Bridgesrindom6968

Impaired wound healing represents an unsolved medical need with a high impact on patients´ quality of life and global health care. Even though its causes are diverse, ischemic-hypoxic conditions and exacerbated inflammation are shared pathological features responsible for obstructing tissue restoration. In line with this, it has been suggested that promoting a normoxic pro-regenerative environment and accelerating inflammation resolution, by reinstating the lymphatic fluid transport, could allow the wound healing process to be resumed. Our group was first to demonstrate the functional use of scaffolds seeded with photosynthetic microorganisms to supply tissues with oxygen. Moreover, we previously proposed a photosynthetic gene therapy strategy to create scaffolds that deliver other therapeutic molecules, such as recombinant human growth factors into the wound area. In the present work, we introduce the use of transgenic Synechococcus sp. PCC 7002 cyanobacteria (SynHA), which can produce oxygen and lymphangioglymphangiogenic photosynthetic scaffolds for dermal regeneration. Our results confirmed that SynHA cyanobacteria maintain their photosynthetic capacity under standard human cell culture conditions and efficiently proliferate when seeded inside fibrin-collagen scaffolds. Moreover, we show that SynHA supported the viability of co-cultured lymphatic endothelial cells (LECs) under hypoxic conditions by providing them with photosynthetic-derived oxygen, while cyanobacteria-derived hyaluronic acid stimulated the lymphangiogenic capacity of LECs. Since tissue hypoxia and impaired lymphatic drainage are two key factors that directly affect wound healing, our results suggest that lymphangiogenic photosynthetic biomaterials could become a treatment option for chronic wound management.

Adolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk-factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level.

A subsample of a multi-site longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder (MDD) onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into 1) developing an MDD diagnosis or subthreshold MDD and 2) healthy controls. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental and structural magnetic resonance imaging [MRI]) at age 14 were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (N=407). The features contributing highest to the prediction were validated in an independent hold-out sample (3 independent IMAGEN sites; N=137).

The area under the receiver operating characteristics curve (AUROC) for predicting depression onset ranged between 0.70-0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression with an AUROC between 0.68-0.72 in the independent validation sample.

This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical, life events, personality traits, brain structure variables.

This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical, life events, personality traits, brain structure variables.Presynaptic neurotransmitter release is strictly regulated by SNARE proteins, Ca2+ and a number of Ca2+ sensors including synaptotagmins (Syts) and Double C2 domain proteins (Doc2s). More than seventy years after the original description of spontaneous release, the mechanism that regulates this process is still poorly understood. Syt-1, Syt7 and Doc2 proteins contribute predominantly, but not exclusively, to synchronous, asynchronous and spontaneous phases of release. The proteins share a conserved tandem C2 domain architecture, but are functionally diverse in their subcellular location, Ca2+-binding properties and protein interactions. In absence of Syt-1, Doc2a and -b, neurons still exhibit spontaneous vesicle fusion which remains Ca2+-sensitive, suggesting the existence of additional sensors. Here, we selected Doc2c, rabphilin-3a and Syt-7 as three potential Ca2+ sensors for their sequence homology with Syt-1 and Doc2b. We genetically ablated each candidate gene in absence of Doc2a and -b and investigated spontaneous and evoked release in glutamatergic hippocampal neurons, cultured either in networks or on microglial islands (autapses). The removal of Doc2c had no effect on spontaneous or evoked release. Syt-7 removal also did not affect spontaneous release, although it altered short-term plasticity by accentuating short-term depression. The removal of rabphilin caused an increased spontaneous release frequency in network cultures, an effect that was not observed in autapses. Taken together, we conclude that Doc2c and Syt-7 do not affect spontaneous release of glutamate in hippocampal neurons, while our results suggest a possible regulatory role of rabphilin-3a in neuronal networks. These findings importantly narrow down the repertoire of synaptic Ca2+ sensors that may be implicated in the spontaneous release of glutamate.

To understand the genetic diversity and molecular epidemiology characteristics of group A Rotavirus (RVA) in domestic sewage through next generation sequencing (NGS), and to explore the feasibility and necessity of NGS method for RVA environmental surveillance.

In this study, two sewage samples from Jinan each quarter in 2019 were selected for concentration, RNA extraction, and then RT-PCR reaction. Selleckchem StemRegenin 1 The amplified positive products were subjected to NGS. Finally, the results were analyzed for diversity and phylogeny.

A total of 9G-genotypes and 13 P-genotypes were detected. The Simpson diversity indices in autumn and winter were relatively high. Phylogenetic analysis showed that the dominant types G9 and P[8] were closely related to human-derived sequences.

This study proves that environmental surveillance as a means to understand the prevalence of RVA in the population is not only feasible but necessary. NGS based environmental surveillance greatly improves our understanding on RVA genetic diversity, and should be encouraged as a sensitive surveillance tool.