Notes

[Ultrasound-Guided Peritendinous Hydrodissection, an alternative choice to Tenolysis Adhesions?
To extract texture features from magnetic resonance imaging (MRI) scans of patients with brain tumors and use them to train a classification model for supporting an early diagnosis.

Two groups of regions (control and tumor) were selected from MRI scans of 40 patients with meningioma or glioma. These regions were analyzed to obtain texture features. Statistical analysis was conducted using SPSS (version 20.0), including the Shapiro-Wilk test and Wilcoxon signed-rank test, which were used to test significant differences in each feature between the tumor and healthy regions. Baf-A1 ic50 T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the data distribution so as to avoid tumor selection bias. The Gini impurity index in random forests (RFs) was used to select the top five out of all features. Based on the five features, three classification models were built respectively with three machine learning classifiers RF, support vector machine (SVM), and back propagation (BP) neural network.

Sixteen of the 25 features were significantly different between the tumor and healthy areas. Through the Gini impurity index in RFs, standard deviation, first-order moment, variance, third-order absolute moment, and third-order central moment were selected to build the classification model. The classification model trained using the SVM classifier achieved the best performance, with sensitivity, specificity, and area under the curve of 94.04%, 92.3%, and 0.932, respectively.

Texture analysis with an SVM classifier can help differentiate between brain tumor and healthy areas with high speed and accuracy, which would facilitate its clinical application.
Texture analysis with an SVM classifier can help differentiate between brain tumor and healthy areas with high speed and accuracy, which would facilitate its clinical application.The blood-brain barrier (BBB) hinders the distribution of therapeutics intended for treatment of diseases of the brain. Our previous studies demonstrated that that a soluble form of melanotransferrin (MTf; Uniprot P08582; also known as p97, MFI2, and CD228), a mammalian iron-transport protein, is an effective carrier for delivery of drug conjugates across the BBB into the brain and was the first BBB targeting delivery system to demonstrate therapeutic efficacy within the brain. Here, we performed a screen to identify peptides from MTf capable of traversing the BBB. We identified a highly conserved 12-amino acid peptide, termed MTfp, that retains the ability to cross the intact BBB intact, distributes throughout the parenchyma, and enter endosomes and lysosomes within neurons, astrocytes and microglia in the brain. This peptide may provide a platform for the transport of therapeutics to the CNS, and thereby offers new avenues for potential treatments of neuropathologies that are currently refractory to existing therapies.To sustain competitiveness in contemporary, fast-paced markets, organizations increasingly focus on innovating their business models to enhance current value propositions or to explore novel sources of value creation. However, business model innovation is a complex task, characterized by shifting characteristics in terms of uncertainty, data availability and its impact on decision making. To cope with such challenges, business model evaluation is advocated to make sense of novel business models and to support decision making. Key performance indicators (KPIs) are frequently used in business model evaluation to structure the performance assessment of these models and to evaluate their strategic implications, in turn aiding business model decision making. However, given the shifting characteristics of the innovation process, the application and effectiveness of KPIs depend significantly on how such KPIs are defined. The techniques proposed in the existing literature typically generate or use quantitatively oriented KPIs, which are not well-suited for the early phases of the business model innovation process. Therefore, following a design science research methodology, we have developed a novel method for defining business model KPIs, taking into account the characteristics of the innovation process, offering holistic support toward decision making. Building on theory on linguistic summarization, we use a set of structured templates to define qualitative KPIs that are suitable to support early-phase decision making. In addition, we show how these KPIs can be gradually quantified to support later phases of the innovation process. We have evaluated our method by applying it in two real-life business cases, interviewing 13 industry experts to assess its utility.Fossil fuels are the primary energy source of almost all societies and economies, but it is finite and scarce. The use of non-renewable fossil fuels threatens earth's environment. At the same time, waste from agricultural and industrial activities is increasing. Most of this waste is discarded or poorly managed, causing many other environmental issues. Converting waste to energy is a promising route to address these challenges. We investigated the hydrothermal liquefaction (HTL) of high moisture content, tobacco-processing waste in a multiple batch thermal reactor to produce biocrude oil. The effects of operating conditions were studied and optimized for maximum liquid biocrude oil yield. HTL operating conditions considered were temperatures from 280 to 340 °C and residence times from 15 to 45 min for a fixed ratio of biomass to deionized water of 13. The reaction temperature was found to affect the yields and distribution of products significantly. The maximum yield of the liquid biocrude oil obtained was more than 52% w/w at 310 °C and 15 min. Under these conditions, almost 90% of the energy was recovered in biocrude oil