High-quality Data Research Articles

Lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses

BackgroundThe integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards.MethodsWe developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings.Resultslab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm’s efficacy in handling large-scale data sets.Conclusionslab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management.Graphical

Toward Ensuring Data Quality in Multi-Site Cancer Imaging Repositories

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools.

High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.

Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for effective treatment. This study developed a high-performance artificial intelligence (AI) model to detect osteosarcoma from X-ray images using highly accurate annotated data to improve diagnostic accuracy at initial consultations. Traditional models trained on unannotated data have shown limited success, with sensitivities of approximately 60%-70%. In contrast, our model used a data-centric approach with annotations from an experienced oncologist, achieving a sensitivity of 95.52%, specificity of 96.21%, and an area under the curve of 0.989. The model was trained using 468 X-ray images from 31 osteosarcoma cases and 378 normal knee images with a strategy to maximize diversity in the training and validation sets. It was evaluated using an independent dataset of 268 osteosarcoma and 554 normal knee images to ensure generalizability. By applying the U-net architecture and advanced image processing techniques such as renormalization and affine transformations, our AI model outperforms existing models, reducing missed diagnoses and enhancing patient outcomes by facilitating earlier treatment. This study highlights the importance of high-quality training data and advocates a shift towards data-centric AI development in medical imaging. These insights can be extended to other rare cancers and diseases, underscoring the potential of AI in transforming diagnostic processes in oncology. The integration of this AI model into clinical workflows could support physicians in early osteosarcoma detection, thereby improving diagnostic accuracy and patient care.

Trust as moral currency: Perspectives of health researchers in sub-Saharan Africa on strategies to promote equitable data sharing.

Groundbreaking data-sharing techniques and quick access to stored research data from the African continent are highly beneficial to create diverse unbiased datasets to inform digital health technologies and artificial intelligence in healthcare. Yet health researchers in sub-Saharan Africa (SSA) experience individual and collective challenges that render them cautious and even hesitant to share data despite acknowledging the public health benefits of sharing. This qualitative study reports on the perspectives of health researchers regarding strategies to mitigate these challenges. In-depth interviews were conducted via Microsoft Teams with 16 researchers from 16 different countries across SSA between July 2022 and April 2023. Purposive and snowball sampling techniques were used to invite participants via email. Recorded interviews were transcribed, cleaned, coded and managed through Atlas.ti.22. Thematic Analysis was used to analyse the data. Three recurrent themes and several subthemes emerged around strategies to improve governance of data sharing. The main themes identified were (1) Strategies for change at a policy level: guideline development, (2) Strengthening data governance to improve data quality and (3) Reciprocity: towards equitable data sharing. Building trust is central to the promotion of data sharing amongst researchers on the African continent and with global partners. This can be achieved by enhancing research integrity and strengthening micro and macro level governance. Substantial resources are required from funders and governments to enhance data governance practices, to improve data literacy and to enhance data quality. High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. Engagement with multiple stakeholders including researchers and research communities is necessary to establish an equitable data sharing approach based on reciprocity and mutual benefit.

