Accelerating the discovery of anticancer peptides targeting lung and breast cancers with the Wasserstein autoencoder model and PSO algorithm.

In the development of targeted drugs, anticancer peptides (ACPs) have attracted great attention because of their high selectivity, low toxicity and minimal non-specificity. In this work, we report a framework of ACPs generation, which combines Wasserstein autoencoder (WAE) generative model and Particle Swarm Optimization (PSO) forward search algorithm guided by attribute predictive model to generate ACPs with desired properties. It is well known that generative models based on Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN) are difficult to be used for de novo design due to the problems of posterior collapse and difficult convergence of training. Our WAE-based generative model trains more successfully (lower perplexity and reconstruction loss) than both VAE and GAN-based generative models, and the semantic connections in the latent space of WAE accelerate the process of forward controlled generation of PSO, while VAE fails to capture this feature. Finally, we validated our pipeline on breast cancer targets (HIF-1) and lung cancer targets (VEGR, ErbB2), respectively. By peptide-protein docking, we found candidate compounds with the same binding sites as the peptides carried in the crystal structure but with higher binding affinity and novel structures, which may be potent antagonists that interfere with these target-mediated signaling.

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space.

Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.

Transformer-based deep learning method for optimizing ADMET properties of lead compounds.

A successful drug needs to exhibit both effective pharmacodynamics (PD) and safe pharmacokinetics (PK). However, the coordinated optimization of PD and PK properties in molecule generation tasks remains a great challenge for most existing methods, especially when they focus on the pursuit of affinity and selectivity for the lead compound. Thus, molecular optimization for PK properties is a critical step in the drug discovery pipeline, in which absorption, distribution, metabolism, excretion and toxicity (ADMET) property predictive models play an increasingly important role by providing an effective method to assess multiple PK properties of compounds. Here, we proposed a Graph Bert-based ADMET prediction model that achieves state-of-the-art performance on the public dataset Therapeutics Data Commons (TDC) by combining molecular graph features and descriptor features, with 11 tasks ranked first and 20 tasks ranked in the top 3. Based on this prediction model, we trained a Transformer model with multiple properties as constraints for learning the structural transformations involved in MMP and the accompanying property changes. The experimental results show that the trained Constraints-Transformer can implement targeted modifications to the starting molecule, while preserving the core scaffold. Moreover, molecular docking and binding mode analysis demonstrate that the optimized molecules still retain the activity and selectivity for biological targets. Therefore, the proposed method accounts for biological activity and ADMET properties simultaneously. Finally, a webserver containing ADMET property prediction and molecular optimization functions is provided, enabling chemists to improve the properties of starting molecules individually.

Comparison of life quality in older adults living in traditional family versus nursing home: a systematic review and meta-analysis.

To assess and compare the QoL of the older people dwelling in traditional family versus nursing home/institution. A comprehensive literature search was performed on 10 January 2018 to identify studies that investigated the QoL of older adults dwelling in family versus nursing home settings. Analyses were run using random-effects meta-analyses. A total of six cross-sectional studies with 1623 people were included. The quality of included studies was moderate. Meta-analysis showed that compared with nursing home support, the family support could significantly improve the physical health (6 studies, SMD = 0.50, 95%CI: 0.32-0.68, p < 0.05), mental status (6 studies, SMD = 0.45, 95%CI: 0.26-0.65, p < 0.05), and social relationship (5 studies, SMD = 0.51, 95%CI: 0.19-0.83, p < 0.05). Traditional family support model demonstrated a significant improvement in the physical health, psychological status and social relationships among older adults. The conclusions were driven by cross-sectional studies, Larger, adequately powered RCTs are required to confirm our finding.

Prognosis biomarker and potential therapeutic target CRIP2 associated with radiosensitivity in NSCLC cells.

