Coronary flow capacity and survival prediction after revascularization: physiological basis and clinical implications.

BACKGROUND AND AIMS: Coronary flow capacity (CFC) is associated with an observed 10-year survival probability for individual patients before and after actual revascularization for comparison to virtual hypothetical ideal complete revascularization. METHODS: Stress myocardial perfusion (mL/min/g) and coronary flow reserve (CFR) per pixel were quantified in 6979 coronary artery disease (CAD) subjects using Rb-82 positron emission tomography (PET) for CFC maps of artery-specific size-severity abnormalities expressed as percent left ventricle with prospective follow-up to define survival probability per-decade as fraction of 1.0. RESULTS: Severely reduced CFC in 6979 subjects predicted low survival probability that improved by 42% after revascularization compared with no revascularization for comparable severity (P = .0015). For 283 pre-and-post-procedure PET pairs, severely reduced regional CFC-associated survival probability improved heterogeneously after revascularization (P < .001), more so after bypass surgery than percutaneous coronary interventions (P < .001) but normalized in only 5.7%; non-severe baseline CFC or survival probability did not improve compared with severe CFC (P = .00001). Observed CFC-associated survival probability after actual revascularization was lower than virtual ideal hypothetical complete post-revascularization survival probability due to residual CAD or failed revascularization (P < .001) unrelated to gender or microvascular dysfunction. Severely reduced CFC in 2552 post-revascularization subjects associated with low survival probability also improved after repeat revascularization compared with no repeat procedures (P = .025). CONCLUSIONS: Severely reduced CFC and associated observed survival probability improved after first and repeat revascularization compared with no revascularization for comparable CFC severity. Non-severe CFC showed no benefit. Discordance between observed actual and virtual hypothetical post-revascularization survival probability revealed residual CAD or failed revascularization.

A Review on Pyrazolines as Colorimetric Fluorescent Chemosensors for Cu2.

The Pyrazoline derivatives display promising potential as sensitive and selective chemosensors for detecting Cu2+ ions. It has undergone screening for its sensing behavior with various metals using absorption, emission spectroscopic techniques. Their unique structure incorporates both donating and accepting sites, characterized by delocalized orbitals. These derivatives exhibit notable chromogenic and fluorogenic capabilities facilitated by intramolecular charge transfer. The sensors based on pyrazoline demonstrate exceptional selectivity, low detection limits, and precise detection of metal ions, particularly Cu2+. This review offers a comprehensive summary of recent discoveries concerning as pyrazoline-based "On-Off" chemosensors. The discussion places emphasis on exploring the design and photophysical properties of these chemosensors, with the primary objective of detecting Cu2+ metal ions. The unique features of pyrazoline derivatives make them promising candidates for practical applications in environmental and biological monitoring, showcasing their potential significance in advancing sensing technologies.

Synthesis of Fluorescent Pyrazoline Sensors as Versatile Tool for Zinc ion Detection: A Mini-Review.

Zinc ions are one of the 2nd most abundant mineral after iron and it is important for immune system, enzymatic catalysis, DNA synthesis, and maintaining structural integrity in humans. But, monitoring the Zn levels in human body poses more challenges. This review paper investigates (paper from 2010 to 2023) the synthesis of pyrazoline derivatives by different methods, including conventional methods and green chemistry protocol. These Pyrazoline derivatives highlighted for their potential application as chemo-sensor for Zn2+ ions recognition. Pyrazoline compounds exhibit excellent sensitivity & selectivity and emitting blue-light with high quantum yields and electroluminescence, along with a superior limit of detection. These derivatives are stable bioactive molecule, with well-known diverse biological activities. This review not only gives valuable insights into the essential role of Zinc in human physiology but also provides a practical method for accurate Zinc detection in various samples. Which holds the potential for advancements in health diagnostics and environmental monitoring. Because of their significant biological application and selectivity as sensors, researchers have much more attention to prepare green environmentally-friendly pyrazoline derivatives.

Synthesis, Physicochemical and Third Order Nonlinear Optical Properties of Bis-Chalcone (BBDP) as Donor-pi Acceptor Chromophore in Organize Medium.

