Hollow Nanoreactors Unlock New Possibilities for Persulfate-Based Advanced Oxidation Processes.

As a novel type of catalytic material, hollow nanoreactors are expected to bring new development opportunities in the field of persulfate-based advanced oxidation processes due to their peculiar void-confinement, spatial compartmentation, and size-sieving effects. For such materials, however, further clarification on basic concepts and construction strategies, as well as a discussion of the inherent correlation between structure and catalytic activity are still required. In this context, this review aims to provide a state-of-the-art overview of hollow nanoreactors for activating persulfate. Initially, hollow nanoreactors are classified according to the constituent components of the shell structure and their dimensionality. Subsequently, the different construction strategies of hollow nanoreactors are described in detail, while common synthesis methods for these construction strategies are outlined. Furthermore, the most representative advantages of hollow nanoreactors are summarized, and their intrinsic connections to the nanoreactor structure are elucidated. Finally, the challenges and future prospects of hollow nanoreactors are presented.

Single-base tiled screen unveils design principles of PspCas13b for potent and off-target-free RNA silencing.

The development of precise RNA-editing tools is essential for the advancement of RNA therapeutics. CRISPR (clustered regularly interspaced short palindromic repeats) PspCas13b is a programmable RNA nuclease predicted to offer superior specificity because of its 30-nucleotide spacer sequence. However, its design principles and its on-target, off-target and collateral activities remain poorly characterized. Here, we present single-base tiled screening and computational analyses that identify key design principles for potent and highly selective RNA recognition and cleavage in human cells. We show that the de novo design of spacers containing guanosine bases at precise positions can greatly enhance the catalytic activity of inefficient CRISPR RNAs (crRNAs). These validated design principles (integrated into an online tool, https://cas13target.azurewebsites.net/ ) can predict highly effective crRNAs with ~90% accuracy. Furthermore, the comprehensive spacer-target mutagenesis revealed that PspCas13b can tolerate only up to four mismatches and requires ~26-nucleotide base pairing with the target to activate its nuclease domains, highlighting its superior specificity compared to other RNA or DNA interference tools. On the basis of this targeting resolution, we predict an extremely low probability of PspCas13b having off-target effects on other cellular transcripts. Proteomic analysis validated this prediction and showed that, unlike other Cas13 orthologs, PspCas13b exhibits potent on-target activity and lacks collateral effects.

Frailty and all-cause and cancer-related mortality in cancer patients: A prospective cohort study.

PURPOSE: To evaluate the associations between frailty and all-cause and cancer-related mortality. Additionally, the objective is to compare the magnitude of these associations between older adults and younger adults. METHODS: We gathered baseline data from NHANES (1999-2018) and developed a cumulative index consisting of 39 items to evaluate frailty. The National Death Index database was utilized to track the survival status of individuals. The Cox regression model was employed to estimate the associations between frailty status and all-cause and cancer-related mortality. RESULTS: Ultimately, 3398 cancer patients were included in the analysis, comprising 910 younger adults and 2488 older adults. Compared to non-frail patients, the elevated all-cause and cancer-related mortality among pre-frail patients was not statistically significant (HRs = 1.312, 95%CI: 0.956-1.800, P = 0.092; HRs = 1.462, 0.811-2.635, P = 0.207). However, a significant elevation of both all-cause and cancer-related mortality risk was observed among frail patients (HRs = 2.213, 1.617-3.030, P < 0.001; HRs = 2.463, 95%CI = 1.370-4.429, P = 0.003). Frailty individuals demonstrated a more pronounced association with the prediction of all-cause mortality in younger (HRs = 2.230, 1.073-4.634, P = 0.032) than in older adults (HRs = 2.090, 1.475-2.960, P < 0.001). Sensitivity analysis consistently revealed robust results. RCS plots suggested a progressively escalating dose-response correlation between frailty and both all-cause and cancer-related mortality risk. CONCLUSIONS: Pre-frailty did not result in an increase in mortality risks compared to non-frailty. However, frailty caused a higher all-cause and cancer-related mortality risk than non-frailty. Identifying those at risk and implementing targeted interventions may contribute to extending healthy life expectancy, regardless of age.

