AIM: To explore the risk factors for postoperative anterior chamber exudation in cataract patients and construct a nomogram prediction model.METHODS: Retrospective study. From July 2019 to October 2024, 450 patients(467 eyes)with age-related cataract who underwent surgery in our hospital were collected as the study subjects. They were randomly grouped into a modeling group(315 cases, 327 eyes)and a validation group(135 cases, 140 eyes)roughly estimated at a 7:3 ratio using the random number table method. Both groups were separated into a non-exudative group and an exudative group based on whether anterior chamber exudation occurred after surgery. Clinical basic data was collected; multivariate Logistic regression was applied to analyze the influencing factors of anterior chamber exudation in patients with age-related cataract after surgery; R software was applied to draw a nomogram prediction model of anterior chamber exudation in patients with age-related cataract after surgery; the calibration curve and Hosmer Lemeshow(H-L)test were applied to evaluate the calibration of the column plot model in predicting the occurrence of anterior chamber exudation in patients with age-related cataract after surgery; ROC was applied to evaluate the efficacy of anterior chamber exudation in patients with age-related cataract after surgery.RESULTS:The clinical characteristics of the modeling group and the validation group were comparable. The high myopia, history of uveitis, preoperative intraocular pressure, lens nuclear grade, intraoperative cumulative dissipated energy, and intraoperative posterior capsular rupture of the lens were the influencing factors for postoperative anterior chamber exudation in patients with age-related cataract(all P<0.05). The results of the modeling group verifying the occurrence of anterior chamber exudation in patients with age-related cataract after surgery showed that the area under the ROC curve(AUC)was 0.986(95% CI: 0.966-0.996), the H-L test was χ2=6.494, P=0.592, indicating that the risk of anterior chamber exudation in patients with age-related cataract after surgery predicted by model had good consistency with actual risks, the AUC of postoperative anterior chamber exudation in patients with age-related cataract based on external validation was 0.982(95% CI: 0.960-0.994); and the H-L test suggested that the risk of anterior chamber exudation in CAT patients after surgery predicted by model had good consistency with actual risks(χ2=6.117, P=0.634).CONCLUSION:High myopia, history of uveitis, preoperative intraocular pressure, lens nuclear grade, intraoperative cumulative dissipated energy, and intraoperative posterior capsular rupture of the lens are risk factors for postoperative anterior chamber exudation in patients with age-related cataract; the nomogram prediction model constructed based on this has high predictive value, and can provide reference for individualized prevention of anterior chamber exudation in patients with age-related cataract after surgery.

Objective: To evaluate the clinical efficacy and safety of injectable platelet-rich fibrin (i-PRF) in the treatment of mild to moderate female pattern hair loss (FPHL), and to analyze the related clinical factors affecting the therapeutic efficacy of i-PRF. Methods: This was a secondary analysis of a prospective, randomized controlled trial. A total of 29 FPHL patients with Sinclair grade Ⅱ-Ⅲ, who attended the outpatient clinics of Beijing Friendship Hospital, Capital Medical University and Peking University Third Hospital from March 2024 to April 2025, were enrolled. All patients received i-PRF treatment once every 4 weeks for a total of 4 sessions, with follow-up up to 24 weeks. Standardized clinical photographs were taken at baseline, week 12 and week 24. Trichoscopic examination was performed to measure the non-vellus target area hair count (TAHC) and hair diameter (HD) in the target area, and all adverse events were recorded. The Mann-Whitney U test was used to compare the intergroup differences in efficacy for categorical influencing factors; Spearman rank correlation analysis was employed to assess the correlation between continuous influencing factors and efficacy; and multiple linear regression (enter method) was performed to identify independent influencing factors of efficacy. Results: A total of 27 patients completed the trial. At week 12, TAHC significantly increased from 112 (105, 120) hairs/cm at baseline to 128 (110, 150) hairs/cm , with a median increase of 12 (5, 28) hairs/cm and a median growth rate of 10.5 (4.2, 24.5)% (P=0.045); hair diameter significantly increased from 42.5 (39.8, 45.2) μm to 52.0 (47.0, 58.0) μm, with a median increase of 7.5 (3.0, 12.5) μm and a median growth rate of 18.0 (7.5, 29.0)% (P<0.001). At week 24, TAHC further significantly increased to 141 (125, 158) hairs/cm (P<0.001), with a median growth rate of 24.8 (18.5, 32.0)%; hair diameter continued to improve to 55.0 (50.0, 61.0) μm (P=0.008), with a median growth rate of 21.5 (16.0, 27.0)%. The overall incidence of adverse events was 10.34% (3/29), mainly presenting as mild scalp pruritus and transient headache, without severe adverse effects. Multiple linear regression analysis revealed that baseline TAHC was an independent influencing factor for i-PRF efficacy (P<0.05), while age, BMI, disease duration, Sinclair grade, family history of AGA, and baseline HD showed no significant independent effect on efficacy in this model (P>0.05). Conclusion: This preliminary study indicates that i-PRF can improve hair density and hair shaft diameter in patients with FPHL with a favorable safety profile. Patients with FPHL and lower baseline TAHC may achieve greater clinical improvement.

