Coccydynia is defined as pain in the coccyx. We investigated the effect of radial extracorporeal shockwave therapy (rESWT) in the management of coccydynia.In this prospective study, patients (≥18 years) diagnosed with coccydynia at a sports clinic located in Thessaloniki, Greece, were eligible for rESWT treatment when they reported a visual analogue scale (VAS) pain level ≥6. Treatment sessions were once weekly and ended when VAS pain levels decreased to ≤3. Recurrence rates were documented at 3-month and 12-month follow-ups.r=0.701, P=0.005). Pairwise comparison highlighted significant reductions in VAS pain levels between each stage of treatment (P<0.001).Fourteen patients were treated using rESWT. The mean age and symptom duration of our cohort was 33.6±7.9 (range: 20–45) years and 9.4±8.5 (range: 3–36) months, respectively. The mean number of treatment sessions per patient was 6.4±1.6 (range: 4–8). The mean device pressure, frequency, and number of pulses was 1.2±0.1 (range: 1–1.4) bar, 5.0±0.1 (range: 5–6) Hz, and 2082±74.8 (range: 2000–2300) pulses, respectively. Treatment alleviated pain in all patients, and no recurrence of symptoms was reported during follow-up. There was a positive correlation between symptom duration and the number of treatment sessions (Our study affirms the safety and efficacy of rESWT in managing coccydynia. HighlightsRadial extracorporeal shockwave therapy (rESWT) offers an alternative and non-invasive means of treatment for coccydynia.Treatment can be adapted by altering device parameters in line with patients’ pain tolerance and treating patients over a variable number of sessions.Pain-adapted intervention protocols may optimise pain management and lower the risk of symptom recurrence.Randomised controlled trials comparing rESWT efficacy with other surgical and non-surgical modalities are needed.1. It serves as the attachment site for several pelvic structures and possesses an extensive nerve supply2. Coccydynia is five times more common in women than men, although its exact prevalence is unclear4. Factors associated with the development of coccydynia include obesity, prolonged sitting, and childbirth4. Degenerative joint disease is also thought to increase risk1. The most common presentation is following a traumatic event, typically a backwards fall1.Coccydynia, or coccygodynia, refers to pain in the coccyx (tailbone). The coccyx is a triangular-shaped bone in the lowermost region of the spine and is composed of three to five individual segments3. Physiotherapy and manipulative therapy are also popular treatment options5. Steroid and anaesthetic injections may be useful in patients who do not respond to these methods, but there is controversy surrounding the optimal site for injection1. Surgical coccygectomy is performed in treatment-resistant cases, and whilst outcomes are generally good-to-excellent, the incision is made close to the anal canal and can make subsequent care challenging7.Most cases of coccydynia can be managed conservatively with rest, anti-inflammatory medications, seating aids, and warm baths8. Devices are placed against the skin and deliver impulses of energy to injured tissue at a given pressure (bar) and frequency (Hz)11. The exact mechanism of action of ESWT remains unknown; however, biological responses are proposed to be mediated by mechanotransduction and include tissue regeneration, wound healing, angiogenesis, and neovascularisation15. Altogether, this can result in significant pain alleviation12. There are two types of ESWT: focused (fESWT) and radial (rESWT). These differ marginally in terms of shockwave production and energy delivery8. In practice, rESWT devices are smaller, cheaper, and easier to use8. Moreover, rESWT has proven to be successful in the management of musculoskeletal disorders such as plantar fasciitis18, calcific shoulder tendinopathy20, trigger digit21, sesamoid osteonecrosis22, patellar tendinopathy23, medial tibial stress syndrome26, and lateral epicondylitis28.Extracorporeal shockwave therapy (ESWT) is a physical agent modality that offers an alternative and non-invasive means of treatment for coccydynia32. One study found rESWT to be more effective than steroid injections for pain relief over 6 months of observation32. A recent systematic review reported that shockwave therapy offered the second-greatest improvements in pain among all available treatment options33. However, research on the subject remains limited. Furthermore, most of the available studies adopt a ‘one size fits all’ approach by treating patients at a single dose over a predetermined number of sessions. The research objectives of this study were: (i) determine whether a pain-adapted protocol of rESWT is effective in treating coccydynia, (ii) assess for a relationship between treatment duration and symptom intensity, and (iii) monitor patients for possible