Contemporary deep learning-based decision systems are well-known for requiring high-volume datasets in order to produce generalized, reliable, and high-performing models. However, the collection of such datasets is challenging, requiring time-consuming processes involving also expert clinicians with limited time. In addition, data collection often raises ethical and legal issues and depends on costly and invasive procedures. Deep generative models such as generative adversarial networks and variational autoencoders can capture the underlying distribution of the examined data, allowing them to create new and unique instances of samples. This study aims to shed light on generative data augmentation techniques and corresponding best practices. Through in-depth investigation, we underline the limitations and potential methodology pitfalls from critical standpoint and aim to promote open science research by identifying publicly available open-source repositories and datasets. Scarce and limited available data in oncology are burdensome for deep learning architectures, which tends to lead to poor decision systems.Heterogeneity in MRI images is a fundamental hurdle for generalization.Generative models are an emerging technology that could address these drawbacks through synthetic data augmentation.Evaluation metrics such as quantitative algorithms, qualitative assessment by experts, and a downstream task are essential for the validity of synthetic images.Deep learning (DL) architectures gained immense popularity in the past few years and specifically when AlexNet  has showApplications of ConvNets in medical image analysis were first applied but with limited success in the early 1990s by , 14 mainA key differentiating factor from other imaging domains is that magnetic resonance imaging (MRI) poses some significant challenges regarding the data collection because of the lack of tissue-specific values, different anatomical areas, varying imaging modalities Fig.  differenThe scarcity of large and diverse patient cohorts with high-quality clinical data for specific clinical outcomes has been reported to be the most significant drawback of using DL in medical imaging tasks . Data auG that creates new samples from noise and the discriminator D that distinguishes among the valid and invalid synthetic samples. The convergence of these models is achieved simultaneously by an algorithm that is based on game theory with a minimax loss. VAEs are commonly used for feature extraction by compressing the input to a compact representation, but they have lately been employed for generative properties by manipulating the latent space.Unsupervised learning techniques such as generative adversarial networks (GANs)  and variGeneration of synthetic data for cross-sectional imaging modalities in oncology is highly challenging since there is a significant underlying biological variability that leads to multiple phenotypic and genetic subtypes of cancerous tumors . AdditioThe current literature review presents from a critical standpoint studies that implement novel deep generative data augmentation techniques on cancer MRI examinations, aiming to highlight the best techniques for synthetic tumor representations, identify best practices for data analysis, promote open science approaches based on widely available public data and open-access source code repositories for improved reproducibility. Another key element of this study is to report the most robust evaluation techniques of the generated samples, which include a visual assessment by experts, direct quantitative metrics prior to analysis and indirect methods from the performance enhancements of the downstream analysis.The sections of the rest of the review are organized as follows: in sect. 2  the search methodology is presented, in sect. 3  key generative architectures used by the examined studies are analyzed, in sect. 4  details about the studies included in this review are reported, in sect. 5  the adopted evaluation methods, limitations and future remarks are discussed, and the final section concludes the review.A systematic review of studies that use data augmentation techniques that enhance cancer differentiation via deep generative models was performed to assess the impact of GANs and VAE applications in oncology. This systematic review was conducted by following the reporting checklist of the Preferred Reporting Items for Reviews and Meta-Analyses (PRISMA) . For theTwo reviewers screened the titles, abstracts and conclusions of the records independently and papers that were clearly not related to the subject matter were discarded. During the first screening phase, the abstracts and conclusions of papers were carefully assessed. Despite the fact that the majority of these papers contained a subset of the searched keywords, a second full-text screening showed that the methodology presented in many of these works was not relevant to deep learning architectures for data augmentation, and thus they were removed. In total, the current study reviewed 36 papers that were based either on widely used, open datasets or on custom in-house data. Most of the custom datasets had data from patients with neoplasms that had been verified by a biopsy, and one study was comprised of data from patients who had either a biopsy or a follow-up imaging examination within the past twelve months. Notably, these papers included different imaging protocols as depicted in Fig. The updated QUADAS-2   criteriaFormulating the objective loss function of a GAN model leads the researchers to create a plethora of such models Fig. , Fig. 6 The pix2pix GAN  Fig. 6  6 a is aBeers et al.  were theHan et al.  proposedShin et al.  used theThe Asynchronized Discriminator GAN  is Medical imaging datasets are frequently limited in terms of size and diversity, especially in oncology since the natural prevalence of the disease leads to imbalanced sets. Deepak et al.  applied VAE is stable during the training process, but blurry images can be produced. On the other hand, GANs can synthesize realistic images, but they are unstable during training. Kwon et al.  proposednt}α-GAN  architecThe combination of auto-encoders and GANs  was propLesion segmentation in medical images is a challenging task and can be achieved by automated or semi-automated detection of lesions or organs within 2D or 3D examinations. The high variability of tumors in terms of shape and texture is the major challenge for segmentation tasks. Generative models are suitable for diversifying limited datasets with new samples. In particular, to address the issue of overlapping pixel intensities of regions of interest (ROI) with other tissue types in brain MRI sequences which can make challenging the automatic pixelwise segmentation, Hamghalam et al.  proposedQi et al.  highlighGuo et al.  proposedIsocitrate dehydrogenase 1 (IDH1) mutation information is crucial for diagnosis, prognosis and guidance in clinical decisions due to observation that IDH1 mutated gliomas have an improved overall survival rate rather than with IDH1 wild type , 65. HowA large number of examinations in similar datasets are not annotated on a pixel-basis, which makes training supervised DL models difficult. In  the authCarver et al.  proposedMok et al.  proposedDikici et al.  proposedlgorithm , which wKamli et al.  incorporPseudoprogression (PsP) and true tumor progression (TTP) in glioblastoma multiforme (GBM) can occur after standard treatment. The distinction between them is mainly based on MRI analysis of the lesion area, which is a time-consuming procedure for clinicians. Li et al.  merged DThe number of patients, lesion types, modalities, pre-processing and downstream tasks are demonstrated in Table DCGANs and cGANs are novel variants of generative models published after their first appearance . Kitchenu et al.  employedADC values derived from diffusion-weighted MRI are useful non-invasive biomarkers for accurately assessing the clinical significance (CS) of suspicious glands for prostate cancer (PCa) . HoweverYang et al.  presenteFernandez-Quilez et al.  proposedTaking into consideration the DCGANs drawbacks, Yu et al.  proposedTo address the cross-client variation problem in medical image data, Yan et al.  proposedGao et al.  employedGenerative models have been implemented for synthesizing multi-parametric breast MRI image patches by Haarburger et al. . Two cusSun et al.  implemenDetails about the examined datasets, pre-processing methods, deep generative model architectures, and a comparative analysis are shown in Tables Deep learning applications regarding medical image analysis applications require big databases from different acquisition protocols in order to effectively capture the intra-class variability in lesion types. This is especially relevant in oncology, where in many cases the natural prevalence of the disease, anatomical variability and tumor heterogeneity cannot be modeled due to the limited available data. Deep generative models can potentially alleviate this drawback by capturing the distribution of each lesion and synthetically augmenting the patient cohort with a robust and diverse sample distribution.Three types of synthetic data evaluation and their combinations were followed in the examined studies: a) direct by specific image quality metrics : b) indirect by examining the delta of the performance prior and after samples generation: and c) qualitative by either expert clinicians or by plotting cluster distribution of the generated samples.A direct assessment of the generative task using statistical metrics was performed by twelve studies –85, 91 pOn the contrary, the majority of the examined studies, as illustrated in Fig. A significant number (nineteen) , 89–92 oThis study has some limitations. It was particularly difficult to identify studies that incorporated GANs or VAE for data augmentation since, in many papers, it was merely a component of their overall study pipeline or was only referenced briefly in a paragraph with little information. Furthermore, in the majority of studies, the authors did not provide the necessary information  that would allow us to fully assess the quality of each study and objectively evaluate their findings. As a result, most of these experiments are impossible to replicate. The original source code or custom datasets are not publicly available for the examined manuscripts, but only in a small number of studies, making extraction of the required hyperparameter problematic.There are different strategies to mitigate the limited population of the original dataset, including subsampling of examinations at a slice level  –84, 88 aThe inherent heterogeneity of cancer images emanating from specific genetic traits, local mutational diversity, varying shape attributes, unclear boundaries, multiple subtypes and stages can significantly affect clinical outcomes. Because of all these parameters, capturing discriminative imaging markers for assessing the output variables can become challenging when generating images that include highly heterogeneous regions of interest.Random noise vectors have been utilized as input for data generation in twenty-one , 88–91 aThe reproducibility of deep learning models in medical imaging is a major challenge since decision support systems must adhere to the relevant legislation and be licensed by the respective regulatory bodies. Many studies, as evident by tables Class imbalance on a patient-basis regarding the examined disease can potentially result in lower performance in the minority class in both the generative model and downstream tasks , 90. ConA trade-off between signal quality and noise in the generated MRI examinations is a key element during the convergence of generative models to capture the granular imaging patterns in each class distribution. Constraints during generation might be implemented to ensure that the intensities of pixels are uniform and realistic . There aDeep models trained on a single medical center might capture biased distributions. Thus, external validation sets are of paramount importance for generalization , 89, 90.Novel quantitative and qualitative methods should be