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Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers tremendous potential for the discovery of new biology, that, by definition, is not known and lacks ground truth. Herein, we describe the application of weakly supervised paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles., (© 2024 Nabi et al.)

Novel portable diffuse optical tomography (DOT) devices for breast cancer lesions hold great promise for non-invasive, non-ionizing breast cancer screening. Critical to this capability is not just the identification of lesions but rather the complex problem of discriminating between malignant and benign lesions. To accurately reconstruct the highly heterogeneous tissue of a cancer lesion in healthy breast tissue using DOT, multiple wavelengths can be leveraged to maximize signal penetration while minimizing sensitivity to noise. However, these wavelength responses can overlap, capture common information, and correlate, potentially confounding reconstruction and downstream end tasks. We show that an orthogonal fusion loss regularizes multi-wavelength DOT leading to improved reconstruction and accuracy of end-to-end discrimination of malignant versus benign lesions. We further show that our raw-to-task model significantly reduces computational complexity without sacrificing accuracy, making it ideal for real-time throughput, desired in medical settings where handheld devices have severely restricted power budgets. Furthermore, our results indicate that image reconstruction is not necessary for unbiased classification of lesions with a balanced accuracy of 77% and 66% on the synthetic dataset and clinical dataset, respectively, using the raw-to-task model. Code is available at https://github.com/sfu-mial/FuseNet., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hanene Ben Yedder reports financial support was provided by Natural Sciences and Engineering Research Council of Canada. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Identification and morphological analysis of mitochondria-ER contacts (MERCs) by fluorescent microscopy is limited by subpixel resolution interorganelle distances. Here, the membrane contact site (MCS) detection algorithm, MCS-DETECT, reconstructs subpixel resolution MERCs from 3D super-resolution image volumes. MCS-DETECT shows that elongated ribosome-studded riboMERCs, present in HT-1080 but not COS-7 cells, are morphologically distinct from smaller smooth contacts and larger contacts induced by mitochondria-ER linker expression in COS-7 cells. RiboMERC formation is associated with increased mitochondrial potential, reduced in Gp78 knockout HT-1080 cells and induced by Gp78 ubiquitin ligase activity in COS-7 and HeLa cells. Knockdown of riboMERC tether RRBP1 eliminates riboMERCs in both wild-type and Gp78 knockout HT-1080 cells. By MCS-DETECT, Gp78-dependent riboMERCs present complex tubular shapes that intercalate between and contact multiple mitochondria. MCS-DETECT of 3D whole-cell super-resolution image volumes, therefore, identifies novel dual control of tubular riboMERCs, whose formation is dependent on RRBP1 and size modulated by Gp78 E3 ubiquitin ligase activity., (© 2023 Cardoen et al.)

Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce DataCurator , a portable software package that verifies arbitrarily complex datasets of mixed formats, working equally well on clusters as on local systems. Human-readable TOML recipes are converted into executable, machine-verifiable templates, enabling users to easily verify datasets using custom rules without writing code. Recipes can be used to transform and validate data, for pre- or post-processing, selection of data subsets, sampling and aggregation, such as summary statistics. Processing pipelines no longer need to be burdened by laborious data validation, with data curation and validation replaced by human and machine-verifiable recipes specifying rules and actions. Multithreaded execution ensures scalability on clusters, and existing Julia, R and Python libraries can be reused. DataCurator enables efficient remote workflows, offering integration with Slack and the ability to transfer curated data to clusters using OwnCloud and SCP. Code available at: https://github.com/bencardoen/DataCurator.jl., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press.)

