Structure of cryptophyte photosystem II-light-harvesting antennae supercomplex.

Cryptophytes are ancestral photosynthetic organisms evolved from red algae through secondary endosymbiosis. They have developed alloxanthin-chlorophyll a/c2-binding proteins (ACPs) as light-harvesting complexes (LHCs). The distinctive properties of cryptophytes contribute to efficient oxygenic photosynthesis and underscore the evolutionary relationships of red-lineage plastids. Here we present the cryo-electron microscopy structure of the Photosystem II (PSII)-ACPII supercomplex from the cryptophyte Chroomonas placoidea. The structure includes a PSII dimer and twelve ACPII monomers forming four linear trimers. These trimers structurally resemble red algae LHCs and cryptophyte ACPI trimers that associate with Photosystem I (PSI), suggesting their close evolutionary links. We also determine a Chl a-binding subunit, Psb-γ, essential for stabilizing PSII-ACPII association. Furthermore, computational calculation provides insights into the excitation energy transfer pathways. Our study lays a solid structural foundation for understanding the light-energy capture and transfer in cryptophyte PSII-ACPII, evolutionary variations in PSII-LHCII, and the origin of red-lineage LHCIIs.

Study on key parameters of buckling deformation instability and fracture of rock beams and asymmetric distribution law of stope stress.

The moving deformation of the strata and the redistribution of stope stress after mining show asymmetrical characteristics, which do not conform to the symmetrical structural characteristics of the original rock beam fracture. To further analyze the deformation of rock beams and the asymmetry law of stope pressure distribution after strata caving, the detailed process of instability and deformation of composite rock beams before failure was revealed through similar material simulation, theoretical analysis, and numerical simulation. Through similar simulation experiments, the structural characteristics of strata caving were observed. After excavation, the caving angle near the open-off cut side of the model is greater than that on the stop-mining line side. The maximum bending moment of the rock beam is located at the open-off cut side. The rock beam fracture is located on the partial open-off cut side in the middle of the rock beam. The rock beam on the open-off cut side is easy to shear slip and not easy to hinge. The rock beam in front of the advancing direction of the working face is easily hinged. Based on the structural characteristics of strata caving, considering the thickness of the composite rock beam, the two-hinged arch mechanical model for rock beam fracture is established. On this basis, the key parameters of rock beam instability and fracture such as limit load, additional horizontal stress, limit break distance, and break position are analyzed. Based on the deformation characteristics of two hinged arches, the caving structure and the asymmetric distribution mechanism of stress redistribution during the deformation of overburden in stope are explained. Finally, the deformation of rock beam and the asymmetry of stress distribution in stope are verified by numerical calculation. The results show that the concentrated stress value of the coal pillar at the open-off cut side is greater than that in front of the working face. There is a pressure relief area behind the working face, and the pressure relief area has a certain range. The range of stress concentration area, pressure relief area, and stress value tend to be stable, and only the range of the original rock stress zone expands when the working face is advanced to a certain distance. The asymmetric distribution of compaction stress in goaf is related to the buckling deformation of strata.

Uncertainty-guided label correction with wavelet-transformed discriminative representation enhancement.

Label noises, categorized into closed-set noise and open-set noise, are prevalent in real-world scenarios and can seriously hinder the generalization ability of models. Identifying noise is challenging because noisy samples closely resemble true positives. Existing approaches often assume a single noise source, oversimplify closed-set noise, or treat open-set noise as toxic and eliminate it, resulting in limited practical effects. To address these issues, we present a novel approach named uncertainty-guided label correction with wavelet-transformed discriminative representation enhancement (Ultra), designed to mitigate the effects of mixed noise. Specifically, our approach considers a more practical noise setting. To achieve robust mixed-noise identification, we initially look into a learnable wavelet filter for obtaining discriminative features and filtering spurious cues automatically at the representation level. Subsequently, we introduce a two-fold uncertainty estimation to stably locate noise within the corrupted supervised signal level. These insights pave the way for a simple yet potent label correction technique, enabling comprehensive utilization of open-set noise, which can be rendered non-toxic in a specific manner, in contrast to harmful closed-set noise. Experimental validation on datasets with synthetic mixed noise, web noise corruption, and a real-world dataset confirms the effectiveness and generality of Ultra. Furthermore, our approach enhances the application of efficient techniques (e.g., supervised contrastive learning) within label noise scenarios.

