Features Of Instrument Research Articles

Left-handed use of surgical instruments: improved function through understanding instrument design.

To explain design features of scissors and surgical instruments that work against left-handed users and demonstrate how the user can adapt their technique for ambidextrous use of standard instruments. Any species. Standard instruments are designed for maximal efficiency with the use of a right-handed grip. A gripping motion results in pushing force from the thumb and pulling of the fingers toward the palm. Both scissor blades and hemostat/needle driver box locks are oriented such that these natural forces act to push the scissor blades against each other or open the box locks. Using standard scissors with a left-handed grip is less efficient and more likely to cause tissue trauma. To use a right-handed instrument with the left hand, the thumb must instead be pulled towards the palm. This is effective but less ergonomic and requires more conscious practice. Sharp, well-maintained scissors are essential to mitigate tissue trauma. Understanding how pressure needs to be applied for opening of box locks and apposition of scissor blades results in improved efficiency of instrument use and precision of scissor cuts. Left-handed people are often adept at adjusting to equipment optimized to right-handed users. However, learning surgical skills from right-handed surgeons can be frustrating, requiring individual trial and error. Understanding the design features of surgical instruments improves efficiency for left-handed users and facilitates teaching of left-handed trainees by right-handed surgeons.

Predictors of Early Death in Patients With Wild-Type Transthyretin Cardiac Amyloidosis.

For the time being, tafamidis is the only approved treatment for wild-type transthyretin cardiac amyloidosis. However, benefits on all-cause death only emerge after ≈18 months. The current available staging systems are unable to specifically discriminate patients at high risk of death within 18 months from diagnosis, and the selection of patients who are expected to benefit from tafamidis is left to the clinical judgment of treating physicians, being often based primarily (and sometimes only) on age. We searched baseline variables able to discriminate patients at risk of death within 18 months. We randomly divided our prospectively maintained database in 2 cohorts, a testing (two thirds) and a validating (one third) set, respectively. We used the Eastern Cooperative Oncology Group-Performance Status as a simple scoring of frailty. We analyzed the clinical, laboratory and instrumental features of 691 consecutive wild-type transthyretin cardiac amyloidosis patients diagnosed between 2006 and 2021. Median follow-up was 43 months, and 367 patients (53%) died. Eighteen-month death was predicted by NT-proBNP (N-terminal pro-B-type natriuretic peptide; cutoff 4200 ng/L), cardiac troponin I (cutoff 92 ng/L), and age (cutoff 80 years). At multivariable analysis, Eastern Cooperative Oncology Group-Performance Status >1 along with the selected variables were independent prognostic determinants and outperformed New York Heart Association functional class. The combination of these variables identified standard- and high-risk patients (all variables above the cutoffs) with a median survival of 57 and 17 months, respectively (P<0.001). This was confirmed in the validation set. A simple scoring system including age, cardiac biomarkers, and Eastern Cooperative Oncology Group-Performance Status identifies subjects with cardiac wild-type transthyretin cardiac amyloidosis at high risk of early death.

Exploring How Older Adults Experience semAPP, a 360° Media-Based Tool for Memory Assessment: Qualitative Study.

