Rapid Optimization Research Articles

CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules.

Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at http://codd.iddd.group/.

Safety Indicators in Patients Receiving High-intensity Care After Hospital Admission for Acute Heart Failure: The STRONG-HF Trial

BackgroundSafety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP Testing, of Heart Failure Therapies (STRONG-HF) demonstrated the safety and efficacy of rapid up-titration of guideline-directed medical therapy (GDMT) with high-intensity care (HIC) compared with usual care in patients hospitalized for acute heart failure (HF). In the HIC group, the following safety indicators were used to guide up-titration: estimated glomerular filtration rate of <30 mL/min/1.73 m2, serum potassium of >5.0 mmol/L, systolic blood pressure (SBP) of <95 mmHg, heart rate of <55 bpm, and N-terminal pro-B-type natriuretic peptide concentration of >10% higher than predischarge values. Methods and ResultsWe examined the impact of protocol-specified safety indicators on achieved dose of GDMT and clinical outcomes. Three hundred thirteen of the 542 patients in the HIC arm (57.7%) met ≥1 safety indicator at any follow-up visit 1–6 weeks after discharge. As compared with those without, patients meeting ≥1 safety indicator had more severe HF symptoms, lower SBP, and higher heart rate at baseline and achieved a lower average percentage of GDMT optimal doses (mean difference vs the HIC arm patients not reaching any safety indicator, –11.0% [95% confidence interval [CI] –13.6 to –8.4%], P < .001). The primary end point of 180-day all-cause death or HF readmission occurred in 15.0% of patients with any safety indicator vs 14.2% of those without (adjusted hazard ratio 0.84, 95% CI 0.48–1.46, P = .540). None of each of the safety indicators, considered alone, was significantly associated with the primary end point, but an SBP of <95 mm Hg was associated with a trend toward increased 180-day all-cause mortality (adjusted hazard ratio 2.68, 95% CI 0.94–7.64, P = .065) and estimated glomerular filtration rate decreased to <30 mL/min/1.73 m2 with more HF readmissions (adjusted hazard ratio 3.60, 95% CI 1.22–10.60, P = .0203). The occurrence of a safety indicator was associated with a smaller 90-day improvement in the EURO-QoL 5-Dimension visual analog scale (adjusted mean difference –3.32 points, 95% CI –5.97 to –0.66, P = .015). ConclusionsAmong patients with acute HF enrolled in STRONG-HF in the HIC arm, the occurrence of any safety indicator was associated with the administration of slightly lower GDMT doses and less improvement in quality of life, but with no significant increase in the primary outcome of 180-day HF readmission or death when appropriately addressed according to the study protocol.

Artificial Neural Network Based Wear and Tribological Analysis of Al 7010 Alloy Reinforced with Nanoparticles of SIC for Aerospace Application

The current study investigates the wear behavior of three distinct composite compositions designated as C1, C2, and C3, with direct implications for aerospace applications. Critical factors such as the Coefficient of Friction (Cf), Specific Rate of Wear (Sw), and Frictional Force (FF) were meticulously analyzed using a systematic experimental approach and the Taguchi L27 array design. Significant relationships between input factors and responses emerged after subjecting these responses to Taguchi signal-to-noise ratio analysis. The optimal parameter combination of a 5% composition, 14.5 N Applied Load (Ap), 150 rpm Rotational Speed (Rs), and 40.5 m Distance of Sliding (Ds) highlights the interplay of factors in improving wear resistance. An Artificial Neural Network (ANN) was used as a predictive tool to boost research efficiency, achieving an impressive 99.663% accuracy in response predictions. The result shows comparison of the ANN's efficacy with actual experimental results. These findings hold great promise for aerospace applications where wear-resistant materials are critical for long-term performance under harsh operating conditions. The incorporation of ANN predictions allows for rapid material optimization while adhering to the stringent requirements of aerospace environments. This research contributes to the evolution of tailored composite materials, poised to improve aerospace applications with increased reliability, efficiency, and durability by advancing wear analysis methodologies and predictive technologies.

