Practical Method Research Articles

Comparison of Standard and Vacuum Specimen Mammography in the Detection of Margin Status in Conservative Surgery for Breast Cancer: a Cross-Sectional Diagnostic Study

Background: One of the most important factors that increases breast cancer (BC) recurrence after Breast-conserving surgery (BCS) is the positivity of the margins, which is found in permanent histological exams. Intra-operative specimen mammography (SM) can reduce the rate of margin positivity and re-operation. Our aim was to examine whether vacuum SM (VSM) is more accurate than standard SM (SSM) in detecting the positivity of the margins. Methods: For this cross-sectional study, in the operating room, excised specimens of 55 women with breast cancer who underwent lumpectomy were oriented by metallic staples and sutures for radiologic and histological assessment, respectively. In the radiology ward, SSM was first taken; then, the specimen was vacuum packed and VSM was performed. Afterwards, the specimen was sent for histopathological analysis as a gold standard for the assessment of surgical margins. Specimens’ margins were classified according to the size of clear margins in millimeters as zero or >0; and ≤1 or >1. Results: The mean age of all participants was 51.22 ± 10.58 years. Totally, 220 margins were assessed. According to classified margins (zero and 1 mm), for the detection of affected margins, the accuracy values of the VSM method were 90.52%, and 87.20% while these figures were 91.51% and 88.68% for SSM. There was substantial agreement between the two methods of detecting the affected margins (VSM and SSM), with Cohen's κ =0.66, 95% CI: 0.34-0.97, P-value <0.001). Finally, there was not a statistically significant difference in the proportion of detecting margin between SSM and VSM (McNemar test P-value =0.63). Conclusion: Specimen mammography with an adequate orientation of the tissue is an accurate and practical method for immediate intraoperative examination of the margin status in BCS, and VSM is not superior to SSM in the detection of affected margins.

PELATIHAN KETERAMPILAN DAN PENGETAHUAN HANDUK LEMBAB (OSHIBORI)

Moist towel (osihibori) skills and knowledge training aims to increase knowledge and skills in making and managing it. Oshibori is wet towel used to clean hands in hotels, restaurants and other public places. This training was carried out with the aim of improving service quality and guest satisfaction. Skills and knowledge training consists of understanding, folding techniques and presentation techniques. Training is carried out using lecture, demonstration and direct practice methods. The results show that participants can improve their knowledge and skills in managing oshibori well.

Research and Appropriate Implementation on Vehicle Tracking System using IoT

The development and implementation of vehicle tracking systems based on Internet of Things (IoT) technology have attracted considerable interest due to the rising demand for effective and secure monitoring solutions. This study investigates the investigation and development of a vehicle monitoring system that utilizes Internet of Things (IoT) capabilities to provide real-time tracking, data collection, and analysis. By combining existing literature, IoT principles, and modern technologies, this study seeks to present a comprehensive and practical method for implementing an effective vehicle tracking system that satisfies the requirements of contemporary fleet management. This document provides a thorough investigation and practical application of a vehicle tracking system utilizing Internet of Things (IoT) technology. The research covers a comprehensive examination of relevant articles and resources regarding vehicle tracking systems and Internet of Things (IoT) applications. It explores into the fundamental principles of IoT and its significance in the context of vehicle tracking. The implementation phase encompasses the creation of a bespoke vehicle tracking system that integrates GPS modules, microcontrollers, wireless communication, and cloud-based data storage to enable real-time tracking and monitoring. The integration of advanced data analytics and machine learning techniques enhances the capabilities of the system, resulting in valuable insights, optimized routing, and predictive maintenance. This integration contributes to improved fleet management. The ethical implications pertaining to the protection of data privacy, security, and compliance to regulatory requirements are carefully taken into account. The results of this study make a valuable contribution to the field of Internet of Things (IoT)-based vehicle tracking systems. Additionally, it provide a practical framework that can be utilized by organizations in search of effective and scalable solutions.

