Electrochemical Properties of Mo4VC4Tx MXene in Aqueous Electrolytes.

M5C4Tx MXenes represent the most recently discovered and least studied subfamily of out-of-plane ordered double transition metal carbides with 11 atomic layers, probably the thickest of all 2D materials. Molybdenum (Mo) and vanadium (V) in Mo4VC4Tx offer multiple oxidation states, making this MXene potentially attractive for electrochemical energy storage applications. Herein, we evaluated the electrochemical properties of Mo4VC4Tx free-standing thin films in acidic, basic, and neutral aqueous electrolytes and observed the highest gravimetric capacitance of 219 F g-1 at 2 mV s-1 in a 3 M H2SO4. Further, we investigated the intercalation states of four different cations (H+, Li+, Na+, and K+) in MXenes through ab initio molecular dynamics (AIMD) simulation and used density functional theory (DFT) calculations to assess the charge storage mechanisms in different electrolytes. These studies show hydrated Li+, Na+, and K+ ions forming an electric double layer (EDL) at the MXene surface as the primary charge storage mechanism. This work shows the promise of Mo4VC4Tx MXene for energy storage in aqueous electrolytes.

Vascular calcification in chronic kidney disease associated with pathogenic variants in ABCC6.

Vascular calcification is prevalent in chronic kidney disease (CKD). Genetic causes of CKD account for 10-20% of adult-onset disease. Vascular calcification is thought to be one of the most important risk factors for increased cardiovascular morbidity and mortality in CKD patients and is detectable in 80% of patients with end stage kidney disease (ESKD). Despite the high prevalence of vascular calcification in CKD, no single gene cause has been described. We hypothesized that variants in vascular calcification genes may contribute to disease pathogenesis in CKD, particularly in families who exhibit a predominant vascular calcification phenotype. We developed a list of eight genes that are hypothesized to play a role in vascular calcification due to their involvement in the ectopic calcification pathway: ABCC6, ALPL, ANK1, ENPP1, NT5E, SLC29A1, SLC20A2, and S100A12. With this, we assessed exome data from 77 CKD patients, who remained unsolved following evaluation for all known monogenic causes of CKD. We also analyzed an independent cohort (Ontario Neurodegenerative Disease Research Initiative (ONDRI), n = 520) who were screened for variants in ABCC6 and compared this to a control cohort of healthy adults (n = 52). We identified two CKD families with heterozygous pathogenic variants (R1141X and A667fs) in ABCC6. We identified 10 participants from the ONDRI cohort with heterozygous pathogenic or likely pathogenic variant in ABCC6. Replication in a healthy control cohort did not reveal any variants. Our study provides preliminary data supporting the hypothesis that ABCC6 may play a role in vascular calcification in CKD. By screening CKD patients for genetic causes early in the diagnostic pathway, patients with genetic causes associated with vascular calcification can potentially be preventatively treated with new therapeutics with aims to decrease mortality.

Non-surgical interventions to control bleeding from arteriovenous fistulas and grafts inside and outside the hemodialysis unit: a scoping review.

Background: Prolonged bleeding from arteriovenous fistulas (AVF) and arteriovenous grafts (AVG) associates with worse outcomes; Within the hemodialysis unit these outcomes include anemia and quality of life disruptions, and outside the hemodialysis unit includes fatal hemorrhage. However, various guidelines for AVF/AVG bleeding management inside and outside the hemodialysis unit lack consensus. Methods: A scoping review was conducted of four databases, from inception to 17 February 2024. The study population was hemodialysis patients experiencing bleeding from AVF or AVG. Studies that assessed non-operative management were included. Results: Sixteen studies met inclusion criteria. Most (14/16) addressed post-cannulation bleeding from AVF/AVG within the dialysis unit. Compared with standard dressings, hemostatic dressings (chitosan-, cellulose- or thrombin-based) decreased post-cannulation bleeding time at arterial and venous site 35.7%-84.0% (P < .05) and 38.5%-78.7% (P < .05), respectively. Use of chitosan-based dressings decreased percentage of patients bleeding 4-min post-cannulation by 16.3%-39.2%. One pilot observational study demonstrated no access thromboses or infections with short-term use of a compression device within the hemodialysis unit. However, the role of compression devices and tourniquets within the dialysis unit remains unclear, despite widespread use. Long-term AVF/AVG survival was not reported in any study. Limited research confirms that devices are effective in prevention of catastrophic out-of-hospital bleeding. It remains uncertain if device availability enhances patient confidence in managing out-of-hospital bleeding. This may impact patient choices around dialysis modality, access and transplant, but this remains uncertain. Conclusions: In hemodialysis patents with bleeding from AVF/AVG, several alternative dressings or devices decrease post-cannulation bleeding time within the hemodialysis unit. Existing research has not established criteria on when it might be appropriate to use specialized dressings. There is very limited research on methods to control bleeding from AVF/AVG outside the hemodialysis unit. More data are required before evidence-based guidelines can be made. Recommendations for future research are provided.

