Prediction of immunotherapy responsiveness in melanoma through single-cell sequencing-based characterization of the tumor immune microenvironment.

Immune checkpoint inhibitors (ICB) therapy have emerged as effective treatments for melanomas. However, the response of melanoma patients to ICB has been highly heterogenous. Here, by analyzing integrated scRNA-seq datasets from melanoma patients, we revealed significant differences in the TiME composition between ICB-resistant and responsive tissues, with resistant or responsive tissues characterized by an abundance of myeloid cells and CD8+ T cells or CD4+ T cell predominance, respectively. Among CD4+ T cells, CD4+ CXCL13+ Tfh-like cells were associated with an immunosuppressive phenotype linked to immune escape-related genes and negative regulation of T cell activation. We also develop an immunotherapy response prediction model based on the composition of the immune compartment. Our predictive model was validated using CIBERSORTx on bulk RNA-seq datasets from melanoma patients pre- and post-ICB treatment and showed a better performance than other existing models. Our study presents an effective immunotherapy response prediction model with potential for further translation, as well as underscores the critical role of the TiME in influencing the response of melanomas to immunotherapy.

Nutrient-Based Approaches for Melanoma: Prevention and Therapeutic Insights.

Melanoma, a prevalent and lethal form of skin cancer, remains a formidable challenge in terms of prevention and treatment. While significant progress has been made in understanding its pathogenesis and treatment, the quest for effective prevention strategies and therapeutic approaches remains ongoing. Considering the increased advancements in understanding the dynamic interplay between nutrients and melanoma, we aim to offer a refreshed perspective on nutrient-based approaches for melanoma prevention and adjunctive therapy. In contrast to other studies, we have innovatively provided a detailed exposition of the nutrients' influences on melanoma prognosis and treatment. This review firstly examines various nutrients, including antioxidants (namely vitamins A, D, C, and E; selenium; and caffeine), polyunsaturated fatty acids, and flavonoids, for their effects and underlying mechanisms in reducing melanoma risk. Among these nutrients, caffeine shows the most promising potential, as it is supported by multiple cohort studies for its protective effect against melanoma. In contrast, there is a certain degree of inconsistency in the research of other nutrients, possibly due to inherent differences between animal studies and epidemiological research, as well as variations in the definition of nutrient intake. To comprehensively investigate the impact of nutrients on melanoma progression and therapeutic approaches, the following sections will explore how nutrients influence immune responses and other physiological processes. While there is robust support from cell and animal studies regarding the immunomodulatory attributes of vitamins D and zinc, the anti-angiogenic potential of polyphenols, and the cell growth-inhibitory effects of flavonoids, the limited availability of human-based research substantially constrains their practical relevance in clinical contexts. As for utilizing nutrients in adjuvant melanoma treatments, multiple approaches have garnered clinical research support, including the utilization of vitamin D to decrease the postoperative recurrence rates among melanoma patients and the adoption of a high-fiber diet to enhance the effectiveness of immunotherapy. In general, the effects of most nutrients on reducing the risk of melanoma are not entirely clear. However, several nutrients, including vitamin D and dietary fiber, have demonstrated their potential to improve the melanoma prognosis and enhance the treatment outcomes, making them particularly deserving of clinical attention. A personalized and interdisciplinary approach, involving dermatologists, oncologists, nutritionists, and researchers, holds the promise of optimizing melanoma treatment strategies.

Relationship between Bladder Cancer, Nutritional Supply, and Treatment Strategies: A Comprehensive Review.

Bladder cancer (BC) is the predominant neoplasm affecting the urinary system and ranks among the most widespread malignancies globally. The causes of bladder cancer include genetic factors; age; sex; and lifestyle factors, such as imbalanced nutrition, obesity, and metabolic disorders. The lack of proper nutrient intake leads to the development of bladder cancer because insufficient nutrients are consumed to prevent this disease. The purpose of this review was to analyze the nutrients closely linked to the onset and advancement of bladder cancer and to explore the relationship between dietary nutrients and bladder cancer. Particular emphasis was placed on nutrients that are frequently ingested in daily life, including sugar, fat, protein, and others. The focus of this research was to analyze how nutritional intake before and after surgery affects the recovery process of patients who have been diagnosed with bladder cancer. This article seeks to increase awareness among both society and the medical community about the significance of implementing appropriate dietary nutrition to reduce the chances of developing bladder cancer, enhance perioperative care for patients with bladder cancer, and aid in their recuperation.

