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In the past decade, a growing amount of evidence has demonstrated that organelles do not act autonomously and independently but rather communicate with each other to coordinate different processes for proper cellular function. With a highly extended network throughout the cell, the endoplasmic reticulum (ER) plays a central role in interorganelle communication through membrane contact sites. Here, we highlight recent evidence indicating that the ER also forms contacts with membrane-less organelles. These interactions contribute to the dynamic assembly and disassembly of condensates and controlled protein secretion. Additionally, emerging evidence suggests their involvement in mRNA localization and localized translation. We further explore exciting future directions of this emerging theme in the organelle contact site field.

Local mRNA translation in axons is critical for the spatiotemporal regulation of the axonal proteome. A wide variety of mRNAs are localized and translated in axons; however, how protein synthesis is regulated at specific subcellular sites in axons remains unclear. Here, we establish that the axonal endoplasmic reticulum (ER) supports axonal translation in developing rat hippocampal cultured neurons. Axonal ER tubule disruption impairs local translation and ribosome distribution. Using nanoscale resolution imaging, we find that ribosomes make frequent contacts with axonal ER tubules in a translation-dependent manner and are influenced by specific extrinsic cues. We identify P180/RRBP1 as an axonally distributed ribosome receptor that regulates local translation and binds to mRNAs enriched for axonal membrane proteins. Importantly, the impairment of axonal ER-ribosome interactions causes defects in axon morphology. Our results establish a role for the axonal ER in dynamically localizing mRNA translation, which is important for proper neuron development.

Dawood Sayed,1 Timothy R Deer,2,3 Vinicius Tieppo Francio,1 Christopher M Lam,1 Kamil Sochacki,4 Nasir Hussain,5 Tristan E Weaver,5 Jay Karri,6,7 Vwaire Orhurhu,8,9 Natalie Holmes Strand,10 Jacqueline Soicher Weisbein,11 Jonathan M Hagedorn,12 Ryan S D’Souza,12 Ryan R Budwany,2 Ahish Chitneni,13 Kasra Amirdelfan,14 Michael J Dorsi,15 Dan TD Nguyen,16 Christopher Bovinet,17 Alaa Abd-Elsayed18 1Anesthesiology and Pain Medicine, The University of Kansas Medical Center, Kansas City, KS, USA; 2Pain Services, Spine and Nerve Center of the Virginias, Charleston, WV, USA; 3Anesthesiology and Pain Medicine, West Virginia University School of Medicine, Charleston, WV, USA; 4Department of Anesthesiology and Perioperative Medicine, Rutgers Robert Wood Johnson, New Brunswick, NJ, USA; 5Anesthesiology, the Ohio State University Wexner Medical Center, Columbus, OH, USA; 6Department of Orthopedic Surgery, University of Maryland School of Medicine, Baltimore, MD, USA; 7Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA; 8Department of Anesthesiology, University of Pittsburgh Medical Center, Williamsport, PA, USA; 9Pain Medicine, MVM Health, East Stroudsburg, PA, USA; 10Anesthesiology and Pain Medicine, Mayo Clinic, Phoenix, AZ, USA; 11Interventional Pain Management, Napa Valley Orthopaedic Medical Group, Napa, CA, USA; 12Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA; 13Department of Rehabilitation & Regenerative Medicine, New York Presbyterian – Columbia & Cornell, New York, NY, USA; 14Director of Clinical Research, Boomerang Healthcare, Walnut Creek, CA, USA; 15Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA; 16Neuroradiology & Pain Solutions of Oklahoma, Edmond, OK, USA; 17The Spine Center of SE Georgia, Brunswick, GA, USA; 18Anesthesiology, University of Wisconsin, Madison, WI, USACorrespondence: Dawood Sayed, Anesthesiology and

Dawood Sayed,1 Timothy R Deer,2,3 Vinicius Tieppo Francio,1 Christopher M Lam,1 Kamil Sochacki,4 Nasir Hussain,5 Tristan E Weaver,5 Jay Karri,6,7 Vwaire Orhurhu,8,9 Natalie Holmes Strand,10 Jacqueline Soicher Weisbein,11 Jonathan M Hagedorn,12 Ryan S D’Souza,12 Ryan R Budwany,2 Ahish Chitneni,13 Kasra Amirdelfan,14 Michael J Dorsi,15 Dan TD Nguyen,16 Christopher Bovinet,17 Alaa Abd-Elsayed18 1Anesthesiology and Pain Medicine, The University of Kansas Medical Center, Kansas City, KS, USA; 2Pain Services, Spine and Nerve Center of the Virginias, Charleston, WV, USA; 3Anesthesiology and Pain Medicine, West Virginia University School of Medicine, Charleston, WV, USA; 4Department of Anesthesiology and Perioperative Medicine, Rutgers Robert Wood Johnson, New Brunswick, NJ, USA; 5Anesthesiology, the Ohio State University Wexner Medical Center, Columbus, OH, USA; 6Department of Orthopedic Surgery, University of Maryland School of Medicine, Baltimore, MD, USA; 7Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA; 8Department of Anesthesiology, University of Pittsburgh Medical Center, Williamsport, PA, USA; 9Pain Medicine, MVM Health, East Stroudsburg, PA, USA; 10Anesthesiology and Pain Medicine, Mayo Clinic, Phoenix, AZ, USA; 11Interventional Pain Management, Napa Valley Orthopaedic Medical Group, Napa, CA, USA; 12Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA; 13Department of Rehabilitation & Regenerative Medicine, New York Presbyterian – Columbia & Cornell, New York, NY, USA; 14Director of Clinical Research, Boomerang Healthcare, Walnut Creek, CA, USA; 15Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA; 16Neuroradiology & Pain Solutions of Oklahoma, Edmond, OK, USA; 17The Spine Center of SE Georgia, Brunswick, GA, USA; 18Anesthesiology, University of Wisconsin, Madison, WI, USACorrespondence: Dawood Sayed, Anesthesiology and Pain Medicine, The Universit

