Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates.

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.

Prediction of protein-RNA interactions from single-cell transcriptomic data.

Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.

Structural insights into the morpholino nucleic acid/RNA duplex using the new XNA builder Ducque in a molecular modeling pipeline.

The field of synthetic nucleic acids with novel backbone structures [xenobiotic nucleic acids (XNAs)] has flourished due to the increased importance of XNA antisense oligonucleotides and aptamers in medicine, as well as the development of XNA processing enzymes and new XNA genetic materials. Molecular modeling on XNA structures can accelerate rational design in the field of XNAs as it contributes in understanding and predicting how changes in the sugar-phosphate backbone impact on the complementation properties of the nucleic acids. To support the development of novel XNA polymers, we present a first-in-class open-source program (Ducque) to build duplexes of nucleic acid analogs with customizable chemistry. A detailed procedure is described to extend the Ducque library with new user-defined XNA fragments using quantum mechanics (QM) and to generate QM-based force field parameters for molecular dynamics simulations within standard packages such as AMBER. The tool was used within a molecular modeling workflow to accurately reproduce a selection of experimental structures for nucleic acid duplexes with ribose-based as well as non-ribose-based nucleosides. Additionally, it was challenged to build duplexes of morpholino nucleic acids bound to complementary RNA sequences.

The usability of an AAC pain description system for patients with acquired expressive communication disorders.

Augmentative and alternative communication (AAC) has been used by patients with acquired expressive communication disorders as an alternative to natural speech. The use of symbols to express pain, which is intangible, is challenging because designing a series of comprehensible symbols to represent personal experiences such as pain is not straightforward. This study describes (a) the development of symbols to express pain that were derived from Chinese pain-related similes and metaphors for an AAC mobile application developed specifically for this study known as PainDiary and (b) an assessment of the appropriateness of the app compared to conventional methods of collecting pain information. The symbols depicted headache pain and discomfort, which is prevalent among neurosurgical patients. The participants were 31 patients diagnosed with acquired expressive communication disorders who were receiving treatment in a neurosurgery general ward of Chang Gung Memorial Hospital in Taiwan and 14 nurses who worked on the ward. Pain information was collected by nurses using conventional methods and the PainDiary app. Assessment data, including the accuracy and efficiency of and user satisfaction with PainDiary, are compared. The results show that use of the app was effective in reporting pain and that patients required less time to report a pain event. The results further indicate that the PainDiary app was better received by younger individuals than by their older counterparts.

Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence.

OBJECTIVES: To externally validate the performance of a commercial AI software program for interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres. METHODS: A total of 3047 CXRs were collected from two primary healthcare centres, characterised by low disease prevalence, between January and December 2018. All CXRs were labelled as normal or abnormal according to CT findings. Four radiology residents read all CXRs twice with and without AI assistance. The performances of the AI and readers with and without AI assistance were measured in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: The prevalence of clinically significant lesions was 2.2% (68 of 3047). The AUROC, sensitivity, and specificity of the AI were 0.648 (95% confidence interval [CI] 0.630-0.665), 35.3% (CI, 24.7-47.8), and 94.2% (CI, 93.3-95.0), respectively. AI detected 12 of 41 pneumonia, 3 of 5 tuberculosis, and 9 of 22 tumours. AI-undetected lesions tended to be smaller than true-positive lesions. The readers' AUROCs ranged from 0.534-0.676 without AI and 0.571-0.688 with AI (all p values < 0.05). For all readers, the mean reading time was 2.96-10.27 s longer with AI assistance (all p values < 0.05). CONCLUSIONS: The performance of commercial AI in these high-volume, low-prevalence settings was poorer than expected, although it modestly boosted the performance of less-experienced readers. The technical prowess of AI demonstrated in experimental settings and approved by regulatory bodies may not directly translate to real-world practice, especially where the demand for AI assistance is highest. KEY POINTS: • This study shows the limited applicability of commercial AI software for detecting abnormalities in CXRs in a health screening population. • When using AI software in a specific clinical setting that differs from the training setting, it is necessary to adjust the threshold or perform additional training with such data that reflects this environment well. • Prospective test accuracy studies, randomised controlled trials, or cohort studies are needed to examine AI software to be implemented in real clinical practice.

Micapipe: A pipeline for multimodal neuroimaging and connectome analysis.

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.

Assessing a New Prescreening Score for the Simplified Evaluation of the Clinical Quality and Relevance of eHealth Apps: Instrument Validation Study.

