Background/Aims: Dysregulated cholesterol metabolism is a hallmark of hepatocellular carcinoma (HCC) that drives tumor initiation and progression. However, clinical targeting of cholesterol metabolism has yielded limited benefits due to stringent feedback in tumor cells. Identifying a central mediator capable of restoring cholesterol homeostasis within the cell’s intrinsically fine-tuned regulatory framework is urgently needed. Methods: We integrated a proteomic dataset from patients with cholesterol-dysregulated HCC into a global cholesterol metabolic regulatory network to identify potential therapeutic targets for disrupted cholesterol homeostasis. The prognostic significance of the candidate targets was further validated in an independent cohort through immunohistochemistry. Functional and mechanistic studies were conducted in vitro using HCC cell lines and in vivo using mouse models. The pharmacological efficacy of the candidate agent was evaluated in both subcutaneous and orthotopic HCC mouse models. Results: ER lipid raft-associated 1 (ERLIN1), a pivotal regulator of cholesterol metabolism reprogramming, was identified as an independent favorable prognostic indicator in HCC. ERLIN1 constrains HCC progression both in vitro and in vivo by stabilizing the INSIG1–SCAP–SREBP2 axis and maintaining the metabolic balance of intracellular cholesterol. Under hypoxia, impaired factor-inhibiting hypoxia-1-dependent hydroxylation of ASB11 at asparagine residues 90 and 92 enhances ASB11-mediated ERLIN1 degradation. Pharmacological targeting of this axis using zoledronic acid (ZoA) attenuated HCC progression by weakening the ASB11–ERLIN1 interaction and restoring cholesterol homeostasis. Conclusions ERLIN1 represents a druggable metabolic vulnerability in cholesterol-dysregulated HCC. Targeting the ASB11–ERLIN1 axis with the clinically approved ZoA reestablishes cholesterol homeostasis and offers a promising therapeutic strategy to overcome the current limitations of cholesterol-targeted HCC therapies.

Objective:To investigate the longitudinal trajectories of symptom clusters in elderly patients with laparoscopic surgery for colon cancer, and explore the predictive factors for each trajectories subgroup.Methods:Using a longitudinal survey, elderly patients with laparoscopic surgery for colon cancer patients from Shanxi Bethune Hospital from January 2021 to January 2024 were collected by convenient sampling method. The Chinese Version of the MD Anderson Symptom Inventory Gastrointestinal Cancer Module was used to conduct follow-up surveys of the selected patients at 2 weeks, 1 month, 3 months and 6 months after surgery. The symptom cluster was extracted by exploratory factor analysis, and the latent category growth model was conducted to identify the trajectory subgroups of each symptom cluster, the predictive factors of each trajectory subgroup was analyzed by multiple Logistic regression analysis.Results:A total of 118 cases were concluded in the present study, there were 69 males and 49 females with an age of (74.85 ± 3.29) years old. There were 4 symptom clusters after surgery, which were named as energy deficit symptom cluster, digestive tract symptom cluster, sleep mental symptom cluster, and psychological fatigue symptom cluster, the variance contribution rates were respectively 62.486%, 71.209%, 73.937%, 63.476%. The results from latent class growth model showed that there were 3 trajectory subgroups in the symptom cluster: high level-slow decline group accounted for 33.0% (39/118), moderate level-stable decline group accounted for 39.0% (46/118), low level-rapid decline group accounted for 28.0% (33/118). College education or above tends to developed into low level-rapid decline group ( OR=0.365, 95% CI 0.083-0.603, P<0.05), while patients undergoing traditional laparoscopic surgery ( OR=3.679, 95% CI 1.297-4.432, P<0.05) and primary tumor stages of Ⅲ-Ⅳ were more likely to developed into high level-slow decline group ( OR=0.333, 95% CI 0.120-0.920, P<0.05). Conclusions:There are 4 symptom clusters in elderly patients with laparoscopic surgery for colon cancer, and the characteristics of postoperative symptom cluster changes demonstrate significant heterogeneity, medical staff should pay attention to the management of symptom clusters trajectory categories, dynamically adjust intervention plans to improve nursing quality.

