Physical rehabilitation medicine is a diverse specialty; its main objective is to provide comprehensive rehabilitation involving multiple health care professionals to optimize function and improve the quality of life for people with disabilities. There is an increase in the number of people with disabilities, and people with disabilities in lower income countries do not receive the required rehabilitation. Rehabilitation intervention includes functional assessment, rehabilitation goal setting, composition of the focused team and coordination of the team works, all of which require a highly skilled team leader. No single professional is likely to have all the necessary skills to achieve optimal results alone. There is a knowledge gap between the theory, existing situation, and practice in rehabilitation team functioning. In this short communication challenges for quality rehabilitation services were highlighted, including the importance of the leadership role of team functioning. The continued occurrence of injuries and the emergence of noncommunicable chronic diseases increases the number of people with disabilities, particularly in developing countries, and the growing need for rehabilitation remains largely unmet globally , 2. PeopMedical rehabilitation is a diverse specialty. The conditions commonly dealt with include but are not limited to musculoskeletal disorders including joint problems, neurological disorders including stroke, spinal cord injury, brain injury, cardio-respiratory conditions, metabolic conditions including neuromusculoskeletal complications presenting in acute, postacute or chronic care settings .The rehabilitation interventions include exercises; modifying an individual's home environment; training and education on healthy living; prescribing medicines; providing psychological support; fitting an orthosis, prosthesis, or assistive devices . ClearlyThis was a short communication based on minireview of the available online literatures made during the period April to Sept 2022. The principal focus was on Developing country, Rehabilitation and Team works.Effective rehabilitation team working has been recognized and described since World War 1 . TeamworEffective team management is considered essential for rehabilitation goal setting team meetings and subsequent team coordination. Many of the developing countries' rehabilitation is not a health priority and faces multiple challenges in rehabilitation team functioning , 15, 16.Multiple challenges were identified for developing a physician-led rehabilitation team functioning in developing countries presented in BOX 1•Lack of relevant course contents of the rehabilitation professional curriculum•Poor training monitoring system and feedback options•A scarcity of trained and experienced rehabilitation professionals, a lack of infrastructure development, and inadequate funding in the rehabilitation sector•Overburdened rehabilitation professionals with poor attitudes toward service providers•Low rate of education of the persons with disability with political interference of administration•Potential ethical issues and dilemmas in team functioning•Minimal or no preparedness for disaster related onslaught casualtiesThere is a knowledge gap between the theory, existing situation, and practice in rehabilitation team functioning. Rehabilitation team functions better when it is led by a physiatrist. Multiple challenges exist for developing a physician-led rehabilitation team in developing countries, which may be addressed with local arrangements.


It is well known that arterial stiffening is one of the earliest detectable signs of structural and functional alterations of the arterial wall. It occurs as a result of aging but also occurs prematurely, caused by atherosclerosis as a result of different cardiovascular risk factors, e.g., hypertension, diabetes, smoking, obesity or chronic kidney disease, because of which media of the arterial wall undergoes structural and functional changes, resulting in reduced distensibility. The loss of elasticity and increased rigidity results in increased velocity of pulse waves since they travel faster in stiff arteries. Many studies have reported different physiological and pathophysiological conditions associated with increased arterial stiffness . For insSome of the studies on arterial stiffness have been published most recently shedding a new light on this topic. Literature data suggested a correlation between serum magnesium levels in the upper part of the distribution and lower arterial stiffness parameters . HoweverA very interesting recently published systematic review suggested an association between gut microbiota composition and arterial stiffness, with two patterns in most animal and human studies—a direct correlation between arterial stiffness and abundances of bacteria associated with altered gut permeability and inflammation; an inverse relationship between arterial stiffness, microbiota diversity, and abundances of bacteria associated with most fit microbiota composition. However, interventional studies showed a stable association between microbiota modification and arterial stiffness only in animals but no human interventional trial was able to demonstrate this relationship .It has been mentioned already that obesity is also associated with an increased arterial stiffening. Therefore, it is not a surprise that a recent meta-analysis has shown that the arterial stiffness is improved after bariatric surgery in extremely obese subjects . A recenParticularly interesting question is whether arterial stiffness, as an established independent predictor of cardiovascular risk, is increased in patients with familial hypercholesterolemia (FH). FH is, because of life-long exposure to elevated serum levels of LDL-cholesterol, the most important risk factor for accelerated and premature atherosclerotic cardiovascular disease. A number of studies demonstrated a significant positive relationship between artery stiffness and total cholesterol and/or LDL-cholesterol and they particularly showed an increased arterial stiffness in patients with FH . A meta-Nevertheless, increased arterial stiffness is associated not only with risk factors for atherosclerosis. It has been shown recently that arterial stiffness might be a strong prognostic parameter in heart failure patients discharged after an acute heart failure decompensation .As a conclusion, it could be stated that arterial stiffness is more and more in the focus of interest of different studies trying to find whether this might be another reliable biomarker for early detection of increased cardiovascular risk and the accumulated data seem to be very much in favor of a positive answer.


Subcortical pathology is known to be highly prevalent in dementia and motor neurodegenerative conditions, however, how these subcortical changes impact on disease presentation, progression and symptomology is still highly debated. This is not trivial since virtually all cortical regions project or have feedback loops to subcortical structures, indicating that subcortical structures contribute to the well-known “cortical symptomology.”in-vivo characterization of regional and subnuclei changes within subcortical brain structures and their respective neural connections. Increasingly, the importance of subcortical pathology is being recognized in the early and even asymptomatic stage of neurodegenerative disorders, with overlapping patterns of involvement identified with increasing disease progression.Over the past decade, there have been significant advancements of The focus of this Research Topic was to highlight current advances in the understanding of subcortical pathology across the spectrum of dementia and motor neurodegenerative syndromes. In particular, it addresses how do these subcortical changes: (i) relate to early clinical diagnosis and phenotyping; (ii) are potential disease markers of motor, cognitive and behavioral impairment; (iii) relate to disease progression, and (iv) can be characterized using cutting-edge technologies. We present five articles highlighting the critical role of subcortical pathology across the spectrum of neurodegenerative conditions, including mild cognitive impairment (MCI), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).Jung et al. presented a novel 5 time-point longitudinal PET/MRI study of MCI patients in South Korea demonstrating a mediating effect of progressive subcortical abnormality on memory and executive function. Notably, time-varying change in thalamic volume, but not subcortical vascular burden, mediated progressive cognitive dysfunction. This builds upon the growing focus of thalamic abnormality as an important imaging biomarker of early and dissociable neurodegenerative pathology  that there is an important need to standardize imaging approaches toward subcortical structures. Such standardization, in the form of harmonization of acquisition and/or analysis protocols would allow more systematic investigation of subcortical changes across time and facilitate multi-center observational and intervention studies for neurodegenerative syndromes. Such standardization processes have been established for some time for cortical imaging but remain overlooked for subcortical regions.The compiled studies show the increasing progress and sophistication applied to subcortical analyses across neurodegenerative conditions and the importance of regional subcortical abnormality as imaging targets for capturing sub-clinical pathology, clinical phenotyping, and disease progression. Despite this promising progress, several limitations still emerge which need to be addressed in future studies. Specifically, the heterogeneity of imaging methods and their application makes it difficult to compare findings across studies, even within the same disease pathophysiology. It highlights a problem which has been recognized by several international working groups (hippocampus subfield group, for details see Temp et al. provide a timely perspective article introducing the unique advantages of Bayes theorem and applied parameter estimation as a methodological alternative to the more commonly applied p value focused Fisher statistics for detecting statistical significance. Specifically, the ability to allow for comparisons of multiple alternative hypotheses for small effects is of interest when analyzing subcortical structures, as it allows for discrimination between groups of patients or at the individual patient level. With recent neuroimaging publications of population-based brain development trajectories across the human life-span (Touching upon the impact of standardized analysis protocols, ife-span , it has Taken together, it emerges that cortical-subcortical loops and the integrity of subcortical structures are a critical aspect for further investigation, in particular for determining how subcortical changes contribute to “cortical symptomology.” There is still a common notion that subcortical changes in many neurodegenerative conditions emerge from Wallerian degeneration caused by cortical changes, based on the significant body of literature reporting cortical findings. However, clearly this is not yet established since subcortical changes might precede cortical changes for some neurodegenerative condition or certain symptoms. Better standardization and modeling of inter-individual differences will allow us to establish the relationship of cortical and subcortical changes in disease progression and symptomology across neurodegenerative conditions. Only then will it be possible to delineate cortical and subcortical contributions to neurodegenerative syndromes, without the current, inherent focus on cortical changes.We hope the readers enjoy our Research Topic submission as it highlights an important but under-researched area, which has great potential for future neurodegenerative disease diagnosis and management.ST, JM, and MH contributed equally to the interpretation and views expressed in the editorial. All authors contributed to the article and approved the submitted version.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.