Bis-Chalcone (BBDP) has been prepared by condensation of N, N-dimethyl benzaldehyde and 1,1'-([1,1'-biphenyl]-4,4'-diyl) di (ethan-1-one), and structure of BBDP was characterized by Mass Spectra, 13C-NMR, 1H-NMR, and IR. Physicochemical properties including Dipole-moments, Stoke-Shifts, Oscillator-strength, dielectric constant and quantum-yields of fluorescence of BBDP were investigated by the emission and absorbances in different solvents. Compound (BBDP) displayed bathochromic shift upon increasing the solvent polarity (from n-Hexane to DMSO). Furthermore, we have exploited third-order nonlinear optical characteristics of the bisChalone were invigilated by the Z-scan techniques in Chloroform. The measurements were taken with a continuous-wave (CW) diode laser having a wavelength of 520 nm in CHCl3 solvent. The third-order nonlinear optical properties, such as the nonlinear refractive index (NLRI) n2, nonlinear absorption coefficient (NLAC) ß, and nonlinear susceptibility χ(3), were measured at various solution concentrations and laser powers. The obtained values of n2, ß, and χ(3) were estimated to be high, of the order of 10-7(cm2/W), 10-3 (cm/W), and 10-6 (esu), respectively. As a result, bis-chalcone (BBDP) is considered as a promising candidate for applications in nonlinear optical (NLO) devices and optical limiting (OL).

Ultrasound-Assisted Synthesis of Chalcone: A Highly Sensitive and Selective Fluorescent Chemosensor for the Detection of Fe3+ in Aqueous Media.

The chalcone compound DHPO was synthesized through a chemical reaction between 1-(2-hydroxyphenyl)-ethanone and 3,4-dimethoxy benzaldehyde under ultrasound irradiation. The interaction between the DHPO compound and several metal ions was studied using fluorescence behavior, revealing that the chalcone function as a "turn on and turn off" switch fluorescent sensor, for selectively and sensitively detecting Fe3+ ions. The process of fluorescence quenching and complexation of DHPO with Fe3+ ion was further studied using methods such as Benesi-Hildebrand, Stern-Volmer plot, and job plot.

New paracetamol hybrids as anticancer and COX-2 inhibitors: Synthesis, biological evaluation and docking studies.

Drug repurposing is an emerging field in drug development that has provided many successful drugs. In the current study, paracetamol, a known antipyretic and analgesic agent, was chemically modified to generate paracetamol derivatives as anticancer and anticyclooxygenase-2 (COX-2) agents. Compound 11 bearing a fluoro group was the best cytotoxic candidate with half-maximal inhibitory concentration (IC50 ) values ranging from 1.51 to 6.31 µM and anti-COX-2 activity with IC50 = 0.29 µM, compared to the standard drugs, doxorubicin and celecoxib. The cell cycle and apoptosis studies revealed that compound 11 possesses the ability to induce cell cycle arrest in the S phase and apoptosis in colon Huh-7 cells. These results were strongly supported by docking studies, which showed strong interactions with the amino acids of the COX-2 protein, and in silico pharmacokinetic predictions were found to be favorable for these newly synthesized paracetamol derivatives. It can be concluded that compound 11 could block cell growth and proliferation by inhibiting the COX-2 enzyme in cancer therapy.

A Review on Schiff Base as a Versatile Fluorescent Chemo-sensors Tool for Detection of Cu2+ and Fe3+ Metal Ion.

Schiff bases as fluorescent Chemo-sensors fluorogenic cation sensors is scientifically exigent work. Recently Schiff bases derivatives gained more attention because of their structural variability. Donor and acceptor group joint with delocalized [Formula: see text]-orbital display excellent fluorescent properties due to intramolecular charge transfer (ICT). Schiff bases coordinate with the metal ions resulting in a small change of photophysical properties and typical sensing behavior hypochromic/hyperchromic or hypsochromic/bathochromic shift. In this review, we endeavored to converge the beforehand reported Schiff baes-derived sensors and investigated the probes' design, synthesis, ON/OFF & OFF/ON Fe 3+ and Cu2+ metal ion sensing mechanism, and practical application. This review focuses on the synthesis of Schiff baes as fluorescent Chemosensor for investigation of Fe and Cu metal ions during the period of over the two past decade (2003-2022). We anticipate that this review will provide a fundamental framework for researchers to further investigate the topic of Schiff-based sensors for metal ion detection.

An Evaluation of e-Health Service Performance through the Integration of 5G IoT, Fog, and Cloud Computing.

In recent years, Internet of Things (IoT) advancements have led to the development of vastly improved remote healthcare services. Scalability, high bandwidth, low latency, and low power consumption are all essential features of the applications that make these services possible. An upcoming healthcare system and wireless sensor network that can fulfil these needs is based on fifth-generation network slicing. For better resource management, organizations can implement network slicing, which partitions the physical network into distinct logical slices according to quality of service (QoS) needs. Based on the findings of this research, an IoT-fog-cloud architecture is proposed for use in e-Health services. The framework is made up of three different but interconnected systems: a cloud radio access network, a fog computing system, and a cloud computing system. A queuing network serves as a model for the proposed system. The model's constituent parts are then subjected to analysis. To assess the system's performance, we run a numerical example simulation using Java modelling tools and then analyze the results to identify the key performance parameters. The analytical formulas that were derived ensure the precision of the results. Finally, the results show that the proposed model improves eHealth services' quality of service in an efficient way by selecting the right slice compared to the traditional systems.

