Comparable Accuracy Research Articles

P0507 pTRACK generated normalised average T2 signal is an accurate and sensitive measure of Perianal Crohn’s disease activity

Abstract Background Magnetic resonance imaging (MRI) is the gold standard for diagnosing, classifying, and monitoring perianal Crohn’s disease (pCD). Fistula T2 signal intensity and volume/complexity are predictive of treatment response. We developed pTRACK, an interactive tool that enables radiologists to measure fistula volume, normalised average T2 signal intensity, and anatomical relationships to generate 3D digital models of pCD within minutes. This study aimed to evaluate pTRACK-generated MRI metrics against MAGNIFI-CD and assess their predictive value for clinical outcomes. Methods We retrospectively included consecutive adult patients (>18 years) with pCD who underwent pelvic MRI and clinical assessment. Two independent, blinded abdominal radiologists scored MAGNIFI-CD and pTRACK metrics (figure 1) on axial T2 STIR TSE sequences. Clinical outcomes (remission, response, non-response) were scored using the fistula drainage assessment (FDA). Correlations, unpaired two-tailed t-tests, and leave-one-patient-out cross-validation (LOPOCV) performance metrics were used to compare MRI metrics (fistula volume, normalised average T2 signal intensity, MAGNIFI-CD score) and predict FDA categories. T2 signal intensity was normalised using an ROI in adjacent pelvic muscles. Results 36 patients having 77 MRI scans were included. Mean C-reactive protein at the time of assessment was 12.9 ± 22 mg/L. 38.2% of patients had class 1 disease, 42.7% had class 2a disease with a minority having more complex disease. 58.4% of patients experienced non-response, with 14.3% experiencing response and 27.3% experiencing remission. Significant differences were observed across metrics when dichotomized into remission and active groups (responding and non-responding): normalised T2 signal (1.65 ± 1.10 vs. 2.98 ± 1.52, p<0.001), fistula volume (1.58 ± 3.08 mL vs. 4.55 ± 8.66 mL, p=0.03), and MAGNIFI-CD scores (5.86 ± 4.70 vs. 12.41 ± 5.80, p<0.001). Fistula volume and average T2 signal intensity showed moderate correlation to MAGNIFI-CD (r=0.72, p<0.001; r=0.49, p<0.001 respectively). LOPOCV AUC for normalised average T2 and fistula volume were 0.77 and 0.74, respectively, with sensitivities of 87% and 95%, and specificities of 62% and 52%. For MAGNIGI-CD, LOPOCV AUC was 0.84, with a sensitivity of 77% and specificity of 81%. Conclusion pTRACK’s normalised T2 signal and fistula volume demonstrated comparable accuracy to MAGNIFI-CD, with higher sensitivity for predicting pCD activity and distinguishing active from non-active disease. These findings underscore pTRACK’s potential utility, warranting prospective validation in cohorts with more severe disease.

Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for Δ-machine learning.

We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d0. To account for variations in distance, we combine AHA with this ansatz for the electronic binding potential, E(d)=(Eu-Es)Ec-EsEu-Esd/d0+Es, where Eu, Ec, Es correspond to the energies of the united atom, calibration at d0, and the sum of infinitely separated atoms, respectively. Our model covers the two-dimensional electronic potential energy surface spanned by distances of 0.7-2.5Å and differences in nuclear charge from which only one single point (with elements of nuclear charge Z1, Z2, and distance d0) is drawn to calibrate Ec. Using reference data from pbe0/cc-pVDZ, we present numerical evidence for the electronic ground-state of all neutral diatomics with 8, 10, 12, and 14 electrons. We assess the validity of our model by comparison to legacy interatomic potentials (harmonic oscillator, Lennard-Jones, and Morse) within the most relevant range of binding (0.7-2.5Å) and find comparable accuracy if restricted to single diatomics and significantly better predictive power when extrapolating to the entire iso-electronic series. We also investigated Δ-learning of the electronic absolute energy using our model as a baseline. This baseline model results in a systematic improvement, effectively reducing training data needed for reaching chemical accuracy by up to an order of magnitude from ∼1000 to ∼100. By contrast, using AHA+Morse as a baseline hardly leads to any improvement and sometimes even deteriorates the predictive power. Inferring the energy of unseen CO converges to a prediction error of ∼0.1 Ha in direct learning and ∼0.04 Ha with our baseline.

Tensor Train Optimization for Conformational Sampling of Organic Molecules.