developed  to proviGe et al.  suggest GANs trained on multicentric MRI data can benefit from scanner variability and further improve generalization of the targeted task . AccordiRegional legislative frameworks for privacy are posing significant challenges in medical data analysis. Deep generative models can assist in providing full anonymity of medical data , 75, 88,Despite the active research of generative models on MRI image analysis, non-trivial challenges still remain. Future studies should examine a more diverse patient cohort to capture the tumor variability in terms of shape, location, anatomical region, genetic background, histological subtypes and other clinically significant parameters.In particular, 35% of the examined studies, as illustrated in Fig. The existing generative models have been developed to converge with selection criteria that are ROI size-restricted. The introduction of architectures that can capture the high variability of tumors is crucial. In particular, more effort should be invested into generating MRI examinations with small lesion ROIs such as low-grade gliomas, lung nodules and other similar-sized neoplasms. Oncology imaging is also characterized by the fact that the population, scanner manufacturers and acquisition methods at different sites vary a lot. Generative models fitted on a diverse set of data could achieve an improved and generalized representation of data distributions that are invariant to these differences.However, the heterogeneity of data might not be preserved in the generated distribution and artifacts might be introduced due to the drawbacks of current cost functions and architectures. Thus, future architectures should be employed on tumor datasets along with new metrics that are better suited for robust evaluation of generative models, not just for image quality but also for assessing diversity in the generated dataset.The computational cost and the required time for developing 3D generative models are high. This limits the majority of studies to 2D models and, therefore, key volumetric features cannot be captured by the synthetic distribution. Only two studies , 92 syntDeep generative modeling is a key technology for alleviating important limiting factors that render data collection challenging, such as the natural prevalence of several cancer types, morphological diversity of lesions and lack of standardization of MRI protocols. Although there are some trustworthiness issues in many of the presented studies, we argue that when implemented properly by strictly following the corresponding best practices and recent advances in this field, generative models have the potential to revolutionize medicine by correcting the class imbalances of the disease in the dataset, diversifying anatomically the available region of interest, providing vendor-specific samples and supporting downstream tasks with larger training sets.


Although skilled goals of care (GOC) conversations are known to reduce aggressive futile end-of-life care, they have not been widely implemented nor standardized in the care of gynecologic malignancies. Clinicians express concern regarding patient readiness and willingness to participate in these conversations, which may be a barrier to GOC discussions.This is a qualitative study, conducted at an academic institution in the United States, of patients with gynecologic malignancies at high risk of death within six months and who had recently completed a GOC discussion with their oncology clinician during an ambulatory visit. Within 10 days of this conversation, patients were approached for potential participation in an hour-long semistructured interview. Patients enrolled in hospice or who were non-English speaking were excluded. Participants were enrolled until thematic saturation was reached. Interviews were transcribed and coded using the five-stage thematic approach.Ten women were consented and participated in semistructured interviews, which occurred a median of 4 (range 1–18) days after the index GOC discussion. The median age was 64 (range 37–78), and the most common diagnosis (50%) was recurrent platinum-resistant ovarian cancer. Four themes were identified: (1) delivery of the GOC conversation, (2) importance of prioritizing individual values, (3) involving family in decision making, and (4) openness to discussing discontinuation of anticancer treatment and hospice. Patients generally felt these GOC conversations were useful, providing a space to express their values and did not compromise the patient–clinician relationship.Patients seemed willing to engage in GOC conversations and were appreciative of their clinicians' communication skills. Often, they used this conversation as an opportunity to convey personal values affecting their care. These individualized discussions between patients, their families, and their health care team allow for opportunities to revisit prognosis, elucidate an individual's values and goals for their care, and subsequently allow for a goal-concordant treatment recommendation from the clinician.10Patients with gynecologic cancers are a unique population as they are traditionally cared for by a single clinician, who provides both surgical intervention and medical management of their cancer throughout the duration of their cancer trajectory. Although this has many benefits it may also present a barrier as clinicians with long-term relationships with their patients may hesitate to initiate these challenging conversations for fear of degrading trust or rapport.5 In addition, 20% of patients did not have a single documented GOC discussion before death, identifying a gap in our then current practice pattern.5In a retrospective 2013 study at our institution, more than one-third of gynecologic oncology patients participated in their first documented GOC discussion during their final hospital admission, usually during their last month of life.11 We hoped that the systematic implementation of GOC conversation would enhance the clinician–patient relationship and allow for the receipt of goal-concordant cancer care. The focus of this qualitative study is to explore the patient experience following earlier outpatient GOC conversations.In August 2017, we implemented a successful quality improvement initiative at our academic institution to systematically implement a framework for early, outpatient GOC discussions to address this deficiency, which has been reported elsewhere.This study was approved by the Institutional Review Board (Pro00092891). In August 2017, a quality improvement project to systematically implement outpatient GOC conversations at a single academic institution in North Carolina was initiated. Gynecologic oncology patients at high risk for death in six months were systematically and prospectively identified based on their current disease status and in accordance with clinical criteria .For the purposes of this study, “high-risk” patients were identified by trained nursing staff and the patient's primary gynecologic oncologist was prompted to initiate a GOC conversation within three outpatient gynecologic oncology visits. Suggested content of the GOC discussion was integrated within an electronic medical record template for ease of use and standardization of documentation . GOC disPatients eligible for this qualitative study included English-speaking patients at least 18 years of age with an outpatient GOC discussion in the last three weeks that was documented using the electronic medical record (EMR) template. Exclusion criteria included patients with current hospice enrollment or those who participated in a GOC conversation during an inpatient admission. Inpatient and hospice patients were excluded as this study focused on patients participating in GOC conversations in outpatient settings. Non-English-speaking patients were excluded given that Spanish-language consents were not available. We purposefully sampled eligible patients by race, partner status, and cancer diagnosis to ensure a breadth of perspective. Participating subjects received $50 for their time.Informed consent was obtained from all participants. Basic demographics and cancer data were abstracted from the medical record (L.A.G. and N.C.Z.). Experienced qualitative researchers from the institution's Behavioral Health and Survey Research Core completed the semistructured hour-long interviews (M.F. and L.F.). An interview guide was used to guide conversations . RecruitTen subjects were consented for participation between October 2018 and May 2019. Although no formal screening log was kept during the duration of the study, ∼20 patients were approached to recruit our study cohort. Baseline demographics are demonstrated in The majority (80%) of the participants were interviewed by phone. The median time between the index GOC conversation and the interview was four days (range 1–18).Four major themes were independently identified by two coders: (1) delivery of the GOC conversation, (2) importance of prioritizing individual values, (3) involving family in decision making, and (4) openness to discussing discontinuation of anticancer treatment and hospice. Quotations from participants highlighting each theme are displayed in Yet [they were] gentle in [their] wording you know, [they] would touch me, [they] would reach out and put [their] hand on my hand when I started crying and you know, would be like, okay get tissues you know, and [they] just, it was very compassionate and I felt like I was dealing with someone that saw my pain and felt sorry you know, they felt bad about it.—37-year-old participant with cervical cancerParticipants emphasized the importance of the technique of content delivery, feeling as if their clinician heard and understood them, and the importance of honesty throughout each of the 10 interviews. Most participants (8 of 10) discussed how the style of delivery of the GOC conversations impacted their experience with their clinician. The majority of participants felt that the GOC conversations were appropriately delivered. A few identified opportunities for improvement in the clinician's delivery, namely inclusion of empathic statements throughout the conversation. These sentiments were reflected with statements such as the following:Most of all that [they were] going to be honest with me. That was really critical to me, that when you know in your mind that the gig is up, I want to know that and no fancy words.—77-year-old participant with ovarian cancerMost participants reported that the GOC conversation allowed the opportunity for their clinician to hear and understand them as individuals. In addition, several participants discussed the importance of honesty from their clinician during GOC conversations with statements such as the following:During the interview, participants were asked to share the content of their GOC conversation with their clinician. In all 10 interviews, the importance of prioritizing ones' individual values was highlighted. Within this discussion, the following four values were highlighted as priorities: (1) relationships with others, (2) specific hobbies/leisurely activities, (3) side effect/pain tolerability, and (4) treatment options. In addition, patients readily proposed prioritizing their time for leisurely activities or completion of “bucket list” items.Each participant was asked the questions “Who came with you to your appointment?” and “After the appointment, did you talk with family or close friends about the GOC conversation? Why or why not?” Only 1 out of the 10 patients did not have any family participate in the GOC conversation. Responses to these questions were divided into two groups: (1) patients who regarded their families as integral and active participants in their GOC conversations and decisions and (2) patients who regarded their families as well-intentioned although holding discordant opinions in matters of GOC. Specifically, four patients noted that their family dynamics were often a barrier during GOC conversations. In each of the four cases, the family members were inclined to pursue ongoing treatment and the patients themselves intended to focus primarily on symptom management.I told [them] I trusted [them] to let me know when it reached the point where I would be at, where I could still back out gracefully so to speak.