BACKGROUND: In 2020, more than 250 eHealth solutions were added to app stores each day, or 90,000 in the year; however, the vast majority of these solutions have not undergone clinical validation, their quality is unknown, and the user does not know if they are effective and safe. We sought to develop a simple prescreening scoring method that would assess the quality and clinical relevance of each app. We designed this tool with 3 health care stakeholder groups in mind: eHealth solution designers seeking to evaluate a potential competitor or their own tool, investors considering a fundraising candidate, and a hospital clinician or IT department wishing to evaluate a current or potential eHealth solution. OBJECTIVE: We built and tested a novel prescreening scoring tool (the Medical Digital Solution scoring tool). The tool, which consists of 26 questions that enable the quick assessment and comparison of the clinical relevance and quality of eHealth apps, was tested on 68 eHealth solutions. METHODS: The Medical Digital Solution scoring tool is based on the 2021 evaluation criteria of the French National Health Authority, the 2022 European Society of Medical Oncology recommendations, and other provided scores. We built the scoring tool with patient association and eHealth experts and submitted it to eHealth app creators, who evaluated their apps via the web-based form in January 2022. After completing the evaluation criteria, their apps obtained an overall score and 4 categories of subscores. These criteria evaluated the type of solution and domain, the solution's targeted population size, the level of clinical assessment, and information about the provider. RESULTS: In total, 68 eHealth solutions were evaluated with the scoring tool. Oncology apps (22%, 20/90) and general health solutions (23%, 21/90) were the most represented. Of the 68 apps, 32 (47%) were involved in remote monitoring by health professionals. Regarding clinical outcomes, 5% (9/169) of the apps assessed overall survival. Randomized studies had been conducted for 21% (23/110) of the apps to assess their benefit. Of the 68 providers, 38 (56%) declared the objective of obtaining reimbursement, and 7 (18%) out of the 38 solutions seeking reimbursement were assessed as having a high probability of reimbursement. The median global score was 11.2 (range 4.7-17.4) out of 20 and the distribution of the scores followed a normal distribution pattern (Shapiro-Wilk test: P=.33). CONCLUSIONS: This multidomain prescreening scoring tool is simple, fast, and can be deployed on a large scale to initiate an assessment of the clinical relevance and quality of a clinical eHealth app. This simple tool can help a decision-maker determine which aspects of the app require further analysis and improvement.

[How to: Basics of programming cardiac pacemakers]. / How to: Basics der Schrittmacherprogrammierung.

Programming of implantable cardiac devices, especially dual-chamber pacemakers, can be challenging in daily clinical practice. Precise knowledge of programmable parameters is important; furthermore, one should also be familiar with the specific algorithms of each manufacturer. During programming, the patient's individual requirements should be taken into account, but out-of-the-box programming should be avoided. Another important goal of programming should be to stimulate as much as needed but as little as possible to provide the patient good exercise capacity while not being aware of the pacing. Manufacturers' algorithms can help reach these aims but need to be understood and-in case of inappropriate behavior-to be deactivated.

Comparison of prostate cancer detection rates in patients undergoing MRI/TRUS fusion prostate biopsy with two different software-based systems.

BACKGROUND: Magnetic resonance imaging (MRI)-targeted prostate biopsy is a routinely used diagnostic tool for prostate cancer (PCa) detection. However, a clear superiority of the optimal approach for software-based MRI processing during biopsy procedures is still unanswered. To investigate the impact of robotic approach and software-based image processing (rigid vs. elastic) during MRI/transrectal ultrasound (TRUS) fusion prostate biopsy (FBx) on overall and clinically significant (cs) PCa detection. METHODS: The study relied on the instructional retrospective biopsy data collected data between September 2013 and August 2017. Overall, 241 men with at least one suspicious lesion (PI-RADS ≥ 3) on multiparametric MRI underwent FBx. The study protocol contains a systematic 12-core sextant biopsy plus 2 cores per targeted lesion. One experienced urologist performed 1048 targeted biopsy cores; 467 (45%) cores were obtained using rigid processing, while the remaining 581 (55%) cores relied on elastic image processing. CsPCa was defined as International Society of Urological Pathology (ISUP) grade ≥ 2. The effect of rigid versus elastic FBx on overall and csPCa detection rates was determined. Propensity score weighting and multivariable regression models were used to account for potential biases inherent to the retrospective study design. RESULTS: In multivariable regression analyses, age, prostate-specific antigen (PSA), and PIRADS ≥ 3 lesion were related to higher odds of finding csPCa. Elastic software-based image processing was independently associated with a higher overall PCa (odds ratio [OR] = 3.6 [2.2-6.1], p < 0.001) and csPCa (OR = 4.8 [2.6-8.8], p < 0.001) detection, respectively. CONCLUSIONS: Contrary to existing literature, our results suggest that the robotic-driven software registration with elastic fusion might have a substantial effect on PCa detection.

