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With the steady progress of China’s opening-up policy, how to avoid the financial risks brought by opening-up is a valuable research topic at present while promoting economic development. As an innovative business model connecting the real economy and the virtual economy, the Internet of Things (IoT) finance provides standardized technical support for the expansion of trade and finance. In financial data analysis, deep learning (DL) has become an important means to predict financial market movements, process text information and improve trading strategies. Analysis is conducted on the influence of trade and financial opening on the volatility of real exchange rate. Through the empirical test of panel data of 45 major countries in the world, the pooled ordinary least square (OLS) method and instrumental variable method are used to evaluate the influence of trade and financial opening of sample countries on the volatility of real exchange rate. The main conclusions are that trade openness is negatively correlated with the volatility of real exchange rate, and financial openness is positively correlated with the volatility of real exchange rate. A certain reference is provided for reducing the fluctuation of real exchange rate in the process of opening to the world.

Multidrug resistance (MDR) occurs frequently after long‐term chemotherapy, resulting in refractory cancer and tumor recurrence. Therefore, combatting MDR is an important issue. Autophagy, a self‐degradative system, universally arises during the treatment of sensitive and MDR cancer. Autophagy can be a double‐edged sword for MDR tumors: it participates in the development of MDR and protects cancer cells from chemotherapeutics but can also kill MDR cancer cells in which apoptosis pathways are inactive. Autophagy induced by anticancer drugs could also activate apoptosis signaling pathways in MDR cells, facilitating MDR reversal. Therefore, research on the regulation of autophagy to combat MDR is expanding and is becoming increasingly important. We summarize advanced studies of autophagy in MDR tumors, including the variable role of autophagy in MDR cancer cells.

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies. Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Breast cancer is the second leading cause of death for women worldwide. The heterogeneity of this disease presents a big challenge in its therapeutic management. However, recent advances in molecular biology and immunology enable to develop highly targeted therapies for many forms of breast cancer. The primary objective of targeted therapy is to inhibit a specific target/molecule that supports tumor progression. Ak strain transforming, cyclin-dependent kinases, poly (ADP-ribose) polymerase, and different growth factors have emerged as potential therapeutic targets for specific breast cancer subtypes. Many targeted drugs are currently undergoing clinical trials, and some have already received the FDA approval as monotherapy or in combination with other drugs for the treatment of different forms of breast cancer. However, the targeted drugs have yet to achieve therapeutic promise against triple-negative breast cancer (TNBC). In this aspect, immune therapy has come up as a promising therapeutic approach specifically for TNBC patients. Different immunotherapeutic modalities including immune-checkpoint blockade, vaccination, and adoptive cell transfer have been extensively studied in the clinical setting of breast cancer, especially in TNBC patients. The FDA has already approved some immune-checkpoint blockers in combination with chemotherapeutic drugs to treat TNBC and several trials are ongoing. This review provides an overview of clinical developments and recent advancements in targeted therapies and immunotherapies for breast cancer treatment. The successes, challenges, and prospects were critically discussed to portray their profound prospects.

Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets. Both traditional medicine-related approaches and modern drug discovery platforms have been used in an attempt to develop m6A-targeted drugs. Here, we provide an update of the latest findings on m6A modification and the critical roles of m6A modification in cancer progression, and we summarize rational sources for the discovery of m6A-targeted anticancer agents from traditional medicines and computer-based chemosynthetic compounds. This review highlights the potential agents targeting m6A modification for cancer treatment and proposes the advantage of artificial intelligence (AI) in the discovery of m6A-targeting anticancer drugs. Graphical abstract Three stages of m6A-targeting anticancer drug discovery: traditional medicine-based natural products, modern chemical modification or synthesis, and artificial intelligence (AI)-assisted approaches for the future.

A bstract The gravitational path integral suggests a striking result: the Hilbert space of closed universes in each superselection sector, a so-called α -sector, is one-dimensional. We develop an abstract formalism encapsulating recent proposals that modify the gravitational path integral in the presence of observers and allow larger Hilbert spaces to be associated with closed universes. Our formalism regards the gravitational path integral as a map from abstract objects called sources to complex numbers, and introduces additional objects called partial sources, which form sources when glued together. We apply this formalism to treat, on equal footing, universes with spatial boundaries, closed universes with prescribed observer worldlines, and closed universes containing observers entangled with external systems. In these contexts, the relevant gravitational Hilbert spaces contain states prepared by partial sources and can consequently have nontrivial α -sectors supporting noncommuting operators. Within our general framework, the positivity of the gravitational inner product implies a bound on the Hilbert space trace of certain positive operators over each α -sector. The trace of such operators, in turn, quantifies the effective size of this Hilbert space.