Analysis of the sodium pump subunit ATP1A3 in glioma patients: Potential value in prognostic prediction and immunotherapy.

The ATP1A3 gene is associated with the development and progression of neurological diseases. However, the pathological function and therapeutic value of ATP1A3 in glioblastoma (GBM) remains unknown. In this study, we tried to explore the correlation between the ATP1A3 gene expression and immune features in GBM samples. We found that ATP1A3 gene expression levels showed significant negative correlation with immune checkpoints such as PD-L1, CTLA-4 and IDO1. Next, ATP1A3 gene expression levels showed significant negative correlation with the anti-cancer immune cell process, the immune score and stromal score. By grouping ATP1A3 expression levels, we found that that immunomodulator-related genes and tumor-associated immune cell effector gene expression levels were associated with lower ATP1A3 expression. In addition, immunotherapy prediction pathway activity and a majority of the anti-cancer immune cell process activity levels were also showed to be correlated with lower ATP1A3 gene expression. Further, nine prognostic factors were identified by prognostic analysis, and a GBM prognostic model (risk score) was established. We applied the model to the TCGA GBM training set sample and the GSE4412 validation set sample and found that patients in the high risk score subgroup had significantly shorter survival time, demonstrating the prognostic value and prognostic efficacy of the risk score. Furthermore, ATP1A3 overexpression has also been found to sensitize cancer cells to anti-PD-1 therapy. In conclusion, we showed that ATP1A3 is a highly promising treatment target in GBM and the risk score is an independent prognostic factor for cancer and can be used to help guide the prediction of survival time in patients with GBM.

Microbial species pool-mediated diazotrophic community assembly in crop microbiomes during plant development.

Plant-associated diazotrophs strongly relate to plant nitrogen (N) supply and growth. However, our knowledge of diazotrophic community assembly and microbial N metabolism in plant microbiomes is largely limited. Here we examined the assembly and temporal dynamics of diazotrophic communities across multiple compartments (soils, epiphytic and endophytic niches of root and leaf, and grain) of three cereal crops (maize, wheat, and barley) and identified the potential N-cycling pathways in phylloplane microbiomes. Our results demonstrated that the microbial species pool, influenced by site-specific environmental factors (e.g., edaphic factors), had a stronger effect than host selection (i.e., plant species and developmental stage) in shaping diazotrophic communities across the soil-plant continuum. Crop diazotrophic communities were dominated by a few taxa (~0.7% of diazotrophic phylotypes) which were mainly affiliated with Methylobacterium, Azospirillum, Bradyrhizobium, and Rhizobium. Furthermore, eight dominant taxa belonging to Azospirillum and Methylobacterium were identified as keystone diazotrophic taxa for three crops and were potentially associated with microbial network stability and crop yields. Metagenomic binning recovered 58 metagenome-assembled genomes (MAGs) from the phylloplane, and the majority of them were identified as novel species (37 MAGs) and harbored genes potentially related to multiple N metabolism processes (e.g., nitrate reduction). Notably, for the first time, a high-quality MAG harboring genes involved in the complete denitrification process was recovered in the phylloplane and showed high identity to Pseudomonas mendocina. Overall, these findings significantly expand our understanding of ecological drivers of crop diazotrophs and provide new insights into the potential microbial N metabolism in the phyllosphere.IMPORTANCEPlants harbor diverse nitrogen-fixing microorganisms (i.e., diazotrophic communities) in both belowground and aboveground tissues, which play a vital role in plant nitrogen supply and growth promotion. Understanding the assembly and temporal dynamics of crop diazotrophic communities is a prerequisite for harnessing them to promote plant growth. In this study, we show that the site-specific microbial species pool largely shapes the structure of diazotrophic communities in the leaves and roots of three cereal crops. We further identify keystone diazotrophic taxa in crop microbiomes and characterize potential microbial N metabolism pathways in the phyllosphere, which provides essential information for developing microbiome-based tools in future sustainable agricultural production.

Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms.