Understanding chemical pathways of brown centre formation in laboratory induced and conventionally dried nut-in-shell macadamia kernels

World tree nut production has increased rapidly by around 50 % in the past decade; however, nut defects cause losses. For example, we know that brown centres are a major internal discolouration defect in macadamia nuts and are linked to the storage of nut-in-shell under improper conditions at high temperature and humidity. However, key chemical changes in brown centre kernels have not been described. In this study, we compared brown centres and white kernels from: 1) samples that were “induced” in the laboratory by storing at high moisture concentration; and 2) samples that were dried immediately after harvest using industry best practice methods recommended by the Australian Macadamia Society (AMS). We measured the moisture concentration, sugar concentration, fatty acid concentration, peroxide value, nutrient concentration and volatile compounds of induced and AMS samples. Our results showed that storing nut-in-shell macadamia under wet and hot conditions increased brown centres compared with samples immediately dried using the AMS regime, 10.33 % vs 1.44 %, respectively. Induced brown centres had significantly higher moisture concentrations than induced white centres. Volatile compounds including nonanoic acid, octanoic acid and 2,3 butanediol were identified and associated with brown centre formation in macadamia kernels and the initiation of lipid oxidation. Our results suggest sugar hydrolysis and the Maillard reaction are associated with brown centres both in laboratory induced samples and those formed using industry best practice drying methods. Our study suggests improper drying and storage at high temperature and high humidity are likely to result in brown centre formation. We recommend brown centre losses can be reduced by appropriate drying and storage practices.

Fibre orientation atlas guided rapid segmentation of white matter tracts.

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

Evaluation of the practice of contraception among adult female traders at international market Orlu, Imo state, South east Nigeria

Aim: To evaluate the practice of contraception among adult female traders at Orlu International Market Background: Contraception is the use of any practice methods or devices to prevent pregnancy from occurring in sexually active women. Currently in Nigeria, there are concerns about the rise in maternal mortality rate and the increase in unwanted pregnancies. This trend is said to be on the rise among the less educated women. This necessitated our study to evaluate the practice of contraception among adult female traders in the international market Orlu Imo State of Nigeria Methodology: An observational descriptive retrospective study involving adult female traders of Orlu International Market, selected by convenience sampling, which is a non probability sampling method. Both self and interviewer administered questionnaires were used collaged data was analysed using statistical package for soual sciences (SPSS) version 20 IBM, USA. Results were presented in frequency tables, bar charts and pie charts Result: A total of 308 respondents participated in the study. Majority were within the age range of 30-39years (36%). Majority of the respondents (68%) had good knowledge of contraception, however, only 34% has used one or more forms of contraception in the past; disappointingly, majority (64%) do not use any form of contraception Conclusion: There is high level of knowledge of the practice of contraception but the usage rate is abysmally low.

Efficient designs and analysis of two-phase studies with longitudinal binary data.

Researchers interested in understanding the relationship between a readily available longitudinal binary outcome and a novel biomarker exposure can be confronted with ascertainment costs that limit sample size. In such settings, two-phase studies can be cost-effective solutions that allow researchers to target informative individuals for exposure ascertainment and increase estimation precision for time-varying and/or time-fixed exposure coefficients. In this paper, we introduce a novel class of residual-dependent sampling (RDS) designs that select informative individuals using data available on the longitudinal outcome and inexpensive covariates. Together with the RDS designs, we propose a semiparametric analysis approach that efficiently uses all data to estimate the parameters. We describe a numerically stable and computationally efficient EM algorithm to maximize the semiparametric likelihood. We examine the finite sample operating characteristics of the proposed approaches through extensive simulation studies, and compare the efficiency of our designs and analysis approach with existing ones. We illustrate the usefulness of the proposed RDS designs and analysis method in practice by studying the association between a genetic marker and poor lung function among patients enrolled in the Lung Health Study (Connett et al, 1993).

