True Value Research Articles

Assessing the Effect of Changing the Average Hematocrit in Red Blood Cell (RBC) Units on the Post- Procedure Hematocrits of Patients Undergoing Erythrocytapheresis

Abstract Introduction/Objective Red cell exchange (RCE) is utilized as a therapeutic or prophylactic procedure for patients with sickle cell disease (SCD). When performing RCE, three hematocrits (Hct) are taken into account: pre-procedure and goal Hct for the patient and average Hct (avg-Hct) of the transfused RBC units. Although there is no definitive community consensus, many transfusion services employ RBC unit avg-Hct of 55% per available guidance. Our center anecdotally observed that utilizing RBC unit avg-Hct of 55% often resulted in post-procedure patient Hcts higher than intended goal Hct, potentially causing hyperviscosity. This study aims to examine the effect of changing the avg-Hct from 55% to 60% on the accuracy of post-procedure HCTs, with the 60% avg-Hct employed based on prior quality studies of RBC units at our facility. Methods/Case Report We performed a retrospective chart review for SCD patients receiving chronic, prophylactic RCE at a large academic hospital, collecting the following for each procedure: goal procedure Hct, number of RBC units exchanged, avg-Hct used, and pre-/post-procedure patient Hcts. Differences between goal Hcts and post- procedure Hcts for each procedure in the avg-Hct=55% and avg-Hct=60% groups were calculated. Next, mean calculated differences for both groups were compared using a two-sample t-test (P<0.05 was considered significant). Results (if a Case Study enter NA) From 1/2023–2/2024, 293 prophylactic RCEs were performed for SCD patients. Median number of RBC units per procedure was 7, with 51% of procedures (n=149) employing unit avg-Hct=55%, while 49% of procedures (n=144) used a unit avg-Hct=60%. Post-procedure patient Hct and goal Hct differences were statistically different according to unit avg-Hct values utilized (mean difference=1.31% for avg-Hct=55% vs mean difference=0.52% for avg-Hct=60%; P<0.0001). Conclusion Changing our protocol to use RBC unit avg-Hct=60% resulted in post-procedure patient Hcts significantly closer to intended goals. Utilization of a more accurate average-Hct value has a substantial impact on post-procedure Hct in RCEs and may limit adverse events.

Deep Learning-Assisted Compound Bioactivity Estimation Framework

Drug Discovery is a highly complicated process. On average, it takes six to twelve years to manufacture a new drug and have the product released in the market. It is of utmost importance to find methods that would accelerate the manufacturing process. This significant challenge in drug development can be addressed using deep learning techniques. The aim of this paper is to propose a deep learning-based framework that can help chemists examine compound biological activity in a more accurate manner. The proposed framework employs autoencoder for data representation of the compounds data, which is then classified using deep neural network followed by building a customized deep regression model to estimate an accurate value of the compound bioactivity. The proposed framework achieved an accuracy of 89% in autoencoder reconstruction error, 79.01% in classification, and MAE of 2.4 while predicting compound bioactivity using deep regression model.

Charge Carrier Density in Organic Semiconductors Modulates the Effective Capacitance: A Unified View of Electrolyte Gated Organic Transistors.

A framework for electrolyte-gated organic transistors (EGOTs) that unifies the view of interfacial capacitive coupling of electrolyte-gated organic field-effect transistors (EGOFETs) with the volumetric capacitive coupling in organic electrochemical transistors (OECTs) is proposed. The EGOT effective capacitance arises from in-series capacitances of the electrolyte/gate electrode and electrolyte/channel interfaces, and the chemical capacitance of the organic semiconductor channel whose weight with respect to the interfacial capacitance is modulated by the charge carrier density, hence by the gate voltage. The expression for chemical capacitance is derived from the DOS of the organic semiconductor, which it is assumed to exhibit exponential energy disorder in the HOMO-LUMO gap. The analytical expression of the EGOT current is assessed on experimental data and shown to accurately predict the shape of the whole transfer curve of an EGOT thus allowing to extract accurate values for the switch-on voltage and the interfacial transconductance, without assumptions on specific response regime and, in OECT, without invoking the volumetric capacitance. Interestingly, the EGOT model recovers EGOFET and OECT as limit cases and, in the latter case, explicitly represents the volumetric capacitance in terms of the energy disorder and the bandgap of the organic semiconductor.

