Early Signals Research Articles

TMOD-02. PATIENT-DERIVED GLIOBLASTOMA ORGANOIDS AS REAL-TIME AVATARS FOR ASSESSING RESPONSES TO CLINICAL CAR-T CELL THERAPY

Abstract Patient-derived tumor organoids have been increasingly leveraged for disease modeling and preclinical studies, but rarely in real-time to aid with the interpretation of patient treatment responses in the clinical setting. Whether tumor organoids can be applied to mirror clinical activity in patients and even to predict responses for personalized medicine remains uncertain. We recently demonstrated promising early efficacy signals in a first-in-human, phase 1 study of dual-targeting chimeric antigen receptor T cells (EGFR-IL13Rα2 CAR-T cells) in patients with recurrent glioblastoma (clinical trial identifier: NCT05168423). Determination of meaningful clinical efficacy, in particular for CAR-T cell therapy, may take months to manifest. Given this challenge, real-time analysis of patient-derived glioblastoma organoids (GBOs) could help to provide early assessment of clinical efficacy and guide patient management. We analyzed six sets of GBOs generated on the same timeline as the production of patients’ CAR-T cell products. GBOs demonstrated the presence and retention of both targeted tumor-associated antigens EGFR and IL13Rα2 by immunohistochemistry. These GBOs were then treated with the same autologous CAR-T cell products received by patients in our phase 1 study in parallel with patient treatment. CAR-T cell treatment led to target antigen reduction assessed by immunohistochemistry and flow cytometry. Furthermore, we confirmed cytolysis of tumor cells in GBOs by cleaved caspase-3 immunostaining and by co-culture in the Axion Maestro Cellular Impedance platform. Importantly, the degree of cytolysis ex vivo significantly correlated with CAR-T cell engraftment detected in patients’ cerebrospinal fluid (CSF) for all six patients. Furthermore, cytotoxic activity of CAR-T cells as measured by TNFα, IL-2, and IFNγ release kinetics in GBOs mirrored cytokine levels in patient CSF samples over time. Our findings highlight a unique trial design and suggest GBOs as a valuable platform for real-time assessment of CAR-T cell bioactivity and for insights into immunotherapy efficacy.

Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model

ABSTRACTAccurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.

Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk.

Sepsis is a clinical syndrome caused by infection, leading to organ dysfunction due to a dysregulated host response. In recent years, its high mortality rate has made it a significant cause of death and disability worldwide. The pathophysiological process of sepsis is related to the body's dysregulated response to infection, with microcirculatory changes serving as early warning signals that guide clinical treatment. The Peripheral Perfusion Index (PI), as an indicator of peripheral microcirculation, can effectively evaluate patient prognosis. This study aims to develop two new prediction models using PI and other common clinical indicators to assess the mortality risk of sepsis patients during hospitalization and within 28 days post-ICU admission. This retrospective study analyzed data from sepsis patients treated in the Intensive Care Unit of Peking Union Medical College Hospital between December 2019 and June 2023, ultimately including 645 patients. LASSO regression and logistic regression analyses were used to select predictive factors from 35 clinical indicators, and two clinical prediction models were constructed to predict in-hospital mortality and 28-day mortality. The models' performance was then evaluated using ROC curve, calibration curve, and decision curve analyses. The two prediction models performed excellently in distinguishing patient mortality risk. The AUC for the in-hospital mortality prediction model was 0.82 in the training set and 0.73 in the validation set; for the 28-day mortality prediction model, the AUC was 0.79 in the training set and 0.73 in the validation set. The calibration curves closely aligned with the ideal line, indicating consistency between predicted and actual outcomes. Decision curve analysis also demonstrated high net benefits for the clinical utility of both models. The study shows that these two prediction models not only perform excellently statistically but also hold high practical value in clinical applications. The models can help physicians accurately assess the mortality risk of sepsis patients, providing a scientific basis for personalized treatment.

Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

AbstractTo address the difficulty in extracting early fault feature signals of rolling bearings, this paper proposes a novel weak fault diagnosis method for rolling bearings. This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and the Improved Maximum Correlated Kurtosis Deconvolution (IMCKD). Utilizing the kurtosis criterion, the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD, which significantly reduces noise in the measured signal. This approach maximizes the energy amplitude at the fault characteristic frequency, facilitating fault feature identification. Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components. Compared with traditional methods, it significantly improves the signal‐to‐noise ratio and more accurately identifies fault features, meeting the requirements for discriminating rolling bearing faults. The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high‐speed wire department of a Long Products Mill. It successfully extracted weak characteristic information of early bearing faults, achieving the expected diagnostic results. This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications, demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.