adverse effects of treatment.Previous literature has highlighted the benefit of rESWT for coccydynia34, Supplemental Digital Content 1, http://links.lww.com/MS9/A206.This prospective study was reported in line with the PROCESS (Preferred Reporting Of CasE Series in Surgery) Guideline35. Anteroposterior and lateral radiographs of the lumbar spine and coccyx were obtained to assess the position of the coccyx and rule out coccygeal dislocations. Magnetic resonance imaging scans were also performed to exclude other pathologies of the lumbar spinal cord, coccyx, and associated soft tissue. Treatment options, including the use of rESWT, were discussed with patients if they reported a visual analogue scale (VAS) pain level ≥6. Patients aged ≥18 years were eligible for the study. Exclusion criteria were: local or systemic neurological disease, rheumatological disease, malignant disease, previous spinal surgery or spinal disease, coagulopathies, pregnancy, recent (≤6 weeks) trauma to the coccyx, and coccygeal dislocations.Patients presenting to a sports clinic located in Thessaloniki, Greece, between January 2017 and December 2020 were diagnosed with coccydynia by a consultant sports physician (N.M.) following focused clinical history, examination, and imaging. Physical examination included inspection of the overlying skin and palpation, which revealed localised focal tenderness in the region of the coccyx36. All treatments were performed after verbal and written informed consent had been obtained. The probe was placed on the skin at 90° to the coccyx in the sagittal plane29. Ultrasound gel was applied to the skin before treatment to reduce energy loss from the device. Local anaesthesia is known to lower the efficacy of treatment and was therefore not used37. Treatment sessions took place once a week to allow for adequate periods of recovery and healing for at least three sessions. Any ongoing treatments for coccydynia were stopped, and patients were advised against the use of non-steroidal anti-inflammatory drugs.Treatment was administered to the maximal point of tenderness exactly over and posterior to the coccyx by the same consultant sports physician, using the Storz Medical Masterpuls MP200  rESWT device22. Pressure, frequency, and the number of pulses were adjusted between sessions in line with patients’ pain tolerance. Pain tolerance is the duration of time until an individual can no longer withstand an unpleasant stimulus and reflects the greatest level of discomfort they are willing to endure39. Tolerance is of particular value in coccydynia, given that pain is the most common presenting symptom and its management is the primary goal of treatment.The rESWT device was set to a minimum pressure and frequency of 1 bar and 5 Hz, respectively. Each session included the delivery of at least 2000 pulses. These parameters were set based on the experience of our authors with previous cases of coccydynia and mirror those utilised in similar rESWT research41. Patients were asked their pain level before treatment at each session, although this did not form part of our formal data collection. Treatment continued until patients reported a VAS pain level ≤3, at which point they were discharged from the clinic42.VAS pain levels were used to monitor patient response and clinical improvement throughout treatment, with patients ranking the severity of their pain at rest between ‘no pain’ (0) and ‘worst imaginable pain’ (10)The main outcome measure of this study was pain management. Each patient provided a baseline VAS pain level based on their initial symptomology. Follow-up measurements were taken immediately after the cessation of treatment, at 3 months posttreatment, and at 12 months posttreatment. Symptom resolution and disease recurrence were monitored during this period. The mean device pressure and frequency, number of pulses, and number of rESWT sessions were also documented using electronic device records.P<0.001). The repeated measures ANOVA  procedure (Friedman) was used to test for significant reductions in VAS pain levels. Pairwise comparisons using Durbin–Conover measured differences in VAS at each stage of follow-up when compared with baseline scores. Spearman’s rank correlation coefficient was used to assess the correlation between variables. A predefined confidence level of 95% (α=0.05) was set for all statistical analyses.Descriptive statistical analysis was carried out using Microsoft Excel , IBM SPSS Statistics Version 27 , and Jamovi . The variables analysed did not meet the normality assumption, which was performed using the Shapiro–Wilk test (Fourteen patients were included in the study. The cohort comprised five (36%) men and nine (64%) women, with a mean age of 33.6±7.9 (range: 20–45) years. The average duration of symptoms before rESWT was 9.4±8.5 (range: 3–36) months Table .r=0.701, P=0.005).The mean number