Erratum: Age, motivation, and emotion regulation skills predict treatment outcome in an internet-based self-help intervention for COVID-19 related psychological distress.

[This corrects the article DOI: 10.3389/fpubh.2022.835356.].

Supporting sustainability projects at neighbourhood scale: Green visions for the San Salvario district in Turin guided by a combined assessment framework

A resilient city includes multiple energy carriers, high-efficiency infrastructure, lower resource demand to decarbonize and sustain the urban system in accordance with the Paris Agreement, the United Nations 2030 Agenda for Sustainable Development, and the various recovery plans following the COVID-19 pandemic period. To achieve these goals, a key role is played by all urban sectors, which can reduce environmental impacts and accelerate the green transition at larger scale. Intervening on a district scale obviously requires the evaluation of different aspects, taking into account both economic and non-economic criteria, as well as different points of view, involving all stakeholders. This paper proposes a multi-step evaluation procedure that extends the European manual-based Cost-Benefit Analysis (CBA) to include the extra-economic benefits and the stakeholders' opinion in the evaluation, according to the COmpoSIte Model for Assessment (COSIMA) method. This is the first application of COSIMA in the urban design sectors (i.e. buildings, water, public lighting, transportation and waste management) where different sustainable measures for a real case study located in Turin (Italy) were compared to define the most suitable transformation scenario according to multiple criteria. The results have shown how invasive scenarios allow achieving the greatest benefits, despite the huge initial costs of realization. © 2022 Elsevier Ltd

Design of a machine learning to classify health beverages preferences for elderly people: an empirical study during COVID-19 in Thailand

This research designed a decision support system based upon a machine learning (DSS-ML) model for classifying health beverage preferences for elderly people. A neural network was designed involving training using particle swarm optimisation (PSO) in comparison with two ML models: logistic regression (LR) and a neural network (NN). The DSS-ML model was able to classify accurately and autonomously the preference complexities associated with the health beverage preferences for elderly people in accordance with the WHO's recommendation. In terms of contribution, the results demonstrated that NN training with PSO resulted in a higher ability to classify the preferences for health beverages than for the two ML models. Furthermore, NN training with PSO achieved faster convergence than NN. The benefits of this research can be separated into two parts. First, manufacturers can introduce beverages that satisfy elderly people's preferences. Second, elderly people can be made aware of appropriate health beverages. Copyright © 2022 Inderscience Enterprises Ltd.

Multi-criteria performance analysis based on Physics of Decision - Application to COVID-19 and future pandemics

The purpose of this study is to present a novel perspective on decision support based on the conventional SEIR pandemic model paradigm considering the risks and opportunities as physical forces deviating the expected performance trajectory of a system. The impact of a pandemic is measured by the deviation of the social system’s performance trajectory within the geometrical framework of its Key Performance Indicators (KPIs). According to the overall premise of utilizing Ordinary Differential Equations to simulate epidemics, the deviations are connected to several alternative interventions. The model is essentially built on two sets of parameters: (i) social system parameters and (ii) pandemic parameters. The ultimate objective is to propose a multi-criteria performance framework to control pandemics that includes a combination of timely measures. On the one hand, the current study optimizes prospective strategies to manage the potential future pandemic, while on the other hand, it explores the COVID-19 epidemic in the state of Georgia (USA). IEEE

An application of the PROMETHEE II method for the comparison of energy requalification strategies to design Post-Carbon Cities

A resilient, diversified, and efficient energy system, comprising multiple energy carriers and high-efficiency infrastructure, is the way to decarbonise the European economy in line with the Paris Agreement, the UN 2030 Agenda for Sustainable Development, and the various recovery plans after the COVID-19 pandemic period. To achieve these goals, a key role is played by the private construction sector, which can reduce economic and environmental impacts and accelerate the green transition. Nevertheless, while traditionally decision-making problems in large urban transformations were supported by economic assessment based on Life Cycle Thinking and Cost-Benefit Analysis (CBA) approaches, these are now obsolete. Indeed, the sustainable neighbourhood paradigm requires the assessment of different aspects, considering both economic and extra-economic criteria, as well as different points of view, involving all stakeholders. In this context, the paper proposes a multi-stage assessment procedure that first investigates the energy performance, through a dynamic simulation model, and then the socio-economic performance of regeneration operations at the neighbourhood scale, through a Multi-Criteria Decision Analysis (MCDA). The model based on the proposed Preference Ranking Organisation Method for Enrichment Evaluations II (PROMETHEE II) aims to support local decision makers (DMs) in choosing which retrofit operations to implement and finance. The methodology was applied to a real-world case study in Turin (Italy), where various sustainable measures were ranked using multiple criteria to determine the best transformation scenario.

Decision Support System of Direct Cash-Village Fund Recipients Using Multi Attribute Utility Theory

Direct Village Fund Cash Assistance (BLT-Dana Desa) is a form of assistance from the government in the form of cash to poor families in villages sourced from the Village Fund to reduce the impact of the COVID-19 pandemic. To facilitate village officials in determining aid recipients quickly, accurately and on target, the MAUT method was chosen which was deemed suitable for use in the Decision Support System (DSS) which had many criteria so that it could easily calculate each alternative based on the many types of criteria and sub-criteria used and with a predetermined weight. There are 148 data samples of BLT recipients registered in the Social Welfare Integrated Data (DTKS) of Loa Janan Ulu village. The criteria in this study are building floor Size, type of house floor, types of house walls, sanitary facilities, power source, source of drinking water, cooking fuel, consumption of chicken/meat/milk, clothing needs, consumption in a day, do not have savings max. 500.000 rupiah. Based on the results of calculations using the MAUT method, a recommendation for direct cash assistance recipients was obtained with an accuracy value of 92.57%.