Exploring the conformational space of molecules remains a challenge of fundamental importance to quantum chemistry: identification of relevant conformers at ambient conditions enables predictive simulations of almost arbitrary properties. Here, we propose a novel approach, called TTConf, to enable conformational sampling of large organic molecules where the combinatorial explosion of possible conformers prevents the use of a brute-force systematic conformer search. We employ tensor trains as a highly efficient dimensionality reduction algorithm, effectively reducing the scaling from exponential to polynomial. In our approach, the conformational search is expressed as global energy minimization task in a high-dimensional grid of dihedral angles. Dimensionality reduction is achieved through a tensor train representation of the high-dimensional torsion space. The performance of the approach is assessed on a variety of drug-like molecules in direct comparison to the state-of-the-art metadynamics based conformer search as implemented in CREST. The comparison shows significant acceleration of up to an order of magnitude, while maintaining comparable accuracy. More importantly, the presented approach allows treatment of larger molecules than typically accessible with metadynamics.

Association of Liver Fibrosis Markers with Mortality Outcomes in Patients with Chronic Kidney Disease and Coronary Artery Disease: Insights from the NHANES 1999-2018 Data.

The objective of this research is to explore the possible link between markers of liver fibrosis and survival rates in a group of adults who have been diagnosed with both chronic kidney disease (CKD) and coronary artery disease (CAD). The National Health and Nutrition Examination Survey (NHANES) data (1999-2018) for participants with both CAD and CKD were analyzed. The Fibrosis-4 Index (FIB-4), Nonalcoholic Fatty Liver Score (NFS), Forns index and Aspartate Aminotransferase/Alanine Aminotransferase (AST/ALT) ratio were identified as crucial biomarkers. All-cause and cardiovascular disease (CVD) mortality were primary outcomes, assessed using Cox models, Kaplan-Meier curves, and ROC analysis. A total of 1,192 CKD and CAD patients were included. The Cox regression analysis revealed substantial correlations between elevated FIB-4, NFS, Forns index and AST/ALT levels and a heightened risk of all-cause (HR 1.188, 95%CI 1.108-1.274; HR 1.145, 95%CI 1.069-1.227; HR 1.142, 95%CI 1.081-1.201; HR 1.316, 95%CI 1.056-1.639, respectively) and CVD mortality (HR 1.133, 95%CI 1.007-1.275; HR 1.155, 95%CI 1.024-1.303; HR 1.208, 95%CI 1.109-1.316 and HR 1.636, 95%CI 1.203-2.224, respectively). The ROC analysis indicated comparable predictive accuracy for all three biomarkers, with AST/ALT showing slightly superior performance. Liver fibrosis markers, including AST/ALT, NFS, Forns index and FIB-4, are significant mortality predictors in CAD-CKD patients. The AST/ALT ratio, being easily measurable, may serve as an effective predictive tool for risk stratification in this population.

Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar

This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is 3.98±5.81 mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is 6.52±7.51 mmHg.

Boundary sample extraction support vector data description: a novel anomaly detection method for large-scale data

Abstract Support vector data description (SVDD) has been effectively used in many anomaly detection problems. As equipped with kernel functions, its training complexity grows exponentially with the increase of training data, which makes it less practical for large-scale dataset. In this paper, we proposed a boundary samples extraction SVDD(BSE-SVDD) anomaly detection method based on data reduction, aiming for the large-scale data. Firstly, the BSE mechanism is established on the basis of demonstrating that the spatial position of the sample is related to its corresponding Lagrange multiplier. Then, the BSE mechanism is used to search for the local optimal solution of the Lagrange multiplier, and all the training samples are sorted. Finally, the top p of ranked samples are extracted as boundary samples for training, while most of the training samples that may be Non-SVs (NSVs) are removed. Compared with SVDD studies based on data reduction, the experimental results on large-scale datasets show that BSE-SVDD can obtain comparable classification accuracy with greatly improved training speed.