—77-year-old participant with ovarian cancerMost participants acknowledged that although discussing discontinuation of anticancer treatment and/or hospice was a challenging conversation, they felt favorably about how the discussion was approached and handled by their clinician. Participants often highlighted the importance of quality versus quantity of time. In addition, trust in their clinician to let them know when it was time to stop treatment and start hospice was a common theme and is reflected by statements such as the following:Although most participants trusted their clinician to provide guidance on discontinuing anticancer treatment and initiating hospice, some responses reflect confusion surrounding the appropriate timing of hospice, with some participants feeling rushed into hospice, whereas others preferring to delay hospice enrollment until death was imminent.12–14 The findings of this qualitative study reinforced the notion that clinician–patient communication is an essential component in the treatment of patients with gynecologic malignancies, particularly near the EOL. Participants discussed four themes: qualities of the GOC conversation, prioritizing values, involving family in decision making, and discussing discontinuation of anticancer treatment and hospice.Prior research demonstrates that clinician communication through GOC discussions is associated with better quality of life and positive caregiver experiences, reduces use of futile medical care, and is cost-effective.15 GOC conversations vary in their detail, with some limited to a discussion of patient's code status, whereas others are a more nuanced narrative entry of a patient's knowledge of their disease, prognosis, and elicitation of goals with subsequent goal-concordant treatment recommendation. Despite both quantitative and qualitative evidence supporting GOC discussions, barriers exist in integrating these conversations into clinical care, including the perception of lack of clinician time and communication skills training and clinician fear of patient nonreadiness for these conversations.Despite data demonstrating numerous benefits of GOC discussions in the care of patients with cancer, these conversations are neither standard nor widely adapted. In one study, only 14% of women with ovarian cancer recalled a structured GOC conversation as part of their care.Although the sample size of our study was small, this qualitative study addresses the latter point and reinforced prior study that patients are willing to engage in these conversations, are appreciative of their clinicians' communication efforts, and may have concerns regarding the involvement of family members in these discussions. Our data support explicit questioning of the patient regarding who they want to participate (or not participate) in these discussions to ensure patient comfort and candidness.In addition, patients reported differing opinions on the appropriate timing of hospice care. The potential for patient misconception of what hospice care entails is important for providers to be aware of and plan for. This confusion could represent a knowledge gap that may be a target for future interventions, potentially increasing patient comfort, knowledge base, and willingness to engage in GOC discussions earlier in the disease process.8 Owing to the perceived gravity of the conversation, clinicians may fear patient willingness and readiness to participate in a GOC, especially when discussions may focus on prognosis, offers to discontinue cancer-directed treatment, and pursue hospice care. There are several themes coded in this study that suggest the opposite. None of the patients in this study expressed doubt about the utility or need for this conversation.In a multicenter survey of 1256 clinicians caring for seriously ill patients, “family members' or patients' difficulty accepting a poor prognosis” was identified as the single most important barrier to initiating GOC conversation.16Prioritizing values is foundational to medical decision making and the delivery of goal-concordant care. With the patient able to identify and express their values, the clinician is then given the opportunity to recommend treatments that are in line with those wishes and, most importantly, forego therapies that may pose a barrier to attaining the individual's goals. These qualitative findings are consistent with previous study in patients with nongynecologic malignancies. In one prior qualitative study of patients with advanced cancer , patients viewed GOC conversation as an important tool to maintain a trusting relationship with their clinician.12Several questions still remain, including the timing of GOC conversations and the specific content discussed. Our study adds to existing evidence refuting the concern that discussing EOL topics contributes to patient distress and supports a wider implementation of GOC conversations for patients with gynecologic malignancies.Our study has several limitations. As participants had to agree to take part in an interview, participation bias is inevitable. Subjects who chose to participate in the study may have been more reflective or willing to discuss EOL topics compared with those who declined participation. In addition, some eligible patients were not well enough to be interviewed, limiting the generalizability of the study. Finally, the study population included only English speakers, which introduces the potential for cultural bias if patients from non-English-speaking backgrounds and those who require use of an interpreter have different openness to GOC conversations.A strength of this study was the clinical timeframe for enrollment. Patients were eligible only if a GOC conversation occurred within three weeks of their scheduled study interview, intended to limit the effect of recall bias. In addition, interviews were conducted among participants representing a diverse set of gynecologic malignancies, whereas the previous literature has focused primarily on patients with ovarian cancer.Our study demonstrates that high-risk patients with gynecologic malignancies near the EOL were not only receptive to GOC discussions with their health care team, but also identified these as opportunities to share their values. The conversations helped highlight the importance of trust between clinicians and patients and did not appear to compromise this relationship. Efforts aimed to improve clinician communication skills could also further improve effectiveness of GOC conversations, as patients felt the style of delivery of the GOC conversations, as well as clinician understanding, impacted their overall experience.


In the past few years, there has been extraordinary interest in how the gut communicates with the brain. This is because substantial and gathering data has emerged to suggest that sensory nerve pathways between the gut and brain may contribute much more widely in heath and disease, than was originally presumed. In the skin, the different types of sensory nerve endings have been thoroughly characterized, including the morphology of different nerve endings and the sensory modalities they encode. This knowledge is lacking for most types of visceral afferents, particularly spinal afferents that innervate abdominal organs, like the gut. In fact, only recently have the nerve endings of spinal afferents in any visceral organ been identified. What is clear is that spinal afferents play the major role in pain perception from the gut to the brain. Perhaps surprisingly, the majority of spinal afferent nerve endings in the gut express the ion channel TRPV1, which is often considered to be a marker of “nociceptive” neurons. And, a majority of gut-projecting spinal afferent neurons expressing TRPV1 are activated at low thresholds, in the “normal” physiological range, well below the normal threshold for detection of painful sensations. This introduces a major conundrum regarding visceral nociception. How should we define a “nociceptor” in the gut? We discuss the notion that nociception from the gut wall maybe a process encrypted into multiple different morphological types of spinal afferent nerve ending, rather than a single class of sensory ending, like free-endings, suggested to underlie nociception in skin. Nociceptors are primary afferent neurons that are capable of detecting and responding to potentially damaging stimuli . In the A variety of stimuli can activate sensory nerve endings from within the gut wall which initiate the sensation of pain. The visceromotor response (VMR) to colorectal distension  or bladdThere is no doubt that in vertebrate animals, noxious or potentially damaging stimuli applied to the gut can trigger the sensation of pain . Howeverex vivo preparations  , but alsThe problem with using terminology like “visceral nociceptors” in the gut is that well accepted nociceptive ion channels, like TRPV1 are highly expressed in the vast majority (∼90%) of spinal afferent neuron that innervate the colon . In thisTransgenic reporter mice have been useful for bulk labeling of large populations of axons in peripheral organs, like the gut. However, they have not been useful for identifying the detailed morphology of the nerve endings of putative “nociceptors” in any visceral organs. This is because firstly, many of the neurochemical markers expressed in spinal afferents are also expressed in vagal afferents or enteric neurons, making interpretation of the origin of the cell bodies of the labeled axons uncertain. And, as mentioned, in reporter mice, labeling of so many axons precludes identification of the fine morphological features that may arise from a single primary afferent. The use of intersectional neurogenetics to narrow the targeted populations in transgenic mice by excluding enteric neuronal populations may overcome this issue in the coming years as the approach becomes more popular. However, all reporter mice will likely label numerous axons that overlap each other making it very difficult to resolve endings that arise from one primary afferent neuron.Anterograde technique from single DRG in live mice has enabled unequivocal identification of spinal afferent nerve endings . Using tIn a recent study, a survival surgical technique was developed to remove specific populations of DRG unilaterally or bilaterally, from live mice . This apTaken together, it is suggested that there may well be no exclusive nociception-dedicated primary afferent neuronor transduction site(s) in the viscera. Rather, nociception may arise from bulk signaling across all visceral afferent morphological and neurochemical subclasses. Indeed, stimuli such as noxious distension that may recruit afferents with high-distension thresholds inevitably recruit all low-threshold afferents too, which can continue to encode higher firing intensities into the noxious range.via primary sensory nerves, it is important we develop a clearer understanding of which class(es) of afferent fibers that lie between the gut and brain may contribute to nociception. This is an essential step for development of novel therapeutics to potentially target visceral (gut) nociception. The discovery large populations of TRPV1 expressing spinal afferent neurons underlie a major component of the mechanically activated pain pathway from colon to brain suggests that low threshold mechanically activated ion channels play major roles in encoding both physiological (non-noxious) and noxious pain signals to the brain. What is clear is that there is an extraordinarily complex array of different morphological types of spinal afferent ending in the gut; and it is becoming increasingly unlikely that a single population of sensory neuron underlies nociception and pain-signaling along the gut-brain axis.As more scientists are turning their attention to understanding gut-brain communication The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.NS wrote the manuscript in collaboration with TH, ZX, and HH. All authors corrected and approved the final manuscript.


