Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

Cancer stem cells,a small population of cells with self-renewal and multidirectional differenti-ation potential in tumor tissues,can initiate primary tumors and mediate treatment resistance,tumor re-currence,and metastasis,but the mechanism of how they affect colorectal cancer at the cellular level is unknown.Therefore,in this study,we explored the effect of cancer stem cells and their exosomes on the malignant phenotype of colorectal cancer.First,CD 166+CD44+cancer stem cells(CSCs)were isolated from colorectal cancer tumor tissues,and then cancer stem cell-derived exosomes(CSCexo)and colorec-tal cancer SW480 cell-derived exosomes(Sexo)were extracted by ultracentrifugation.Then,exosomes were subjected to NTA particle size analysis,electron microscopic observation and identification by West-ern blotting.Subsequently,the successfully isolated CSC and CSCexo were co-cultured with colorectal cancer SW480 cells.The apoptosis rate of SW480 cells after co-culture was found to decrease from 20％to about 13％by CCK-8,apoptosis assay(P＜0.01)and the invasive ability was significantly increased(P＜0.001)after co-culture with CSC or CSCexo.In addition,in vivo animal experiments revealed that the tumor growth rate of the S-exo treatment group was slower than that of the CSCexo treatment group,and that CSCexo inhibited the drug efficacy of 5-FU against colorectal cancer tumors.PET/CT imaging,immunohistochemical analysis,and Western blotting experiments showed that CSCexo enhanced the up-take of the glucose analog 18F-FDG and the expression of the glycolytic enzymes HK2,PFKFB2,PKM2,and LDHA in colorectal cancer.In addition,interfering with the expression of glycolytic enzymes with siRNAs blocked the drug resistance induced by CSCexo.In summary,this study demonstrates that color-ectal cancer stem cells deliver exosomes that affect tumor glucose metabolism pathways and promote chemotherapy resistance and invasive ability,revealing the mechanism of formation and dynamic changes in the malignant tumor microenvironment.

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks (GNN) models on the internal test set. Furthermore, it also exhibited much better performance on two external test sets, suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity. Then, a naïve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms, and the consistency of the interpretability results for 5 typical epidermal growth factor receptor (EGFR) inhibitors with their structure‒activity relationships could be observed. Finally, a free online web server called KIP was developed to predict the kinome-wide polypharmacology effects of small molecules (http://cadd.zju.edu.cn/kip).

To investigate the factors affecting the sperm retrieval rate of microdissection testicular sperm extraction (micro-TESE) in patients with nonmosaic Klinefelter syndrome (KS), 64 patients with nonmosaic KS who underwent micro-TESE in the Center for Reproductive Medicine of Peking University Third Hospital (Beijing, China) between January 2016 and December 2017 were included in the study. Data on medical history, physical examination and laboratory examination results, and micro-TESE outcomes were collected. Patients were divided into two groups according to micro-TESE outcomes. The following factors were compared between the two groups by the Mann‒Whitney U test or Student's t-test based on the distribution (nonnormal or normal) of the factors: age, testicular size, follicle-stimulating hormone level, luteinizing hormone level, testosterone level, and anti-Müllerian hormone level. The overall success rate of sperm retrieval was 50.0%. Correlation analysis showed that testicular volume was positively correlated with testosterone level. Using a logistic regression model, age and anti-Müllerian hormone levels were found to be better predictors for the sperm retrieval rate than the other parameters.
Humans ; Male ; Sperm Retrieval ; Klinefelter Syndrome ; Microdissection ; Anti-Mullerian Hormone ; Semen ; Testis ; Spermatozoa ; Testosterone ; Azoospermia ; Retrospective Studies

