Employing machine learning using ferroptosis-related genes to construct a prognosis model for patients with osteosarcoma

Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed via a subgroup analysis. A nomogram was constructed using clinical markers with independent prognostic significance and risk score results. The CIBERSORT algorithm was used to detect the correlation between prognostic genes and 22 tumor-infiltrating lymphocytes. The expression of prognostic genes in erastin-treated OS cell lines was verified via real-time PCR. Six prognostic FRGs (ACSL5, ATF4, CBS, CDO1, SCD, and SLC3A2) were obtained and used to construct the risk prognosis model. Subjects were divided into high- and low-risk groups. Prognosis was worse in the high-risk group, and the model had satisfactory prediction performance for patients younger than 18 years, males, females, and those with non-metastatic disease. Univariate COX regression analysis showed that metastasis and risk score were independent risk factors for patients with OS. Nomogram was built on independent prognostic factors with superior predictive power and patient benefit. There was a significant correlation between prognostic genes and tumor immunity. Six prognostic genes were differentially expressed in ferroptosis inducer-treated OS cell lines. The identified prognostic genes can regulate tumor growth and progression by affecting the tumor microenvironment

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Stochastic Controls in Competitions and Mean Field Games

Problems combining games and controls for multiple players become widely studied due to the complexity of the world and the interactions among populations. In this thesis, we propose two models for fund managers who compete with their relative performance and one Mean Field Game model with common noise for cost minimization. For the first model, we consider a group of managers competing for the cash flows based on their relative performance by choosing between an idiosyncratic and a common risky investment opportunity. Since investors may choose to invest or withdraw continuously conditional on the real-time performance of funds, the model is of continuous competition. The unique constant equilibrium is derived in closed form, which implies that funds generally decrease the investments in their idiosyncratic risky assets under competition, in order to lower the risk of the relative performance. It pushes all funds to herd and hurts their after-fee performance. However, sufficiently disadvantaged funds with poor idiosyncratic investment opportunities or highly risk-averse managers may take the excessive risk for a better chance of attracting new investments, and their performance may improve compared to the case without competition, which benefits the investors. For the second model, we propose a principle agent model where the principle is a policy maker who decides the optimal capital gain tax rate and agents are fund managers who choose optimal portfolios in their investment opportunities. The optimal tax rate and unique portfolios are derived for one policy maker and one representative fund. Moreover, with one policy maker but N funds competing with each other based on the terminal relative performance, there exist multiple Nash equilibria and a unique Pareto optimal equilibrium can be found. Our findings also suggest that managers may take more risks with the higher tax rate, which is different from the existing literature. For the third model, we study Mean Field Games with a common noise given by a continuous time Markov chain or an independent Brownian motion under a quadratic cost structure. The theory implies that the optimal path under the equilibrium is a Gaussian process conditional on the common noise. Interestingly, it reveals the Markovian structure of the random equilibrium measure flow, which can be characterized via a deterministic finite dimensional system. Moreover, the counterpart N-player game can be embedded in the probability space generated by two Brownian motions, which concludes the convergence of the N-player game to Mean Field Games, both in the sense of the processes and the empirical measure

LQG Mean Field Games with a Markov Chain as its common noise

This paper studies Mean Field Games with a common noise given by a continuous time Markov chain under a quadratic cost structure. The theory reveals the Markovian structure of the random equilibrium measure flow, which can be used to characterize the equilibrium via a finite dimensional system. The counterpart is the N-player game characterized via an ODE system, whose dimension has a polynomial growth in the number of players. The convergence of the N-player game towards Mean Field Games is proved by explicitly embedding the N-player game into one specific probability space.Comment: 25 pages, 9 figure

Synergy of Prediction Rule and Total Synthesis in Solving the Stereochemical Puzzle of Valactamides