The general mechanism of calcium-triggered chemical transmitter release from neuronal synapses has been intensely studied, is well-known, and highly conserved between species and synapses across the nervous system. However, the structural and functional details within each transmitter release site (or active zone) are difficult to study in living tissue using current experimental approaches owing to the small spatial compartment within the synapse where exocytosis occurs with a very rapid time course. Therefore, computer simulations offer the opportunity to explore these microphysiological environments of the synapse at nanometer spatial scales and on a sub-microsecond timescale. Because biological reactions and physiological processes at synapses occur under conditions where stochastic behavior is dominant, simulation approaches must be driven by such stochastic processes. MCell provides a powerful simulation approach that employs particle-based stochastic simulation tools to study presynaptic processes in realistic and complex (3D) geometries using optimized Monte Carlo algorithms to track finite numbers of molecules as they diffuse and interact in a complex cellular space with other molecules in solution and on surfaces . In this review we discuss MCell-based spatially realistic models of the mammalian and frog neuromuscular active zones that were developed to study presynaptic mechanisms that control transmitter release. In particular, these models focus on the role of presynaptic voltage-gated calcium channels, calcium sensors that control the probability of synaptic vesicle fusion, and the effects of action potential waveform shape on presynaptic calcium entry. With the development of these models, they can now be used in the future to predict disease-induced changes to the active zone, and the effects of candidate therapeutic approaches. Synaptic communication at neuromuscular junctions (NMJs) occurs at specialized sites called active zones (AZs). The NMJ AZ structure and function represents an ideal model system that has been studied using a wide variety of experimental techniques including electrophysiology, freeze-fracture electron microscopy, electron microscopic tomography, and immunohistochemistry. Not only is the NMJ an easily accessible synapse for these experimental studies, but the well-organized structure and organization of AZs at neuromuscular synapses facilitates comparisons of AZ structure and function. These studies have helped to shape our understanding of how the structural and functional organization of presynaptic AZs are coupled. Before discussing specific use of MCell computer modeling at this synapse, we will first describe the experimental work that constrains MCell models of the NMJ AZ, and the gaps in our understanding for which MCell modeling can be applied.The frog NMJ has been used for decades as a model synapse not only for specific features of neuromuscular synaptic transmission, but also for the study of conserved mechanisms of neurotransmission in general  has been implicated as the primary sensor for fast, synchronous calcium-triggered vesicle fusion  simulation tool is a Monte Carlo reaction-diffusion modeling tool that uses spatially realistic 3-dimensional geometries and allows realistic synaptic microphysiology simulations. It specifically tracks each molecule in the system in space and time as they diffuse and interact with other molecules  was required. Ma et al.  predicteOne important question that MCell modeling can address is the impact of differences in AZ structural organization on synaptic function. It is clear when one compares AZ structures from different synapses that there are many ways to organize these transmitter release structures , Laghaei et al.  simply rFunctionally, the most striking physiological differences between a frog and mouse NMJ are the probability of release and short-term synaptic plasticity. Frog NMJs have a relatively low probability of release per docked synaptic vesicle (0.022), and very strong paired-pulse facilitation  and tetanic potentiation during 5 action potentials at 100 Hz stimulation (2.51-fold increase). Mouse NMJs have a stronger probability of release per docked synaptic vesicle (0.11), display no significant change during paired-pulse stimuli (1.03-fold with a 10 ms ISI), and mild tetanic depression during 5 action potentials at 100 Hz stimulation (10–20% reduction). Interestingly, using MCell models, all of these striking differences in function were predicted to emerge as a property of AZ organization  Laghaei.Synaptic delay has been shown to be considerably variable across individual neuromuscular synapses at the frog NMJ , howeverThe MCell modeling results from Homan et al.  predict RL and SM manuscript conceptualization and writing and editing of the manuscript. Both authors contributed to the article and approved the submitted version.This work was supported by NSF Collaborative Research in Computational Neuroscience Program Award Number: 2011616. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which was supported by National Science Foundation grant number ACI-1548562. Specifically, it used the Bridges-2 system, which was supported by NSF award number ACI-1928147, at the Pittsburgh Supercomputing Center (PSC).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Active participation as a citizen depends on fluent decoding and production of written language. Efficient processing of graphsFernández-López et al. investigated the early precursors of precise letter position coding in pre-schoolers via the transposed-letter effect, i.e., failing to efficiently differentiate between CHOLOCATE and CHOCOLATE. Results highlighted Eberhard-Moscicka et al. They investigated 1st-grade children with EEG and tested reading skills in the same children 3 years later. N1 print tuning, measured as an N1 increase in response to words compared to false-font strings, together with the mismatch negativity (MMN) improved the prediction of future reading skills compared to behavioral measures alone .This Research Topic begins with two studies focusing on orthographic processing , a key interface between low-level visual processes and higher-level processing of words during reading , inferior frontal gyrus (IFG), and vOTC during audiovisual processing of single letter-speech sound correspondences.The second part of this Research Topic comprises six studies investigating letter-speech sound integration as an emergent property of learning to read. In a review article, Calabrich et al.; Fraga-González et al.; Xia et al.) and of letter strings and spoken words  by dyslexic readers. Specifically, Xia et al. provided evidence that the IFG and STG regions are also involved in the audiovisual processing of morpho-syllabic Chinese. While audiovisual integration effects in these regions did not differ between children with and without dyslexia for Chinese characters, a different correlational pattern of these effects with cognitive measures suggested that different neurocognitive networks shape the integration effects in children with and without dyslexia. Moreover, the same study also found a different audiovisual integration pattern for alphabetic pinyin compared to characters, which may reflect the specific role of pinyin as a scaffolding mechanism for learning Chinese characters. In an eye-tracking study, Calabrich et al. showed that adults with dyslexia recognized and recalled fewer newly learned letter-speech sound bindings than control readers. Dyslexics also showed an overreliance on (seemingly irrelevant) episodic cues during stimulus exposure to aid memory retrieval, specifically on the consistency of contextual stimulus properties, which “may be indicative of a more fragile memory representation” (p. 12). Fraga-González et al. adopted a graph theoretical approach for assessing EEG activity in dyslexic and typical readers during an artificial audiovisual learning task. Dyslexic were as able as control adults to accurately learn the novel bindings , but showed lower theta connectivity during task performance and lower theta degree correlation over task and rest recordings, suggesting reduced (long distance) network integration and less communication between network nodes compared to typical readers. Finally, at the word level, using an implicit same-different perceptual-matching task, Varga et al. found that, whilst reading groups did not differ in ERP correlates of letter identity and letter position encoding in the visual modality, only typical adult readers but not those with dyslexia seemed to show automatic phonological processing and audiovisual integration when the visual letters and speech sounds were presented simultaneously .In turn, four articles provided evidence regarding the audiovisual integration of single characters  underpinning this handwriting benefit and proposed new directions to disentangle and investigate them. Seyll and Content provided empirical evidence in preschool children for the proposal that detailed visual analysis, which is inherent to handwriting, could be the key to the benefit of this learning experience in subsequent visual graph recognition rather than the graphic motor programs per se. Guan et al. showed that, relative to a view-only control training, the contribution of handwriting to visual word recognition also holds in a non-alphabetic script, both at behavioral and electrophysiological levels. Children with dyslexia did not benefit from such a multisensory graph integration. Note, however, that this conclusion might be premature, given that participants were engaged in handwriting for a few seconds and in a single exposure.The last part of this Research Topic focused on the other cross-modal binding promoted when learning to read, that is, between the visual representation of graphs and the corresponding writing gestures. It has been demonstrated that handwriting training during learning of visual graphs is more beneficial for subsequent visual graph recognition than are other learning experiences  may still be developing in young children at the earliest stages of learning to read. A positive correlation further indicated that the response in these dorsal motor regions during handwriting may be related to children's emerging literacy skills .Fischer and Luxembourger addressed the challenge of mirror-image discrimination that beginners face when learning to read and write . These authors tested three candidate models for explaining the almost systematic reversal errors  found in writing by learners, using the data made publicly available by Torres et al. .