Design, synthesis, pharmacological evaluation, and computational study of benzo[d] isothiazol-based small molecule inhibitors targeting PD-1/PD-l1 interaction.

Blockade of the programmed cell death-1 (PD-1)/programmed cell death ligand 1 (PD-L1) pathway is an attractive strategy for immunotherapy, but the clinical application of small molecule PD-1/PD-L1 inhibitors remains unclear. In this work, based on BMS-202 and our previous work YLW-106, a series of compounds with benzo[d]isothiazol structure as scaffold were designed and synthesized. Their inhibitory activity against PD-1/PD-L1 interaction was evaluated by a homogeneous time-resolved fluorescence (HTRF) assay. Among them, LLW-018 (27c) exhibited the most potent inhibitory activity with an IC50 value of 2.61 nM. The cellular level assays demonstrated that LLW-018 exhibited low cytotoxicity against Jurkat T and MDA-MB-231. Further cell-based PD-1/PD-L1 blockade bioassays based on PD-1 NFAT-Luc Jurkat cells and PD-L1 TCR Activator CHO cells indicated that LLW-018 could interrupt PD-1/PD-L1 interaction with an IC50 value of 0.88 µM. Multi-computational methods, including molecular docking, molecular dynamics, MM/GBSA, MM/PBSA, Metadynamics, and QM/MM MD were utilized on PD-L1 dimer complexes, which revealed the binding modes and dissociation process of LLW-018 and C2-symmetric small molecule inhibitor LCH1307. These results suggested that LLW-018 exhibited promising potency as a PD-1/PD-L1 inhibitor for further investigation.

Infant urinary tract infection in Sweden - A national study of current diagnostic procedures, imaging and treatment.

BACKGROUND: Urinary tract infection (UTI) in infants is a common, potentially life-threatening bacterial infection, and must be managed carefully through the entire chain of care from diagnosis, choice of treatment, follow-up and risk stratification of future complications. This Swedish nationwide study of infant UTI was conducted to evaluate the current management of infant UTI, yield of investigations and the Swedish UTI guidelines' ability to detect abnormalities of importance in the urinary tract. METHODS: Infants < 1 year with a first episode of UTI were included in a prospective multicenter study. Treatment and follow-up were provided by local pediatricians. Clinical and laboratory findings and imaging results were reported to the coordinating center. The current management and results were compared with a previous Swedish study. RESULTS: One thousand three hundred six infants were included. Urine sampling was performed with clean catch technique in 93% of patients. Initial oral antibiotic treatment was used in 63%, predominantly third generation cephalosporines. Permanent kidney abnormalities were found in 10% and dilating vesicoureteral reflux (VUR) in 8%. Higher rates of male gender, non-E. coli infection and ultrasound dilatation were seen in infants < 1 month. UTI recurrences were reported in 18%. CONCLUSIONS: Infant UTI is still generating a considerable amount of follow-up examinations. There is a significant shift towards clean catch as the main urine sampling method. Voiding cystourethrography is performed less frequently reducing the findings of low grade VUR. The incidence of renal scarring is comparable with earlier studies which suggests that the Swedish guidelines are able to identify individuals with risk for long-term complications.

Experiences of Self-Sampling and Future Screening Preferences in Non-Attenders Who Returned an HPV Vaginal Self-Sample in the YouScreen Study: Findings From a Cross-Sectional Questionnaire.

BACKGROUND: We assessed experiences of human papillomavirus (HPV) vaginal self-sampling and future screening preferences in an ethnically and socio-economically diverse group of women overdue for cervical screening. SETTING AND PARTICIPANTS: A postal questionnaire was embedded in the YouScreen self-sampling trial in England: 32.5% (2712/8338) of kit completers returned the survey. Kit non-completers were encouraged to return a questionnaire, but no responses were received. Participants were ethnically diverse (40.3% came from ethnic minority backgrounds), and 59.1% came from the two most deprived quintiles. Differences in confidence in kit completion, trust in the test results and intention to attend a follow-up test if HPV-positive were evaluated using Pearson's χ2 analyses. Binary logistic regression models explored predictors of a future screening choice and preferences for urine versus vaginal self-sampling. RESULTS: Most kit-completers reported high confidence in self-sampling (82.6%) and high trust in the results (79.9%), but experiences varied by ethnicity and screening status. Most free-text comments were positive but some reported difficulties using the device, pain or discomfort. Most women would opt for self-sampling in the future (71.3% vs. 10.4% for a clinician-taken test) and it was more often preferred by ethnic minority groups, overdue screeners and never attenders. Urine self-tests were preferred to vaginal tests (41.9% vs. 15.4%), especially among women from Asian, Black or Other Ethnic backgrounds. CONCLUSIONS: Kit-completers were confident, found the test easy to complete, and trusted the self-sample results. However, experiences varied by ethnic group and some women highlighted difficulties with the kit. Most women would prefer self-sampling in the future, but it was not a universal preference, so offering a choice will be important. PATIENT OR PUBLIC CONTRIBUTION: We did not have direct patient and public involvement and engagement (PPIE) in the questionnaire design. However, patients and public representatives did input into the design of the YouScreen trial and reviewed the wider study materials (e.g. participant information sheet). TRIAL REGISTRATION: This questionnaire study was embedded in the YouScreen trial. The protocol for the YouScreen trial is available at https://www.isrctn.com/ISRCTN12759467. The National Institute for Health Research 43 Clinical Research Network (NIHR CRN) Central Portfolio Management System (CPMS) ID is 4441934.

