Similar Precision Research Articles

PRECISION ALS—an integrated pan European patient data platform for ALS

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative condition. Despite significant advances in pre-clinical models that enhance understanding of disease pathobiology, translation of candidate drugs to effective human therapies has been disappointing. There is increasing recognition of the need for a precision medicine approach toward drug development, as many failures in translation can be attributed in part to disease heterogeneity in humans. PRECISION-ALS is an academic industry collaboration between clinicians, Computer Scientists, Information engineers, technologists, data scientists and industry partners that will address the key clinical, computational, data science and technology associated research questions to generate a sustainable precision medicine based approach toward new drug development. Using extant and prospectively collected population based clinical data across nine European sites, PRECISION-ALS provides a General Data Protection Regulation (GDPR) compliant framework that seamlessly collects, processes and analyses research-quality multimodal and multi-sourced clinical, patient and caregiver journey, digitally acquired data through remote monitoring, imaging, neuro-electric-signaling, genomic and biomarker datasets using machine learning and artificial intelligence. PRECISION-ALS represents a first-in-kind modular transferable pan-European ICT framework for ALS that can be easily adapted to other regions that face similar precision medicine related challenges in multimodal data collection and analysis.

Development and Validation of Green and High-Throughput Microwell Spectrophotometric Assay for the Determination of Selective Serotonin Reuptake Inhibitors in Their Pharmaceutical Dosage Forms.

This study describes the development and validation of a new green and high-throughput microwell spectrophotometric assay (MW-SPA) for the determination of three selective serotonin reuptake inhibitors (SSRIs) in their pharmaceutical dosage forms. These SSRIs are fluoxetine, fluvoxamine, and paroxetine, the most prescribed drugs for the treatment of depression. The proposed assay was based on the formation of orange-colored N-substituted naphthoquinone derivatives upon the reaction of SSRIs with 1,2-naphthoquinone-4-sulphonate (NQS) in alkaline media. The assay was conducted in 96-microwell assay plates, and the absorbances of the reaction products were measured by a microplate reader at their maximum absorbance wavelengths. The optimum conditions of the reaction were refined and established. Under these conditions, calibration curves were generated, and linear regression equations were computed. The linear relations between the absorbances and drug concentrations were linear with good correlation coefficients (0.9992-0.9997) in the range of 2-80 µg/mL. The assay limits of detection were in the range of 1.5-4.2 µg/mL. The precision was satisfactory as the values of relative standard deviation did not exceed 1.70%. The accuracy of the assay was ≥98.2%. The proposed MW-SPA was successfully applied to the analysis of the SSRIs in their pharmaceutical dosage forms with acceptable accuracy and precision; the label claims were 99.2-100.5% (±0.96-1.35%). The results of the proposed MW-SPA were compared with those of the official/pre-validated assays by statistical analysis with respect to the accuracy (by t-test) and precision (by F-test). No significant differences were found between the calculated and theoretical values of the t- and F-tests at the 95% confidence level, proving similar accuracy and precision in the determination of SSRIs by both assays. The greenness of the proposed assay was confirmed by two metric tools. In addition, the assay is characterized with a high-throughput property which enables the simultaneous analysis of many samples in a short time. Therefore, the assay is a valuable tool for rapid routine application in pharmaceutical quality control units for the determination of the investigated SSRIs.

Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction

BackgroundRenin-angiotensin-aldosterone system inhibitors (RAASi) are commonly used medications. Renal adverse events associated with RAASi are hyperkalemia and acute kidney injury. We aimed to evaluate the performance of machine learning (ML) algorithms in order to define event associated features and predict RAASi associated renal adverse events. Materials and MethodsData of patients recruited from five internal medicine and cardiology outpatient clinics were evaluated retrospectively. Clinical, laboratory, and medication data were acquired via electronic medical records. Dataset balancing and feature selection for machine learning algorithms were performed. Random forest (RF), k-nearest neighbor (kNN), naïve Bayes (NB), extreme gradient boosting (xGB), support vector machine (SVM), neural network (NN), and logistic regression (LR) were used to create a prediction model. Results409 patients were included, and 50 renal adverse events occurred. The most important features predicting the renal adverse events were the index K and glucose levels, as well as having uncontrolled diabetes mellitus. Thiazides reduced RAASi associated hyperkalemia. kNN, RF, xGB and NN algorithms have the highest and similar AUC (≥ 98%), recall (≥ 94%), specifity (≥ 97%), precision (≥ 92%), accuracy (≥ 96%) and F1 statistics (≥ 94%) performance metrics for prediction. ConclusionRAASi associated renal adverse events can be predicted prior to medication initiation by machine learning algorithms. Further prospective studies with large patient numbers are needed to create scoring systems as well as for their validation.

Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks.

The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, sufficient open-source data of neonates on the NICU is lacking. We set out to train neural networks with the thermal-RGB-fusion data of neonates. We propose a novel indirect fusion approach including the sensor fusion of a thermal and RGB camera based on a 3D time-of-flight (ToF) camera. Unlike other approaches, this method is tailored for close distances encountered in neonatal incubators. Two neural networks were used with the fusion data and compared to RGB and thermal networks. For the class "head" we reached average precision values of 0.9958 (RetinaNet) and 0.9455 (YOLOv3) for the fusion data. Compared with the literature, similar precision was achieved, but we are the first to train a neural network with fusion data of neonates. The advantage of this approach is in calculating the detection area directly from the fusion image for the RGB and thermal modality. This increases data efficiency by 66%. Our results will facilitate the future development of non-contact monitoring to further improve the standard of care for preterm neonates.

Validation and psychometric properties of the Arabic version of Hamilton Depression Rating Scale 7 items (HAMD-7) among non-clinical and clinical samples of Lebanese adults.

The Hamilton Depression Rating Scale (HDRS or HAMD) is widely used scale for depression assessment. A shortened version of the HDRS, composed of 7 items, was implemented. The latter is timesaving compared to the original version, while still providing similar precision. Our objective in this study was to assess the psychometric properties of the Arabic HAMD-7 scale among non-clinical and clinical samples of Lebanese adults. In study 1, 443 Lebanese citizens enrolled in this cross-sectional study (June-September 2021). The total sample in study 1 was divided into two subsamples to conduct the exploratory-to-confirmatory factor analysis (EFA-to-CFA). Another cross-sectional study was conducted in September 2022 on another sample of Lebanese patients (independent from the sample of study 1) and included 150 patients attending two psychology clinics. The Montgomery-Asberg Depression Rating Scale (MADRS), Lebanese Depression Scale (LDS), Hamilton Anxiety Scale (HAM-A) and Lebanese Anxiety Scale (LAS) were used to assess the validity of the HAMD-7 scale. The results of the EFA (subsample 1; study 1) showed that the HAM-D-7 items converged into a one-factor solution (McDonald's ω = .78). The CFA (subsample 2; study 1) confirmed the one-factor solution obtained in the EFA (ω = .79). CFA indicated that fit of the one-factor model of the HAM-D-7 was acceptable: χ2/df = 27.88/14 = 1.99, RMSEA = .066 (90% CI = .028, .102), SRMR = .043, CFI = .960, TLI = .939. All indices suggested that configural, metric, and scalar invariance was supported across gender. The HAMD-7 scale score positively correlated with the MADRS (r = 0.809; p<0.001), LDS (r = 0.872; p<0.001), HAM-A (r = 0.645; p<0.001) and LAS (r = 0.651; p<0.001) scales scores. The optimal cutoff score between healthy individuals and depressive patients for the HAMD-7 was at a value = 5.50 (Se = 82.8% and Sp = 62.4%). The positive and negative predicted values for the HAMD-7 were 25.1% and 96.0%, respectively. The positive and negative likelihood ratios were 2.20 and 0.28 respectively. No significant difference was found between the non-clinical total sample (study 1) and the clinical sample (study 2) in terms of HAM-D-7 scores (5.24 ± 4.43 vs 4.54 ± 5.06; t(589) = 1.609; p = .108). Psychometric properties of the Arabic HAMD-7 scale are satisfactory, warranting its use clinically and in research. This scale seems highly efficient for ruling out depression; however, those with positive scores need a referral to a mental health professional for deeper evaluation. HAMD-7 might be self-administered by non-clinical subjects. Future studies are recommended to additionally confirm our results.