To investigate the awareness of cognitive impairment disorders among residents of the Xizang Autonomous Region and its influencing factors, thereby providing a basis for targeted prevention and treatment efforts.

The somatosensory system, including modalities such as touch, temperature, and pain, is essential for perceiving and interacting with the environment. When individuals encounter different somatosensory modalities, they interact through a process called multimodal somatosensory integration. This integration is essential for accurate perception, motor coordination, pain management, and adaptive behavior. Disruptions in this process can lead to a variety of sensory disorders and complicate rehabilitation efforts. However, research on the behavioral patterns and neural mechanisms underlying multimodal somatosensory integration remains limited. According to previous studies, multimodal somatosensory integration can result in facilitative or inhibitory effects depending on factors like stimulus type, intensity, and spatial proximity. Facilitative effects are observed primarily when stimuli from the same sensory modality (e.g., two touch or temperature stimuli) are presented simultaneously, leading to amplified perceptual strength and quicker reaction times. Additionally, certain external factors, such as cooling, can increase sensitivity to other sensory inputs, further promoting facilitative integration. In contrast, inhibitory effects may also emerge when stimuli from different sensory modalities interact, particularly between touch and pain. Under such conditions, one sensory input (e.g., vibration or non-noxious temperature stimulation) can effectively reduce the perceived intensity of the other, often resulting in reduced pain perception. These facilitative and inhibitory interactions are critical for efficient processing in a multi-stimulus environment and play a role in modulating the experience of somatosensory inputs in both normal and clinical contexts. The neural mechanisms underlying multimodal somatosensory integration are multi-tiered, encompassing peripheral receptors, the spinal cord, and various cortical structures. Facilitative integration relies on the synchronous activation of peripheral receptors, which transmit enhanced signals to higher processing centers. At the cortical level, areas such as the primary and secondary somatosensory cortex, through multimodal neuron responses, facilitate combined representation and amplification of sensory signals. In particular, the thalamus is a significant relay station where multisensory neurons exhibit superadditive responses, contributing to facilitation by enhancing signal strength when multiple inputs are present. Inhibitory integration, on the other hand, is mediated by mechanisms within the spinal cord, such as gating processes that limit transmission of competing sensory signals, thus diminishing the perceived intensity of certain inputs. At the cortical level, lateral inhibition within the somatosensory cortex plays a key role in reducing competing signals from non-target stimuli, enabling prioritized processing of the most relevant sensory input. This layered neural architecture supports the dynamic modulation of sensory inputs, balancing facilitation and inhibition to optimize perception. Understanding the neural pathways involved in somatosensory integration has potential clinical implications for diagnosing sensory disorders and developing therapeutic strategies. Future research should focus on elucidating the specific neural circuitry and mechanisms that contribute to these complex interactions, providing insights into the broader implications of somatosensory integration on behavior and cognition. In summary, this review highlights the importance of multimodal somatosensory integration in enhancing sensory perception. It also underscores the need for further exploration into the neural underpinnings of these processes to advance our understanding of sensory integration and its applications in clinical settings.

Objective To analyse the clinical efficiency of endolymphatic sac surgery (ESS) in the management of Meniere’s disease (MD). Methods A retrospective analysis was conducted on 274 patients with MD who were hospitalized for treatment in Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University from January 2009 to August 2023. All patients received lifestyle management and drug treatment such as diuretics. For those whose conditions were not well controlled 3 to 6 months after the initial treatment, intratympanic glucocorticoid (ITG) or ESS treatment was carried out. Six months after the treatment, the classes of vertigo relief and hearing changes in the patients were evaluated. After adjusting the confounding factors through propensity score matching (PSM), the impact of ESS on the prognosis of MD patients was evaluated. Results Among 274 patients, 194 and 80 patients underwent ITG and ESS, respectively. Eighty patients were enrolled into each group after PSM. Before and after PSM, the rate of patients reaching vertigo relief class A in ESS group was higher than that in the ITG group (P＝0.004); there was no significant difference in hearing preservation between the two groups. Kaplan-Meier curve analysis showed that vertigo relief in the ESS group was better than that in the ITG group (P=0.029); there was no statistically significant difference in hearing preservation between the two groups. Conclusion When the initial treatment for patients with MD is ineffective, choosing ESS is more beneficial than ITG for controlling vertigo.