OBJECTIVE: To develop and validate a user-friendly risk score for older mitral regurgitation (MR) patients, referred to as the Elder-MR score. METHODS: The China Senile Valvular Heart Disease (China-DVD) Cohort Study functioned as the development cohort, while the China Valvular Heart Disease (China-VHD) Study was employed for external validation. We included patients aged 60 years and above receiving medical treatment for moderate or severe MR (2274 patients in the development cohort and 1929 patients in the validation cohort). Candidate predictors were chosen using Cox's proportional hazards model and stepwise selection with Akaike's information criterion. RESULTS: Eight predictors were identified: age ≥ 75 years, body mass index < 20 kg/m², NYHA class III/IV, secondary MR, anemia, estimated glomerular filtration rate < 60 mL/min per 1.73 m², albumin < 35 g/L, and left ventricular ejection fraction < 60%. The model displayed satisfactory performance in predicting one-year mortality in both the development cohort (C-statistic = 0.73, 95% CI: 0.69-0.77, Brier score = 0.06) and the validation cohort (C-statistic = 0.73, 95% CI: 0.68-0.78, Brier score = 0.06). The Elder-MR score ranges from 0 to 15 points. At a one-year follow-up, each point increase in the Elder-MR score represents a 1.27-fold risk of death (HR = 1.27, 95% CI: 1.21-1.34, P < 0.001) in the development cohort and a 1.24-fold risk of death (HR = 1.24, 95% CI: 1.17-1.30, P < 0.001) in the validation cohort. Compared to EuroSCORE II, the Elder-MR score demonstrated superior predictive accuracy for one-year mortality in the validation cohort (C-statistic = 0.71 vs. 0.70, net reclassification improvement = 0.320, P < 0.01; integrated discrimination improvement = 0.029, P < 0.01). CONCLUSIONS The Elder-MR score may serve as an effective risk stratification tool to assist clinical decision-making in older MR patients.

Objective: Based on the diagnostic model established and validated by the machine learning algorithm, to investigate the value of seven tumor-associated autoantibodies (TAABs), namely anti-p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE antibodies in the diagnosis of non-small cell lung cancer (NSCLC) and to differentiate between NSCLC and benign lung nodules. Methods: This was a retrospective study of clinical cases. Model building queue: a total of 227 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from November 2018 to June 2021 were collected as the NSCLC group, and 120 cases of benign lung nodules, 122 cases of pneumonia and 120 healthy individuals were selected as the control groups. External validation queue: a total of 100 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from May 2022 to December 2022 were collected as the NSCLC group, and 36 cases of benign lung nodules, 32 cases of pneumonia and 44 healthy individuals were selected as the control groups. In addition, NSCLC was divided into early (stage 0-ⅠB) and mid-to-late (stage ⅡA-ⅢB) subgroups. The levels of 7-TAABs were detected by enzyme immunoassay, and serum concentrations of CEA and CYFRA21-1 were detected by electrochemiluminescence. Four machine learning algorithms, XGBoost, Lasso logistic regression, Naïve Bayes, and Support Vector Machine are used to establish classification models. And the best performance model was chosen based on evaluation metrics and a multi-indicator combination model was established. In addition, an online risk evaluation tool was generated to assist clinical applications. Results: Except for p53, the levels of rest six TAABs, CEA and CYFRA21-1 were significantly higher in the NSCLC group (P<0.05). Serum levels of anti-SOX2 [1.50 (0.60, 10.85) U/ml vs. 0.8 (0.20, 2.10) U/ml, Z=2.630, P<0.05] and MAGEA1 antibodies [0.20 (0.10, 0.43) U/ml vs. 0.10 (0.10, 0.20) U/ml, Z=2.289, P<0.05], CEA [3.13 (2.12, 5.64) ng/ml vs. 2.11 (1.25, 3.09) ng/ml, Z=3.970, P<0.05] and CYFRA21-1 [4.31(2.37, 7.14) ng/ml vs. 2.53(1.92, 3.48) ng/ml, Z=3.959, P<0.05] were significantly higher in patients with mid-to late-stage NSCLC than in early stages. XGBoost model was used to establish a multi-indicator combined detection model (after removing p53). 6-TAABs combined with CYFRA21-1 was the best combination model for the diagnosis of NSCLC and early NSCLC. The optimal diagnostic thresholds were 0.410, 0.701 and 0.744, and the AUC was 0.828, 0.757 and 0.741, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in model building queue, and the AUC was 0.760, 0.710 and 0.660, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in external validation queue. Conclusion: In the diagnosis of NSCLC, 6-TAABs is superior to that of traditional tumor markers CEA and CYFRA21-1, and can compensate for the shortcomings of traditional tumor markers. For the differential diagnosis of NSCLC and benign lung nodule, "6-TAABs+CYFRA21-1" is the most cost-effective combination, and plays an important role in prevention and screening for early lung cancer.
Humans ; Carcinoma, Non-Small-Cell Lung/surgery* ; Lung Neoplasms/diagnosis* ; Retrospective Studies ; Autoantibodies ; Bayes Theorem ; Tumor Suppressor Protein p53 ; Carcinoembryonic Antigen ; Antigens, Neoplasm ; Biomarkers, Tumor ; Algorithms ; Pneumonia