Impurity reduction in lightly doped n-type gallium nitride layer grown via halogen-free vapor-phase epitaxy

The development of gallium nitride (GaN) vertical-type metal-oxide-semiconductor field-effect transistors and p–i–n diode devices has gathered increasing attention. These devices require an n-type drift layer with a low doping level of 1016 cm−3 or less, minimized point defects inhibiting electron conduction, and a layer approximately 10 μm thick. Therefore, a practical method with a growth rate of at least several tens of μm/h and impurity concentrations of less than 1015 cm−3, except for that of dopants, is necessary. Halogen-free vapor-phase epitaxy (HF-VPE) has a high growth rate suitable for fabricating thick drift layers and utilizes a simple reaction between Ga vapor and ammonia gas (without a corrosive halogen gas), resulting in lower impurity levels. Herein, we eliminated the quartz content from the high-temperature zone to reduce the excess unintentional Si doping and identified that the nitrile gloves used for the growth preparation are other impurity contamination sources. We obtained a lightly n-type ([Si]=∼1016 cm−3) GaN layer, in which C, O, B, Fe, Mg, Al, Ca, Cr, Zn, Ni, Mn, and Ti impurity contents were below the detection limits of secondary ion mass spectrometry. Deep-level transient spectroscopy revealed that electron traps at EC − 0.26 and at EC − 0.59 eV were 2.7 × 1013 and 5.2 × 1014 cm−3, respectively. Moreover, the Hall effect analysis showed the acceptor-type defect-compensating donor content as approximately 2.7 × 1015 cm−3, resulting in a high electron mobility of HF-VPE GaN in the 30–710 K temperature range. Furthermore, we identified the Ca impurity as a deep acceptor, another killer defect leading to mobility collapse.

Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra-tissue with low perfusion yet intact cellular integrity-making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders.

Pd(II)‐Catalyzed Aerobic Alcohol Oxidation for Multicomponent Synthesis of 2,3‐Dihydroquinazolin‐4(1H)‐ones in Water

AbstractWe describe a multicomponent reaction between isatoic anhydrides, alkylamines and benzylic alcohols using Pd(II)/TPPMS catalyst with cocatalytic Cu(II) salt in water. This cascade reaction involves the catalytic dehydrogenation of benzyl alcohol to benzaldehyde under aerobic conditions, followed by cyclocondensation with in situ generated 2‐aminobenzamides, allowing rapid access to 2,3‐dihydroquinazolin‐4(1H)‐ones (DHQs). This practical method enables direct access to a series of DHQs without strong bases and hazardous oxidants. Mechanistic studies reveal that the multicomponent reaction is first order with respect to the concentrations of benzyl alcohol and catalysts, and proceeds independently of the concentration of the 2‐aminobenzamide intermediate.

Implementation of the scenario simulation teaching method in the clinical teaching practice of oral medicine for nursing students

Implementation of the scenario simulation teaching method in the clinical teaching practice of oral medicine for nursing students

Current state and future directions for veterinary antimicrobial resistance research.

Antimicrobial resistance (AMR) is a critical One Health concern with implications for human, animal, plant, and environmental health. Antimicrobial susceptibility testing (AST), antimicrobial resistance testing (ART), and surveillance practices must be harmonized across One Health sectors to ensure consistent detection and reporting practices. Veterinary diagnostic laboratory stewardship, clinical outcomes studies, and training for current and future generations of veterinarians and laboratorians are necessary to minimize the spread of AMR and move veterinary medicine forward into an age of better antimicrobial use practices. The purpose of this article is to describe current knowledge gaps present in the literature surrounding ART, AST, and clinical or surveillance applications of these methods and to suggest areas where AMR research can fill these knowledge gaps. The related Currents in One Health by Maddock et al, JAVMA, March 2024, addresses current limitations to the use of genotypic ART methods in clinical veterinary practice.

Elevated glutathione in researchers exposed to engineered nanoparticles due to potential adaptation to oxidative stress.

