Regulating Molecular Interactions in Polybenzimidazole Membrane for Efficient Vanadium Redox Flow Battery.

The tightly bonded structure of polybenzimidazole (PBI) membrane is the origin of its poor proton conductivity, which severely hinders achieving a cost-effective membrane for vanadium redox flow battery (VRFB). It desires a strategy to relax the membrane structure to significantly improve the proton conductivity and maintain its structure stability. Therefore, this work proposes a novel strategy through regulating molecular interactions within PBI membrane to loosen up the structure of PBI membrane and dramatically enhance the proton conductivity. The interactions in PBI membrane are switched by DMSO/water and acid through sequentially treating membrane with these solutions. The efficient PBI membrane prepared using this strategy demonstrates an outstanding performance for VRFB, with the proton conductivity enhanced by 3850% (from 1.9 to 76.3 mS cm-1), and VRFB achieves a high energy efficiency of 80.5% under 200 mA cm-2. More importantly, this work shed lights on the structure-property relationship of PBI membrane, and the mechanism in enhancing proton conductivity is unraveled, which is of great significance for the development of VRFB membranes.

Establishment and validation of the prognostic risk model based on the anoikis-related genes in esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a malignant condition in humans. Anoikis-related genes (ARGs) are crucial to cancer progression. Therefore, more studies on the relationship between ARGs and ESCC are warranted. METHODS: The study acquired ESCC-related transcriptome data from TCGA. Differentially expressed ARGs (DE-ARGs) were obtained by differential analysis and candidates were filtered out by survival analysis. Prognostic genes were determined by Cox and LASSO regression. A risk model was constructed based on prognostic gene expressions. An immune infiltration study was done to explain how these genes contribute to ESCC development. The IC50 test was adopted to assess the clinical response of chemotherapy drugs. Single cell analysis was performed on the GSE145370 dataset. Moreover, the prognostic gene expressions were detected by qRT-PCR. RESULTS: 53 DE-ARGs were screened and four candidate genes including PBK, LAMC2, TNFSF10 and KL were obtained. Cox and LASSO regression identified the two prognostic genes, TNFSF10 and PBK. Immuno-infiltration analysis revealed positive associations of PBK with Macrophages M0 cells, and TNFSF10 with Macrophages M1 cells. The IC50 values of predicted drugs, in the case of Tozasertib 1096 and WIKI4 1940, were significantly variant between risk groups. Single cell analysis revealed that TNFSF10 and PBK levels were higher in epithelial cells than in other cells. The prognostic genes expression results by qRT-PCR were compatible with the dataset analysis. CONCLUSION: The study established an ARG prognosis model of ESCC. It provided a reference for the research of ARGs in ESCC.

The role of hyperthermia in the treatment of tumor.

Despite recent advancements in the diagnosis and treatment options for cancer, it remains one of the most serious threats to health. Hyperthermia (HT) has emerged as a highly promising area of research due to its safety and cost-effectiveness. Currently, based on temperature, HT can be categorized into thermal ablation and mild hyperthermia. Thermal ablation involves raising the temperature within the tumor to over 60°C, resulting in direct necrosis in the central region of the tumor. In contrast, mild hyperthermia operates at relatively lower temperatures, typically in the range of 41-45°C, to induce damage to tumor cells. Furthermore, HT also serves as an immune adjuvant strategy in radiotherapy, chemotherapy, and immunotherapy, enhancing the effectiveness of radiotherapy, increasing the uptake of chemotherapy drugs, and reprogramming the tumor microenvironment through the induction of immunogenic cell death, thereby promoting the recruitment of endogenous immune cells. This article reviews the current status and development of hyperthermia, outlines potential mechanisms by which hyperthermia inhibits tumors, describes clinical trial attempts combining hyperthermia with radiotherapy, chemotherapy, and immunotherapy, and discusses the relationship between nanoparticles and hyperthermia.

Uncovering the effects of non-lethal oxidative stress on replication initiation in Escherichia coli.