and solid products. The liquid fraction was mainly composed of phenols, ketones, and nitrogenous compounds. This study provides a potential framework for eco-technologies for biomass waste-to-energy conversion with respect to converting tobacco processing residues to liquid biofuels and biochemicals.The prevalence of COVID-19 has caused global dysfunction in terms of public health, sustainability, and socio-economy. While vaccination shows potential in containing the spread, the development of surfaces that effectively reduces virus transmission and infectivity is also imperative, especially amid the early stage of the pandemic. However, most virucidal surfaces are operated under harsh conditions, making them impractical or potentially unsafe for long-term use. link2 Here, it is reported that laser-induced graphene (LIG) without any metal additives shows marvelous antiviral capacities for coronavirus. Under low solar irradiation, the virucidal efficacy of the hydrophobic LIG (HLIG) against HCoV-OC43 and HCoV-229E can achieve 97.5% and 95%, respectively. The photothermal effect and the hydrophobicity of the HLIG synergistically contribute to the superior inactivation capacity. link3 The stable antiviral performance of HLIG enables its multiple uses, showing advantages in energy saving and environmental protection. This work discloses a potential method for antiviral applications and has implications for the future development of antiviral materials.This paper combines SHARE Corona Survey and SHARE Wave 7 data for 25 European countries and Israel (N = 40,919) with institutional and epidemic-related country characteristics to investigate healthcare access for Europeans aged 50+ during the outbreak of COVID-19. We use a micro-macro approach to examine whether and to what extent barriers to accessing healthcare measured by reported unmet healthcare needs vary within and between countries. We consider various aspects of barriers and distinguish among (1) respondents who forewent medical treatment because they were afraid of becoming infected with the Coronavirus; (2) respondents who had pre-scheduled medical appointments postponed by health providers due to the outbreak; and (3) respondents who tried to arrange a medical appointment but were denied one. Limited access to healthcare during the initial outbreak was more common for the occupationally active, women, the more educated and those living in urban areas. A bad economic situation, poor overall health and higher healthcare utilisation were robust predictors of unmet healthcare. People aged 50+ in countries of 'Old' Europe, countries with higher universal health coverage and stricter containment and closure policies were more likely to have medical services postponed. Policymakers should address the healthcare needs of older people with chronic health conditions and a poor socio-economic status who were made more vulnerable by this pandemic. In the aftermath of the health crisis, public health systems might experience a great revival in healthcare demand, a challenge that should be mitigated by careful planning and provision of healthcare services.This paper explores the impact of infrastructure on trade connectivity among ASEAN and three Asian countries-India, China, and Japan. Our study is mainly motivated by the increased infrastructure investment and trade among these countries in recent years. The main results of trade network analysis include high trade density and interconnectedness among ASEAN, India, China, and Japan. There are specific "trade intensive paths" among the few countries in the group. It highlights the "export hubs" or main "suppliers of intermediate goods" in the region. Further, the paper analyzed the nexus between trade connectivity and infrastructure by applying the panel fixed effects method and Poisson pseudo-maximum likelihood. Moreover, the robustness of the results is tested by estimating two-stage least square. Hard infrastructure, foreign direct investment plays a crucial role in bringing the nexus to trade connectivity. Reducing trade barriers and improving infrastructure quality are essential for deepening regional trade integration.The Comprehensive Agreement on Investment (CAI) that was borne but not formally blessed by China and the European Union in late December 2020 is unlikely to survive in its current form, if it survives at all. In fact, there is good reason to believe that the CAI is DOA - dead on arrival - due to EU sanctions and Chinese countersanctions related to China's persecution of its Uyghur minority; criticism of the negotiated agreement; and changing political calculations by Beijing and among EU member states.
Electrocardiogram (ECG) is one of the most essential tools for detecting heart problems. Till today most of the ECG records are available in paper form. It can be challenging and time-consuming to manually assess the ECG paper records. Hence, automated diagnosis and analysis are possible if we digitize such paper ECG records.

The proposed work aims to convert ECG paper records into a 1-D signal and generate an accurate diagnosis of heart-related problems using deep learning. Camera-captured ECG images or scanned ECG paper records are used for the proposed work. Effective pre-processing techniques are used for the removal of shadow from the images. A deep learning model is used to get a threshold value that separates ECG signal from its background and after applying various image processing techniques threshold ECG image gets converted into digital ECG. These digitized 1-D ECG signals are then passed to another deep learning model for the automated diagnosis of heart diseases into different classes such as ST-segment elevation myocardial infarction (STEMI), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and T-wave abnormality.
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