Compulsory treatment at home: an interview study exploring the experiences of an early group of patients, relatives and mental-health workers

BackgroundWhen introduced in 2020, the Netherlands’ Compulsory Mental Healthcare Act included provisions for compulsory community treatment (CCT) and compulsory treatment in patients’ homes (CTH). Although CCT has been incorporated into mental health care in many countries, its effectiveness is debated. We know of no other countries in which CTH has been adopted.The aim of this study is to evaluate how an early group of participants experienced CTH. They were drawn from three stakeholder groups: patients, relatives and mental-health workers.MethodsIn total, 17 open interviews were conducted with six patients, five relatives and six mental-health workers. All had experience with CTH. Thematic analysis was used to analyze the interviews.ResultsFive themes were identified: 1). Reasons for applying for a court order with options for CTH. The reasons included preventing harm, avoiding hospitalization, and providing a safety net. 2.) Participants’ experiences with CTH in practice. The four most noteworthy experiences were related to the process of applying for a court order; compulsory home visits and the compulsory use of medication; the involvement of relatives during treatment; and the influence of CTH on the relationship between patients and relatives. 3.) The advantages and disadvantages of CTH. The most important advantages were avoiding hospitalization; improving medication adherence; facilitating easy access to care; early signaling of deterioration; early intervention; and regained autonomy. The most important disadvantages were restricted autonomy; fewer options for monitoring compared to hospitalization; and problems regarding control of patient behavior. 4.) Participants’ preferences. All preferred CTH to hospitalization. 5.) Participants’ suggestions for improving CTH. These included the need not only to provide patients with better information, but also to improve the involvement of relatives during treatment.ConclusionThe interviewees found that CTH might help to avoid hospitalization by providing stakeholders with more options for arranging effective care at home. Although this suggests that initial experiences of CTH under the new Dutch mental health law were positive, it is still uncertain whether CTH as currently implemented really differs from CCT.

Strategic management of tax disclosure: asymmetric timeliness of tax footnote modifications

AbstractPrevious studies have shown that qualitative disclosure contains information that facilitates the assessment of future firm outcomes. We first connect to this literature on early signals by showing that qualitative disclosure also specifically contains information about future ETR as one relevant firm outcome. Using the tax footnote from 10-K filings of all publicly traded U.S. firms over the period from 2000 to 2021, we show that year-over-year tax footnote modifications are an early signal of impending changes in ETR. When specifically considering the timeliness of this signal, we find that tax footnote modifications are associated with future changes in ETR at least three years in advance. Second, we investigate, as our main focus of analysis, asymmetry in timeliness. Here, we find that tax footnote modifications preceding increases in ETR occur relatively earlier (and are greater) than do (are) tax footnote modifications preceding decreases in ETR. We additionally use word lists to directly identify relevant underlying processes for changes in ETR and to investigate asymmetric timeliness conditional on the underlying process. Overall, our results indicate that firms strategically manage their tax disclosure in accordance with the ”accounting conservatism” hypothesis proposed by Basu (1997).

Rapid reagent-free anaemia screening using plant-derived “HemoQR” paper-strips and smartphone: A study on 200 human subjects

Detecting anaemia in resource-limited settings is challenging due to limited access to diagnostic tools, healthcare infrastructure, and trained personnel, often resulting in underdiagnosis and delayed treatment. This study aims to evaluate HemoQR, a novel point-of-care (POC) diagnostic method for haemoglobin (Hb) level detection using a smartphone-based system integrated with a paper strip without needing any reagent or additional auxiliary device. The goal is to assess the efficacy of this portable, user-friendly, rapid and accurate alternative for Hb measurement in large-scale intervention programmes such as Anaemia-mukt Bharat (AMB), which is crucial for diagnosing anaemia and monitoring various health conditions. The method involves the transfer of one drop of the blood sample to a paper strip that is made indigenously using plant based cellulosic sources for optimal functionality. The colour signal around the test spot is quantitatively analysed using a smartphone camera equipped with a custom application. The application processes the images through a machine learning algorithm to determine the Hb concentration. The system was evaluated in a clinical trial involving 200 participants, comparing its performance with the gold-standard laboratory Hb assay using automated haematology analyser. The smartphone-based method demonstrated high sensitivity (87.09 %) and specificity (96.11 %) for anaemia screening, as compared with laboratory results. The average detection time was significantly reduced to under q minute, and the system showed excellent user-friendliness and portability with no auxiliary device needed beyond the smartphone. Clinical trial results indicated that the device was accurate and reliable across different Hb levels. Summarily, HemoQR evidenced to be a promising tool for POC diagnostics. Its high accuracy, rapid results, and ease of use make it suitable for both clinical settings and remote areas with limited access to laboratory facilities. Future research will prioritize further validation and integration of the anaemia screening outcome with electronic health records from a large population cohort. This will help identify early indicators and signals of numerous emerging disease burdens that could escalate into secondary and tertiary health crises if not addressed in time.