Technology is already a part of our daily lives, and its influence is growing rapidly. This evolution has not spared the health care field. Nowadays, a crucial challenge is considering aspects such as design, development, and implementation, highlighting their functionality, ease of use, compatibility, performance, and safety when a new technological tool is developed. As noted in many works, the abandonment rate is usually higher when a user has a terrible experience with these instruments. It would be appropriate to incorporate the final users-whether they are patients, health care professionals, or both-in the stages of instrument design to understand their needs and preferences. Since most apps that fail did not include end users and health care professionals in the development phase, their involvement at all stages of app development may increase their commitment and improve integration, self-management, and health outcomes. This study aims (1) to develop semAPP (spatial and episodic memory assessment application), a 360° media-based tool, to assess memory in aging by simulating a real-life situation and (2) to test the usability of the app and the connected experience in an end-user population. A total of 34 older adults participated in the study: 16 (47%) healthy individuals and 18 (53%) patients with mild cognitive impairment. They used semAPP and completed qualitative and quantitative measures. The app includes 2 parts: object recognition and spatial memory tasks. During the first task, users have to navigate in an apartment freely and visit rooms, and then they must recognize the right map of the house. In the second task, users are immersed in a living room, and they have to encode and then recall some target objects, simulating a relocation. We deployed this app on an 11.2-inch iPad, and we tested its usability and the experience of users interacting with the app. We conducted descriptive analyses for both the entire sample and each subgroup; we also conducted parametric and correlation analyses to compare groups and to examine the relationship between task execution and the virtual experience, as well as the acceptance of technology. Both groups judged the app as an easy-to-use tool, and they were willing to use it. Moreover, the results match the idea that usability might be influenced by different factors depending on instrument and personal features, such as presentation, functionality, system performance, interactive behavior, attitudes, skills, and personality. The findings support the possibility of using semAPP in older patients, as well as the importance of designing and evaluating new technological tools, considering not only the general population but also the specific target ones.

Методологічна специфіка метафор Фридриха Ніцше: «нова форма» для «нескінченного прояснення»

Nietzsche’s metaphorical statements often are used to show the vagueness, contradiction, and radicalism of his ideas. The author of the article proves that Nietzsche's metaphorical style is a part of philosophical tools, and it constitutes the philosopher's methodology of knowledge. The main attention is focused on: (1) a set of factors that shaped Nietzsche's linguistic criticism – ideological motivations of an anti-metaphysical nature, key trends in the comprehension of language, and a fascination with music – and their role in the formation of the philosopher's metaphorical style is shown; (2) instrumental features of Nietzsche's metaphor in the process of cognition. In the first part of the article, the author concludes that the problem of concepts as a key linguistic problem for Nietzsche is the falsity of their content and proves that this was the reason for the philosopher's “search for a new form” of writing and, ultimately, his use of metaphors. In the second part of the article, the author interprets Nietzsche's metaphor as a possibility of “infinite clarification”, which made it possible to substantiate the perspective nature of Nietzsche's cognition.

Enhancing the selection of heart failure patients for invasive procedures: how machine learning can predict the diagnostic yield of endomyocardial biopsy

Abstract Background Determining the etiology of heart failure (HF) is crucial for appropriate treatment. Endomyocardial biopsy (EMB) is an invasive procedure that complements clinical assessment and imaging in diagnosing various cardiac disorders. The diagnostic value of EBM depends upon the anticipated yield of the procedure. Whether results from non-invasive examinations may predict the diagnostic yield from EMB is unclear. Purpose We aimed to investigate the relative importance of clinical and instrumental features in predicting a diagnostic EMB result, using a machine learning (ML) approach. Methods We retrospectively examined 665 consecutive adult patients with HF symptoms who underwent right EBM as part of their diagnostic work-up. The primary outcome was a positive biopsy result with a clear diagnosis. The ML algorithm consisted of Gradient Boosting Machines (GBM), with computation of relative variable importance using conditional importance. We calculated the conditional variable importance for 53 clinical or instrumental variables. Model performance was evaluated using a receiver operating characteristic curve, which was compared to a logistic regression model using the same variables. Validation was performed using a 10-fold cross-validation model. Results Among the 665 patients (50±14 years, 67% male), the EMB provided a diagnosis in 138 cases (20.8%). Patients attaining diagnosis were more often older, had a shorter disease duration, presented with ventricular arrhythmia or cardiac arrest, had a pacemaker or defibrillator, displayed higher creatinine, NT-proBNP and high-sensitive troponin T levels, and had smaller ventricular volumes with higher ejection fraction at CMR. All patients with a diagnostic EMB showed late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in the right ventricle, the right atrium or in the left atrium. The relative importance of different variables predicting outcomes is presented in Figure 1. The top six included four distinct CMR features and two circulating biomarkers (NT-proBNP and high-sensitive troponin T). LGE in the right ventricle and the presence of left ventricular hypertrophy were stronger predictors compared to the remaining variables. The GBM model performed significantly better than the logistic regression model (AUC GBM 0.98, AUC logistic regression 0.93, DeLong’s test p&amp;lt;0.001) (Figure 2). Conclusion Using a ML algorithm, CMR LGE, left ventricular hypertrophy, and elevated cardiac biomarkers, stand out as strong overall predictors of an endomyocardial biopsy with a high diagnostic yield.Figure 1Figure 2

