Great Impact Research Articles

The burden of executive dysfunction after ischemic stroke (Bulgaria, 2019-2021)

Abstract Background Executive dysfunctions have great impact on behavior and quality of life in elderly. They also play important role for post-stroke functional outcome. The aim of our study was to examine the frequency, severity and types of executive dysfunctions at the third month after the ischemic stroke. Methods We examined 108 patients 3 months after ischemic stroke (66.67±9.03 years old, 66 males and 42 females) via 128-Wisconsin Card Sorting Test (WCST). WCST is a standardized instrument for detecting various executive dysfunctions. It is applicable for patients after stroke, because the test doesn’t require preserved arm motor functions. It measures global executive functioning (GEF), inhibitory control system (ICS), executive attention (EA), cognitive flexibility (CF) and working memory (WM). Our patients’ results were compared to the normal population test results. SPSS 24 was used for statistical analysis. Results Impaired GEF were found in 46% of our patients, most of them had mild or moderate impairment. One fourth of them showed impaired ICS, although the impairment in most of the cases was mild. EA deficits were found in 43% of our cases (17% had mild, 15% mild to moderate, 7% moderate and 4% severe impairment). WM dysfunction was found in 1/3 of them (the average result was about 27 percentiles; 29% had mild and 5% had mild to moderate deficits) and about ¼ of our patients had borderline results. CF was impaired in 68% of our patients (11% had mild, 28% mild to moderate, 16% moderate and 13% severe impairment). Conclusions GEF impairment is frequent after stroke. Patients had problems in ICS, EA, CF and WM. However, in most of the cases the dysfunction is mild or moderate and can be found only if it is actively searched and accessed. The most impaired functions were CF and EA. Patients with executive dysfunction require cognitive rehabilitation and specific social support. Key messages • Executive dysfunctions are frequent after ischemic stroke. The impairment is mild or moderate, but however it impacts the quality of life, behavior, prognosis and outcome of post-stroke survivors. • Patients with executive dysfunctions require specific cognitive rehabilitation and social support.

The burden of psoriasis in Belgium, 2021

Abstract Psoriasis is a common, chronic, inflammatory skin disease with a great impact on healthcare systems. Given the limitations of the currently available burden estimates, estimates of the Belgian national burden of disease study are necessary in order to guide decision-making processes within the health sector. According to the Global Psoriasis Atlas, the prevalence of psoriasis is rising globally. The current study aims to estimate the burden of psoriasis in Belgium using national data. As there is no single comprehensive data source on prevalence of psoriasis in Belgium, a critical appraisal of existing local and national data sources was conducted. We estimated the non-fatal psoriasis burden in terms of years lived with disability (YLD). Estimated prevalence data were combined with disability weights to yield YLDs imposed by psoriasis. Mortality attributable to psoriasis was assumed to be zero, in line with the Global Burden of Disease study (GBD) 2019. We assessed three data sources containing information on existing cases with psoriasis in Belgium. Based on qualitative and quantitative appraisal, a widespread network of general practitioners was identified as the best available national data source. In 2021, the prevalence of psoriasis in Belgium was 2.1%, leading to 13084 DALYs (113 DALYs per 100 000). The burden of psoriasis was in line with national disease burden estimates for bipolar disorder, multiple myeloma, and hearing loss. Our prevalence, YLD, and DALY estimates for psoriasis in Belgium were however lower than those of the GBD study. Psoriasis imposes a non-negligible burden on population health in Belgium, despite it not being a cause of death. Along progression in psoriasis management, objective evaluation of psoriasis burden is critical to track the trends at the population level. Key messages • Psoriasis is a common skin disease with a relatively high morbidity burden. • Information on psoriasis prevalence and burden are crucial for disease management and appropriate health-care planning.