Variability in findings related to non-invasive brain stimulation (NIBS) have increasingly been described as a result of differences in neurophysiological state. Additionally, there is some evidence suggesting that individual differences in psychological states may correlate with the magnitude and directionality of effects of NIBS on the neural and behavioural level. In this narrative review, it is proposed that the assessment of baseline affective states can quantify non-reductive properties which are not readily accessible to neuroscientific methods. Particularly, affective-related states are theorized to correlate with physiological, behavioural and phenomenological effects of NIBS. While further systematic research is needed, baseline psychological states are suggested to provide a complementary cost-effective source of information for understanding variability in NIBS outcomes. Implementing measures of psychological state may potentially contribute to increasing the sensitivity and specificity of results in experimental and clinical NIBS studies. Owing to its unique ability to influence nerve tissue, non-invasive brain stimulation (NIBS) provides an imperative means to study the workings of the human brain. In addition to investigating the basic neural properties underlying mental processes and behaviour, the modulatory effects of NIBS on neurophysiology that can outlast the stimulation period make tES and TMS suitable candidates for the treatment of mental and neurological disorders. NIBS has proven invaluable for expanding our knowledge about the neurobiological underpinnings of human behaviour in health and disease, yet fluctuations in, for example, ongoing neural activity can greatly influence the outcomes of NIBS on brain and behaviour e.g., . Within mulation . Acknowlmulation .Variation in neural activity due to external  or internal  factors has been demonstrated to predict magnitude and directionality of the effects of NIBS on brain physiology and behaviour . Informapsychological state. The added value of measuring psychological states is proposed to lie in (1) providing unique information, such as the phenomenological (subjective) experience of anxiety that cannot be disclosed with existing neuroimaging techniques, and (2) not having to rely on expensive neuroimaging equipment and expertise. Within the psychological framework of embodiment, psychological states can be understood as subjective phenomenological read-outs determined by sensory signals from the external surroundings (exteroception),  signals from within the body (interoception), and prior experience  over the right dorsolateral prefrontal cortex (DLPFC) in healthy female volunteers . Results. Higher levels of baseline anhedonia  were observed in patients who failed to respond to rTMS as compared to patients who showed an antidepressant effect. Moreover, higher anhedonia levels were associated with reduced resting-state functional connectivity in the brain's reward circuit, suggesting a potential neurobiological link between anhedonic state and antidepressant response to rTMS. Research in clinical settings also provides evidence for affective state-dependent effects. For example, an open-label adjuvant study investigated the antidepressant effects of bilateral high-frequency rTMS of the prefrontal cortex in medication-resistant patients with a major depressive episode Following the empirical confirmation that weak electric currents applied to the scalp induce changes in cortical excitability , transcrIn a sham-controlled study, tDCS over the frontal cortex was combined with a revised Taylor aggression paradigm in healthy volunteers . State aThe importance of considering individual differences is further underlined by a study showing that individuals scoring high on the introversion personality dimension are more susceptible to the modulatory effects of tDCS on emotional reactivity than individuals scoring high on extraversion . In a moIn clinical populations, there is increasing evidence for associations between affective state and NIBS-induced effects as well. A study on alpha hyperactivity in MDD patients  showed tlower intracortical facilitation and higher intracortical inhibition  from different finger muscles following a suprathreshold TMS pulse administered over the motor cortex. Associations between resting-state cortical excitability and affective states have been extensively studied. For example, a previous study has shown that asymmetries in left and right cortical excitability are correlated with approach and avoidance-related motivational tendencies . Moreovehibition . In contpatients .In sum, previous research indicates that affective states are linked to cortical excitability levels, thereby providing a psychobiological basis for interactions between affective states and NIBS interventions.Different findings have been found for self-reported state and trait anxiety as assessed by Spielberger's State and Trait Anxiety Inventory (STAI). For example, Self-report questionnaires are not without shortcomings and can include response biases , and a lack of a person's ability for introspection can have a negative impact on the reliability and validity of the measurement. Differences in construct validity of self-report scales and questionnaires as well as contextual factors like subject's expectations and motivation to participate could therefore potentially explain inconsistencies observed in the literature so far. Higher levels of anxiety have, for instance, been associated both with increased and decreased intracortical facilitation, but in the first case the association was based on the Hamilton anxiety rating scale in generalized anxiety patients  and in tIt is important to emphasise that we do not aim to equate affective state with disease-specific predictors . Instead, we propose that fluctuating experiences of, for example, anxiety or anger, which are specific to a given stimulation session, may be more informative to anticipate the observed NIBS-induced effect. Crucially, based on the reviewed studies, such a pattern has been observed in both clinical and non-clinical samples, suggesting that it could be a generic mechanism not restricted to specific (psycho)pathologies. Still, it needs to be stressed that assessing individual affective states to better explain NIBS effects has predominantly been done in a post-hoc manner so far. Arguably, this increases the risk of false positives. It is therefore suggested to systematically investigate the predictive value of affective states on the direction and size of NIBS effects to scrutinize whether this concept is tenable. In addition, assessing psychological states beyond cross-sectional self-reports may include longitudinal follow-ups to monitor changes in psychological states and adding behavioural correlates by administering psychological tasks may further contribute to the validity of affective states.during multiple sessions lead to more predictable outcomes of NIBS.Furthermore, the studies reviewed here included measures of either natural fluctuations  or experimentally induced  affective states see also  and 2. ILastly, based on the limited existing evidence it is not yet viable to determine whether effects of affective state reflect, for example, arousal and/or are driven by valence . In light of improving the predictive value of affective state-dependency, it may be worthwhile to try to disentangle these two aspects empirically. For example, anxiety and anger are both affective states associated with increased arousal, but the specific emotional experience of the two states is different. At this point it remains unclear whether the measured affective state needs to be aligned with the study outcome measure, or whether a generic arousal measurement suffices. Gaining more insight into this association may shed more light on the link between affective states and NIBS effects prospectively. Administering validated questionnaires, such as a shortened version of the Profile of Mood States , the BehUnravelling the physiological mechanisms by which NIBS techniques establish their effects in the brain remains critical for addressing basic neuroscientific research questions and developing effective neuromodulation-based treatments. In addition to studying neural states directly, evaluation of affective states could be a cost-effective way to obtain valuable non-reductive readouts which together with neurophysiological markers offer important mediators of the effects of NIBS both in basic research as well as in applied settings. Here, empirical evidence was presented to illustrate this idea and to raise the possibility that affective states can have added value in explaining intra- and interindividual study-outcome variability. Systematic assessment of affective states in experimental and clinical research may contribute to further increasing the sensitivity and specificity of tES- and TMS-based interventions.Dutch Research Foundation .This work was supported by the The authors have no competing interests to declare.


The past 20 years of research has elucidated new innate immune sensing and cell death pathways with disease relevance. Future molecular characterization of these pathways and their crosstalk and functional redundancies will aid in development of therapeutic strategies. The past 20 years of research has revealed several new innate immune sensing and cell death pathways with disease relevance. This Perspective looks back over the impact these discoveries have had and forwards toward their future therapeutic potential. PLOS Biology 20th Anniversary Collection.This article is part of the Innate immunity provides the first line of defense against disease. To carry out this critical function, the innate immune system is made up of sensor molecules called pattern recognition receptors (PRRs) that detect pathogen-associated and damage-associated molecular patterns (PAMPs and DAMPs) to initiate signaling pathways that activate a broader immune response and regulated cell death (RCD). There are several families of PRRs, including Toll-like receptors (TLRs), NOD-like receptors (NLRs), and RIG-I-like receptors (RLRs), and they are highly evolutionarily conserved . For insBecause of their essential and multifaceted functions in health and disease, innate immune sensors and their downstream pathways have been a major focus of recent research. The advent of new technologies and genetic tools to study the immune system has greatly advanced this field and enabled cutting-edge breakthroughs in our understanding of innate immune-mediated cell death. Until approximately 20 years ago, apoptosis was the only widely accepted form of RCD, and it was thought to be a developmental process that allowed nonlytic, immunologically silent clearance of cells that were no longer needed. There was a gradual shift in this understanding as studies began to show that infections could also cause RCD . But thiBuilding upon these findings, the innate immune sensing and cell death fields have grown exponentially. As a prime example of this growth, there were only 92 publications in PubMed on pyroptosis in its first 10 years of study (2001 to 2011), but there were 2,204 just last year (2022). As a result of the increased mechanistic understanding of RCD, cell death molecules are now widely implicated across the disease spectrum, from infections and autoinflammatory diseases to cancers, and these molecules are prime targets for drug development [Clinical translation continues to be a top priority in innate immunity and cell death fields, leading investigators to ask fundamental questions about RCD pathways to improve therapeutic strategies. As research in these areas has grown, so too has the understanding that there are mechanistic connections and functional redundancies among multiple forms of RCD. This may be one of the key factors influencing the lack of clinical effectiveness for therapeutics targeting cell death machinery, such as caspases and inflammasome components . StudiesRecent progress to fill this gap has identified regulatory mechanisms controlling crosstalk between RCD pathways and the central roles of caspases in these connections. Caspase-1 was characterized as a component of the inflammasome and pyroptosis, but it can also cleave apoptotic substrates, including caspase-7 and PARP . AdditioPANoptosomes form when innate immune sensors detect pathogens, PAMPs, DAMPs, homeostatic perturbations, or the cytokines produced downstream. The first PANoptosome identified is formed by Z-DNA binding protein 1 (ZBP1), an innate immune sensor that detects influenza A virus to activate the NLRP3 inflammasome and PANoptosis . These POverall, the fields of innate immune sensing and cell death have progressed extensively in the past 20 years, highlighting key molecular similarities and differences between RCD pathways. Looking to the future, work to improve the translational application of these discoveries is critically important. Current molecular evidence suggests that many disease phenotypes cannot be explained by previously identified RCD pathways individually . Future Future work to identify new therapeutic targets in innate immunity and cell death complexes and to translate these findings to the clinic should leverage the power of new technological advancements. The advent of CRISPR–Cas9, single-cell analysis technologies, advanced imaging capabilities, and cryo-EM have already rapidly accelerated progress in the field and paved the way for continued discovery. It has never been a better time to investigate both the evolutionarily conserved and distinct aspects of innate immune signaling and cell death pathways in health and disease to translate these mechanistic findings into therapeutic strategies.


Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources  requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful. Over the past decade, computational models have become more prominent in psychiatric research and—aligned with the fourth industrial revolution—are also finding their way into clinical and commercial solutions for psychiatry. In this Series paper, we chart the landscape of computational models in psychiatry, highlight the communalities and differences between different types of computational models, discuss their advantages and disadvantages for research and clinical practice, and distinguish between mechanism-driven and mechanism-agnostic models, which have traditionally served different purposes. Mechanism-driven models are biology-inspired models that mimic processes in the brain and are interpretable in their mechanisms. Conversely, mechanism-agnostic models use complex machine-learning methods to distil information from large datasets and often provide little insight into the relevant mechanisms. Here we show that these model types are complementary and describe how models from both domains can be brought together to build more interpretable models that are more likely to find a place in clinical practice than using each model-type in isolation.2 These changes have not gone unnoticed in the corporate sector. Mental health solutions are more popular than ever and startups in mental health have become a hot commodity. Companies that pursue automated and online-based solutions have gained much attraction from investors, and technology giants, such as Apple, have ventured into predicting mental health problems using our ever-present smartphones.3The COVID-19 pandemic has inadvertently put mental health into the spotlight. Psychiatric symptoms have strongly increased and the demand for remedies is higher than ever.4 In the first paper in this Series, we will selectively review the different computational approaches and their respective data sources that have been used in academic research. Rather than present a systematic literature review, we will provide a narrative description of the field and illustrate what we consider important contributions using selected examples from computational psychiatry and precision psychiatry. Although a delineation of these two fields is not clear cut and the terms are sometimes used interchangeably, traditionally computational psychiatry has focused more on understanding the mechanisms underlying mental disorders whereas precision psychiatry has focused on prediction and individualised treatment. We discuss how different modelling approaches can be meaningfully brought together to overcome limitations and move towards clinically useful models. As academics, clinicians, and the industry are moving closer together, computational approaches could be greatly beneficial, but an in-depth crosstalk between these different fields is essential to build meaningful models.At the core of this excitement is the promise that computational approaches can help improve and broaden access to mental illness detection, prediction, and intervention. However, computational approaches to psychiatry are already well established in academic research, with the fields of computational psychiatry  and prec7Computational modelling in psychiatry aims to achieve different objectives that can be roughly divided into four categories.7Many academic studies aim to understand the biological mechanisms that cause mental illness, often investigating the neural mechanisms that underpin mental disorders. The goal of these approaches is to understand how processes in the brain go wrong, which can facilitate the development of better biomarkers for diagnosis, prevention, and therapeutic intervention.8 Therefore, there is hope that computational models will be able to deconvolve the heterogeneity of psychiatric disease taxonomy by generating new measures that are more objective and biologically driven.5 These approaches largely rely on unsupervised models, such as clustering, aimed at discovering meaningful patterns in the data that are then evaluated against external measures, like treatment outcomes.A longstanding challenge for psychiatry is that we know little about the biological causes of mental health problems. Current diagnostic manuals are not informed by any neurobiological mechanisms, and their purely descriptive analyses of symptoms have been criticised because of doubts of the validity of diagnostic labels.9 Predicting mental illness before its development is particularly important because it might allow the prevention of adverse disease courses in a timely and efficient manner. These endeavours are most commonly used in the early psychosis field, in which high-risk states are well established, providing highly valuable windows of opportunity for preventive interventions.10An important goal is to predict a mental health status, either concurrently or before the development of disease to predict the changes that are about to emerge.vs medication) or to select the specific form of treatment .From a therapeutic perspective, predicting which patient will benefit from a particular treatment is essential. Psychiatry has developed a variety of non-pharmacological and pharmacological treatments, but a substantial proportion of patients will not benefit from these treatments. Finding out which patients benefit from a specific treatment is often a tedious and slow trial and error process. Therefore, the hope is that computational models can help improve treatment predictions, be it either to select between different types of therapeutic strategies  and oneThe elegance of computational models is primarily in their ability to detect meaningful hidden patterns in complex data. Often, mental health-relevant information is not directly observable in collected raw data , but only through aggregating this input data can one extract clinically useful patterns . Therefore, the function of computational models is to condense and aggregate data, but also to determine the structure of meaningful variation, which can help forecast clinically relevant developments.20 On the other hand are mechanism-driven models, also known as white box or glass box models,21 for which the link between input and output variables is clearly described and directly observable from the model formulation.In this Series paper, we sort computational models according to how they are mechanistically formulated . On one A key goal of academic research in mental health is to understand why psychiatric disorders arise and what the neural underpinnings and mechanisms are. To this end, researchers combine neuroscience methods  with computational modelling. These models are inspired by our knowledge about the brain function and imitate the information processing that takes place in the brain.23 Thus far, computational psychiatry has seen approaches at many different levels of abstraction,27 but a superiority for one level of abstraction has yet to be shown.Due to brain complexity, most computational neuro-scientists do not attempt to replicate the brain one to one, but use abstractions based on principles that are known to guide brain function. This allows the computational models to remain interpretable. A key challenge for this approach in modelling mental ill health is to determine the right level of abstraction. If a psychiatric disorder arises from an ion channel impairment, then these channels should be explicitly characterised in the model. However, if a breakdown takes place at the level of communication between different hierarchically organised brain regions, then modelling single synapses and neurons is probably not necessary and they can be approximated as entire ensembles.28 Such models of neuronal populations can then be used to go beyond single brain regions and model the interactions between regions and even whole brain connectivity ,40A second set of mechanism-driven modelling approaches focuses on capturing behaviour as closely as possible and is less tightly connected to the specific brain implementation. Specifically, reinforcement learning, Bayesian, and similar models are promising for representing complex behaviours and behavioural biases in patients and linking behaviour with subjective experiences and clinically relevant symptoms.44 is traditionally assessed using clinical interviews; by contrast patients with schizophrenia who show a jumping to conclusions.47 To objectively measure patient indecisiveness, we and others have used information gathering tasks . However, how well these mechanism-driven models perform compared with mechanism-agnostic models, and how they can be supplemented with other data sources is yet to be determined.Although mechanism-driven models facilitate a better understanding of which neural or cognitive processes are impaired in patients these models are not yet used to predict psychiatric phenotypes (diagnoses and outcomes) in clinical practice. Most models are used to find differences between groups, rather than using these model parameters to estimate an individual’s psychiatric status. Studies suggest that mechanism-driven, model-derived parameters are better at predicting disease status or longer-term outcomes than standard neural, behavioural, or sometimes even clinical predictors55Since the advent of modern machine learning methods there has been considerable enthusiasm for their use, including deep learning, for precision psychiatry. Unlike mechanism-driven strategies, mechanism-agnostic models are usually complex with hundreds or thousands of free parameters. These models have achieved previously unseen performance in a wide range of tasks, from image classification to predicting protein structures.56In mental health, mechanism-agnostic models are being used together with different forms of data, including clinical records, brain-based measures, and passively collected smartphone or social media data . The aim11 several studies have used mechanism-agnostic models with the primary aim of condensing and distilling information about mental health status and symptoms.57With an ongoing digitalisation of health-care records across health-care systems, large clinical datasets for mental health are becoming available for interrogation. Although these datasets are sometimes limited in terms of data quality, organisation, and accessibility, as described by Koutsouleris and colleagues in Series paper 2,59 and risk of suicide61 (with out-of-sample area under the receiver operating characteristic curve prediction from 0·58 to >0·80). However, these studies also make another key challenge apparent: what language features should these algorithms be trained on? Training NLP algorithms on specific language features relevant to psychiatry, such as research domain criteria-related content, might help improve these predictive models over standard semantic corpus labels.In psychiatry, large amounts of clinical notes and medical records are difficult to condense because much of the relevant information is captured in the clinician’s notes, rather than in laboratory test indicators . Studies have successfully used natural language processing (NLP) algorithms, which allow the extraction of specific information from written text to help predicting outcomes, such as hospitalisation