Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

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.

Selenoproteins, as the active form of selenium, play an important role in various physiological and pathological processes, such as anti-oxidation, anti-tumor, immune response, metabolic regulation, reproduction and aging. Although the expression level of selenoproteins in adipose tissue is significantly influenced by dietary selenium intake, it is closely related to the homeostasis of adipose tissue. In this review, we summarized the role of selenoproteins in the physiological function of adipose tissue and the pathogenesis of obesity in recent years, in order to provide a rationale for developing potential therapeutic agents for the treatment of obesity and related metabolic diseases.
Selenoproteins/metabolism* ; Adipose Tissue/physiology* ; Obesity/metabolism* ; Humans ; Animals ; Selenium

In this experiment, self-assembled nanoparticles(SANs) were prepared by the pH-driven method, and Her-HCP SAN was constructed by using herpetrione(Her) and Herpetospermum caudigerum polysaccharides(HCPs). The average particle size and polydispersity index(PDI) were used as evaluation indexes for process optimization, and the quality of the final formulation was evaluated in terms of particle size, PDI, Zeta potential, and microstructure. The proposed Her-HCP SAN showed a spheroid structure and uniform morphology, with an average particle size of(244.58±16.84) nm, a PDI of 0.147 1±0.014 8, and a Zeta potential of(-38.52±2.11) mV. Her-HCP SAN significantly increased the saturation solubility of Her by 2.69 times, with a cumulative release of 90.18% within eight hours. The results of in vivo unidirectional intestinal perfusion reveal that Her active pharmaceutical ingredient(API) is most effectively absorbed in the jejunum, where both K_a and P_(app) are significantly higher compared to the ileum(P<0.001). However, the addition of HCP leads to a significant reduction in the P_(app) of Her in the jejunum(P<0.05). Furthermore, the formation of the Her-HCP SAN results in a notably lower P_(app) in the jejunum compared to Her API alone(P<0.001), while both K_a and P_(app) in the ileum are significantly increased(P<0.001, P<0.05). The absorption of Her-HCP SAN at different concentrations in the ileum shows no significant differences, and the pH has no significant effect on the absorption of Her-HCP SAN in the ileum. The addition of the transporter protein inhibitors(indomethacin and rifampicin) significantly increases the absorption parameters K_a and P_(app) of Her-HCP SAN in the ileum(P<0.05,P<0.01), whereas the addition of verapamil has no significant effect on the intestinal absorption parameters of Her-HCP SAN, suggesting that Her may be a substrate for multidrug resistance-associated protein 2 and breast cancer resistance proteins but not a substrate of P-glycoprotein.
Nanoparticles/metabolism* ; Polysaccharides/pharmacokinetics* ; Intestinal Absorption/drug effects* ; Animals ; Rats ; Particle Size ; Drugs, Chinese Herbal/pharmacokinetics* ; Male ; Rats, Sprague-Dawley ; Drug Carriers/chemistry* ; Drug Compounding ; Cucurbitaceae/chemistry*

OBJECTIVE: To investigate the biological behavior, differentiation ability, and differential gene expression of lymph node mesenchymal stem cells (MSCs) in patients with diffuse large B-cell lymphoma (DLBCL) and reactive lymphoid hyperplasia (RLH), providing a theoretical basis for clinical chemotherapy resistance. METHODS: Lymph node MSCs from patients with DLBCL and RLH were separated, passaged and cultured. The cell morphology and growth status were observed. Flow cytometry was performed to detect the immune phenotype of MSCs. The in vitro directed differentiation ability of the two types of MSCs was observed. High-throughput sequencing was used to analyze the differential gene expression and enrichment of two groups of MSCs. RESULTS: The lymph node MSCs of patients with DLBCL and RLH had similar cell morphology and growth characteristics, and both groups of MSCs expressed CD90, CD105, and CD73 on the cell surface. Compared with lymph node MSCs derived from patients with RLH, lymph node MSCs derived from DLBCL patients showed stronger osteogenic and adipogenic differentiation abilities. High-throughput sequencing results displayed that lymph node MSCs derived from DLBCL patients significantly upregulated some genes such as TOP2A, LFNG, GRIA3, SEC14L2, SPON2, AURKA, LRRC15, FOXD1, HOXC9, CDC20 and remarkably downregulated some genes such as TBC1D8, LDLR, PCDHAC2, POLH, PKP2, ANKRD37, DMKN, HSD11B1, ARHGAP20, PTGS1,etc. CONCLUSION Lymph node MSCs in DLBCL patients exhibit unique biological behavior and gene expression profiles, which may be closely related to clinical chemotherapy resistance.
Humans ; Mesenchymal Stem Cells/cytology* ; Lymphoma, Large B-Cell, Diffuse/pathology* ; Cell Differentiation ; Lymph Nodes/pathology* ; Pseudolymphoma/pathology*

