In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer’s disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient’s cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer’s disease progression.The online version contains supplementary material available at 10.1186/s40708-022-00165-5. Alzheimer’s disease (AD) is a neurodegenerative pathology caused by multiple factors whereby neurological changes in the brain could occur several decades before cognitive impairment arises . TypicalSeveral tests provide information about the neuropsychological conditions of patients and are commonly adopted to assess the severity of the most important symptoms of AD . The mosAlthough ML models for the diagnosis and prognosis of cognitive decline and Alzheimer’s disease have reached very high-performance levels, most works have devoted little attention to the explainability of these models. Indeed, although these models achieved tremendous predictive performance, they are not gaining popularity in clinical practice due to their high inner complexity. Explainability is the ability to explain AI decision-making in human-understandable ways to a wider variety of end-users . Of courWhile these XAI approaches have proved useful in certain medical AI fields, notably in visualization, virtual reality and other Big data problems , 38, theIn this paper, we present a framework based on ML, XAI and statistical analysis to perform a three-classes classification between healthy control subjects, individuals with different levels of cognitive impairment, and subjects with dementia by using different cognitive indexes and to analyze the variability of the explainability values associated with the decisions taken by the predictive models. The present work aims to: (i) relate the performance of the ML algorithm to the variability of the extracted XAI values by applying a local post-hoc algorithm; (ii) analyze any potential differences between the explainability values associated with both correctly and incorrectly classified subjects; (iii) identify subgroups of subjects by applying unsupervised algorithms to their explainability values in order to provide the physicians with a set of predictors most associated with the subgroups; (iv) examine the longitudinal variations of the XAI values to identify the cognitive indexes whose importance is most variable in subjects converting from one diagnostic category to another and provide the clinicians with a list of cognitive indexes best suited to track cognitive variations across the spectrum of neurodegeneration.We hypothesise that the explainability indexes could themselves be considered markers to describe both the change in cognitive status of patients during nominal and pathological ageing and to reliably quantify the contribution of each functional cognitive domain to the overall condition of each patient. Thus, XAI scores could provide personalized neurodegeneration patterns, explaining the heterogeneous contributions of the functional domains to AD. This latter aspect is particularly crucial to identifying an optimal set of predictors to classify AD.So far, XAI methods have been applied to computational neuroscience in a few cases to explore how different brain regions affect age prediction or to predict the onset of cognitive decline. These aspects are particularly relevant given the risk that cognitive impairment could convert to AD.Specifically, Beebe-Wang et al.  developeEl-Sappagh et al.  developeIn a previous work , we explIn addition, different visual XAI techniques have been developed to quantify the interpretability of the latent representations of CNNs for the classification of dementia and cognitive impairment, such as the layer-wise Relevance Propagation (LRP) technique , saliencIn this study, we exploited a dataset obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). ADNI researchers collected several types of data from study volunteers throughout their participation in the study. Data collection was performed using a standard set of protocols and procedures to eliminate inconsistencies. Subjects have been recruited from over 57 sites across the US and Canada. The study was conducted according to the Good Clinical Practice guidelines, the Declaration of Helsinki, and US 21 CFR Part 50 (Protection of Human Subjects), and Part 56 . Subjects were willing and able to undergo test procedures, including neuroimaging and follow-up, and written informed consent was obtained from participants.We used the ADNIMERGE R package to download the data on June 1, 2021 (see www.adni-info.org). Up to the date of June 1, 2021, 15,304 samples belonging to 2306 adults, ranging in age from 55 to 90, have been collected. In this study, we employed data collected at baseline visits and longitudinal data at successive visits. Initial preprocessing of the data revealed that the pattern of data missingness was correlated with the diagnosis, as, for example, some measurements were missing more often in healthy subjects. We, therefore, kept clinical and neuropsychological variables and samples with no missing entries. This step led to a reduction in the number of variables to 10 and the number of samples from 15,304 to 6285.The 6285 samples  were categorized into three groups according to the diagnosis given at the exam date: (a) normal controls (NC): 2408 samples (38.3%), (b) MCI: 2912 samples diagnosed as MCI (46.3%), (c) AD: 965 samples diagnosed with dementia (15.4%). Demographic, clinical and neuropsychological information of the samples is reported in Table The ADNI database contains more than 40 variables resulting from different cognitive and functional assessments. In this work, the 10 indexes: ADAS11, ADAS13, MMSE, MoCA, FAQ, RAVLT-immediate, RAVLT-learning, RAVLT-percforgetting, ECogPt total and ECogSP total were selected to reflect the condition and functionality of each subject at each visit. The selected indexes encompass scores of different neuropsychological tests’ ratings and standard questionnaires both of participant or study partner (SP), and are widely adopted as screening tools to detect memory deficits and behavioral symptoms associated with dementia. The indexes are briefly described in Table The clinical and neuropsychological indexes are used to train a Random Forest (RF) classifier and predict the diagnosis of each subject at each visit ;The SHAP algorithm is applied to explain the decisions made by RF for each sample. The output of SHAP is a vector of feature importance for each subject at a specific visit;Several statistical analyses are carried out within the framework of both a non-retrospective and a retrospective study. As part of the non-retrospective analysis, statistical comparisons were performed to: (i) relate the classification probabilities associated with the diagnostic category (as it was established by RF) for each example to the explanations associated with that decision; (ii) examine the similarity of SHAP indexes between groups of samples (both correctly and incorrectly classified); (iii) identify potential subgroups in the diagnostic groups provided by the RF classifier. Regarding the retrospective investigation, a statistical analysis was performed to compare the similarity of SHAP values between the first and the last visit of each participant in order to identify the cognitive index that best reflects the potential conversion of subjects from one diagnostic category to another.Each step is detailed in the following sections.Figure S features and i, RF is an ensemble learner of decision tree base learners that individually predict the target response to output both the final prediction probability score and the target class of each sample by using a majority-voting mechanism [mtry candidate predictors. Random forest is one of the most widely used machine learning algorithms for the detection and classification of AD and MCI as it overcomes some of the disadvantages of decision trees such as multicollinearity and overfitting problems [In this work, the RF algorithm was selected to predict the diagnostic label of each sample from a set of echanism . Each deproblems , 55.In order to obtain clinically unbiased results, we adopted a leave-one-subject-out cross-validation strategy. According to this validation scheme, the dataset is split into as many sets as the number of subjects: one subject is randomly selected for testing while the others are used to train the model, and the procedure is repeated until all the subjects have been used as test , 57. In mtry), and an outer loop for evaluating the trained model with the selected parameters on the unseen fold. This nested CV strategy has been adopted to avoid the use of the same data for parameter tuning and model evaluation and therefore to avoid overfitting. The tuned model was tested on each sample of the test subject to predict the diagnostic class, producing Moreover, since in general, the ML algorithms can be sensitive to changes in the training set, returning unstable performance, for each cross-validation round we randomly under-sampled the training set j was evaluated by using the following metrics:Accuracy: Sensitivity: Specificity: Precision: Additionally, we reported the receiver operating characteristic (ROC) curves and the area under the ROCs (AUROCs) of one-versus-rest (OVR) decisions [The performance of the models for each class ecisions .We selected the SHAP algorithm to explain the predictions of RF models for each sample. Indeed, SHAP is a local model-agnostic post-hoc explainer algorithm based on the Shapley value concept from game theory , 60. It D be a dataset of samples, i and f be a classifier and i which corresponds to the predicted label.Let j among the S features as the average marginal contribution of the feature value across all possible coalitions, i.e., all possible sets of feature values with and without the feature j. In particular, a coalition, F, is defined to be a subset of S  NC-NC (NC subjects correctly classified as NC), (b) NC-MCI (NC subjects incorrectly classified as MCI), (c) MCI-NC (MCI subjects incorrectly classified as NC), (d) MCI-MCI (MCI subjects correctly classified as MCI), (e) MCI-AD (MCI subjects incorrectly classified as AD), (f) AD-MCI (AD subjects incorrectly classified as MCI) and (g) AD-AD (AD subjects correctly classified as AD). The classes NC-AD and AD-NC were not considered due to the small number of subjects belonging to them.We then assessed the similarity between each couple of samples within each class by computing the cosine distance between their SHAP vectors. A similarity network for each class has been defined for each class by including all the similarity scores between the samples within the class. Significant differences between