Publications by authors named "X Y Zhang"

Background: Primary pulmonary lymphoepithelioma-like carcinoma (PLELC) is a rare subtype of non-small-cell lung cancer. This study aims to compare the efficacy and safety of perioperative PD-1/PD-L1 inhibitor plus chemotherapy versus chemotherapy alone in stage II-IIIB PLELC patients.

Patients And Methods: This retrospective study included stage II-IIIB PLELC patients.

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  • The study aimed to explore how non-drug treatments affect sarcopenic obesity (SO) through a meta-analysis of randomized controlled trials (RCTs).
  • Results indicated that exercise and nutrition can improve various physical health metrics, such as body fat percentage (PBF) and grip strength, but individual interventions showed limited effectiveness.
  • The combination of exercise and nutritional interventions proved to be the most effective approach for improving outcomes related to SO, highlighting the potential role of physical therapy as well.
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  • - Plant lipid transfer proteins (LTPs) are crucial for moving lipids between membranes, impacting pollen wall development, including the pollen aperture structure.
  • - The study focuses on a rice mutant called pollen aperture defect 1 (Ospad1), which shows male sterility due to abnormal pollen grain development linked to a non-specific LTP that fails to properly bind lipids.
  • - Researchers found that OsPAD1 interacts with a gene involved in pollen development, providing new insights into how LTPs function in forming pollen apertures, which could have broader implications for other cereal crops.
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Purpose: To explore if recentering the humeral head could improve shoulder abduction in shoulders with irreparable massive rotator cuff tears (IMRCTs).

Methods: Nine fresh-frozen cadaveric shoulders were used to simulate the intact condition and IMRCTs as controls. Four 'recenter' interventions were then sequentially applied: subacromial interposition (SI) using 4- and 8-mm spacers and superior capsule reconstruction (SCR) using two- and four-layer fascia lata grafts.

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Background: Medical Visual Question Answering (MedVQA) enhances diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret medical images. This study aims to redefine MedVQA as a generation task that mirrors human-machine interaction and to develop a model capable of integrating complex visual and textual information.

Methods: We constructed a large-scale medical visual-question answering dataset, PMC-VQA, containing 227,000 VQA pairs across 149,000 images that span various modalities and diseases.

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