Rooftop solar is increasingly promoted as a source of low-carbon household energy; however, there is a plausible concern that solar installations might influence energy consumption patterns in ways that undermine potential environmental benefits. In this study, we examine the impact of residential rooftop solar panels on energy usage in Vietnam. Leveraging a comprehensive and unique panel dataset, we employ a difference-in-differences identification strategy to estimate the effects of solar installation on consumption. Our models reveal that households installing solar panels reduce grid consumption (typically carbon-intensive) by approximately 3.6 %. This reduction occurs concomitantly with an increase in total consumption of around 16 %, indicating a substantial rebound effect from solar panels. Nonetheless, dynamic models suggest a diminishing trend over time for both the decline in grid usage and the rebound effect, leveling off to 1.5 % and 3.5 %, respectively, within one year of solar installation. Acknowledging the marked differences in household consumption behavior and electricity demand dynamics between developing and developed nations, our research provides valuable insights into the understanding of the solar rebound effect and its dynamics over time.

BACKGROUND: Whether initial invasive management in older vs younger adults with chronic coronary disease and moderate or severe ischemia improves health status or clinical outcomes is unknown. OBJECTIVES The goal of this study was to examine the impact of age on health status and clinical outcomes with invasive vs conservative management in the ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) trial. METHODS: One-year angina-specific health status was assessed with the 7-item Seattle Angina Questionnaire (SAQ) (score range 0-100; higher scores indicate better health status). Cox proportional hazards models estimated the treatment effect of invasive vs conservative management as a function of age on the composite clinical outcome of cardiovascular death, myocardial infarction, or hospitalization for resuscitated cardiac arrest, unstable angina, or heart failure. RESULTS: Among 4,617 participants, 2,239 (48.5%) were aged <65 years, 1,713 (37.1%) were aged 65 to 74 years, and 665 (14.4%) were aged $75 years. Baseline SAQ summary scores were lower in participants aged <65 years. Fully adjusted differences in 1-year SAQ summary scores (invasive minus conservative) were 4.90 (95% CI: 3.56-6.24) at age 55 years, 3.48 (95% CI: 2.40-4.57) at age 65 years, and 2.13 (95% CI: 0.75-3.51) at age 75 years (Pinteraction = 0.008). Improvement in SAQ Angina Frequency was less dependent on age (Pinteraction = 0.08). There were no age differences between invasive vs conservative management on the composite clinical outcome (Pinteraction = 0.29). CONCLUSIONS: Older patients with chronic coronary disease and moderate or severe ischemia had consistent improvement in angina frequency but less improvement in angina-related health status with invasive management compared with younger patients. Invasive management was not associated with improved clinical outcomes in older or younger patients.

Though early ASD diagnosis is highly stable, this case report describes a rare situation in which symptoms resolved without intervention over a 4 month period. We do not recommend delaying diagnosis in symptomatic children who meet criteria but when major behavioral changes are reported after diagnosis, reevaluation may be beneficial.

Timothy Deer,1 Ankur A Patel,2 Dawood Sayed,3 Ashley Bailey-Classen,4 Ashley Comer,1 Benjamin Gill,5 Kiran Patel,6 Alaa Abd-Elsayed,7 Natalie Strand,8 Jonathan M Hagedorn,9 Zohra Hussaini,3 Nasir Khatri,10 Ryan Budwany,1 Melissa Murphy,11 Dan Nguyen,12 Vwaire Orhurhu,13 Morteza Rabii,14 Douglas Beall,15 Stephen Hochschuler,16 Michael E Schatman,17,18 Timothy Lubenow,19 Richard Guyer,16,20 Ahmed M Raslan21 1The Spine and Nerve Center of the Virginias, Charleston, WV, USA; 2Weill Cornell Tri-Institutional Pain Medicine Program, Department of Anesthesiology, Weill Medical College of Cornell University, New York, NY, USA; 3Department of Anesthesiology and Pain Medicine, the University of Kansas Medical Center, Kansas City, KS, USA; 4Trinity Pain Medicine Associates, Fort Worth, TX, USA; 5Department of Physical Medicine and Rehabilitation, University of Missouri, Columbia, MO, USA; 6Lenox Hill Hospital, Northwell Health, New York, NY, USA; 7Department of Anesthesiology, University of Wisconsin, Madison, WI, USA; 8Interventional Pain Management, Mayo Clinic, Scottsdale, AZ, USA; 9Department of Anesthesiology and Perioperative Medicine, Division of Pain Medicine, Mayo Clinic, Rochester, MN, USA; 10Novant Health Spine Specialists, Charlotte, NC, USA; 11North Texas Orthopedics and Spine Center, Grapevine, TX, USA; 12Neuroradiology and Pain Solutions of Oklahoma, Oklahoma City, OK, USA; 13Department of Anesthesia, University of Pittsburgh Medical Center, Susquehanna, PA, USA; 14Crimson Pain Management, Overland Park, Kansas, KS, USA; 15Comprehensive Specialty Care, Edmond, OK, USA; 16Texas Back Institute, Plano, TX, USA; 17Department of Anesthesiology, Perioperative Care and Pain Medicine, NYU Grossman School of Medicine, New York, NY, USA; 18Department of Population Health – Division of Medical Ethics, NYU Grossman School of Medicine, New York, NY, USA; 19Department of Anesthesiology, Rush University Medical Center, Chicago, IL, USA; 20Department of Orthopedic Surgery, Uni

Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Materials and methods: 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results: The DSC, ASD, and HD95 values for the test dataset were 62.2%, 2.54mm, and 7mm, respectively. AI segmented contours were dosimetrically equivalent to the expert physician's contours. The observer study showed that expert physicians' scored AI contours (mean=3.7) higher than inexperienced physicians' contours (mean=3.1). When inexperienced physicians started with AI contours, the score improved to 3.7. Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

