Predictive models for personalized precision medical intervention in spontaneous regression stages of cervical precancerous lesions.

BACKGROUND: During the prolonged period from Human Papillomavirus (HPV) infection to cervical cancer development, Low-Grade Squamous Intraepithelial Lesion (LSIL) stage provides a critical opportunity for cervical cancer prevention, giving the high potential for reversal in this stage. However, there is few research and a lack of clear guidelines on appropriate intervention strategies at this stage, underscoring the need for real-time prognostic predictions and personalized treatments to promote lesion reversal. METHODS: We have established a prospective cohort. Since 2018, we have been collecting clinical data and pathological images of HPV-infected patients, followed by tracking the progression of their cervical lesions. In constructing our predictive models, we applied logistic regression and six machine learning models, evaluating each model's predictive performance using metrics such as the Area Under the Curve (AUC). We also employed the SHAP method for interpretative analysis of the prediction results. Additionally, the model identifies key factors influencing the progression of the lesions. RESULTS: Model comparisons highlighted the superior performance of Random Forests (RF) and Support Vector Machines (SVM), both in clinical parameter and pathological image-based predictions. Notably, the RF model, which integrates pathological images and clinical multi-parameters, achieved the highest AUC of 0.866. Another significant finding was the substantial impact of sleep quality on the spontaneous clearance of HPV and regression of LSIL. CONCLUSIONS: In contrast to current cervical cancer prediction models, our model's prognostic capabilities extend to the spontaneous regression stage of cervical cancer. This model aids clinicians in real-time monitoring of lesions and in developing personalized treatment or follow-up plans by assessing individual risk factors, thus fostering lesion spontaneous reversal and aiding in cervical cancer prevention and reduction.

Microencapsulated polyphenol extracts from Georgia-grown pomegranate peels delay lipid oxidation in salad dressing during accelerated and ambient storage conditions.

Lipid oxidation is a major cause of quality deterioration in salad dressings. This study evaluated the effect of incorporating microencapsulated polyphenol extracts via spray drying from pomegranate peels (MPP) to delay lipid oxidation in Italian-style salad dressings (ISD) during accelerated (55°C) and ambient (25°C) storage conditions. ISDs, prepared at high (5000 rpm) and low (250 rpm) shear rates conditions, were formulated with unencapsulated polyphenol extracts from pomegranate peels (PPP), MPP, and/or grape seed extract (GSE). Lipid oxidation in ISDs was evaluated by measuring peroxide value (PV), iodine value (IV), and TBARS, stored in accelerated and ambient conditions for 21 days and 8 weeks, respectively. Tannis in extracts were measured via HPLC-DAD and the total hydrolyzable tannin content of PPP and MPP was 283.09 and 427.74 (mg/g extract), respectively. Condensed tannins were not detected in PPP and MPP but were found in GSE (348.53 mg/g extract). Salad dressings prepared at high shear rates had significantly (p < .05) higher emulsion stability than those homogenized at low shear rates. Mixing conditions did not affect the lipid oxidative stability of IDSs. Salad dressing stored under accelerated storage had higher lipid oxidation (higher PV, lower IV, and higher TBARS) after 21 days than IDSs stored under ambient conditions for 8 weeks. ISDs prepared with MPPP showed significantly (p < .05) lower lipid oxidation than the other ISDs at the end of the shelf life studies. Results from the accelerated storage suggested that incorporating MPP could have extended the shelf life of IDSs by 20% compared to using unencapsulated polyphenol extracts. The study demonstrated that MPP delays lipid oxidation in ISDs during storage more effectively than unencapsulated extracts. MPP may serve as a natural and effective functional food ingredient for controlling lipid oxidation in high-lipid and acidified foods.

IMD-Net: Interpretable multi-scale detection network for infrared dim and small objects.

This study proposed an interpretable multi-scale infrared small object detection network (IMD-Net) design method to improve the precision of infrared small object detection and contour segmentation in complex backgrounds. To this end, a multi-scale object enhancement module was constructed, which converted artificially designed features into network structures. The network structure was used to enhance actual objects and extract shallow detail and deep semantic features of images. Next, a global object response, channel attention, and multilayer feature fusion modules were introduced, combining context and channel information and aggregated information, selected data, and decoded objects. Finally, the multiple loss constraint module was constructed, which effectively constrained the network output using multiple losses and solved the problems of high false alarms and high missed detections. Experimental results showed that the proposed network model outperformed local energy factor (LEF), self-regularized weighted sparse model (SRWS), asymmetric contextual modulation (ACM), and other state of the art methods in the intersection-over-union (IoU) and Fmeasure values by 10.8% and 11.3%, respectively. The proposed method performed best on the currently available datasets, achieving accurate detection and effective segmentation of dim and small objects in various infrared complex background images.