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Drosophila melanogaster strains with different geographic origins, how these patterns are affected by mitochondrial DNA (mtDNA) variation, and how breaking up co-evolved mito-nuclear gene combinations affect the studied activity traits. Our results demonstrate that Drosophila strains from different locations differ in sleep and activity, and that females are generally more active than males. By comparing activity and sleep of mtDNA variants introgressed onto a common nuclear background in cytoplasmic hybrid (cybrid) strains, we were able to quantify the among-line variance attributable to mitochondrial DNA, and we establish that mtDNA variation affects both activity and sleep, in a sex-specific manner. Altogether our study highlights the important role that mitochondrial genome variation plays on organismal physiology and behaviour.Mitochondria are organelles that produce cellular energy in the form of ATP through oxidative phosphorylation, and this primary function is conserved among many taxa. Locomotion is a trait that is highly reliant on metabolic function and expected to be greatly affected by disruptions to mitochondrial performance. To this end, we aimed to examine how activity and sleep vary between  Mitochondria are key organelles in a range of critical metabolic processes and are the primary energy producers for the eukaryotic cell. In addition to this primary role, they are also involved in a range of other vital processes that control key aspects of cellular growth and regulation such as signalling  and V   was used to collect all experimental data on fly activity and sleep cycles  generated by the DAM was used to quantify locomotor activity. Sleep was defined as a 5 min time-bin with no registered activity , we were able to evaluate how much of the variance in locomotor activity was affected by the mtDNA and newly created mito-nuclear combinations Figs. . In geneND1; V190M, ND2; I277L, ND5; M502I) and V   and cIV (COIII) when compared to other cybrids (Salminen et al. CYTB mutations in muscles have been shown to cause complex III deficiency and exercise intolerance in humans (Andreu et al. CYTB mutation in mtKSA2 may partially explain the lower activity rates when compared to other strains.mtKSA2 is the only African variant, and it possesses two unique amino acid replacement variants in OXPHOS cIII (Drosophila sleep and activity research (Isaac et al. mtKSA2 is also associated with low mtDNA copy number in ORT nuclear background when compared to other cybrid lines (Salminen et al. Tigriopus californicus (Willett and Burton Previous studies have demonstrated that disrupting co-evolved mito-nuclear interactions can lead to decreased OXPHOS function (Sackton et al. Natural selection is blind to deleterious mitochondrial phenotypes which manifest in males as mtDNA is maternally inherited (Carnegie et al. Drosophila sleep and activity research will enable further understanding of the causes of abnormal sleep and activity patterns in humans and the role of mitochondrial variation in these traits. Further studies of mito-nuclear interactions and mtDNA variation are essential in recognising and eventually preventing mito-nuclear mismatches that might occur during mitochondrial replacement therapy.All datasets and full R code for all analyses can be found at 10.5281/zenodo.5573904 and is fully citable as Vale PF  Data andSupplementary Figures and tables


Bordetella pertussis (Bp) is the highly transmissible etiologic agent of pertussis, a severe respiratory disease that causes particularly high morbidity and mortality in infants and young children. Commonly known as “whooping cough,” pertussis is one of the least controlled vaccine-preventable diseases worldwide with several countries experiencing recent periods of resurgence despite broad immunization coverage. While current acellular vaccines prevent severe disease in most cases, the immunity they confer wanes rapidly and does not prevent sub clinical infection or transmission of the bacterium to new and vulnerable hosts. The recent resurgence has prompted new efforts to generate robust immunity to Bp in the upper respiratory mucosa, from which colonization and transmission originate. Problematically, these initiatives have been partially hindered by research limitations in both human and animal models as well as potent immunomodulation by Bp. Here, we consider our incomplete understanding of the complex host-pathogen dynamics occurring in the upper airway to propose new directions and methods that may address critical gaps in research. We also consider recent evidence that supports the development of novel vaccines specifically designed to generate robust mucosal immune responses capable of limiting upper respiratory colonization to finally halt the ongoing circulation of Bordetella pertussis. Bordetella pertussis (Bp) is the highly transmissible etiologic agent of pertussis, a severe respiratory disease that causes particularly high morbidity and mortality in infants and young children  the most severe forms of pertussis disease  titers decay rapidly within the first-year post-vaccination and continue to decline at slower rates thereafter –37. HoweBp infection. This has been repeatedly observed from conventional murine models, as high antibody titers were detected in aP vaccinated animals with no reduction in CFU in the nasal cavity, resulting in prolonged carriage  is produced in response to natural infection, but not from current intramuscular aP or wP vaccines. However, conflicting reports regarding the role of IgA in infection with ry tract . In addi and IgG , 42. Howty to Bp .Bp was reported from experiments investigating a recent live-attenuated vaccine candidate, BPZE1. Designed to be administered intranasally, BPZE1 is reported to generate a robust secretory IgA response which appears to correlate with protection against challenge with Bp. Importantly, BPZE1-vaccinated IgA-/- mice had significantly higher colonization on days 7 and 21 post-challenge relative to vaccinated wildtype mice , which is a positive indicator for applicability of this concept in human immunization  cells required for translocation into nasopharynx-associate lymphoid tissue (NALT)  . This in B cells Figure 1ectively –50.Bp warrant additional investigations into sIgA and intranasal vaccination strategies that deliver antigen directly to the site of infection  pertussis , which are typically maintained in respiratory tissue and respond rapidly to secondary encounter with Bp , IL-17C , IL-6, and IFNγ recruit additional phagocytes to the site of infection including dendritic cells and macrophages which ultimately clear the infection via opsonization-enhanced phagocytosis  immunity that are critical in the mucosal response to respiratory pathogens. However, the observation that intranasal administration of aP vaccines and candidate vaccines like BPZE1 are able to generate these responses suggest we have much to learn from continued investigations in this area  and cellular  and may downregulate host genes associated with cell death . Adenylabilities .Bp immunity, such as pertussis toxin. It is likely that immunomodulation is particularly important in mucosal immunity to Bp, as the tightly controlled inflammatory responses in the nasopharynx may be further reduced to enable persistence and extend the window of transmissibility  infection, where deletion of a regulator of immunomodulators (btrS) generated robust sterilizing immunity and identified eosinophils as unexpected contributors in pulmonary responses to Bb (Disrupting immunomodulation in animal models may be informative as a tool to evaluate which aspects of the host response are actively suppressed by Bp . This ides to Bb .Bp, and the development of vaccines that prime IgG, IgA, and Th1/Th-17 responses in the respiratory mucosa. Excitingly, recent investigations into intranasal vaccination support our ability to develop next-generation vaccines to generate robust sterilizing mucosal and systemic immunity sufficient to reduce carriage and the ongoing circulation of Bp in vaccinated populations.In an era of widespread resurgence, imperfect waning immunity, and rapid expansion of vaccine-escape mutants, research initiatives have ambitiously shifted toward generating complete protection in the nasal mucosa. Accomplishing this goal will require an improved understanding of the complex immunobiology of upper respiratory infection with The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.AC and MC conceived the perspective. AC and EH wrote the manuscript. MC generated the figures. All authors contributed to the article and approved the submitted version.


