In 2019/2020, the emergence of coronavirus disease 2019 (COVID-19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID-19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi-drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose-finding to achieve drug synergy. This approach is widely-used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI-based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top-ranked combination being optimally and sub-optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism-agnostic, and potentially applicable to the systematic N-of-1 and population-wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID-19 and future pandemics.
The optimization of materials is challenging as it often involves simultaneous manipulation of an assembly of condition parameters, which generates an enormous combinational space. Thus, optimization models and algorithms are widely adopted to accelerate material design and optimization. However, most optimization strategies can poorly handle multiple parameters simultaneously with limited prior knowledge. Herein, we describe a novel systematic optimization strategy, namely, machine-learningassisted differential evolution, which combines machine learning and the evolutionary algorithm together, for zero-prior-data, rapid, and simultaneous optimization of multiple objectives. The strategy enables the evolutionary algorithm to "learn" so as to accelerate the optimization process, and also to identify quantitative interactions between the condition parameters and functional characteristics of the material. The performance of the strategy is verified by in silico simulations, as well as an application on simultaneously optimizing three characteristics, namely, water contact angle, oil absorption capacity, and mechanical strength, of an electrospun polystyrene/polyacrylonitrile (PS/PAN) material as a potential sorbent for a marine oil spill. With only 50 tests, the optimal fabrication parameters were successfully located from a combinatorial space of 50 000 possibilities. The presented platform technique offers a universal enabling technology to identify the optimal conditions rapidly from a daunting parameter space to synthesize materials with multiple desired functionalities.
The incidence of global head and neck cancer has increased markedly in the last 10 years, and its prognosis is poor, which seriously endangers people’s life and health. At present, there are few studies on its pathogenesis. Golgi integral membrane protein 4 (GOLIM4) is a major member of the Golgi apparatus transporter complex, and its role in tumor is unclear. The present study found that GOLIM4 was the key target protein downstream of stromal interaction molecule 1 (STIM1), which can inhibit the proliferation of head and neck cancer cells FaDu (human pharyngeal squamous carcinoma cell) and Tca-8113 (human tongue squamous carcinoma cell) with knockdown of GOLIM4 by lentivirus. And the decreased expression of GOLIM4 induced cellular apoptosis. Further experiments revealed that FaDu cell cycle progression was changed after GOLIM4 silence, G1 phase arrest and the number of G2/M cells decreased significantly. It was also found that the cells in S-phase decreased markedly after GOLIM4 was knocked down compared with the control group by 5-bromo-2′-deoxyuridine (BrdU) incorporation experiment. In conclusion, we found that GOLIM4, as the target gene downstream of STIM1, inhibited the proliferation of head and neck cancer, promoted apoptosis, and regulated cell cycle progression, and GOLIM4 is a novel oncogene in head and neck cancer and might help in developing promising targetted therapies for head and neck cancer patients.
Colon cancer (CC) is one of the leading causes of cancer related mortality. Research over past decades have profoundly enhanced our understanding of immunotherapy, a major clinical accomplishment, and its potential role toward treating CC. However, studies investigating the expression of these immune checkpoints, such as epithelial cell adhesion molecule (EpCAM), programmed death-1 (PD-1), and programmed death-ligand 1 (PD-L1), by peripheral blood mononuclear cells (PBMCs) is lacking. Here, high-dimensional mass cytometry (CyTOF) is used to investigate immune alterations and promising immunotherapeutic targets expression by PBMCs of CC patients. Results reveal that expression of EpCAM and PD-L1 on CD4 + T cells significantly increased in patients with CC, compared with age- and sex- matching healthy controls and patients with colonic polyps. These differences are also validated in an independent patient cohort using flow cytometry. Further analysis revealed that EpCAM + CD4 + T cells are PD-L1 + CCR5 + CCR6 + . Immunofluorescence staining results demonstrate that the increase of EpCAM + CD4 + T cells is also observed in tumor tissues, rather than para-cancerous tissues. To ascertain the functional disorders of the identified cell subset, phosphorylated signaling protein levels are assessed using imaging mass cytometry. Increases in pp38 MAPK and pMAPKAPK2 are observable, indicating abnormal activation of pp38 MAPK-pMAPKAPK2 signaling pathway. Results in this study indicate that EpCAM + CD4 + T cells may play a role in CC development. Detailed knowledge on the functionality of EpCAM + CD4 + T cells is of high translational relevance.
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