Cascaded domain multiphoton spatial frequency modulation imaging.

Multiphoton microscopy is a powerful imaging tool for biomedical applications. A variety of techniques and respective benefits exist for multiphoton microscopy, but an enhanced resolution is especially desired. Additionally multiphoton microscopy requires ultrafast pulses for excitation, so optimization of the pulse duration at the sample is critical for strong signals. We aim to perform enhanced resolution imaging that is robust to scattering using a structured illumination technique while also providing a rapid and easily repeatable means to optimize group delay dispersion (GDD) compensation through to the sample. Spatial frequency modulation imaging (SPIFI) is used in two domains: the spatial domain (SD) and the wavelength domain (WD). The WD-SPIFI system is an in-line tool enabling GDD optimization that considers all material through to the sample. The SD-SPIFI system follows and enables enhanced resolution imaging. The WD-SPIFI dispersion optimization performance is confirmed with independent pulse characterization, enabling rapid optimization of pulses for imaging with the SD-SPIFI system. The SD-SPIFI system demonstrates enhanced resolution imaging without the use of photon counting enabled by signal to noise improvements due to the WD-SPIFI system. Implementing SPIFI in-line in two domains enables full-path dispersion compensation optimization through to the sample for enhanced resolution multiphoton microscopy.

Багатоцентрове опитування практикуючих лікарів Вінницької області щодо дотримання рекомендацій із передопераційного голодування та профілактики аспірації

Reducing the period of preoperative fasting is one of the most important principles of rapid metabolic optimization of patients within the concept of enhanced recovery after caesarean section (ERAS). Purpose - to analyze compliance by anesthesiologists with the recommendations of current protocols regarding preoperative fasting and prevention of aspiration in obstetric hospitals/departments of the city of Vinnytsia and the Vinnytsia region. Materials and methods. We conducted a multicenter survey in which 70 anesthesiologists from 21 medical institutions of Vinnytsia and Vinnytsia region, which provide assistance to obstetric patients, took part. Statistical data processing was carried out using the SPSS 21 program. Results. The results of our study show that most respondents (57.1%) recommend pregnant women to stop eating solid food 6-8 hours before cesarean section. It has been found that only 10% of anesthesiologists recommend to stop fluid intake as stated by current guidelines, i.e. 2 hours before surgery. According to the data obtained, the vast majority of respondents (85.8%) prescribe pharmacological prevention of aspiration during planned surgical interventions. Conclusions. The vast majority of anesthesiologists in Vinnytsia and Vinnytsia region follow the current recommendations of clinical guidelines regarding preoperative fasting and prevention of aspiration in obstetric patients. The research was carried out in accordance with the principles of the Declaration of Helsinki. The research protocol was approved by the Local Ethics Committee of the institution mentioned in the work. Respondents informed consent was obtained for the study. No conflict of interests was declared by the authors.

Mesh optimization using an improved self-organizing mechanism

As more powerful computing hardware enables higher resolution simulations, a fast and flexible mesh optimization method is becoming increasingly indispensable for Computational Fluid Dynamics (CFD), which unfortunately remains a bottleneck in the current CFD workflows. In this paper, a novel mesh optimization method based on an improved self-organizing map (SOM) neural network is proposed to improve the accuracy and efficiency of numerical simulation while maintaining constant computational cost. During an improved competitive learning procedure in SOM, the node distribution with constant connectivity rapidly matches the characteristics of the flow field, which is predicted by a Multilayer Perceptron (MLP). Based on the local element volume and flow solution variations, annealing schemes for self-adaptation of important SOM parameters are designed to ensure the convergence of the proposed algorithm. Specially, a feasible region constraint and a smoothing constraint are embedded into the node movement to avoid mesh tangling and excessive mesh skewness, and make the transition between nodes gradual. The proposed approach is applicable to various types of meshes and is easy to implement without code intrusiveness. Comparative results on benchmark examples and typical CFD examples demonstrate that the proposed method attributes to both the improvement in the computational accuracy and efficiency. It exhibits the potential to be a flexible and promising tool for rapid mesh optimization in CFD and other engineering fields.

Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.

Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied. This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity-modulated radiation therapy (IMRT) treatment planning. A double-UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high-dose region-controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O-P)] and [body avoids OARs & PTV(B-OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two-step process. In the beginning, the plans of two extra cases with and without low-dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O-P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O-P FEs from first step were constrained again but weakly and the dose of the B-OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double-UNet dose prediction model trained by low and high region-controlled plans (method 2). The mean differences in dose metrics between the UNet-predicted dose and the clinical plans were: 0.47Gy for bladder D50% ; 0.62Gy for rectum D50% ; 0% for small intestine V30Gy ; 1% for small intestine V40Gy ; 4% for left femoral head V30Gy ; and 6% for right femoral head V30Gy . The reductions in mean dose (p<0.001) after FE-based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7Gy), rectum (1.0Gy), and small intestine (0.6Gy), while maintaining PTV homogeneity and conformity. FE-based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low-dose regions.

An efficient multi-objective optimization strategy for Halbach array permanent magnet synchronous machines based on nonlinear semi-analytical model

In this article, an efficient multi-objective optimization strategy for the Halbach array permanent magnet synchronous machine (PMSM) is developed by taking into consideration the nonlinear B-H behavior of soft magnetic materials. Based on the harmonic modeling (HM) technology, the electromagnetic performances (EPs) of the Halbach array PMSM can be computed. To specifically model the local magnetic saturation, the stator teeth are separated into several annular layers, and each tooth is further divided into several regions along the tangential direction. Then, the parameters of the Halbach array PMSM are optimized utilizing combined nonlinear semi-analytical model (SAM) and non-dominated sorting genetic algorithm II (NSGA-II). To validate the effectiveness and accuracy of the developed optimization scheme, a Halbach array prototype is then manufactured in accordance with the optimization results. The multi-objective rapid optimization strategy developed in this article, which includes but is not limited to Halbach array permanent magnet (PM) machines, serves as a reference for the design and optimization of various PM machines.

Robust trajectory optimization of re-entry flight with prescribed endpoint region via sparse grid ensemble pseudospectral optimal control

This paper introduces a sampling-based computational framework with a pseudospectral transcription, designed to investigate performance-robustness tradeoffs for constrained re-entry flights with a non-Gaussian uncertainty propagation. Ensemble of trajectories which are generated with sparse grids with Conjugate Unscented Transformation have been integrated into the nonlinear programming via a vectorized and parallelized formulation. A cross-range maximization problem is formulated to assess the influences of uncertainties in entry interface conditions and model parametric uncertainties. A comprehensive analysis, facilitated by rapid gradient-based optimization convergence and warm-starting, highlights the significant effects of initial speed uncertainties and drag coefficient on problem feasibility, offering insights into optimal control behavior under different degrees and combinations of uncertainties. Proper scaling of controls and the objective function proves pivotal for achieving convergence. Furthermore, a novel phenomenon called “contact arcs” is discovered, composed of contact points that form an arc at the edge of the path constraint in the constrained ensemble optimal control as a counterpart to the boundary arcs in deterministic optimal control problems. Simulation results show excellent agreement with the derived optimality conditions.

Continuous Online Titer Monitoring in CHO Cell Culture Supernatant Using a Herringbone Nanofluidic Filter Array.

