Search Results (34)

Understanding the mechanisms of immune activation and deactivation is paramount. A host must initiate effective immunity against pathogenic infections while avoiding triggering immunity against self-antigens, which can lead to detrimental autoimmune disorders. Host immunological pathways can be categorized as Immunoglobulin (Ig)G-dominant eradicable immune reactions and IgA-dominant tolerable immune reactions. Eradicable immune reactions include Th1, Th2, Th22, and Thαβ immune responses against four different types of pathogens. Tolerable immune reactions include Th1-like, Th9, Th17, and Th3 immune responses against four different types of pathogens. Here, we try to determine the mechanisms of activation and deactivation of host immune reactions. The spleen and liver play contrasting roles in mediating immune responses: the spleen is primarily involved in immune activation, whereas the liver is responsible for immune deactivation. Similarly, the sympathetic and parasympathetic nervous systems have opposing functions in immune modulation, with the sympathetic system promoting pro-inflammatory responses and the parasympathetic system facilitating anti-inflammatory processes. Furthermore, adrenocorticotropic hormone (ACTH) and glucocorticosteroids exhibit contrasting effects on immune regulation: ACTH is involved in activating adaptive immunity while inhibiting innate immunity, whereas glucocorticosteroids activate natural IgM antibody associated with innate immunity while inhibiting adaptive immunity. Heat shock proteins, particularly molecular chaperones induced by fever, play pivotal roles in immune activation. Conversely, IgD B cells and gamma/delta T cells contribute to immune deactivation through mechanisms such as clonal anergy. Understanding these mechanisms provides insights into immunological pathways, aiding in the better management of infectious diseases and autoimmune disorders. Full article

The COVID-19 pandemic, driven by the high transmissibility and immune evasion caused by SARS-CoV-2 and its variants (e.g., Alpha, Delta, Omicron), has led to massive casualties worldwide. As of November 2024, the International Committee on Taxonomy of Viruses (ICTV) has identified 14,690 viral species across 3522 genera. The increasing infectious and resistance to FDA and EMA-approved antivirals, such as 300-fold efficacy reduction in Nirmatrelvir against the SARS-CoV-2 3CLpro, highlight the need for mutation-stable antivirals, likewise targeting the essential host proteins like kinases, heat shock proteins, lipid metabolism proteins, immunological pathway proteins, etc. Unlike direct-acting antivirals, HDAs reduce the risk of resistance by targeting conserved host proteins essential for viral replication. The proposal for repurposing current FDA-approved drugs for host-directed antiviral (HDA) approach is not new, such as the Ouabain, a sodium-potassium ATPase inhibitor for herpes simplex virus (HSV) and Verapamil, a calcium channel blocker for influenza A virus (IAV), to name a few. Given the colossal potential of the mutation-stable HDA approach to exterminate the virus infection, it has been increasingly studied on SARS-CoV-2. This review aims to unravel the interaction between viruses and human hosts and their successfully proposed host-directed antiviral approach to provide insight into an alternative treatment to the rampant mutation in SARS-CoV-2. The benefits, limitations, and potential of host protein-targeted antiviral therapies and their prospects are also covered in this review. Full article

Background: The delta neutrophil index (DNI) represents the immature granulocyte fraction and is determined by subtracting the fraction of mature polymorphonuclear leucocytes from the sum of myeloperoxidase-reactive cells. The DNI has been proposed as a useful prognostic marker for sepsis. This study evaluated the clinical utility of DNI as a predictive marker in patients with pneumonia-induced sepsis in the intensive care unit (ICU). Methods: We conducted a retrospective study of pneumonia-induced sepsis in patients who were admitted to the Kangdong Sacred Heart Hospital’s medical ICUs from July 2022 to March 2024. The DNI was measured on three consecutive days after ICU admission. The primary outcome of this study was a 28-day mortality. Results: A total of 227 patients with pneumonia-induced sepsis were included in this study. A 28-day mortality occurred 20.3% of the time in our study. In a univariate analysis, age (p = 0.05), lymphocyte (p = 0.02), DNI 1 (p = 0.01), DNI 2 (p = 0.00), DNI 3 (p = 0.00), and lactic acid (p = 0.00) were significantly associated with 28-day mortality. In a multivariable analysis, lactate (adj. OR: 0.86, 95% CI: 0.78–0.95, p = 0.002), and DNI 3 (adj. OR: 0.94, 95% CI: 0.89–0.99, p = 0.048) were significantly associated with 28-day mortality. In our study, the most appropriate cut-off values were DNI 1 (7.15), DNI 2 (8.9), and DNI 3 (2.6). Patients with higher DNI 3 (≥2.6) showed higher 28-day mortality than patients with lower DNI 3 values of <2.6 (67.4% vs. 32.6%; p < 0.001). However, those aged ≥70 did not show statistically significantly different DNI 1 values between the survivor and non- survivor groups. Conclusions: The DNI at 72 h after ICU admission is a promising predictive prognostic marker of 28-day mortality in patients with pneumonia-induced sepsis in the ICU. However, the interpretation of the DNI in sepsis patients aged 70 and older on the first day of hospitalization should be approached with caution. Full article

