To provide a practical and descriptive comparison of contemporary clinical planning software for guided implant surgery, focusing on digital workflow feasibility, user experience, system requirements, cost models, and the availability of prosthetic component libraries, based on real‑world expert use. Seven implant planning software platforms (DentiqGuide, BlueSkyPlan, CoDiagnostix, Implant Studio, R2Gate, ExoPlan, and RealGuide) were evaluated by experienced clinicians using two predefined clinical scenarios representing moderate and high planning complexity. User interface (UI) friendliness was assessed using a Likert scale, and planning time was recorded according to standardized task definitions. Hardware requirements, pricing models, available functionalities, and workflow completeness were compared. All quantitative outcomes were analysed descriptively. Descriptive values of UI scores ranged from 5.3 to 10, while planning times ranged from 10 to 67 minutes depending on workflow completeness and task availability. Based on a predefined complete workflow (CW), only DentiqGuide and BlueSkyPlan enabled completion of all planning steps within a single clinical software environment, whereas other platforms required additional modules or external tools. System requirements and cost structures differed substantially, including license‑based and pay‑per‑use models. Semi‑automated functions, such as wax‑up-guided implant positioning, were available in several platforms. However, none provided fully automated planning. A consistent limitation across all evaluated software was restricted availability of prosthetic components within integrated libraries. Within the limits of this descriptive, expert‑based evaluation, the findings should be interpreted as exploratory and hypothesis‑generating rather than as comparative performance rankings. However, current clinical implant planning software may demonstrate substantial variability in workflow integration, usability and cost structure, and a common limitation in the availability of prosthetic component libraries. These findings highlight a persistent gap in achieving fully prosthetically driven digital workflows in clinical planning environments. The present findings reflect expert-dependent interaction with individual software platforms and were not validated using inter-rater reliability assessment. Therefore, the reported outcomes should not be interpreted as standardized or directly comparable measures of usability or workflow performance. Understanding practical digital workflow limitations, particularly restricted prosthetic component libraries, may support clinicians in selecting implant planning software that aligns with their experience level, case complexity, and clinical setting, while emphasizing the need for careful verification of prosthetic feasibility during digital treatment planning.
A major problem with reviewing the statistical methodology in published medical articles is that extracting the necessary details from large sample sets is time-consuming. This paper demonstrates how a novel automated procedure can extract information about statistical reporting from literature. To illustrate this, we searched the PubMed Central database for original research articles published in 2021 and 2023 to identify the statistical software packages used for data analysis. A key element in terms of transparency and reproducibility is the reporting of the software used for statistical analysis. A freely available Shiny App was created with the help of generative artificial intelligence, and it was used to retrieve automatically information from randomly selected samples of articles indexed in PubMed Central. We analyzed a large sample of articles (n = 1740) to determine the reporting of statistical software for nine study designs. We found that, across different study types, proprietary software such as IBM SPSS Statistics still dominates. Despite multiple calls for greater use of open-source research software, these programs are not used as frequently. In addition, a surprising number of articles did not report the software used. Furthermore, this is the first application of the recent Vibe Coding concept to statistical research methods.
Computational methods are central to the life sciences. The rapid growth and diversification of software tools and databases make it difficult to find, compare, and reuse methods for a given task. bio.tools is a community-driven registry designed to improve the visibility of research software and allow researchers to simplify access to the software ecosystem through structured, interoperable, and accessible metadata. Tools are annotated using the EDAM ontology and additional controlled vocabularies, enabling users to search and filter by scientific topics, operations, input/output data types, and data formats. bio.tools supports interactive exploration via rich tool landing pages and provides programmatic access through a documented API for search, retrieval, and registry statistics. The registry has expanded to almost 33,000 annotated tools through the combined contributions of thousands of community members and semi-automated literature mining that keep the registry up to date. Recent improvements to the registry include machine-assisted scoring to prioritise curator review, and consolidation of both its standards stack and software architecture. bio.tools has also become a foundational upstream metadata source that is reused by other services in the ELIXIR Research Software Ecosystem and beyond, to support synchronisation, cross-linking, and additional downstream services. bio.tools is freely available at https://bio.tools.