Introduction: Currently, the healthcare sector is under tremendous financial pressure, and many acknowledge that a dramatic shift is required as the current system is not sustainable. Furthermore, the quality of care that is delivered varies strongly. Several solutions have been proposed of which the conceptual framework known as value-based healthcare (VBHC) is further explored in this study for psoriasis. Psoriasis is a chronic inflammatory skin disease, which is associated with a high disease burden and high treatment costs. The objective of this study is to investigate the feasibility of using the VBHC framework for the management of psoriasis., Methods and Analysis: This is a prospective clinical study in which new patients attending the psoriasis clinic (PsoPlus) of the Ghent University Hospital will be followed up during a period of 1 year. The main outcome is to determine the value created for psoriasis patients. The created value will be considered as a reflection of the evolution of the value score (ie, the weighted outputs (outcomes) divided by weighted inputs (costs)) obtained using data envelopment analysis. Secondary outcomes are related to comorbidity control, outcome evolution and treatment costs. In addition, a bundled payment scheme will be determined as well as potential improvements in the treatment process. A total of 350 patients will be included in this trial and the study initiation is foreseen on 1 March 2023., Ethics and Dissemination: This study has been approved by the Ethics Committee of the Ghent University Hospital. The findings of this study will be disseminated by various means: (1) publication in one or more peer-reviewed dermatology and/or management journals, (2) (inter)national congresses, (3) via the psoriasis patient community and (4) through the research team's social media channels., Trial Registration Number: NCT05480917., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Identification of small objects in fluorescence microscopy is a non-trivial task burdened by parameter-sensitive algorithms, for which there is a clear need for an approach that adapts dynamically to changing imaging conditions. Here, we introduce an adaptive object detection method that, given a microscopy image and an image level label, uses kurtosis-based matching of the distribution of the image differential to express operator intent in terms of recall or precision. We show how a theoretical upper bound of the statistical distance in feature space enables application of belief theory to obtain statistical support for each detected object, capturing those aspects of the image that support the label, and to what extent. We validate our method on 2 datasets: distinguishing sub-diffraction limit caveolae and scaffold by stimulated emission depletion (STED) super-resolution microscopy; and detecting amyloid-β deposits in confocal microscopy retinal cross-sections of neuropathologically confirmed Alzheimer's disease donor tissue. Our results are consistent with biological ground truth and with previous subcellular object classification results, and add insight into more nuanced class transition dynamics. We illustrate the novel application of belief theory to object detection in heterogeneous microscopy datasets and the quantification of conflict of evidence in a joint belief function. By applying our method successfully to diffraction-limited confocal imaging of tissue sections and super-resolution microscopy of subcellular structures, we demonstrate multi-scale applicability., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Cardoen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Mitochondria are major sources of cytotoxic reactive oxygen species (ROS), such as superoxide and hydrogen peroxide, that when uncontrolled contribute to cancer progression. Maintaining a finely tuned, healthy mitochondrial population is essential for cellular homeostasis and survival. Mitophagy, the selective elimination of mitochondria by autophagy, monitors and maintains mitochondrial health and integrity, eliminating damaged ROS-producing mitochondria. However, mechanisms underlying mitophagic control of mitochondrial homeostasis under basal conditions remain poorly understood. E3 ubiquitin ligase Gp78 is an endoplasmic reticulum membrane protein that induces mitochondrial fission and mitophagy of depolarized mitochondria. Here, we report that CRISPR/Cas9 knockout of Gp78 in HT-1080 fibrosarcoma cells increased mitochondrial volume, elevated ROS production and rendered cells resistant to carbonyl cyanide m-chlorophenyl hydrazone (CCCP)-induced mitophagy. These effects were phenocopied by knockdown of the essential autophagy protein ATG5 in wild-type HT-1080 cells. Use of the mito-Keima mitophagy probe confirmed that Gp78 promoted both basal and damage-induced mitophagy. Application of a spot detection algorithm (SPECHT) to GFP-mRFP tandem fluorescent-tagged LC3 (tfLC3)-positive autophagosomes reported elevated autophagosomal maturation in wild-type HT-1080 cells relative to Gp78 knockout cells, predominantly in proximity to mitochondria. Mitophagy inhibition by either Gp78 knockout or ATG5 knockdown reduced mitochondrial potential and increased mitochondrial ROS. Live cell analysis of tfLC3 in HT-1080 cells showed the preferential association of autophagosomes with mitochondria of reduced potential. Xenograft tumors of HT-1080 knockout cells show increased labeling for mitochondria and the cell proliferation marker Ki67 and reduced labeling for the TUNEL cell death reporter. Basal Gp78-dependent mitophagic flux is, therefore, selectively associated with reduced potential mitochondria promoting maintenance of a healthy mitochondrial population, limiting ROS production and tumor cell proliferation., (© 2022. The Author(s).)

