Single-Cell Sequencing of Peripheral Mononuclear Cells Reveals Distinct Immune Response Landscapes of COVID-19 and Influenza Patients.

The coronavirus disease 2019 (COVID-19) pandemic poses a current world-wide public health threat. However, little is known about its hallmarks compared to other infectious diseases. Here, we report the single-cell transcriptional landscape of longitudinally collected peripheral blood mononuclear cells (PBMCs) in both COVID-19- and influenza A virus (IAV)-infected patients. We observed increase of plasma cells in both COVID-19 and IAV patients and XIAP associated factor 1 (XAF1)-, tumor necrosis factor (TNF)-, and FAS-induced T cell apoptosis in COVID-19 patients. Further analyses revealed distinct signaling pathways activated in COVID-19 (STAT1 and IRF3) versus IAV (STAT3 and NFκB) patients and substantial differences in the expression of key factors. These factors include relatively increase of interleukin (IL)6R and IL6ST expression in COVID-19 patients but similarly increased IL-6 concentrations compared to IAV patients, supporting the clinical observations of increased proinflammatory cytokines in COVID-19 patients. Thus, we provide the landscape of PBMCs and unveil distinct immune response pathways in COVID-19 and IAV patients.

Structural and mechanistic insights into fungal ß-1,3-glucan synthase FKS1.

The membrane-integrated synthase FKS is involved in the biosynthesis of ß-1,3-glucan, the core component of the fungal cell wall1,2. FKS is the target of widely prescribed antifungal drugs, including echinocandin and ibrexafungerp3,4. Unfortunately, the mechanism of action of FKS remains enigmatic and this has hampered development of more effective medicines targeting the enzyme. Here we present the cryo-electron microscopy structures of Saccharomyces cerevisiae FKS1 and the echinocandin-resistant mutant FKS1(S643P). These structures reveal the active site of the enzyme at the membrane-cytoplasm interface and a glucan translocation path spanning the membrane bilayer. Multiple bound lipids and notable membrane distortions are observed in the FKS1 structures, suggesting active FKS1-membrane interactions. Echinocandin-resistant mutations are clustered at a region near TM5-6 and TM8 of FKS1. The structure of FKS1(S643P) reveals altered lipid arrangements in this region, suggesting a drug-resistant mechanism of the mutant enzyme. The structures, the catalytic mechanism and the molecular insights into drug-resistant mutations of FKS1 revealed in this study advance the mechanistic understanding of fungal ß-1,3-glucan biosynthesis and establish a foundation for developing new antifungal drugs by targeting FKS.

CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data.

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low-dimensional representations of the lncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi-view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

Parallel T Cell Immunogenic Regions in Influenza B and A Viruses with Distinct Nuclear Export Signal Functions: The Balance between Viral Life Cycle and Immune Escape.

Influenza A viruses (IAVs) and influenza B viruses (IBVs) cause annual epidemics in human populations with seasonal circulation spikes. Peptide AM58-66GL9 located at residues 58-66 of M1 protein of IAVs has been recognized as an immunodominant T cell epitope with HLA-A*0201 restriction and broadly used as a positive reference in influenza immunity. This peptide also almost completely overlaps with a nuclear export signal (NES) 59-68 in IAV M1, which explains the limited escape mutations under the T cell immune pressure in this region. In this study, we investigated the potential immunogenicity and NES in the corresponding region of IBV. The long peptide covering this region can be recognized by specific T cells and induce robust expression of IFN-γ among HLA-B*1501 donors in vivo, but not in HLA-A*0201 donors. Among a series of truncated peptides derived from this region, we identified an immunodominant HLA-B*1501-restricted T cell epitope BM58-66AF9 (ALIGASICF) in the M1 protein of IBV. Furthermore, the structure of the HLA-B*1501/BM58-66AF9 complex shows that BM58-66AF9 performs a flat and featureless conformation that is similar to AM58-66GL9 presented by HLA-A*0201. In contrast with IAV, the sequence around residues 55-70 of IBV M1 does not contain an NES. Our comparative study on IBVs and IAVs provides new insights into the immune and evolution characteristics of IBVs and may shed light on vaccine development for influenza viruses.

Embryo sac development relies on symplastic signals from ovular integuments in Arabidopsis.

