Assessment of Patient Care Activities and Readiness for Advanced Pharmacy Practice Experiences in Revised Third-Year Introductory Pharmacy Practice Experiences.

OBJECTIVE: To quantify the impact of a revised third-year (P3) introductory pharmacy practice experience (IPPE) curriculum on student opportunities for direct patient care and to evaluate student and preceptor perceptions of advanced pharmacy practice experience (APPE) readiness. METHODS: An intentional, structured curriculum redesign shifted 50 IPPE hours from each of the first- and second-years into the P3 year. A survey was developed and administered to students in the graduating classes of 2023 (original curriculum) and 2024 (revised curriculum) at the end of their first APPE rotation. The survey quantified the frequency of patient care activities completed during P3 IPPEs and assessed student perceptions of the effectiveness of P3 IPPEs in preparation for APPEs. At the conclusion of the first APPE, preceptors answered a single question assessing student APPE readiness. RESULTS: A total of 213/226 (94%) students responded to the optional survey. A significantly higher proportion of students in the 2024 cohort had the opportunity to complete several direct patient care activities compared to the 2023 cohort in community, institutional, and elective IPPEs. Additionally, the 2024 cohort was provided with greater access to the electronic health record (EHR). Although the 2024 cohort had higher perceived APPE readiness in areas of navigating the EHR and administering vaccines, student- and preceptor-perceived overall APPE readiness was similar between the 2 cohorts. CONCLUSION: Transferring more IPPE hours into the last didactic year can increase student opportunities for direct patient care while promoting APPE readiness. Activity quantification could be used by other pharmacy programs to optimize IPPEs.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Vigor King mitigates spermatogenic disorders caused by environmental estrogen zearalenone exposure.

Zearalenone (ZEN) has been shown to cause reproductive damage by inducing oxidative stress. Astaxanthin and L-carnitine are widely used to alleviate oxidative stress and promote sperm maturation. However, it remains uncertain whether they are effective in mitigating spermatogenesis disorders induced by ZEN. This study aimed to investigate the therapeutic efficacy and potential mechanisms of Vigor King (Vig), a compound preparation primarily consisting of astaxanthin and L-carnitine, in alleviating ZEN-induced spermatogenesis disorders. In the experiment, mice received continuous oral gavage of ZEN (80 µg/kg) for 35 days, accompanied by a rescue strategy with Vig (200 mg/kg). The results showed that Vig effectively reduced the negative impact on semen quality and improved the structural and functional abnormalities of the seminiferous epithelium caused by ZEN. Additionally, the accumulation of reactive oxygen species (ROS), DNA double-strand breaks, apoptosis, and autophagy abnormalities were all significantly ameliorated. Intriguingly, the GSK3ß-dependent BTRC-NRF2 signaling pathway was found to play an important role in this process. Furthermore, testing of offspring indicated that Vig could extend its protective effects to the next generation, effectively combating the transgenerational toxic effects of ZEN. In summary, our research suggests that Vig supplementation holds considerable promise in alleviating spermatogenesis disorders induced by zearalenone.

Circulating Small Extracellular Vesicles Involved in Systemic Regulation Respond to RGC Degeneration in Glaucoma.

Glaucoma is a leading cause of irreversible blindness worldwide and is characterized by progressive retinal ganglion cell (RGC) degeneration and vision loss. Since irreversible neurodegeneration occurs before diagnosable, early diagnosis and effective neuroprotection are critical for glaucoma management. Small extracellular vesicles (sEVs) are demonstrated to be potential novel biomarkers and therapeutics for a variety of diseases. In this study, it is found that intravitreal injection of circulating plasma-derived sEVs (PDEV) from glaucoma patients ameliorated retinal degeneration in chronic ocular hypertension (COH) mice. Moreover, it is found that PDEV-miR-29s are significantly upregulated in glaucoma patients and are associated with visual field defects in progressed glaucoma. Subsequently, in vivo and in vitro experiments are conducted to investigate the possible function of miR-29s in RGC pathophysiology. It is showed that the overexpression of miR-29b-3p effectively prevents RGC degeneration in COH mice and promotes the neuronal differentiation of human induced pluripotent stem cells (hiPSCs). Interestingly, engineered sEVs with sufficient miR-29b-3p delivery exhibit more effective RGC protection and neuronal differentiation efficiency. Thus, elevated PDEV-miR-29s may imply systemic regulation to prevent RGC degeneration in glaucoma patients. This study provides new insights into PDEV-based glaucoma diagnosis and therapeutic strategies for neurodegenerative diseases.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Peer Ambassador Perspectives in a Culturally Tailored Self-Management Intervention for African Americans with Type 2 Diabetes: A Qualitative Study.