the similarity networks across the classes were evaluated based on post-hoc comparisons  following the diagnostic categories assigned to patients at each visit based on the ADNI consortium criteria. Although a reduced subset of features was selected in this study for the benefit of larger sample size, the RF model achieves state-of-the-art performances , 66. In tegories . In ordet-test, significant at We investigated the heterogeneity of the impact of different cognitive and neuropsychological indexes on the prediction of the clinical outcome using the local XAI SHAP method. The main objective of this step was to understand if the whole XAI vector of feature importance can be exploited as a new derived cognitive marker across the cognitive spectrum instead of using a set of original features. Indeed, this method would allow a more conscious use of artificial intelligence methods in clinical practice. As a first step, we, therefore, examined the intra-group similarity of XAI vectors through the cosine distance measure. The statistical analysis carried out on the different groups of subjects showed that the healthy subjects  presented the lowest intra-group variability of the XAI vectors as reported in Fig. mentclass2pt{minimWe showed the variable importance plots in Fig. A retrospective analysis of the SHAP scores was performed to investigate the longitudinal variation of the SHAP vectors for different diagnostic categories. In particular, we compared the similarity of SHAP values between the first and the last visit of each participant for both converting and stable clinical classes. In this case, stable NC subjects show the least variability see Fig. , indicatIn this work, SHAP achieves to explain the internals of a RF classifier trained on cognitive and clinical information, thus showing a possible link between diagnosis and patterns of feature relevancy. However, neuropsychological assessment represents a preliminary step for the clinical diagnosis of AD and using other exams and neuroimaging data might largely improve the diagnostic results and add validity to the interpretation of predictions. Several works have shown that the accuracy resulting from the three-class (AD vs. MCI vs. HC) classification task can be considerably increased by using MEG data, PET, SPECT and MRI imaging modalities properly integrated with convolutional architectures and deep learning models –73. In fIn this work, we provided a ML framework to explore the contribution of cognitive and clinical measures for the automatic classification of mild cognitive impairment and Alzheimer’s disease. We demonstrated that explainability must be related to ML efficiency in order to obtain reliable characterisations of patients’ cohorts. This is of paramount importance for XAI to provide valuable insights about the disease progression. Hence, we developed a robust ML algorithm with a XAI module to shed more light on the impact of cognitive and clinical indexes on the diagnostic category assigned to patients at each visit. A statistical analysis of the XAI vectors revealed that diagnostic categories in the last part of the neurodegenerative spectrum have the greatest variability, which can be explained by the existence of different subcategories within those considered in the study. Moreover, a retrospective analysis clearly outlined that the impact of cognitive and clinical indexes does not vary substantially for stable subjects, regardless of the starting diagnostic category, and that for specific converting categories of subjects, it might be more appropriate to consider only the limited set of indexes that show a significant longitudinal change in their impact on the final prediction of the diagnosis. Our findings allow us to state that the SHAP values can effectively characterise the impact of each index on the cognitive status of patients and quantify the variation of such impact over time, keeping track of longitudinal changes and providing continuous information about progression to AD, in line with the current 2018 NIA-AA research framework that has transitioned to defining AD as a continuum.Additional file 1: Table S1. Mean and standard deviation values for the two clusters identified in the similarity network for each group. Significant differences resulting from Student's t-test are shaded in gray.


Chiloscyllium plagiosum, during suction feeding. As the lower jaw begins to depress for suction expansion, both the upper jaw  and lower jaw (Meckel's cartilage) evert, such that their toothed surfaces move laterally, and then they invert with jaw closing. Eversion has been shown to tense the dental ligament and erect the teeth in some sharks, but it is not clear how this tooth erection would contribute to suction feeding in bamboo sharks. Two recent XROMM studies have shown LAR of the lower jaws during mastication in mammals and stingrays and our study extends LAR to suction feeding and confirms its presence in shark species. Examples of LAR of the jaws are becoming increasingly widespread across vertebrates with unfused mandibular symphyses. Unfused lower jaws are the plesiomorphic condition for most vertebrate lineages and therefore LAR may be a common component of jaw mechanics unless the mandibular symphysis is fused.Long-axis rotation (LAR) of the jaws may be an important component of vertebrate feeding mechanisms, as it has been hypothesized to occur during prey capture or food processing across diverse vertebrate groups including mammals, ray-finned fishes, and sharks and rays. LAR can affect tooth orientation as well as muscle fiber direction and therefore muscle power during feeding. However, to date only a handful of studies have demonstrated this LAR in vivo. Here, we use XROMM to document LAR of the upper and lower jaws in white-spotted bamboo sharks,  Long-axis rotation (LAR) of the jaws can modify feeding mechanics by changing the orientation of teeth and the directions of muscle fibers during jaw abduction or adduction. This extra axis of rotation could allow animals to feed on a wide variety of foods or improve feeding performance beyond what is possible without LAR. Distinct from depression or medio-lateral rotation of the upper or lower jaw at the jaw joint, LAR describes the rotation of a skeletal element around its long axis . During Potamotrygon motoro, Tenrec  expanding the behavioral and taxonomic breadth of LAR within vertebrates, and strengthening the possibility that LAR is a common component of jaw mechanics among vertebrates.Advances in X-ray video recording have improved the reconstruction of three-dimensional kinematics of feeding among vertebrates . Recent The dataset analyzed for this study was also used for a study of hyoid arch kinematics , food trC.plagiosum Bennett) were anaesthetized using 0.033 gL–1 of MS-222 tricaine methanesulfonate in buffered seawater for marker implantation and CT scan procedures. Four conical tungsten carbide markers (White-spotted bamboo sharks ( markers  were impvAvg tool in the XROMM MayaTools (bitbucket.org/xromm/xromm_mayatools).Sharks were CT scanned using a FIDEX CT scanner  at an isotropic resolution of 0.185 mm. Mesh models of the cranium, upper and lower jaws, hyomandibula, and ceratohyal as well as implanted markers were created with Osirix  and exported as .obj files to Maya . The centroid for the vertices of each marker was calculated using the Feeding trials were recorded at the W. M. Keck Foundation XROMM Facility, Brown University (Imaging Systems and Service). Food was withheld for one week before data collection. Individuals were fed one piece of squid or herring for each trial and each piece was cut to roughly 50% of gape width. The motion of implanted markers was recorded at 320 Hz or 330 Hz, with X-ray energies of 110–120kV and 100mA. Video data are stored on xmaportal.org with their essential metadata in accordance with best practices for video data management in organismal biology .imp). The mean standard deviations of marker-to-marker distance pairs in each rigid body were calculated to estimate the precision of marker tracking  and negative rotations are eversions . In this study, we measured the difference in motion between the lower jaw and the chondrocranium, and between the upper jaw and the chondrocranium. JCS data was output as Euler angles using Food was captured by inertial suction and transported to the oral cavity in a single motion for all trials analyzed except one. For the single exception the food item was drawn to the edge of the mouth, grasped, and then transported in another suction event. While the magnitude of hyoid motions of that trial was lower than other trials  no obvioFeeding was initiated with simultaneous depression and eversion of the lower jaw Fig. , 81 ms. LAR of the upper and the lower jaws can be interpreted as slight medial and lateral displacements of the ventral margins relative to the jaw joint (lower jaw) or cranium (upper jaw) in anterior views Fig. B and areMotions of the mandibular arch are more sequential compared to the hyoid arch, where the majority of the motion happens at the same time . MaximumWhen LAR of the jaws is recorded in vivo it is associated with unfused symphyses of the jaws e.g., . This asThe impact of LAR on force production or muscle orientation has not been considered in models of feeding. For models of speed and force, LAR has the potential to alter the orientation of muscle fibers during feeding as well as the application of stress and strain. For example, eversion can lengthen muscle fibers. Longer fibers increase jaw-closing forces and fiber length can be a significant component of bite force models e.g., . Future C. plagiosum and large oceanic shark species to tense the dental ligament, erecting the teeth during prey capture. The pattern of LAR we observed is consistent with tensing the dental ligament in the upper jaw during prey capture because eversion occurs during food capture  with strong eversion and inversion of the lower jaws and eversion of the upper jaws during food capture. Jaw LAR has also been hypothesized for biting, grasping, and ant eating, based on specimen manipulations and modeling (White spotted bamboo sharks demonstrate LAR of the upper and the lower jaws during suction feeding. Examples of LAR of the jaws are becoming increasingly widespread across vertebrates and across feeding strategies. Recent kinematic analyses using XROMM have found LAR during food processing , and thimodeling  and coul


