Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Annealed fractional Lévy-Itō diffusion models for protein generation

Protein generation has numerous applications in designing therapeutic antibodies and creating new drugs. Still, it is a demanding task due to the inherent complexities of protein structures and the limitations of current generative models. Proteins possess intricate geometry, and sampling their conformational space is challenging due to its high dimensionality. This paper introduces novel Markovian and non-Markovian generative diffusion models based on fractional stochastic differential equations and the Lévy distribution, allowing for a more effective exploration of the conformational space. The approach is applied to a dataset of 40 , 000 proteins and evaluated in terms of Fréchet distance, fidelity, and diversity, outperforming the state-of-the-art by 25.4%, 35.8%, and 11.8%, respectively.

A deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma

Anatomical pathology is undergoing its third revolution, transitioning from analogical to digital pathology and incorporating new artificial intelligence technologies into clinical practice. Aside from classification, detection, and segmentation models, predictive models are gaining traction since they can impact diagnostic processes and laboratory activity, lowering consumable usage and turnaround time. Our research aimed to create a deep-learning model to generate synthetic Ki-67 immunohistochemistry from Haematoxylin and Eosin (H&E) stained images. We used 175 oral squamous cell carcinoma (OSCC) from the University Federico II's Pathology Unit's archives to train our model to generate 4 Tissue Micro Arrays (TMAs). We sectioned one slide from each TMA, first stained with H&E and then re-stained with anti-Ki-67 immunohistochemistry (IHC). In digitised slides, cores were disarrayed, and the matching cores of the 2 stained were aligned to construct a dataset to train a Pix2Pix algorithm to convert H&E images to IHC. Pathologists could recognise the synthetic images in only half of the cases in a specially designed likelihood test. Hence, our model produced realistic synthetic images. We next used QuPath to quantify IHC positivity, achieving remarkable levels of agreement between genuine and synthetic IHC. Furthermore, a categorical analysis employing 3 Ki-67 positivity cut-offs (5%, 10%, and 15%) revealed high positive-predictive values. Our model is a promising tool for collecting Ki-67 positivity information directly on H&E slides, reducing laboratory demand and improving patient management. It is also a valuable option for smaller laboratories to easily and quickly screen bioptic samples and prioritise them in a digital pathology workflow.

Examining technology use and mental health among parents with newborns in the intensive care unit during the COVID-19 pandemic: A cross-sectional study

Objectives

To investigate the relationship between pandemic-related stressors, mental health, and technology use among parents of hospitalized infants during the COVID-19 pandemic.

Methods

A cross-sectional study of 47 participants who had an infant in the Neonatal Intensive Care Unit (NICU) during the pandemic was completed. Participants ranked several statements on a Likert scale to assess mental health, technology use, and COVID-19-related stress during their infant's stay in the NICU.

Results

Mental health wellness scores were negatively associated with COVID-19-related stress (rs - 0.40, p = .015). The most prevalent stressor was hospital visitation restriction. Higher COVID-19-related stress was associated with greater use of text and video chat [(rs0.35, p = 0.016) and (rs0.33, p = .025)]. Enjoyment of technology use and access to technology were positively associated with higher mental health wellness scores [(rs0.42, p = .003) and (rs0.38, p = .009)].

Conclusions

Social uses of technology were valuable in a cohort of parents with infants hospitalized during the COVID-19 pandemic.

Innovation

Technology is a tool that can help parents cope with the stress of having a hospitalized infant. Digital literacy and technology access should be promoted in the post-pandemic landscape to help parents of infants in the NICU attain more benefit from these resources.

Australian and Canadian financial wellbeing policy landscape during COVID-19: An equity-informed policy scan

Background

This targeted and comprehensive policy scan examined how different levels of governments in Australia and Canada responded to the financial crisis brought on by the COVID-19 pandemic. We mapped the types of early policy responses addressing financial strain and promoting financial wellbeing. We also examined their equity considerations.

Methods

Through a systematic search, snowballing, and manual search, we identified Canadian and Australian policies at all government levels related to financial strain or financial wellbeing enacted or amended in 2019-2020. Using a deductive-inductive approach, policies were categorized by jurisdiction level, focal areas, and target population groups.

