Dynamic changes of the immune microenvironment in the development of gastric cancer caused by inflammation.

Precancerous lesions typically precede gastric cancer (GC), but the molecular mechanisms underlying the transition from these lesions to GC remain unclear. Therefore, it is urgent to understand this transition from precancerous lesions to GC, which is crucial for the early diagnosis and treatment of GC. In this study, we merged multiple single-cell RNA sequencing datasets to investigate the molecular changes in distinct cell types associated with the progression of GC. First, we observed an increasing abundance of immune cells and a decrease in non-immune cells from non-atrophic gastritis to GC. Five immune cell types were significantly enriched in GC compared to precancerous lesions. Moreover, we found that the interleukin (IL)-17 signaling pathway and Th17 cell differentiation were significantly up-regulated in immune cell subsets during GC progression. Some genes in these processes were predominantly expressed at the GC stage, highlighting their potential as diagnostic markers. Furthermore, we validated our findings using bulk RNA sequencing data from The Cancer Genome Atlas and confirmed consistent immune cell changes during GC progression. Our study provides insights into the immune infiltration and signaling pathways involved in the development of GC, contributing to the development of early diagnosis and targeted treatment strategies for this malignancy.

Artificial selection of mutations in two nearby genes gave rise to shattering resistance in soybean.

Resistance to pod shattering is a key domestication-related trait selected for seed production in many crops. Here, we show that the transition from shattering in wild soybeans to shattering resistance in cultivated soybeans resulted from selection of mutations within the coding sequences of two nearby genes - Sh1 and Pdh1. Sh1 encodes a C2H2-like zinc finger transcription factor that promotes shattering by repressing SHAT1-5 expression, thereby reducing the secondary wall thickness of fiber cap cells in the abscission layers of pod sutures, while Pdh1 encodes a dirigent protein that orchestrates asymmetric lignin distribution in inner sclerenchyma, creating torsion in pod walls that facilitates shattering. Integration analyses of quantitative trait locus mapping, genome-wide association studies, and allele distribution in representative soybean germplasm suggest that these two genes are primary modulators underlying this domestication trait. Our study thus provides comprehensive understanding regarding the genetic, molecular, and cellular bases of shattering resistance in soybeans.

Agonist Discovery for Membrane Proteins on Live Cells by Using DNA-encoded Libraries.

Identifying biologically active ligands for membrane proteins is an important task in chemical biology. We report an approach to directly identify small molecule agonists against membrane proteins by selecting DNA-encoded libraries (DELs) on live cells. This method connects extracellular ligand binding with intracellular biochemical transformation, thereby biasing the selection toward agonist identification. We have demonstrated the methodology with three membrane proteins: epidermal growth factor receptor (EGFR), thrombopoietin receptor (TPOR), and insulin receptor (INSR). A ∼30 million and a 1.033 billion-compound DEL were selected against these targets, and novel agonists with subnanomolar affinity and low micromolar cellular activities have been discovered. The INSR agonists activated the receptor by possibly binding to an allosteric site, exhibited clear synergistic effects with insulin, and activated the downstream signaling pathways. Notably, the agonists did not activate the insulin-like growth factor 1 receptor (IGF-1R), a highly homologous receptor whose activation may lead to tumor progression. Collectively, this work has developed an approach toward "functional" DEL selections on the cell surface and may provide a widely applicable method for agonist discovery for membrane proteins.

Scoping review protocol: what is the state of evidence for the use of communication apps with immigrant seniors in long-term care and community settings?