The different strains of SARS-CoV-2 were detected and labeled in 2021. Each strain differs in both clinical symptoms and severity. Previous studies found different clinical symptoms and treatment outcomes between outbreak waves; however, data in Southeast Asia were limited. This study collected data of hospitalized COVID-19 patients from a Tertiary hospital in Thailand between January 2020 and December 2023 and analyzed patients’ data in each outbreak wave using Pearson’s chi-square. A total of 1084 inpatients were included for analysis. The median age was 64 (IQR, 0.4–100) years. The patients were hospitalized in predominantly Alpha (22.78%), Delta (21.68%), and Omicron (5.07%) periods of the virus outbreak. The largest age group was elderly (over 65 years old) in all three variant of concern (VOC) periods; 82.65% of the patients had comorbidities, including 58.5% hypertension, 46.5% dyslipidemia, and 42.0% diabetes mellitus (DM). The study found pneumonia at 67.53%, septic shock at 4.61%, acute respiratory distress syndrome (ARDS) at 2.86%, and congestive heart failure at 0.83% in all age groups with no significant difference between outbreak periods. The overall mortality rate was 16.14%. A total of 75% of deaths occurred in patients over 65 years old. The mortality rates in each VOC period were 20.0% Delta, 19.83% Alpha, and 13.23% Omicron. In the elderly group, the mortality rates were Delta 15.32%, Alpha 11.75%, and Omicron 10.88%. The Omicron VOC was less severe than other variants, particularly in the elderly (≥65 years). There were no significant differences in the younger (<65 years) age group. The elderly still had more severe symptoms and the highest mortality rates in every wave of outbreak. Full article

The interaction of two simple delta shock waves for a pressureless gas dynamic system is considered. The result of the interaction is a delta shock wave with constant speed. This interaction is approximated by letting the perturbed parameter in the Euler equations for isentropic fluids go to zero. Each delta shock wave is approximated by two shock waves of the first and second family when the perturbed parameter goes to zero. These shock waves are solutions of two Riemann problems at time $t=0$. The solution of the Riemann problem for $t>0$ can also contain rarefaction waves. If the perturbed parameter approaches 0, the strength of the rarefaction waves increases and the number of interactions of the rarefaction waves increases, as well. When two split rarefaction waves interact, the number of Riemann problems to be solved is $m_1·m_2$, where $m_i$ is the number of ith rarefaction waves. The main topic of this paper is to develop an algorithm that reduces the number of these Riemann problems. The algorithm is based on the determination of the intermediate states that make the Rankine–Hugoniot deficit small. The approximated wave front tracking algorithm was used for the numerical verification of these interactions. The theoretical background was the concept of the shadow wave solution. Full article

Dracocephalum moldavica is widely used as an ornamental, medicine, and perfume in industry. Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) is widely and accurately utilized for gene expression evaluations. Selecting optimal reference genes is essential for normalizing RT-qPCR results. However, the identification of suitable reference genes in D. moldavica has not been documented. A total of 12 reference genes in D. moldavica were identified by PEG6000 (15%) treatment under hypertonia conditions in different tissues (roots, stem, leaves, flower, seeds and sepal) and during three stages of flower development, then used to validate the expression stability. There were four algorithms (delta Ct, geNorm, NormFinder, and BestKeeper) used to analyze the stability. Finally, the RefFinder program was employed to evaluate the candidate reference genes’ stability. The results showed that ACTIN, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and EF1α (elongation factor-1α) were stable reference genes under the PEG6000 treatment. Heat shock protein 70 (HSP70) was the most stable gene across different flower development stages. ADP-ribosylation factor (ARF) was the most stable gene in different tissues and total samples. This study provides reliable gene expression studies for future research in D. moldavica. Full article

