Effectiveness of mobile health applications on clinical outcomes and health behaviors in patients with coronary heart disease: A systematic review and meta-analysis.

Objective: Mobile health applications (apps) have gained significant popularity and widespread utilization among patients with coronary heart disease (CHD). The objective of this study is to evaluate the effects of mHealth apps on clinical outcomes and health behaviors in patients with CHD. Methods: Databases were searched from inception until December 2023, including Cochrane Library, PubMed, EMBASE, Web of Science, CINAHL, China National Knowledge Infrastructure (CNKI), Chinese BioMedical Literature Service System (SinoMed), Wanfang Data, China Science and Technology Journal Database (VIP), for randomized controlled trials (RCTs) regarding the effectiveness of mHealth apps in patients with CHD. Two researchers conducted a comprehensive review of the literature, extracting relevant data and evaluating each study's methodological quality separately. The meta-analysis was performed utilizing Review Manager v5.4 software. Results: A total of 34 RCTs were included, with 5,319 participants. The findings demonstrated that using mHealth apps could decrease the incidence of major adverse cardiac events (RR = 0.68, P = 0.03), readmission rate (RR = 0.56, P < 0.001), total cholesterol (WMD = -0.19, P = 0.03), total triglycerides (WMD = -0.24, P < 0.001), waist circumference (WMD = -1.92, P = 0.01), Self-Rating Anxiety Scale score (WMD = -6.70, P < 0.001), and Self-Rating Depression Scale score (WMD = -7.87, P < 0.001). They can also increase the LVEF (WMD = 6.50, P < 0.001), VO2 max (WMD = 1.89, P < 0.001), 6-min walk distance (6MWD) (WMD = 19.43, P = 0.004), Morisky Medication Adherence Scale-8 score (WMD = 0.96, P = 0.004), and medication adherence rate (RR = 1.24, P = 0.03). Nevertheless, there is no proof that mHealth apps can lower low-density lipoprote in cholesterol, blood pressure, BMI, or other indicator (P > 0.05). Conclusion: Mobile health apps have the potential to lower the incidence of major adverse cardiac events (MACEs), readmission rates, and blood lipids in patients with CHD. They can also help enhance cardiac function, promote medication adherence, and alleviate symptoms of anxiety and depression. To further corroborate these results, larger-scale, multi-center RCTs with longer follow-up periods are needed.

Optimization of lipid nanoparticles for gene editing of the liver via intraduodenal delivery.

Lipid nanoparticles (LNPs) have recently emerged as successful gene delivery platforms for a diverse array of disease treatments. Efforts to optimize their design for common administration methods such as intravenous injection, intramuscular injection, or inhalation, revolve primarily around the addition of targeting ligands or the choice of ionizable lipid. Here, we employed a multi-step screening method to optimize the type of helper lipid and component ratios in a plasmid DNA (pDNA) LNP library to efficiently deliver pDNA through intraduodenal delivery as an indicative route for oral administration. By addressing different physiological barriers in a stepwise manner, we down-selected effective LNP candidates from a library of over 1000 formulations. Beyond reporter protein expression, we assessed the efficiency in non-viral gene editing in mouse liver mediated by LNPs to knockdown PCSK9 and ANGPTL3 expression, thereby lowering low-density lipoprotein (LDL) cholesterol levels. Utilizing an all-in-one pDNA construct with Strep. pyogenes Cas9 and gRNAs, our results showcased that intraduodenal administration of selected LNPs facilitated targeted gene knockdown in the liver, resulting in a 27% reduction in the serum LDL cholesterol level. This LNP-based all-in-one pDNA-mediated gene editing strategy highlights its potential as an oral therapeutic approach for hypercholesterolemia, opening up new possibilities for DNA-based gene medicine applications.

Feasibility of thoracoscopic monosubsegmentectomy for small ground-glass opacity dominant lung cancer.

