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The mitochondrion is an organelle that plays a vital role in the regulation of hepatic cellular redox, lipid metabolism, and cell death. Mitochondrial dysfunction is associated with both acute and chronic liver diseases with emerging evidence indicating that mitophagy, a selective form of autophagy for damaged/excessive mitochondria, plays a key role in the liver’s physiology and pathophysiology. This review will focus on mitochondrial dynamics, mitophagy regulation, and their roles in various liver diseases (alcoholic liver disease, non-alcoholic fatty liver disease, drug-induced liver injury, hepatic ischemia-reperfusion injury, viral hepatitis, and cancer) with the hope that a better understanding of the molecular events and signaling pathways in mitophagy regulation will help identify promising targets for the future treatment of liver diseases.

This study aims to utilize our hospital's existing Stereo Electroencephalography (SEEG) examination results combined with other clinical data to systematically analyze the risk factors for epilepsy comorbid with depression, and to establish a model for predicting the risk of developing depression in epilepsy patients. Clinically, this model can be used to predict the risk of comorbid depression in epilepsy patients, thereby enhancing the identification of this condition and providing a theoretical basis for proactive intervention in depressive symptoms among epilepsy patients. A retrospective analysis was conducted on the clinical data of patients diagnosed with epilepsy in the Department of Neurosurgery at Tongde Hospital Of Zhejiang Province from 01/01/2020-31/12/2024, all of whom underwent Electroencephalography (EEG) examinations. According to the C-NDDI-E scores and clinical manifestations, the epilepsy patients were divided into an epilepsy with comorbid depression group (study group) and epilepsy without depression group (control group). Univariate analysis was performed using SPSS 26.0 software to screen for potential factors contributing to depression comorbid with epilepsy. Variables with a univariate P ≤ 0.05 were entered into a linear Lasso regression analysis. Those with statistical significance were then used to construct a nomogram model for predicting the risk of depression comorbid with epilepsy using R software. A total of 152 epilepsy patients were enrolled, including 43 in the study group and 109 in the control group. Univariate analysis showed statistically significant (P < 0.05) differences between the groups in terms of age, employment status, marital status, age of onset, frequency of epileptic seizures, type of drug treatment, scalp EEG-determined epileptogenic zone, SEEG-determined epileptogenic zone, and Activities of Daily Living (ADL) score. Lasso regression analysis revealed that marital status (p = 0.0008), Enrollment age (OR = 0.9152, P = 0.0003, 95% CI: 0.8673-0.9562), frequency of epileptic seizures (OR =5.9946, P = 0.0030, 95% CI: 1.8952-20.6541), type of drug treatment (OR = 44.4062, P = 0.0157, 95% CI: 1.3629-15.6702), SEEG results indicating the epileptogenic zone (hippocampal onset: OR = 12.3489, P = 0.0026, 95% CI: 2.5902-70.9811), and ADL score (OR = 0.9358, P = 0.0314, 95% CI: 0.8785-0.9930) were independent risk factors for depression comorbid with epilepsy. The area under the ROC curve (AUC) was 0.895, indicating strong discriminative ability and high predictive accuracy. Independent risk factors for depression comorbid with epilepsy include: hippocampal origin of epilepsy as identified by SEEG, unstable marital status, younger age at the time of enrollment, higher frequency of epileptic seizures (>4 times/month), use of specific anti-seizure medications (such as topiramate, phenobarbital, levetiracetam, and perampanel), and lower activities of daily living (ADL) scores. The nomogram model established based on these factors performs well in relatively accurately predicting the risk of depression comorbid with epilepsy. This facilitates early identification of high-risk patients in clinical practice, enabling timely interventions to prevent the severe consequences of depressive episodes, improving patient adherence to epilepsy treatment, and emphasizing the link between psychological and neuroscientific aspects in epilepsy management to foster interdisciplinary collaboration for more comprehensive patient care.

The risk factors for plasma cell mastitis (PCM) remain unclear. Understanding and mitigating these factors to prevent PCM before its onset has become a significant concern. This study identifies PCM risk factors, develops a predictive nomogram, and offers insights for targeted prevention and awareness in high-risk groups. We retrospectively analyzed the clinical data of 82 patients diagnosed with PCM at Hangzhou Women's Hospital's Breast Surgery Department from 01/01/2019 to 01/01/2022. A control group was randomly selected, consisting of 82 healthy women aged between 20-60 years who had undergone routine health check-ups during the same period. Using SPSS 26.0 software for univariate analysis, significant risk factors for PCM were identified. R software was used for multivariate logistic regression analysis, and a nomogram prediction model for the risk of developing PCM was established. The average age of patients in the study group was 32.37 ± 6.64 years, the control group was 29.54 ± 5.33 years, with no statistically significant difference between the groups (P = 0.176). The onset time after childbirth or miscarriage was 3.37 ± 1.91 years. Univariate analysis revealed significant differences in BMI, nipple retraction, number of pregnancies, recent trauma history, and hyperlipidemia (P < 0.05). Multivariate logistic regression analysis identified nipple retraction (OR=20.128, P = 0.000, 95% CI: 5.952-68.072), number of pregnancies (OR=0.343, P = 0.000, 95% CI: 0.189-0.624), and recent trauma history (within two weeks) (OR=11.154, P = 0.000, 95% CI: 2.936-42.382) as independent risk factors for PCM. Nipple retraction, recent trauma history, and the number of pregnancies were identified as independent risk factors for PCM. Targeted education for high-risk groups, particularly women within 3 years postpartum/post-abortion, improves disease prevention. The nomogram model had a C-index of 0.809, indicating strong discriminatory power and high prediction accuracy.

