AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

The association between XPD rs13181 and rs1799793 polymorphism and oral cancer risk: evidence from a meta-analysis.

OBJECTIVE: Single nucleotide polymorphisms (SNPs) are common in genes and can lead to dysregulation of gene expression in tissues, which can affect carcinogenesis. Many studies reporting the association between xeroderma pigmentosum group D (XPD) polymorphisms of rs13181 and rs1799793 with oral cancer risk, but with conflicting and inconclusive results. METHODS: We performed a comprehensive and systematic search through the PubMed, Elsevier, Web of science, and Embase databases, twelve studies were included in the meta-analysis to determine whether XPD rs13181 and rs1799793 polymorphism contributed to the risk of oral cancer. RESULTS: The pooled date indicated a significant association between the rs13181 polymorphism and oral cancer risk for the allele comparison model (odds ratio, OR = 1.60, 95% confidence intervals, CI = 1.09-2.35, P = 0.02), the dominant model (OR = 1.74, 95% CI = 1.08-2.82, P = 0.02), and the heterozygote model (OR = 1.59, 95% CI = 1.02-2.49, P = 0.04). For the XPD rs1799793 polymorphism, it is not associated with the incidence of oral cancer under any model. Subgroup analyses based on ethnicity indicated that the rs13181 polymorphism increased the risk of oral cancer among Asians according to the allele comparison model (OR = 1.97, 95% CI = 1.10-3.51, P = 0.02), the dominant model (OR = 2.35, 95% CI = 1.25-4.44, P = 0.008), the heterozygote model (OR = 2.05, 95% CI = 1.15-3.66, P = 0.01), and the homozygous model (OR = 2.47, 95% CI = 1.06-5.76, P = 0.04). CONCLUSION: Our meta-analysis suggests a positive correlation between XPD rs13181polymorphism and the development of oral cancer among Asians, but a negative correlation among Caucasians populations.

Integrated Anchored Stapling and Hierarchical Dynamics: MSICDA-Driven CREBBP Bromodomain Inhibition.

Despite recent success in the computational approaches of cyclic peptide design, current studies face challenges in modeling noncanonical amino acids and nonstandard cyclizations due to limited data. To address this challenge, we developed an integrated framework for the tailored design of stapled peptides (SPs) targeting the bromodomain of CREBBP (CREBBP-BrD). We introduce a powerful combination of anchored stapling and hierarchical molecular dynamics to design and optimize SPs by employing the MultiScale integrative conformational dynamics assessment (MSICDA) strategy, which involves an initial virtual screening of over 1.5 million SPs, followed by comprehensive simulations amounting to 154.54 µs across 5418 of instances. The MSICDA method provides a detailed and holistic stability view of peptide-protein interactions, systematically isolated optimized peptides and identified two leading candidates, DA#430 and DA#99409, characterized by their enhanced stability, optimized binding, and high affinity toward the CREBBP-BrD. In cell-free assays, DA#430 and DA#99409 exhibited 2- to 12-fold greater potency than inhibitor SGC-CBP30. Cell studies revealed higher peptide selectivity for cancerous versus normal cells over small molecules. DA#430 combined with (+)-JQ-1 showed promising synergistic effects. Our approach enables the identification of peptides with optimized binding, high affinity, and enhanced stability, leading to more precise and effective cyclic peptide design, thereby establishing MSICDA as a generalizable and transformative tool for uncovering novel targeted drug development in various therapeutic areas.

Can market-oriented and government-led spatial agglomeration of factories reduce carbon emission intensity? Evidence from China.