Louis Cantor,1 Dan Lindfield,2 Federico Ghinelli,3 Anna W &Sacute;wider,3 Francesca Torelli,3 Carolyn Steeds,3 Jaime E Dickerson Jr,4,5 Dan Q Nguyen6 1Eugene and Marilyn Glick Eye Institute, Indiana School of Medicine, Indianapolis, IN, USA; 2Royal Surrey County Hospital, Guildford, England, UK; 3Valid Insight, Macclesfield, England, UK; 4Sight Sciences, Menlo Park, CA, USA; 5North Texas Eye Research Institute, University of North Texas Health Science Center, Fort Worth, TX, USA; 6Mid-Cheshire Hospitals NHS Foundation Trust, Crewe, UKCorrespondence: Francesca Torelli, Valid Insight, Ropewalks, Newton St, Macclesfield, SK11 6QJ, UK, Tel +44 203 750 9833 Ext 703, Email ftorelli@validinsight.comIntroduction: Selective laser trabeculoplasty (SLT) and minimally invasive glaucoma surgery (MIGS) are increasingly used options for mild-to-moderate open-angle glaucoma (OAG) care. While most MIGS devices are indicated for use in combination with cataract surgery only, with phacoemulsification playing a role in lowering IOP, newer technologies can also be used as standalone glaucoma surgery.Methods: This systematic literature review (SLR) aimed to assess the clinical, economic, and humanistic outcomes of MIGS and SLT for the treatment of OAG and was conducted according to PRISMA guidelines. Studies that assessed MIGS or SLT in at least one treatment arm versus any other glaucoma treatment in adults with mild-to-moderate OAG were included. Clinical, humanistic (health-related quality of life [HRQoL] and patient burden), and economic data were extracted, and the methodological quality of included studies was evaluated.Results: A total of 2720 articles were screened, and 81 publications were included. Fifty-eight reported clinical outcomes. The majority assessed iStent or iStent inject (n=41), followed by OMNI (n=9), gonioscopy-assisted transluminal trabeculotomy (GATT) or the Kahook Dual Blade (KDB) (n=7), Hydrus (n=6), SLT (n=5), Xen Gel Stent (n=2), PreserFlo (n=1), and iTr

The primary cilium is a central signaling component during embryonic development. Here we focus on CROCCP2, a hominid-specific gene duplicate from ciliary rootlet coiled coil (CROCC), also known as rootletin, that encodes the major component of the ciliary rootlet. We find that CROCCP2 is highly expressed in the human fetal brain and not in other primate species. CROCCP2 gain of function in the mouse embryonic cortex and human cortical cells and organoids results in decreased ciliogenesis and increased cortical progenitor amplification, particularly basal progenitors. CROCCP2 decreases ciliary dynamics by inhibition of the IFT20 ciliary trafficking protein, which then impacts neurogenesis through increased mTOR signaling. Loss of function of CROCCP2 in human cortical cells and organoids leads to increased ciliogenesis, decreased mTOR signaling, and impaired basal progenitor amplification. These data identify CROCCP2 as a human-specific modifier of cortical neurogenesis that acts through modulation of ciliary dynamics and mTOR signaling., SCOPUS: ar.j, info:eu-repo/semantics/published

Estimating the uncertainty of deep learning models in a reliable and efficient way has remained an open problem, where many different solutions have been proposed in the literature. Most common methods are based on Bayesian approximations, like Monte Carlo dropout (MCDO) or Deep ensembling (DE), but they have a high inference time (i.e. require multiple inference passes) and might not work for out-of-distribution detection (OOD) data (i.e. similar uncertainty for in-distribution (ID) and OOD). In safety critical environments, like medical applications, accurate and fast uncertainty estimation methods, able to detect OOD data, are crucial, since wrong predictions can jeopardize patients safety. In this study, we present an alternative direct uncertainty estimation method and apply it for a regression U-Net architecture. The method consists in the addition of a branch from the bottleneck which reconstructs the input. The input reconstruction error can be used as a surrogate of the model uncertainty. For the proof-of-concept, our method is applied to proton therapy dose prediction in head and neck cancer patients. Accuracy, time-gain, and OOD detection are analyzed for our method in this particular application and compared with the popular MCDO and DE. The input reconstruction method showed a higher Pearson correlation coefficient with the prediction error (0.620) than DE and MCDO (between 0.447 and 0.612). Moreover, our method allows an easier identification of OOD (Z-score of 34.05). It estimates the uncertainty simultaneously to the regression task, therefore requires less time or computational resources., Comment: 11 pages, 3 figures and 3 Tables

Phase separation of components of ER exit sites (ERES) into membraneless compartments, the Sec bodies, occurs in Drosophila cells upon exposure to specific cellular stressors, namely, salt stress and amino acid starvation, and their formation is linked to the early secretory pathway inhibition. Here, we show Sec bodies also form in secretory mammalian cells upon the same stress. These reversible and membraneless structures are positive for ERES components, including both Sec16A and Sec16B isoforms and COPII subunits. We find that Sec16A, but not Sec16B, is a driver for Sec body formation, and that the coalescence of ERES components into Sec bodies occurs by fusion. Finally, we show that the stress-induced coalescence of ERES components into Sec bodies precedes ER exit inhibition, leading to their progressive depletion from ERES that become non-functional. Stress relief causes an immediate dissolution of Sec bodies and the concomitant restoration of ER exit. We propose that the dynamic conversion between ERES and Sec body assembly, driven by Sec16A, regulates protein exit from the ER during stress and upon stress relief in mammalian cells, thus providing a conserved pro-survival mechanism in response to stress.

