Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

Background: Aspiration pneumonia (AP), which can be caused by gastric content inhalation into the lower airways, causes acute lung injury (ALI) through complex mechanisms, including inflammation, oxidative stress, and apoptosis. Here, we evaluated the efficacy of exosomes derived from human placental mesenchymal stem cells (hpMSCs) in mitigating ALI in a murine model of AP. We also investigated the role of hsa-let-7i-5p, the most abundant miRNA in hpMSC-derived exosomes, in this respect. Methods: Adult male C57BL/6 mouse AP models were administered hpMSC-derived exosomes (APExo group) or phosphate-buffered saline (AP group) intra-tracheally. After 48 h, the mice were euthanized and evaluated. The effects of hsa-let-7i-5p were assessed by specific inhibition or overexpression. Results: Compared with the APExo group, the AP group exhibited significantly greater ALI, as evidenced by histological damage, increased lung injury scores, impaired lung function, increased leukocyte infiltration, and elevated tissue edema (all P < 0.05). The untreated AP group also showed more inflammation, characterized by nuclear factor-κB upregulation, macrophage M1 polarization, and cytokine level elevation (tumor necrosis factor-α, interleukin-1β, and interleukin-6), as well as increased oxidation and activation of the apoptosis pathway (all P < 0.05). Notably, the therapeutic effects of hpMSC-derived exosomes were compromised by specific inhibition of hsa-let-7i-5p. Furthermore, engineered exosomes derived from genetically modified RAW264.7 overexpressing hsa-let-7i-5p demonstrated therapeutic effects against AP similar to those obtained with hpMSC-derived exosomes. Conclusions In a murine AP model, intra-tracheal administration of hpMSC-derived exosomes has ALI-mitigating effects, involving inflammation, oxidation, and apoptosis modulation, with hsa-let-7i-5p playing a pivotal mediating role.

Background: Type 2 diabetes mellitus (T2DM) is a complex, multifactorial disease with a significant global burden. Although genome-wide association studies (GWAS) have identified many T2DM-associated variants, most lie in non-coding regions, making it difficult to interpret their functional roles. Methods: We aimed to identify genetically regulated Cytosine–phosphate–Guanine (CpG) sites associated with T2DM by conducting a methylome-wide association study (MWAS), followed by Mendelian randomization (MR) and functional validation using human pancreatic cells and mouse models. MWAS was performed using summary statistics from large-scale GWAS and a DNA methylation (DNAm) prediction model to test associations between genetically predicted DNAm and T2DM. Results: We identified 111 CpG sites significantly associated with T2DM in Europeans, including 8 novel sites near genes not previously linked to T2DM. These findings were replicated in independent datasets. Many CpGs also showed associations with cardiometabolic traits, highlighting shared epigenetic mechanisms. Trans-ethnic MR analysis confirmed consistent effects for six CpGs in East Asians. Functional analysis revealed that several CpGs regulate gene expression in human pancreatic α- and β-cells. Among them, 2´-5´-oligoadenylate synthetase like (OASL) expression, regulated by a significant CpG, was differentially expressed in α-cells of T2DM cases compared to controls. Supporting evidence from mouse models suggests a role for OASL in glucose regulation. Conclusion Our study identifies novel genetically regulated CpG sites associated with T2DM risk and highlights OASL as a potential epigenetic regulator of glucose metabolism in α-cells. These findings provide mechanistic insights into the epigenetic architecture of T2DM and suggest potential targets for cross-ethnic biomarker development and therapeutic intervention.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To explore an experimental protocol for differentiating human-induced pluripotent stem cells(iPSCs)into highly pure midbrain dopaminergic(DA)neurons.Methods By optimizing a blend of small molecules and recombinant human growth factors,iPSCs were induced to differentiate into ventral midbrain floor plate DA progenitor cells and subsequently into mature substantia nigra pars compacta DA neurons.Throughout the differentiation process,Real-time PCR and immunofluorescent staining were utilized as a method for quality assessment.Results iPSCs firstly differentiate into dopaminergic precursor cells,and then gradually differentiate into DA neurons expressing tyrosine hydroxylase(TH).Conclusion The protocol successfully yields approximately high purity tyrosine hydroxylase-positive(TH+)DA neurons.This differentiation technique offers an effective cellular model for studying the physiological mechanisms and pathogenesis of Parkinson's disease,providing valuable insights for future research and potential therapeutic strategies.