Approach for evaluating the effectiveness of ex vivo root canal instrumentation using microtomography

Endodontic treatment of a tooth involves effective elimination of the infectious process localized in the tooth cavity and root canals. The process of root canal treatment is carried out using specialized rotary instruments (files). In this paper, an approach is proposed for evaluating the effectiveness of root canal treatment using the ProTaper Next file system ex vivo using computed X-ray microtomography and VGSTUDIO MAX 3.5 software. This approach involves comparison of three-dimensional tooth models before and after the endodontic instrumentation procedure, segmentation, calculation of the root canal volume using software according to a specially developed protocol, as well as a study of their microgeometry. As an example of using the approach, a study of the features of root canal instrumentation of two- and three-rooted human molars is presented. The developed approach demonstrated high effectiveness in the process of examining human teeth, while the endodontic procedure was effective for both two- and three-rooted molars in terms of removing the internal biological content of the canals, which indicates the reliability of the applied method. The developed approach can be applied to evaluate the effectiveness of liquid dental materials and antibacterial solutions used during endodontic treatment of teeth.

TWINKLE: An open-source two-photon microscope for teaching and research.

Many laboratories use two-photon microscopy through commercial suppliers, or homemade designs of considerable complexity. The integrated nature of these systems complicates customization, troubleshooting as well as grasping the principles of two-photon microscopy. Here, we present "Twinkle": a microscope for Two-photon Imaging in Neuroscience, and Kit for Learning and Education. It is a fully open, high-performance and cost-effective research and teaching microscope without any custom parts beyond what can be fabricated in a university machine shop. The instrument features a large field of view, using a modern objective with a long working distance and large back aperture to maximize the fluorescence signal. We document our experiences using this system as a teaching tool in several two week long workshops, exemplify scientific use cases, and conclude with a broader note on the place of our work in the growing space of open-source scientific instrumentation.

Dual-stage semantic segmentation of endoscopic surgical instruments.

Endoscopic instrument segmentation is essential for ensuring the safety of robotic-assisted spinal endoscopic surgeries. However, due to the narrow operative region, intricate surrounding tissues, and limited visibility, achieving instrument segmentation within the endoscopic view remainschallenging. This work aims to devise a method to segment surgical instruments in endoscopic video. By designing an endoscopic image classification model, features of frames before and after the video are extracted to achieve continuous and precise segmentation of instruments in endoscopicvideos. Deep learning techniques serve as the algorithmic core for constructing the convolutional neural network proposed in this study. The method comprises dual stages: image classification and instrument segmentation. MobileViT is employed for image classification, enabling the extraction of key features of different instruments and generating classification results. DeepLabv3+ is utilized for instrument segmentation. By training on distinct instruments separately, corresponding model parameters are obtained. Lastly, a flag caching mechanism along with a blur detection module is designed to effectively utilize the image features in consecutive frames. By incorporating specific parameters into the segmentation model, better segmentation of surgical instruments can be achieved in endoscopicvideos. The classification and segmentation models are evaluated on an endoscopic image dataset. In the dataset used for instrument segmentation, the training set consists of 7456 images, the validation set consists of 829 images, and the test set consists of 921 images. In the dataset used for image classification, the training set consists of 2400 images and the validation set consists of 600 images. The image classification model achieves an accuracy of 70% on the validation set. For the segmentation model, experiments are conducted on two common surgical instruments, and the mean Intersection over Union (mIoU) exceeds 98%. Furthermore, the proposed video segmentation method is tested using videos collected during surgeries, validating the effectiveness of the flag caching mechanism and blur detection module. Experimental results on the dataset demonstrate that the dual-stage video processing method excels in performing instrument segmentation tasks under endoscopic conditions. This advancement is significant for enhancing the intelligence level of robotic-assisted spinal endoscopic surgeries.