INTRODUCTION: First language care is critical for older immigrant adults with limited English proficiency, especially in long-term care settings where most residents require staff assistance and experience complex chronic conditions, resulting in multiple communication interactions where language poses a barrier. Although there are a myriad of cultural-language translation apps and devices available, there is a gap in both research and practice on the acceptability and feasibility of these digital resources within the context of long-term care and community settings for older immigrant adults, from a cultural relevance and digital health equity perspective. Our paper outlines a scoping review protocol to examine the state of the literature on the extent to which cultural-language translation apps are used in long-term care settings and community-based elder care. We will also examine the extent to which such apps bridge or further gaps in equitable, accessible and acceptable care for older immigrant adults with limited English language proficiency. METHODS AND ANALYSIS: This scoping review protocol will employ an adapted five-stage framework outlined by Arksey and O'Malley guided by enhancements recommended by Levac et al and Colquhoun et al. Using the Joanna Briggs Institute's population, concept and context framework, we defined the scope of the scoping review by identifying the target population, concepts for investigation and the context within which the research is situated. We will conduct a search of the literature from 2005 to 2024 using five bibliographic databases from health sciences (Healthstar OVID, MEDLINE OVID and Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO), engineering (Engineering Village Elsevier) and a cross-disciplinary database (Web of Science Clarivate). The research team will adopt a critical, equity-focused approach for the scoping review by integrating Richardson et al's framework for Digital Health Equity into our analysis of the findings. This will ensure that health and social equity perspectives are integrated within our methodology and analytical lens. Our analysis will specifically examine selected studies for their engagement with health equity and their ability to address issues such as ageism, ableism and the digital divide within geriatric care. ETHICS AND DISSEMINATION: Ethics approval is not required for this scoping review as it involves secondary analysis of published works and no primary data collection involving human subjects. Findings of the review will be shared with community partners and disseminated through publications, conferences and peer-reviewed publications.

Robotic surgery reduces the consumption of medical consumables: cost analysis of robotic pancreatic surgery from a tertiary hospital in China.

Robotic surgery has been increasingly adopted in various surgical fields, but the cost-effectiveness of this technology remains controversial due to its high cost and limited improvements in clinical outcomes. This study aims to explore the health economic implications of robotic pancreatic surgery, to investigate its impact on hospitalization costs and consumption of various medical resources. Data of patients who underwent pancreatic surgery at our institution were collected and divided into robotic and traditional groups. Statistical analyses of hospitalization costs, length of stay, costs across different service categories, and subgroup cost analyses based on age, BMI class, and procedure received were performed using t tests and linear regression. Although the total hospitalization cost for the robotic group was significantly higher than that for the traditional group, there was a notable reduction in the cost of medical consumables. The reduction was more prominent among elderly patients, obese patients, and those undergoing pancreaticoduodenectomy, which could be attributed to the technological advantages of the robotic surgery platform that largely facilitate blood control, tissue protection, and suturing. The study concluded that despite higher overall costs, robotic pancreatic surgery offers significant savings in medical consumables, particularly benefiting certain patient subgroups. The findings provide valuable insights into the economic viability of robotic surgery, supporting its adoption from a health economics perspective.

O-Carboxymethyl chitosan nanoparticles: A novel approach to enhance water stress tolerance in maize seedlings.

Water stress, a significant abiotic stressor, significantly hampers crop growth and yield, posing threat to food security. Despite the promising potential of nanoparticles (NPs) in enhancing plant stress tolerance, the precise mechanisms underlying the alleviation of water stress using O-Carboxymethyl chitosan nanoparticles (O-CMC-NPs) in maize remain elusive. In this study, we synthesized O-CMC-NPs and delved into their capacity to mitigate water stress (waterlogging and drought) in maize seedlings. Structural characterization revealed spherical O-CMC-NPs with a size of approximately 200 nm. These NPs accumulated near the seed embryo and root tip, resulting in a substantial increase in fresh and dry weights. The application of O-CMC-NPs to water-stressed maize seedlings remarkedly elevated the chlorophyll content and activity of various antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and polyphenol oxidase (PPO). The malondialdehyde (MDA) content was significantly reduced compared to the untreated control. Additionally, the expression of stress-responsive genes, such as ZmSOD, ZmCAT, ZmPOD, ZmTIFY, ZmACO, ZmPYL2, ZmNF-YC12, and ZmEREB180, were significantly upregulated in the O-CMC-NPs treated seedlings. These findings unveil the novel role of O-CMC-NPs in enhancing plant stress tolerance, suggesting their potential application in safeguarding maize seedlings under water stress conditions and facilitating the recovery from oxidative damage.

Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study.

