Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Objective:To use machine learning to predict the efficacy of accelerated corneal collagen cross-linking (A-CXL) surgery, identify prognostic factors, and construct models to predict postoperative disease progression.Methods:A single-center retrospective study was conducted.A total of 82 keratoconus patients (112 eyes) who underwent A-CXL surgery at the West China Hospital of Sichuan University between March and December 2021 were enrolled.Preoperative and follow-up examinations included anterior segment evaluation by slit-lamp microscopy, corneal topography using Pentacam, and corneal biomechanical indices using Corvis ST.Disease progression was defined as an increase in maximum keratometry (Kmax) of ≥1 D from the preoperative level at the last follow-up.Various machine learning algorithms were employed to analyze corneal topography, biomechanical parameters and corneal densitometry values to identify prognostic factors and construct models for predicting postoperative disease progression.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (No.2023496).Written informed consent was obtained from each subject.Results:During follow-up, 15.1% (17/112) of the eyes showed progression after A-CXL.The preoperative astigmatism and stress-strain index (SSI) in the progression group were (-5.41±2.72)D and 1.41±0.78, respectively, which were significantly higher than (-3.30±2.54)D and 0.95±0.98 in the non-progression group ( t=2.80, 2.03; both P<0.05).Cox regression analysis identified preoperative astigmatism (hazard ratio [HR]=1.20), SSI (HR=1.10), and anterior corneal densitometry of 2-6 mm (CDA6) (HR=2.10) as significant risk factors for post-A-CXL progression.Among various machine learning models developed and validated, the area under the curve (AUC) values for logistic regression, multilayer perceptron (MLP) model, and random forest (RF) exceeded 0.700.For F1-score, the AUC values for logistic regression, MLP, and RF were 0.870, 0.880, and 0.880, respectively.The network structure of the visualized MLP was a single-layer, 24-neurons neural network with 80% accuracy in predicting whether progression occurred after A-CXL.The clinical nomogram developed in conjunction with astigmatism, SSI, and CDA6 predicted the cumulative probability of progression at 0.5, 1, and 2 years postoperatively based on the sum of the specified values for each variable, and based on the optimal cutoff value, keratoconus corneas could be classified into high-, intermediate-, and low-risk groups, respectively.The time-dependent subject operating characteristic curves of the nomogram showed AUCs of 0.734, 0.685, and 0.935 at 0.5, 1, and 2 years postoperatively, respectively, all of which performed well in predicting progression. Conclusions:Preoperative astigmatism, SSI, and CDA6 are significant risk factors for post-A-CXL progression in keratoconus.The MLP model can accurately predict postoperative disease progression, and the clinical nomogram combining preoperative astigmatism, SSI, and CDA6 can effectively differentiate between low-, medium-, and high-risk postoperative progression outcomes.

Objective To develop a toolkit suitable for assisting community health institutions in the early identification and inter-vention of mild cognitive impairment(MCI)among older adults.Methods A literature review was conducted to construct a draft of the identification and intervention toolkit.Tools with an expert approval rate above 70%were included after expert consultation.The final version of the toolkit was developed by integrating these tools with officially recommended tools in China.Results The expert consultation yielded an authority coefficient of 0.84.The finalized toolkit included the assessment tools of Mini-Mental State Examination,Montreal Cognitive Assessment,General Practitioner Assessment of Cognition,Cognitive Abilities Screening Instrument and Clock Drawing Test,and 18 intervention measures in-cluding pharmacological treatment,cognitive training and psychological interventions,etc.Conclusion The MCI Identification-Intervention Toolkit may serve as a reference for guiding the identification and inter-vention of MCI among older adults for community health institutions.

Objective:To explore the effectiveness of nursing based on the Roy model in patients with Guillain-Barre syndrome (GBS) .Methods:Convenience sampling was used to select 55 patients with GBS at the First People's Hospital of Shangqiu between January 2020 and May 2024 as study subjects. Patients were divided into an intervention group ( n=28) and a control group ( n=27). Control group received conventional nursing, while intervention group received nursing based on Roy adaptation model. The recovery time, psychological state, and social support of the two groups of patients were compared. Results:Intervention group demonstrated shorter recovery times, lower anxiety and depression scores, and higher Social Support Rating Scale scores compared to control group, with all differences being statistically significant ( P<0.05) . Conclusions:The Roy model nursing in this study of GBS patients, effectively improves recovery outcomes, psychological state, and social support among GBS patients through comprehensive assessment, personalized nursing intervention, and ongoing evaluation.