Online monitoring of monoclonal antibody product titers throughout biologics process development and production enables rapid bioprocess decision-making and process optimization. Conventional analytical methods, including high-performance liquid chromatography and turbidimetry, typically require interfacing with an automated sampling system capable of online sampling and fractionation, which suffers from increased cost, a higher risk of failure, and a higher mechanical complexity of the system. In this study, a novel nanofluidic system for continuous direct (no sample preparation) IgG titer measurements was investigated. Tumor necrosis factor α (TNF-α), conjugated with fluorophores, was utilized as a selective binder for adalimumab in the unprocessed cell culture supernatant. The nanofluidic device can separate the bound complex from unbound TNF-α and selectively concentrate the bound complex for high-sensitivity detection. Based on the fluorescence intensity from the concentrated bound complex, a fluorescence intensity versus titer curve can be generated, which was used to determine the titer of samples from filtered, unpurified Chinese hamster ovary cell cultures continuously. The system performed direct monitoring of IgG titers with nanomolar resolution and showed a good correlation with the biolayer interferometry assays. Furthermore, by variation of the concentration of the indicator (TNF-α), the dynamic range of the system can be tuned and further expanded.

Application of hybrid intelligent algorithm for multi-objective optimization of high performance concrete in complex alpine environment highway

Concrete mix proportion affects concrete performance and cost. Therefore, it arouses an increase of interest in the research on performance improvements in concrete through the optimization of the raw material mix proportion, especially in the high cold, high-salinity and other complex environment. In this paper, an intelligent multi-objective optimization framework is constructed based on Bayesian optimization Random Forest (BO-RF) algorithm and fast non-dominated sorting genetic algorithm (NSGA-III). The key factors affecting concrete performance are taken as the input indexes, the freezing resistance, impermeability, carbonization depth and cost are taken as the evaluation indexes of concrete. Based on 400 groups of freeze–thaw cycle of salt invasion experiments in cold environment, the orthogonal experiment method is used to obtain the index system of the original data sets, then, the RF prediction algorithm is adopted to establish the key factors and performance index of a nonlinear mapping relationship and the Bayesian optimization random forest is used to establish a high-precision fitness function. Ultimately, the BO-RF-NSGA-III model is applied for multiobjective optimization. The results show that (1) Based on the proposed framework, the rapid prediction and optimization of concrete performance can be realized, which can well meet the needs of the complex alpine environment (2) Accurate model prediction can be made through BO-RF. The R2 values obtained at the time of prediction range from 0.944 to 0.998, the mean absolute error (MAE) range from 0.001 to 0.09. (3) Based on BO-RF-NSGA-III and grey correlation method, the comprehensive optimal mix proportion of concrete is obtained.

Dose Individualization of Cefepime for Febrile Neutropenia in Patients With Lymphoma or Multiple Myeloma: Implications for Therapeutic Drug Monitoring.

Optimal cefepime dosing is a challenge because of its dose-dependent neurotoxicity. This study aimed to determine individualized cefepime dosing for febrile neutropenia in patients with lymphoma or multiple myeloma. This prospective study enrolled 16 patients receiving cefepime at a dose of 2 g every 12 hours. Unbound concentrations were determined at 0.5 hours, 7.2 hours [at the 60% time point of the 12 hours administration interval (C7.2h)], and 11 hours (trough concentration) after the first infusion (rate: 2 g/h). The primary and secondary end points were the predictive performance of the area under the unbound concentration-time curve (AUC unbound ) and the effect of unbound cefepime pharmacokinetic parameters on clinical response, respectively. The mean (SD) AUC unbound was 689.7 (226.6) mcg h/mL, which correlated with C7.2h (R 2 = 0.90), and the Bayesian posterior AUC unbound using only the trough concentration (R 2 = 0.66). Although higher exposure was more likely to show a better clinical response, each parameter did not indicate a statistical significance between positive and negative clinical responses ( P = 0.0907 for creatinine clearance (Ccr), 0.2523 for C7.2h, 0.4079 for trough concentration, and 0.1142 for AUC unbound ). Cutoff values were calculated as 80.2 mL/min for Ccr (sensitivity: 0.889, specificity: 0.714), 18.6 mcg/mL for C7.2h (sensitivity: 0.571, specificity: 1.000), and 9.2 mcg/mL for trough concentration (sensitivity: 0.571, specificity: 1.000). When aiming for a time above 100% the minimum inhibitory concentration, both continuous infusion of 4 g/d and intermittent infusion of 2 g every 8 hours achieved a probability of approximately 100% at a minimum inhibitory concentration of 8 mcg/mL. Therapeutic drug monitoring by sampling at C7.2h or trough can facilitate rapid dose optimization. Continuous infusion of 4 g/d was recommended. Intermittent dosing of 2 g every 8 hours was alternatively suggested for patients with a Ccr of 60-90 mL/min.

