Different Levels Of Sensitivity Research Articles

Prevalence estimates for COVID-19-related health behaviors based on the cheating detection triangular model

BackgroundSurvey studies in medical and health sciences predominantly apply a conventional direct questioning (DQ) format to gather private and highly personal information. If the topic under investigation is sensitive or even stigmatizing, such as COVID-19-related health behaviors and adherence to non-pharmaceutical interventions in general, DQ surveys can lead to nonresponse and untruthful answers due to the influence of social desirability bias (SDB). These effects seriously threaten the validity of the results obtained, potentially leading to distorted prevalence estimates for behaviors for which the prevalence in the population is unknown. While this issue cannot be completely avoided, indirect questioning techniques (IQTs) offer a means to mitigate the harmful influence of SDB by guaranteeing the confidentiality of individual responses. The present study aims at assessing the validity of a recently proposed IQT, the Cheating Detection Triangular Model (CDTRM), in estimating the prevalence of COVID-19-related health behaviors while accounting for cheaters who disregard the instructions.MethodsIn an online survey of 1,714 participants in Taiwan, we obtained CDTRM prevalence estimates via an Expectation-Maximization algorithm for three COVID-19-related health behaviors with different levels of sensitivity. The CDTRM estimates were compared to DQ estimates and to available official statistics provided by the Taiwan Centers for Disease Control. Additionally, the CDTRM allowed us to estimate the share of cheaters who disregarded the instructions and adjust the prevalence estimates for the COVID-19-related health behaviors accordingly.ResultsFor a behavior with low sensitivity, CDTRM and DQ estimates were expectedly comparable and in line with official statistics. However, for behaviors with medium and high sensitivity, CDTRM estimates were higher and thus presumably more valid than DQ estimates. Analogously, the estimated cheating rate increased with higher sensitivity of the behavior under study.ConclusionsOur findings strongly support the assumption that the CDTRM successfully controlled for the validity-threatening influence of SDB in a survey on three COVID-19-related health behaviors. Consequently, the CDTRM appears to be a promising technique to increase estimation validity compared to conventional DQ for health-related behaviors, and sensitive attributes in general, for which a strong influence of SDB is to be expected.

Zinc finger domain of p62/SQSTM1 is involved in the necroptosis of human cisplatin‑resistant ovarian cancer cells treated with sulfasalazine.

Cisplatin resistance in ovarian cancer cells is mainly apoptosis resistant. Although other types of programmed cell death are highly involved in chemoresistance, which type can overcome cisplatin resistance remains unclear. The present study observed that cisplatin-sensitive SKOV3 cells and cisplatin-resistant SKOV3/DDP cells had different levels of sensitivity to sulfasalazine (SAS). The present study aimed to investigate the effect of SAS on necroptosis under the same inhibition rate in these two types of cells. Necroptosis inhibitor Necrostatin-1 (Nec-1) attenuated SAS-induced SKOV3/DDP cytotoxicity. SAS decreased SKOV3/DDP cells survival rate, accompanied by decreased cell adhesion and spreading. SAS treatment activated necrosome formation in SKOV3/DDP cells, suggesting the possibility of necroptosis. p62/sequestosome-1 (SQSTM1) protein expression levels were also increased over the same time period. The transfection of small interfering (si)-p62 could decrease the ratios of phosphorylated (p)-receptor-interacting serine/threonine kinase 1 (RIP1)/RIP1, p-receptor-interacting serine/threonine kinase 3 (RIP3)/RIP3 and p-mixed lineage kinase domain-like protein (MLKL)/MLKL proteins in SKOV3/DDP cells. Under the si-p62 condition, there was no increase in the rate of cell survival in Nec-1 and SAS combination group compared with SAS. The zinc finger domain deletion of p62/SQSTM1 effectively decreased the expression levels of necroptosis-related p-proteins. Collectively, certain drugs were able to induce necroptosis in SKOV3/DDP, while p62/RIP1/RIP3/MLKL was associated with the induction of necroptosis and with increasing the sensitivity of cisplatin-resistant ovarian cancer cells, which provided evidence for potential as a therapeutic target for overcoming resistance.