Cooperative-Aware Radio Resource Allocation Scheme for 5G Network Slicing in Cloud Radio Access Networks.

The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model's performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm.

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks.

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study's training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew's correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively.

Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication.

In this paper, we present a hybrid frequency shift keying and frequency division multiplexing (i.e., FSK-FDM) approach for information embedding in dual-function radar and communication (DFRC) design to achieve an improved communication data rate. Since most of the existing works focus on merely two-bit transmission in each pulse repetition interval (PRI) using different amplitude modulation (AM)- and phased modulation (PM)-based techniques, this paper proposes a new technique that doubles the data rate by using a hybrid FSK-FDM technique. Note that the AM-based techniques are used when the communication receiver resides in the side lobe region of the radar. In contrast, the PM-based techniques perform better if the communication receiver is in the main lobe region. However, the proposed design facilitates the delivery of information bits to the communication receivers with an improved bit rate (BR) and bit error rate (BER) regardless of their locations in the radar's main lobe or side lobe regions. That is, the proposed scheme enables information encoding according to the transmitted waveforms and frequencies using FSK modulation. Next, the modulated symbols are added together to achieve a double data rate using the FDM technique. Finally, each transmitted composite symbol contains multiple FSK-modulated symbols, resulting in an increased data rate for the communication receiver. Numerous simulation results are presented to validate the effectiveness of the proposed technique.

In Vivo and In Silico Analgesic Activity of Ficus populifolia Extract Containing 2-O-ß-D-(3',4',6'-Tri-acetyl)-glucopyranosyl-3-methyl Pentanoic Acid.

Natural product-based structural templates have immensely shaped small molecule drug discovery, and new biogenic natural products have randomly provided the leads and molecular targets in anti-analgesic activity spheres. Pain relief achieved through opiates and non-steroidal anti-inflammatory drugs (NSAIDs) has been under constant scrutiny owing to their tolerance, dependency, and other organs toxicities and tissue damage, including harm to the gastrointestinal tract (GIT) and renal tissues. A new, 3',4',6'-triacetylated-glucoside, 2-O-ß-D-(3',4',6'-tri-acetyl)-glucopyranosyl-3-methyl pentanoic acid was obtained from Ficus populifolia, and characterized through a detailed NMR spectroscopic analysis, i.e., 1H-NMR, 13C-DEPT-135, and the 2D nuclear magnetic resonance (NMR) correlations. The product was in silico investigated for its analgesic prowess, COX-2 binding feasibility and scores, drug likeliness, ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, possible biosystem's toxicity using the Discovery Studio®, and other molecular studies computational software programs. The glycosidic product showed strong potential as an analgesic agent. However, an in vivo evaluation, though at strong levels of pain-relieving action, was estimated on the compound's extract owing to the quantity and yield issues of the glycosidic product. Nonetheless, the F. populifolia extract showed the analgesic potency in eight-week-old male mice on day seven of the administration of the extract's dose in acetic acid-induced writhing and hot-plate methods. Acetic acid-induced abdominal writhing for all the treated groups decreased significantly (p < 0.0001), as compared to the control group (n = 6) by 62.9%, 67.9%, and 70.9% of a dose of 100 mg/kg (n = 6), 200 mg/kg (n = 6), and 400 mg/kg (n = 6), respectively. Similarly, using the analgesia meter, the reaction time to pain sensation increased significantly (p < 0.0001), as compared to the control (n = 6). The findings indicated peripheral and central-nervous-system-mediated analgesic action of the product obtained from the corresponding extract.

Alternating Direction Method of Multipliers-Based Constant Modulus Waveform Design for Dual-Function Radar-Communication Systems.

In this paper, we design constant modulus waveforms for dual-function radar-communication (DFRC) systems based on a multi-input multi-output (MIMO) configuration of sensors for a far-field scenario. At first, we formulate a non-convex optimization problem subject to waveform synthesis for minimizing the interference power while maintaining a constant modulus constraint. Next, we solve this non-convex problem, iteratively, using the alternating direction method of multipliers (ADMM) algorithm. Importantly, the designed waveforms approximate a desired beampattern in terms of a high-gain radar beam and a slightly high gain communication beam while maintaining a desired low sidelobe level. The designed waveforms ensure an improved detection probability and an improved bit error rate (BER) for radar and communications parts, respectively. Finally, we demonstrate the effectiveness of the proposed method through simulation results.