OBJECTIVE: Diabetes disproportionately affects African Americans, leading to higher morbidity and mortality. This study explores the experiences of African American adults who successfully self-manage their type 2 diabetes (called Peer Ambassadors) and provided phone-based peer support in a 6-month culturally tailored diabetes self-management program for African Americans guided by the information-motivation-behavioral skills model. DESIGN: A group discussion using a semi-structured discussion guide was conducted. Qualitative content analysis was used to identify the facilitators and barriers to completing the role of a Peer Ambassador and to develop strategies for overcoming possible challenges in the future. SETTING: Key informant discussions were conducted in a community location to gain insights into Ambassadors' motivations and challenges in delivering peer support. PARTICIPANTS: Three Peer Ambassadors completed ethics training and peer mentor training and received a phone call guide before providing support to their peers. RESULTS: There were four core themes related to Peer Ambassador experiences: (1) Motivation to be a Peer Ambassador, (2) program elements that supported Peer Ambassador role, (3) key elements of achieving engagement, and (4) challenges related to being a Peer Ambassador. CONCLUSIONS: This study showed Peer Ambassadors in a culturally tailored peer supported self-management program found fulfillment in sharing experiences and supporting peers. They highly valued educational group sessions for knowledge updates and sustaining their health-related goals, suggesting the potential benefits of recognizing milestones or providing advanced training for future program sustainability. Findings suggest the importance of recruiting motivated patients and providing effective facilitation for peer support roles, including addressing barriers such as time commitment and lack of socialization opportunities.

Advancements and Practical Considerations for Biophysical Research: Navigating the Challenges and Future of Super-resolution Microscopy.

The introduction of super-resolution microscopy (SRM) has significantly advanced our understanding of cellular and molecular dynamics, offering a detailed view previously beyond our reach. Implementing SRM in biophysical research, however, presents numerous challenges. This review addresses the crucial aspects of utilizing SRM effectively, from selecting appropriate fluorophores and preparing samples to analyzing complex data sets. We explore recent technological advancements and methodological improvements that enhance the capabilities of SRM. Emphasizing the integration of SRM with other analytical methods, we aim to overcome inherent limitations and expand the scope of biological insights achievable. By providing a comprehensive guide for choosing the most suitable SRM methods based on specific research objectives, we aim to empower researchers to explore complex biological processes with enhanced precision and clarity, thereby advancing the frontiers of biophysical research.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Numerical Optimization Study of Combustion and Heat Transfer Characteristics in Municipal Solid Waste Incinerators.

The efficient and clean utilization of urban waste can substitute for partial fossil fuels and reduce total carbon emissions. Fuel combustion is divided into three stages. Before the fire, the fuel is put into the furnace to reach the preparation stage of the fire temperature, the combustion stage takes place after the ignition temperature is reached, and finally, the combustion is completed. This article employs numerical simulation methods to comprehensively study the effects of various factors on the combustion characteristics of waste in a mechanical grate incinerator, including the inclination angle of the front arch, fuel properties, height of the front and rear arches, air distribution methods, and speed of the grate chain rotation. The results indicate that when the rear arch angle is set at 25°, the airflow distribution within the furnace is uniform and the high-temperature flue gas exhibits an ideal "L" shaped flow, achieving favorable characteristics of airflow distribution inside the furnace. With this structure, the airflow from the rear arch can adequately penetrate deep into the front arch area, thereby forming an efficient T-shaped combustion flame.

