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During animal development, the spatiotemporal properties of molecular events largely determine the biological outcomes. Conventional gene analysis methods lack the spatiotemporal resolution for precise dissection of developmental mechanisms. Although optogenetic tools exist for manipulating designer proteins in cultured cells, few have been successfully applied to endogenous proteins in live animals. Here, we report OptoTrap, a light-inducible clustering system for manipulating endogenous proteins of diverse sizes, subcellular locations, and functions in Drosophila. This system turns on fast, is reversible in minutes or hours, and contains variants optimized for neurons and epithelial cells. By using OptoTrap to disrupt microtubules and inhibit kinesin-1 in neurons, we show that microtubules support the growth of highly dynamic dendrites and that kinesin-1 is required for patterning of low- and high-order dendritic branches in differential spatiotemporal domains. OptoTrap allows for precise manipulation of endogenous proteins in a spatiotemporal manner and thus holds promise for studying developmental mechanisms in a wide range of cell types and developmental stages.

Sensory neurons enable an organism to perceive external stimuli, which is essential for survival. The sensory capacity of a neuron depends on the elaboration of its dendritic arbor and the localization of sensory ion channels to the dendritic membrane. However, it is not well understood when and how ion channels localize to growing sensory dendrites and whether their delivery is coordinated with growth of the dendritic arbor. We investigated the localization of the DEG/ENaC/ASIC ion channel Pickpocket (Ppk) in the peripheral sensory neurons of developing fruit flies. We used CRISPR-Cas9 genome engineering approaches to tag endogenous Ppk1 and visualize it live, including monitoring Ppk1 membrane localization via a novel secreted split-GFP approach. Fluorescently tagged endogenous Ppk1 localizes to dendrites, as previously reported, and, unexpectedly, to axons and axon terminals. In dendrites, Ppk1 is present throughout actively growing dendrite branches and is stably integrated into the neuronal cell membrane during the expansive growth of the arbor. Although Ppk channels are dispensable for dendrite growth, we found that an over-active channel mutant severely reduces dendrite growth, likely by acting at an internal membrane and not the dendritic membrane. Our data reveal that the molecular motor dynein and recycling endosome GTPase Rab11 are needed for the proper trafficking of Ppk1 to dendrites. Based on our data, we propose that Ppk channel transport is coordinated with dendrite morphogenesis, which ensures proper ion channel density and distribution in sensory dendrites.

Influenza remains a major cause of death and disability with limited treatment options. Studies of acute lung injury have identified angiopoietin-2 (Ang-2) as a key prognostic marker and a potential mediator of Acute respiratory distress syndrome. However, the role of Ang-2 in viral pneumonia remains poorly defined. This study characterized the time course of lung Ang-2 expression in severe influenza pneumonia and tested the therapeutic potential of Ang-2 inhibition. We inoculated adult mice with influenza A (PR8 strain) and measured angiopoietin-1 (Ang-1), Ang-2, and Tie2 expressions during the evolution of inflammatory lung injury over the first 7 days post-infection (dpi). We tested a peptide-antibody inhibitor of Ang-2, L1-7, administered at 2, 4, and 6 dpi and measured arterial oxygen saturation, survival, pulmonary edema, inflammatory cytokines, and viral load. Finally, we infected primary human alveolar type II epithelial (AT2) cells grown in air-liquid interface culture with influenza and measured Ang-2 RNA expression. Influenza caused severe lung injury between 5 and 7 dpi in association with increased Ang-2 lung RNA and a dramatic increase in Ang-2 protein in bronchoalveolar lavage. Inhibition of Ang-2 improved oxygenation and survival and reduced pulmonary edema and alveolar-capillary barrier permeability to protein without major effects on inflammation or viral load. Finally, influenza increased the expression of Ang-2 RNA in human AT2 cells. The increased Ang-2 levels in the airspaces during severe influenza pneumonia and the improvement in clinically relevant outcomes after Ang-2 antagonism suggest that the Ang-1/Ang-2 Tie-2 signaling axis is a promising therapeutic target in influenza and potentially other causes of viral pneumonia.

The current recommendation for postoperative radiotherapy for esophageal cancer in China is mainly based on the data of incomplete two-field dissection of open left thoracotomy. At present, the type of surgery for esophageal cancer gradually transitions from open left thoracotomy to open right thoracotomy and from open esophagectomy to minimally invasive esophagectomy (MIE). Patients with early-stage esophageal cancer are selected as candidates for MIE. MIE is less invasive than open esophagectomy, and the right thoracic approach is conducive to more thorough lymph node dissection. However, few data and related studies are available on the patterns of failure after MIE in esophageal cancer, and guiding an adjuvant therapy is difficult. The feasibility of an adjuvant therapy for selective high-risk patients and the optimized treatment after MIE remains to be explored in clinical practice. In this regard, this article aims to review the safety of MIE, long-term survival outcomes, postoperative recurrence patterns, and recurrence rates of patients to discuss the value of postoperative adjuvant therapy and guide clinical treatment.

Abstract Purpose With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI. Methodology We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity. Result A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance. Conclusion According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.