Delta parallel robots have been widely used in precision processing, handling, sorting, and the assembly of parts, and their high efficiency and motion stability are important indexes of their performance.Corners created by small line segments in trajectory planning cause abrupt changes in a tangential discontinuous trajectory, and the vibration and shock caused by such changes seriously affect the robot’s high-speed and high-precision performance. In this study, a trajectory-planning method combining Cartesian space and joint space is proposed. Firstly, the vector method and microelement integration method were used to establish the complete kinematic and dynamic equations of a delta parallel robot, and an inverse kinematic/dynamic model-solving program was written based on the MATLAB software R2020a. Secondly, the end-effector trajectory of the delta parallel robot was planned in Cartesian space, and the data points and inverse control points of the end effector’s trajectory were obtained using the normalization method. Finally, the data points and control points were mapped to the joint space through the inverse kinematic equation, and the fifth-order B-spline curve was adopted for quadratic trajectory planning, which allowed the high-order continuous smoothing of the trajectory planning to be realized. The simulated and experimental results showed that the trajectory-smoothing performance in continuous high-order curvature changes could be improved with the proposed method. The peak trajectory tracking error was reduced by $10.53\%$, $41.18\%$, and $44.44\%$, respectively, and the peak torque change of the three joints was reduced by $3.5\%$, $11.6\%$, and $1.6\%$, respectively. Full article

Improving the damping capacity of metal matrix composites is crucial, especially for applications in the aerospace industry where reliable performance against vibrations and shocks is mandatory. The main objective of the present study is the numerical prediction of the damping behavior of alpha titanium matrix nanocomposites reinforced with hollow carbon nano-onions at various volume fractions. According to the proposed numerical scheme, a structural transient analysis is implemented using the implicit finite element method (FEM). The metal matrix nanocomposites are modeled via the utilization of appropriate representative volume elements. To estimate the mechanical and damping behavior of the nanocomposite representative volume elements, axial sinusoidally time-varying loads are applied to them. The damping capacity of the metal matrix nanocomposites is then estimated by the arisen loss factor, or equivalently the tan delta, which is computed by the time delay between the input stress and output strain. The analysis shows that the loss factor of alpha titanium may be improved up to 60% at 100 Hz by adding 5 wt% carbon nano-onions. The numerical outcome regarding the dynamic properties of the carbon nano-onions/alpha titanium nanocomposites is used in a second-level analysis to numerically predict their damping performance when they are additionally reinforced with unidirectional carbon fibers, using corresponding representative volume elements and time-varying loadings along the effective direction. Good agreement between the proposed computational and other experimental predictions are observed regarding the stiffness behavior of the investigated metal matrix nanocomposites with respect to the mass fraction of the carbon-onion nanofillers in the titanium matrix. Full article

In the early 1960s, heat shock proteins (HSPs) were first identified as vital intracellular proteinaceous components that help in stress physiology and reprogram the cellular responses to enable the organism’s survival. By the early 1990s, HSPs were detected in extracellular spaces and found to activate gamma-delta T-lymphocytes. Subsequent investigations identified their association with varied disease conditions, including autoimmune disorders, diabetes, cancer, hepatic, pancreatic, and renal disorders, and cachexia. In cardiology, extracellular HSPs play a definite, but still unclear, role in atherosclerosis, acute coronary syndromes, and heart failure. The possibility of HSP-targeted novel molecular therapeutics has generated much interest and hope in recent years. In this review, we discuss the role of Extracellular Heat Shock Proteins (Ec-HSPs) in various disease states, with a particular focus on cardiovascular diseases. Full article

