BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.

Objective To investigate the 30-day mortality risk factors in elderly patients with heart failure with reduced ejection fraction (HFrEF) after isolated coronary artery bypass grafting (CABG) and to construct a nomogram for predicting mortality risk. Methods A retrospective analysis of elderly (≥70 years) HFrEF patients undergoing isolated CABG at Tianjin Chest Hospital from 2010 to 2024 was performed. Simple random sampling in R software was used to divide the dataset into training and validation sets in a 7 : 3 ratio. The training set was further divided into survivors and non-survivors. Univariate logistic regression was performed to identify differences between groups, followed by multivariate logistic regression to select independent risk factors for death and to establish a death-risk nomogram, which underwent internal validation. The predictive value of the nomogram was assessed by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision-curve analyses for both the training and validation sets. Results A total of 656 patients were included. The training set consisted of 458 patients (survivors 418, deaths 40); the validation set consisted of 198 patients (survivors 180, deaths 18). In the training set, univariate analysis showed significant differences between survivors and deaths for creatinine (Cr) level, brain natriuretic peptide (BNP), maximum Cr, intra-aortic balloon pump (IABP) use, assisted ventilation, reintubation, hyperlactatemia, low cardiac output syndrome, and renal failure (P<0.05). After multivariable logistic regression, five independent risk factors were identified: IABP use (OR=3.391, 95%CI 1.065-11.044, P=0.038), reintubation (OR=15.991, 95%CI 4.269-67.394, P<0.001), hyperlactatemia (OR=8.171, 95%CI 2.057-46.089, P=0.007), Cr (OR=4.330, 95%CI 0.997-6.022, P=0.024), and BNP (OR=1.603, 95%CI 1.000-2.000, P=0.010). Accordingly, a nomogram predicting mortality risk was constructed. The ROC and calibration analyses indicated good predictive value: area under the curve (AUC) in the training set was 0.898 (95%CI 0.831-0.966) and in the validation set was 0.912 (95%CI 0.805-1.000). Calibration and decision-curve analyses showed good agreement and clinical utility. Conclusion The nomogram incorporating IABP use, reintubation, hyperlactatemia, creatinine, and BNP provides good predictive value for 30-day mortality after CABG in elderly patients with HFrEF and demonstrates potential clinical utility.

BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.

Objective To evaluates the effects of cross-balance training on knee function,dynamic balance,and rectus femoris(RF)activation in patients after anterior cruciate ligament reconstruction(ACLR).Methods Forty ACLR patients at 5th-6th week after operation were randomly divided into experimental group and control group.The experimental group received the cross-balance training on the basis of standard rehabilitation,while the control group received only standard rehabilitation.Knee function was assessed with the Lysholm score,dynamic balance,and root mean square(RMS)of RF surface electromyography.The correlation between RMS and dynamic balance was also examined.Results After intervention,the Lysholm score of the experimental group was significantly higher than that of the control group(P<0.01).Regarding balance function,both the gait line length and single support line length of the experimental group were significantly greater than those of the control group(P<0.01).Conversely,the mediolateral displacement of the experimental group was significantly lower than that of the control group(P<0.01).Furthermore,the RF RMS of the experimental group was significantly larger than that of the control group(P<0.01).The RF RMS was positively correlated with the gait line length and single support line length,whereas it was negatively correlated with the mediolateral displacement(P<0.05).Conclusions Cross-balance training significantly enhances knee function,dynamic balance,and RF activation in post-ACLR patients,supports the theory of cross-education.Cross-balance training has certain application values in ACL postoperative rehabilitation.