Transformer models for quantum gate set tomography

Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challenges of manufacturing high-fidelity and scalable quantum processors. Quantum gate set tomography (QGST) is a critical method for characterizing quantum processors and understanding their operational capabilities and limitations. This paper introduces Ml4Qgst as a novel approach to QGST by integrating machine learning techniques, specifically utilizing a transformer neural network model. Adapting the transformer model for QGST addresses the computational complexity of modeling quantum systems. Advanced training strategies, including data grouping and curriculum learning, are employed to enhance model performance, demonstrating significant congruence with ground-truth values. We benchmark this training pipeline on the constructed learning model, to successfully perform QGST for 2 and 3 gates on single-qubit and two-qubit systems, with over-rotation error and depolarizing noise estimation with comparable accuracy to pyGSTi. This research marks a pioneering step in applying deep neural networks to the complex problem of quantum gate set tomography, showcasing the potential of machine learning to tackle nonlinear tomography challenges in quantum computing.

Taming large-scale genomic analyses via sparsified genomics

Searching for similar genomic sequences is an essential and fundamental step in biomedical research. State-of-the-art computational methods performing such comparisons fail to cope with the exponential growth of genomic sequencing data. We introduce the concept of sparsified genomics where we systematically exclude a large number of bases from genomic sequences and enable faster and memory-efficient processing of the sparsified, shorter genomic sequences, while providing comparable accuracy to processing non-sparsified sequences. Sparsified genomics provides benefits to many genomic analyses and has broad applicability. Sparsifying genomic sequences accelerates the state-of-the-art read mapper (minimap2) by 2.57-5.38x, 1.13-2.78x, and 3.52-6.28x using real Illumina, HiFi, and ONT reads, respectively, while providing comparable memory footprint, 2x smaller index size, and more correctly detected variations compared to minimap2. Sparsifying genomic sequences makes containment search through very large genomes and large databases 72.7-75.88x (1.62-1.9x when indexing is preprocessed) faster and 723.3x more storage-efficient than searching through non-sparsified genomic sequences (with CMash and KMC3). Sparsifying genomic sequences enables robust microbiome discovery by providing 54.15-61.88x (1.58-1.71x when indexing is preprocessed) faster and 720x more storage-efficient taxonomic profiling of metagenomic samples over the state-of-the-art tool (Metalign).

Assessment of viable tumours by [68Ga]Ga-FAPI-04 PET/CT after local regional treatment in patients with hepatocellular carcinoma.

To investigate the efficacy of [68Ga]Ga-FAPI-04 PET/CT for assessing viable tumours (VTs) after local regional treatment (LRT) in hepatocellular carcinoma (HCC) patients. The related imaging features of HCC after LRT are preliminarily discussed. A cohort of 37 LRT patients with HCC (encompassing 51 lesions) was retrospectively included from a prospective parent study (ChiCTR2000039099), and sequential PET/CT using [18F]FDG and [68Ga]Ga-FAPI-04 was performed. The diagnostic accuracies of [68Ga]Ga-FAPI-04 and [18F]FDG PET/CT and multiphasic CT/MRI for detecting VTs after LRT were calculated and analysed. Pathological examination was considered the gold standard for VT diagnosis, and clinical follow-up was used as the reference standard. The SUVmax and tumour-to-background ratio (TBR) derived from [18F]FDG and [68Ga]Ga-FAPI-04 PET/CT were calculated and compared. Moreover, the SUVmax, target-to-normal liver ratio (TNR) of VT, tumour necrosis (TN), benign rim (BR), and normal liver (NL) from different imaging modalities after LRT for HCC were compared. Both the sensitivity (96.0% [24/25] vs. 36.0% [9/25], p < 0.001) and accuracy (94.1%, [48/51] vs. 68.6% [35/51], p = 0.004) of [68Ga]Ga-FAPI-04 PET/CT for detecting VTs after LRT were greater than those of [18F]FDG PET/CT, whereas their specificities were comparable (92.3% [24/26] vs. 100% [26/26]). Notably, [68Ga]Ga-FAPI-04 PET/CT had a greater SUVmax (9.80 vs. 3.60) and TBR (9.93 vs. 1.57) than [18F]FDG PET/CT in VT patients (all p < 0.001). Furthermore, VT had a greater SUVmax and TNR than did TN, BR, and NL on [68Ga]Ga-FAPI-04 PET/CT (all p < 0.001) and exhibited morphologic nodular, mass-like, or irregular tracer uptake. Although no significant differences were observed for VT detection (all p > 0.05), [68Ga]Ga-FAPI-04 PET/CT and multiphasic CT/MRI complemented each other in some cases. [68Ga]Ga-FAPI-04 PET/CT not only presented higher sensitivity and accuracy than [18F]FDG PET/CT for diagnosing VTs after LRT for HCC but also showed comparable diagnostic accuracy and complementary roles with multiphasic CT/MRI. Overall, [68Ga]Ga-FAPI-04 PET/CT may play an essential role in surveillance after LRT for HCC. Chinese Clinical Trial Registry ChiCTR2000039099. Registered 17 October 2020.