Development and identification of a novel wheat-Thinopyrum ponticum disomic substitution line DS5Ag(5D) with new genes conferring resistance to powdery mildew and leaf rust

BackgroundPowdery mildew (caused by Blumeria graminis f. sp. tritici (Bgt)) and leaf rust (caused by Puccinia triticina (Pt)) are prevalent diseases in wheat (Triticum aestivum L.) production. Thinopyrum ponticum (2n = 10x = 70, EeEeEbEbExExStStStSt) contains genes that confer high levels of resistance to these diseases.ResultsAn elite wheat-Th. ponticum disomic substitution line, DS5Ag(5D), was developed in the Bainong Aikang 58 (AK58) background. The line was assessed using genomic in situ hybridization (GISH), oligo-nucleotide probe multiplex (ONPM) fluorescence in situ hybridization (FISH), and molecular markers. Twenty eight chromosome-specific molecular markers were identified for the alien chromosome, and 22 of them were co-dominant. Additionally, SNP markers from the wheat 660 K SNP chip were utilized to confirm chromosome identification and they provide molecular tools for tagging the chromosome in concern. The substitution line demonstrated high levels of resistance to powdery mildew throughout its growth period and to leaf rust at the adult stage. Based on the resistance evaluation of five F5 populations between the substitution lines and wheat genotypes with different levels of sensitivity to the two diseases. Results showed that the resistance genes located on 5Ag confered stable resistance against both diseases across different backgrounds. Resistance spectrum analysis combined with diagnostic marker detection of known resistance genes of Th. ponticum revealed that 5Ag contained two novel genes, Pm5Ag and Lr5Ag, which conferred resistance to powdery mildew and leaf rust, respectively.ConclusionsIn this study, a novel wheat-Th. ponticum disomic substitution line DS5Ag(5D) was successfully developed. The Th. ponticum chromosome 5Ag contain new resistance genes for powdery mildew and leaf rust. Chromosomic—specific molecular markers were generated and they can be used to track the 5Ag chromosome fragments. Consequently, this study provides new elite germplasm resources and molecular markers to facilitate the breeding of wheat varieties that is resistant to powdery mildew and leaf rust.

Change point detection of events in molecular simulations using dupin

Particle tracking is commonly used to study time-dependent behavior in many different types of physical and chemical systems involving constituents that span many length scales, including atoms, molecules, nanoparticles, granular particles, and even larger objects. Behaviors of interest studied using particle tracking information include disorder-order transitions, thermodynamic phase transitions, structural transitions, protein folding, crystallization, gelation, swarming, avalanches and fracture. A common challenge in studies of these systems involves change detection. Change point detection discerns when a temporal signal undergoes a change in distribution. These changes can be local or global, instantaneous or prolonged, obvious or subtle. Moreover, system-wide changes marking an interesting physical or chemical phenomenon (e.g. crystallization of a liquid) are often preceded by events (e.g. pre-nucleation clusters) that are localized and can occur anywhere at anytime in the system. For these reasons, detecting events in particle trajectories generated by molecular simulation is challenging and typically accomplished via ad hoc solutions unique to the behavior and system under study. Consequently, methods for event detection lack generality, and those used in one field are not easily used by scientists in other fields. Here we present a new Python-based tool, dupin, that allows for universal event detection from particle trajectory data irrespective of the system details. dupin works by creating a signal representing the simulation and partitioning the signal based on events (changes within the trajectory). This approach allows for studies where manual annotating of event boundaries would require a prohibitive amount of time. Furthermore, dupin can serve as a tool in automated and reproducible workflows. We demonstrate the application of dupin using three examples and discuss its applicability to a wider class of problems. Program summaryProgram Title:dupinCPC Library link to program files:https://doi.org/10.17632/kjcn97zc46.1%Developer's repository link::https://github.com/glotzerlab/dupinLicensing provisions: BSD 3-clauseProgramming language: PythonNature of problem: In the field of molecular simulations, detecting structural transitions or events within trajectories can be both challenging and time-consuming for larger studies due to the requirement of a manual approach. This issue is particularly pronounced in studies involving hundreds or thousands of simulations, where manual detection and analysis of transitions become infeasible. Our goal is to develop an automated, accurate and efficient method for detecting transition points in simulation trajectories, which both saves time and aids researchers in uncovering important events and their underlying causes in various systems. Additionally, we aim to facilitate new machine learning applications to important materials problems such as predicting and designing crystallization pathways, predicting defect formation, and describing the behavior of active matter, all of which involve structural transitions occurring over time. The developed method should be applicable to offline and online detection, enabling event-dependent triggers for advanced simulation/experimental protocols and efficient processing and storing of data.Solution method: We develop a versatile python package called dupin for detecting molecular events and structural transitions in simulation trajectories. dupin's workflow pipeline includes three major stages: data preprocessing, data augmentation, and detection. The components of this pipeline collectively improve the accuracy and efficiency of identifying structural changes in particle trajectory data. In data preprocessing, we generate and aggregate data into a comprehensive representation of the system. Data augmentation techniques such as feature selection and dimensionality reduction counteract the noise arising from high-dimensional data and enhance computational performance. We detect change points within the trajectory indicating transition events using a cost-based event detection method. In dupin, we implement two cost functions based on piecewise linear fits, which offer different levels of sensitivity to sudden shifts and changes in the signal. The package can use any cost-based detection algorithm but has a special interface for the Python package ruptures. Regardless of detection algorithm, we use the cost function and “elbow” detection to determine the correct number of change points. The detection scheme can be applied both offline and online, enabling real-time analysis of molecular events as simulations progress. As an example, dupin may be used to trigger a high frequency storage of frames within a simulation upon nucleation and subsequent solidification of a liquid into a crystal. Our method demonstrates a high degree of accuracy in detecting transition points within simulation trajectories when provided with informative descriptors. By automating the detection process, our solution enables efficient change point detection for studies with large-scale simulations.Additional comments including restrictions and unusual features: Our package, dupin, has great promise in detecting transition points within simulation trajectories with a high degree of accuracy; nonetheless, it is essential to note that it relies heavily on the selection of informative descriptors. The accuracy of the detection may be compromised if the chosen descriptors do not effectively capture the changes in a system's properties. However, this restriction can be mitigated by selecting a diverse range of descriptors and applying a feature selection tool to refine the signal.

