High-quality Data Research Articles

Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis

Time series data are widely used and provide a wealth of information for countless applications. However, some applications are faced with a limited amount of data, or the data cannot be used due to confidentiality concerns. To overcome these obstacles, time series can be generated synthetically. For example, electrocardiograms can be synthesized to make them available for building models to predict conditions such as cardiac arrhythmia without leaking patient information. Although many different approaches to time series synthesis have been proposed, evaluating the quality of synthetic time series data poses unique challenges and remains an open problem, as there is a lack of a clear definition of what constitutes a “good” synthesis. To this end, we present a comprehensive literature survey to identify different aspects of synthesis quality and their relationships. Based on this, we propose a definition of synthesis quality and a systematic evaluation procedure for assessing it. With this work, we aim to provide a common language and criteria for evaluating synthetic time series data. Our goal is to promote more rigorous and reproducible research in time series synthesis by enabling researchers and practitioners to generate high-quality synthetic time series data.

TOMGPT: Reliable Text-Only Training Approach for Cost-Effective Multi-modal Large Language Model

Multi-modal large language models (MLLMs), such as GPT-4, exhibit great comprehension capabilities on human instruction, as well as zero-shot ability on new downstream multi-modal tasks. To integrate the different modalities within a unified embedding space, previous MLLMs attempted to conduct visual instruction tuning with massive and high-quality image-text pair data, which requires substantial costs in data collection and training resources. In this article, we propose TOMGPT (Text-Only training Multi-modal GPT), a cost-effective MLLM tuned solely on easily accessible text data with much fewer resources. Along with pre-trained visual-linguistic coupled modality space (e.g., CLIP and ALIGN model), a text-only training strategy is devised to further project the aligned multi-modal latent space to that of LLM, endowing the LLM with visual comprehension capabilities in an efficient manner. Instead of enormous image-text training data required by previous MLLMs, we find that TOMGPT can be well-tuned with fewer yet diverse GPT-generated free-form text data, as we establish the semantic connection between LLM and pre-trained vision-language model. A quantitative evaluation is conducted on both MME and LVLM, which are recently released and extensively utilized MLLM benchmarks. The experiments reveal that TOMGPT achieved reliable performance compared to numerous models trained on a large amount of image-text pair data. Case studies are also presented, demonstrating TOMGPT’s broad understanding and dialogue capabilities across diverse image categories.

Federated Momentum Contrastive Clustering

Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a central entity, considering data privacy requirements and computational resources. Federated Learning (FL) has been developed successfully to aggregate a global model while training on distributed local data, respecting the data privacy of edge devices. However, most FL research effort focuses on supervised learning algorithms. A fully unsupervised federated clustering scheme has not been considered in the existing literature. We present federated momentum contrastive clustering (FedMCC), a generic federated clustering framework that can not only cluster data automatically but also extract discriminative representations training from distributed local data over multiple users. In FedMCC, we demonstrate a two-stage federated learning paradigm where the first stage aims to learn differentiable instance embeddings and the second stage accounts for clustering data automatically. The experimental results show that FedMCC not only achieves superior clustering performance but also outperforms several existing federated self-supervised methods for linear evaluation and semi-supervised learning tasks. Additionally, FedMCC can easily be adapted to ordinary centralized clustering through what we call momentum contrastive clustering (MCC). We show that MCC achieves state-of-the-art clustering accuracy results in certain datasets such as STL-10 and ImageNet-10. We also present a method to reduce the memory footprint of our clustering schemes.

Change in Striatal Functional Connectivity Networks Across Two Years Due to Stimulant Exposure in Childhood ADHD: Results from the ABCD Sample.