Brief Overview on Pathology of Blood and Urine

Clinical pathology is recognized as an essential component of the preclinical safety evaluation of test articles (new chemical entities, exploratory novel medicines, and xenobiotics), particularly in short- and medium-term toxicity studies. Traditionally, this evaluation includes clinical chemistry, coagulation, hematology, and urinalysis assessments. The findings of these assessments include details on the general state of health of the animals as well as target organs and general metabolic, adaptive, or harmful processes linked to test material exposure. Consequently, in the preclinical safety assessment of a test article, clinical pathology evaluations aid in the establishment of toxicological dose-response correlations. These findings hold significance and relevance for dose selection in long-term research, as well as for risk assessment and management when applied to humans. Guidance on the fundamentals of clinical pathology testing and interpretation in the species utilized in toxicological investigations is the aim of this chapter. In order to increase testing capacity and enhance its ability to identify more sensitive, early signals of drug-induced target organ toxicity or pathophysiology, the application of clinical pathology testing in preclinical safety assessment as well as the areas of development for novel biomarker implementation within the laboratory are discussed. Lastly, the significance of interpreting clinical pathology and combining the results with data from other studies is emphasized when assessing overall safety or risk assessment

Leveraging Systemic Contributors and Adaptations to Sharpen Interventions: The IMPActS Workshop

A common pattern among intervention implementations is that, in many cases, initial adoption and early signals of success are followed by dwindling participation, signals that the intervention is not achieving its goals, and abandonment of the intervention as it was originally conceived. The IMPActS Framework facilitates anticipation of misalignment among stakeholders during the design, pitch, implementation, and sustainability of a candidate intervention’s implementation. We adapted the IMPActS Framework to design an IMPActS Workshop to occur within the context of an ongoing research with a partner organization to develop their proactive performance monitoring and sustained adaptability management program. The IMPActS Workshop prospectively tests the implementation sustainability of candidate interventions and sharpened intervention design to improve the likelihood of sustainment across the organization. We report the motivation, methods, results, and lessons learned from this effort.

Novel cardiovascular magnetic resonance imaging heterogeneity biomarkers to diagnose and predict cardiovascular disease: a UK Biobank CMR strain study