Uncertainty Quantification Analysis in the Blade Element Momentum Method

The blade element momentum (BEM) method is a widely used non-linear model for the efficient performance evaluation and design of wind turbines. The aim of this paper is to quantify the uncertainty related to BEM model inputs and sub-models, and investigate how these propagate through the model. Uncertainties related to viscous dissipation in the wake, aerofoil force coefficients, and tip-loss models are considered. Global sensitivity to these parameters is analysed using non-intrusive polynomial chaos expansion, which provides a structured method for uncertainty propagation and sensitivity quantification. Sobol indices are employed to rank the relative importance of each factor to the overall uncertainty in the system. By analysing the NREL 5 MW and DTU 10 MW reference wind turbines, we observe that the different rotors may exhibit different levels of sensitivity to input parameters. The effect of viscous mixing in the turbine wake is found to have a significant impact on predicted rotor performance. Uncertainty in tip-loss model coefficients is also found to generally be important, particularly when evaluating spanwise variations in rotor loads. Uncertainty quantification has the potential to improve understanding of BEM modelling, and provide guidance on the importance of sub-model improvements.

Optimisation of energy efficiency of ambient backscatter communication and reconfigurable intelligent surfaces in non‐orthogonal multiple access downlink

AbstractThe authors study the energy efficiency (EE) of ambient backscatter communication (AmBC) device‐assisted and reconfigurable intelligent surfaces (RIS)‐assisted non‐orthogonal multiple access (NOMA) downlinks. The authors establish two optimisation problems based on the two collaborative devices (AmBC devices, RIS) with the objective of maximising the EE of the system, taking into account the requirements of power limitation and rate limitation, etc. and also obtain the solutions of two problems by optimising the relevant performance metrics based on the alternating optimisation algorithm. For the backscatter device (BD)‐aided downlink NOMA network, the problem is first decoupled into three subproblems, where the power allocation optimisation subproblem is solved by using the quadratic transformation method and the subgradient algorithm. The maximum EE is obtained by iterating according to the Dinkelbach's algorithm. For the RIS‐aided downlink NOMA network, the power allocation problem is solved by the same method as above and the phase optimisation problem is solved by the successive convex approximation method. Numerical results show that the proposed algorithm can achieve convergence after several iterations, and the EE of systems with BD‐assisted and RIS‐assisted have different levels of sensitivity to different influencing factors.

Toxic Effects of Arsenic on Four Freshwater Aquatic Species and Its Transformation Metabolism in Crucian Carp (Carassius auratus).

Inorganic arsenic is a well-known carcinogen that is much more toxic than its organic counterpart. While much is known about the accumulation and transformation of arsenic in marine organisms, little is known regarding these processes in freshwater aquatic species. In this study, the acute toxicity and toxicological effects of inorganic arsenic on four freshwater organisms (Cyprinus carpio, Misgurnus anguillicaudatus, Pseudorasbora parva, Eriocheir sinensis) commonly found in rice-fish farming systems were investigated. The organisms exhibited different levels of sensitivity to inorganic arsenic, with crustaceans being more sensitive than fish. Fish were found to be more tolerant to As(V) than As(III). The study also investigated the accumulation, transformation, and release of inorganic arsenic in crucian carp, an omnivorous species with high environmental tolerance. The fish accumulated As(III) rapidly in various tissues, and were able to transport it to other tissues through gills, intestines, and skin. The accumulated As(III) was converted into less toxic forms, such as monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA), via methylation. The fish also converted As(III) into arsenate (AsV) via enzymatic and oxidative reactions. After the transferal to clean water, the forms of arsenic in the various tissues decreased rapidly, but the rates of excretion of the four forms of arsenic were not the same among the different tissues. Our results suggest that crucian carp can reduce the environmental toxicity of As(III) at certain concentrations by transforming it into less toxic forms within their bodies.