Preliminary study on the computer-based optokinetic nystagmus analyzer to detect the visual acuity of preschool children.

The purpose of this study is to examine the viability, precision, and consistency of a computer-based optokinetic nystagmus analyzer (nystagmus meter) for diagnosing eyesight in preschoolers. A total of 59 subjects who could pass the log of minimum angle of resolution (LogMAR) visual acuity chart were divided into three groups by age, 4-, 5-, and 6-year-old groups, and their visual acuity was tested with nystagmus meter. The percentage of children in each age group that could be detected by nystagmus was recorded along with the differences between these groups. The correlation between the test results from the two methods was found for each age group using the correlation coefficient method. Repeated measurements were used to assess the two visual acuity values of the measured nystagmus, and the repeatability of the two measurement techniques for different age groups was compared. The overall measurability of the visual acuity detected by nystagmus was 93.22%, and the measurability of the 4-, 5-, and 6-year-old groups was 90%, 95%, and 94.74%, respectively. There was no statistically significant difference in the measurability of subjects among all age groups (P = 1.0). The outcomes of the LogMAR visual acuity chart had a negative correlation with the visual acuity measured by the nystagmus meter. The overall correlation coefficient R value was -0.80, and the correlation coefficient R value of the 4-, 5-, and 6-year-old groups was -0.79, -0.76, and -0.87, respectively. The nystagmus meter has good feasibility, accuracy, and stability in visual acuity testing and can be used for visual acuity testing in children.

Multidimensional progressive single-cell sequencing reveals cell microenvironment composition and cancer heterogeneity in lung cancer.

Despite substantial advances in cancer biology and treatment, the clinical outcomes of patients with lung cancer remain unsatisfactory. The tumor microenvironment (TME) is a potential target. Using single-cell RNA sequencing, we could distinguish eight distinct cell types in the lung cancer microenvironment, demonstrating substantial intratumoral heterogeneity in 19 different lung cancer tumor samples. Through the re-dimensional grouping of cancer-associated fibroblasts (CAFs), myeloid cells, epithelial cells, natural killer (NK) cells, and T cells, the difference in the TME of lung cancer was revealed. We discovered SFTPB, SFN, and KRT8 as possible predictive biomarkers for lung cancer by assessing the gene expression patterns in epithelial cells. Examining cell-to-cell communications showed a robust association between the quantity of matrix CAFs, epithelial cells, and macrophages in the thrombospondin signaling pathway. Additionally, we found that the amyloid precursor protein signaling pathway primarily originated from the matrix, and inflammatory cancer-associated endothelial and fibroblast cells showed a co-expression relationship with myeloid cells and B cells. Through cell-to-cell correlation analysis, we found positive regulation between NK cells, regulatory T cells, GZMB-CD8 T cells, and GZMK-CD8 T cells, which could play a role in developing immune TMEs. These findings support studies on cancer heterogeneity and add to our understanding of lung cancer's cellular microenvironment.

Adverse Event Profile Differences between Trastuzumab Emtansine and Trastuzumab Deruxtecan: A Real-world, Pharmacovigilance Study.

