Discrete Mathematics in Dynamic Network Analysis: Long-Term Efficacy Evaluation of Fotona Laser Therapy for Overactive Bladder Syndrome Using Clustering-Based Patient Subgroup Identification.

Dynamic network analysis, a state-of-the-art application of discrete mathematics, offers unprecedented insight into complex, time-evolving clinical data. This technical report demonstrates the value of evaluating the long-term efficacy of Fotona laser therapy (Fotona d.o.o., Ljubljana, Slovenia) in overactive bladder (OAB) syndrome. We analyzed data from 101 female patients aged ≥60 years who underwent Fotona laser treatment, including Vaginal Erbium Laser (VEL) and Urethral Erbium Laser (UEL), between 2020 and 2022. OAB symptom scores (OABSS) were collected at baseline (T0) and at six, 12, 18, and 24 months post treatment. Network graphs were constructed, representing patients as nodes, and symptom similarities as edges. Clustering techniques identify patient subgroups, whereas principal component analysis reduces dimensionality. The dynamic evolution of patient clusters was visualized through changes in average degree centrality over time. This approach revealed three distinct patient clusters with unique treatment response patterns. The results showed a progressive reduction in OABSS, with the total score decreasing by 2.82 ± 3.03 at 24 months. Our method provides novel visualization and analysis of complex longitudinal clinical data, offering insights into personalized treatment strategies for OAB. This report presents one of the first applications of discrete mathematics and dynamic network analysis to evaluate the long-term outcomes of Fotona laser therapy for OAB and introduces an innovative perspective for clinical decision-making in urogynecology.

Comparative effectiveness trial: Modular behavior approach for young autistic children compared to comprehensive behavioral intervention.

This 24-week single-blind trial tested a modular approach for young autistic children (MAYAC) that was delivered for fewer hours per week and modified based on child progress and parental input compared to comprehensive behavioral intervention treatment as usual (CBI, TAU). Participants were autistic children, ages 18-60 months of age. MAYAC was initially 5 h of intervention per week, one of which was parent training and the other four direct therapy focusing on social communication and engagement, but additional modules could be added for up to 10 h per week. Comprehensive behavior intervention was delivered for ≥15 h per week. Outcome measures included the Vineland Adaptive Behavior Scales; VABS, the Ohio Autism Clinical Improvement Scale - Autism Severity; OACIS - AS and the Pervasive Developmental Disorder Behavior Inventory - Parent; PDDBI-P. Implementation and parent satisfaction measures were also collected. Fifty-six children, mean age of 34 months, were randomized. Within-group analysis revealed significant improvements from baseline to week 24 for both MAYAC (p < 0.0001) and CBI, TAU (p < 0.0001) on the VABS. The noninferiority test was performed to test between group differences and MAYAC was not inferior to CBI, TAU on the VABS (p = 0.0144). On the OACIS - AS, 48.0% of MAYAC and 45.5% of CBI were treatment responders there were no significant changes on the PDDBI-P, for either group. Treatment fidelity was high for both groups (>95%) as was parent satisfaction. Findings from this small trial are promising and suggest MAYAC may be an alternative for some young autistic children and their families to CBI, TAU.

A discrete choice experiment to understand preferences of patients with type 2 diabetes about the attributes of GLP1 receptor agonists in Spain.

OBJECTIVE: To determine the preferences regarding injection, medication frequency and complexity of GLP1 receptor agonists among patients with type 2 diabetes, treatment-naïve for such drugs in Spain. Additionally, patients' willingness to pay according to these attributes was evaluated. METHODS: A discrete-choice experiment survey designed to evaluate patients' preferences over three attributes discriminating by age, sex and patients experience with previous injectable treatment was fulfilled by patients. The resulting model was analyzed using a conditional (fixed-effects) logistic regression. RESULTS: A total of 180 patients (63.35 ± 11.49 years, 63.28% men, 48.41% with previous cardiovascular disease, 54.69% with a time of evolution of diabetes >10 years) recruited from 5 health care centers in Spain completed the survey. Patients viewed positively weekly injections (vs daily injections), but rated negatively a complex preparation of the dose (vs simple preparation). Whereas naïve patients for injectable medications did not consider administration timing of importance, no naïve patients considered it relevant. No relevant differences were observed according to age or gender. Patients were willing to pay 83.25€for a "no preparation required" dose. No naïve and naïve patients were willing to pay 34.61€ and 14.35€; p = 0.000, to change daily injection for a weekly injection. CONCLUSIONS: Patients highly valued the avoidance of injections, with weekly dosing clearly preferred over daily dosing, as well as reducing the treatment complexity. These findings may provide a better understanding of what patients prefer and value in their treatment and provide guidance for clinicians making therapeutic decisions regarding treatments of patients with type 2 diabetes.