The XMM-Newton and NuSTAR view of IRASF11119+3257

Context. IRASF11119+3257 is an ultra-luminous infrared galaxy with a post-merger morphology, hosting a type-1 quasar at z = 0.189. It shows a prominent ultra-fast outflow (UFO) absorption feature (vout ∼ 0.25c) in its 2013 Suzaku spectrum. This is the first system in which the energy released by the UFO was compared to that of the known galaxy-scale molecular outflow to investigate the mechanism driving active galactic nuclei (AGN) feedback. Aims. In 2021, we obtained the first XMM-Newton long look of the target, coordinated with a simultaneous NuSTAR observation, with the goal of constraining the broad band continuum and the nuclear wind physical properties and energetics with an unprecedented accuracy. Methods. The new high-quality data allowed us to clearly detect at a confidence level P > 99.8% multiple absorption features associated with the known UFO at the 9.1 and 11.0 keV rest frames. Furthermore, an emission plus absorption feature at 1.1 − 1.3 keV reveals the presence of a blueshifted P-Cygni profile in the soft band. Results. We associate the two hard band features with blends of FeXXV and FeXXVI Heα-Lyα and Heβ-Lyβ line pairs and infer a large column (NH ∼ 1024 cm−2) of highly ionized (log ξ ∼ 5) gas outflowing at vout = 0.27 ± 0.01c. The 1.3 keV absorption line can be associated with a blend of Fe and Ne transitions, produced by a lower column (NH ∼ 3 × 1021 cm−2) and ionization (log ξ ∼ 2.6) gas component outflowing at the same speed. Using a radiative-transfer disk wind model to fit the highly ionized UFO, we derive a mass outflow rate comparable with the mass accretion rate and the Eddington limit (Ṁout = 4.25−0.73+1.11 M⊙/yr, ∼1.6 Ṁacc and ∼1.0 ṀEdd), and kinetic energy (Ėkin = 1.21−0.20+0.32 Lbol and ∼0.7LEdd) and momentum flux (Ṗout = 6.37−1.09+1.67 Lbol/c) among the highest reported in the literature. We measured an extremely low high-energy cutoff (Ecut ∼ 25 − 30 keV). This and several other cases in the literature suggest that a steep X-ray continuum may be related to the formation of powerful winds. We also analyzed the ionized [OIII] component of the large-scale outflow through optical spectroscopy and derived a large outflow velocity (vout ∼ 3000 km/s) and energetics comparable with the large-scale molecular outflows. Finally, we observe a trend of decreasing outflow velocity from forbidden optical emission lines of decreasing ionization levels, interpreted as the outflow decelerating at large distances from the ionizing source. Conclusions. The lack of a significant momentum boost between the nuclear UFO and the different phases of the large-scale outflow, observed in IRASF11119 and in a growing number of similar sources, can be explained by (i) a momentum-driven expansion, (ii) an inefficient coupling of the UFO with the host interstellar medium, or (iii) by repeated energy-driven expansion episodes with a low duty cycle, that average out on long timescales to produce the observed large-scale outflow.

See one, do one, teach one - Trends in resident autonomy and teaching assistant cases during general surgery residency in the United States: A nationwide retrospective analysis

IntroductionAutonomy during residency is crucial to the training and development of competent surgeons. An essential component of this process is the ‘teaching assistant (TA)’ case, an indispensable opportunity for residents to gain confidence and hone intraoperative skills. However, high-quality data on the volume and diversity of cases that graduates perform are scarce. MethodsA retrospective analysis was performed from publicly collected data of operative case logs from general surgery residents graduating from ACGME-accredited programs from 2006 to 2023. Data on the median overall number of surgeon chief and TA cases were retrieved. Collected data were organized based on sub-specialties. The Mann-Kendall trend test was used to investigate trends in TA cases and surgeon chief operative volume. ResultsBetween 2007 and 2023, the surgeon chief cases gradually increased from 229 to 274 (19.6 ​% increase; τ ​= ​0.610, p ​= ​0.001). There was a concurrent 72.7 ​% increase in TA cases from a median of 22–38 (τ ​= ​0.574, p ​= ​0.001). Surgeon chief (283 per resident) and TA cases (43 per resident) peaked in 2018–2019 and 2016–2017.The uptrend in TA cases was associated with the significant increase in colorectal (τ ​= ​0.559, p ​= ​0.001), general surgery-other (τ ​= ​0.404, p ​= ​0.018), and hepatopancreaticobiliary (HPB) (τ ​= ​0.596, p ​= ​0.001) subspecialties. Trauma and vascular surgery did not change significantly. With respect to total chief cases, general surgery-other (τ ​= ​0.956, p=<0.001), HPB (τ ​= ​0.713, p=<0.001) and colorectal (τ ​= ​0.522, p ​= ​0.004) volume increased. There was no significant change in trauma and foregut volume, while the volume of endocrine (τ ​= ​−0.485, p ​= ​0.006) and vascular surgery (τ ​= ​0.603, p ​= ​0.001) dropped significantly.The procedural category with the highest chief and TA volume was ‘colorectal tract - large intestine.’ Most procedural categories (53.49 ​%) retained a median of 0 teaching cases. No chief cases were logged for the specialties generally not considered part of general surgery (genitourinary, nervous system, orthopedics, and gynecology), although a median of 1 surgeon chief genitourinary case was recorded from 2018 to 2023. ConclusionsOver the past seventeen years, there has been a gradual uptrend in the number of surgeon chief and TA cases. While this is a positive indicator of improved autonomy, further research must focus on strategies to improve resident autonomy to train well-rounded surgeons safely.