Extrachromosomal DNA (ecDNA) in cancer: mechanisms, functions, and clinical implications.

Extrachromosomal DNA (ecDNA) is circular DNA that plays an important role in the development and heterogeneity of cancer. The rapid evolution of methods to detect ecDNA, including microscopic and sequencing approaches, has greatly enhanced our knowledge of the role of ecDNA in cancer development and evolution. Here, we review the molecular characteristics, functions, mechanisms of formation, and detection methods of ecDNA, with a focus on the potential clinical implications of ecDNA in cancer. Specifically, we consider the role of ecDNA in acquired drug resistance, as a diagnostic and prognostic biomarker, and as a therapeutic target in the context of cancer. As the pathological and clinical significance of ecDNA continues to be explored, it is anticipated that ecDNA will have broad applications in the diagnosis, prognosis, and treatment of patients with cancer.

Global analysis of the COVID-19 policy activity levels and evolution patterns: A cross-sectional study.

Background and Aims: Since the beginning of the coronavirus disease 2019 (COVID-19), a large number of government policies have been implemented worldwide in response to the global spread of COVID-19. This paper aims at developing a data-driven analysis to answer the three research questions: (a) Compared to the pandemic development, are the global government COVID-19 policies sufficiently active? (b) What are the differences and characteristics in the policy activity levels at the country level? (c) What types of COVID-19 policy patterns are forming? Methods: Using the Oxford COVID-19 Government Response Tracker data set, we present a global analysis of the COVID-19 policy activity levels and evolution patterns from January 1, 2020 to June 30, 2022, based on the differential expression-sliding window analysis (DE-SWAN) algorithm and the clustering ensemble algorithm. Results: Within the period under study, the results indicate that (a) the global government policy responses to COVID-19 are very active, and the policy activity levels are significantly higher than those of global pandemic developments; (b) a high activity of policy is positively correlated to pandemic prevention at the country level; and (c) a high human development index (HDI) score is negatively correlated to the country policy activity level. Furthermore, we propose to categorize the global policy evolution patterns into three categories: (i) Mainstream (152 countries); (ii) China; and (iii) Others (34 countries). Conclusion: This work is one of the few studies that quantitatively explores the evolutionary characteristics of global government policies on COVID-19, and our results provide some new perspectives on global policy activity levels and evolution patterns.

Three-dimensional nickel nanowires modified by amorphous Fe nanosheets as electrocatalysts for the oxygen evolution reaction.

The development of electrode materials with abundant active surface sites is important for large-scale hydrogen production by water electrolysis. In this study, Fe/Ni NWs/NF catalysts were prepared by hydrothermal and electrochemical deposition of iron nanosheets on nickel chain nanowires, initially grown on nickel foam. The synthesized Fe/Ni NWs/NF electrode possessed a 3D layered heterostructure and crystalline-amorphous interfaces, containing amorphous Fe nanosheets, which demonstrated excellent activity in the oxygen evolution reaction (OER). The newly prepared electrode material has a large specific surface area, and its electrocatalytic performance is characterized by a small Tafel slope and an oxygen evolution overpotential of 303 mV at 50 mA cm-2. The electrode was highly stable in alkaline media with no degradation observed after 40 h of continuous OER operation at 50 mA cm-2. The study demonstrates the significant promise of the Fe/Ni NWs/NF electrode material for large-scale hydrogen production by water electrolysis and provides a facile and low-cost approach for the preparation of highly active OER electrocatalysts.

High-Density NiCu Bimetallic Phosphide Nanosheet Clusters Constructed by Cu-Induced Effect Boost Total Urea Hydrolysis for Hydrogen Production.

