Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions.

Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.

Selection of an alternate cementitious mortar using ceramic tile dust waste: a hybrid MCDM approach.

Selection of a suitable alternative material from a pool of alternatives with many conflicting criteria becomes a Multi-Criteria Decision Making (MCDM) problem. In the present study, ternary blended mortars were prepared using ceramic tile dust waste (CTD), fly ash (FA), and ground granulated blast furnace slag (GGBFS) as binder components. Crusher dust (CD) was used as a fine aggregate component. Binder to aggregate ratios of 1:3 and 1:1 were prepared considering suitable flow. A total of 16 mortar mixes were cast. These mortars were tested for various conflicting criteria compressive strength, flexural strength, porosity, water absorption, bulk density, thermal conductivity, specific heat, thermal diffusivity, and thermal effusivity whose weightages obtained were 29.09%, 20.08%, 12.77%, 10.60%, 8.74%, 6.74%, 5.54%, 4.47%, and 1.97%, respectively, as per AHP analysis. Later, considering these different criteria and alternate mortars, it was observed that a 1:1 mortar with 20% CTD, 30% FA, and 50% GGBFS (RC20F30G50) is found to be the suitable mortar with the highest relative closeness coefficient of 0.861 and the highest net outranking flow of 0.316 with respect to MCDM techniques: technique for order of preference by similarity to ideal solution (TOPSIS) and preference ranking organization method for enrichment of evaluations (PROMETHEE-II), respectively. The ranking of the mortar in both methods complies with the relative weightages of the criteria and the performance of the mortars with respect to the above criteria.

Primary care and abortion provider perspectives on mail-order medication abortion: a qualitative study.

BACKGROUND: This qualitative study aims to assess perspectives of clinicians and clinic staff on mail-order pharmacy dispensing for medication abortion. METHODS: Participants included clinicians and staff involved in implementing a mail-order dispensing model for medication abortion at eleven clinics in seven states as part of a prospective cohort study, which began in January 2020 (before the FDA removed the in-person dispensing requirement for mifepristone). From June 2021 to July 2022, we invited participants at the participating clinics, including six primary care and five abortion clinics, to complete a semi-structured video interview about their experiences. We then conducted qualitative thematic analysis of interview data, summarizing themes related to perceived benefits and concerns about the mail-order model, perceived patient interest, and potential barriers to larger-scale implementation. RESULTS: We conducted 24 interviews in total with clinicians (13 physicians and one nurse practitioner) and clinic staff (n = 10). Participants highlighted perceived benefits of the mail-order model, including its potential to expand abortion services into primary care, increase patient autonomy and privacy, and to normalize abortion services. They also highlighted key logistical, clinical, and feasibility concerns about the mail-order model, and specific challenges related to integrating abortion into primary care. CONCLUSION: Clinicians and clinic staff working in primary care and abortion clinics were optimistic that mail-order dispensing of medication abortion can improve the ability of some providers to provide abortion and enable more patients to access services. The feasibility of mail-order pharmacy dispensing of medication abortion following the Supreme Court Dobbs decision is to be determined. TRIAL REGISTRATION: Registry: Clinicaltrials.gov. TRIAL REGISTRATION NUMBER: NCT03913104. Date of registration: first submitted on April 3, 2019 and first posted on April 12, 2019.

Prediction of frequency response of sub-frame bushing and study of high-order fractional derivative viscoelastic model.

This paper presents experimental and dynamic modeling research on the rubber bushings of the rear sub-frame. The Particle Swarm Optimization algorithm was utilized to optimize a Backpropagation (BP) neural network, which was separately trained and tested across two frequency ranges: 1-40 Hz and 41-50 Hz, using wideband frequency sweep dynamic stiffness test data. The testing errors at amplitudes of 0.2 mm, 0.3 mm, and 0.5 mm were found to be 1.03%, 3.05%, and 1.96%, respectively. Subsequently, the trained neural network was employed to predict data within the frequency range of 51-70 Hz. To incorporate the predicted data into simulation software, a dynamic model of the rubber bushing was established, encompassing elastic, friction, and viscoelastic elements. Additionally, a novel model, integrating high-order fractional derivatives, was proposed based on the frequency-dependent model for the viscoelastic element. An enhanced Particle Swarm Optimization algorithm was introduced to identify the model's parameters using the predicted data. In comparison to the frequency-dependent model, the new model exhibited lower fitting errors at various amplitudes, with reductions of 3.84%, 3.61%, and 5.49%, respectively. This research establishes a solid foundation for subsequent vehicle dynamic modeling and simulation.

Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks.

This article provides a unified analysis of the multistability of fraction-order multidimensional-valued memristive neural networks (FOMVMNNs) with unbounded time-varying delays. Firstly, based on the knowledge of fractional differentiation and memristors, a unified model is established. This model is a unified form of real-valued, complex-valued, and quaternion-valued systems. Then, based on a unified method, the number of equilibrium points for FOMVMNNs is discussed. The sufficient conditions for determining the number of equilibrium points have been obtained. By using 1-norm to construct Lyapunov functions, the unified criteria for multistability of FOMVMNNs are obtained, these criteria are less conservative and easier to verify. Moreover, the attraction basins of the stable equilibrium points are estimated. Finally, two numerical simulation examples are provided to verify the correctness of the results.

Mittag-Leffler stability and application of delayed fractional-order competitive neural networks.

In the article, the Mittag-Leffler stability and application of delayed fractional-order competitive neural networks (FOCNNs) are developed. By virtue of the operator pair, the conditions of the coexistence of equilibrium points (EPs) are discussed and analyzed for delayed FOCNNs, in which the derived conditions of coexistence improve the existing results. In particular, these conditions are simplified in FOCNNs with stepped activations. Furthermore, the Mittag-Leffler stability of delayed FOCNNs is established by using the principle of comparison, which enriches the methodologies of fractional-order neural networks. The results on the obtained stability can be used to design the horizontal line detection of images, which improves the practicability of image detection results. Two simulations are displayed to validate the superiority of the obtained results.

Kinetic and Mechanistic Modelling of Pyrimethanil Fungicide Adsorption onto Soils of Varying Physico-chemical Properties.

Pesticide transport in the environment is impacted by the kinetics of its adsorption onto soil. The adsorption kinetics of pyrimethanil was investigated in ten soil samples of varying physicochemical properties. The highest adsorption was in the soil having the maximum silt and CaCO3 contents, pH and electrical conductance but the lowest amorphous Fe oxides and CaCl2 extractable Mn. Pseudo-second order kinetics and intra-particle diffusion model best accounted the adsorption kinetics of pyrimethanil. The equilibrium adsorption estimated by pseudo-second order kinetics (q02) was significantly and positively correlated with CaCl2 extractable Cu content (r = 0.709) while rate coefficient (k02) had a negative correlation with crystalline iron oxides content (r = -0.675). The intra-particle diffusion coefficient (ki.d.) had inverse relationship with CaCl2 extractable Mn content in soils (r = -0.689). FTIR spectra showed a significant interaction of pyrimethanil with micronutrient cations. Adsorption kinetic parameters of pyrimethanil could be successfully predicted by soil properties. The findings may help to evolve fungicide management decisions.

Familiar Sequences Are Processed Faster Than Unfamiliar Sequences, Even When They Do Not Match the Count-List.

In order processing, consecutive sequences (e.g., 1-2-3) are generally processed faster than nonconsecutive sequences (e.g., 1-3-5) (also referred to as the reverse distance effect). A common explanation for this effect is that order processing operates via a memory-based associative mechanism whereby consecutive sequences are processed faster because they are more familiar and thus more easily retrieved from memory. Conflicting with this proposal, however, is the finding that this effect is often absent. A possible explanation for these absences is that familiarity may vary both within and across sequence types; therefore, not all consecutive sequences are necessarily more familiar than all nonconsecutive sequences. Accordingly, under this familiarity perspective, familiar sequences should always be processed faster than unfamiliar sequences, but consecutive sequences may not always be processed faster than nonconsecutive sequences. To test this hypothesis in an adult population, we used a comparative judgment approach to measure familiarity at the individual sequence level. Using this measure, we found that although not all participants showed a reverse distance effect, all participants displayed a familiarity effect. Notably, this familiarity effect appeared stronger than the reverse distance effect at both the group and individual level; thus, suggesting the reverse distance effect may be better conceptualized as a specific instance of a more general familiarity effect.

Do actions structure auditory memory? Action-based event segmentation effects on sensory responses, pupil dilation and sequential memory.