In recent decades, the prevalence of hyperuricemia and gout has increased dramatically due to lifestyle changes. The drugs currently recommended for hyperuricemia are associated with adverse reactions that limit their clinical use. In this study, we report that berberine (BBR) is an effective drug candidate for the treatment of hyperuricemia, with its mechanism potentially involving the modulation of gut microbiota and its metabolite, succinic acid. BBR has demonstrated good therapeutic effects in both acute and chronic animal models of hyperuricemia. In a clinical trial, oral administration of BBR for 6 months reduced blood uric acid levels in 22 participants by modulating the gut microbiota, which led to an increase in the abundance of Bacteroides and a decrease in Clostridium sensu stricto_1. Furthermore, Bacteroides fragilis was transplanted into ICR mice, and the results showed that Bacteroides fragilis exerted a therapeutic effect on uric acid similar to that of BBR. Notably, succinic acid, a metabolite of Bacteroides, significantly reduced uric acid levels. Subsequent cell and animal experiments revealed that the intestinal metabolite, succinic acid, regulated the upstream uric acid synthesis pathway in the liver by inhibiting adenosine monophosphate deaminase 2 (AMPD2), an enzyme responsible for converting adenosine monophosphate (AMP) to inosine monophosphate (IMP). This inhibition resulted in a decrease in IMP levels and an increase in phosphate levels. The reduction in IMP led to a decreased downstream production of hypoxanthine, xanthine, and uric acid. BBR also demonstrated excellent renoprotective effects, improving nephropathy associated with hyperuricemia. In summary, BBR has the potential to be an effective treatment for hyperuricemia through the gut-liver axis.

Ovulatory dysfunction (OD) is one of the main causes of infertility in women of childbearing age, which not only affects their reproductive ability, but also physical and mental health. Traditional treatment strategies have limited efficacies, and the emergence of biomedicines provides a promising alternative solution via the strategies of combining engineered design with modern advanced technology. This review explores the pathophysiological characteristics and related induction mechanisms of OD, and evaluates the current cutting-edge advances in its treatments. It emphasizes the potentials of biomedicines strategies such as hydrogels, nanoparticles and extracellular vesicles in improving therapeutic precision and efficacy. By mimicking natural physiological processes, and achieving controlled drug release, these advanced drug carriers are expected to address the challenges in ovarian microenvironment reprogramming, tissue repair, and metabolic and immune regulation. Despite the promising progress, there are still challenges in terms of biomedical complexity, differences between animal models and human physiology, and the demand for intelligent drug carriers in the therapy of OD. Future researches are mainly dedicated to developing precise personalized biomedicines in OD therapy through interdisciplinary collaboration, promoting the development of reproductive regenerative medicine.

Objective To investigate the diagnostic value of CT radiomics features in pulmonary alveolar proteinosis(PAP).Methods The general data and clinical characteristics of 24 patients with PAP in the Chinese PLA General Hospital from November 2008 to August 2022 were retrospectively collected and analyzed.Another 53 patients with other diffuse lung diseases except for PAP during the same period served as control group.The differences in the 10 conventional CT signs(semantic features)and 107 radiomics features between the two groups were compared.All patients were randomly divided into the training group(n=53)and the validation group(n=24)at a ratio of 7:3.CT semantic feature model,radiomics model and combined model to diagnose PAP were constructed in training group,and the diagnostic efficacy of models was compared using the receiver operating characteristic(ROC)curve in validation group.Decision curve analysis(DCA)was used to assess the value of models for practical clinical application.Radscore was calculated for the model with the highest diagnostic efficacy.Results A total of 24 patients with pathologically confirmed PAP were enrolled,with a male to female ratio of 3:1 and an average age of(44.6±15.2)years.The main clinical symptoms of patients with PAP included shortness of breath,cough,sputum and chest tightness.Compared with control group,the incidence of pleural effusion in PAP group was significantly lower(P＜0.05),while no significant differences were observed in other CT features(P＞0.05).The areas under the curve(AUC)of the semantic feature model for diagnosing PAP in training and validation group were 0.590 and 0.594,respectively,and in validation group,the accuracy,sensitivity,and specificity for diagnosis of PAP were 0.188,1.000,and 0.188,respectively.The AUCs of the radiomics model in training group and validation group were 0.845 and 0.867,respectively,and in validation group,the accuracy,sensitivity,and specificity were 0.641,0.938,and 0.703,respectively.The AUCs of the combined model in training group and validation group were 0.850 and 0.883,respectively,and in validation group,the accuracy,sensitivity,and specificity were 0.688,0.750,and 0.938,respectively.The AUCs of the radiomics model and the combined model were significantly greater than that of the semantic feature model,but there was no significant difference in the AUCs between the first two models.The decision curve analysis showed that both the radiomics model and the combined model had high application value for predicting PAP.Conclusion CT radiomics shows higher clinical value in the diagnosis of PAP compared with conventional CT features.