Patient preferences for the provision of NHS medicines helpline services: a discrete choice experiment.

Introduction: Patient medicines helpline services (PMHS) can reduce harm and improve medicines adherence and patient satisfaction after hospital discharge. There is little evidence of which PMHS attributes are most important to patients. This would enable PMHS providers to prioritise their limited resources to maximise patient benefit. Methods: Patient preferences for PMHS attributes were measured using a discrete choice experiment. Seven attributes were identified from past research, documentary analysis and stakeholder consultation. These were used to produce a D-efficient design with two blocks of ten choice sets incorporated into an online survey. Adults in the UK who took more than one medicine were eligible to complete the survey and were recruited via the Research for the Future database. Preferences were estimated using conditional logistic regression. Associations between participant characteristics and preferences were investigated with latent class models. Results: 460 participants completed the survey. The most valued attributes were weekend opening (willingness-to-pay, WTP: £11.20), evening opening (WTP: £8.89), and receiving an answer on the same day (WTP: £9.27). Alternative contact methods, immediate contact with a pharmacist and helpline location were valued less. Female gender and full-time work were associated with variation in preferences. For one latent class containing 27% of participants, PMHS location at the patient's hospital was the most valued attribute. Discussion: PMHS providers should prioritise extended opening hours and answering questions on the same day. Limitations include a non-representative sample in terms of ethnicity, education and geography, and the exclusion of people without internet access.

PD-DEM hybrid modeling of leading edge erosion in wind turbine blades under controlled impact scenarios.

This paper addresses the critical issue of leading edge erosion (LEE) on modern wind turbine blades (WTBs) caused by solid particle impacts. LEE can harm the structural integrity and aerodynamic performance of WTBs, leading to reduced efficiency and increased maintenance costs. This study employs a novel particle-based approach called hybrid peridynamics-discrete element method (PD-DEM) to model the impact of solid particles on WTB leading edges and target material failure accurately. It effectively captures the through-thickness force absorption and the propagation of stresses within the leading edge coating system composed of composite laminates. The amount of mass removed and the mean displacement of the target material points can be reliably calculated using the current method. Through a series of tests, the research demonstrates the method's ability to predict impact force changes with varying particle size, velocity, impact angles and positions. Moreover, this study offers a significant improvement in erosion prediction capability and the development of design specifications. This work contributes to the advancement of WTB design and maintenance practices to mitigate LEE effectively.

Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling.

The Discrete element method (DEM) is a robust numerical tool for simulating crack propagation and wear in granular materials. However, the computational cost associated with DEM hinders its applicability to large domains. To address this limitation, we employ DEM to model regions experiencing crack propagation and wear, and utilize the finite element method (FEM) to model regions experiencing small deformation, thus reducing the computational burden. The two domains are linked using a FEM-DEM coupling, which considers an overlapping region where the deformation of the two domains is reconciled. We employ a "strong coupling" formulation, in which each DEM particle in the overlapping region is constrained to an equivalent position obtained by nodal interpolation in the finite element. While the coupling method has been proved capable of handling propagation of small-amplitude waves between domains, we examine in this paper its accuracy to efficiently model for material failure events. We investigate two cases of material failure in the DEM region: the first one involves mode I crack propagation, and the second one focuses on rough surfaces' shearing leading to debris creation. For each, we consider several DEM domain sizes, representing different distances between the coupling region and the DEM undergoing inelasticity and fracture. The accuracy of the coupling approach is evaluated by comparing it with a pure DEM simulation, and the results demonstrate its effectiveness in accurately capturing the behavior of the pure DEM, regardless of the placement of the coupling region. Supplementary Information: The online version contains supplementary material available at 10.1007/s40571-024-00788-x.

Coupled Discrete Phase and Eulerian Wall Film Models for Drug Deposition Efficacy Analysis in Human Respiratory Airways.