BackgroundA baseline of hepatitis C virus (HCV) burden and other HCV epidemiological profiles is necessary for HCV micro-elimination in Ho Chi Minh City (HCMC), Viet Nam. This study aimed to determine HCV exposure and prevalence of HCV viremia as well as the proportion of HCV testing and treatment uptake among participants.MethodsFrom 2019 to 2020, the probability proportionate to size sampling method was deployed to representatively invite approximately 20,000 adults (18 or older) throughout HCMC to free screening and linkage to care for HCV.FindingsIn HCMC, the weighted prevalence of anti-HCV was 1·3% (95% CI, 1·1%-1·6%). Individuals born from 1945 to 1964 had the anti-HCV prevalence of 3·6% (95% CI, 3·0%-4·2%) and represented 40·4% of all HCV cases. There were wide variations in anti-HCV prevalence in HCMC, including variations between districts, risk factors, and socioeconomic statuses. A baseline HCV continuum of care for the city demonstrated that only 28·5% (85/298, 95%CI 23·4-33·7%) of persons with anti-HCV (+) were aware of their HCV status, with 77.6% (66/85, 95%CI 68·8-86·5%) diagnosing HCV incidentally, 82·7% (62/75, 95%CI 74·1-91·2%) initiating anti-HCV therapy, and 53.6% (30/56, 95%CI 40·5-66·6%) achieving HCV cures.InterpretationThere remains a considerable disease burden of HCV in HCMC of which a significant proportion was in the age group born between 1945 to 1964. Additionally, there were significant gaps in HCV awareness, screening, and access to care in the community in Viet Nam. Thus, future interventions must have pragmatic targets, be tailored to the local needs, and emphasise screening.FundingThis work was supported by investigator-sponsored research grants from Gilead Sciences Inc. (Grant No: IN-US-987-5382); Roche Diagnostic International Ltd. (Grant No. SUB-000196); and in-kind donations from Abbott Diagnostic Viet Nam; Hepatitis B Foundation; Medic Medical Center, Viet Nam; Johns Hopkins University School of Medicine's Center of Excellenc

Volvo Construction Equipment (VCE) has invested in an advanced lasermachine that can cut sheet metal parts with angled edges. As a result, thedevice provides expanded possibilities in the design and production of sheetmetal products.The material cost component is significant in the production of sheet metalproducts. This cost can be reduced by reducing the plate's thickness andchanging its dimensions.This study examines the effects of a thinner plate thickness combined withchanged height and width dimensions and a more robust weld joint inmanufacturing a so-called A stay, a part of the articulated hauler from VCE.The study was conducted with the Design for Six Sigma (DFSS) process,product research, development, and R&D methodology, which falls under theoverall scientific, technical research methodology "Systems Engineering."The study's conclusion shows that material saving with a thinner sheet is at theexpense of reduced quality and product cycle life. In addition,recommendations for in-depth analyses have also been proposed in the study'sdiscussion section.

The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.

BackgroundA baseline of hepatitis C virus (HCV) burden and other HCV epidemiological profiles is necessary for HCV micro-elimination in Ho Chi Minh City (HCMC), Viet Nam. This study aimed to determine HCV exposure and prevalence of HCV viremia as well as the proportion of HCV testing and treatment uptake among participants.MethodsFrom 2019 to 2020, the probability proportionate to size sampling method was deployed to representatively invite approximately 20,000 adults (18 or older) throughout HCMC to free screening and linkage to care for HCV.FindingsIn HCMC, the weighted prevalence of anti-HCV was 1·3% (95% CI, 1·1%-1·6%). Individuals born from 1945 to 1964 had the anti-HCV prevalence of 3·6% (95% CI, 3·0%-4·2%) and represented 40·4% of all HCV cases. There were wide variations in anti-HCV prevalence in HCMC, including variations between districts, risk factors, and socioeconomic statuses. A baseline HCV continuum of care for the city demonstrated that only 28·5% (85/298, 95%CI 23·4-33·7%) of persons with anti-HCV (+) were aware of their HCV status, with 77.6% (66/85, 95%CI 68·8-86·5%) diagnosing HCV incidentally, 82·7% (62/75, 95%CI 74·1-91·2%) initiating anti-HCV therapy, and 53.6% (30/56, 95%CI 40·5-66·6%) achieving HCV cures.InterpretationThere remains a considerable disease burden of HCV in HCMC of which a significant proportion was in the age group born between 1945 to 1964. Additionally, there were significant gaps in HCV awareness, screening, and access to care in the community in Viet Nam. Thus, future interventions must have pragmatic targets, be tailored to the local needs, and emphasise screening.FundingThis work was supported by investigator-sponsored research grants from Gilead Sciences Inc. (Grant No: IN-US-987-5382); Roche Diagnostic International Ltd. (Grant No. SUB-000196); and in-kind donations from Abbott Diagnostic Viet Nam; Hepatitis B Foundation; Medic Medical Center, Viet Nam; Johns Hopkins University School of Medicine's Center of Excellenc

Volvo Construction Equipment (VCE) has invested in an advanced lasermachine that can cut sheet metal parts with angled edges. As a result, thedevice provides expanded possibilities in the design and production of sheetmetal products.The material cost component is significant in the production of sheet metalproducts. This cost can be reduced by reducing the plate's thickness andchanging its dimensions.This study examines the effects of a thinner plate thickness combined withchanged height and width dimensions and a more robust weld joint inmanufacturing a so-called A stay, a part of the articulated hauler from VCE.The study was conducted with the Design for Six Sigma (DFSS) process,product research, development, and R&D methodology, which falls under theoverall scientific, technical research methodology "Systems Engineering."The study's conclusion shows that material saving with a thinner sheet is at theexpense of reduced quality and product cycle life. In addition,recommendations for in-depth analyses have also been proposed in the study'sdiscussion section.

The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.