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

Objectives: Studies on variability drivers of treatment costs in hospitals can provide the necessary information for policymakers and healthcare providers seeking to redesign reimbursement schemes and improve the outcomes-over-cost ratio, respectively. This systematic literature review, focusing on the hospital perspective, provides an overview of studies focusing on variability in treatment cost, an outline of their study characteristics and cost drivers, and suggestions on future research methodology., Methods: We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane Handbook for Systematic Reviews of Interventions. We searched PubMED/MEDLINE, Web of Science, EMBASE, Scopus, CINAHL, Science direct, OvidSP and Cochrane library. Two investigators extracted and appraised data for citation until October 2020., Results: 90 eligible articles were included. Patient, treatment and disease characteristics and, to a lesser extent, outcome and institutional characteristics were identified as significant variables explaining cost variability. In one-third of the studies, the costing method was classified as unclear due to the limited explanation provided by the authors., Conclusion: Various patient, treatment and disease characteristics were identified to explain hospital cost variability. The limited transparency on how hospital costs are defined is a remarkable observation for studies wherein cost variability is the main focus. Recommendations relating to variables, costs, and statistical methods to consider when designing and conducting cost variability studies were provided., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021. Published by Elsevier B.V.)

The endoplasmic reticulum (ER) is a complex subcellular organelle composed of diverse structures such as tubules, sheets and tubular matrices. Flaviviruses such as Zika virus (ZIKV) induce reorganization of ER membranes to facilitate viral replication. Here, using 3D super resolution microscopy, ZIKV infection is shown to induce the formation of dense tubular matrices associated with viral replication in the central ER. Viral non-structural proteins NS4B and NS2B associate with replication complexes within the ZIKV-induced tubular matrix and exhibit distinct ER distributions outside this central ER region. Deep neural networks trained to distinguish ZIKV-infected versus mock-infected cells successfully identified ZIKV-induced central ER tubular matrices as a determinant of viral infection. Super resolution microscopy and deep learning are therefore able to identify and localize morphological features of the ER and allow for better understanding of how ER morphology changes due to viral infection.

Study Design: Retrospective, single-center analysis., Objective: To calculate the total clinical hospital cost of the Adult Spinal Deformity (ASD) care trajectory, to explain cost variability by patient and surgery characteristics, and to identify areas of process improvement opportunities., Summary of Background Data: ASD is associated with a high financial and clinical burden on society. ASD care thus requires improved insights in costs and its drivers as a critical step toward the improvement of value, i.e., the ratio between delivered health outcome and associated costs., Methods: Patient characteristics and surgical variables were collected following ethical approval in a cohort of 139 ASD patients, treated between December, 2014 and January, 2018. Clinical hospital costs were calculated, including all care activities, from initial consultation to 1 year after initial surgery (excl. overhead) in a university hospital setting. Multiple linear regression analysis was performed to analyze the impact of patient and surgical characteristics on clinical costs., Results: 75.5% of the total clinical hospital cost (&OV0556;27,865) was incurred during initial surgery with costs related to the operating theatre (80.3%), nursing units (11.9%), and intensive care (2.9%) being the largest contributors. 57.5% of the variation in total cost could be explained in order of importance by surgical invasiveness, age, coronary disease, single or multiple-staged surgery, and mobility status. Revision surgery, unplanned surgery due to complications, was found to increase average costs by 87.6% compared with elective surgeries (&OV0556; 44,907 (± &OV0556; 23,429) vs. &OV0556; 23,944 (± &OV0556; 7302))., Conclusion: This study identified opportunities for process improvement by calculating the total clinical hospital costs. In addition, it identified patient and treatment characteristics that predict 57.5% of cost variation, which could be taken into account when developing a payment system. Future research should include outcome data to assess variation in value., Level of Evidence: 4.