duration, readmission likelihood,62It is relevant to note that mechanism-agnostic models are not confined to written notes. These approaches also hold great promise for more complex data, such as audio and video recordings from assessments and therapy sessions. These algorithms could assist clinicians by alerting them to subtle  reactions and other features that might go unnoticed.Scientific investigations of patients with psychiatric disorders often generate large data sets with many datapoints per participant. Neuroimaging  data contain tens of thousands of datapoints per participant. This high dimensionality poses considerable challenges for analysing the data with traditional statistical approaches. Mechanism-agnostic models have been used mostly in two distinct approaches, either using data directly to classify and predict participants’ mental health, or using unsupervised  algorithms to create lower dimensional features, which can then be used for linkage with mental health status.64 and deployed a wide range of machine learning models . Although these methods can discriminate between groups , newer studies have shown that these predictions improve significantly when integrating neuroimaging data with other data sources, such as clinician ratings, genetic data, and neuropsychological tests.64 This complementarity of neuroimaging data to other data sources also has implications for interpretability, because it allows a better understanding of the degree to which different sources are complementary, and how mechanism-driven features might shed light onto mechanism-agnostic features.To predict current or future mental health status, many studies have used different variants of MRI data70 An advantage of these methods is that the intermediary brain fingerprints are often more interpretable and less noisy than when predicting mental health status directly from raw data, which can also help us to better understand the mechanisms underlying a specific status. For example, by using deep autoencoders of diffusion tensor imaging data, Chamberland and colleagues66 were not only able to predict various neurological and psychiatric disorders (area under the receiver operating characteristic curve from >0·6 to >0·8), but also generate anomaly metrics that allowed them to establish which fibre tracts were most relevant for each disorder.An alternative approach to analysing neuroimaging data is to use unsupervised models to generate low-dimensional brain organisation patterns, which can then be used to predict mental health status. Various methods have been used to generate such brain fingerprints, from clustering algorithms to canonical correlation analyses combining brain and behaviour to deep autoencoders.70 thus, smartphones are becoming ideal companions for data-hungry models. Many different types of measures can be extracted from digital data ,78 Another limitation of mechanism-driven models is that many rely on complex data collection, which substantially restricts their use outside of academic settings.For mechanism-driven models, a key challenge is their predictive performance. Traditionally, mechanism-driven models are developed and optimised to capture behaviour or neural responses. Because these models are not optimised to predict mental health status, ideal therapeutic response, or long-term outcomes, these parameters often display a more restricted predictive power than models optimised to predict mental health-related phenotypes. Attempts to overcome this weakness use generative embedding strategies, which use mechanism-driven algorithms as a dimensionality reduction step before the subsequent generation of optimally predictive mechanism-agnostic models.79 but improving our understanding of them is crucial for three reasons: (1) only through understanding mechanism-agnostic models will we be able to establish which input variables are relevant and which could be removed, which is challenging in complex and non-linear mechanism-agnostic models; (2) understanding enables us to detect biases and faults of the model that arise through biased training sets;11 (3) predictions from unexplainable models can pose a substantial challenge when used in clinics because the uptake of model predictions strongly depends on clinical staff understanding and trusting them. We propose to use three strategies that could help alleviate these limitations.For mechanism-agnostic models, the key challenge is understanding how these models operate and what they predict. Their complexity renders them opaque,80 have only been evaluated in small samples of highly selected participants, and little is known about their potential for predicting mental health status in real-world clinical cohorts. Therefore, we need to examine the use of mechanistic assessments outside of overly selective laboratory samples in large, epidemiologically sampled populations.11 This is crucial because these assessments still rely heavily on the experimenters’ instructions. For any assessment that should be applied to clinical practice, assessments that are robust to experimenter biases are required  has proven to be promising.93Although gamified approaches are unable to replace neuroimaging markers directly, they can help to inexpensively approximate potential mechanisms. By using similar tasks used in neuroimaging scanners, we can use computational models to infer the probable neural mechanisms relevant for imbalanced processing. Moreover, by using pharmacological manipulations, we can obtain relevant information about possible neurotransmitter involvement that can be helpful for pharmacological treatment predictions.94 might provide additional crucial information.A key advantage of mobile assessment platforms is that they are more amenable for repeated and triggered assessments. They can be combined with self-reports collected as ecological momentary assessments. In addition, bringing assessments together with passive data collection or physiological data, such as pupillometry,95 and attention maps96). However, these explanations are not undisputed because they only provide an approximation to the true model. This means that they are unable to fully capture the model and could provide false explanations for a considerable number of cases.79The inability to understand many mechanism-agnostic models not only challenges their usability, but also threatens their uptake in clinics and might become a regulatory requirement. Over the past few years, various techniques, predominantly in image classification, have been developed trying to explain black box models .Complementarily, an important new trend in machine learning is the use of causal models that allow advancement beyond simple correlational effects. This is particularly relevant in psychiatry to identify factors that are causal for mental health and not simply coincidental. Although there are several different forms of models that allow the assessment of causality,10 Therefore, it is important to carefully consider the complexity of a model and to balance interpretability and complexity in accordance with the demand.Another method to increase interpretability is to use dimensionality reduction approaches before using these lower dimensional features for prediction. this modularisation is useful to assess the performance of each compartment independently and exploit the relatively low dimensionality of the final prediction model to establish better understandability. An example of such an approach is the prediction of psychosis onset, in which a combination of separately aggregated clinical, neuroimaging, and neuropsychological predictors have revealed partly additive and explainable effects.101 and digital markers103 could complement such data and improve performance.Thus far, the computational modelling in psychiatry mainly consists of many scattered, independent approaches to explain mental health, but these different promising attempts have not yet been brought together. Building clinically useful models will require us to overcoming these fragmented aspirations to pursuing the integration of different data sources following modelling strategies that maximise complementarity and interpretability . For exaWhen approaching data integration, it is crucial to be aware of the complementarity of the data. Data sources that capture entirely distinct data types  are likely to be non-overlapping and thus add meaningful new dimensions that can help elucidate mental health heterogeneity. Therefore, by combining these different data sources and models, we might be able to more comprehensively parametrise a person’s mental health.96 but their clinical usefulness is yet to be determined. Moreover, by bringing together mechanism-driven and mechanism-agnostic models, we can detect shortcomings of our mechanism-driven models and improve our mechanistic understanding.104Focus should be directed towards mechanism-driven models and data sources that extract meaningful features of rich data; by bringing these data sources together in shallower and interpretable mechanism-agnostic models, we will be able to identify the role of each of these condensed features. Such approaches also allow us to assess which data sources contribute to prediction the most, and which can be eliminated without losing predictive power. The first attempts for fusing different data and modelling modalities show promise,A wealth of computational approaches to psychiatry make navigating this complex, rapidly evolving space challenging and understanding the uniqueness versus the relatedness of these models more difficult. A stricter standardisation of modelling strategies and enforcement of comparability is needed to achieve a transparent landscape of computational modelling in psychiatry. In this Series paper, we show how to dissociate these models based on their purpose. Moreover, we have highlighted the importance of bringing these disparate models and data sources together to increase both prediction and interpretability. In particular, the combination of mechanism-driven and mechanism-agnostic models hold great promise to derive biologically informed and transparent prediction models, which could help to develop novel treatments and interventions.


Escherichia coli by random mutagenesis for improved lycopene’ by Hailin Chen et al., RSC Adv., 2018, 8, 15021–15028.Correction for ‘Directed evolution of mevalonate kinase in  Microbial Cell Factories,1 which should have been cited in this RSC Advances paper.The authors wish to draw the readers’ attention to their closely related paper, published in RSC Advances paper and RSC Advances article.The authors regret that there is unattributed overlap in text between this RSC Advances paper, a directed evolution strategy was used to optimize the activity of MK to enhance the tolerance for farnesyldiphosphate (FPP) and geranylgeranyldiphosphate (GGPP), to enhance the affinity of mevalonate and MK, and to improve lycopene production. The catalytic mechanisms of both enzymes are very different; however improving their activities can improve lycopene production.Two different rate-limiting enzymes in the lycopene synthetic pathway were studied using the same methods, mevalonate kinase (MK) in this paper and isopentenyl diphosphate isomerase (IDI) in The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.


