Anti-DNA antibody-targeted D-peptide nanoparticles ameliorate lupus nephritis in MRL/lpr mice.

Peptide ALW (ALWPPNLHAWVP) targeting anti-dsDNA antibodies has shown promising therapeutic effects in alleviating lupus nephritis, but is potentially limited by poor stability and non-kidney targeting. We recently developed a D-form modified ALW, called D-ALW, which has the capacity to widely inhibit pathogenic polyclonal anti-dsDNA antibody reactions. Further modification of D-ALW using PEG-PLGA nanoparticles to enhance good kidney-targeting ability and extend half-life. Here, we demonstrate that the D-form modified ALW maintains higher binding and inhibition efficiencies and achieves higher stability. Most importantly, D-ALW nanoparticles exhibit excellent kidney-targeting ability and prolong the half-life of the peptides in BALB/c mice. Additionally, compared to D-ALW, D-ALW nanoparticles significantly reduce the glomerular deposition of IgG and C3, improve renal histopathologies, such as glomerular proliferation and inflammatory cells infiltration, and markedly prolong lifespan in MRL/lpr lupus-prone mice. Overall, these results establish that the D-ALW nanoparticles offer synergistic benefits in both safety and efficacy, providing long-term renal preservation and treatment advantages in lupus nephritis.

Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.

OBJECTIVES: To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). METHODS: In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. RESULTS: Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. CONCLUSION: DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. CLINICAL RELEVANCE STATEMENT: Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. KEY POINTS: • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.

OBJECTIVE: This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs). METHODS: A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m2), 100-kVp group (BMI 24-28.9 kg/m2), and 120-kVp group (BMI ≥ 29 kg/m2). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared. RESULTS: DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups. CONCLUSION: For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both. CLINICAL RELEVANCE STATEMENT: The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs. KEY POINTS: • DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.

Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study.

OBJECTIVES: To develop a CT-based radiomics model for preoperative prediction of lymph node (LN) metastasis in perihilar cholangiocarcinoma (pCCA). METHODS: The study enrolled consecutive pCCA patients from three independent Chinese medical centers. The Boruta algorithm was applied to build the radiomics signature for the primary tumor and LN. The k-means algorithm was employed to cluster the selected LNs based on the radiomics signature LN. Support vector machines were used to construct the prediction models. The diagnostic efficiency was measured by the area under the receiver operating characteristic curve (AUC). The optimal model was evaluated in terms of calibration, clinical usefulness, and prognostic value. RESULTS: A total of 214 patients were included in the study (mean age: 61.6 years ± 9.4; 130 male). The selected LNs were classified into two clusters, which were significantly correlated with LN metastasis in all cohorts (p < 0.001). The model incorporated the clinical risk factors, radiomics signature primary tumor, and the LN cluster obtained the best discrimination, with AUC values of 0.981 (95% CI: 0.962-1), 0.896 (95% CI: 0.810-0.982), and 0.865 (95% CI: 0.768-0.961) in the training, internal validation, and external validation cohorts, respectively. High-risk patients predicted by the optimal model had shorter overall survival than low-risk patients (median, 13.7 vs. 27.3 months, p < 0.001). CONCLUSIONS: The study proposed a radiomics model with good performance to predict LN metastasis in pCCA. As a noninvasive preoperative prediction tool, this model may help in patient risk stratification and personalized treatment. CLINICAL RELEVANCE STATEMENT: A CT-based radiomics model accurately predicts lymph node metastasis in perihilar cholangiocarcinoma patients. This noninvasive preoperative tool can aid in patient risk stratification and personalized treatment, potentially improving patient outcomes. KEY POINTS: • The radiomics model based on contrast-enhanced CT is a useful tool for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma. • Radiomics features extracted from lymph nodes show great potential for predicting lymph node metastasis. • The study is the first to identify a lymph node phenotype with a high probability of metastasis based on radiomics.

FXR activation remodels hepatic and intestinal transcriptional landscapes in metabolic dysfunction-associated steatohepatitis.