BACKGROUND: Monosubsegmentectomy (MSS) involves removal of less lung parenchyma than monosegmentectomy (MS) does. However, the clinical efficacy of MSS in lung cancer treatment remains unclear, with concerns regarding insufficient surgical margins and increased complications. METHODS: Between February 2015 and December 2019, patients who underwent thoracoscopic MSS (n = 126) or MS (n = 678) for small ground-glass opacity (GGO) dominant lung cancer were examined. The primary endpoints were the procedure success rate (defined as resection with a surgical margin ≥2 cm or tumor size) and surgical margin. RESULTS: There were no significant differences in age, sex, smoking history, or comorbidities between the groups. Both groups achieved a success rate of 100%. No significant group differences were observed in the number of lymph nodes removed (p = 0.060), overall complications (p = 0.147), or major complications (p = 0.450). The MSS group had a smaller surgical margin (median, 17 vs. 21mm, p < 0.001) and longer operative time (median, 138 vs. 130 min, p = 0.005) than the MS group did. Propensity score-matched analysis of 122 pairs of patients revealed consistent results. Surgical margins were further compared based on the number of resected subsegments. The 1 subsegment group had the smallest median surgical margin, followed by the 2 and 3 subsegments groups (17, 20, and 23 mm, p < 0.001). CONCLUSIONS: Thoracoscopic MSS is an acceptable option for treating patients with small-sized GGO-dominant lung cancer. However, the close surgical margins and prolonged operative time should be considered.

Research progress in water quality prediction based on deep learning technology: a review.

Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.

The association between alcohol consumption and all-cause mortality: An umbrella review of systematic reviews using lifetime abstainers or low-volume drinkers as a reference group.

BACKGROUND AND AIMS: Systematic reviews of the relationship between alcohol consumption and all-cause mortality have reported different relative risk (RR) curves, possibly due to the choice of reference group. Results have varied from 'J-shaped' curves, where low-volume consumption is associated with reduced risk, to monotonically increased risk with increasing consumption. We summarised the evidence on alcohol consumption and all-cause mortality exclusively from systematic reviews using lifetime abstainers or low-volume/occasional drinkers as the reference group. METHODS: We conducted a systematic umbrella review of systematic reviews of the relationship between alcohol consumption and all-cause mortality in prospective cohort studies using a reference group of lifetime abstainers or low-volume/occasional drinkers. Several databases (PubMed/Medline/Embase/PsycINFO/Cochrane Library) were searched to March 2022. Reviews were assessed for risk of bias, and those with reference groups containing former drinkers were excluded. RESULTS: From 2149 articles retrieved, 25 systematic reviews were identified, and five did not include former drinkers in the reference group. Four of the five included reviews had high risk of bias. Three reviews reported a J-shaped relationship between alcohol consumption and all-cause mortality with significant decreased risk for low-volume drinking (RR range 0.84 to 0.95), while two reviews did not. The one review at low risk of bias reported monotonically increased risk with greater consumption (RRs = 1.02, 1.13, 1.33 and 1.52 for low-, medium-, high- and higher-volume drinking, respectively, compared with occasional drinking). All five reviews reported significantly increased risk with higher levels of alcohol consumption (RR range 1.28 to 3.70). Sub-group analyses were reported by sex and age; however, there were evidence gaps for many important factors. Conversely, 17 of 20 excluded systematic reviews reported decreased mortality risk for low-volume drinking. CONCLUSIONS: Over 70% of systematic reviews and meta-analyses published to March 2022 of all-cause mortality risk associated with alcohol consumption did not exclude former drinkers from the reference group and may therefore be biased by the 'sick-quitter effect'.

Liter-scale manufacturing of shelf-stable plasmid DNA/PEI transfection particles for viral vector production.

The transfection efficiency and stability of the delivery vehicles of plasmid DNA (pDNA) are critical metrics to ensure high-quality and high-yield production of viral vectors. We previously identified that the optimal size of pDNA/poly(ethylenimine) (PEI) transfection particles is 400-500 nm and developed a bottom-up assembly method to construct stable 400-nm pDNA/PEI particles and benchmarked their transfection efficiency in producing lentiviral vectors (LVVs). Here, we report scale-up production protocols for such transfection particles. Using a two-inlet confined impinging jet (CIJ) mixer with a dual syringe pump set-up, we produced a 1-L batch at a flow rate of 100 mL/min, and further scaled up this process with a larger CIJ mixer and a dual peristaltic pump array, allowing for continuous production at a flow rate of 1 L/min without a lot size limit. We demonstrated the scalability of this process with a 5-L lot and validated the quality of these 400-nm transfection particles against the target product profile, including physical properties, shelf and on-bench stability, transfection efficiency, and LVV production yield in both 15-mL bench culture and 2-L bioreactor runs. These results confirm the potential of this particle assembly process as a scalable manufacturing platform for viral vector production.

A model-based direct inversion network (MDIN) for dual spectral computed tomography.