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

NAD+ precursors promote the restoration of spermatogenesis in busulfan-treated mice through inhibiting Sirt2-regulated ferroptosis.

Rationale: In recent years, nicotinamide adenine dinucleotide (NAD+) precursors (Npre) have been widely employed to ameliorate female reproductive problems in both humans and animal models. However, whether and how Npre plays a role in the male reproductive disorder has not been fully clarified. Methods: In the present study, a busulfan-induced non-obstructive azoospermic mouse model was used, and Npre was administered for five weeks following the drug injection, with the objective of reinstating spermatogenesis and fertility. Initially, we assessed the NAD+ level, germ cell types, semen parameters and sperm fertilization capability. Subsequently, testis tissues were examined through RNA sequencing analysis, ELISA, H&E, immunofluorescence, quantitative real-time PCR, and Western blotting techniques. Results: The results indicated that Npre restored normal level of NAD+ in blood and significantly alleviated the deleterious effects of busulfan (BU) on spermatogenesis, thereby partially reestablishing fertilization capacity. Transcriptome analysis, along with recovery of testicular Fe2+, GSH, NADPH, and MDA levels, impaired by BU, and the fact that Fer-1, an inhibitor of ferroptosis, restored spermatogenesis and semen parameters close to CTRL values, supported such possibility. Interestingly, the reduction in SIRT2 protein level by the specific inhibitor AGK2 attenuated the beneficial effects of Npre on spermatogenesis and ferroptosis by affecting PGC-1α and ACLY protein levels, thus suggesting how these compounds might confer spermatogenesis protection. Conclusion: Collectively, these findings indicate that NAD+ protects spermatogenesis against ferroptosis, probably through SIRT2 dependent mechanisms. This underscores the considerable potential of Npre supplementation as a feasible strategy for preserving or restoring spermatogenesis in specific conditions of male infertility and as adjuvant therapy to preserve male fertility in cancer patients receiving sterilizing treatments.

Exploring the diversity, bioactivity of endophytes, and metabolome in Synsepalum dulcificum.

Synsepalum dulcificum exhibits high edible and medicinal value; however, there have been no reports on the exploration of its endophyte resources. Here, we conducted analyses encompassing plant metabolomics, microbial diversity, and the biological activities of endophytic metabolites in S. dulcificum. High-throughput sequencing identified 4,913 endophytic fungal amplicon sequence variants (ASVs) and 1,703 endophytic bacterial ASVs from the roots, stems, leaves, flowers, and fruits of S. dulcificum. Fungi were classified into 5 phyla, 24 classes, 75 orders, 170 families, and 313 genera, while bacteria belonged to 21 phyla, 47 classes, 93 orders, 145 families, and 232 genera. Furthermore, there were significant differences in the composition and content of metabolites in different tissues of S. dulcificum. Spearman's correlation analysis of the differential metabolites and endophytes revealed that the community composition of the endophytes correlated with plant-rich metabolites. The internal transcribed spacer sequences of 105 isolates were determined, and phylogenetic analyses revealed that these fungi were distributed into three phyla (Ascomycota, Basidiomycota, and Mucoromycota) and 20 genera. Moreover, 16S rDNA sequencing of 46 bacteria revealed they were distributed in 16 genera in three phyla: Actinobacteria, Proteobacteria, and Firmicutes. The antimicrobial activities (filter paper method) and antioxidant activity (DPPH and ABTS assays) of crude extracts obtained from 68 fungal and 20 bacterial strains cultured in different media were evaluated. Additionally, the α-glucosidase inhibitory activity of the fungal extracts was examined. The results showed that 88.6% of the strains exhibited antimicrobial activity, 55.7% exhibited antioxidant activity, and 85% of the fungi exhibited α-glucosidase inhibitory activity. The research suggested that the endophytes of S. dulcificum are highly diverse and have the potential to produce bioactive metabolites, providing abundant species resources for developing antibiotics, antioxidants and hypoglycemic drugs.