Both market-oriented and government-led spatial agglomeration of factories profoundly affect China's carbon emission intensity. However, few studies have comprehensively examined the effect of spatial agglomeration of factories driven by different motivations on carbon emission intensity, and often ignore the moderating effect of local government attention to the environment. This study attempts to incorporate market-oriented and government-led spatial agglomeration of factories into the same analytical framework and examine their interaction effects on carbon emission intensity. We first calculate an Agglomeration Index based on the geographic coordinates of micro-factories to measure market-oriented agglomeration of factories, and take development zone policies as a proxy for government-led agglomeration of factories. Based on city-level data from 1998 to 2013, we empirically analyze their impacts on carbon emission intensity and further explore regional heterogeneity and mechanisms. The results show that both the market-oriented and government-led spatial agglomeration of factories can significantly reduce carbon emission intensity through scale effect, innovation effect and structure effect. However, the agglomeration of factories caused by government forces has weakened the original effect of market-oriented agglomeration of factories in reducing carbon emission intensity. Heterogeneity analysis indicates that government-led agglomeration of factories is more effective in reducing carbon emission intensity in less-developed regions. Furthermore, the improvement of the governments' attention to the environment can further exert the positive effect of spatial agglomeration of factories on carbon emission intensity. Our study deepens the understanding of the effects of spatial agglomeration of factories caused by different motivations, and provides a reference for China and other developing countries to formulate relevant policies for reducing carbon emission intensity.

Design and Implementation of Pavlovian Associative Memory Based on DNA Neurons.

In the field of biocomputing and neural networks, deoxyribonucleic acid (DNA) strand displacement (DSD) technology performs well in computation, programming, and information processing. In this article, the multiplication gate, addition gate, and threshold gate based on DSD are used to cascade into a single DNA neuron. Multiple DNA neurons can be cascaded to form different neural networks. The DNA neural networks are designed to implement seven classical conditioned reflexes from Pavlovian associative memory experiments. A classical conditioned reflex is a combination of a conditioned stimulus (CS) and another un CS with a reward or punishment. So that the individual develops a conditioned reflex that is similar to an unconditioned reflex in the use of CS alone. The seven classical conditioned reflexes include acquisition and forgetting, interstimulus interval effect, blocking, conditioned inhibition, overshadowing, generation, and differentiation. The simulations are verified by the software visual DSD. This article provides a direction for the integration of biology and psychology.

Models predicting mortality risk of patients with burns to ≥ 50% of the total body surface.

BACKGROUND: Several models predicting mortality risk of burn patients have been proposed. However, models that consider all such patients may not well predict the mortality of patients with extensive burns. METHOD: This retrospective multicentre study recruited patients with extensive burns (≥ 50% of the total body surface area [TBSA]) treated in three hospitals of Eastern China from 1 January 2016 to 30 June 2022. The performances of six predictive models were assessed by drawing receiver operating characteristic (ROC) and calibration curves. Potential predictors were sought via "least absolute shrinkage and selection operator" regression. Multivariate logistic regression was employed to construct a predictive model for patients with burns to ≥ 50% of the TBSA. A nomogram was prepared and the performance thereof assessed by reference to the ROC, calibration, and decision curves. RESULT: A total of 465 eligible patients with burns to ≥ 50% TBSA were included, of whom 139 (29.9%) died. The FLAMES model exhibited the largest area under the ROC curve (AUC) (0.875), followed by the models of Zhou et al. (0.853) and the ABSI model (0.802). The calibration curve of the Zhou et al. model fitted well; those of the other models significantly overestimated the mortality risk. The new nomogram includes four variables: age, the %TBSA burned, the area of full-thickness burns, and blood lactate. The AUCs (training set 0.889; internal validation set 0.934; external validation set 0.890) and calibration curves showed that the nomogram exhibited an excellent discriminative capacity and that the predictions were very accurate. CONCLUSION: For patients with burns to ≥ 50％of the TBSA, the Zhou et al. and FLAMES models demonstrate relatively high predictive ability for mortality. The new nomogram is sensitive, specific, and accurate, and will aid rapid clinical decision-making.

Molecular Dynamics and In Vitro Studies Elucidating the Tunable Features of Reconfigurable Nanodiscs for Guiding the Optimal Design of Curcumin Formulation.