Joint torque estimation method of industrial robot by combining error compensation model based on LSTM and iterative weighted parameter identification

It is essential for human-robot interaction to accurate joint torque value estimation of industrial robots without force/torque (F/T) sensors. The most common method to estimate the value of robot joint torque is based on dynamic model parameter identification. However, no matter how elaborate the modeling methods are used, there are always uncertainty errors when robot’s joint rotation direction changes. In this paper, an identification framework is proposed to estimate optimal model parameters, and a deep learning network is proposed to compensate for the uncertainty errors caused by the unmodeled dynamics part. At first, the dynamic parameters are estimated during the Least Squares (LS) identification process considering the noise influence and data outliers, and the nonlinear friction model is unified into the identification framework. Secondly, based on Long-Short Term Memory (LSTM) network, an error compensation model (ECM) is proposed to establish the mapping relationship between the joint motion and the identification errors. Finally, a 6-DOF robot is used for parameter identification and ECM validation. Experimental results show that the proposed identification method is better than the LS method. Compared with the torque value estimated by the identification method, the proposed ECM can compensate for the uncertainty errors and improve the torque estimation accuracy.

The Impact of Machine Learning-based Student Behaviour Detection on Teaching in Teaching Classroom Scenarios

The purpose of this study is to address the issue of low efficiency and time-consuming monitoring of student classroom behaviour in teaching classroom scenarios, as well as how to apply deep learning to student classroom behaviour detection. Classroom teaching is the main way for students to acquire and master knowledge, and teachers need to pay attention to students’ behaviour in the classroom and make timely corrections. In this study, the teaching classroom of undergraduate students was used as an example for application and analysis. Based on the You Only Look Once v5s network, a student behaviour detection algorithm was designed by combining the ghosting module and optimised feature fusion structure, and a student behaviour association technology suitable for continuous teaching classrooms was designed. Through the visualisation results, it can be seen that the research method can accurately detect the behaviour categories of students, and the effect is better in target dense scenes. Through quantitative analysis using correlation technology, it can be concluded that there are very few students with the lowest level of seriousness, while the majority of students have a level of seriousness above the qualified level. The overall situation of the teaching classroom is good. These student behaviour analyses can energy the level of seriousness of students, form effective and objective classroom feedback, provide teachers with objective and accurate reference value, and promote the construction of smart classrooms.

Exploring Public Perception of News and Citizenship and Its Relationship with News Use

ABSTRACT This survey study (N = 1065) examines Americans’ perception of news and citizenship and its relationship with consumption of news via diverse media—traditional news media, cable news channels and political satire—that include features of news yet differ from one another in their adherence to journalistic norms, such as fairness, accuracy and objectivity, to varying degrees. Findings suggest that Americans still hold high expectations for news to be fair, accurate and objective and value the role of news in our democracy and democratic citizenship, yet have negative assessments of their performance, such that news media fall short of the democratic ideal. These findings may speak to the declining public trust in media and growing perception of media bias.

A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization.

The propagation rate coefficient (kp) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The kp in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict kp more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monomers, which indicates that the model exhibits strong generalization capabilities and provides reliable and robust predictions. In addition, this model can accurately predict the influence of the ester side chain length of (meth)acrylates on kp, aligning well with established scientific knowledge. This approach offers a straightforward and practical model for other researchers to rapidly obtain accurate kp values by employing monomer structural information. The model is sufficiently general to apply to a wide range of (meth)acrylate and butadiene FRP monomers, thereby supporting kinetic modeling of polymerization reactions.