Our actions shape our everyday experience: what we experience, how we perceive, and remember it are deeply affected by how we interact with the world. Performing an action to deliver a stimulus engages neurophysiological processes which are reflected in the modulation of sensory and pupil responses. We hypothesized that these processes shape memory encoding, parsing the experience by grouping self- and externally generated stimuli into differentiated events. Participants encoded sound sequences, in which either the first or last few sounds were self-generated and the rest externally generated. We tested recall of the sequential order of sounds that had originated from the same (within event) or different sources (across events). Memory performance was not higher for within-event sounds, suggesting that actions did not structure the memory representation. However, during encoding, we observed the expected electrophysiological response attenuation for self-generated sounds, together with increased pupil dilation triggered by actions. Moreover, at the boundary between events, physiological responses to the first sound from the new source were influenced by the direction of the source switch. Our results suggest that introducing actions creates a stronger contextual shift than removing them, even though actions do not directly contribute to memory performance. This study contributes to our understanding of how interacting with sensory input shapes experiences by exploring the relationships between action effects on sensory responses, pupil dilation, and memory encoding. Importantly, it challenges the notion of a meaningful contribution from low-level neurophysiological mechanisms associated with action execution in the modulation of the self-generation effect.

How intervention studies measure the effectiveness of medication safety-related clinical decision support systems in primary and long-term care: a systematic review.

BACKGROUND: Medication errors and associated adverse drug events (ADE) are a major cause of morbidity and mortality worldwide. In recent years, the prevention of medication errors has become a high priority in healthcare systems. In order to improve medication safety, computerized Clinical Decision Support Systems (CDSS) are increasingly being integrated into the medication process. Accordingly, a growing number of studies have investigated the medication safety-related effectiveness of CDSS. However, the outcome measures used are heterogeneous, leading to unclear evidence. The primary aim of this study is to summarize and categorize the outcomes used in interventional studies evaluating the effects of CDSS on medication safety in primary and long-term care. METHODS: We systematically searched PubMed, Embase, CINAHL, and Cochrane Library for interventional studies evaluating the effects of CDSS targeting medication safety and patient-related outcomes. We extracted methodological characteristics, outcomes and empirical findings from the included studies. Outcomes were assigned to three main categories: process-related, harm-related, and cost-related. Risk of bias was assessed using the Evidence Project risk of bias tool. RESULTS: Thirty-two studies met the inclusion criteria. Almost all studies (n = 31) used process-related outcomes, followed by harm-related outcomes (n = 11). Only three studies used cost-related outcomes. Most studies used outcomes from only one category and no study used outcomes from all three categories. The definition and operationalization of outcomes varied widely between the included studies, even within outcome categories. Overall, evidence on CDSS effectiveness was mixed. A significant intervention effect was demonstrated by nine of fifteen studies with process-related primary outcomes (60%) but only one out of five studies with harm-related primary outcomes (20%). The included studies faced a number of methodological problems that limit the comparability and generalizability of their results. CONCLUSIONS: Evidence on the effectiveness of CDSS is currently inconclusive due in part to inconsistent outcome definitions and methodological problems in the literature. Additional high-quality studies are therefore needed to provide a comprehensive account of CDSS effectiveness. These studies should follow established methodological guidelines and recommendations and use a comprehensive set of harm-, process- and cost-related outcomes with agreed-upon and consistent definitions. PROSPERO REGISTRATION: CRD42023464746.

Microwave-assisted synthesis of Ni-doped europium hydroxide for photocatalytic degradation of 4-nitrophenol.

Microwave-assisted synthesis method was used to prepare europium hydroxide (Eu(OH)3) and different percentages of 1, 5, and 10 % nickel-doped Eu(OH)3 (Ni-Eu(OH)3) nanorods (NRs). X-ray diffraction study showed a hexagonal phase with an average crystallite size in the range of 21 - 35 nm for Eu(OH)3 and Ni-Eu(OH)3 NRs. FT-IR and Raman studies also confirmed the synthesis of Eu(OH)3 and Ni-Eu(OH)3. The synthesized materials showed rod-like morphology with an average length and diameter between 27 - 50 nm and 8 - 13 nm, respectively. The band gap energies of Ni-Eu(OH)3 NRs were reduced (4.06 - 3.50 eV), which indicates that the doping of Ni2+ ions has influenced the band gap energy of Eu(OH)3. The PL study exhibited PL quenching with Ni doping. The photocatalytic degradation of 4-nitrophenol (4-NP) by the synthesized materials under UV light irradiation was investigated, in which 10 % Ni-Eu(OH)3 NRs showed the best response. A kinetic study was also conducted which shows pseudo-first-order kinetics. Based on this, Ni-Eu(OH)3 NRs have shown a potential to be a UV-light active material for photocatalysis.