INTRODUCTION: Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post-challenge hyperglycemia (IPH), that is., patients with normal fasting plasma glucose (<7.0 mmoL/L) and abnormal 2-h postprandial blood glucose (≥11.1 mmoL/L). We aimed to develop a model to differentiate individuals with IPH from the normal population. METHODS: Data from 54301 eligible participants were obtained from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a longitudinal (REACTION) study in China. Data from 37740 participants were used to develop the diagnostic system. External validation was performed among 16561 participants. Three machine learning algorithms were used to create the predictive models, which were further evaluated by various classification algorithms to establish the best predictive model. RESULTS: Ten features were selected to develop an IPH diagnosis system (IPHDS) based on an artificial neural network. In external validation, the AUC of the IPHDS was 0.823 (95% CI 0.811-0.836), which was significantly higher than the AUC of the Taiwan model [0.799 (0.786-0.813)] and that of the Chinese Diabetes Risk Score model [0.648 (0.635-0.662)]. The IPHDS model had a sensitivity of 75.6% and a specificity of 74.6%. This model outperformed the Taiwan and CDRS models in subgroup analyses. An online site with instant predictions was deployed at https://app-iphds-e1fc405c8a69.herokuapp.com/. CONCLUSIONS: The proposed IPHDS could be a convenient and user-friendly screening tool for diabetes during health examinations in a large general population.

Engineering Yeast Peroxisomes for α-Bisabolene Production from Sole Methanol with the Aid of Proteomic Analysis.

Microbial metabolic engineering provides a feasible approach to sustainably produce advanced biofuels and biochemicals from renewable feedstocks. Methanol is an ideal feedstock since it can be massively produced from CO2 through green energy, such as solar energy. However, engineering microbes to transform methanol and overproduce chemicals is challenging. Notably, the microbial production of isoprenoids from methanol is still rarely reported. Here, we extensively engineered Pichia pastoris (syn. Komagataella phaffii) for the overproduction of sesquiterpene α-bisabolene from sole methanol by optimizing the mevalonate pathway and peroxisomal compartmentalization. Furthermore, through label-free quantification (LFQ) proteomic analysis of the engineered strains, we identified the key bottlenecks in the peroxisomal targeting pathway, and overexpressing the limiting enzyme EfmvaE significantly improved α-bisabolene production to 212 mg/L with the peroxisomal pathway. The engineered strain LH122 with the optimized peroxisomal pathway produced 1.1 g/L α-bisabolene under fed-batch fermentation in shake flasks, achieving a 69% increase over that of the cytosolic pathway. This study provides a viable approach for overproducing isoprenoid from sole methanol in engineered yeast cell factories and shows that proteomic analysis can help optimize the organelle compartmentalized pathways to enhance chemical production.

Fugacity- and biotransformation-based mechanistic insights into the trophic transfer of organophosphate flame retardants in a subtropical coastal food web from the Northern Beibu Gulf of China.

The bioaccumulation and trophic transfer of organophosphate flame retardants (OPFRs) in marine ecosystems have attracted great attention in recent research, but our understanding of the trophic transfer mechanisms involved is limited. In this study, we investigated the trophodynamics of OPFRs and their metabolites in a subtropical coastal food web collected from the northern Beibu Gulf, China, and characterized their trophodynamics using fugacity- and biotransformation-based approaches. Eleven OPFRs and all seven metabolites were simultaneously quantified in the shellfish, crustacean, pelagic fish, and benthic fish samples, with total concentrations ranging from 164 to 4.11 × 104 and 4.56-4.28 × 103 ng/g lipid weight, respectively. Significant biomagnification was observed only for tris (phenyl) phosphate (TPHP) and tris (2-ethylhexyl) phosphate (TEHP), while other compounds except for tris(2-chloroethyl) phosphate (TCEP) displayed biomagnification trends based on Monte Carlo simulations. Using a fugacity-based approach to normalize the accumulation of OPFRs in biota to their relative biological phase composition, storage lipid is the predominant biological phase for the mass distribution of 2-ethylhexyl diphenyl phosphate (EHDPHP) and TPHP. The water content and structure protein are equally important for TCEP, whereas lipid and structure protein are the two most important phases for other OPFRs. The mass distribution of these OPFRs along with TLs can explain their trophodynamics in the food web. The organophosphate diesters (as OPFR metabolites) also displayed biomagnification trends based on bootstrapped estimation. The correlation analysis and Korganism-water results jointly suggested the metabolites accumulation in high-TL organisms was related to biotransformation processes. The metabolite-backtracked trophic magnification factors for tri-n­butyl phosphate (TNBP) and TPHP were both greater than the values that accounted for only the parent compounds. This study highlights the incorporation of fugacity and biotransformation analysis to characterize the trophodynamic processes of OPFRs and other emerging pollutants in food webs.