The current study explores the effectiveness of drug particle deposition into human respiratory airways to cure various pulmonary-bound ailments. It has been assumed that drug solutions are inhaled in the form of tiny droplets or mist, which after striking create a thin layer along the inner surface of airways where the virus initially resides to infect the human body. A coupled Eulerian wall film (EWF) and discrete phase model (DPM) based simulation approach is used to capture these dynamics. Here, the Lagrangian DPM technique tracks the dynamics of tiny droplets, while the liquid layer formation after striking is captured using the Eulerian thin film approximations or the EWF model. Previous studies in this field primarily employed only the DPM method, which is inadequate to predict the poststriking dynamics of drug layer deposition and their spread to neutralize the respiratory virus. The drug delivery effectiveness is characterized by three different particle sizes, 1, 5, and 10 µm at the inhalation rates of 15, 30, and 60 L per minute (LPM). It has been found that the size of the drug particles significantly influences drug delivery effectiveness. The film thickness increases monotonically with particle sizes and inhalation rates. However, this increase in averaged film thickness is prominent in the range 5 to 10 µm (≈60%) compared to 1 to 5 µm (≈10%) droplet sizes at generation level 4 (G4). The other deposition parameters, e.g., deposition fraction, deposition density, and area coverage) roughly show similar behavior with the increase in droplet sizes. Therefore, it is recommended to vary the droplet sizes between 5 and 10 µm for better deposition effectiveness. The sizes of more than 10 µm mostly stuck into the oral cavity and cannot reach the targeted generations. In contrast, less than 5 µm may reach much deeper generations than the targeted one.

Effect of N-7 alkylation on Cu (II) binding with 6-chloropurine: Influence of anions and magnetic studies.

This study investigates the anion-directed assembly of discrete copper (II) complexes. The ligands of choice are two unusual N7-alkyl-purine-based neutral ligands. These ligands facilitate the exclusive coordination through the N3 and N9 positions, preventing polymeric chain formation. Perchlorate ions promoted the formation of discrete paddlewheel-like complexes with the general formula [Cu2(µ-Pur)4(CH3CN)2]4+, while chloride ions yielded chloride-bridged dimers of the form [Cu2(Pur)2(µ-Cl)2Cl2]. Copper-copper bond distances within these complexes ranged from 2.92 to 2.98 Å. Magnetic susceptibility measurement of chloride-bridged complexes exhibited antiferromagnetic coupling, whereas paddlewheel complexes displayed more complex alternating ferromagnetic and antiferromagnetic interactions. Chloride-bridged compounds exhibited strong near-infrared absorption.

Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. The authors believe the processes can detect various neurodevelopmental problems in children utilizing EEG signals.

Time Crystals from Single-Molecule Magnet Arrays.

Time crystals, a unique nonequilibrium quantum phenomenon with promising applications in current quantum technologies, mark a significant advance in quantum mechanics. Although traditionally studied in atom-cavity and optical lattice systems, pursuing alternative nanoscale platforms for time crystals is crucial. Here we theoretically predict discrete time crystals in a periodically driven molecular magnet array, modeled by a spin-S Heisenberg Hamiltonian with significant quadratic anisotropy, taken with realistic and experimentally relevant physical parameters. Surprisingly, we find that the time crystal response frequency correlates with the energy levels of the individual magnets and is essentially independent of the exchange coupling. The latter is unexpectedly manifested through a pulse-like oscillation in the magnetization envelope, signaling a many-body response. These results show that molecular magnets can be a rich platform for studying time-crystalline behavior and possibly other out-of-equilibrium quantum many-body dynamics.

Preferences for attributes of oral antipsychotic treatments: results from a discrete-choice experiment in respondents with schizophrenia or bipolar I disorder.