Radiotherapy plays a major role in non-small cell lung cancer (NSCLC) treatment. The curative efficacy of advanced NSCLC is unsatisfactory because of its radioresistance to conventional radiotherapy. The biomarkers which can be used to diagnose radiosensitivity or predict for prognosis are beneficial in promoting curative effects. In this study, NSCLC cell lines with acquired radioresistance to X-rays were obtained through fractionated irradiation. The differentially expressed proteins (DEPs) between the self-established radioresistant NSCLC cell line A549-R11 and control (A549-CK) were measured by proteomic analysis. Among the detected DEPs, CRIP2, ARHGDIB, and PADI3 were validated to be up-regulated in radioresistant cells, in mRNA and protein levels. Further analysis of bioinformatics deciphered that CRIP2, as a potential biomarker for diagnosis and a key biomarker for prediction of prognosis, may impact the X-ray radiosensitivity of NSCLC by regulating the occurrence of apoptosis and cell cycle arrest; as such, it may serve as a potent therapeutic target to facilitate NSCLC radiotherapy. CRIP2 and other DEPs may shed new light on the recognition of complex factors associated with radiation-responsiveness and finally be beneficial in the advancement of personalized therapies and precision medical treatment.

Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer.

Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs. We find that the networks possess a two-level bipartite structure: common competing endogenous network (CCEN) composed of an invariant set of microRNAs over all the stages and stage-dependent, unique competing endogenous networks (UCENs). A systematic enrichment analysis of the pathways of the mRNAs in CCEN reveals that they are strongly associated with cancer development. We also find that the microRNA-linked mRNAs from UCENs have a higher enrichment efficiency. A key finding is six microRNAs from CCEN that impact patient survival at all stages, and four microRNAs that affect the survival from a specific stage. The ten microRNAs can then serve as potential biomarkers and prognostic tools for LUAD.

Relationship between antidepressive agents and incidence risk of breast cancer: systematic review and meta-analysis.

Purpose: This study aimed to review the association between antidepressive agent (AD) use and the incidence risk of breast cancer. Methods: CBM, WOS, Embase, PubMed and Cochrane Library were systematically searched in July 2019. The methodological quality of the studies was assessed through the Newcastle-Ottawa Scale. Results: We included 19 studies from six countries or regions with relationships between breast cancer and ADs. Subgroup analysis showed no significant association in nested case-control or case-control studies; however, cohort studies revealed a significant association (odds ratio = 1.11; 95% CI: 1.04-1.17). Conclusions: This meta-analysis indicates that breast cancer was not associated with the use of ADs when considering all types of studies, but an association was observed if we considered cohort studies.

Non-pharmacological interventions for older adults with depressive symptoms: a network meta-analysis of 35 randomized controlled trials.

Objective: To assess the effectiveness of non-pharmacological interventions for seniors with depressive symptoms.Methods: A comprehensive literature search was performed. We conducted network meta-analysis in two ways, intervention classes (psychosocial, psychotherapy, physical activity, combined, treatment as usual) and individual intervention (11 categories). Whenever included studies used different scales, the different instruments were converted to the units of the scale most frequently used (the Geriatric Depression Scale), such that the effect size was reported as a mean difference (MD) with 95% confidence interval (CI). The risk of bias of RCTs included in this review was assessed according to the Cochrane Handbook. Bayesian NMA was conducted using R-3.4.0 software.Results: A total of 35 RCTs with 3,797 enrolled patients were included. Compared to conventional treatment, physical activity and psychotherapy resulted in significant improvements in depressive symptoms (MD: 2.25, 95%CrI: 0.99-3.56; SUCRA = 86.07%; MD: 1.75, 95% CrI: 0.90-2.64; SUCRA = 66.44%, respectively). Similar results were obtained for music (MD: 2.6; 95% CrI: 0.84-4.35;SUCRA = 80.53%), life review (MD:1.92; 95% CrI:0.71-3.14; SUCRA = 65.62%), cognitive behavioral therapy (MD: 1.27; 95% CrI: 0.23-2.38; SUCRA = 45.4%), aerobic (MD: 1.84; 95% CrI: 0.39-3.36; SUCRA = 63%) and resistance training (MD: 1.72; 95% CrI: 0.06-3.42; SUCRA = 59.24%). Network meta-regression showed that there were no statistically significant subgroup effects.Conclusions: Physical activity and psychotherapy demonstrated statistically significant superiority over conventional treatment. Music and life review therapy proved the most promising individual interventions. However, conclusions are limited by the lack of sufficient sample size and consensus regarding intervention categories and so an adequately powered study is necessary to consolidate these findings.