BACKGROUND:It has been shown that Gushukang affects bone metabolism by regulating nucleotide and amino acid metabolism and immune mechanisms.Current research on the mechanism of Gushukang in the treatment of osteoporosis primarily focuses on osteoblast regulation and requires further improvement from the perspective of osteoclasts. OBJECTIVE:To investigate the mechanism by which Gushukang interferes with osteoclasts in the treatment of osteoporosis using RAW264.7 cells as the research model. METHODS:Twenty-four 8-week-old female Sprague-Dawley rats were randomly divided into four groups(n=6 per group):the three experimental groups were given 1,2 and 4 g/kg osteoporosis solution by gavage(2 times per day),and the control group was given an equal amount of distilled water by gavage(2 times per day).After 7 days of intragastric administration,aortic blood samples were extracted to collect serum samples using centrifugation,and serum samples from the same groups were combined to obtain the low-,medium-,and high-concentration Gushukang-containing and normal sera for the subsequent experiments.(1)RAW264.7 cells were cultured in six groups:normal serum was added to the control group;low,medium,and high concentration groups were added with low,medium,and high concentrations of Gushukang-containing serum,respectively;ML385,a nuclear factor erythroid 2-related factor 2(Nrf2)inhibitor was given in the Nrf2 inhibitor group;and t-BHQ,a Nrf2 activator,was added in the Nrf2 activator group.Cell viability was detected using the cell counting kit-8 assay.(2)The 3rd generation RAW 264.7 cells were cultured and divided into five groups:the blank control group was added with normal serum,the osteoclast group was added with receptor activator of nuclear factor κB ligand(RANKL),and the low-,medium-,and high-concentration groups were added with low-,medium-,and high-concentration Gushukang-containing serum based on the addition of RANKL.Tartrate-resistant acid phosphate staining was performed after 5 days of culture.(3)RAW264.7 cells were cultured and divided into five groups:blank control group was cultured with normal serum,osteoclast group cultured with normal serum and RANKL,high concentration+osteoclast group cultured with RANKL+high concentration Gushukang-containing serum,osteoclast+Nrf2 agonist group cultured with RANKL+t-BHQ,and high concentration+osteoclast+Nrf2 inhibitor group cultured with RANKL+high concentration Gushukang-containing serum+ML385.Western blot assay and determination of reactive oxygen content were performed after 5 days of culture. RESULTS AND CONCLUSION:The cell counting kit-8 results indicated that Gushukang-containing serum,NRF2 inhibitor or agonist had no significant effect on RAW264.7 cell viability.Tartrate-resistant acid phosphate staining results demonstrated that Gushukang-containing serum exhibited a concentration-dependent inhibitory effect on osteoclast differentiation.Western blot analysis and determination of reactive oxygen species revealed that compared with the blank control group,Nrf2 protein expression was decreased in the osteoclast group(P<0.05),while c-Fos and NFATc1 protein expression and reactive oxygen species content were elevated(P<0.05);compared with the osteoclast group,Nrf2 protein expression was elevated and reactive oxygen species content was decreased in the high-concentration+osteoclast group,osteoclast+Nrf2 agonist group,and high-concentration+osteoclast+Nrf2 inhibitor group(P<0.05),while c-Fos and NFATc1 protein expression was decreased in the high concentration+osteoclast group and osteoclast+Nrf2 agonist group(P<0.05);compared with the high concentration+osteoclast group,Nrf2 protein expression was decreased(P<0.05)and reactive oxygen species content was elevated(P<0.05)in the high concentration+osteoclast+Nrf2 inhibitor group.To conclude,Gushukang reduces reactive oxygen species production by activating Nrf2,thereby inhibiting downstream of the c-Fos/NFATc1 pathway and suppressing osteoclast differentiation.

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.

OBJECTIVE: To investigate the effect of zedoarondiol on neovascularization of atherosclerotic (AS) plaque by exosomes experiment. METHODS: ApoE^-/- mice were fed with high-fat diet to establish AS model and treated with high- and low-dose (10, 5 mg/kg daily) of zedoarondiol, respectively. After 14 weeks, the expressions of anti-angiogenic protein thrombospondin 1 (THBS-1) and its receptor CD36 in plaques, as well as platelet activation rate and exosome-derived miR-let-7a were detected. Then, zedoarondiol was used to intervene in platelets in vitro, and miR-let-7a was detected in platelet-derived exosomes (Pexo). Finally, human umbilical vein endothelial cells (HUVECs) were transfected with miR-let-7a mimics and treated with Pexo to observe the effect of miR-let-7a in Pexo on tube formation. RESULTS: Animal experiments showed that after treating with zedoarondiol, the neovascularization density in plaques of AS mice was significantly reduced, THBS-1 and CD36 increased, the platelet activation rate was markedly reduced, and the miR-let-7a level in Pexo was reduced (P<0.01). In vitro experiments, the platelet activation rate and miR-let-7a levels in Pexo were significantly reduced after zedoarondiol's intervention. Cell experiments showed that after Pexo's intervention, the tube length increased, and the transfection of miR-let-7a minics further increased the tube length of cells, while reducing the expressions of THBS-1 and CD36. CONCLUSION Zedoarondiol has the effect of inhibiting neovascularization within plaque in AS mice, and its mechanism may be potentially related to inhibiting platelet activation and reducing the Pexo-derived miRNA-let-7a level.
Animals ; MicroRNAs/genetics* ; Exosomes/drug effects* ; Plaque, Atherosclerotic/genetics* ; Neovascularization, Pathologic/genetics* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Blood Platelets/drug effects* ; Apolipoproteins E/deficiency* ; Thrombospondin 1/metabolism* ; CD36 Antigens/metabolism* ; Platelet Activation/drug effects* ; Male ; Mice ; Mice, Inbred C57BL

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.