Skin cancer has emerged as the fifth most commonly reported cancer in the world, causing a burden on global health and the economy. The enormously rising environmental changes, industrialization, and genetic modification have further exacerbated skin cancer statistics. Current treatment modalities such as surgery, radiotherapy, conventional chemotherapy, targeted therapy, and immunotherapy are facing several issues related to cost, toxicity, and bioavailability thereby leading to declined anti-skin cancer therapeutic efficacy and poor patient compliance. In the context of overcoming this limitation, several nanotechnological advancements have been witnessed so far. Among various nanomaterials, nanoparticles have endowed exorbitant advantages by acting as both therapeutic agents and drug carriers for the remarkable treatment of skin cancer. The small size and large surface area to volume ratio of nanoparticles escalate the skin tumor uptake through their leaky vasculature resulting in enhanced therapeutic efficacy. In this context, the present review provides up to date information about different types and pathology of skin cancer, followed by their current treatment modalities and associated drawbacks. Furthermore, it meticulously discusses the role of numerous inorganic, polymer, and lipid-based nanoparticles in skin cancer therapy with subsequent descriptions of their patents and clinical trials. Graphical Abstract

Cancer is a grievous disease whose treatment requires a more efficient, non-invasive therapy, associated with minimal side effects. Gold nanoparticles possessing greatly impressive optical properties have been a forerunner in bioengineered cancer therapy. This theranostic system has gained immense popularity and finds its application in the field of molecular detection, biological imaging, cancer cell targeting, etc. The photothermal property of nanoparticles, especially of gold nanorods, causes absorption of the light incident by the light source, and transforms it into heat, resulting in tumor cell destruction. This review describes the different optical features of gold nanoparticles and summarizes the advance research done for the application of gold nanoparticles and precisely gold nanorods for combating various cancers including breast, lung, colon, oral, prostate, and pancreatic cancer.

A bstract It has previously been shown how the gravitational thermal partition function can be obtained from a Lorentzian path integral. Unlike the Euclidean case, the integration contour over Lorentzian metrics is not immediately ruled out by the conformal factor problem. One can then ask whether this contour can be deformed to pick up nontrivial contributions from various saddle points. In Einstein-Maxwell theory, we argue that the relevance of each black hole saddle to the thermal partition function depends on its thermodynamic stability against variations in energy, angular momentum, and charge. The argument involves consideration of constrained saddles where area and quantities associated with angular momentum and charge are fixed on a codimension-two surface. Consequently, this surface possesses not only a conical singularity, but two other types of singularities. The latter are characterized by shifts along the surface and along the Maxwell gauge group acquired as one winds around near the surface in a metric-orthogonal and connection-horizontal manner. We first study this enlarged class of codimension-two singularities in generality and propose an action for singular configurations. We then incorporate these configurations into the path integral calculation of the partition function, focusing on three-dimensional spacetimes to simplify the treatment of angular momentum.

Resistance to cancer therapies has been a commonly observed phenomenon in clinical practice, which is one of the major causes of treatment failure and poor patient survival. The reduced responsiveness of cancer cells is a multifaceted phenomenon that can arise from genetic, epigenetic, and microenvironmental factors. Various mechanisms have been discovered and extensively studied, including drug inactivation, reduced intracellular drug accumulation by reduced uptake or increased efflux, drug target alteration, activation of compensatory pathways for cell survival, regulation of DNA repair and cell death, tumor plasticity, and the regulation from tumor microenvironments (TMEs). To overcome cancer resistance, a variety of strategies have been proposed, which are designed to enhance the effectiveness of cancer treatment or reduce drug resistance. These include identifying biomarkers that can predict drug response and resistance, identifying new targets, developing new targeted drugs, combination therapies targeting multiple signaling pathways, and modulating the TME. The present article focuses on the different mechanisms of drug resistance in cancer and the corresponding tackling approaches with recent updates. Perspectives on polytherapy targeting multiple resistance mechanisms, novel nanoparticle delivery systems, and advanced drug design tools for overcoming resistance are also reviewed. The reduced responsiveness of cancer cells can be associated with various mechanisms, including drug inactivation, reduced intracellular drug accumulation by reduced uptake or increased efflux, drug target alteration, activation of compensatory pathways for cell survival, regulation of DNA repair and cell death, tumor plasticity, and the regulation from tumor microenvironments. Various targeting strategies against different resistance mechanisms have been developed. The purpose of overcoming the drug resistance of cancer cells is to optimize the sensitivity of the therapy. This can be achieved by polytherapy using the combination of at least two drugs; immunotherapy using checkpoint inhibitors or monoclonal antibodies; antibody‐drug conjugates improving the selectivity of cancer treatment; gene technology modifying the epigenetic sequence; targeted therapy targeting the overexpression of drug efflux transporter or vital proteins for the cancer cell apoptosis; and nanoparticle delivery system improving the efficacy of the drug and reducing the side effect.