bacteria and Faecalibacterium prausnitzii, a butyrate producer), and Tobacco mosaic virus (TMV) were enriched in the COVID-19 group, suggesting a severe microbiota dysbiosis. The significant correlation between Rothia mucilaginosa, TMV, and SARS-CoV-2 revealed drastic inflammatory battles between the host, SARS-CoV-2, and other microbes in the lungs. Notably, TMV only existed in the COVID-19 group, while human respirovirus 3 (HRV 3) only existed in the healthy group. Our study provides insights into the active microbiota in the lungs of COVID-19 patients and would contribute to the understanding of the infection mechanism of SARS-CoV-2 and the treatment of the disease and complications. Conclusion: SARS-COV-2 infection deeply altered the lung microbiota of COVID-19 patients. The enrichment of several other pathogens, immunomodulatory probiotics (lactic acid or butyrate producers), and TMV in the COVID-19 group suggests a complex and active lung microbiota disorder.

Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records.

BACKGROUND: Established prediction models of Diabetic kidney disease (DKD) are limited to the analysis of clinical research data or general population data and do not consider hospital visits. Construct a 3-year diabetic kidney disease risk prediction model in patients with type 2 diabetes mellitus (T2DM) using machine learning, based on electronic medical records (EMR). METHODS: Data from 816 patients (585 males) with T2DM and 3 years of follow-up at the PLA General Hospital. 46 medical characteristics that are readily available from EMR were used to develop prediction models based on seven machine learning algorithms (light gradient boosting machine [LightGBM], eXtreme gradient boosting, adaptive boosting, artificial neural network, decision tree, support vector machine, logistic regression). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best performing model. RESULTS: The LightGBM model had the highest AUC (0.815, 95% CI 0.747-0.882). Recursive feature elimination with random forest and SHAP plot based on LightGBM showed that older patients with T2DM with high homocysteine (Hcy), poor glycemic control, low serum albumin (ALB), low estimated glomerular filtration rate (eGFR), and high bicarbonate had an increased risk of developing DKD over the next 3 years. CONCLUSIONS: This study constructed a 3-year DKD risk prediction model in patients with T2DM and normo-albuminuria using machine learning and EMR. The LightGBM model is a tool with potential to facilitate population management strategies for T2DM care in the EMR era.

A design method based on Bayesian decision for routing-based digital microfluidic biochips.

With the continuous application and development of the digital microfluidic technology in various fields, many researchers have studied the design of digital microfluidic chips. Module-based chip design methods greatly simplify the design process but waste resources, including through the inadequate use of electrodes within the module and guard cells. To address this problem, a routing-based synthesis method based on a digital microfluidic biochip (DMFB) platform is presented. Routing-based DMFBs ensure a much higher chip utilization factor by removing the virtual modules on the chip and the extra electrodes needed as guard cells. Many previous works focused only on the problems of synthesis completion times, bioassay completion times, and electrode utilization rates. However, the reliability of chips has not been fully studied, and this factor is extremely important because faulty chips affect the test results. Thus, the influence of chip reliability should be fully considered. This paper proposes a design method based on Bayesian decision-making (BBD) for routing-based DMFBs that can fully consider the reliability of chips during the DMFB design process. Simulated experimental results showed that the method can address the reliability problems of chips. The efficiency and convergence performance of the algorithm were very good. The proposed method can achieve an average assay completion time that is shorter than those of the moduleless synthesis (MLS) and modified-MLS (MMLS) methods. The electrode usage rate of the proposed method is better than that of the module-based and improved Dijkstra and improved particle swarm optimization (ID-IPSO) methods.

Down-conversion luminescence and temperature sensing characteristics of LaSrGaO4 :Er3.

Erbium(III) ion (Er3+ ) has abundant energy levels that can emit light covering a quite broad wavelength range in many hosts. Here we synthesized LaSrGaO4 :Er3+ phosphors by a high-temperature solid-state method. Upon excitation at the ultraviolet (UV) band, LaSrGaO4 :Er3+ phosphors could emit green, red and near-infrared emission simultaneously. The temperature dependent emission characteristics of the as-prepared samples was then studied and two kinds of luminescent ratiometric thermometry were constructed. The first one is on the basis of two green emission bands that stems from the 2 H11/2 → 4 I15/2 and 4 S3/2 → 4 I15/2 transitions of Er3+ . The intensity ratio between these two emission bands was found to follow well with the Boltzmann distribution, and its maximum relative sensitivity was calculated to be 0.84% K-1 at 299 K. The other one depends on the 4 F9/2 → 4 I15/2 transition of Er3+ and self-luminescence of the host LaSrGaO4 , considering that these two emission lines have different temperature response. The relative sensitivity of this type of luminescence intensity ratio (LIR) thermometry could reach 1.86% K-1 at 299 K, we have successfully developed materials with one of the largest relative sensitivities to date, which provides some basis for the subsequent development of a new type of non-contact temperature sensor.