Abstract Background Myocardial strain evaluation on cardiovascular magnetic resonance imaging (CMR) shows significant promise to investigate cardiac function in vivo. There is concern that derived global longitudinal strain (GLS) and global circumferential strain (GCS) are less sensitive to early pathological cardiac remodelling, especially when regional. By quantifying contractile variability across the myocardium, the absolute longitudinal and circumferential strain coefficients of variation, |CoVLS| and |CoVCS| may facilitate earlier diagnosis and improved prognostication. Purpose We compared experimental heterogeneity biomarkers, |CoVLS| and |CoVCS|, to established global strain markers, GLS and GCS, to discriminate prevalent cardiovascular disease (CVD) and predict major adverse cardiovascular events (MACE). Methods Prevalent and incident CVD were defined among 44,955 participants from the UK Biobank (UKBB), who had feature-tracking (FT-CMR) derived strain markers. Prevalent CVD was the sum of established cardiovascular diagnoses at the time of baseline CMR, including hypertension, diabetes, atrial fibrillation, coronary artery disease, stroke and heart failure (HF). Incident CVD was newly identified MACE, including death from any cause, myocardial infarction, HF and stroke. Biomarkers were separately regressed against said endpoints, using ordinal logistic and Cox proportional hazards regression respectively. Statistical adjustment was performed for important baseline co-variables, including age, sex, ethnicity, smoking status, body mass index (BMI), low-density lipoprotein (LDL), left atrial volume, left ventricular mass and ejection fraction. Results Overall, 30,289 (67.4%) participants had no prevalent CVD, 10,362 (23.0%) had 1 CVD and 29 (0.06%) had ≥5 CVD. |CoVLS|, GLS, |CoVCS| and GCS were associated with a higher odds of prevalent CVD diagnoses (Figure 1). GLS had the greatest strength of association (OR 1.25, 95% CI 1.20-1.29), followed by |CoVCS| (OR 1.14, 95% CI 1.10-1.17). Over median 2.7 years (Q1-Q3 1.9-4.1 years), 966 (2.1%) participants experienced MACE, including 379 (0.8%) deaths, 263 (0.6%) MI, 246 (0.5%) HF, 237 (0.5%) stroke. |CoVLS|, |CoVCS| and GLS were comparable risk markers for MACE (Figure 2). The largest and smallest effect sizes were in predicting HF and death from any cause respectively. Conclusion Subclinical cardiac disease is often patchy, diffuse and regional; measures of heterogeneity may be more sensitive to these early pathological signals. We demonstrated that |CoVLS| and |CoVCS| are comparable to GLS and better than GCS in discriminating prevalent cardiac disease and predicting future cardiovascular events. These findings need to be replicated in other population studies and diseased cohorts.

Study on the characteristics of acoustic-thermal precursors of destabilization damage in coal-rock combination bodies with different proportions

The instability and failure processes of coal-rock combinations are accompanied by the release of acoustic emission (AE) and infrared radiation (IR) signals. To investigate the characteristics of AE and IR signals during the failure process in coal-rock combinations with different ratios, and to analyze the effectiveness and applicability of the monitoring methods for these two signals in various specimen ratios. In this paper, the uniaxial compression tests were conducted on seven coal-rock combinations with different ratios by using the rock mechanics loading system and the cooperative monitoring platform of infrared and acoustic emission. The results show that (1) the AE counts for failure precursors in coal-rock combinations are positively correlated with the coal-rock ratio, whereas the AE peak counts are negatively correlated. Moreover, as the coal-rock ratio decreases, the slope of the cumulative AE count curve during the peak failure stage approaches 1. (2) During the elastic and yield stages, the maximum infrared radiation temperature (MIRT) curve fluctuates, and just before peak failure, the curve displays a distinctive “V” shape. This “V” shape becomes more pronounced as the coal-rock ratio decreases. (3) In the monitoring of damage precursors of coal-rock combinations, when the ratio of coal to rock is 1:3 or less, IR monitoring is better than AE monitoring. Conversely, when the ratio of coal to rock is greater than 1:3, AE monitoring is more suitable. Consequently, the combined monitoring of AE and IR signals can increase the reliability and precision of early warning signals for coal-rock assemblage damage precursors.

Bayesian metamodeling of early T-cell antigen receptor signaling accounts for its nanoscale activation patterns

T cells respond swiftly, specifically, sensitively, and robustly to cognate antigens presented on the surface of antigen presenting cells. Existing microscopic models capture various aspects of early T-cell antigen receptor (TCR) signaling at the molecular level. However, none of these models account for the totality of the data, impeding our understanding of early T-cell activation. Here, we study early TCR signaling using Bayesian metamodeling, an approach for systematically integrating multiple partial models into a metamodel of a complex system. We inform the partial models using multiple published super-resolution microscopy datasets. Collectively, these datasets describe the spatiotemporal organization, activity, interactions, and dynamics of TCR, CD45 and Lck signaling molecules in the early-forming immune synapse, and the concurrent membrane alterations. The resulting metamodel accounts for a distinct nanoscale dynamic pattern that could not be accounted for by any of the partial models on their own: a ring of phosphorylated TCR molecules, enriched at the periphery of early T cell contacts and confined by a proximal ring of CD45 molecules. The metamodel suggests this pattern results from limited activity range for the Lck molecules, acting as signaling messengers between kinetically-segregated TCR and CD45 molecules. We assessed the potential effect of Lck activity range on TCR phosphorylation and robust T cell activation for various pMHC:TCR association strengths, in the specific setting of an initial contact. We also inspected the impact of localized Lck inhibition via Csk recruitment to pTCRs, and that of splicing isoforms of CD45 on kinetic segregation. Due to the inherent scalability and adaptability of integrating independent partial models via Bayesian metamodeling, this approach can elucidate additional aspects of cell signaling and decision making.