Background: Sepsis is a time-dependent disease: the early recognition of patients at risk for poor outcome is mandatory. Aim: To identify prognostic predictors of the risk of death or admission to intensive care units in a consecutive sample of septic patients, comparing different statistical models and machine learning algorithms. Methods: Retrospective study including 148 patients discharged from an Italian internal medicine unit with a diagnosis of sepsis/septic shock and microbiological identification. Results: Of the total, 37 (25.0%) patients reached the composite outcome. The sequential organ failure assessment (SOFA) score at admission (odds ratio (OR): 1.83; 95% confidence interval (CI): 1.41–2.39; p < 0.001), delta SOFA (OR: 1.64; 95% CI: 1.28–2.10; p < 0.001), and the alert, verbal, pain, unresponsive (AVPU) status (OR: 5.96; 95% CI: 2.13–16.67; p < 0.001) were identified through the multivariable logistic model as independent predictors of the composite outcome. The area under the receiver operating characteristic curve (AUC) was 0.894; 95% CI: 0.840–0.948. In addition, different statistical models and machine learning algorithms identified further predictive variables: delta quick-SOFA, delta-procalcitonin, mortality in emergency department sepsis, mean arterial pressure, and the Glasgow Coma Scale. The cross-validated multivariable logistic model with the least absolute shrinkage and selection operator (LASSO) penalty identified 5 predictors; and recursive partitioning and regression tree (RPART) identified 4 predictors with higher AUC (0.915 and 0.917, respectively); the random forest (RF) approach, including all evaluated variables, obtained the highest AUC (0.978). All models’ results were well calibrated. Conclusions: Although structurally different, each model identified similar predictive covariates. The classical multivariable logistic regression model was the most parsimonious and calibrated one, while RPART was the easiest to interpret clinically. Finally, LASSO and RF were the costliest in terms of number of variables identified. Full article

The current regional development crisis and opportunities coexist. On the one hand, the economic environment is complex and volatile, with more and more crisis shocks testing the resilience of urban development, while on the other hand, the rapid development of science and technology such as the digital economy has affected all areas of the economy, life, and governance of cities, bringing opportunities for urban development. The use of digital transformation to enhance urban resilience is therefore an obvious and important topic. Based on panel data of 27 cities in the Yangtze River Delta from 2011 to 2020, this study empirically analyses the impact of digital transformation on urban resilience by constructing a fixed effects model, a mediated effects model and a spatial Du bin model. The study finds that: (1) In terms of time, the urban resilience and digital transformation capacity of the Yangtze River Delta region are both on the rise; From a spatial point of view, the urban resilience of the Yangtze River Delta region basically shows a spatial distribution pattern of “high in the central cities, and low in the peripheral cities”, while the digital transformation capacity basically shows a pattern of “high in the east-central region, and low in the west”. (2) Digital transformation has a significant positive impact on improving urban resilience; (3) Digital transformation enhances urban resilience through three main paths: technological innovation capacity, new economic sector development momentum, and innovation and entrepreneurship development vitality; (4) Digital transformation has a spatial spillover effect on the urban resilience of neighboring regions. Full article

The structures of industrial linkages form an essential basis for the economy and have an important impact on urban economic resilience. By analyzing the impact of COVID-19 on China’s urban economy in 2020, this study uses China’s national input–output table to measure the centrality and diversity of industrial linkage structures. Extracted data from 298 cities in China are used to explore the impact of centrality and diversity on urban economic resilience. The results show that the cities in East China, Central China, and the Chengdu–Chongqing area in western China have a high centrality with respect to industrial linkage structures. Cities in the Yangtze River Delta, Pearl River Delta, the middle reaches of the Yangtze River city cluster, and the Chengdu–Chongqing city cluster have a high diversity of industrial linkages structures. During the shock of the COVID-19 pandemic, most cities in China have shown high economic resilience. For cities across the country, diversity shows a significant and positive correlation with economic resilience, and centrality shows a significant and positive correlation with economic resilience. The latter displays an inverted U-shaped relationship between centrality and economic resilience. For cities with different population sizes, there are differences in the impacts of centrality and diversity on urban economic resilience. Different industrial policies can be developed to adjust the centrality and diversity of the cities to enhance urban economic resilience. Full article

Blockchain can support the food supply chain in several aspects. Particularly, food traceability and trading across pre-existing contracts can make the supply chain fast, error-free, and support in detecting potential fraud. A proper algorithm, keeping in mind specific geographic, demographic, and additional essential parameters, would let the automated market maker (AMM) supply ample liquidity to pre-determined orders. AMMs are usually run by a set of sequential algorithms called a ‘smart contract’ (SM). Appropriate use of SM reduces food waste, contamination, extra or no delivery in due course, and, possibly most significantly, increases traceability. However, SM has definite vulnerabilities, making it less adaptable at times. We are investigating whether they are genuinely vulnerable during stressful periods or not. We considered seven SM platforms, namely, Fabric, Ethereum (ETH), Waves, NEM (XEM), Tezos (XTZ), Algorand (ALGO), and Stellar (XLM), as the proxies for food supply-chain-based smart contracts from 29 August 2021 to 5 October 2022. This period coincides with three stressed events: Delta (Covid II), Omicron (Covid III), and the Russian invasion of Ukraine. We found strong traces of risk transmission, comovement, and interdependence of SM return among the diversified SMs; however, the SMs focused on the food supply chain ended up as net receivers of shocks at both of the extreme tails. All these SMs share a stronger connection in both positive shocks (bullish) and negative shocks (bearish). Full article