Constructing and validating a predictive nomogram for osteoporosis risk among Chinese single-center male population using the systemic immune-inflammation index.

Osteoporosis (OP) is a bone metabolism disease that is associated with inflammatory pathological mechanism. Nonetheless, rare studies have investigated the diagnostic effectiveness of immune-inflammation index in the male population. Therefore, it is interesting to achieve early diagnosis of OP in male population based on the inflammatory makers from blood routine examination. We developed a prediction model based on a training dataset of 826 Chinese male patients through a retrospective study, and the data was collected from January 2022 to May 2023. All participants underwent the dual-energy X-ray absorptiometry (DXEA) and blood routine examination. Inflammatory markers such as systemic immune-inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) was calculated and recorded. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. Multivariable logistic regression analysis was applied to construct a predicting model incorporating the feature selected in the LASSO model. This predictive model was displayed as a nomogram. Receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance. Internal validation was test by the bootstrapping method. This study was approved by the Ethic Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (Ethic No. JY2023012) and conducted in accordance with the relevant guidelines and regulations. The predictive factors included in the prediction model were age, BMI, cardiovascular diseases, cerebrovascular diseases, neuropathy, thyroid diseases, fracture history, SII, PLR, C-reactive protein (CRP). The model displayed well discrimination with a C-index of 0.822 (95% confidence interval: 0.798-0.846) and good calibration. Internal validation showed a high C-index value of 0.805. Decision curve analysis (DCA) showed that when the threshold probability was between 3 and 76%, the nomogram had a good clinical value. This nomogram can effectively predict the incidence of OP in male population based on SII and PLR, which would help clinicians rapidly and conveniently diagnose OP with men in the future.

Body mass index and pressure injuries risk in hospitalized adult patients: A dose-response analysis.

BACKGROUND: The association between underweight and pressure injuries (PIs) has been established in several studies. However, there is a lack of well-designed research investigating the connection between overweight and obesity with these injuries. OBJECTIVE: This meta-analysis aims to investigate the dose-response relationship between body mass index (BMI) and the risk of PIs in adult hospitalized patients. METHODS: PubMed, Web of Science, and MEDLINE Databases were searched from inception to May 2024. Observational articles with at least three BMI categories were included in the study. BMI was defined as underweight, normal weight, overweight, and morbid obesity for the meta-analysis. The non-linear relationship between BMI and the risk of PIs in hospitalized adults was investigated using restricted cubic spline models. Fractional polynomial modeling was used. RESULTS: Eleven articles reporting at least 3 categories of BMI met the inclusion criteria, including 31,389 participants. Compared to patients with normal weight, those with underweight, obesity, and morbid obesity exhibited an increased risk of PIs, with odds ratios of 1.70 (95%CI:1.50-1.91), 1.12 (95%CI:1.02-1.24), 1.70 (95%CI:1.13-2.55), respectively. A J-shaped dose-response model was established for the relationship between PI risk and BMI (Pnon-linearity < 0.001, Plinearity = 0.745). CONCLUSION: The J-shaped dose-response pattern revealed that underweight, obesity and morbid obesity heightened the risk of PIs in hospitalized adults. Lower and higher BMI values may signify an increased risk for PIs, particularly among the elderly with lower BMI, providing valuable guidance for medical staff.

Unraveling the Atomistic Mechanisms Underlying Effective Reverse Osmosis Filtration by Graphene Oxide Membranes.