The escalating obesity epidemic and aging population have propelled metabolic dysfunction-associated steatohepatitis (MASH) to the forefront of public health concerns. The activation of FXR shows promise to combat MASH and its detrimental consequences. However, the specific alterations within the MASH-related transcriptional network remain elusive, hindering the development of more precise and effective therapeutic strategies. Through a comprehensive analysis of liver RNA-seq data from human and mouse MASH samples, we identified central perturbations within the MASH-associated transcriptional network, including disrupted cellular metabolism and mitochondrial function, decreased tissue repair capability, and increased inflammation and fibrosis. By employing integrated transcriptome profiling of diverse FXR agonists-treated mice, FXR liver-specific knockout mice, and open-source human datasets, we determined that hepatic FXR activation effectively ameliorated MASH by reversing the dysregulated metabolic and inflammatory networks implicated in MASH pathogenesis. This mitigation encompassed resolving fibrosis and reducing immune infiltration. By understanding the core regulatory network of FXR, which is directly correlated with disease severity and treatment response, we identified approximately one-third of the patients who could potentially benefit from FXR agonist therapy. A similar analysis involving intestinal RNA-seq data from FXR agonists-treated mice and FXR intestine-specific knockout mice revealed that intestinal FXR activation attenuates intestinal inflammation, and has promise in attenuating hepatic inflammation and fibrosis. Collectively, our study uncovers the intricate pathophysiological features of MASH at a transcriptional level and highlights the complex interplay between FXR activation and both MASH progression and regression. These findings contribute to precise drug development, utilization, and efficacy evaluation, ultimately aiming to improve patient outcomes.

Evaluation of modified clear Twin Block aligner in treating adolescents with skeletal class II malocclusion: A two-centre cephalometric study.

INTRODUCTION: The purpose of this study was to evaluate the therapeutic effect of modified clear Twin Block (CTB) aligner and traditional twin block (TB) appliance from skeletal, dentoalveolar and soft tissue changes in adolescents with skeletal class II malocclusion. METHODS: A total of 80 adolescents, included in this study from two medical centres, were distributed into CTB group, TB group and control group based on the treatment they received. Lateral cephalograms at pre-treatment (T1) and post-treatment (T2) were measured by modified Pancherz's cephalometric analysis, and dentoskeletal and soft tissue changes were analysed by independent-sample t-test, paired-sample t-test, ANOVA test and Scheffe's Post Hoc test. RESULTS: Seventy-five adolescents completed the study, including 32 in the CTB group, 32 in the TB group and 11 in the control group. Both CTB and TB treatment showed significant differences in most dentoskeletal and soft tissue measurements. Compared with the control group, improvements were observed in class II molar relationship through significant different in S Vert/Ms-S Vert/Mi in the CTB group (P < .01) and the TB group (P < .001), as well as deep overjet through significant different in S Vert/Is-S Vert/Ii in the CTB group (P < .001) and the TB group (P < .001). Besides, the CTB group also showed less protrusion of lower incisors and resulted in a more significant improvement in profile with fewer adverse effects on speaking, eating and social activities. CONCLUSIONS: For adolescents with skeletal class II malocclusion, CTB appliance was as effective as TB on improving dentoskeletal and soft tissue measurements, featuring more reliable teeth control and patient acceptance.

Latent group detection in functional partially linear regression models.

In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.

Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

OBJECTIVES: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). METHODS: A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. RESULTS: The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001). CONCLUSION: DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. KEY POINTS: • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).

Transition-Linker Containing Detergents for Membrane Protein Studies.

It is a pressing need, but still challenging to explore the structure and function of membrane proteins (MPs). One of the main obstacles is the limited availability of matched detergents for the handling of specific MPs. We describe herein the design of new detergents by incorporation of a transition linker between the hydrophilic head and the hydrophobic tail. This design allows a gradual change of hydrophobicity between the outside and inside of micelles, in contrast to the abrupt switch in conventional detergents. Notably, many of these detergents assembled into micelles in while retaining low critical micelle concentrations. Meanwhile, thermal stabilizing evaluation identified superior detergents for representative MPs, including G protein-coupled receptors and a transporter protein. Among them, further improved the NMR study of MPs. We anticipate these that results will encourage future detergent expansion through new remodeling on the traditional detergent scaffold.

