OBJECTIVE To establish the method for determining the contents of five active components in raw and charred herb pairs of Gardenia jasminoides-Scutellaria baicalensis ， construct their fingerprints， and investigate the effects of G. jasminoides processing on chemical constituents in the h erb pairs. METHODS The HPLC method was used to determine the contents of genipin 1- β -D-gentiobioside， geniposide， crocin Ⅰ， crocin Ⅱ and baicalin in ten batches of raw G. jasminoides-S. baicalensis herb pairs and ten batches of charred G. jasminoides-S. baicalensis herb pairs. Using the same HPLC method， chromatographic fingerprints for the ten batches of raw herb pairs and ten batches of processed herb pairs were established with the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicine （2012 edition）. Principal component analysis and orthogonal partial least squares-discriminant analysis were performed. RESULTS Compared with the raw G. jasminoides-S. baicalensis herb pair， the average content of genipin 1- β -D-gentiobioside in the charred G. jasminoides-S. baicalensis herb pair increased by 12.65%； while the average contents of geniposide， crocin Ⅰ， crocin Ⅱ and baicalin decreased by 7.86%， 60.62%， 62.07% and 0.15%， respectively. Chromatographic fingerprints of ten batches of raw herb pairs and ten batches of processed herb pairs shared 18 common peaks with similarities ranging from 0.997 to 1.000 and 0.988 to 1.000， respectively. Five common peaks were identified： genipin 1- β -D-gentiobioside （peak 2）， geniposide （peak 3）， crocin Ⅰ （peak 6）， crocin Ⅱ （peak 9） and baicalin （peak 10）. Principal component analysis and orthogonal partial least squares-discriminant analysis results showed that the raw G. jasminoides-S. baicalensis herb pair and the charred G. jasminoides-S. baicalensis herb pair could be clearly distinguished. Variable importance in the projection （VIP） values for crocin Ⅰ and crocin Ⅱ were both greater than one. CONCLUSIONS A method has been successfully developed for the determination of five active c omponents， including genipin 1- β -D-gentiobioside， in the G. jasminoides-S. baicalensis herb pair， and its fingerprint has been drawn. Processing is found to increase the content of genipin 1- β -D-gentiobioside， while decreasing the levels of geniposide， crocin Ⅰ， crocin Ⅱ and baicalin in the herb pair. Furthermore， crocin Ⅰ and crocin Ⅱ could serve as potential quality markers for the quality control of this herb pair.

Work-related musculoskeletal disorders (WMSDs) represent a significant occupational health challenge among healthcare professionals globally, posing substantial threats to physical and mental well-being as well as work sustainability. Adopting preventive behaviors—including ergonomic postural adjustments, optimized work-rest scheduling, proper use of protective and assistive equipment, and regular physical activity—is essential for mitigating the risk of WMSDs. Guided by the social ecological model, the review synthesized current evidence on the determinants of WMSDs preventive behaviors across four levels: intrapersonal characteristics, work environment conditions, interpersonal support, and policy/institutional factors. The findings suggest that higher educational attainment, favorable health-related behavioral patterns, optimized ergonomic work environments, adoption of supportive collaborative systems, strong organizational support, as well as policy safeguards facilitate preventive behavior adoption. Conversely, limited prevention-related knowledge, low risk perception, insufficient physical activity, excessive workload, lack of appropriate protective equipment, inadequate ergonomic training, a prevailing culture of presenteeism, and inadequate policy implementation constitute significant barriers. Multi-dimensional intervention strategies targeting these determinants are warranted to enhance preventive behaviors, reduce the risk of WMSDs, and strengthen occupational health protection for healthcare professionals.

Objective: To develop a prediction model for mild cognitive impairment (MCI) risk among the elderly, so as to provide a tool for MCI early screening. Methods : From July 2022 to September 2024, a multi-stage stratified random cluster sampling method was used to recruit permanent residents aged ≥65 years from the Xinjiang Uygur Autonomous Region as study participants. Data on sociodemographic characteristics, nutritional status, body composition indices, bone mineral density, and handgrip strength were collected through questionnaires and physical examinations. Sarcopenia was defined based on appendicular skeletal muscle index and handgrip strength. MCI was assessed using the Mini-Mental State Examination, with adjustments for educational level. Participants were randomly divided into a training set and a validation set in a 7∶3 ratio. LASSO regression and multivariable logistic regression models were employed to screen for predictors and construct an MCI risk prediction model. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 1 641 participants were surveyed, including 755 males (46.01%) and 886 females (53.99%). The majority of participants were aged 65-<75 years, comprising 1 154 individuals (70.32%). MCI was detected in 517 participants, corresponding to a detection rate of 31.51%. Resultsfrom LASSO regression and multivariate logistic regression analysis showed that residence (rural, OR = 2.323, 95% CI: 1.682-3.210), age (75-<85 years, OR = 1.405, 95% CI: 1.019-1.937; ≥85 years, OR = 3.655, 95% CI: 1.696-7.875), educational level (primary school, OR = 0.341, 95% CI: 0.247-0.472; junior high school, OR = 0.255, 95% CI: 0.160-0.408; high school, OR = 0.286, 95% CI: 0.154-0.531; bachelor's degree or above, OR = 0.120, 95% CI: 0.041-0.351), history of alcohol consumption (yes, OR = 3.216, 95% CI: 2.164-4.779), risk of malnutrition (yes, OR = 1.464, 95% CI: 1.064-2.014), sarcopenia (yes, OR = 3.197, 95% CI: 2.332-4.385), and waist-to-hip ratio (abnormal, OR = 1.540, 95% CI: 1.159-2.048) were identified as predictive factors for MCI among the elderly. In the training set, the area under the ROC curve, sensitivity, and specificity were 0.788, 0.719, and 0.712, respectively. In the validation set, the corresponding values were 0.784, 0.913, and 0.542, respectively. DCA demonstrated that the model provided a higher clinical net benefit for predicting MCI risk when the risk threshold probability ranged from 0.124 to 0.764. Conclusion The prediction model developed in this study demonstrates good discriminative ability and clinical utility, indicating its substantial value for predicting the MCI risk among the elderly.

Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of neural populations and closely tied to cognitive and behavioral states. In contrast, aperiodic fluctuations exhibit a power-law decaying spectral trend, revealing the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in studying brain aperiodic dynamics. These studies demonstrate that aperiodic activity holds significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. Aperiodic activity serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent has emerged as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with activities at the levels of individual neurons, neuronal ensembles, and neural networks collectively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic signals. Aperiodic dynamics currently boasts broad application prospects. It not only provides a novel perspective for investigating brain neural dynamics but also holds immense potential as a neural marker in neuromodulation or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and based on these findings, elaborates on the research prospects of aperiodic dynamics.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

BACKGROUND:Prolyl hydroxylase domain 2(PHD2)inhibitors can regulate bone metabolism and relieve osteoporosis in ovariectomized rats.cpd17 is a small molecule oral PHD2 inhibitor newly developed by China Pharmaceutical University.It is effective in the treatment of renal anemia with few side effects,but its effect on bone formation and bone resorption is still unclear. OBJECTIVE:To investigate the effects of cpd17 on mouse osteogenic precursor cells. METHODS:Osteogenic precursor cells were treated with cpd17.Alkaline phosphatase activity and extracellular matrix mineralization were measured,and the expression levels of osteogenesis-and osteoclastogenesis-related markers,as well as PHD2 and hypoxia-inducible factor 1α,were detected.After inhibition of the hypoxia-inducible factor 1α pathway using LW6(a hypoxia-inducible factor 1α pathway inhibitor),alkaline phosphatase activity and extracellular matrix mineralization were detected again,as well as the expression levels of osteogenesis-and osteoclastogenesis-related markers,PHD2 and hypoxia-inducible factor 1α. RESULTS AND CONCLUSION:cpd17 significantly enhanced alkaline phosphatase activity and extracellular matrix mineralization,up-regulated the expression of osteogenesis-related markers,down-regulated the expression of osteoclastogenesis-related markers,up-regulated the expression of hypoxia-inducible factor 1α,down-regulate the expression of PHD2.However,cpd17's effects were significantly attenuated by LW6.To conclude,the PHD2 inhibitor cpd17 promotes osteogenic differentiation and inhibits osteoclastic differentiation through activation of the hypoxia-inducible factor 1α signaling pathway.

Triple-negative breast cancer (TNBC) represents a distinctive subtype, characterized by the absence of estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2 (HER2). Due to its high inter-tumor and intra-tumor heterogeneity, TNBC poses significant chanllenges for personalized diagnosis and treatment. The advant of clustered regular interspaced short palindromic repeats (CRISPR) technology has profoundly enhanced our understanding of the structure and function of the TNBC genome, providing a powerful tool for investigating the occurrence and development of diseases. This review focuses on the application of CRISPR/Cas technology in the personalized diagnosis and treatment of TNBC. We begin by discussing the unique attributes of TNBC and the limitations of current diagnostic and treatment approaches: conventional diagnostic methods provide limited insights into TNBC, while traditional chemotherapy drugs are often associated with low efficacy and severe side effects. The CRISPR/Cas system, which activates Cas enzymes through complementary guide RNAs (gRNAs) to selectively degrade specific nucleic acids, has emerged as a robust tool for TNBC research. This technology enables precise gene editing, allowing for a deeper understanding of TNBC heterogeneity by marking and tracking diverse cell clones. Additionally, CRISPR facilitates high-throughput screening to promptly identify genes involved in TNBC growth, metastasis, and drug resistance, thus revealing new therapeutic targets and strategies. In TNBC diagnostics, CRISPR/Cas was applied to develop molecular diagnostic systems based on Cas9, Cas12, and Cas13, each employing distinct detection principles. These systems can sensitively and specifically detect a variety of TNBC biomarkers, including cell-specific DNA/RNA and circulating tumor DNA (ctDNA). In the realm of precision therapy, CRISPR/Cas has been utilized to identify key genes implicated in TNBC progression and treatment resistance. CRISPR-based screening has uncovered potential therapeutic targets, while its gene-editing capabilities have facilitated the development of combination therapies with traditional chemotherapy drugs, enhancing their efficacy. Despite its promise, the clinical translation of CRISPR/Cas technology remains in its early stages. Several clinical trials are underway to assess its safety and efficacy in the treatment of various genetic diseases and cancers. Challenges such as off-target effects, editing efficiency, and delivery methods remain to be addressed. The integration of CRISPR/Cas with other technologies, such as 3D cell culture systems, human induced pluripotent stem cells (hiPSCs), and artificial intelligence (AI), is expected to further advance precision medicine for TNBC. These technological convergences can offer deeper insights into disease mechanisms and facilitate the development of personalized treatment strategies. In conclusion, the CRISPR/Cas system holds immense potential in the precise diagnosis and treatment of TNBC. As the technology progresses and becomes more costs-effective, its clinical relevance will grow, and the translation of CRISPR/Cas system data into clinical applications will pave the way for optimal diagnosis and treatment strategies for TNBC patients. However, technical hurdles and ethical considerations require ongoing research and regulation to ensure safety and efficacy.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.