Cell cycle adaptability assists bacteria in response to adverse stress. The effect of oxidative stress on replication initiation in Escherichia coli remains unclear. This work examined the impact of exogenous oxidant and genetic mutation-mediated oxidative stress on replication initiation. We found that 0-0.5 mM H2O2 suppresses E. coli replication initiation in a concentration-dependent manner but does not lead to cell death. Deletion of antioxidant enzymes SodA-SodB, KatE, or AhpC results in delayed replication initiation. The antioxidant N-acetylcysteine (NAC) promotes replication initiation in ΔkatE and ΔsodAΔsodB mutants. We then explored the factors that mediate the inhibition of replication initiation by oxidative stress. MutY, a base excision repair DNA glycosylase, resists inhibition of replication initiation by H2O2. Lon protease deficiency eliminates inhibition of replication initiation mediated by exogenous H2O2 exposure but not by katE or sodA-sodB deletion. The absence of clpP and hslV further delays replication initiation in the ΔktaE mutant, whereas hflK deletion promotes replication initiation in the ΔkatE and ΔsodAΔsodB mutants. In conclusion, non-lethal oxidative stress inhibits replication initiation, and AAA+ proteases are involved and show flexible regulation in E. coli.

Green Tea Diet Can Effectively Antagonize the Toxicity Induced by Environmental-Related Concentrations of BPA: An Implication from In Vivo and In Silico Studies.

The neurotoxicity of bisphenol A (BPA) exposure has been confirmed in vitro and in vivo, and inflammatory response is considered the main pathway. Green tea is a healthy life habit as it is rich in various anti-inflammatory components. To confirm that green tea diet is an effective measure to antagonize BPA-induced neurotoxicity, mice were treated with 0.5 and 5000 µg/kg/day of BPA from postnatal days (PNDs) 10-50 and supplemented with green tea on PND 21. From PND 51, behavioral tests were conducted on mice to assess their emotional, cognitive, and spatial learning memory capabilities. The open field test and elevated plus maze test indicated anxiety-like behaviors induced by BPA. Interestingly, green tea diet significantly alleviated BPA-induced anxiety-like behaviors. Meanwhile, the green tea diet effectively reversed BPA-induced microglia activation and morphological changes in the hippocampus of mice. Molecularly, green tea inhibited hippocampal neuroinflammation of mice by reducing BPA-induced expressions of NLRP3, ASC, cleaved-caspase-1, GSDMD-N, IL-6, and IL-1ß, as well as significantly reducing the expression of Bak1, Bax, caspase-9, and Cytc c genes (p < 0.05). Molecular docking suggests that various anti-inflammatory components of green tea can competitively bind to the estrogen receptors with BPA. In general, a green tea diet alleviates BPA-induced emotional disorders by inhibiting microglial polarization and hippocampal pyroptosis, indicating its effective antagonistic ability against the neurotoxicity induced by environmental BPA exposure.

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.

BACKGROUND: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combining deep learning (DL) and machine learning (ML). METHODS: The model features multi-modality input and multi-task output, measuring LV ejection fraction (LVEF), left atrial end-systolic volume (LAESV), and Doppler parameters: mitral E wave velocity (E), A wave velocity (A), mitral annulus e' velocity (e'), and tricuspid regurgitation velocity (TRmax). The algorithm was trained and tested on two internal datasets (862 and 239 echocardiograms) and validated using three external datasets, including EchoNet-Dynamic and CAMUS. The ASE diastolic function decision tree and total probability theory were used to provide diastolic grading probabilities. RESULTS: The algorithm, named MMnet, demonstrated high accuracy in both test and validation datasets, with Dice coefficients for segmentation between 0.922 and 0.932 and classification accuracies between 0.9977 and 1.0. The mean absolute errors (MAEs) for LVEF and LAESV were 3.7 % and 5.8 ml, respectively, and for LVEF in external validation, MAEs ranged from 4.9 % to 5.6 %. The diastolic function grading accuracy was 0.88 with hard criteria and up to 0.98 with soft criteria which account for the top two probability in total probability theory. CONCLUSIONS: MMnet can automatically grade ASE diastolic function with high accuracy and efficiency by annotating 2D videos and Doppler images.