The neural stem cell niche is a key regulator participating in the maintenance, regeneration, and repair of the brain. Within the niche neural stem cells (NSC) generate new neurons throughout life, which is important for tissue homeostasis and brain function. NSCs are regulated by intrinsic and extrinsic factors with cellular metabolism being lately recognized as one of the most important ones, with evidence suggesting that it may serve as a common signal integrator to ensure mammalian brain homeostasis. The aim of this review is to summarize recent insights into how metabolism affects NSC fate decisions in adult neural stem cell niches, with occasional referencing of embryonic neural stem cells when it is deemed necessary. Specifically, we will highlight the implication of mitochondria as crucial regulators of NSC fate decisions and the relationship between metabolism and ependymal cells. The link between primary cilia dysfunction in the region of hypothalamus and metabolic diseases will be examined as well. Lastly, the involvement of metabolic pathways in ependymal cell ciliogenesis and physiology regulation will be discussed. The mammalian brain cortex originates from neuroepithelial stem cells (NESCS) or primary neural stem cells (pNSCs) , which dThe specific cytoarchitecture of the V-SVZ niche is tightly associated with a specific and uniquely organized extracellular matrix (ECM) that regulates cell proliferation, differentiation, and migration throughout adulthood. The brain ECM alters its composition based on the developmental stage , but as Identifying the mechanisms that govern pNSC and aNSC behavior is a multifactorial process which includes intrinsic and extrinsic mechanisms in the NSCN. The role of metabolism and specifically mitochondrial metabolism has been recently implicated as a regulator of NSC self-renewal and differentiation . There iAs mentioned previously, neurogenesis is an important and intricate process carried on postnatally in distinct niches such as SVZ and SGZ . UnderstFocusing on NSCs, cell metabolism has been shown to play a crucial role in a variety of processes, such as NSC proliferation, differentiation, and quiescence  Figure . In partLipid metabolism has also been found to be important in aNSC fate and in particular in hippocampal neurogenesis where it has been shown to control the proliferative activity of aNSCs. Fatty acid oxidase (FAO) seems to maintain NSC stemness whereas lipogenesis drives them toward differentiation , a findiin vitro models of human brain development studies suggesting that altered lipid metabolism contributes to intellectual disability  in the SVZ producing enzymes required for fatty acid oxidation show constant increases in oxygen consumption upon treatment with a polyunsaturated fatty acid . FAO inhsability  highlighsability .From all the above, the importance of lipid metabolism was highlighted. Nevertheless, the energetic demands are different between actively proliferating and quiescent NSCs in the adult brain as specific metabolic processes were found different in the two categories of neural stem cells . In ordeGlutaminolysis has also been shown to play a crucial role in NSC fate decisions. Specifically, glutaminolysis is required for the Rho GTPase Activating Protein 11B (ARHGAP11B)-mediated promotion of SVZ basal progenitors (BPs) proliferation during the embryonic life . What maOverall, there is evidence supporting the role of metabolism in the NSC fate and function regulation, reinforcing the notion that lipid and fatty acid metabolism contribute to human neurogenesis and behavior. It should also be noted that the transition from NSC to a neuronal lineage is accompanied by increased mitochondrial biogenesis, as well as downregulation of glycolysis and fatty acid oxidation pathways, reduction in glycolysis-related proteins, such as hexokinase 2 (HK2) and isoform A of lactate dehydrogenase (LDHA), switch from M2 isoform of the pyruvate kinase (PKM2) to its constitutively active M1 isoform (PKM1) and upregulation of OXPHOS-related genes . UnderstMitochondria are bioenergetic organelles involved in a variety of anabolic and catabolic functions . Metabol+/NADH ratio regulation that can dictate the fate of stem cells via sirtuins  activation  supplementation , an important transcription factor for fatty acid and cholesterol biosynthesis, whose aberrant activation has been shown to suppress ciliary formation . DysreguGlucose metabolism also plays a significant role in primary cilium function, as glucose deprivation induces primary cilia formation through mammalian target of rapamycin complex 1 (mTORC1) inactivation and p27 activation . AdditioAs previously stated, the role of primary cilia is important in determining aNSC  and EC fThe SVZ is sandwiched between a basement membrane on the one end (attached to blood capillaries) and ECs on the other end . EC haveStudies in renal epithelial cells have shown that primary cilia detect shear stress and stimulate lipophagy, promote mitochondrial biogenesis and increases ATP production which provides the energy required for metabolic reprogramming and cellular adaptation . SimilarEpendymal cells (EC) act as a barrier as well as selective transporter of molecules such as fatty acids which enter ependymal cells and incorporated into triacyl glycerol compounds. This sequestration of fatty acids in the form of triacyl glycerol seem to be part of an active transport mechanism of essential fatty acids from CSF to central nervous system (CNS) . On the In the study of Lalioti et al. deletion of GemC1 in mice triggered cell fate changes of neural progenitor cells of the brain into adult NSCs instead of the brain ependymal cells. These cell fate alterations were followed by global changes in chromatin accessibility of several genes promoters resulting in higher accessibility of NSCs-related promoters. Among others fatty acid binding protein 7 (Fabp7) and lactate dehydrogenase B (Ldhb) gene promoters which are important for fatty acid metabolism and lipid biosynthesis were found to display increased accessibility in GemC1-knockout mice . The linHere, we comprehensively reviewed the existing literature on Neural stem cell Niche regarding the role of metabolism in NSC fate with special emphasis in ciliogenesis. Over the years, significant progress has been made to unravel the mechanisms by which NSCs are differentiated toward neurons during embryonic and adult neurogenesis. Recently it has been discovered that cellular metabolism plays an important role in maintaining quiescent or proliferative NSCs and in modulating self-renewal and differentiation of NSCs. The main goal for future research is to unravel the metabolic adaptations that occur in NSCs that maintain their stemness or initiate their differentiation. Cell metabolism may serve as a signal integrator that “translates” a variety of signals into an integrated metabolic response that may affect cells at multiple levels, e.g., energy status, fuel utilization source, oxidative stress, and epigenetics. Thus, studying the interplay between transcriptional programs, morphogenic signaling, and its down-stream or up-stream regulation of the metabolic state may substantially improve our understanding of how NSPCs orchestrate the construction of the brain. Given that cilia, primary or motile, are in the interface between extracellular and intracellular space, their contribution in metabolic processes is not unexpected as discussed here. Although we do not fully understand the mechanisms of metabolic control of NSPC behavior yet, there is strong evidence that the metabolic state is a novel key player regulating the balance between stem cell quiescence/activation and subsequent differentiation. Identification and subsequent targeting of the compensatory metabolic pathways create opportunities for more efficient therapeutic strategies. Forward and unbiased metabolomics approaches should facilitate understanding this metabolic rewiring. Developing further knowledge on other aspects of neural stem cell metabolism such as metabolic compartmentalization, metabolic crosstalk with other cell types, and EC subtype-specific metabolic traits is another future challenge in this young field and a prerequisite to the development of novel therapeutic strategies.IA conceived the idea of this review and wrote together with GG, KB, and IH the first draft of this manuscript. CK, EH, KK, ZL, and ST supervised the writing of the manuscript, helped with writing, and provided their specialized insights based on their different backgrounds. IA produced all the schematic illustrations for the figures. All authors contributed to the article and approved the submitted version.


Our brains synthesize vast and varied sensory inputs to represent an event or place that can later be recalled freely. The term “engram” refers to a group of neurons activated in association with a memory or event, suggesting that if one were to activate a subset of cells in the ensemble, one would trigger the recall of a memory. The medial temporal lobes, including the hippocampus, are often studied as the primary region of spatial engram formation . HoweverThe recent article builds on their previous study , where aThe current study  adopted Having established that they could tag memory-related circuits and that similar networks were activated by PTZ, they tagged neurons in control and T-maze-trained TRAP2 mice followed by acute seizure induction a week later to test the overlap between memory and seizure circuits. They found that colocalization between neurons tagged during the T-maze task and seizure-activated neurons, immunolabeled with the immediate early gene ARC, was significantly greater than that expected by chance or observed in controls. The enhanced overlap occurred in the RSC and mPFC but not in the PVT, demonstrating a strong intersection between neurons activated during spatial memory and seizures. These findings suggest that seizures contribute to retrograde amnesia by involving memory ensembles in abnormal, unrelated activity. Indeed, neurons with higher intrinsic or circuit-driven excitability are likely to be more active in engrams  and to bThe study highlights how the TRAP2 strategy can be elegantly adapted to label and analyze processes at the intersection of transient phenomenon of memory formation and seizures. However, the potential issues posed by the longer window for tagging “active” neurons rather than the tasks and needs for habituation for animal handling and injection to minimize task-independent neuronal activation need to be considered. Although cFOS expression has been shown to be greater than that of ARC in certain tasks , the oveCurrently, we have an incomplete understanding of the pathophysiology of seizure disorders and thus the lack of proper therapeutics. By demonstrating that seizures and memory occupy the same brain regions, potentially perturbing hippocampal–cortical interactions involved in consolidation, this study moves us a step closer to fully understanding seizure-related memory issues. The global approach of the study is notable and necessary to fully understand how seizures disrupt learning and memory circuits, resulting in retrograde amnesia.