Widely prescribed for Attention-Deficit/Hyperactivity Disorder (ADHD), stimulants (e.g., methylphenidate) have been studied for their chronic effects on the brain in prospective designs controlling dosage and adherence. While controlled approaches are essential, they do not approximate real-world stimulant exposure contexts where medication interruptions, dosage non-compliance, and polypharmacy are common. Brain changes in real-world conditions are largely unexplored. To fill this gap, we capitalized on the observational design of the Adolescent Brain Cognitive Development (ABCD) study to examine effects of stimulants on large-scale bilateral cortical networks' resting-state functional connectivity (rs-FC) with 6 striatal regions (left and right caudate, putamen, and nucleus accumbens) across two years in children with ADHD. Bayesian hierarchical regressions revealed associations between stimulant exposure and change in rs-FC of multiple striatal-cortical networks, affiliated with executive and visuo-motor control, which were not driven by general psychotropic medication. Of these connections, three were selective to stimulants versus stimulant naive: reduced rs-FC between caudate and frontoparietal network, and between putamen and frontoparietal and visual networks. Comparison with typically developing children in the ABCD sample revealed stronger rs-FC reduction in stimulant-exposed children for putamen and frontoparietal and visual networks, suggesting a normalizing effect of stimulants. 14% of stimulant-exposed children demonstrated reliable reduction in ADHD symptoms, and were distinguished by stronger rs-FC reduction between right putamen and visual network. Thus, stimulant exposure for a two-year period under real-world conditions modulated striatal-cortical functional networks broadly, had a normalizing effect on a subset of networks, and was associated with potential therapeutic effects involving visual attentional control.

CHEOPS in-flight performance. A comprehensive look at the first 3.5 years of operations

Since the discovery of the first exoplanet almost three decades ago, the number of known exoplanets has increased dramatically. By beginning of the 2000s it was clear that dedicated facilities to advance our studies in this field were needed. The CHaracterising ExOPlanet Satellite ( is a space telescope specifically designed to monitor transiting exoplanets orbiting bright stars. In September 2023 completed its nominal mission duration of 3.5 years and remains in excellent operational conditions. As a testament to this, the mission has been extended until the end of 2026. Scientific and instrumental data have been collected throughout in-orbit commissioning and nominal operations, enabling a comprehensive analysis of the mission's performance. In this article, we present the results of this analysis with a twofold goal. First, we aim to inform the scientific community about the present status of the mission and what can be expected as the instrument ages. Secondly, we intend for this publication to serve as a legacy document for future missions, providing insights and lessons learned from the successful operation of CHEOPS. To evaluate the instrument performance in flight, we developed a comprehensive monitoring and characterisation (M C) programme. It consists of dedicated observations that allow us to characterise the instrument's response and continuously monitor its behaviour. In addition to the standard collection of nominal science and housekeeping data, these observations provide valuable input for detecting, modelling, and correcting instrument systematics, discovering and addressing anomalies, and comparing the instrument's actual performance with expectations. The precision of the measurements has enabled the mission objectives to be met and exceeded. The satellite's performance remains stable and reliable, ensuring accurate data collection throughout its operational life. Careful modelling of the instrumental systematics allows the data quality to be significantly improved during the light curve analysis phase, resulting in more precise scientific measurements. is compliant with the driving scientific requirements of the mission. Although visible, the ageing of the instrument has not affected the mission's performance. The satellite's capabilities remain robust, and we are confident that we will continue to acquire high-quality data during the mission extension.

TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

Analysis and authentication of avocado oil by low-field benchtop NMR spectroscopy and chemometrics.

Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low-field (LF) NMR spectroscopy allow for collection of high-quality data despite the use of low magnetic fields produced by non-superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly-, mono-, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46-5.53 and root mean squared error of prediction CV ranging 0.46-2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. PRACTICAL APPLICATION: Here, we describe the application of LF-NMR for fatty acid analysis and avocado oil authentication. LF-NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user-friendly, and sustainable analysis solutions.

Cellular pharmacokinetic of methotrexate and its modulation by folylpolyglutamate synthetase and γ-glutamyl hydrolase in tumor cells.

Clinical studies showed that prolonged infusion of methotrexate (MTX) leads to more severe adverse reactions than short infusion of MTX at the same dose. We hypothesized that it is the saturation of folate polyglutamate synthetase (FPGS) at high MTX concentration that limits the intracellular synthesis rate of methotrexate polyglutamate (MTX-PG). Due to a similar accumulation rate, a longer infusion duration may increase the concentration of MTX-PG and, result in more serious adverse reactions. In this study, we validated this hypothesis. A549, BEL-7402 and MHCC97H cell lines were treated with MTX at gradient concentrations. Liquid chromatograph-mass spectrometer (UPLC-MS/MS) was used to quantify the intracellular concentration of MTX-PG and the abundance of FPGS and γ-glutamyl hydrolase (GGH). High quality data were used to fit the cell pharmacokinetic model. Both cell growth inhibition rate and intracellular MTX-PG concentration showed a nonlinear relationship with MTX concentration. The parameter Vmax in the model, which represents the synthesis rate of MTX-PG, showed a strong correlation with the abundance of intracellular FPGS. According to the model fitting results, it was confirmed that the abundance of FPGS is a decisive factor limiting the synthesis rate of MTX-PG. The proposed hypothesis was verified in this study. In addition, based on the intracellular metabolism, a reasonable explanation was provided for the correlation between the severity of adverse reactions of MTX and infusion time. This study provides a new strategy for the individualized treatment and prediction of efficacy/side effects of MTX.