OBJECTIVE: To construct a prognostic model of a breast cancer-related oxidative stress-related gene (OSRG) signature using machine learning algorithms. METHODS: The OSRGs of breast cancer were constructed by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The Cancer Genome Atlas (TCGA) was used to analyse the gene expression and prognostic value. The Human Protein Atlas was used to analyse the protein expression of hub genes. Receiver operating characteristic analysis, calibration curve and decision curve analysis were used to predict the stability of this model. RESULTS: The area under the curve of 1-, 3- and 5-year overall survival were 0.751, 0.707 and 0.645 in the TCGA training dataset; and 0.692, 0.678 and 0.602 in the TCGA testing dataset, respectively. Calibration plot showed good agreement between predicted probabilities and observed outcomes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) pathway analysis indicated that multiple cancer-related pathways were highly enriched in the high-risk group. Immune infiltration analysis showed immune cells and their functions may play a key role in the development and mechanism of breast cancer. CONCLUSIONS: This new OSRG signature was associated with the immune infiltration and it might be useful in predicting the prognosis in patients with breast cancer.

Lactylation-driven FTO targets CDK2 to aggravate microvascular anomalies in diabetic retinopathy.

Diabetic retinopathy (DR) is a leading cause of irreversible vision loss in working-age populations. Fat mass and obesity-associated protein (FTO) is an N6-methyladenosine (m6A) demethylase that demethylates RNAs involved in energy homeostasis, though its influence on DR is not well studied. Herein, we detected elevated FTO expression in vitreous fibrovascular membranes of patients with proliferative DR. FTO promoted cell cycle progression and tip cell formation of endothelial cells (ECs) to facilitate angiogenesis in vitro, in mice, and in zebrafish. FTO also regulated EC-pericyte crosstalk to trigger diabetic microvascular leakage, and mediated EC-microglia interactions to induce retinal inflammation and neurodegeneration in vivo and in vitro. Mechanistically, FTO affected EC features via modulating CDK2 mRNA stability in an m6A-YTHDF2-dependent manner. FTO up-regulation under diabetic conditions was driven by lactate-mediated histone lactylation. FB23-2, an inhibitor to FTO's m6A demethylase activity, suppressed angiogenic phenotypes in vitro. To allow for systemic administration, we developed a nanoplatform encapsulating FB23-2 and confirmed its targeting and therapeutic efficiency in mice. Collectively, our study demonstrates that FTO is important for EC function and retinal homeostasis in DR, and warrants further investigation as a therapeutic target for DR patients.

Angiogenic and Fibrogenic Dual-effect of Gremlin1 on Proliferative Diabetic Retinopathy.

Ocular angiogenic diseases, such as proliferative diabetic retinopathy (PDR), are often characterized by pathological new vessels and fibrosis formation. Anti-vascular endothelial growth factor (VEGF) therapy, despite of its efficiency to inhibit new vessels, has limitations, including drug resistance and retinal fibrosis. Here, we identified that Gremlin1, a novel angiogenesis and fibrosis inducer, was secreted from Müller glial cells, and its expression increased in the vitreous fluid from patients with PDR. Mechanistically, Gremlin1 triggered angiogenesis by promoting endothelial-mesenchymal transition via the EGFR/RhoA/ROCK pathway. In addition, Gremlin1 activated microglia to present profibrotic and fibrogenic properties. Further, anti-Gremlin1 antibody inhibited ocular angiogenesis and microglia fibrosis in mouse models. Collectively, Gremlin1 could be a potential therapeutic target in the treatment of ocular angiogenic diseases.

Association between Urinary Lead and Female Breast Cancer: A Population-Based Cross-Sectional Study.