Ovules are female reproductive organs of angiosperms, consisting of sporophytic integuments surrounding female gametophytes, that is, embryo sacs. Synchronization between integument growth and embryo sac development requires intracellular communication. However, signaling routes through which cells of the two generations communicate are unclear. We report that symplastic signals through plasmodesmata (PDs) of integuments are critical for the development of female gametophytes. Genetic interferences of PD biogenesis either by functional loss of CHOLINE TRANSPORTER-LIKE1 (CTL1) or by integument-specific expression of a mutated CALLOSE SYNTHASE 3 (cals3m) compromised PD formation in integuments and reduced fertility. Close examination of pINO:cals3m or ctl1 ovules indicated that female gametophytic development was either arrested at various stages after the formation of functional megaspores. In both cases, defective ovules could not attract pollen tubes, leading to the failure of fertilization. Results presented here demonstrate a key role of the symplastic route in sporophytic control of female gametophytic development.

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD), with a preliminary investigation into their clinical significance and underlying molecular mechanisms. Transcriptome data for SARS-CoV-2 infection and LUAD were sourced from public databases. Differentially expressed genes (DEGs) associated with SARS-CoV-2 infection were identified and subsequently overlapped with TCGA-LUAD DEGs to discern the crosstalk genes (CGs). In addition, CGs pertaining to both diseases were further refined using LUAD TCGA and GEO datasets. Univariate Cox regression was conducted to identify genes associated with LUAD prognosis, and these genes were subsequently incorporated into the construction of a prognosis signature using 10 different machine learning algorithms. Additional investigations, including tumor mutation burden assessment, TME landscape, immunotherapy response assessment, as well as analysis of sensitivity to antitumor drugs, were also undertaken. We discovered the risk stratification based on the prognostic signature revealed that the low-risk group demonstrated superior clinical outcomes (p < 0.001). Gene set enrichment analysis results predominantly exhibited enrichment in pathways related to cell cycle. Our analyses also indicated that the low-risk group displayed elevated levels of infiltration by immunocytes (p < 0.001) and superior immunotherapy response (p < 0.001). In our study, we reveal a close association between CGs and the immune microenvironment of LUAD. This provides preliminary insight for further exploring the mechanism and interaction between the two diseases.

Enantiomer-Specific Colorimetric Tandem Assays for Salivary d-Alanine Associated with Gastric Cancer.

Salivary d-alanine (d-Ala) and d-proline (d-Pro) are of concern for their potential in the noninvasive diagnosis of gastric cancer (GC). Most reports have succeeded in determining the total concentration of d-Ala and d-Pro. However, for personalized diagnosis and better elucidation of the underlying specific correlation of d-Ala (or d-Pro) with GC, it is desirable to determine the specific concentration of d-Ala or d-Pro. Herein, we propose an enantiomer-specific tandem assay of d-Ala based on the colorimetric reaction between 2,4-dinitrophenylhydrazine and pyruvic acid generated from the deamination of d-Ala catalyzed by d-amino acid oxidase, which is easily distinguished from l-form amino acids, d-Pro, and many other species. A linear concentration range is established from 20 to 400 µmol/L with a limit of detection of 1.01 µmol/L. Real saliva sample tests reveal that the levels of d-Ala in GC cases are remarkably higher than those in healthy individuals, which offers a simple and low-cost strategy for GC diagnosis. Simultaneously, the total concentrations of d-Ala and d-Pro in saliva are determined. Hence, the concentration of d-Pro and the proportion of d-Ala could be calculated, which further provides more molecule- and individual-specific information. This research may offer a convenient method for noninvasive diagnosis of GC and pave a new route to explore the potentials of rare d-form amino acids in disease diagnosis and treatment.

Distance-related functional reorganization predicts motor outcome in stroke patients.

BACKGROUND: Analyzing distance-dependent functional connectivity density (FCD) yields valuable insights into patterns of brain activity. Nevertheless, whether alterations of FCD in non-acute stroke patients are associated with the anatomical distance between brain regions remains unclear. This study aimed to explore the distance-related functional reorganization in non-acute stroke patients following left and right hemisphere subcortical lesions, and its relationship with clinical assessments. METHODS: In this study, we used resting-state fMRI to calculate distance-dependent (i.e., short- and long-range) FCD in 25 left subcortical stroke (LSS) patients, 22 right subcortical stroke (RSS) patients, and 39 well-matched healthy controls (HCs). Then, we compared FCD differences among the three groups and assessed the correlation between FCD alterations and paralyzed motor function using linear regression analysis. RESULTS: Our findings demonstrated that the left inferior frontal gyrus displayed distance-independent FCD changes, while the bilateral supplementary motor area, cerebellum, and left middle occipital gyrus exhibited distance-dependent FCD alterations in two patient subgroups compared with HCs. Furthermore, we observed a positive correlation between increased FCD in the bilateral supplementary motor area and the motor function of lower limbs, and a negative correlation between increased FCD in the left inferior frontal gyrus and the motor function of both upper and lower limbs across all stroke patients. These associations were validated by using a longitudinal dataset. CONCLUSIONS: The FCD in the cerebral and cerebellar cortices shows distance-related changes in non-acute stroke patients with motor dysfunction, which may serve as potential biomarkers for predicting motor outcomes after stroke. These findings enhance our comprehension of the neurobiological mechanisms driving non-acute stroke. TRIAL REGISTRATION: All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (NCT05648552, registered 05 December 2022, starting from 01 January 2022).

Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype-genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes-receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)-of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.

Simultaneous reconstruction of 3D fluorescence distribution and object surface using structured light illumination and dual-camera detection.

Fluorescence molecular tomography (FMT) serves as a noninvasive modality for visualizing volumetric fluorescence distribution within biological tissues, thereby proving to be an invaluable imaging tool for preclinical animal studies. The conventional FMT relies upon a point-by-point raster scan strategy, enhancing the dataset for subsequent reconstruction but concurrently elongating the data acquisition process. The resultant diminished temporal resolution has persistently posed a bottleneck, constraining its utility in dynamic imaging studies. We introduce a novel system capable of simultaneous FMT and surface extraction, which is attributed to the implementation of a rapid line scanning approach and dual-camera detection. The system performance was characterized through phantom experiments, while the influence of scanning line density on reconstruction outcomes has been systematically investigated via both simulation and experiments. In a proof-of-concept study, our approach successfully captures a moving fluorescence bolus in three dimensions with an elevated frame rate of approximately 2.5 seconds per frame, employing an optimized scan interval of 5 mm. The notable enhancement in the spatio-temporal resolution of FMT holds the potential to broaden its applications in dynamic imaging tasks, such as surgical navigation.

Artificial intelligence in COPD CT images: identification, staging, and quantitation.

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

Pd-Catalyzed Cascade Cyclization/Thiocarbonylation with Thioformates: Synthesis of Thioester-Functionalized Oxindoles.

A Pd-catalyzed thiocarbonylative cyclization of N-(o-iodoaryl)acrylamides with easily accessible thioformates has been developed. The reaction has a wide substrate scope with good yields and represents a powerful route to the synthesis of thioester-functionalized oxindoles. Both S-aryl and alkyl thioformates as the thioester sources were well tolerated. The active Pd-CO intermediate may play an important role in the transformation process.

Validation of China Health-Related Outcomes Measures-Cardiovascular Disease.

OBJECTIVES: China Health-Related Outcomes Measures (CHROME) was an initiative aimed at developing a system of preference-based health-related quality of life instruments for China. CHROME-cardiovascular disease (CVD) is a CVD-specific instrument with 14 items developed under this initiative. This study aimed to test the psychometric properties of CHROME-CVD. METHODS: This validation study was conducted using cross-sectional questionnaire survey in China. Eligible patients with CVD were recruited and asked to complete the CHROME-CVD, the EQ-5D-5L, and a CVD-specific nonpreference-based health-related quality of life instrument selected according to the confirmed diagnosis of the patients. Item evaluation, internal consistency, measurement invariance, test-retest reliability, structural validity, and construct validity were tested using classic test theory. Item response theory was used to evaluate item-level performance. RESULTS: A total of 444 patients with CVD (coronary artery disease, n = 276, heart failure, n = 104, angina, n = 33, and atrial fibrillation, n = 16) from 6 provinces in China were enrolled for the validation. Exploratory factor analysis identified 4 factors: chest pain, other symptoms, physical health, and mental and social health. Cronbach's alpha and intraclass correlation coefficient were >0.8. A total of 20 of 26 (76.9%), and 90 of 95 (94.7%) predefined hypotheses were met for convergent and discriminant validities, respectively. No important difference was identified between subgroups of gender and residency. Response options of 10 items were found overlapped based on categorical response curves, which led to modification to 4-level response options. Wording of 3 items were modified by referring wordings of reference instruments. CONCLUSION: The validation of the CHROME-CVD demonstrated generally good psychometric properties. Further validation on the modified CHROME-CVD is needed.

Ligand-Induced Red-Emitting Copper Nanoclusters for Selective Fluorescence Determination of Aluminum Ions.