When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic difference between the model predictions and the final contours revised and approved by clinicians for an initial group of patients. The learned style feature differences are concatenated with the new patients (query) features and then decoded to get the style-adapted segmentations. The model is independent of practice styles and anatomical structures. It meta-learns with simulated style differences and does not need to be exposed to any real clinical stylized structures during training. Once trained on the simulated data, it can be deployed for clinical use to adapt to new practice styles and new anatomical structures without further training. To show the proof of concept, we tested the Prior-guided DDL network on six different practice style variations for three different anatomical structures. Pre-trained segmentation models were adapted from post-operative clinical target volume (CTV) segmentation to segment CTVstyle1, CTVstyle2, and CTVstyle3, from parotid gland segmentation to segment Parotidsuperficial, and from rectum segmentation to segment Rectumsuperior and Rectumposterior. The mode performance was quantified with Dice Similarity Coefficient (DSC). With adaptation based on only the first three patients, the average DSCs were improved from 78.6, 71.9, 63.0, 52.2, 46.3 and 69.6 to 84.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice styles

We evaluated the temporal performance of a deep learning (DL) based artificial intelligence (AI) model for auto segmentation in prostate radiotherapy, seeking to correlate its efficacy with changes in clinical landscapes. Our study involved 1328 prostate cancer patients who underwent definitive radiotherapy from January 2006 to August 2022 at the University of Texas Southwestern Medical Center. We trained a UNet based segmentation model on data from 2006 to 2011 and tested it on data from 2012 to 2022 to simulate real world clinical deployment. We measured the model performance using the Dice similarity coefficient (DSC), visualized the trends in contour quality using exponentially weighted moving average (EMA) curves. Additionally, we performed Wilcoxon Rank Sum Test to analyze the differences in DSC distributions across distinct periods, and multiple linear regression to investigate the impact of various clinical factors. The model exhibited peak performance in the initial phase (from 2012 to 2014) for segmenting the prostate, rectum, and bladder. However, we observed a notable decline in performance for the prostate and rectum after 2015, while bladder contour quality remained stable. Key factors that impacted the prostate contour quality included physician contouring styles, the use of various hydrogel spacer, CT scan slice thickness, MRI-guided contouring, and using intravenous (IV) contrast. Rectum contour quality was influenced by factors such as slice thickness, physician contouring styles, and the use of various hydrogel spacers. The bladder contour quality was primarily affected by using IV contrast. This study highlights the challenges in maintaining AI model performance consistency in a dynamic clinical setting. It underscores the need for continuous monitoring and updating of AI models to ensure their ongoing effectiveness and relevance in patient care.

Prediction uncertainty estimation has clinical significance as it can potentially quantify prediction reliability. Clinicians may trust 'blackbox' models more if robust reliability information is available, which may lead to more models being adopted into clinical practice. There are several deep learning-inspired uncertainty estimation techniques, but few are implemented on medical datasets -- fewer on single institutional datasets/models. We sought to compare dropout variational inference (DO), test-time augmentation (TTA), conformal predictions, and single deterministic methods for estimating uncertainty using our model trained to predict feeding tube placement for 271 head and neck cancer patients treated with radiation. We compared the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) trends for each method at various cutoffs that sought to stratify patients into 'certain' and 'uncertain' cohorts. These cutoffs were obtained by calculating the percentile "uncertainty" within the validation cohort and applied to the testing cohort. Broadly, the AUC, sensitivity, and NPV increased as the predictions were more 'certain' -- i.e., lower uncertainty estimates. However, when a majority vote (implementing 2/3 criteria: DO, TTA, conformal predictions) or a stricter approach (3/3 criteria) were used, AUC, sensitivity, and NPV improved without a notable loss in specificity or PPV. Especially for smaller, single institutional datasets, it may be important to evaluate multiple estimations techniques before incorporating a model into clinical practice., Comment: 12 pages, 5 figures

Phase separation of components of ER exit sites (ERES) into membraneless compartments, the Sec bodies, occurs in Drosophila cells upon exposure to specific cellular stressors, namely, salt stress and amino acid starvation, and their formation is linked to the early secretory pathway inhibition. Here, we show Sec bodies also form in secretory mammalian cells upon the same stress. These reversible and membraneless structures are positive for ERES components, including both Sec16A and Sec16B isoforms and COPII subunits. We find that Sec16A, but not Sec16B, is a driver for Sec body formation, and that the coalescence of ERES components into Sec bodies occurs by fusion. Finally, we show that the stress-induced coalescence of ERES components into Sec bodies precedes ER exit inhibition, leading to their progressive depletion from ERES that become non-functional. Stress relief causes an immediate dissolution of Sec bodies and the concomitant restoration of ER exit. We propose that the dynamic conversion between ERES and Sec body assembly, driven by Sec16A, regulates protein exit from the ER during stress and upon stress relief in mammalian cells, thus providing a conserved pro-survival mechanism in response to stress.

Medical image registration is a fundamental and vital task which will affect the efficacy of many downstream clinical tasks. Deep learning (DL)-based deformable image registration (DIR) methods have been investigated, showing state-of-the-art performance. A test time optimization (TTO) technique was proposed to further improve the DL models' performance. Despite the substantial accuracy improvement with this TTO technique, there still remained some regions that exhibited large registration errors even after many TTO iterations. To mitigate this challenge, we firstly identified the reason why the TTO technique was slow, or even failed, to improve those regions' registration results. We then proposed a two-levels TTO technique, i.e., image-specific optimization (ISO) and region-specific optimization (RSO), where the region can be interactively indicated by the clinician during the registration result reviewing process. For both efficiency and accuracy, we further envisioned a three-step DL-based image registration workflow. Experimental results showed that our proposed method outperformed the conventional method qualitatively and quantitatively.

