Many priority bacterial pathogens such as
P. aeruginosa
encode both PPK1 and PPK2 enzymes to maintain polyphosphate homeostasis. While PPK1 and PPK2 have distinct structures and catalytic mechanisms, they are both capable of synthesizing and consuming polyphosphate; thus, PPK2 enzymes can compensate for the loss of PPK1 and vice versa.
Background and Aims:
Monocarboxylate transporter (MCT) 4 is a high‐affinity lactate transporter that is primarily involved in the maintenance of intracellular pH homeostasis and highly expressed in different tumors. However, the role of MCT4 in modulating immune responses against HCC remains unknown.
Approach and Results:
In this study, we demonstrated that MCT4 was overexpressed in HCC, which was associated with poor prognosis in patients. Genetic or pharmacological inhibition of MCT4 using VB124 (a highly potent MCT4 inhibitor) suppressed HCC tumor growth in immunocompetent mice model by enhancing CD8+ T cell infiltration and cytotoxicity. Such improved immunotherapy response by MCT4 targeting was due to combined consequences characterized by the alleviated acidification of tumor microenvironment and elevated the chemokine (C‐X‐C motif) ligand (CXCL) 9/CXCL10 secretion induced by reactive oxygen species/NF‐κB signaling pathway. Combining MCT4 inhibition improved the therapeutic benefit of anti–programmed cell death 1 immunotherapy in HCC and prolonged mice survival. Moreover, higher MCT4 expression was observed in tumor tissues from nonresponder patients with HCC receiving neoadjuvant therapy with toripalimab.
Conclusions:
Our results revealed that lactate exportation by MCT4 has a tumor‐intrinsic function in generating an immunosuppressive HCC environment and demonstrated the proof of the concept of targeting MCT4 in tailoring HCC immunotherapeutic approaches.
We developed a novel spin-labeled terbium complex Tb(3+)/cs124-DTPA-TEMPO (1) by covalently labeling a nitroxide radical on the terbium complex for monitoring free radicals of various areas. This lanthanide complex probe shows a high EPR signal which resulted from the nitroxide radical moiety, and is weakly luminescent which resulted from the intramolecular quenching effect of the nitroxide radical on sensitised terbium luminescence. The intensity of both the EPR and luminescence can be modulated by eliminating the paramagnetism of the nitroxide radical through recognition of a carbon-centered radical analyte and thus gives a quantification of the analyte. We have preliminarily applied this probe in the luminescent detection of model carbon-centered radicals and hydroxyl radicals (·OH). This probe is water-soluble and contains lanthanide-luminescence properties, favorable for the time-resolved luminescence technique. The investigation of the intramolecular quenching process has showed that the labeled nitroxide radical quenches multiple excited states of the terbium complex, resulting in highly efficient quenching of terbium luminescence. This probe is the first example of intramolecular modulation of lanthanide luminescence by a nitroxide radical.
In-Memory Computing (IMC), which takes advantage of analog multiplication-accumulation (MAC) insides memory, is promising to alleviate the Von-Neumann bottleneck and improve the energy efficiency of deep neural networks (DNNs). Since the time-domain (TD) computing is also an energy-efficient analog computing paradigm, we present an 8kb mixed-signal IMC macro, TD-SRAM, by combining IMC with TD computing. A dual-edge single input (DESI) TD computing topology is proposed, which can significantly improve the area and power efficiencies of TD cell. The TD-SRAM bitcell consisting of a 6T DESI based TD cell and a 6T-SRAM cell supports binary DNNs. In the IMC mode, 60 columns work in parallel and 96-input binary-MAC operations are processed in each column. Implemented in a standard 40-nm CMOS process, the TD-SRAM achieves the high energy efficiency of 537 TOPS/W at 0.9-V supply. With different DNN topologies, the test chips achieve the accuracy of 95.90%-98.00% with a dual 2-bit time-to-digital converter (TDC) in the MNIST dataset.
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