Comparative transcriptome profiling and co-expression network analysis uncover the key genes associated with pear petal defense responses against Monilinia laxa infection.

Pear brown rot and blossom blight caused by Monilinia laxa seriously affect pear production worldwide. Here, we compared the transcriptomic profiles of petals after inoculation with M. laxa using two pear cultivars with different levels of sensitivity to disease (Sissy, a relatively tolerant cultivar, and Kristalli, a highly susceptible cultivar). Physiological indexes were also monitored in the petals of both cultivars at 2 h and 48 h after infection (2 HAI and 48 HAI). RNA-seq data and weighted gene co-expression network analysis (WGCNA) allowed the identification of key genes and pathways involved in immune- and defense-related responses that were specific for each cultivar in a time-dependent manner. In particular, in the Kristalli cultivar, a significant transcriptome reprogramming occurred early at 2 HAI and was accompanied either by suppression of key differentially expressed genes (DEGs) involved in the modulation of any defense responses or by activation of DEGs acting as sensitivity factors promoting susceptibility. In contrast to the considerably high number of DEGs induced early in the Kristalli cultivar, upregulation of specific DEGs involved in pathogen perception and signal transduction, biosynthesis of secondary and primary metabolism, and other defense-related responses was delayed in the Sissy cultivar, occurring at 48 HAI. The WGCNA highlighted one module that was significantly and highly correlated to the relatively tolerant cultivar. Six hub genes were identified within this module, including three WRKY transcription factor-encoding genes: WRKY 65 (pycom05g27470), WRKY 71 (pycom10g22220), and WRKY28 (pycom17g13130), which may play a crucial role in enhancing the tolerance of pear petals to M. laxa. Our results will provide insights into the interplay of the molecular mechanisms underlying immune responses of petals at the pear-M. laxa pathosystem.

Spatial sequestration of activated-caspase 3 in aggresomes mediates resistance of neuroblastoma cell to bortezomib treatment

Neuroblastoma (NB) is the most common pediatric tumor and is currently treated by several types of therapies including chemotherapies, such as bortezomib treatment. However, resistance to bortezomib is frequently observed by mechanisms that remain to be deciphered. Bortezomib treatment leads to caspase activation and aggresome formation. Using models of patients-derived NB cell lines with different levels of sensitivity to bortezomib, we show that the activated form of caspase 3 accumulates within aggresomes of NB resistant cells leading to an impairment of bortezomib-induced apoptosis and increased cell survival. Our findings unveil a new mechanism of resistance to chemotherapy based on an altered subcellular distribution of the executioner caspase 3. This mechanism could explain the resistance developed in NB patients treated with bortezomib, emphasizing the potential of drugs targeting aggresomes.

Cleanroom-Free Microfluidic Device for Natural Induction of Hypoxia in 2D and 3D Tumor Models.

Hypoxia is a common and critical feature of solid tumors that contributes to the plasticity and heterogeneity of the cancer cells. Cancer cell populations take on a region-specific adaptation induced by hypoxia, and each cancer cell population will show different levels of sensitivity and resistance to cancer therapeutics. Therefore, a faithful recapitulation of tumor hypoxia that allows for accurate assessments of hypoxia-induced adaptations, heterogeneity, and response to therapy is needed to develop new therapeutic approaches. The existing hypoxic tumor models rely on complex fabrication methods and external gas sources that make them unfavorable for the early-stage screening of new therapeutics. Here, we demonstrate how to establish a cleanroom-free microfluidic device that supports both 2D and 3D hypoxic tumor modeling through natural cancer cell metabolism and confirm the induction of the hypoxic gradient.

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.

ANALYSIS OF INVESTMENT SENSITIVITY IN RUSUNAWA TAMBAK WEDI CITY SURABAYA

Based on the Surabaya City RPJMD for 2021-2026, strategic issues in government affairs in the field of housing and residential areas focus on providing Rusunawa capacity which is not proportional to the number of applicants. To overcome this problem, the Surabaya City Government took the initiative to build Rusunawa Tambak Wedi in the City of Surabaya. This research aims to evaluate the amount of investment receipts and determine the acceptance limits, with the aim of minimizing risks in Rusunawa investment activities. The research results show that the Medium Tariff Structure 1, Lower Tariff Structure 1, and Lower Tariff Structure 2 are considered feasible if analyzed based on the Net Present Value (NPV), Internal Rate of Return (IRR), Benefit Cost Ratio (BCR), and Payback Period methods. (PP) but the Medium Tariff Structure 2 and the Upper Tariff Structure were declared unfeasible if analyzed using the same method. Sensitivity analysis shows that each Rusunawa rental rate structure has a different level of sensitivity to changes in variables, such as residential unit rental rates, management fees, and management fee subsidies provided by the Government.