Detecting the risk of bullying victimization among adolescents: A large-scale machine learning approach

There is an increasing interest in using machine learning methods to identify risk factors for problematic behaviors. The current study tested and compared six machine learning algorithms: Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Light Gradient Boosting Machine (LightGBM), to detect risk factors for both traditional bullying victimization and cyberbullying victimization among Chinese adolescents. The Random Forest algorithm and LightGBM algorithm obtained similar accuracy and precision, and outperformed other four algorithms. We then combined the feature importance of LightGBM and Random Forest algorithms to evaluate the predictive power of 40 potentially relevant personal, educational, social and psychological factors in predicting bullying victimization, achieving better accuracy and higher performance. These results showed that the combined model can distinguish high-risk and low-risk adolescents for both types of bullying victimization based on a few easy-to-find variables. By comparing the relative significance of each factor, the current study also found mental illness, physical illness, and unhealthy living environments as having the highest values in predicting bullying victimization. Thus, the recommended model has a great application value in preventing bullying victimization among Chinese adolescents.

Improvement of a Simple Coupled VOF with LS (S-CLSVOF) Method

In this study, an efficient conversion algorithm of level set function ϕ from volume fraction α is developed. This method makes the calculation of ϕ easier than the conventional CLSVOF method, and the precision of surface tension can be improved by the application of ϕ. The two numerical tests are shown to demonstrate the proposed scheme. As a result of two demonstration tests, it is obtained that (a) the proposed method gives better ϕ than that of the existing simple coupled method because this can take into account multi-dimensional profiles of α, (b) a surface tension evaluated by using the proposed method can contribute to much lower spurious currents, (c) the proposed method can simulate bubble generation with a similar precision of rigorous CLSVOF method.

Weighing the Local Interstellar Medium Using Gamma Rays and Dust.

Cold gas forms a significant mass fraction of the MilkyWay disk, but is its most uncertain baryonic component. The density and distribution of cold gas is of critical importance for MilkyWay dynamics, as well as models of stellar and galactic evolution. Previous studies have used correlations between gas and dust to obtain high-resolution measurements of cold gas, but with large normalization uncertainties. We present a novel approach that uses Fermi-LAT γ-ray data to measure the total gas density, achieving a similar precision as previous works, but with independent systematic uncertainties. Notably, our results have sufficient precision to probe the range of results obtained by current world-leading experiments.

Estimating human papillomavirus vaccine efficacy from a single-arm trial: proof-of-principle in the Costa Rica Vaccine Trial.

The World Health Organization recommends a 1- or 2-dose human papillomavirus (HPV) vaccination schedule for females aged 9 to 20 years. Studies confirming the efficacy of a single dose and vaccine modifications are needed, but randomized controlled trials are costly and face logistical and ethical challenges. We propose a resource-efficient single-arm trial design that uses untargeted and unaffected HPV types as controls. We estimated HPV vaccine efficacy (VE) from a single arm by comparing 2 ratios: the ratio of the rate of persistent incident infection with vaccine-targeted HPV 16 and 18 (HPV 16/18) and cross-protected types HPV 31, 33, and 45 (HPV 31/33/45) to vaccine-unaffected types HPV 35, 39, 51, 52, 56, 58, 59, and 66 (HPV 35/39/51/52/56/58/59/66) vs the ratio of prevalence of these types at the time of trial enrollment. We compare VE estimates using only data from the bivalent HPV 16/18 vaccine arm of the Costa Rica Vaccine Trial with published VE estimates that used both the vaccine and control arms. Our single-arm approach among 3727 women yielded VE estimates against persistent HPV 16/18 infections similar to published 2-arm estimates from the trial (according-to-protocol cohort: 91.0% , 95% CI = 82.9% to 95.3% [single-arm] vs 90.9% , 95% CI = 82.0% to 95.9% [2-arm]; intention-to-treat cohort: 41.7%, 95% CI = 32.4% to 49.8% [single-arm] vs 49.0% , 95% CI = 38.1% to 58.1% [2-arm]). VE estimates were also similar in analytic subgroups (number of doses received; baseline HPV serology status). We demonstrate that a single-arm design yields valid VE estimates with similar precision to a randomized controlled trial. Single-arm studies can reduce the sample size and costs of future HPV vaccine trials while avoiding concerns related to unvaccinated control groups. ClinicalTrials.gov Identifier: NCT00128661.

