Current Tools Research Articles

Development and Internal Validation of a Clinical and Genetic Risk Score for Rheumatoid Arthritis-Associated Interstitial Lung Disease.

Although clinical and genetic risk factors have been identified for rheumatoid arthritis-associated interstitial lung disease (RA-ILD), there are no current tools allowing for risk stratification. We sought to develop and validate an ILD risk model in a large, multicentre, prospective RA cohort. Participants in the Veterans Affairs RA (VARA) registry were genotyped for 12 single nucleotide polymorphisms (SNPs) associated with idiopathic pulmonary fibrosis. ILD was validated through systematic record review. A genetic risk score (GRS) was computed from minor alleles weighted by effect size with ILD, using backward selection. The GRS was combined with clinical risk factors within a logistic regression model. Internal validation was completed using bootstrapping, and model performance was assessed by the area under the receiver operating curve (AUC). Of 2,386 participants (89% male, mean age 69.5 years), 9.4% had ILD. Following backward selection, five SNPs contributed to the GRS. The GRS and clinical factors outperformed clinical factors alone in discriminating ILD (AUC 0.675 vs 0.635, p< 0.001). The shrinkage-corrected performance for combined and clinical-only models was 0.667 (95% CI 0.628, 0.712) and 0.623 (95% CI 0.584, 0.651), respectively. Twenty percent of the cohort had a combined risk score below a cut-point with >90% sensitivity. A clinical and genetic risk model discriminated ILD in a large, multicentre RA cohort better than a clinical-only model, excluding 20% of the cohort from low-yield testing. These results demonstrate the potential utility of a GRS in RA-ILD and support further investigation into individualized risk stratification and screening.

Comprehensive framework for overcoming scientific challenges related to assessing radioactive ultra-fine (nano/micro) particles transfer at the atmosphere-leaf interface

Food products are prone into contamination after a nuclear emission of radionuclides. While the mechanisms of emission and deposition of ultrafine radioactive particles are well documented, the transfer of these species from the atmosphere into plants is poorly assessed. This is evident in the lack of quantification of particles distributed within plants, especially regarding particles physical-chemical criteria to plant of different properties. Such knowledge gaps raise the concern about the representativeness of risk assessment tools designed for the transfer evaluation of ionic/soluble species to be qualified for simulating insoluble species exposure and proposes a possible underestimation. This highlights the possible need for special particle codes development to be implemented in models for future emissions. In addition, the later tools utilize transfer factors aggregating relevant sub-processes, suggesting another weak point in their overall reliability. As researchers specialized in the nuclear safety and protection, we intend in this perspective, to develop a compressive analysis of the interaction of ultrafine particles with plants of different specificities at different level processes starting from particles retention and gradual translocation to sink organs. This analysis is leveraged in providing insights for possible improvements in the current modeling tools for better real-life scenarios representation.

Diversity‐Based Topology Optimization of Soft Robotic Grippers

Soft grippers are ideal for grasping delicate, deformable objects with complex geometries. Universal soft grippers have proven effective for grasping common objects, however complex objects or environments require bespoke gripper designs. Multi‐material printing presents a vast design‐space which, when coupled with an expressive computational design algorithm, can produce numerous, novel, high‐performance soft grippers. Finding high‐performing designs in challenging design spaces requires tools that combine rapid iteration, simulation accuracy, and fine‐grained optimization across a range of gripper designs to maximize performance, no current tools meet all these criteria. Herein, a diversity‐based soft gripper design framework combining generative design and topology optimization (TO) are presented. Compositional pattern‐producing networks (CPPNs) seed a diverse set of initial material distributions for the fine‐grained TO. Focusing on vacuum‐driven multi‐material soft grippers, several grasping modes (e.g. pinching, scooping) emerging without explicit prompting are demonstrated. Extensive automated experimentation with printed multi‐material grippers confirms optimized candidates exceed the grasp strength of comparable commercial designs. Grip strength, durability, and robustness is evaluated across 15,170 grasps. The combination of fine‐grained generative design, diversity‐based design processes, high‐fidelity simulation, and automated experimental evaluation represents a new paradigm for bespoke soft gripper design which is generalizable across numerous design domains, tasks, and environments.

