Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective To elucidate the mechanisms of trauma-induced coagulopathy(TIC),clarify the specific pathogenic factors and pathophysiological processes,and discover the effective diagnostic indicators and therapeutic targets.Methods Transcriptomic data of traumatic hemorrhagic shock patients were obtained from the Gene Expression Omnibus(GEO)to identify differentially expressed genes(DEGs).Coagulation-related genes(CRGs)from the Kyoto Encyclopedia of Genes and Genomes(KEGG)were intersected with DEGs.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO)and random forest(RF),were applied to identify key genes.The CIBERSORT algorithm was used to analyze the correlation between key genes and immune cell infiltration.Through consensus clustering,subtype analysis was conducted on trauma patients to compare the infiltration of immune cells.A rat model of traumatic hemorrhagic shock was established to validate coagulation function and the expression of key genes.Results The dataset included samples from 17 healthy controls and 478 patients with traumatic hemorrhagic shock.A total of 6 315 DEGs were identified under the screening criterion of corrected P<0.05.Gene set enrichment analysis(GSEA)showed that the up-regulated DEGs were significantly enriched in the glucose metabolism pathway,while the down-regulated DEGs were enriched in the immune reaction-related pathways.Through cross-analysis of DEGs and CRGs,a total of 65 differentially expressed coagulation-related genes(DE-CRGs)were screened out.GO functional enrichment showed that these genes were mainly located in secreting granular membranes and platelet α-granules,and were involved in physiological processes such as blood coagulation,regulation of body fluid levels,and wound healing.KEGG pathway analysis revealed that these genes were significantly enriched in pathways such as platelet activation,complement and coagulation cascade reactions,Rap1 signaling pathway,and human cytomegalovirus infection.Six key DE-CRGs were identified through machine learning.Receiver operating characteristic(ROC)curve analysis indicated that these genes had good diagnostic efficacy.CIBERSORT analysis revealed a significant correlation between key genes and immune cell infiltration.Patients were classified into two subtypes based on the six key genes:subtype A was rich in CD8+T cells and activated NK cells,presented an immune-active state;subtype B was mainly composed of monocytes and resting NK cells,with insufficient activation of immune pathways.Animal experiments on rats showed that hemorrhagic shock can lead to coagulation dysfunction.The results of qRT-PCR further confirmed that the expression trend of key genes was consistent with the results of bioinformatics analysis.Conclusion In this study,through transcriptomics and machine learning methods,six key genes closely related to TIC were systematically screened out,namely GNA13,PIK3R3,ITGAM,MAPK14,PPP1CC and LYN,and their close connections with coagulation function and immune infiltration were revealed.Animal experiments have further verified the value of these genes as potential diagnostic and therapeutic targets.

AIM:To analyze the expression of se-creted phosphoprotein 1(SPP1)and programmed cell death-ligand 1(PD-L1)in SMARCA4-deficient non-small cell lung cancer,and to provide a scientif-ic basis for the study of the follow-up treatment of this rare pathological type of lung cancer.METH-ODS:The clinical and pathological characteristics of 12 patients with this disease were analyzed retro-spectively,and the patients were divided into two groups of adenocarcinomas and poorly differentiat-ed carcinomas according to their morphological characteristics,and the relationship between the expression of SPP1 and PD-L1 was analyzed in the two groups.RESULTS:SPP1 expression was detect-ed in all patients and Its expression level was signif-icantly higher in the poorly differentiated carcino-ma group compared with the adenocarcinoma group(P=0.015);PD-L1 expression was found in 6/7 patients(5 cases were not measured),compared with the adenocarcinoma group,PD-L1 was also highly expressed in the poorly differentiated carci-noma group(P=0.048)and the PD-L1 difference be-tween the two groups suggested that the results were similar to those of SPP1.CONCLUSION:SMARCA4-deficient non-small cell lung cancer has high positive expression of SPP1 and PD-L1.It was more pronounced in patients with poorly differenti-ated carcinoma.There may be a positive correla-tion between SPP1 and PD-L1 expression in SMAR-CA4-deficient non-small cell lung cancer and the mechanism of the correlation needs to be further verified in subsequent studies.