In this study, we advance our exploration of Apolipoprotein A-I (apoA-I) peptide analogs (APAs) for their application in nanodisc (ND) assembly, focusing on the dynamic conformational characteristics and the potential for drug delivery. We explore APA-ND interactions with an emphasis on curcumin encapsulation, utilizing molecular dynamic simulations and in vitro assessments to evaluate the efficacy of various APA-ND formulations as drug carriers. The methodological approach involved the generation of three unique apoA-I α-11/3 helical mimics, resulting in fifteen distinct APAs. Their structural integrity was rigorously assessed using ColabFold-AF2, with particular attention to pLDDT and pTM scores. Extensive molecular dynamics simulations, covering 1.7 µs across 17 ND systems, were conducted to investigate the influence of APA sequence variations on ND stability and interactions. This study reveals that the composition of APAs, notably the presence of Proline, Serine, and Tryptophan, significantly impacts ND stability and morphology. Oligomeric APAs, in particular, demonstrated superior stability and distinct interaction patterns compared to their monomeric counterparts. Additionally, hydrodynamic diameter measurements over eight weeks indicated sequence-dependent stability, highlighting the potential of specific APA configurations for sustained colloidal stability. In vitro study successfully encapsulated curcumin in [AA]3/DMPC ND formulations, revealing concentration-dependent stability and interaction dynamics. The findings underscore the remarkable capability of APA-NDs to maintain structural integrity and efficient drug encapsulation, positioning them as a promising platform for drug delivery. The study concludes by emphasizing the tunability and versatility of APA-NDs in drug formulation, potentially revolutionizing nanomedicine by enabling customized APA sequences and ND properties for targeted drug delivery.

The prognostic value and model construction of inflammatory markers for patients with non-small cell lung cancer.

The aim of this study was to investigate and analyse the predictive value of systemic inflammatory markers based on peripheral blood biomarkers for the prognosis of non-small cell lung cancer (NSCLC) patients. Based on a retrospective monitoring cohort of 973 NSCLC patients from an Affiliated Tumor Hospital from 2012 to 2023. The log-rank test and Cox proportional risk regression model were used to identify independent prognostic inflammatory markers. Subsequently, a nomogram prediction model was constructed and evaluated. The results of multivariate Cox regression analysis showed that patients with high NLR group (HR = 1.238, 95% CI 1.015-1.510, P = 0.035), and high CAR group (HR = 1.729, 95% CI 1.408-2.124, P < 0.001) were risk factors affecting the prognosis of NSCLC patients. The nomogram that includes age, tumor stage, smoking history, BMI, NLR, and CAR can effectively predict the prognosis of NSCLC patients.The inflammatory markers NLR and CAR, which combine inflammatory and nutritional status, are effective predictors of the prognosis of NSCLC patients. The combination of clinical information and these easily accessible inflammatory markers has significant research value for prognostic assessment, clinical treatment, and follow-up monitoring of NSCLC patients.

Association Studies of Serum Levels of TNF- α, IL-10, IFN-γ and CXCL 5 with Latent Tuberculosis Infection in Close Contacts.

Purpose: Early recognition and treatment of latent tuberculosis infection(LTBI) is key to tuberculosis(TB) prevention. However, the emergence of LTBI is influenced by a combination of factors, of which the role of individual immune cytokines remains controversial. The aim of this study is to explore the influencing factors of LTBI and their effects with cytokines on LTBI. Patients and Methods: Close contacts of tuberculosis in Urumqi City from 2021 to 2022 were selected for the study to conduct a field survey. It used logistic regression model to analyse the influencing factors of LTBI, principal component analysis to extract a composite indicators of cytokines, and structural equation modelling to explore the direct and indirect effects of cytokines and influencing factors on LTBI. Results: LTBI infection rate of 33.3% among 288 TB close contacts. A multifactorial Logistic model showed that factors influencing LTBI included education, daily contact hours, eating animal liver, and drinking coffee (P<0.05); After controlling for confounding factors and extracting composite indicators of cytokines using principal component analysis, CXCL5 and IFN-γ is a protective factor for LTBI(OR=0.572, P=0.047), IL-10 and TNF-α is a risk factor for LTBI(OR=2.119, P=0.010); Structural equation modelling shows drinking coffee, eating animal liver, daily contact hours, and IL-10 and TNF-α had direct effects on LTBI and educations had indirect effects on LTBI(P<0.05). Conclusion: IL-10 and TNF-α are involved in the immune response and are directly related to LTBI. By monitoring the cytokine levels of TB close contacts and paying attention to their dietary habits and exposure, we can detect and intervene in LTBI at an early stage and control their progression to TB.

Ultra-restrictive red blood cell transfusion strategies in extensively burned patients.

