Haikou City People's Hospital

Background: Periprostatic adipose tissue (PPAT) fibrosis is a histological feature potentially linked to prostate cancer (PCa) aggressiveness, though its role is not fully understood. This study investigates the correlation between PPAT fibrosis and PCa aggressiveness and develops a radiomics model based on PPAT MRI features for non-invasive prediction. Methods: This retrospective study included 51 patients who underwent radical prostatectomy. PPAT samples were collected, stained with Sirius Red and quantitatively evaluated for fibrosis using 12 indices via 3D reconstruction with Imaris software. Patients were stratified by cancer aggressiveness based on Grade Groups. Radiomic features were extracted from T1-weighted MRIs of the PPAT. An XGBoost model was developed to predict aggressiveness using these features. Results: Significant correlations were found between multiple PPAT fibrosis indices and PCa aggressiveness (p < 0.05), with more aggressive tumors showing increased fiber complexity. PPAT fibrosis was also significantly associated with primary tumor location in the peripheral zone (p < 0.05). Conversely, PPAT volume showed no significant correlation with aggressiveness (p = 0.616). The radiomics model based on PPAT features achieved an AUC of 0.86 in predicting cancer aggressiveness. Conclusions: PPAT fibrosis is a promising marker of PCa aggressiveness, superior to PPAT volume. The significant link with tumor location provides new insights into the tumor microenvironment (TME). MRI-based radiomics of PPAT offers a potential non-invasive method for assessing fibrosis and aggressiveness, aiding in early diagnosis and personalized treatment.

BackgroundStroke-associated infection (SAI) adversely affects the prognosis of acute ischemic stroke (AIS) patients, contributing to poorer functional outcomes and survival. The absence of validated tools for early SAI diagnosis and risk stratification in AIS remains a critical clinical gap. This study aims to develop and validate a machine learning-based prediction model that leverages phase-rectified signal averaging (PRSA) indicators closely linked to SAI pathogenesis for timely risk assessment in emergency settings.MethodsThis derivative cohort comprised 392 patients diagnosed with AIS between 2021 and 2023. The variables considered in this study included age, sex, heart rate variability (HRV) parameters, and PRSA parameters. Variable selection was performed using the Boruta algorithm and correlation analysis. Ten machine learning methods were employed to construct the SAI diagnostic model, and its performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), sensitivity, specificity, and an internal validation cohort. The predictive model outcomes were interpreted using Shapley Additive Explanations (SHAP).ResultsThrough variable screening, 16 indicators were identified as independent predictive factors for SAI in AIS patients. Utilizing these indicators, 10 machine learning models were developed. Among the machine learning algorithms, the Categorical Boosting (CAT) model demonstrated superior performance, achieving an accuracy of 91%, sensitivity of 88%, specificity of 92%, F1-score of 74%, and an AUC of 0.939 (95% CI: 0.894–0.984). Furthermore, SHAP identified cardiac deceleration capacity (DC) and the National Institute of Health Stroke Scale (NIHSS) at admission as the primary determinants influencing the predictions of the machine learning models.ConclusionMachine learning algorithms, when integrated with demographic and clinical factors, demonstrated accurate prediction of SAI in patients with AIS. The CAT model exhibited robust performance, highlighting its potential to enhance early detection and treatment in clinical practice. Additionally, PRSA markers may serve as potential targets for preventive interventions, enabling more judicious, timely, and targeted use of antibiotics. This approach opens new avenues for research into the prophylactic management of SAI.

Psoriasis is a chronic, recurrent, inflammatory autoimmune skin disease. The advent of biologics, such as ixekizumab, has provided a breakthrough for patients with moderate-to-severe disease by specifically inhibiting the interleukin-17A (IL-17A) mediated inflammatory cascade. The human papillomavirus (HPV) vaccine is a cornerstone in the prevention of HPV-associated malignancies, with well-established safety and efficacy profiles. However, due to the unique immune status of patients with psoriasis, cutaneous adverse events following HPV vaccination during biologic therapy have rarely been reported. Seborrheic keratosis (SK) is a common benign epidermal tumour. Its eruptive variant, known as the Leser-Trélat sign, has traditionally been associated with internal malignancies; however, recent studies suggest potential links to inflammation, pharmacological agents, and immune alterations. We report the case of a 37-year-old female with psoriasis who, after achieving stable disease control on ixekizumab, developed widespread eruptive brown macules, patches, and papules within one month of receiving a quadrivalent HPV vaccine. Histopathological examination confirmed SK, and malignancy, as well as other associated disorders, were excluded. The lesions faded following oral acitretin therapy. This case highlights the possibility that ixekizumab in combination with HPV vaccination may synergistically alter immune homeostasis, thereby precipitating eruptive SK. We discuss potential mechanisms, propose clinical management considerations, and provide insights that may inform future research and clinical practice. The ultimate aim is to contribute to the refinement of guidelines on vaccination in patients receiving biologic therapy for psoriasis, ensuring both therapeutic efficacy and patient safety.