Dr. Reddy's Laboratories (United States)

Background: Anemia affects over 50–60% of women of reproductive age, children, and pregnant women in many parts of India (per NFHS and WHO data), contributing to maternal mortality, low birth weight, impaired child development, and reduced productivity. Early detection through hemoglobin (Hb) screening is essential for interventions like iron supplementation, deworming, and nutritional programs. However, traditional lab-based methods (e.g., automated hematology analyzers using cyanmethemoglobin or flow cytometry) are expensive, require trained personnel, infrastructure, and sample transport—often impractical in rural or primary health centers (PHCs), antenatal clinics, schools, or blood donation camps. Accurate and rapid hemoglobin (Hb) estimation is critical for anemia screening and clinical decision-making. This study evaluates the analytical performance of the Hemo Spark+ Hemoglobin meter point-of-care (POC) meter, based on dual wavelength absorption photometry, compared to the HemoCue Hb 201 reference standard. Performance was assessed through statistical interpretation of accuracy, precision, and agreement using both venous and capillary blood samples. Objective: To assess the analytical reliability and correlation of the HemoSpark+ Hemoglobin meter, hemoglobin levels were estimated in parallel with the Hemo Cue Hb 201 hemoglobin meter with both venous and blood samples. To determine if Hemo Spark+ meter is a viable, cost-effective alternative for anemia screening, blood donation, and maternal health programs, especially where portability and ease-of-use are critical. Materials and Methods: This prospective, method-comparison study was conducted to evaluate the diagnostic performance of the Hemo Spark+ against the HemoCue Hb 201 (Reference Method). The study protocol adhered to the CLSI EP09-A3 guidelines for method comparison and bias estimation using patient samples. A total of 321 participants were enrolled, including: Adults (N=236): Stratified by gender (Male/Female). Children (N=85): Defined as individuals under 12 years of age. Two types of blood matrices were collected from each participant to evaluate matrix effects: Capillary Blood: Obtained via a standard finger-prick using a high-flow lancet. Venous Blood: Collected via venipuncture into K2-EDTA vacuum tubes. Venous samples were analyzed within 4 hours of collection to ensure cellular integrity. Results: The Hemo Spark+ hemoglobin meter meets and exceeds the performance requirements for point-of-care anemia screening. With a correlation of 0.9989 and diagnostic sensitivity and specificity exceeding 95%, it provides a reliable, low-bias alternative to the HemoCue Hb 201 for both adult and pediatric populations. The device demonstrated high repeatability and reproducibility across the three critical hemoglobin levels. Intra-Sample (Repeatability): Testing the same sample 20 times on one meter. Low (8.2 g/dL): CV = 1.46; Medium (13.5 g/dL): CV = 0.89; High (17.1g/dL): CV=0.70%; Inter-Sample (Reproducibility): Testing the same sample across 5 different meters. Low (8.2 g/dL): CV = 2.20%; Medium (13.5 g/dL): CV = 1.33%; High (17.1 g/dL): CV = 1.05%; There was no statistically significant difference (p > 0.05) between venous and capillary blood performance. While capillary samples showed a slightly higher standard deviation (+/- 0.12 g/dL) compared to venous samples (+/- 0.08 g/dL), both remained well within the target accuracy and sensitivity/specificity thresholds (> 95%). Conclusion: The Hemo Spark+ hemoglobin meter meets and exceeds the performance requirements for point-of-care anemia screening. With a correlation of 0.9989 and diagnostic sensitivity and specificity exceeding 95%, it provides a reliable, low-bias alternative to the HemoCue Hb 201 for both adult and pediatric populations.