Assessment of validity and reliability of the feedback quality instrument

BackgroundThe purpose was to investigate the psychometric features of the Feedback Quality Instrument (FQI) in medical students, emphasizing the instrument’s utility for evaluating the quality of feedback provided in clinical contexts and the importance of performing so for medical trainees.Methods and materialThe Persian version of the FQI was evaluated for content validity through a focus group of medical education experts. The questionnaire’s face, content, and construct validity were assessed using Confirmatory Factor Analysis, internal consistency, and inter-rater reliability. The questionnaire was revised and pilot-tested, with medical students’ feedback in different clinical situations. The data was analyzed using AMOS26.ResultsThe content validity index equaled 0.88(> 0.79). The content validity ratio representing the proportion of participants who agreed on a selected item was 0.69(> 0.42). According to experts, item 25 is the only modified item, while items 23 and 24 are presented as one item. For reliability, Cronbach alpha was equaled to 0.98.ConclusionsThe Persian version of the Feedback Quality Instrument (FQI) was valid, reliable, and fair in assessing feedback quality in medical students, providing valuable insights for other institutions. Establishing a basis for systematically analyzing how certain educator behaviors affect student outcomes is practical.

Measuring the quality of philosophical dialogue: a high-inference rating instrument for research and teacher education

Various studies have shown that philosophizing with children at school can have a positive effect on cognitive, language and social skills. However, previous studies have not considered how the quality of the dialogue influences these outcomes. Addressing this gap, our article introduces a high-inference rating instrument to assess the quality of philosophical dialogue. This instrument features four quality dimensions: Philosophical Richness, Co-construction, Focus, and Restrained Facilitation. It was applied to evaluate 63 class dialogues from a Swiss study involving secondary-school students. The article presents the instrument using excerpts from the study, along with initial validation information, showing its reliability in measuring philosophical dialogue quality. The significance of this research lies in its focus on the quality of philosophical dialogue, an aspect often overlooked in educational settings. By providing a tool to assess this quality, we open up new pathways for analyzing the effectiveness of philosophical dialogues in schools. This is especially relevant as educational systems increasingly recognize the importance of developing students' critical thinking and discussion skills. The findings highlight the potential of high-quality philosophical dialogues to enrich students' educational experiences and offer insights for educators and researchers into enhancing these interactions. This study contributes to a deeper understanding of how philosophical dialogue can be used as a powerful educational tool, not just for engaging students but for fostering meaningful and impactful learning.

An Identification Potential of Legal Values in a Transitive Society

The article examines the role of legal values in the social identification of citizens in a transitive society. Based on the analysis of the value foundations of legislation and its role in the construction of social identification, a conclusion is made about the primacy of the axiological level of legal reality over ontological ones in a transitive society. The constitutional identification of a person in these conditions is determined not by belonging to a certain nation and the implementation of the rules developed by it, but by positioning identification with the community on value grounds. Often, such identification is not so much an identification as an imitation, since it involves only articulating compliance with the rules, and not following them. Based on this, identification in a transitive society is situational and fragmented. In these conditions, the role of legislation in social identification is changing. It becomes a tool that the addressee of communication uses to broadcast to the addressee a system of values that ensure not only the identification of citizens, but also lawful behavior. At the same time, legislation, due to its instrumental features, is able to overcome transitivity and bring society to stable development and functioning.