Methodological precision of in situ and in vitro algal density measurements in the model cnidarian, Exaiptasia diaphana

In cnidarian symbiosis research, studying algal uptake, maintenance, and expulsion typically requires quantification of algal density in host tissue. Multiple methods are used to measure algal density including in vitro cell counts of holobiont homogenate and in situ cell counts of tentacle clippings. The relative precision of both types of measurement has not previously been reported for the model cnidarian Exaiptasia diaphana in the fully symbiotic state. The objective of this study was to evaluate the precision of in vitro and in situ algal density measurement protocols using light, fluorescent, and confocal microscopy and an automated cell counter. In situ algal density was quantified as algal area fraction (%) using confocal images of tentacle clippings mounted on two types of slides. In vitro algal density of holobiont homogenate was quantified as algal cells/µl of holobiont homogenate using an automated cell counter and a hemocytometer viewed using light and fluorescent microscopy. Triplicate measurements of each method for ten anemones were collected and the coefficient of variation was calculated and compared across the ten anemones within each method. The algal density measurements were equally precise when they were obtained by quantifying in vitro cell counts using a hemocytometer and when they were obtained by quantifying in situ cell counts. While both light and fluorescent microscopy yielded similar measurement precision of in vitro cell counts, use of a fluorescent microscope was more efficient and convenient than use of a light microscope, and both methods required terminal sampling. Conversely, in situ methods required more sophisticated equipment (namely a confocal microscope) but involved non-terminal sampling. An automated cell counter was ineffective for in vitro quantification of algal density, although the potential utility of this technology warrants future attempts using a more robust algal cell purification process that could include filtering homogenate prior to analysis. This study demonstrated that in vitro and in situ methods yield estimates of algal density with comparable precision, which is information that researchers can use for future studies when making decisions about methodology.

Abstract 5402: Using computer vision to resolve proliferative dynamics within therapeutic responses in large scale screens of patient derived models

Abstract IC50, Emax, and AUC values are commonly used to assess susceptibility for a given therapeutic candidate but are subject to the number of cell divisions for a given in vitro model. When comparing multiple in vitro models and especially patient-derived models such as patient-derived tumor organoids, the degree of intrinsic cell proliferation or doubling time substantially differs and therefore limits conventional metrics as above. Furthermore, assays that directly measure cellular viability can oftentimes overlook therapeutic agents that have cytostatic, versus cytotoxic mechanisms of action and require assays with similar sensitivity and precision to account for this phenotype. Finally, terminal endpoint assays can require tedious temporal optimization to capture the full dynamic range of a given therapeutic which makes cross-therapeutic and cross-model comparisons difficult to interpret. An assay that incorporates these multiple features and in vitro specific doubling time would lead to highly accurate measurements of drug effects and allow the calculation of growth-adjusted IC50 values i.e. GI50 values. We, therefore, sought to develop a computer vision model that utilizes label-free longitudinal light microscopy images as input to report the total number of nuclei present within a given experimental well, allowing for the monitoring of cell division and any cytostatic effects. To train the neural network, we use Hoechst-stained images as our ground truth. The model used is an extension of the Regularized Conditional Adversarial Network (PMID: 34320344). It takes as input Brightfield images along with the registered Hoescht stained wells. The label for each well is obtained using commercial image processing software to count the total number of nuclei present in each well from the Hoescht stained channel. The model was trained to predict the virtually stained Hoescht well for every Brightfield, as well as the aggregate nuclei count for all organoids present inside the well. The model was trained on 29000 individual sites across 9 distinct cancer types. Five experiments were conducted during training to test for bit depth and tonality effects of the input training data. Inference on 9240 images showed a pearsonR score of 0.81 on the best-performing model. Results show that the model performs well and can be applied in real-time using only experimental data to correct for differing cell division rates and measure cytostatic phenotypes all while using simple light microscopy. The tool will enable cross-comparison of different therapeutic MoAs as well as enable cross-cancer type/indication comparison for a therapeutic in development to inform early development clinical strategy. Citation Format: Madhavi Kannan, Brian Larsen, Chi-Sing Ho, Jagadish Venkataraman, Martin Stumpe, Ameen Salahudeen. Using computer vision to resolve proliferative dynamics within therapeutic responses in large scale screens of patient derived models. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5402.