Lung cancer is a disease associated with significant morbidity and mortality on a global setting. This form of cancer commonly gives raise to metastatic lesions the brain, which can further worsen outcomes. In this focused review, we discuss an overview of lung cancers that metastasize to the brain: known risk factors; means of detection and diagnosis; and options for treatment including a comparison between surgical resection, stereotactic radiosurgery, and whole-brain radiation therapy. These interventions are still being assessed by clinical trials and continue to be modified through evidence-based practice. Lung cancer is the most common form of malignancy and cause of cancer-related deaths in the world, and the second leading form of cancer-related deaths in the United States.,2 SeveraBrMs are often initially detected from imaging as part of a metastatic tumor workup, or following the advent of clinical symptoms; with a definitive diagnosis later being confirmed via biopsy.,17 BrMs,Previous research has identified several risk factors specific for the presence of BrMs in NSCLC: being the female gender; concurrent lymphatic metastases; specific microRNA signatures; a high neutrophil to lymphocyte (NLR) ratio; elevated levels of neurofilament light chain; presence of EGFR driver mutation; and elevated serum levels of CEA, S100B, ProApolipoprotein A1 (apo A-1), Ki-67, VEGF-C, caspace-3, and calcium.–37 Sun, Preclinical studies using rodent models have demonstrated early detection of BrMs by employing molecular MRI with contrast agents that highlight tumor vascular factors ALCAM21 and VCAM-1.–40 RoutiThe development of machine learning algorithms has also been shown as a promising method of early detection. Machine learning is performed by teaching a machine a dataset with known predictors and outcomes using algorithms. What the machine then learns can be used in diagnosis in datasets where the diagnosis is unknown.,44 Cho  is the modality primarily used in the diagnosing and localization of brain tumors in patients with brain lesions, as high level of availability, comparatively high resolution, and excellent capabilities for the characterization of soft tissues are provided by this device; additionally, with specific sequences, supplementary biological information like apoptosis, cell density, or angiogenesis can be measured (diffusion-weighted MRIs or perfusion-weighted MRIs),49 CertaA molecular imaging technique called Positron Emission Tomography (PET), which detects emitted photons from radiotracers, is another advanced imaging technique widely used in brain cancer patients. Using PET imaging, metabolic processes, like glucose metabolism and amino acid uptake, can be measured noninvasively and quantitatively. Despite Hybrid PET/MRI scanners enable high-resolution metabolic and anatomical imaging.,56 This Radiopharmaceuticals should be selected based on the characteristics of the tumor being examined. PET tracers like [18F] fluorodeoxyglucose (FDG) are most used because tumor cells exhibit a higher glucose metabolism than healthy tissues,59 In caCurrent treatments for lung cancer patients with BrMs include supportive care, surgical resection, radiotherapies. The inteThe discussion of surgically resecting BrMs is best understood by first outlining the distinct subsets of clinical presentations. One subset is when the tumor size is large and causing severe neurologic symptoms, such as mass effect.,70 The fAnother subset of patients who meet criteria for surgical resection are those with a brain metastasis that is not large or causing severe neurologic symptoms, but in patients in which the intracranial disease is limited, systemic disease is controlled, and the patient is functionally independent.,76 FurthRadiotherapies in the treatment of metastatic brain lesions are primarily groups into Stereotactic radiosurgery (SRS), and whole brain radiation therapy (WBRT).,85 SRS iSRS and WBRT may be used exclusively as well as in conjunction with other modalities. LiteratuTreatment via SRS and WBRT differs in dosage and number of fractions. A fraction refers to dividing up the total radiation dosage into multiple treatments and maximizes the effectiveness of radiotherapy. This is accomplished by administering radiation on regularly time intervals which correlate with radiosensitive stages in the cell cycle of cancer cells. SRS treaWhen ionizing radiation is introduced to tissues, a large quantity of free radicals is created, and these free radicals combined with oxygen in the blood and destroy surrounding tissues.–101 StudThe development of brain metastases in lung cancer patients continues to be a major health concern on a global scale. These metastatic tumors significantly increase both morbidity and mortality rates among patients. Despite advances in medical technology, no treatment yet exists without adverse effects, or low remission rates: surgical resection alone leaves concern for untreated micrometases; and radiotherapies are associated with gross cognitive decline. Optimum dosing and fractioning in both stereotactic radiosurgery and whole brain radiation therapy have been investigated to find an optimal approach, but results are not without there drawbacks. Ultimately, the most promising option for improving mortality and morbidity rates lies in the detection of brain metastases as early as possible; thereby minimizing the intensity of treatment—and therefore adverse consequences—needed.


Our study investigates the well-known observation/quandary that cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. This has motivated us to explore whether these sex biases may have a common basis. To study that, we assembled and analyzed a large collection of cancer and AID incidence datasets, including matched data from 29 countries. We first, quite strikingly, find that the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. To our knowledge, this is the first time this across-tissue relationship has been quantitatively demonstrated. Second, we find by analyzing healthy human tissue gene expression data that the sex bias in the expression of mitochondrial-encoded genes stands out as the common key factor whose levels across human tissues are most strongly and positively associated with both cancer and AID incidence rate sex biases, pointing to the key potential role of these genes in determining sex bias in both disorders. These findings may further prompt researchers to explore how pertaining findings in cancer studies could cross fertilize AID studies and vice versa, potentially enhancing our ability to prevent and treat these diseases.Cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. To explore whether these sex biases have a common basis, we collected 167 AID incidence studies from many countries for tissues that have both a cancer type and an AID that arise from that tissue. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that, indeed, the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. The common key factor whose levels across human tissues are most strongly associated with these incidence rate sex biases is the sex bias in the expression of the 37 genes encoded in the mitochondrial genome. Both autoimmune diseases (AIDs) and cancers have notably sex-biased incidence rates. Most AIDs occur more often in women ,2, and macross human tissues. This question is of fundamental interest, since an affirmative answer may suggest that there are common factors underlying their incidence. Establishing such a link between AIDs and cancers could further prompt researchers to explore how pertaining findings in AIDs could cross fertilize cancer risk studies and vice versa, potentially enhancing our ability to prevent and treat these diseases.Given these observations, we asked whether the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are correlated To explore whether these sex biases are correlated across human tissues, we collected population-based AID incidence studies for tissues that have both a cancer type and an AID that arise from that tissue. For countries for which we collected AID incidence data, we gathered incidence data for corresponding cancer types from national cancer registries. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that the incidence rate sex biases observed for AIDs and cancers that occur in the same tissue are positively correlated across human tissues. In addition, we analyzed gene expression data from non-diseased tissue samples to determine if sex biases in gene set expression in these tissues are correlated with AID and cancer incidence rate sex biases in the same tissues. We find that the top positively enriched gene set across human tissues whose expression sex bias is most strongly associated with the incidence rate sex biases for AIDs, cancers, and AIDs and cancers considered jointly, is the set of 37 genes encoded in the mitochondrial genome.Our analysis is divided into two main parts: curation and analysis of disease incidence rate data; and investigation of associations between incidence rates and gene expression in corresponding non-diseased human tissue samples. First, we studied the association of incidence rates for AIDs and cancers occurring in the same tissue. We collected incidence data for AIDs from published studies, and for each country for which we found incidence data for a given AID, we collected incidence data from that country’s national cancer registry for cancers occurring in the same tissue as the AID. We matched AID and cancer incidence data by tissue and by country to produce country-specific tissue-matched AID-cancer incidence rate data pairs. We then computed across-tissue correlations between AIDs and cancers for male incidence rates, female incidence rates, overall incidence rates, and incidence rate sex biases, at both the individual country level and the across-country global level.Next, we used non-diseased human tissue transcriptomic data from GTEx version 8 ), which might introduce geographic, ethnic, or social biases. This geographic bias in our AID-cancer data pairs is largely due to a paucity of suitable AID epidemiological studies based on populations outside of North America and Europe, particularly on populations in low- and middle-income countries ,36. For It has been hypothesized that chronic inflammation leads to cancer ,42. IndeAs in humans, sex differences have been reported in animal studies of diseases, which has prompted us to search the literature and survey previous studies of sex bias in disease incidence in rodent models of cancers and AIDs. We focused on studies of sex difference in spontaneous and/or autochthonous carcinogenesis by either carcinogen treatment or genetic engineering, excluding transplantation of syngeneic animals because these animals do not model disease development  stands out as a key factor whose aggregate level across human tissues is quite strongly associated with these incidence rate sex biases. Our findings thus call for further mechanistic studies on the role of mitochondrial gene expression in determining cancer and AID incidence and their incidence rate sex biases.