Objective To investigate the correlation between the level of N-terminal pro-Brain natriuretic peptide(NT-proBNP)and cardiorespiratory fitness following acute exposure to high altitude.Methods Forty-six subjects were recruited from the Second Affiliated Hospital of Army Medical University in June 2022,including 19 males and 27 females.After completing cardiopulmonary exercise test(CPET),serological detection of myocardial cell-related markers,and multiple metabolites at a plain altitude(300 meters above sea level),all subjects flew to a high-altitude location(3900 meters above sea level).Biomarker testing and CPET were repeated on the second and third days after arrival at high altitude.Changes in serum biomarker and key CPET indicators before and after rapid ascent to high altitude were compared,and the correlation between serum levels of various myocardial cell-related markers and metabolites and high altitude cardiorespiratory fitness was analyzed.Results Compared with the plain altitude,there was a significant decrease in maximal oxygen uptake after rapid ascent to high altitude[(25.41±6.20)ml/(kg.min)vs.(30.17±5.01)ml/(kg.min),P<0.001].Serum levels of NT-proBNP,Epinephrine(E),plasma renin activity(PRA),angiotensin Ⅱ(Ang Ⅱ),angiotensin-converting enzyme 2(ACE2)and leptin(LEP)significantly increased,with all differences being statistically significant(P<0.05)after acute high altitude exposure.In contrast,no statistically significant differences were observed for creatine kinase MB(CK-MB),cardiac troponin I(cTnI),myoglobin(Myo)and norepinephrine(NE)(P>0.05).Correlation analysis showed a significant negative correlation between NT-proBNP at plain altitude(r=-0.768,P<0.001)and at high altitude(r=-0.791,P<0.001)with maximal oxygen uptake at high altitude.Multivariate linear regression analysis indicated that maximal oxygen uptake at plain altitude(t=2.069,P=0.045),NT-proBNP at plain altitude(t=-2.436,P=0.020)and at high altitude(t=-3.578,P=0.001)were independent influencing factors of cardiorespiratory fitness at high altitude.Conclusion Cardiorespiratory fitness significantly decreases after rapid ascent to high altitude,and the baseline NT-proBNP level at plain altitude is closely related to cardiorespiratory fitness at high altitude,making it a potential predictor indicator for high altitude cardiorespiratory fitness.

Objective To propose a cell phone fingertip video-based heart rate extraction algorithm to relieve the influences of environmental factors.Methods Firstly,the fingertip video was captured and extracted at a frame rate of 30 frames/s,and the images were separated into three channels:R,G,and B.The brightness changes of the three channels were compared,the G channel,which was the most sensitive to the fingertip blood flow signal,was selected as the signal source,and the brightness change information was extracted to generate a volumetric pulse waveform with a duration of 10 s.Secondly,the waveform underwent signal processing such as digital filtering,removal of baseline drift and Fourier transform,then the heart rate was predicted based on the position information of the maximum spectral peak.Finally,the heart rate prediction efficacy by the proposed algorithm was validated on Brno University of Technology Smartphone PPG Database(BUT PPG dataset).Results The predicted heart rate by the algorithm and the actual value had the mean square deviation,root mean square deviation and mean absolute error being 3.71,1.92 and 1.2 beats/min,respectively.Condusion The algorithm proposed has high accuracy for heart rate prediction,and can be invovled in cell phones for daily heart rate monitoring.[Chinese Medical Equipment Journal,2024,45(8):16-20]