The graphene oxide (GO) membrane displays promising potential in efficiently filtering ions from water. However, the precise mechanism behind its effectiveness remains elusive, particularly due to the lack of direct experimental evidence at the atomic scale. To shed light on this matter, state-of-the-art techniques are employed such as integrated differential phase contrast-scanning transmission electron microscopy and electron energy loss spectroscopy, combined with reverse osmosis (RO) filtration experiments using GO membranes. The atomic-scale observations after the RO experiments directly reveal the binding of various ions including Na+, K+, Ca2+, and Fe3+ to the defects, edges, and functional groups of GO. The remarkable ion-sieving capabilities of GO membranes are confirmed, which can be attributed to a synergistic interplay of size exclusion, electrostatic interactions, cation-π, and other non-covalent interactions. Moreover, GO membranes modified by external pressure and cation also demonstrated further enhanced filtration performance for filtration. This study significantly contributes by uncovering the atomic-scale mechanism responsible for ion sieving in GO membranes. These findings not only enhance the fundamental understanding but also hold substantial potential for the advancement of GO membranes in reverse osmosis (RO) filtration.

Photoaffinity probe-based antimalarial target identification of artemisinin in the intraerythrocytic developmental cycle of Plasmodium falciparum.

Malaria continues to pose a serious global health threat, and artemisinin remains the core drug for global malaria control. However, the situation of malaria resistance has become increasingly severe due to the emergence and spread of artemisinin resistance. In recent years, significant progress has been made in understanding the mechanism of action (MoA) of artemisinin. Prior research on the MoA of artemisinin mainly focused on covalently bound targets that are alkylated by artemisinin-free radicals. However, less attention has been given to the reversible noncovalent binding targets, and there is a paucity of information regarding artemisinin targets at different life cycle stages of the parasite. In this study, we identified the protein targets of artemisinin at different stages of the parasite's intraerythrocytic developmental cycle using a photoaffinity probe. Our findings demonstrate that artemisinin interacts with parasite proteins in vivo through both covalent and noncovalent modes. Extensive mechanistic studies were then conducted by integrating target validation, phenotypic studies, and untargeted metabolomics. The results suggest that protein synthesis, glycolysis, and oxidative homeostasis are critically involved in the antimalarial activities of artemisinin. In summary, this study provides fresh insights into the mechanisms underlying artemisinin's antimalarial effects and its protein targets.

Transmission Patterns of Co-Circulation of Omicron Sub-Lineages in Hong Kong SAR, China, a City with Rigorous Social Distancing Measures, in 2022.

OBJECTIVE: This study aimed to characterize the changing landscape of circulating SARS-CoV-2 lineages in the local community of Hong Kong throughout 2022. We examined how adjustments to quarantine arrangements influenced the transmission pattern of Omicron variants in a city with relatively rigorous social distancing measures at that time. METHODS: In 2022, a total of 4684 local SARS-CoV-2 genomes were sequenced using the Oxford Nanopore GridION sequencer. SARS-CoV-2 consensus genomes were generated by MAFFT, and the maximum likelihood phylogeny of these genomes was determined using IQ-TREE. The dynamic changes in lineages were depicted in a time tree created by Nextstrain. Statistical analysis was conducted to assess the correlation between changes in the number of lineages and adjustments to quarantine arrangements. RESULTS: By the end of 2022, a total of 83 SARS-CoV-2 lineages were identified in the community. The increase in the number of new lineages was significantly associated with the relaxation of quarantine arrangements (One-way ANOVA, F(5, 47) = 18.233, p < 0.001)). Over time, Omicron BA.5 sub-lineages replaced BA.2.2 and became the predominant Omicron variants in Hong Kong. The influx of new lineages reshaped the dynamics of Omicron variants in the community without fluctuating the death rate and hospitalization rate (One-way ANOVA, F(5, 47) = 2.037, p = 0.091). CONCLUSION: This study revealed that even with an extended mandatory quarantine period for incoming travelers, it may not be feasible to completely prevent the introduction and subsequent community spread of highly contagious Omicron variants. Ongoing molecular surveillance of COVID-19 remains essential to monitor the emergence of new recombinant variants.

Prevalence of anxiety, depression, sleeping problems, cognitive impairment, and suicidal ideation in people with autoimmune skin diseases.