Abstract Activating mutations in KRAS occur at high frequency in colorectal, lung, and pancreatic cancers, which together account for a substantial proportion of global cancer mortality. Mutant KRAS is constitutively biased toward the GTP-bound state, driving persistent proliferative signaling, but simultaneously imposes oncogenic stresses that threaten cellular homeostasis. To sustain transformation, KRAS-mutant cells engage adaptive stress-response mechanisms, many of which converge on translational control mediated by the eIF2-eIF2B axis. While eIF2B is classically known as a guanine nucleotide exchange factor (GEF) for eIF2 during translation initiation, its potential role in directly regulating oncogenic signaling pathways has remained unexplored. Here, we identify a non-canonical function of eIF2B as a direct activator of mutant KRAS signaling. We demonstrate that eIF2B forms a tripartite complex with SOS and mutant KRAS at the plasma membrane (PM), thereby enhancing KRAS activation and tumorigenic signaling. Biochemical assays and structural modeling support an interaction between the catalytic ε subunit of eIF2B and the allosteric Ras-binding site of SOS, stabilizing SOS in an active conformation. This interaction potentiates SOS-mediated GDP/GTP exchange on mutant KRAS and promotes KRAS nanoclustering at the PM. Importantly, eIF2B exhibits marked specificity for mutant KRAS4B, but not KRAS4A, HRAS, or NRAS. This selectivity arises from KRAS4B’s unique polybasic membrane-anchoring domain and from eIF2B-dependent remodeling of plasma membrane lipid composition. eIF2B enhances glycosphingolipid (GSL) biosynthesis, particularly GM3 and SM4, through translational upregulation of B4GALT5, generating a membrane environment that preferentially supports mutant KRAS4B anchoring and signaling. Disruption of GSL synthesis impairs formation of the eIF2B:SOS:KRAS complex and selectively reduces mutant KRAS activation. Notably, eIF2B’s stimulation of mutant KRAS signaling occurs independently of eIF2α phosphorylation, separating its translational stress-response function from its oncogenic signaling role. Functionally, eIF2B promotes tumor growth specifically in KRAS-mutant cancer models, including human xenografts and an autochthonous KRAS G12C-driven lung adenocarcinoma model. Clinically, high expression of eIF2Bε correlates with poorer outcomes in patients with KRAS-mutant tumors. Our findings identify eIF2B as a previously unrecognized regulator of mutant KRAS-driven tumorigenesis that links translational control, membrane lipid remodeling, and oncogenic signaling. By coordinating SOS activation, KRAS membrane nanoclustering, and selective translation, eIF2B emerges as a central modulator of KRAS oncogenic output and a potential therapeutic and prognostic target. In vivo targeting of eIF2B supports its use as a combinatorial strategy with KRAS inhibition to broaden the therapeutic window in KRAS-mutant cancers. This abstract was edited and refined with the assistance of generative artificial intelligence to improve clarity and conciseness. Citation Format: Hyungdong Kim, Shiqi Diao, Kwang-Jin Cho, Hyun-Ro Lee, Junchen Liu, Pascal Egea, Tatu Pantsar, Milla Kurki, Nour Ghaddar, Shuo Wang, Jia Yi Zou, Mehdi Amiri, Ritchel Gannaban, John F. Hancock, Kylie M. Rice, Atsuo Sasaki, John Asara, Brajendra Tripathi, Douglas Lowy, Rosalie Lawrence, Maria Hatzoglou, Carlos R. Azpilcueta-Nicolas, Jean-Philip Lumb, John Columbus, Thomas J. Turbyville, Christopher B. Marshall, Mitsuhiko Ikura, Jay T. Groves, Nahum Sonenberg, Peter Walter, Antonis E. Koromilas. eIF2B Selectively Anchors and Activates Mutant KRAS4B [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RAS Oncogenesis and Therapeutics; 2026 Mar 5-8; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(5_Suppl_1):Abstract nr B001.

The rapid evolution of 6G networks has brought new challenges in the domain of task offloading (TO), particularly within edge computing environments that are heavily reliant on the internet of things (IoT). Traditional TO methods that based on rule-based heuristics or shallow learning techniques fail to adapt efficiently to the dynamic, unpredictable network conditions, resource heterogeneity, and varying task demands. The proliferation of edge computing, the IoT, and 6G networks has introduced new challenges in TO due to dynamic network conditions, resource heterogeneity, and unpredictable task demands. To address these challenges, this work proposes an innovative TO method that integrates deep reinforcement learning (DRL) with heuristic search methods. The combination of DRL and heuristic algorithms enhances adaptability, convergence speed, and decision-making efficiency, making it well-suited for real-time TO in complex and unpredictable environments This paper proposes a novel hybrid TO framework that integrates DRL with genetic algorithms (GA) to address these challenges. The proposed hybrid optimization technique offer promising solutions by leveraging the strengths of individual approaches to balance competing objectives, such as energy consumption, task completion time, and resource utilization. This method explores optimization strategies to enhance TO efficiency in decentralized environments mainly focusing on optimizing energy use while ensuring performance metrics like latency, throughput, and task deadlines are met.

