Efficacy of the posterior nasal nerve resection combined with hormone transnasal nebulization on difficult-to-treat rhinosinusitis: a retrospective analysis.

OBJECTIVE: A retrospective analysis was performed to explore the clinical effect of the Posterior Nasal Nerve (PNN) resection combined with hormone transnasal nebulization on Difficult-to-Treat Rhinosinusitis (DTRS). METHODS: A total of 120 DTRS patients were selected and divided into a control group (n = 60) and a study group (n = 60) according to different treatments. The control group patients were treated via PNN resection, followed by normal saline transnasal nebulization; the study group patients were given PNN resection and then treated with budesonide suspension transnasal nebulization. Subsequently, the comparison was performed between the two groups in terms of (1) Clinical baseline characteristics; (2) Sino-nasal Outcome Test (SNOT)-22 scores before treatment and after 3-months, 6-months and 12-months of treatment; (3) Lund-MacKay scores before treatment and after 10, 30, 90, and 180 days of treatment; (4) Incidence of adverse reactions during treatment. RESULTS: There was no significant difference in SNOT-22 or Lund-Kennedy scores between the two groups before treatment (p > 0.05). After treatment, the SNOT-22 and Lund-Kennedy scores of the control and the study groups were decreased, and compared with the control group, the SNOT-22 and Lund-Kennedy scores in the study group improved more significantly (p < 0.05). In addition, the study group and the control group presented with 1 and 4 cases of nasal adhesion, 2 and 3 cases of epistaxis, 1 and 4 cases of sinus orifice obstruction, 1 and 3 cases of lacrimal duct injuries, respectively. The incidence of adverse reactions in the study group was significantly lower than that in the control group (8.3% vs. 23.3%) (p < 0.05). CONCLUSION: PNN resection combined with hormone transnasal nebulization treatment can improve the symptoms and quality of life of DTRS patients, with good clinical efficacy but few adverse reactions. Therefore, such combination treatment deserves a promotion and application clinically. LEVEL OF EVIDENCE: Level 3.

Functional co-delivery nanoliposomes based on improving hypoxia for increasing photoimmunotherapy efficacy of cold tumors.

Cold tumors lack T cells infiltration and have low immunogenicity, resulting insufficient immunotherapy response. Therefore, how to realize the transformation from cold tumor to hot tumor is an urgent problem to be solved. Photodynamic therapy can induce immunogenic death of tumor cells (ICD) and activate T lymphocytes to produce tumor immune response. However, hypoxia in the cold tumor microenvironment limits the effectiveness of photodynamic therapy. So in this article, MET-HMME/CAT-HMME@Nlip as a functional co-delivery nanoliposomes was constructed based on overcoming the above problems. Firstly, the oxygen-deficient state could be improved by the following two ways, one is catalase loaded in CAT-HMME@Nlip can decompose high concentration hydrogen peroxide to produce oxygen, and the other is metformin loaded in MET-HMME@Nlip can decrease oxygen consumption by inhibiting of mitochondrial respiration. And then with the increase of substrate oxygen concentration, the sensitivity of photodynamic therapy can be greatly improved and the anti-tumor immune response by PDT-induced ICD can also be enhanced obviously. In addition, metformin could act as a small molecule immune checkpoint inhibitor to reduce the expression of PD-L1 on the surface of tumor cells, thereby effectively improving the specific killing ability of cytotoxic T cells to tumor cells which could not only erasing the primary tumor, but also inhibiting the growth of simulated distant tumors through the immune memory function. This study provides a new idea for improving the clinical treatment effect of hypoxic cold tumors, especially for tumors that could not benefit from immunotherapy due to low or no expression of PD-L1 protein on the surface of tumor cells.

Childhood maltreatment and alcohol and tobacco use trajectories in rural Chinese adolescents.

