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.

With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
Artificial Intelligence ; Humans ; Biomedical Research

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Redox-sensitive cysteine(RSC)thiol plays an important role in many biological processes such as photosynthesis,cellular metabolism,and transcription.Therefore,it is necessary to identify red-ox-sensitive cysteine accurately.However,traditional redox-sensitive cysteine identification is very ex-pensive and time-consuming.At present,there is an urgent need for a mathematical calculation method to identify sequence information and redox-sensitive cysteines quickly and accurately.Here,we devel-oped an effective predictor called iRSC-PseAAC,which used the dimension reduction algorithm LDA combined with the support vector machine to predict redox-sensitive cysteine sites.In the cross-validation results,the specificity(Sp),sensitivity(Sn),accuracy(Acc)and Matthews correlation coefficient(MCC)were 0.841,0.868,0.859 and 0.692 respectively.In the independent dataset results,the Sp,Sn,Acc and MCC were 0.906,0.882,0.890 and 0.767 respectively.compared with existing prediction methods,iRSC-PseAAC had obvious improvement effect.The method proposed for this study can also be used for many problems in computational proteomics.

Objective:To explore the trends and outcomes for heart transplantation (HT) in elderly recipients and further examine the related risk factors.Methods:Between June 2004 and December 2021, retrospective review was conducted for the relevant clinical data and age distribution of 1044 HT recipients aged ≥18 year at Fuwai Hospital. The study population was assigned into two groups of elder (≥60 year, n=877) and non-elder (<60 year, n=157). Subgroup analysis was made between recipients aged <65 year (n=107) and those aged ≥ 65 year (n=50) in elder group. Baseline demographic profiles, clinical data, in-hospital and one-year post-transplant mortality and long-term survival were compared between two groups. Then a further comparison of long-term survival was conducted among the groups of non-elder, elder aged <65 year and elder aged ≥65 year. Cox proportional risk regression and multivariate Logistic regression models were utilized for examining the relevant risk factors for cumulative survival rate and short-term mortality. Kaplan-Meier analysis was employed for plotting survival curves and Log-rank test for comparison. Multivariate Cox proportional risk regression model was utilized for examining the relevant risk factors for cumulative survival rate and multivariate Logistic regression model for analyzing the relevant risk factors for short-term mortality. After adjusting for other confounding factors, the impact of recipient age on survival post-HT was determined.Results:The number of elderly HT recipients spiked annually at our center while average age of adult recipients and average age of elderly recipients have remained relatively constant. The median follow-up period was 6.5 years. Regarding baseline data, statistically significant differences existed in ratio of males [84.7%(113/157) vs 77.5%(687/877)], hypertension history [20.4%(32/157) vs 8.9%(79/877)], smoking history [47.1%(74/157) vs 36.1%(320/877)], diabetic history [33.8%(53/157) vs 14.7%(130/877)], preoperative ICD/CRT/CRT-D implantation [28.0%(44/157) vs 18.0%(160/877)], value of creatinine [(105.3±25.3) vs (96.8±35.0) μmol/L], IMPACT score [(6.9±2.4) vs (4.2±2.9) point], serum total bilirubin [19.7(13.6, 30.3) vs 23.7(15.8, 36.8) μmol/L], mean pulmonary arterial pressure [(26.0±10.3) vs (29.7±11.0) mmHg (1 mmHg=0.133 kPa)] and ischemic duration [(274.7±105.6) vs (296.0±120.4) min] (all P<0.05). No significant inter-group difference existed in in-hospital mortality [4.5%(7/157) vs 4.7%(42/887)] or 1-year mortality [5.7%(9/157) vs 6.5%(58/887)] ( P=0.88, P=0.70); in-hospital mortality and 1-year postoperative mortality of recipients aged ≥65 years 10.0%(5/50) and 14.0%(7/50) were both higher than those aged <65 year [1.9%(2/107), 1.9%(2/107)]. The differences were both statistically significant ( P=0.02, P<0.01). Kaplan-Meier survival analysis indicated that long-term survival rate was lower in elder group than that in non-elder group and the difference was statistically significant ( P=0.046). Long-term survival rate of elders aged ≥65 year was lower than that of non-elders aged <65 year and the difference was statistically significant ( P<0.01). Regression analysis indicated that age of recipient ≥65 year, preoperative creatinine ≥133 μmol/L, preoperative total bilirubin ≥25.65 μmol/L and preoperative support of extracorporeal membrane oxygenation (ECMO) were independent risk factors for short/long-term mortality post-HT. Conclusion:Although long-term prognosis of elderly recipients is slightly worse than that of non-elderly ones, in-hospital mortality and one-year postoperative mortality are similar between two groups. For elderly recipients with fewer comorbidities and better preoperative status, they should not be excluded from HT based solely upon age. The long-term prognosis of recipients aged ≥65 year remains poor and HT decisions should be made carefully.

Objective:To explore the morbidity and risk factors of de novo malignancy after heart transplantation (HT).Methods:From June 2004 to August 2021, 995 patients undergoing HT were selected and followed up.The epidemiological characteristics, the morbidity of de novo malignancy (DNM) and its risk factors were examined.Kaplan-Meier survival analysis was performed for calculating the cumulative incidence and mortality of DNM.Log rank test was utilized for comparing the survival rate of each subgroup.Cox regression model was employed for examining the relationship between the included factors and the endpoint of DNM.Results:The median follow-up period was 6.36(3.64, 10.18) years.Thirty-six patients (3.6%) developed DNM during follow-up.Lung cancer accounted for 22.2%(8/36) of DNM while digestive system tumors accounted for 38.9% (including gastric cancer 6/36, 16.7%; liver cancer 3/36, 8.3%; colon cancer 2/36, 5.6%). The cumulative morbidity of DNM at Year 1/5/10/15 post-HT was 0.1%, 2.3%, 4.9% and 7.6% respectively.The median survival time of DNM recipients was 83.32 months.The mean survival time was significantly lower than those without DNM[(115.32±13.12) vs.(194.22±2.58), P<0.001]. The mortality of DNM recipients was around 6.57 folds higher ( HR=6.57, 95% CI: 4.06-10.64, P<0.01). Age was an independent risk factor for an occurrence of DNM.Hypertension and diabetes were also correlated with DNM. Conclusions:DNM after HT is associated with shorter survival time.And age is an independent risk factor for DNM after HT.

Overexpression of ABCG2 transporter in cancer cells has been linked to the development of multidrug resistance (MDR), an obstacle to cancer therapy. Our recent study uncovered that the MET inhibitor, tepotinib, is a potent reversal agent for ABCB1-mediated MDR. In the present study, we reported for the first time that the MET inhibitor tepotinib can also reverse ABCG2-mediated MDR in vitro and in vivo by directly binding to the drug-binding site of ABCG2 and reversibly inhibiting ABCG2 drug efflux activity, therefore enhancing the cytotoxicity of substrate drugs in drug-resistant cancer cells. Furthermore, the ABCB1/ABCG2 double-transfected cell model and ABCG2 gene knockout cell model demonstrated that tepotinib specifically inhibits the two MDR transporters. In mice bearing drug-resistant tumors, tepotinib increased the intratumoral accumulation of ABCG2 substrate drug topotecan and enhanced its antitumor effect. Therefore, our study provides a new potential of repositioning tepotinib as an ABCG2 inhibitor and combining tepotinib with substrate drugs to antagonize ABCG2-mediated MDR.