Theory of Moiré Magnetism and Multidomain Spin Textures in Twisted Mott Insulator-Semimetal Heterobilayers.

Motivated by the recent experimental developments in van der Waals heterostructures, we investigate the emergent magnetism in Mott insulator-semimetal moiré superlattices by deriving effective spin models and exploring their phase diagram by Monte Carlo simulations. Our analysis indicates that the stacking-dependent interlayer Kondo interaction can give rise to different types of magnetic order, forming domains within the moiré unit cell. In particular, we find that the AB (AA) stacking regions tend to order (anti)ferromagnetically for an extended range of parameters. The remaining parts of the moiré unit cell form ferromagnetic chains that are coupled antiferromagnetically. We show that the decay length of the Kondo interaction can control the extent of these phases. Our results highlight the importance of stacking-dependent interlayer exchange and the rich magnetic spin textures that can be obtained in van der Waals heterostructures.

Fractional-order Sprott K chaotic system and its application to biometric iris image encryption.

Fractional-order (FO) chaotic systems exhibit random sequences of significantly greater complexity when compared to integer-order systems. This feature makes FO chaotic systems more secure against various attacks in image cryptosystems. In this study, the dynamical characteristics of the FO Sprott K chaotic system are thoroughly investigated by phase planes, bifurcation diagrams, and Lyapunov exponential spectrums to be utilized in biometric iris image encryption. It is proven with the numerical studies the Sprott K system demonstrates chaotic behaviour when the order of the system is selected as 0.9. Afterward, the introduced FO Sprott K chaotic system-based biometric iris image encryption design is carried out in the study. According to the results of the statistical and attack analyses of the encryption design, the secure transmission of biometric iris images is successful using the proposed encryption design. Thus, the FO Sprott K chaotic system can be employed effectively in chaos-based encryption applications.

Hybrid controller with neural network PID/FOPID operations for two-link rigid robot manipulator based on the zebra optimization algorithm.

The performance of the robotic manipulator is negatively impacted by outside disturbances and uncertain parameters. The system's variables are also highly coupled, complex, and nonlinear, indicating that it is a multi-input, multi-output system. Therefore, it is necessary to develop a controller that can control the variables in the system in order to handle these complications. This work proposes six control structures based on neural networks (NNs) with proportional integral derivative (PID) and fractional-order PID (FOPID) controllers to operate a 2-link rigid robot manipulator (2-LRRM) for trajectory tracking. These are named as set-point-weighted PID (W-PID), set-point weighted FOPID (W-FOPID), recurrent neural network (RNN)-like PID (RNNPID), RNN-like FOPID (RNN-FOPID), NN+PID, and NN+FOPID controllers. The zebra optimization algorithm (ZOA) was used to adjust the parameters of the proposed controllers while reducing the integral-time-square error (ITSE). A new objective function was proposed for tuning to generate controllers with minimal chattering in the control signal. After implementing the proposed controller designs, a comparative robustness study was conducted among these controllers by altering the initial conditions, disturbances, and model uncertainties. The simulation results demonstrate that the NN+FOPID controller has the best trajectory tracking performance with the minimum ITSE and best robustness against changes in the initial states, external disturbances, and parameter uncertainties compared to the other controllers.

Three-Month Comprehensive Outcomes of Topography-Guided LASIK Versus Keratorefractive Lenticule Extraction (KLEx): A Prospective Contralateral Study.

INTRODUCTION: The aim of this study was to compare outcomes of topography-guided laser-assisted in situ keratomileusis (TG-LASIK) and small incision lenticule extraction (SMILE®) for correcting myopia and myopic astigmatism. METHODS: In this prospective, randomized contralateral study, 34 patients (68 eyes) received TG-LASIK in one eye, and SMILE in their fellow eye. Efficacy, safety, predictability, higher-order aberrations, corneal tomography, biomechanics, and patient-reported outcomes (PROs) were assessed preoperatively and up to 3 months postoperatively. RESULTS: Both platforms showed comparable efficacy at 3 months (TG-LASIK 1.08 ± 0.19; SMILE 0.98 ± 0.17, p = 0.055). However, TG-LASIK demonstrated quicker visual recovery, with 63% and 89% achieving uncorrected distance visual acuity (UDVA) of 20/16 or better at 1 day and 1 week, respectively, compared to SMILE (34% and 63%; p < 0.05). Safety index at 3 months did not significantly differ between TG-LASIK and SMILE (p = 0.223). TG-LASIK and SMILE had 44% and 56% of eyes within 0.13 D of spherical equivalent (SEQ) target, respectively. SMILE induced more total higher-order aberrations (HOAs), vertical coma, and oblique trefoil than TG-LASIK at 3 months (p < 0.05). Both platforms showed similar increases in epithelial remodeling, but SMILE induced thicker epithelium than TG-LASIK at the 7.0-mm nasal zonal area. No significant differences were found in corneal hysteresis (CH) or corneal resistance factor (CRF) at 3 months (p > 0.05). Reported symptoms of glare, halos, rings, starbursts, or dry eye did not significantly differ between groups at 3 months (p > 0.05). Overall, 59% of patients preferred their TG-LASIK treated eye at 1 month, but 65% of patients had no specific eye preference at 3 months. CONCLUSION: TG-LASIK and SMILE demonstrate excellent and comparable efficacy, safety, and predictability at 3 months, but TG-LASIK offers faster postoperative visual recovery at 1 day and 1 week. TG-LASIK induces fewer HOAs than SMILE, but both procedures affect corneal biomechanics similarly. TRIAL REGISTRATION: ClincialTrials.gov identifier, NCT05611294.