Results

In total, 213 and 97 policies in Canada and Australia, respectively, were included. Comparisons between Canadian and Australian policies indicated a more diversified and equity-targeted policy landscape in Canada. In both countries, most policies focused on individual and family finances, followed by housing and employment areas.

Conclusions

The policy scan identified gaps and missed opportunities in the early policies related to financial strain and financial wellbeing. While fast, temporary actions addressed individuals' immediate needs, we recommend governments develop a longer-term action plan to tackle the root causes of financial strain and poor financial wellbeing for better health and non-health crisis preparedness.

Statement on Ethics and Informed Consent

This research reported in this paper did not require ethical clearance or patient informed consent as the data sources were published policy documents. This study did not involve data collection with humans (or animals), nor any secondary datasets involving data provided by humans (or from animal studies).

In vivo dynamics of hard tissue-forming cell origins: Insights from Cre/loxP-based cell lineage tracing studies

Bone tissue provides structural support for our bodies, with the inner bone marrow (BM) acting as a hematopoietic organ. Within the BM tissue, two types of stem cells play crucial roles: mesenchymal stem cells (MSCs) (or skeletal stem cells) and hematopoietic stem cells (HSCs). These stem cells are intricately connected, where BM-MSCs give rise to bone-forming osteoblasts and serve as essential components in the BM microenvironment for sustaining HSCs. Despite the mid-20th century proposal of BM-MSCs, their in vivo identification remained elusive owing to a lack of tools for analyzing stemness, specifically self-renewal and multipotency. To address this challenge, Cre/loxP-based cell lineage tracing analyses are being employed. This technology facilitated the in vivo labeling of specific cells, enabling the tracking of their lineage, determining their stemness, and providing a deeper understanding of the in vivo dynamics governing stem cell populations responsible for maintaining hard tissues. This review delves into cell lineage tracing studies conducted using commonly employed genetically modified mice expressing Cre under the influence of LepR, Gli1, and Axin2 genes. These studies focus on research fields spanning long bones and oral/maxillofacial hard tissues, offering insights into the in vivo dynamics of stem cell populations crucial for hard tissue homeostasis.

Robotic mirror therapy for stroke rehabilitation through virtual activities of daily living

Mirror therapy is a standard technique of rehabilitation for recovering motor and vision abilities of stroke patients, especially in the case of asymmetric limb function. To enhance traditional mirror therapy, robotic mirror therapy (RMT) has been proposed over the past decade, allowing for assisted bimanual coordination of paretic (affected) and contralateral (healthy) limbs. However, state-of-the-art RMT platforms predominantly target mirrored motions of trajectories, largely limited to 2-D motions. In this paper, an RMT platform is proposed, which can facilitate the patient to practice virtual activities of daily living (ADL) and thus enhance their independence. Two similar (but mirrored) 3D virtual environments are created in which the patients operate robots with both their limbs to complete ADL (such as writing and eating) with the assistance of the therapist. The recovery level of the patient is continuously assessed by monitoring their ability to track assigned trajectories. The patient's robots are programmed to assist the patient in following these trajectories based on this recovery level. In this paper, the framework to dynamically monitor recovery level and accordingly provide assistance is developed along with the nonlinear controller design to ensure position tracking, force control, and stability. Proof-of-concept studies are conducted with both 3D trajectory tracking and ADL. The results demonstrate the potential use of the proposed system to enhance the recovery of the patients.

Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma

In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis.

Designing eHealth interventions for children with complex care needs requires continuous stakeholder collaboration and co-creation

Objective

Hospital-to-home (H2H) transitions challenge families of children with medical complexity (CMC) and healthcare professionals (HCP). This study aimed to gain deeper insights into the H2H transition process and to work towards eHealth interventions for its improvement, by applying an iterative methodology involving both CMC families and HCP as end-users.

Methods

For 20-weeks, the Dutch Transitional Care Unit consortium collaborated with the Amsterdam University of Applied Sciences, HCP, and CMC families. The agile SCREAM approach was used, merging Design Thinking methods into five iterative sprints to stimulate creativity, ideation, and design. Continuous communication allowed rapid adaptation to new information and the refinement of solutions for subsequent sprints.