The development of urea electrolysis technologies toward energy-saving hydrogen production can alleviate the environmental issues caused by urea-rich wastewater. In the current practices, the development of high-performance electrocatalysts in urea electrolysis remains critical. In this work, the NiCu-P/NF catalyst is prepared by anchoring Ni/Cu bimetallic phosphide nanosheets onto Ni foam (NF). In the experiments, the micron-sized elemental Cu polyhedron is first anchored on the surface of the NF substrate to provide more space for the growth of bimetallic nanosheets. Meanwhile, the Cu element adjusted the electron distribution within the composite and formed Ni/P orbital vacancies, which in turn accelerated the kinetic process. As a result, the optimal NiCu-P/NF sample exhibits excellent catalytic activity and cycling stability in a hybrid electrolysis system for the urea oxidation reaction (UOR) and hydrogen evolution reaction (HER). Further, the alkaline urea-containing electrolyzer is assembled with NiCu-P/NF as two electrodes reached a current density of 50 mA cm-2 with a low driving potential of 1.422 V, which outperforms the typical commercial noble metal electrolyzer (RuO2||Pt/C). Those findings suggest the feasibility of the substrate regulation strategy to increase the growth density of active species in preparation of an efficient bifunctional electrocatalyst for cracking the urea-containing wastewater.

A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors.

This paper proposes a method to assist patients in finding the most appropriate doctor for online medical consultation. To do that, it constructs an online doctor selection decision-making method that considers the correlation attributes, in which the measure of attribute correlation is derived from the history real decision data. To combine public and personal preference with correlated attributes, it proposes a Choquet integral based comprehensive online doctor ranking method. In detail, a two stage classification model based on BERT (Bidirectional Encoder Representations from Transformers) is used to extract service features from unstructured text reviews. Then, 2-additive fuzzy measure is adopted to represent the patient public group aggregated attribute preference. Next, a novel optimization model is proposed to combine the public preference and personal preference. Finally, a case study of dxy.com is carried out to illustrate the procedure of the method. The comparison result between proposed method and other traditional MADM (multi-attribute decision-making) methods prove its rationality.

Kirkendall effect Strengthened-Superhydrophilic/superaerophobic Co-Ni3N/NF heterostructure as electrode catalyst for High-current hydrogen production.

The development of efficient electrocatalysts for large-scale water electrolysis is crucial and challenging. Research efforts towards interface engineering and electronic structure modulation can be leveraged to enhance the electrochemical performance of the developed catalysts. In this work, a surface-engineered Co-Ni3N/NF heterostructure electrode was prepared based on Kirkendall effect for high-current water electrolysis. In the experiments, the textural feature and intrinsic activity of the Co-Ni3N/NF heterostructure were tuned through cobalt-doping and the creation of structural defects. As a result, the increased surface energy endowed Co-Ni3N/NF heterostructure with superhydrophilic and superaerophobic properties. Meanwhile, the contact area of the gas-liquid-solid three phases was optimized. With a large underwater bubble contact angle (CA) of 169°, the electrolyte solution can infiltrate the Co-Ni3N/NF electrode within 150 ms. Sequentially, the generated gas bubbles were able to detach at high frequency, which ensured the rapid mass exchange. The performance tests showed that the optimal Co-Ni3N/NF electrode sample reached current densities of 100 mA cm-2 and 500 mA cm-2 at the overpotentials of 98 mV and 123 mV, respectively. Benefiting from the reduction of hydrogen embrittlement, the HER performance of the prepared Co-Ni3N/NF electrode sample decreased slightly after 100 h durability test, but the overall structure remained well. Those results allowed us to conclude that the prepared Co-Ni3N/NF electrocatalyst holds the promises for large-scale water electrolysis in industries. More specifically, this work provided a new perspective that the efficiency of electrocatalysts for large-scale water electrolysis can be enhanced by constructing a heterostructure with good wettability and gas repellency.

Biomimicry-inspired fish scale-like Ni3N/FeNi3N/NF superhydrophilic/superaerophobic nanoarrays displaying high electrocatalytic performance.

The mass transfer efficiency and structural stability of the electrode are critical for industrialized water electrolysis operations. Herein, the biomimicry-inspired design of Ni3N/FeNi3N/NF nanoarrays with a fish scale-like structure, which endowed the Ni3N/FeNi3N/NF nanoarrays with rapid infiltration of aqueous solution within 60 ms and 169° bubble contact angle, is demonstrated. The optimal Ni3N/FeNi3N/NF sample displayed catalytic activity with hydrogen evolution reaction (HER) overpotentials of only 48 mV at 10 mA cm-2 and 102 mV at 100 mA cm-2. Similarly, the overpotential of the anodic-coupled urea oxidation reaction (UOR) was only 1.3 V at 10 mA cm-2 and 1.35 V at 100 mA cm-2. Besides, the small impact resulting from the rapid bubble extraction within the Ni3N/FeNi3N/NF nanoarrays ensured excellent HER cycling stability over 100 h at a current density of 50 mA cm-2. The further scale-up experiment suggests the industrialization prospects of the prepared Ni3N/FeNi3N/NF electrocatalysts.

