BackgroundEffective therapeutic options are limited for patients with advanced esophageal squamous cell carcinoma (ESCC). The incorporation of an immune checkpoint inhibitor and a molecular anti‐angiogenic agent into the commonly adopted chemotherapy may produce synergistic effects. Therefore, we aimed to investigate the efficacy and safety of camrelizumab plus apatinib combined with chemotherapy as the first‐line treatment of advanced ESCC.MethodsIn this single‐arm prospective phase II trial, patients with unresectable locally advanced or recurrent/metastatic ESCC received camrelizumab 200 mg, liposomal paclitaxel 150 mg/m2, and nedaplatin 50 mg/m2 on day 1, and apatinib 250 mg on days 1‐14. The treatments were repeated every 14 days for up to 9 cycles, followed by maintenance therapy with camrelizumab and apatinib. The primary endpoint was objective response rate (ORR) according to the Response Evaluation Criteria in Solid Tumors (version 1.1). Secondary endpoints included disease control rate (DCR), progression‐free survival (PFS), overall survival (OS), and safety.ResultsWe enrolled 30 patients between August 7, 2018 and February 23, 2019. The median follow‐up was 24.98 months (95% confidence interval [CI]: 23.05‐26.16 months). The centrally assessed ORR was 80.0% (95% CI: 61.4%‐92.3%), with a median duration of response of 9.77 months (range: 1.54 to 24.82+ months). The DCR reached 96.7% (95% CI: 82.8%‐99.9%). The median PFS was 6.85 months (95% CI: 4.46‐14.20 months), and the median OS was 19.43 months (95% CI: 9.93 months – not reached). The most common grade 3‐4 treatment‐related adverse events (AEs) were leukopenia (83.3%), neutropenia (60.0%), and increased aspartate aminotransferase level (26.7%). Treatment‐related serious AEs included febrile neutropenia, leukopenia, and anorexia in one patient (3.3%), and single cases of increased blood bilirubin level (3.3%) and toxic epidermal necrolysis (3.3%). No treatment‐related deaths occurred.ConclusionsCamrelizumab plus apatinib combined with liposomal paclitaxel and nedaplatin as first‐line treatment demonstrated feasible anti‐tumor activity and manageable safety in patients with advanced ESCC. Randomized trials to evaluate this new combination strategy are warranted.Trial registrationThis trial was registered on July 27, 2018, at ClinicalTrials.gov (identifier: NCT03603756).

Background: Alzheimer's disease (AD) currently lacks a cure. Because substantial neuronal damage usually occurs before AD is advanced enough for diagnosis, the best hope for disease-modifying AD therapies likely relies on early intervention or even prevention, and targeting multiple pathways implicated in early AD pathogenesis rather than focusing exclusively on excessive production of β-amyloid (Aβ) species.Methods: Coniferaldehyde (CFA), a food flavoring and agonist of NF-E2-related factor 2 (Nrf2), was selected by multimodal in vitro screening, followed by investigation of several downstream effects potentially involved. Furthermore, in the APP/PS1 AD mouse model, the therapeutic effects of CFA (0.2 mmol kg-1d-1) were tested beginning at 3 months of age. Behavioral phenotypes related to learning and memory capacity, brain pathology and biochemistry, including Aβ transport, were assessed at different time intervals.Results: CFA promoted neuron viability and showed potent neuroprotective effects, especially on mitochondrial structure and functions. In addition, CFA greatly enhanced the brain clearance of Aβ in both free and extracellular vesicle (EV)-contained Aβ forms. In the APP/PS1 mouse model, CFA effectively abolished brain Aβ deposits and reduced the level of toxic soluble Aβ peptides, thus eliminating AD-like pathological changes in the hippocampus and cerebral cortex and preserving learning and memory capacity of the mice.Conclusion: The experimental evidence overall indicated that Nrf2 activation may contribute to the potent anti-AD effects of CFA. With an excellent safety profile, further clinical investigation of coniferaldehyde might bring hope for AD prevention/therapy.

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Due to the increasing demand for microbially manufactured products in various industries, it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products. Recently, with the gradual cross-fertilization between computer science and bioinformatics fields, machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models (GSMMs) based on constrained optimization methods, and many high-quality related works have been published. Therefore, this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering, with special emphasis on GSMMs. Specifically, the development history of GSMMs is first reviewed. Then, the analysis methods of GSMMs based on constraint optimization are presented. Next, this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models. In addition, the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.