A method combining LDA and neural networks for antitumor drug efficacy prediction.

Background: Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task. Objective: This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data. Methods: This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network to predict drug response in cancer patients treated with platinum drugs. To evaluate the effectiveness of the proposed method, we gathered and organized clinical records of lung and bowel cancer patients who underwent platinum-based treatment. The prediction performance is assessed using the following metrics: Precision, Recall, F1-score, Accuracy, and Area Under the ROC Curve (AUC). Results: The study analyzed a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. The proposed method achieved a stratified 5-fold cross-validation average Precision of 0.81, Recall of 0.89, F1-score of 0.85, Accuracy of 0.77, and AUC of 0.81 for cisplatin efficacy prediction on the data, which most are better than those of previous methods. Of these, the AUC value is at least 4% higher than those of the previous. At the same time, the superior result over the previous method persisted on an independent dataset of 266 bowel cancer patients, showing the generalizability of the proposed method. These results demonstrate the potential value of precise tumor treatment in clinical practice. Conclusions: Combining LDA and neural networks can help predict the efficacy of antitumor drugs based on clinical text. Our approach outperforms previous methods in predicting drug clinical efficacy.

Unveiling microbial dynamics in lung adenocarcinoma and adjacent nontumor tissues: insights from nicotine exposure and diverse clinical stages via nanopore sequencing technology.

Background: Lung is the largest mucosal area of the human body and directly connected to the external environment, facing microbial exposure and environmental stimuli. Therefore, studying the internal microorganisms of the lung is crucial for a deeper understanding of the relationship between microorganisms and the occurrence and progression of lung cancer. Methods: Tumor and adjacent nontumor tissues were collected from 38 lung adenocarcinoma patients and used nanopore sequencing technology to sequence the 16s full-length sequence of bacteria, and combining bioinformatics methods to identify and quantitatively analyze microorganisms in tissues, as well as to enrich the metabolic pathways of microorganisms. Results: the microbial composition in lung adenocarcinoma tissues is highly similar to that in adjacent tissues, but the alpha diversity is significantly lower than that in adjacent tissues. The difference analysis results show that the bacterial communities of Streptococcaceae, Lactobacillaceae, and Neisseriales were significantly enriched in cancer tissues. The results of metabolic pathway analysis indicate that pathways related to cellular communication, transcription, and protein synthesis were significantly enriched in cancer tissue. In addition, clinical staging analysis of nicotine exposure and lung cancer found that Haemophilus, paralinfluenzae, Streptococcus gordonii were significantly enriched in the nicotine exposure group, while the microbiota of Cardiobactereae and Cardiobacterales were significantly enriched in stage II tumors. The microbiota significantly enriched in IA-II stages were Neisseriaeae, Enterobacteriales, and Cardiobacterales, respectively. Conclusion: Nanopore sequencing technology was performed on the full length 16s sequence, which preliminarily depicted the microbial changes and enrichment of microbial metabolic pathways in tumor and adjacent nontumor tissues. The relationship between nicotine exposure, tumor progression, and microorganisms was explored, providing a theoretical basis for the treatment of lung cancer through microbial targets.

Synthesis and Antiosteoporotic Characterization of Diselenyl Maleimides: Discovery of a Potent Agent for the Treatment of Osteoporosis by Targeting RANKL.

To discover new osteoclast-targeting antiosteoporosis agents, we identified forty-six diselenyl maleimides, which were efficiently prepared using a novel, simple, and metal-free method at room temperature in a short reaction time. Among them, 3k showed the most marked inhibition of osteoclast differentiation with an IC50 value of 0.36 ± 0.03 µM. Moreover, 3k significantly suppressed RANKL-induced osteoclast formation, bone resorption, and osteoclast-specific genes expression in vitro. Mechanistic studies revealed that 3k remarkably blocked the RANKL-induced mitogen-activated protein kinase (MAPK) and NF-κB signaling pathways. In ovariectomized mice, intragastric administration of 3k significantly alleviated bone loss, exhibiting an effect similar to that of alendronate. Surface plasmon resonance assay and microscale thermophoresis assay results suggested that RANKL might be a potential molecular target for 3k. Collectively, the findings presented above provided a novel candidate for further development of bone antiresorptive drugs that target RANKL.