Objectively measured medication adherence using assays for carvedilol and enalaprilat in patients with heart failure in Mozambique and Nigeria

BackgroundPoor medication adherence leads to poor health outcomes and increased healthcare costs among patients with heart failure (HF). This study aimed to objectively assess medication adherence by measuring carvedilol and enalaprilat plasma concentrations among patients with HF. MethodsThe present sub-study of the Safety, Tolerability, and Efficacy of Rapid Optimization, helped by NT-proBNP testing, of Heart Failure therapies (STRONG-HF) study involved adult patients with acute HF admitted in two Mozambican and two Nigerian hospitals who were not optimally treated with oral enalapril and carvedilol. Patients in the high-intensity arm of the STRONG-HF study, and those not meeting the biomarker criteria for persistent congestion, were included in the “frequent visit” (FV) arm. In the FV arm, blood for bioanalysis of plasma enalaprilat or/and carvedilol was drawn at the 2,6,12th week post-discharge. Patients in the usual care arm of STRONG-HF were included in the “standard visit” (SV) arm, which followed the usual local practice with blood sampling in week 12. ResultsThe study involved 113 (79 FV and 34 SV) participants with a mean age of 48.6 years and a mean left ventricular (LV) ejection fraction of 33.1%. Theenalaprilat below the lower level of quantification (LLOQ) was documented in 7.7%, 11.9%, and 15.6% of participants in FV during the 2,6 and 12th weeks. Carvedilol concentration below LLOQ was documented in 37%, 30%, and 44.4% of participants in the FV arm during the 2,6 and 12th weeks, respectively. For the SV arm, enalaprilat and carvedilol concentrations below LLOQ in the twelfth week were documented in 37.3% and 42.9% of patients, respectively. ConclusionUp to a third of patients using enalapril and carvedilol did not take any medication during the 12 weeks of follow-up. Non adherence was more common in patients who had less follow up, emphasizing the importance of close follow up to adherence. No adherence was also more common in medications know to have more side effects such as carvedilol.

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

AbstractThe hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

A Laboratory-Developed Assay for Clade II Human Mpox Virus on the Panther Fusion Open Access System.

This paper describes the methods for developing and optimizing a laboratory-developed assay (LDA) for detecting clade II human mpox virus using the automated Panther Fusion platform and Open Access software. Various concentrations of reagents in a primer-probe mix were tested to optimize the LDA. The LDA was validated using 10 previously characterized positive and 10 negative human mpox samples, resulting in 95% accuracy and 100% precision. The LDA resulted in 100% specificity among previously tested HSV1-, HSV2-, and VZV-positive human samples. Several spiked media extensions were also validated and achieved 98% accuracy and 100% precision across all collection media types. The assay's limit of detection was calculated to be 1.475 copies/reaction, and the polymerase chain reaction efficiency resulted in 89.87% (slope, -3.5911; R2 = 0.9947). The methods described here can be applied to the rapid optimization and development of LDAs for many possible pathogens of public health importance.