In flowering plants, mature pollen grains are produced within the developing anthers of the flowers in two successive phases, microsporogeneseis and microgametogenesesis. Anther and pollen development involves the coordinated growth and differentiation of tissues and cell types required for the formation of viable male gametes, but also, to permit pollen release and ensure successful fertilization. The identification of the genetic networks and regulatory molecules involved in the formation of the anther remains a highly unexplored topic in many crops. Evolutionary and functional studies in crops are helping to unravel new functions for genes originally identified in model plants , a pea anther-specific gene that encodes a protein containing four hemopexin domains. Gain and loss-of-function experiments showed that PsEND1 is a central player in the maintenance of balanced redox levels during pollen and anther development. Zhou L. et al. demonstrated pollen-specific regulatory function of ZmLarp6c1, maize ortholog of Arabidopsis AtLARP6C, particularly during the progamic phase. The respective Ds-GFP transposable element insertion line showed reduced transmission through the male wich was associated with altered germination dynamics and slower growth of mutant pollen tubes. Considering that LARP6C also orchestrates posttranscriptional reprogramming of gene expression during hydration in Arabidopsis  ortholog from the basal eudicot Eschscholzia californica , a gene involved in pollen aperture formation. This study substantiates the importance of INP1 homologs for aperture formation across angiosperms and opens up new avenues for functional studies of other aperture candidate genes. The role of the Arabidopsis Ankyrin-repeat protein , during male and female gametogenesis is analyzed by Kulichová et al. using the new mutant allele akrp-3. AKRP is a plastid-localized protein with a putative function in plastid differentiation and morphogenesis. The findings provide insight into the role of this protein in both the differentiation of gametophytes and the coupling of embryo development with chlorophyll synthesis. In the research article by Dong et al., the authors reported a new major gene Cla006625 controlling “Genic Male Sterility” (GMS) in watermelon. Molecular and genomics studies revealed that this gene renamed as ClaPEX1 encodes a leucine-rich repeat protein, and the recessive mutant of this locus causes pollen abortion conferring GMS. The targeted RNAi based evidence further confirmed the functionality for this gene in GMS, and the authors propose potential applications in hybrid seed production technology to capture heterosis in watermelon. Kakui et al. studied pollen number variation in Arabidopsis and discovered the first gene responsible for pollen number, REDUCED POLLEN NUMBER1 (RDP1), encoding the large ribosomal subunit assembly factor. CRISPR/Cas9-generated rdp1-3 mutants revealed the pleiotropic effect of RDP1 in flowering and pollen development. Subsequent transcriptome analysis supported the hypothesis that ribosome biogenesis, critical for pollen development, is disturbed in the rdp1-3 mutant pollen and highlighted three key bHLH transcription factors .New advances in the functional characterization of genes involved in different aspects of pollen development and function are described in the following articles of this Research Topic. In the original research article by Záveská Drábková et al. in several Nicotiana species. The article describes the dynamic changes in endogenous phytohormones during pollen ontogeny, highlighting that unequal levels of endogenous hormones and the presence of specific derivatives which may be characteristic for pollen development in different phylogenetic plant groups. In the original research article by Zhou D. et al., the author used male-sterile systems of Brassica campestris (Chinese cabbage) to study anther and pollen development in this species. Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) were identified providing new insights into molecular regulation especially the ncRNA interaction during pollen development in Brassica crops.The identification and characterization of regulatory molecules during anther or pollen development is the subject of the remaining articles in this Research Topic. The role of hormone dynamics during microgametogenesis has been explored by Xue et al., developed several live imaging methods for the study of anther development. They created the marker line ProUBQ10:H2B:VENUS and used it to study the development of the middle layer in the anther of Arabidopsis thaliana. The results showed that the middle layer was derived from both inner and outer secondary parietal cells, indicating that the cell fate determination of the middle layer was non-cell-autonomous in Arabidopsis. In the new research report by Xiao et al., the authors investigated the adaptive and evolutionary features of “Delayed Autonomous Selfing” (DAS) in Salvia umbractica. The observations and findings from the field and controlled experiments showed outcrossing using insect pollinators first, wich failed to fertilize, lead to execution of DAS to ensure fertilization for successful fruit and seed production. The authors presented strong supporting evidence by detailed documentation of changes in the reproductive organs' specific behaviors linked with morphological and developmental activities of these two built-in alternative pollination options in Salvia species. In the research article by Calić et al., the authors investigated the influence of long-term storage temperature on pollen viability of four Serbian autochthon apple cultivars. Interestingly, the pollen could be efficiently maintained at −20°C and later used for breeding purposes. The results will surely contribute to the preservation of these old autochthon cultivars as unique genetic resources with important ecological and economic value. Jaffri and MacAlister utilized histology and immunostaining to show the structure of the tomato pollen wall, characterized dynamic changes in pectin composition, and established a developmental timeline of its formation. Following meiosis, the microspores losing their cellulose primary wall remain connected by a temporary callose wall in tetrads. Release of early microspores initiates sporopollenin secretion to form exine, wich is completed in late microspores. The tomato pollen wall formation is finished by the formation of intine from pollen mitosis I to pollen maturation. Grienenberger and Quilichini highlighted significant progress in the field of sporopollenin research; they examined the cross-disciplinary efforts to solve the sporopollenin composition puzzle and presented a working model of sporopollenin's molecular structure and biosynthesis. They further discussed the controversies and remaining knowledge gaps, including the degree of aromaticity, cross-linkage profiles, and extent of chemical conservation of sporopollenin among land plants. Finally, the authors highlighted opportunities for practical utilization of this extraordinary biomaterial.Several of the articles submitted to this Research Topic investigated global developmental aspects of pollen development and its impact on plant performance.  et al., the authors recapitulated past and present research on obtaining male-derived haploid progeny by microspore embryogenesis. The authors evaluated basic breeding applications of this process, explored the utility of genomics and gene editing technologies for protocol development, and provided considerations to overcome genotype specificity and morphogenic recalcitrance in non-model plant systems. In the article by Pandey et al., the authors reviewed the molecular mechanisms controlling the alternation of generations between the sporophytic and gametophytic stages from an evolutionary perspective. The article compares the genetic factors and mechanisms regulating the separation of the two developmental programs and discusses its biotechnological applications for accelerating the breeding of crop plants.Pollen biotechnology offers a wide range of possibilities for plant breeding. Doubled-haploid technology, based on the reprogramming of immature pollen grains or microspores toward embryogenesis, promises to accelerate crop breeding programs and shorten the time to obtain new varieties. Investigations in recent years have shown the complex regulatory mechanisms underlying microspore embryogenesis (Testillano, The goal of this Research Topic was to highlight the latest advances in pollen research and the potential of pollen in the development of biotechnological applications for plant breeding. Fifteen articles have been published on this Research Topic including two reviews, one mini-review, and 12 original research articles covering different aspects of pollen biology, biotechnology, and breeding applications. Remarkably, many of the research articles were carried out on important crops including studies in apple, pea, maize, watermelon, or tomato. Globally this Research Topic of articles successfully represents some major advances in pollen research across different plant species contributing to increasing knowledge in the field and to the generation of new opportunities to implement crop improvement programs in the coming years.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.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.


Previous study has identified increased antithrombin III (ATIII) in patients with major depressive disorder (MDD), supporting ATIII as a potential biomarker for depression diagnosis.This study aimed to reveal the alteration of ATIII after occipital repetitive transcranial magnetic stimulation (rTMS), and illuminate its power to evaluate and predict the curative effects in MDD treatment.A total of 90 MDD patients were recruited and further intervened with rTMS in occipital for individualized, standard or sham treatment for five days. Those of 74 patients underwent entire detection, including clinical assessments, blood collection and protein measurement.After treatment, decreased ATIII were detected in both the individualized and the standard group  instead of the sham one. Especially, the reduction in ATIII in the individualized group was associated with improvements in several neuropsychological assessments. Besides, ATIII at baseline in the standard group and after the individualized rTMS showed high performance to evaluate or predict the response to the 5-day treatment  and the remission in follow-up . Furthermore, both baseline ATIII and change in ATIII involved in the prediction of 24-item Hamilton Depression Rating Scale in the follow-up study with significant predictive values .This study detected a reduction in ATIII after occipital rTMS, further revealed the relationships between change in ATIII and therapeutic response, and ultimately provided evidence for the potential of ATIII as a biomarker for the evaluation and prediction of antidepressive effect.No significant relationships.