Objectives:To explore the value of non-invasive myocardial work combined with myocardial contrast echocardiography(MCE)in the early diagnosis of coronary artery disease and its efficacy in stratifying the severity of coronary vessel lesions.Methods:A total of 130 patients with suspected coronary artery disease admitted to the First Affiliated Hospital of Dalian Medical University from June 2024 to January 2025 were enrolled in this study.All patients underwent echocardiography and MCE after admission,and coronary angiography(CAG).Based on CAG results,patients were divided into non-CAD group(n=45,coronary artery stenosis<50%)and CAD group(n=85,coronary artery stenosis≥50%).Patients in CAD group were further divided into low-score CAD group(≤49 points,n=43)and high-score CAD group(>49 points,n=42)according to the median of Gensini score(49 points).Non-invasive MW indices and quantitative MCE parameters were assessed.A binary logistic regression model was used to construct a combined diagnostic model,and the value of each parameter in diagnosing CAD and evaluating the severity of coronary lesions was calculated.The receiver operating characteristic(ROC)curve of subjects was estimated,and the area under the curve(AUC)was calculated to evaluate its sensitivity and specificity for early diagnosis of coronary heart disease.Results:Compared with the non-CAD group,the global longitudinal strain,global work index(GWI),and global constructive work(GCW)in both low-score and high-score CAD groups were significantly lower(all P<0.05),and the global work efficiency in the high-score CAD group was significantly reduced(P<0.05).MCE indices in both low-score and high-score CAD groups were significantly lower than those in the non-CAD group(all P<0.05).The multivariate logistic stepwise regression analysis and ROC curve showed that GWI(OR=0.997,95%CI:0.995-0.999,P=0.003)and A value(representing the peak intensity of the curve,reflecting myocardial blood volume(OR=0.415,95%CI:0.246-0.698,P=0.001)were independent predictors of low-score CAD.The combined diagnostic sensitivity and specificity for low-score coronary artery disease were 72.1%and 88.9%respectively,with an AUC of 0.851.GCW(OR=0.997,95%CI:0.995-1.000,P=0.019)and β-value(OR=0.000,95%CI:0.000-0.003,P<0.001)were independent predictors of high-score CAD.The combined diagnostic sensitivity and specificity for high-score coronary artery disease were 88.1%and 88.9%respectively,with an AUC of 0.934.Conclusions:Both non-invasive myocardial work parameters and MCE parameters have high diagnostic efficacy for coronary artery lesions of various degrees.The combined application of the two methods significantly improves the accuracy of coronary artery disease diagnosis,with improved sensitivity and specificity than single technique.Our results provide a new non-invasive comprehensive diagnostic model for clinical early diagnosis and risk stratification of coronary artery disease.

Objective To evaluates the effects of cross-balance training on knee function,dynamic balance,and rectus femoris(RF)activation in patients after anterior cruciate ligament reconstruction(ACLR).Methods Forty ACLR patients at 5th-6th week after operation were randomly divided into experimental group and control group.The experimental group received the cross-balance training on the basis of standard rehabilitation,while the control group received only standard rehabilitation.Knee function was assessed with the Lysholm score,dynamic balance,and root mean square(RMS)of RF surface electromyography.The correlation between RMS and dynamic balance was also examined.Results After intervention,the Lysholm score of the experimental group was significantly higher than that of the control group(P<0.01).Regarding balance function,both the gait line length and single support line length of the experimental group were significantly greater than those of the control group(P<0.01).Conversely,the mediolateral displacement of the experimental group was significantly lower than that of the control group(P<0.01).Furthermore,the RF RMS of the experimental group was significantly larger than that of the control group(P<0.01).The RF RMS was positively correlated with the gait line length and single support line length,whereas it was negatively correlated with the mediolateral displacement(P<0.05).Conclusions Cross-balance training significantly enhances knee function,dynamic balance,and RF activation in post-ACLR patients,supports the theory of cross-education.Cross-balance training has certain application values in ACL postoperative rehabilitation.

Objectives:To explore the value of non-invasive myocardial work combined with myocardial contrast echocardiography(MCE)in the early diagnosis of coronary artery disease and its efficacy in stratifying the severity of coronary vessel lesions.Methods:A total of 130 patients with suspected coronary artery disease admitted to the First Affiliated Hospital of Dalian Medical University from June 2024 to January 2025 were enrolled in this study.All patients underwent echocardiography and MCE after admission,and coronary angiography(CAG).Based on CAG results,patients were divided into non-CAD group(n=45,coronary artery stenosis<50%)and CAD group(n=85,coronary artery stenosis≥50%).Patients in CAD group were further divided into low-score CAD group(≤49 points,n=43)and high-score CAD group(>49 points,n=42)according to the median of Gensini score(49 points).Non-invasive MW indices and quantitative MCE parameters were assessed.A binary logistic regression model was used to construct a combined diagnostic model,and the value of each parameter in diagnosing CAD and evaluating the severity of coronary lesions was calculated.The receiver operating characteristic(ROC)curve of subjects was estimated,and the area under the curve(AUC)was calculated to evaluate its sensitivity and specificity for early diagnosis of coronary heart disease.Results:Compared with the non-CAD group,the global longitudinal strain,global work index(GWI),and global constructive work(GCW)in both low-score and high-score CAD groups were significantly lower(all P<0.05),and the global work efficiency in the high-score CAD group was significantly reduced(P<0.05).MCE indices in both low-score and high-score CAD groups were significantly lower than those in the non-CAD group(all P<0.05).The multivariate logistic stepwise regression analysis and ROC curve showed that GWI(OR=0.997,95%CI:0.995-0.999,P=0.003)and A value(representing the peak intensity of the curve,reflecting myocardial blood volume(OR=0.415,95%CI:0.246-0.698,P=0.001)were independent predictors of low-score CAD.The combined diagnostic sensitivity and specificity for low-score coronary artery disease were 72.1%and 88.9%respectively,with an AUC of 0.851.GCW(OR=0.997,95%CI:0.995-1.000,P=0.019)and β-value(OR=0.000,95%CI:0.000-0.003,P<0.001)were independent predictors of high-score CAD.The combined diagnostic sensitivity and specificity for high-score coronary artery disease were 88.1%and 88.9%respectively,with an AUC of 0.934.Conclusions:Both non-invasive myocardial work parameters and MCE parameters have high diagnostic efficacy for coronary artery lesions of various degrees.The combined application of the two methods significantly improves the accuracy of coronary artery disease diagnosis,with improved sensitivity and specificity than single technique.Our results provide a new non-invasive comprehensive diagnostic model for clinical early diagnosis and risk stratification of coronary artery disease.