A-078 Harmonization of Immunoassay Reference Intervals with LC-MS/MS for Total and Calculated Free Testosterone Measurement

Abstract Background Management of male hypogonadism relies on accurate serum testosterone testing and reference values. This study aimed to establish harmonized reference ranges for total and calculated free testosterone in adults using the Siemens Atellica immunoassay, validated against a reference method. Methods The Siemens Atellica IM total testosterone assay is a fully automated competitive chemiluminescent immunoassay. We conducted 25 de-identified serum samples. Inter-laboratory correlation studies for total testosterone were performed on in-house Siemens Atellica and liquid chromatography-tandem mass spectrometry (LC-MS/MS) at a reference laboratory. Free testosterone was calculated using the International Society for the Study of the Aging Male (ISSAM) Free Testosterone calculator, which can be accessed from the ISSAM website, http://www.issam.ch/freetesto.htm, using the assayed concentrations of testosterone and Sex Hormone Binding Globulin (SHBG) on the Siemens Atellica IM system. The Siemens Atellica IM SHBG assay is a sandwich monoclonal immunoassay. Calculated free testosterone was compared with LC-MS/MS. Deming and linear regression, and Bland-Altman plot were employed for data analysis. Reference intervals were adopted from the reference laboratory and verified using 20 male and 20 female healthy samples. Total testosterone: Male 240-950 ng/dL, Female 8-60 ng/dL; Free testosterone: Male 2.29-20.7 ng/dL, Female 0-1.08 ng/dL. Results Strong correlations were observed between Siemens Atellica and LC-MS/MS for total testosterone (R^2 = 0.9915, slope = 0.988, intercept = 35) and for calculated free testosterone (R^2 = 0.9645, slope = 0.988, intercept = -0.4). Adopted reference intervals were verified using local healthy samples. Conclusions Harmonized reference intervals for total and calculated free testosterone, validated on Siemens Atellica, offer consistent results comparable to LC-MS/MS. This study demonstrates the feasibility of establishing standardized reference ranges for testosterone assays across laboratories.

A-092 Siemens Healthineers Commitment to Quality - A Focus on Continuous Improvement and Customer Experience

Abstract Background It is necessary that each step of in vitro diagnostic assay development and manufacturing processes be documented and executed in alignment with established quality management systems. Here, we describe how Siemens Healthineers (SHS) maintains assay traceability and focus on continuous improvement using the Atellica® CH A-LYTE® Integrated Multisensor Technology (IMT) assays measuring electrolytes sodium (Na), potassium (K), and chloride (Cl) as a prime example. Methods Establishing an unbroken chain of comparison from the National Institute of Standards and Technology (NIST) higher standard references to the product calibrators ensures accurate value assignment within stated measurement uncertainties. Traceability and standardization are achieved by creating a panel of 10 serum anchor pools spanning the analytical measuring interval for each electrolyte: Na, K, and Cl. The concentrations of the anchor pools are determined at a reference laboratory using methods calibrated with standard solutions derived from NIST reference material. These serum anchor pools are used by SHS, assigning values to product calibrator material which maintains the chain of traceability to the primary reference methods. In addition to traceability, onboard stability for the A-LYTE® IMT multisensor is continuously monitored and evaluated against allowable drift limits for each electrolyte. Although observed drift was within allowable limits, a design change was implemented to improve recovery drift over the use life of the sensor. Results To ensure consistent, robust, and accurate performance of the A-LYTE IMT, a strict set of testing procedures are used to verify the accuracy of value assignments for each manufactured lot of master standards. The recovery of the NIST standard reference material must fall within manufacturer established acceptable bias limits. Consistency over nine master lots is shown with bias ranging from -0.9% to +1.9%, -2.4% to +1.8% and -1.8 to +0.5%, for Na, K and Cl, respectively. By further optimizing the amount of Na ionophore and the polymer matrix of the Na sensor, drift over the use life was minimized and onboard stability improved. Prior to making the sensor change, customer Na QC data at 116 mmol/L showed an SD of 1.1 mmol/L (n=585), whereas the precision improved to an SD of 0.6 mmol/L (n=470) post-optimization. As Manufacturers optimize assay performance, it may become necessary for laboratories to re-evaluate reference intervals for their patient populations. For example, one large integrated delivery network established a new reference interval for patient anion gap (AG). A re-evaluation of the historical AG reference interval which was 5-15 mmol/L identified the need for an update. AG values were calculated from 16,948 healthy patients and used to establish a new reference interval of 2-10 mmol/L. Conclusions Accurate clinical laboratory measurements are critical for diagnosing, monitoring, and treating diseases. A-LYTE assays have an unbroken chain of traceability to NIST reference material and minimal drift over the life of the multisensor. Continuous monitoring to identify improvement projects and rigorous control systems ensure sensor performance over time. This provides confidence that the patient results produced by the laboratory are accurate and consistent over time and across different laboratory locations.