Recent studies have recognized the importance of characterizing the extent of lung disease in pulmonary hypertension patients by using Computed Tomography. The trustworthiness of an artificial intelligence system is linked with the depth of the evaluation in functional, operational, usability, safety and validation dimensions. The safety and validation of an artificial tool is linked to the uncertainty estimation of the model’s prediction. On the other hand, the functionality, operation and usability can be achieved by explainable deep learning approaches which can verify the learning patterns and use of the network from a generalized point of view. We developed an artificial intelligence framework to map the 3D anatomical models of patients with lung disease in pulmonary hypertension. To verify the trustworthiness of the framework we studied the uncertainty estimation of the network’s prediction, and we explained the learning patterns of the network. Therefore, a new generalized technique combining local explainable and interpretable dimensionality reduction approaches  was developed. Our open-source software framework was evaluated in unbiased validation datasets achieving accurate, robust and generalized results. Recent work in the medical literature has highlighted the need to better characterize and quantify lung disease in pulmonary hypertension3. There is a prognostic significance of lung parenchymal disease on CT with presence of emphysema and ground glass predictive of early mortality4.Pulmonary hypertension (PH) is a complex condition characterized by elevated pulmonary arterial pressures and presenting with a varying degree of lung parenchymal disease. Computation Tomography (CT) imaging is the gold-standard imaging modality for non-invasive assessment of lung disease and is recommended by the latest European Respiratory Society/European Society of Cardiology PH guidelines5, and emphysema regions using High-Resolution Computed Tomography scans of patients with chronic obstructive pulmonary disease6. Moreover, deep learning tries to automate the detection of PH existence or absence8 and predict elevated pulmonary artery pressure9. The current trend is the use of patch-based approaches for texture extraction and feature classification to either segment or classify medical pathologies and regions of interest in a variety of different organs12. For instance, Tang et al.13 proposed a patch-based network with random spatial initialization and statistical fusion on overlapping regions of interest, for three-dimensional abdominal organ segmentation on high-resolution computed tomography. Ben naceu et al.10 utilized a deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy to segment fully automatically a brain tumour. Borne et al.14 developed an automatic labelling of cortical sulci using patch and CNN-based segmentation techniques combined with bottom-up geometric constraints. Lastly, Aswathy et al.15 used a Cascaded 3D U-net architecture for segmenting COVID-19 infections from lung CT volume images.Deep learning approaches are used to quantify pulmonary ground-glass opacity nodules detection16. However, the use of AI in clinical applications always gives rise to the limitation of introducing bias, and the limitations posed by privacy and security constraints, and lack of transparency and explainability of the networks18. Translating AI networks from the prototyping version to support clinical stakeholders during routine care brings challenges, especially as decisions impact human lives. It has been observed that when experts interact with AI frameworks, they became biased to reach decisions, and they may be disproportionately inclined to follow the AI’s predictions19. That may be problematic because of the AI’s lack of generalization and confidence prediction combine with the risk of learning wrong patterns during the training process. These circumstances motivate the need for transparent AI systems19. The latest review of Ciecierski-Holme et al.20 highlights that the main limitations of the existing AI studies are related with the lack of successful development and adaptation of well-performing AI tools, the limited available data, the lack of transparent and cost-effective AI tools in low-income and middle-income countries. Shad et al.21 state that studies of explainability, uncertainty and bias should be core components of any clinical AI tool studies. Even though there are studies using explainability techniques to increase the transparency of their AI tools they lack generalized approaches as they mainly use local explainable techniques like salience maps, GradCam, or feature engineering approaches24.Artificial intelligence (AI) approaches show great promise in clinical application particularly in their ability to automatically quantify different radiological lung disease featuresTo this end, we developed a transparent AI considering the lack of prediction in high uncertainty circumstances and validating the usability of the system by verifying the correct patterns of learning during the training process. We estimated the epistemic and aleatoric uncertainty of the framework and we developed a new generalized local explainable and interpretable dimension reduction technique  to study and validate the prediction of the AI framework. Moreover, we comprehensively studied a pulmonary hypertension multi-classification task by using different deep learning networks . We used the deep learning classifier to develop the pathological ratios of lung diseases and to map the 3D anatomical lung models of patients with evidence of pulmonary hypertension. To the authors knowledge, this is the first study to develop a transparent artificial intelligence framework to map and diagnose a patient’s pulmonary hypertension profile in three dimensions.We evaluated the results of the multi-classification pulmonary hypertension task in the ‘seen’ validation and test datasets. Moreover, we implemented an ablation study of the framework for different 3D patch sizes, to observe how the variety of the patch sizes influence the performance of the AI framework. Lastly, we validated the AI framework in the ‘unseen’ dataset which includes patients with a challenging pathological pulmonary hypertension profile.The AUC-ROC curves, precision, recall, and f1-score metrics have been used to evaluate the generalization and accuracy of the networks’ classification. Supplementary Fig. Figure To evaluate the 3D-patch framework in the ‘unseen’ cohort we used the measurements of Jaccard score, Hamming distances, Root Mean Square Error (RMSE), f1-score, recall, precision, Matthews correlation coefficient (MCC), and accuracy. Table entclass1pt{minimaFigure The most accurate deep learning network was the DenRes-131 with the Figure The correlation coefficient of positive and negative pixels ratio is a way to evaluate the false positive or negative pixels and the true positive or negative pixels of the network’s learning patterns  was developed. We studied PCA analysis of different component numbers  and we concluded that the eight components of the PCA-Shape  and the PCA-GradCam  analysis were the most robust. Moreover, we computed the correlation coefficient of the PCA-Shape and PCA-GradCam and the negative and positive pixels ratio of the correlation between them. In this way, we evaluated the generalized correct and wrong learning pattern of the network in each class. The correlation coefficient of positive and negative pixels ratio was an approach to evaluate the false positive or negative pixels and the true positive or negative pixels of the network’s learning patterns. The network learned correct patterns in the emphysema and honeycomb classes and wrong patterns in the ground glass and ground glass reticulation classes. By using augmentation techniques with Monte Carlo simulations and Monte Carlo dropout layers we estimated the aleatoric and epistemic uncertainty of each class. The dataset had high aleatoric uncertainty in the ground glass, unhealthy, healthy and honeycomb diseases and the framework predicts high epistemic uncertainty in the ground glass, ground glass reticulation and healthy diseases.Even if we delivered a transparent AI framework there is a limitation about the performance of the AI in the classes with high uncertainty like the ground glass and healthy classes. We can solve this problem by applying different pre-processing techniques, by increasing the samples variability of the high uncertainty classes and by applying domain adaptation techniques to increase the AI framework performance in the out of the distribution samples. Therefore as future work we will increase the variability of the datasets including more clear cases of ground glass and ground glass reticulation patients to reduce the aleatoric uncertainty. Furthermore, we will apply domain adaptation techniques in the classifiers like few shots, to increase the accuracy of the prediction and decrease the uncertainty in the ground glass and ground glass reticulation classes. Lastly, we aim to create a thresholding validation protocol to identify the appropriate threshold difference between the positive and negative pixels ratio of the correlation coefficient method of our combined local explainable and global interpretable techniques to justify uniquely and unbiasedly the correct and wrong learning patterns of the networks.Our AI framework was tested in an unbiased validation protocol which accurately captures ordinary clinical trials, and it delivered accurate robust and generalized performance with uncertainty prediction probabilities and generalized explanations Fig. d.The dataset protocol and methods were performed in accordance with relevant guidelines and regulations and approved by ASPIRE registry , reference c06/Q2308/8; REC 17/YH/0016. This study complies with the Declaration of Helsinki. We confirm that all experiments were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardian(s).To train and evaluate the networks in the multi-classification task, we used a cohort of 84 patients (‘seen’ cohort). From the ‘seen’ cohort 75 patients were used for training/validation and 9 for testing. As the multi-classification task was based on a patch oriented deep learning approach, we utilized four different patch sizes  registry. Around 17,500 CT slices were divided into six regions: healthy lungs, ground glass, ground glass reticulation, honeycomb, emphysema, unhealthy lungs. These classes were manually labelled in each slice by two specialist radiologists and differences resolved through consensus. The unhealthy class was a combination of lung diseases observations with low frequency in the dataset . We combined these diseases in the ‘unhealthy’ class, as we needed to extract a balance training dataset with same number of samples in each class. We used these regions to train and validate the multi-classification task. The ‘unseen’ cohorts included a collection of 19 patients with a diagnosis of PH from the ASPIRE registry. The unseen cohort was a collection of five anatomical CT slice levels exhaustively labeled by specialist radiologists (KD and AS with 3-years and 10-years experience) for each patient. The anatomical levels chosen were top of the aortic arch, bifurcation of the trachea, main pulmonary artery bifurcation, mitral valve, and diaphragm.25. Moreover, we normalized the images and we have used data augmentation techniques including rotation , using noise filters such as curvature anisotropic diffusion image filtersWe developed a patch-oriented AI framework to map the 3D anatomical models of patients with lung disease in pulmonary hypertension. The framework has four steps: the extraction of the 3D patches, the classification of each patch, the volume rebuild of the 3D anatomical model, and the analysis and evaluation of the pulmonary hypertension profile of the lungs Fig. .27 and we used a variate of different sizes of the volume patch sizes to evaluate the performance of the classifiers. We have used four different patch sizes of To train the classifiers