To present a feasible workflow for artificial intelligence (AI)-assisted software engineering in dentistry as a technical innovation report. The use of this workflow is illustrated through three self-developed open-source dental applications. Four AI-assisted development approaches were employed: chat-based interfaces of large language models, command-line interface tools, integrated development environments with AI assistance, and agent-based architectures. The dental software applications were created by a single clinician without formal programming training. Three applications were created: (1) VirtualEndo Converter, a Blender add-on for automated CBCT derived STL conversion for augmented/virtual reality (AR/VR), (2) MeshComparisonTool, a 3D Slicer extension for quantitative 3D morphology comparisons, and (3) DentalEmergencyTrainer, an application for simulating dental trauma emergency calls. All the applications are publicly available under the MIT license on GitHub. This report demonstrates that AI-assisted software development can enable dental practitioners without formal programming training to create functional prototypes of applications for research, education, and potentially clinical support. However, the reproducibility of this approach remains to be established, as the three tools were developed by a single clinician, and their clinical deployment would require thorough validation, security auditing, and regulatory assessment. AI-assisted development can help dental practitioners prototype tools that address unmet needs in clinical workflows, research, and education, but clinical use requires cautious separation from validated medical software. Before deployment, such tools require defined intended use, safety evaluation, data-protection safeguards, maintenance plans, and regulatory assessment.
Accurate surgical case duration estimation (CDE) is critical for operating room efficiency, staffing, resource allocation, and patient safety. Traditional approaches-historical case averages, surgeon estimates, and variable use of electronic scheduling tools-frequently produce substantial under- or overestimation, leading to workflow disruption, overtime, and reduced access. This study developed and evaluated a machine learning (ML)-based CDE model and conducted a parallel human-factor analysis to identify contributors to scheduling variability at a high-volume tertiary cancer center. The authors curated 40,656 surgical cases across 22 service lines (2016-2024) and trained a gradient-boosted tree model (termed "ORchestra") using patient-, procedure-, and surgeon-specific features. Model performance was compared with existing scheduling practices, including originally scheduled duration, historical averages, and Epic's CDE tool under both user-adjusted and vendor-recommended configurations. Evaluation metrics included mean absolute error (MAE magnitude), mean signed error (MSE bias), and the proportion of cases scheduled within ± 30 min of actual duration (f30min) or within ± 10% of actual duration (f10%). Human-factor investigations assessed variation in scheduling behavior, definitions of case duration, and choice of software parameters. A silent-trial deployment examined real-world feasibility. Baseline scheduling practices demonstrated substantial systematic underscheduling bias (MSE = -31 min), wide variability across service lines, and low accuracy: overall MAE = 35.3 min, f30min = 0.52, and f10% = 0.09. Standardizing workflow elements-specifically, consistent inclusion of prep/wrap time and use of vendor-recommended Epic settings-substantially improved performance (MAE = 20.1 min, f30min = 0.81, and f10% = 0.37). The ORchestra ML model further reduced error (MAE = 19.8 min), eliminated systematic bias (+1.2 min), and decreased overscheduling outliers, with the largest gains observed in high-variability services. Notably, workflow standardization alone approached ML-level accuracy for many procedures, highlighting human-factor variability as a dominant source of error. ML-based CDE improves predictive accuracy and reduces disruptive scheduling outliers; however, real-world performance depends equally on standardized workflows, consistent software configuration, and unified operational definitions. This study demonstrates that successful deployment requires not only technical optimization but also organizational alignment, governance, and disciplined practice change. Integrating predictive tools into perioperative operations provides measurable benefit but must be paired with structured workflow redesign to ensure reliability, safety, and sustainable impact.
Shaking table testing is a crucial method for evaluating the seismic performance of structures; however, the resulting data are typically characterized by massive volumes, high sampling rates, and complex multi-channel arrays. Traditional manual processing methods relying on commercial spreadsheet software (e.g., Excel, Origin) present significant limitations regarding processing efficiency, mathematical transparency, and result reproducibility. To address these methodological gaps, this paper proposes a novel, fully automated data processing and analysis framework tailored for high-density structural dynamic testing using an open-source Python toolchain. Unlike conventional "black-box' commercial software, this method provides a transparent, end-to-end pipeline-from automated raw multi-channel data alignment and signal pre-processing to advanced time-frequency domain analysis and standardized visualization. The framework's efficacy is validated using a shaking table test of a 1:2 scaled village masonry structure. The extracted experimental results clearly indicate that the masonry structure exhibits a significant low-pass filtering effect on high-frequency inputs (5-15 Hz), with response energy concentrated within the natural frequency range of 2-4 Hz. Furthermore, the pipeline integrates an automated structural health evaluation module; by comparing the Power Spectral Density (PSD) of white noise sweeps before and after seismic inputs, the method successfully and rapidly identified that while the structure exhibited displacement amplification under the 0.2 g operating condition, no significant stiffness degradation occurred. Ultimately, this study contributes a scalable, reproducible, and highly efficient methodological blueprint for big data analysis in structural seismic evaluation.