USING ALTMAN Z-SCORE MODEL OF FINANCIAL SOUNDNESS OF BANKING INDUSTRY IN INDIA BY – A CASE STUDY ON SELECT PUBLIC SECTOR BANKS IN INDIA-AN EMPIRICAL EVIDENCE

Nearly three quarters of the countrys financial activity is conducted by these nationalised institutions. The government owns the majority of these banks. Since merging with its five affiliate banks (as of 1st April 2017), SBI has grown to become one of the worlds top 50 banks, and it is also the biggest public sector bank in India in terms of volume. The stability of a countrys financial system is essential to its overall economic health. The collapse of multinational financial institutions has the potential to wreak havoc on economies all across the world. A recent example of this infectious impact is the collapse of the Lehman Brothers. As such, assessing the stability of domestic banks finances is of the utmost importance. Financial metrics such as the CAMEL rating, which incorporates capital adequacy ratio, profitability, and liquidity are some of the current tools that could be useful for analysing banks financial situations. When assessing the health or instability of a companys finances, the Altman Z score model is a crucial tool. Unfortunately, scholars did not delve into it much while examining the financial stability of institutions. The current research makes an effort to adapt the Altman model for use in the Indian banking sector based on this reference. The majority of Indian banks financial situations were deemed adequate by the research, with only two outliers. were both discovered to be in a precarious financial situation. But when compared to other banks in their industry, both of these ones had a sufficient capital adequacy ratio. In order to draw any firm-specific conclusions about its viability, the research recommends using a hybrid model. KEYWORDS: Altman Model, Financial Position, Hybrid Model, Public Sector Banks.

Is trans-laryngeal ultrasound (TLUS) an accurate and acceptable tool in the assessment of inducible laryngeal obstruction? A protocol for the ILOTUS: A cross-sectional, two-stage feasibility study

BACKGROUND: Inducible laryngeal obstruction (ILO) describes an inappropriate narrowing of the larynx during breathing in. ILO can lead to several respiratory symptoms and be misidentified and treated as asthma, with associated patient morbidity and healthcare cost. ILO is also known as vocal cord dysfunction, or paradoxical vocal fold movement. The current reference assessment tool for diagnosing ILO is a video-laryngoscopy. However, timely access to specialist diagnostic laryngoscopy services may be challenging, and laryngoscopy may be poorly tolerated. AIMS: The aim of this cross-sectional, two-stage feasibility study is to determine whether abnormal vocal fold movements associated with ILO can be adequately visualised using trans-laryngeal ultrasound (TLUS) namely, the ILOTUS study: (ILO assessment via trans- laryngeal ultrasound). The second aim is to compare TLUS with a reference standard diagnostic tool to ascertain whether it may serve as a less invasive screening tool to assess ILO. Participants’ opinions about the acceptability of TLUS will also be gathered and compared with responses to a validated symptom score questionnaire. METHODS: In stage one, healthy volunteers (n = 30) will undergo TLUS to assess whether the vocal folds can be visualised,and whether narrowing of the vocal folds can be seen when they mimic ILO. Stage 2 will be conducted with a patient population(n = 30) referred to a tertiary airways service for breathlessness assessment. Participants in stage 2 will undergo the reference standard laryngoscopy as well as simultaneous TLUS. The level of agreement between TLUS and laryngoscopy will be evaluated, as well as comparison of symptom scores. DISCUSSION: This study will help inform future studies of the utility of TLUS in the assessment of ILO. This will be valuable for understanding whether TLUS could be offered as a non-invasive option for patients in the assessment of ILO if they are not able to tolerate or access laryngoscopy diagnostic services. It is not intended that TLUS will replace the current reference standard of video-laryngoscopy but may have potential as an early screening tool if found to be non-inferior.

Accessory genes define species-specific routes to antibiotic resistance.

A deeper understanding of the relationship between the antimicrobial resistance (AMR) gene carriage and phenotype is necessary to develop effective response strategies against this global burden. AMR phenotype is often a result of multi-gene interactions; therefore, we need approaches that go beyond current simple AMR gene identification tools. Machine-learning (ML) methods may meet this challenge and allow the development of rapid computational approaches for AMR phenotype classification. To examine this, we applied multiple ML techniques to 16,950 bacterial genomes across 28 genera, with corresponding MICs for 23 antibiotics with the aim of training models to accurately determine the AMR phenotype from sequenced genomes. This resulted in a >1.5-fold increase in AMR phenotype prediction accuracy over AMR gene identification alone. Furthermore, we revealed 528 unique (often species-specific) genomic routes to antibiotic resistance, including genes not previously linked to the AMR phenotype. Our study demonstrates the utility of ML in predicting AMR phenotypes across diverse clinically relevant organisms and antibiotics. This research proposes a rapid computational method to support laboratory-based identification of the AMR phenotype in pathogens.

Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data

Expression Quantitative Trait Loci (eQTLs) are critical to understanding the mechanisms underlying disease-associated genomic loci. Nearly all protein-coding genes in the human genome have been associated with one or more eQTLs. Here we introduce a multi-variant generalization of allelic Fold Change (aFC), aFC-n, to enable quantification of the cis-regulatory effects in multi-eQTL genes under the assumption that all eQTLs are known and conditionally independent. Applying aFC-n to 458,465 eQTLs in the Genotype-Tissue Expression (GTEx) project data, we demonstrate significant improvements in accuracy over the original model in estimating the eQTL effect sizes and in predicting genetically regulated gene expression over the current tools. We characterize some of the empirical properties of the eQTL data and use this framework to assess the current state of eQTL data in terms of characterizing cis-regulatory landscape in individual genomes. Notably, we show that 77.4% of the genes with an allelic imbalance in a sample show 0.5 log2 fold or more of residual imbalance after accounting for the eQTL data underlining the remaining gap in characterizing regulatory landscape in individual genomes. We further contrast this gap across tissue types, and ancestry backgrounds to identify its correlates and guide future studies.

Management Information Systems and Correlation Between E-Business and Information Security from a Business Intelligence Perspective

The research focus was motivated by the emergence of electronic business which increased during and after the COVID-19 pandemic. The research is a literature review and its purpose is to build awareness about the importance of information security and to analyze the correlation between information security and electronic business in an environment where electronic business is emerging and the gap between electronic business and information security is enlarging. The research is a review of peer-reviewed articles retrieved from case studies, empirical research, case analysis, literature reviews, comparative studies, systematic reviews, and conceptual analysis dating from the years of 2018 to 2023. This literature review defines four business intelligence concepts: management information systems, value driven business, electronic business, and information security. This literature review also reveals the effects of all four business intelligence concepts on organizations’ decision-making and financial objectives. Findings of this literature review revealed that electronic businesses need a stronger risk management approach regarding information security. The conclusion shows that the current technological approach and information security tools such as encryption key management, mantraps, and network intrusion detection systems do not ensure trust and/or eliminate digital security risks.

Imaging actin organisation and dynamics in 3D.

The actin cytoskeleton plays a critical role in cell architecture and the control of fundamental processes including cell division, migration and survival. The dynamics and organisation of F-actin have been widely studied in a breadth of cell types on classical two-dimensional (2D) surfaces. Recent advances in optical microscopy have enabled interrogation of these cytoskeletal networks in cells within three-dimensional (3D) scaffolds, tissues and in vivo. Emerging studies indicate that the dimensionality experienced by cells has a profound impact on the structure and function of the cytoskeleton, with cells in 3D environments exhibiting cytoskeletal arrangements that differ to cells in 2D environments. However, the addition of a third (and fourth, with time) dimension leads to challenges in sample preparation, imaging and analysis, necessitating additional considerations to achieve the required signal-to-noise ratio and spatial and temporal resolution. Here, we summarise the current tools for imaging actin in a 3D context and highlight examples of the importance of this in understanding cytoskeletal biology and the challenges and opportunities in this domain.

RPA-CRISPR/Cas9-based method for the detection of Toxoplasma gondii: A proof of concept

Toxoplasma gondii is a widespread and specialized intracellular protozoan pathogen that affects one third of the world’ s population, posing a great threat to public health. As the definitive host, cats excrete oocysts and play a crucial role in the transmission of toxoplasmosis. The current diagnostic tools usually require bulky equipment and expertize, which hinders the efficient diagnosis and intervention of Toxoplasma infection in cats. In this study, we combined (RPA) with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 technique to establish an easier method for the detection of T. gondii oocysts in cat fecal samples. The sensitivity, specificity, and practicability of the established RPA-CRISPR/Cas9 method were evaluated using a lateral flow strip, with the limitation of detection determined at 10 plasmid copies/μL (corresponding to about one oocyst), cross reactivity to none of Giardia lamblia, Cryptosporidium sp., Microsporidium biberi and Blastocystis hominis that also commonly found in cats, and comparable performance in detecting T. gondii in clinical samples to conventional PCR amplification. This RPA-CRISPR/Cas9 method provides an alternative to conventional molecular tools used in the clinical diagnosis of Toxoplasma infection in cats and other animals.