Phosphoproteomics of PLN R14del heart tissue reveals major myocyte degenerate phenotype, and is attenuated by PLN antisense treatment in vitro

Abstract Background/Purpose: Phospholamban (PLN) p.Arg14del (R14del, R14Δ/+) is a pathogenic variant that can cause cardiomyopathy, characterized by PLN protein aggregation, ultimately leading to heart failure (HF). The exact pathophysiology is unknown, we aim to uncover R14Δ/+ disease mechanisms and study potential treatment using PLN antisense oligonucleotides (PLN-ASOs). Methods Phosphoproteomics was performed on human heart tissue from end-stage R14Δ/+ patients (N=6) versus other etiologies of HF (N=10) as a control. CRISPR-Cas9 engineered R14Δ/+ and isogenic control (WT) induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) were extensively characterized, including the phosphoproteome, calcium transients (FLUO 4-AM), mitochondrial respiration (Seahorse Mito Stress Test) and contractility in dynamic engineered heart tissues (dyn-EHTs) before and after PLN-ASO treatment, and the results were validated in R14Δ/+ iPSC-CMs derived from R14Δ/+ patients. Results Phosphoproteomics of heart tissue from R14Δ/+ vs. other etiologies of HF identified 138 differentially expressed proteins (DEPs) and 317 differentially expressed phosphoproteins (DEPPs), therewith establishing a disease-specific signature. Phosphoproteomics of WT vs. R14Δ/+ iPSC-CMs identified 451 DEPs and 1046 DEPPs. Gene ontology analysis of end-stage R14Δ/+ heart tissue and iPSC-CMs revealed a large overlap. Here, R14Δ/+ showed to have great impact on contraction (e.g. SYNPO2L, MYH10, TTN) and calcium (e.g. AHNAK, HRC, RYR2). In line with this, functional characterization revealed that R14Δ/+ iPSC-CMs have an increased Ca2+ systolic velocity and accelerated calcium transient (decreased T50 and T90), that associated with altered calcium-related DEPPs (e.g. RYR2). In dyn-EHTs, R14Δ/+ dyn-EHTs have a decreased contraction duration, time-to-peak and ability to pace, associated with altered contractility-related DEPPs (e.g. SYNPO2L, MYH7, TTN). PLN-ASOs dose-dependently altered PLN mRNA expression and protein levels in iPSC-CMs, and 62 DEPs and 372 DEPPs were identified. PLN-ASOs increased Ca2+ diastolic velocity, which associated with altered calcium-related DEPPs (e.g. PLN, AHNAK, HRC). PLN-ASOs increased the ability of dyn-EHTs to pace and therewith improved contractility, which associated with altered contractility-related DEPPs (e.g. SYNPO2L, MYH7, TTN). PLN-ASOs dose-dependently increased basal respiration and ATP production, which associated with altered mitophagy-related DEPs (GABARAPL2 and MAP1LC3). These functional characteristics were validated in R14Δ/+ patient iPSC-CMs. Discussion: R14Δ/+ cardiomyocytes have a degenerative phenotype characterized by altered calcium transients and reduced contractility and PLN-ASOs attenuate these characteristics and improve metabolism.

Predicting diabetes prognosis using machine learning techniques in a Hungarian clinical database

Abstract Background Chronic conditions such as type 2 diabetes mellitus have great impact on patients’ quality of life. Although clinical databases provide a perfect base for research aimed at improving diabetes care, analyzing such databases requires extensive pre-processing, mainly due to the large amount of unstructured data. Our study aimed to present the steps of generating a dataset from a large clinical database, and to apply machine learning-based analytical techniques regarding. Methods Data of the Clinical Center of University of Debrecen was used. To structure the unstructured data, regular expressions and natural language processing methods were used. The main machine learning models were as follows: Gradient Boosting Machines to predict the risk of complication development; Long Short-Term Memory Networks to forecast future health outcomes. All analysis and procedures were done using Python. Results The database contains approximately 1600 tables, with a total size of 1.9 terabytes, where the largest table is 21.07 gigabytes with approximately 44.64 million rows. The final dataset consisted of 40,332 patients. Most variables originate from the unstructured data, including complications and comorbidities of diabetes, as well as physical and laboratory parameters. Related to laboratory parameters, the number of measurements and the median value for every half-year were available. The diagnosis time of the complications’ occurrence is also presented. Machine learning methods were more accurate compared with traditional statistical methods in predicting the prognosis (p < 0.05). Discussion Our research highlights the importance of clinical data in chronic disease management. There are challenges in pre-processing and managing datasets, but machine learning-based methods are very efficient not only in extracting useful information from unstructured data but also in predicting the prognosis and identifying potential intervention points for better care. Key messages • Natural language processing can be used to obtain useful information from unstructured clinical data. • Using machine learning techniques on clinical data could improve diabates care.