Diabetes mellitus developed against the background of pancreatitis: clinical, laboratory and instrumental features

Diabetes mellitus developed against the background of pancreatitis: clinical, laboratory and instrumental features

Financing infrastructure projects: a review of definitional issues and thematic areas

The purpose of the article is to identify current problems and thematic areas of financing infrastructure projects. To do this, both a systematization of approaches is carried out on the basis of thematic analysis and a review of Russian and foreign sources is done. A methodological plurality of definitions of the main categories was discovered. Inductively identified thematic areas of research cover: general trends in the development of types of economic infrastructure, microanalysis of the creation of specific infrastructure facilities and local needs for infrastructure development, local economic impact of individual types of infrastructure, cross-sectoral effects of infrastructure development, integration aspects of the creation of infrastructure facilities, infrastructure in the context of sustainable development, features of instruments and forms of financing infrastructure projects, infrastructure facilities as a separate class of investment assets.

Legal regulation of the monetary system of the Russian Federation in the digital economy: financial and legal problems

The article discusses current problems of digitalization of the monetary system of the Russian Federation. A conclusion is made about the place and content of the sub-growth of the rights of the monetary system in the structure of financial law. It is argued that digitalization does not change the essence of financial relations, the essence of money as a financial and legal category, it only gives them an electronic form. The author’s assessments of the legal nature and qualifying features of new financial payment instruments that are a response to the challenges of the digital economy are given: electronic money, digital currencies, digital ruble. It is argued that they are subtypes of non-cash money circulation along with classic bank money. Proposals for improving the current legislation on the national payment system are substantiated in terms of systemic legal regulation of all types of digital forms of monetary circulation.

Survey of Students’ Experiences with Electronic Communication for Writing Improvement

The introduction of electronic communication has caused a considerable evolution in the dynamic interaction between teachers and students in today's educational environments. In order to improve writing skills in school settings, this study explores the transformative potential of internet communication. The present abstract outlines the complex features of this revolutionary teaching instrument, clarifying its many aspects and significant consequences for promoting writing abilities in learning environments. This investigation, which is based on constructivist pedagogy and cognitive learning theories, reveals the mutually beneficial connection between internet communications and increasing writing ability through leveraging the collaborative relationship between technology and education. Teachers can enable learners to reach their full creative potential and produce a generation of articulate speakers ready to handle the challenges of the digital age.

Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.

In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early-stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression. Three hundred eighty-five samples from four independent cohorts were systematically retrieved. The weighted gene co-expression network analysis, differential expression analysis (|log2(foldchange)|>0.5 and adjusted P<0.05), and protein-protein interaction network were sequentially performed to identify HCM-related hub genes. With a machine learning algorithm, the least absolute shrinkage and selection operator regression algorithm, a stable diagnostic model was developed. The immune-cell infiltration and biological functions of HCM were also explored to characterize its underlying pathogenic mechanisms and the immune signature. Two key modules were screened based on weighted gene co-expression network analysis. Pathogenic mechanisms relevant to extracellular matrix and immune pathways have been discovered. Twenty-seven co-regulated genes were recognized as HCM-related hub genes. Based on the least absolute shrinkage and selection operator algorithm, a stable HCM diagnostic model was constructed, which was further validated in the remaining three cohorts (n=385). Considering the tight association between HCM and immune-related functions, we assessed the infiltrating abundance of various immune cells and stromal cells based on the xCell algorithm, and certain immune cells were significantly different between high-risk and low-risk groups. Our study revealed a number of hub genes and novel pathways to provide potential targets for the treatment of HCM. A stable model was developed, providing an efficient tool for the diagnosis of HCM.