Exosomal circ-PTPN22 and circ-ADAMTS6 mark T cell exhaustion and neutrophil extracellular traps in Asian intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a liver tumor featured by challenges of non-invasive early diagnosis and a higher prevalence rate in Asian countries. These characteristics necessitate the development of liquid biopsy and immunotherapy methods to improve the prognosis of patients with ICC. Herein, we conducted a pilot study on the transcriptome of tumor tissues, adjacent normal tissues, and plasma exosomes of Asian patients with ICC from northern and southern China. We identified a subgroup of immunogenic Asian ICC, which is different from Caucasian ICC and is characterized by T cell exhaustion and neutrophil extracellular traps. The levels of circ-PTPN22 (hsa_circ_0110529) and circ-ADAMTS6 (hsa_circ_0072688), potential circRNA biomarkers, were elevated in the ICC tumor tissues and plasma exosomes of this subgroup than in the other subgroups and normal controls. These circRNAs were derived from post-transcriptional backsplicing of PTPN22 and ADAMTS6 that were expressed in T cells and endothelial cells, respectively, in the ICC microenvironment. Our results revealed a subgroup of Asian ICC characterized by T cell exhaustion and neutrophil extracellular traps and marked by elevated levels of circ-PTPN22 and circ-ADAMTS6 in tumor tissues and plasma exosomes. This subgroup is potentially detectable by plasma exosomal circRNAs and treatable with immune checkpoint blockade.

A Minimum Cost Consensus-Based Failure Mode and Effect Analysis Framework Considering Experts' Limited Compromise and Tolerance Behaviors.

This study proposes a minimum cost consensus-based failure mode and effect analysis (MCC-FMEA) framework considering experts' limited compromise and tolerance behaviors, where the first behavior indicates that a failure mode and effect analysis (FMEA) expert might not tolerate modifying his/her risk assessment without limitations, and the second behavior indicates that an FMEA expert will accept risk assessment suggestions without being paid for any cost if the suggested risk assessments fall within his/her tolerance threshold. First, an MCC-FMEA with limited compromise behaviors is presented. Second, experts' tolerance behaviors are added to the MCC-FMEA with limited compromise behaviors. Theoretical results indicate that in some cases, this MCC-FMEA with limited compromise and tolerance behaviors has no solution. Thus, a minimum compromise adjustment consensus model and a maximum consensus model with limited compromise behaviors are developed and analyzed, and an interactive MCC-FMEA framework, resulting in an FMEA problem consensual collective solution, is designed. A case study, regarding the assessment of COVID-19-related risk in radiation oncology, and a detailed sensitivity and comparative analysis with the existing FMEA approaches are provided to verify the effectiveness of the proposed approach to FMEA consensus-reaching.

Medical capacity investment for epidemic disease: The effects of policymaker's confidence and public trust.

Due to the server bed shortage, which has raised ethical dilemmas in the earliest days of the COVID-19 crisis, medical capacity investment has become a vital decision-making issue in the attempt to contain the epidemic. Furthermore, economic strength has failed to explain the significant performance difference across countries in combatting COVID-19. Unlike common diseases, epidemic diseases add substantial unpredictability, complexity, and uncertainty to decision-making. Knowledge miscalibration on epidemiological uncertainties by policymaker's over- and underconfidence can seriously impact policymaking. Ineffective risk communication may lead to conflicting and incoherent information transmission. As a result, public reactions and attitudes could be influenced by policymakers' confidence due to the level of public trust, which eventually affects the degree to which an epidemic spreads. To uncover the impacts of policymakers' confidence and public trust on the medical capacity investment, we establish epidemic diffusion models to characterize how transmission evolves with (and without) vaccination and frame the capacity investment problem as a newsvendor problem. Our results show that if the public fully trusts the public health experts, the policymaker's behavioral bias is always harmful, but its effect on cost increment is marginal. If a policymaker's behavior induces public reactions due to public trust, both the spread of the epidemic and the overall performance will be significantly affected, but such impacts are not always harmful. Decision bias may be beneficial when policymakers are pessimistic or highly overconfident. Having an opportunity to amend initially biased decisions can debias a particular topic but has a limited cost-saving effect.