How stop smoking outreach works: a co-inquiry informed, complex intervention model

Abstract Background Across Europe, smoking prevalence is concentrating in underserved communities with high levels of deprivation. And yet, there is little research on how stop smoking services (SSS) can engage these communities. This study presents the findings of a 12-month collaboration involving an external public health research team and staff of an English SSS. Staff worked with community partners (e.g., schools) to raise awareness of the service, which included a newly introduced e-cigarette offer. Methods A complex intervention model was developed and refined through 6 facilitated co-inquiry sessions involving 13 SSS staff. Staff also completed 32 reflection forms on outreach events and rapid ethnography was conducted, by the research team, to evaluate staff’s working assumptions. This included 8 observations of outreach events and interviews with 10 SSS staff and 8 community members, from the service target groups. Data were organised into themes. Findings The model presents a picture of stop smoking outreach as challenging, unpredictable and resource intensive yet often having great impact. The themes enrich this to highlight 1) the appreciation for yet unpredictability of outreach referrals for people disconnected from health services; 2) the importance of staff adopting a person-centred approach; 3) the challenges and opportunities presented by e-cigarettes; and 4) the role of strategic community partnerships in optimising staff time on outreach. Conclusions Stop smoking outreach can generate considerable interest in SSS, particularly when delivered in combination with an e-cigarette offer. Future research should explore the economic and social return on investment in community outreach: while time consuming, referrals generated proactively may become more important for SSS as smoking concentrates in underserved communities. Key messages • Stop smoking outreach is an effective way to raise awareness and generate referrals but must be done non-judgementally and via effective community partnerships. • Staff’s careful discussion of an e-cigarette offer can change the perception of SSS without contributing to recreational normalization.

The Mediating Effect of Service Quality on Total Quality Management and Customer Loyalty in Banking Sector

The aim of the research is to scrutinize the effect of TQM on customer loyalty through the mediation of quality service. The data was collected from workers of private banks in Iraq. Cluster sampling technique was used to collect the data. There were 390 employees taken as a sample size. The finding demonstrates that the effect of TQM practices on customer loyalty is significant. The significant mediating effect of quality service between the association of TQM and customer loyalty. In the end, the implication of the study is the private banks implement TQM practice effectively so that it has more a great impact on customer loyalty.

Study on the Damage Characteristics of Red Sandstone Foundation Under Rainfall Infiltration in the Red-Bed Area of the Sichuan Basin—Taking Zhongjiang County as an Example

The Sichuan Basin in China is one of the most concentrated areas of red beds in China. In the red-bed area, abundant rainfall can easily cause natural disasters, such as landslides, mudslides, collapses, and subsidence. This has had a great impact on the safety of people and property and sustainable modernization in the area. Zhongjiang County of Sichuan Province is a typical red-bed area, and red sandstone is one of the main foundation rocks in this area. Under the influence of rainfall, the strength of red sandstone foundation easily decays, causing disasters such as house collapse. Therefore, in order to explore the influence of rainfall on the mechanical properties of red sandstone, this paper takes the red sandstone in Zhongjiang County, Sichuan Province, China, as the research object and conducts acoustic-emission uniaxial compression experiments under different water contents. The strength characteristics, instability precursor characteristics, fracture types, and damage characteristics of red sandstone in different water-bearing states are obtained. The abovementioned results provide a reference for the Zhongjiang County Government to consider the impact of rainfall on the red sandstone foundation during modernization and emergency management.