Lipid levels and multiple myeloma risk: insights from Meta-analysis and mendelian randomization.

BACKGROUND: Lipid levels have been suggset to be correlated with multiple myeloma (MM) risk, though causality remains unconfirmed. To explore this further, a detailed study combining meta-analysis and Mendelian randomization (MR) was conducted. METHODS: Literature searches were performed on PubMed and Embase; summary data for plasma lipid traits were extracted from the IEU and MM data from the FinnGen database. Meta-analysis and MR were utilized to analyze the link of lipids with MM risk, including mediator MR to identify potential mediators. The study was conducted in accordance with PRISMA and STROBE-MR guidelines. RESULTS: Observational studies analyzed through meta-analysis showed that elevated levels of LDL, HDL, total cholesterol (TC), and triglycerides correlate with a lower risk of MM, with HRs of 0.73, 0.59, 0.60, and 0.84, respectively. MR analysis confirmed a potential causal link of triglyceride with a reduced MM risk (OR: 0.67, 95% CI: 0.46-0.98), independent of BMI. Mediation analysis pointed to X-11,423-O-sulfo-L-tyrosine and neuropilin-2 as potential mediators. CONCLUSIONS: The findings suggest that higher lipid levels (LDL, HDL, TC, and triglycerides) are linked with a reduced MM risk, and higher triglyceride levels are causally associated with a reduced MM risk. This suggests new avenues for therapeutic interventions targeting MM.

Assessing a machine learning-based downscaling framework for obtaining 1km daily precipitation from GPM data.

Hydro-meteorological monitoring through satellites in arid and semi-arid regions is constrained by the coarse spatial resolution of precipitation data, which impedes detailed analyses. The objective of this study is to evaluate various machine learning techniques for developing a downscaling framework that generates high spatio-temporal resolution precipitation products. Focusing on the Hai River Basin, we evaluated three machine learning approaches-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagation (BP) neural networks. These methods integrate environmental variables including land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Precipitable Water Vapor (PWV), and albedo, to downscale the 0.1° spatial resolution Global Precipitation Measurement (GPM) product to a 1 km resolution. We further refined the results with residual correction and calibration using terrestrial rain gauge data. Subsequently, utilizing the 1 km annual precipitation, we employed the moving average window method to derive monthly and daily precipitation. The results demonstrated that the XGBoost method, calibrated with Geographical Difference Analysis (GDA) and Kriging spatial interpolation, proved to be the most accurate, achieving a Mean Absolute Error (MAE) of 58.40 mm for the annual product, representing a 14 % improvement over the original data. The monthly and daily products achieved MAE values of 11.61 mm and 1.79 mm, respectively, thus enhancing spatial resolution while maintaining accuracy comparable to the original product. In the Hai River Basin, key factors including longitude, latitude, DEM, LST_night, and PWV demonstrated greater importance and stability than other factors, thereby enhancing the model's precipitation prediction capabilities. This study provides a comprehensive assessment of the annual, monthly, and daily high-temporal and high-spatial resolution downscaling processes of precipitation, serving as an important reference for hydrology and related fields.

Factors associated with concomitant prostate cancer in benign prostatic hyperplasia patients aged 60 years and above.