OBJECTIVE To explore the clinical characteristics of Tropheryma whipplei(TW)infection and observe the application of pathogenic metagenomic next-generation sequencing(mNGS)in diagnosis of TW infection.METHODS The clinical data were collected from 1 patient who was diagnosed by mNGS in the Affiliated Hospital of Jining Medical University on Apr.9,2022.The data including the results of laboratory tests and treatment out-comes were summarized,and a literature review was conducted.RESULTS A 50-year-old woman presented to the hospital with chest tightness and chest pain lasting for 3 days,accompanied by dyspnea,palpitations,and expec-toration.The chest plain CT scan and magnetic resonance imaging(MRI)scan suggested a high probability of pul-monary infection.Normal flora were isolated by culture of bronchoalveolar lavage fluid(BALF);TW and human βherpes virus type 7 were detected in BALF by mNGS,with the sequence numbers 327 000 and 9,respectively.The pulmonary symptoms of the patient were improved after joint treatment of the infection with etimicin,levo-floxacin and minocycline.The patient repeatedly sought for medical treatment due to the pain of shoulder joint and limitation of motion.CONCLUSIONS TW is one of major pathogens leading to the infections of systemic multiple systems,and it is necessary to attach great importance to the diagnosis and treatment.The traditional laboratory test method can not achieve ideal diagnosis effect and is more likely to make a missed diagnosis.mNGS is more ac-curate and more efficient than the traditional detection method in diagnosis of TW-induced diseases.Early use of mNGS can make a rapid identification of pathogens and facilitate the reasonable clinical use of antibiotics.It is of great significance for control of the disease progression,improvement of prognosis and prevention of recurrence.

Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective:To construct a curriculum system for malignant fungating wounds based on Kolb experience learning theory.Methods:The first draft of a curriculum system for malignant fungating wounds was constructed based on literature search and group discussion using Kolb experience learning theory as a guide. Between February and May 2024, purposive sampling was used to select 16 experts for two rounds of expert consultation based on the Delphi method to form the final draft of the curriculum system for malignant fungating wounds.Results:A total of 16 questionnaires were distributed in both rounds of expert consultation and 16 questionnaires were effectively recovered with an effective recovery rate of 100.00%. The expert authority coefficients in two rounds of consultation were all 0.916, and Kendall's W values ranged from 0.089 to 0.192 (all P<0.05). The final curriculum system for malignant fungating wounds included four primary indicators, 10 secondary indicators, and 32 tertiary indicators. Conclusions:The curriculum system for malignant fungating wounds constructed under the guidance of Kolb experience learning theory is scientific and practical, and can provide a basis for conducting malignant fungating wound training.

Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective To clarify the evidence of the frequency and risk factors for falls in knee osteoarthritis(KOA)of adults by meta-analysis.Methods Computerized searches of the CNKI,VIP,Wanfang data,CBM,PubMed,Cochrane Library,Embase,Web of Science were conducted for literature on risk factors for falls in adults with KOA from the inception of the databases to August 2024.After literature screening,data extraction,and quality evaluation,RevMan 5.4 software was used for Meta-analysis.Results A total of 26 articles were involved.Meta-analysis result showed that the rate of falls was 29.0％.Factors associated with increased risk of falls included being female(OR=1.35),decreased lower limb muscle strength(OR=1.72),decreased knee flexion muscle strength(OR=7.05),decreased static posture stability(OR=1.28),opioid use(OR=1.79),antidepressant use(OR=1.69),frequent stair climbing(OR=7.58),combined neurological disease(OR=1.77),history of falls(OR=3.29)and fear of falling(OR=2.54).Conclusion The rate of falls of patients with KOA is high.The adults with KOA who are women,have lower muscle strength of lower limbs and knee flexion muscle strength,poorer static posture stability,use opioids,antidepressant,frequent stair climbing,combined neurological disorders,previous falls in the past year and fear of falls are at higher risk of falls.Healthcare professionals should dynamically assess and detect the risk of falls in the patients with KOA and adopt targeted,individualized interventions to prevent falls.