Objective. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Additionally, several existing DSCT methods rely heavily on the information of the spectra, which is often not readily available in applications. To address this problem, in this study, we aim to develop a novel approach to improve the DSCT reconstruction performance.Approach. A model-based direct inversion network (MDIN) is proposed for DSCT, which can directly predict the basis material images from the collected polychromatic projections. The all operations are performed in the network, requiring neither the conventional algorithms nor the information of the spectra. It can be viewed as an approximation to the inverse procedure of DSCT imaging model. The MDIN is composed of projection pre-decomposition module (PD-module), domain transformation layer (DT-layer), and image post-decomposition module (ID-module). The PD-module first performs the pre-decomposition on the polychromatic projections that consists of a series of stacked one-dimensional convolution layers. The DT-layer is designed to obtain the preliminary decomposed results, which has the characteristics of sparsely connected and learnable parameters. And the ID-module uses a deep neural network to further decompose the reconstructed results of the DT-layer so as to achieve higher-quality basis material images.Main results. Numerical experiments demonstrate that the proposed MDIN has significant advantages in substance decomposition, artifact reduction and noise suppression compared to other methods in the DSCT reconstruction.Significance. The proposed method has a flexible applicability, which can be extended to other CT problems, such as multi-spectral CT and low dose CT.

Path towards mRNA delivery for cancer immunotherapy from bench to bedside.

Messenger RNA (mRNA) has emerged as a promising therapeutic agent for the prevention and treatment of various diseases. mRNA vaccines, in particular, offer an alternative approach to conventional vaccines, boasting high potency, rapid development capabilities, cost-effectiveness, and safe administration. However, the clinical application of mRNA vaccines is hindered by the challenges of mRNA instability and inefficient in vivo delivery. In recent times, remarkable technological advancements have emerged to address these challenges, utilizing two main approaches: ex vivo transfection of dendritic cells (DCs) with mRNA and direct injection of mRNA-based therapeutics, either with or without a carrier. This review offers a comprehensive overview of major non-viral vectors employed for mRNA vaccine delivery. It showcases notable preclinical and clinical studies in the field of cancer immunotherapy and discusses important considerations for advancing these promising vaccine platforms for broader therapeutic applications. Additionally, we provide insights into future possibilities and the remaining challenges in mRNA delivery technology, emphasizing the significance of ongoing research in mRNA-based therapeutics.

A comprehensive review on the source, ingestion route, attachment and toxicity of microplastics/nanoplastics in human systems.

Microplastics (MPs)/nanoplastics (NPs) are widely found in the natural environment, including soil, water and the atmosphere, which are essential for human survival. In the recent years, there has been a growing concern about the potential impact of MPs/NPs on human health. Due to the increasing interest in this research and the limited number of studies related to the health effects of MPs/NPs on humans, it is necessary to conduct a systematic assessment and review of their potentially toxic effects on human organs and tissues. Humans can be exposed to microplastics through ingestion, inhalation and dermal contact, however, ingestion and inhalation are considered as the primary routes. The ingested MPs/NPs mainly consist of plastic particles with a particle size ranging from 0.1 to 1 µm, that distribute across various tissues and organs within the body, which in turn have a certain impact on the nine major systems of the human body, especially the digestive system and respiratory system, which are closely related to the intake pathway of MPs/NPs. The harmful effects caused by MPs/NPs primarily occur through potential toxic mechanisms such as induction of oxidative stress, generation of inflammatory responses, alteration of lipid metabolism or energy metabolism or expression of related functional factors. This review can help people to systematically understand the hazards of MPs/NPs and related toxicity mechanisms from the level of nine biological systems. It allows MPs/NPs pollution to be emphasized, and it is also hoped that research on their toxic effects will be strengthened in the future.

Cerebral neurotoxicity of amino-modified polystyrene nanoplastics in mice and the protective effects of functional food Camellia pollen.