In recent years, due to the shortage of blood products, some extensive burn patients were forced to adopt an "ultra-restrictive" transfusion strategy, in which the hemoglobin levels of RBC transfusion thresholds were < 7 g/dl or even < 6 g/dl. This study investigated the prognostic impacts of ultra-restrictive RBC transfusion in extensive burn patients. This retrospective multicenter cohort study recruited extensive burns (total body surface area ≥ 50%) from three hospitals in Eastern China between 1 January 2016 and 30 June 2022. Patients were divided into an ultra-restrictive transfusion group and a restrictive transfusion group depending on whether they received timely RBC transfusion at a hemoglobin level < 7 g/dl. 1:1 ratio propensity score matching (PSM) was performed to balance selection bias. Modified Poisson regression and linear regression were conducted for sensitive analysis. Subsequently, according to whether they received timely RBC transfusion at a hemoglobin level < 6 g/dl, patients in the ultra-restrictive transfusion group were divided into < 6 g/dl group and 6-7 g/dl group to further compare the prognostic outcomes. 271 eligible patients with extensive burns were included, of whom 107 patients were in the ultra-restrictive transfusion group and 164 patients were in the restrictive transfusion group. The ultra-restrictive transfusion group had a significantly lower RBC transfusion volume than the restrictive transfusion group (11.5 [5.5, 21.5] vs 17.3 [9.0, 32.5] units, p = 0.004). There were no significant differences between the two groups in terms of in-hospital mortality, risk of infection, hospital length of stay, and wound healing time after PSM or multivariate adjustment (p > 0.05). Among the ultra-restrictive transfusion group, patients with RBC transfusion threshold < 6 g/dl had a significantly higher hospital mortality than 6-7 g/dl (53.1% vs 21.3%, p = 0.001). For extensive burn patients, no significant adverse effects of ultra-restrictive RBC transfusion were found in this study. When the blood supply is tight, it is acceptable to adopt an RBC transfusion threshold of < 7 g/dL but not < 6 g/dL.

The causal effect and autonomous buffering mechanisms of large-scale internal migration on carbon emissions: evidence from China.

Large-scale internal migration and unprecedented urbanization have dramatically promoted economic growth in China, resulting in a rapid surge in carbon emissions in urban areas. However, few studies have investigated the causal effect of mass internal migration on carbon emissions or examined the effects of autonomous mitigation mechanisms, such as population agglomeration and technological innovation. This study identifies the causal effect of internal migration on prefectural-level cities' carbon emissions in China by employing an instrumental variable and further investigates the buffering effect of population agglomeration and technological innovation using mediating effect models. The results show that mass internal migration has a substantial impact on increasing carbon emissions in prefectural-level cities. If the proportion of inflowed migrants rises by 1% point, prefectural-level cities' carbon emissions per capita will increase by 1.9%. A series of robustness tests confirms the result. Population migration also promotes population agglomeration and technological innovation in urban areas. Two autonomous mechanisms buffer 11.9% and 5.4% of prefectural-level cities' incremental carbon emissions per capita caused by population migration, respectively. This study highlights the crucial role of population agglomeration and technological innovation in mitigating carbon emissions in cities experiencing significant migrant inflows and provides several implications for formulating relevant policies.

Accelerating Globally Optimal Consensus Maximization in Geometric Vision.

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an n-1 dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to four fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, point set registration, and rotation and focal length estimation. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios.

Associated predictors of prolonged length of stay in patients surviving extensive burns: A large multicenter retrospective study.