Circ-Bptf Ameliorates Learning and Memory Impairments via the miR-138-5p/p62 Axis in APP/PS1 Mice.

Alzheimer's disease (AD) is a common age-associated progressive neurodegenerative disorder that is implicated in the aberrant regulation of numerous circular RNAs (circRNAs). Here, we reported that circ-Bptf, a conserved circRNA derived from the Bptf gene, showed an age-dependent decrease in the hippocampus of APP/PS1 mice. Overexpression of circ-Bptf significantly reversed dendritic spine loss and learning and memory impairment in APP/PS1 mice. Moreover, we found that circ-Bptf was predominantly localized to the cytoplasm and upregulated p62 expression by binding to miR-138-5p. Furthermore, the miR-138-5p mimics reversed the decreased expression of p62 induced by the silencing of circ-Bptf. Together, our findings suggested that circ-Bptf ameliorated learning and memory impairments via the miR-138-5p/p62 axis in APP/PS1 mice. It may act as a potential player in AD pathogenesis and therapy.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

Implementation and evaluation of a pharmacy family program to foster community and inclusion.

BACKGROUND AND PURPOSE: Learning communities in the form of student-faculty families in pharmacy education provide a structure to foster community and inclusion. The purpose of this work is to describe how a new Pharmacy Family (PF) program was implemented and to evaluate the impact on students. EDUCATIONAL ACTIVITY AND SETTING: Our PF program was developed with the goals of building community, promoting a sense of belonging, providing students with opportunities to share and receive advice, and providing a venue for surveillance of student concerns. Each family was comprised of one to two faculty/instructor leaders and three to four doctor of pharmacy students from each cohort and met longitudinally over the course of the academic year. Quantitative and qualitative survey data were collected to assess student perceptions and program satisfaction. FINDINGS: A total of 233 students (66.2%) completed the survey and the majority (66%) were satisfied with the program. Thematic analysis of open-ended questions revealed four themes that contributed to students' satisfaction ratings: meeting content, relationships, atmosphere, and timing. Students with high satisfaction frequently noted that the program fostered connections, mentoring opportunities, and a safe space to share concerns. Students that were neutral or dissatisfied frequently commented on the timing of meetings and inability to form deeper connections. SUMMARY: Student-faculty families can be implemented to improve community and engagement within pharmacy education. Our program was most successful in providing a venue for students to share concerns. Addressing meeting times and adjusting the structure to promote community building is warranted to achieve program goals.

Understanding the medication experience of patients with advanced non-small cell lung cancer taking epidermal growth factor receptor-tyrosine kinase inhibitors: A phenomenological study.

BACKGROUND AND OBJECTIVE: Patients with cancer taking oral antineoplastic medications may encounter problems including suboptimal adherence as well as physical and psychological disease burden. Despite increase in the use of oncology pharmacy services, there are wide variations between healthcare professionals and patient perceptions of patients' medication experiences. The objective of the study was to explore the medication experience of taking oral targeted therapy in patients with advanced non-small cell lung cancer (NSCLC). METHOD: We purposively sampled advanced stage (stage III or IV) NSCLC patients taking epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in a medical center in Taiwan. Face-to-face interviews using semi-structured interview guides were conducted. Interviews were transcribed verbatim and thematic analysis was applied. A phenomenological methodology was adopted to explore the underlying meaning of patients' lived experience. RESULTS: A total of 19 participants with a mean age of 68.2 years were interviewed. The duration of EGFR-TKIs use ranged from 2 weeks to 5 years. When first learned about the unexpected yet 'treatable' cancer, participants expressed strong emotional responses based on their intrinsic beliefs of the terminal disease and therapy. They walked along an unfamiliar trail while confronting physical and psychological challenges and made compromises to treatment. Gaining experiences from cancer journey, patients with cancer continuously seek the ultimate goals-'return to normal'. CONCLUSIONS: This study also revealed medication experiences of participants' journey from seeking information in the initial phase and living with cancer, to taking back control of their own lives. Healthcare professionals could better empathize with patients' loss of control and understand their perspectives when making clinical decisions. These findings can guide interdisciplinary teams to integrate patients' beliefs and conduct pre-screening assessments of health literacy levels to tailor communication. Subsequent interventions should be developed to identify barriers to medication self-management and empower patients by building social networks.