The global transition to current low levels of habitual physical activity among children and adolescents began in the second half of the last century. Low physical activity harms health in both the short term (during childhood and adolescence) and long term (during adulthood). In turn, low physical activity could limit progress towards several sustainable development goals, undermine noncommunicable disease prevention, delay physical and mental health recovery from the coronavirus disease 2019 pandemic, increase health-care costs and hinder responses to climate change. However, despite the importance of physical activity, public health surveillance among children and adolescents is very limited globally and low levels of physical activity in children is not on the public health agenda in many countries, irrespective of their level of economic development. This article details proposals for improvements in global public health surveillance of physical activity from birth to adolescence based on recent systematic reviews, international collaborations and World Health Organization guidelines and strategies. Empirical examples from several countries illustrate how improved surveillance of physical activity can lead to public health initiatives. Moreover, better surveillance raises awareness of the extent of physical inactivity, thereby making an invisible problem visible, and can lead to greater capacity in physical activity policy and practice. The time has arrived for a step change towards more systematic physical activity surveillance from infancy onwards that could help inform and inspire changes in public health policy and practice globally. We argue that “what gets measured gets done” and “if you don’t measure, it you can’t change it.” Improved surveillance is essential for good public health policy and practice.–,In the past decade, international studies and surveillance initiatives have found that typical physical activity levels among school-age children and adolescents globally are much lower than recommended, even in low- and middle-income countries.,,,Children and adolescents were much more physically active in the distant past.–The environmental changes that have reduced physical activity among children and adolescents have also led to biological changes, including reduced motor competence , reduced physical fitness and high body fat, even among those who are not overweight or obese as defined by their body mass index. In turn, these biological changes further reduce physical activity by producing feedback loops that amplify adverse environmental impacts on physical activity .20–25,,,,The main limitations of existing global surveillance programmes of physical activity among children and adolescents are summarized in –,,The validity, reliability and cultural appropriateness of the methods used to measure physical activity in many surveys are not supported by good-quality evidence and the variation in methods used by different surveys renders it difficult, or even impossible, to make international comparisons .7 In addBox 1,In China, an upper-middle-income country, gaps in physical activity surveillance of children and adolescents and in physical activity policy implementation and evaluation were reported between 2017 and 2022.Beginning in 2007, the Chinese government has released a series of policies and strategies to promote physical activity among children and adolescents.–In the Philippines, a lower-middle-income country where adolescent physical activity is very low, no surveillance of physical activity in children was conducted between 2018 and 2022, there was limited capacity for physical activity surveillance and little physical activity policy was either established or implemented.As in many low- and middle-income countries, surveillance of child and adolescent physical activity in the Philippines has been restricted to adolescents, as reported in the country’s global school-based student health surveys.–In Scotland, physical activity policy and practice between 2008 and 2022 has been misinformed by inaccurate measurement of physical activity during surveillance.,,The Scottish physical activity strategy is well-developed and has been guided by surveillance evidence derived largely from nationally representative Scottish Health Surveys.,Table 1).For children younger than 4 years and preadolescents, the limitations of self- and proxy-reporting of physical activity mean that device-based measurement is an essential component of surveillance.,,The global megatrends  that hav,,Guidance for public health policy-makers and practitioners on tackling low physical activity among children and adolescents is available from WHO and is summarized in our key points for policy-makers and practitioners in Box 2Increasing physical activity is more complex and challenging than it seems.Increasing physical activity requires informed action on its upstream environmental influences, such as transportation, the built environment, planning, the cultural environment and the regulation of screen time – these lie outside the domain of physical activity itself .Focusing only on downstream individual policy targets (e.g. through educating the population) is unlikely to be very effective and may increase inequalities.Increasing physical activity meaningfully by targeting the most obvious individual physical activity domains (e.g. school and sport) will require informed and effective action in these domains, not just single policy targets. For example, targeting physical education alone or the school environment alone is unlikely to increase physical activity meaningfully because children are typically at school for only around half of all days annually and are generally less active on non-school days.Meaningful increases in physical activity will require changes beyond the single domain of sport because physical activity does not occur in sport alone.Physical activity is very hard to measure accurately in children and adolescents. Self-reports and parental proxy reports of physical activity should be treated with caution unless there is good evidence of validity, reliability and cultural appropriateness. In addition to physical activity, policy and action are required on sedentary behaviour (e.g. screen time) and sleep.Extensive guidance on physical activity policy and strategy, support and resources are available but could be used much more widely.Global action plan on physical activity 2018–2030 and Guidelines on physical activity, sedentary behaviour and sleep for children under 5 years of age.,WHO has produced many practical, evidence-based strategies and guidelines, such as the WHO strategies and other initiatives suggest useful actions that can be taken to establish informed policy and practice, though these actions may need to be adapted to particular circumstances and may require dedicated resources.Developing policy, even informed policy, is insufficient. Successful policy requires implementation and evaluation, which are often lacking globally.At present, policy-makers have limited awareness of the key public health concepts in child and adolescent physical activity and of the evidence available.,,,International collaborations that focus on surveillance Existing surveillance data on physical activity in children and adolescents, though limited, are useful for guiding policy and practice. Making national survey data available as soon as possible would be helpful: in many low- and middle-income countries, global school-based student health survey data only become available after many years and are, therefore, much less useful than they could be.Assuming there are specific children or adolescents who are at a particularly high risk of insufficient physical activity is usually wrong. A group should only be labelled high-risk when there is robust surveillance evidence that the risk is higher than in the general population of children and adolescents.All children and adolescents are now at a high risk of insufficient physical activity at all ages from infancy onwards and in all places, including rural areas and low- and middle-income countries.Adolescence does not cause a decline in physical activity – physical activity declines well before adolescence and the decline continues throughout adolescence.Children have a fundamental right to physically active play – environments that do not support physical activity deny them that right.The benefits of physical activity for children and adolescents extend well beyond the substantial health effects and include, for example, educational and cognitive benefits.Increased physical activity in children and adolescents also benefits society: it contributes to the achievement of several SDGs, aids noncommunicable disease prevention, helps counter the climate emergency and assists recovery from the COVID-19 pandemic.COVID-19: coronavirus disease 2019; SDGs: sustainable development goals; WHO: World Health Organization.,,Physical activity in children and adolescents is a relatively new academic discipline and, at present, capacity is largely concentrated in developed countries.,,The development of national report cards on physical activity among children and adolescents by the Active Healthy Kids Global Alliance has made a substantial contribution to physical activity surveillance and to public health policy and practice.Several European nations are now developing, or have recently developed, school-based physical fitness surveillance programmes that involve relatively simple, low-cost measures. These nations have formed an international collaboration, called FitBack, that aims to build capacity in fitness surveillance and to share examples of how best to change policy and practice to maintain or improve child and adolescent fitness.,Increasing physical activity and combating sedentary behaviour among children and adolescents globally will require a step change in surveillance. However, this step should not be simply a monitoring exercise. Our experience, supported by many international examples, suggests that improved surveillance will also inform and inspire changes in public health policy and practice. The time has arrived for making such a step change because: (i) physical activity is now recognized as important globally for achieving several SDGs and for preventing many noncommunicable diseases; (ii) WHO has produced new global guidelines on physical activity from infancy to adulthood;