Metabolic activity of gut microbial enrichment cultures from different marine species and their transformation abilities to plastic additives.

The role of the gut microbiota in host physiology has been previously elucidated for some marine organisms, but little information is available on their metabolic activity involved in transformation of environmental pollutants. This study assessed the metabolic profiles of the gut microbial cultures from grouper (Epinephelus coioides), green mussel (Perna viridis) and giant tiger prawn (Penaeus monodon) and investigated their transformation mechanisms to typical plastic additives. Community-level physiological profiling analysis confirmed the utilization profiles of the microbial cultures including carbon sources of carbohydrates, amines, carboxylic acids, phenolic compounds, polymers and amino acids, and the plastic additives of organophosphate flame retardants, tetrabromobisphenol A derivates and bisphenols. Using in vitro incubation, triphenyl phosphate (TPHP) was found to be rapidly metabolized into diphenyl phosphate by the gut microbiota as a representative ester-containing plastic additive, whereas the transformation of BPA (a representative phenol) was relatively slower. Interestingly, all three kinds of microbial cultures efficiently transformed the hepatic metabolite of BPA (BPA-G) back to BPA, thereby increasing its bioavailability in the body. The specific enzyme analysis confirmed the ability of the gut microbiota to perform the metabolic reactions. The results of 16S rRNA sequencing and network analysis revealed that the genera Escherichia-Shigella, Citrobacter, and Anaerospora were functional microbes, and their collaboration with fermentative microbes played pivotal roles in the transformation of the plastic additives. The structure-specific transformations by the gut microbiota and their distinct bioavailability deserve more attention in the future.

Removal of artificial sweeteners in wastewater treatment plants and their degradation during sewage sludge composting with micro- and nano-sized kaolin.

This study surveyed the fates of artificial sweeteners in influent, effluent, and sewage sludge (SS) in wastewater treatment plant, and investigated the effects of Micro-Kaolin (Micro-KL) and Nano-Kaolin (Nano-KL) on nitrogen transformation and sucralose (SUC) and acesulfame (ACE) degradation during SS composting. Results showed the cumulative rate of ACE and SUC in SS was ∼76 %. During SS composting, kaolin reduced NH3 emissions by 30.2-45.38 %, and N2O emissions by 38.4-38.9 %, while the Micro-KL and Nano-KL reduced nitrogen losses by 14.8 % and 12.5 %, respectively. Meanwhile, Micro-KL and Nano-KL increased ACE degradation by 76.8 % and 84.2 %, and SUC degradation by 75.3 % and 77.7 %, and significantly shifted microbial community structure. Furthermore, kaolin caused a positive association between Actinobacteria and sweetener degradation. Taken together, kaolin effectively inhibited nitrogen loss and promoted the degradation of ACE and SUC during the SS composting, which is of great significance for the removal of emerging organic pollutants in SS.

A DIRIGENT Gene GmDIR26 Regulates Pod Dehiscence in Soybean.

Pod dehiscence brings much loss for modern agricultural production, and multiple pod dehiscence components have been identified in many plant species. However, the pod dehiscence regulation factors in soybean are limited. In this study, we investigate the function of GmDIR26, a close homologues gene of pod dehiscence genes GmPdh1, PvPdh1, and CaPdh1, in the regulation of pod dehiscence in soybean. The secondary and tertiary structure analysis reveals that GmDIR26 protein has a similar structure with GmPdh1, PvPdh1, and CaPdh1 proteins. Synteny analysis of soybean and chickpea genomes shows that the genomic region surrounding GmDIR26 and CaPdh1 might be evolved from the same ancestor, and these two genes might have similar function. GmDIR26 shows an increased expression pattern during pod development and reaches a peak at beginning seed stage. Meanwhile, GmDIR26 exhibits high expression levels in dorsal suture and pod wall, but low expression pattern in ventral suture. In addition, GmDIR26 shows higher expression levels in pod dehiscence genotype than that in pod indehiscence accessions. Overexpression of GmDIR26 in soybean increases pod dehiscence in transgenic plants, of which the lignin layer in inner sclerenchyma pods is thicker and looser. The expression levels of several pod dehiscence genes are altered. Our study provides important information for further modification of pod dehiscence resistance soybean and characterization of soybean pod dehiscence regulation network.