Efficient risk-based collection of biospecimens in cohort studies: Designing a prospective study of diagnostic performance for multicancer detection tests.

In cohort studies, it can be infeasible to collect specimens on an entire cohort. For example, to estimate sensitivity of multiple Multi-Cancer Detection (MCD) assays, we desire an extra 80mL of cell-free DNA (cfDNA) blood, but this much extra blood is too expensive for us to collect on everyone. We propose a novel epidemiologic study design that efficiently oversamples those at highest baseline disease risk from whom to collect specimens, to increase the number of future cases with cfDNA blood collection. The variance reduction ratio from our risk-based subsample versus a simple random (sub)sample (SRS) depends primarily on the ratio of risk model sensitivity to the fraction of the cohort selected for specimen collection subject to constraining the risk model specificity. In a simulation where we chose 34% of Prostate, Lung, Colorectal, and Ovarian Screening Trial cohort at highest risk of lung cancer for cfDNA blood collection, we could enrich the number of lung cancers 2.42-fold and the standard deviation of lung-cancer MCD sensitivity was 31-33% reduced versus SRS. Risk-based collection of specimens on a subsample of the cohort could be a feasible and efficient approach to collecting extra specimens for molecular epidemiology.

Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose Estimation, and Performance Parameter Analysis.

BACKGROUND: Two-dimensional (2D) video is a common tool used during sports training and competition to analyze movement. In these videos, biomechanists determine key events, annotate joint centers, and calculate spatial, temporal, and kinematic parameters to provide performance reports to coaches and athletes. Automatic tools relying on computer vision and artificial intelligence methods hold promise to reduce the need for time-consuming manual methods. OBJECTIVE: This study systematically analyzed the steps required to automate the video analysis workflow by investigating the applicability of a threshold-based event detection algorithm developed for 3D marker trajectories to 2D video data at four sampling rates; the agreement of 2D keypoints estimated by an off-the-shelf pose estimation model compared with gold-standard 3D marker trajectories projected to camera's field of view; and the influence of an offset in event detection on contact time and the sagittal knee joint angle at the key critical events of touch down and foot flat. METHODS: Repeated measures limits of agreement were used to compare parameters determined by markerless and marker-based motion capture. RESULTS: Results highlighted that a minimum video sampling rate of 100 Hz is required to detect key events, and the limited applicability of 3D marker trajectory-based event detection algorithms when using 2D video. Although detected keypoints showed good agreement with the gold-standard, misidentification of key events-such as touch down by 20 ms resulted in knee compression angle differences of up to 20°. CONCLUSION: These findings emphasize the need for de novo accurate key event detection algorithms to automate 2D video analysis pipelines.

The spatial extent and the dispersal strategy of species shape the occupancy frequency distribution of stream insect assemblages.

Several theoretical models have been proposed as the underlying mechanisms behind occupancy frequency distribution (OFD) patterns. For instance, the metapopulation dynamic model predicts bimodal OFD pattern indicating the dominance of dispersal processes in structuring the assemblages, while the niche-based model predicts unimodal right-skewed OFD pattern, and thus assemblages are driven mostly by niche processes. However, it is well known that the observed OFD pattern reflects the interplay of several other factors (e.g. habitat heterogeneity, species specificity and sampling protocol parameters). It follows that the individual contribution of each factor to the OFD pattern is rather complicated to explore. Our main objective was to examine the role of the spatial extent of the sampling and the dispersal strategies of species in shaping OFD pattern. For this, we collected samples of stream insect assemblages inhabiting near-natural streams in the Pannon Ecoregion. We formed groups of species representing contrasting dispersal strategies (referred to as dispersal groups). Applying a computer program algorithm, we produced samples with different spatial extent. We found that with increasing spatial extent, the OFD pattern changed from bimodal to unimodal for active dispersers. Insect groups with different dispersal strategies differed in the strength of support for OFD patterns within all spatial extent. Furthermore, the strength of support for OFD patterns varied across dispersal groups differently as the spatial extent increased. Our results reflected underlying changes in mechanisms structuring assemblages along an increasing spatial extent. We also assumed that the stream insect dispersal strategy influences the relative role of dispersal and niche processes particularly as spatial extent increases from stream reaches to the extent of adjacent valleys. We could define spatial extents and dispersal strategies within which unique metacommunity processes could underlie the organisation of assemblages.