Assessing feasibility and sex-related inequity in the cardiac rehabilitation quality indicators in Manitoba.

The cardiac rehabilitation quality indicators (CRQIs) developed by the Canadian Cardiovascular Society provide a means to standardize program assessment and identify sex-related inequities. No formal evaluation of the CRQIs has been conducted in Manitoba. An environmental scan for the CRQIs was performed using data in the electronic medical record at two cardiac rehabilitation (CR) sites in Winnipeg for 2016-2019 referrals. Of the 8116 referrals, 7758 (5491 males and 2267 females) had geographical access and were eligible for CR. The Manitoba Centre for Health Policy Data Quality Framework informed the data quality assessment. Thirteen CRQIs were available; four were considered high quality; nine demonstrated moderate to significant missing data. In addition to missing values, potential misclassification of risk (CR-4) and physiologically implausible and invalid dates were assessed and identified (CR-13 and CR-17). Each site had a physician medical director (CR-31) and a documented emergency response strategy (CR-32). Only high-quality data were evaluated for sex-related differences using chi-square and median tests. Women had lower enrollment (CR-3), and more women enrolled after the median of 41 days (CR-2b). Engagement with CR partners, including frontline staff, and utilizing strategies to assess and limit physiologically implausible values and dates will enhance data capture and quality.

Mechanisms and causes of death after abdominal surgery in low-income and middle-income countries: a secondary analysis of the FALCON trial

Death after surgery is devasting for patients, families, and communities, but remains common in low-income and middle-income countries (LMICs). We aimed to use high-quality data from an existing global randomised trial to describe the causes and mechanisms of postoperative mortality in LMICs. To do so, we developed a novel framework, learning from both existing classification systems and emerging insights during data analysis. This study was a preplanned secondary analysis of the FALCON trial in 54 hospitals across seven LMICs (Benin, Ghana, India, Mexico, Nigeria, Rwanda, and South Africa). FALCON was a pragmatic, 2 × 2 factorial, randomised controlled trial that compared the effectiveness of two types of interventions for skin preparation (10% aqueous povidone-iodine vs 2% alcoholic chlorhexidine) and sutures (triclosan-coated vs uncoated). Patients who did not have surgery or were lost to follow-up were excluded (n=231). The primary outcomes of the present analysis were the mechanism and cause of death within 30-days of surgery, determined using a modified verbal autopsy strategy from serious adverse event reports. Factors associated with mortality were explored in a mixed-effects Cox proportional hazards model. The FALCON trial is registered with ClinicalTrials.gov, NCT03700749. This preplanned secondary analysis of the FALCON trial included 5558 patients who underwent abdominal surgery, of whom 4248 (76·4%) patients underwent surgery in tertiary, referral centres and 1310 (23·6%) underwent surgery in primary referral (ie, district or rural) hospitals. 3704 (66·7%) of 5558 surgeries were emergent. 306 (5·5%) of 5558 patients died within 30 days of surgery. 226 (74%) of 306 deaths were due to circulatory system failure, which included 173 (57%) deaths from sepsis and 29 (9%) deaths from hypovolaemic shock including bleeding. 47 (15%) deaths were due to respiratory failure. 60 (20%) of 306 patients died without a clear cause of death: 45 (15%) patients died with sepsis of unknown origin and 15 (5%) patients died of an unknown cause. 46 (15%) of 306 patients died within 24 h, 111 (36%) between 24 h and 72 h, 57 (19%) between >72 h and 168 h, and 92 (30%) more than 1 week after surgery. 248 (81%) of 306 patients died in hospital and 58 (19%) patients died out of hospital. The adjusted Cox regression model identified age (hazard ratio 1·01, 95% CI 1·01-1·02; p<0·0001), ASA grade III-V (4·93, 3·45-7·03; p<0·0001), presence of diabetes (1·47, 1·04-2·41; p=0·033), being an ex-smoker (1·59, 1·10-2·30; p=0·013), emergency surgery (2·08, 1·45-2·98; p<0·0001), cancer (1·98, 1·42-2·76; p<0·0001), and major surgery (3·94, 2·30-6·75; p<0·0001) as risk factors for postoperative mortality INTERPRETATION: Circulatory failure leads to most deaths after abdominal surgery, with sepsis accounting for almost two-thirds. Variability in timing of death highlights opportunities to intervene throughout the perioperative pathway, including after hospital discharge. A high proportion of patients without a clear cause of death reflects the need to improve capacity to rescue and cure by strengthening perioperative systems. National Institute for Health and Care Research Global Health Research Unit.

