Growing Crystalline Zinc-1,3,5-benzenetricarboxylate Metal–Organic Frameworks in Different Surfactants

Six new zinc-1,3,5-benzenetricarboxylate-based metal–organic frameworks (MOFs) have been successfully synthesized using three different surfactants (PEG 400, octanoic acid, and hexadecyltributylphosphonium bromide) as reaction media. These surfactants with different characteristics, such as being neutral, acidic, and cationic, have been demonstrated to show strong effects on directing the crystals’ growth and resulted in different secondary building units (SBUs) including an unusual SBU unit [Zn₄(μ₄-O)­(CO₂)₇]. Our results clearly indicated that the surfactant–thermal method could offer exciting opportunities for preparing novel MOFs or other inorganic crystalline materials with diverse structures and interesting properties

MOESM2 of Global comparison of chromosome X genes of pulmonary telocytes with mesenchymal stem cells, fibroblasts, alveolar type II cells, airway epithelial cells, and lymphocytes

Additional file 2. Details of up-regulated or down-regulated gene expression variations in chromosome X

Image_1_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Data_Sheet_1_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.docx

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_2_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Data_Sheet_2_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.docx

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Antigenic Subversion: A Novel Mechanism of Host Immune Evasion by Ebola Virus

<div><p>In addition to its surface glycoprotein (GP_1,2), Ebola virus (EBOV) directs the production of large quantities of a truncated glycoprotein isoform (sGP) that is secreted into the extracellular space. The generation of secreted antigens has been studied in several viruses and suggested as a mechanism of host immune evasion through absorption of antibodies and interference with antibody-mediated clearance. However such a role has not been conclusively determined for the Ebola virus sGP. In this study, we immunized mice with DNA constructs expressing GP_1,2 and/or sGP, and demonstrate that sGP can efficiently compete for anti-GP₁₂ antibodies, but only from mice that have been immunized by sGP. We term this phenomenon “antigenic subversion”, and propose a model whereby sGP redirects the host antibody response to focus on epitopes which it shares with membrane-bound GP_1,2, thereby allowing it to absorb anti-GP_1,2 antibodies. Unexpectedly, we found that sGP can also subvert a previously immunized host's anti-GP_1,2 response resulting in strong cross-reactivity with sGP. This finding is particularly relevant to EBOV vaccinology since it underscores the importance of eliciting robust immunity that is sufficient to rapidly clear an infection before antigenic subversion can occur. Antigenic subversion represents a novel virus escape strategy that likely helps EBOV evade host immunity, and may represent an important obstacle to EBOV vaccine design.</p> </div

Image_5_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_3_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Ability of sGP to divert antibody responses against GP_1,2.

<p>(A) Immunization study design. Female BALB/C mice were immunized IM with 50 µg of total DNA per immunization according to the schedule. Two groups of mice (n = 12) were primed and boosted as in previous experiments with either sGP Edit or GP_1,2 Edit in pCAGGS vector. Each group was divided in two and subgroups were boosted at week 10 with either the same construct against which they had initially been immunized, or with the opposite editing site mutant construct. (B) Comparison of antibody response against GP_1,2. Sera collected at week 12 were analyzed for antibodies against GP_1,2 by ELISA using GP_1,2 as coating antigen. (C) sGP competition ELISA. The ability of sGP to compete for anti-GP_1,2 antibodies was determined by competition ELISA as described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003065#ppat-1003065-g003" target="_blank">Figure 3B</a>. Pooled antisera were analyzed from mice immunized with sGP Edit and then boosted at week 10 with either GP_1,2 Edit (red), or sGP Edit (purple), and from mice immunized with GP_1,2 Edit and then boosted at week 10 with either GP_1,2Edit (blue) or sGP Edit (green). All ELISA experiments were performed in duplicate at least three times and representative results shown. (D) Interference of EBOV GP pseudovirus neutralization by sGP. The ability of sGP to interfere with antibody-dependent neutralization was determined as in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003065#ppat-1003065-g004" target="_blank">Figure 4B</a>. Pooled sGP-primed, GP_1,2-boosted (red) and GP_1,2-primed, sGP-boosted (green) antisera were fixed at the dilution corresponding to 50% neutralization. Antisera were co-incubated with increasing dilutions of His-tagged sGP (solid markers) or His-tagged influenza PR8 HA (open markers), and rescue of infectivity was measured as described in methods. (E) Comparison of 50% neutralization titers. Antiserum titers corresponding to 50% pseudovirus neutralization activity (NT₅₀) were calculated for week 6 (fine checkered) and week 12 (coarse checkered) mice. Error bars correspond to 95% confidence interval as determined by Student's t-test.</p