BACKGROUND: Autoimmune skin diseases (ASDs) such as psoriasis and vitiligo, in addition to causing visible skin symptoms, are closely associated with psychological health issues. However, a comprehensive understanding of the prevalence of these psychological comorbidities in affected individuals is lacking. This study aims to identify the prevalence of anxiety, depression, sleeping problems, cognitive impairment, and suicidal ideation in people with ASDs. METHOD: PubMed, MEDLINE, Web of Science, and Cochrane Library searches were conducted from 1993 to May 2024. Observational studies reporting prevalence data for anxiety, depression, sleeping problems, cognitive impairment, and suicidal ideation among people with ASDs were included in the analysis. The Newcastle-Ottawa scale was used to evaluate the quality of studies. RESULTS: The study included 114 studies from 37 countries including 823,975 participants. The estimated pooled prevalence of anxiety in patients with ASDs was 33.3% (95% CI: 27.3-29.3%). The estimated pooled prevalence of depression was 33.7% (95% CI: 29.2-38.1%). The estimated pooled prevalence of sleeping problems was 45.0% (95% CI:31.6-58.4%). The estimated pooled prevalence of cognitive impairment and suicidal ideation was 30.8% (95% CI:15.0-46.7%) and 21.6% (95% CI:13.4-29.8%), respectively. The most common mental disorder in patients with systemic lupus erythematosus and psoriasis was sleeping problems at 55.9% (95% CI: 35.6-76.1%, I2 = 97%) and 39.0% (95% CI: 21.1-56.9%, I2 = 99%). CONCLUSION: Among patients with ASDs, anxiety, depression, sleeping problems, cognitive impairment, and suicidal ideation were common. The most prevalent mental disorder among patients with systemic lupus erythematosus and psoriasis was sleeping problems. Those with ASDs may experience considerable psychological burdens, and integrated mental health support is necessary for their treatment.

RNA sequencing-based approaches to identifying disulfidptosis-related diagnostic clusters and immune landscapes in osteoporosis.

Disulfidptosis, a newly recognized cell death triggered by disulfide stress, has garnered attention for its potential role in osteoporosis (OP) pathogenesis. Although sulfide-related proteins are reported to regulate the balance of bone metabolism in OP, the precise involvement of disulfidptosis regulators remains elusive. Herein, leveraging the GSE56815 dataset, we conducted an analysis to delineate disulfidptosis-associated diagnostic clusters and immune landscapes in OP. Subsequently, vertebral bone tissues obtained from OP patients and controls were subjected to RNA sequencing (RNA-seq) for the validation of key disulfidptosis gene expression. Our analysis unveiled seven significant disulfidptosis regulators, including FLNA, ACTB, PRDX1, SLC7A11, NUBPL, OXSM, and RAC1, distinguishing OP samples from controls. Furthermore, employing a random forest model, we identified four diagnostic disulfidptosis regulators including FLNA, SLC7A11, NUBPL, and RAC1 potentially predictive of OP risk. A nomogram model integrating these four regulators was constructed and validated using the GSE35956 dataset, demonstrating promising utility in clinical decision-making, as affirmed by decision curve analysis. Subsequent consensus clustering analysis stratified OP samples into two different disulfidptosis subgroups (clusters A and B) using significant disulfidptosis regulators, with cluster B exhibiting higher disulfidptosis scores and implicating monocyte immunity, closely linked to osteoclastogenesis. Notably, RNA-seq analysis corroborated the expression patterns of two disulfidptosis modulators, PRDX1 and OXSM, consistent with bioinformatics predictions. Collectively, our study sheds light on disulfidptosis patterns, offering potential markers and immunotherapeutic avenues for future OP management.

Identifications of novel host cell factors that interact with the receptor-binding domain of the SARS-CoV-2 spike protein.