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Purpose: The aim of the study was to examine the role of machine learning in enhancing risk management strategies in financial institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that integration of machine learning into risk management strategies within financial institutions has demonstrated significant potential for enhancing decision-making processes and mitigating various risks. The study have consistently shown that machine learning algorithms outperform traditional statistical methods in areas such as credit risk assessment, fraud detection, market risk management, and loan portfolio optimization. These advancements have led to improved accuracy, efficiency, and timeliness in risk assessment, enabling financial institutions to make more informed decisions while reducing losses and enhancing overall performance. Unique Contribution to Theory, Practice and Policy: Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH) & Agency Theory may be used to anchor future studies on role of machine learning in enhancing risk management strategies in financial institutions. Invest in building robust data infrastructure and governance frameworks to support the implementation of machine learning models in risk management practices. High-quality data is crucial for training accurate and reliable machine learning algorithms. Establish regulatory guidelines and standards for the responsible use of machine learning in risk management within the financial industry. These guidelines should address issues such as model transparency, fairness, and accountability to ensure ethical and responsible practices.

Analyzing the Effectiveness of Non-remote Monitoring Telemedicine Within the United States After 2019, Including Future Implications as a Healthcare Medium, Regarding Its Statistical and Analytical Disciplines: A Systematic Review

Telemedicine is the utilization of healthcare services remotely via numerous applications that connect the patient with a qualified physician. Telemedicine plays a vital role in providing accessible healthcare to individuals who are unable to do an in-person visit, and its involvement in healthcare is analyzed through the analytical/statistical lens, financial lens, and ethical lens. Telemedicine systems have demonstrated their potential to serve as a functional healthcare medium, but many barriers to an increase in its implementation and efficacy still exist after the COVID-19 pandemic. Despite the importance of telemedicine, knowledge of its implied efficacy and efficiency in providing healthcare is poor, and already existing research doesn’t address solutions to the barriers of telemedicine. The research aims to address this gap by doing a comprehensive, systematic literature review on the efficacy, barriers to implementation, practicality, and future implications of telemedicine’s utilization in the healthcare sphere. Furthermore, by adding a unique perspective on this topic and consolidating research data for the general healthcare field to utilize, this further addresses missing medical knowledge regarding telemedicine consultations. This research screened 737 different articles and papers to gather information regarding telemedicine and its efficacy. Results were filtered and screened using the PRISMA method to maintain a standard of high-quality, impartial, and accurate data throughout the research paper. Only the sources which pertained to the research question were chosen to be included in the paper. Trends were identified in the chosen articles, which were viewed by the contributors and referenced in the paper. Telemedicine has demonstrated profound evidence of benefits for both patients and healthcare administrations. However, concerns regarding its accessibility and efficacy remain. Suggestions are made for medical administrations and for public knowledge to provide solutions for these concerns.

Dilute-and-shoot approach for the high-throughput LC-MS/MS determination of illicit drugs in the field of wastewater-based epidemiology

The determination of illicit drugs in urban influent wastewater (IWW) enables the monitoring of spatial and temporal drug usage trends and assessment of community lifestyle habits. The increasing number of wastewater surveillance studies has emphasized the necessity for the development of rapid, high-throughput methods that maintain high quality data. This work evaluates the use of a dilute-and-shoot methodology, based on direct injection (DI) of centrifuged samples, as an alternative approach to the widely applied sample pre-treatment based on solid-phase extraction, for the liquid chromatography-tandem mass spectrometry determination of seven widely consumed illicit drugs and their metabolites in IWW (amphetamine; cocaine metabolite, benzoylecgonine; ketamine; 3,4-methylenedioxymethamphetamine (MDMA); methamphetamine; cannabis metabolite, 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THCCOOH); heroin metabolite, 6-acetylmorphine (6-MAM)). Comparison of both approaches in terms of matrix effects, sensitivity and accuracy, demonstrates the DI method suitability to correctly quantify these analytes in IWW, with a limit of quantification lower than 30 ng L−1 for most compounds. After validation of the method and participation in an interlaboratory exercise, the DI method was applied to the analysis of 54 IWW samples collected from different Spanish wastewater treatment plants. Additionally, quality controls were incorporated in each analysis batch to support the DI method applicability and robustness. The use of a 10 μL-DI reduces time-consuming sample preparation, analysis time and measurement uncertainty. Moreover, it supports green chemistry by reducing the consumption of organic solvents and it facilitates logistics by collecting, transporting, and storing less sample volume. The methodology is therefore especially appropriate for monitoring illicit drugs in large wastewater-based epidemiology sampling campaigns or when fast near real-time results are needed.