Analytic continuation of differential cross-sections as a way to determine asymptotic normalization coefficients. Application to the 16O(d,p)17O reaction

Asymptotic normalization coefficients (ANC) are important nuclear characteristics. This paper discusses a method for determining ANC values from experimental data on differential cross-sections of nuclear transfer reactions. The method is based on the use of the analytic continuation of these cross-sections to the pole point of the reaction amplitude with respect to the variable [Formula: see text], where [Formula: see text] is the center-of-mass scattering angle. The method under consideration is used to determine the ANC for the channel [Formula: see text]O(1/2[Formula: see text]; 0.871[Formula: see text]MeV) [Formula: see text]O(0[Formula: see text]; 0[Formula: see text]MeV) [Formula: see text] from the data on the [Formula: see text]O(0[Formula: see text]; 0[Formula: see text]MeV)[Formula: see text]O (1/2[Formula: see text]; 0.871[Formula: see text]MeV) reaction. When determining the ANC, the corrections caused by the Coulomb interaction in the initial, final and intermediate states of the reaction were taken into account. It is shown that these corrections have a great impact on the extracted ANC value.

Cellular Senescence: A Bridge Between Diabetes and Microangiopathy

Cellular senescence is a state of permanent cell cycle arrest and plays an important role in many vascular lesions. This study found that the cells of diabetic patients have more characteristics of senescence, which may cause microvascular complications. Cell senescence, as one of the common fates of cells, links microangiopathy and diabetes. Cell senescence in a high-glucose environment can partially elucidate the mechanism of diabetic microangiopathy, and various types of cellular senescence induced by it can promote the progression of diabetic microangiopathy. Still, the molecular mechanism of microangiopathy-related cellular senescence has not yet been clearly studied. Building on recent research evidence, we herein summarize the fundamental mechanisms underlying the development of cellular senescence in various microangiopathies associated with diabetes. We gradually explain how cellular senescence serves as a key driver of diabetic microangiopathy. At the same time, the treatment of basic senescence mechanisms such as cellular senescence may have a great impact on the pathogenesis of the disease, may be more effective in preventing the development of diabetic microangiopathy, and may provide new ideas for the clinical treatment and prognosis of diabetic microangiopathy.

Integrated Machine Learning and Region Growing Algorithms for Enhanced Concrete Crack Detection: A Novel Approach

In the field of construction engineering, the cracking of concrete structures is a common engineering problem, which has a great impact on the overall stability and service life of the engineered structure. During structural repair, crack detection is the most critical step. Automatic detection significantly reduces the engineering cost and human factor error compared with manual detection. However, due to the changeable environment of the project site and different image specifications, using a single algorithm makes it difficult to balance high efficiency and high accuracy. In this study, we designed a combined recognition method including the region growth algorithm and machine learning regression that can achieve a tradeoff between accuracy and efficiency. Firstly, the regression method learns the image features of the dataset and the specific region growth threshold, and the regression function is trained by using the open-source dataset to determine the region growth threshold using the characteristics of the images included in the tests. The region growth algorithm is used to expand the threshold from the seed points of the image to obtain the crack recognition results. The results show that this method improves the accuracy of SSIM by 7% compared with the traditional region growth algorithm, and does not significantly increase the computational cost, with an increase of 0.78 s per photo process. Compared with the deep learning method, the recognition accuracy of SSIM is decreased by 5.96%, but it takes less resources and has high efficiency.

Plugging biologging into animal welfare: An opportunity for advancing wild animal welfare science

Abstract Animal welfare science is currently expanding beyond its traditional boundaries, from captive animals to those living in the wild. This current development is conceptually and methodologically challenging, but it could benefit from adjacent and more established research fields. Among these fields, biologging appears to be a strong candidate, as most intrinsic, location and environmental variables collected through biologging approaches could be used to assess animal welfare in the wild. To provide an objective view of the suitability of biologging to assess wild animal welfare, biologging was evaluated against the criteria that are currently recommended to assess animal welfare. This evaluation shows that biologging approaches could enhance animal welfare assessments in terms of completeness, informativeness and feasibility in the wild. However, their full implementation may be complicated by limitations in terms of validity, representativeness and disturbance, and by the different welfare perspectives taken by wildlife biologists using biologging approaches and animal welfare biologists. To exploit the full potential that biologging approaches could offer to assess wild animal welfare, their current limitations need to be overcome. Towards this end, recommendations are explicitly provided to enhance the validity and the representativeness of biologging measurements as welfare indicators, while reducing disturbance. To increase the visibility and the impact of biologging studies examining wild animal welfare, we also encourage wildlife biologists using biologging approaches to adopt the same language and perspectives as those used by animal welfare biologists. If current limitations are overcome, biologging is likely to be instrumental for the future study of animal welfare in the wild. Reciprocally, integrating animal welfare in biologging studies is expected to have a great impact on the whole biologging field by extending its current scope to a new and promising research area.

Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor

Gas detection and monitoring are critical to protect human health and safeguard the environment and ecosystems. Chemiresistive sensors are widely used in gas monitoring due to their ease of fabrication, high customizability, mechanical flexibility, and fast response time. However, with the rapid development of industrialization and technology, the main challenges faced by chemiresistive gas sensors are poor selectivity and insufficient anti-interference stability in complex application environments. In order to overcome these shortcomings of chemiresistive gas sensors, the pattern recognition method is emerging and is having a great impact in the field of sensing. In this review, we focus systematically on the advancements in the field of data processing methods for feature extraction, such as the methods of determining the characteristics of the original response curve, the curve fitting parameters, and the transform domain. Additionally, we emphasized the developments of traditional recognition algorithms and neural network algorithm in gas discrimination and analyzed the advantages through an extensive literature review. Lastly, we summarized the research on chemiresistive gas sensors and provided prospects for future development.

A Robust PCC Voltage Feedforward Control Strategy of LCL‐Type Converters under Weak Grid Condition

The point of common coupling (PCC) voltage feedforward control strategy can effectively reduce the startup inrush current and improve the anti‐interference ability of the LCL‐type converters, which is widely used in the renewable energy converters control field. With the high penetration of the new energy, the increase of grid impedance leads to the weakening of the grid. Under weak grid conditions, the PCC voltage feedforward control strategy will generate an additional feedback loop of grid‐side current, which has a great impact on the stability of the system. In this paper, the damping characteristic of the PCC voltage feedforward control strategy and the effect on the open loop characteristic of the system have been deeply analyzed. An improved PCC voltage feedforward control strategy based on a proportional plus band‐pass filter is proposed, which can effectively improve the phase margin and achieve active damping under weak grid conditions, therefore, the robustness of the converters can be effectively improved. A three‐phase LCL‐type AC/DC converter platform is built and comparative experiments are carried out to verify the effectiveness of the proposed algorithm. The experimental results are consistent with the theoretical analysis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Application of Path Planning and Tracking Control Technology in Mower Robots

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

Characterization of dairy goat production systems in coastal valleys of the Lima region

Goat farming in Peru is a husbandry activity that, although it is considered secondary in the country, has a great economic and social impact on the rural population, that is why government efforts to develop is so important. The objective of this study was to characterize dairy goat rearing systems in the coastal valleys of the Lima region to identify gaps and opportunities for improvement. This cross-sectional research was conducted in four provinces located in the Lima region, Peru. A total of 62 goat farmers participated in the trial. For data collection, a standard survey was prepared with open and closed questions distributed across two components (socioeconomic and productive). The surveys were processed for qualitative variables using a multiple correspondence analysis (MCA) followed by a hierarchical cluster analysis (HCA) to differentiate the types of farming systems prevalent based on the survey population. The hierarchical cluster analysis resulted in the formation of three separate groups of goat farmers, which can be classified as extensive systems differentiated by management practices and their production and marketing objectives. The test showed a significant difference; therefore, it can be affirmed that they are associated with the groups or clusters formed. These results will allow actors related to goat farming, such as state and regional entities, to focus efforts on addressing specific demands of the different types of goat farmers found in this study.

Political View at the St. Petersburg EAEU Summit

In the world of the 21st century, international economic structures are increasingly increasing their influence on the world market, which directly affects the economic potential of the member countries of these organizations and the development and prosperity of the national economy. EAEU is considered one of such structures, which during its nearly 10 years of activity has had a great impact on the internal output of the member states, as well as on ensuring interstate economic cooperation and the economic integration process. The St. Petersburg meeting had a special role and significance within the framework of EAEU, during which a strategy regulating and coordinating the structure's further work was promoted and a document was signed that created visible prospects in the near future in order to clarify its actions and upcoming steps. The authors of the scientific work, taking into account the importance and strategic significance of this document, considered it necessary to present in the form of an article what prospective positive impact the document can have for the organization and its member countries and what its goals are.