Objective To investigate the neuroprotective effect of rhein(RHE)on ischemic mice and its potential mechanism of reducing inflammatory response and neuronal apoptosis by inhibiting Notch/nuclear factor(NF)-κB signaling pathway.Methods The classical middle cerebral artery occlusion(MCAO)method was used to construct ischemic stroke mouse models.The mice were randomly divided into 5 groups including the sham operation group(sham),the model group(MCAO),the MCAO+edaravone group(Eda),the MCAO+RHE-treated group(RHE),and the MCAO+RHE+Notch activitor Jagged 1 group(RHE+J).Each group has 18 mice.The Bederson scoring system,balance beam walking test and accelerated rotating rod test were used to assess the neurological function and locomotor ability of mice in each group.2,3,5-triphenyltetrazolium chloride(TTC)staining,hematoxylin eosin staining,and TUNEL method were used to assess cerebral infarction,hippocampal morphological damage,and neuronal apoptosis.ELISA was used to analysis the levels of interleukin(IL)-6 and tumor necrosis factor α(TNF-α)in hippocampal tissue.Western blotting was used to analysis caspase-3,Notch 1,Hes1,and NF-κB p65 protein expression.Results Compared with the sham group,Bederson score,balance beam score,cerebral infarct volume,IL-6 and TNF-α levels,the proportion of TUNEL-positively stained cells,caspase-3,Notch1,Hes1,and p-NF-κB p65 protein expression were significantly increased in the MCAO group,whereas the latency to fall decreased significantly(P＜0.05).Compared with the MCAO group,Bederson score,balance beam score,cerebral infarct volume,IL-6 and TNF-α levels,proportion of TUNEL-positively stained cells,caspase-3,Notch1,Hes1,and p-NF-κB p65 protein expression were significantly lower in both Eda and RHE groups,whereas the latency to fall increased significantly(P＜0.05).Compared with the RHE group,Bederson score,balance beam score,cerebral infarction volume,IL-6 and TNF-α levels,the proportion of TUNEL-positively stained cells,caspase-3,Notch1,Hes1,and p-NF-κB p65 protein expression increased significantly in the RHE+J group,whereas the latency to fall decreased significantly(P＜0.05).Conclusion Rhein can significantly improve nerve function in ischemic mice by inhibiting Notch/NF-κB signaling pathway activation,suggesting that rhein has potential clinical application value.

Facing of mounting resource constraints and rising demands for personalization in medical education,regional anatomy teaching urgently requires transformation.In this paper,we focus on the regional anatomy of the thoracic wall,in order to explore a novel AI-driven teaching paradigm.Anchored in the core principle of"virtual-real integration with cadaveric dissection as the cornerstone,"the paradigm redefines educational objective and constructs an intelligent,closed-loop teaching model integrating students,computers,and instructors.Leveraging the robust support of digital intelligence(e.g.,DeepSeek),this paradigm incorporates interactive method including group collaboration,branching instruction,and gamified assessments.It achieves a comprehensive intelligent transformation of the entire teaching process-from goal setting and plan customization to activity implementation,task completion,outcome exchange,multidimensional evaluation,and reflective iteration.This new paradigm centers on medical students and leverages digital intelligence to activate deep personalized learning potential.It seamlessly integrates fundamental anatomical knowledge with clinical scenarios(e.g.,key anatomy in breast cancer surgery,flap design in breast reconstruction),and significantly enhances clinical decision-making abilities,scientific research and innovative thinking,as well as medical humanistic literacy,paving a new path for intelligent medical education.

Curcumae Rhizoma, derived from the rhizome of Curcuma phaeocaulis, Curcuma kwangsiensis and Curcuma wenyujin, was called Ezhu in China. In the past, Curcumae Rhizoma extracts were obtained through water decoction or alternative methods, which showed significant anti-cancer effects. However, the mixed extracts contain various compound components of Curcumae Rhizoma, leading to an ambiguous mechanism of action for Curcumae Rhizoma extracts anti-cancer. Contemporary researchers have extracted the chemical components of Curcumae Rhizoma separately for experimental verification of its active ingredients in the anti-cancer field. Numerous studies demonstrated that curcumol, germacrone, β-elemene, and curcumin in Curcumae Rhizoma extracts have significant governing effects in anti-cancer activities. Pharmacological studies have shown that Curcumae Rhizoma suppresses cancer cell proliferation, invasion, and migration, triggering apoptosis and regulating cellular autophagy to achieve anticancer effects. Here, we summarized the research progress of Curcumae Rhizoma on anti-cancer effects from 2013 to 2022, aiming to explore the deeper molecular mechanisms of Curcumae Rhizoma's active components in cancer treatment.