Background: This research aims to evaluate and contrast the effectiveness and safety profiles of Aspirin with Rivaroxaban (RA) and Clopidogrel with Apixaban (CA) in minimizing thromboembolic risks among patients diagnosed with Deep Vein Thrombosis (DVT). Methodology: A forward-looking observational study was conducted involving 40 participants with confirmed DVT, allocated evenly between two treatment groups. Parameters assessed included recurrence and progression of DVT, bleeding events, death rates, and relevant laboratory markers. Data analysis involved Chi-square and T-tests using SPSS v26.0. Findings: The patients in the CA group had a significantly lower rate of DVT recurrence (5% vs. 45%; p= 0.0035) and a tendency towards less disease progression (10% vs. 35%; p = 0.051). The incidence of major and mild hemorrhage, as well as fatality rates, did not exhibit significant differences. However, the baseline INR, haemoglobin, and platelet count were significantly different (p<0.05) from the normal range. Conclusion: Clopidogrel-apixaban seemed to be more effective in the prevention of DVT recurrence without increasing the risk to the patients’ safety compared to Aspirin-Rivaroxaban. This study needs to be followed by broader, randomised trials to confirm these findings.

This study presents a novel methodology for developing a conservation Blueprint in the western Amazon, focusing on the Marañón, Napo, and Ucayali basins. Our approach highlights the critical role of fluvial integrity in shaping ecological functions and biodiversity through floodplain reshaping, habitat interconnecting across forests, and the transport and distribution of sediments, nutrients, and minerals throughout the region. We introduced two ecological attributes, the Modern Morphodynamics Index (MOR) and the Suspended Sediment Index (SSI), which together provide an improved representation of the temporal dynamics and connectivity that rivers impart to the landscape, as well as their influence on biotic dynamics. Our methodology integrates quantification and classification of biotic (five taxa) and abiotic variables across spatially nested hierarchies to assess ecological conditions and human-induced pressure. The resulting Ecological System (ES) units prioritizes areas with high ecological condition scores (i.e., high integrity) and low anthropogenic impact, enabling the development of more targeted conservation strategies. Our findings underscore (1) the value of incorporating MOR and SSI, which identify regions characterized by high dynamism and substantial sediment and nutrient loads as major drivers of biotic patterns; and (2) the urgent need for expanded species diversity surveys to better understand spatiotemporal distribution patterns across these complex river systems. Overall, this comprehensive framework offers a robust foundation for informing conservation planning and infrastructure development, supporting the long-term sustainable management of these critical Amazonian ecosystems.

Pumped fluid loops (PFLs) are critical for thermal and environmental control systems in satellites and in manned spacecraft like the International Space Station (ISS). However, a significant percentage of Western spacecraft PFLs experience failures (~17\%), and about half of those are due to debris in the system, prompting an increase in filtration research in microgravity environments. Current state-of-the-art (SOTA) solutions rely on active filtration, which requires the use sieve type filters (e.g. stainless steel mesh) or porous media, such as activated carbon or sintered ceramic; all of which actively block fluid flow to remove debris. Over time these filters become clogged, incurring a significant increase in pressure drop across the device, and a corresponding higher risk of system failure. The Micro-Gravity Sediment Trap (MGST) offers an innovative, high-efficiency passive filtration alternative, designed to augment or potentially replace active filter components in spacecraft PFLs. MGST has no orifices, bypass lines, or porous media, resulting in a very low pressure drop compared to active filters. Ground tests conducted with the current MGST design indicated exceptionally high particle trapping efficiencies (>94%) with particle sizes ranging from ~1 µm to 150+ µm. A flight test unit was subsequently designed and constructed to test the viability of MGST in a microgravity environment, specifically on the ISS. This paper focuses on flight unit design, post-flight data analysis techniques, and results obtained from the on-orbit test.