Suitability of the animated activity questionnaire for use as computer adaptive test: establishing the AAQ-CAT

PurposeThe animated activity questionnaire (AAQ) is a computer-based measure of activity limitations. To answer a question, patients choose the animation of a person performing an activity that matches their own level of limitation. The AAQ has not yet been tested for suitability to be applied as computer-adaptive test (CAT). Thus, the objective of this study was to develop and evaluate an AAQ-based CAT to facilitate the application of the AAQ in daily clinical care.MethodsPatients (n = 1408) with hip/knee osteoarthritis from Brazil, Denmark, France, The Netherlands, Norway, Spain, and the UK responded to all 17 AAQ items. Assumptions of item-response theory (IRT) modelling were investigated. To establish item parameters for the CAT, a graded response model was estimated. To evaluate the performance of post-hoc simulated AAQ-based CATs, precision, test length, and construct validity (correlations with well-established measures of activity limitations) were evaluated.ResultsUnidimensionality (CFI = 0.95), measurement invariance (R2-change < 2%), and IRT item fit (S-X2p > .003) of the AAQ were supported. Performing simulated CATs, the mean test length was more than halved (≤ 8 items), while the range of precise measurement (standard error ≤ 0.3) was comparable to the full AAQ. The correlations between original AAQ scores and three AAQ-CAT versions were ≥ 0.95. Correlations of AAQ-CAT scores with patient-reported and performance measures of activity limitations were ≥ 0.60.ConclusionThe almost non-verbal AAQ-CAT is an innovative and efficient tool in patients with hip/knee osteoarthritis from various countries, measuring activity limitations with lower respondent burden, but similar precision and construct validity compared to the full AAQ.

MINIMIZATION OF RISK AND MAXIMIZATION OF PROFIT ON BEHALF OF BANK BY USING MACHINE LEARNING ALGORITHM

The main aim to develop a loan approval prediction model for a bank by utilizing machine learning algorithms. [Objective] The primary objective is to minimize the bank's loss by developing a decision rule for approving or rejecting a loan application based on an applicant's demographic and socio-economic profile. [Method] Three machine learning algorithms, namely Logistic Regression, Random Forest, and Decision Tree, were applied to a German credit dataset to achieve this objective. The data were preprocessed, including converting the target variable to binary, converting character variables to factors, and selecting only numeric variables. The accuracy, sensitivity, and precision of the models were evaluated using crossvalidation, and the results were compared. [Result] The Random Forest model showed the highest accuracy, with an average accuracy of 76.07%, followed by the Decision Tree model with an average accuracy of 71.99%, and Logistic Regression with an average accuracy of 71.62%. The sensitivity evaluation showed that the Decision Tree model had the highest sensitivity, with an average sensitivity of 70.88%, followed by the Logistic Regression model with an average sensitivity of 68.35%, and the Random Forest model with an average sensitivity of 65.86%.[Conclusion] In conclusion, the Random Forest model showed the highest accuracy, while the Decision Tree model had the highest sensitivity. However, all three models showed similar precision scores. Therefore, based on the objective of minimizing the bank's loss, the Decision Tree model may be more suitable as it has higher sensitivity, which means it has a lower chance of approving a loan for a potentially risky applicant. However, further research and testing are necessary before implementing these models in real-world applications.

DARU-Net: A dual attention residual U-Net for uterine fibroids segmentation on MRI.

Uterine fibroid is the most common benign tumor in female reproductive organs. In order to guide the treatment, it is crucial to detect the location, shape, and size of the tumor. This study proposed a deep learning approach based on attention mechanisms to segment uterine fibroids automatically on preoperative Magnetic Resonance (MR) images. The proposed method is based on U-Net architecture and integrates two attention mechanisms: channel attention of squeeze-and-excitation (SE) blocks with residual connections, spatial attention of pyramid pooling module (PPM). We did the ablation study to verify the performance of these two attention mechanisms module and compared DARU-Net with other deep learning methods. All experiments were performed on a clinical dataset consisting of 150 cases collected from our hospital. Among them, 120 cases were used as the training set, and 30 cases are used as the test set. After preprocessing and data augmentation, we trained the network and tested it on the test dataset. We evaluated segmentation performance through the Dice similarity coefficient (DSC), precision, recall, and Jaccard index (JI). The average DSC, precision, recall, and JI of DARU-Net reached 0.8066 ± 0.0956, 0.8233 ± 0.1255, 0.7913 ± 0.1304, and 0.6743 ± 0.1317. Compared with U-Net and other deep learning methods, DARU-Net was more accurate and stable. This work proposed an optimized U-Net with channel and spatial attention mechanisms to segment uterine fibroids on preoperative MR images. Results showed that DARU-Net was able to accurately segment uterine fibroids from MR images.