Results

This iterative process revealed three domains of care - care coordination, social wellbeing, and emotional support - that were important to all stakeholders. These domains informed the development of our final prototype, 'Our Care Team', an application tailored to meet the H2H transition needs for CMC families and HCP.

Conclusion

Complex processes like the H2H transition for CMC families require adaptive interventions that empower all stakeholders in their respective roles, to promote transitional care that is anticipatory, rather than reactive.

Innovation

A collaborative methodology is needed, that optimizes existing resources and knowledge, fosters innovation through collaboration while using creative digital design principles. This way, we might be able to design eHealth solutions with end-users, not just for them.

Frequently used extraoral maxillofacial prosthetic materials and their longevity - A comprehensive review

The longevity of an extraoral prosthesis depends on its physical and mechanical properties and user maintenance. Faced with multiple outcome measures, researchers find it difficult to determine the most appropriate extraoral prosthetic material. This comprehensive review evaluates the most used extraoral prosthesis materials and qualitatively assesses their longevity and function. The study aims to identify and interpret the results of current updates on the factors that affect longevity and functionality. This comprehensive review summarizes and evaluates differences in the properties of commonly used extraoral maxillofacial prosthetic materials. The review was planned to focus on all factors related to the longevity and function of the extraoral maxillofacial prosthetics. An electronic search covered English articles in PubMed, Scopus, Google Scholar, Web of Science, and grey literature. Manual searching was also performed. Six authors participated in the screening. Search engines extracted 1107 records, and 88 studies were included for qualitative and bias assessments. Silicones are the most frequently used extraoral maxillofacial prosthetic materials. Heat-cured silicones are more color-stable than those cured at room temperature. Additional ingredients and processing techniques affect prosthesis longevity.

"Active carbon" is more advantageous to the bacterial community in the rice rhizosphere than "stable carbon"

Carbon materials are commonly used for soil carbon sequestration and fertilization, which can also affect crop growth by manipulating the rhizosphere bacterial community. However, the comparison of the differences between active carbon (e.g., organic fertilizers) and stable carbon (e.g., biochar) on rhizosphere microdomains is still unclear. Hence, a trial was implemented to explore the influence of control (CK, no fertilizer; NPK, chemical fertilizer), organic fertilizer (CF-O, organic fertilizer; CF-BO, biochar-based organic fertilizer) and biochar material (CF-B, perishable garbage biochar; CF-PMB, pig manure biochar) on the diversity, composition, and interaction of rice rhizosphere bacterial community through 16 S rRNA gene high-throughput sequencing. Our results demonstrate that organic fertilizer increases bacterial alpha-diversity compared to no-carbon supply treatment to the extend, whereas biochar has the opposite effect. The rhizosphere bacterial community composition showed pronounced variations among the various fertilization treatments. The relative abundance in Firmicutes decreased with organic fertilizer application, whereas that in Chloroflexi and Actinobacteria decreased with biochar application. Bacterial network analysis demonstrate that organic fertilizer enhances the complexity and key taxa of bacterial interactions, while biochar exhibits an opposing trend. The findings of our study indicate that organic fertilizer may contribute to a positive and advantageous impact on bacterial diversity and interaction in rice rhizosphere, whereas the influence of biochar is not as favorable and constructive. This study lays the foundation for elucidating the fate of the rhizosphere bacterial community following different carbon material inputs in the context of sustainable agricultural development.

Perceived helpfulness of caregiver support resources: Results from a state-wide poll

Objective

Our goal was to identify specific types of services desired by caregivers and determine subgroups most interested in each service type.

Methods

Caregiving questions were added to a state-wide poll conducted in a majority-rural state. Those who identified as caregivers (n = 428) were asked to report on the helpfulness of 6 domains of services. Descriptive analysis and logistic regressions were conducted.

Results

Top resources caregivers identified as potentially helpful included hands-on services (33.9%), help coordinating care from multiple providers (21.5%), help with finances (18.9%), and help managing emotional stress (17.8%). Only 15% indicated no caregiver resources would be helpful. Younger caregivers endorsed several service domains as more helpful than older caregivers; caregivers reporting higher stress were more likely to endorse most domains as helpful.

Conclusion

Data reinforces the overwhelming need to offer caregiver services. Navigation and integrated and tailored service models may be beneficial to help caregivers identify and access appropriate services within healthcare systems.