Photogrammetry technique may provide a promising approach compared to conventional techniques for multiple implants. However, the accuracy of photogrammetric technique for implant-supported fixed complete dentures in clinical scenarios remains unclear. This study aimed to evaluate the accuracy of photogrammetric technique in horizontal impressions for implant-supported fixed complete dentures compared to conventional impression technique in edentulous jaws. Between March and December 2023, twenty edentulous arches (10 maxillary and 10 mandibular), each consisting of four to eight dental implants, were selected. For the photogrammetric technique, specialized scan bodies were placed on dental implants, followed by a digital scan using a photogrammetric camera. An intraoral scanner was then applied to obtain the information of soft tissue, which was subsequently aligned with photogrammetric data in the software using a best-fit algorithm. After one month, half of the participants were selected to repeat the procedure to assess the precision of photogrammetric technique. For conventional impressions, a two-step technique was employed. The initial impression was first completed using polyvinyl siloxane impression material to create a model. After pouring dental stone, several impression posts were attached to analogs, and a perforated custom tray was subsequently fabricated to facilitate the open-tray splinted final impressions. The final impressions, made with polyvinyl siloxane impression material, were subsequently poured with dental stone and scanned using a laboratory scanner. The deviations in distances and angles between photogrammetric technique and conventional impression were measured in the software. Distance deviations were recorded as the main outcome, while angular deviations were calculated as the secondary outcome. Deviations between photogrammetric technique versus conventional impression technique were compared using the Wilcoxon test. The significance level was set at 0.05. The overall deviation in distance was 34.46 ± 24.65 mm for the maxilla and 49.80 ± 39.09 mm for the mandible. In terms of angular parameters, deviation was 0.36 ± 0.28 degrees for the maxilla and 0.44 ± 0.33 degrees for the mandible. The results of the Wilcoxon test indicated no significant differences in distance and angles between photogrammetric technique and conventional impression technique, demonstrating the acceptable trueness of the photogrammetric technique for implant-supported fixed complete dentures (P < 0.05). Additionally, no significant difference was found between two measurements using the photogrammetry technique over a one-month interval, indicating the promising precision of the technique (P < 0.05). The photogrammetric technique could serve as a promising alternative for implant-supported fixed complete dentures in edentulous patients, demonstrating acceptable trueness and precision in the clinical environment.
We develop a freely-available Python package Vardetector (https://github.com/julijselb/vardetector/tree/main/vardetector) used for detecting DNA called mutations in aligned RNA reads. We benchmark it by comparing it to industry standard variant caller (GATK HaplotypeCaller; r = 0.88896/0.88859 (supporting-reads/all-reads)) and demonstrate the functionality by comparing two RNA-seq library preparation protocols for formalin fixed paraffin embedded (FFPE) tumor samples. One protocol relies on exome-capture and the other on ribosome-depletion (ribodepletion) chemistry. We call somatic mutations from DNA of tumor/normal samples of two individuals with non-small cell lung cancer and test the difference between the two protocols by quantifying all RNA reads (all-reads) and somatic mutation supporting RNA reads (supporting-reads) over the positions of the DNA-called mutations. We show that the ribodepletion protocol produces significantly higher number of all (p < 0.001) and of supporting (p < 0.001) reads over the mutations of interest. Moreover, the ribodepletion protocol produces significantly (p < 0.001) wider breath of somatic mutation position coverage. The Vardetector software package and our results display a meaningful potential of the approach to improve neoantigen prioritisation pipelines.