Fourier energy spectrum centroid: a robust and efficient approach for shear wave speed estimation in ω–k space

Objective.The propagation speed of a shear wave, whether externally or internally induced, in biological tissues is directly linked to the tissue's stiffness. The group shear wave speed (SWS) can be estimated using a class of time-of-flight (TOF) methods in the time-domain or phase speed-based methods in the frequency domain. However, these methods suffer from biased estimations or time-consuming computations, and they are especially prone to wave distortions inin vivocases. In this work, we present a parameter-free, robust, and efficient group SWS estimation method coined as Fourier energy spectrum centroid (FESC).Approach.The proposed FESC method is based on the center of mass inω-kspace. It was evaluated on data from computer simulations with additive Gaussian noise, a commercial elasticity phantom, anex vivopig liver, andin vivobiceps brachii muscles of three young healthy male subjects. The FESC method was compared with two 2D frequency-domain methods: Max-fre, which considers phase SWS at the peak ofk-space, and Fre-regre, which applies linear regression of phase SWS within a fixed bandwidth. Two additional benchmarks included time-domain methods based on cross-correlation (X-Corr) and radon sum transformation (RD).Main results.Statistical results showed that our FESC method and the RD method had comparable accuracy and robustness, outperforming the other benchmark methods. In the simulation and phantom studies, when the signal-to-noise ratio was higher than 25 dB, our FESC showed higher accuracy than RD. In thein vivostudy, our FESC method had better repeatability than RD. Furthermore, the proposed FESC method was 100 times faster than the runner-up method, X-Corr, and 3,000 times faster than the least efficient method, RD.Significance.All results indicated that our proposed Fourier-based method shows promise in reliably and efficiently providing reference values for group SWS in homogeneous bulk media.

A Facile Electrode Modification Approach Based on Metal-Free Carbonaceous Carbon Black/Carbon Nanofibers for Electrochemical Sensing of Bisphenol A in Food.

Bisphenol A (BPA) is a typical environmental estrogen that is distributed worldwide and has the potential to pose a hazard to the ecological environment and human health. The development of an efficient and sensitive sensing strategy for the monitoring of BPA residues is of paramount importance. A novel electrochemical sensor based on carbon black and carbon nanofibers composite (CB/f-CNF)-assisted signal amplification has been successfully constructed for the amperometric detection of BPA in foods. Herein, the hybrid CB/f-CNF was prepared using a simple one-step ultrasonication method, and exhibited good electron transfer capability and excellent catalytic properties, which can be attributed to the large surface area of carbon black and the strong enhancement of the conductivity and porosity of carbon nanofibers, which promote a faster electron transfer process on the electrode surface. Under the optimized conditions, the proposed CB/f-CNF/GCE sensor exhibited a wide linear response range (0.4-50.0 × 10-6 mol/L) with a low limit of detection of 5.9 × 10-8 mol/L for BPA quantification. Recovery tests were conducted on canned peaches and boxed milk, yielding satisfactory recoveries of 86.0-102.6%. Furthermore, the developed method was employed for the rapid and sensitive detection of BPA in canned meat and packaged milk, demonstrating comparable accuracy to the HPLC method. This work presents an efficient signal amplification strategy through the utilization of carbon/carbon nanocomposite sensitization technology.

Accuracy Comparison of Impression by Alginate Materials between Conventional and New Methods (Injection)—an In-vitro Study

Purpose: In this research, we used a new technique (injection) to improve the accuracy of recording the surface details of alginate impression material. This research aims to compare the new method (injection) accuracy with the conventional method using alginate impression material. Material and Method: In this in vitro study, the Typodont Tooth model of the Maxillary Arch was used. The second molar tooth was prepared from the second quadrant of the maxillary arch model, and ten points were identified for examination with an extra-oral laboratory 3D scanner. Poly Works, SPSS, and EXO CAD software were used for analysis and comparison. Results: Among the ten reference points used on the trimmed teeth after impression, casting, and scanning, the casts made by the new method contain the most recorded points, with the highest accuracy in recording details. By using Poly-Works software, the degree of matching of the scanned plaster casts with the original model was compared, and the highest match was related to casts number 1, 2, and 3 (injection) and cast No. 4, 5, and 6 (conventional) had the minor match. Conclusion: Based on the results obtained from this study, the injection technique was more accurate than the traditional technique.