Accumulating evidence suggests that nanoplastics contribute to an increased risk of brain damage, however, the precise underlying mechanisms remain unclear. Here, we subjected mice to long-term exposure to amino-modified polystyrene nanoplastics (APS-NPs). These nanoplastics were detected in the mouse brain; coupled with the observed upregulation of Alzheimer's disease-associated genes (APP and MAPT). To further explore nanoplastic damage mechanisms and the corresponding protective strategies against these mechanisms in vitro, we used hCMEC/D3 and HT22 cells. Results showed that APS-NPs disrupted tight junction proteins (Occludin and ZO-1) via TLR2/MMP9 axis, resulting in blood-brain barrier permeation; this was significantly mitigated by functional food Camellia pollen treatment. APS-NPs initiated iNOS and nNOS upregulation within neurons resulting in Sirtuin 1 deacetylase inactivation and CBP acetyltransferase stimulation, ultimately leading to Ac-Tau formation. This process was attenuated by Camellia pollen, which also ameliorated the APS-NPs-induced neuronal apoptosis mediated by the p53/Bax/Bcl-2 axis. Network pharmacology analysis of Camellia pollen offered a further theoretical understanding of its potential applications in preventing and treating nervous system disorders, such as Alzheimer's disease. This study established that Camellia pollen protects the brain against APS-NPs-mediated blood-brain barrier damage and alleviates neuronal apoptosis and Alzheimer's disease-like neurotoxicity. This study elucidates the mechanisms underlying polystyrene-induced brain damage and can be used to inform future prevention and treatment strategies.

Screening for lipid nanoparticles that modulate the immune activity of helper T cells towards enhanced antitumour activity.

Lipid nanoparticles (LNPs) can be designed to potentiate cancer immunotherapy by promoting their uptake by antigen-presenting cells, stimulating the maturation of these cells and modulating the activity of adjuvants. Here we report an LNP-screening method for the optimization of the type of helper lipid and of lipid-component ratios to enhance the delivery of tumour-antigen-encoding mRNA to dendritic cells and their immune-activation profile towards enhanced antitumour activity. The method involves screening for LNPs that enhance the maturation of bone-marrow-derived dendritic cells and antigen presentation in vitro, followed by assessing immune activation and tumour-growth suppression in a mouse model of melanoma after subcutaneous or intramuscular delivery of the LNPs. We found that the most potent antitumour activity, especially when combined with immune checkpoint inhibitors, resulted from a coordinated attack by T cells and NK cells, triggered by LNPs that elicited strong immune activity in both type-1 and type-2 T helper cells. Our findings highlight the importance of optimizing the LNP composition of mRNA-based cancer vaccines to tailor antigen-specific immune-activation profiles.

Machine Learning Elucidates Design Features of Plasmid DNA Lipid Nanoparticles for Cell Type-Preferential Transfection.

For cell and gene therapies to become more broadly accessible, it is critical to develop and optimize non-viral cell type-preferential gene carriers such as lipid nanoparticles (LNPs). Despite the effectiveness of high throughput screening (HTS) approaches in expediting LNP discovery, they are often costly, labor-intensive, and often do not provide actionable LNP design rules that focus screening efforts on the most relevant chemical and formulation parameters. Here we employed a machine learning (ML) workflow using well-curated plasmid DNA LNP transfection datasets across six cell types to maximize chemical insights from HTS studies and has achieved predictions with 5-9% error on average depending on cell type. By applying Shapley additive explanations to our ML models, we unveiled composition-function relationships dictating cell type-preferential LNP transfection efficiency. Notably, we identified consistent LNP composition parameters that enhance in vitro transfection efficiency across diverse cell types, such as ionizable to helper lipid ratios near 1:1 or 10:1 and the incorporation of cationic/zwitterionic helper lipids. In addition, several parameters were found to modulate cell type-preferentiality, including the ionizable and helper lipid total molar percentage, N/P ratio, cholesterol to PEGylated lipid ratio, and the chemical identity of the helper lipid. This study leverages HTS of compositionally diverse LNP libraries and ML analysis to understand the interactions between lipid components in LNP formulations; and offers fundamental insights that contribute to the establishment of unique sets of LNP compositions tailored for cell type-preferential transfection.

Night-time radiative warming using the atmosphere.

Night-time warming is vital for human production and daily life. Conventional methods like active heaters are energy-intensive, while passive insulating films possess restrictions regarding space consumption and the lack of heat gain. In this work, a nanophotonic-based night-time warming strategy that passively inhibits thermal radiation of objects while actively harnessing that of atmosphere is proposed. By using a photonic-engineered thin film that exhibits high reflectivity (~0.91) in the atmospheric transparent band (8-14 µm) and high absorptivity (~0.7) in the atmospheric radiative band (5-8 and 14-16 µm), temperature rise of 2.1 °C/4.4 °C compared to typical low-e film and broadband absorber is achieved. Moreover, net heat loss as low as 9 W m-2 is experimentally observed, compared to 16 and 39 W m-2 for low-e film and broadband absorber, respectively. This strategy suggests an innovative way for sustainable warming, thus contributes to addressing the challenges of climate change and promoting global carbon neutrality.