Integrative Gene Expression Profiling Analysis to Investigate Potential Prognostic Biomarkers for Colorectal Cancer.

BACKGROUND Despite noteworthy advancements in the multidisciplinary treatment of colorectal cancer (CRC) and deeper understanding in the molecular mechanisms of CRC, many of CRC patients with histologically identical tumors present different treatment response and prognosis. Thus, more evidence on novel predictive and prognostic biomarkers for CRC remains urgently needed. This study aims to identify potential prognostic biomarkers for CRC with integrative gene expression profiling analysis. MATERIAL AND METHODS Differential expression analysis of paired CRC and adjacent normal tissue samples in 6 microarray datasets was independently performed, and the 6 datasets were integrated by the robust rank aggregation method to detect consistent differentially expressed genes (DEGs). Aberrant expression patterns of these genes were further validated in RNA sequencing data. Then, gene set enrichment analysis (GSEA) was performed to investigate significantly dysregulated biological functions in CRC. Finally, univariate, LASSO and multivariate Cox regression models were built to identify key prognostic genes in CRC patients. RESULTS A total of 990 DEGs (495 downregulated and 495 upregulated genes) were acquired after integratedly analyzing the 6 microarray datasets, and 4131 DEGs (2050 downregulated and 2081 upregulated genes) were obtained from the RNA sequencing dataset. Subsequently, these DEGs were intersected and 885 consistent DEGs were finally identified, including 458 downregulated and 427 upregulated genes. Two risky prognostic genes (TIMP1 and LZTS3) and 5 protective prognostic genes (AXIN2, CXCL1, ITLN1, CPT2 and CLDN23) were identified, which were significantly associated with the prognosis of CRC. CONCLUSIONS The 7 genes that we identified would provide more evidence for further applying novel diagnostic and prognostic biomarkers in clinical practice to facilitate personalized treatment of CRC.

Health education interventions for older adults with hypertension: A systematic review and meta-analysis.

OBJECTIVE: The study aimed to evaluate the effectiveness of health education intervention for the control of blood pressure (BP) in older adults with hypertension. METHODS: Five databases were searched in March 2018 for randomized controlled trials to manage hypertension in older adults by health education. The primary outcomes were changes in systolic and diastolic BP. RevMan5 was used for meta-analysis. RESULTS: Seven articles with 1,105 participants were included. In them, 393 (35.56%) older adults participated in health education interventions in the form of courses, and 226 (20.45%), in health education sessions. The meta-analysis suggested an overall reduction in systolic BP after health education courses (SMD, standardized mean difference = 4.80, 95% CI: 7.01-2.59, p < .05). Similar results were observed after health education sessions (SMD = 11.73, 95% CI: 17.63-5.82, p < .05). The diastolic BP reduction showed no difference after the health education course (p = .09). The random effects meta-analysis suggested an overall reduction in diastolic BP after health education sessions (SMD = 5.39, 95% CI: 7.98-2.79, p < .05). CONCLUSION: Although different health education methods had different effects on hypertension control, overall, educational interventions can potentially lead to improved BP control.

Identification of 17 mRNAs and a miRNA as an integrated prognostic signature for lung squamous cell carcinoma.