Exclusive Benefits of Sustained MRD-Negative in High-Risk Multiple Myeloma

Introduction: Achievement of minimal residual disease (MRD) negativity is now recognized as a strong prognostic indicator for favorable clinical outcomes in multiple myeloma (MM) patients. Nonetheless, the optimal duration of MRD-negativity as a surrogate endpoint across different levels of sensitivity remains unclear. Additionally, it is imperative to explore whether sustained MRD-negativity holds the same prognostic value for MM patients with varying risk stratifications. Methods: In this study, 587 newly diagnosed MM patients were assessed for MRD using multiparameter flow cytometry (sensitivity of 10^-5 or 10^-6) at Zhongshan Hospital Fudan University between January 2014 and Janyary 2022. Patients were categorized as MRD-negativity not obtained (NON-MRD), obtained and sustained (SUS-MRD), or obtained and lost (LOS-MRD) based on their MRD status at given landmark point(4, 6, 12, 18, 24, and 36 months) after initiation of anti-myeloma treatment. The impact of MRD-negativity on survival outcomes was analyzed at increasing landmark times. Univariate and multivariate regression analyses were performed to examine the prognostic implications of time-dependent MRD status on overall survival in the context of different risk-stratification systems which were identified by versions of International Staging System (ISS). Results: A total of1718 MRD tests were performed on MM patients, the median number was 3. Among survival analyses at differnet landmark time, MM patients at 12 months showed greatest separation in subsequent survival between 3 groups no matter the sensitivity of 10^-5 or 10^-6 ( p=0.032, p=0.03). Pooled analysis indicated that patients who were in SUS-MRD category with a sensitivity ≥10^-5 had superior overall survival (OS) at 12 months landmark point ( p<0.001), with comparable survival between patients in Non-MRD and LOS-MRD category ( p=0.096, Figure 1). SUS-MRD group of MM patients at 12 months did not show better OS than the rest part patients in low-risk subgroup(ISS/R-ISS/R2-ISS=I, p=0.73, p=0.28, p=0.41,respectively). Among high-risk subgroup (ISS/R-ISS=III, R2-ISS=IV), landmark survival analyses at 12 months indicated that SUS-MRD benefited patients most, as shown the greatest OS in the SUS-MRD part ( p=0.013, p=0.011, p=0.056,respectively). Multivariate cox regression analysis showed that SUS-MRD was an independent favorable variable for patients in the ISS/R-ISS=III group ( p=0.021, HR:0.429; p=0.002, HR: 0.161). Conclusions: No matter the sensitivity of 10^-5 or 10^-6, the sustained of MRD negative lasts for 12 months is a robust marker of superior survival outcomes. MM patients who lost MRD negative has similar OS with those constant MRD positive in the long term. Benefit of Sustained MRD negative in MM limited to high risk subgroup MM patients.

Natural Killer Cells Are Active Against Myeloma, Leukemia or Lymphoma Cells with Loss of Tumor Suppressor Genes