SARS-CoV-2 entry into host cells is facilitated by the interaction between the receptor-binding domain of its spike protein (CoV2-RBD) and host cell receptor, ACE2, promoting viral membrane fusion. The virus also uses endocytic pathways for entry, but the mediating host factors remain largely unknown. It is also unknown whether mutations in the RBD of SARS-CoV-2 variants promote interactions with additional host factors to promote viral entry. Here, we used the GST pull-down approach to identify novel surface-located host factors that bind to CoV2-RBD. One of these factors, SH3BP4, regulates internalization of CoV2-RBD in an ACE2-independent but integrin- and clathrin-dependent manner and mediates SARS-CoV-2 pseudovirus entry, suggesting that SH3BP4 promotes viral entry via the endocytic route. Many of the identified factors, including SH3BP4, ADAM9, and TMEM2, show stronger affinity to CoV2-RBD than to RBD of the less infective SARS-CoV, suggesting SARS-CoV-2-specific utilization. We also found factors preferentially binding to the RBD of the SARS-CoV-2 Delta variant, potentially enhancing its entry. These data identify the repertoire of host cell surface factors that function in the events leading to the entry of SARS-CoV-2.

The Effects of a Nonpharmacological Intervention Practice for Older Adults with Mild Cognitive Impairment and Their Family Caregivers in China.

Mild cognitive impairment (MCI) marks a critical phase in the progression to dementia. In our study, social workers utilized the Multicomponent Nonpharmacological Intervention Approach (MCNIA) to aid MCI participants (N = 52) and their caregivers, dividing into intervention and control groups. The intervention group underwent an additional regimen of non-pharmacological therapies besides pharmacological treatment. Our findings highlighted that: 1) MCNIA significantly enhanced cognitive and daily living abilities in the intervention group; 2) Caregivers experienced reduced burdens and improved social support; 3) Correlation analyses involving biomarkers indicated that MCNIA was particularly effective in alleviating depression in those with slightly more severe cognitive impairment.

Efficacy of recombinant human epidermal growth factor in pressure injury healing: evidence from Chinese randomized controlled trials.

OBJECTIVE: To evaluate the efficacy of recombinant human epidermal growth factor (rhEGF) in healing pressure injuries (PIs). METHODS: A meta-analysis was conducted of randomized controlled trials (RCTs) involving rhEGF in the treatment of PIs that were identified in PubMed, Web of Science, the Cochrane Library, and China National Knowledge Infrastructure (CNKI). The population, intervention, comparison, outcomes, study design (PICOS) strategy was applied to determine analysis eligibility. The Cochrane risk of bias tool was used, and statistical analysis, including sensitivity analysis, was performed of 3 outcomes indicators: the primary outcome was total efficacy of rhEGF in treating PIs, and the secondary outcomes were the proportion of complete healing and the time to complete healing. Total efficacy refers to the proportion of cases that have been cured, obviously effective, or effective. Complete healing refers to cases where the wound has healed, scabbed, and the scab has sloughed off. RESULTS: Sixteen RCTs were included, comprising a total of 1,206 patients. Study and control group size varied by outcomes. The total effective healing rate in rhEGF group was 97.18%, which was significantly higher than 83.38% in control group (OR: 5.69, [95% CI: 3.61, 8.97], z=7.49, P < .001). The proportion of complete healing in the rhEGF group was 73.30%, which was higher than 39.52% in control group (OR: 3.88, [95% CI: 3.01, 5.01], z=10.39, P < .001). Furthermore, the healing time using rhEGF was shorter (SMD: -2.14 days, [95% CI: -2.60, -1.67], z=9.07, P < .001). Sensitivity analyses indicated that the results were robust. CONCLUSIONS: The meta-analysis indicated that rhEGF was effective in healing PIs with few negative effects. Further research beyond Chinese populations involving larger studies and studies that distinguish between results found in using rhEGF alone or in combination are recommended.

Restoring bone-fat equilibrium: Baicalin's impact on P38 MAPK pathway for treating diabetic osteoporosis.