GEPREP: A comprehensive data atlas of RNA-seq-based gene expression profiles of exercise responses

BackgroundPhysical activity can regulate and affect gene expression in multiple tissues and cells. Recently, with the development of next-generation sequencing, a large number of RNA-sequencing (RNA-seq)-based gene expression profiles about physical activity have been shared in public resources; however, they are poorly curated and underutilized. To tackle this problem, we developed a data atlas of such data through comprehensive data collection, curation, and organization. MethodsThe data atlas, termed gene expression profiles of RNA-seq-based exercise responses (GEPREP), was built on a comprehensive collection of high-quality RNA-seq data on exercise responses. The metadata of each sample were manually curated. Data were uniformly processed and batch effects corrected. All the information was well organized in an easy-to-use website for free search, visualization, and download. ResultsGEPREP now includes 69 RNA-seq datasets of pre- and post-exercise, comprising 26 human datasets (1120 samples) and 43 mouse datasets (1006 samples). Specifically, there were 977 (87.2 %) human samples of skeletal muscle and 143 (12.8 %) human samples of blood. There were also samples across 9 mice tissues with skeletal muscle (359, 35.7 %) and brain (280, 27.8 %) accounting for the main fractions. Metadata—including subject, exercise interventions, sampling sites, and post-processing methods—are also included. The metadata and gene expression profiles are freely accessible at http://www.geprep.org.cn/. ConclusionGEPREP is a comprehensive data atlas of RNA-seq-based gene expression profiles responding to exercise. With its reliable annotations and user-friendly interfaces, it has the potential to deepen our understanding of exercise physiology.

Thermodynamic investigation of reduced barium molybdates

Precise identification and thermodynamic assessment of the various phases in spent nuclear fuel are necessary for the accurate dynamic modeling of nuclear fuel. The Thermodynamics of Advanced Fuels – International Database (TAF-ID) compiles high-quality, self-consistent, and reliable thermodynamic data of various phases in chemical systems of interest for the international nuclear industry, such as the Ba-Mo-O ternary system. The thermodynamic properties of compounds in the Ba-Mo-O ternary system, especially those of hexavalent Mo, have been extensively studied and included in computational thermodynamic assessments. In contrast, compounds with Mo in reduced oxidation states, such as BaMo6O10 and Ba3Mo18O28, have been excluded from such assessments due to a lack of experimentally assessed thermodynamic data. In this work, high-quality samples of the aforementioned compounds were synthesized, and the first experimental assessment of their heat capacities in the temperature range 300–600 K were obtained. Furthermore, a reversible phase transition was identified at ∼380 K for BaMo6O10. The collected experimentally-assessed thermodynamic data from this work and initial calculated estimations of standard enthalpies of formation and entropies for these compounds were successfully integrated into a preliminary, updated Ba-Mo-O thermodynamic assessment using the CALPHAD method. Key thermodynamic properties of the newly included compounds (i.e., standard enthalpy of formation and entropy) were calculated using the new Ba-Mo-O assessment.

Network pharmacology for traditional Chinese medicine in era of artificial intelligence

AbstractTraditional Chinese Medicine Network Pharmacology (TCM-NP) is an interdisciplinary discipline that integrates information science, systems biology, network science and pharmacology, providing a systematic research methodology for TCM studies. With the development of artificial intelligence (AI) and multi-omics technologies, TCM-NP has entered a new era and can incorporate multimodal and high-dimensional data in the context of big data to enhance both theoretical foundations and technical capabilities. Despite its advancement, TCM-NP still faces challenges, particularly in ensuring the quality of data and research, as well as achieving more profound scientific discoveries. The field needs further innovation to obtain more precise and biomedically meaningful results. Overall research progress in TCM-NP depends on developing more accurate algorithms together with utilizing higher-quality and larger-scale data. This paper gives a perspective on the trends and characteristics of TCM-NP development and application in the era of AI.

A registered report of a two-site study of variations of the flanker task: ERN experimental effects and data quality.