Energy and nutrient intake by 11-13-year-old young adolescents attending private schools in Delhi, India.

There are no high-quality data on dietary behavior of adolescents in India. This study aimed to assess the intake of energy (E), macronutrients and selected micronutrients in a sample of 11-13-year-old schoolchildren in Delhi, India. Participants from private schools (n=10) recorded dietary intake using a 3-day food diary. Information was entered into the dietary assessment tool, Intake24, to ascertain portion size and convert data into nutrient intake through integrated food tables. Of the 514 consenting participants, 393 (76.4%) (169 girls, 224 boys) aged 11.4 (± 1.8) years completed the study. The median (interquartile range (IQR) daily E intake was 2580 (2139.3-2989.8) kcal [10.8 (9.0 -12.5) MJ] for girls, and 2941.5 (2466.7- 3599.3) kcal [12.3 (10.3- 15.2) MJ] for boys. The median (IQR) daily nutrient intakes for girls and boys respectively were: protein 64.6 (54.8-79.3) g, 74.4 (61.4; 89.4) g; carbohydrate 336.5 (285.3- 393.6) g, 379.6 (317.8-461.8) g; and saturated fat 45.6 (34.8-58.3) g, 54.6 (41.9-69.5) g. There were no significant between-gender differences in percent E from protein (10.2 (9.2 - 11.4)), or carbohydrate (52.4 (48.7- 56.7)). Girls obtained less percent E from saturated fat (16.1 (11.0-18.2) compared with boys 16.3 (14.2 - 19.1) (P<0.05). E from saturated fat was above Food and Agriculture Organization recommendations in >74% participants. The EAR for iron was achieved by < 40% of girls. In conclusion, strategies to optimize dietary intake of adolescents in India should focus on preventing excess intakes of E and saturated fat, and improving iron intake in girls.

Increase in Modulation Speed of Silicon Photonics Modulator with Quantum-Well Slab Wings: New Insights from a Numerical Study

A Silicon Photonics modulator is a high-speed photonic integrated circuit for optical data transmission in high-capacity optical networks. Silicon Photonics modulators in the configuration of a Mach–Zehnder interferometer, in which a PN-junction rib-waveguide phase shifter is inserted in each arm of the interferometer, are studied in this paper because of their superior performance of high-quality optical data generation in a wide range of spectral bands and their simplicity in fabrication processes suitable to production in foundries. Design, fabrication, and fundamental characteristics of Silicon Photonics Mach–Zehnder modulators are reviewed as an introduction to these high-speed PICs on the Silicon Photonics platform. Modulation speed, or modulation bandwidth, is a key performance item, as well as optical loss, in the application to high-speed optical transmitters. Limiting factors on modulation speed are addressed in equations. Electrical resistance–capacitance coupling, which causes optical modulation bandwidth–optical loss trade-off, is the most challenging limiting factor that limits high-speed modulation. Expansion of modulation bandwidth is not possible without increasing optical loss in the conventional approaches. A new idea including quantum-mechanical effect in the design of Silicon Photonics modulators is proposed and proved in computational analysis to resolve the bandwidth loss trade-off. By adding high-mobility quantum-well overlayers to the side slab wings of the rib-waveguide phase shifter, the modulation bandwidth is doubled without increasing optical loss to achieve a 200 Gbaud modulation rate.