Objectives: Using a standardised diagnostic and generic treatment path for breast cancer, and the molecular subtype perspective, we aim to measure the impact of several patient and disease characteristics on the overall treatment cost for patients. Additionally, we aim to generate insights into the drivers of cost variability within one medical domain., Design, Setting and Participants: We conducted a retrospective study at a breast clinic in Belgium. We used 14 anonymous patient files for conducting our analysis., Results: Significant cost variations within each molecular subtype and across molecular subtypes were found. For the luminal A classification, the cost differential amounts to roughly 166%, with the greatest treatment cost amounting to US$29 780 relative to US$11 208 for a patient requiring fewer medical activities. The major driver for these cost variations relates to disease characteristics. For the luminal B classification, a cost difference of roughly 242% exists due to both disease-related and patient-related factors. The average treatment cost for triple negative patients amounted to US$26 923, this is considered to be a more aggressive type of cancer. The overall cost for HER2-enriched is driven by the inclusion of Herceptin, thus this subtype is impacted by disease characteristics. Cost variability across molecular classifications is impacted by the severity of the disease, thus disease-related factors are the major drivers of cost., Conclusions: Given the cost challenge in healthcare, the need for greater cost transparency has become imperative. Through our analysis, we generate initial insights into the drivers of cost variability for breast cancer. We found evidence that disease characteristics such as severity and more aggressive cancer forms such as HER2-enriched and triple negative have a significant impact on treatment cost across the different subtypes. Similarly, patient factors such as age and presence of gene mutation contribute to differences in treatment cost variability within molecular subtypes., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fluorescent labelled proteins, SMLM is capable of nanometer scale reconstruction of cellular structures. Precise localization of proteins in 3D astigmatic SMLM is dependent on parameter sensitive preprocessing steps to select regions of interest. With SMLM acquisition highly variable over time, it is non-trivial to find an optimal static parameter configuration. The high emitter density required for reconstruction of complex protein structures can compromise accuracy and introduce artifacts. To address these problems, we introduce two modular auto-tuning pre-processing methods: adaptive signal detection and learned recurrent signal density estimation that can leverage the information stored in the sequence of frames that compose the SMLM acquisition process. We show empirically that our contributions improve accuracy, precision and recall with respect to the state of the art. Both modules auto-tune their hyper-parameters to reduce the parameter space for practitioners, improve robustness and reproducibility, and are validated on a reference in silico dataset. Adaptive signal detection and density prediction can offer a practitioner, in addition to informed localization, a tool to tune acquisition parameters ensuring improved reconstruction of the underlying protein complex. We illustrate the challenges faced by practitioners in applying SMLM algorithms on real world data markedly different from the data used in development and show how ERGO can be run on new datasets without retraining while motivating the need for robust transfer learning in SMLM.

In many hospitals there are patients who receive surgery later than what is medically indicated. In one of Europe's largest hospitals, the University Hospital Leuven, this is the case for approximately every third patient. Serving patients late cannot always be avoided as a highly utilized OR department will sometimes suffer capacity shortage, occasionally leading to unavoidable delays in patient care. Nevertheless, serving patients late is a problem as it exposes them to an increased health risk and hence should be avoided whenever possible. In order to improve the current situation, the delay in patient scheduling had to be quantified and the responsible mechanism, the scheduling process, had to be better understood. Drawing from this understanding, we implemented and tested realistic patient scheduling methods in a discrete event simulation model. We found that it is important to model non-elective arrivals and to include elective rescheduling decisions made on surgery day itself. Rescheduling ensures that OR related performance measures, such as overtime, will only loosely depend on the chosen patient scheduling method. We also found that capacity considerations should guide actions performed before the surgery day such as patient scheduling and patient replanning. This is the case as those scheduling strategies that ensure that OR capacity is efficiently used will also result in a high number of patients served within their medically indicated time limit. An efficient use of OR capacity can be achieved, for instance, by serving patients first come, first served. As applying first come, first served might not always be possible in a real setting, we found it is important to allow for patient replanning.

This paper presents an overview of the operating theatre planning and scheduling practice of hospitals in Flanders (Belgium). An electronic survey was sent to 95 hospitals in which surgeries are performed, which eventually resulted in a response set of 52 hospitals (55%). The questionnaire did not only focus on issues related to the elective (inpatient and outpatient) planning and scheduling process, but also questioned how hospitals currently deal with the occurrence of non-elective surgeries (urgencies and emergencies). We indicate what goals health managers try to achieve and how this planning is established. We furthermore pay attention to some possible disruptions to the schedule and the corresponding anticipatory methods. Despite the proliferation of computerized planning and scheduling procedures proposed by the scientific community, the implementation rate of satisfying technological planning or evaluation systems still seems to be low. In order to increase the operating theatre efficiency, a closer cooperation between the academic institutions and the practitioners should be encouraged.

This paper presents a software system that visualizes the impact of the master surgery schedule on the demand for various resources throughout the rest of the hospital. The master surgery schedule can be seen as the engine that drives the hospital. Therefore, it is very important for decision makers to have a clear image on how the demand for resources is linked to the surgery schedule. The software presented in this paper enables schedulers to instantaneously view the impact of, e.g., an exchange of two block assignments in the master surgery schedule on the expected resource consumption pattern. A case study entailing a large Belgian surgery unit illustrates how the software can be used to assist in building better surgery schedules.