This narrative review provides a succinct exploration of prolactinoma, the most common pituitary adenoma, focusing on its epidemiology, clinical manifestations, and therapeutic interventions. Beginning with an overview of its prevalence and aetiology, the review delves into the gender distribution and familial associations of prolactinoma. Clinical presentations, including endocrine disruptions, reproductive health issues, and metabolic disturbances, are examined, emphasizing their impact on hormonal regulation and cardiovascular health. The narrative then navigates through pharmacological treatments, surgical interventions, and radiation therapy, highlighting their efficacy, side effects, and long-term management challenges. Strategies to mitigate side effects and optimize treatment outcomes are discussed, emphasizing the importance of multidisciplinary collaboration in prolactinoma management. This review is a concise yet comprehensive resource for healthcare professionals and researchers, providing insights into prolactinoma\'s clinical complexities and therapeutic nuances to guide optimal patient care strategies.

Cell sheet technology (CST) has primarily been applied in tissue engineering for repair purposes. Our preliminary research indicates that an in vivo prostate cancer model established using CST outperforms traditional cell suspension methods. However, the potential for CST to be used with bladder cancer cells has not yet been explored. In this study, we investigated the ability of two bladder cancer cell lines, T24 and 5637, to form cell sheets. We found that T24 cells successfully formed cell sheets. We then performed staining to evaluate the integrity, specific markers, and proliferation characteristics of the T24 cell sheets. Our findings demonstrate that bladder cancer cell sheets can be established, providing a valuable tool for both in vivo and in vitro bladder cancer studies and for personalized drug selection for patients.

UNASSIGNED: Repetitive transcranial magnetic stimulation (rTMS) is an effective noninvasive neuromodulation technique for Parkinson\'s disease (PD). However, the efficacy of rTMS varies widely between individuals. This study aimed to investigate the factors related to the response to rTMS in PD patients.
UNASSIGNED: We retrospectively analyzed the response of 70 idiopathic PD patients who underwent rTMS for 14 consecutive days targeting the supplementary motor area (SMA) in either an open-label trail (n = 31) or a randomized, double-blind, placebo-controlled trial (RCT) (n = 39). The motor symptoms of PD patients were assessed by the United Parkinson\'s Disease Rating Scale Part III (UPDRSIII). Based on previous studies, the UPDRSIII were divided into six symptom clusters: axial dysfunction, resting tremor, rigidity, bradykinesia affecting right and left extremities, and postural tremor. Subsequently, the efficacy of rTMS to different motor symptom clusters and clinical predictors were analyzed in these two trails.
UNASSIGNED: After 14 days of treatment, only the total UPDRSIII scores and rigidity scores improved in both the open-label trial and the RCT. The results of multiple linear regression analysis indicated that baseline rigidity scores (β = 0.37, p = 0.047) and RMT (β = 0.30, P = 0.02) positively predicted the improvement of UPDRSIII. The baseline rigidity score (β = 0.55, P < 0.0001) was identified as an independent factor to predict the improvement of rigidity.
UNASSIGNED: This study demonstrated significant improvements in total UPDRSIII scores and rigidity after 14-day treatment, with baseline rigidity scores and RMT identified as predictors of treatment response, underscoring the need for individualized therapy.

This study aimed to assess the predictive capacity of emerging serological markers, serum HBV RNA and HBcrAg, for HBeAg seroconversion in children with HBeAg-positive chronic hepatitis B (CHB). Treatment-naïve HBeAg-positive CHB children who admitted to the Liver Disease Center of Hunan Children\'s Hospital between April 2021 and September 2022 and received treatment with the combined entecavir and interferon-alpha treatment were recruited. Serum HBV RNA and HBcrAg were measured at baseline and Weeks 12, 24, and 48 of treatment. Our study showed that serum HBV RNA (HR = 0.71, 95% CI: 0.56-0.91, p = 0.006), HBcrAg (HR = 0.60, 95% CI: 0.43-0.84, p = 0.003), and HBsAg (HR = 0.49, 95%CI: 0.36-0.69, p < 0.001) at Week 12 were independent predictors of HBeAg seroconversion. ROC curve analysis presented that serum HBV RNA decline value (ΔHBV RNA) at Week 36 and HBcrAg decline value (ΔHBcrAg) at Week 12 (AUC = 0.871, p = 0.003 and AUC = 0.810, p = 0.003, respectively) could effectively predict HBeAg seroconversion. Furthermore, the optimal critical values were determined and the children with ΔHBV RNA > 3.759 log10 copies/mL at Week 36 or ΔHBcrAg >0.350 log10 U/mL at Week 12 more likely to achieve HBeAg seroconversion. The serum HBV RNA and HBcrAg provide new insights into the treatment of CHB in children. Early assessment of serum HBV RNA and HBcrAg during treatment can assist clinical decision-making and optimize individualized therapeutic approaches.