Monitoring levels of excessive aluminum ions (Al3+) is crucial as it can harm the immune system, reduce enzyme activity, cause cell death, and damage environmental and biological systems. Developing a fast and efficient Al3+ ion determination method is the key to addressing this issue. In this work, red-emitting fluorescent copper nanoclusters (CuNCs) were synthesized using N-acetyl-l-cysteine (NAC) as a ligand and CuCl2·2H2O through a facile procedure. The NAC-CuNCs exhibited a large Stokes shift and displayed remarkable luminescence properties. A method for detecting Al3+ through a fluorescence probe was proposed. Its fluorescence mechanism was also explored. The probe showed rapid responsiveness (within 1 min) to Al3+ ion determination. The detection limit for Al3+ was found to be 19.7 nM, which is significantly lower than the WHO's value and most reports, with a linear range of 0-52.9 µM. The determination of Al3+ concentrations in actual water using the fluorescence probe yielded satisfactory outcomes. Moreover, the visual detection of Al3+ ions was also achieved through a smartphone, which can enhance its fast and practical detection.

Fundamental Insights into the Direct Electron Transfer Mechanism on Ag Atomic Cluster.

The nonradical oxidation pathway for pollutant degradation in Fenton-like catalysis is favorable for water treatment due to the high reaction rate and superior environmental robustness. However, precise regulation of such reactions is still restricted by our poor knowledge of underlying mechanisms, especially the correlation between metal site conformation of metal atom clusters and pollutant degradation behaviors. Herein, we investigated the electron transfer and pollutant oxidation mechanisms of atomic-level exposed Ag atom clusters (AgAC) loaded on specifically crafted nitrogen-doped porous carbon (NPC). The AgAC triggered a direct electron transfer (DET) between the terminal oxygen (Oα) of surface-activated peroxodisulfate and the electron-donating substituents-containing contaminants (EDTO-DET), rendering it 11-38 times higher degradation rate than the reported carbon-supported metal catalysts system with various single-atom active centers. Heterocyclic substituents and electron-donating groups were more conducive to degradation via the EDTO-DET system, while contaminants with high electron-absorbing capacity preferred the radical pathway. Notably, the system achieved 79.5% chemical oxygen demand (COD) removal for the treatment of actual pharmaceutical wastewater containing 1053 mg/L COD within 30 min. Our study provides valuable new insights into the Fenton-like reactions of metal atom cluster catalysts and lays an important basis for revolutionizing advanced oxidation water purification technologies.

Enzyme-Responsive Micelles with High Drug-Loading Capacity for Antitumor Therapy.

To overcome the poor targeting of conventional chemotherapeutic drugs and the defects of low drug-loading capacity of conventional drug delivery systems, novel drug delivery systems with high drug-loading capacity are developed. Herein, the high drug-loaded mPEG79-GFLGDDD-DOX copolymer is first synthesized via an amide reaction, which can bond multiplex DOX. After PEGylation, the drug can resist the adsorption of proteins in the plasma in blood circulation, avoid being rapidly cleared out of the body, and prolong the circulation time of the drug in the blood, which is conducive to the enrichment of micelles in tumor tissues through the EPR effect. In tumor tissues, the peptide Glycine- Phenylalanine- Leucine- Glycine (GFLG) is recognized and sheared by overexpressed cathepsin B, which stripped the outer layer of methoxy polyethylene glycol (mPEG) and made it more readily available for uptake by tumor cells. After entering the tumor cells, the bonded DOX and the physically encapsulated DOX in the micelles played a synergistic role, realizing the killing of tumor cells, thus effectively enhancing the therapeutic effect on tumors. The findings in this work suggest that a high drug-loading drug delivery system has great potential in the clinical treatment of tumors.

A novel conserved B-cell epitope in pB602L of African swine fever virus.