BACKGROUND: Antipsychotic medications are effective treatments for schizophrenia (SZ) and bipolar I disorder (BD-I), but when presented with different treatment options, there are tradeoffs that individuals make between clinical improvement and adverse effects. As new options become available, understanding the attributes of antipsychotic medications that are valued and the tradeoffs that individuals consider when choosing among them is important. METHODS: A discrete-choice experiment (DCE) was administered online to elicit preferences across 5 attributes of oral antipsychotics: treatment efficacy (i.e., improvement in symptom severity), weight gain over 6 months, sexual dysfunction, sedation, and akathisia. Eligible respondents were aged 18-64 years with a self-reported clinician diagnosis of SZ or BD-I. RESULTS: In total, 144 respondents with SZ and 152 with BD-I completed the DCE. Of those with SZ, 50% identified themselves as female and 69.4% as White, with a mean (SD) age of 41.0 (10.1) years. Of those with BD-I, most identified themselves as female (69.7%) and as White (77.6%), with a mean (SD) age of 40.0 (10.7) years. In both cohorts, respondents preferred oral antipsychotics with better efficacy, less weight gain, no sexual dysfunction or akathisia, and lower risk of sedation. Treatment efficacy was the most important attribute, with a conditional relative importance (CRI) of 31.4% for respondents with SZ and 31.0% for those with BD-I. Weight gain (CRI = 21.3% and 23.1%, respectively) and sexual dysfunction (CRI = 23.4% and 19.2%, respectively) were adverse effects in this study that respondents most wanted to avoid. Respondents with SZ were willing to accept 9.8 lb of weight gain or > 25% risk of sedation for symptom improvement; those with BD-I were willing to accept 8.5 lb of weight gain or a > 25% risk of sedation. CONCLUSIONS: In this DCE, treatment efficacy was the most important attribute of oral antipsychotic medications among respondents with SZ and BD-I. Weight gain and sexual dysfunction were the adverse effects respondents most wanted to avoid; however, both cohorts were willing to accept some weight gain or sedation to obtain better efficacy. These results highlight features that patients value in antipsychotic medications and how they balance benefits and risks when choosing among treatments.

Cannabis consumers' preferences for legal and illegal cannabis: evidence from a discrete choice experiment.

BACKGROUND: In U.S. states that legalized and commercialized recreational cannabis, cannabis sales in illegal markets are still sizable or even larger than those in legal markets. This study aimed to assess cannabis consumers' preferences for purchasing cannabis from legal and illegal markets and estimate the trade-offs under various policy scenarios. METHODS: 963 adults were recruited, who used cannabis in the past year and lived in a state with recreational cannabis legalization. In a discrete choice experiment, participants chose purchasing cannabis from a legal dispensary or an illegal dealer with varying levels in product attributes including quality, safety, accessibility, potency, and price. Mixed logit models were used to analyze preferences. RESULTS: The likelihood of choosing legal cannabis increased with a higher quality, the presence of lab test, a shorter distance to seller, a higher tetrahydrocannabinol level, and a lower price. The likelihood of choosing illegal cannabis increased with a higher quality, a shorter distance to seller, and a lower price. Among product attributes, quality and accessibility were perceived to be the most important for legal cannabis and price was perceived to be the most important for illegal cannabis. Policy simulations predicted that improving quality, ensuring safety, allowing delivery services, increasing dispensary density, and lowering prices/taxes of legal cannabis may reduce illegal cannabis market share. CONCLUSIONS: In the U.S., cannabis consumers' preferences for illegal cannabis were associated with both legal and illegal cannabis product attributes. Policies regulating legal cannabis markets should consider potential spillover effects to illegal markets.

Brain tumor segmentation by combining MultiEncoder UNet with wavelet fusion.

BACKGROUND AND OBJECTIVE: Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long-range relationships between voxels cannot be captured due to the localized character of the convolution procedure. METHOD: Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context-aware module was introduced to capture the long-range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance. RESULT: Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state-of-the-art methods. CONCLUSION: The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.

Ergodic Rate Analysis of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface-Assisted Rate-Splitting Multiple Access Systems Based on Discrete Phase Shifts.

In this paper, we combine simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA) technology and investigate the ergodic rate performance of an STAR-assisted RSMA system. Considering the discrete phase shifts of the STAR-RIS in practice, the downlink performance of STAR-RIS-assisted RSMA with discrete phase shifts is compared to that with continuous phase shifts. Firstly, the cumulative distribution function of signal-to-interference-plus-noise ratio (SINR) of users is analyzed. Then, the total ergodic rate of the system and its approximate closed-form solution are, respectively, derived based on the cumulative distribution function of users. The simulation results validate the effectiveness of the theoretical analysis, showing good agreement between the derived theoretical ergodic rate and the corresponding simulations. Although the system performance with discrete phase shifts is inferior to that with continuous phase shifts due to quantization errors, the performance of the continuous phase shift system is well approximated when the quantization bit of the phase shift system reaches 3 in the simulations. Additionally, the impact of the number of STAR-RIS elements on the system's performance is analyzed.

