CMR University

ObjectivesThis review compiles to provide an overview of non-small cell lung cancer (NSCLC) and the therapeutic rationale for antibody-drug conjugates (ADCs), followed by a summary of current evidence highlighting the efficacy, safety, and adverse event management of trastuzumab deruxtecan (T-DXd) in HER2-mutant metastatic NSCLC.Data SourcePUBMED, ClinicalTrials.gov, and Google Scholar were searched to retrieve relevant clinical studies including randomized controlled trials, non-randomized studies, observational studies, and real-world evidence from its approval to July, 2025 for the treatment of patients with HER2-mutant metastatic NSCLC received prior systemic therapy. The search strategy was guided by the target therapeutic area, intervention type, and population studied.Data SummaryT-DXd, a HER2-targeted ADC and second line treatment, has shown significant clinical benefit in HER2-mutant metastatic NSCLC, as evidenced by key clinical trials (NCT04644237 and NCT04644237) demonstrating high objective response rates (ORRs) and disease control rates (DCRs). Although it exhibits a manageable safety profile, interstitial lung disease (ILD) remains a notable adverse event (AE), with improved tolerability observed at the lower dose of 5.4 mg/kg. Moreover, multidisciplinary Strategies are also used to manage T-DXd associated AEs. Additionally, ongoing trials are evaluating the combination of T-DXd with immunotherapy and targeted agents to optimize safety outcomes and efficacy in high-risk NSCLC populations.ConclusionsT-DXd at 5.4 mg/kg offers substantial efficacy with better tolerability than 6.4 mg/kg. Incorporating AE mitigation, real-world insights, and combination trial data may further optimize its safety and clinical benefit in this biomarker-defined population.

This study presents a comprehensive investigation of blood flow embedded with magnetic nanoparticles (〖Fe〗_2 O_3 and Fe_3 O_4) over an exponentially stretching surface, incorporating the effects of a tilted magnetic field, Joule heating, and thermal radiation. The exponential stretching model captures nonlinear vascular wall deformation and stent expansion, while the tilted magnetic field offers a more realistic representation of biomedical device orientations. A mathematical model governing the flow is formulated and transformed into a system of ordinary differential equations using suitable similarity transformations, which are solved numerically using the MATLAB bvp4c solver. Numerical simulations elucidate the influence of magnetic-field inclination, thermal radiation, and Joule heating on velocity and temperature distributions. Results reveal that thermal convection and radiation enhance flow velocity, whereas increased magnetic field strength and local porosity induce significant flow resistance due to enhanced drag forces. The study highlights the critical role of optimizing nanoparticle properties and external magnetic stimuli to regulate thermal behavior while minimizing hydrodynamic resistance. These findings contribute to improved heat transfer performance in microfluidic systems, advanced thermal management in electronic devices, and optimized biomedical applications such as magnetic hyperthermia and targeted drug delivery, where precise control of blood-flow dynamics is essential.

Space structures are a common element in modern architecture because of their expressive form and intrinsic geometric complexity, which allow architects to produce designs that are both aesthetically pleasing and structurally sound. The majority of studies and research on space structures have traditionally concentrated on their structural behaviour, highlighting elements like geometric stability, load distribution, and material efficiency. However, it is becoming more and more crucial to look at space structures from both an engineering and an architectural design standpoint as they continue to acquire traction in architectural practice. In the present study. Researches on dynamic analysis of space structures were widely carried out across the world. The study on structural behavior of grid, domes, vaults which are subjected to earthquake were carried out through many analytical and experimental works. The present work focuses on the study of orientation of stiffness of the supporting roof, and the effect of peripheral cross-bracings on overall lateral response. Further, dynamic analysis has been carried out for grid space structure with different horizontal bracings. Finally, criticality and its locations in various configurations of the space structures are identified. Based on the results and discussions, it is concluded that, the presence of horizontal and vertical bracings will resist the lateral load efficiently and particularly in model 5 where cross bracings are provided. From linear time history analysis, it can be concluded that, the presence of fixed base, vertical bracings and horizontal bracings has significant effect on the vibration characteristics. Presence of horizontal and vertical bracings will contribute significantly in resisting the lateral load of grid space structures.