Diagnostic Accuracy Comparison between Automatic and Conventional Blood Pressure Measuring Devices in Adults with Hypertension

Background Blood pressure measurement is a main clinical procedure used for the detection of Systemic Arterial Hypertension. Mercury sphygmomanometer, in combination with auscultation technique, is still recognized as the gold standard for non-invasive blood pressure measurement. Objective The objective of this study is to compare the diagnostic accuracy between automatic non-invasive blood pressure measurement devices versus mercury sphygmomanometers in hospitalized patients. Methods A prospective, analytical and cross-sectional diagnostic method study was designed to compare the concordance, positive and negative predictive value, the sensitivity and specificity of automatic non-invasive blood pressure measurement devices and the mercury sphygmomanometer. The devices were divided into two groups: the reference standard which was mercury sphygmomanometer (n=150), versus the index tests, that were wrist device (n=150), semi-automatic sphygmomanometer (n=150) and vital signs monitor (n=150). Results The monitor detected more cases (69%) of systemic arterial hypertension (p 0.0019, OR 5.3). The semiautomatic sphygmomanometer identified more true positives (n=22) and true negatives (n=113). It also showed a higher diagnostic accuracy: 67% sensitivity (p 0.0576, OR 0.5) and 97% specificity (p 0.088, OR 3.2). A mean arterial pressure variation &lt;5mmHg of 65% was found with the semiautomatic sphygmomanometer (p &lt;0.0001, OR 0.02) and 61% with the vital signs monitor (p &lt;0.0001, OR 0.12). Best concordance of blood pressure between reference standard versus index test was obtained in the following order: Semi-automatic sphygmomanometer, Vital signs monitor, and Wrist device. Conclusion The use of vital signs monitor to measure blood pressure is recommended in all patients, although semiautomatic sphygmomanometer is more sensitive for the detection of systemic arterial hypertension.

Capturing Strong Correlation in Molecules with Phaseless Auxiliary-Field Quantum Monte Carlo Using Generalized Hartree-Fock Trial Wave Functions.

Generalized Hartree-Fock (GHF) is a long-established electronic structure method that can lower the energy (compared to spin-restricted variants) by breaking physical wave function symmetries, namely and . After an exposition of GHF theory, we assess the use of GHF trial wave functions in phaseless auxiliary field quantum Monte Carlo (ph-AFQMC-G) calculations of strongly correlated molecular systems including symmetrically stretched hydrogen rings, carbon dioxide, and dioxygen. Imaginary time propagation is able to restore symmetry and yields energies of comparable or better accuracy than CCSD(T) with unrestricted HF and GHF references, and consistently smooth dissociation curves─a remarkable result given the relative scalability of ph-AFQMC-G to larger system sizes. The present exploration of model strongly correlated systems marks a promising starting point for future studies of more chemically relevant molecules, and demonstrates that ph-AFQMC-G provides a highly accurate (and, in contrast to active-space-based trials, relatively black box and always size-consistent) description of challenging systems exhibiting, e.g., antiferromagnetic coupling and/or geometric spin frustration.

Comparing ARFIMA and ARIMA Models in Forecasting under Five Mortality Rate in Tanzania

Tanzania has been taking various measures to drop the Under-Five Mortality Rate (UFMR), but the pace to meet national and global UFMR targets has been slow. Nevertheless, the decline for the past years has continued to be low as compared to the Sustainable Development Goals (SDGs) target which is set at 25 deaths/1000 live births by 2030. The lack of statistical modeling-based forecast values of UFMR results into setting targets that are not SMART towards the realization of national and international goals of the health sector. Thus, the current study uses both ARFIMA and ARIMA to make forecasts of UFMR in Tanzania from 2021 to 2030 by using data extracted from the World Databank - World Development Indicators (WDI). Also, an accuracy comparison between the ARFIMA and ARIMA best-fit models in forecasting UFMR was conducted. The forecasts from the best ARFIMA (1, 0.284243, 2) model indicate that by June 2026 the rate will on average be 41 deaths/1,000 live births as compared to the Tanzanian Five Year Development Plan Phase III (TFYDP-III) target of 40 deaths/1,000 live births; whereas the best fit ARIMA (1, 2, 0) model forecasts depict that the rate will be 40.1 deaths/1,000 live births as compared to the TFYDP-III target. In relation to the UN SDGs target of 25 deaths/1,000 live births by 2030, the ARFIMA (1, 0.284243, 2) model forecast values indicate that by 2030, Tanzania will experience a decrease in UFMR to 35.2 deaths/1,000 live births. The ARIMA (1, 2, 0) forecast values indicate that by 2030, Tanzania will experience a decrease in UFMR to 32.9 deaths/1,000 live births. The results of using RMSE and MAPE forecasting model accuracy measures reveal that the ARFIMA (1, 0.284243, 2) model performs better than ARIMA (1, 2, 0) in forecasting UFMR.

Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition.

Accurate identification of surgical instruments is crucial for efficient workflows and patient safety within the operating room, particularly in preventing complications such as retained surgical instruments. Artificial Intelligence (AI) models have shown the potential to automate this process. This study evaluates the accuracy of publicly available Large Language Models (LLMs)-ChatGPT-4, ChatGPT-4o, and Gemini-and a specialized commercial mobile application, Surgical-Instrument Directory (SID 2.0), in identifying surgical instruments from images. The study utilized a dataset of 92 high-resolution images of 25 surgical instruments (retractors, forceps, scissors, and trocars) photographed from multiple angles. Model performance was evaluated using accuracy, weighted precision, recall, and F1 score. ChatGPT-4o exhibited the highest accuracy (89.1%) in categorizing instruments (e.g., scissors, forceps). SID 2.0 (77.2%) and ChatGPT-4 (76.1%) achieved comparable accuracy, while Gemini (44.6%) demonstrated lower accuracy in this task. For precise subtype identification of instrument names (like "Mayo scissors" or "Kelly forceps"), all models had low accuracy, with SID 2.0 having an accuracy of 39.1%, followed by ChatGPT-4o (33.69%). Subgroup analysis revealed ChatGPT-4 and 4o recognized trocars in all instances. Similarly, Gemini identified surgical scissors in all instances. In conclusion, publicly available LLMs can reliably identify surgical instruments at the category level, with ChatGPT-4o demonstrating an overall edge. However, precise subtype identification remains a challenge for all models. These findings highlight the potential of AI-driven solutions to enhance surgical-instrument management and underscore the need for further refinements to improve accuracy and support patient safety.

Computationally efficient machine-learned model for GST phase change materials via direct and indirect learning.

Phase change materials such as Ge2Sb2Te5 (GST) are ideal candidates for next-generation, non-volatile, solid-state memory due to the ability to retain binary data in the amorphous and crystal phases and rapidly transition between these phases to write/erase information. Thus, there is wide interest in using molecular modeling to study GST. Recently, a Gaussian Approximation Potential (GAP) was trained for GST to reproduce Density Functional Theory (DFT) energies and forces at a fraction of the computational cost [Zhou et al., Nat. Electron. 6, 746 (2023)]; however, simulations of large length and time scales are still challenging using this GAP model. Here, we present a machine-learned (ML) potential for GST implemented using the Atomic Cluster Expansion (ACE) framework. This ACE potential shows comparable accuracy to the GAP potential but performs orders of magnitude faster. We train the ACE potentials both directly from DFT and also using a recently introduced indirect learning approach where the potential is trained instead from an intermediate ML potential, in this case, GAP. Indirect learning allows us to consider a significantly larger training set than could be generated using DFT alone. We compare the directly and indirectly learned potentials and find that both reproduce the structure and thermodynamics predicted by the GAP and also match experimental measures of GST structure. The speed of the ACE model, particularly when using graphics processing unit acceleration, allows us to examine repeated transitions between crystal and amorphous phases in device-scale systems with only modest computational resources.

Assessment of Cognitive Functions in Multimorbid Patients in Lithuanian Primary Care Settings: A Cross-Sectional Study Using MMSE and LT-GPCOG.