This study investigates the crucial role of immune- and epithelial-mesenchymal transition (EMT)-associated genes and non-coding RNAs in glioma development and diagnosis, given the challenging 5-year survival rates associated with this prevalent CNS malignant tumor. Clinical and RNA data from glioma patients were meticulously gathered from CGGA databases, and EMT-related genes were sourced from dbEMT2.0, while immune-related genes were obtained from MSigDB. Employing consensus clustering, novel molecular subgroups were identified. Subsequent analyses, including ESTIMATE, TIMER, and MCP counter, provided insights into the tumor microenvironment (TIME) and immune status. Functional studies, embracing GO, KEGG, GSVA, and GSEA analyses, unraveled the underlying mechanisms governing these molecular subgroups. Utilizing the LASSO algorithm and multivariate Cox regression, a prognostic risk model was crafted. The study unveiled two distinct molecular subgroups with significantly disparate survival outcomes. A more favorable prognosis was linked to low immune scores, high tumor purity, and an abundance of immune infiltrating cells with differential expression of non-coding RNAs, including miRNAs. Functional analyses illuminated enrichment of immune- and EMT-associated pathways in differentially expressed genes and non-coding RNAs between these subgroups. GSVA and GSEA analyses hinted at abnormal EMT status potentially contributing to glioma-associated immune disorders. The risk model, centered on OS-EMT-ICI genes, exhibited promise in accurately predicting survival in glioma. Additionally, a nomogram integrating the risk model with clinical characteristics demonstrated notable accuracy in prognostic predictions for glioma patients. In conclusion, OS-EMT-ICI gene and non-coding RNA expression emerges as a valuable indicator intricately linked to immune microenvironment dysregulation, offering a robust tool for precise prognosis prediction in glioma patients within the OBMRC framework.

Diabetic nephropathy (DN) represents a significant microvascular complication in diabetes, entailing intricate molecular pathways and mechanisms associated with cardiorenal vascular diseases. Prolonged hyperglycemia induces renal endothelial dysfunction and damage via metabolic abnormalities, inflammation, and oxidative stress, thereby compromising hemodynamics. Concurrently, fibrotic and sclerotic alterations exacerbate glomerular and tubular injuries. At a macro level, reciprocal communication between the renal microvasculature and systemic circulation establishes a pernicious cycle propelling disease progression. The current management approach emphasizes rigorous control of glycemic levels and blood pressure, with renin-angiotensin system blockade conferring renoprotection. Novel antidiabetic agents exhibit renoprotective effects, potentially mediated through endothelial modulation. Nonetheless, emerging therapies present novel avenues for enhancing patient outcomes and alleviating the disease burden. A precision-based approach, coupled with a comprehensive strategy addressing global vascular risk, will be pivotal in mitigating the cardiorenal burden associated with diabetes.

Breast cancer (BC) is the most prevalent malignancy among women worldwide. Traditional research models such as primary cancer cell and patient-derived tumor xenografts (PDTXs) have limitations. Cancer cells lack a tumor microenvironment (TME) and genetic diversity, whereas PDTXs are expensive and have a time-consuming preparation protocol. Therefore, alternative research models are warranted. Patient-derived organoids (PDOs) are a promising in vitro model. They mimic the TME, gene expression, and cell types of original cancer tissues. PDOs have been successfully developed from various cancers, including BC. In this review, we focused on the value and limitations of PDOs in BC research, including their characteristics and potential in drug development, personalized therapy, immunotherapy, and the application prospects of PDOs in drug testing and prognosis.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People\'s Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

Methods accurately predicting the responses of colorectal cancer (CRC) and colorectal cancer liver metastasis (CRLM) to personalized chemotherapy remain limited due to tumor heterogeneity. This study introduces an innovative patient-derived CRC and CRLM tumor model for preclinical investigation, utilizing 3d-bioprinting (3DP) technology. Efficient construction of homogeneous in vitro 3D models of CRC/CRLM is achieved through the application of patient-derived primary tumor cells and 3D bioprinting with bioink. Genomic and histological analyses affirm that the CRC/CRLM 3DP tumor models effectively retain parental tumor biomarkers and mutation profiles. In vitro tests evaluating chemotherapeutic drug sensitivities reveal substantial tumor heterogeneity in chemotherapy responses within the 3DP CRC/CRLM models. Furthermore, a robust correlation is evident between the drug response in the CRLM 3DP model and the clinical outcomes of neoadjuvant chemotherapy. These findings imply a significant potential for the application of patient-derived 3DP cancer models in precision chemotherapy prediction and preclinical research for CRC/CRLM.

UNASSIGNED: Gastric cancer (GC) is a prevalent malignancy of the digestive tract globally, demonstrating a substantial occurrence of relapse and metastasis, alongside the absence of efficacious treatment. Tumor progression and the development of cancer are linked to oxidative stress. Our objective was twofold: first, to determine distinct subcategories based on oxidative stress in GC patients, and second, to establish oxidative stress-related genes that would aid in stratifying the risk for GC patients.
UNASSIGNED: TCGA-STAD and GSE84437 datasets were utilized to obtain the mRNA expression profiles and corresponding clinical information of GC patients. Through consensus clustering analysis, distinct subgroups related to oxidative stress were identified. To uncover the underlying mechanisms, GSEA and GSVA were performed. xCell, CIBERSORT, MCPCounter, and TIMER algorithms were employed to evaluate the immune microenvironment and immune status of the different GC subtypes. A prognostic risk model was developed using the TCGA-STAD dataset and substantiated using the GSE84437 dataset. Furthermore, qRT-PCR was employed to validate the expression of genes associated with prognosis.
UNASSIGNED: Two distinct subtypes of oxidative stress were discovered, with markedly different survival rates. The C1 subtype demonstrated an activated immune signal pathway, a significant presence of immune cell infiltration, high immune score, and a high microenvironment score, indicating a poor prognosis. Moreover, a prognostic signature related to oxidative stress (IMPACT and PXDN) was able to accurately estimate the likelihood of survival for patients with gastric cancer. A nomogram incorporating the patients\' gender, age, and risk score was able to predict survival in gastric cancer patients. Additionally, the expression of IMPACT and PXDN showed a strong correlation with overall survival and the infiltration of immune cells.
UNASSIGNED: Based on signatures related to oxidative stress, we developed an innovative system for categorizing patients with GC. This stratification enables accurate prognostication of individuals with GC.