The full transcription map of cottontail rabbit papillomavirus in tumor tissues.

Cottontail rabbit papillomavirus (CRPV), the first papillomavirus associated with tumor development, has been used as a powerful model to study papillomavirus pathogenesis for more than 90 years. However, lack of a comprehensive analysis of the CRPV transcriptome has impeded the understanding of CRPV biology and molecular pathogenesis. Here, we report the construction of a complete CRPV transcription map from Hershey CRPV-induced skin tumor tissues. By using RNA-seq in combination with long-reads PacBio Iso-seq, 5' and 3' RACE, primer-walking RT-PCR, Northern blot, and RNA in situ hybridization, we demonstrated that the CRPV genome transcribes its early and late RNA transcripts unidirectionally from at least five distinct major promoters (P) and polyadenylates its transcripts at two major polyadenylation (pA) sites. The viral early transcripts are primarily transcribed from three "early" promoters, P90, P156, and P907 and polyadenylated at nt 4368 by using an early polyadenylation signal (PAS) at nt 4351. Like other low-risk human papillomaviruses and animal papillomaviruses, CRPV E6 and E7 transcripts are transcribed from three separate early promoters. Transcripts from two "late" promoters, P7525, and P1225, utilize either an early PAS for E1^E4 or a late PAS at 7399 for L2 and L1 RNA polyadenylation at nt 7415 to express capsid L2 and L1 proteins respectively. By using the mapped four 5' splice sites and three 3' splice sites, CRPV RNA transcripts undergo extensive alternative splicing to produce more than 33 viral RNA isoforms for production of at least 12 viral proteins, some of which without codon optimization are expressible in rabbit RK13 and human HEK293T cells. The constructed full CRPV transcription map in this study for the first time will enhance our understanding of the structures and expressions of CRPV genes and their contribution to molecular pathogenesis and tumorigenesis.

Model Test on Acoustic Emission Monitoring of Loess Slope Failure

The three stages of loess collapse are characterized by notable concealment and sudden onset due to the sudden nature of loess collapse and the prolonged duration of the peristaltic deformation stage. Traditional displacement monitoring methods struggle to detect early signals of instability and failure, leading to poor timeliness in disaster warnings. This project begins by examining non-force field information related to the loess collapse process. It focuses on acoustic emission monitoring and employs model tests to identify effective waveguide rods for monitoring loess collapse. Additionally, the project investigates the evolution anomalies of acoustic emission parameters before and after loess collapse failure, aiming to establish early warning criteria for loess collapse based on acoustic emission. This work provides a theoretical basis for monitoring and early warning of loess collapses. This study evaluates five parameters of the active waveguide system: sensor installation method, filling material, waveguide rod wall thickness, outer wrapping material, and outer wrapping wall thickness. The densities of the filler materials were tested using the optimal parameters derived from the tests to identify the best configurations for active acoustic emission (AE) waveguide systems suitable for monitoring loess collapse. Subsequently, a one-sided connected loess collapse model was employed for indoor tests, integrating real-time AE monitoring with the active waveguide method. This model facilitates the exploration of AE response characteristics during loess collapse and the analysis of destructive forms of loess collapse and time-sequence evolution of AE ringing counts throughout the deformation and destruction process. Results indicate that using filler materials with high elasticity modulus, high compactness, and low Poisson’s ratio, along with thin outer wrapping and waveguide rod walls, leads to strong AE signals. As deformation damage of loess collapse intensifies, the number of AE ringing counts notably increases. A rapid rise in cumulative ringing counts can indicate a “sudden increase”, or the b-value may stabilize, providing precursor information for loess collapse.