Two-Dimensional Detergent Expansion Strategy for Membrane Protein Studies.

Detergents are the most frequently applied reagents in membrane protein (MP) studies. The limited diversity of one-head-one-tailed traditional detergents, however, is far from sufficient for structurally distinct MPs. Expansion of detergent repertoire has a continuous momentum. In line with the speculation that detergent pre-assembly exerts superiority, herein we report for the first time cross-conjugation of two series of monomeric detergents for constructing a two-dimensional library of dimeric detergents. Optimum detergents stood out with unique preferences in the systematic evaluation of individual MPs. Furthermore, unprecedented hybrid detergents 14M8G and 14M9G enabled high-quality EM study of transporter MsbA and NMR study of G protein-coupled receptor A2A AR, respectively. Given the abundance of cross-coupling chemistries, comprehensive diversity could be readily covered that would facilitate the finding of new detergents for the manipulation of thorny MPs and innovation of the functional and structural study in future.

G Protein-coupled Receptor (GPCR) Reconstitution and Labeling for Solution Nuclear Magnetic Resonance (NMR) Studies of the Structural Basis of Transmembrane Signaling.

G protein-coupled receptors (GPCRs) are a large membrane protein family found in higher organisms, including the human body. GPCRs mediate cellular responses to diverse extracellular stimuli and thus control key physiological functions, which makes them important targets for drug design. Signaling by GPCRs is related to the structure and dynamics of these proteins, which are modulated by extrinsic ligands as well as by intracellular binding partners such as G proteins and arrestins. Here, we review some basics of using nuclear magnetic resonance (NMR) spectroscopy in solution for the characterization of GPCR conformations and intermolecular interactions that relate to transmembrane signaling.

Involvement of the cytokine TWEAK in the pathogenesis of psoriasis vulgaris, pustular psoriasis, and erythrodermic psoriasis.

Psoriasis is a common chronic inflammatory dermatitis in which various cytokines play a detrimental role. The cytokine tumor necrosis factor-related weak inducer of apoptosis (TWEAK) is involved in the pathogenesis of multiple inflammatory disorders. However, the potential role of TWEAK in various subtypes of psoriasis has not been studied in depth. To investigate whether the levels of TWEAK are associated with clinical traits and the levels of some known psoriasis-related cytokines, such as interleukin (IL)-17A, IL-22, interferon (IFN)-γ, and IL-36γ, 20 patients with psoriasis vulgaris (PV), 8 patients with pustular psoriasis (PP), 8 patients with erythrodermic psoriasis (EP), and 20 healthy controls (HCs) were recruited into this study. The levels of serum cytokines were detected by commercial enzyme-linked immunosorbent assay kits. The average levels of TWEAK, IL-17A, IL-22, IFN-γ, and IL-36γ were significantly higher in the psoriasis groups than in the HC group. Furthermore, there was a statistically significant correlation between TWEAK and IL-17A/IFN-γ in PV and IL-36γ in EP, but there was no correlation between TWEAK and IL-22 in any subtype of psoriasis. This study suggests that TWEAK may have a role in the pathogenesis of PV, PP, and EP via synergy with IL-17A, IFN-γ, or IL-36γ, but not with IL-22.