Reconstruction of temperature field in nanofluid-filled annular receiver with fins using deep hybrid transformer-convolutional neural network

This study proposes a deep Hybrid Transformer-Convolutional Neural Network (HTCNN) to reconstruct the temperature field in the nanofluid-filled Parabolic Trough Collector receivers. The proposed HTCNN is improved from U-net architecture by replacing all the skip-connections with residual blocks and embedding a Transformer module. The novel method tackles the challenges of high computational cost inherent in numerical simulation, and the constraints posed by limited data availability in data-driven approaches. The results observed that the HTCNN enables estimating the temperature field with high accuracy and remarkably fast speed; furthermore, it doesn't depend on the dataset scale: trained with only 18 data, the HTCNN reaches an average prediction accuracy higher than 99.92% on the test set and a prediction speed of more than 1000 times faster than numerical simulation. Our results show the impressive potential of applying the HTCNN for rapid and efficient 3D fin configuration optimization of nanofluid-filled receivers with limited data.

Diastereoselective Reductive Etherification Via High-Throughput Experimentation: Access to Pharmaceutically Relevant Alkyl Ethers.

This manuscript describes the development of the first diastereoselective intermolecular synthesis of alkyl ethers via reductive etherification of diverse ketones or aldehydes with alcohols. Key to this development was the use of low-temperature high-throughput experimentation (HTE) technologies that enabled rapid reaction optimizations and parallel synthesis. A broad scope of pharmaceutically relevant substrates was surveyed, which formed alkyl ethers effectively. In addition, we demonstrated that the diastereoselectivity of this transformation can be readily modulated by prudent selection of the reductant.

Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects.

Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability.

Registration between 2D and 3D Ultrasound Images to Track Liver Blood Vessel Movement.

For the accurate positioning of surgical tools, conventional intraoperative navigation systems have been developed to recognize the relationship between target positions and the tools. However, since an internal organ is deformed during the operation, registration between realtime two-dimensional (2D) ultrasound images and three-dimensional (3D) CT or MRI images is not always effective. Therefore, this study developed image registration between 2D and 3D ultrasound images considering deformation for tracking target vessel movement in the liver. 3D ultrasound image was obtained in advance with 3D coordinates, including the target vessel. Then real-time 2D images and ultrasound probe position were simultaneously acquired using a 3D position sensor. We applied multiple image resolution registration, where rapid and fine optimizations can be expected at higher and lower levels, respectively. Meanwhile, the gradient descent method was adopted for the optimization, which determines the relative arrangements to obtain maximum similarity between 2D and 3D images. We experimentally established resolution level parameters using a phantom before applying it to track liver blood vessel movements in a normal healthy subject. Comparing the 2D images and the registered images, although the approach has some limitations in tracking large displacement, we confirmed that the cross-section of the target blood vessel was clearly visualized. This method has the potential for an ultrasound therapy targeting blood vessels under natural respiration conditions.

Ultrafast Detection of Monoamine Oxidase A in Live Cells and Clinical Glioma Tissues Using an Affinity Binding‐Based Two‐Photon Fluorogenic Probe

AbstractAbnormal expression of monoamine oxidase A (MAO−A) has been implicated in the development of human glioma, making MAO−A a promising target for therapy. Therefore, a rapid determination of MAO−A is critical for diagnosis. Through in silico screening of two‐photon fluorophores, we discovered that a derivative of N,N‐dimethyl‐naphthalenamine (pre‐mito) can effectively fit into the entrance of the MAO−A cavity. Substitutions on the N‐pyridine not only further explore the MAO−A cavity, but also enable mitochondrial targeting ability. The aminopropyl substituted molecule, CD1, showed the fastest MAO−A detection (within 20 s), high MAO−A affinity and selectivity. It was also used for in situ imaging of MAO−A in living cells, enabling a comparison of the MAO−A content in human glioma and paracancerous tissues. Our results demonstrate that optimizing the affinity binding‐based fluorogenic probes significantly improves their detection rate, providing a general approach for rapid detection probe design and optimization.