Introduction: Trastuzumab emtansine(T-DM1) and trastuzumab deruxtecan (T-DXd, formerly DS-8201a), the human epidermal growth factor receptor 2 (HER2)-targeted antibody-drug conjugate (ADC), are commonly used in metastatic breast cancer. However, their real-world safety profile has not been adequately compared. Objective: We aimed to investigate the adverse event (AE) profile of T-DM1 and T-DXd reported by the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: All indications were searched for T-DM1 and T-DXd, as primary suspected drugs, from FAERS data (January 2004 to June 2023). Disproportionality analyses were performed by reporting odds ratios (ROR) and proportional reporting ratio (PRR). The odds ratio (OR) of fatal AEs associated with T-DM1 and T-DXd under different exposure factors were performed by univariate and multivariate logistical regression analysis. Results: 3723 and 2045 reports of T-DM1 and T-DXd were submitted to FAERS. Finally, 94 and 61 significant signals for T-DM1 and T-DXd were systematically analyzed. The valid AEs with the highest frequency and the strongest signal intensity for T-DM1 were platelet count decreased (n=108) and hepatopulmonary syndrome (ROR=680.42), respectively. Interstitial lung disease (n=262, ROR=82.55) and pneumonitis (n=89, ROR = 48.34) showed both high frequency and strong signal intensity for T-DXd. The proportion of AEs in each SOC system was different. T-DM1 had a greater proportion of valid AEs in the nervous system, musculoskeletal system, hepatobiliary system, ocular system, cardiac system and hematologic system(p<0.05). T-DXd had a greater proportion of valid AEs in the skin disorders, respiratory system, infestations, general system and gastrointestinal system(p<0.05). Furthermore, the analysis of fatal AEs in four systems revealed that T-DXd exhibited a significantly higher proportion of fatal outcomes in the hematologic and respiratory system compared to T-DM1. Conversely, T-DM1 had a significantly higher proportion of fatal outcomes in the hepatobiliary system. Neither T-DM1 nor T-DXd exhibited a high mortality ratio in the cardiac system. Logistic regression analysis indicated that advanced age (≥65 years) and male gender were identified as independent risk factors of fatal AEs for both T-DM1 and T-DXd. Additionally, the drug combination therapy, particularly with a CYP3A4 inhibitor, was found to be a risk factor for fatal AEs specifically related to T-DXd. Conclusions: Hematological and respiratory toxicity of T-DXd and hepatobiliary toxicity of T-DM1 exhibited a high incidence of fatal outcomes. It is crucial to identify high-risk factors and enhance the monitoring of AEs during clinical application.

Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study.

Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine-learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010-2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K-nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self-Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.

Impact of COVID-19 lockdown on air quality analyzed through machine learning techniques.

After February 2020, the majority of the world's governments decided to implement a lockdown in order to limit the spread of the deadly COVID-19 virus. This restriction improved air quality by reducing emissions of particular atmospheric pollutants from industrial and vehicular traffic. In this study, we look at how the COVID-19 shutdown influenced the air quality in Lahore, Pakistan. HAC Agri Limited, Dawn Food Head Office, Phase 8-DHA, and Zeenat Block in Lahore were chosen to give historical data on the concentrations of many pollutants, including PM2.5, PM10 (particulate matter), NO2 (nitrogen dioxide), and O3 (ozone). We use a variety of models, including decision tree, SVR, random forest, ARIMA, CNN, N-BEATS, and LSTM, to compare and forecast air quality. Using machine learning methods, we looked at how each pollutant's levels changed during the lockdown. It has been shown that LSTM estimates the amounts of each pollutant during the lockout more precisely than other models. The results show that during the lockdown, the concentration of atmospheric pollutants decreased, and the air quality index improved by around 20%. The results also show a 42% drop in PM2.5 concentration, a 72% drop in PM10 concentration, a 29% drop in NO2 concentration, and an increase of 20% in O3 concentration. The machine learning models are assessed using the RMSE, MAE, and R-SQUARE values. The LSTM measures NO2 at 4.35%, O3 at 8.2%, PM2.5 at 4.46%, and PM10 at 8.58% in terms of MAE. It is observed that the LSTM model outperformed with the fewest errors when the projected values are compared with the actual values.

Prevalence of mobile phone addiction among medical students: a systematic review.

The incidence and factors related to mobile phone addiction among Chinese medical students were analyzed through meta-analysis. Chinese literature databases (such as China Knowledge Network and VIP Information Resource System) and English literature databases (such as PubMed and Web of Science) were searched for cross-sectional studies on the incidence and factors related to mobile phone addiction, and the required data were extracted. Meta-analysis was performed using a random effects model with RevMan 5.3 statistical software, and publication bias was tested with Stata 12.0. A total of 20 studies were included, including 36,365 study subjects. Among them, there were 10,597 cases of mobile phone addiction with an incidence of 29.14%. The results of the meta-analysis showed that the combined OR values (95% CI) of the factors were: gender 1.070 (1.030-1.120), residence 1.118 (1.090-1.146), school type 1.280 (1.241-1.321), mobile phone use time 1.098 (1.068-1.129), sleep quality 1.280 (1.288-1.334), self-perception of learning 0.737 (0.710-0.767), and family relationship 0.821 (0.791-0.852). The study showed that being a male student from cities and towns, being at a vocational college, excessive use of mobile phones, and poor sleep quality were the risk factors for mobile phone addiction among medical students in China. Positive self-perception of learning and family relationships were protective factors, and more related factors are still controversial and need to be further explored and confirmed.