Innovation

This study uses an innovative approach to identifying needs of caregivers, who are often invisible within the healthcare system. Our findings suggest a paradigm shift is needed to broaden the scope and depth of services offered to caregivers.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Impact of a patient-centered tool to reduce misconceptions about coronary artery disease and its treatment: The CAD roadmap

Objective

Health misinformation is common and can lead to harmful behaviors such as medication non-adherence. We assessed the impact of a novel patient educational tool focused on overcoming misconceptions among patients with coronary artery disease (CAD).

Methods

We developed the CAD Roadmap, an educational tool aimed at explaining the disease trajectory and overcoming common disease misconceptions (such as that statin medications are not beneficial). We designed a pilot survey to assess patients' 1) CAD-related knowledge, 2) medication-taking behavior, and 3) acceptability of the Roadmap. Survey participants were recruited online. CAD knowledge scores were compared with repeated measures t-tests.

Results

Among 114 patients with CAD (mean age 67 years, 63% male), average CAD-related knowledge was 79.0% pre-test and 89.7% after review of the CAD Roadmap (p < .001). After review of the Roadmap, 24% indicated they planned to take their medications more regularly, 93% agreed it was helpful in understanding medication benefits, and 77% felt more empowered to participate in medical decisions.

Conclusion

The CAD Roadmap was evaluated positively, improved disease-related knowledge, and has the potential to improve adherence to treatments.

Innovation

Unlike many other interventions, the CAD Roadmap is specifically designed to overcome common misconceptions to improve health behaviors.

Reasons for choosing sober living houses and their associations with substance use recovery outcomes

Background

Sober living houses (SLHs) offer abstinence-based housing for people in recovery. Studies have shown that these supportive environments are associated with positive outcomes, yet little is known about why residents choose SLHs and their relationship to recovery outcomes.

Methods

Longitudinal data were collected from SLH residents who completed an interview six months after baseline (N = 462). Participants rated the importance of eight reasons for choosing SLHs. Multilevel models assessed whether reasons for choosing were associated with outcomes abstinence on the Timeline Followback, psychiatric distress via the Psychiatric Diagnostic Screening Questionnaire (PDSQ), employment problems severity on Addiction Severity Index (ASI), and length of stay (LOS).

Results

The most frequently cited reasons residents chose SLHs were affordability (74.4 %) and wanting to live with others in recovery (63.2 %). Reasons for choosing were not associated with neither LOS nor abstinence, except for not wanting to live with others in recovery predicting abstinence from all drugs except marijuana. Choosing SLHs due to affordability was associated with less psychiatric distress; no other place to live was associated with increased psychiatric distress (Ps < 0.05). Severity of employment problems was associated with choosing SLHs based on location, transportation, and someone else paying fees (Ps < 0.01).

Conclusion

Residents seek entry into SLHs to live affordably with others in recovery. Those who had no other option had greater psychiatric distress, thus supporting findings of housing instability being related to mental health. Reasons for choosing related to employment problems severity may reflect how concerns about employment impact housing choices.

Enriched human embryonic stem cells-derived CD133+, CD24+ renal progenitors engraft and restore function in a gentamicin-induced kidney injury in mice

Introduction

Acute kidney injury (AKI) is a common health problem that leads to high morbidity and potential mortality. The failure of conventional treatments to improve forms of this condition highlights the need for innovative and effective treatment approaches. Regenerative therapies with Renal Progenitor Cells (RPCs) have been proposed as a promising new strategy. A growing body of evidence suggests that progenitor cells differentiated from different sources, including human embryonic stem cells (hESCs), can effectively treat AKI.

Methods

Here, we describe a method for generating RPCs and directed human Embryoid Bodies (EBs) towards CD133+CD24+ renal progenitor cells and evaluate their functional activity in alleviating AKI.

Results

The obtained results show that hESCs-derived CD133+CD24+ RPCs can engraft into damaged renal tubules and restore renal function and structure in mice with gentamicin-induced kidney injury, and significantly decrease blood urea nitrogen levels, suppress oxidative stress and inflammation, and attenuate histopathological disturbances, including tubular necrosis, tubular dilation, urinary casts, and interstitial fibrosis.