BACKGROUND: There is a high prevalence of childhood maltreatment among Chinese children and adolescents, but little is known about its impact on alcohol and tobacco use trajectories and how positive school and neighborhood environments moderate the associations. The objective of this study was to assess the association between multiple forms of childhood maltreatment and longitudinal alcohol and tobacco use trajectories, and to assess the possibility that perceived connections to school and neighborhood moderate these associations. METHODS: This longitudinal cohort study included 2594 adolescents (9 to 13 years) from a low-income rural area in China. Childhood exposure to abuse and neglect was assessed using the Childhood Trauma Questionnaire. Participants reported past-month alcohol and tobacco use at three time points over 1 year. RESULTS: Growth curve models revealed that childhood sexual abuse was associated with a higher risk of past-month drinking (OR = 1.53, 95% CI 1.19-2.03, p < 0.001) and smoking (OR = 1.82, 95% CI 1.30-2.55, p < 0.001). Neglect was associated with a higher risk of past-month drinking (OR = 1.52, 95% CI 1.06-1.90, p < 0.05) and smoking (OR = 2.02, 95% CI 1.34-3.02, p < 0.001). None of the maltreatment forms predicted a faster increase in either drinking or smoking. These associations were found independent of personal, family, and contextual characteristics. School and neighborhood connection moderated the association between physical abuse and past-month drinking, such that physical abuse was associated with a greater risk of drinking only for youth who perceived low school or neighborhood connections. CONCLUSIONS: Findings demonstrate the importance of early experiences of childhood maltreatment for adolescent alcohol and tobacco use. Enhancing school and neighborhood connectedness for physically abused youth may help protect them from alcohol use.

Comparison of risk prediction models for the progression of pelvic inflammatory disease patients to sepsis: Cox regression model and machine learning model.

Introduction: The present study presents the development and validation of a clinical prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to predict the probability of pelvic inflammatory disease (PID) progressing to sepsis. Methods: A retrospective cohort study was conducted, gathering clinical data of patients diagnosed with PID between 2008 and 2019 from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who met the Sepsis 3.0 diagnostic criteria were selected, with sepsis as the outcome. Univariate Cox regression and stepwise Cox regression were used to screen variables for constructing a nomogram. Moreover, an RSF model was created using machine learning algorithms. To verify the model's performance, a calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve were utilized. Furthermore, the capabilities of the two models for estimating the incidence of sepsis in PID patients within 3 and 7 days were compared. Results: A total of 1064 PID patients were included, of whom 54 had progressed to sepsis. The established nomogram highlighted dialysis, reduced platelet (PLT) counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts as significant predictive factors. The areas under the curve (AUCs) of the nomogram for prediction of PID progression to sepsis at 3-day and 7-day (3-/7-day) in the training set and the validation set were 0.886/0.863 and 0.824/0.726, respectively, and the C-index of the model was 0.8905. The RSF displayed excellent performance, with AUCs of 0.939/0.919 and 0.712/0.571 for 3-/7-day risk prediction in the training set and validation set, respectively. Conclusion: The nomogram accurately predicted the incidence of sepsis in PID patients, and relevant risk factors were identified. While the RSF model outperformed the Cox regression models in predicting sepsis incidence, its performance exhibited some instability. On the other hand, the Cox regression-based nomogram displayed stable performance and improved interpretability, thereby supporting clinical decision-making in PID treatment.

Gallbladder polypoid lesions: Current practices and future prospects.

ABSTRACT: Gallbladder polypoid lesions (GPLs) refer to any elevated lesion of the mucosal surface of the gallbladder wall, and the prevalence is estimated to be between 0.9% and 12.1%. GPLs include benign polyps and malignant polyps. Benign polyps are further classified as non-neoplastic polyps and neoplastic polyps. Cholesterol polyps are the most common benign polyps and adenocarcinoma is the main type of malignant polyp. Hepatitis B virus infection, liver function abnormalities, dyslipidemia, and obesity are the main risk factors for GPLs. Studies of biological mechanisms have focused on malignant gallbladder polyps, the development of which is regulated by hormone levels in vivo , gut microbiota, inflammation, oxidative stress, Salmonella typhimurium , and related molecules. Diagnostic modalities include chemical examination and imaging examination, with imaging examination currently being the mainstay. Treatment of patients with GPLs is based on the presence or absence of symptoms, age, size of the polyps, tendency of the polyp to increase, and risk factors for symptomatic malignancy to determine whether surgery should be performed.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.