Chest pain (angina), heart attack, and stroke can occur as a result of disorders affecting constricted or clogged arteries, collectively referred to as cardiovascular disease (CVD). This study is focused on the performance of various machine learning classifiers in predicting CVD dependent on symptoms of the patients. (Data cleansing (e.g., the removal of noise from data, the migration of misplaced data, the substantial in of non-attendance values when appropriate, and the categorization of attributes for various levels of prediction and decision making)): Do not required in the anticipated examine since we include only analyzed informative accumulation as of Kaggle. Classification, accuracy, understanding, and specificity analysis techniques are used to determine the diagnosis model presentation. These studies suggested an overarching prediction representation that can assess the individual’s risk of diagnosing or raising awareness of cardiovascular disease. Specifically, relevant rules are assessed for their relevancy to SVM and KNN model results. Comparisons are done based on Accuracy and AUC-ROC.

As the reliance on networked devices and software applications grows, there is a need for robust, secure, and cost-effective software update management approaches. To facilitate safe software updates, this paper proposes a blockchain-enabled ecosystem that generates keys, payment, and QR codes for delivery and authorization. The proposed Cost-Efficiency, Scalable, Stable ecosystem was thoroughly analyzed and evaluated. It is shown that the key generation systems provide strong protection against software piracy, while the blockchain backbone prevents unwanted manipulation. Safe banking integration paves the way for smooth monetization while QR-code-based delivery makes it easier for users to engage while reducing errors and increasing adoption rates. While the individual formation of these towers has greatly improved the overall level of security, they have inherent disadvantages as structures. Simple, secure payment methods are often not enough, and CP- ABE can be to monitor and implemented quite difficult because of its complexity. To resolve these drawbacks, we proposed a new ecosystem, where a secure payment method is combines with the resilience of Advanced Encryption Standard (AES) cryprographic algorithm. Instead of relying on OTPs, our solution uses a unique approach of encrypting OTPs and showing them as QR codes. This innovative technique effectively minimizes the risk of third-party applications compromising payment transactions, ensuring a secure and successful currency transfer.

Advances in artificial intelligence (AI) are changing the nature of worker-machine interaction, which is moving toward a model where humans and machines interact fluidly, completing tasks together and adjusting to the user and each other in real-time.Even taking advantage of recent innovative technologies (NLP, gangs, and adaptive learning) this paper consists of a service that allows the user to be in easy contact with any machine. In reality, they are actually following a linguistically driven form which combined deep learning with a rule-based system to give them a better context and response accuracy. It focused on issues related to communication such as user intent ambiguity, style differences between human and ML-based communication, and accessibility to different user populations. There is a new approach where emerging AI technologies are harnessed for developing such a system, they include natural language processing (NLP) that can parse spoken input and written text inputs and using computer vision technique to predetermine environmental modalities, taking into account voice inputs recognized through NLP.

Accurate channel-estimation algorithms are critical for enhancing the throughput of wireless communication systems, including millimetre wave (mmWave) multiple-input multiple-output (MIMO) systems, where precise channel knowledge enables reliable signal detection and beamforming. In practical wireless environments, impulsive non-Gaussian noise with unknown statistics often occurs due to electromagnetic interference and harsh propagation conditions, significantly degrading estimation accuracy and overall system performance. In this context, the maximum correntropy criterion (MCC) has emerged as an attractive solution for robust channel estimation that outperforms state-of-the-art algorithms. However, the MCC-based algorithm’s performance is sensitive to the tuning of hyperparameters, which is challenging in the presence of non-Gaussian noise, such as impulsive noise (IN). Furthermore, a recent genre of kernel width sampling methods makes MCC hyperparameter-free and allows for asymptotic convergence to the squared-error performance of MCC with the ideal kernel width. To ensure their practical applicability, convergence analysis is essential to theoretically guarantee stability and performance under various IN scenarios. This study presents convergence analysis of hyperparameter-free MCC-based channel estimation for mmWave MIMO systems considering various IN scenarios. To validate the theoretical analysis, simulations are conducted on practical mmWave MIMO system models. Simulation results closely match the analytical findings, which confirms the accuracy and effectiveness of the analysis we here present.