we extracted volume patches from the initial NIFTI and DICOM files of the CT images. We segmented the lung region using a nn-unet networkmentclass2pt{minim28. VGG-16 is a well-established convolutional neural network (CNNs) with a combination of pooling and convolution layers29. ResNet-50 is a deep network, in which all layers have the same number of filters as the number of the output feature size. In case the output feature size is halved, the number of filters is doubled, thus reducing the time complexity per layer30. DenseNet-121 is an efficient convolutional network. The network comprises of deep layers, each of which implements a nonlinear transformation. Hauang et al.31 introduced a unique connectivity pattern information flow between layers to direct connecting any layer to all subsequent layers. DenRes-131 is a modified version of28 network which has two dropout layers to estimate the epistemic uncertainty of the model and to reduce the overfitting of the model . The ImageNet dataset consists of over 14 million images and the task were to classify the images into one of almost 22, 000 different categories . We trained the DenRes-131 for 25 epochs and the other three networks for 100 epochs.After random shuffling each dataset had been partitioned into 32. However the use of only local explainability techniques can be biased. Thus we tried to remove the bias effect by using an interpretable non-linear dimensionality reduction technique, the principal component analysis (PCA). We utilized the PCA to study the variability and generalization of all the GradCam outputs (PCA-GradCam) and all the input patch images (PCA-Shape) of the testing cohort to evaluate the learning pattern of the deep learning network. We tested three different values of components for the PCA analysis , and we studied their differences. We extracted the correlation coefficient of the PCA-GradCam and PCA-Shape to compare the positive and negative ratio between them. Therefore, we evaluated the similarities, and the accuracy in correct and wrong learning patterns of the networks.The explainable analysis and uncertainty estimation of a network is a very important part of a classification study as it validates the functional, operational, safety and usability dimensions of a transparent AI tool. In this study we used an established local explainable technique in medical imaging applications, the GRAD-CAM method33. Epistemic uncertainty refers mainly to lack of knowledge of the way to solve a specific medical problem (features and parameters involved in study). For example, when someone develops a machine learning network to solve a cancer risk assessment problem, he takes into consideration specific biomarkers (features) related with the prediction of the severity level of each sample. Because there is a gap of knowledge of other possible biomarkers which can contribute to the risk assessments of each sample, he needs to include the possibility that other networks with different parameters can solve the same problem. Monte Carlo dropout method samples the training data for limited iterations and it generates an estimation of the posterior distribution (network with trained parameters). Thus, we estimated the posterior distribution providing information on whether the input data exists in the learned distribution. MC dropout is the most common way to estimate the epistemic uncertainty in Bayesian networks33. To estimate the aleatoric uncertainty we used a Monte Carlo test-time augmentation method34. The uncertainty can be estimated by using the variance or the entropy distribution The uncertainty of our multi-classification task was separated into aleatoric and epistemic uncertainty. Aleatoric uncertainty captures noise inherent during the data collection. This noise can be product of different reasons such as variation of biological  and environmental  conditions, social status  or variability in the scanning machines or medical tools were used for the medical data collection. On the other hand, epistemic uncertainty studies the uncertainty in the model’s prediction based on the variability of the network’s parametersm unique value is We used a Monte Carlo simulation of The frequency of the prediction of a specific patch is given by x denotes the input features from training images, the predictive mean E[y] denotes the expected model output given the input x, and T times for T independent identical distributions The epistemic uncertainty was estimated by using Monte Carlo dropout layers in the multi-preceptor level of the networks Fig. b. The si35) and R libraries36.Continuous variables were presented as proportions, means ± standard deviations, or median and interquartile range for data not following a normal distribution. We used an ANOVA analysis with The methods were performed in accordance with relevant guidelines and regulations and approved by ASPIRE registry , reference c06/Q2308/8; REC 17/YH/0016.Supplementary Information.


Operation of the Philippine Antimicrobial Resistance Surveillance Program (ARSP) has been affected by the coronavirus disease 2019 (COVID-19) pandemic, during which time difficulties in maintaining laboratory functions, staffing levels and participation were reported.The COVID-19 pandemic has increased pressure on most health systems and programmes in the Philippines, including ARSP. As ARSP is the source of national data on antimicrobial resistance (AMR) trends, there are concerns that the negative effects of the pandemic may have impacted the quality of data produced.We describe disruptions to laboratory operations, personnel availability and participation in ARSP surveillance, and their impact on reported data for 2020.Surveillance operations were challenged by reallocation of human, infrastructure and financial resources for pandemic response among both the sentinel sites and the coordinating laboratory, the Antimicrobial Resistance Surveillance Reference Laboratory. There was a decrease in the amount of data submitted to the surveillance system, as well as in the number of isolates sent to the reference laboratory for confirmation of bacterial identification and antimicrobial susceptibility testing. Nevertheless, overall performance scores of the sentinel sites for most parameters were comparable to 2019, the year before the pandemic.The impact of operational changes to ARSP due to the pandemic needs to be considered when analysing AMR surveillance data from 2020. Automation of data submission, good working relationships between the coordinating laboratory and sentinel sites, and supply chain system strengthening were identified as key to maintaining AMR surveillance during the COVID-19 pandemic. By the end of 2020, the coronavirus disease 2019 (COVID-19) pandemic had caused around 3.2 million excess deaths worldwide and continues to pose major challenges globally.  – is based at the Research Institute for Tropical Medicine (RITM). Case finding is based on specimens sent to sentinel sites’ clinical laboratories for diagnostic purposes.RITM has been at the forefront of the Philippine COVID-19 response for both patient care and laboratory testing. From January to June 2020, RITM served as the primary COVID-19 testing centre for the Philippines, responsible for more than 90% of all COVID-19 tests conducted in the country. In March 2020, to accommodate the surge in demand for reverse transcription polymerase chain reaction (RT–PCR) testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ARSRL, together with RITM’s other laboratories, was directed to assist the national testing effort by undertaking some of the procedures required, such as sample inactivation and DNA extraction. This directive resulted in the reassignment of ARSRL laboratory staff to the COVID-19 testing team. Facilities, workspaces, equipment and critical resources  were also repurposed to meet the demand for COVID-19 testing. Directives were also made to postpone scheduled trainings, monitoring visits and face-to-face meetings – activities usually performed by the reference laboratory – until the end of June 2020. Consequently, the number of laboratory technicians who underwent training for AMR bacteriology procedures decreased from 27 to 6.By July 2020, sufficient additional staff had been recruited to cover the COVID-19 response tasks, allowing existing reference laboratory staff to return to their routine AMR surveillance work. However, until the end of 2020 about 20% of ARSRL laboratory space remained allocated to the COVID-19 response. The annual ARSP Program Implementation Meeting (ARSP PIM) was held virtually via Zoom instead of the usual face-to-face format and the publication of the ARSP 2019 annual surveillance report was delayed by 5 months.The impacts of the above-mentioned institutional directives on the operations of RITM’s ARSRL were reviewed. In addition, the challenges experienced by the surveillance sentinel sites participating in ARSP during the course of 2020 were summarized.To assess the impact of the pandemic, ARSP performance indicators (and corresponding targets for participation and number of isolates reported to the surveillance system) for 2020 were compared to those for 2019  of the sentinel sites cited delays in the usual schedule of delivery of reagents and supplies due to transportation issues. Another commonly reported supply challenge stemmed from the new requirement to use extra layers of PPE, which resulted in episodes of low or depleted supplies of PPE at nearly two thirds  of sites. In some areas, suspension of courier services had reportedly delayed the referral of bacterial isolates to RITM for confirmatory testing.Half  of all sentinel sites reported a decrease in the number of specimens requiring routine culture and sensitivity testing. Four reported periodic closure of their outpatient departments. Sites also reported a low influx of patients, and the two nongovernment sentinel sites experienced a reduction in revenue for the laboratory.The majority  of sentinel sites reported being designated as COVID-19 referral hospitals during the pandemic and having to establish or expand their molecular biology facilities. Even though this provided opportunities for acquisition of new equipment and increasing laboratory staff capacity for molecular detection of pathogens, it also meant temporary reallocation of space and human resources for RT–PCR testing for COVID-19.The majority of sentinel sites also reported encountering various challenges relating to laboratory personnel. Seventeen sites (65.4%) experienced a reduction in the number of active-duty staff over several periods due to reassignment to other hospital units for COVID-19 sample processing and testing. Furthermore, around one third  reported that on several occasions staff work schedules had to be reduced to a skeletal workforce to maintain physical distancing in the laboratory. Staff in high-risk groups were ordered to work from home, further decreasing the number of staff available to work in bacteriology laboratories. Eleven sentinel sites (42.3%) reported that they were provided with additional manpower to help overcome staffing challenges.The health status of frontline workers at ARSP sites was also affected, with some infected with COVID-19 and some experiencing anxiety because of the requirement to be at work. Laboratory personnel from four sites in the National Capital Region (NCR) reported experiencing difficulties in getting to work due to travel restrictions and suspension of transport services.Delays in transporting isolates to ARSRL for confirmatory testing, typically because of the lack of courier services, was a common challenge among sentinel sites . Moreover, the decrease in the number of samples requiring testing at sentinel sites also reduced the demand for confirmatory testing of isolates.Although automated transmission of data from the sentinel sites to ARSRL had already been established before the pandemic, many sentinel sites still reported experiencing delays in encoding identification and susceptibility data to WHONET due