Home hospital (HH), a care delivery model of providing hospital-grade care to patients in their homes, has become increasingly common in medical settings, though surgical uptake has been limited. HH programs have been shown to be safe and effective in a variety of medical contexts, with increased usage of this care pathway during the COVID-19 pandemic. Though surgical patients have unique clinical considerations, surgical Home Hospital (SHH) programs may have important benefits for this population. Various technologies exist for the delivery of hospital care in the home, such as clinical risk prediction models and remote patient monitoring platforms. Here, we use institutional experiences at Brigham and Women’s Hospital (BWH) to discuss the utility of technology in enabling SHH programs and highlight current limitations. Additionally, we comment on the importance of data interoperability, access for all patients, and clinical workflow design in successfully implementing SHH programs. Delivering care at home has been shown to be safe and effective for medical patients, though the introduction of HH programs for surgical patients has been limited2. Many components of postoperative management, such as fluid repletion and medication administration, are similar to existing home hospital care for medical patients. This suggests that for surgical patients of lower severity, who do not require mechanical ventilation or repeated imaging, care can be undertaken in the home3. For example, home-based management of postoperative surgical site infections could be conducted with intravenous antibiotics, and postoperative vomiting in bariatric patients could be managed at home with fluids and electrolyte repletion3. HH programs for surgical patients may provide benefits, including reductions in nosocomial complications and inpatient volume, as well as cost containment3. At our institution, Brigham and Women’s Hospital , SHH has been piloted in bariatric surgery, with ten patients enrolled to date, and additional protocols being developed in plastic surgery, general surgery, and urology. A wide variety of technologies, including remote patient monitoring devices, clinical risk prediction models, and virtual care platforms, exist to facilitate the delivery of care in the home. In this piece, we comment on the utility of these technologies for enabling surgical home hospital (SHH) leveraging our institutional experience. We trace the flow of data from a collection by devices at the point of care to signal processing, analysis, and data integration with health records , also known as Hospital-at Home, is a care model of delivering hospital-level care in patients’ homes. It has become increasingly common since the start of the COVID-19 pandemic and the subsequent establishment of the Acute Hospital Care at Home waiver by the Centers for Medicare & Medicaid Services (CMS)4.Remote patient monitoring (RPM) technologies are an important component of HH programs and have the potential to further expand the scope of surgical care delivery in the home. RPM tools record and transmit patient data to providers and can come in the form of wearable or ambient monitoring technologies that collect continuous or intermittent data5. Wearable technologies have been used to monitor patients with chronic diseases and patients with COVID receiving care at home8. Despite its use in medical patients, wearable usage in surgical care remains limited10.Wearable tools include devices such as pulse oximeters and heart rate monitorsTM platform from Biofourmis, which integrates data from wearable devices and uses an AI algorithm to predict health deterioration or improvement11. These devices include the Vital Connect VitalPatch, which collects temperature, heart rate, and respiratory rate, a Bluetooth-enabled blood-pressure cuff, and a Bluetooth-enabled pulse oximeter. All patients enrolled in SHH have complied with device usage. Data from these devices are displayed on a secure portal on the Biofourmis website or via a smartphone app accessible to clinicians. Vital sign abnormalities are detected and prompt virtual or in-person visits, depending on severity. The platform also includes a patient-facing interface with daily reminders. Compared to medical complications, surgical complications are relatively infrequent but potentially catastrophic. The ability to detect physiologic signs of stress that serve as early warning signs is critical for the surgical home hospital effort. Real-time temperature, EKG, respiratory rate, blood pressure, and pulse oximetry together provide the advantages of frequent vital checks in a less intrusive manner. Given the array of wearable devices and platforms, surgical home hospitals will need to develop guidelines to assess the types of wearable devices best suited to their patient populations and infrastructure.In our experience at BWH treating bariatric patients in SHH, we use the biovitals12. While more nascent, these technologies have been used in the ICU to monitor vital signs and in lower acuity contexts such as at-home elder care and rehabilitation15. Ambient methods capture patient data by using technologies like cameras, thermal sensors that capture infrared waves, and radio sensors12. Sensors have been developed that use radio frequencies to track gait, sleep patterns, and progression of movement disorders16. Given the breadth of available technology, there is an opportunity to expand the usage of these tools in surgical care. For example, the usage of ambient monitoring to quantify postoperative mobility in patients after joint replacement may help clinicians identify patients struggling to return to baseline function17. Challenges remain in implementing these technologies, as the accuracy of monitoring devices, particularly ambient sensors, may be reduced in complex settings like patients’ homes12. While these technologies may function in a more static inpatient environment, the heterogeneity of real-world environments may limit the current utility of these tools.Ambient monitoring technologies use modalities like radar or video for contactless monitoring of vital signs and fallsThough the usage of remote and ambient monitoring tools in surgery has been limited thus far, use cases can be envisioned based on the needs of surgical patients Table . For exa18. As HH programs integrate more technologies, often produced by different manufacturers, ensuring data interoperability between devices and data access for remote teams is essential19. At BWH, we use a single vendor, Biofourmis, which provides wearable devices and interfaces for remote data access, though data from wearables are not currently integrated into the electronic health record11. The approach of using one technology vendor may not be feasible for all hospital systems, and relying on a single vendor could limit the integration of different tools in the future. RPM platforms for surgery must also include the ability to integrate with existing surgical monitoring technologies, such as ViOptix for tissue flap perfusion monitoring20. In addition to emphasizing interoperability, increasing the clinical utility of data collected from RPM devices may be accomplished using statistical learning methods. More importantly, HH programs will need new workflows developed by providers to safely integrate patient monitoring tools and data into care delivery. In the BWH experience, the creation of a clinical framework detailing important steps on each postoperative day, including visits from providers and data collection via remote tools and lab testing, has helped to structure the process of postoperative care at home. It is crucial to design workflows that allow remote monitoring data to be accessible to all members of the care team, particularly during rounds and other team-based care events.There are challenges associated with remote patient monitoring that must be addressed as these tools are integrated into SHH programs. Both wearable and ambient technologies present challenges, including signal fidelity, data interoperability, and integration into clinical decision-making. Quality and accuracy of data derived from wearables and ambient monitoring devices are crucial in ensuring the utility of these tools. Determining which vital signs need to be monitored, as well as the appropriate frequency, is also crucial due to concerns about false alarms. Separating true alerts from false alarms could be considered even more critical in HH settings than in traditional care, given the lack of proximity to patients as well as the challenge that alert fatigue presents to patient safety21. These models have the potential to add value to hospital-at-home programs in two capacities: (1) dynamic prediction of complications to help care teams preempt a patient’s clinical deterioration and mobilize resources accordingly and (2) predict who may benefit from hospital-at-home care versus traditional inpatient care. The use of machine learning in interpreting clinical data can provide unique benefits for patients in SHH. For surgical patients at risk of acute clinical events, it is helpful to have machine learning tools constantly monitoring patient data. Machine learning-based tools that learn with patients’ baseline biometrics can adjust for various activities, such as ambulation in the home, can detect subclinical deviations from this baseline, and can alert a physician when a patient is at elevated risk of an adverse event.Clinical risk prediction models are tools that use retrospective observational data and statistical methods—sometimes machine learning-based methods—to predict an individual patient’s likelihood of some clinical outcome5. More broadly, in medicine, few published prediction models have been successfully incorporated into practice22. Multiple explanations have been put forth for the lack of these tools’ real-world utility, from their development and validation to the complexity of implementation within existing clinical workflows23. In the BWH experience, clinical risk prediction for in-hospital complications, including pneumonia, sepsis, and death, is used to screen patients for surgical home hospital. Patients are deemed high-risk through analysis of demographic factors and comorbidities, and are triaged to inpatient care in the hospital in order to minimize the risk of delays in care escalation or access to additional resources.While clinical risk prediction models have been proposed as a potentially useful component of HH programs, they are not yet widely used in these care settings25. Prediction of rare clinical events presents the risk of alert fatigue and wasted resources if a model flags too many false positives, and unnecessary excess morbidity and mortality for a model with too many false negatives. In addition to accuracy, the risk cutoff threshold used in these models will determine their real-world utility: common risk cutoff selection approaches that equally weight false positives and false negatives may not be ideal in these scenarios. Tools used to predict risk in inpatient settings will likely require careful recalibration to be useful in SHH programs26.When using clinical risk prediction in the care of surgical patients at home, data quality and clinical complications present important considerations. The clinical fidelity of data collected from RPM devices is crucial, as a model’s predictive performance is limited by the reliability of its data inputs. The nature of surgical complications presents another unique challenge. Acute postoperative surgical complications include general complications such as sepsis and myocardial infarction, as well as procedure-specific complications like anastomotic leak after colorectal surgery or duodenal stump leak after gastrectomy—events that are low frequency but potentially catastrophic and time-sensitive when they occurlies in translating prediction model outputs into real-world actionable insights. There is a lack of consensus around how prediction models should be incorporated into the clinical decision-making process27. Few would argue that these models should fully supplant human judgment - but then how should the provider incorporate the model’s insights into their clinical decision, particularly in cases where the model’s prediction diverges from the provider’s? And what resources should be mobilized or what workflow should be triggered if a patient’s risk of clinical deterioration is high? Most published reports of SHH programs make little use of prediction models in their clinical decision-making28. More research is needed on the optimal integration of clinical prediction models into SHH models.Another important challenge 5 Our model at BWH combines virtual surgeon visits with in-person RN and paramedic visits to provide patients with appropriate observation. Other potential models could include in-person visits from physician assistants, surgicalists, or medical hospitalists in combination with virtual surgeon visits. While performing a physical exam virtually presents some challenges, these issues can be mitigated by having a trained professional (physician assistant or nurse) perform the exam and report findings29. Emerging technologies, such as connected auscultation or using built-in smartphone accelerometers to measure abdominal palpation, can allow patients to examine themselves and report findings31. Additionally, SHH programs must fulfill the need for inpatient consultation by building ways to connect patients with inpatient consult teams. A seamless connection between primary and consulting teams is necessary to ensure appropriate clinical oversight of home hospital patients.Virtual visits between patients and clinicians are critical components of existing home hospital programs and will be crucial to the success of HH within the surgery. Established medical HH programs have created hybrid models which mix in-person and virtual provider visits, and surgical HH programs will need to establish