Natural products with diverse functional groups and stereogenic centers have inspired therapeutics and underpin the modern drug discovery process. Their three-dimensional molecular structures need to be unambiguously determined in order to be realized as clinical candidates or to achieve further activity-guided structural optimization. Although recent advances in spectroscopic methods have made it possible for researchers to determine the structures of microgram samples of complex natural products, there is no universally accepted method for determining the relative and absolute configuration of a naturally occurring compound. We report the determination of the full stereostructure of valactamide A, an eight-stereogenic-center-containing fungal metabolite by the synergy of prediction rule-guided analysis and chemical synthesis. The expedient total synthesis resulted in unambiguous verification of the predicted stereochemistry for valactamide A

Revolutionizing cancer treatment: The power of cell-based drug delivery systems

Intravenous administration of drugs is a widely used cancer therapy approach. However, the efficacy of these drugs is often hindered by various biological barriers, including circulation, accumulation, and penetration, resulting in poor delivery to solid tumors. Recently, cell-based drug delivery platforms have emerged as promising solutions to overcome these limitations. These platforms offer several advantages, including prolonged circulation time, active targeting, controlled release, and excellent biocompatibility. Cell-based delivery systems encompass cell membrane coating, intracellular loading, and extracellular backpacking. These innovative platforms hold the potential to revolutionize cancer diagnosis, monitoring, and treatment, presenting a plethora of opportunities for the advancement and integration of pharmaceuticals, medicine, and materials science. Nevertheless, several technological, ethical, and financial barriers must be addressed to facilitate the translation of these platforms into clinical practice. In this review, we explore the emerging strategies to overcome these challenges, focusing specifically on the functions and advantages of cell-mediated drug delivery in cancer treatment.ISSN:0168-3659ISSN:1873-499

Plumbagin Enhances the Anticancer Efficacy of Cisplatin by Increasing Intracellular ROS in Human Tongue Squamous Cell Carcinoma

Cisplatin is widely used in the treatment of tongue squamous cell carcinoma (TSCC), but its clinical efficacy is limited by drug resistance and toxic side effects. Hence, a novel compound capable of enhancing the anticancer effect of cisplatin while reducing the side effects is urgently needed. We have previously shown that plumbagin (PLB), an anticancer phytochemical, is able to inhibit the growth of TSCC in vitro and in vivo. The objective of this study was to investigate the effect of PLB in reversing the resistance of TSCC to cisplatin as well as its molecular mechanisms. Here, we found that PLB enhances cisplatin-induced cytotoxicity, apoptosis, and autophagy in CAL27 and cisplatin-resistant CAL27/CDDP cells. PLB could inhibit the viability and growth of TSCC cells by increasing the production of intracellular reactive oxygen species (ROS). In addition, the combination treatment of PLB and cisplatin resulted in a synergistic inhibition of TSCC viability, apoptosis, and autophagy by increasing intracellular ROS, which may be achieved by activating JNK and inhibiting AKT/mTOR signaling pathways. Finally, the synergistic treatment was also demonstrated in vivo. Therefore, PLB combined with cisplatin is a potential therapeutic strategy against therapy TSCC cisplatin resistance

Structural insights into TSC complex assembly and GAP activity on Rheb.

Tuberous sclerosis complex (TSC) integrates upstream stimuli and regulates cell growth by controlling the activity of mTORC1. TSC complex functions as a GTPase-activating protein (GAP) towards small GTPase Rheb and inhibits Rheb-mediated activation of mTORC1. Mutations in TSC genes cause tuberous sclerosis. In this study, the near-atomic resolution structure of human TSC complex reveals an arch-shaped architecture, with a 2:2:1 stoichiometry of TSC1, TSC2, and TBC1D7. This asymmetric complex consists of two interweaved TSC1 coiled-coil and one TBC1D7 that spans over the tail-to-tail TSC2 dimer. The two TSC2 GAP domains are symmetrically cradled within the core module formed by TSC2 dimerization domain and central coiled-coil of TSC1. Structural and biochemical analyses reveal TSC2 GAP-Rheb complimentary interactions and suggest a catalytic mechanism, by which an asparagine thumb (N1643) stabilizes γ-phosphate of GTP and accelerate GTP hydrolysis of Rheb. Our study reveals mechanisms of TSC complex assembly and GAP activity