BACKGROUND: Gene signatures for predicting the outcome of lung squamous cell carcinoma (LUSC) have been employed for many years. However, various signatures have been applied in clinical practice. Therefore, in the present study, we aimed to filter out an effective LUSC prognostic gene signature by simultaneously integrating mRNA and microRNA (miRNA). METHODS: First, based on data from the Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga), mRNAs and miRNAs that were related to overall survival of LUSC were obtained by the least absolute shrinkage and selection operator method. Subsequently, the predicting effect was tested by time-dependent receiver operating characteristic curve analysis and Kaplan-Meier survival analysis. Next, related clinical indices were added to evaluate the efficiency of the selected gene signatures. Finally, validation and comparison using three independent gene signatures were performed using data from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo). RESULTS: Our data showed that the prognostic index (PI) contained 17 mRNAs and one miRNA. According to the best normalized cut-off of PI (0.0247), the hazard ratio of the PI was 3.40 (95% confidence interval = 2.33-4.96). Moreover, when clinical factors were introduced, the PI was still the most significant index. In addition, only two Gene Ontology terms with p < 0.05 were reported. Furthermore, validation implied that, using our 18-gene signature, only hazard ratio = 1.36 (95% confidence interval = 1.01-1.83) was significant compared to the other three groups of gene biomarkers. CONCLUSIONS: The 18-gene signature selected based on data from the TCGA database had an effective prognostic value for LUSC patients.

A network approach to quantifying radiotherapy effect on cancer: Radiosensitive gene group centrality.

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends to be more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients.

Investigate the mechanisms of Chinese medicine Fuzhengkangai towards EGFR mutation-positive lung adenocarcinomas by network pharmacology.

BACKGROUND: Chinese traditional herbal medicine Fuzhengkangai (FZKA) formulation combination with gefitinib can overcome drug resistance and improve the prognosis of lung adenocarcinoma patients. However, the pharmacological and molecular mechanisms underlying the active ingredients, potential targets, and overcome drug resistance of the drug are still unclear. Therefore, it is necessary to explore the molecular mechanism of FZKA. METHODS: A systems pharmacology and bioinformatics-based approach was employed to investigate the molecular pathogenesis of EGFR-TKI resistance with clinically effective herb formula. The differential gene expressions between EGFR-TKI sensitive and resistance cell lines were calculated and used to find overlap from targets as core targets. The prognosis of core targets was validated from the cancer genome atlas (TCGA) database by Cox regression. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment is applied to analysis core targets for revealing mechanism in biology. RESULTS: The results showed that 35 active compounds of FZKA can interact with eight core targets proteins (ADRB2, BCL2, CDKN1A, HTR2C, KCNMA1, PLA2G4A, PRKCA and LYZ). The risk score of them were associated with overall survival and relapse free time (HR = 6.604, 95% CI: 2.314-18.850; HR = 5.132, 95% CI: 1.531-17.220). The pathway enrichment suggested that they involved in EGFR-TKI resistance and non-small cell lung cancer pathways, which directly affect EGFR-TKI resistance. The molecular docking showed that licochalcone a and beta-sitosterol can closely bind two targets (BCL2 and PRKCA) that involved in EGFR-TKI resistance pathway. CONCLUSIONS: This study provided a workflow for understanding mechanism of CHM for against drug resistance.

Evolving protein-protein interaction networks: A model based on duplication and mutation at different rates.

We present a model describing the evolution of protein-protein interaction networks. The model is based on gene duplication and mutation. Considering rates of gene duplication and mutation, the average node degree and cluster coefficient are calculated for different parameters. The predicted degree distribution and cluster coefficient are in good agreement with the observed statistical properties obtained from the analysis of the yeast proteome.

Proteomic analysis of effects by x-rays and heavy ion in HeLa cells.