Genome-wide association study and expression of candidate genes for Fe and Zn concentration in sorghum grains

Sorghum germplasm showed grain Fe and Zn genetic variability, but a few varieties were biofortified with these minerals. This work contributes to narrowing this gap. Fe and Zn concentrations along with 55,068 high-quality GBS SNP data from 140 sorghum accessions were used in this study. Both micronutrients exhibited good variability with respective ranges of 22.09–52.55 ppm and 17.92–43.16 ppm. Significant marker-trait associations were identified on chromosomes 1, 3, and 5. Two major effect SNPs (S01_72265728 and S05_58213541) explained 35% and 32% of Fe and Zn phenotypic variance, respectively. The SNP S01_72265728 was identified in the cytochrome P450 gene and showed a positive effect on Fe accumulation in the kernel, while S05_58213541 was intergenic near Sobic.005G134800 (zinc-binding ribosomal protein) and showed negative effect on Zn. Tissue-specific in silico expression analysis resulted in higher levels of Sobic.003G350800 gene product in several tissues such as leaf, root, flower, panicle, and stem. Sobic.005G188300 and Sobic.001G463800 were expressed moderately at grain maturity and anthesis in leaf, root, panicle, and seed tissues. The candidate genes expressed in leaves, stems, and grains will be targeted to improve grain and stover quality. The haplotypes identified will be useful in forward genetics breeding.

A Seismic Nodal Deployment to Understand Magmatic Structure in the Vicinity of the Pāhala Earthquake Swarm

Abstract In Summer–Fall 2022, 80 three-component SmartSolo IGU-BD3C-5 nodal seismometers were deployed surrounding the Pāhala seismic swarm on the Island of Hawai‘i, with the goal of improving seismicity catalogs and seismic velocity images of the crust and upper mantle in this region. The Pāhala swarm, located south of Mauna Loa and Kīlauea, has been the site of a multiyear sustained swarm of seismicity at the depths of ∼25–40 km, with order of magnitude increases in rate in 2015, and then again in 2019. This seismicity is possibly related to the input of magma from the mantle plume below, which may then be subsequently transported to volcanic edifices. However, these processes remain enigmatic, in part due to a lack of precise earthquake locations and seismic velocity models in this region. Here, we provide an overview of the deployment, an assessment of the quality of the collected data, and discuss the viability of the data set for local earthquake relocation, tomography, and teleseismic receiver functions. Through comparisons with proximal permanent broadband and short-period instruments, we find that the nodes produce high-quality data, particularly at periods shorter than 5 s, although we find, document, and correct discrepancies with the gain and polarities of the instruments. We successfully record signals from teleseismic earthquakes, even at periods longer than 5 s (the corner of the flat response of the nodes). We also record local earthquakes, including details related to source characteristics. This indicates that the data are likely to prove useful for investigations using both local and teleseismic earthquake signals to better understand the connections between the deep and shallow magmatic systems of Hawai‘i. Although this deployment provides a snapshot in time, its success may provide a useful benchmark for future studies as the volcanic systems of Hawai‘i continue to evolve in the future.

A Simple Water Sample Storage Test for Water Isotope Analysis

Water is pivotal for human societies’ sustainability and resilience. Isotope hydrology and hydrogeology research plays an important role in understanding and managing water resources. Reliable scientific results hinge on high-quality data. Preventing water sample evaporation is essential for accurate isotopic analysis. In this study, the impacts on the quality of isotopic data were tested for the storage of water samples and the repetitive opening of a laboratory reference material (LRM) sub-sample replica during daily operation. Twenty 15 mL water samples were stored in high-density polyethylene (HDPE) bottles at room temperature and humidity to simulate storage conditions. One 60 mL water sample was collected from the same starting batch to simulate the LRM sub-sample. Each 15 mL sample was analysed once over 80 days for the isotopic composition of oxygen (δ18O) and hydrogen (δ2H). The 60 mL sample was repeatedly analysed in the same period. The data were tested to identify shifts in the isotopic composition induced by evaporative processes. The main results of the work are the following: (i) storage of the 15 mL water samples did not cause detectable evaporation in the testing period; (ii) the 60 mL δ18O values showed evidence of evaporation as proved by the positive shift of the isotopic data; (iii) the repetitive opening of the 60 mL sample was the main cause of evaporation; (iv) five openings can already cause detectable isotopic enrichment. Careful manipulation and frequent replacement of the LRM are thus necessary to prevent deterioration of the quality of the analyses.