CiteSpace-based Survey of International Advances in Chinese Traditional Culture Research in 1999-2019

With the globalization strategy and the initiative “One Belt and One Road” in China, Chinese traditional culture has a great impact on the world. This study, based on CiteSpace, explores the total numbers, hotspots, characteristics and tendency of international research on Chinese traditional culture, by retrieving 866 articles from SSCI and A&HCI in the Web of Science (Core Collection). The results of the study revealed that: there is a strong increases in total output from 1999 to 2019 and the research on the theme of Chinese traditional culture has become interdisciplinary; the most productive authors aren’t the ones in the most cited documents and the journals on which the most cited documents publish aren’t the prolific journals; most of the researchers are from Hong Kong, Taiwan, mainland of China, Canada and USA. In view of the results, it is obvious that it is a good way to spread Chinese traditional culture over the world. It is strongly recommended that we should do more analysis on the international literatures with different paradigms, methods and metrics in order to get a more comprehensive and correct domain visualization map of Chinese traditional culture research.

Research on a Multi-Scale Clustering Method for Buildings Taking into Account Visual Cognition

Building clustering is a key problem that needs to be solved in the realization of the automatic synthesis of large-scale maps. The selection of different feature and spatial distance calculation methods has a great impact on the clustering results, and the need to manually select appropriate feature and distance metrics leads to the problem of not being able to fully consider the complexity and diversity of buildings. In this paper, we propose a multi-scale clustering method for buildings that takes visual perception into account using the Gestalt principle to simulate how humans classify buildings through visual perception. Moreover, by analyzing the spatial features and texture attributes of buildings, a visual distance is designed to be used as a condition for building classification to assess the similarity between buildings, solving the complexity of manually selecting feature vectors and spatial distances and realizing the adaptive selection of features. Through experimental validation at different scales (macro, meso and micro), the present method is able to achieve the accurate clustering of buildings, and a frequency threshold of 91% is found, which is able to determine the optimal clustering results. The experimental results show that the proposed method can not only fully consider the complexity and diversity of buildings but also effectively support the understanding and analysis of urban spatial structure and provide a scientific decision-making basis for urban planning and management.

Egyptian aquaponic celery (Apium graveolens): Phenolic and volatile profiles analysed using HPLC-MS/ms and gc-ms/ms

Recently, water scarcity has been a substantial problem facing agriculture in Egypt, with a great impact on food security and hence makes it a challenge to satisfy food demand. An aquaponic system with minimum water needs is considered a substitute technique to meet the demand of food shortages. Celery (Apium graveolens) is a widely cultivated herb belonging to the family Apiaceae and widely consumed as a popular ingredient in a wide range of dishes and food recipes. A total of 13 phenolic metabolites were detected and quantified using HPLC-ESI-MS/MS. Chlorogenic acid was detected as the most abundant phenolic compound accounting for 3471.58 mg kg−1. Moreover, 65 volatile compounds belonging to 12 different classes were detected using GS-MS with an abundance of aromatic hydrocarbons at ca.68.15%. and the main constituents were 6-phenyltridecane, 6-phenyldodecane, and 5-phenylundecane at ca.6.78%, 6.62%, and 6.13% respectively. It is the first time to report metabolic profile in celery grown in an aquaponic system.

Chemical Capture and Sensing of Bovine Serum Albumin with Phenothiazinylcarbaldehyde-Labeled 2′-Deoxyuridine

AbstractChemical-capture-mediated sensing has had a great impact on proteomic research. Toward this end, we demonstrate the chemical trapping of BSA by the reactive formyl functionality of a newly developed fluorescent nucleoside probe, formylphenothiazine-labeled-2′-deoxyuridine. The probe is capable of trapping BSA via Schiff base formation leading to fluorescence ‘switch-on’ sensing with a large hypsochromic shift of ca. 100 nm. The α-amylase does not show any significant change in fluorescence response, demonstrating the efficiency of the probe in selective sensing of BSA. Docking studies suggest the preferential interaction of the phenothiazinylcarbaldehyde-labeled dU with the residual amino acids in site I of the BSA protein as compared to site II.