Whole-infrared-band camouflage with dual-band radiative heat dissipation.

Advanced multispectral detection technologies have emerged as a significant threat to objects, necessitating the use of multiband camouflage. However, achieving effective camouflage and thermal management across the entire infrared spectrum, especially the short-wave infrared (SWIR) band, remains challenging. This paper proposes a multilayer wavelength-selective emitter that achieves effective camouflage across the entire infrared spectrum, including the near-infrared (NIR), SWIR, mid-wave infrared (MWIR), and long-wave infrared (LWIR) bands, as well as the visible (VIS) band. Furthermore, the emitter enables radiative heat dissipation in two non-atmospheric windows (2.5-3 µm and 5-8 µm). The emitter's properties are characterized by low emittance of 0.270/0.042/0.218 in the SWIR/MWIR/LWIR bands, and low reflectance of 0.129/0.281 in the VIS/NIR bands. Moreover, the high emittance of 0.742/0.473 in the two non-atmospheric windows ensures efficient radiative heat dissipation, which results in a temperature decrement of 14.4 °C compared to the Cr reference at 2000 W m-2 input power density. This work highlights the role of solar radiance in camouflage, and provides a comprehensive guideline for developing multiband camouflage compatible with radiative heat dissipation, from the visible to LWIR.

Unsupervised learning-based dual-domain method for low-dose CT denoising.

Objective. Low-dose CT (LDCT) is an important research topic in the field of CT imaging because of its ability to reduce radiation damage in clinical diagnosis. In recent years, deep learning techniques have been widely applied in LDCT imaging and a large number of denoising methods have been proposed. However, One major challenge of supervised deep learning-based methods is the exactly geometric pairing of datasets with different doses. Therefore, the aim of this study is to develop an unsupervised learning-based LDCT imaging method to address the aforementioned challenges.Approach. In this paper, we propose an unsupervised learning-based dual-domain method for LDCT denoising, which consists of two stages: the first stage is projection domain denoising, in which the unsupervised learning method Noise2Self is applied to denoise the projection data with statistically independent and zero-mean noise. The second stage is an iterative enhancement approach, which combines the prior information obtained from the generative model with an iterative reconstruction algorithm to enhance the details of the reconstructed image.Main results. Experimental results show that our proposed method outperforms the comparison method in terms of denoising effect. Particularly, in terms of SSIM, the denoised results obtained using our method achieve the highest SSIM.Significance. In conclusion, our unsupervised learning-based method can be a promising alternative to the traditional supervised methods for LDCT imaging, especially when the availability of the labeled datasets is limited.

A Meta-Analysis of Influencing Factors on the Activity of BiVO4-Based Photocatalysts.

With the continuous advancement of global industrialization, a large amount of organic and inorganic pollutants have been discharged into the environment, which is essential for human survival. Consequently, the issue of water environment pollution has become increasingly severe. Photocatalytic technology is widely used to degrade water pollutants due to its strong oxidizing performance and non-polluting characteristics, and BiVO4-based photocatalysts are one of the ideal raw materials for photocatalytic reactions. However, a comprehensive global analysis of the factors influencing the photocatalytic performance of BiVO4-based photocatalysts is currently lacking. Here, we performed a meta-analysis to investigate the differences in specific surface area, kinetic constants, and the pollutant degradation performance of BiVO4-based photocatalysts under different preparation and degradation conditions. It was found that under the loading condition, all the performances of the photocatalysts can be attributed to the single BiVO4 photocatalyst. Moreover, loading could lead to an increase in the specific surface area of the material, thereby providing more adsorption sites for photocatalysis and ultimately enhancing the photocatalytic performance. Overall, the construct heterojunction and loaded nanomaterials exhibit a superior performance for BiVO4-based photocatalysts with 136.4% and 90.1% improvement, respectively. Additionally, within a certain range, the photocatalytic performance increases with the reaction time and temperature.

Difficulty of Video-Assisted Thoracoscopic Surgery Segmentectomy: Proposal for a New Classification.