Heart disease remains a leading cause of global mortality, underscoring the importance of early and accurate detection methods. This study investigates the application of machine learning techniques for heart attack detection using a clinical dataset. A comprehensive methodology was adopted, beginning with data preprocessing that included missing value imputation, normalization of numerical features, and categorical encoding through one-hot transformation. Multiple models were developed and analyzed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Model evaluation was conducted using stratified train-test splits and cross-validation, with performance measured across accuracy, precision, recall, F1-score, and ROC-AUC. The results revealed that ensemble-based models significantly outperformed traditional classifiers. Random Forest, Gradient Boosting, and XGBoost each achieved an accuracy of 98.48 %, with precision, recall, and F 1 -score values of 0.9877, and ROC-AUC values above 0.98. Notably, Random Forest achieved the highest ROC-AUC of 0.9958, indicating superior discriminative ability. In contrast, Logistic Regression attained moderate performance (79.9 % accuracy), while KNN <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(66.7 \%)$</tex> and SVM (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$74.6 \%)$</tex> lagged behind, reflecting limitations in handling the dataset's complexity. Comparative analysis underscores the reliability and robustness of ensemble learning approaches for clinical prediction tasks. This work highlights the potential of machine learning, particularly ensemble methods, as decision-support tools in healthcare, aiding early detection and reducing the risk of misdiagnosis. Future research may extend this work by incorporating hybrid deep learning models, larger datasets, and advanced feature selection techniques to further enhance prediction accuracy and interpretability.

An image-based modeling technique for microstructure was created to simulate the coating wear process and to aid in the composition design of the abradable coating. A range of analysis tools were utilized for microstructure modeling, meshing, and wear analysis to develop a model of an abradable coating and analyze its wear under controlled conditions. The correlation between the percentage change in the form and composition of metal, oxide, and voids, as well as mechanical qualities like hardness, is investigated using computational models based on actual micrographic pictures of abradable coatings. Abrasion-resistant coatings such as NiCrAl-bentonite and other CoNiCrAl-BN polyester coatings were examined using microstructure photos. Various techniques were used to construct, mesh, and refine 3D coating finite element models from microstructure images, which were then enhanced by adding the blade. After that, the coating models were put through the simulated rub rig test. The effect of material parameter variation such as the changes in porosity level and hardness level on the abradability level of coatings is also presented here. As porosity increases, so does the erosion rate and abradability. A decrease in abradability occurs with an increase in hardness. Increased coating wear or decreased blade wear results in increased abradability. The abradability number of the CoNiCrAl-BN polyester coating coating, as determined by simulation, is higher than that of the NiCrAl-bentonite coating. Main outcomes of this investigation reveal that coatings with higher porosity (56%) and lower hardness (48 HR15Y) exhibit superior abradability (up to 11.34), enabling efficient, controlled material removal with minimal blade wear. In contrast, denser microstructures (46% porosity, 71 HR15Y hardness) show reduced abradability (∼6.56), resulting in higher blade damage due to increased contact stresses. Statistical analysis confirms abradability is directly proportional to porosity and erosion rate, and inversely proportional to hardness and interfacial bond strength. These findings provide a predictive, microstructure-driven design framework for optimizing abradable seal performance in gas turbine engines.

In recent years, model-informed strategies such as the model integrated bioequivalence (MIBE) approach have gained significance due to their ability to enable decision making, streamline drug product development, and to waive bioequivalence studies, as applicable. In this work, we demonstrate the successful application of the MIBE approach that led to the waiver of a repeat fasting and fed bioequivalence studies for clopidogrel bisulfate 300 mg tablets. In the pivotal bioequivalence study, pharmacokinetic sampling in fasting and fed conditions was performed until 12 h, shorter than the recommended sampling up to at least three half-lives of the drug. The mean half-life of reference listed drug clopidogrel was 6 h. During dossier review, a query was received from the regulatory agency to assess the impact of early termination of the study on AUC0-inf and bioequivalence assessment, although the pivotal study demonstrated average bioequivalence. To address this, a population pharmacokinetic model was developed under fasting and fed conditions based on the pilot study data that included data until 24 h. Further, the pivotal study pharmacokinetic parameters were predicted based on a model developed with pilot data and complete profiles until 10 half-lives were simulated. The model-derived parameters were utilized to assess preserving Type-1 error rate at 5% and bioequivalence was simulated. The simulations demonstrated average bioequivalence under fasting and fed conditions, and the 90% confidence intervals within 80%-125%. Overall, the MIBE approach demonstrated that truncated sampling till 12 h did not compromise the interpretability of pivotal study, thereby alleviating the need for a repeat bioequivalence study.