A Review of Numerical Models of Radiation Injury and Repair Considering Subcellular Targets and the Extracellular Microenvironment.

Astronauts in space are subject to continuous exposure to ionizing radiation. There is concern about the acute and late-occurring adverse health effects that astronauts could incur following a protracted exposure to the space radiation environment. Therefore, it is vital to consider the current tools and models used to describe and study the organic consequences of ionizing radiation exposure. It is equally important to see where these models could be improved. Historically, radiobiological models focused on how radiation damages nuclear deoxyribonucleic acid (DNA) and the role DNA repair mechanisms play in resulting biological effects, building on the hypotheses of Crowther and Lea from the 1940s and 1960s, and they neglected other subcellular targets outside of nuclear DNA. The development of these models and the current state of knowledge about radiation effects impacting astronauts in orbit, as well as how the radiation environment and cellular microenvironment are incorporated into these radiobiological models, aid our understanding of the influence space travel may have on astronaut health. It is vital to consider the current tools and models used to describe the organic consequences of ionizing radiation exposure and identify where they can be further improved.

Effects of Game-Related Tasks for the Diagnosis and Classification of Gaming Disorder.

Gaming disorder (GD) is an addictive behavior characterized by an insatiable need to play video games and shares similar symptoms with the failure of self-control due to a decline in cognitive function. Current GD diagnostic and screening tools rely on questionnaires and behavioral observations related to cognitive functions to assess an individual's capacity to maintain self-control in everyday life. However, current GD screening approaches rely on subjective symptoms, and a reliable diagnosis requires long-term clinical follow-up. Recent studies have measured biosignals along with cognitive functional tasks to provide objectivity to GD diagnosis and to acquire immediate results. However, people with GD are hypersensitive to game-related cues, so their responses may vary depending on the type of stimuli, and the difference in response to stimuli might manifest as a difference in the degree of change in the biosignal. Therefore, it is critical to choose the correct stimulus type when performing GD diagnostic tasks. In this study, we investigated the task dependence of cognitive decline in GD by comparing two cognitive functional tasks: a continuous performance task (CPT) and video game play. For this study, 69 young male adults were classified into either the gaming disorder group (GD, n = 39) or a healthy control group (HC, n = 30). CPT score, EEG signal (theta, alpha, and beta), and HRV-HF power were assessed. We observed differences in the left frontal region (LF) of the brain between the GD and HC groups during online video game play. The GD group also showed a significant difference in HF power of HRV between CPT and online video gaming. Furthermore, LF and HRV-HF significantly correlated with Young's Internet Addiction Test (Y-IAT) score, which is positively associated with impulsivity score. The amount of change in theta band activity in LF and HRV-HF-both biomarkers for changes in cognitive function-during online video game play suggests that people with GD express task-dependent cognitive decline compared with HC. Our results demonstrate the feasibility of quantifying individual self-regulation ability for gaming and underscore its importance for GD classification.

Biomimetic leaves with immobilized catalase for machine learning-enabled validating fresh produce sanitation processes

Washing and sanitation are vital steps during the postharvest processing of fresh produce to reduce the microbial load on the produce surface. Although current process control and validation tools effectively predict sanitizer concentrations in wash water, they have significant limitations in assessing sanitizer effectiveness for reducing microbial counts on produce surfaces. These challenges highlight the urgent need to improve the validation of sanitation processes, especially considering the presence of dynamic organic contaminants and complex surface topographies. This study aims to provide the fresh produce industry with a novel, reliable, and highly accurate method for validating the sanitation efficacy on the produce surface. Our results demonstrate the feasibility of using a food-grade, catalase (CAT)-immobilized biomimetic leaf in combination with vibrational spectroscopy and machine learning to predict microbial inactivation on microgreen surfaces. This was tested using two sanitizers: sodium hypochlorite (NaClO) and hydrogen peroxide (H2O2). The developed CAT-immobilized leaf-replicated PDMS (CAT@L-PDMS) effectively mimics the microscale topographies and bacterial distribution on the leaf surface. Alterations in the FTIR spectra of CAT@L-PDMS, following simulated sanitation processes, indicate chemical changes due to CAT oxidation induced by NaClO or H2O2 treatments, facilitating the subsequent machine learning modeling. Among the five algorithms tested, the competitive adaptive reweighted sampling partial least squares discriminant analysis (CARS-PLSDA) algorithm was the most effective for classifying the inactivation efficacy of E. coli on microgreen leaf surfaces. It predicted bacterial reduction on microgreen surfaces with 100% accuracy in both training and prediction sets for NaClO, and 95% in the training set and 86% in the prediction set for H2O2. This approach can improve the validation of fresh produce sanitation processes and pave the way for future research.