The Transcription Factor IRF9 Promotes Colorectal Cancer via Modulating the IL-6/STAT3 Signaling Axis.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, and innate immune responses and inflammation are known to affect the course of disease. Interferon (IFN) signaling in particular is critical for modulating inflammation-associated diseases including CRC. While the effects of IFN signaling in CRC have been studied, results have been conflicting. Furthermore, individual molecules in the IFN pathway that could be therapeutically targeted have distinct functions, with many of their diverse roles in CRC remaining unclear. Here, we found that IRF9 had an oncogenic effect in CRC; loss of IRF9 reduced tumorigenesis in both azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced and spontaneous CRC models. IRF9 also reduced DSS-induced colitis and inflammation in the colon, but it had no effect on the NF-κB and MAPK signaling activation. Instead, IRF9 enhanced the transcription and production of the inflammatory cytokine IL-6. By promoting IL-6 release, IRF9 drove the activation of pro-oncogenic STAT3 signaling in the colon. Overall, our study found that IRF9 promoted the development of CRC via modulation of the IL-6/STAT3 signaling axis, identifying multiple potential targets and suggesting new therapeutic strategies for the treatment of CRC.

Decision support system for cash direct assistance for victims of the covid-19 outbreak based on cloud computing

The Covid-19 outbreak came suddenly and shocked all humans who live on earth because the rate of spread was so fast and resulted in high mortality and an extremely bad economic impact. The economic impact is very big, especially for the poor. Therefore, to protect the poor and vulnerable from the impact of the COVID-19 pandemic, the government of Indonesia has designed several social protection security programs. One of them is the Village Fund Direct Cash Assistance or “BLT” (Bantuan Langsung Tunai -Dana Desa), which is financial assistance sourced from the Village Fund and is aimed at poor and vulnerable people who have difficulty fulfilling their daily needs, especially due to the COVID-19 outbreak. So far, the process of selecting people who receive assistance has been done manually by considering several criteria that must be met by these community members. Because the feasibility assessment process is carried out manually, the process is less effective and less efficient. Thus, this research aims to assist the assessment process to determine assistance for the poor by using a decision support system so that the process can be carried out more efficiently and effectively.. The result of the research is a DSS model for determining direct cash transfers for the poor. © IEOM Society International.

The SARS-CoV-2 host cell receptor ACE2 correlates positively with immunotherapy response and is a potential protective factor for cancer progression.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 29 million people and has caused more than 900,000 deaths worldwide as of September 14, 2020. The SARS-CoV-2 human cell receptor ACE2 has recently received extensive attention for its role in SARS-CoV-2 infection. Many studies have also explored the association between ACE2 and cancer. However, a systemic investigation into associations between ACE2 and oncogenic pathways, tumor progression, and clinical outcomes in pan-cancer remains lacking. Using cancer genomics datasets from the Cancer Genome Atlas (TCGA) program, we performed computational analyses of associations between ACE2 expression and antitumor immunity, immunotherapy response, oncogenic pathways, tumor progression phenotypes, and clinical outcomes in 13 cancer cohorts. We found that ACE2 upregulation was associated with increased antitumor immune signatures and PD-L1 expression, and favorable anti-PD-1/PD-L1/CTLA-4 immunotherapy response. ACE2 expression levels inversely correlated with the activity of cell cycle, mismatch repair, TGF-ß, Wnt, VEGF, and Notch signaling pathways. Moreover, ACE2 expression levels had significant inverse correlations with tumor proliferation, stemness, and epithelial-mesenchymal transition. ACE2 upregulation was associated with favorable survival in pan-cancer and in multiple individual cancer types. These results suggest that ACE2 is a potential protective factor for cancer progression. Our data may provide potential clinical implications for treating cancer patients infected with SARS-CoV-2.

Alpha-MSH Targeted Liposomal Nanoparticle for Imaging in Inflammatory Bowel Disease (IBD).

BACKGROUND: The purpose of our study was to find a novel targeted imaging and drug delivery vehicle for inflammatory bowel disease (IBD). IBD is a common and troublesome disease that still lacks effective therapy and imaging options. As an attempt to improve the disease treatment, we tested αMSH for the targeting of nanoliposomes to IBD sites. αMSH, an endogenous tridecapeptide, binds to the melanocortin-1 receptor (MC1-R) and has anti-inflammatory and immunomodulating effects. MC1-R is found on macrophages, neutrophils and the renal tubule system. We formulated and tested a liposomal nanoparticle involving αMSH in order to achieve a specific targeting to the inflamed intestines. METHODS: NDP-αMSH peptide conjugated to Alexa Fluor™ 680 was linked to the liposomal membrane via NSuccinyl PE and additionally loaded into the lumen of the liposomes. Liposomes without the αMSH-conjugate and free NDP-αMSH were used as a control. The liposomes were also loaded with ICG to track them. The liposomes were tested in DSS treated mice, which had received DSS via drinking water order to develop a model IBD. Inflammation severity was assessed by the Disease Activity Index (DAI) score and ex vivo histological CD68 staining of samples taken from different parts of the intestine. The liposome targeting was analyzed by analyzing the ICG and ALEXA 680 fluorescence in the intestine compared to the biodistribution. RESULTS: NPD-αMSH was successfully labeled with Alexa and retained its biological activity. Liposomes were identified in expected regions in the inflamed bowel regions and in the kidneys, where MC1-R is abundant. In vivo liposome targeting correlated with the macrophage concentration at the site of the inflammation supporting the active targeting of the liposomes through αMSH. The liposomal αMSH was well tolerated by animals. CONCLUSION: This study opens up the possibility to further develop an αMSH targeted theranostic delivery to different clinically relevant applications in IBD inflammation but also opens possibilities for use in other inflammations like lung inflammation in Covid 19.