Bat4RCT: A suite of benchmark data and baseline methods for text classification of randomized controlled trials.

Randomized controlled trials (RCTs) play a major role in aiding biomedical research and practices. To inform this research, the demand for highly accurate retrieval of scientific articles on RCT research has grown in recent decades. However, correctly identifying all published RCTs in a given domain is a non-trivial task, which has motivated computer scientists to develop methods for identifying papers involving RCTs. Although existing studies have provided invaluable insights into how RCT tags can be predicted for biomedicine research articles, they used datasets from different sources in varying sizes and timeframes and their models and findings cannot be compared across studies. In addition, as datasets and code are rarely shared, researchers who conduct RCT classification have to write code from scratch, reinventing the wheel. In this paper, we present Bat4RCT, a suite of data and an integrated method to serve as a strong baseline for RCT classification, which includes the use of BERT-based models in comparison with conventional machine learning techniques. To validate our approach, all models are applied on 500,000 paper records in MEDLINE. The BERT-based models showed consistently higher recall scores than conventional machine learning and CNN models while producing slightly better or similar precision scores. The best performance was achieved by the BioBERT model when trained on both title and abstract texts, with the F1 score of 90.85%. This infrastructure of dataset and code will provide a competitive baseline for the evaluation and comparison of new methods and the convenience of future benchmarking. To our best knowledge, our study is the first work to apply BERT-based language modeling techniques to RCT classification tasks and to share dataset and code in order to promote reproducibility and improvement in text classification in biomedicine research.

Vanadium(IV) and vanadium(V) complexation by succinic acid studied by affinity capillary electrophoresis. Simultaneous injection of two analytes in equilibrium studies

The interaction of V(IV) and V(V) with succinic acid was investigated by affinity capillary electrophoresis (ACE) in aqueous acid solutions at pH values 1.5, 2.0 and 2.4, and different ligand concentrations. V(IV) and V(V) form protonated complexes with succinic acid ligand at this pH range. The logarithms of the stability constants, measured at 0.1 mol L-1 (NaClO4/HClO4) ionic strength and 25 °C, are logβ111=7.4 ± 0.2 and logβ122=14.1 ± 0.5 for V(IV), and logβ111=7.3 ± 0.1 for V(V), respectively. The stability constant values, extrapolated to zero ionic strength with the Davies equation, are logβ°111=8.3 ± 0.2 and logβ°122=15.6 ± 0.5 for V(IV) and logβ°111=7.9 ± 0.1 for V(V). The application of ACE to the simultaneous equilibria of V(IV) and V(V) (injection of two analytes) was also attempted. When the results were compared with those obtained when introducing only one analyte in the capillary, using the traditional version of the method, similar stability constants and precision are obtained. The possibility of studying two analytes simultaneously decreases the time needed for the determination of the constants; this feature is especially valuable when working with hazardous materials or when only small quantities of ligand are available.

CA‐UNet: Convolution and attention fusion for lung nodule segmentation

AbstractLung cancer is one of the deadliest cancers in the world and is a serious threat to human life. Lung nodules are an early manifestation of lung cancer, early detection and treatment of which can improve the survival rate of patients. In order to accurately segment the lung nodule regions in lung CT images, CA‐UNet, an encoding and decoding structure based on convolution and attention fusion, is proposed based on the U‐Net network. It has improved on two points: First, at the skip connection, the global feature information is extracted using the Swin Transformer block and then fused with the pre‐extraction features and subsequently fed into the corresponding layer of the decoder; second, each channel information is reweighted in the decoder by the channel attention module so that the network focuses on more important channels. Experimental results on the LIDC‐IDRI public database of lung nodules showed that the intersection of union, dice similarity coefficient, precision, and recall of the algorithm were 82.42%, 89.86%, 89.07%, and 92.44%, respectively. The algorithm has better segmentation performance compared to other segmentation methods.

Thyroid function in causal relation to MRI markers of cerebral small vessel disease: a Mendelian randomization analysis.