BACKGROUND: Carbon ion therapy may be better against cancer than the effects of a photon beam. To investigate a biological advantage of carbon ion beam over X-rays, the radioresistant cell line HeLa cells were used. Radiation-induced changes in the biological processes were investigated post-irradiation at 1 h by a clinically relevant radiation dose (2 Gy X-ray and 2 Gy carbon beam). The differential expression proteins were collected for analysing biological effects. MATERIALS AND METHODS: The radioresistant cell line Hela cells were used. In our study, the stable isotope labelling with amino acids (SILAC) method coupled with 2D-LC-LTQ Orbitrap mass spectrometry was applied to identity and quantify the differentially expressed proteins after irradiation. The Western blotting experiment was used to validate the data. RESULTS: A total of 123 and 155 significantly changed proteins were evaluated with treatment of 2 Gy carbon and X-rays after radiation 1 h, respectively. These deregulated proteins were found to be mainly involved in several kinds of metabolism processes through Gene Ontology (GO) enrichment analysis. The two groups perform different response to different types of irradiation. CONCLUSIONS: The radioresistance of the cancer cells treated with 2 Gy X-rays irradiation may be largely due to glycolysis enhancement, while the greater killing effect of 2 Gy carbon may be due to unchanged glycolysis and decreased amino acid metabolism.

pH-responsive resveratrol-loaded ZIF-8 nanoparticles modified with tannic acid for promoting colon cancer cell apoptosis.

Resveratrol (Res) is known for its potential in treating various types of cancers, with a particular advantage of causing minimal toxic side effects. However, its clinical application is constrained by challenges such as poor bioavailability, low water solubility, and chemical instability in neutral and alkaline environments. In light of these limitations, we have developed a pH-responsive drug delivery nanoplatform, Res@ZIF-8/TA NPs, which exhibits good biocompatibility and shows promise for in vitro cancer therapy. Benefiting from the mild reaction conditions provided by zeolitic imidazolate frameworks (ZIFs), a "one-pot method" was used for drug synthesis and loading, resulting in a satisfactory loading capacity. Notably, Res@ZIF-8/TA NPs respond to acidic environments, leading to an improved drug release profile with a controlled release effect. Our cell-based experiments indicated that tannic acid (TA) modification enhances the biocompatibility of ZIFs. 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di- phenytetrazoliumromide (MTT assay), Hoechst 33342/PI staining, cell scratch assay, Transwell and Reverse Transcription quantitative PCR (RT-qPCR) assays further demonstrated that Res@ZIF-8/TA NPs inhibited colon cancer cell migration and invasion, and promoted apoptosis of colon cancer cells, suggesting a therapeutic potential and demonstrating anti-cancer properties. In conclusion, the Res@ZIF-8/TA NPs pH-responsive drug delivery systems we developed may offer a promising avenue for cancer therapy. By addressing some of the challenges associated with Res-based treatments, this system could contribute to advancements in cancer therapeutics.

Effectiveness of Mindfulness-Based Stress Reduction on Mental Health and Psychological Quality of Life among University Students: A GRADE-Assessed Systematic Review.

Background: Psychological distress is a progressive health problem that has been linked to decreased quality of life among university students. This meta-analysis reviews existing randomized controlled trials (RCTs) that have examined the effects of mindfulness-based stress reduction (MBSR) on the relief of psychosomatic stress-related outcomes and quality of life among university students. Methods: The PubMed, EMBASE, Web of Science, PsycINFO (formerly PsychLit), Ovid MEDLINE, ERIC, Scopus, Google Scholar, ProQuest, and Cochrane Library databases were searched in November 2023 to identify the RCTs for analysis. Data on pathology (anxiety, depression, and perceived stress), physical capacity (sleep quality and physical health), and well-being (mindfulness, self-kindness, social function, and subjective well-being) were analyzed. Results: Of the 276 articles retrieved, 29 met the inclusion criteria. Compared with control therapies, the pooled results suggested that MBSR had significant effects, reducing anxiety (SMD = -0.29; 95% CI: -0.49 to -0.09), depression (SMD = -0.32; 95% CI: -0.62 to -0.02), and perceived stress (SMD = -0.41; 95% CI: -0.60 to -0.29) and improving mindfulness (SMD = 0.34; 95% CI: 0.08 to 0.59), self-kindness (SMD = 0.57; 95% CI: 0.30 to 1.12), and physical health (SMD = -0.59; 95% CI: -1.14 to -0.04). No significant differences were observed in sleep quality (SMD = -0.20; 95% CI: -0.06 to 0.20), social function (SMD = -0.71; 95% CI: -2.40 to 0.97), or subjective well-being (SMD = 0.07; 95% CI: -0.18 to 0.32). The quality of the evidence regarding sleep quality and physical health outcomes was low. Conclusions: MBSR therapy appears to be potentially useful in relieving functional emotional disorders. However, additional evidence-based large-sample trials are required to definitively determine the forms of mindfulness-based therapy that may be effective in this context and ensure that the benefits obtained are ongoing. Future studies should investigate more personalized approaches involving interventions that are tailored to various barriers and students' clinical characteristics. To optimize the effects of such interventions, they should be developed and evaluated using various designs such as the multiphase optimization strategy, which allows for the identification and tailoring of the most valuable intervention components.