To investigate the predictors of anatomical response in patients with diabetic macular edema (DME) following anti-vascular endothelial growth factor (anti-VEGF) therapy and establish a nomogram model for predicting the probability of anatomical response. This study enrolled 200 DME patients treated with anti-VEGF regimen. Based on the reduction rate of central macular thickness (CMT) following treatment, patients were classified into an anatomical weak responder group (CMT reduction < 20%) and an anatomical responder group (CMT reduction ≥ 20%). Baseline clinical data and OCT biomarkers were analyzed with multivariate logistic regression. A nomogram model was constructed by using R software. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model. Predictors for anatomical response in DME patients are serum creatinine (Scr), CMT, photoreceptor outer segment length (PROSL), and cystoid macular edema (CME) presence as independent variables. The nomogram prediction model based on the above four predictors had good representativeness (Bootstrap method: precision: 0.820), differentiation [the area under curve (AUC) value: 0.819], and the DCA analysis showed that the prediction model, whose threshold probability was in the range of 0 to 1, had clinical practical value. The anatomical response to anti-VEGF treatment for DME is independently associated with baseline Scr, CMT, PROSL, and the presence of CME.
Chronic pain is a severe burden affecting 20% of the population worldwide. To develop novel analgesics, in vivo preclinical assessment of the pain threshold is inevitable. Investigation of the nociception in rodents is still challenging, since most of the currently available methods are manually operated. So, the results highly depend on the experience of the examiner and can be significantly biased by subjective human factors. To improve this translational research paradigm, advanced tools are needed in this field. Therefore, the aim of the present study was to develop a new generation automated pain assessment device. In collaboration with Z-Elektronika Ltd., Pécs, Hungary we have designed and validated high-precision automated dynamic plantar aesthesiometer (ADPA) that is suitable for the assessment of mechanonociceptive threshold in rats and mice. It utilizes artificial intelligence (AI) to automatically recognize the animals investigated. The system's software controls the mechanical stimulation of the hindpaws with simultaneous video recording of the nocifensive reaction and analysis of the pain thresholds. The main advantage of ADPA is the automated, computer-controlled induction and evaluation of the pain threshold, increasing the quality, comparability, reproducibility, and objectivity of the results. This device may significantly enhance the accuracy of pain assessment in animal models and contribute to improved preclinical pain research.
Intra-abdominal infections are a common complication of colorectal cancer surgery. Postoperative abdominal infections can cause systemic inflammatory response syndrome, which seriously affects the prognosis of patients. With the widespread application of antibiotics, the detection rate of drug-resistant bacteria has increased annually, resulting in increased pressure on antibiotic treatment selection. To improve the prognosis of postoperative patients with colorectal cancer, it is important to actively search for risk factors leading to postoperative abdominal infection and formulate effective intervention measures according to these risk factors. A comprehensive search was conducted using several databases, including China National Knowledge Infrastructure, Wanfang Data, VIP, CBM, PubMed, Embase, and OVID, until September 2025. Case-control studies focusing on postoperative abdominal infections in colorectal cancer were conducted, and a meta-analysis was performed using the RevMan 5.4 software. A total of 21 case-control studies were included, and 42 risk factors for infection were identified. The results indicated that significant differences (P < .05) existed between the postoperative abdominal infection and non-infection groups concerning various factors, including diabetes mellitus, hypertension, cardiovascular disease, hypoproteinemia, tumor-node-metastasis stage I, tumor location, and several perioperative variables: operation time exceeding 150 minutes, hospital stay of 30 days or more, drainage tube indentation lasting over 10 days, serum albumin levels, preoperative hemoglobin levels, incision length > 15 cm, blood loss exceeding 300 mL, laparoscopic surgery, postoperative fistula, preoperative intestinal obstruction, anemia, anastomotic fistula, combined organ resection, preoperative ASA score, perioperative blood transfusion, and reoperation. Given the multitude of identified risk factors for postoperative abdominal infections in colorectal cancer, medical institutions should prioritize the prevention and control of hospital infections. This includes developing targeted strategies based on identified risk factors, careful assessment of surgical indications for colorectal cancer patients during clinical diagnosis and treatment, strict adherence to surgical protocols, and enhancing organ function support for patients post-surgery to reduce the incidence of postoperative abdominal infections.