Observational studies provided insufficient information on the association between thyroid function and the risk of cerebral small vessel disease (CSVD); moreover, the causality of this link was unclear. This study aimed to investigate whether genetically predicted variation within thyroid function was causally associated with the risk of CSVD using two-sample Mendelian randomization (MR) analysis. In this two-sample MR study with genome-wide association variants, we estimated the causal effects of genetically predicted thyrotropin (TSH; N = 54,288), free thyroxine (FT4; N = 49,269), hypo (N = 51,823), and hyperthyroidism (N = 51,823) on three neuroimaging markers of CSVD, including white matter hyperintensities (WMH; N= 42,310), mean diffusivity (MD; N = 17,467), and fractional anisotropy (FA, N = 17,663). The primary analysis used inverse-variance-weighted MR method, followed by sensitivity analyses using MR-PRESSO, MR-Egger, weighted median, and weighted mode methods. Genetically increased TSH was associated with increased MD (β = 0.311, 95% CI = [0.0763, 0.548], P = 0.01). Genetically increased FT4 was associated with increased FA (β = 0.540, 95% CI = [0.222, 0.858], P < 0.001). Sensitivity analyses using different MR methods showed similar directions but lower precision. No significant associations of hypo and hyperthyroidism with WMH, MD, or FA were found (all P > 0.05). This study indicated that genetically predicted increased TSH was associated with increased MD, as well as increased FT4 with increased FA, implying the causal effect of thyroid dysfunction on white matter microstructural damages. There was no evidence for causal relationships of hypo or hyperthyroidism with CSVD. Further investigations should verify these findings and clarify the underlying pathophysiological mechanisms.

Beyond the bulge–halo conspiracy? Density profiles of early-type galaxies from extended-source strong lensing

ABSTRACTObservations suggest that the dark matter and stars in early-type galaxies ‘conspire’ to produce a surprisingly simple distribution of total mass, ρ(r) ∝ ρ−γ, with γ ≈ 2. We measure the distribution of mass in 48 early-type galaxies that gravitationally lens a resolved background source. By fitting the source light in every pixel of images from the Hubble Space Telescope, we find a mean $\langle \gamma \rangle =2.075_{-0.024}^{+0.023}$ with an intrinsic scatter between galaxies of $\sigma _\gamma =0.172^{+0.022}_{-0.032}$ for the overall sample. This is consistent with and has similar precision to traditional techniques that employ spectroscopic observations to supplement lensing with mass estimates from stellar dynamics. Comparing measurements of γ for individual lenses using both techniques, we find a statistically insignificant correlation of $-0.150^{+0.223}_{-0.217}$ between the two, indicating a lack of statistical power or deviations from a power-law density in certain lenses. At fixed surface mass density, we measure a redshift dependence, $\partial \langle \gamma \rangle /\partial z=0.345^{+0.322}_{-0.296}$, that is consistent with traditional techniques for the same sample of Sloan Lens ACS and GALaxy-Lyα EmitteR sYstems (GALLERY) lenses. Interestingly, the consistency breaks down when we measure the dependence of γ on the surface mass density of a lens galaxy. We argue that this is tentative evidence for an inflection point in the total mass-density profile at a few times the galaxy effective radius – breaking the conspiracy.

Discrete Oligocarbamates Exhibit Sequence-Dependent Fluorescence Emission and Quenching.

The encoded precision of biological polymers enables a few simple monomers (e.g., four nucleotides in nucleic acids) to create complex macromolecular structures that accomplish a myriad of functions. Similar spatial precision in synthetic polymers and oligomers can be harnessed to create macromolecules and materials with rich and tunable properties. Recent exciting advances in iterative solid- and solution-phase synthetic strategies have led to the scalable production of discrete macromolecules, which in turn has enabled the study of sequence-dependent material properties. Our recent example of a scalable synthetic strategy using inexpensive vanillin-based monomers to create sequence-defined oligocarbamates (SeDOCs) enabled the preparation of isomeric oligomers with different thermal and mechanical properties. We show that unimolecular SeDOCs also exhibit sequence-dependent dynamic fluorescence quenching that persists from solution to the solid phase. We detail the evidence for this phenomenon and show that changes in fluorescence emissive properties are dependent on macromolecular conformation, which in turn is driven by sequence.