OBJECTIVE: Prostate hyperplasia and cancer are more prevalent in middle-aged and elderly men. Previous studies have linked both disorders to androgen receptors. Herein, efforts were made to identify factors associated with prostate cancer in patients ≥60 years, aiming to enhance their health management. METHODS: An analytical framework was established utilizing the "Prostate Cancer Early Warning Dataset" from the National Clinical Medical Science Data Center. Variables selection was conducted through LASSO regression, followed by multifactorial logistic stepwise regression to construct a predictive model. RESULTS: A total of 1,502 patients with BPH and 294 with combined PCa were hereby included. Multivariate regression delineated several independent predictors of PCa coexistence, including age (OR [95% CI]: 1.06 [1.04-1.09], p < 0.001), fPSA/tPSA ratio (OR [95% CI]: 0.01 [0.002-0.05], p < 0.001), serum inorganic phosphorus (OR [95% CI]: 5.85 [2.61-13.15], p < 0.001), globulin levels (OR [95% CI]: 1.06 [1.02-1.11], p = 0.005), serum potassium (OR [95% CI]: 0.58 [0.40-0.86], p = 0.006), low-density lipoprotein (LDL) cholesterol (OR [95% CI]: 1.28 [1.06-1.54], p = 0.009), among others. CONCLUSION: The analysis revealed connections between PCa occurrence in men aged over 60 and BPH, along with specific serum biomarkers such as inorganic phosphorus, globulin, LDL cholesterol, lower fPSA/tPSA ratios and serum potassium.

Factor structure of the Chinese version of Emotion Regulation Goals Scale.

Recent studies in Western cultures suggested emotion regulation goals have important implications for mental health. This study aimed to test the factor structure of Emotion Regulation Goals Scale (ERGS) in a Chinese cultural context. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were first used to examine the factor structure of the ERGS, and then reliability and validity tests were conducted to examine the psychometric properties of the ERGS. Results showed that the original five-factor model demonstrated fit during both EFA and CFA, and was thus adopted for further psychometric analyses. Most of the five factors were significantly associated with emotion regulation tendencies and negative emotional outcomes (e.g., depression), except for the non-significant associations between pro-hedonic goals and expressive suppression, and pro-social and impression management goals with depression. The ERGS also showed good internal consistency and split-half reliability. However, the test-retest reliabilities varied substantially across the five factors. The pro-hedonic goal had a higher test-retest reliability, whereas the contra-hedonic, performance, pro-social, and impression management goals showed lower values, especially the latter two. In brief, the ERGS showed a promising five-factor structure in assessing emotion regulation goals in Chinese cultural context.

Cyclic-di-GMP induces inflammation and acute lung injury through direct binding to MD2.

BACKGROUND: Severe bacterial infections can trigger acute lung injury (ALI) and acute respiratory distress syndrome, with bacterial pathogen-associated molecular patterns (PAMPs) exacerbating the inflammatory response, particularly in COVID-19 patients. Cyclic-di-GMP (CDG), one of the PAMPs, is synthesized by various Gram-positve and Gram-negative bacteria. Previous studies mainly focused on the inflammatory responses triggered by intracellular bacteria-released CDG. However, how extracellular CDG, which is released by bacterial autolysis or rupture, activates the inflammatory response remains unclear. METHODS: The interaction between extracellular CDG and myeloid differentiation protein 2 (MD2) was investigated using in vivo and in vitro models. MD2 blockade was achieved using specific inhibitor and genetic knockout mice. Site-directed mutagenesis, co-immunoprecipitation, SPR and Bis-ANS displacement assays were used to identify the potential binding sites of MD2 on CDG. RESULTS: Our data show that extracellular CDG directly interacts with MD2, leading to activation of the TLR4 signalling pathway and lung injury. Specific inhibitors or genetic knockout of MD2 in mice significantly alleviated CDG-induced lung injury. Moreover, isoleucine residues at positions 80 and 94, along with phenylalanine at position 121, are essential for the binding of MD2 to CDG. CONCLUSION: These results reveal that extracellular CDG induces lung injury through direct interaction with MD2 and activation of the TLR4 signalling pathway, providing valuable insights into bacteria-induced ALI mechanisms and new therapeutic approaches for the treatment of bacterial co-infection in COVID-19 patients.

Unlocking Fe(III) Ions Improving Oxygen Evolution Reaction Activity and Dynamic Stability of Anodized Nickel Foam.