Stochastic dynamics mass spectrometric structural analysis of poly(methyl methacrylate)

Polymers such as poly(methyl methacrylate), comprising of all-carbon backbones are regarded as ubiquitous functional materials mainly due to their low cost, unique physical properties, and impressive robustness. The latter properties are results from relative chemical inertness of the polymer backbone carbon–carbon bonds. The highlighted stability crucially challenges not only innovations to plastic recycling polymer materials, but also developments of reliable analytical protocols for determining both quantitatively and structurally polymers. Despite, the fact that there are numerous applications of mass spectrometric methods to polymer science, the analysis involves chiefly annotation of monomers of polymers and additives, owing to the fact that the additives are low molecular weight analytes. The exact structural determining of end-chain groups, particularly highlighting chemically substituted end carbon–carbon bond represents significant challenge even utilizing the superior features of the analytical mass spectrometric instrumentation. This study, in the latter context, illustrates innovative stochastic dynamics approach and model equations capable of not only determining unambiguously substituted carbon–carbon end chain of the entitled polymer but also to predict mass spectra of its derivatives: thus, extending crucially the applicability of the method to many fields of the fundamental scientific and industrial research. The latter claim is argued still at the beginning of the study with the achieved method performances showing |r|=0.99998. The study utilizes experimental and theoretical mass spectrometric data on high resolution electrospray ionization mass spectrometry; high accuracy quantum chemical static methods, molecular dynamics; and chemometrics.

Teaching Experimental Instrumental Analytical Chemistry Are we forming professionals, training operators or illuding students (and ourselves)?

Teaching Experimental Instrumental Analytical Chemistry Are we forming professionals, training operators or illuding students (and ourselves)?

SurgNet: Self-Supervised Pretraining With Semantic Consistency for Vessel and Instrument Segmentation in Surgical Images.

Blood vessel and surgical instrument segmentation is a fundamental technique for robot-assisted surgical navigation. Despite the significant progress in natural image segmentation, surgical image-based vessel and instrument segmentation are rarely studied. In this work, we propose a novel self-supervised pretraining method (SurgNet) that can effectively learn representative vessel and instrument features from unlabeled surgical images. As a result, it allows for precise and efficient segmentation of vessels and instruments with only a small amount of labeled data. Specifically, we first construct a region adjacency graph (RAG) based on local semantic consistency in unlabeled surgical images and use it as a self-supervision signal for pseudo-mask segmentation. We then use the pseudo-mask to perform guided masked image modeling (GMIM) to learn representations that integrate structural information of intraoperative objectives more effectively. Our pretrained model, paired with various segmentation methods, can be applied to perform vessel and instrument segmentation accurately using limited labeled data for fine-tuning. We build an Intraoperative Vessel and Instrument Segmentation (IVIS) dataset, comprised of ~3 million unlabeled images and over 4,000 labeled images with manual vessel and instrument annotations to evaluate the effectiveness of our self-supervised pretraining method. We also evaluated the generalizability of our method to similar tasks using two public datasets. The results demonstrate that our approach outperforms the current state-of-the-art (SOTA) self-supervised representation learning methods in various surgical image segmentation tasks.

Prospects of the Multi-channel Photometric Survey Telescope in the Cosmological Application of Type Ia Supernovae

The Multi-channel Photometric Survey Telescope (Mephisto) is a real-time, three-color photometric system designed to capture the color evolution of stars and transients accurately. This telescope system can be crucial in cosmological distance measurements of low-redshift (low-z, z ≲0.1) Type Ia supernovae (SNe Ia). To optimize the capabilities of this instrument, we perform a comprehensive simulation study before its official operation is scheduled to start. By considering the impact of atmospheric extinction, weather conditions, and the lunar phase at the observing site involving the instrumental features, we simulate light curves of SNe Ia obtained by Mephisto. The best strategy in the case of SN Ia cosmology is to take the image at an exposure time of 130 s with a cadence of 3 days. In this condition, Mephisto can obtain hundreds of high-quality SNe Ia to achieve a distance measurement better than 4.5%. Given the on-time spectral classification and monitoring of the Lijiang 2.4 m Telescope at the same observatory, Mephisto, in the whole operation, can significantly enrich the well-calibrated sample of supernovae at low-z and improve the calibration accuracy of high-z SNe Ia.