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It is often difficult to close and repair irregular wounds such as gums with traditional dressings that have a fixed shape, while injectable hydrogels that can be molded in situ offer tremendous advantages in treating complex, large wounds and can better seal or fill a variety of irregular wounds. While the majority of injectable hydrogels developed in current research have poor mechanical properties and biocompatibility and obstacles to clinical applications, this paper reports an injectable hydrogel that can be molded in situ with appropriate mechanical properties, excellent biocompatibility, and antibacterial activity. Under UV irradiation, a methacrylate-gelatin/poly-(vinyl alcohol) loaded with copper ion cross-linked tannic acid nanoparticle (GelMA/PVA-TA/Cu2+NPs) precursor solution could rapidly gel within 45 s and adhere stably to the wound tissue by topological adhesion and hydrogen bonding. Through hydrogen bonding and physical entanglement, TA/Cu2+NPs are anchored in the GelMA/PVA interpenetrating network to form the GelMA/PVA-TA/Cu2+NPs composite hydrogel. This injectable hydrogel, which can quickly achieve photocurable in situ molding, can not only achieve closure and repair of irregular wounds but also has appropriate swelling characteristics, absorbing tissue exudate and blocking bacteria, while providing a good moist environment for wounds and continuously promoting wound healing. Cytotoxicity tests and wound healing experiments have shown that GelMA/PVA-TA/Cu2+NPs injectable hydrogels have good biocompatibility and wound closure and repair ability and have promising applications in the treatment of irregularly wounded orifices. [ABSTRACT FROM AUTHOR]

Background: Poly (ADP‐ribose) polymerase (PARP) inhibitors (PARPi) are approved for the treatment of various solid tumors. In gastric cancer, genes commonly harbor mutations in the homologous recombination DNA repair pathway, potentially increasing sensitivity to PARPi. Pamiparib (BGB‐290) is a small molecule inhibitor of PARP1 and PARP2. Methods: The PARALLEL‐303 study (NCT03427814) investigated the efficacy and safety of pamiparib 60 mg orally (PO) twice daily (BID) versus placebo PO BID as maintenance therapy in patients with inoperable locally advanced or metastatic gastric cancer that responded to platinum‐based first‐line chemotherapy. The primary endpoint of this double‐blind, randomized, global phase 2 study was progression‐free survival (PFS) (RECIST version 1.1; per investigator assessment). Secondary endpoints included overall survival (OS) and safety. Results: In total, 136 patients were randomized 1:1 to receive pamiparib (n = 71) or placebo (n = 65). Median PFS was numerically longer with pamiparib versus placebo but did not reach statistical significance (3.7 months [95% confidence interval (CI): 1.9, 5.3] vs. 2.1 months [95% CI: 1.9, 3.8]; hazard ratio 0.8 [95% CI: 0.5, 1.2]; p = 0.1428). Median OS was 10.2 months (95% CI: 8.7, 16.3) in the pamiparib arm versus 12.0 months (95% CI: 8.2, not estimable) in the placebo arm. Overall, 8 patients (11.3%) in the pamiparib arm and 2 patients (3.1%) in the placebo arm experienced ≥1 TEAE leading to treatment discontinuation. Conclusions: Maintenance pamiparib did not meet statistical significance for superiority versus placebo for PFS, but was well tolerated with few treatment discontinuations; no unexpected safety signals were identified. [ABSTRACT FROM AUTHOR]

Nine imported Zika virus (ZIKV) infections (four through temperature monitoring and epidemiological investigation at entry and five by active surveillance tracking of index case contacts during follow-up; from Venezuela [n = 5], Samoa [n = 3] and both Samoa and Fiji [n = 1]) were detected in mainland China from February 1 to 29, 2016. The minimal incubation period lasted 5.2 days, with mean lag time to diagnosis of 2.6 days. Diagnosis relied on positive real-time reverse transcriptase polymerase chain reaction for ZIKV RNA in serum (n = 7), urine (n = 4) or saliva (n = 3), respectively. All cases recovered rapidly without serious complications. [ABSTRACT FROM AUTHOR]

Background: Assessing the 3-dimensional (3D) relationship between critical anatomical structures and the surgical channel can help select percutaneous endoscopic lumbar discectomy (PELD) approaches, especially at the L5/S1 level. However, previous evaluation methods for PELD were mainly assessed using 2-dimensional (2D) medical images, making the understanding of the 3D relationship of lumbosacral structures difficult. Artificial intelligence based on automated magnetic resonance (MR) image segmentation has the benefit of 3D reconstruction of medical images., Objectives: We developed and validated an artificial intelligence-based MR image segmentation method for constructing a 3D model of lumbosacral structures for selecting the appropriate approach of percutaneous endoscopic lumbar discectomy at the L5/S1 level., Study Design: Three-dimensional reconstruction study using artificial intelligence based on MR image segmentation., Setting: Spine and radiology center of a university hospital., Methods: Fifty MR data samples were used to develop an artificial intelligence algorithm for automatic segmentation. Manual segmentation and labeling of vertebrae bone (L5 and S1 vertebrae bone), disc, lumbosacral nerve, iliac bone, and skin at the L5/S1 level by 3 experts were used as ground truth. Five-fold cross-validation was performed, and quantitative segmentation metrics were used to evaluate the performance of artificial intelligence based on the MR image segmentation method. The comparison analysis of quantitative measurements between the artificial intelligence-derived 3D (AI-3D) models and the ground truth-derived 3D (GT-3D) models was used to validate the feasibility of 3D lumbosacral structures reconstruction and preoperative assessment of PELD approaches., Results: Artificial intelligence-based automated MR image segmentation achieved high mean Dice Scores of 0.921, 0.924, 0.885, 0.808, 0.886, and 0.816 for L5 vertebrae bone, S1 vertebrae bone, disc, lumbosacral nerves, iliac bone, and skin, respectively. There were no significant differences between AI-3D and GT-3D models in quantitative measurements. Comparative analysis of quantitative measures showed a high correlation and consistency., Limitations: Our method did not involve vessel segmentation in automated MR image segmentation. Our study's sample size was small, and the findings need to be validated in a prospective study with a large sample size., Conclusion: We developed an artificial intelligence-based automated MR image segmentation method, which effectively segmented lumbosacral structures (e.g., L5 vertebrae bone, S1 vertebrae bone, disc, lumbosacral nerve, iliac bone, and skin) simultaneously on MR images, and could be used to construct a 3D model of lumbosacral structures for choosing an appropriate approach of PELD at the L5/S1 level.