Fe has been reported to play a crucial role in improving the catalytic activity and stability of Ni/Co-based electrocatalysts for the oxygen evolution reaction (OER), while the Fe effect remains intangible. Here, we design several experiments to identify the activity and stability improvement using porous anodized nickel foam (ANF) as the electrode and 1.0 M KOH containing 1000 µM Fe(III) ions as the electrolyte. Systematic investigations reveal that Ni sites serve as hosts to capture Fe ions to create active FeNi-based intermediates on the surface of ANF to improve the OER activity significantly, and Fe ions regulate catalytic equilibrium and maintain the stability for a long time. The system exhibits 242 and 343 mV overpotentials to reach 10 and 1000 mA cm-2 current densities and a robust stability of 360 h at an industrially suitable current density (1000 mA cm-2). This work expands insights into the Fe(III) catalysis effect on the OER efficiency of Ni-based catalysts and provides an economical and practical way to commercial application.

Directional Construction of Low-Coordination Fe-N3 Coupled with Intrinsic Carbon Defects for High-Efficiency Oxygen Reduction.

Regulating the coordination environment of Fe-Nx sites is an efficient but challenging approach for promoting the intrinsic catalytic activity of single-atom Fe/N-codoped carbon (Fe-N-C) toward the oxygen reduction reaction (ORR). Herein, low-coordination Fe-N3 sites coupled with carbon vacancies (Fe-N3/CV) are directionally constructed in Fe-N-C via pyrolysis of a metal-organic framework (MOF) precursor with N3-Zn-O-Fe moieties, which are delicately prefabricated by chemically anchoring Fe3+ onto a H2O-etching induced linker-missing Zn-N3 site in the MOF precursor. The optimized Fe-N-C with the Fe-N3/CV sites displays a high ORR half-wave potential of 0.92 V (vs RHE), which is attributed to the optimized electronic structure and binding strengths of the active Fe center toward the ORR intermediates stemming from the synergy of the asymmetric configuration of Fe-N3 as well as the adjacent carbon vacancies. This work could be enlightening for the design and construction of high-activity coupling sites in metal and nitrogen-codoped carbon catalysts.

The role of reactive oxygen species in gastric cancer.

Gastric cancer (GC) ranks fifth in cancer incidence and fourth in cancer-related mortality worldwide. Reactive oxygen species (ROS) are highly oxidative oxygen-derived products that have crucial roles in cell signaling regulation and maintaining internal balance. ROS are closely associated with the occurrence, development, and treatment of GC. This review summarizes recent findings on the sources of ROS and the bidirectional regulatory effects on GC and discusses various treatment modalities for GC that are related to ROS induction. In addition, the regulation of ROS by natural small molecule compounds with the highest potential for development and applications in anti-GC research is summarized. The aim of the review is to accelerate the clinical application of modulating ROS levels as a therapeutic strategy for GC.

Cognitive dysfunction in schizophrenia patients caused by down-regulation of γ-aminobutyric acid receptor subunits.

BACKGROUND: The expression pattern of gamma aminobutyric acid (GABA) receptor subunits are commonly altered in patients with schizophrenia, which may lead to nerve excitation/inhibition problems, affecting cognition, emotion, and behavior. AIM: To explore GABA receptor expression and its relationship with schizophrenia and to provide insights into more effective treatments. METHODS: This case-control study enrolled 126 patients with schizophrenia treated at our hospital and 126 healthy volunteers who underwent physical examinations at our hospital during the same period. The expression levels of the GABA receptor subunits were detected using 1H-magnetic resonance spectroscopy. The recognized cognitive battery tool, the MATRICS Consensus Cognitive Battery, was used to evaluate the scores for various dimensions of cognitive function. The correlation between GABA receptor subunit downregulation and schizophrenia was also analyzed. RESULTS: Significant differences in GABA receptor subunit levels were found between the case and control groups (P < 0.05). A significant difference was also found between the case and control groups in terms of cognitive function measures, including attention/alertness and learning ability (P < 0.05). Specifically, as the expression levels of GABRA1 (α1 subunit gene), GABRB2 (ß2 subunit gene), GABRD (δ subunit), and GABRE (ε subunit) decreased, the severity of the patients' condition increased gradually, indicating a positive correlation between the downregulation of these 4 receptor subunits and schizophrenia (P < 0.05). However, the expression levels of GABRA5 (α5 subunit gene) and GABRA6 (α6 subunit gene) showed no significant correlation with schizophrenia (P > 0.05). CONCLUSION: Downregulation of the GABA receptor subunits is positively correlated with schizophrenia. In other words, when GABA receptor subunits are downregulated in patients, cognitive impairment becomes more severe.