To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model’s superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.

Submerged plants are an important part of aquatic ecosystems, and the restoration of submerged plants is a key step in the reconstruction of aquatic ecosystems. However, little is known about the role of modified sediments in helping submerged plants recover under low light. In this study, we set up four sediment types and two light intensities to explore the effects of modified sediments on the growth of Vallisneria natans under two low light conditions. The results showed that the independent and interactive effects of light intensity and sediment type significantly affected the biomass, morphology, photosynthetic pigment content and antioxidant enzyme activity of V. natans . At 5% and 20% natural light intensity, the sediment modified with 40% peat soil had a larger root biomass and the highest leaf and root C/N ratio, the sediment modified with 40% vermiculite had a longer root length and more ramets. At 5% natural light intensity, the sediments modified with fly ash had shorter root length and smaller leaf biomass. The sediments modified with fly ash had the greatest chlorophyll content at 20% natural light intensity. It can be concluded that the addition of 40% peat soil or 40% vermiculite in sediment is conducive to the growth of V. natans under low light conditions. Our study indicates the positive effects of the modified sediment on the growth of V. natans under low light conditions, and our study will provide a reference for the restoration of submerged plants in aquatic ecosystems.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease characterized by lipid accumulation and endoplasmic reticulum (ER) stress, while effective therapies targeting the specific characteristics of NAFLD are limited. Ufmylation is a newly found post-translational modification process that involves the attachment of the Ubiquitin-fold modifier 1 (UFM1) protein to its substrates via ufmylation modification system. Ufmylation regulates ER stress via modifying UFM1 binding protein 1 (UFBP1), suggesting a potential role for ufmylation in NAFLD pathogenesis. However, the precise role of ufmylation in NAFLD remains unclear. Herein, we aim to elucidate the impact of ufmylation on UFBP1 in NAFLD and explore the underlying mechanisms involved. We observed increased expression of UFM1-conjugated proteins and ufmylation modification system components in livers with steatosis derived from NAFLD patients and NAFLD models. Upregulation of ufmylation on hepatic proteins appeared to be an adaptive response to hepatic ER stress in NAFLD. In vitro, knocking down UFBP1 resulted in increased lipid accumulation and lipogenesis in hepatocytes treated with free fatty acids (FFA), which could be rescued by wild-type UFBP1 (WT UFBP1) but not by a mutant form of UFBP1 lacking the main ufmylation site lys267 (UFBP1 K267R). In vivo, ufmylation on UFBP1 ameliorated obesity, hepatic steatosis, hepatic lipogenesis, dyslipidemia, insulin resistance and liver damage in mice with NAFLD induced by a high fat diet (HFD). We also demonstrated that the downregulation of UFBP1 induced ER stress, whereas the reintroduction or overexpression of UFBP1 alleviated ER stress in a manner dependent on ufmylation in NAFLD. This mechanism could be responsible for the amelioration of aberrant hepatic lipogenesis and insulin resistance in NAFLD. Our data reveal a protective role of ufmylation on UFBP1 against NAFLD and offer a specific target for NAFLD treatment.

Ferroptosis, a new iron- and reactive oxygen species–dependent form of regulated cell death, has attracted much attention in the therapy of various types of tumors. With the development of nanomaterials, more and more evidence shows the potential of ferroptosis combined with nanomaterials for cancer therapy. Recently, there has been much effort to develop ferroptosis-inducing nanomedicine, specially combined with the conventional or emerging therapy. Therefore, it is necessary to outline the previous work on ferroptosis-inducing nanomedicine and clarify directions for improvement and application to cancer therapy in the future. In this review, we will comprehensively focus on the strategies of cancer therapy based on ferroptosis-inducing nanomedicine currently, elaborate on the design ideas of synthesis, analyze the advantages and limitations, and finally look forward to the future perspective on the emerging field.