BACKGROUND: Patients with extensive burns are critically ill and have long treatment periods. Length of stay (LOS) is a good measure for assessing treatment. This study sought to identify predictors of prolonged LOS in patients with extensive burns (≥50% TBSA). METHODS: This retrospective multicenter cohort study included adults aged ≥ 18 years who survived extensive burns in three burn centers in Eastern China between January 2016 and June 2022. Epidemiological, demographic and clinical outcomes data were extracted from electronic medical records and compared between patients with/without prolonged LOS, which was defined as LOS greater than the median. Logistic regression analysis was used to identify predictors of prolonged LOS. RESULTS: The study sample included 321 patients, of whom 156 (48.6%) had an LOS of 58 days (IQR 41.0-77.0). Univariate regression analysis showed that increased total burn area and increased full-thickness burn area; electrical, chemical and other burns; increased erythrocytes, leukocytes, platelets or serum creatinine within 24 h of admission; concomitant inhalation injury, pulmonary edema, sepsis, bloodstream infection, wound infection, pulmonary infection, urinary tract infection, or HB < 70 g/L during hospitalization were associated with prolonged LOS in patients with extensive burns. Increased number of surgical operations, mechanical ventilation and renal replacement therapy were also associated with prolonged LOS (P < 0.05 or P < 0.001). Multivariate regression analysis revealed that increased total burn area (ratio 1.032, 95%CI 1.01-1.055; P = 0.004), electrical and chemical or other burns (3.282, 1.335-8.073; P = 0.01), development of wound infection (2.653 1.285-5.481; P = 0.008) and increased number of operative procedures (1.714, 1.388-2.116, P < 0.001) were significant predictors. CONCLUSIONS: Increased area of full-thickness burn,occurrence of electrical and chemical or other burns,occurrence of wound infection and increased number of surgeries are the best predictors of prolonged LOS in patients with extensive burns. Clarifying relevant predictors of burn patients' LOS provides a reliable reference for clinical treatment.

SMURF1 controls the PPP3/calcineurin complex and TFEB at a regulatory node for lysosomal biogenesis.

Macroautophagy/autophagy is a homeostatic process in response to multiple signaling, such as the lysosome-dependent recycling process of cellular components. Starvation-induced MTOR inactivation and PPP3/calcineurin activation were shown to promote the nuclear translocation of TFEB. However, the mechanisms via which signals from endomembrane damage are transmitted to activate PPP3/calcineurin and orchestrate autophagic responses remain unknown. This study aimed to show that autophagy regulator SMURF1 controlled TFEB nuclear import for transcriptional activation of the lysosomal biogenesis. We showed that blocking SMURF1 affected lysosomal biogenesis in response to lysosomal damage by preventing TFEB nuclear translocation. It revealed galectins recognized endolysosomal damage, and led to recruitment of SMURF1 and the PPP3/calcineurin apparatus on lysosomes. SMURF1 interacts with both LGALS3 and PPP3CB to form the LGALS3-SMURF1-PPP3/calcineurin complex. Importantly, this complex further stabilizes TFEB, thereby activating TFEB for lysosomal biogenesis. We determined that LLOMe-mediated TFEB nuclear import is dependent on SMURF1 under the condition of MTORC1 inhibition. In addition, SMURF1 is required for PPP3/calcineurin activity as a positive regulator of TFEB. SMURF1 controlled the phosphatase activity of the PPP3CB by promoting the dissociation of its autoinhibitory domain (AID) from its catalytic domain (CD). Overexpression of SMURF1 showed similar effects as the constitutive activation of PPP3CB. Thus, SMURF1, which bridges environmental stress with the core autophagosomal and autolysosomal machinery, interacted with endomembrane sensor LGALS3 and phosphatase PPP3CB to control TFEB activation.Abbreviations: ATG: autophagy-related; LLOMe: L-Leucyl-L-Leucine methyl ester; ML-SA1: mucolipin synthetic agonist 1; MTOR: mechanistic target of rapamycin kinase; PPP3CB: protein phosphatase 3 catalytic subunit beta; RPS6KB1/p70S6K: ribosomal protein S6 kinase B1; SMURF1: SMAD specific E3 ubiquitin protein ligase 1; TFEB: transcription factor EB.

Translational Challenges and Prospective Solutions in the Implementation of Biomimetic Delivery Systems.

Biomimetic delivery systems (BDSs), inspired by the intricate designs of biological systems, have emerged as a groundbreaking paradigm in nanomedicine, offering unparalleled advantages in therapeutic delivery. These systems, encompassing platforms such as liposomes, protein-based nanoparticles, extracellular vesicles, and polysaccharides, are lauded for their targeted delivery, minimized side effects, and enhanced therapeutic outcomes. However, the translation of BDSs from research settings to clinical applications is fraught with challenges, including reproducibility concerns, physiological stability, and rigorous efficacy and safety evaluations. Furthermore, the innovative nature of BDSs demands the reevaluation and evolution of existing regulatory and ethical frameworks. This review provides an overview of BDSs and delves into the multifaceted translational challenges and present emerging solutions, underscored by real-world case studies. Emphasizing the potential of BDSs to redefine healthcare, we advocate for sustained interdisciplinary collaboration and research. As our understanding of biological systems deepens, the future of BDSs in clinical translation appears promising, with a focus on personalized medicine and refined patient-specific delivery systems.