Application Effectiveness of Segment IV Portal Vein Reconstruction for Early Postoperative Liver Function Recovery in Split Liver Transplantation.

The objective of this study was to investigate the significance of portal vein reconstruction in segment IV of the liver on early postoperative liver function recovery in split liver transplantation. The clinical data of patients of right trilobe split liver transplantation in our center were analyzed and divided into two groups, including a group without portal vein reconstruction and a group with portal vein reconstruction. Clinical data of alanine aminotransferase (ALT), aspartate transaminase (AST), albumin (ALB), creatinine (Cr), total bilirubin (TB), alkaline phosphatase (ALP), gamma-glutamyl Transferase (GGT), lactic acid (Lac), and international normalized ratio (INR) levels were analyzed. The technique of segment IV portal vein reconstruction is beneficial to the early postoperative recovery of liver function. Statistically, there was no significant effect of portal vein reconstruction in the IV segment of the liver on the recovery of liver function within 1 week after split liver transplantation. There was no significant difference in survival rate between the control group and reconstruction group over the 6 months follow-up period after surgery.

Blockchain enabled data security in vehicular networks.

Recently, researchers have applied blockchain technology in vehicular networks to take benefit of its security features, such as confidentiality, authenticity, immutability, integrity, and non-repudiation. The resource-intensive nature of the blockchain consensus algorithm makes it a challenge to integrate it with vehicular networks due to the time-sensitive message dissemination requirements. Moreover, most of the researchers have used the Proof-of-Work consensus algorithm, or its variant to add a block to a blockchain, which is a highly resource-intensive process with greater latency. In this paper, we propose a consensus algorithm for vehicular networks named as Vehicular network Based Consensus Algorithm (VBCA) to ensure data security across the network using blockchain that maintains a secured pool of confirmed messages exchanged in the network. The proposed scheme, based on a consortium blockchain, reduces average transaction latency, and increases the number of confirmed transactions in a decentralized manner, without compromising the integrity and security of data. The simulation results show improved performance in terms of confirmed transactions, transaction latency, number of blocks, and block creation time.

Synthesis of cerium oxide embedded perovskite type bismuth ferrite nanocomposites for sonophotocatalysis of aqueous micropollutant ibuprofen.

Ibuprofen is potentially toxic and carcinogenic for freshwater ecosystems and poses a serious threat to human health by affecting kidney function. The present study focused on the sunlight-controlled degradation of ibuprofen from water using a novel magnetically separable cerium oxide-embedded bismuth ferrite heterostructure. Catalysts were synthesized by solvothermal and co-precipitation methods and characterized by X-ray diffractometry, transmission electron microscopy, X-ray photoelectron spectroscopy, UV-vis optical absorption spectroscopy, and nitrogen adsorption. This study investigated the effect of photocatalysis, sonolysis, sonophotolysis, and sonophotocatalysis on the degradation of ibuprofen in water. Pseudo-first-order and second-order kinetics were applied to evaluate the rate of reaction for ibuprofen degradation. The addition of 5% CeO2 to the BiFeO3 significantly increased the surface area and pore volume of bismuth ferrite, which enhanced their photocatalytic degradation efficiency by 2.28 times in terms of ibuprofen mineralization. Sonolysis treatment alone and in combination with photolysis led to the degradation of ibuprofen, but with the formation of intermediate products. Positive synergy was observed when sonolysis was combined with photocatalysis in terms of the mineralization of ibuprofen and the degradation of intermediates along with their parent compound. It was proposed that, compared to photocatalytic mineralization, the ultrasound-assisted advanced oxidation process resulted in the conversion of ibuprofen to its mineralization products.

The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer.

Machine learning (ML) can enhance a dermatologist's work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

Functionalized Graphene Oxide-Based Lamellar Membranes with Tunable Nanochannels for Ionic and Molecular Separation.

Graphene oxide (GO)-based membranes with tunable microstructure and controlled nanochannels have attracted an increasing interest for various applications in wastewater treatment, desalination, gas separation, organic nanofiltration, etc. However, they showed limited use in water desalination due to their lower stability and separation efficiency. In this work, a class of two-dimensional (2D) GO lamellar membranes have been prepared with controlled pores for efficient and fast separation of ions and dye molecules. The GO membranes are fucntionalized with a star-like 6-armed poly(ethylene oxide) using the simple amidation route under mild conditions. The as-prepared covalently cross-linked networks are chemically steady in aqueous medium and show remarkable selectivity (∼100%) for several probe molecules and 10-100 higher permeance than those of the reported GO-based membranes. Further, such membranes are also used for salt separation and show more than 80% rejection for Pb2+ and Ni2+ salts. Moreover, a 1360 nm-thick membrane shows >99% rejection for NaCl with a good water permeance of up to 120 L m-2 h-1 bar-1. Additionally, these membranes are stable for more than 20 days under different conditions.