The therapeutic targeting of tumor cells with loss of function (LOF) for tumor suppressor genes (TSGs) is challenging across cancers, including hematologic neoplasias, because pharmacological mechanisms to restore the function(s) of such genes are not readily feasible, in contrast to e.g., inhibition of oncogenic drivers. We reasoned, however, that, although LOF for TSGs leads to de-repressed growth of neoplastic hematopoietic cells, it may not necessarily protect them from immune attack. We thus explored the hypothesis that CRISPR-based loss of function of TSGs in cells from multiple myeloma (MM), leukemias or lymphoma may still be associated with substantial response to immune effector cells such as NK cells, which have the advantage to kill tumor cells across HLA barriers. We have conducted CRISPR-based studies in 7 cell lines that represent different hematologic malignancies and different levels of sensitivity to natural killer (NK) cells, namely the B-cell lymphoma (SUDHL4), precursor B cell acute lymphoblastic leukemia (NALM6), multiple myeloma (MM1.S, LP1, KMS11), chronic myeloid leukemia (K562), and acute myeloid leukemia (MOLM14). In these genome-scale or focused CRISPR screens for LOF (CRISPR-based gene editing) or gain of function (GOF, CRISPR activation), the blood cancer lines were exposed to allogeneic donor-derived NK cells (vs. control cultures without NK cells). We evaluated the performance of genes known to represent recurrent TSGs based on genomic data of patient samples or cell lines; as well as candidate TSGs, based on results from genome-scale CRISPR gene editing screens (e.g., CERES or CHRONOS scores >0.4 in multiple DepMap releases and TPM>1 [RNA-seq]) in the same cell lines as the NK cell resistance screens. These analyses sought to identify any TSGs whose LOF may potentially alter the response of blood cancer cells to NK cells. We also evaluated genes identified as top recurrent TSGs in patient samples from MM and other hematologic neoplasias (e.g., based on prior genomic studies). On aggregate, we evaluated a collection of known and recurrent TSGs (e.g., PTEN, TP53, RB1, CDKN2C, CDKN1B, TENT5C/FAM46C) as well as other, previously underappreciated candidate genes with TSG properties in hematologic neoplasias (e.g., HIF1A, DEPDC5). Perturbation of none of these genes was identified to meet criteria for association with significant resistance to allogeneic donor-derived NK cells (e.g., log2FC>1.0, at least 3-4 sgRNAs with enrichment upon CRISPR KO or depletion with CRISPR activation, p-value <0.05, enrichment rank <100, based on rank aggregation algorithm) in any of the MM, leukemia or lymphoma cell lines examined in LOF or GOF CRISPR screens for NK cell resistance. In fact, for a limited set of cases (e.g., PTEN in KMS11 cells), KO of a TSG was associated with sensitization to NK cell treatment. To probe the mechanistic basis for this lack of effect of TSG loss, we examined the molecular sequelae of CRISPR-based KO for one of these recurrent TSGs, PTEN, by performing scRNAseq using the CROP-seq platform. Pools of MM1.S and LP1 cells expressing sgRNAs targeting select hits from our CRISPR screens and also PTEN were co-cultured with NK cells for 24 h or left untreated, followed by scRNA-seq and sgRNA detection, differential gene-expression analysis. We observed in these studies that the transcriptional changes induced in MM cells by KO of PTEN included limited, if any, changes in the expression of key genes involved in regulation of NK cell responses of tumor cells, e.g., ligands for activating or inhibitor receptors in NK cells, death receptors (e.g., TRAIL, Fas) or their downstream effectors/regulators or other molecules identified from our aforementioned genome-scale and focused CRISPR LOF and GOF studies. Notably, our in-house results from studies of NK exposure of cell lines from hematologic neoplasias are concordant with results for these TSGs in CRISPR screens of non-hematological cancer cells treated with cytotoxic T-cells (e.g., 4T1 or RENCA cells; GSE149933). Overall, our observations indicate that MM, leukemia or lymphoma cells that become deficient for diverse TSGs based on CRISPR-based gene editing are equally responsive to NK cells as their TSG-proficient counterparts. NK cell-based therapies may thus be a promising approach to target TSG-deficient hematologic malignancies for which specific pharmacological therapies are not currently available.

O036 Measuring Sleep-wake State Discrepancy in the Home across Multiple Nights

Abstract Introduction Sleep-wake state discrepancy, the mismatch between subjective and objective measurements of sleep/wake status, is estimated to affect 9-50% of individuals with insomnia, often undetected due to lack of objective measures. Consumer sleep devices allow efficient measurement of sleep-wake state across multiple nights in the habitual sleep environment. This study assessed sleep-wake state discrepancies obtained by two devices across multiple nights in individuals with insomnia. Methods Individuals with insomnia completed daily sleep diaries, wore a Fitbit Charge 4 device, and used a Withings Sleep Analyser under-mattress sensor for 14-days. Sleep metrics were obtained from all three data sources to calculate sleep-wake state discrepancy (device metric − diary metric). Results 15 people participated in the study. Fitbit data were available for 12 and mattress data for 11 participants. There were no significant differences between the sleep-wake state discrepancy values derived from Fitbit and Withings devices for total sleep time (−78.7±111.8 and −95.7±124.4 minutes respectively; p .123) or wake after sleep onset (−15.4±52.9 and −17.6±110.6 minutes respectively; p .826). Significant differences were found in the sleep-wake state discrepancy values derived from Fitbit (−17.5±23.3, p <.001) and Withings devices for number of awakenings (3.2±23.1, p .175). Discussion Both devices comparably detected sleep-wake state discrepancy in people with insomnia, noting a device-difference for number of awakenings. These preliminary findings suggest that consumer devices may be useful to detect sleep-wake state discrepancy. Larger studies may reveal devices do so with different levels of sensitivity. Future studies should include polysomnography to assess relative magnitudes across devices.

Automatic Tuning of Privacy Budgets in Input-Discriminative Local Differential Privacy

LDP (Local Differential Privacy) and its variants have been recently studied to analyze personal data collected from IoT (Internet of Things) devices while strongly protecting user privacy. In particular, a recent study proposes a general privacy notion called ID-LDP (Input-Discriminative LDP), which introduces a privacy budget for each input value to deal with different levels of sensitivity. However, it is unclear how to set an appropriate privacy budget for each input value, especially in current situations where re-identification is considered a major risk, e.g., in GDPR. Moreover, the possible number of input values can be very large in IoT. Consequently, it is also extremely difficult to manually check whether a privacy budget for each input value is appropriate. In this paper, we propose algorithms to automatically tune privacy budgets in ID-LDP so that obfuscated data strongly prevent re-identification. We also propose a new instance of ID-LDP called OneID-LDP (One-Budget Input-Discriminative LDP) to prevent re-identification with high utility. Through comprehensive experiments using four real datasets, we show that existing instances of ID-LDP lack either utility or privacy – they overprotect personal data or are vulnerable to re-identification attacks. Then we show that our OneID-LDP mechanisms with our privacy budget tuning algorithm provide much higher utility than LDP mechanisms while strongly preventing re-identification.