workflows that provide the appropriate schedule of virtual visits based on illness acuity and procedural complexity.While digital technologies are just a few of the many essential elements of an SHH program Fig. , they wi32. The Office of the National Coordinator for Healthcare IT (ONC) Final Rule, which requires health systems to make Fast Healthcare Interoperability Resources (FHIR)-based application programming interfaces (APIs) available for transmitting and retrieving patient data by December 31, 2022, is an encouraging development that will hopefully make the US healthcare data ecosystem more interoperable and thereby benefit surgical HH programs that depend on communication between RPM devices and EHRs33. The Food and Drug Administration (FDA) also supports non-binding consensus standards that encourage medical device interoperability, providing manufacturers with a framework to facilitate device interoperability34.The range of patient monitoring devices is increasing, making it crucial that these devices can communicate seamlessly with each other and EHRs35. The American Telemedicine Association (ATA) recently put forth a framework for eliminating disparities using telehealth that includes not only connectivity, affordability, and literacy of new digital technologies but also inclusivity and trust. It includes not only clinical care teams but also vendors, policymakers, and community members as key stakeholders36. HH programs can similarly be used to tackle disparities in access to quality postoperative care if they are similarly designed with these elements and stakeholders in mind. Additionally, HH programs could potentially decrease disparities in surgical care by increasing provider knowledge of social determinants of health that patients face, information that could be used to connect patients to resources and improve surgical outcomes37.A final consideration in establishing HH programs within surgery involves ensuring that these programs are designed in a manner that works towards eliminating health disparities instead of widening them. Given disparities in access to surgery and surgical outcomes that racial minorities and patients of lower socioeconomic status experience, it is critical that SHH programs be designed to optimize communication between providers and patients and to account for differences in access to technology38. 30% of telehealth visits are conducted as audio-only conversations due to infrastructural constraints or technology literacy constraints39. These real-world limitations need to be taken into consideration when deploying HH programs as well. Patient-facing technologies should be easy to use for a wide range of literacies and compatible with a wide range of devices. Of particular concern is to ensure that telehealth used in the SHH setting incorporates translation capabilities for patients with limited English language proficiency. Broadband and other IT infrastructure to support these technologies should also be made more widely available and affordable40. While some early studies suggest HH can be delivered successfully to patients with lower socioeconomic statuses, surgical HH programs must intentionally design protocols to ensure equal access for all patients37. Surgical HH programs designed with equity in mind may not only improve access to care, but may also promote patient inclusivity in research studying post-surgical outcomes41. Furthermore, as connected devices proliferate on unsecured networks in non-traditional care settings such as the home, the security of the information being transmitted to and from the patient becomes increasingly relevant. Health systems deploying HH programs should establish strong relationships with their vendors and security standards for the technology that they adopt.As the technological sophistication of HH programs increases, the tension between access and site-of-care optimization arises. In particular, successful implementation of hospital-at-home programs that depend heavily on technology assumes high levels of patient digital literacy and strong internet connectivity, all of which are not guaranteed, particularly for patients from marginalized groupsA successful surgical HH program is one that redesigns clinical workflows instead of entirely replacing them with technology. Current surgical HH programs use many new digital health technologies to enable the delivery of quality care in the home. While there is considerable room for improvement in RPM, clinical risk prediction, and data interoperability, the current functionality of these existing technologies should not be considered a significant barrier to deploying surgical HH programs. The bulk of the innovation in these programs instead lies in developing and operationalizing complex new workflows for providers and staff. As new digital health technologies become more sophisticated, their ability to add clinical value to surgical HH programs will likely increase as well. SHH models need to be rigorously evaluated to ensure that the primary goal of improving patient experience and quality, and not just cost savings, are achieved. The goal of SHH digital health technologies should be to extend, not replace, the clinical presence in patients’ homes. With the thoughtful implementation of remote patient monitoring, SHH care models can do just that: extend clinical expertise into patients’ homes to truly bring new advances in digital health to the patient’s actual bedside.Further information on research design is available in the Reporting Summary


These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image.Advances in bioimaging now permit We develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics.www.github.com/murphy17/HPA-SimCLR.Code and trained model are available at Bioinformatics online. Contrastive learning generates positive examples for a given In short, this work makes the following contributions:We show how to effectively apply self-supervised learning to IHC images, by developing a sampling procedure that generates meaningful positive and negative examples, rather than having to engineer complex data augmentations;We show that the learned embeddings can be combined with labelled data from a different source—single-cell transcriptomics—to obtain a classifier of cell type specificity, without requiring human labelling of images; andWe show applying both steps to IHC of kidney yields a representation that clusters images according to cell type specificity, and better classifies known proteomic markers than a purely transcriptomic approach.Unsupervised and self-supervised learning have been employed previously to generate biologically informative representations of genes and proteins from high-throughput imaging data  an antibody passes orthogonal validation if its overall staining intensity matches expression of its nominal gene target in bulk RNASeq across at least two tissues. Both criteria are determined qualitatively by a human evaluator. In principle, it is unlikely for an antibody to satisfy both of these criteria yet bind to something other than its nominal target  an antibody passes In this work, due to storage and processing limitations we restrict our scope to IHC images from version 21 of the HPA taken in a single tissue: healthy kidney. Kidney was selected in particular because it is a complex organ that displays substantial anatomical and cellular diversity . This does not result in a visualization with clear clustering structure, and images tend to colocate with other images derived from the same donor. In comparison, our approach of sampling positive examples from other images of the same gene (middle) imparts clearly visible global clustering structure.However, there remain some clusters exhibiting visible imbalance with respect to the donor label. This is fixed when we additionally restrict negative examples to images derived from the same donor (right). We quantify invariance of our representation to the identity of the tissue donor via fivefold cross validation accuracy of a logistic regression trained to predict the donor label from the embedding. When sampling negatives uniformly, the donor label can be accurately classified 39.7% of the time; our negative sampling procedure makes this task more difficult, reducing to 29.5% accuracy.T-tests and Wilcoxon tests  ranks PTH2R last among the 126 marker genes by glomerular specificity, instead assigning it to proximal tubules (ranked 8th of 126).in situ hybridization, We suggest an explanation for the misclassification based on scRNA: using Rather than a mistake of our classifier, we suggest this may be a limitation of relying on IHC: visual inspection of the images of that gene also indicates staining of distal tubules, highlighted with red arrows in supervised learning algorithm trained on images of testis that were manually labelled for cell-type specificity by human experts.We also provide in In this article, we develop a contrastive learning algorithm for learning representations of IHC images in the HPA. We demonstrate our approach to sampling positive and negative examples leads to a representation that captures biological semantics of IHC images, without needing to engineer complex data augmentations. When applied to images of the kidney, the resulting embeddings yield clusters that display specificity for different cell types and anatomical regions. We then incorporate auxiliary labels from an independent single-cell transcriptomics dataset to train an image classifier that predicts cell type specificity without requiring human annotation of those cell types. This classifier better recovers known proteomic markers than prioritization solely via differential transcriptional expression.One important potential application of our method is toward designing multiplexed spatial proteomics experiments, for which antibody panel selection is an important consideration. The ability to screen candidate marker genes beforehand for proteomic specificity, as opposed to solely transcriptomic specificity, should save time during validation of such panels.While we only consider images of kidney here, our contrastive learning procedure is applicable to any tissue represented in the HPA, and the embeddings can be used for any prediction or visualization task for which invariances of the sort we learn are desirable. We also point out that while training the model does necessitate the unique scale of the HPA specifically, in principle it can be subsequently applied to any IHC image acquired in a tissue that was represented in the training set. Our subsequent classification step can also use any data type that associates genes or proteins with cell type specificity: it is not limited to our particular choice of single-cell transcriptomics dataset, nor to the granularity of cell type definitions therein. It therefore will benefit from efforts to discover and catalog finer distinctions between cell types, such as the Human Cell Atlas (In addition to cell type specificity, we also observe our approach is sensitive to subcellular localization of markers. This result is unsurprising, as multiple previous works demonstrate effective prediction of subcellular localization from IHC images in the supervised context : here weWe note some limitations with our method in its current implementation. We had to downsample the high-resolution images in the HPA substantially to accommodate our choice of architecture, and GPU memory constraints prevented us from using larger batch sizes as recommended for SimCLR (Finally, our approach is fundamentally limited by antibody cross-reactivity: while we filter our training set using the HPA’s reliability criteria to counteract this, in principle an approach that attempts to predict cell type specificity of proteins from immunohistochemical images will fail when antibodies bind to proteins other than their nominal targets. On the other hand, genes that exhibit systematic disagreement between immunohistochemical and transcriptomic estimates of cell type specificity across multiple antibodies and tissues may present interesting candidates for biological follow-up in their own right.btac263_Supplementary_DataClick here for additional data file.