A-037 A1c by separation and enzymatic methods: what's the difference?

Abstract Background Accurate values for hemoglobin A1c (HbA1c) are crucial for diagnosing and monitoring diabetes mellitus. Two common methods to measure HbA1c are capillary electrophoresis (CE) and immunoassay (IA). Many methods used to measure HbA1c are prone to interferences from hemoglobin variants and abnormalities affecting the red blood cell’s (RBC) turnover. Consequently, the average estimated glucose levels using the HbA1c value can be unreliable in diabetics with hemoglobinopathies and other RBC lifetime disorders. Separation methods such as CE can capture hemoglobinopathy information, while IA methods may not. We performed this study in a population with relatively high levels of hemoglobin disorders to evaluate the performance of HbA1c testing on the Alinity platform (IA) against the Sebia Capillarys 3 Tera instrument (CE). Methods Samples were identified retrospectively based on a search of the Electronic Medical Record for all patients with an ordered and resulted HbA1c and complete blood count in the Montefiore Medical Center the day prior to sampling. Routine HbA1c testing was performed by an Abbott Immunoassay method on the Abbott Alinity Instrument and CBC results were obtained from a Sysmex XN2100 instrument. Records were compared by patient name, medical record number, and sample collection time to identify all patients with CBC and HbA1c drawn within one hour of each other. Those samples were retrieved from storage (a 4° automated refrigerator) within 48 hours of draw and tested via the Sebia Capillarys 3 Tera instrument using HBA1c kits. Results In this large representative population study (N=2075), there was overall excellent agreement between IA and CE. The correlation coefficient (R2) of the whole dataset was 0.989, with an average bias of 2.7% between the two methods. Additional analysis was performed on those with possible RBC turnover disorder (patients with RBC counts below 4x106/uL or above 6x106/uL, MCVs above 96 fL or below 80 fL, RDW above 17%, or abnormal A2 value: total of 638 samples), those with a hemoglobin variant as detected by the Capillarys 3 instrument (162 samples), or with a measurable hemoglobin F value detected by the Capillarys 3 instrument (37 samples). Samples were sorted into these groups independently and thus there was significant overlap between groups, notably with 23 of 37 samples with elevated HbF also having a hemoglobin variant. In both the RBC disorder and hemoglobinopathies group, similar low bias and high correlations were seen. The elevated F samples showed a clear negative bias (5.6%) for all patients on the IA method as compared with CE. The IA method states that samples with HbF above 5% should not be tested. However, as this is not a separation method, there is no way to test samples for HbF before analysis, and bias was seen for samples below 5%. Conclusions The IA and CE methods showed similar performance across patients regardless of RBC lifetime or hemoglobin disorder status, though presence of HbF is a potential interference and should be considered. Further questions remain for these instruments in terms of correlation with average glucose levels as calculated through CGM instruments.

Specific tension of human muscle in vivo: a systematic review.