Angiogenesis-related lncRNAs predict the prognosis signature of stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD), which accounts for approximately 95% of gastric cancer types, is a malignancy cancer with high morbidity and mortality. Tumor angiogenesis plays important roles in the progression and pathogenesis of STAD, in which long noncoding RNAs (lncRNAs) have been verified to be crucial for angiogenesis. Our study sought to construct a prognostic signature of angiogenesis-related lncRNAs (ARLncs) to accurately predict the survival time of STAD. METHODS: The RNA-sequencing dataset and corresponding clinical data of STAD were acquired from The Cancer Genome Atlas (TCGA). ARLnc sets were obtained from the Ensemble genome database and Molecular Signatures Database (MSigDB, Angiogenesis M14493, INTegrin pathway M160). A ARLnc-related prognostic signature was then constructed via univariate Cox and multivariate Cox regression analysis in the training cohort. Survival analysis and Cox regression were performed to assess the performance of the prognostic signature between low- and high-risk groups, which was validated in the validation cohort. Furthermore, a nomogram that combined the clinical pathological characteristics and risk score conducted to predict the overall survival (OS) of STAD. In addition, ARLnc-mRNA coexpression pairs were constructed with Pearson's correlation analysis and visualized to infer the functional annotation of the ARLncs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of four ARLncs in STAD and their correlation with the angiogenesis markers, CD34 and CD105, were also validated by RT-qPCR in a clinical cohort. RESULTS: A prognostic prediction signature including four ARLncs (PVT1, LINC01315, AC245041.1, and AC037198.1) was identified and constructed. The OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (p < 0.001). The values of the time-dependent area under the curve (AUC) for the ARLnc signature for 1-, 3-, and 5- year OS were 0.683, 0.739, and 0.618 in the training cohort and 0.671, 0.646, and 0.680 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the ARLnc signature was an independent prognostic factor for STAD patients (p < 0.001). Furthermore, the nomogram and calibration curve showed accurate prediction of the survival time based on the risk score. In addition, 262 mRNAs were screened for coexpression with four ARLncs, and GO analysis showed that mRNAs were mainly involved in biological processes, including angiogenesis, cell adhesion, wound healing, and extracellular matrix organization. Furthermore, correlation analysis showed that there was a positive correlation between risk score and the expression of the angiogenesis markers, CD34 and CD105, in TCGA datasets and our clinical sample cohort. CONCLUSION: Our study constructed a prognostic signature consisting of four ARLnc genes, which was closely related to the survival of STAD patients, showing high efficacy of the prognostic signature. Thus, the present study provided a novel biomarker and promising therapeutic strategy for patients with STAD.

Quantile regression for survival data with covariates subject to detection limits.

With advances in biomedical research, biomarkers are becoming increasingly important prognostic factors for predicting overall survival, while the measurement of biomarkers is often censored due to instruments' lower limits of detection. This leads to two types of censoring: random censoring in overall survival outcomes and fixed censoring in biomarker covariates, posing new challenges in statistical modeling and inference. Existing methods for analyzing such data focus primarily on linear regression ignoring censored responses or semiparametric accelerated failure time models with covariates under detection limits (DL). In this paper, we propose a quantile regression for survival data with covariates subject to DL. Comparing to existing methods, the proposed approach provides a more versatile tool for modeling the distribution of survival outcomes by allowing covariate effects to vary across conditional quantiles of the survival time and requiring no parametric distribution assumptions for outcome data. To estimate the quantile process of regression coefficients, we develop a novel multiple imputation approach based on another quantile regression for covariates under DL, avoiding stringent parametric restrictions on censored covariates as often assumed in the literature. Under regularity conditions, we show that the estimation procedure yields uniformly consistent and asymptotically normal estimators. Simulation results demonstrate the satisfactory finite-sample performance of the method. We also apply our method to the motivating data from a study of genetic and inflammatory markers of Sepsis.

Inhibition of fibroblast growth factor-inducible 14 attenuates experimental tubulointerstitial fibrosis and profibrotic factor expression of proximal tubular epithelial cells.

