Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns.

BACKGROUND: Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT). OBJECTIVE: We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially. MATERIALS AND METHODS: Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models. RESULTS: 1 The success of the VGG16 model in the test phase was 95.02% accuracy. 2 Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model. CONCLUSION: Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

The connectedness and risk spillovers between bitcoin spot and futures markets: evidence from intraday data.

This paper examines the dynamic relation between Bitcoin spot and futures markets during the Covid-19 pandemic. Using hourly data from 2020 combined with quantile impulse response analysis and predictability in the distribution test, we attempt to ascertain whether spot or futures markets lead in the price discovery process under a variety of market conditions. Granger predictability based on the left tail, the right tail, and the center of the distribution show bidirectional predictability between spot and futures markets suggesting significant feedback effects following normal and extreme gains/losses where neither market dominates in price discovery. Using a CAViaR model and the associated impulse response functions with estimates for dynamic tail dependence, we document spillovers between quantiles of spot and futures returns. Estimates of impulse response functions at various risk levels show the futures market has an edge in influencing the spot market and figures more prominently in the price discovery process.

Investor sentiments and stock markets during the COVID-19 pandemic.

This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using various methods, including panel regression with fixed effects, panel quantile regressions, a panel vector autoregression (PVAR) model, and country-specific regressions. We proxy for negative and positive investor sentiments using the Google Search Volume Index for terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significant relationships between positive and negative investor sentiments and stock market returns and volatility. Specifically, an increase in positive investor sentiment leads to an increase in stock returns while negative investor sentiment decreases stock returns at lower quantiles. The effect of investor sentiment on volatility is consistent across the distribution: negative sentiment increases volatility, whereas positive sentiment reduces volatility. These results are robust as they are corroborated by Granger causality tests and a PVAR model. The findings may have portfolio implications as they indicate that proxies for positive and negative investor sentiments seem to be good predictors of stock returns and volatility during the pandemic.

Does military expenditure impact environmental sustainability in developed Mediterranean countries?

This study aims to examine the relationship between military expenditure and environmental sustainability in developed Mediterranean countries: Greece, France, Italy, and Spain. Sustainable economic growth is strictly related to energy consumption which leads to producing a higher level of carbon emissions. Besides, there may be a nexus between military expenditures and environmental pollution. This study focuses on developed Mediterranean countries since carbon emissions and greenhouse gas emissions are relatively high in these countries. Furthermore, France and Italy are the top countries in terms of total military spending. We investigate the relationship between military expenditure and carbon emissions using the Global Vector Autoregression model proposed by Pesaran et al. (J Bus Econ Stat 22 129:162, Pesaran et al., J Bus Econ Stat 22:129-162, 2004) and Dees et al. (J Appl Econ 22(1):38, Dees et al., J Appl Econ 22:1-38, 2007) between 1965 and 2019. The empirical findings indicated that the relationship between carbon emission and military expenditure should be taken into account from a global perspective for environmental sustainability, and an increase in the global military expenditure seems to be very harmful to the global environment. It can be concluded that country-based prevention cannot provide the desired solution in combating environmental pollution.

Relationship between oil price volatility and military expenditures in GCC countries.

Natural resource-rich countries transfer more sources to military expenditures due to extreme security concerns. As public revenues have declined due to the decline in oil prices, military expenditures have been cut in many countries. Nevertheless, this is not valid for all countries. Even in some countries, despite the decrease in oil prices and volatility, military expenditures increase. The aim of this study is to investigate the relationship between volatility in oil prices and military expenditures in GCC countries (United Arab Emirates, Bahrain, Qatar, Kuwait, Saudi Arabia, and Oman). The analysis period was determined differently for each country depending on the availability of data. UAE and Qatar were excluded from the analysis as the defense expenditures data of these countries could not be provided regularly. ARDL model was preferred for the research. According to the bound test results, there is a cointegration relationship between the variables in all countries. Besides, the long-term results showed that the volatility in oil prices in all countries, except for Bahrain, positively affects military expenditures. The error correction model indicated that there is a reverse relationship between oil price volatility and military expenditures. These findings indicated that despite the volatility in oil prices, military expenditures in GCC countries are not reduced.

Clonal analysis of the quasispecies of antiviral-resistant HBV genomes in patients with entecavir resistance during rescue treatment and successful treatment of entecavir resistance with tenofovir.

BACKGROUND: Clonal analysis of quasispecies of resistant HBV genomes in patients with entecavir (ETV) resistance receiving lamivudine (3TC) plus adefovir (ADV) rescue therapy has never been performed. METHODS: A sample of 10 patients with ETV resistance who were switched to 3TC+ADV treatment were analysed for changes in viral quasispecies. Serum samples at baseline, and at months 3 and 6 of 3TC+ADV treatment could be clonally analysed in 7 of 10 patients; 3-82 clones per sample (total 1,068 clones, mean 63) were sequenced. RESULTS: 3TC+ADV therapy led to a modest decline in HBV DNA. Almost all clones had L180M and M204V 3TC resistance mutations before and during combination therapy. All clones had ≥1 of the S202G, T184F, T184A, T184L, T184I and M250V ETV resistance mutations. The percentages of detected clones bearing 3TC (rtL180M and rtM204V) and ETV mutations did not change with rescue 3TC+ADV therapy. In 7 of 8 patients with detectable HBV DNA (median 5.17 log(10) copies/ml) after a median 24 months of ADV therapy, HBV DNA became undetectable with 3TC plus tenofovir after 6 months of treatment. CONCLUSIONS: In patients with ETV resistance tenofovir is effective. Clonal analysis data indicate no selection of specific HBV mutants during rescue 3TC+ADV.

Complete genome sequences and phylogenetic analysis of hepatitis delta viruses isolated from nine Turkish patients.

Hepatitis delta virus (HDV) is a subviral agent of hepatitis B virus (HBV), and its life cycle is dependent on HBV. It is commonly accepted that HDV has eight distinct genotypes. In this study, the complete nucleotide sequences of HDV genomes isolated from nine Turkish patients were obtained by RT-PCR using two pairs of primers that cover the entire HDV genome. PCR products were sequenced directly. The results showed that these 9 isolates were approximately 1680 base pairs in length and clustered in the genotype HDV-1 branch when phylogenetic analysis was done with the sequences together with the complete sequences of HDV genomes representing each genotype retrieved from GenBank. Analysis of a portion of the large hepatitis D antigen (L-HDAg) gene showed that sequence similarity among these Turkish isolates is between 87.4 and 97.1%, and the Turkish isolates have the most sequence similarity to HDV-1 (90.5%), while they have the least sequence similarity to HDV-3 (64.1%). Full-genome analysis indicates that the sequence similarity is between 80.7 and 95.4%, and the highest sequence similarity is 84.8% (between the Turkish isolates and HDV-1). The lowest sequence similarity is 56.4% (between the Turkish isolates and HDV-3). In conclusion, phylogenetic analysis shows that the Turkish HDV isolates belong to HDV-1.