Large numbers of people with psychiatric illness receive treatment in jails and prisons, yet the experience of medical learners in carceral settings is underexplored. This study analyzed medical student reflections following a carceral psychiatry rotation to understand students' pre-rotation beliefs and whether these ideas were changed by the clinical experience. The authors sought to understand whether the clinical experience motivated students or deterred them from pursuing work in carceral medicine. Two independent coders reviewed deidentified student reflections (n = 19) from July 2022 to October 2025 and developed a codebook of themes using an inductive approach. Reflections were qualitatively coded in ATLAS.ti software using the agreed-upon codebook. Codes were revised as further themes emerged and discrepancies were resolved by consensus. Five main categories were identified in student reflections: (1) preconceived notions; (2) perceptions of the prison environment; (3) perceptions of the people in prisons; (4) perceptions of the prison system; and (5) insights gained. Most students found that their clinical experience did not align with their preconceived beliefs, specifically concerning safety or the perceived aggressiveness of incarcerated people. Many recognized the shortcomings of the carceral system while simultaneously acknowledging the rewarding elements of their experience, including how many clinical interactions humanized incarcerated individuals. Students' preconceived notions were challenged most during direct student-patient interactions, suggesting that clinical rotations involving direct patient care may hold unique value as compared to lecture-based curricula about carceral health care. Ultimately, the elective led many students to consider working with incarcerated people in the future.
Body composition is emerging as a prognostic biomarker in cancer and may be associated with treatment tolerance, side-effects, and health-related quality of life (HRQoL). It can be measured from imaging routinely acquired during patient care. We evaluated whether body composition metrics were associated with radiotherapy-related side-effects and HRQoL in patients with prostate or lung cancer using a prospective multicentre dataset. Radiotherapy planning computed tomography (CT) scans, patient and disease characteristics, and clinician- and patient-reported side-effects up to 24 months post-treatment were obtained from the REQUITE study. Skeletal muscle and intramuscular adipose tissue were segmented at the L3 and T12 vertebrae for prostate and lung patients respectively using in-house software. Standardised total average toxicity scores captured composite acute and late clinician- and patient-reported side-effects and HRQoL. Gradient boosted machine models were developed for all endpoints with and without body composition variables. Predictor importance rankings and model performance (root mean squared error (RMSE)) were assessed. 279 lung and 848 prostate patients were available for analysis. Body composition variables were ranked in the top five most important variables for 9 of 12 endpoints. Body composition variables were ranked higher than body mass index for 9 of 12 endpoints. Adding body composition variables was associated with statistically significant (p < 0.01) but small reductions in apparent/in-sample RMSE across endpoints. Body composition variables were frequently ranked among important predictors of radiotherapy-related side-effects and HRQoL, but their incremental improvement in apparent model fit was small. These findings suggest that CT-derived body composition may warrant further investigation as an exploratory imaging biomarker, but external validation and demonstration of clinically meaningful incremental value are required before clinical implementation.
To analyze the current research status and trends in the management of cancer-related muscle pain worldwide from 2015 to 2024 using bibliometric methods. In this study, a literature search was conducted in the Web of Science Core Collection for the period from January 1, 2015, to January 21, 2025. The inclusion criteria were restricted to English-language original articles and reviews. We excluded non-English publications and other document types, such as editorial materials, book chapters, proceeding papers, letters, and news items. Ultimately, 909 valid publications (671 articles and 238 reviews) were retrieved. Data analysis was carried out using CiteSpace, VOSviewer, and the bibliometrix package in R software, focusing on main bibliometric indicators including the number of publications, total citation counts, country and institutional contributions, author productivity, journal influence, and keyword co-occurrence clusters. China and the United States were the main contributors in this field. The number of published articles increased continuously, peaking at 148 publications in 2023. Most of the highly productive authors were from China, forming a close collaboration network. In terms of journal influence, Medicine (n = 77) and Cancers (n = 31) published the most articles, while The Lancet (1990 citations) and Pain (974 citations) received the highest total citation counts. Through keyword clustering analysis, 4 dominant clusters were identified: basic research and clinical management of cancer-related muscle pain, the psychological state and quality of life of patients, evaluation of the safety and effectiveness of treatment, and comprehensive management strategies. This study systematically summarizes the research progress in the management of cancer-related muscle pain from 2015 to 2024, and reveals the research hotspots and future trends in this field. Future research should further strengthen international cooperation and improve the research quality, especially in the research on pain mechanisms, comprehensive management, and the quality of life of patients.