Diabetes can lead to a disorder of bone-fat balance, a significant cause of osteoporosis due to changes in environmental factors. Baicalin (Bai), an active ingredient of Scutellaria baicalensis, has been confirmed to possess antioxidant, hypoglycemic, and anti-osteoporotic effects. However, a comprehensive understanding of Bai's influence on diabetic osteoporosis (DOP), including its effects and underlying mechanisms, remains elusive. This study investigated Bai's impact on the bone-fat equilibrium in rats with DOP. The results indicated that Bai alleviated bone damage in DOP by promoting osteogenesis and inhibiting adipogenesis. Concurrently, through bioinformatics analysis, it was suggested that Bai's mechanism of action might involve the P38-MAPK pathway. In vitro, Bai was found to enhance the development of bone marrow mesenchymal stem cells (BMSCs) towards osteogenic lineages while suppressing their differentiation towards adipogenic lineages. It was discovered that Bai's promotion of BMSC osteogenic differentiation depends on the P38-MAPK pathway. Additionally, the synergistic effect mediated by Bai and P38-MAPK inhibitor suppressed BMSC adipogenic differentiation. Our research indicates that the P38-MAPK pathway play a role in Bai's effects on the osteogenic-adipogenic differentiation of BMSCs, showcasing the potential for DOP treatment. This study highlights Bai's ability to regulate the equilibrium between bone and fat, presenting a novel approach to adressing DOP.

Exploring Novel Fentanyl Analogues Using a Graph-Based Transformer Model.

The structures of fentanyl and its analogues are easy to be modified and few types have been included in database so far, which allow criminals to avoid the supervision of relevant departments. This paper introduces a molecular graph-based transformer model, which is combined with a data augmentation method based on substructure replacement to generate novel fentanyl analogues. 140,000 molecules were generated, and after a set of screening, 36,799 potential fentanyl analogues were finally obtained. We calculated the molecular properties of 36,799 potential fentanyl analogues. The results showed that the model could learn some properties of original fentanyl molecules. We compared the generated molecules from transformer model and data augmentation method based on substructure replacement with those generated by the other two molecular generation models based on deep learning, and found that the model in this paper can generate more novel potential fentanyl analogues. Finally, the findings of the paper indicate that transformer model based on molecular graph helps us explore the structure of potential fentanyl molecules as well as understand distribution of original molecules of fentanyl.

Folic Acid Functionalized AQ4N/Gd@PDA Nanoplatform with Real-Time Monitoring of Hypoxia Relief and Enhanced Synergistic Chemo/Photothermal Therapy in Glioma.

Purpose: Hypoxia is often associated with glioma chemoresistance, and alleviating hypoxia is also crucial for improving treatment efficacy. However, although there are already some methods that can improve efficacy by alleviating hypoxia, real-time monitoring that can truly achieve hypoxia relief and efficacy feedback still needs to be explored. Methods: AQ4N/Gd@PDA-FA nanoparticles (AGPF NPs) were synthesized using a one-pot method and were characterized. The effects of AGPF NPs on cell viability, cellular uptake, and apoptosis were investigated using the U87 cell line. Moreover, the effectiveness of AGPF NPs in alleviating hypoxia was explored in tumor-bearing mice through photoacoustic imaging. In addition, the diagnosis and treatment effect of AGPF NPs were evaluated by magnetic resonance imaging (MRI) and bioluminescent imaging (BLI) on orthotopic glioma mice respectively. Results: In vitro experiments showed that AGPF NPs had good dispersion, stability, and controlled release. AGPF NPs were internalized by cells through endocytosis, and could significantly reduce the survival rate of U87 cells and increase apoptosis under irradiation. In addition, we monitored blood oxygen saturation at the tumor site in real-time through photoacoustic imaging (PAI), and the results showed that synergistic mild-photothermal therapy/chemotherapy effectively alleviated tumor hypoxia. Finally, in vivo anti-tumor experiments have shown that synergistic therapy can effectively alleviate hypoxia and inhibit the growth of orthotopic gliomas. Conclusion: This work not only provides an effective means for real-time monitoring of the dynamic feedback between tumor hypoxia relief and therapeutic efficacy, but also offers a potential approach for the clinical treatment of gliomas.

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems.

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.