Impact of Debt Composition and Accounting Conservatism on Financial Distress in Emerging Markets

Aims: This study investigates the impact of debt composition, specifically short-term and long-term debt, on corporate financial distress, focusing on the moderating role of accounting conservatism. It aims to understand how firms in emerging markets, particularly Kenya, can achieve financial stability through prudent debt structuring and conservative accounting practices. Study Design: The research adopted an explanatory research design. Place and Duration of Study: The study utilized financial data from firms listed on the Kenyan Securities Exchange between 2008 and 2021. Methodology: The study analyzed a sample of 45 firms trading at the Nairobi Stock Exchange over 14 years from 2008 to 2021. The study used panel logistic regression, a statistical method for analyzing data with multiple observations over time, to test its hypotheses. Results: The results show that both short-term and long-term debt increase the likelihood of financial distress, with accounting conservatism further intensifying the risk for firms heavily reliant on short-term debt while mitigating it for those with long-term debt. This suggests that conservative accounting practices, which promote early recognition of losses, may exacerbate financial distress for firms heavily reliant on short-term debt. In contrast, for long-term debt, accounting conservatism has a mitigating effect helping firms manage their debt obligations more effectively over time. This highlights a nuanced role of accounting conservatism, where it increases the pressure on firms with short-term debt while providing stability for those with long-term obligations. Conclusion: The findings suggest that corporate managers in emerging markets should carefully balance short- and long-term debt to minimize financial distress risks. Additionally, adopting conservative accounting practices can serve as a safeguard, providing early warning signals for financial trouble and enhancing corporate stability. Managers should therefore carefully balance short-term and long-term debt to reduce financial distress while adopting conservative accounting practices to enhance financial transparency and risk management. Policymakers can support firms by promoting responsible debt management and offering incentives for conservative financial reporting to strengthen overall financial stability.

Enhanced cell stress response and protein degradation capacity underlie artemisinin resistance in Plasmodium falciparum.

Malaria remains a global health burden, killing over half a million people each year. Decreased therapeutic efficacy to artemisinin, the most efficacious antimalarial, has been detected in sub-Saharan Africa, a worrying fact given that over 90% of deaths occur on this continent. Mutations in Kelch13 are the most well-established molecular marker for artemisinin resistance, but these do not explain all artemisinin-resistant isolates. Understanding the biological underpinnings of drug resistance is key to curbing the emergence and spread of artemisinin resistance. Artemisinin-mediated non-specific alkylation leads to the accumulation of misfolded and damaged proteins and activation of the parasite unfolded protein response (UPR). In addition, the parasite proteasome is vital to artemisinin resistance, as we have previously shown that chemical inhibition of the proteasome or mutations in the β2 proteasome subunit increase parasite susceptibility to dihydroartemisinin (DHA), the active metabolite of artemisinins. Here, we investigate parasites with mutations at the Kelch13 and/or 19S and 20S proteasome subunits with regard to UPR regulation and proteasome activity in the context of artemisinin resistance. Our data show that perturbing parasite proteostasis kills parasites, early parasite UPR signaling dictates DHA survival outcomes, and DHA susceptibility correlates with impairment of proteasome-mediated protein degradation. Importantly, we show that functional proteasomes are required for artemisinin resistance in a Kelch13-independent manner, and compound-selective proteasome inhibition demonstrates why artemisinin-resistant Kelch13 mutants remain susceptible to the related antimalarial peroxide OZ439. These data provide further evidence for targeting the parasite proteasome and UPR to overcome existing artemisinin resistance.IMPORTANCEDecreased therapeutic efficacy represents a major barrier to malaria treatment control strategies. The malaria proteasome and accompanying unfolded protein response are crucial to artemisinin resistance, revealing novel antimalarial therapeutic strategies.

Seeking for effectiveness of sustainability entrepreneurial education programs: A multiple case analysis

ABSTRACT The urgency of environmental challenges necessitates educational programs that equip future entrepreneurs with practical sustainability-specific skills and knowledge. Accordingly, this article compares sustainability entrepreneurial education program cases using action research at the University of California San Diego (startBlue and Innovation-CORPS [I-CORPS]) and Cranfield University (Ideas-to-Innovation and [i2i] Energy Entrepreneurship) specific to pedagogical approaches like experiential learning, transdisciplinary collaboration, community mentors, and practical examples. Findings highlight multiple achievements—underrepresented founder involvement, United Nations Sustainable Development Goal alignment, and early value development signal identification. Practically, such insights can guide educators, policymakers, and program designers in enhancing sustainability entrepreneurial education, fostering innovation, and addressing local and regional sustainability challenges through collaboration and practical applications. Also, there are actionable recommendations to facilitate knowledge, skill, and competency development with entrepreneurs, investors, and other stakeholders, thereby aiding sustainability-focused venture development, growth, and success.