Population size and structure of beisa oryx and gerenuk in Geralle National Park, southeastern Ethiopia.

The study was conducted to determine the population size of endangered Oryx beisa (Rüppell, 1835), and near-threatened Litocranius walleri (Brooke, 1878) of uncertain global population estimates in Geralle National Park, southeastern Ethiopia. Systematic line transects were established with a transect length range of 2.3 to 6.8 km long (a total of 165.4 km long with a sighting distance of 150 m after truncation). The combination of AIC and chi-square p-values was used as model selection criteria for density/population size estimation in distance sampling software. The lowest AIC, ∆AIC (close to zero), and Chi-square tests (p-value > .05) were selected with adequate model fit. The minimum observation was 67 individuals of beisa oryx in the dry season. The maximum observation was 349 individuals of gerenuk during the wet season. The minimum detection probability of oryx was in the wet season (p â = 76 ± 26), and the minimum detection probability of gerenuk was (p â = 75 ± 1) in both seasons. The two-season pooled density analyzed for studied species indicated (0.85 ± 0.34, 1.24 ± 0.47, beisa oryx/km2), and (3.82 ± 0.6, 4.88 ± 0.7 gerenuk/km2) in dry and wet seasons, respectively. It can be concluded from the results of the study that GNP is home to previously undiscovered healthy populations of the endangered beisa oryx and near-threatened gerenuk. So it is recommended to undergo in-depth population studies, including other species available in the national park and their habitat components, so as to design sound, sustainable conservation measures for the wildlife resources in the area.

Endocannabinoid analysis in GlucoEXACT plasma: Method validation and sample handling recommendations.

Endocannabinoids (ECs), such as anandamide and 2-arachidonyl glycerol (2-AG), contribute to the pathology of inflammatory, malignant, cardiovascular, metabolic and mental diseases. The reliability of quantitative analyses in biological fluids of ECs and endocannabinoid-like (EC-like) substances depends on pre-analytical conditions such as temperature and "time-to-centrifugation". Standardization of these parameters is critical for valid quantification and implementation in clinical research. In this study, we compared concentrations obtained with GlucoEXACT blood collection tubes versus K3EDTA tubes and employed the optimized procedure to assess ECs profiles in patients with inflammatory skin disease and healthy controls. A UHPLC-MS/MS method was validated for human plasma from GlucoEXACT blood collection tubes according to EMA and FDA guidelines, and pre-analytical conditions were systematically modified to assess analyte stability and optimize the procedures. The results showed significantly lower concentrations of ECs and EC-like substance concentrations with GlucoEXACT tubes compared with K3EDTA tubes, and GlucoEXACT extended the time window of stable concentrations. The strongest method-disagreement occurred for 1/2-AG suggesting that GlucoEXACT delayed ex vivo isomer rearrangement. Hence, GlucoExact tubes were superior in terms of stability and reliability. However, although absolute concentrations obtained with GlucoExact and K3EDTA differed, linear regression studies showed high agreement (except for 1/2-AG), and both methods showed similar EC profiles and similar disease-dependent pro-inflammatory patterns in dermatology patients. Hence, despite the obstacles in EC analyses, implementation of optimized pre-analytical blood collection and sample processing procedures provide reliable insight into peripheral ECs.

Reducing discrepancies between actual and ideal affect across adulthood: the roles of activity flow conduciveness, pleasantness, and familiarity.