Prospective, non-blinded, randomized controlled trial on early administration of pulmonary surfactant guided by lung ultrasound scores in very preterm infants: study protocol.

Background: Bedside lung ultrasonography has been widely used in neonatal intensive care units (NICUs). Lung ultrasound scores (LUS) may predict the need for pulmonary surfactant (PS) application. PS replacement therapy is the key intervention for managing moderate to severe neonatal respiratory distress syndrome (NRDS), with early PS administration playing a positive role in improving patient outcomes. Lung ultrasonography aids in the prompt diagnosis of NRDS, while LUS offers a semi-quantitative assessment of lung health. However, the specific methodologies for utilizing LUS in clinical practice remain controversial. This study hypothesizes that, in very preterm infants [<32 weeks gestational age (GA)] exhibiting respiratory distress symptoms, determining PS application through early postnatal LUS combined with clinical indicators, as opposed to relying solely on clinical signs and chest x-rays, can lead to more timely PS administration, reduce mechanical ventilation duration, improve patient outcomes, and lower the occurrence of bronchopulmonary dysplasia (BPD). Methods and design: This is a protocol for a prospective, non-blinded, randomized controlled trial that will be conducted in the NICU of a hospital in China. Eligible participants will include very preterm infants (< 32 weeks GA) exhibiting signs of respiratory distress. Infants will be randomly assigned in a 1:1 ratio to either the ultrasound or control group. In the ultrasonography group, the decision regarding PS administration will be based on a combination of lung ultrasonography and clinical manifestations, whereas in the control group, it will be determined solely by clinical signs and chest x-rays. The primary outcome measure will be the mechanical ventilation duration. Statistical analysis will employ independent sample t-tests with a significance level set at α = 0.05 and a power of 80%. The study requires 30 infants per group (in total 60 infants). Results: This study aims to demonstrate that determining PS application based on a combination of LUS and clinical indicators is superior to traditional approaches. Conclusions: This approach may enhance the accuracy of NRDS diagnosis and facilitate early prediction of PS requirements, thereby reducing the duration of mechanical ventilation. The findings of this research may contribute valuable insights into the use of LUS to guide PS administration.

OTUD1 Deficiency Alleviates LPS-Induced Acute Lung Injury in Mice by Reducing Inflammatory Response.

The ovarian tumor (OTU) family consists of deubiquitinating enzymes thought to play a crucial role in immunity. Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) pose substantial clinical challenges due to severe respiratory complications and high mortality resulting from uncontrolled inflammation. Despite this, no study has explored the potential link between the OTU family and ALI/ARDS. Using publicly available high-throughput data, 14 OTUs were screened in a simulating bacteria- or LPS-induced ALI model. Subsequently, gene knockout mice and transcriptome sequencing were employed to explore the roles and mechanisms of the selected OTUs in ALI. Our screen identified OTUD1 in the OTU family as a deubiquitinase highly related to ALI. In the LPS-induced ALI model, deficiency of OTUD1 significantly ameliorated pulmonary edema, reduced permeability damage, and decreased lung immunocyte infiltration. Furthermore, RNA-seq analysis revealed that OTUD1 deficiency inhibited key pathways, including the IFN-γ/STAT1 and TNF-α/NF-κB axes, ultimately mitigating the severity of immune responses in ALI. In summary, our study highlights OTUD1 as a critical immunomodulatory factor in acute inflammation. These findings suggest that targeting OTUD1 could hold promise for the development of novel treatments against ALI/ARDS.