A new Schizophyllum commune strain as a potential biocontrol agent against blueberry root rot.

In recent years, blueberry root rot has been caused mainly by Fusarium commune, and there is an urgent need for a green and efficient method to control this disease. To date, research on Schizophyllum commune has focused on antioxidant mechanisms, reactive dye degradation, etc., but the mechanism underlying the inhibition of pathogenic microorganisms is still unclear. Here, the control effects of S. commune on F. commune and blueberry root rot were studied using adversarial culture, tissue culture, and greenhouse pot experiments. The results showed that S. commune can dissolve insoluble phosphorus and secrete various extracellular hydrolases. The results of hyphal confrontation and fermentation broth antagonism experiments showed that S. commune had a significant inhibitory effect on F. commune, with inhibition rates of 70.30% and 22.86%, respectively. Microscopy results showed distortion of F. commune hyphae, indicating that S. commune is strongly parasitic. S. commune had a significant growth-promoting effect on blueberry tissue-cultured seedlings. After inoculation with S. commune, inoculation with the pathogenic fungus, or inoculation at a later time, the strain significantly reduced the root rot disease index in the potted blueberry seedlings, with relative control effects of 79.14% and 62.57%, respectively. In addition, S. commune G18 significantly increased the antioxidant enzyme contents in the aboveground and underground parts of potted blueberry seedlings. We can conclude that S. commune is a potential biocontrol agent that can be used to effectively control blueberry root rot caused by F. commune in the field.

Factors Influencing Delayed Onset of Lactogenesis: A Scoping Review.

Purpose: Delayed onset of lactogenesis is a significant barrier to achieving the WHO-recommended 50% exclusive breastfeeding rate in the first six months. This study maps the main factors influencing this condition, addressing gaps in the current research landscape. Methods: Following Arksey and O'Malley's scoping review framework, databases such as PubMed, Web of Science (WOS), Embase, Cochrane Library, CINAHL plus with full text, China National Knowledge Infrastructure (CNIK), Weipu Chinese Journal Service Platform (VIP), Wanfang Data Knowledge Service Platform, and China Biomedical Literature Database (CBM) were searched on February 1, 2023. Studies in Chinese and English involving pregnant and postpartum women, focusing on delayed onset of lactogenesis, were included without restrictions on publication date or geography. Results: Forty-six studies published between 2002 and 2022 met the inclusion criteria, revealing variable incidences of delayed lactogenesis among different groups. Thirty-four influencing factors were identified and organized into five themes: maternal-infant characteristics, perinatal mental state, physical activity participation during pregnancy, breastfeeding behaviors, and medical staff interventions. Within eighteen major factors highlighted, factors such as age, pre-pregnancy BMI, gestational weight gain, average LATCH score within 24 hours postpartum, labor analgesia, sleep, frequency of postpartum breastfeeding, and timing of initial breast suckling/pumping showed inconsistent or conflicting conclusions. Conclusion: High and variable incidences of delayed lactogenesis underline its multifactorial nature. Effective interventions require strong advocacy from healthcare professionals and adherence by pregnant women. Further research using standardized methods is essential to clarify inconsistent or conflicting findings on the influencing factors.

Ultrastrong and ductile medium-entropy alloys via hierarchical ordering.

Long-range ordered phases in most high-entropy and medium-entropy alloys (HEAs/MEAs) exhibit poor ductility, stemming from their brittle nature of complex crystal structure with specific bonding state. Here, we propose a design strategy to severalfold strengthen a single-phase face-centered cubic (fcc) Ni2CoFeV MEA by introducing trigonal κ and cubic L12 intermetallic phases via hierarchical ordering. The tri-phase MEA has an ultrahigh tensile strength exceeding 1.6 GPa and an outstanding ductility of 30% at room temperature, which surpasses the strength-ductility synergy of most reported HEAs/MEAs. The simultaneous activation of unusual dislocation multiple slip and stacking faults (SFs) in the κ phase, along with nano-SF networks, Lomer-Cottrell locks, and high-density dislocations in the coupled L12 and fcc phases, contributes to enhanced strain hardening and excellent ductility. This work offers a promising prototype to design super-strong and ductile structural materials by harnessing the hierarchical ordered phases.