Acurácia da análise da discrepância de Bolton proposta por método manual e digital com softwares ortodônticos / ccuracy of Bolton discrepancy analysis proposed by manual and digital method with orthodontic software

objetivo desse estudo foi avaliar o desempenho diagnóstico da Análise da Discrepância de Bolton (DB) anterior e total proposta pelo programa ClinCheck®, bem como dos softwares ortodônticos Dolphin Imaging® e 3D Slicer, comparado com mensurações manuais realizados em modelos impressos tridimensionalmente. Cinquenta e cinco pares de modelos digitais da fase inicial de pacientes tratados com sistema Invisalign® (Align, San Jose, Califórnia, EUA) foram impressos tridimensionalmente por estereolitografia e usados para mensuração da DB por método manual, com paquímetro digital, por um operador previamente calibrado. Os valores da DB fornecidos pelo software de planejamento ClinCheck® foram tabulados e os arquivos da amostra em formato STL foram medidos utilizando os softwares Dolphin Imaging® e 3D Slicer. Para avaliar a confiabilidade das análises usadas como referência das comparações seguintes, o erro do método foi calculado utilizando vinte por cento da amostra, com intervalo de 2 semanas. A análise estatística foi executada no software Jamovi (versão 2.0) e um nível de significância de 5% foi adotado. Os dados em milímetros foram avaliados pelo coeficiente de correlação intraclasse (ICC), fórmula de Dahlberg, teste t pareado e método de Bland-Altman. Os resultados indicaram que os três métodos evidenciaram medidas confiáveis (ICC ≥ 0,7), sendo a DB anterior ligeiramente mais confiável. No entanto, apenas as medidas da DB anterior realizadas pelo ClinCheck e Dolphin Imaging® foram acuradas (p>0,5, sem viés de proporção). Para o DB total, todos os programas pesquisados apresentaram diferença estatisticamente significativa em relação ao método manual (p<0.001), embora essas diferenças não tenham implicação clínica relevante. Conclui-se que as ferramentas disponíveis apresentam validade para aplicação no diagnóstico e planejamento ortodôntico no que diz respeito a DB, especialmente as medidas indicadas pelo ClinCheck® e Dolphin Imaging® para região anterior do arco. (AU)

This study aimed to evaluate the diagnostic performance of the analysis of anterior and overall Bolton Discrepancy (BD) proposed by the ClinCheck® program, as well as the orthodontic software Dolphin Imaging® and 3D Slicer, compared with manual measurements performed on three-dimensionally printed models. Fifty-five pairs of early-stage digital models of patients treated with the Invisalign® system (Align, San Jose, California, USA) were three-dimensionally printed by stereolithography and used to measure BD by manual method with a digital caliper by a previously calibrated operator. DB values provided by ClinCheck® planning software were tabulated and sample files in STL format were measured using Dolphin Imaging® and 3D Slicer software. To assess the reliability of the analyzes used as a reference for the following comparisons, the method error was calculated using twenty percent of the sample, with an interval of 2 weeks. Statistical analysis was performed using the Jamovi software (version 2.0) and a significance level of 5% was adopted. Data in millimeters were evaluated using the intraclass correlation coefficient (ICC), Dahlberg formula, paired t-test, and the Bland-Altman method. The results indicated that the three methods showed reliable measurements (ICC ≥ 0.7), with the anterior BD slightly more reliable. However, only anterior BD measurements performed by ClinCheck® and Dolphin Imaging® were accurate (p>0.5, without proportion bias). For overall BD, all programs surveyed showed a statistically significant difference from the manual method (p<0.001), although these differences have no relevant clinical implications. The available tools studied are valid for application in the diagnosis and orthodontic planning concerning BD, especially the measures indicated by ClinCheck® and Dolphin Imaging® for the anterior region of the arch. (AU)

Conversatorio respuesta digital a COVID-19 usando software libre

Como parte de la respuesta de los gobiernos nacionales y sub-nacionales a la pandemia de COVID-19, se han utilizado las tecnologías digitales para apoyar la misma, generando una avalancha de soluciones y servicios digitales por parte de la industria del Software. Sin embargo, muchos países se han apoyado en el Software Libre y Código Abierto, sobre todo en soluciones con ya una historia sólida en respuesta a temas de salud pública. En este conversatorio virtual, conoceremos las distintas perspectivas de las soluciones líderes.

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.

To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.