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dose-volume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH criteria. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for a conventional planning model. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. In the interest of reproducibility, our data and code is freely available at https://github.com/ababier/open-kbp-opt., Comment: 19 pages, 7 tables, 6 figures

Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images. Propagating expert-drawn contours from the pre-treatment planning CT (pCT) through traditional or deep learning (DL) based deformable image registration (DIR) can achieve improved results in many situations. Typical DL-based DIR models are population based, that is, trained with a dataset for a population of patients, so they may be affected by the generalizability problem. In this paper, we propose a method called test-time optimization (TTO) to refine a pre-trained DL-based DIR population model, first for each individual test patient, and then progressively for each fraction of online ART treatment. Our proposed method is less susceptible to the generalizability problem, and thus can improve overall performance of different DL-based DIR models by improving model accuracy, especially for outliers. Our experiments used data from 239 patients with head and neck squamous cell carcinoma to test the proposed method. Firstly, we trained a population model with 200 patients, and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models. We compared each of the individualized models with the population model in terms of segmentation accuracy. The number of patients with at least 0.05 DSC improvement or 2 mm HD95 improvement by TTO averaged over the 17 selected structures for the state-of-the-art architecture Voxelmorph is 10 out of 39 test patients. The average time for deriving the individualized model using TTO from the pre-trained population model is approximately four minutes. When adapting the individualized model to a later fraction of the same patient, the average time is reduced to about one minute and the accuracy is slightly improved.

Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians to edit contours. However, deep learning (DL)-based direct segmentation of CBCT images is a challenging task, mainly due to the poor image quality and lack of well-labelled large training datasets. Deformable image registration (DIR) is often used to propagate the manual contours on the planning CT (pCT) of the same patient to CBCT. In this work, we undertake solving the problems mentioned above with the assistance of DIR. Our method consists of three main components. First, we use deformed pCT contours derived from multiple DIR methods between pCT and CBCT as pseudo labels for initial training of the DL-based direct segmentation model. Second, we use deformed pCT contours from another DIR algorithm as influencer volumes to define the region of interest for DL-based direct segmentation. Third, the initially trained DL model is further fine-tuned using a smaller set of true labels. We found that DL-based direct segmentation on CBCT trained with pseudo labels and without influencer volumes shows poor performance compared to DIR-based segmentation. However, adding deformed pCT contours as influencer volumes in the direct segmentation network dramatically improves segmentation performance, reaching the accuracy level of DIR-based segmentation. The DL model with influencer volumes can be further improved through fine-tuning using a smaller set of true labels. Experiments showed that 7 out of 19 structures have an at least 0.2 Dice similarity coefficient increase compared to DIR-based segmentation. A DL-based direct CBCT segmentation model can be improved to outperform DIR-based segmentation models by using deformed pCT contours as pseudo labels and influencer volumes for initial training, and by using a smaller set of true labels for model fine tuning.

Binh PDU, An PL, Nguyen NA, et al. J Multidiscip Healthc. 2021;14:2229–2238. Table 4 Mean Scores of Professional Attributes Across Academic Years and Between Two Faculties Page 2231, Data Analysis section, first sentence, the text “Statistical analysis was performed using SPSS Statistics (SPSS Statistics Inc., Chicago, US) version 20.0” should read “Statistical analysis was performed using SPSS Statistics (SPSS Statistics Inc., Chicago, US) version 20.0 and STATA version 16.0”. Pages 2232 and 2234, Differences in Students’ Perception of Professional Attributes Relating to QOC Between the Two Faculties section, text has been deleted from paragraphs 2 and 3, the text should read as follows: “In both faculties, the trait Self-Awareness was reported as the most important attribute, the next being Quality of Care and Professional Habit in each year. Social Duty received the lowest mean scores, which made it the least important trait in both faculties almost at all years. Students’ perception in most attributes did not differ significantly between the two faculties in their clinical years (4th - 6th year). However, significant differences were found in the pre-clinical phase. 2nd year medical students 15significantly assigned higher scores for Self-Awareness and Social Duty over 2nd year TM students (t(356) = −3.12, p=0.002; t(356) = −3.53, p < 0.001).” Page 2235, errors have been found in Table 4. The correct Table 4 is shown in Download Article. Page 2236, Discussion section, left column, text has been deleted from paragraph 2, the text should read as follows: “This is the first study in Vietnam to reveal both Medical and TM students’ perceptions on QOC-relating to professional attributes. No differences were found between the faculties in the order of these 4 attributes, being from the most to the least important, which was Self-Aware

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. The proposed model uses the PB dose and computed tomography image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1 mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 s. The trained model can be adapted to new datasets through transfer learning. Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.

Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: 1) accelerate the L-COSY experiment and 2) quantify L-COSY spectra. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiment

PurposeA previously developed ordinary differential equation (ODE) that models the dynamic interaction and distinct radiosensitivity between cancer stem cells (CSC) and differentiated cancer cells (DCC) was used to explain the definitive treatment failure in Glioblastoma Multiforme (GBM) for conventionally and hypo-fractionated treatments. In this study, optimization of temporal dose modulation based on the ODE equation is performed to explore the feasibility of improving GBM treatment outcome.MethodsA non-convex optimization problem with the objective of minimizing the total cancer cell number while maintaining the normal tissue biological effective dose (BEDnormal) at 100 Gy, equivalent to the conventional 2 Gy × 30 dosing scheme was formulated. With specified total number of dose fractions and treatment duration, the optimization was performed using a paired simulated annealing algorithm with fractional doses delivered to the CSC and DCC compartments and time intervals between fractions as variables. The recurrence time, defined as the time point at which the total tumor cell number regrows to 2.8×109 cells, was used to evaluate optimization outcome. Optimization was performed for conventional treatment time frames equivalent to currently and historically utilized fractionation schemes, in which limited improvement in recurrence time delay was observed. The efficacy of a super hyperfractionated approach with a prolonged treatment duration of one year was therefore tested, with both fixed regular and optimized variable time intervals between dose fractions corresponding to total number of fractions equivalent to weekly, bi-weekly, and monthly deliveries (n = 53, 27, 13). Optimization corresponding to BEDnormal of 150 Gy was also obtained to evaluate the possibility in further recurrence delay with dose escalation.ResultsFor the super hyperfractionated schedules with dose fraction number equivalent to weekly, bi-weekly, and monthly deliveries, the recurrence time p

