Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach.

Objectives: The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients.

Methods: Clinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed.

Initial [18F]DCFPyL PET/CT in treatment-naïve prostate cancer: correlation with post-ADT PSA outcomes and recurrence.

Purpose: Positron emission tomography (PET) with prostate-specific membrane antigen (PSMA) targeting tracers has emerged as a valuable diagnostic tool for prostate cancer (PCa), androgen deprivation therapy (ADT) stands as the cornerstone treatment for advanced PCa, yet forecasting the response to hormonal therapy poses a significant clinical hurdle.

Methods: In a prospective cohort of 86 PCa patients undergoing short-term ADT, this study evaluated the prognostic potential of [18F]DCFPyL PET/CT scans. Comprehensive data encompassing clinical profiles, baseline prostate-specific antigen (PSA) levels, and imaging metrics were assessed.

Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects.

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis.

Performance of F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study.

Unlabelled: This study aimed to investigate the performance of F-DCFPyL positron emission tomography/computerized tomography (PET/CT) models for predicting benign-vs-malignancy, high pathological grade (Gleason score > 7), and clinical D'Amico classification with machine learning. The study included 138 patients with treatment-naïve prostate cancer presenting positive F-DCFPyL scans. The primary lesions were delineated on PET images, followed by the extraction of tumor-to-background-based general and higher-order textural features by applying five different binning approaches.

Effectiveness of Elastic Band Exercises on the Functional Fitness of Older Adults in Long-Term Care Facilities.

Background: Population aging has caused a rise in the institutionalization, disability, and mortality rates of older adults worldwide. Older adults are able to engage in muscle training. Elastic band exercises can safely and effectively improve the upper and lower muscle strength and balance of older adults.

Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using Deep Reinforcement Learning.

We study the foot plantar sensor placement by a deep reinforcement learning algorithm without using any prior knowledge of the foot anatomical area. To apply a reinforcement learning algorithm, we propose a sensor placement environment and reward system that aims to optimize fitting the center of pressure (COP) trajectory during the self-selected speed running task. In this environment, the agent considers placing eight sensors within a 7 × 20 grid coordinate system, and then the final pattern becomes the result of sensor placement.

A case-control study about the association between vascular endothelial growth inhibitor gene polymorphisms and breast cancer risk in female patients in Northeast China.

Objective: The inhibition of the neovascularization in tumors is a potential therapeutic target of cancer. Vascular endothelial growth inhibitor (VEGI) is a member of the TNF superfamily which has the ability to suppress the formation of new vessels in tumors. In order to study the association between VEGI gene polymorphisms and breast cancer risk, a case-control study was conducted in Chinese Han women in Northeast China.

Association of TNF-α, TNFRSF1A and TNFRSF1B gene polymorphisms with the risk of sporadic breast cancer in northeast Chinese Han women.

Background: The interaction of tumor necrosis factor-α (TNF-α) with its receptors: TNFRSF1A and TNFRSF1B is critical for the promotion of tumor growth, invasion and metastasis. To better understand the roles of single nucleotide polymorphisms (SNPs) in the TNF-α, TNFRSF1A and TNFRSF1B genes in the development of breast cancer, we explored the associations between SNPs in these three genes and breast cancer susceptibility in northeast Chinese Han women.

Methodology/principal Findings: This case-control study was conducted among 1016 breast cancer patients and 806 age-matched healthy controls.