Urban agglomerations are becoming an increasingly important factor in advancing regional development and reshaping a new pattern of regional competition. However, few studies are focused on the impact of expanding urban agglomerations on reducing carbon emissions and its possible mechanism. Based on 285 city-level panel data from 2006 to 2017, this paper uses a staggered Difference-in-Differences (DID) model to explore the reduction effect and its possible mechanism of sustainable regional development policy, characterized by urban agglomeration expansion policy in the Yangtze River Delta, on carbon emissions with policy shocks in 2010 and 2013. The results are as follows: (1) The urban agglomeration expansion policy shows a significant marginal contribution to the reduction of carbon emissions, especially for the later joined (new) cities, and the reduction effect is particularly significant in the first and third years after the expansion, indicating that there are significant short-term and long-term reduction effects of the expansion policy. (2) The heterogeneities of reduction effect among three provinces are significant. Zhejiang Province enjoys the largest proportion carbon emission reduction effect, followed by Anhui and Jiangsu provinces. To be specific, urban agglomeration expansion in Zhejiang Province reduced carbon emissions and carbon emissions intensity in the overall, incumbent cities and new cities, while it only increased the total carbon emissions of the incumbent cities in Jiangsu province. (3) The heterogeneities of reduction effect brought by 2010 and 2013 are also significant. The urban agglomeration expansion policy in 2010 reduced carbon emissions on the whole cities and the incumbent cities with later joined cities excluded, while it had a significant reduction effect on the total, incumbent cities, and the new cities in 2013. (4) There are two possible mechanisms of this reduction effect. One is the strengthening of economic ties and enhanced environmental synergy between governments, called the market integration mechanism, which only has a significant effect on carbon emission reduction in the incumbent cities. Another is through the upgrade of the structure of regional industries, which has a significant effect in both the incumbent and new cities. These findings suggest that when formulating urban agglomerations polices, governments must take into account the carbon emissions effect, and advance the upgrading of industrial structure in the urban agglomeration. Full article

Ductal carcinoma in situ (DCIS) of the breast is often managed by lumpectomy and radiation or mastectomy, despite its indolent features. Effective non-invasive treatment strategies could reduce the morbidity of DCIS treatment. We have exploited the high heat shock protein 90 (HSP90) activity in premalignant and malignant breast disease to non-invasively detect and selectively ablate tumors using photodynamic therapy (PDT). PDT with the HSP90-targeting photosensitizer, HS201, can not only ablate invasive breast cancers (BCs) while sparing non-tumor tissue, but also induce antitumor immunity. We hypothesized that HS201-PDT would both non-invasively ablate DCIS and prevent progression to invasive BC. We tested in vitro selective uptake and photosensitivity of HS201 in DCIS cell lines compared to the non-selective parental verteporfin, and assessed in vivo antitumor efficacy in mammary fat pad and intraductal implantation models. Selective uptake of HS201 enabled treatment of intraductal lesions while minimizing toxicity to non-tumor tissue. The in vivo activity of HS201-PDT was also tested in female MMTV-neu mice prior to the development of spontaneous invasive BC. Mice aged 5 months were administered HS201, and their mammary glands were exposed to laser light. HS201-PDT delayed the emergence of invasive BC, significantly prolonged disease-free survival (DFS) (p = 0.0328) and tended to improve overall survival compared to the no-treatment control (p = 0.0872). Systemic administration of anti-PD-L1 was combined with HS201-PDT and was tested in a more aggressive spontaneous tumor model, HER2delta16 transgenic mice. A single PDT dose combined with anti-PD-L1 improved DFS compared to the no-treatment control, which was significantly improved with repetitive HS201-PDT given with anti-PD-L1 (p = 0.0319). In conclusion, a non-invasive, skin- and tissue-sparing PDT strategy in combination with anti-PD-L1 antibodies effectively prevented malignant progression of DCIS to invasive BC. This non-invasive treatment strategy of DCIS may be safe and effective, while providing an option to reduce the morbidity of current conventional treatment for patients with DCIS. Clinical testing of HS201 is currently underway. Full article