Error-related negativity is a widely used measure of error monitoring, and many projects are independently moving ERN recorded during a flanker task toward standardization, optimization, and eventual clinical application. However, each project uses a different version of the flanker task and tacitly assumes ERN is functionally equivalent across each version. The routine neglect of a rigorous test of this assumption undermines efforts to integrate ERN findings across tasks, optimize and standardize ERN assessment, and widely apply ERN in clinical trials. The purpose of this registered report was to determine whether ERN shows similar experimental effects (correct vs. error trials) and data quality (intraindividual variability) during three commonly used versions of a flanker task. ERN was recorded from 172 participants during three versions of a flanker task across two study sites. ERN scores showed numerical differences between tasks, raising questions about the comparability of ERN findings across studies and tasks. Although ERN scores from all three versions of the flanker task yielded high data quality and internal consistency, one version did outperform the other two in terms of the size of experimental effects and the data quality. Exploratory analyses of the error positivity (Pe) provided tentative support for the other two versions of the task over the paradigm that appeared optimal for ERN. The present study provides a roadmap for how to statistically compare psychometric characteristics of ERP scores across paradigms and gives preliminary recommendations for flanker tasks to use for ERN- and Pe-focused studies.

AI-based R&D for frozen and thawed meat: Research progress and future prospects.

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

Early alterations of thalami- and hippocampi-cortical functional connectivity as biomarkers of seizures after traumatic brain injury

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx, project 3) is a prospective multicenter clinical observational study to identify early biomarkers of epileptogenesis after moderate-to-severe traumatic brain injury (TBI). We used a seed-based approach applied to acute (i.e., ≤14 days) fMRI imaging data, directly testing the hypothesis that the presence of seizures up to two years following brain trauma is associated with functional changes within hippocampi and thalami-cortical networks. Additionally, we hypothesized that the network connectivity involving thalami and hippocampi circuits underlying early and late-onset seizures would differ. Approximately 30% of the initial dataset was deemed unusable due to MRI issues. Approximately 50% of the enrolled sample was lost to a 2-year follow-up. After preprocessing the fMRI data, approximately 40% of the follow-up sample had to be excluded from the analysis due to excessive in-scanner movements, as assessed by state-of-the-art quality control protocols. Only 37 patients provided data that was suitable for the seed-based analysis. Despite these challenges, the remaining, high-quality data returned noteworthy findings. We identified specific hippocampi and thalami biomarkers associated with both early and late seizures following TBI (p < .05, FWE-corrected at the cluster level). The predictive capability for the development of late seizures after TBI, when adding fMRI data to demographic and clinical data, provided 88% accuracy — an additional 8% improvement compared to using demographic and clinical data alone. Our findings highlight the potential of fMRI for uncovering, in hippocampal and thalamic cortical networks, biomarkers of early and late seizures following TBI. However, they also highlight the important challenges that need to be overcome in order for fMRI to become an effective biomarker and prognostic tool in the intensive care context.

HCS-hierarchical algorithm for simulation of omics datasets.

Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducing the dataset to include only those variables that are related to the studied phenomena. Existing simulators of the omics data were mostly developed with the goal of improving the methods for generations of high-quality data, that correspond with the highest possible fidelity to the real level of molecular markers in the biological materials. The current study aims to simulate the data on a higher level of generalization. Such datasets can then be used to perform tests of the feature selection and ML algorithms on systems that have structures mimicking those of real data, but where the ground truth may be implanted by design. They can also be used to generate contrast variables with the desired correlation structure for the feature selection. We proposed the algorithm for the reconstruction of the omic dataset that, with high fidelity, preserves the correlation structure of the original data with a reduced number of parameters. It is based on the hierarchical clustering of variables and uses principal components of the clusters. It reproduces well topological descriptors of the correlation structure. The correlation structure of the principal components of the clusters then is used to obtain datasets with correlation structures similar to the original data but not correlated with the original variables. The code and data is available at: https://github.com/p100mma/hcrs_omics.