Artificial intelligence in healthcare forecasting: Enhancing market strategy with predictive analytics

This research examines the transformative role of artificial intelligence (AI) and predictive analytics in healthcare market forecasting, with a specific focus on their application at Eli Lilly. AI-driven insights are becoming critical tools in anticipating market trends, assessing the impact of regulatory changes, and optimizing product positioning in the competitive global healthcare landscape. By leveraging machine learning algorithms and vast datasets, AI can identify patterns and correlations that traditional forecasting methods may overlook, offering companies like Eli Lilly a more comprehensive understanding of future market dynamics. The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act. This legislative change, aimed at reducing drug prices and increasing market access, presents significant challenges and opportunities for pharmaceutical companies. AI allows firms to model the potential outcomes of such regulatory changes and adjust their market strategies accordingly. For example, predictive analytics can forecast the pricing pressures on specific drugs, helping companies develop competitive pricing models while maintaining profitability. Moreover, the research highlights the use of AI in mitigating risks associated with market volatility. By continuously analyzing real-time data, AI can detect early signals of potential disruptions—whether from economic downturns, competitor actions, or public health crises—allowing companies to take proactive measures. Additionally, AI-driven forecasting helps identify growth opportunities in emerging healthcare markets by analyzing demographic trends, disease prevalence, and healthcare infrastructure development. In conclusion, AI and predictive analytics are revolutionizing how healthcare companies like Eli Lilly approach market forecasting and strategy development. By offering deeper insights into future market conditions, these technologies not only reduce risks but also optimize product positioning and unlock growth opportunities in the global healthcare market. This research underscores the critical importance of AI in shaping the future of healthcare market forecasting. Keywords: Artificial Intelligence, Predictive Analytics, Market Forecasting, Regulatory Impact, Healthcare Strategy

Influence of Contaminants Mercury and PAHs on Somatic Indexes of the European Hake (Merluccius merluccius, L. 1758).

This research investigates the dynamics of contaminant exposure in European hake (Merluccius merluccius, L. 1758) from the Adriatic Sea (Central Mediterranean Sea) by examining the levels of total mercury (THg) and polycyclic aromatic hydrocarbons (PAHs) in the muscle fish tissues. The study explores the correlations between these pollutants and somatic indexes to identify the early warning signals of pollution and ecological effects. The levels of pollutants are influenced by season and sex. Lipids appear to have a minimal effect on the PAH levels, whereas they exhibit a positive correlation with mercury levels in the muscle. No significant relationships between the pollutants and condition indexes were observed, except for a positive correlation between THg and the gonadosomatic index, indicating a potential impact on the reproductive health of fish. In contrast, PAHs showed no meaningful correlation with condition indexes. Differences in contaminant accumulations and lipid levels between sexes reflect variations in metabolic activity, reproductive costs, and adaptive strategies to seasonal changes and energy demands. This study highlights the importance of long-term monitoring to improve pollution management, environmental conservation, and the protection of marine organisms' health.

Evaluating the impact of demographic characteristics on residential mortgage default risk: Evidence from Lebanon

Purpose: This study aims to examine the relationship between borrower's demographic characteristics and default risk in mortgage loans to help financial institutions develop more effective lending policies. Design/Methodology/Approach: Cross-sectional data were elicited from randomly selected 6743 individual accounts from Lebanese housing banks. This study applied the binary logistic and stepwise regression models to analyze the dataset using the Stata statistical software. Model diagnosis is performed using the Hosmer-Lemeshow goodness of fit test, likelihood ratio test, model accuracy classification table and statistically significant test-ROC curve. Findings: The findings revealed that there is a significant relationship between residential loan default risk and borrower’s marital status, nature of job occupation, job economic sector, job location and loan purpose. The performance of the binary logistic regression analysis demonstrates the overall percentage who is correctly classified is 91.61%. Conclusion: The log odds of default risk for widowed borrowers are about 90 percent higher than those of divorced borrowers and that of self-employed borrowers is about 54 percent higher than that of employed borrowers. Borrowers working in the banking and real estate sectors have lower default rates than borrowers working in other economic sectors. In addition, loans granted for renovation purposes have the lowest default rates compared to loans provided for purchase, under-construction and construction purposes. Practical Implications: The empirical results help financial institutions to have early warning signals in detecting financial distress and to differentiate between a high- and low-risk group of borrowers, helping in the development of tailored risk mitigation strategies and adjusting the lending criteria.