The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis.

Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.

Mental Health Assessments in Emergency Department Patients: Assessing Longitudinal Follow-up and Retest Reliability (MCAT-2) [Provisional Baseline Analysis

Background/Objective:The prevalence of anxiety and depression in adults in the United States is high with many experiencing ≥1 episode of anxiety or depression over a given year. Despite this, diagnoses of anxiety and depression in patients presenting to the ED for somatic complaints are seldom, relative to the population prevalence. Current fixed-item anxiety and depression screening tools have variable specificity and sensitivity, but adaptive screening tools such as the Computerized Adaptive Testing-Mental Health tool (CAT-MH) are thought to provide improved diagnostic characteristics and precision over fixed item tools. Our objective is to establish the reliability ofits depression and anxiety severity assessments in patients initially screened in an ED setting and reassessed over 30 days.&#x0D; Methods:We are conducting a longitudinal observational study among adult ED patients presenting with somatic, non-mental health complaints. The CAT-MH, Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-8 (PHQ-8), are administered to randomly selected patients meeting enrollment criteria after informed consent. Participants are reassessed at 1-, 2-, and 4 weeks after ED discharge and analyzed for longitudinal test-retest reliability and sensitivity to change in score severity over time.&#x0D; Results:In this ongoing study, we present baseline anxiety and depression severity assessments for the first 49 enrolled patients (Mean age of 37, 73% female, 49% white, 47% black). Fixed item tools (GAD-7 and PHQ-8) categorized 16% and 6% of patients as moderate and severe anxiety respectively vs 19% and 15% as moderate, and moderately-severe/severe depression. In comparison, CAT-MH categorized 8% and 10% as moderate and severe anxiety.10% and 6% were categorized as moderate and severe depression.&#x0D; Conclusion:In this longitudinal observational study, we present baseline data for the first 49 out of 100 planned patients. Upon completion of the study, we hope to find that the severity assessments from CAT-MH remain stable over 30 days.

Facilitating business model transformation: Theory, operators, and patterns

Understanding Business Models (BMs) has become an essential capability for organizations seeking to drive growth, revitalize a lagging core or defend against industry disruption. However, this concept still has multiple interpretations, as it is often intertwined with others, like business strategy (BS), making it difficult for stakeholders to understand and re-design their own BMs. In fact, current tools including taxonomies, have encountered difficulties in providing mechanisms to guide this process. Seeking to contribute with the strengthening of the theoretical basis and with tools to transform BMs, in this work, we follow a Design Science Research (DSR) approach to provide a theoretical foundation for the concepts of BM, Business Model Patterns (BMPs), and BS under a formal conceptual representation. In doing so, we propose a language to describe a BM and we introduce a set of mechanisms to transform BMs. Finally, we provide a collection of BMPs that serve as the foundation for the application of these mechanisms. Thus, we aim to contribute to the development of a solid knowledge base to be used as a source of inspiration when restructuring, specializing, or creating new BMs.

An exploration of operational excellence methodologies implementation in the logistics sectors: a global study

PurposeThe aim of this study reported in this paper was to explore the application of operational excellence methodologies in a global context.Design/methodology/approachA qualitative interview approach was used to understand the current state, benefits, challenges, success factors, tools and techniques of operational excellence methodology implementation with relevance to logistics companies worldwide. About 16 interviews were undertaken with practitioners working in leading companies and with leading academics in Asia, Europe, Africa, North America, South America and Australia.FindingsThe findings show that operational excellence methodologies including Lean, Six Sigma, Lean Six Sigma and Agile can apply in logistics firms to improve operations and productivity and save costs. Top management support and involvement play an important role in the success of operational excellence projects in the logistics service.Research limitations/implicationsThe findings will be of interest to top and middle managers and logistics practitioners, with a dual aim of improving logistics performance and saving costs.Originality/valueThe present study has been one of the first global study attempts to explore the implementation of operational excellence methodologies in the logistics sectors.