to decreased staff numbers in their bacteriology sections.Of the 10 reported performance indicators, five were unchanged between 2019 and 2020 .The challenges to ARSP laboratory operations, including staffing, experienced during the COVID-19 pandemic did not appear to reduce overall participation of the sentinel sites in ARSP. This could be due, at least in part, to the fact that all sentinel sites, having been involved in ARSP for more than 5 years, have surveillance activities ingrained in their operations such that the disruptions of the COVID-19 pandemic did not result in lessened participation in surveillance activities. However, we recognize the limitation that the activity reports provided by each site may not have included other relevant aspects of laboratory operations. Estimated changes in the performance indicators reported here cannot therefore be conclusively attributed to the actions described by the sentinel sites in their activity reports.The good working relationships between RITM and sentinel site personnel, formed through partnerships that have been in place since 1988, may have been a contributory factor in maintaining high levels of participation in ARSP. The practice of recognizing and incentivizing sites with top participation scores, which was established in 2012, may have also encouraged continued participation in ARSP during the COVID-19 pandemic.The process of automated data transfer, whereby WHONET encoded data from the sentinel sites are automatically transmitted to the ARSRL server, facilitated the data submission from sentinel sites throughout 2020. ARSRL, as the coordinating laboratory, adapted to the challenges caused by the pandemic by adjusting staff schedules and activities, holding the annual PIM online and delaying the publication of the annual surveillance report.There was, however, a reduction in the amount of surveillance data submitted in 2020, a factor that must be considered when interpreting the overall AMR rates reported by ARSP for 2020. It is possible that the decrease in submitted data was due to the shift towards remote outpatient consultations (to lessen the risk of infection at the sentinel sites), a trend which could introduce bias in patient and testing denominators for the 2020 ARSP surveillance data, and which should be considered in the analysis of AMR data. (The actions implemented by the participating sentinel sites alleviated much of the negative impact of the pandemic on laboratory operations and logistics. Performance indicators revealed that despite the ongoing health crisis, sentinel sites were able to perform their tasks as the primary contributors to national AMR data collection. It is imperative for ARSRL to disseminate information and encourage other facilities to adopt the good practices observed at these sites. The success and continuity of ARSP is contingent upon the collaborative efforts of the reference laboratory and the sentinel sites. The COVID-19 pandemic has the potential to exacerbate the AMR situation in the Philippines and put stewardship efforts at risk. It is imperative therefore that efforts against the development of AMR should not cease.Further studies should be conducted to provide more information on the impact of the COVID-19 pandemic on AMR emergence and spread. Moreover, the expanded molecular biology facilities established to meet the needs of the COVID-19 response should be used to enhance national AMR surveillance through genomic epidemiology.


C. elegans have revealed that the common neurons that exist in both sexes (sex-shared neurons) also play significant roles in generating sex differences in the structure and function of neural circuits. Here, we review the anatomical and functional differences in the sex-shared neurons of C. elegans. These sexually dimorphic characteristics include morphological differences in neurite projection or branching patterns with substantial changes in synaptic connectivity, differences in synaptic connections without obvious structural changes, and functional modulation in neural circuits with no or minimal synaptic connectivity changes. We also cover underlying molecular mechanisms whereby these sex-shared neurons contribute to the establishment of sexually dimorphic circuits during development and function differently between the sexes.Studies on sexual dimorphism in the structure and function of the nervous system have been pivotal to understanding sex differences in behavior. Such studies, especially on invertebrates, have shown the importance of neurons specific to one sex (sex-specific neurons) in shaping sexually dimorphic neural circuits. Nevertheless, recent studies using the nematode  Sexually reproducing animals often show innate, stereotyped sexual behaviors for mating. These innate behaviors are differently displayed in the two sexes and result from the sexual dimorphism in the structure and function of the nervous system  and Doublesex (Dsx), are responsible for the sexual differentiation of the brain between males and females  in addition to the neurons specific to each sex (sex-specific neurons)—eight in hermaphrodites and 93 in males  and temperature sensation  has been shown to be expressed in the male PHC and required for the axon fasciculation with other neurons in male C. elegans  promotes DVB neurite branching in a cell-autonomous manner, whereas NLG-1 (the ortholog of neuroligin), expressed in the postsynaptic DVB target cells suppresses the effect of NRX-1. The antagonistic functions of NRX-1 and NLG-1 are not likely involved in their canonical functions in synapse formation  or iBLINC (in vivo biotin labeling of intercellular contacts) technique  which in turn regulate the expression of target effector protein genes . However, even the regulation by TRA-1 is presumably not as simple as previously thought since recent studies have identified substantial variations in TRA-1 expression among sex-shared neurons depending on age, sex, or environmental conditions (Bayer et al., In DK and BK developed the concept. Both have written and edited the text. 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.


The Western Cape Pregnancy Exposure Registry (PER) was established at two public sector healthcare sentinel sites in the Western Cape province, South Africa, to provide ongoing surveillance of drug exposures in pregnancy and associations with pregnancy outcomes.Established in 2016, all women attending their first antenatal visit at primary care obstetric facilities were enrolled and followed to pregnancy outcome regardless of the site . Routine operational obstetric and medical data are digitised from the clinical stationery at the healthcare facilities. Data collection has been integrated into existing services and information platforms and supports routine operations. The PER is situated within the Provincial Health Data Centre, an information exchange that harmonises and consolidates all health-related electronic data in the province. Data are contributed via linkage across a unique identifier. This relationship limits the missing data in the PER, allows validation and avoids misclassification in the population-level data set.Approximately 5000 and 3500 pregnant women enter the data set annually at the urban and rural sites, respectively. As of August 2021, >30 000 pregnancies have been recorded and outcomes have been determined for 93%. Analysis of key obstetric and neonatal health indicators derived from the PER are consistent with the aggregate data in the District Health Information System.This represents significant infrastructure, able to address clinical and epidemiological concerns in a low/middle-income setting. The Western Cape Pregnancy Exposure Registry (PER) provides ongoing surveillance of drug exposures in pregnancy and associations with pregnancy outcomes.Data collection is integrated into existing services and information platforms and supports routine operations.The PER is situated within the Provincial Health Data Centre, an information exchange that harmonises and consolidates all health-related electronic data which limits missing data, allows validation and avoids misclassification in the population-level data set.The PER digitises clinical data recorded in operational stationery and we cannot control for data quality nor account for missing data in the source documents nor for unmeasured confounders.Medicines obtained outside the public sector systems and traditional and complementary medicines are not included unless they are documented in the clinical stationery.Assessing medicine and vaccine safety in pregnancy requires ongoing surveillance across multiple settings. In high-income countries, reviews of outpatient prescriptions and self-medication during pregnancy estimated exposure rates of up to 93% and 43%, respectively, excluding vitamins and supplements.3Pregnant women have been systematically excluded from pharmaceutical trials and the efficacy, dosing and safety of many medicines used during pregnancy are uncertain or findings are delayed until after the product is licensed and in use. Post-authorisation safety assessments have historically relied on passive reporting of suspected medicine-related adverse events. Such systems have been limited by their dependence on voluntary reporting, variable data quality, absence of background rates of adverse birth outcomes including common congenital disorders, and lack of data to establish a denominator.Recently, pharmacovigilance in pregnancy has drawn public and political attention following concerns about the initial signal of potential association observed between the antiretroviral integrase inhibitor, dolutegravir, and neural tube defects,11–14Pregnancy Exposure Registries (PER) are a form of surveillance, designed to iteratively detect adverse events within a defined pregnant population. Importantly, the prospective nature of PER allows collection of exposure and other data before the pregnancy outcome is known. The pharmaceutical industry maintains drug-specific registries for medicines and/or drug classes with known/suspected teratogenic effects  or as part of post-marketing commitments .all women presenting to the site to allow concurrent establishment of background rates and assessment of multiple potential exposures.20The UNICEF/UNDP/World Bank/WHO Special Programme for Research & Training in Tropical Diseases (TDR) has developed a PER approach for resource-limited settings aimed at prospective data collection on exposures in a cohort of pregnant women attending antenatal care services at sentinel sites. Important for validity and causality determination, the approach recommends inclusion of The Western Cape (WC) PER was established in Cape Town in 2016, adapted from the TDR template. It was nested within the province-wide health information exchange, a component within a larger project designed to assess the impact of WHO Option B+ for vertical HIV transmission prevention  for pregnant and breastfeeding women) at the population and individual levels.We took a pragmatic approach to the establishment of the PER based on the availability of resources and the desire to integrate into existing systems and operational routines, avoiding a parallel infrastructure and supporting longevity. Data generated by the initiative are available for the evaluation and improvement of clinical care as well as epidemiological review.The PER has been established at two sentinel sites in the WC. Gugulethu Midwife Obstetrics Unit (GMOU) provides obstetric care to approximately 5000 women annually in Gugulethu, Cape Town a low-income area with high unemployment and an antenatal HIV prevalence of approximately 30%. GMOU refers patients to Mowbray Maternity Hospital (secondary) and Groote Schuur Hospital (tertiary). About half of all women who attend GMOU are referred to hospital, antenatally or perinatally. Worcester MOU (WMOU) is situated adjacent to the Worcester Provincial Hospital in Worcester, a town of approximately 230 000 in a farming community 120 km outside Cape Town. WMOU provides delivery