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revision. Here, we present a novel concept called artificial intelligence assisted contour revision (AIACR) and demonstrate its feasibility. The proposed clinical workflow of AIACR is as follows given an initial contour that requires a clinicians revision, the clinician indicates where a large revision is needed, and a trained deep learning (DL) model takes this input to update the contour. This process repeats until a clinically acceptable contour is achieved. The DL model is designed to minimize the clinicians input at each iteration and to minimize the number of iterations needed to reach acceptance. In this proof-of-concept study, we demonstrated the concept on 2D axial images of three head-and-neck cancer datasets, with the clinicians input at each iteration being one mouse click on the desired location of the contour segment. The performance of the model is quantified with Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff Distance (HD95). The average DSC/HD95 (mm) of the auto-generated initial contours were 0.82/4.3, 0.73/5.6 and 0.67/11.4 for three datasets, which were improved to 0.91/2.1, 0.86/2.4 and 0.86/4.7 with three mouse clicks, respectively. Each DL-based contour update requires around 20 ms. We proposed a novel AIACR concept that uses DL models to assist clinicians in revising contours in an efficient and effective way, and we demonstrated its feasibility by using 2D axial CT images from three head-and-neck cancer datasets.

Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance. We propose a site-agnostic, 3D dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset.

Volumetric modulated arc therapy planning is a challenging problem in high-dimensional, non-convex optimization. Traditionally, heuristics such as fluence-map-optimization-informed segment initialization use locally optimal solutions to begin the search of the full arc therapy plan space from a reasonable starting point. These routines facilitate arc therapy optimization such that clinically satisfactory radiation treatment plans can be created in about 10 minutes. However, current optimization algorithms favor solutions near their initialization point and are slower than necessary due to plan overparameterization. In this work, arc therapy overparameterization is addressed by reducing the effective dimension of treatment plans with unsupervised deep learning. An optimization engine is then built based on low-dimensional arc representations which facilitates faster planning times.

In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training dataset augmented from the prior information available in an ART workflow - an approach we term Intentional Deep Overfit Learning (IDOL). Implementing the IDOL framework in any task in radiotherapy consists of two training stages: 1) training a generalized model with a diverse training dataset of N patients, just as in the conventional DL approach, and 2) intentionally overfitting this general model to a small training dataset-specific the patient of interest (N+1) generated through perturbations and augmentations of the available task- and patient-specific prior information to establish a personalized IDOL model. The IDOL framework itself is task-agnostic and is thus widely applicable to many components of the ART workflow, three of which we use as a proof of concept here: the auto-contouring task on re-planning CTs for traditional ART, the MRI super-resolution (SR) task for MRI-guided ART, and the synthetic CT (sCT) reconstruction task for MRI-only ART. In the re-planning CT auto-contouring task, the accuracy measured by the Dice similarity coefficient improves from 0.847 with the general model to 0.935 by adopting the IDOL model. In the case of MRI SR, the mean absolute error (MAE) is improved by 40% using the IDOL framework over the conventional model. Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by utilizing the IDOL framework.

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19-positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We collected one dataset at UT Southwestern (UTSW), and three external datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran), and MosMedData (Russia). We divided the data into 2 classes: COVID-19-positive and COVID-19-negative patients. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on a single dataset achieved accuracy/area under the receiver operating characteristics curve (AUC) values of 0.87/0.826 (UTSW), 0.97/0.988 (CC-CCCI), and 0.86/0.873 (COVID-CTset) when evaluated on their own dataset. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, the performance dropped close to an AUC of 0.5 (random guess) for all models when evaluated on a different dataset outside of its training datasets. Including the MosMedData, which only contained positive labels, into the training did not necessarily help the performance on the other datasets. Multiple factors likely contribute to these results, such as patient demographics and differences in image acquisition or reconstruction, causing a data shift among different study cohorts.

Automatic segmentation of medical images with DL algorithms has proven to be highly successful. With most of these algorithms, inter-observer variation is an acknowledged problem, leading to sub-optimal results. This problem is even more significant in post-operative clinical target volume (post-op CTV) segmentation due to the absence of macroscopic visual tumor in the image. This study, using post-op CTV segmentation as the test bed, tries to determine if physician styles are consistent and learnable, if there is an impact of physician styles on treatment outcome and toxicity; and how to explicitly deal with physician styles in DL algorithms to facilitate its clinical acceptance. A classifier is trained to identify which physician has contoured the CTV from just the contour and corresponding CT scan, to determine if physician styles are consistent and learnable. Next, we evaluate if adapting automatic segmentation to physician styles would be clinically feasible based on a lack of difference between outcomes. For modeling different physician styles of CTV segmentation, a concept called physician style-aware (PSA) segmentation is proposed which is an encoder-multidecoder network trained with perceptual loss. With the proposed physician style-aware network (PSA-Net), Dice similarity coefficient (DSC) accuracy increases on an average of 3.4% for all physicians from a general model that is not style adapted. We show that stylistic contouring variations also exist between institutions that follow the same segmentation guidelines and show the effectiveness of the proposed method in adapting to new institutional styles. We observed an accuracy improvement of 5% in terms of DSC when adapting to the style of a separate institution.

Low Dose Computed Tomography (LDCT) is clinically desirable due to the reduced radiation to patients. However, the quality of LDCT images is often sub-optimal because of the inevitable strong quantum noise. Inspired by their unprecedent success in computer vision, deep learning (DL)-based techniques have been used for LDCT denoising. Despite the promising noise removal ability of DL models, people have observed that the resolution of the DL-denoised images is compromised, decreasing their clinical value. Aiming at relieving this problem, in this work, we developed a more effective denoiser by introducing a high-resolution network (HRNet). Since HRNet consists of multiple branches of subnetworks to extract multiscale features which are later fused together, the quality of the generated features can be substantially enhanced, leading to improved denoising performance. Experimental results demonstrated that the introduced HRNet-based denoiser outperforms the benchmarked UNet-based denoiser in terms of superior image resolution preservation ability while comparable, if not better, noise suppression ability. Quantitative metrics in terms of root-mean-squared-errors (RMSE)/structure similarity index (SSIM) showed that the HRNet-based denoiser can improve the values from 113.80/0.550 (LDCT) to 55.24/0.745 (HRNet), in comparison to 59.87/0.712 for the UNet-based denoiser.

