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Abstract The intertwining between the life cycle of plants and their pathogens made the plant circadian clock an integral constituent of the plant immune system. Reciprocally, pathogens were also found to perturb the expression pattern of certain clock genes. However, how pathogens influence clock components remains largely unknown. Here we show that an oomycete effector HaRxL10 directly targets Arabidopsis central clock component CCA1 HIKING EXPEDITION (CHE) to manipulate its function. HaRxL10 stabilises CHE by disrupting E3 ligase ZEITLUPE-mediated CHE protein degradation. Surprisingly, the accumulation of CHE does not enhance but rather suppresses CHE function, inhibiting its binding to the downstream gene promoter. HaRxL10 triggers reprogramming of the transcriptome including expression of genes related to circadian oscillations. Moreover, HaRxL10 hijacks CHE to repress plant immunity and manipulate physiological processes, including hypocotyl growth and flowering. Taken together, our study discovers the first plant pathogen effector that directly targets a plant circadian clock component and elucidates the underlying molecular mechanism.

Abstract Pelvic inflammatory disease (PID) is a critical global health concern with the potential to lead to adverse outcomes, including infertility and chronic pelvic pain. Since PID is often caused by ascending vaginal infections or urinary tract infections, understanding the treatment of both is critical to preventing PID. Meanwhile, the emergence of drug‐resistant and persistently infected strains poses a growing challenge. This review discusses current clinical treatments for the prevention of PID from the physiologic basis of PID, as well as summarizes the advantages and research progress of hydrogels in the prevention of PID. In contrast to conventional treatments, hydrogels serve as excellent vehicles for vaginal drug delivery, maintaining the presence of the drug at the target site and controlling its release. In the context of urinary tract infections (UTIs), hydrogels are employed primarily as coatings on catheters to prevent and treat catheter‐associated UTIs. Finally, this review summarizes the limitations of hydrogels in PID prevention and future directions for development with the aim of elucidating avenues for clinical treatment of PID and informing further research.

ObjectiveBy extracting early chest CT radiomic features of COVID-19 patients, we explored their correlation with laboratory indicators and oxygenation index (PaO2/FiO2), thereby developed an Artificial Intelligence (AI) model based on radiomic features to predict the deterioration of oxygenation function in COVID-19 patients.MethodsThis retrospective study included 384 patients with COVID-19, whose baseline information, laboratory indicators, oxygenation-related parameters, and non-enhanced chest CT images were collected. Utilizing the PaO2/FiO2 stratification proposed by the Berlin criteria, patients were divided into 4 groups, and differences in laboratory indicators among these groups were compared. Radiomic features were extracted, and their correlations with laboratory indicators and the PaO2/FiO2 were analyzed, respectively. Finally, an AI model was developed using the PaO2/FiO2 threshold of less than 200 mmHg as the label, and the model’s performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Group datas comparison was analyzed using SPSS software, and radiomic features were extracted using Python-based Pyradiomics.ResultsThere were no statistically significant differences in baseline characteristics among the groups. Radiomic features showed differences in all 4 groups, while the differences in laboratory indicators were inconsistent, with some PaO2/FiO2 groups showed differences and others not. Regardless of whether laboratory indicators demonstrated differences across different PaO2/FiO2 groups, they could all be captured by radiomic features. Consequently, we chose radiomic features as variables to establish an AI model based on chest CT radiomic features. On the training set, the model achieved an AUC of 0.8137 (95% CI [0.7631–0.8612]), accuracy of 0.7249, sensitivity of 0.6626 and specificity of 0.8208. On the validation set, the model achieved an AUC of 0.8273 (95% CI [0.7475–0.9005]), accuracy of 0.7739, sensitivity of 0.7429 and specificity of 0.8222.ConclusionThis study found that the early chest CT radiomic features of COVID-19 patients are strongly associated not only with early laboratory indicators but also with the lowest PaO2/FiO2. Consequently, we developed an AI model based on CT radiomic features to predict deterioration in oxygenation function, which can provide a reliable basis for further clinical management and treatment.

Abstract The rapid evolution of clinical medicine, materials science, and regenerative medicine has rendered traditional implantable scaffolds inadequate for addressing the complex therapeutic demands of various diseases. Currently, implantable scaffolds in clinical practice are mainly made of metal, with the disadvantages of high stiffness, poor toughness, and low deformation. This paper offers a thorough review of shape memory scaffolds (SMSs), emphasizing their distinctive self‐recovery and adaptive functionalities that enhance compatibility with injured tissues, surpassing the capabilities of conventional metallic biomaterials. It delves into the limitations of current clinical scaffolds and the requisite performance metrics for effective implants and outlines the essential materials and fabrication methods for SMSs. Moreover, we enumerate the biomedical applications of SMMs with different response types, including thermology‐responsive, water‐responsive, and light‐responsive. The discussion extends to the burgeoning applications of SMSs in biomedical engineering, including their utility in bone tissue engineering, cardiovascular stenting, tubular structures, and cardiac patches, which underscore their potential in minimally invasive procedures and dynamic tissue interactions. This review concludes with an analysis of current challenges and prospects, providing valuable insights for developing and applying SMSs in the biomedical sector.

To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations.

Introduction Psychotherapy is considered part of the standard treatment of cancer in Western countries. However, there is no literature on the attitudes of Chinese cancer patients toward psychotherapy. Methods: In a multicenter, cross-sectional study in China, a homemade questionnaire was delivered to cancer patients. The targeted population was Chinese hospitalized cancer patients who were informed of their state of illness. Results Five hundred and fifty cancer patients received our questionnaire, and 83.3% completed the questionnaire. Among the 458 patients, 43.2% indicated that they had never heard of psychotherapy before the survey. However, after a brief introduction of psychotherapy, most (92.1%) cancer patients indicated that psychotherapy is essential for cancer patients, and over half of patients (57.4%) were willing to take psychotherapy on the advice of the oncologist in charge. Participants aged 45 years or younger, had a family income > 10000 yuan per month, and had an ECOG PS (Eastern Cooperative Oncology Group Performance Status) of 2-4 were more willing to receive psychotherapy. Of all patients, 59.2% and 57.6% were willing to participate in individual and group psychotherapy clinical trials, respectively. Participants who had a bachelor’s degree or higher (odds ratio, OR = 2.09) and were aged 45 years or younger (OR = 1.67) were more willing to participate in individual and group psychotherapy clinical trials, respectively. Conclusion The unmet psychological needs of cancer patients in China remain high, and doctors’ advice is likely to positively impact the patients’ acceptance of psychotherapy. Psychological education for Chinese cancer patients should be strengthened. More high-quality clinical trials of psychotherapy should be conducted in China to achieve greater benefits for cancer patients and their families.