The objective of this study was to develop a rapid and precise detection technique for por-cine Senecavirus A(SVA)employing reverse transcription recombinase polymerase amplification(RT-RAA)in conjunction with CRISPR Cas13a technology.Additionally,the study aimed to opti-mize the assay's reaction conditions to enhance amplification efficiency.Eight RT-RAA primer sets were designed based on the conserved gene sequence of porcine SVA,and a series of reaction condi-tions were evaluated to refine the RT-RAA reaction system.Subsequently,CRISPR-derived RNA(crRNA)sequences were developed and selected to construct the RT-RAA-CRISPR reaction sys-tem.The method's specificity was determined by examining six prevalent porcine pathogenic nucleic acids,while its sensitivity was assessed using SVA cRNA standards quantified by digital PCR.The method's stability and the consistency of clinical sample analysis were also evaluated.The findings revealed that the optimized RT-RAA and CRISPR reaction systems exhibited the highest amplifi-cation efficiency at a reaction temperature of 37 ℃.Among the eight crRNAs,five were identified as exhibiting the strongest detection signals.The formulated RT-RAA-CRISPR Cas13a method demonstrated exceptional specificity,showing no cross-reactivity with other common porcine disea-ses,including ASFV,PRRSV,PEDV,PCV2,CSFV,and PRV.The method achieved high sensitivi-ty,detecting as low as 0.86 copies/μL of SVA,and exhibited stable fluorescence output,robust re-producibility,and the ability to complete clinical sample analysis within 50 minutes.Consistency e-valuation with six positive and 58 negative samples indicated 100％agreement in outcomes.These results substantiate that the study successfully established a rapid and specific RT-RAA-CRISPR Cas13a detection method for the on-site identification of porcine Senecavirus A,demonstrating high specificity and sensitivity,and holds promise for application in SVA monitoring and control initia-tives.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To evaluate the diagnostic value of native T1 mapping in differentiating Oxford classification (MEST-C) scores in patients with IgA nephropathy.Methods:In this prospective study, patients who underwent both T1 mapping and renal biopsy at the First Medical Center of the Chinese PLA General Hospital between April 2023 and October 2024 were consecutively enrolled. Two radiologists, blinded to clinical and pathological information, measured renal T1 mapping parameters, including cortical T1 (cT1), medullary T1 (mT1), the corticomedullary difference (ΔT1), and the corticomedullary ratio (T1 ratio). Clinical and renal biopsy data based on the Oxford classification from patients with IgA nephropathy were collected. The Oxford classification includes five indicators: Mesangial hypercellularity (M), Endocapillary hypercellularity (E), Segmental glomerulosclerosis or adhesion (S), Tubular atrophy/interstitial fibrosis (T), and Cellular or fibrocellular crescents (C). Spearman correlation analysis was applied to evaluate the associations between MEST-C scores and T1 parameters. The diagnostic performance of T1 parameters for discriminating among scores of the Oxford classification was analyzed using the receiver operating characteristic (ROC) curve.Results:A total of 124 patients with IgA nephropathy were included in this study [66 males, 58 females; age 19-70 years, 39 (30, 51) years]. Except for the E indicator, M, S, T, and C were significantly correlated with renal T1 values ( ρ=0.177-0.414, all P<0.05). cT1 showed the best diagnostic efficacy for the S score, with an area under the curve (AUC) of 0.798, a sensitivity of 68.7%, and a specificity of 88.0%. The best T1 parameter for differentiating the T score was the T1 ratio, with an AUC of 0.687, a sensitivity of 57.9%, and a specificity of 79.1%. Conclusion:Native T1 mapping can be used for the non-invasive assessment of the S and T scores in the Oxford classification of patients with IgA nephropathy.