Timely palliative care can reduce the disease burden and improve quality of life in patients with cancer. Although several studies have developed assessment models for palliative care needs in cancer patients, the quality and clinical applicability of these models remain unclear. To systematically review existing assessment models for palliative care needs in patients with cancer, with a focus on their characteristics, predictors, risk of bias, and applicability. A systematic search was conducted in PubMed, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, China National Knowledge Infrastructure (CNKI) through September 12, 2025. Data extraction and evaluation were rigorously performed by two researchers based on the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 5714 articles were identified, and eight studies were included, which covered 24 models for assessing palliative care needs. The sample size of the included studies ranged from 179 to 54,628, with areas under the curve ranging from 0.724 to 0.998. The models in all the included studies encompassed four categories of predictive factors: general demographic data, symptom/functional assessments, laboratory indicators, and treatment status. Five studies were rated as having a high risk of bias, primarily due to high risks associated with participants and conclusions, with generally low applicability. Existing models demonstrate potential for identifying patients with cancer who have increased palliative care needs using routinely collected clinical data. Commonly included predictors were symptom burden, functional status, laboratory parameters, treatment-related factors, and demographic characteristics. However, the overall body of evidence is constrained by a substantial risk of bias, particularly arising from inappropriate data sources, limited sample sizes, suboptimal handling of continuous variables, insufficient reporting of missing data, and the lack of robust internal or external validation. In addition, many models adopted mortality-based surrogate outcomes rather than clinically meaningful indicators of palliative care needs. Therefore, the currently available models should be interpreted with caution, and further high-quality model development and external validation are required before they can support broader routine clinical implementation. Future research should prioritize clinically actionable outcomes and incorporate patient-, caregiver-, and family-level factors to improve the relevance of these models for referral decisions and care planning.
The presence of upper limb dysfunction is a common complication following surgical interventions for breast cancer patients. This study aims to investigate the impact of digital rehabilitation therapy on the recovery of upper limb function in breast cancer patients after surgery. This research enrolled 52 breast cancer patients who underwent modified radical mastectomy at the Affiliated Xinhua Hospital of Shanghai Jiao Tong University School of Medicine between September 2022 and October 2023. A comparative analysis was conducted between the control group and the experimental group regarding their upper limb movements (flexion, extension, abduction, adduction), as well as their quality of life indicators. Following rehabilitation training, the extent of improvement in flexion, extension, abduction, and adduction of the affected upper limbs in the experimental group was significantly higher than that observed in the control group (p < 0.05). Notably, after the same 6-week period, the range of motion in flexion and abduction of the affected limbs in the experimental group did not significantly differ from that of their contralateral limbs (p > 0.05). According to scores on the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30, the experimental group also showed significant improvements in role functioning, emotional functioning, and social functioning compared to the control group (p < 0.05). The application of digital rehabilitation therapy in the recovery process of upper limb function among breast cancer patients post-surgery has proven efficacious in enhancing objective recovery of shoulder range of motion and improving several domains of quality of life, although no significant between-group differences were observed in patient-reported functional outcomes. Chinese Clinical Trial Registry (ChiCTR), ChiCTR2500107529, registered on August 13, 2025.
Herein a hierarchical tubular Fe,Co-MoS2/Ag@NCMTs composite was engineered via a sacrificial-template strategy utilizing tubular polypyrrole (PPy) to fabricate nitrogen-doped carbon microtubes (NCMTs), which serve as supports for Fe, Co-doped MoS2/Ag nanosheets (NSs). This unique architecture synergistically integrates the advantages of Fe, Co co-doped MoS2 NSs, one-dimensional(1D) tubular geometry, and Ag nanoparticles (NPs) incorporation, resulting in enhanced electrical conductivity, structural stability, and catalytic activity. The hierarchical design effectively mitigates the aggregation of MoS2 NSs during the preparation processes, while the transition metals doping and Ag NPs facilitate efficient electron transfer and reaction kinetics. Consequently, the composite demonstrates exceptional catalytic efficiency in converting 4-nitrophenol (4-NP) to 4-aminophenol (4AP). Furthermore, the Fe,Co-MoS2@NCMTs composites exhibit universal in-situ loading capability for noble Surfaces Physicochemical Engineering Aspects metal NPs (e.g., Au, Pd, Ru), enabling tailored functionalities for diverse catalytic applications. This scalable platform, combining hierarchical tubular architecture with synergistic component interactions, exemplifies a versatile and high-performance catalytic system with broad applicability in environmental remediation, organic synthesis, and beyond.
Green hydrogels hold promise for biomedical applications but are limited by weak mechanical properties. Here, we report a chitosan (CS)-reinforced polyvinyl alcohol hydrogel (PVA) fabricated via eco-friendly freeze-thaw cycles, achieving dual enhancement in mechanical strength and antibacterial efficacy. Incorporating 3 wt% chitosan increased compressive strength from 0.36 MPa in pure PVA to 2.92 MPa at 80% strain, while maintaining over 80% recovery after cyclic compression. The porous structure of the fabricated hydrogel and hydrogen-bonded network enabled sustained ciprofloxacin (CIP) release over 72 h. Antibacterial performance against Escherichia coli and Staphylococcus aureus improved significantly with CS addition, reaching 99% efficiency at 3% CS content, while CIP integration achieved complete bacterial eradication (100% efficiency). Synergistic action between the contact-based bactericidal activity of CS and CIP sustained release eliminated colony formation, demonstrating superior inhibition of both Gram-negative and Gram-positive bacteria. This green strategy delivers a mechanically robust, multifunctional hydrogel for infection-resistant biomedical applications.
Plant root-associated proteins promote plant growth and enhance stress tolerance. They participate in signaling and plant growth regulation. It is clear that they play key roles in plant growth, development and environmental adaptation. At present, the root-associated proteins have not been fully discovered. It is essential to identify latent root-associated proteins. Traditional methods (proteomic analysis, transcriptome and expression analysis) for determining root-associated proteins are highly relied on the data generated by biochemical experiments, which are always expensive and time-consuming. On the other hand, the current computational models show weak ability, providing great spaces for improvement. In this study, we propose a new computational model, Hypergraph-Root, for predicting root-associated proteins. The model employed several feature types to represent proteins, which were derived from proteins BLOSUM62 and position-specific scoring matrices as well as by a protein language model. These features were improved by hypergraph convolutional network and multi-head attention. The final predicted result was yielded by a fully connected layer. The model yielded high performance with AUC about 0.9 on training and independent datasets. It had evident advantages compared with existing models. Some additional tests were conducted to prove the rationality of the model’s structure.