Inter-observer variation is a significant problem in clinical target volume(CTV) segmentation in postoperative settings, where there is no gross tumor present. In this scenario, the CTV is not an anatomically established structure, but one determined by the physician based on the clinical guideline used, the preferred tradeoff between tumor control and toxicity, their experience and training background, and other factors. This results in high inter-observer variability between physicians. This variability has been considered an issue, but the absence of multiple physician CTV contours for each patient and the significant amount of time required for dose planning have made it impractical to study its dosimetric consequences. In this study, we analyze the impact that variations in physician style have on dose to organs-at-risk(OAR) by simulating the clinical workflow via deep learning. For a given patient previously treated by one physician, we use deep learning-based tools to simulate how other physicians would contour the CTV and how the corresponding dose distributions would look for this patient. To simulate multiple physician styles, we use a previously developed in-house CTV segmentation model that can produce physician style-aware segmentations. The corresponding dose distribution is predicted using another in-house deep learning tool, which, can predict dose within 3% of the prescription dose, on average, on the test data. For every test patient, four different physician style CTVs are considered, and four different dose distributions are analyzed. OAR dose metrics are compared, showing that even though physician style variations result in organs getting different doses, all the important dose metrics except Maximum Dose point are within the clinically acceptable limit.

Pham Duong Uyen Binh,1,2 Pham Le An,3 Nghia An Nguyen,3 Dan Van Nguyen,4 Giao Huynh,5 Harumi Gomi,2 Motofumi Yoshida2 1Department of Educational Quality Assurance, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam; 2Department of Social Medical Sciences, Graduate School of Medicine, International University of Health and Welfare, Tokyo, Japan; 3Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam; 4Faculty of Traditional Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam; 5Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamCorrespondence: Pham Duong Uyen BinhDepartment of Social Medical sciences, Graduate School of Medicine, International University of Health and Welfare, Tokyo, JapanTel +84 961156505Email p.d.uyenbinh@gmail.comPurpose: Health science students need to be professional to improve quality of care (QOC) in the current Vietnamese healthcare system. Therefore, we aim to evaluate medicine and traditional medicine (TM) students’ perception of the professional attributes relating to QOC for improving inter-disciplined professionalism training that promotes QOC in Vietnam.Methods: The cross-sectional study was carried on 2039 students of 6 years at the University of Medicine and Pharmacy at Ho Chi Minh City (HUMP) from the medical and TM faculty in March, 2021. The Vietnamese American Board of Internal Medicine (ABIM) questionnaire (2011) was used as the survey instrument. The confirmatory factor analysis (CFA) was performed to confirm the validity of the scale in TM students. Mean, Min–Max, standard deviation and sample paired t-test were performed for Likert scale. The one-way ANOVA was used for inferential statistics.Results: The CFA demonstrated the validity of the Vietnamese questionnaire in measuring 4 QOC-relating professional attributes, previously fou

DNA organization within a cell is multifaceted and dynamic. Not only does chromatin structure vary with position, as there is significant heterogeneity in the degree of local compaction within the global DNA complex, but it also changes with time, showing dependence on, e.g., the growth stage of a cell. The condensation state of DNA is intimately coupled to gene accessibility and expression: changes to DNA organization can modulate transcription and, in turn, transcribed RNA can reorganize chromatin structure. We sought to develop a more tractable, in vitro platform to probe this complex relationship and utilized a self-assembling DNA nanostar (NS) as the principal component of a chromatin mimic. In this dissertation, I will discuss our research with condensed phases of self-assembled NSs, describing structural properties of the system's two phases (i.e. NS-networks & NS-liquids), features of NS-liquids that enable application as a model of membraneless organelles, and initial work examining the transcription of genes integrated within NS-liquids.

DNA organization within a cell is multifaceted and dynamic. Not only does chromatin structure vary with position, as there is significant heterogeneity in the degree of local compaction within the global DNA complex, but it also changes with time, showing dependence on, e.g., the growth stage of a cell. The condensation state of DNA is intimately coupled to gene accessibility and expression: changes to DNA organization can modulate transcription and, in turn, transcribed RNA can reorganize chromatin structure. We sought to develop a more tractable, in vitro platform to probe this complex relationship and utilized a self-assembling DNA nanostar (NS) as the principal component of a chromatin mimic. In this dissertation, I will discuss our research with condensed phases of self-assembled NSs, describing structural properties of the system's two phases (i.e. NS-networks & NS-liquids), features of NS-liquids that enable application as a model of membraneless organelles, and initial work examining the transcription of genes integrated within NS-liquids.

PURPOSE: This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model, via transfer learning with minimal input data, to three different internal treatment planning styles and one external institution planning style. METHODS: We built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles, 14-29 cases per style, in the same institution and 20 cases treated in a different institution to adapt the source model to four target models in total. We compared the dose distributions predicted by the source model and the target models with the corresponding clinical plan dose used for patient treatments and quantified the improvement in the prediction quality for the target models over the source model using the Dice similarity coefficients (DSC) of 0% to 100% isodose volumes and the dose-volume-histogram (DVH) parameters of the planning target volume and the organs-at-risk. RESULTS: The source model accurately predicts dose distributions for plans generated in the same source style, but performs sub-optimally for the three different internal and one external target styles, with the mean DSC ranging between 0.81-0.94 and 0.82-0.91 for the internal and the external styles, respectively. With transfer learning, the target model predictions improved the mean DSC to 0.88-0.95 and 0.92-0.96 for the internal and the external styles, respectively. Target model predictions significantly improved the accuracy of the DVH parameter predictions to within 1.6%. CONCLUSION: We demonstrated the problem of model generalizability for DL-based dose prediction and the feasibility of using transfer learning to solve this problem. With 14-29 cases per style, we successfully adapted the source model into several different practice st