SpliceVarDB: A comprehensive database of experimentally validated human splicing variants

Variants that alter gene splicing are estimated to comprise up to a third of all disease-causing variants, yet they are hard to predict from DNA sequencing data alone. To overcome this, many groups are incorporating RNA-based analyses, which are resource intensive, particularly for diagnostic laboratories. There are thousands of functionally validated variants that induce mis-splicing; however, this information is not consolidated, and they are under-represented in ClinVar, which presents a barrier to variant interpretation and can result in duplication of validation efforts. To address this issue, we developed SpliceVarDB, an online database consolidating over 50,000 variants assayed for their effects on splicing in over 8,000 human genes. We evaluated over 500 published data sources and established a spliceogenicity scale to standardize, harmonize, and consolidate variant validation data generated by a range of experimental protocols. According to the strength of their supporting evidence, variants were classified as "splice-altering" (∼25%), "not splice-altering" (∼25%), and "low-frequency splice-altering" (∼50%), which correspond to weak or indeterminate evidence of spliceogenicity. Importantly, 55% of the splice-altering variants in SpliceVarDB are outside the canonical splice sites (5.6% are deep intronic). These variants can support the variant curation diagnostic pathway and can be used to provide the high-quality data necessary to develop more accurate in silico splicing predictors. The variants are accessible through an online platform, SpliceVarDB, with additional features for visualization, variant information, in silico predictions, and validation metrics. SpliceVarDB is a very large collection of splice-altering variants and is available at https://splicevardb.org.

Safety and Efficacy of a Preemptive Mycophenolate Mofetil Dose Reduction Strategy in Kidney Transplant Recipients.

There are no high-quality data to guide long-term mycophenolate mofetil (MMF) dosing in kidney transplant recipients (KTRs) to balance the long-term risks of allograft rejection with that of infections and malignancy. At our center, KTRs are managed with either a "preemptive" dose reduction strategy, where the MMF dose is reduced after the first year before the development of adverse events, or with a "reactive" dosing strategy, where they are maintained on the same MMF dose and only reduced if they develop an adverse event. We hypothesized that a preemptive MMF dosing strategy after the first year of transplantation is associated with decreased infections without increasing alloimmune complications. We conducted a retrospective cohort study of all KTRs receiving MMF from January 1, 2015, to December 31, 2020. The primary outcome was the incidence of infections requiring hospitalization. One hundred forty-two KTRs met the inclusion criteria, of whom 44 (31%) were in the preemptive group and 98 (69%) were in the reactive group. The median follow-up was 4 y (interquartile range, 3.8-4.0). Multivariable analysis showed that a preemptive MMF dose reduction strategy was associated with a lower risk of infections requiring hospitalization (adjusted hazard ratio = 0.39; 95% confidence interval, 0.16-0.92). There was no difference in graft loss, rejection, or estimated glomerular filtration rate slope. Preemptive MMF dose reduction in KTRs may be an effective strategy to prevent infections without increasing the risk of allograft rejection. Randomized clinical trials are needed to confirm these findings.

Artificial urinary sphincter for neurogenic urinary incontinence: a narrative review.

The artificial urinary sphincter (AUS) is most known for its use in the treatment of moderate to severe post-prostatectomy stress urinary incontinence. However, another lesser-known indication includes stress incontinence related to intrinsic sphincter deficiency (ISD) in the neurogenic bladder population. The purpose of this review is to discuss specific technical considerations related to device implantation in this population, efficacy, durability, and complications. We performed a non-systematic literature review using the PubMed Database to identify articles specifically related to treatment of neurogenic urinary incontinence using an artificial urinary sphincter. More proximal placement of the cuff at the bladder neck is preferred in the neurogenic population due to higher rates of erosion and complications related to frequent clean intermittent catheterization or cystoscopic procedures when placed along the bulbar urethra. Robotic-assisted laparoscopic cuff placement has emerged as a safe and effective alternative to open surgery in select patients. Although continence rates are highly variable due the subjectivity of the term, functional continence (≤1 pad, ± nighttime incontinence) is reported to be between 75-90%. The need for secondary surgery for explanation with or without revision/replacement is higher in neurogenic patients compared to non-neurogenic patients. Neurogenic urinary incontinence is a complex condition due to the interplay of urethral resistance and bladder function/compliance. While there are a variety of strategies to treat neurogenic incontinence, high quality data from direct comparisons are lacking. Although AUS comes with a high revision rate, functional outcomes for continence with bladder neck placement are promising in this population.

Preventing Recurrence of Crohn's Disease Post-Ileocaecal Surgery in Paediatric Patients: A Therapy Guide Based on Systematic Review of the Evidence.