P11.39.B PREDICTING RESPONSE TO CHEMOTHERAPY IN RECURRENT HIGH GRADE GLIOMAS USINGEX VIVO 3D CELL CULTURE FUNCTIONAL PRECISION ONCOLOGY PLATFORM

Abstract BACKGROUND Treatment of recurrent high-grade glioma remains a clinical challenge. There are very few agents active in this disease and short of enrollment in a clinical trial, the choice of therapy for a patient at the time of recurrence remains challenging. Several groups, including our own have developed ex vivo drug sensitivity assays to evaluate susceptibility of the tumor tissue to specific chemotherapeutics. To our knowledge, there is only one commercially available platform from the US based company Kiyatec. This tool, in addition to the molecular analysis of the tumor might provide another layer of confidence in choosing treatments for patients with recurrent high-grade gliomas. MATERIAL AND METHODS We present the data from our initial experience with the 3D PredictTM platform (Kiyatec, Greenville, SC, USA). We used the platform for 6 patients with recurrent high-grade gliomas undergoing tumor resection at the time of recurrence. We followed the company protocol for obtaining, preserving and shipping the tissue. RESULTS Out of the 6 specimens shipped for analysis, 4 assays were completed with full results available for analysis and clinical use. In two cases, the ex vivo tissue expansion was unsuccessful and drug testing was not completed. For the remaining 4 cases, spheroids created from the expansion of the harvested tissue were exposed to 12 drugs (temozolomide, lomustine, procarbazine, abemaciclib, carboplatin, dabrafenib, etoposide, everolimus, irinotecan, osimertinib, rucaparib and trametinib). We have observed different levels of sensitivity to several agents graded as: NO RESPONSE, MODERATE RESPONSE and RESPONSE. In 3 cases the results were used for clinical decision making and 2 patients were started on temozlomidede and carboplatin respectively and remain on treatment. In one case where sensitivity to etoposide was detected, we were not able to proceed with therapy due to insurance denial for the chosen drug. CONCLUSION In our experience, this platform is easy to use in a standard clinical setting, the success rate for creating viable spheroids for testing is high and the information derived from the drug sensitivity testing can be useful in clinical practice. Large, prospective clinical trial is need to validate this approach for routine clinical use.

Search strategies (filters) to identify systematic reviews in MEDLINE and Embase.

Bibliographic databases provide access to an international body of scientific literature in health and medical sciences. Systematic reviews are an important source of evidence for clinicians, researchers, consumers, and policymakers as they address a specific health-related question and use explicit methods to identify, appraise and synthesize evidence from which conclusions can be drawn and decisions made. Methodological search filters help database end-users search the literature effectively with different levels of sensitivity and specificity. These filters have been developed for various study designs and have been found to be particularly useful for intervention studies. Other filters have been developed for finding systematic reviews. Considering the variety and number of available search filters for systematic reviews, there is a need for a review of them in order to provide evidence about their retrieval properties at the time they were developed. To review systematically empirical studies that report the development, evaluation, or comparison of search filters to retrieve reports of systematic reviews in MEDLINE and Embase. We searched the following databases from inception to January 2023: MEDLINE, Embase, PsycINFO; Library, Information Science & Technology Abstracts (LISTA) and Science Citation Index (Web of Science). We included studies if one of their primary objectives is the development, evaluation, or comparison of a search filter that could be used to retrieve systematic reviews on MEDLINE, Embase, or both. Two review authors independently extracted data using a pre-specified and piloted data extraction form using InterTASC Information Specialist Subgroup (ISSG) Search Filter Evaluation Checklist. We identified eight studies that developed filters for MEDLINE and three studies that developed filters for Embase. Most studies are very old and some were limited to systematic reviews in specific clinical areas. Six included studies reported the sensitivity of their developed filter. Seven studies reported precision and six studies reported specificity. Only one study reported the number needed to read and positive predictive value. None of the filters were designed to differentiate systematic reviews on the basis of their methodological quality. For MEDLINE, all filters showed similar sensitivity and precision, and one filter showed higher levels of specificity. For Embase, filters showed variable sensitivity and precision, with limited study reports that may affect accuracy assessments. The report of these studies had some limitations, and the assessments of their accuracy may suffer from indirectness, considering that they were mostly developed before the release of the PRISMA 2009 statement or due to their limited scope in the selection of systematic review topics. Search filters for MEDLINE Three studies produced filters with sensitivity > 90% with variable degrees of precision, and only one of them was developed and validated in a gold-standard database, which allowed the calculation of specificity. The other two search filters had lower levels of sensitivity. One of these produced a filter with higher levels of specificity (> 90%). All filters showed similar sensitivity and precision in the external validation, except for one which was not externally validated and another one which was conceptually derived and only externally validated. Search filters for Embase We identified three studies that developed filters for this database. One of these studies developed filters with variable sensitivity and precision, including highly sensitive strategies (> 90%); however, it was not externally validated. The other study produced a filter with a lower sensitivity (72.7%) but high specificity (99.1%) with a similar performance in the external validation. Studies reporting the development, evaluation, or comparison of search filters to retrieve reports of systematic reviews in MEDLINE showed similar sensitivity and precision, with one filter showing higher levels of specificity. For Embase, filters showed variable sensitivity and precision, with limited information about how the filter was produced, which leaves us uncertain about their performance assessments. Newer filters had limitations in their methods or scope, including very focused subject topics for their gold standards, limiting their applicability across other topics. Our findings highlight that consensus guidance on the conduct of search filters and standardized reporting of search filters are needed, as we found highly heterogeneous development methods, accuracy assessments and outcome selection. New strategies adaptable across interfaces could enhance their usability. Moreover, the performance of existing filters needs to be evaluated in light of the impact of reporting guidelines, including the PRISMA 2009, on how systematic reviews are reported. Finally, future filter developments should also consider comparing the filters against a common reference set to establish comparative performance and assess the quality of systematic reviews retrieved by strategies.