Background Breast cancer stem cell (CSC) expansion results in tumor progression and chemoresistance; however, the modulation of CSC pluripotency remains unexplored. Transmembrane protein 120B (TMEM120B) is a newly discovered protein expressed in human tissues, especially in malignant tissues; however, its role in CSC expansion has not been studied. This study aimed to determine the role of TMEM120B in transcriptional coactivator with PDZ-binding motif (TAZ)-mediated CSC expansion and chemotherapy resistance. Methods Both bioinformatics analysis and immunohistochemistry assays were performed to examine expression patterns of TMEM120B in lung, breast, gastric, colon, and ovarian cancers. Clinicopathological factors and overall survival were also evaluated. Next, colony formation assay, MTT assay, EdU assay, transwell assay, wound healing assay, flow cytometric analysis, sphere formation assay, western blotting analysis, mouse xenograft model analysis, RNA-sequencing assay, immunofluorescence assay, and reverse transcriptase-polymerase chain reaction were performed to investigate the effect of TMEM120B interaction on proliferation, invasion, stemness, chemotherapy sensitivity, and integrin/FAK/TAZ/mTOR activation. Further, liquid chromatography–tandem mass spectrometry analysis, GST pull-down assay, and immunoprecipitation assays were performed to evaluate the interactions between TMEM120B, myosin heavy chain 9 (MYH9), and CUL9. Results TMEM120B expression was elevated in lung, breast, gastric, colon, and ovarian cancers. TMEM120B expression positively correlated with advanced TNM stage, lymph node metastasis, and poor prognosis. Overexpression of TMEM120B promoted breast cancer cell proliferation, invasion, and stemness by activating TAZ-mTOR signaling. TMEM120B directly bound to the coil-coil domain of MYH9, which accelerated the assembly of focal adhesions (FAs) and facilitated the translocation of TAZ. Furthermore, TMEM120B stabilized MYH9 by preventing its degradation by CUL9 in a ubiquitin-dependent manner. Overexpression of TMEM120B enhanced resistance to docetaxel and doxorubicin. Conversely, overexpression of TMEM120B-∆CCD delayed the formation of FAs, suppressed TAZ-mTOR signaling, and abrogated chemotherapy resistance. TMEM120B expression was elevated in breast cancer patients with poor treatment outcomes (Miller/Payne grades 1–2) than in those with better outcomes (Miller/Payne grades 3–5). Conclusions Our study reveals that TMEM120B bound to and stabilized MYH9 by preventing its degradation. This interaction activated the β1-integrin/FAK-TAZ-mTOR signaling axis, maintaining stemness and accelerating chemotherapy resistance.

Machine learning technology has been extensively applied in the medical field, particularly in the context of disease prediction and patient rehabilitation assessment. Acute spinal cord injury (ASCI) is a sudden trauma that frequently results in severe neurological deficits and a significant decline in quality of life. Early prediction of neurological recovery is crucial for the personalized treatment planning. While extensively explored in other medical fields, this study is the first to apply multiple machine learning methods and Shapley Additive Explanations (SHAP) analysis specifically to ASCI for predicting neurological recovery. A total of 387 ASCI patients were included, with clinical, imaging, and laboratory data collected. Key features were selected using univariate analysis, Lasso regression, and other feature selection techniques, integrating clinical, radiomics, and laboratory data. A range of machine learning models, including XGBoost, Logistic Regression, KNN, SVM, Decision Tree, Random Forest, LightGBM, ExtraTrees, Gradient Boosting, and Gaussian Naive Bayes, were evaluated, with Gaussian Naive Bayes exhibiting the best performance. Radiomics features extracted from T2-weighted fat-suppressed MRI scans, such as original_glszm_SizeZoneNonUniformity and wavelet-HLL_glcm_SumEntropy, significantly enhanced predictive accuracy. SHAP analysis identified critical clinical features, including IMLL, INR, BMI, Cys C, and RDW-CV, in the predictive model. The model was validated and demonstrated excellent performance across multiple metrics. The clinical utility and interpretability of the model were further enhanced through the application of patient clustering and nomogram analysis. This model has the potential to serve as a reliable tool for clinicians in the formulation of personalized treatment plans and prognosis assessment.

Thyroid cancer (THCA) remains a prevalent endocrine malignancy, with limited molecular markers for accurate diagnosis and targeted therapy. This study aimed to identify key biomarkers and therapeutic targets for THCA using integrative bioinformatics and experimental validation. We used differential gene expression analysis, gene enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms to identify potential biomarkers. Receiver operating characteristic (ROC) curve analysis, immunohistochemical data from the Human Protein Atlas (HPA), quantitative real-time PCR (qPCR), and immune infiltration analysis were used for further validation. Additionally, ROC and multivariate Cox regression analyses were conducted to quantitatively evaluate the diagnostic and prognostic performance of candidate genes. Four candidate biomarkers (VEGFA, TYK2, NRP1, and C5AR1) were identified, but only C5AR1 showed statistical significance in ROC and prognostic analyses. Specifically, C5AR1 demonstrated a strong diagnostic value (AUC = 0.873, 95% CI: 0.822–0.924) and was significantly associated with poorer overall survival (hazard ratio [HR] = 2.41, 95% CI: 1.15–5.06, p  = 0.021). Further qPCR and immune infiltration analyses confirmed that C5AR1 expression was associated with immune cell infiltration, potentially influencing THCA progression. This study identifies C5AR1 as a key biomarker in THCA, suggesting its role in immune-related tumor progression. These findings indicate statistical associations rather than causal mechanisms, highlighting the need for further experimental validation. The results provide a foundation for targeted immunotherapy strategies in THCA treatment.