Ileocaecal resection (ICR) is frequent in paediatric patients with Crohn's disease (pCD). Despite rates of reoperation being low, the risk of clinical or endoscopic post-operative recurrence (POR) is high; effective medical strategies to prevent POR are thus needed. The aim of this systematic review(SR) was to identify and evaluate the published literature on post-operative medical prevention of POR in pCD to draft a possible therapy guide for pCD patients undergoing ICR. We performed an SR according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards and registered it in the PROSPERO database (ID: CRD42024533855). The population, intervention, control, outcome (PICO) model was focussed on post-surgical medical prevention of POR in pCD with clearly expressed definition of recurrence (endoscopically using a standardized scoring system (e.g. Rutgeerts score) or by laboratory markers, for example, faecal calprotectin (F-CPT), C-reactive protein (CRP) or by histological findings or by clinical activity indexes [e.g. weighted paediatric Crohn's disease activity index - (w)PCDAI]. From inception until 29 February 2024, the following databases were searched: PubMed/MEDLINE, Scopus/Embase, Web of Sciences, Evidence-Based Medicine Reviews (including Cochrane), Cochrane Central Registrar of controlled Trials (CENTRAL), ClinicalTrials.gov and EudraCT. Retrieved articles were evaluated for eligibility and finally selected publications for risk of bias using ROBINS-I tool. Out of 811 publications identified by the search, only 5 fulfilled inclusion criteria of the SR. None of the studies fully answered our PICO question. The studies were overall of poor quality and the heterogeneity of the data did not allow us to perform meta-analysis, detailed statistical analysis or formal synthesis of data. Adverse events of post-operative medication were not described in any of the included studies. Existing guidelines of European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN), North American Society for Paediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN), European Crohn's and Colitis Organisation (ECCO) and American Gastroenterological Association (AGA) were reviewed and paediatric therapy guide for pCD undergoing ICR was drafted with respect to recent SRs and meta-analyses in adult population and including scarce paediatric data identified by our SR. As pCD patientsundergoing ICR are a high-risk population, they should not be left untreated post-operatively. Anti-tumour necrosis factor (anti-TNF) drugs should be considered as first-line therapy in the majority of patients. Non-anti-TNF biologics should be considered in case of anti-TNF failure. Regular endoscopic monitoring starting at 6 months after the surgery and supported by regular F-CPT evaluation should be used to identify early endoscopic recurrence and to escalate the treatment. Our SR revealed that there is wide variability in treatment strategies in children, and high-quality data are generally lacking. At the moment, paediatric prophylaxis of POR should be guided by available adult evidence with respect to the high-risk nature of pCD. Extensive research in pCD should be encouraged.

Impact of AI on Manufacturing and Quality Assurance in Medical Device and Pharmaceuticals Industry

Global health and well-being largely depend on the pharmaceutical and medical device industries. Manufacturing and quality assurance (QA) processes are crucial to maintaining product efficacy, safety, and regulatory compliance in these sectors. Artificial intelligence (AI) integration presents ground-breaking opportunities to enhance these processes. This study aims to systematically assess the impact of AI on manufacturing and QA in these pharmaceutical and medical device industries. It examines the benefits, challenges, and ethical and legal implications of integrating AI. It offers a thorough understanding of how AI technology can and has been successfully integrated to enhance business operations. An extensive literature analysis was carried out to investigate AI's application, role, benefits, and challenges in manufacturing and quality assurance processes in both industries. Research was also conducted on emerging trends, future developments, and regulatory issues. Increased productivity, early detection of defects, safer and higher-quality goods, improved regulatory compliance, reduced costs, and more flexibility and scalability are some advantages of AI technologies. However, significant obstacles are also to overcome, such as high capital costs, data quality and availability issues, legacy system integration, ethical concerns about bias and data privacy, difficulties with regulatory compliance, and a lack of AI-skilled workers. Case studies show how AI has been utilized to guarantee regulatory compliance and optimize processes. AI integration has much to offer the pharmaceutical and medical device industries in terms of improved manufacturing and quality assurance procedures. By addressing restrictions and seizing novel opportunities, these industries can use AI's transformative potential to support innovation, enhance product quality and safety, ensure regulatory compliance, and improve global health outcomes.