The intrinsic force production capability of human muscle can be expressed as "Specific Tension," or, the maximum force generated per cross-sectional area of muscle fibers. This value can be used to determine, for example, whether muscle quality changes during exercise, atrophy, disease, or hypertrophy. A value of 22.5 N/cm2 for mammalian muscle has generally become accepted based on detailed studies of small mammals. Determining the specific tension of human muscle is much more challenging as almost all determinations are indirect. Calculation of human muscle specific tension requires an understanding of that muscle's contribution to joint torque, its activation magnitude, tendon compliance, and joint moment arm. Determining any of these parameters is technically challenging in humans and thus, it is no surprise that human specific tension values reported vary from 2 to 73 N/cm2. In this systematic review, we screened 1,506 published papers and identified the 30 studies published between 1983 and 2023 that used appropriate methods and which reported 96 human specific tension values. We weighted each parameter based on whether it was directly measured, estimated, or calculated based on the literature, with decreasing weighting used, the more indirect the methods. Based on this exhaustive review of the relevant human literature, we suggest that the most accurate value that should be used for human muscle specific tension is 26.8 N/cm2.

Blowin' in the Wind: Mapping the Dispersion of Metal(loid)s From Atacama Mining.

The Atacama Desert's naturally elevated metal(loid)s pose a unique challenge for assessing the environmental impact of mining, particularly for indigenous communities residing in these areas. This study investigates how copper mining influences the dispersion of these elements in the wind-transportable fraction (<75μm) of surface sediments across an 80km radius. We employed a multi-pronged approach, utilizing spatial modeling to map element distributions, exponential decay analysis to quantify concentration decline with distance, regime shift modeling to identify dispersion pattern variations, and pollution assessment to evaluate impact. Our results reveal significant mining-driven increases in surface concentrations of copper (Cu), molybdenum (Mo), and arsenic (As). Notably, within the first 20km, concentrations peaked at 1,016mgkg⁻1 for Cu, 31mgkg⁻1 for Mo, and a remarkable 165mgkg⁻1 for As. Cu and Mo displayed significant dispersion, extending up to 50km from the source. However, As exhibited the most extensive reach, traveling up to 70km downwind, highlighting the far-reaching ecological footprint of mining operations. Mineralogical analyses corroborated these findings, identifying mining-related minerals in surface sediments far beyond the immediate mining area. Although pollution indices based on the proposed Local Geochemical Background reveal significant contamination across the study area, establishing accurate pre-industrial baseline values is essential for a more reliable assessment. This study challenges the concept of "natural pollution" by demonstrating that human activities exacerbate baseline metal(loid)s levels. Expanding monitoring protocols is imperative to comprehensively assess the combined effects of multiple emission sources, including mining and natural processes, in safeguarding environmental and human health for future generations.

The clinical value of serum long non-coding RNA human leukocyte antigen complex group 11/microRNA-532-3p in the diagnosis and prognosis of patients with acute myocardial infarction undergoing percutaneous coronary intervention

BackgroundAcute myocardial infarction (AMI) is a cardiovascular disease with the highest morbidity and mortality rate in the world. Several studies have suggested that abnormal regulation of non-coding RNAs (ncRNAs) may play a vital role in the occurrence and progress of AMI.ObjectiveThe purpose of this study was to investigate the clinical values of human leukocyte antigen complex group 11 (HCG11) or miR-532-3p in the diagnosis and prognosis of patients with AMI after percutaneous coronary intervention (PCI).MethodsThe clinical data of 100 AMI patients who underwent PCI were analyzed retrospectively. According to whether major adverse cardiovascular events (MACE) occurred after PCI, they were divided into MACE group (n = 38) and non-MACE group (n = 62). Basic clinical data and serum HCG11 and miR-532-3p levels were analyzed. Multivariate Cox regression analysis was performed to evaluate the risk factors for MACE, and the receiver operator characteristic (ROC) curve was constructed to assess the clinical predictive value of HCG11 and miR-532-3p for MACE.ResultsCompared with the control group, the serum HCG11 level and miR-532-3p in AMI patients were significantly increased or decreased, and the serum levels of HCG11 and miR-532-3p in the MACE group were significantly increased and decreased, compared with those in non-MACE group. Multivariate Cox regression showed that HCG11 and miR-532-3p were risk factors for MACE occurrence. ROC curve investigated that HCG11 combined with miR-532-3p has accurate predictive value for MACE.ConclusionThis study showed that serum HCG11 and miR-532-3p have certain predictive value for MACE after PCI in patients with AMI.