BACKGROUND: Segmentectomy has classically been distinguished as "simple" or "complex" based on the number of intersegmental planes (ISPs) dissected. However, with the increasing variety and complexity of segmentectomies, it is clear that a classification based on the number of ISPs alone is inadequate. This study aimed to develop a new classification to predict the surgical difficulty of video-assisted thoracoscopic surgery (VATS) segmentectomy. METHODS: The study retrospectively reviewed 1868 patients who underwent VATS segmentectomy between January 2014 and December 2019. Uni- and multivariate analyses were performed to identify predictors associated with prolonged operative time (>140 min), and a scoring system was constructed to classify the surgical difficulty of VATS segmentectomy. RESULTS: Altogether, 1868 VATS segmentectomies were divided into three groups: group 1 (low difficulty, including segmentectomy with only one intersegmental plane [ISP] dissection), group 2 (intermediate difficulty, including a single segmentectomy with more than one ISP dissection and a single subsegmentectomy), group 3 (high difficulty level, including combined resection with more than one ISP dissection). This classification effectively differentiated the three groups in terms of operative time, estimated blood loss, major complications, and overall complications (all p < 0.001). For receiver operating characteristic analysis, the new classification showed significantly better differentiation performance in terms of operative time (p < 0.001), estimated blood loss (p = 0.004), major complications (p = 0.002), and overall complications (p = 0.012) than the simple/complex classification. CONCLUSIONS: This new three-level classification accurately predicted the surgical difficulty of VATS segmentectomy.

Mendelian Randomization and Transcriptome-Wide Association Analysis Identified Genes That Were Pleiotropically Associated with Intraocular Pressure.

BACKGROUND: Intraocular pressure (IOP) is a major modifiable risk factor for glaucoma. However, the mechanisms underlying the controlling of IOP remain to be elucidated. OBJECTIVE: To prioritize genes that are pleiotropically associated with IOP. METHODS: We adopted a two-sample Mendelian randomization method, named summary-based Mendelian randomization (SMR), to examine the pleiotropic effect of gene expression on IOP. The SMR analyses were based on summarized data from a genome-wide association study (GWAS) on IOP. We conducted separate SMR analyses using Genotype-Tissue Expression (GTEx) and Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data. Additionally, we performed a transcriptome-wide association study (TWAS) to identify genes whose cis-regulated expression levels were associated with IOP. RESULTS: We identified 19 and 25 genes showing pleiotropic association with IOP using the GTEx and CAGE eQTL data, respectively. RP11-259G18.3 (PSMR = 2.66 × 10-6), KANSL1-AS1 (PSMR = 2.78 × 10-6), and RP11-259G18.2 (PSMR = 2.91 × 10-6) were the top three genes using the GTEx eQTL data. LRRC37A4 (PSMR = 1.19 × 10-5), MGC57346 (PSMR = 1.19 × 10-5), and RNF167 (PSMR = 1.53 × 10-5) were the top three genes using the CAGE eQTL data. Most of the identified genes were found in or near the 17q21.31 genomic region. Additionally, our TWAS analysis identified 18 significant genes whose expression was associated with IOP. Of these, 12 and 4 were also identified by the SMR analysis using the GTEx and CAGE eQTL data, respectively. CONCLUSIONS: Our findings suggest that the 17q21.31 genomic region may play a critical role in the regulation of IOP.

Cloaking Mesoporous Polydopamine with Bacterial Membrane Vesicles to Amplify Local and Systemic Antitumor Immunity.

As adjuvants or antigens, bacterial membranes have been widely used in recent antibacterial and antitumor research, but they are often injected multiple times to achieve therapeutic outcomes, with limitations in biosafety and clinical application. Herein, we leverage the biocompatibility and immune activation capacity of Salmonella strain VNP20009 to produce double-layered membrane vesicles (DMVs) for enhanced systemic safety and antitumor immunity. Considering the photothermal effect of polydopamine upon irradiation, VNP20009-derived DMVs are prepared to coat the surface of mesoporous polydopamine (MPD) nanoparticles, leading to the potential synergies between photothermal therapy mediated by MPD and immunotherapy magnified by DMVs. The single dose of MPD@DMV can passively target tumors and activate the immune system with upregulated T cell infiltration and secretion levels of pro-inflammatory factors as well as antitumor related cytokines. All of these promoted immune responses result in malignant melanoma tumor regression and extended survival time on local or distant tumor-bearing mouse models. Importantly, we further explore the advantages of intravenous injection of the MPD@DMV agent compared with its intratumoral injection, and the former demonstrates better long-term immune effects on animal bodies. Overall, this formulation design brings broader prospects for the autologous vaccine adjuvant by bacterial membrane vesicles in cancer therapy.