Aim To explore the mechanism of the syn-ergistic effect of the ferroptosis inducer erastin com-bined with shikonin in promoting ferroptosis in human hypertrophic scar fibroblasts(HSFBs).Methods Hypertrophic scar tissues provided by the General Hos-pital of Ningxia Medical University were collected,and HSFBs were extracted.HSFBs were identified by HE staining and immunofluorescence.The inhibitory rates of Era and SHK on HSFBs at different concentrations were detected by CCK-8 assay,and the IC50 value was calculated.CompuSyn software was used to calculate the co-use index(CI).Control group,Erastin(Era)group,shikonin(SHK)group and Era+SHK group were set up,and the number and morphological chan-ges of cells were observed after 24 hours of interven-tion.The ability of cell migration and invasion was de-tected by scratch test and Transwell test.The changes of malondialdehyde(MDA),total iron ion and reactive oxygen species(ROS)were detected by corresponding biochemical kits.The expressions of collagen I,α-SMA and GOT1,SLC7A11,GPX4 and FTH1 were detected by Western blot.Results The IC50 value of Era and SHK of primary HSFBs was 2.22 μmol·L-1 and 3.94μmol·L-1 respectively,which was used as the single drug concentration for subsequent experiments.The CompuSyn software was employed to calculate the CI value when the two drugs were used in combination,and the concentrations corresponding to CI=0.39597(Era:1.2 μmol·L-1+SHK:1.5 μmol·L-1)were selected as subsequent combination concentrations(Because when CI was equal to 0.395 97,the concen-tration of each drug was lower than the concentration of single drug,and the inhibition rate of combined drug was greater than 50％).Compared with the monother-apy group,the number of HSFBs in the SHK+Era group was significantly reduced,cell membrane showed breakage and vesiculation,cell wrinkling became smal-ler,and cytoplasm was concentrated.The migration and invasion ability of HSFBs in the SHK+Era group were obviously weakened(P＜0.05),and the expres-sion of fibrosis-related proteins collagen Ⅰ and α-SMA was reduced(P＜0.05);the contents of MDA,total i-ron ions,and ROS in HSFBs of the SHK+Era group increased(P＜0.05),and the protein expression lev-els of SLC7A11,GOT1,GPX4,and FTH1 further de-creased(P＜0.05).Conclusions Erastin in combi-nation with shikonin can synergistically inhibit the pro-liferation,migration and fibrosis levels of HSFBs.The mechanism may be that erastin enhances the inhibition of shikotin on GOT1,increases the levels of cellular i-ron ions,ROS,and lipid peroxides,thereby promoting ferroptosis in HSFBs.

Objective:To use machine learning to predict the efficacy of accelerated corneal collagen cross-linking (A-CXL) surgery, identify prognostic factors, and construct models to predict postoperative disease progression.Methods:A single-center retrospective study was conducted.A total of 82 keratoconus patients (112 eyes) who underwent A-CXL surgery at the West China Hospital of Sichuan University between March and December 2021 were enrolled.Preoperative and follow-up examinations included anterior segment evaluation by slit-lamp microscopy, corneal topography using Pentacam, and corneal biomechanical indices using Corvis ST.Disease progression was defined as an increase in maximum keratometry (Kmax) of ≥1 D from the preoperative level at the last follow-up.Various machine learning algorithms were employed to analyze corneal topography, biomechanical parameters and corneal densitometry values to identify prognostic factors and construct models for predicting postoperative disease progression.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (No.2023496).Written informed consent was obtained from each subject.Results:During follow-up, 15.1% (17/112) of the eyes showed progression after A-CXL.The preoperative astigmatism and stress-strain index (SSI) in the progression group were (-5.41±2.72)D and 1.41±0.78, respectively, which were significantly higher than (-3.30±2.54)D and 0.95±0.98 in the non-progression group ( t=2.80, 2.03; both P<0.05).Cox regression analysis identified preoperative astigmatism (hazard ratio [HR]=1.20), SSI (HR=1.10), and anterior corneal densitometry of 2-6 mm (CDA6) (HR=2.10) as significant risk factors for post-A-CXL progression.Among various machine learning models developed and validated, the area under the curve (AUC) values for logistic regression, multilayer perceptron (MLP) model, and random forest (RF) exceeded 0.700.For F1-score, the AUC values for logistic regression, MLP, and RF were 0.870, 0.880, and 0.880, respectively.The network structure of the visualized MLP was a single-layer, 24-neurons neural network with 80% accuracy in predicting whether progression occurred after A-CXL.The clinical nomogram developed in conjunction with astigmatism, SSI, and CDA6 predicted the cumulative probability of progression at 0.5, 1, and 2 years postoperatively based on the sum of the specified values for each variable, and based on the optimal cutoff value, keratoconus corneas could be classified into high-, intermediate-, and low-risk groups, respectively.The time-dependent subject operating characteristic curves of the nomogram showed AUCs of 0.734, 0.685, and 0.935 at 0.5, 1, and 2 years postoperatively, respectively, all of which performed well in predicting progression. Conclusions:Preoperative astigmatism, SSI, and CDA6 are significant risk factors for post-A-CXL progression in keratoconus.The MLP model can accurately predict postoperative disease progression, and the clinical nomogram combining preoperative astigmatism, SSI, and CDA6 can effectively differentiate between low-, medium-, and high-risk postoperative progression outcomes.