Counterterrorism resource allocation during a pandemic: The effects of dynamic target valuations when facing a strategic terrorist.

The outbreak of pandemics such as COVID-19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well-being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender-attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.

Consensus Reaching Based on Social Influence Evolution in Group Decision Making.

A key issue in social network group decision making (SNGDM) is to determine the weights (i.e., social influences) of individuals. Notably, in some SNGDM scenarios, the social influences of individuals may evolve over time. Meanwhile, consensus reaching is another important issue in SNGDM. In this article, we are dedicated to disclosing the natural evolution process of social influence, and further to discussing the consensus reaching issue in SNGDM. First, we establish the social influence evolution model, where the individual's social influence is obtained by combining his/her intrinsic influence and network influence. Afterward, we design the consensus reaching process based on social influence evolution (CRP-SIE) to assist the individuals to reach a consensus. Furthermore, we use a hypothetical application to show the applicability of the proposed CRP-SIE. Finally, simulation analysis is adopted to investigate the effects of social influence evolution on consensus reaching in SNGDM, and comparative analysis is conducted to demonstrate the advantages of our proposal.

In-site hydrogen bubble template method to prepare Ni coated metal meshes as effective bi-functional electrodes for water splitting.

Metal substrates are frequently used as current collectors and supports for electrochemically active materials, but their effect on the physical and electrochemical performance of electrocatalysts is rarely investigated. In this study, the electrodeposition method was used to coat four different metal meshes with three-dimensional nickel porous structures using hydrogen bubbles as a template. The significant influence of the metal substrates on the morphology of deposited nickel was demonstrated. 3D porous structures formed on nickel, iron, copper, and titanium meshes via the hydrogen bubble template method varied significantly. It was found that differences in the physical adsorption of hydrogen and electrochemical hydrogen evolution on metal substrates are the fundamental reasons behind the diverse morphology of the coatings. Lattice matching of the substrate and the active material also plays an important role during the electrodeposition process. Electrocatalytic performance of the newly prepared materials in water electrolysis was evaluated using the hydrogen and oxygen evolution reactions (HER and OER). The results demonstrate the high electrocatalytic activity of Ni/FeM in the OER and HER, and the good stability of Ni/TiM in HER.

Synthesis of C3-functionalized indole derivatives via Brønsted acid-catalyzed regioselective arylation of 2-indolylmethanols with guaiazulene.

A Brønsted acid catalyzed method for the construction of guaiazulenyl C3-functionalized indole derivatives is reported for the first time. The reactions proceeded smoothly at ambient temperature by using (±)-10-camphorsulfonic acid (CSA) as a catalyst, 2-indolylmethanols and guaiazulene as substrates, and the desired products were obtained in high yields with excellent regioselectivities.

Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making.

Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided.

Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics.

In linguistic decision-making problems, there may be cases when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, that is, decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this article, we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs-based incomplete linguistic preference estimation method.

Bounded Confidence Evolution of Opinions and Actions in Social Networks.

Inspired by the continuous opinion and discrete action (CODA) model, bounded confidence and social networks, the bounded confidence evolution of opinions and actions in social networks is investigated and a social network opinions and actions evolutions (SNOAEs) model is proposed. In the SNOAE model, it is assumed that each agent has a CODA for a certain issue. Agents' opinions are private and invisible, that is, an individual agent only knows its own opinion and cannot obtain other agents' opinions unless there is a social network connection edge that allows their communication; agents' actions are public and visible to all agents and impact other agents' actions. Opinions and actions evolve in a directed social network. In the limitation of the bounded confidence, other agents' actions or agents' opinions noticed or obtained by network communication, respectively, are used by agents to update their opinions. Based on the SNOAE model, the evolution of the opinions and actions with bounded confidence is investigated in social networks both theoretically and experimentally with a detailed simulation analysis. Theoretical research results show that discrete actions can attract agents who trust the discrete action, and make agents to express extreme opinions. Simulation experiments results show that social network connection probability, bounded confidence, and the opinion threshold of action choice parameters have strong impacts on the evolution of opinions and actions. However, the number of agents in the social network has no obvious influence on the evolution of opinions and actions.