In-house validation of an LC-MS method for the multiplexed quantitative determination of total allergenic food in chocolate.

Mass spectrometry has been widely accepted as a confirmatory tool for the sensitive detection of undeclared presence of allergenic ingredients. Multiple methods have been developed so far, achieving different levels of sensitivity and robustness, still lacking harmonization of the analytical validation and impairing comparability of results. In this investigation, a quantitative method has been validated in-house for the determination of six allergenic ingredients (cow's milk, hen's egg, peanut, soybean, hazelnut, and almond) in a chocolate-based matrix. The latter has been produced in a food pilot plant to provide a real and well-characterized matrix for proper assessment of method performance characteristics according to official guidelines. In particular, recent considerations issued by the European Committee for Standardization have been followed to guide a rigorous single-laboratory validation and to feature the main method performance, such as selectivity, linearity, and sensitivity. Synthetic surrogates of the peptide markers have been used both in native and labelled forms in matrix-matched calibration curves as external calibrants and internal standards, respectively. A two-order of magnitude range was investigated, focusing on the low concentration range for proper assessment of the detection and quantification limits (LOD and LOQ) by rigorous calibration approach. Conversion factors for all six allergenic ingredients have been determined for the first time to report the final quantitative information as fraction of total allergenic food protein (TAFP) per mass of food (µgTAFP/gfood), since such areporting unit isexploitable in allergenic risk assessment plans. The method achieved good sensitivity with LOD values ranging between 0.08 and 0.2 µgTAFP/gfood, for all ingredients besides egg and soybean, whose quantitative markers reported a slightly higher limit (1.1 and 1.2 µgTAFP/gfood, respectively). Different samples of chocolate bar incurred at four defined concentration levels close to the currently available threshold doses have been analyzed to test the quantitative performance of the analytical method, with a proper estimate of the measurement uncertainty from different sources of variability. The sensitivity achieved resulted in compliance with the various threshold doses issued or recommended worldwide.

Investigation of Metal Toxicity on Microalgae Phaeodactylum tricornutum, Hipersaline Zooplankter Artemia salina, and Jellyfish Aurelia aurita.

The escalating global anthropogenic activities associated with industrial development have led to the increased introduction of heavy metals (HMs) into marine environments through effluents. This study aimed to assess the toxicity of three HMs (Cr, Cu, and Cd) on organisms spanning different trophic levels: Phaeodactylum tricornutum (a primary producer), Artemia salina (a primary consumer), and Aurelia aurita (a secondary consumer). The EC50 values obtained revealed varying relative toxicities for the tested organisms. Phaeodactylum tricornutum exhibited the highest sensitivity to Cu, followed by Cd and Cr, while Artemia salina displayed the highest sensitivity to Cr, followed by Cu and Cd. A. aurita, on the other hand, demonstrated the highest sensitivity to Cu, followed by Cr and Cd. This experimental investigation further supported previous studies that have suggested A. aurita as a suitable model organism for ecotoxicity testing. Our experiments encompassed sublethal endpoints, such as pulsation frequency, acute effects, and mortality, highlighting different levels of sensitivity among the organisms.