Background and Objectives: The aging population has led to a rise in cognitive impairments, including dementia, often associated with multimorbidity. Early diagnosis of cognitive decline is crucial, especially in primary care, where time constraints and the limitations of diagnostic tools may hinder accurate detection. This study aims to assess the cognitive functions of multimorbid patients using the Mini-Mental State Examination (MMSE) and the Lithuanian version of the General Practitioner Assessment of Cognition (LT-GPCOG). We hypothesized that the LT-GPCOG would perform similarly to the MMSE in suspecting cognitive impairments. Materials and Methods: This cross-sectional study, conducted from 2021 to 2022, included 796 patients aged 40-85, with arterial hypertension and at least one other chronic disease, recruited from seven Lithuanian primary health care centers. Cognitive function was assessed using the MMSE and LT-GPCOG, and statistical analyses were performed using SPSS to determine the association between cognitive impairment and various demographic and clinical variables. Results: Out of 796 participants, 793 completed the study. Cognitive impairment was suspected in 5.1% of participants based on MMSE and 4.2% based on the LT-GPCOG. Statistically significant associations were found between cognitive impairment and chronic obstructive pulmonary disease (COPD) (p = 0.008 and p = 0.003) in both tests and chronic kidney disease (CKD) (p = 0.005) while testing with the MMSE. Lower education and unemployment were also correlated with cognitive impairment (p = 0.008 and p < 0.001). Conclusions: The findings suggest that regular cognitive assessments should be integrated into the management of multimorbid patients, particularly those with COPD and CKD. The LT-GPCOG proved to be an efficient alternative to the MMSE in primary care settings, demonstrating comparable diagnostic accuracy. Further studies are also needed to assess the sensitivity and specificity of the LT-GPCOG test.

Beyond the baseline: Quantification of two Phosphatidylethanol (PEth) homologues in Whole Blood by LC-MS/MS and Retrospective Data Analysis from a National Reference Laboratory.

Quantification of phosphatidylethanol in whole blood was accomplished using an LC-MS/MS assay. Isotopically labeled internal standard for the two PEth homologues was added to the whole blood specimen, followed by protein precipitation with a mixture of acetonitrile and isopropyl alcohol. After centrifugation, an aliquot of the supernatant was buffered with ammonium acetate before LC-MS/MS analysis on an Agilent 6470 triple quadrupole mass spectrometer coupled to an Agilent 1260 Infinity II LC system. This LC-MS/MS assay was validated for clinical use in accordance with CLSI guidelines. The analytical measurement range, 10 - 2000 ng/mL, was linear with R2 of 0.999. The within-run and total imprecision was < 5% CV for the low (20 ng/mL), medium (200 ng/mL), and high QC (1000 ng/mL). Results from accuracy and method comparison experiments met the bias criteria of ±15%. Retrospective data analysis showed ~27% of patients had PEth concentrations greater than 20 ng/mL. Males and females had similar positivity rates for PEth and the positivity rate of women of reproductive age (15-44 years old) was 35% in comparison to 25% in women 45-89 years old. This study's LC-MS/MS method showed acceptable analytical performance in quantifying PEth as a sensitive and specific biomarker for evaluating alcohol consumption. Results from this study may provide an opportunity to educate women of reproductive age on drinking during pregnancy and the long-term effects of alcohol use.

Multiclass Steroid Profiling in Short-Finned Pilot Whale Blubber Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS).

Wildlife scientists are quantifying steroid hormones in a growing number of tissues and employing novel methods that must undergo validation before application. This study tested the accuracy and precision of liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods for use on blubber samples from short-finned pilot whales (Globicephala macrorhynchus). We expanded upon a method for corticosteroid quantification by adding analytes and optimizing internal standard (IS) application. We optimized a method for the quantification of seven steroid hormones using LC-MS/MS with a C18 column. We assessed the accuracy and precision of this updated C18 method and an existing biphenyl method for use with pilot whale blubber by conducting a spike recovery experiment and calculating percent recovery and relative standard deviation (RSD) for each analyte. To explore the potential for running this method with fewer matched ISs, we compared the performance of multiple ISs for each analyte. All 11 adrenal and gonadal analytes showed good accuracy and precision in the spike recovery experiment, with recoveries between 82% and 110% and recovery RSDs below 10%. The C18 method detected all analytes at endogenous concentrations, except aldosterone. Although endogenous DHEA was detected, variability was high. IS comparisons showed 10 of 11 analytes could be calculated with comparable accuracy and precision using an IS substitute, but some substitutions significantly altered the analyte concentrations calculated. The C18 method was not sensitive enough for endogenous aldosterone detection. DHEA, which has not been previously quantified in blubber, was detected in all samples, but with high variability at lower concentrations. The methods in this study provide reliable detection and quantification of the other nine hormones tested and can be used for assessments of adrenal and gonadal steroid hormones from whales. Laboratories can reduce costs through IS substitution but should consider how these substitutions affect results.