Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation

[This corrects the article DOI: 10.1016/j.apsb.2019.01.013.].

The vascular endothelial growth factor (VEGF) family and the VEGF receptor (VEGFR) pathway play an important role in regulating tumor-induced angiogenesis. Consequently, inhibitors targeting the VEGF/VEGFR pathway have demonstrated potential as anti-angiogenic agents in cancer therapy. However, the clinical application of these inhibitors is currently limited by off-target toxicity and the heterogeneity of VEGF/VEGFR expression levels. Radiolabeled molecular probes targeting the VEGF/VEGFR pathway offer a non-invasive means to detect VEGF/VEGFR expression in tumors, aiding in therapeutic decision-making and efficacy evaluation of anti-angiogenic treatments. This article reviews the research progress of radiopharmaceuticals targeting the VEGF/VEGFR pathway in tumor diagnosis and treatment, with a focus on the role of structural modification methods in optimizing the performance of VEGF/VEGFR-targeting compounds, and provides an outlook on future research directions.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To systematically evaluate the unmet needs and challenges of caregivers of cancer patients at home and clarify their unmet needs.Methods:A computerized search was conducted in PubMed, Web of Science, Embase, Cochrane Library, ProQuest, Scopus, CINAHL, China National Knowledge Infrastructure, Wanfang Data, VIP, and China Biology Medicine disc for qualitative studies on the unmet needs and challenges of caregivers of cancer patients in the home environment. The search time limit was from the establishment of the databases to April 30, 2024. The Joanna Briggs Institute Centre for Evidence-Based Healthcare's quality assessment criteria for qualitative research were used to evaluate the quality of the literatures, and the aggregative integration method was used to summarize and integrate the research results.Results:A total of 17 articles were finally included, which were summarized into eight new categories. Further synthesis yielded three integrated results: caregivers in the home environment face multi-dimensional unmet needs and challenges related to patients; caregivers in the home environment face challenges and unmet needs in self-care; caregivers in the home environment are hesitant to obtain external support, and continuity and sustainability are hindered.Conclusions:Medical staff should accurately assess the unmet needs of caregivers of cancer patients at home, pay attention to their negative experiences, promote positive coping, optimize the patient-and family-centered cancer care model, and strengthen social support.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To systematically evaluate the unmet needs and challenges of caregivers of cancer patients at home and clarify their unmet needs.Methods:A computerized search was conducted in PubMed, Web of Science, Embase, Cochrane Library, ProQuest, Scopus, CINAHL, China National Knowledge Infrastructure, Wanfang Data, VIP, and China Biology Medicine disc for qualitative studies on the unmet needs and challenges of caregivers of cancer patients in the home environment. The search time limit was from the establishment of the databases to April 30, 2024. The Joanna Briggs Institute Centre for Evidence-Based Healthcare's quality assessment criteria for qualitative research were used to evaluate the quality of the literatures, and the aggregative integration method was used to summarize and integrate the research results.Results:A total of 17 articles were finally included, which were summarized into eight new categories. Further synthesis yielded three integrated results: caregivers in the home environment face multi-dimensional unmet needs and challenges related to patients; caregivers in the home environment face challenges and unmet needs in self-care; caregivers in the home environment are hesitant to obtain external support, and continuity and sustainability are hindered.Conclusions:Medical staff should accurately assess the unmet needs of caregivers of cancer patients at home, pay attention to their negative experiences, promote positive coping, optimize the patient-and family-centered cancer care model, and strengthen social support.

The vascular endothelial growth factor (VEGF) family and the VEGF receptor (VEGFR) pathway play an important role in regulating tumor-induced angiogenesis. Consequently, inhibitors targeting the VEGF/VEGFR pathway have demonstrated potential as anti-angiogenic agents in cancer therapy. However, the clinical application of these inhibitors is currently limited by off-target toxicity and the heterogeneity of VEGF/VEGFR expression levels. Radiolabeled molecular probes targeting the VEGF/VEGFR pathway offer a non-invasive means to detect VEGF/VEGFR expression in tumors, aiding in therapeutic decision-making and efficacy evaluation of anti-angiogenic treatments. This article reviews the research progress of radiopharmaceuticals targeting the VEGF/VEGFR pathway in tumor diagnosis and treatment, with a focus on the role of structural modification methods in optimizing the performance of VEGF/VEGFR-targeting compounds, and provides an outlook on future research directions.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.