The telomere structure in the cell nucleus is crucial for maintaining the stability and functions of chromosomes.Telomerase is a ribonucleoprotein reverse transcriptase,which catalyzes the elongation of telomeres using its own RNA as a template,thereby counteracting the shortening of telomeres caused by chromosome replication and cell division.Due to its overexpression in over 85％of malignant tumor cells,telomerase has emerged as a highly promising biomarker and a novel target for cancer therapy.In recent years,given the importance of precise quantification of telomerase activity in guiding medical diagnosis and treatment strategies,researchers have developed various high-performance telomerase detection techniques.Among these,electrochemical biosensing technique has cause much attention due to its high sensitivity,operational convenience,rapid response,and ease of miniaturization.This paper focused on the latest advances in electrochemical sensing technique for detection of telomerase activity,aiming to provide inspiration for designing novel telomerase activity detection strategies by elucidating three unique properties of telomerase primer extension products.

OBJECTIVE: To explore the anti-photoaging properties of salvianolic acid B (Sal B). METHODS: The optimal photoaging model of human immortalized keratinocytes (HaCaT cells) were constructed by expose to ultraviolet B (UVB) radiation. The cells were divided into control, model and different concentrations of Sal B groups. Cell viability was measured via cell counting kit-8. Subsequently, the levels of oxidative stress, including reactive oxygen species (ROS), hydroxyproline (Hyp), catalase (CAT), and glutathione peroxidase (GSH-Px) were detected using the relevant kits. Silent information regulator 1 (SIRT1) protein level was detected using Western blot. The binding pattern of Sal B and SIRT1 was determined via molecular docking. RESULTS: Sal B significantly increased the viability of UVB-irradiated HaCaT cells (P<0.05 or P<0.01). Sal B effectively scavenged the accumulation of ROS induced by UVB (P<0.05 or P<0.01). In addition, Sal B modulated oxidative stress by increasing the intracellular concentrations of Hyp and CAT and the activity of GSH-Px (P<0.05 or P<0.01). The Western blot results revealed a substantial increase in SIRT1 protein levels following Sal B administration (P<0.05). Moreover, Sal B exhibited good binding affinity toward SIRT1, with a docking energy of -7.5 kCal/mol. CONCLUSION Sal B could improve the repair of photodamaged cells by alleviating cellular oxidative stress and regulating the expression of SIRT1 protein.
Humans ; Sirtuin 1/metabolism* ; Ultraviolet Rays ; Oxidative Stress/radiation effects* ; Keratinocytes/metabolism* ; Molecular Docking Simulation ; Benzofurans/pharmacology* ; Skin Aging/radiation effects* ; Reactive Oxygen Species/metabolism* ; Cell Survival/radiation effects* ; HaCaT Cells ; Hydroxyproline/metabolism* ; Glutathione Peroxidase/metabolism* ; Catalase/metabolism* ; Depsides

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

OBJECTIVE: To investigate the short-and med-term clinical efficacy of unicompartmental knee arthroplasty(UKA)for the treatment of medial knee osteoarthritis (OA) in elderly patients with anterior cruciate ligament deficiency(ACLD). METHODS: A retrospective analysis was conducted on 31 patients aged over 75 years old with primary medial knee OA and ACLD who underwent UKA between January 2018 and December 2022. The cohort included 12 males and 19 females, aged from 75 to 91 years with an average age of (79.56±4.54) years, with 13 left knee, 16 right knee, and 2 bilateral knees. Clinical outcomes were assessed preoperatively and at final follow-up using the visual analogue scale (VAS), Hospital for Special Surgery(HSS) score, range of motion (ROM), hip-knee-ankle angle (HKA), and tibial component posterior slope angle (TCPSA). Complications such as infection, prosthesis wear, prosthesis loosening, and dislocation were also recorded. RESULTS: All 31 patients were followed up from 12 to 63 months with an average of (28.34±10.56) months. The average postoperative TCPSA was (4.83±1.31)° ranged from 2.5° to 6.8°. At the final follow-up, there was significant improvement in VAS (3.24±0.53) vs. (6.59±0.69), HSS score (85.19±4.45) vs. (64.38±5.94), ROM (118.83±5.38)° vs. (98.85±4.08)°, and HKA (176.83±5.16)° vs. (169.57±6.28)° compared to preoperative values (P<0.05). No cases of infection, prosthesis loosening, or dislocation were reported. CONCLUSION UKA provides favorable short-and mid-term outcomes for elderly patients with medial knee OA and ACLD . However, long-term clinical efficacy needs further investigation through extended follow-up.
Humans ; Male ; Female ; Arthroplasty, Replacement, Knee/methods* ; Aged ; Osteoarthritis, Knee/physiopathology* ; Retrospective Studies ; Aged, 80 and over ; Range of Motion, Articular ; Anterior Cruciate Ligament Injuries/surgery*

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

Depression is a prevalent mental and emotional disor-der that often results in significant emotional disturbances,cog-nitive dysfunction,and memory impairments.It is characterized by a high incidence rate,a substantial disability burden,and limited therapeutic efficacy.Currently,the long-term use of medications for the treatment of depression can result in a range of adverse reactions,highlighting the urgent need to explore no-vel approaches that can effectively alleviate depressive symptoms while minimizing side effects.Curcumin,a natural polyphenolic compound derived from the rhizome of turmeric,demonstrates considerable potential in the prevention and treatment of depres-sion,owing to its diverse array of biological activities.In recent years,numerous studies have investigated the use of curcumin for the treatment of depression.This article aims to provide a comprehensive review of the mechanisms of action underlying curcumin's efficacy in treating depression.Specifically,it focu-ses on its ability to improve neurotransmitter imbalances,restore neural plasticity,alleviate neural damage,mitigate dysfunction of the hypothalamic-pituitary-adrenal(HPA)axis,regulate in-flammatory factors and neuroinflammatory signaling pathways,and inhibit oxidative stress.This review is intended to offer in-sights and methodological references for basic research on curcu-min,as well as for the development of novel therapeutic agents for the treatment of depression.