Halving Time of BCR-ABL Transcripts as a Precise Predictor for Deep Molecular Response in Patients with Chronic Myeloid Leukemia Treated with TKI

To investigate the early predictive value of halving time (HT) of BCR-ABLIS for deep molecular response (DMR) in patients with chronic myeloid leukemia (CML) treated with tyrosine kinase inhibitor (TKI). The continuous data of newly diagnosed CML patients with complete case data and first-line imatinib treatment admitted to the Affiliated Hospital of Inner Mongolia Medical University from January 2014 to June 2022 were retrospectively analyzed. Combined with the clinical characteristics of the patients and the efficacy analysis at each time point, a logistic regression model was used to explore the independent influencing factors of DMR, and combined HT of BCR-ABLIS with BCR-ABLIS level at 3 months to predict DMR of the patients. Univariate and multivariate analyses showed that HT and 3-month BCR-ABLIS levels were independent influencing factors for MR4, MR4.5, and stable MR4.5 ( P < 0.05). ROC curve analysis determined that the best cut-off value of HT was 28 days. Compared with patients with HT>28 d, patients with HT≤28 d were more likely to obtain DMR at 2, 3, and 5 years, respectively (74.2% vs 27.3%, 71.2% vs 22.7%, and 63.6% vs 25.0%, all P < 0.001). The patients were divided into 4 groups according to BCR-ABLIS levels at 3 months and HT. Kaplan-Meier analysis showed that the patients in the BCR-ABLIS≤10% and HT≤28 d group had a higher probability of obtaining cumulative MR4 and MR4.5 than those in the BCR-ABLIS≤10% and HT>28 d group (P < ＜0.05); Patients in the BCR-ABLIS>10% and HT≤28 d group had a higher probability of obtaining cumulative MR4 and MR4.5 than those in the BCR-ABLIS＞10% and HT>28 d group ( P < 0.05). In addition to BCR-ABLIS level, HT of BCR-ABLIS can be used as another important predictor of treatment efficacy in CML patients. The combination of BCR-ABLIS level and HT has a more accurate predictive value for long-term molecular response of CML patients after TKI treatment.

A Deep Learning Application of Capsule Endoscopic Gastric Structure Recognition Based on a Transformer Model.

Gastric structure recognition systems have become increasingly necessary for the accurate diagnosis of gastric lesions in capsule endoscopy. Deep learning, especially using transformer models, has shown great potential in the recognition of gastrointestinal (GI) images according to self-attention. This study aims to establish an identification model of capsule endoscopy gastric structures to improve the clinical applicability of deep learning to endoscopic image recognition. A total of 3343 wireless capsule endoscopy videos collected at Nanfang Hospital between 2011 and 2021 were used for unsupervised pretraining, while 2433 were for training and 118 were for validation. Fifteen upper GI structures were selected for quantifying the examination quality. We also conducted a comparison of the classification performance between the artificial intelligence model and endoscopists by the accuracy, sensitivity, specificity, and positive and negative predictive values. The transformer-based AI model reached a relatively high level of diagnostic accuracy in gastric structure recognition. Regarding the performance of identifying 15 upper GI structures, the AI model achieved a macroaverage accuracy of 99.6% (95% CI: 99.5-99.7), a macroaverage sensitivity of 96.4% (95% CI: 95.3-97.5), and a macroaverage specificity of 99.8% (95% CI: 99.7-99.9) and achieved a high level of interobserver agreement with endoscopists. The transformer-based AI model can accurately evaluate the gastric structure information of capsule endoscopy with the same performance as that of endoscopists, which will provide tremendous help for doctors in making a diagnosis from a large number of images and improve the efficiency of examination.