Comparing the Self-Reported Acceptability of Discrete Choice Experiment and Best-Worst Scaling: An Empirical Study in Patients with Type 2 Diabetes Mellitus.

Purpose: Discrete choice experiment (DCE) and profile case (case 2) best-worst scaling (BWS) present uncertainties regarding the acceptability of quantifying individual healthcare preferences, which may adversely affect the validity of responses and impede the reflection of true healthcare preferences. This study aimed to assess the acceptability of these two methods from the perspective of patients with type 2 diabetes mellitus (T2DM) and examine their association with specific characteristics of the target population. Patients and Methods: This cross-sectional study was based on a nationally representative survey; data were collected using a multistage stratified cluster-sampling procedure between September 2021 and January 2022. Eligible adults with confirmed T2DM voluntarily participated in this study. Participants completed both the DCE and case 2 BWS (BWS-2) choice tasks in random order and provided self-reported assessments of acceptability, including task completion difficulty, comprehension of task complexity, and response preference. Logistic regression and random forest models were used to identify variables associated with acceptability. Results: In total, 3286 patients with T2DM were included in the study. Respondents indicated there was no statistically significant difference in completion difficulty between the DCE and BWS-2, although the DCE scores were slightly higher (3.07 ± 0.68 vs 3.03 ± 0.67, P = 0.06). However, 1979 (60.2%) respondents found the DCE easier to comprehend. No significant preferences were observed between the two methods (1638 (49.8%) vs 1648 (50.2%)). Sociodemographic factors, such as residence, monthly out-of-pocket costs, and illness duration were significantly associated with comprehension complexity and response preference. Conclusion: This study yielded contrasting results to most of previous studies, suggesting that DCE may be less cognitively demanding and more suitable for patients with T2DM from the perspective of self-reported acceptability of DCE and BWS. This study promotes a focus on patient acceptability in quantifying individual healthcare preferences to inform tailored optimal stated-preference method for a target population.

Stated preference methodologies such as the discrete choice experiment (DCE) and case 2 best-worst scaling (BWS-2) are gaining popularity as methods for quantifying individual preferences in healthcare. However, the acceptability of the two methods to participants must be considered in practice to reduce cognitive burden and ensure the validity of preference elicitation.DCE was perceived to be less cognitively burdensome than BWS-2. In contrast to patients who thought that DCE was more acceptable, BWS-2 was more accepted by rural patients, patients who lived with the disease for a longer period, and those who had lower monthly out-of-pocket costs.These findings demonstrate potential differences in the acceptability of DCE and BWS-2 for patients with type 2 diabetes mellitus. To improve efficiency, it would be useful for researchers to consider the optimal stated preference method for identifying target populations according to sociodemographic and disease-related characteristics.

Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features.

Computer-aided diagnosis (CAD) system assists ophthalmologists in early diabetic retinopathy (DR) detection by automating the analysis of retinal images, enabling timely intervention and treatment. This paper introduces a novel CAD system based on the global and multi-resolution analysis of retinal images. As a first step, we enhance the quality of the retinal images by applying a sequence of preprocessing techniques, which include the median filter, contrast limited adaptive histogram equalization (CLAHE), and the unsharp filter. These preprocessing steps effectively eliminate noise and enhance the contrast in the retinal images. Further, these images are represented at multi-scales using discrete wavelet transform (DWT), and center symmetric local binary pattern (CSLBP) features are extracted from each scale. The extracted CSLBP features from decomposed images capture the fine and coarse details of the retinal fundus images. Also, statistical features are extracted to capture the global characteristics and provide a comprehensive representation of retinal fundus images. The detection performances of these features are evaluated on a benchmark dataset using two machine learning models, i.e., SVM and k-NN, and found that the performance of the proposed work is considerably more encouraging than other existing methods. Furthermore, the results demonstrate that when wavelet-based CSLBP features are combined with statistical features, they yield notably improved detection performance compared to using these features individually.

A discrete time-to-event model for the meta-analysis of full ROC curves.