Expand available targets for CAR-T therapy to overcome tumor drug resistance based on the "Evolutionary Traps".

Based on the concept of "Evolutionary Traps", targeting survival essential genes obtained during tumor drug resistance can effectively eliminate resistant cells. While, it still faces limitations. In this study, lapatinib-resistant cells were used to test the concept of "Evolutionary Traps" and no suitable target stand out because of the identified genes without accessible drug. However, a membrane protein PDPN, which is low or non-expressed in normal tissues, is identified as highly expressed in lapatinib-resistant tumor cells. PDPN CAR-T cells were developed and showed high cytotoxicity against lapatinib-resistant tumor cells in vitro and in vivo, suggesting that CAR-T may be a feasible route for overcoming drug resistance of tumor based on "Evolutionary Trap". To test whether this concept is cell line or drug dependent, we analyzed 21 drug-resistant tumor cell expression profiles reveal that JAG1, GPC3, and L1CAM, which are suitable targets for CAR-T treatment, are significantly upregulated in various drug-resistant tumor cells. Our findings shed light on the feasibility of utilizing CAR-T therapy to treat drug-resistant tumors and broaden the concept of the "Evolutionary Trap".

Severity of COVID-19 infection in patients with COVID-19 combined with diabetes.

PURPOSE: This study aimed to analyse the correlation between blood glucose control and the severity of COVID-19 infection in patients with diabetes. METHODS: Clinical and imaging data of a total of 146 patients with diabetes combined with COVID-19 who visited our hospital between December 2022 and January 2023 were retrospectively collected. The patients were divided into the 'good blood glucose control' group and the 'poor blood glucose control' group based on an assessment of their blood glucose control. The clinical data, computed tomography (CT) appearance and score and the severity of COVID-19 infection of the two groups were compared, with the severity of COVID-19 infection being the dependent variable to analyse other influencing factors. RESULTS: The group with poor blood glucose control showed a higher lobar involvement degree and total CT severity score (CTSS) than the group with good blood glucose control (13.30 ± 5.25 vs. 10.38 ± 4.84, p < 0.05). The two groups exhibited no statistically significant differences in blood lymphocyte, leukocyte, C-reaction protein, pleural effusion, consolidation, ground glass opacity or crazy-paving signs. Logistic regression analysis showed that the total CTSS significantly influences the clinical severity of patients (odds ratio 1.585, p < 0.05), whereas fasting plasma glucose and blood glucose control are not independent factors influencing clinical severity (both p > 0.05). The area under the curve (AUC) of CTSS prediction of critical COVID-19 was 0.895 with sensitivity of 79.3% and specificity of 88.1% when the threshold value is 12. CONCLUSION: Blood glucose control is significantly correlated with the CTSS; the higher the blood glucose is, the more severe the lung manifestation. The CTSS can also be used to evaluate and predict the clinical severity of COVID-19.

CRISPR screens in mechanism and target discovery for AML.

CRISPR-based screens have discovered novel functional genes involving in diverse tumor biology and elucidated the mechanisms of the cancer pathological states. Recently, with its randomness and unbiasedness, CRISPR screens have been used to discover effector genes with previously unknown roles for AML. Those novel targets are related to AML survival resembled cellular pathways mediating epigenetics, synthetic lethality, transcriptional regulation, mitochondrial and energy metabolism. Other genes that are crucial for pharmaceutical targeting and drug resistance have also been identified. With the rapid development of novel strategies, such as barcodes and multiplexed mosaic CRISPR perturbation, more potential therapeutic targets and mechanism in AML will be discovered. In this review, we present an overview of recent progresses in the development of CRISPR-based screens for the mechanism and target identification in AML and discuss the challenges and possible solutions in this rapidly growing field.

Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state.

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.