BACKGROUND AND AIM: As a proinflammatory cytokine, tumor necrosis factor-like weak inducer of apoptosis (TWEAK) participates in the progression of renal fibrosis by binding to its receptor, fibroblast growth factor-inducible 14 (Fn14). However, the effect of Fn14 inhibition on tubular epithelial cell-mediated tubulointerstitial fibrosis remains unclear. This study aimed to elucidate the role of TWEAK/Fn14 interaction in the development of experimental tubulointerstitial fibrosis as well as the protective effect of Fn14 knockdown on proximal tubular epithelial cells. METHODS: A murine model of unilateral ureteral obstruction was constructed in both wild-type and Fn14-deficient BALB/c mice, followed by observation of the tubulointerstitial pathologies. RESULTS: Fn14 deficiency ameliorated the pathological changes, including inflammatory cell infiltration and cell proliferation, accompanied by reduced production of profibrotic factors and extracellular matrix deposition. In vitro experiments showed that TWEAK dose-dependently enhanced the expression of collagen I, fibronectin, and α-smooth muscle actin in proximal tubular epithelial cells. Interestingly, TWEAK also upregulated the expression levels of Notch1/Jagged1. Fn14 knockdown and Notch1/Jagged1 inhibition also mitigated the effect of TWEAK on these cells. CONCLUSIONS: In conclusion, TWEAK/Fn14 signals contributed to tubulointerstitial fibrosis by acting on proximal tubular epithelial cells. Fn14 inhibition might be a therapeutic strategy for protecting against renal interstitial fibrosis.

Adenosine A2B receptor activation stimulates alveolar fluid clearance through alveolar epithelial sodium channel via cAMP pathway in endotoxin-induced lung injury.

Clinical studies have established that the capacity of removing excess fluid from alveoli is impaired in most patients with acute respiratory distress syndrome. Impaired alveolar fluid clearance (AFC) correlates with poor outcomes. Adenosine A2B receptor (A2BAR) has the lowest affinity with adenosine among four adenosine receptors. It is documented that A2BAR can activate adenylyl cyclase (AC) resulting in elevated cAMP. Based on the understanding that cAMP is a key regulator of epithelial sodium channel (ENaC), which is the limited step in sodium transport, we hypothesized that A2BAR signaling may affect AFC in acute lung injury (ALI) through regulating ENaC via cAMP, thus attenuating pulmonary edema. To address this, we utilized pharmacological approaches to determine the role of A2BAR in AFC in rats with endotoxin-induced lung injury and further focused on the mechanisms in vitro. We observed elevated pulmonary A2BAR level in rats with ALI and the similar upregulation in alveolar epithelial cells exposed to LPS. A2BAR stimulation significantly attenuated pulmonary edema during ALI, an effect that was associated with enhanced AFC and increased ENaC expression. The regulatory effects of A2BAR on ENaC-α expression were further verified in cultured alveolar epithelial type II (ATII) cells. More importantly, activation of A2BAR dramatically increased amiloride-sensitive Na+ currents in ATII cells. Moreover, we observed that A2BAR activation stimulated cAMP accumulation, whereas the cAMP inhibitor abolished the regulatory effect of A2BAR on ENaC-α expression, suggesting that A2BAR activation regulates ENaC-α expression via cAMP-dependent mechanism. Together, these findings suggest that signaling through alveolar epithelial A2BAR promotes alveolar fluid balance during endotoxin-induced ALI by regulating ENaC via cAMP pathway, raising the hopes for treatment of pulmonary edema due to ALI.

5-aminolaevulinic acid-based photodynamic therapy induces both necrosis and apoptosis of keratinocytes in plantar warts.

BACKGROUND: 5-aminolaevulinic acid (ALA)-based photodynamic therapy (ALA-PDT) is an effective treatment for proliferative skin diseases. Many studies revealed that ALA-PDT induces apoptosis of cancer cells. However, the mechanism of PDT in warts is not fully elucidated. OBJECTIVE: This study was designed to explore the efficacy of ALA-PDT for plantar warts and its possible mechanism. METHODS: Twenty-five patients with plantar warts underwent four rounds of ALA-PDT at weekly intervals. Therapeutic effects were observed 1 month since last treatment. The adverse reactions were also recorded during and after the procedure. To observe histological changes, 5 patients accepted tissue biopsies before and 24 h after first PDT treatment. RESULTS: Twenty-two patients (88%) showed a complete response. Twenty patients complained of a mild burning sensation during treatment, and four patients developed local edema immediately after red light exposure. Five patients had burns after treatment, which disappeared within 24 h. Histologically, epidermal keratinocytes demonstrated remarkable necrocytosis and apoptosis after PDT. CONCLUSIONS: ALA-PDT is highly effective for treatment of plantar warts. The most common adverse effects observed in patients are pain and edema. ALA-PDT might eradicate plantar warts by activating both necrosis and apoptosis to trigger the death of proliferating keratinocytes.