African swine fever virus (ASFV) is a complex DNA virus and the only member of the Asfarviridae family. It causes high mortality and severe economic losses in pigs. The ASFV pB602L protein plays a key role in virus assembly and functions as a molecular chaperone of the major capsid protein p72. In addition, pB602L is an important target for the development of diagnostic tools for African swine fever (ASF) because it is a highly immunogenic antigen against ASFV. In this study, we expressed and purified ASFV pB602L and validated its immunogenicity in serum from naturally infected pigs with ASFV. Furthermore, we successfully generated an IgG2a κ subclass monoclonal antibody (mAb 7E7) against pB602L using hybridoma technology. Using western blot and immunofluorescence assays, mAb 7E7 specifically recognized the ASFV Pig/HLJ/2018/strain and eukaryotic recombinant ASFV pB602L protein in vitro. The 474SKENLTPDE482 epitope in the ASFV pB602L C-terminus was identified as the minimal linear epitope for mAb 7E7 binding, with dozens of truncated pB602l fragments characterized by western blot assay. We also showed that this antigenic epitope sequence has a high conservation and antigenic index. Our study contributes to improved vaccine and antiviral development and provides new insights into the serologic diagnosis of ASF. KEY POINTS: • We developed a monoclonal antibody against ASFV pB602L, which can specifically recognize the ASFV Pig/HLJ/2018/ strain. • This study found one novel conserved B-cell epitope 474SKENLTPDE482. • In the 3D structure, 474SKENLTPDE482 is exposed on the surface of ASFV pB602L, forming a curved linear structure.

Glutamic-acid grafted hyaluronic acid inhibits inflammatory factors via fibroblast and skin model tests.

BACKGROUND: Excessive inflammation may cause tissue damage and disrupt the function of the skin barrier. Hyaluronic acid (HA), an endogenous component, was found to regulate multiple inflammatory factors for skin health. This work aims to further enhance its efficacy by grafting amino acid onto its molecule. METHODS: Glutamic acid (Glu) was selected as the ligand to react with low-molecular-weight HA. Fibroblast tests and a 3D skin model were used to investigate the anti-inflammation efficacy of HA-Glu. RESULTS: For IL-1α, IL-6 and TNF-α, the grafted compound presents stronger inhibition ability versus native HA. Moreover, HA-Glu could promote the repair of damaged skin by improving the compactness of the stratum corneum and increasing the thickness of the living cell layer. CONCLUSION: The application of HA-Glu compound in skin care formulas would be effective to alleviate inflammation-induced skin symptoms and skin aging.

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning. METHODS: The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier. RESULTS: 104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87. CONCLUSIONS: The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

Construction and validation of a risk prediction model for postoperative ICU admission in patients with colorectal cancer: clinical prediction model study.

BACKGROUND: Transfer to the ICU is common following non-cardiac surgeries, including radical colorectal cancer (CRC) resection. Understanding the judicious utilization of costly ICU medical resources and supportive postoperative care is crucial. This study aimed to construct and validate a nomogram for predicting the need for mandatory ICU admission immediately following radical CRC resection. METHODS: Retrospective analysis was conducted on data from 1003 patients who underwent radical or palliative surgery for CRC at Ningxia Medical University General Hospital from August 2020 to April 2022. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Independent predictors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression in the training cohort to construct the nomogram. An online prediction tool was developed for clinical use. The nomogram's calibration and discriminative performance were assessed in both cohorts, and its clinical utility was evaluated through decision curve analysis (DCA). RESULTS: The final predictive model comprised age (P = 0.003, odds ratio [OR] 3.623, 95% confidence interval [CI] 1.535-8.551); nutritional risk screening 2002 (NRS2002) (P = 0.000, OR 6.129, 95% CI 2.920-12.863); serum albumin (ALB) (P = 0.013, OR 0.921, 95% CI 0.863-0.982); atrial fibrillation (P = 0.000, OR 20.017, 95% CI 4.191-95.609); chronic obstructive pulmonary disease (COPD) (P = 0.009, OR 8.151, 95% CI 1.674-39.676); forced expiratory volume in 1 s / Forced vital capacity (FEV1/FVC) (P = 0.040, OR 0.966, 95% CI 0.935-0.998); and surgical method (P = 0.024, OR 0.425, 95% CI 0.202-0.891). The area under the curve was 0.865, and the consistency index was 0.367. The Hosmer-Lemeshow test indicated excellent model fit (P = 0.367). The calibration curve closely approximated the ideal diagonal line. DCA showed a significant net benefit of the predictive model for postoperative ICU admission. CONCLUSION: Predictors of ICU admission following radical CRC resection include age, preoperative serum albumin level, nutritional risk screening, atrial fibrillation, COPD, FEV1/FVC, and surgical route. The predictive nomogram and online tool support clinical decision-making for postoperative ICU admission in patients undergoing radical CRC surgery. TRIAL REGISTRATION: Despite the retrospective nature of this study, we have proactively registered it with the Chinese Clinical Trial Registry. The registration number is ChiCTR2200062210, and the date of registration is 29/07/2022.