Aim: To find a practical biomonitoring method for researchers exposed to nanoparticles causing oxidative stress. Methods: In a continuation of a study in 2016-2018, biological samples (plasma, urine and exhaled breath condensate [EBC]) were collected in 2019-2020 from 43 researchers (13.8±3.0years of exposure) and 45 controls. Antioxidant status was assessed using glutathione (GSH) and ferric-reducing antioxidant power, while oxidative stress was measured as thiobarbituric acid reactive substances, all using spectrophotometric methods. Researchers' personal nanoparticle exposure was monitored. Results: Plasma GSH was elevated in researchers both before and after exposure (p<0.01); postexposure plasma GSH correlated with nanoparticle exposure, and GSH in EBC increased. Conclusion: The results suggest adaptation to chronic exposure to nanoparticles, as monitored by plasma and EBC GSH.

Resorbable bilayer membrane made of L-lactide-ε-caprolactone in guided bone regeneration: an in vivo experimental study

PurposeGuided bone regeneration (GBR) is an accepted method in dental practice that can successfully increase the bone volume of the host at sites chosen for implant placement; however, existing GBR membranes exhibit rapid absorption and lack of adequate space maintenance capabilities. We aimed to compare the effectiveness of a newly developed resorbable bilayer membrane composed of poly (l-lactic acid) and poly (-caprolactone) (PLACL) with that of a collagen membrane in a rat GBR model.MethodsThe rat calvaria was used as an experimental model, in which a plastic cylinder was placed. We operated on 40 male Fisher rats and subsequently performed micro-computed tomography and histomorphometric analyses to assess bone regeneration.ResultsSignificant bone regeneration was observed, which was and similar across all the experimental groups. However, after 24 weeks, the PLACL membrane demonstrated significant resilience, and sporadic partial degradation. This extended preservation of the barrier effect has great potential to facilitate optimal bone regeneration.ConclusionsThe PLACL membrane is a promising alternative to GBR. By providing a durable barrier and supporting bone regeneration over an extended period, this resorbable bilayer membrane could address the limitations of the current membranes. Nevertheless, further studies and clinical trials are warranted to validate the efficacy and safety of The PLACL membrane in humans.

A Research on the Dynamization Effect of Brand Visual Identity Design: Mediated by Digital Information Smart Media

The article utilizes the literature research method, case study method, and practical verification method. The article discusses brand visual identity and motion graphics design principles. The article outlines dynamic brand visual identity design trends that digital information and AI enable. It explains AI generative models like GAN and diffusion models that generate graphics and effects. Examples like Stable Diffusion and Midjourney show AI's potential for diverse, abstract visuals in motion graphics. AI could also enable interactive effects by combining with AR/VR. Overall, AI can empower dynamic, personalized graphic design and branding. Key points are that dynamic design brings interactivity and better conveys brand meaning. Brand visual design is diversifying, with core brand image and dynamic performance reinforcing each other. AI can boost efficiency, innovation, and meaning in dynamic design. Though mainstream, 2D branding remains relevant. The article highlights the future potential of AI in motion graphics and visual storytelling, as it can generate new interpretations and experiences.

How to understand limitations of generative networks

Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks.

Fabrication of a new type of polyvinyl alcohol/sulfosuccinic acid/glutaric anhydride cation exchange membrane for desalination using membrane capacitive deionization