AIM To investigate the effects of Rutong Ruanjian Tablets on angiogenesis in cancer tissues of rats with preneoplastic breast cancer(PBC).METHODS 60 female SD rats were randomly divided into a blank group of 10 rats and a model group of 50 rats for the establishment of the PBC models of Liver-Qi Stagnation and Blood Stasis Pattern with 9 weeks of oral administration of 7,12-dimethylbenz[a]anthracene(DMBA)and cervical ligation.After successful modeling,the rats were randomly divided into the model group,the tamoxifen group(3.2 mg/kg),the Rutong Ruanjian Tablets group(128 mg/kg),the 3,5-difluorobenzoyl group(DAPT,5 mg/kg),and the Rutong Ruanjian Tablets(128 mg/kg via gavage)+DAPT(5 mg/kg intraperitoneal injection)group,for 1 month corresponding drug administration,with 10 rats in each group.Then the rats had their cancer progression and syndrome scores observed;their angiogenesis evaluated by assessment of microvascular density(MVD);their vascular endothelial growth factor(VEGF)expression assessed by immunohistochemistry;and their mRNA and protein expressions of proteins related to the DLL4/Notch1/Hes1 pathway measured using RT-qPCR,immunohistochemistry and Western blot.RESULTS During carcinogenesis of rats induced by DMBA,there was gradual disappearance of E-cadherin expression and consistency of HE staining result with the PBC progression confirming the success of the modeling.Compared with the blank group,the model group showed increased MVD values,mRNA expression of Notch1 and Hes1,and protein expressions of VEGF,DLL4,Notch1 and Hes1(P＜0.05,P＜0.01).Compared with the model group,the Rutong Ruanjian Tablets group exhibited reduced MVD values,mRNA expression of Notch1 and Hes1,and protein expressions of VEGF,DLL4,Notch1 and Hes1(P＜0.05,P＜0.01).The Rutong Ruanjian Tablets+DAPT group showed reduced mRNA expression of Notch1 and Hes1,and protein expressions of DLL4,Notch1 and Hes1 compared to the Rutong Ruanjian Tablets group(P＜0.05,P＜0.01).CONCLUSION Rutong Ruanjian Tablets can inhibit angiogenesis and attenuate cancer progression in PBC rats of Liver-Qi Stagnation and Blood Stasis Pattern,and the mechanism may lie in the downregulation of DLL4/Notch1/Hes1 signaling pathway related proteins.

Objective To explore the advantages of high-frequency ultrasound-guided"cross-shaped"positioning method in accurate positioning and surgical effect in open reduction and internal fixation of rib fractures.Methods Retrospective analysis of 97 cases of multiple rib fractures treated surgically in our department from october 2019 to october 2024.Patients were divided into three groups based on different localization methods.The control group designed the surgical incision according to the results of chest three-dimensional reconstruction,the ultrasound group determined the incision based on the control group combined with high-frequency ultrasound,and Based on the method used in the ultrasound group,the"cross-localization"technique was combined to determine the incision in the cross-shaped group.The comparison of surgical-related data included fracture positioning accuracy,incision length of per singer rib,postoperative drainage volume,duration of drainage tube retention,postoperative pain score,operative time,intraoperative blood loss,length of hospital stay and incision infection rate.Additionally,commonly used inflammatory markers(white blood cell count,neutrophil percentage,CRP,IL-6,NF-κB)and pain-related biomarkers(PGE2,5-HT)were also included in the comparative analysis.Results In the control group,ultrasound group,and crossover group,aside from wound infections,the respective accuracies of fracture localization were 68.97％vs 87.50％vs 97.22％,the length of single rib incision was(3.50±1.23)cm vs(2.70±0.62)cm vs(2.45±0.58)cm,the volume of drainage on the day of surgery was(170.55±27.85)ml vs(150.69±24.67)ml vs(120.34±18.45)ml,the duration of drainage tube retention was(4.17±1.12)days vs(3.67±0.95)days vs(3.12±0.65)days,the postoperative pain scores were(6.86±2.15)vs(5.54±1.15)vs(4.89±1.53),the operation times were(75.84±15.62)minutes vs(67.74±11.85)minutes vs(57.35±9.36)minutes,the intraoperative blood loss was(85.78±11.78)ml vs(72.65±8.92)ml vs(62.23±9.63)ml,and the length of hospital stay was(8.42±1.47)days vs(7.12±1.14)days vs(6.56±1.32)days.The crossover group showed superior results in all metrics compared to the other two groups,with statistically significant differences(P＜0.05).The inflammatory and pain stress indicators for the control,ultrasound,and crossover groups were as follows:white blood cell counts[(16.39±4.15)× 109/Lvs(13.25±2.45)× 109/L vs(12.02±2.77)× 109/L],neutrophil percentages[(80.14±12.21)vs(72.36±10.34)vs(65.73±8.83)],CRP[(31.86±6.87)mg/L vs(27.72±5.65)mg/L vs(24.69±4.11)mg/L],IL-6[(46.35±11.42)pg/L vs(41.42±8.75)pg/L vs(35.53±9.51)pg/L],NF-B[(22.55±4.98)pg/L vs(17.34±3.62)pg/L vs(15.91±3.84)pg/L],PGE2[(240.37±21.65)ng/L vs(209.45±23.24)ng/L vs(180.21±18.72)ng/L],and 5-HT[(290.62±34.37)ng/L vs(270.85±26.98)ng/L vs(210.62±19.64)ng/L].The crossover group demonstrated statistically significant differences compared to both the control and ultrasound groups(P＜0.05).There was no statistically significant difference in incision infection among the three groups(P＞0.05).Conclusion The ultrasound-guided"cross"positioning method for rib fracture open reduction and internal fixation can more accurately locate the incision,shorten the incision length and hospitalization time,reduce postoperative inflammatory response and patient pain,and facilitate rapid recovery after surgery.