Disulfide-Containing Detergents (DCDs) for the Structural Biology of Membrane Proteins.

Disulfide-containing detergents (DCDs) are introduced, which contain a disulfide bond in the hydrophobic tail. DCDs form smaller micelles than corresponding detergents with linear hydrocarbon chains, while providing good solubilization and reconstitution of membrane proteins. The use of this new class of detergents in structural biology is illustrated with solution NMR spectra of the human G protein-coupled receptor A2A AR, which is an α-helical protein, and the ß-barrel protein OmpX from E. coli.

Copula-based semiparametric models for spatiotemporal data.

The joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non-Gaussian. In this paper, we introduce a copula-based spatiotemporal model for analyzing spatiotemporal data and propose a semiparametric estimator. The proposed algorithm is computationally simple, since it models the marginal distribution and the spatiotemporal dependence separately. Instead of assuming a parametric distribution, the proposed method models the marginal distributions nonparametrically and thus offers more flexibility. The method also provides a convenient way to construct both point and interval predictions at new times and locations, based on the estimated conditional quantiles. Through a simulation study and an analysis of wind speeds observed along the border between Oregon and Washington, we show that our method produces more accurate point and interval predictions for skewed data than those based on normality assumptions.

Efficient Removal of Ultrafine Particles from Diesel Exhaust by Selected Tree Species: Implications for Roadside Planting for Improving the Quality of Urban Air.

Human exposure to airborne ultrafine (≪1 µm) particulate pollution may pose substantial hazards to human health, particularly in urban roadside environments where very large numbers of people are frequently exposed to vehicle-derived ultrafine particles (UFPs). For mitigation purposes, it is timely and important to quantify the deposition of traffic-derived UFPs onto leaves of selected plant species, with particularly efficient particle capture (high deposition velocity), which can be installed curbside, proximal to the emitting vehicular sources. Here, we quantify the size-resolved capture efficiency of UFPs from diesel vehicle exhaust by nine temperate-zone plant species, in wind tunnel experiments. The results show that silver birch (79% UFP removal), yew (71%), and elder (70.5%) have very high capabilities for capture of airborne UFPs. Metal concentrations and metal enrichment ratios in leaf leachates were also highest for the postexposure silver birch leaves; scanning electron microscopy showed that UFPs were concentrated along the hairs of these leaves. For all but two species, magnetic measurements demonstrated substantial increases in the concentration of magnetic particles deposited on the leaves after exposure to the exhaust particulates. Together, these new data show that leaf-deposition of UFPs is chiefly responsible for the substantial reductions in particle numbers measured downwind of the vegetation. It is critical to recognize that the deposition velocity of airborne particulate matter (PM) to leaves is species-specific and often substantially higher (∼10 to 50 times higher) than the "standard" Vd values (e.g., 0.1-0.64 cm s-1 for PM2.5) used in most modeling studies. The use of such low Vd values in models results in a major under-estimation of PM removal by roadside vegetation and thus misrepresents the efficacy of selected vegetation species in the substantial (≫20%) removal of PM. Given the potential hazard to health posed by UFPs and the removal efficiencies shown here (and by previous roadside measurements), roadside planting (maintained at or below head height) of selected species at PM "hotspots" can contribute substantially and quickly to improve in urban air quality and reductions in human exposure. These findings can contribute to the development and implementation of mitigation policies of traffic-derived PM on an international scale.