Recent Development in Laminar Transition Metal Dichalcogenides-Based Membranes Towards Water Desalination: A Review.

Transition metal dichalcogenides (TMDCs)-based laminar membranes have gained significant interest in energy storage, fuel cell, gas separation, wastewater treatment, and desalination applications due to single layer structure, good functionality, high mechanical strength, and chemical resistivity. Herein, we review the recent efforts and development on TMDCs-based laminar membranes, and focus is given on their fabrication strategies. Further, TMDCs-based laminar membranes for water purification and seawater desalination are discussed in detail. Finally, present their merits, limits and future challenges needed in this area.

Lightweight Internet of Things Botnet Detection Using One-Class Classification.

Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation.

A hybrid group-based movie recommendation framework with overlapping memberships.

Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.

Facile synthesis of Zn-doped CdS nanowires with efficient photocatalytic performance.

In this study, one-dimensional zinc (Zn)-doped cadmium sulphide (CdS) nanowires were synthesised by a solvothermal method. The Zn doping concentrations were varied from 1 to 5 mol% (ZnxCd1-xS where x = 0.001, 0.003 and 0.005). As-prepared materials were characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray (EDX) spectroscopy and UV-visible spectroscopy. Electrochemical impedance spectroscopy (EIS) was conducted to measure the charge transfer resistance. The photocatalytic performance of prepared materials was evaluated by the photodegradation of methylene blue (MB) dye. The result showed that 5% Zn-doped CdS is more photoactive as compared to other corresponding doped and undoped CdS. The increase in photocatalytic performance is due to improvement in the charge separation.

Two-Dimensional Transition Metal Carbides and Nitrides (MXenes) for Water Purification and Antibacterial Applications.

Two-dimensional (2D) materials such as graphene, graphene oxide (GO), metal carbides and nitrides (MXenes), transition metal dichalcogenides (TMDS), boron nitride (BN), and layered double hydroxide (LDH) metal-organic frameworks (MOFs) have been widely investigated as potential candidates in various separation applications because of their high mechanical strength, large surface area, ideal chemical and thermal stability, simplicity, ease of functionalization, environmental comparability, and good antibacterial performance. Recently, MXene as a new member of the 2D polymer family has attracted significant attention in water purification, desalination, gas separation, antibacterial, and antifouling applications. Herein, we review the most recent progress in the fabrication, preparation, and modification methods of MXene-based lamellar membranes with the emphasis on applications for water purification and desalination. Moreover, the antibacterial properties of MXene-based membranes show a significant potential for commercial use in water purification. Thus, this review provides a directional guide for future development in this emerging technology.

Visible-light excitable Eu3+-induced hyaluronic acid-chitosan aggregates with heterocyclic ligands for sensitive and fast recognition of hazardous ions.

Water-soluble luminescent lanthanide complexes that can be excited with visible light could enable rapid detection of toxic anions and cations in biological systems. Eu3+-induced hyaluronic acid-chitosan aggregates (EIHCA) can improve the stability, biocompatibility, efficiency, and light absorption of luminescent Eu3+ complexes. Visible-range excitation may avoid phototoxicity associated with overexposure to UV light in biological and ecological applications. In this work, we synthesized and characterized series of EIHCA complexes having three N-donor heterocyclic ligands: 2,9-dimethyl-4,7-diphenyl-1,10-phenanthroline (Dphen), 2,2': 6',2″-terpyridine (Tpy) and 1,10-phenanthroline monohydrate (Phen). These complexes possessed bright red fluorescence with a visible range excitation maximum. The photophysical properties of one formulation (we denote as EDL6) include fast quenching response (20 s) of the fluorescence, multi-selectivity, low limit of detection, and high quenching (Ksv) values, enabling selective, rapid and sensitive recognition of Cr2O72- and Fe3+ in aqueous solution. Furthermore, EDL6 exhibits cytocompatibility with mammalian cells that make these complexes promising biocompatible candidate as a safe replacement of organic fluorophores for fluorescence sensing applications. Thus, these new EIHCA complexes were successfully employed for the selective detection of hazardous materials in biological and aqueous environment samples.