Objective: Based on the diagnostic model established and validated by the machine learning algorithm, to investigate the value of seven tumor-associated autoantibodies (TAABs), namely anti-p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE antibodies in the diagnosis of non-small cell lung cancer (NSCLC) and to differentiate between NSCLC and benign lung nodules. Methods: This was a retrospective study of clinical cases. Model building queue: a total of 227 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from November 2018 to June 2021 were collected as the NSCLC group, and 120 cases of benign lung nodules, 122 cases of pneumonia and 120 healthy individuals were selected as the control groups. External validation queue: a total of 100 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from May 2022 to December 2022 were collected as the NSCLC group, and 36 cases of benign lung nodules, 32 cases of pneumonia and 44 healthy individuals were selected as the control groups. In addition, NSCLC was divided into early (stage 0-ⅠB) and mid-to-late (stage ⅡA-ⅢB) subgroups. The levels of 7-TAABs were detected by enzyme immunoassay, and serum concentrations of CEA and CYFRA21-1 were detected by electrochemiluminescence. Four machine learning algorithms, XGBoost, Lasso logistic regression, Naïve Bayes, and Support Vector Machine are used to establish classification models. And the best performance model was chosen based on evaluation metrics and a multi-indicator combination model was established. In addition, an online risk evaluation tool was generated to assist clinical applications. Results: Except for p53, the levels of rest six TAABs, CEA and CYFRA21-1 were significantly higher in the NSCLC group (P<0.05). Serum levels of anti-SOX2 [1.50 (0.60, 10.85) U/ml vs. 0.8 (0.20, 2.10) U/ml, Z=2.630, P<0.05] and MAGEA1 antibodies [0.20 (0.10, 0.43) U/ml vs. 0.10 (0.10, 0.20) U/ml, Z=2.289, P<0.05], CEA [3.13 (2.12, 5.64) ng/ml vs. 2.11 (1.25, 3.09) ng/ml, Z=3.970, P<0.05] and CYFRA21-1 [4.31(2.37, 7.14) ng/ml vs. 2.53(1.92, 3.48) ng/ml, Z=3.959, P<0.05] were significantly higher in patients with mid-to late-stage NSCLC than in early stages. XGBoost model was used to establish a multi-indicator combined detection model (after removing p53). 6-TAABs combined with CYFRA21-1 was the best combination model for the diagnosis of NSCLC and early NSCLC. The optimal diagnostic thresholds were 0.410, 0.701 and 0.744, and the AUC was 0.828, 0.757 and 0.741, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in model building queue, and the AUC was 0.760, 0.710 and 0.660, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in external validation queue. Conclusion: In the diagnosis of NSCLC, 6-TAABs is superior to that of traditional tumor markers CEA and CYFRA21-1, and can compensate for the shortcomings of traditional tumor markers. For the differential diagnosis of NSCLC and benign lung nodule, "6-TAABs+CYFRA21-1" is the most cost-effective combination, and plays an important role in prevention and screening for early lung cancer.
Humans ; Carcinoma, Non-Small-Cell Lung/surgery* ; Lung Neoplasms/diagnosis* ; Retrospective Studies ; Autoantibodies ; Bayes Theorem ; Tumor Suppressor Protein p53 ; Carcinoembryonic Antigen ; Antigens, Neoplasm ; Biomarkers, Tumor ; Algorithms ; Pneumonia

Objective: To explore the relationship between handgrip strength (HGS) and pulmonary function (PF) in college students with sedentary lifestyle, so as to provide a reference for taking HGS as an essential factor of PF. Methods: In March 1-5, 2023, a total of 44 college students were recruited and were divided into the sedentary group (22) and exercise group (22) according to the International Physical Activity Questionaire (IPAQ), with 22 students in each group. Independent sample t-test was used to compare the indexes between groups, pearson correlation coefficient was used to determine the correlation between HGS and PF. Multiple linear regression analysis was used to determine the predictive model of PF. Results: There were statistical significance of the correlations between HGS and FVC, MEP, PEF, FEV1, FIVC, MIP, and PIF ( r=0.79, 0.47, 0.44, 0.60, 0.72, 0.53 , 0.49, P <0.01). When gender, physical activity, age, height, weight, and HGS were included as predictors in the regression model, height and HGS had significant effects on FVC ( R 2=0.75, F= 60.55 , P <0.01), weight and HGS had a good predictive effect on FIVC ( R 2=0.67, F=41.77, P <0.01). Conclusion HGS is significantly associated with PF in college students with sedentary lifestyle. Therefore, HGS can be used as an important indicator to predict the PF status of habitual sedentary college students.