The development of new statistical models for the meta-analysis of diagnostic test accuracy studies is still an ongoing field of research, especially with respect to summary receiver operating characteristic (ROC) curves. In the recently published updated version of the "Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy", the authors point to the challenges of this kind of meta-analysis and propose two approaches. However, both of them come with some disadvantages, such as the nonstraightforward choice of priors in Bayesian models or the requirement of a two-step approach where parameters are estimated for the individual studies, followed by summarizing the results. As an alternative, we propose a novel model by applying methods from time-to-event analysis. To this task we use the discrete proportional hazard approach to treat the different diagnostic thresholds, that provide means to estimate sensitivity and specificity and are reported by the single studies, as categorical variables in a generalized linear mixed model, using both the logit- and the asymmetric cloglog-link. This leads to a model specification with threshold-specific discrete hazards, avoiding a linear dependency between thresholds, discrete hazard, and sensitivity/specificity and thus increasing model flexibility. We compare the resulting models to approaches from the literature in a simulation study. While the estimated area under the summary ROC curve is estimated comparably well in most approaches, the results depict substantial differences in the estimated sensitivities and specificities. We also show the practical applicability of the models to data from a meta-analysis for the screening of type 2 diabetes.

Physician preferences for treatment of low-density lipoprotein cholesterol among patients with atherosclerotic cardiovascular disease (ASCVD)-A discrete choice experiment.

Objective: Approximately 80 % of patients with atherosclerotic cardiovascular disease (ASCVD) do not achieve the guideline-based target for low-density lipoprotein (LDL-C) levels in current clinical practice, particularly the 95 % of ASCVD patients receiving oral statin monotherapy. The objective was to determine physician prescribing preferences for LDL-C lowering therapies beyond statins for patients with ASCVD. Methods: A discrete choice experiment (DCE) survey was administered to cardiologists and primary care physicians in the United States, presenting a series of treatment choices systematically varied across 8 treatment attributes: % LDL-C reduction, myalgias, other side effects, route and frequency of administration, time to prior authorization, patient monthly out-of-pocket cost (mOOP), and adherence. Data were analyzed using logistic regression to estimate preference weights for each attribute. Results: A total of 200 cardiologists and 50 primary care physicians (PCPs) completed the survey. Both exhibited similar prescribing preferences, highly valuing efficacy in reducing LDL-C levels and minimization of patients OOP cost. Each additional 10 % reduction in LDL-C was associated with a 69 % relative increase in physician preference. By contrast, a 10 % relative decrease in preference was observed for each $10 additional monthly mOOP. Compared to PCPs, cardiologists tended to place more emphasis on LDL-C reduction, being more willing to accept higher mOOP or side effects. Although oral therapies were preferred, injectable therapies, like the PCSK9 siRNA-like drug, administered less frequently that allowed for greater LDL-C reduction were seen as having considerable utility, especially among patients with a history of medication nonadherence. Conclusion: These results document considerable preference similarities among cardiologist and PCP prescribers of LDL-C lowering therapies for ASCVD. Broad availability of several therapies with varying administration frequencies and product profiles are likely of great value to prescribing physicians aiming to achieve target LDL-C concentrations. Considering all aspects of treatment, most participants preferred a PCSK9 siRNA-like drug.

Discrete Wavelet Transformation for the Sensitive Detection of Ultrashort Radiation Pulse with Radiation-induced Acoustics.

Radiation-induced acoustics (RIA) shows promise in advancing radiological imaging and radiotherapy dosimetry methods. However, RIA signals often require extensive averaging to achieve reasonable signal-to-noise ratios, which increases patient radiation exposure and limits real-time applications. Therefore, this paper proposes a discrete wavelet transform (DWT) based filtering approach to denoise the RIA signals and avoid extensive averaging. The algorithm was benchmarked against low-pass filters and tested on various types of RIA sources, including low-energy X-rays, high-energy X-rays, and protons. The proposed method significantly reduced the required averages (1000 times less averaging for low-energy X-ray RIA, 32 times less averaging for high-energy X-ray RIA, and 4 times less averaging for proton RIA) and demonstrated robustness in filtering signals from different sources of radiation. The coif5 wavelet in conjunction with the sqtwolog threshold selection algorithm yielded the best results. The proposed DWT filtering method enables high-quality, automated, and robust filtering of RIA signals, with a performance similar to low-pass filtering, aiding in the clinical translation of radiation-based acoustic imaging for radiology and radiation oncology.