Exogenous myo-inositol increases salt tolerance and accelerates CAM induction in the early juvenile stage of the facultative halophyte Mesembryanthemum crystallinum but not in the late juvenile stage.

Mesembryanthemum crystallinum L. (ice plant) develops salt tolerance during the transition from the juvenile to the adult stage through progressive morphological, physiological, biochemical, and molecular changes. Myo -inositol is the precursor for the synthesis of compatible solute D-pinitol and promotes Na+ transport in ice plants. We previously showed that supplying myo -inositol to 9-day-old seedlings alleviates salt damage by coordinating the expression of genes involved in inositol synthesis and transport, affecting osmotic adjustment and the Na/K balance. In this study, we examined the effects of myo -inositol on physiological parameters and inositol-related gene expression in early- and late-stage juvenile plants. The addition of myo -inositol to salt-treated, hydroponically grown late juvenile plants had no significant effects on growth or photosynthesis. In contrast, supplying exogenous myo -inositol to salt-treated early juvenile plants increased leaf biomass, relative water content, and chlorophyll content and improved PSII activity and CO2 assimilation. The treatment combining high salt and myo -inositol synergistically induced the expression of myo -inositol phosphate synthase (INPS ), myo -inositol O -methyltransferase (IMT ), and inositol transporters (INTs ), which modulated root-to-shoot Na/K ratio and increased leaf D-pinitol content. The results indicate that sufficient myo -inositol is a prerequisite for high salt tolerance in ice plant.

Risk Factors for Level-VII Lymph Node Metastases of Thyroid Neoplasms: A Meta-Analysis.

OBJECTIVE: The present study conducted a meta-analysis to forecast the risk factors associated with level-VII lymph node metastases in case of thyroid neoplasms, intending to assist in determining the requirement for level-VII lymph node lymphadenectomy during the surgery. METHODS: Electronic databases, PubMed, Embase, the Cochrane Library, CNKI, Wanfang Data, VIP, and CBM electronic databases were searched for studies focused on level-VII lymph node metastases in thyroid neoplasms, published up to April 2021. Stata 13.1 software was used for analyses. RESULTS: The literature search identified a total of 997 studies. Among these, 8 studies, involving 1813 patients, were included in the present case. All these studies were case-control studies. Results for meta-analysis showed that male (OR = 1.340, 95% CI: 1.018-1.764, P = .037), age < 45 years (OR = 4.178, 95% CI: 1.601-10.908, P = .003), tumor size ≥ 2.0 cm (OR = 1.960, 95% CI: 1.079-3.562, P = .027), extrathyroidal extension (OR = 2.037, 95% CI: 1.578-2.630, P < .001), distant metastasis (OR = 2.775, 95% CI: 2.005-3.840, P < .001), central lymph node metastasis (OR = 3.500, 95% CI: 1.127-10.874, P = .03), contralateral cervicolateral metastasis (OR = 2.119, 95% CI: 1.514-2.965, P < .001), and bilateral nodal metastasis (OR = 4.651, 95% CI: 2.697-8.020, P < .001) acted as risk factors for level-VII lymph node metastases. In addition to this, sensitivity analyses and bias test showed that the results of meta-analysis were reliable and stable and involved no publication bias. CONCLUSION: In the present study, male gender, age < 45 years, tumor size ≥ 2.0 cm, extrathyroidal extension, distant metastasis, central lymph node metastasis, contralateral cervicolateral metastasis, and bilateral nodal metastasis were identified as risk factors for level-VII lymph node metastases in case of thyroid neoplasms.