The purpose of this study is to evaluate the feasibility and validity of 3D avatar-based anthropometry for assessing anthropometric indicators of overweight and obesity in children based on artificial intelligence-derived 3D body reconstruction. This cross-sectional study included 171 children aged 8-10 years from five primary schools in southern Spain. Due to technical constraints of the reconstruction software, 75 children meeting predefined image quality and body weight requirements (≥ 30 kg) were eligible for 3D avatar analysis. Manual anthropometric measurements were obtained following International Society for the Advancement of Kinanthropometry (ISAK) standards and compared with avatar-derived circumferences. Agreement was assessed by using Wilcoxon tests, Spearman correlations, Bland-Altman analyses, and false discovery rate-adjusted comparisons. Avatar-derived circumferences were consistently greater than manual measurements, although waist-to-hip and waist-to-stature ratios demonstrated no significant differences. Avatar and manual circumferences were moderately to strongly correlated (Spearman's ρ = 0.62-0.72; adjusted p < 0.05). However, the mean absolute errors ranged from 6.21 to 7.11 cm, and Bland-Altman analysis revealed systematic overestimation with wide limits of agreement, particularly for waist and hip circumferences. Despite these discrepancies, both methods similarly discriminated between weight status categories. Image-based 3D avatar anthropometry is a feasible, noninvasive approach for population-level screening and research in pediatric settings. Although the system reliably captures relative differences and body proportions, its current accuracy is insufficient for individual-level clinical assessment. Further algorithmic refinement and validation in lighter and more diverse pediatric populations are needed before clinical implementation. • Childhood obesity remains a major public health challenge requiring accurate monitoring. • Anthropometric assessment still relies mainly on manual measurements, while AI-based 3D body modeling offers a promising non-invasive alternative. • Image-based 3D avatar anthropometry is feasible in school settings but is constrained by technical requirements. • Avatar measurements are correlated with manual anthropometry and preserve weight status discrimination but demonstrate systematic overestimation and limited agreement.
This study aimed to examine the training needs for competency development of novice nurse educators using the Nursing Professional Development (NPD) practice model, with the purpose of providing evidence to inform training program development. A descriptive qualitative approach was applied in accordance with the NPD practice model. Between April and May 2025, 29 nurse educators from a grade A tertiary teaching hospital were recruited using purposive sampling. Participants were organized into four focus groups and engaged in semi-structured interviews. Data were analyzed using directed content analysis with the assistance of NVivo 12.0 software. The training needs of novice nurse educators were classified into three dimensions based on the NPD model. Within the input dimension, adaptation to teaching environments and analysis of learner needs were identified as key requirements for improving role clarity. Within the process dimension, there was a strong demand for systematic instruction in teaching theory, curriculum design, and resource development, as well as structured mentorship and communication mechanisms to address challenges related to role overload and limited visibility of outcomes. Within the output dimension, self-innovation and targeted empowerment were identified as necessary to advance team development and transform teaching practice, thereby promoting the integration of organizational culture and professional values. Competency development training for novice nurse educators should align with the seven core role standards of the NPD model and be supported through appropriate pedagogical strategies. This requires the restructuring of resources during the input phase, implementation of multidimensional empowerment during the process phase, and the establishment of scientific evaluation systems during the output phase. These measures support the progression of novice nurse educators into competent NPD practitioners and enhance the overall quality of talent cultivation.
To establish gestational age-specific reference ranges for the intraplacental vascular index measured using microvascularity-flow ultrasound software (VIMV) and to evaluate its variation by placental location, parity, and pregnancy outcome. This prospective study enrolled 354 singleton pregnancies, of which 243 (68.64%) uncomplicated cases were used to construct reference ranges. A residual bootstrap method with 500 iterations was applied to square-root-transformed VIMV values, followed by back-transformation to derive the 5th, 50th, and 95th percentiles with 95% confidence intervals. Group differences were assessed using quantile regression, and associations with Doppler parameters were examined. VIMV increased significantly with advancing gestational age (linear coefficient 1.88, p = .007), following a curvilinear pattern with mild late-gestation downturn (quadratic coefficient - 0.036, p = .011). Posterior placentas showed slightly higher early values, although this difference was not significant; from 31 weeks onward, anterior placentas demonstrated significantly higher VIMV (mean difference + 15.4, p = .010 at 31-35 weeks; +11.6, p = .003 at 36-40 weeks). Multiparous women showed a modestly higher fitted trajectory and a similar non-significant trend toward higher birthweight, but parity was not independently associated with VIMV. Pregnancies complicated by maternal disease, hypertensive disorders, or small-for-gestational-age neonates exhibited lower trajectories than uncomplicated pregnancies, although these differences were not statistically significant. VIMV was inversely associated with uterine and umbilical artery pulsatility indices and positively associated with umbilical vein time-averaged mean velocity. Gestational age-specific VIMV reference ranges were established. VIMV may provide an adjunctive quantitative marker for assessing placental vascular adaptation and identifying pregnancies with altered placental perfusion.