BACKGROUND: Previous studies have explored the relationship between serum lead levels and the risk of female breast cancer (FBC). However, it is still uncertain whether urinary lead levels are associated with FBC. This study aimed to investigate the potential association between urinary lead and FBC. METHODS: A cross-sectional case-control study was conducted using the National Health and Nutrition Examination Survey (NHANES), which is a series of cross-sectional, nationally representative surveys of the United States population consisting of 10 survey waves from 1999 to 2018. This study analyzed a total of 2795 female participants (≥20 years), consisting of 210 participants with FBC and 2585 healthy controls. Urinary lead was detected using Inductively Coupled Plasma-Mass Spectrometry, which was divided into four levels by using quartiles-defining cut points. Multivariate logistic regression was used to analyze the association between urinary lead and FBC. RESULTS: Multivariate logistic regression revealed that urinary lead was positively correlated with FBC (Odds ratio [OR], 2.16; 95% confidence interval [CI]: [1.18, 3.95], p < 0.05) in a fully adjusted model. There were significantly increased ORs of FBC in quartile 4 (Q4) and quartile 3 (Q3), compared with the lowest quartile 1 (Q1) (Q4, OR = 1.48, 95% CI [0.89, 2.48]; Q3: OR = 1.01, 95% CI [0.59, 1.73], p for trend = 0.021). No significant interaction effects were observed between urinary lead levels and FBC between the subgroups (age, race, educational status, body mass index (BMI), marital status, family income to poverty ratio, hypertension status, diabetes status, renal function status, smoking history, ever been pregnant, oral contraceptive use, occupation classification, etc.) (All interaction p-value > 0.05). CONCLUSIONS: Urinary lead is likely positively associated with FBC in the US population.

Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study.

Introduction: Artificial intelligence (AI) technology has made rapid progress for disease diagnosis and triage. In the field of ophthalmic diseases, image-based diagnosis has achieved high accuracy but still encounters limitations due to the lack of medical history. The emergence of ChatGPT enables human-computer interaction, allowing for the development of a multimodal AI system that integrates interactive text and image information. Objective: To develop a multimodal AI system using ChatGPT and anterior segment images for diagnosing and triaging ophthalmic diseases. To assess the AI system's performance through a two-stage cross-sectional study, starting with silent evaluation and followed by early clinical evaluation in outpatient clinics. Methods and analysis: Our study will be conducted across three distinct centers in Shanghai, Nanjing, and Suqian. The development of the smartphone-based multimodal AI system will take place in Shanghai with the goal of achieving ≥90% sensitivity and ≥95% specificity for diagnosing and triaging ophthalmic diseases. The first stage of the cross-sectional study will explore the system's performance in Shanghai's outpatient clinics. Medical histories will be collected without patient interaction, and anterior segment images will be captured using slit lamp equipment. This stage aims for ≥85% sensitivity and ≥95% specificity with a sample size of 100 patients. The second stage will take place at three locations, with Shanghai serving as the internal validation dataset, and Nanjing and Suqian as the external validation dataset. Medical history will be collected through patient interviews, and anterior segment images will be captured via smartphone devices. An expert panel will establish reference standards and assess AI accuracy for diagnosis and triage throughout all stages. A one-vs.-rest strategy will be used for data analysis, and a post-hoc power calculation will be performed to evaluate the impact of disease types on AI performance. Discussion: Our study may provide a user-friendly smartphone-based multimodal AI system for diagnosis and triage of ophthalmic diseases. This innovative system may support early detection of ocular abnormalities, facilitate establishment of a tiered healthcare system, and reduce the burdens on tertiary facilities. Trial registration: The study was registered in ClinicalTrials.gov on June 25th, 2023 (NCT05930444).

Night-time radiative warming using the atmosphere.

Night-time warming is vital for human production and daily life. Conventional methods like active heaters are energy-intensive, while passive insulating films possess restrictions regarding space consumption and the lack of heat gain. In this work, a nanophotonic-based night-time warming strategy that passively inhibits thermal radiation of objects while actively harnessing that of atmosphere is proposed. By using a photonic-engineered thin film that exhibits high reflectivity (~0.91) in the atmospheric transparent band (8-14 µm) and high absorptivity (~0.7) in the atmospheric radiative band (5-8 and 14-16 µm), temperature rise of 2.1 °C/4.4 °C compared to typical low-e film and broadband absorber is achieved. Moreover, net heat loss as low as 9 W m-2 is experimentally observed, compared to 16 and 39 W m-2 for low-e film and broadband absorber, respectively. This strategy suggests an innovative way for sustainable warming, thus contributes to addressing the challenges of climate change and promoting global carbon neutrality.

Optical Coherence Tomography Angiography for Evaluation of Conjunctival Vessels in Dry Eyes.