Research on Supply Chain Optimization and Management Based on Deep Reinforcement Learning

This research introduces a groundbreaking approach to supply chain optimization and management, termed as Deep Reinforcement Learning based Supply Chain Optimization and Management (DRL-SCOM). At the core of this approach is the utilization of advancements in Deep Reinforcement Learning (DRL), specifically through the integration of Randomized Ensembled Double Q-learning (REDQ) and Trust Region Policy Optimization (TRPO). DRL-SCOM is designed to effectively tackle the inherent complexities and dynamic challenges that are characteristic of supply chain management. One of the key strengths of DRL-SCOM lies in its use of REDQ, which plays a crucial role in mitigating the overestimation bias commonly associated with traditional Q-learning methods. This results in more accurate value estimation and policy improvement, a critical factor in the effective management of supply chains. Additionally, the integration of TRPO into the framework brings the advantage of safe and stable policy updates. Such stability is vital for maintaining the robustness required in the fluctuating environment of supply chain operations. The combination of REDQ and TRPO in DRL-SCOM creates a powerful synergy. REDQ’s ensembled learning approach, when fused with TRPO’s trust-region method, enables the framework to efficiently navigate the complex and high-dimensional decision space typical of supply chains. This allows for real-time optimization of decisions while staying within operational constraints. The DRL-SCOM methodology shows significant potential in addressing various aspects of supply chain management, from demand forecasting and inventory management to logistics, adeptly handling the nonlinearities and uncertainties that are prevalent in these areas. Thus, the DRL-SCOM framework emerges as an innovative solution, pushing the frontiers of traditional supply chain management. It paves the way for a more agile, responsive, and intelligent system, equipped to adapt to changing market demands and operational challenges. This approach represents a significant stride towards transforming supply chain management into a more advanced, data-driven, and adaptive field.

Simplified approach to estimate Lorenz number using experimental Seebeck coefficient for non-parabolic band

Reducing lattice thermal conductivity (κL) is one of the most effective ways for improving thermoelectric properties. However, the extraction of κL from the total measured thermal conductivity can be misleading if the Lorenz (L) number is not estimated correctly. κL is obtained using the Wiedemann–Franz law, which estimates the electronic part of thermal conductivity κe = L σT, where σ and T are electrical conductivity and temperature, respectively. κL is then estimated as κL = κT − L σT. For metallic systems, the Lorenz number has a universal value of 2.44 × 10 −8 WΩ K−2 (degenerate limit), but for non-degenerate semiconductors, the value can deviate significantly for acoustic phonon scattering, the most common scattering mechanism for thermoelectric materials above room temperature. Up until now, L is estimated by solving a series of equations derived from Boltzmann transport equations. For the single parabolic band (SPB) model, an equation was proposed to estimate L directly from the experimental Seebeck coefficient. However, using the SPB model will lead to an overestimation of L in the case of low bandgap semiconductors, which results in an underestimation of κL, sometimes even negative κL. In this article, we propose a simpler equation to estimate L for a non-parabolic band. The experimental Seebeck coefficient, bandgap (Eg), and temperature (T) are the main inputs to the equation, which nearly eliminates the need for solving multiple Fermi integrals besides giving accurate values of L.

A Method for Treating Significant Conformational Changes in Alchemical Free Energy Simulations of Protein-Ligand Binding.

Relative binding free energy (RBFE) simulation is a rigorous approach to the calculation of quantitatively accurate binding free energy values for protein-ligand binding in which a reference binder is gradually converted to a target binder through alchemical transformation during the simulation. The success of such simulations relies on being able to accurately sample the correct conformational phase space for each alchemical state; however, this becomes a challenge when a significant conformation change occurs between the reference and target binder-receptor complexes. Increasing the simulation time and using enhanced sampling methods can be helpful, but effects can be limited, especially when the free energy barrier between conformations is high or when the correct target complex conformation is difficult to find and maintain. Current RBFE protocols seed the reference complex structure into every alchemical window of the simulation. In our study, we describe an improved protocol in which the reference structure is seeded into the first half of the alchemical windows, and the target structure is seeded into the second half of the alchemical windows. By applying information about the relevant correct end point conformations to different simulation windows from the beginning, the need for large barrier crossings or simulation prediction of the correct structures during an alchemical simulation is in many cases obviated. In the diverse cases we examine below, the simulations yielded free energy predictions that are satisfactory as compared to experiment and superior to running the simulations utilizing the conventional protocol. The method is straightforward to implement for publicly available FEP workflows.