Proposed threshold-based and rule-based approaches to detecting duplicates in bibliographic database

Bibliographic databases are used to measure the performance of researchers, universities and research institutions. Thus, high data quality is required and data duplication is avoided. One of the weaknesses of the threshold-based approach in duplication detection is the low accuracy level. Therefore, another approach is required to improve duplication detection. This study proposes a method that combines threshold-based and rule-based approaches to perform duplication detection. These two approaches are implemented in the comparison stage. The cosine similarity function is used to create weight vectors from the features. Then, the comparison operator is used to determine whether the pair of records are grouped as duplication or not. Three research databases: Web of Science (WoS), Scopus, and Google Scholar (GS) on the Science and Technology Index (SINTA) database are investigated. Rule 4 and Rule 5 provide the best performance. For WoS dataset, the accuracy, precision, recall, and F1-measure values were 100.00%. For Scopus dataset, the accuracy and precision values were 100.00%, recall: 98.00%, and the F1-measure value is 98.00%. For GS dataset, the accuracy value was 100.00%, precision: 99.00%, recall: 97.00%, and the F1-measure value is 98.00%. The proposed method is potential tool for accurate detection on duplication records in publication databases.

Enhancements to the National HIV Surveillance System, United States, 2013-2023.

HIV infection is monitored through the National HIV Surveillance System (NHSS) to help improve the health of people with HIV and reduce transmission. NHSS data are routinely used at federal, state, and local levels to monitor the distribution and transmission of HIV, plan and evaluate prevention and care programs, allocate resources, inform policy development, and identify and respond to rapid transmission in the United States. We describe the expanded use of HIV surveillance data since the 2013 NHSS status update, during which time the Centers for Disease Control and Prevention (CDC) coordinated to revise the HIV surveillance case definition to support the detection of early infection and reporting of laboratory data, expanded data collection to include information on sexual orientation and gender identity, enhanced data deduplication processes to improve quality, and expanded reporting to include social determinants of health and health equity measures. CDC maximized the effects of federal funding by integrating funding for HIV prevention and surveillance into a single program; the integration of program funding has expanded the use of HIV surveillance data and strengthened surveillance, resulting in enhanced cluster response capacity and intensified data-to-care activities to ensure sustained viral suppression. NHSS data serve as the primary source for monitoring HIV trends and progress toward achieving national initiatives, including the US Department of Health and Human Services' Ending the HIV Epidemic in the United States initiative, the White House's National HIV/AIDS Strategy (2022-2025), and Healthy People 2030. The NHSS will continue to modernize, adapt, and broaden its scope as the need for high-quality HIV surveillance data remains.

Immune checkpoint inhibitor-related reproductive adverse effects: A pharmacovigilance analysis based on the FAERS database.

e14707 Background: There exists a critical need to systematically understand the extent and magnitude of immune checkpoint inhibitor (ICI)-related reproductive adverse effects, which may not only pose long-term implications for patients who may desire future fertility, but also impact the quality of life. We aim to better understand this underexplored area. Methods: Our focus was on ICIs approved by the FDA as of June 31, 2023, including anti-PD-1 agents (nivolumab, pembrolizumab, cemiplimab, dostarlimab, retifanlimab), anti-PD-L1 agents (atezolizumab, avelumab, durvalumab), anti-CTLA-4 agents (ipilimumab, tremelimumab), and an anti-LAG-3 agent (relatlimab). We analyzed 13,702,373 cases from January 1, 2015, to June 30, 2023. We included adverse reactions under the 'Reproductive system and breast disorders' category in the SOC (System Organ Classes). For signal detection, we used the reporting odds ratio (ROR) method. Results: We identified 133,512 patients administered with ICIs, among whom 568 reported irRAEs. Abnormal spermatogenesis (ROR025: 7.91), scrotal irritation (ROR025: 3.83), scrotal oedema (ROR025: 1.90), perineal rash (ROR025: 1.38), prostatitis (ROR025: 1.25), acquired phimosis (ROR025: 1.20), and scrotal erythema (ROR025: 1.08) in males and genital tract fistula in females (ROR025: 2.72) were detected as significant irRAEs. Among them, the most commonly reported irRAEs were prostatitis (n=29), scrotal oedema (n=12), and abnormal spermatogenesis (n=6) in males and genital tract fistula (n=34) in females. Male patients had a lower risk compared to females (OR=0.68 [0.57-0.81], p&lt;0.0001). There was no marked difference between the patients on polytherapy vs. monotherapy (OR=0.90 [0.71-1.13], p=0.36). PD-1 inhibitors pose the greatest risk when compared with CTLA-4 inhibitors (OR=1.65 [1.05-2.79], p=0.045). Gynecologic cancers in females (OR=3.77 [2.82-4.99], p&lt;0.0001) and urogenital cancers in males (OR=1.56 [1.17-2.06], p=0.0018) were strong risk factors. Additional targeted therapy, particularly lenvatinib (OR=3.79 [2.71-5.15], p&lt;0.0001) and cabozantinib (OR=2.69 [1.66-4.09], p&lt;0.0001) significantly increased the risk (OR=1.87 [1.52-2.29], p&lt;0.0001) and this risk appeared to be more pronounced in females (OR=2.32 [1.76-3.02], p&lt;0.0001). Additional chemotherapy was associated with a significantly reduced risk in males (OR=0.65 [0.42-0.96], p=0.042). However, doxorubicin (OR=2.58 [1.10-5.05], p=0.013) and cyclophosphamide (OR=2.36 [0.93-4.83], p=0.038) were linked to an elevated risk. Conclusions: Our findings demonstrated that ICIs could lead to a wide range of irRAEs in both males and females. Additional therapies could change the risk of irRAEs. Despite the current lack of high-quality clinical data on this topic, it is reasonable to inform patients about the plausible risk of irRAEs.

