Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

Electronic Decision-Making Tool for Smoking Cessation (Pare de Fumar Conosco) Versus Standard of Care: A Cost-Effectiveness Analysis.

OBJECTIVES: The study aimed to evaluate the cost-effectiveness of the Pare de Fumar Conosco software compared with the standard of care adopted in Brazil for the treatment of smoking cessation. METHODS: In the cohort of smokers with multiple chronic conditions, we developed an decision tree model for the benefit measures of smoking cessation. We adopted the perspectives of the Brazilian Unified Health System and the service provider. Resources and costs were measured by primary and secondary sources and effectiveness by a randomized clinical trial. The incremental cost-effectiveness ratio (ICER) was calculated, followed by deterministic and probabilistic sensitivity analyses and deterministic and probabilistic sensitivity analyses. No willingness to pay threshold was adopted. RESULTS: The software had a lower cost and greater effectiveness than its comparator. The ICER was dominant in all of the benefits examined (-R$2 585 178.29 to -R$325 001.20). The cost of the standard of care followed by that of the electronic tool affected the ICER of the benefit measures. In all probabilistic analyses, the software was superior to the standard of care (53.6%-82.5%). CONCLUSION: The Pare de Fumar Conosco software is a technology that results in cost savings in treating smoking cessation.

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.

Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1.

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).

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.