Graph Theoretical Description of Phase Transitions in Complex Multiscale Phases with Supramolecular Assemblies.

Phase transitions are typically quantified using order parameters, such as crystal lattice distances and radial distribution functions, which can identify subtle changes in crystalline materials or high-contrast phases with large structural differences. However, the identification of phases with high complexity, multiscale organization and of complex patterns during the structural fluctuations preceding phase transitions, which are essential for understanding the system pathways between phases, is challenging for those traditional analyses. Here, it is shown that for two model systems- thermotropic liquid crystals and a lyotropic water/surfactant mixtures-graph theoretical (GT) descriptors can successfully identify complex phases combining molecular and nanoscale levels of organization that are hard to characterize with traditional methodologies. Furthermore, the GT descriptors also reveal the pathways between the different phases. Specifically, centrality parameters and node-based fractal dimension quantify the system behavior preceding the transitions, capturing fluctuation-induced breakup of aggregates and their long-range cooperative interactions. GT parameterization can be generalized for a wide range of chemical systems and be instrumental for the growth mechanisms of complex nanostructures.

Optimization of Ray-Tracing-Guided LASIK Outcomes: A Prospective Comparative Study of ZZ InnovEyes Strategy versus Automated Strategy.

Purpose: To evaluate the effectiveness of Zhang and Zheng's InnovEyes (ZZ InnovEyes) strategy for optimizing outcomes of ray-tracing-guided laser in situ keratomileusis (LASIK) compared to the standard automated strategy. Methods: A total of 38 patients (71 eyes) undergoing therapeutic refractive surgery at Hangzhou MSK Eye Hospital were randomly assigned to the ZZ InnovEyes and automated groups using double-masked randomization. The study assessed visual acuity, refractive outcomes, and higher-order aberrations preoperatively and at 1-day, 2-week, 1-month, and 3-month follow-ups. Statistical analysis was done with Microsoft Excel and SPSS 19.0. Results: The exposure and control groups comprised 36 and 35 eyes, respectively. The ZZ InnovEyes group demonstrated significant advantages in manifest refraction spherical equivalent (MRSE) correction compared to the automated approach group (0.13 ± 0.30 D vs 0.62 ± 0.40 D, p < 0.001), achieving 97.22% uncorrected distance visual acuity (UDVA) of 20/16 or better compared to 85.71% in the automated group at the 3-month follow-up (p = 0.08), and achieving 50.00% UDVA of 20/12.5 or better compared to 28.57% in the automated group at the 3-month follow-up (p = 0.06). Loss lines from preoperative corrected distance visual acuity to postoperative UDVA were lower in the ZZ InnovEyes group (0.00%) than the automated group (8.57%; p = 0.07). Both groups exhibited similar astigmatism corrections and higher-order aberrations. Conclusion: The ZZ InnovEyes strategy, which incorporates manifest and wavefront refraction for ray-tracing-guided LASIK, demonstrated superior MRSE correction and potential advantages in visual acuity outcomes compared to the standard automated strategy. This study highlights the need for ongoing optimization and research in refractive surgery. Clinical Trial Registration Number: ChiCTR2300078709.

Unskilled and unaware: second-order judgments increase with miscalibration for low performers.