Benchmarking the transcriptomic predictive biomarkers for immunotherapy plus chemotherapy: Results from phase III RATIONALE-309 and ORIENT-11 randomized trials.

e14624 Background: The combination of PD-(L)1 inhibitors and chemotherapy (IO+Chemo) has become the standard first-line treatment for multiple types of cancer. In contrast to PD-(L)1 monotherapy, there is a lack of established biomarkers to distinguish the outcomes of IO+Chemo. The paucity of high-quality omics data in this setting has not allowed for deep biomarker exploration. We utilized RNA-seq data from two in-house randomized controlled trials (RCTs), RATIONALE-309 and ORIENT-11, with the goal to find transcriptomic biomarkers to identify patients who may not derive benefit from IO+Chemo. Methods: RATIONALE-309 (NCT03924986) was a phase 3 study conducted at 42 sites in Asia. Treatment-naive recurrent/metastatic nasopharyngeal cancer patients were randomly allocated to either PD-1 inhibitor tislelizumab or placebo plus gemcitabine and cisplatin. ORIENT-11 (NCT03607539) was a phase 3 study that enrolled patients from 47 centers in China who had previously untreated locally advanced/metastatic nonsquamous non-small cell lung cancer. Patients were randomized to PD-1 inhibitor sintilimab or placebo plus pemetrexed and platinum. We compared several immune-related biomarkers, including IFN-γ signature, PD-L1, cytolytic score (CYT), T-cell inflamed gene expression profile, B cell infiltration, and ESTIMATE immune score, and explored the optimal predictive threshold. Results: In the RATIONALE-309 and ORIENT-11 trials, 247 and 171 patients with RNA-seq data were analyzed, respectively. Although the addition of Chemo to IO could theoretically benefit tumors with low immune activation, we hypothesized that patients might not benefit from IO+Chemo if their activation level is extremely low. We first tested the predictiveness of several thresholds (20-50th quantiles) for each selected biomarker in the two RCTs. We found that low CYT and B cell are the two best indicators for identifying a lack of survival benefits from IO+Chemo versus Chemo, especially when using 20-30th quantile as threshold. To incorporate their distinct biological information and predictive value, we combined their genes into a single gene signature to represent immune activation. We found that patients in the lower quantile (&lt;20th) showed no benefit of IO+Chemo over Chemo in terms of overall survival (OS) (RATIONALE-309: HR=1.01, P=0.99; ORIENT-11: HR=1.54, P=0.31) and progression-free survival (PFS) (RATIONALE-309: HR=0.76, P=0.42; ORIENT-11: HR=1.30, P=0.52). Moreover, this lack of advantage was maintained in key subgroups such as PD-L1-positive patients and former/current smokers across the two RCTs. Conclusions: Based on the two large-scale RCTs, our findings have implications for the selection of biomarkers when identifying suitable candidates for IO+Chemo treatment. B cells and CYT are crucial components required for the efficacy of IO+Chemo.