SOX9 Promotes Collagen VI Secretion by Upregulating PCOLCE in Neurofibroma.

Neurofibromatosis type 1 (NF1) is caused by NF1 gene mutations. Patients with NF1 often have complications with tumors, such as neurofibroma. In order to investigate the pathogenesis of human neurofibroma, a systematic comparison of protein expression levels between Schwann cell-like sNF96.2 cells, which originated from malignant peripheral nerve sheath tumors (MPNST), and normal Schwann cells was performed using 4-D label-free proteomic analysis. In addition, the expression levels and localization of dysregulated proteins were confirmed using a Gene Expression Omnibus (GEO) transcriptomic dataset, Western blot analysis, and immunofluorescence labeling. The effects of SRY-box transcription factor 9 (SOX9) in the neurofibroma and surrounding microenvironment were evaluated in vivo using a tumor transplantation model. The present study observed that SOX9 and procollagen C-endopeptidase enhancer (PCOLCE) were significantly altered. NF1 mutation promoted the nuclear translocation and transcriptional activity of SOX9 in neurofibromas. SOX9 increased collagen VI secretions by enhancing the activation of PCOLCE in neurofibroma cells. These findings might provide new perspectives on the pathophysiological significance of SOX9 in neurofibromas and elucidate a novel molecular mechanism underlying neurofibromas.

Imageable Brachytherapy with Chelator-Free Radiolabeling Hydrogel.

Brachytherapy stands as an essential clinical approach for combating locally advanced tumors. Here, an injectable brachytherapy hydrogel is developed for the treatment of both local and metastatic tumor. Fe-tannins nanoparticles are efficiently and stably radiolabeled with clinical used therapeutic radionuclides (such as 131I, 90Y, 177Lu, and 225Ac) without a chelator, and then chemically cross-linked with 4-armPEG-SH to form brachytherapy hydrogel. Upon intratumoral administration, magnetic resonance imaging (MRI) signal from ferric ions embedded within the hydrogel directly correlates with the retention dosage of radionuclides, which can real-time monitor radionuclides emitting short-range rays in vivo without penetration limitation during brachytherapy. The hydrogel's design ensures the long-term tumor retention of therapeutic radionuclides, leading to the effective eradication of local tumor. Furthermore, the radiolabeled hydrogel is integrated with an adjuvant to synergize with immune checkpoint blocking therapy, thereby activating potent anti-tumor immune responses and inhibiting metastatic tumor growth. Therefore, this work presents an imageable brachytherapy hydrogel for real-time monitoring therapeutic process, and expands the indications of brachytherapy from treatment of localized tumors to metastatic tumors.

PROTOCOL: Effectiveness of home-based interventions to prevent child neglect: A systematic review.

This is the protocol for a Campbell systematic review. The objectives are as follows. The objectives of the present study are to answer the following questions: (1) What types of home-based interventions are currently being studied to prevent child neglect? (2) How effective are the different home-based interventions for preventing child neglect? (3) What are the causes of heterogeneity among included studies and their impact on study effects?