Nonalcoholic fatty liver disease (NAFLD) is characterized by hepatic steatosis and insulin resistance and there are currently no approved drugs for its treatment. Hyperactivation of mTOR complex 1 (mTORC1) and subsequent impairment of the transcription factor EB (TFEB)-mediated autophagy-lysosomal pathway (ALP) are implicated in the development of NAFLD. Accordingly, agents that augment hepatic TFEB transcriptional activity may have therapeutic potential against NAFLD. The objective of this study was to investigate the effects of nuciferine, a major active component from lotus leaf, on NAFLD and its underlying mechanism of action. Here we show that nuciferine activated ALP and alleviated steatosis, insulin resistance in the livers of NAFLD mice and palmitic acid-challenged hepatocytes in a TFEB-dependent manner. Mechanistic investigation revealed that nuciferine interacts with the Ragulator subunit hepatitis B X-interacting protein and impairs the interaction of the Ragulator complex with Rag GTPases, thereby suppressing lysosomal localization and activity of mTORC1, which activates TFEB-mediated ALP and further ameliorates hepatic steatosis and insulin resistance. Our present results indicate that nuciferine may be a potential agent for treating NAFLD and that regulation of the mTORC1-TFEB-ALP axis could represent a novel pharmacological strategy to combat NAFLD.

Objective: To compare the prognostic evaluation value of preoperative neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII) in rectal cancer patients. Nomogram survival prediction model based on inflammatory markers was constructed. Methods: The clinical and survival data of 585 patients with rectal cancer who underwent radical resection in the First Affiliated Hospital of Xi'an Jiao tong University from January 2013 to December 2016 were retrospectively analyzed. The optimal cut-off values of NLR, PLR, LMR, and SII were determined by the receiver operating characteristic (ROC) curve. The relationship between different NLR, PLR, LMR and SII levels and the clinic pathological characteristics of the rectal cancer patients were compared. Cox proportional risk model was used for univariate and multivariate regression analysis. Nomogram prediction models of overall survival (OS) and disease-free survival (DFS) of patients with rectal cancer were established by the R Language software. The internal validation and accuracy of the nomograms were determined by the calculation of concordance index (C-index). Calibration curve was used to evaluate nomograms' efficiency. Results: The optimal cut-off values of preoperative NLR, PLR, LMR and SII of OS for rectal cancer patients were 2.44, 134.88, 4.70 and 354.18, respectively. There was statistically significant difference in tumor differentiation degree between the low NLR group and the high NLR group (P<0.05), and there were statistically significant differences in T stage, N stage, TNM stage, tumor differentiation degree and preoperative carcinoembryonic antigen (CEA) level between the low PLR group and the high PLR group (P<0.05). There was statistically significant difference in tumor differentiation degree between the low LMR group and the high LMR group (P<0.05), and there were statistically significant differences in T stage, N stage, TNM stage, tumor differentiation degree and preoperative CEA level between the low SII group and the high SII group (P<0.05). The multivariate Cox regression analysis showed that the age (HR=2.221, 95%CI: 1.526-3.231), TNM stage (Ⅲ grade: HR=4.425, 95%CI: 1.848-10.596), grade of differentiation (HR=1.630, 95%CI: 1.074-2.474), SII level (HR=2.949, 95%CI: 1.799-4.835), and postoperative chemoradiotherapy (HR=2.123, 95%CI: 1.506-2.992) were independent risk factors for the OS of patients with rectal cancer. The age (HR=2.107, 95%CI: 1.535-2.893), TNM stage (Ⅲ grade, HR=2.850, 95%CI: 1.430-5.680), grade of differentiation (HR=1.681, 95%CI: 1.150-2.457), SII level (HR=2.309, 95%CI: 1.546-3.447), and postoperative chemoradiotherapy (HR=1.837, 95%CI: 1.369-2.464) were independent risk factors of the DFS of patients with rectal cancer. According to the OS and DFS nomograms predict models of rectal cancer patients established by multivariate COX regression analysis, the C-index were 0.786 and 0.746, respectively. The calibration curve of the nomograms showed high consistence of predict and actual curves. Conclusions: Preoperative NLR, PLR, LMR and SII levels are all correlated with the prognosis of rectal cancer patients, and the SII level is an independent prognostic risk factor for patients with rectal cancer. Preoperative SII level can complement with the age, TNM stage, differentiation degree and postoperative adjuvant chemoradiotherapy to accurately predict the prognosis of rectal cancer patients, which can provide reference and help for clinical decision.
Biomarkers, Tumor ; Carcinoembryonic Antigen ; Humans ; Inflammation/classification* ; Lymphocytes ; Neutrophils ; Nomograms ; Preoperative Period ; Prognosis ; Rectal Neoplasms/surgery* ; Retrospective Studies