In recent decades, thousands of research articles on neurodegenerative diseases (NDs) have been published. Retinoic acid and its analogues play crucial roles in biological processes such as cell proliferation, differentiation, and apoptosis through their interaction with retinoic acid receptors (RARs). While the involvement of RARs in NDs has attracted increasing interest, a further understanding of the current state and future trajectories of RARs research within this field needs to be explored. This study aims to provide a systematic overview through bibliometric and visual analysis. Original research and review articles concerning RARs in NDs were systematically retrieved from 3 databases: Web of Science Core Collection, Scopus, and PubMed. Subsequent statistical analysis and graphical representation of data on country, institution, authorship, journal, and key terms were conducted using advanced software like VOSviewer, CiteSpace, and the bibliometric toolbox within the R programming language. A total of 1094 articles were included in the analysis, with the United States leading in both publication output (n = 254) and total citations (TC = 17,102), followed by China and Germany. The United States also demonstrated the highest total link strength (90), indicating its central role in international collaborations. The University of California System was the most prolific institution. Keyword analysis revealed core research themes including "retinoic acid," "neurodegeneration," "neuroinflammation," "oxidative stress," and "neuronal differentiation," with recent shifts toward mechanisms involving microglia, the blood-brain barrier, and translational models. Research on RARs in NDs represents a dynamically growing and interdisciplinary field. The USA has contributed most substantially to the literature, underscoring the importance of international and institutional collaboration. Current and emerging research hotspots focus on intracellular calcium, cancer, tau protein, and inflammation, highlighting pathways with therapeutic potential. Future studies should further elucidate molecular mechanisms, integrate advanced technologies such as single-cell sequencing, and accelerate the translation of RAR-related findings into clinical applications.
Acute pancreatitis (AP) concurrent with acute kidney injury (AKI) remarkably elevates the risk of adverse outcomes in affected individuals. Abnormal serum magnesium concentrations have been linked to AKI development across diverse patient populations; however, the prognostic significance of serum magnesium levels at multiple time points (60 days, 90 days, 180 days, and 365 days) remains inadequately explored in AP patients with AKI admitted to the intensive care unit (ICU). This study aimed to assess the dynamic prognostic value of serum magnesium at the aforementioned key time points, clarify its clinical utility for risk stratification in this specific cohort, and investigate prognostic disparities among patients stratified by gender, as well as the presence or absence of diabetes mellitus, congestive heart failure, and pre-existing kidney disease. Study data were extracted from the MIMIC-IV database, which was made publicly available in October 2024. Adult patients (≥ 18 years) diagnosed with AP, who had an ICU length of stay (LOS) exceeding 24 h and complete mortality data, were enrolled. Exclusion criteria included missing serum magnesium measurements, ICU LOS < 24 h, incomplete clinical records, and aberrant survival data. Finally, 492 data samples meeting the inclusion criteria were enrolled in the present study. Serum magnesium levels were stratified into three grades using X-tile software, with stratification thresholds determined based on 60-day survival outcomes. Clinical data were retrieved using SQL and PostgreSQL. Intergroup comparisons were performed using statistical methods including the Wilcoxon rank-sum test, chi-square test, and t-test. Survival analyses were conducted to evaluate the association between serum magnesium levels and prognosis. Univariate Cox regression models were used to initially assess the relationship, and multivariate Cox regression models were constructed to adjust for confounding factors based on key patient characteristics. Among the 492 enrolled patients, males accounted for 53.25%. No statistically significant differences were noted in gender distribution or age across the three groups stratified by serum magnesium levels (P > 0.05). The hypermagnesemia group had the longest median ICU length of stay (LOS) (145 h, interquartile range [IQR]: 62-274 h), with intergroup differences approaching statistical significance (H = 5.112, P = 0.078). The incidence rates of sepsis and hypertension increased