services for ~3600 women annually. The antenatal HIV prevalence is approximately 16%. Women requiring more advanced care are referred to Worcester Hospital (secondary) and Tygerberg Hospital (tertiary). The community is structurally disadvantaged, and many depend on seasonal employment on farms. In both areas the population is mobile; women move within the WC province and may deliver outside the proscribed referral axes.Enrolment started at GMOU in Cape Town in September 2016 and at WMOU in January 2018.situating enrolment at the primary care facility allowed us to capture a sample representative of the pregnant population in the geographical drainage area of that facility.All women seeking care at the sentinel primary care sites were included. Most women who use public maternity services, including those with medical and obstetric complications, initially present to primary care, therefore ward-stock without a linked digital record, although details are recorded in paper-based registers.Obstetric care is free at the point of service and approximately 65% of women present at/before 20 weeks gestation.Within resource constraints, the WC endeavours to provide an antenatal ultrasound scan to clients before 22 weeks gestation for determining gestational age. If concerns are identified women are up-referred for formal fetal anomaly review.Antenatal visits, HIV testing, transfers and deliveries are recorded against patient names in individual paper-based registers. Monthly aggregate statistics of key obstetric indicators  are manuThe Maternity Case Record (MCR) is a patient-held paper-based document distributed at the first antenatal visit that serves as a record of all clinical obstetric care until discharge after pregnancy outcome, regardless of level of care. It is used throughout South Africa and archived at the site of outcome. Chronic medication and any agents dispensed during pregnancy should be recorded in the MCR by the attending clinicians. However, medicines received at specialist clinics, during hospital admissions and over-the-counter medicines are often not documented.3 22After birth, live- and stillborn neonates are examined by the attending clinician (nurse midwife/doctor) and the outcome of the limited neonatal surface examination is recorded in the MCR. This examination has been shown to detect most major congenital malformations in neonates, that is, those that are visible and do not require diagnostic tools.In the WC, most women (99%) give birth at a health facility.For the purposes of the PER, the MCR serves as the primary source of prospectively-collected clinical data. Thus, women enter the cohort on first visit to the MOU and are followed up until pregnancy outcome.The PER digitises routinely collected data from the clinical stationery if not already digitised under existing service delivery. In addition to the patient-held MCR, data sources include primary care dating ultrasound reports, and the STI and labour ward delivery registers. As we are using operational data, definitions have been aligned with operational clinical definitions in the WC. Using other routinely collected data elements  we are able to align case definitions with those of the Global Alignment of Immunization Safety Assessment in Pregnancy,Externally-funded PER data clerks are embedded at the facilities and project-augmented data collection is accommodated within the routine patient and document flow without disruption of clinical care.The provincial government of the WC operates as a single provider of public sector health services. A 9-digit numeric folder number which is common across the health platform for a given patient facilitates the harmonisation of all electronic health records within the Provincial Health Data Centre (PHDC), the information exchange that consolidates all electronic administrative, pharmacy, laboratory, and disease-specific information.All MOUs use the Primary Healthcare Information Service (PHCIS) electronic medical records system which records attendance against patient identifiers, and ART in WLHIV. PHCIS automatically generates a unique folder number for live infants at birth, providing electronic linkage between mother and baby. Clinicom performs this function at all hospitals. Data are imported daily by the PHDC.21Electronic dispensing data in the PHDC are augmented by the PER which captures medicine exposures elicited from the women during the clinical consultation and ward-stock medicines recorded by clinicians in the MCR. The PER also records some lifestyle factors  that may act as confounders for certain outcomes. Combining the electronic pharmacy data in the PHDC strengthens the ascertainment of exposures, providing a complete list of medication dispensed from public sector pharmacies. Using multiple data sources for this has been shown to provide a more complete picture of antenatal medicine use essential for pregnancy exposure research.3 29 30Information on neonatal outcomes such as vital status, birth weight, gestational age and APGAR scores tend to be consistently captured across the cohort. The key findings of the neonatal surface examinations, although often perfunctory, usually result in the recording of notable physical anomalies. Internal anomalies such as cleft palate, hip dysplasias and cardiac anomalies as well as more subtle dysmorphic features may be missed at the time of the initial neonatal examination. Details of neonatal deaths, and major congenital disorders often require review of inpatient records at the delivery facilities.PER data are imported daily into the PHDC and linked using patient identifiers, providing a comprehensive electronic clinical record at the level of the individual which is accessible to the attending clinicians. Both systems benefit greatly from this design. The PER allows for validation of the provincial data set as relates to pregnancy and delivery, and the PHDC is able to identify missing outcomes  or exposures (from electronic pharmacy dispensing) not included in the PER.Between 01 September 2016 and 31 August 2021, 31 346 pregnancies were recorded in the PER. To assess robustness of the data set, we analysed data for a subset of women who attended their first visit to antenatal care between 01 January 2018 and 31 December 2019. Over th10.1136/bmjopen-2021-060205.supp1Supplementary dataWhere relevant, we compared rates of key adverse birth outcomes in the PER with official aggregate routine indicator data for the WC,The congenital disorder data are still being cleaned for analysis with pregnancy outcomes.Building Teratovigilance Capacity in Africa, which provided networking and training opportunities to 60 delegates from sub-Saharan Africa https://globalpharmacovigilance.tghn.org/resources/building-teratovigilance-capacity-africa/.We conducted an initial baseline assessment comparing clinical records to dispensing data before the implementation of the PERStepwise Surface Examination of the Newborn (https://www.who.int/tdr/publications/videos/stepwise-surface-examination-newborns/en/) and the training modules for midwives we developed as part of the South African Central Pregnancy Exposure Registry (https://www.ubomibuhle.org.za/training-lessons).In addition to the employment of project-specific staff, embedded with computers at the facilities, the project supports ongoing training of clinical staff to improve and standardise clinical history-taking with an emphasis on exposures, neonatal examination and clinical record keeping. Open resources include the WHO/TDR The integration of the PER within the PHDC greatly increases the completeness of the data. It facilitates identification of pregnancy outcomes at facilities outside our sentinel referral chains reducing loss to follow-up. Harmonisation and triangulation of two data sources for medicine exposures  provides a more robust summary of exposures than either alone.Accurate timing of exposures over the course of pregnancy is crucial to assess potential associations with adverse pregnancy outcomes. Collecting multiple reference points for gestational age  enabled the development of a hierarchy of methods and the allocation of a confidence score to the reported gestational age.In line with the TDR protocol,From the outset, it was important to avoid a parallel system and support project sustainability. The PER has been integrated into the existing clinical and clerical routines and uses local electronic health information platforms. It allows for electronic generation of key monthly indicators at primary care sites that are otherwise collected by hand.As the cohort expands, capacity to conduct nested studies that facilitate signal detection and signal verification of potential or suspected teratogens will improve. The collection of individual-level data in a large prospectively enrolled cohort, representative of both urban and rural WC populations who use public sector services will support more robust analyses that can better account for confounding factors in such observational data.The PER digitises clinical data recorded in operational stationery and we cannot control for data quality nor account for missing data in the source documents, including the risk of under-reporting. To address this, we have engaged in ongoing training at the sites with an emphasis on drug history-taking, medical record-keeping and neonatal examination offering in-person teaching and video tutorials. Clinical staff have been provided with Medicine Identification Aids with photographs of common formulations and packaging, and the WHO Birth Defects Atlas.Notwithstanding the advantages of the individual-level data available within the PHDC, data are limited to those that are entered into one of the electronic medical records systems used in the public sector. In terms of medicine exposures, the PER documents dispensed medication which may not reflect actual use. In addition, medicines obtained outside of the public sector systems, from private doctors or over-the-counter from pharmacies are not included unless they are noted in the clinical records.The PER database is parsimonious by design and necessity and we are unable to account for unmeasured confounders. However, data fields are collected for the entire cohort who are all drawn from the same geographical areas served by the primary care clinics. Additionally, we record limited data on lifestyle factors relevant in pregnancy  which are lacking from equivalent population data sets based on insurance claims data.As with the PHDC within which it is located, the PER can address clinical, operational and research needs, and data access is specific to each. Aggregate reports are available to managers. Data are anonymised using standard protocols for de-identifying records before they are shared with researchers who are not directly engaged in the women’s clinical care. It is anticipated that such de-identified individual-level data may be shared as part of the South African Central Pregnancy Registry21The PER is integrated into the data collection and curation services of the Western Cape Government Department of Health and clinical and other service providers have engaged with the project since its inception. The data are available to managers as aggregate reports and to contribute to the electronic clinical records accessible by clinicians. Feedback from users contributes iteratively to optimisation of the PER to improve health outcomes for pregnant women and infants.Research on medicine safety in pregnancy requires data on individual pregnancies, mother–infant linkage, medication exposure, gestational age at exposure and maternal and birth outcomes. Data completeness and robustness continues to improve with ongoing training, evolution of routine clinical information systems and increasing political focus on pregnancy exposures. The cohort is well-placed to detect large signals in pregnancy outcomes as novel maternal exposures are introduced, and to contribute to cohort harmonisation for rarer outcomes and address the lack of information on congenital disorders in Africa.