Asymmetric Ru/Cinchonine Dual Catalysis for the One-Pot Synthesis of Optically Active Phthalides from Benzoic Acids and Acrylates.

Herein, we report the asymmetric Ru/cinchonine dual catalysis that provides straightforward access to enantioselective synthesis of C-3 substituted phthalides via tandem C-H activation/Michael addition cascade. The use of readily accessible and less expensive [RuCl2(p-cym)]2 and cinchonine catalyst for the one-pot assembly of chiral phthalides greatly overcomes the present trend of using highly sophisticated catalysts. The developed method provides access to both enantiomers of a product using pseudoenantiomeric cinchona alkaloids as catalysts streamlining the synthesis of phthalide in both the optically active forms.

Proactive Caching in D2D Assisted Multitier Cellular Network.

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption.

A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks.

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber-physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information.

Synthesis and Anti-Cancer Activity of New Pyrazolinyl-Indole Derivatives: Pharmacophoric Interactions and Docking Studies for Identifying New EGFR Inhibitors.

Newly designed series of indole-containing pyrazole analogs, pyrazolinylindoles, were synthesized, and their structures were confirmed based on the spectral data of the 1H NMR, 13C NMR, and HR-MS analyses. Preliminary anti-cancer activity testings were carried out by the National Cancer Institute, United States of America (NCI, USA). Compounds HD02, HD05, and HD12 demonstrated remarkable cytotoxic activities against nine categories of cancer types based cell line panels which included leukemia, colon, breast, melanoma, lungs, renal, prostate, CNS, and ovarian cancer cell lines. The highest cytotoxic effects were exhibited by the compounds HD02 [1-(5-(1-H-indol-3-yl)-3-(p-tolyl)-4,5-dihydro-1H-pyrazol-1-yl)-2-phenylethanone], HD05 [1-(3-(4-chlorophenyl)-5-(1H-indol-3-yl)-4,5-dihydro-1H-pyrazol-1-yl)-2-phenoxyethanone], and HD12 [(3-(4-chlorophenyl)-5-(1H-indol-3-yl)-4,5-dihydro-1H-pyrazol-1-yl)(pyridin-4-yl)methanone] against some of the 56 types of NCI-based cell lines in different panels. Compound HD05 showed the maximum range of cancer cell growth inhibitions against all categories of the cell lines in all nine panels. On average, in comparison to the referral standard, imatinib, at a dose level of 10 µM, the HD05 showed significant activity against leukemia in the range of 78.76%, as compared to the imatinib at 9% of cancer cells' growth inhibitions. Molecular docking simulation studies were performed in silico on the epidermal growth factor receptor (EGFR) tyrosine kinase, in order to validate the activity.

Galangin/ß-Cyclodextrin Inclusion Complex as a Drug-Delivery System for Improved Solubility and Biocompatibility in Breast Cancer Treatment.

The purpose of this study was to evaluate the potential of a newly modified cyclodextrin derivative, water-soluble ß-cyclodextrin-epichlorohydrin (ß-CD), as an effective drug carrier to enhance the poor solubility and bioavailability of galangin (GAL), a poorly water-soluble model drug. In this regard, inclusion complexes of GAL/ß-CDP were prepared. UV-VIS spectrophotometry, Fourier-transform infrared spectroscopy (FTIR), X-ray crystallography (XRD), zeta potential analysis, particle size analysis, field emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM) were applied to characterize the synthesized GAL/ß-CD. Michigan Cancer Foundation-7 (MCF-7; human breast cancer cells) and rat embryo fibroblast (REF; normal cells) were employed to examine the in vitro cytotoxic effects of GAL/ß-CD using various parameters. The dye-based tests of MTT and crystal violet clearly exhibited that GAL/ß-CD-treated cells had a reduced proliferation rate, an influence that was not found in the normal cell line. The cells' death was found to follow apoptotic mechanisms, as revealed by the dye-based test of acridine orange/ethidium bromide (AO/EtBr), with the involvement of the mitochondria via caspase-3-mediated events, as manifested by the Rh 123 test. We also included a mouse model to examine possible in vivo toxic effects of GAL/ß-CD. It appears that the inclusion complex does not have a significant influence on normal cells, as indicated by serum levels of kidney and liver enzymatic markers, as well as thymic and splenic mass indices. A similar conclusion was reached on the histological level, as manifested by the absence of pathological alterations in the liver, kidney, thymus, spleen, heart, and lung.