Conclusion

The results suggest that RPCs have a promising regenerative potential in improving renal disease and can lay the foundation for future cell therapy and disease modeling.

A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients

Purpose

It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups.

Methods

Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results

The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79-0.86), 0.77(95 %CI 0.72-0.83) and 0.841(95 %CI 0.78-0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility.

Conclusions

The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

Risk factors for the development of Thunderstorm-associated asthma among indigenous Ghanaians: A matched case-control study

Objectives

Epidemic Thunderstorm asthma (TA) is a serious public health threat with a potential to overwhelm health systems. Being the first documented incidence in Ghana, we sought to determine whether the chronic respiratory risk factors for the development of TA as identified in other countries were similar or different from that in Ghana.

Study design

A matched case-control study involving 41 cases and 82 controls was conducted in two conveniently selected health facilities in the Ashanti Region of Ghana.

Methods

Data were collected from pre-existing patient records and included general demography, a history of allergies and a history of asthma. A chi-square and multiple logistic regression analysis were conducted to identify risk factors for the development of TA.

Results

Overall, 53.7 % of the TA cases and 7.3 % of controls had a previous history of asthma (AOR = 4.53 p = 0.064, 95 % CI = 0.918-22.365). Also, 29.3 % of the cases and 1.2 % of the controls had a previous history of allergies (AOR = 12.48 p = 0.05, 95 % CI = 0.919-169.305).

Conclusions

A previous history of allergy was a significant risk factor for TA. A previous history of asthma though associated with TA, was not a significant risk factor for its development. The recognition and awareness of risk factors for TA, by clinicians and health managers, is essential for health education, case management and preparation for the surge capacity occasioned by the event.

Advanced applications of smart electrospun nanofibers in cancer therapy: With insight into material capabilities and electrospinning parameters

Cancer remains a major global health challenge, and despite available treatments, its prognosis remains poor. Recently, researchers have turned their attention to intelligent nanofibers for cancer drug delivery. These nanofibers exhibit remarkable capabilities in targeted and controlled drug release. Their inherent characteristics, such as a high surface area-to-volume ratio, make them attractive candidates for drug delivery applications. Smart nanofibers can release drugs in response to specific stimuli, including pH, temperature, magnetic fields, and light. This unique feature not only reduces side effects but also enhances the overall efficiency of drug delivery systems. Electrospinning, a widely used method, allows the precision fabrication of smart nanofibers. Its advantages include high efficiency, user-friendliness, and the ability to control various manufacturing parameters. In this review, we explore the latest developments in producing smart electrospun nanofibers for cancer treatment. Additionally, we discuss the materials used in manufacturing these nanofibers and the critical parameters involved in the electrospinning process.

Acidic sphingomyelinase interactions with lysosomal membranes and cation amphiphilic drugs: A molecular dynamics investigation

Lysosomes are pivotal in cellular functions and disease, influencing cancer progression and therapy resistance with Acid Sphingomyelinase (ASM) governing their membrane integrity. Moreover, cation amphiphilic drugs (CADs) are known as ASM inhibitors and have anti-cancer activity, but the structural mechanisms of their interactions with the lysosomal membrane and ASM are poorly explored. Our study, leveraging all-atom explicit solvent molecular dynamics simulations, delves into the interaction of glycosylated ASM with the lysosomal membrane and the effects of CAD representatives, i.e., ebastine, hydroxyebastine and loratadine, on the membrane and ASM. Our results confirm the ASM association to the membrane through the saposin domain, previously only shown with coarse-grained models. Furthermore, we elucidated the role of specific residues and ASM-induced membrane curvature in lipid recruitment and orientation. CADs also interfere with the association of ASM with the membrane at the level of a loop in the catalytic domain engaging in membrane interactions. Our computational approach, applicable to various CADs or membrane compositions, provides insights into ASM and CAD interaction with the membrane, offering a valuable tool for future studies.

Gonorrhea cluster detection in Manitoba, Canada: Spatial, temporal, and spatio-temporal analysis

In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation.