Previous findings demonstrate that people often do not feel how they want to feel, supporting the distinction between "actual affect" and "ideal affect." But are there certain activities that reduce the discrepancy between actual and ideal affect? Based on flow theory and socioemotional selectivity theory, we examined whether the discrepancy between people's actual and ideal positive affect would be smaller during activities that were more conducive to flow (a state of intense absorption and concentration), pleasant, and familiar. In Study 1, U.S. participants aged 17-79 (N = 393) reported their ideal affect and how they felt during activities with varying degrees of challenges and skills. For both low-arousal positive affect (LAP) and high-arousal positive affect (HAP), participants reported smaller actual-ideal affect discrepancies during flow-conducive activities (when skills matched challenges). Study 2 was a 14-day experience sampling study, in which Hong Kong participants aged 18-83 (Nindividual = 109) reported their momentary actual and ideal affect, and how pleasant and familiar their activities were (Nexperience = 3,815). Greater activity familiarity was associated with smaller discrepancies in actual-ideal LAP, while greater activity pleasantness was associated with smaller discrepancies in actual-ideal HAP. These findings provide insights on the activities that help people achieve their ideal affect more easily.

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.

Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: A comparison across tropical countries.

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities.

Concentrations of potentially endocrine disrupting chemicals in car cabin air and dust - Effect of temperature and ventilation.

Materials in car cabins contain performance-enhancing semi-volatile organic compounds (SVOCs). As these SVOCs are not chemically bound to the materials, they can emit from the materials at slow rates to the surrounding, causing human exposure. This study aimed at increasing the understanding on abundance of SVOCs in car cabins by studying 18 potential endocrine disrupting chemicals in car cabin air (gas phase and airborne particles) and dust. We also studied how levels of these chemicals varied by temperature inside the car cabin along with ventilation settings, relevant to human exposure. A positive correlation was observed between temperature and SVOC concentration in both the gas and the particle phase, where average gas phase levels at 80 °C were a factor of 18-16,000 higher than average levels at 25 °C, while average particle phase levels were a factor of 4.6-40,000 higher for the studied substances. This study also showed that levels were below the limit of detection for several SVOCs during realistic driving conditions, i.e., with the ventilation activated. To limit human exposure to SVOCs in car cabins, it is recommended to ventilate a warm car before entering and have the ventilation on during driving, as both temperature and ventilation have a significant impact on SVOC levels.

Comparative analysis of obstetric, perinatal, and neurodevelopmental outcomes following chorionic villus sampling and amniocentesis.

Background: The risks of invasive prenatal tests are reported in previous studies such as miscarriage, fetal anomalies, and bleeding. However, few compare short-term and long-term outcomes between invasive tests. This study aims to investigate obstetric, perinatal, and children's neurodevelopmental outcomes following chorionic villus sampling (CVS) or amniocentesis in singleton pregnancy. Methods: This retrospective cohort study included healthy singleton pregnancies underwent transabdominal CVS (gestational age [GA] at 10-13 weeks) or amniocentesis (GA at 15-21 weeks) at a single medical center between 2012 and 2022. Only cases with normal genetic results were eligible. Short-term and long-term neurodevelopmental outcomes were evaluated. Results: The study included 200 CVS cases and 498 amniocentesis cases. No significant differences were found in body mass index, parities, previous preterm birth, conception method, and cervical length (CL) before an invasive test between the groups. Rates of preterm labor, preterm premature rupture of the membranes, preterm birth, neonatal survival, neonatal short-term morbidities, and long-term neurodevelopmental delay were similar. However, the CVS group had a higher rate of cervical cerclage due to short CL before 24 weeks (7.0%) compared to the amniocentesis group (2.4%). CVS markedly increased the risk of cervical cerclage due to short CL (adjusted odd ratio [aOR] = 3.17, 95%CI [1.23-8.12], p = 0.016), after considering maternal characteristics. Conclusion: Performing CVS resulted in a higher incidence of cerclage due to short cervix or cervical dilatation compared to amniocentesis in singleton pregnancies. This highlights the importance of cautious selection for CVS and the necessity of informing women about the associated risks beforehand.

Multi-Armed Bandit-Based User Network Node Selection.

In the scenario of an integrated space-air-ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a Multi-Armed Bandit (MAB) model and proposes an optimization algorithm leveraging dynamic variance sampling (DVS). The algorithm posits that the prior distribution of each node's network state conforms to a normal distribution, and by constructing the distribution's expected value and variance, it maximizes the utilization of sample data, thereby maintaining an equilibrium between data exploitation and the exploration of the unknown. Theoretical substantiation is provided to illustrate that the Bayesian regret associated with the algorithm exhibits sublinear growth. Empirical simulations corroborate that the algorithm in question outperforms traditional ε-greedy, Upper Confidence Bound (UCB), and Thompson sampling algorithms in terms of higher cumulative rewards, diminished total regret, accelerated convergence rates, and enhanced system throughput.

Towards Robust Decision-Making for Autonomous Highway Driving Based on Safe Reinforcement Learning.