Despite many pharmacological and surgical treatments addressing the symptoms of Parkinson’s disease, there are no approved treatments that slow disease progression. Genetic discoveries in the last 20 years have increased our understanding of the molecular contributors to Parkinson’s pathophysiology, uncovered many druggable targets and pathways, and increased investment in treatments that might slow or stop the disease process. Longitudinal, observational studies are dissecting Parkinson’s disease heterogeneity and illuminating the importance of molecularly defined subtypes more likely to respond to targeted interventions. Indeed, clinical and pathological differences seen within and across carriers of PD-associated gene mutations suggest the existence of greater biological complexity than previously appreciated and increase the likelihood that targeted interventions based on molecular characteristics will be beneficial. This article offers our current perspective on the promise and current challenges in subtype identification and precision medicine approaches in Parkinson’s disease. Death of nigrostriatal dopaminergic neurons is a pathological hallmark of Parkinson’s disease (PD). Indeed, most approved PD therapies today seek to replace or mimic dopamine’s molecular activity. While remarkably effective in treating many of the disease’s troubling motor symptoms, the efficacy of dopaminergic drugs becomes more complicated over time as dopamine neurons continue to degenerate. Moreover, degeneration in other non-dopaminergic systems and emergence of non-motor or treatment-resistant motor symptoms such as cognitive impairment, dementia, autonomic dysfunction, and gait impairment, makes management of PD, especially in later stages, more challenging. Despite tests of interventions to potentially slow disease progression , there asingle disease. However, this idea ignores the fact that research, including human genetic studies, suggests a variety of molecular mechanisms as cellular culprits behind PD neurodegeneration including neuroinflammation, lysosomal dysfunction, and disruption of mitochondrial pathways  and individuals who exhibit bradykinesia, postural instability and gait disturbances (posture gait instability disorder) . This grGenetic studies of PD over the last 20 years have revolutionized our understanding of the causes and contributors to PD and identified druggable molecular targets. Analyses of families with a history of PD uncovered monogenic, presumably causal mutations . InclusiLongitudinal observation of individuals carrying different mutations suggest that clinical features may cluster with certain mutations, offering an additional layer of precision. People with PD carrying LRRK2 mutations tend to progress slower in their motor symptoms over time, while individuals harboring GBA mutations are more likely to develop cognitive impairment . CarrierThis idea is already being pursued in clinical trials as sponsors look to enroll participants based on genetic status, hoping that individuals harboring mutations in specific genes may be most responsive to targeted treatments . In the In all trials enrolling individuals based on the presence of a genetic variant, it will be important to consider variability in clinical phenotype of mutation carriers, even within the same gene. Deeper interrogation of different mutations within the LRRK2 gene reveals a more complex picture where the clinical phenotype may vary based on the specific mutation . UnderstAs the field advances, we expect that the next few years of PD subtyping will leverage additional data modalities including molecular, clinical, neuroimaging, and digital sensor data. The availability of open-access datasets such as One molecular measure of suspected pathology is showing particular promise. The alpha-synuclein protein is a key player in Parkinson’s pathophysiology, and measuring all forms of protein  may uncover more distinct signatures of Parkinson’s subgroups. Assays measuring the alpha-synuclein protein combined with clinical data are yielding intriguing results suggestive of subtypes that may distinguish individuals with presumed high alpha-synuclein pathology burden. Across multiple laboratories and assay platforms, alpha-synuclein seeding amplification assay (SAA) can diagnose individuals with Parkinson’s disease with accuracy of over 95% . As SAA Combining clinical features with genetics offers an exciting opportunity to identify people at the earliest stages of their clinical journey and explore the molecular changes occurring. Olfaction, constipation, and REM sleep behavior disorder (RBD) can help find at-risk individuals and analysis of DNA will help identify molecular subtypes. As genetic data from people with PD continues to increase, we are also seeing advances in how this genetic insight can be leveraged for stratifying risk not just to PD but to specific biological forms. For example, recent work with polygenic risk scores suggests an ability to tease out not only likelihood of PD but also PD associated with pathway-defined risk, such as mitochondrial or autophagy-lysosomal forms . AdvanceTranslating these early molecular indicators of Parkinson’s subtypes into reliable and clinically useful precision medicine tools will require collaboration across the field. Data sharing from both observational studies and clinical trials is essential to validate existing and identify novel disease subtypes. The PPMI, AMP PD, and Critical Path for Parkinson’s Disease (CPP) initiatives are public private consortia built around data sharing of human observational studies and clinical trials with the goal of developing more effective PD treatments. Learning from clinical trials testing therapeutics in enriched PD populations will help the field understand the benefits and risks of targeted treatment strategies in PD. Posthoc analyses of historical trials will reveal features of patients that may have responded more favorable to treatment than others, and this will only improve the stratification of future clinical trials. Encouragingly, the infrastructure for data sharing already exists through these consortia, and a maturing culture among PD researchers is fostering a similar expectation of data sharing and collaboration.The PD research community also needs to engage regulators in conversations about precision medicine approaches. Both the Food and Drug Administration and European Medicines Agency have indicated the importance of measuring clinical meaningfulness in clinical trials testing novel therapeutics. If precision medicine in PD will make progress, early and frequent input from regulators will be critical to understand the data needed to link PD subtypes and targeted treatment with desired clinical outcomes. Fortunately, initiatives like CPP are established venues in which all stakeholders convene and discuss these challenges.biologic staging of disease based on molecular changes rather than definitions based on pure clinical phenomenology. Indeed, PD will require a consensus molecular staging similar to what has been done for Alzheimer’s disease  has invested in developing and optimizing imaging and biochemical measurements across hypothesized pathways linked to PD including neuroinflammation, lysosomal dysfunction, and mitochondrial dysfunction. Expanding the toolkit of such assays and linking to data from multiple patient cohorts should help identify subtypes that could benefit from a therapeutic targeting a specific molecular pathway. However, molecular changes clustering together in subgroups may be orthogonal to clinical features of PD and may not correlate with clinical outcomes. This will require a fundamental shift in how the community defines PD and will result in a  disease .Finally, we must engage all people affected by PD. Data from people living with the disease will critically fuel precision medicine but will only succeed by enlisting their input throughout the research process from study design to data analysis. As most PD data are derived from people of European Caucasian descent, it will also be essential to partner with a wider and more diverse group of individuals to obtain a full picture of the heterogeneity of PD (and ultimately more precise understanding of disease subtypes).The Parkinson’s community including patients, researchers, industry sponsors, regulators, and nonprofits is poised to transform the approach to therapeutic development for Parkinson’s disease. An increasing mindset of data sharing and collaboration (and the platforms to do so), advances in molecular and digital data collection, along with data infrastructure and analytic capabilities are the foundation on which precision medicine for Parkinson’s disease will be built. Much like shifts seen in oncology, where the term ‘cancer’ now encompasses a wide spectrum of molecularly defined tumor types, each with selective treatment, we believe that PD will soon follow a similar path.https://www.ppmi-info.org, https://amp-pd.org/.Publicly available datasets were analyzed in this study. This data can be found at: All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.MF, BF, and TS are employed by The Michael J Fox Foundation for Parkinson’s Research.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.