Objective:To understand the evolution and variation characteristics of the H1N1 influenza virus in Wuxi City from 2018 to 2023.Methods:Real time PCR was used to perform nucleic acid testing on throat swab samples of influenza like cases sent to sentinel hospitals for testing. The influenza A (H1N1) pdm09 positive samples were subjected to cell culture, and nucleic acid was extracted from strains with a red blood cell agglutination test (HA) ≥1∶8. The whole genome sequence was amplified, and a library was constructed. The MiSeq sequencer was used for sequencing on the machine. Using NC_026431.1 as a reference strain, we analyzed the offline data using CLC Genomics Workbench (Version 23) software. MEGA 7.0 software was used to construct a phylogenetic tree, and NetNGlyc 1.0 Server software was used to predict N-glycosylation sites.Results:The nucleotide and amino acid homology between 38 strains of A (H1N1) pdm09 influenza virus from 2018 to 2023 were 96.06%-100% and 96.12%-100%, respectively. From February to May 2023, all 12 strains of A (H1N1) pdm09 had two amino acid mutation sites occurring in the HA antigenic determinant cluster, namely the Ca region (A203T) and the Cb region (K71Q). No mutations were found in the HA receptor binding site and NA resistance site. The strains from January to June 2018 belong to the 6B. 1A evolutionary branch, the strains from December 2018 to January 2020 belong to three evolutionary branches: 6B. 1A. 1, 6B. 1A. 5a, and 6B. 1A. 7, and the strains from February to May 2023 belong to the 6B. 1A. 5a. 2a evolutionary branch. 38 strains of A (H1N1) pdm09 HA gene all have 7 potential N-glycosylation sites, while NA gene has 7-8 potential N-glycosylation sites.Conclusions:There are characteristic amino acid mutation sites of H1N1 influenza A in Wuxi City from 2018 to 2023. The emergence of these mutation sites may affect the virus′s transmission and antigenic changes.

Lean six sigma (LSS) emphasizes patient demand-oriented, and continuously optimizing prolesses to achieve efficiency and standardization in medical services. Starting in 2021, a specialized children′s hospital (comprising one main campus and three branch campuses) introduced the LSS management method. Through define, measure, analyze, improve, and control, the hospital utilized tools such as questionnaire surveys, SIPOC models, and fishbone diagrams to identify pain points in the building of multi-campus child-friendly hospital and develop improvement measures.To address the main issues of insufficient cross-campus collaboration, significant disparities in service quality among different campuses, unclear functional positioning of each campus, and inadequate integration with social security mechanisms, the hospital implemented a " homogeneity-differentiation-coordination" management mode. The hospital implemented unified diagnosis and treatment standards and clinical pathways, carried out remote consultations and expert rotations, completed mutual recognition of examination and testing results, optimized child friendly labeling and child friendly environment, and built a " hospital-to-hospitals, hospital to medical schools, and hospital to community health centers" linkage platform, etc., to promote the standardization of diagnosis and treatment processes, clear functional positioning of the hospital area, and efficient resource allocation. Through practice, the outpatient appointment rate and patient satisfaction rate in the main hospital had increased from 86.72% and 98.64% in January December 2021 to 91.87% and 99.72% in January December 2024, respectively; The patient waiting time had been shortened from 26.54 minutes to 21.94 minutes, and the efficiency of medical treatment and service experience had been significantly improved. As of 2024, mutual recognition of 214 inspection and testing items had been achieved cross hospital campuses, forming a collaborative pattern of " main hospital leading, campuses support, and resource complementarity", and significantly improving the level of collaboration and child friendly connotation among multi-campus. This practice explored the integration path of multi-campus collaborative governance and child-friendly services, which could provide reference and inspiration for the similar hospitals.

Objective To develop a toolkit suitable for assisting community health institutions in the early identification and inter-vention of mild cognitive impairment(MCI)among older adults.Methods A literature review was conducted to construct a draft of the identification and intervention toolkit.Tools with an expert approval rate above 70%were included after expert consultation.The final version of the toolkit was developed by integrating these tools with officially recommended tools in China.Results The expert consultation yielded an authority coefficient of 0.84.The finalized toolkit included the assessment tools of Mini-Mental State Examination,Montreal Cognitive Assessment,General Practitioner Assessment of Cognition,Cognitive Abilities Screening Instrument and Clock Drawing Test,and 18 intervention measures in-cluding pharmacological treatment,cognitive training and psychological interventions,etc.Conclusion The MCI Identification-Intervention Toolkit may serve as a reference for guiding the identification and inter-vention of MCI among older adults for community health institutions.

Objective:To explore the effectiveness of nursing based on the Roy model in patients with Guillain-Barre syndrome (GBS) .Methods:Convenience sampling was used to select 55 patients with GBS at the First People's Hospital of Shangqiu between January 2020 and May 2024 as study subjects. Patients were divided into an intervention group ( n=28) and a control group ( n=27). Control group received conventional nursing, while intervention group received nursing based on Roy adaptation model. The recovery time, psychological state, and social support of the two groups of patients were compared. Results:Intervention group demonstrated shorter recovery times, lower anxiety and depression scores, and higher Social Support Rating Scale scores compared to control group, with all differences being statistically significant ( P<0.05) . Conclusions:The Roy model nursing in this study of GBS patients, effectively improves recovery outcomes, psychological state, and social support among GBS patients through comprehensive assessment, personalized nursing intervention, and ongoing evaluation.