Robotic surgery education in Australia and New Zealand: primetime for a curriculum.

Globally, robotic surgery (RS) has witnessed remarkable growth, yet Australia and New Zealand (ANZ) lack dedicated RS training programs, creating a workforce gap. This narrative review synthesises international research to explore trends and challenges in robotic education. We conducted a comprehensive literature review, searching PubMed, Google Scholar, and MEDLINE using keywords like 'robotic surgery', 'surgical education', 'robotic surgery training', and 'robotic surgery curriculum'. We selected studies contributing to understanding current curricula, training tools, and issues in robotic education, utilising the international experience and how it might apply to the ANZ context. RS in ANZ has grown significantly over two decades, but formal curricula for trainees are absent. North America and Europe employ diverse training tools and curricula. Barriers include cost, access, time constraints, equipment complexity, changing training environments, and competition from emerging robotic surgical systems. Balancing the curriculum's demands with trainees' existing requirements is essential. Developing a tailored RS curriculum within ANZ's surgical training is crucial for RS to become the primary surgical approach in the future. By working towards a national curriculum we can prepare skilled trainees in robotics to meet the rising demand. The most significant barrier is the lack of robotics in public hospital where trainees are based. This curriculum should encompass online teaching modules, bedside assistance, surgical simulation, dual console mentoring, and primary operator experience.

Noninvasive imaging of 4D electrical activation patterns of uterine peristalsis during normal menstrual cycles

Uterine peristalsis, characterized by spontaneous slow-wave contractions of the subendometrial layer of the uterine myometrium, occurs throughout the menstrual cycle. Disruptions in peristalsis patterns may occur in women experiencing abnormal uterine bleeding, endometriosis, and infertility. Current tools to measure uterine peristalsis in humans have limitations that hamper their research or clinical utility. Here, we developed an electrophysiological imaging system with wearable electrical sensors to noninvasively quantify the four-dimensional electrical activation pattern during human uterine peristalsis with high spatial and temporal resolution and coverage. To demonstrate capabilities of this new imaging system, we enrolled 26 pre-menopausal participants ages 18–40 years old with regular menstrual cycles (25–34 days in length) and normal gynecologic anatomy. Participants initially underwent magnetic resonance imaging to obtain the body-uterus geometry. Then, during each of the four phases of a single menstrual cycle, patients underwent noninvasive electrical recording for 30 min. We measured uterine peristalsis frequency, direction, duration, magnitude, and power and found that these parameters differed in different phases of the menstrual cycle. Future use of this uterine peristalsis imaging system may advance our understanding of the role of uterine peristalsis in maintaining normal uterine function and contributing to gynecological pathophysiology.

Efficient and safe therapeutic use of paired Cas9-nickases for primary hyperoxaluria type 1

The therapeutic use of adeno-associated viral vector (AAV)-mediated gene disruption using CRISPR-Cas9 is limited by potential off-target modifications and the risk of uncontrolled integration of vector genomes into CRISPR-mediated double-strand breaks. To address these concerns, we explored the use of AAV-delivered paired Staphylococcus aureus nickases (D10ASaCas9) to target the Hao1 gene for the treatment of primary hyperoxaluria type 1 (PH1). Our study demonstrated effective Hao1 gene disruption, a significant decrease in glycolate oxidase expression, and a therapeutic effect in PH1 mice. The assessment of undesired genetic modifications through CIRCLE-seq and CAST-Seq analyses revealed neither off-target activity nor chromosomal translocations. Importantly, the use of paired-D10ASaCas9 resulted in a significant reduction in AAV integration at the target site compared to SaCas9 nuclease. In addition, our study highlights the limitations of current analytical tools in characterizing modifications introduced by paired D10ASaCas9, necessitating the development of a custom pipeline for more accurate characterization. These results describe a positive advance towards a safe and effective potential long-term treatment for PH1 patients.