Objective: This study aimed to evaluate conjunctival vessels in patients with dry eye disease (DED) using optical coherence tomography angiography (OCTA). Methods: This was a cross-sectional, observational clinical study. Twenty-three eyes of 18 patients with DED and 28 eyes of 23 healthy controls were included for examination in this study. The evaluation included the application of an Ocular Surface Disease Index Questionnaire, Schirmer Basic Secretion Test, and anterior OCTA targeting the temporal conjunctiva. AngioTool software was used to quantify the total vessel length and vessel density in the 3 × 3 mm temporal region of interest. Results: Blood vessel density measurements were compared across the OCTA systems. The total vessel length within the conjunctiva of the DED group (4799.34 ± 834.36) exceeded that of the control eye (3864.89 ± 1455.70) group (P < 0.05). However, the difference in vessel density between the two groups was not statistically significant. Conclusion: Measurement and analysis of conjunctival blood vessels using OCTA exhibited robust repeatability. In dry eyes, the total number of conjunctival blood vessels increased in accordance with disease severity. Hypoxia of conjunctival tissue may be an important cause of dry eye disease.

Whole-infrared-band camouflage with dual-band radiative heat dissipation.

Advanced multispectral detection technologies have emerged as a significant threat to objects, necessitating the use of multiband camouflage. However, achieving effective camouflage and thermal management across the entire infrared spectrum, especially the short-wave infrared (SWIR) band, remains challenging. This paper proposes a multilayer wavelength-selective emitter that achieves effective camouflage across the entire infrared spectrum, including the near-infrared (NIR), SWIR, mid-wave infrared (MWIR), and long-wave infrared (LWIR) bands, as well as the visible (VIS) band. Furthermore, the emitter enables radiative heat dissipation in two non-atmospheric windows (2.5-3 µm and 5-8 µm). The emitter's properties are characterized by low emittance of 0.270/0.042/0.218 in the SWIR/MWIR/LWIR bands, and low reflectance of 0.129/0.281 in the VIS/NIR bands. Moreover, the high emittance of 0.742/0.473 in the two non-atmospheric windows ensures efficient radiative heat dissipation, which results in a temperature decrement of 14.4 °C compared to the Cr reference at 2000 W m-2 input power density. This work highlights the role of solar radiance in camouflage, and provides a comprehensive guideline for developing multiband camouflage compatible with radiative heat dissipation, from the visible to LWIR.

Stable Pyrene-Based Metal-Organic Framework for Cyclization of Propargylic Amines with CO2 and Detection of Antibiotics in Water.

A pyrene-based metal-organic framework, Cd2(PTTB)(H2O)2 (WYU-11), was synthesized from the tetracarboxylic pyrene ligand H4PTTB (H4PTTB = 1,3,6,8-tetrakis(3-carboxyphenyl)pyrene) and Cd(NO3)2·4H2O. Powder X-ray diffraction analysis discloses that the framework is stable in acid, base, and various organic solvent environments. WYU-11 shows excellent catalytic performance on the cyclization reaction of propargylic amines with CO2 into 2-oxazolidinones under mild conditions (60 °C, atmospheric CO2). 1H NMR studies unveiled that WYU-11 and 1,1,3,3-tetramethylguanidine (TMG) can synergistically activate the propargylic amine substrate and promote the reaction. Importantly, WYU-11 represents a rare example of noble metal-free heterogeneous catalyst that can catalyze the cyclization of CO2 with propargylic amines. In addition, by virtue of the excellent water stability and luminescence properties, WYU-11 shows excellent detection performance for sulfathiazole (STZ) and ornidazole (ODZ) in water. Investigation reveals that the coexistence of photoinduced electron transfer and internal filtering effect could reasonably explain the luminescence quenching of WYU-11 by the antibiotics.

Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel disease.

BACKGROUND: Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVD classification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (DL) model based on susceptibility-weighted MR sequence (SWS) to detect CMBs and classify CSVD to assist neurologists in optimizing CSVD management. Patients with arteriolosclerosis (aSVD), cerebral amyloid angiopathy (CAA), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) treated at three centers were enrolled between January 2017 and May 2022 in this retrospective study. The SWSs of patients from two centers were used as the development set, and the SWSs of patients from the remaining center were used as the external test set. The DL model contains a Mask R-CNN for detecting CMBs and a multi-instance learning (MIL) network for classifying CSVD. The metrics for model performance included intersection over union (IoU), Dice score, recall, confusion matrices, receiver operating characteristic curve (ROC) analysis, accuracy, precision, and F1-score. RESULTS: A total of 364 SWS were recruited, including 336 in the development set and 28 in the external test set. IoU for the model was 0.523 ± 0.319, Dice score 0.627 ± 0.296, and recall 0.706 ± 0.365 for CMBs detection in the external test set. For CSVD classification, the model achieved a weighted-average AUC of 0.908 (95% CI 0.895-0.921), accuracy of 0.819 (95% CI 0.768-0.870), weighted-average precision of 0.864 (95% CI 0.831-0.897), and weighted-average F1-score of 0.829 (95% CI 0.782-0.876) in the external set, outperforming the performance of the neurologist group. CONCLUSION: The DL model based on SWS can detect CMBs and classify CSVD, thereby assisting neurologists in optimizing CSVD management.