Aptamer-based kinetically controlled DNA reactions coupled with metal-organic framework nanoprobes for sensitive detection of SARS-CoV-2 spike protein.

Since the outbreak in 2019, COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the deadliest infectious disease worldwide for people of all ages, from children to older adults. As a main structural protein of SARS-CoV-2, spike protein is reported to play a key role in the entry of the virus into host cells and is considered as an effective antigenic marker for COVID-19 diagnosis. Herein, we develop a new aptamer-based fluorescence method for SARS-CoV-2 spike protein detection based on using kinetically controlled DNA reactions and metal-organic framework nanoprobes. Specifically, the binding of SARS-CoV-2 spike protein to its aptamer is designed to precisely control the kinetics of a DNA displacement reaction, leading to the release of free signaling probes. By reasonable integration of magnetic enrichment and exonuclease-fuelled recycling, the released probes efficiently disrupt the interaction within metal-organic framework nanoprobes, thereby generating a remarkable fluorescent response. Experimental results show that the method not only exhibits a wide linear range and a low detection limit of 7.8 fg mL-1 for SARS-CoV-2 spike protein detection but also demonstrates desirable specificity and utility in complex samples. Therefore, the method may provide a valuable tool for the detection of SARS-CoV-2 spike protein, and has bright prospects in the rapid diagnosis of COVID-19, which is of great significance for guiding rational treatment during a pandemic of respiratory infectious diseases and reducing the occurrence of severe disease in children.

Effects of NAD+ precursors on blood pressure, C-reactive protein concentration and carotid intima-media thickness: A meta-analysis of randomized controlled trials.

BACKGROUND: There are contradictory effects regarding the effect of NAD+ precursor on blood pressure and inflammation. In order to obtain a better viewpoint from them, this study aimed to comprehensively investigate the effects of NAD+ precursor supplementation on blood pressure, C-reactive protein (CRP) and carotid intima-media thickness (CIMT). METHODS: PubMed/MEDLINE, Web of Science, SCOPUS and Embase databases were searched using standard keywords to identify all controlled trials investigating the effects of NAD+ precursor on blood pressure, CRP and CIMT. Pooled weighted mean difference (WMD) and 95% confidence intervals (95% CI) were achieved by random-effects model analysis for the best estimation of outcomes. RESULTS: Twenty-nine articles (with 8664 participants) were included in this article. Results from meta-analyses of RCTs from random-effects models indicated a significant reduction in systolic (SBP) (weighted mean difference (WMD): -2.54 mmHg, p < .001) and diastolic blood pressure (DBP) (WMD: -2.15 mmHg, p < .001), as well as in CRP (WMD: -.93 mg/L, 95% CI -1.47 to -.40, p < .001) concentrations and CIMT (WMD: -.01 mm, 95% CI -.02 to -.00, p = .005) with the NAD+ precursors supplementation compared with the control group. In addition, a greater effect of supplementation with NAD+ precursors in reducing blood pressure (BP) were observed with the highest dose (≥2 g) and duration of the intervention (>12 weeks), as well as with NA supplementation when compared to NE. CONCLUSIONS: Overall, these findings suggest that NAD+ precursor supplementation might have a beneficial effect on cardiovascular risk factors such as BP, CRP concentration and CIMT.

SMURF1 attenuates endoplasmic reticulum stress by promoting the degradation of KEAP1 to activate NRF2 antioxidant pathway.