Nowadays, the lack of clean water is becoming a serious problem, due to the increasingdemand for clean water, along with global climate change. Desalination of sea water orbrackish water has been considered as a practicable method to supply clean water. Amongdesalination technologies, membrane capacitive deionization has emerged for the last 15years as an alternative method and received a lot of research attention since then. In thisstudy, low-cost and ecofriendly polyvinyl alcohol/sulfosuccinic acid/glutaric anhydridecomposite membranes were fabricated through the crosslinking reaction of sulfosuccinicacid and glutaric anhydride with polyvinyl alcohol. The effect of sulfosuccinic acid andglutaric anhydride content was investigated. The presence of SSA in the cation exchangemembrane, which was proven by FT-IR, helps improve its water uptake (98.5 % comparedto 38.2 %) as well as cation exchange capacity (1.993 mM/g compared to 0.156 mM/g).The polyvinyl alcohol/sulfosuccinic acid/glutaric anhydride composite solution wascoated on the electrode, and the membrane's ability to desalinate water was assessed. Themembrane-coated electrode exhibited enhanced salt adsorption capacity and shorterdesalination time (13.4 minutes compared to 85.3 minutes) in comparison with theuncoated commercial electrode, indicating that the polyvinyl alcohol/sulfosuccinicacid/glutaric anhydride membrane is a suitable candidate to be utilized in the membranecapacitive deionization systems.® 2022 Journal of Science and Technology - NTTU

Intramuscular Myxoma, Mimicking a Psoas Abscess

We report a clinical observation of intramuscular myxoma, mimicking a psoas abscess. Surgical minimally invasive treatment of the disease has been suggested. This clinical observation shows an example of the effective use of minimally invasive treatment methods in surgical practice, as an alternative to open surgery.

The Promise and Perils of Artificial Intelligence in Health Professions Education Practice and Scholarship.

Artificial intelligence (AI) methods, especially machine learning and natural language processing, are increasingly affecting health professions education (HPE), including the medical school application and selection processes, assessment, and scholarship production. The rise of large language models over the past 18 months, such as ChatGPT, has raised questions about how best to incorporate these methods into HPE. The lack of training in AI among most HPE faculty and scholars poses an important challenge in facilitating such discussions. In this commentary, the authors provide a primer on the AI methods most often used in the practice and scholarship of HPE, discuss the most pressing challenges and opportunities these tools afford, and underscore that these methods should be understood as part of the larger set of statistical tools available.Despite their ability to process huge amounts of data and their high performance completing some tasks, AI methods are only as good as the data on which they are trained. Of particular importance is that these models can perpetuate the biases that are present in those training datasets, and they can be applied in a biased manner by human users. A minimum set of expectations for the application of AI methods in HPE practice and scholarship is discussed in this commentary, including the interpretability of the models developed and the transparency needed into the use and characteristics of such methods.The rise of AI methods is affecting multiple aspects of HPE including raising questions about how best to incorporate these models into HPE practice and scholarship. In this commentary, we provide a primer on the AI methods most often used in HPE and discuss the most pressing challenges and opportunities these tools afford.

Process Algebraic Approach for Probabilistic Verification of Safety and Security Requirements of Smart IoT (Internet of Things) Systems in Digital Twin

Process algebra can be considered one of the most practical formal methods for modeling Smart IoT Systems in Digital Twin, since each IoT device in the systems can be considered as a process. Further, some of the algebras are applied to predict the behavior of the systems. For example, PALOMA (Process Algebra for Located Markovian Agents) and PACSR (Probabilistic Algebra of Communicating Shared Resources) process algebras are designed to predict the behavior of IoT Systems with probability on choice operations. However, there is a lack of analytical methods in the algebras to predict the nondeterministic behavior of the systems. Further, there is no control mechanism to handle undesirable nondeterministic behavior of the systems. In order to overcome these limitations, this paper proposes a new process algebra, called dTP-Calculus, which can be used (1) to specify the nondeterministic behavior of the systems with static probability, (2) verify the safety and security requirements of the nondeterministic behavior with probability requirements, and (3) control undesirable nondeterministic behavior with dynamic probability. To demonstrate the feasibility and practicality of the approach, the SAVE (Specification, Analysis, Verification, Evaluation) tool has been developed on the ADOxx Meta-Modeling Platform and applied to a SEMS (Smart Emergency Medical Service) example. In addition, a miniature digital twin system for the SEMS example was constructed and applied to the SAVE tool as a proof of concept for Digital Twin. It shows that the approach with dTP-Calculus on the tool can be very efficient and effective for Smart IoT Systems in Digital Twin.