Ambient Pressure Drying of Freeze-Cast Ceramics from Aqueous Suspension.

Freeze-casting has been wildly exploited to construct porous ceramics but usually requires costly and demanding freeze-drying (high vacuum, size limit, and supercooled chamber), which can be avoided by the ambient pressure drying (APD) technique. However, applying APD to freeze-cast ceramic based on an aqueous suspension is still challenging due to inert surface chemistry. Herein, a modified APD strategy is developed to improve the drying process of freeze-cast ceramics by exploiting the simultaneous ice etching, ionic cross-linking, and solvent exchange under mild conditions (-10-0 °C, ambient pressure). This versatile strategy is applicable to various ceramic species, metal ions, and freezing techniques. The incorporated metal ions not only enhance liquid-phase sintering, producing ceramics with higher density and mechanical properties than freeze-cast counterparts, but also render customizable coloration and antibacterial property. The cost-/time-efficient APD is promising for mass production and even successive production of large-size freeze-cast ceramics that exceed the size of commercial freeze-dryers.

Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation.

Electroencephalography-to-Text generation (EEG-to-Text), which aims to directly generate natural text from EEG signals has drawn increasing attention in recent years due to the enormous potential for Brain-computer interfaces. However, the remarkable discrepancy between the subject-dependent EEG representation and the semantic-dependent text representation poses a great challenge to this task. To mitigate this, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C- SCL), which effectively recalibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thereby reducing the discrepancy. Specifically, our C- SCL pulls semantically similar EEG representations together while pushing apart dissimilar ones. Besides, in order to introduce more meaningful contrastive pairs, we carefully employ curriculum learning to not only craft meaningful contrastive pairs but also make the learning progressively. We conduct extensive experiments on the ZuCo benchmark and our method combined with diverse models and architectures shows stable improvements across three types of metrics while achieving the new state-of-the-art. Further investigation proves not only its superiority in both the single-subject and low-resource settings but also its robust generalizability in the zero-shot setting. Our codes are available at: https://github.com/xcfcode/contrastive_eeg2text.

Insight into Cancer Immunity: MHCs, Immune Cells and Commensal Microbiota.

Cancer cells circumvent immune surveillance via diverse strategies. In accordance, a large number of complex studies of the immune system focusing on tumor cell recognition have revealed new insights and strategies developed, largely through major histocompatibility complexes (MHCs). As one of them, tumor-specific MHC-II expression (tsMHC-II) can facilitate immune surveillance to detect tumor antigens, and thereby has been used in immunotherapy, including superior cancer prognosis, clinical sensitivity to immune checkpoint inhibition (ICI) therapy and tumor-bearing rejection in mice. NK cells play a unique role in enhancing innate immune responses, accounting for part of the response including immunosurveillance and immunoregulation. NK cells are also capable of initiating the response of the adaptive immune system to cancer immunotherapy independent of cytotoxic T cells, clearly demonstrating a link between NK cell function and the efficacy of cancer immunotherapies. Eosinophils were shown to feature pleiotropic activities against a variety of solid tumor types, including direct interactions with tumor cells, and accessorily affect immunotherapeutic response through intricating cross-talk with lymphocytes. Additionally, microbial sequencing and reconstitution revealed that commensal microbiota might be involved in the modulation of cancer progression, including positive and negative regulatory bacteria. They may play functional roles in not only mucosal modulation, but also systemic immune responses. Here, we present a panorama of the cancer immune network mediated by MHCI/II molecules, immune cells and commensal microbiota and a discussion of prospective relevant intervening mechanisms involved in cancer immunotherapies.