Vascular rings are congenital malformations resulting from abnormal development of the great vessels, with the consequent encircling and compression of the trachea, esophagus, or both. We conducted a review of the current literature to identify the different management strategies that can be implemented based on the prognosis of each of these anomalies.Although most vascular rings occur in isolation, they can also be associated with other congenital cardiac and/or respiratory diseases; therefore, thorough investigation is necessary before definitive surgical repair. Clinical presentation varies from asymptomatic to severe, with both respiratory and digestive symptoms. Although early surgical results are acceptable, the long-term outcome is variable; therefore, there is still controversy regarding the appropriate timing of treatment. This is especially true with regard to the Kommerell diverticulum (KD) and in patients without symptoms at the time of initial surgical evaluation.As more sophisticated diagnostic tools have become available and more studies on adults affected by this condition have been published, understanding of this condition and its additional clinical implications has grown and appears to be tilting management toward earlier intervention. Vascular rings account for less than 1% of all congenital cardiovascular anomalies , GlaevecIn 1787 Bayford described a post-mortem finding of a retro-esophageal right aberrant subclavian artery (ASCA) in the context of chronic dysphagia; later, Arkin named the ASCA as arteria lusoria and the term “dysphagia lusoria” was born –8. In 19Aortic development begins during the third week of gestation after a very intricate ontogenetic process . Most aoDouble aortic archRight arch dominance (80%)Left arch dominance (10%)Balanced arches (10%)Right aortic arch with left ligamentum arteriosusMirror image branching (34%)Retro-esophageal left subclavian artery (65%)Circumflex aortaInnominate artery compressionPulmonary artery slingThe International Congenital Heart Surgery Nomenclature and Database Committee has proposed the following anatomical classification of vascular rings , 14:DoubThe two most common anatomical vascular rings are the double aortic arch and the right aortic arch with left ligamentum arteriosum . The forKD in the left aortic arch with right ASCA.KD in the right aortic arch with left ASCA.KD without ASCA.The KD is a persistent remnant of the fourth primitive aortic arch, which has failed to regress . When thIn contrast, the distal pulmonary arterial system is normally formed from the lung buds and joins separately into the main pulmonary artery, which derives from the tronco-aortic sac . The anoThe classic clinical presentation of patients affected with a vascular ring is stridor along with a barky cough, often referred to as a “seal-bark cough.” Other frequent symptoms are recurrent respiratory tract infections, wheezing, dyspnea on exertion, and dysphagia. Some children may have apparent life-threatening events or apnea, and more rarely those with severe compression and tracheomalacia can develop critical respiratory distress requiring endotracheal intubation . On the LPA sling is usually diagnosed during infancy, since these children often present with significant respiratory insufficiency. It carries a poor prognosis, and it is often associated with intrinsic congenital tracheobronchial malformations  such as The diagnostic work-up of patients with vascular rings has evolved dramatically in the last few decades owing to the advancement in imaging modalities, applicable not only after birth but also during pregnancy. The prenatal diagnosis of vascular rings and pulmonary artery sling begins with a fetal echocardiogram. The new American Institute of Ultrasound in Medicine guidelines for fetal echocardiography specifically direct clinicians to identify normal pulmonary arterial branching at the same time as looking for fetal arch anomalies . Three-dThe limitations of the fetal echocardiogram can be overcome with the use of 3D fetal cardiac magnetic resonance imaging (CMRI) with motion-corrected slice-volume registration, particularly when a reliable visualization of the fetal cardiovascular structures is required to rule out important defects of the fetal vascular system Fig.  31].Fig.Fig31].FThe diagnostic images for the postnatal diagnosis of patients with vascular ring range from a simple chest radiogram (CXR) to most invasive ones such as catheter angiography. The plain CXR of a symptomatic child with vascular ring will always demonstrate some abnormality . The preThe contrast esophagogram or barium study was historically routinely used in the diagnosis of vascular rings and is particularly useful in patients with persistent asthma and aspiration symptoms unresponsive to standard treatment. The finding of an extrinsic pulsatile indentation is highly suggestive of a vascular ring, while its absence effectively excludes this pathology; additionally, it aids to identify other differential diagnoses such as aspiration or tracheoesophageal fistula , 35.Although CXR and esophagogram may confirm the presence of a vascular ring, cross-sectional imaging, in the form of computed tomography angiogram (CTA) and/or MRI, is imperative to establish the exact anatomical configuration, as well as to facilitate surgical planning. It can also enable the diagnosis of other causes of fixed extrinsic esophageal or tracheal compression, such as mediastinal foregut duplication cyst , 36. CTAMRI studies also produce high-quality images, but do not require the use of iodinated contrast or radiation. Additionally, MRI is able to obtain cardiac hemodynamic information, making it more appealing for the pediatric population when compared to the CTA, especially for patients with additional intracardiac lesions. However, the longer study times, the need for sedation in younger patients, and the reduced spatial resolution coupled with the limited availability other than in higher level health centers are all major negative factors that make CTA the preferred imaging modality in some institutions . It is iAlthough the flexible bronchoscopy represented a pillar in the diagnosis of tracheal stenosis of external origin in the past and was frequently used as a screening method for vascular rings prior to confirmatory cardiac catheterization, its role today is debatable . Many stNeonatal and infant airway obstruction is of critical clinical significance. Death from airway obstruction in patients with PAS has been reported as early as 2 days of age, and it remains common before 6 months in untreated patients. Therefore, it is crucial to actively investigate for intrinsic tracheal pathology in this population, employing a combination of bronchoscopy and cross-sectional imaging. Optical coherence tomography (OCT) has also been described for allowing the detection of complete cartilaginous rings in the trachea, as well as for assessing fragmentation or dysplastic features .Vascular rings are purely anatomical pathologies, and therefore no medical treatment can offer a structural correction for affected patients. Nearly all the subjects suffering from a true vascular ring will present clinical cues associated with it, and repair should not be delayed in symptomatic patients once the diagnosis is confirmed, particularly for those found to have double aortic arch and/or pulmonary artery sling. Surgery will prevent the potential complications secondary to unrepaired vascular rings later in life, such as catastrophic bleeding from indwelling nasogastric tubes, aortic dissection particularly in relation to the KD, aortic aneurysms, and persistent tracheomalacia . While iGross was the first to manage dysphagia lusoria in a child by ligation of the ASCA . The traThe thoracotomy side is determined by the structures needed to be resected; hence, double aortic arches with right dominance and right aortic arch with left ligamentum arteriosus or mirror image can be addressed through a left thoracotomy, while a double aortic arch with left dominance will need to be operated on from the right. In the instance of a balanced double aortic arch, it is imperative that careful analysis of the cross-sectional images occurs in order to establish which arch should be divided Fig. .Fig. 4ScThe chest cavity is accessed through the third or fourth intercostal space; and after gentle lung retraction, the vascular structures of the posterior mediastinum will be visible and amenable to dissection. Posterolateral muscle-sparing (MST) thoracotomy has been associated with less pain, better shoulder function, and earlier hospital discharge in the adult population . AlthougAnother surgical strategy is extrapleural access to the structures forming the vascular ring. Preserving the pleural barrier maintains intercostal muscle integrity and diminishes the risk of adhesions and development of chest wall-lung collaterals, which can be advantageous, particularly in patients with additional intracardiac lesions and/or single ventricular physiology . Some grOnce the pertinent anatomic structures have been identified and carefully dissected, the ligamentum arteriosum is divided. In the case of a double aortic arch, it is important to recognize any patent segment, the origin of the supra-aortic branches, and the presence of a KD prior to transection. A KD is present in 65% of all right-sided aortic arches with left ASCA • and itsAfter all the vessels have been separated, it is crucial to perform a fasciolysis around the esophagus to guarantee full mobilization and release of any adhesions, hence preventing further obstruction; additionally, a tracheopexy may be necessary to ensure airway recovery and decompression . MoreoveThe first successful repair of a pulmonary artery sling was described by Dr. Willis Pott in 1953 on a 5-month-old child performed through a right thoracotomy . AlthougGiven the nature of the anatomical derangement, the surgical repair of the PAS comprised different sets of goals than that established for vascular rings due to malformations of the aortic arch and/or its branches. Congenital tracheal stenosis with complete tracheal rings should be addressed at the time of the repair when the stenosis is clinically severe; in that vein, some groups suggest a tracheal diameter of < 3 mm and length ratio > 60%, while others indicate a diameter: length ratio < 5.9 for tracheal stenosis as an objective measurement for indication of simultaneous surgical repair .PAS repair is currently performed via median sternotomy. After the aberrant left pulmonary artery is confirmed, extensive mediastinal dissection and mobilization of the superior vena cava, main pulmonary artery (MPA), right pulmonary artery (RPA), and ascending aorta is carried out. Once the anatomic structures have been mobilized, the patient is placed on CPB, and the pulmonary vessels are isolated. The aberrant LPA is dissected to the left pulmonary hilum and subsequently divided from its origin off the RPA. This is, then, passed through from the right to the left in-between the trachea and esophagus, to finally be reimplanted anterior to the trachea into the MPA with an end-to-side anastomosis . ExtremeThe natural history of unrepaired vascular rings was described by Lodeweges et al. They found that the preoperative values for peak expiratory flow (PEF), forced expiratory flow after 25% of forced vital capacity (FVC) expired, and forced expiratory flow after 50% of FVC expired were significantly lower and below normal in patients with onset of symptoms during childhood. Additionally, 8% of the patients did not show improvement after repair ••.Surgeries for vascular rings are generally safe operations, with a very low early and late mortality; however, additional complex cardiac and tracheal anomalies are associated with an increased risk . AlthougDuring the last decade there has been an increasing awareness of potential underlying aortic disease associated with the KD, with medial degeneration or necrosis being the histological finding in surgically resected specimens from patients with KD in the context of aortic aneurysm or aortic dissection. It is hypothesized that the KD or remnant of the right dorsal arch is abnormal tissue rather than an innocuous aortic diverticulum, as it was programmed to involute and may have only partially done so, resulting in attenuated vascular tissue that can degenerate into an aneurysm, hence the reason to be aggressive in its resection .Children with PA sling who do not require tracheal surgery have excellent clinical outcomes. Mortality in these patients is driven by the need for additional tracheal surgery, which has a combined early and late mortality of ~14% in some series , 62. TheVascular rings are rare congenital cardiovascular malformations whose variety of associated symptoms should prompt early surgical repair. Excluding PAS cases, operative mortality is negligible, and the vast majority of patients experience resolution of their symptoms. However, a minor proportion of repaired subjects will present with recurrence of clinically significant symptoms and require reintervention.The presence of KD in adults with previous diagnosis of vascular ring increases the risk of aortic aneurysm and aortic dissection. As such, it is generally advisable to resect it at the time of initial operation during infancy. If unrepaired, serial follow-up imaging appears warranted.Although being only a partial ring, the LPA sling represents a more complex spectrum of vascular rings. The complication and mortality rate of LPA sling repair are closely related to the degree of tracheal stenosis and the need for concomitant tracheal intervention. However, novel surgical techniques and implementation of a multidisciplinary approach have dramatically improved the outcomes of this cohort.