significantly with elevated serum magnesium levels (sepsis: χ² = 11.496, P = 0.003; hypertension: χ² = 6.065, P = 0.048). Additionally, the utilization rate of continuous renal replacement therapy (CRRT) in the hypermagnesemia group (20.75%) was significantly higher than that in the hypomagnesemia group (9.15%) and normomagnesemia group (11.48%) (χ² = 6.302, P = 0.043). In the hypermagnesemia group, serum creatinine, potassium, sodium, and chloride levels were significantly elevated, while serum calcium levels were markedly decreased (all P < 0.05). Disease severity scores, including the Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS II), and Logistic Organ Dysfunction System (LODS) score, were significantly higher in the hypermagnesemia group compared to the other two groups (all P < 0.05). Regarding prognostic outcomes, the hypermagnesemia group had the shortest median survival times at 60, 180, and 365 days, with statistically significant intergroup differences (H-values: 6.75, 6.033, 9.235; all P < 0.049). Its 365-day mortality rate (37.74%) was more than twice that of the hypomagnesemia group (18.61%). Kaplan-Meier analysis revealed that the hypermagnesemia group had significantly lower survival rates at all time points compared to the hypomagnesemia group (log-rank test, P < 0.05). Multivariate Cox regression analysis indicated that the risk of death gradually increased with rising serum magnesium levels, and hypermagnesemia was associated with a 54% higher risk of 365-day mortality (HR = 1.54, 95% CI: 0.54-4.43). Restricted cubic spline (RCS) analysis demonstrated a significant increase in mortality risk when serum magnesium levels exceeded 1.9 mg/dL. Subgroup analysis confirmed that the association between serum magnesium levels and prognosis was consistent across different subgroups. Furthermore, during the 365-day follow-up, the hypermagnesemia-related mortality risk was significantly elevated in obese patients and those with sepsis (P < 0.05). Elevated serum magnesium levels upon ICU admission are closely correlated with increased risks of adverse events and medium- and long-term mortality in patients with acute pancreatitis complicated by acute kidney injury, and can serve as a valuable early clinical indicator for evaluating disease severity and predicting patient prognosis. Abnormally elevated serum magnesium levels effectively reflect the status of severe concomitant organ dysfunction and possess favorable clinical value for disease assessment and prognostic prediction. The stratified cut-off values of serum magnesium established in this study can be applied to early clinical risk stratification and early warning for such patients. Routine dynamic monitoring of serum magnesium is recommended to facilitate early clinical evaluation and risk assessment. Furthermore, it provides a reliable reference for subsequent mechanistic investigations and prospective clinical trials to explore targeted interventions for improving patient outcomes.
Transgenic crops undergo rigorous safety assessments prior to commercialization, with molecular characterization serving as a critical component of regulatory review. This process establishes the identity, copy number, sequence integrity, absence of unintended foreign DNA, and insert stability across breeding generations. While whole-genome sequencing (WGS) has emerged as a powerful alternative to Southern blotting, the lack of accessible interpretation frameworks can be an entry barrier to those who wish to understand this modernized experimental setup. We developed an analytical workflow based on mapped-read signatures to characterize T-DNA (transfer DNA) insertions using short-read WGS data. Simulated Illumina paired-end datasets representing diverse transformation outcomes were generated and analyzed to define five informative read classes, which when observed mapped to a reference transformation construct provide distinct signatures indicating transformation outcomes. These signatures were applied to identify insertion boundaries, copy number, structural anomalies, and potential contamination. Mapped-read signatures can reliably distinguish single-copy inserts, multiple insertions, backbone co-integrations, and structural rearrangements, aided by coverage profiles and mate-pair orientations. We present representative examples and a practical interpretation to guide practitioners new to WGS-based molecular characterization and regulators assessing these data. This framework standardizes interpretation of short-read paired-end WGS data for molecular characterization without prescribing specific software. The platform-agnostic approach ensures broad applicability while enhancing transparency in regulatory assessments.