Cancer cells consistently utilize the unfolded protein response (UPR) to encounter the abnormal endoplasmic reticulum (ER) stress induced by the accumulation of misfolded proteins. Extreme activation of the UPR could also provoke maladaptive cell death. Previous reports have shown that NRF2 antioxidant signaling is activated by UPR and serves as noncanonical pathway to defense and reduce excessive ROS levels during ER stress. However, the mechanisms of regulating NRF2 signaling upon ER stress in glioblastoma have not been fully elucidated. Here we identify that SMURF1 protects against ER stress and facilitates glioblastoma cell survival by rewiring KEAP1-NRF2 pathway. We show that ER stress induces SMURF1 degradation. Knockdown of SMURF1 upregulates IRE1 and PERK signaling in the UPR pathway and prevents ER-associated protein degradation (ERAD) activity, leading to cell apoptosis. Importantly, SMURF1 overexpression activates NRF2 signaling to reduce ROS levels and alleviate UPR-mediated cell death. Mechanistically, SMURF1 interacts with and ubiquitinates KEAP1 for its degradation (NRF2 negative regulator), resulting in NRF2 nuclear import. Moreover, SMURF1 loss reduces glioblastoma cell proliferation and growth in subcutaneously implanted nude mice xenografts. Taken together, SMURF1 rewires KEAP1-NRF2 pathway to confer resistance to ER stress inducers and protect glioblastoma cell survival. ER stress and SMURF1 modulation may provide promising therapeutic targets for the treatment of glioblastoma.

Application of 3D Transparent Facemasks in Long-Term Outpatient Rehabilitation of Facial Scars After Burns: A Retrospective Cohort Study of Improved Appearance of Target Scars With Different Healing Time.

Severe facial burns may cause scarring problems and affect living quality of patients. With the advent of 3D facemasks, it is being used to treat facial scars; however, its efficacy must be confirmed by adequate studies. A retrospective analysis of 26 patients who visited rehabilitation outpatient clinic from 2017 to 2022. Patients were separated into two groups based on the time to healing (TTH) following burn injury: early healing group (TTH ≤ 21 days) and late healing group (TTH > 21 days). To compare treatment outcomes and differences between the two groups, 3D facemask application was assessed using the Vancouver Scar Scale (VSS), patient satisfaction, and complications. In both groups, there were significant improvements in the total VSS scores (P < .01) and each VSS subscore (P < .01). These scar characteristics improved over time as the treatment progressed. Compared with the late healing group, the early healing group had more obvious effects on improving scar pigmentation (P < .05) and vascularity (P < .05) at similar assessment time points after burns. At the last assessment, there was a significant difference in total VSS scores between groups (P = .009). For the early and late healing groups, respectively, the mean gradient value (SE) of the total VSS scores was 1.550 (0.373) and 1.283 (0.224) over the course of the treatment periods. 3D facemasks are effective in the rehabilitation of facial scars caused by burns, which should be used for prevention and treatment in the initial stages of scar development.

Enhanced liquidity of p62 droplets mediated by Smurf1 links Nrf2 activation and autophagy.

BACKGROUND: Macro-autophagy/Autophagy is an evolutionarily well-conserved recycling process to maintain the balance through precise spatiotemporal regulation. However, the regulatory mechanisms of biomolecular condensates by the key adaptor protein p62 via liquid-liquid phase separation (LLPS) remain obscure. RESULTS: In this study, we showed that E3 ligase Smurf1 enhanced Nrf2 activation and promoted autophagy by increasing p62 phase separation capability. Specifically, the Smurf1/p62 interaction improved the formation and material exchange of liquid droplets compared with p62 single puncta. Additionally, Smurf1 promoted the competitive binding of p62 with Keap1 to increase Nrf2 nuclear translocation in p62 Ser349 phosphorylation-dependent manner. Mechanistically, overexpressed Smurf1 increased the activation of mTORC1 (mechanistic target of rapamycin complex 1), in turn leading to p62 Ser349 phosphorylation. Nrf2 activation increased the mRNA levels of Smurf1, p62, and NBR1, further promoting the droplet liquidity to enhance oxidative stress response. Importantly, we showed that Smurf1 maintained cellular homeostasis by promoting cargo degradation through the p62/LC3 autophagic pathway. CONCLUSIONS: These findings revealed the complex interconnected role among Smurf1, p62/Nrf2/NBR1, and p62/LC3 axis in determining Nrf2 activation and subsequent clearance of condensates through LLPS mechanism.