Overestimation and miscalibration increase with a decrease in performance. This finding has been attributed to a common factor: participants' knowledge and skills about the task performed. Researchers proposed that the same knowledge and skills needed for performing well in a test are also required for accurately evaluating one's performance. Thus, when people lack knowledge about a topic they are tested on, they perform poorly and do not know they did so. This is a compelling explanation for why low performers overestimate themselves, but such increases in overconfidence can also be due to statistical artifacts. Therefore, whether overestimation indicates lack of awareness is debatable, and additional studies are needed to clarify this issue. The present study addressed this problem by investigating the extent to which students at different levels of performance know that their self-estimates are biased. We asked 653 college students to estimate their performance in an exam and subsequently rate how confident they were that their self-estimates were accurate. The latter judgment is known as second-order judgments (SOJs) because it is a judgment of a metacognitive judgment. We then looked at whether miscalibration predicts SOJs per quartile. The findings showed that the relationship between miscalibration and SOJs was negative for high performers and positive for low performers. Specifically, for low performers, the less calibrated their self-estimates were the more confident they were in their accuracy. This finding supports the claim that awareness of what one knows and does not know depends in part on how much one knows.

Electronic alerts to improve management of heparin-induced thrombocytopenia.

Background: Heparin-induced thrombocytopenia (HIT) is a difficult clinicopathologic diagnosis to make and to treat. Delays in identification and appropriate treatment can lead to increased morbidity and mortality. Objectives: To use electronic health alert interventions to improve provider diagnosis and management of heparin-induced thrombocytopenia through guideline-based, accurate care delivery. Methods: This quality improvement initiative developed 3 electronic health record-based interventions at our 750-bed academic medical center to improve the initial management of suspected HIT between 2018 and 2021: 1. an interruptive alert to recommend discontinuation of active heparin products when signing a heparin-platelet factor 4 test (PF4) order, 2. integrated 4T score calculation in the heparin-PF4 test order, and 3. interruptive alert suggesting not to order heparin-PF4 tests when the 4T score is <4. Changes in practice were assessed over defined time periods pre and post each intervention. Results: Intervention 1 resulted in heparin discontinuation in more patients, with 65% (191 heparin orders/293 heparin-PF4 enzyme-linked immunosorbent assay tests) of cases continuing heparin prealert and only 54% (127 heparin orders/235 heparin-PF4 enzyme-linked immunosorbent assay tests) postinterruptive alert (95% CI 2.3-19.9; P = .015). Intervention 2 increased appropriate heparin-PF4 test ordering from 40.4% (110/272) preintervention to 79.1% (246/311) (95% CI 30.9-46.4; P < .00001) postintervention, with inappropriate PF4 ordering defined as testing when 4T score was <4. Intervention 3 did not lead to reduction in heparin-PF4 testing in the control group (96 inappropriate orders/402 total orders, 24%) compared to the randomized alert group (56 inappropriate orders/298 total orders; 19%) (95% CI -1.2 to 11.5; P = .13). Conclusion: Implementation of unique electronic health record interventions, including both diagnostic and management interventions, led to improved guideline-based, accurate care delivery with 4T score calculation and cessation of heparin for patients with suspected HIT.

A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.

To elucidate the genetic basis of complex diseases, it is crucial to discover the single-nucleotide polymorphisms (SNPs) contributing to disease susceptibility. This is particularly challenging for high-order SNP epistatic interactions (HEIs), which exhibit small individual effects but potentially large joint effects. These interactions are difficult to detect due to the vast search space, encompassing billions of possible combinations, and the computational complexity of evaluating them. This study proposes a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI) specifically designed to address this challenge. The algorithm operates in three stages. First, a harmony search algorithm is employed, utilizing four lightweight evaluation functions, such as Bayesian network and entropy, to efficiently explore potential SNP combinations related to disease status. Second, a G-test statistical method is applied to filter out insignificant SNP combinations. Finally, two machine learning-based methods, multifactor dimensionality reduction (MDR) as well as random forest (RF), are employed to validate the classification performance of the remaining significant SNP combinations. This research aims to demonstrate the effectiveness of MTHS-EE-DHEI in identifying HEIs compared to existing methods, potentially providing valuable insights into the genetic architecture of complex diseases. The performance of MTHS-EE-DHEI was evaluated on twenty simulated disease datasets and three real-world datasets encompassing age-related macular degeneration (AMD), rheumatoid arthritis (RA), and breast cancer (BC). The results demonstrably indicate that MTHS-EE-DHEI outperforms four state-of-the-art algorithms in terms of both detection power and computational efficiency. The source code is available at https://github.com/shouhengtuo/MTHS-EE-DHEI.git .