Measurement of electrical conductivity of human tissue: A feasibility study of a novel time-domain approach

A straightforward, non-invasive experimental approach to the extraction of electrical conductivity of human tissue using the transient electromagnetic field coupling between two loop antennas is presented. The main objective of this initial study is to demonstrate the feasibility of the new time-domain (TD) methodology. The designed loop antennas and the experimental setup with its instrumentation are described. The presented results show that the proposed method is capable of obtaining accurate values of the electrical conductivity of human tissue.

Abstract C054: Current anticancer drugs differentially impact Black and White prostate cancer cell lines

Abstract Introduction/Background: In pre-clinical models of prostate cancer (PCa) such as the utilization of cell lines, it is important to address the limited diversity present. This acknowledgment is vital to facilitate equitable assessment of the efficacy and safety of drug development across various races and ethnicities. Of all commercially available PCa cell lines, 98% of them are of European origin. The genetic and biological disparities between PCa cell lines derived from men of European ancestry (MEA) and men of African ancestry (MAA) may underlie the observed enhanced responsiveness of tumours to existing anticancer agents in MEA compared to MAA. This difference is notable given that MAA are 2.5 times more likely to succumb to PCa. This study compared the cytotoxic effects of five anticancer drugs (Docetaxel, Cabazitaxel, Abiraterone, Olaparib, Enzalutamide) and the natural product betaine on MAA (ACRJ-PC28, MDA-PCA-2b) and MEA (DU-145, PC-3) PCa cell lines. Method: After a 72-hour treatment with the anti-cancer drugs and the natural product, the inhibitory effects of these anti-cancer agents on the four cell lines were investigated using three different assays. CellTiter Aqueous, Neutral Red, and Crystal Violet assays were utilized due to distinct growth patterns observed in the various cell lines. By comparing the optical density (OD) readings and examining the cellular morphology through microscopic visualization, we aimed to determine which assay(s) yielded the most reliable results in determining accurate IC50 values. Results: Drug selectivity was observed for Abiraterone and Olaparib that were most effective in ACRJ-PC28 (IC50 = 1.10 μM) and PC-3 (IC50 = 0.28 μM) cells compared to the other PCa cell lines. Enzalutamide differentially impacted MAA, (IC50= 206 μM for ACRJ-PC28 and 104 μM for MDA-PCA-2b) and MEA (IC50 = 37 μM for PC-3 and 48 μM for DU-145) PCa cell lines. However, no distinct impact was observed for Docetaxel (IC50s = 0.05 nM for ACRJ-PC28; 0.08 nM for MDA-PCA-2b, 0.07 nM for PC-3 and 0.08 nM for DU-145) and Cabazitaxel ((IC50s = 0.56 nM for ACRJ-PC28; 0.45 nM for MDA-PCA-2b, 0.43 nM for PC-3 and 0.38 nM for DU-145) as both drugs impacted the cell lines very potently in the nM range. Conclusion: Due to the heterogenous nature of cancers, especially in PCa, a single drug or drug class may not be the best course of treatment. Instead, a combination of drugs with different mechanisms may be more suitable for addressing the diversity in cancer types. This points to the evolving understanding of personalized cancer treatment, where a strategic integration of first, second, and third-line therapies can effectively address the genetic and epigenetic factors that are unique to individuals in cancer management. Citation Format: Simone Badal, Bor-Jang Hwang, Ashlianne Nelson, Kristoff Frank, Rory Thompson, Belinda Morison, Valerie Marah, Camille Ragin. Current anticancer drugs differentially impact Black and White prostate cancer cell lines [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C054.