No other significant correlations were observed. Conclusion: ARS presented moderate reliability and were correlated with the daily sputum expectoration in bronchiectasis. The use of sequential measurements may be an option to achieve greater accuracy when ARS are used to monitor or assess the effects of physiotherapy interventions in this population.Background: Computerized adventitious respiratory sounds (ARS), such as crackles and wheezes, have been poorly explored in bronchiectasis, especially their measurement properties. This study aimed to test the reliability and validity of ARS in bronchiectasis. Methods: Respiratory sounds were recorded twice at 4 chest locations on 2 assessment sessions (7 days apart) in people with bronchiectasis and daily sputum expectoration. The total number of crackles, number of wheezes and wheeze occupation rate (%) were the parameters extracted. Results: 28 participants  were included. Total number of crackles and wheezes showed moderate within-day  and between-day reliability  considering all chest locations and both respiratory phases; wheeze occupation rate showed moderate within-day reliability , but poor between-day reliability . Bland–Altman plots revealed no systematic bias, but wide limits of agreement, particularly in the between-days analysis. All ARS parameters correlated moderately with the amount of daily sputum expectoration (r > 0.4;  Pseudomona aeruginosa infection  for those not normally distributed. The characteristics of the computerized ARS (crackles and wheezes) were described for each of the two recordings performed in each assessment session, stratified for anterior and posterior chest location, and pooled for right and left sides. Variables were checked for normality using the Shapiro–Wilk test. Data were described using absolute and relative frequencies for categorical variables, mean  for continuous variables normally distributed or median . Only 25 participants were included in the between-day reliability and in the convergent validity analysis because two participants did not attend the second assessment session (no reason provided) and one participant had an acute exacerbation.A total of 28 participants were included in the within-day reliability analysis. The characteristics of the participants are described in A summary of crackles’ characteristics obtained during the two assessment sessions is presented in Overall, within-day reliability for all chest locations was moderate for the total number of crackles during expiration and the complete respiratory cycle and poor during inspiration . At the Considering all chest locations, between-day reliability for the total number of crackles was moderate during inspiration and the complete respiratory cycle and poor during expiration . Between1, LCQ, QoL-B, number of lobes affected and BSI ; (iii) expiratory wheezes were clearly more predominant than inspiratory wheezes in our sample, maybe associated with the fact that all of our participants presented daily sputum expectoration as a common clinical feature of the bronchiectasis disease.Wheezes are associated with airflow limitation in abnormally narrowed or compressed airways . The prea et al.  study vsNo systematic differences have been found for the number of crackles and wheezes in the within-day and between-day analysis as it was expected in a stable condition; however, the mean difference between the measurements was large for the occupation rate in both analyses. The limits of agreement were quite wide for all parameters, particularly in the between-day analysis; the only exception was when the average of the crackles did not exceed seven. Therefore, the use of repeat measurements may be a good solution to improve the dispersion around the mean, as previously was reported for other outcome measures ,47. Over1%). As stated above, the less severity of bronchial obstruction in our study may explain why a correlation was not found. Future studies with patients with greater decline of lung function will clarify this issue. Chronic sputum production is a common clinical feature of people with bronchiectasis, and it is known that crackles and wheezes may be related to the presence of excess of mucus in the airways [1%, number of lobes affected) and patient-reported outcomes (LCQ and QoL-B) and, therefore, the use of ARS to monitor people with bronchiectasis or assess the effects of treatments  should be encouraged in combination with other measurements. Future studies evaluating if other respiratory sounds parameters, such as intensity or frequency, present a better relationship with clinical outcome measures, and if ARS change along the disease trajectory  and across different clinical statuses  using matched controls in bronchiectasis are recommended.Daily spontaneous sputum expectoration is the only clinical outcome that correlates moderately with the number of crackles, wheezes and occupation rate in our study, in contrast with a previous study conducted on COPD that found significant correlations between the number of crackles at the posterior chest location and lung function (FEV airways . In fact airways . Our datAlthough we have estimated the sample size needed to determine the reliability of ARS, this estimation was below the threshold of 50 participants recommended by the COSMIN initiative . Future Our data suggest that the number of crackles and wheezes present moderate reliability at short time intervals and are moderately correlated with the daily amount of sputum expectorated in people with stable bronchiectasis. However, the level of agreement, the SEM and SDC provide information on the presence of high variability for all ARS parameters. Therefore, the use of sequential measurements when ARS are evaluated, including all chest locations and both respiratory phases, is recommended to achieve greater accuracy and complete the clinical assessment with other outcome measures when ARS are used to monitor or assess the effects of interventions, such as airway clearance techniques or pulmonary rehabilitation, in people with stable bronchiectasis.


The immediate and short-term impact of the ongoing Covid-19 pandemic on children are already well documented, while its longer-term consequences will eventually reveal themselves. In clinical practice, we have witnessed an unprecedented increase in hospital admissions due to eating disorders . FurtherChild and adolescent psychiatry (CAP) must rise to these challenges and meet the mental health needs of this vulnerable population. Three years ago, representatives from associations relevant to CAP  met and discussed the future of CAP. Subsequently, four consensus priorities for our medical discipline for the next 10 years were published by Skokauskas et al. . These iIn contrast to the rising prevalence of mental disorders among adolescents , the numEuropean Child and Adolescent Psychiatry. Luisa Lázaro documented the long road to the creation of the speciality of child and adolescent psychiatry in Spain [The latter is reported in this issue of in Spain . In 1950The new curriculum is estimated to be approved in 2023. Training in CAP will take 5 years, with educational aspects shared with adult psychiatry. It is estimated that the first  CAP trained doctors will complete their training in 2028. The author comments that this huge endeavour to offer excellent training for future CAP specialists in Spain might have been helped along by the mental health crisis created by Covid-19 .expanding research on the etiology of psychiatric disorders as another CAP priority. It is well established, that psychiatric disorders are multifactorial with both genetic and non-genetic factors underlying their etiology. Their genetic architecture is complex with variants in likely thousands of genes contributing to the disorders pathophysiology. Genome-wide association studies (GWAS) focusing on common variants with small effect sizes provided unprecedented insight into the biological processes at play [In addition to enlarging the CAP workforce, Skokauskas and colleagues  identifi at play . GWAS re at play . The dowChildhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe) [. In this issue of European Child & Adolescent Psychiatry, Rajula and colleagues describe a world-wide unique collection of eight population-based birth and childhood cohorts for which longitudinal, detailed phenotype data  are available. In addition, for more than 70 000 individuals GWAS data and for more than 4 000 individuals epigenetic data are available. CAPICE aims to address the poorly understood interplay between non-genetic, environmental and genetic factors on the onset and course of disorder and the comorbidity patterns of childhood onset psychiatric disorders. The ultimate goal is to identify children at high risk for poorer outcomes and to improve future treatment and prevention options. CAPICE is an international training network for early career scientists. In addition to providing insight into the mechanisms underlying the phenotypic variance in affected children and adolescents, it builds up future leaders in CAP research. The cohorts included so far are mainly from Western Europe and Australia. To the authors knowledge no additional cohorts similar to those in CAPICE exist. However, if there are, they are welcome to join the Training Network [These so far poorly understand aspects of psychiatric disorders are at the heart of the European Union funded Training Network CAPICE ( Europe) . In this Network .