Instance-level medical image classification for text-based retrieval in a medical data integration center

A medical data integration center integrates a large volume of medical images from clinical departments, including X-rays, CT scans, and MRI scans. Ideally, all images should be indexed appropriately with standard clinical terms. However, some images have incorrect or missing annotations, which creates challenges in searching and integrating data centrally. To address this issue, accurate and meaningful descriptors are needed for indexing fields, enabling users to efficiently search for desired images and integrate them with international standards. This paper aims to provide concise annotation for missing or incorrectly indexed fields, incorporating essential instance-level information such as radiology modalities (e.g., X-rays), anatomical regions (e.g., chest), and body orientations (e.g., lateral) using a Deep Learning classification model - ResNet50. To demonstrate the capabilities of our algorithm in generating annotations for indexing fields, we conducted three experiments using two open-source datasets, the ROCO dataset, and the IRMA dataset, along with a custom dataset featuring SNOMED CT labels. While the outcomes of these experiments are satisfactory (Precision of >75%) for less critical tasks and serve as a valuable testing ground for image retrieval, they also underscore the need for further exploration of potential challenges. This essay elaborates on the identified issues and presents well-founded recommendations for refining and advancing our proposed approach.

Unveiling the impact of the SMARTCLAP project on habilitation

This report summarises the SMARTCLAP research project, which employs a user-centred design approach to develop a revolutionary smart product service system. The system offers personalised motivation to encourage children with cerebral palsy to actively participate more during their occupational therapy sessions, while providing paediatric occupational therapists with an optimal tool to monitor children's progress from one session to another. The product service system developed includes of a smart wearable device called DigiClap used to interact with a serious game in an Augmented Reality environment. The report highlights the research methodology used to advance the technology readiness level from 4 to 6, acknowledging the contribution of the consortium team and funding source. As part of the technology's maturity process, DigiClap and the respective serious game were evaluated with target users, to identify the system's impact in supporting the children's overall participation and hand function, and to gather feedback from occupational therapists and caregivers on this novel technology. The outcomes of this study are discussed, highlighting limitations and lessons learned. The report also outlines future work and further funding for the sustainability of the project and to reach other individuals who have upper limb limitations. Ultimately, the potential of DigiClap and the overall achievements of this project are discussed.

Roadmap towards safe and sustainable advanced and innovative materials. (Outlook for 2024-2030)

The adoption of innovative advanced materials holds vast potential, contingent upon addressing safety and sustainability concerns. The European Commission advocates the integration of Safe and Sustainable by Design (SSbD) principles early in the innovation process to streamline market introduction and mitigate costs. Within this framework, encompassing ecological, social, and economic factors is paramount. The NanoSafety Cluster (NSC) delineates key safety and sustainability areas, pinpointing unresolved issues and research gaps to steer the development of safe(r) materials. Leveraging FAIR data management and integration, alongside the alignment of regulatory aspects, fosters informed decision-making and innovation. Integrating circularity and sustainability mandates clear guidance, ensuring responsible innovation at every stage. Collaboration among stakeholders, anticipation of regulatory demands, and a commitment to sustainability are pivotal for translating SSbD into tangible advancements. Harmonizing standards and test guidelines, along with regulatory preparedness through an exchange platform, is imperative for governance and market readiness. By adhering to these principles, the effective and sustainable deployment of innovative materials can be realized, propelling positive transformation and societal acceptance.

Geomembrane-based salt production method to increase the quantity and quality of small-scale salt producer

An appropriate salt production methods must be implemented to increase salt production's quantity and quality. This paper was prepared to introduce and demonstrate a new method, namely a geomembrane-based salt production method, which can contribute directly to increasing the quantity and quality of people's salt production. This method can be applied quickly with simple equipment, so this method is easy to replicate in various salt production center areas. The results of statistical tests directly show that there is a fundamental difference between the quantity and quality of salt produced by salt farmers using conventional salt production methods and geomembrane-based salt production methods, where geomembrane-based salt production methods are capable of producing much higher quantities of salt production with better quality.•The geomembrane-based salt production method is easy to implement because it uses simple equipment and can be made independently by salt farmers.•Using a geomembrane in this method can prevent leaks in the salt crystallization pond and optimize heat from solar energy to optimize the quantity of salt production.•This method prevents direct contact of seawater with soil. The effect is that the quality of salt produced from geomembrane-based salt production methods is higher than conventional salt production methods.