OBJECTIVE To explore the clinical characteristics of Tropheryma whipplei(TW)infection and observe the application of pathogenic metagenomic next-generation sequencing(mNGS)in diagnosis of TW infection.METHODS The clinical data were collected from 1 patient who was diagnosed by mNGS in the Affiliated Hospital of Jining Medical University on Apr.9,2022.The data including the results of laboratory tests and treatment out-comes were summarized,and a literature review was conducted.RESULTS A 50-year-old woman presented to the hospital with chest tightness and chest pain lasting for 3 days,accompanied by dyspnea,palpitations,and expec-toration.The chest plain CT scan and magnetic resonance imaging(MRI)scan suggested a high probability of pul-monary infection.Normal flora were isolated by culture of bronchoalveolar lavage fluid(BALF);TW and human βherpes virus type 7 were detected in BALF by mNGS,with the sequence numbers 327 000 and 9,respectively.The pulmonary symptoms of the patient were improved after joint treatment of the infection with etimicin,levo-floxacin and minocycline.The patient repeatedly sought for medical treatment due to the pain of shoulder joint and limitation of motion.CONCLUSIONS TW is one of major pathogens leading to the infections of systemic multiple systems,and it is necessary to attach great importance to the diagnosis and treatment.The traditional laboratory test method can not achieve ideal diagnosis effect and is more likely to make a missed diagnosis.mNGS is more ac-curate and more efficient than the traditional detection method in diagnosis of TW-induced diseases.Early use of mNGS can make a rapid identification of pathogens and facilitate the reasonable clinical use of antibiotics.It is of great significance for control of the disease progression,improvement of prognosis and prevention of recurrence.

Objective:To investigate the longitudinal trajectories of symptom clusters in elderly patients with laparoscopic surgery for colon cancer, and explore the predictive factors for each trajectories subgroup.Methods:Using a longitudinal survey, elderly patients with laparoscopic surgery for colon cancer patients from Shanxi Bethune Hospital from January 2021 to January 2024 were collected by convenient sampling method. The Chinese Version of the MD Anderson Symptom Inventory Gastrointestinal Cancer Module was used to conduct follow-up surveys of the selected patients at 2 weeks, 1 month, 3 months and 6 months after surgery. The symptom cluster was extracted by exploratory factor analysis, and the latent category growth model was conducted to identify the trajectory subgroups of each symptom cluster, the predictive factors of each trajectory subgroup was analyzed by multiple Logistic regression analysis.Results:A total of 118 cases were concluded in the present study, there were 69 males and 49 females with an age of (74.85 ± 3.29) years old. There were 4 symptom clusters after surgery, which were named as energy deficit symptom cluster, digestive tract symptom cluster, sleep mental symptom cluster, and psychological fatigue symptom cluster, the variance contribution rates were respectively 62.486%, 71.209%, 73.937%, 63.476%. The results from latent class growth model showed that there were 3 trajectory subgroups in the symptom cluster: high level-slow decline group accounted for 33.0% (39/118), moderate level-stable decline group accounted for 39.0% (46/118), low level-rapid decline group accounted for 28.0% (33/118). College education or above tends to developed into low level-rapid decline group ( OR=0.365, 95% CI 0.083-0.603, P<0.05), while patients undergoing traditional laparoscopic surgery ( OR=3.679, 95% CI 1.297-4.432, P<0.05) and primary tumor stages of Ⅲ-Ⅳ were more likely to developed into high level-slow decline group ( OR=0.333, 95% CI 0.120-0.920, P<0.05). Conclusions:There are 4 symptom clusters in elderly patients with laparoscopic surgery for colon cancer, and the characteristics of postoperative symptom cluster changes demonstrate significant heterogeneity, medical staff should pay attention to the management of symptom clusters trajectory categories, dynamically adjust intervention plans to improve nursing quality.

Objective To clarify the evidence of the frequency and risk factors for falls in knee osteoarthritis(KOA)of adults by meta-analysis.Methods Computerized searches of the CNKI,VIP,Wanfang data,CBM,PubMed,Cochrane Library,Embase,Web of Science were conducted for literature on risk factors for falls in adults with KOA from the inception of the databases to August 2024.After literature screening,data extraction,and quality evaluation,RevMan 5.4 software was used for Meta-analysis.Results A total of 26 articles were involved.Meta-analysis result showed that the rate of falls was 29.0％.Factors associated with increased risk of falls included being female(OR=1.35),decreased lower limb muscle strength(OR=1.72),decreased knee flexion muscle strength(OR=7.05),decreased static posture stability(OR=1.28),opioid use(OR=1.79),antidepressant use(OR=1.69),frequent stair climbing(OR=7.58),combined neurological disease(OR=1.77),history of falls(OR=3.29)and fear of falling(OR=2.54).Conclusion The rate of falls of patients with KOA is high.The adults with KOA who are women,have lower muscle strength of lower limbs and knee flexion muscle strength,poorer static posture stability,use opioids,antidepressant,frequent stair climbing,combined neurological disorders,previous falls in the past year and fear of falls are at higher risk of falls.Healthcare professionals should dynamically assess and detect the risk of falls in the patients with KOA and adopt targeted,individualized interventions to prevent falls.