Reinforcement Learning (RL) methods are regarded as effective for designing autonomous driving policies. However, even when RL policies are trained to convergence, ensuring their robust safety remains a challenge, particularly in long-tail data. Therefore, decision-making based on RL must adequately consider potential variations in data distribution. This paper presents a framework for highway autonomous driving decisions that prioritizes both safety and robustness. Utilizing the proposed Replay Buffer Constrained Policy Optimization (RECPO) method, this framework updates RL strategies to maximize rewards while ensuring that the policies always remain within safety constraints. We incorporate importance sampling techniques to collect and store data in a Replay buffer during agent operation, allowing the reutilization of data from old policies for training new policy models, thus mitigating potential catastrophic forgetting. Additionally, we transform the highway autonomous driving decision problem into a Constrained Markov Decision Process (CMDP) and apply our proposed RECPO for training, optimizing highway driving policies. Finally, we deploy our method in the CARLA simulation environment and compare its performance in typical highway scenarios against traditional CPO, current advanced strategies based on Deep Deterministic Policy Gradient (DDPG), and IDM + MOBIL (Intelligent Driver Model and the model for minimizing overall braking induced by lane changes). The results show that our framework significantly enhances model convergence speed, safety, and decision-making stability, achieving a zero-collision rate in highway autonomous driving.

Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing.

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems.

Enhancing Cervical Cancer Screening with 7-Type HPV mRNA E6/E7 Testing on Self-Collected Samples: Multicentric Insights from Mexico.

Cervical cancer remains a significant public health issue, particularly in regions with low screening uptake. This study evaluates the effectiveness of self-sampling and the 7-type HPV mRNA E6/E7 test in improving cervical cancer screening outcomes among a referral population in Mexico. A cohort of 418 Mexican women aged 25 to 65, referred for colposcopy and biopsy due to abnormal cytology results (ASC-US+), participated in this study. Self-samples were analyzed using both the 14-type HPV DNA test and the 7-type HPV mRNA E6/E7 test. The study assessed the sensitivity, specificity, positive predictive value (PPV), and the necessity of colposcopies to detect CIN3+ lesions. Participant acceptability of self-sampling was also evaluated through a questionnaire. The 7-type HPV mRNA E6/E7 test demonstrated equivalent sensitivity but significantly higher specificity (77.0%) and PPV for CIN3+ detection compared to the 14-type HPV DNA test (specificity: 45.8%, p < 0.001). The use of the HPV mRNA test as a triage tool reduced the number of colposcopies needed per CIN3+ case detected from 16.6 to 7.6 (p < 0.001). Self-sampling was highly accepted among participants, with the majority reporting confidence in performing the procedure, minimal discomfort, and willingness to undertake self-sampling at home. Self-sampling combined with the 7-type HPV mRNA E6/E7 testing offers a promising strategy to enhance cervical cancer screening by improving accessibility and ensuring precise diagnostics. Implementing these app roaches could lead to a significant reduction in cervical cancer morbidity and mortality, especially in underserved populations. Future research should focus on the long-term impact of integrating these methods into national screening programs and explore the cost-effectiveness of widespread implementation.

Sampling-rate calibration vs. equilibrium calibration for in vivo solid-phase microextraction: Analysis of neonicotinoids in bananas.

In vivo solid-phase microextraction (in vivo SPME) is an emerging fascinating sample pretreatment technique, but its quantitative correction method is different from the traditional correction methods, which has become a bottleneck limiting its development. At present, the sampling-rate calibration and equilibrium calibration are mainly used, however, their characteristics and applicability are not clear. In this study, the sampling-rate calibration and equilibrium calibration were evaluated in the case of the determination of neonicotinoids in bananas by in vivo SPME. The factors that affect the sampling rate (Rs), such as the matrix states, sampling durations, and individual differences were studied, and they all had impacts on Rs. Conversely, the equilibrium distribution coefficient (Kfs) remained constant after extraction equilibrium and the individual differences were smaller. The highest accuracy and precision were achieved by equilibrium calibration, and the relative recoveries were in the range of 83.2 %-104.3 % with the relative standard deviations below 8.1 % compared to a standard QuEChERS-based method. The lower limits of quantification for 4 neonicotinoids in bananas were below 5 ng g-1, lower than the standard method and the maximum residue levels in China and the European Union. This work clarifies the characteristics, rules and performance of the sampling-rate calibration and equilibrium calibration, which is of crucial importance for the development and application of in vivo SPME. The developed method is convenient, sensitive, and accurate for the determination of pesticide residues, which is of great significance to guide the safe use of pesticides in the field and prevent products with excessive pesticide residues from entering the market.

Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

PURPOSE: For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. METHODS: The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. RESULTS: The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. CONCLUSION: Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.