Objective:To explore the effect of Taichi motor imagination therapy combined with resistance exercise on the recovery of heart function and quality of life in patients with chronic heart failure(CHF).Methods:A total of 118 elderly CHF patients admitted to Huangshan Shoukang Hospital between January 2020 and December 2023 were included in this randomized controlled trial.Participants were randomly divided into intervention group(n=59)and control group(n=59).Participants in the control group received conventional anti-heart failure treatment,while those in the intervention group received additional Taichi motor imagination therapy and resistance exercise.The in-tervention lasted for 3 months.Cardiac function[left ventricular ejection fraction(LVEF),left ventricular end-diastolic diameter(LVEDd)and interventricular septal thickness at diastole(IVSTd)],quality of life[Minnesota Living with Heart Failure Questionnaire(MLHFQ)],markers of heart failure and neurohormones[B-type natri-uretic peptide(BNP),norepinephrine(NE),aldosterone(ALD)and angiotensin Ⅱ(AngⅡ)]levels and the inci-dence of major adverse cardiovascular events(MACE)during intervention were compared between the two groups.Results:Compared to those in the control group,participants in the intervention group had significant higher LVEF[(50.52±1.96)％vs.(45.84±2.21)％],and significant lower LVEDd[(57.12±1.72)mm vs.(60.45±2.14)mm],IVSTd[(8.84±0.82)mm vs.(10.00±1.19)mm],MLHFQ score[(28.86±5.34)points vs.(34.51±6.57)points],BNP[(9.23±1.35)pg/ml vs.(13.62±1.49)pg/ml],NE[(13.09±0.93)pmol/L vs.(16.63±0.86)pmol/L],ALD[(1.82±0.12)ng/L vs.(2.35±0.15)ng/L],AngⅡ[(0.75±0.07)ng/L vs.(0.90±0.06)ng/L]and incidence of MACE(6.90％vs.26.32％)(P＜0.01 all).Conclusion:Taichi motor imagination therapy combined with resistance exercise may improve the cardiac function and quality of life in CHF patients.

Objective To elucidate the mechanisms of trauma-induced coagulopathy(TIC),clarify the specific pathogenic factors and pathophysiological processes,and discover the effective diagnostic indicators and therapeutic targets.Methods Transcriptomic data of traumatic hemorrhagic shock patients were obtained from the Gene Expression Omnibus(GEO)to identify differentially expressed genes(DEGs).Coagulation-related genes(CRGs)from the Kyoto Encyclopedia of Genes and Genomes(KEGG)were intersected with DEGs.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO)and random forest(RF),were applied to identify key genes.The CIBERSORT algorithm was used to analyze the correlation between key genes and immune cell infiltration.Through consensus clustering,subtype analysis was conducted on trauma patients to compare the infiltration of immune cells.A rat model of traumatic hemorrhagic shock was established to validate coagulation function and the expression of key genes.Results The dataset included samples from 17 healthy controls and 478 patients with traumatic hemorrhagic shock.A total of 6 315 DEGs were identified under the screening criterion of corrected P<0.05.Gene set enrichment analysis(GSEA)showed that the up-regulated DEGs were significantly enriched in the glucose metabolism pathway,while the down-regulated DEGs were enriched in the immune reaction-related pathways.Through cross-analysis of DEGs and CRGs,a total of 65 differentially expressed coagulation-related genes(DE-CRGs)were screened out.GO functional enrichment showed that these genes were mainly located in secreting granular membranes and platelet α-granules,and were involved in physiological processes such as blood coagulation,regulation of body fluid levels,and wound healing.KEGG pathway analysis revealed that these genes were significantly enriched in pathways such as platelet activation,complement and coagulation cascade reactions,Rap1 signaling pathway,and human cytomegalovirus infection.Six key DE-CRGs were identified through machine learning.Receiver operating characteristic(ROC)curve analysis indicated that these genes had good diagnostic efficacy.CIBERSORT analysis revealed a significant correlation between key genes and immune cell infiltration.Patients were classified into two subtypes based on the six key genes:subtype A was rich in CD8+T cells and activated NK cells,presented an immune-active state;subtype B was mainly composed of monocytes and resting NK cells,with insufficient activation of immune pathways.Animal experiments on rats showed that hemorrhagic shock can lead to coagulation dysfunction.The results of qRT-PCR further confirmed that the expression trend of key genes was consistent with the results of bioinformatics analysis.Conclusion In this study,through transcriptomics and machine learning methods,six key genes closely related to TIC were systematically screened out,namely GNA13,PIK3R3,ITGAM,MAPK14,PPP1CC and LYN,and their close connections with coagulation function and immune infiltration were revealed.Animal experiments have further verified the value of these genes as potential diagnostic and therapeutic targets.