of rESWT sessions was 6.4±1.6 (range: 4–8). The median number of sessions was 6.5 . Five patients (36%) underwent eight sessions, two (14%) underwent seven sessions, two (14%) underwent six sessions, three 21%) underwent five sessions, and two (14%) underwent four sessions (Table 1% underwhttp://links.lww.com/MS9/A207).The mean pressure and frequency of the rESWT device over the course of all treatments were 1.2±0.1 (range: 1–1.4) bar and 5.0±0.1 (range: 5–6) Hz, respectively. The median pressure and frequency was 1.2  bar and 5.0  Hz, respectively. The mean number of pulses delivered per session was 2082±74.8 (range: 2000–2300) pulses. The median number of pulses per session was 2000  pulses (Table http://links.lww.com/MS9/A207).The mean and median pretreatment VAS was 7.4±0.7 (range: 6–8) and 7.5 , respectively. The mean and median VAS immediately posttreatment was 1.6±0.7 (range: 1–3) and 1.5 , respectively. The mean and median VAS at 3-month follow-up were 0.9±0.7 (range: 0–2) and 1.0 , respectively. The mean and median VAS at 12-month follow-up was 0.3±0.5 (range: 0–1) and 0.0 , respectively  value of 38.7 (P<0.001). The pairwise comparison revealed significant reductions in VAS pain levels between each stage of treatment (P<0.001) , frequency (5–21 Hz), number of pulses (2000–3000 pulses), and number of sessions ranged considerably. To our knowledge, this study is the first to deliver a pain-adapted protocol of rESWT. We achieved this by (i) adjusting rESWT device parameters in line with patients’ pain tolerance and (ii) continuing treatment until a VAS pain level ≤3 was reported. We believe that continuing treatment until a low level of pain is achieved is relevant to the favourable outcome of this study.The value of rESWT for coccydynia has been highlighted in a handful of studies43. To illustrate, if a patient found the same dose of treatment to be less painful at a later session compared to an earlier session, then device parameters may be increased. Alternatively, if a patient finds treatment uncomfortable at a particular dose, then device parameters may be lowered. A 2007 study also observed the greatest reductions in pain when patients with chronic heel pain were treated with ‘maximum tolerable’ energy densities, compared to a fixed protocol44.Treating patients according to pain tolerance is common with the use of shockwave therapy. Numerous regimes in a 2013 systematic review described making dose alterations ‘as tolerated’ when treating soft tissue conditions19. Higher device pressures are said to increase pain; therefore, our approach may also make treatment more comfortable for patients45. The mean device frequency increased by a maximum of 0.2 Hz (5–5.2 Hz), and the mean number of pulses per session increased by a maximum of 8.95% (2000–2179 pulses). An increased number of larger cavitation bubbles – and resultant shockwaves – are generated at lower frequencies, which may explain the findings of one study, where the device frequency was set to 21 Hz, and increases in pain were reported at the 7-month follow-up46. In contrast, no symptom recurrence was present in our study. The number of pulses per session increased only between sessions numbers two and six, and with careful consideration of energy accumulation in those who had more than six sessions.The mean device pressure in this study increased by a maximum of 0.2 bar (1.1–1.3 bar). Whilst this may be an arguably insignificant change, it is worth noting the positive outcomes observed at lower overall device pressures31. Given the symptom control and lack of recurrence in our cohort, we may argue for the number of treatment sessions to be dictated by patients’ pain levels, as this appears to be the most important indicator of successful treatment. Pain levels after treatment and during follow-up were also lower in our cohort compared to others.Patients were discharged from the clinic once their VAS pain level reached ≤3. This meant that they all underwent at least four, and no more than eight, treatment sessions. In contrast, previous studies administered treatment over a predetermined number of sessions29. Equally, we acknowledge that complete symptom resolution (VAS=0) was not achieved among our entire sample, and, in fact, no patient was completely pain-free immediately posttreatment (VAS 1-3). While the long-term effect of rESWT can be observed during follow-up, four patients remained symptomatic (VAS=1) at 12 months. This suggests that while treatment may have been successful, it did not always eradicate pain. A lengthier follow-up period may have highlighted further decreases or recurrences in VAS; however, 12 months can be considered adequate, given the nature and conditions of this study.Yet, inadequate pain relief among patients was observed in another study that continued rESWT until VAS pain levels were ≤3r=0.701). There were seven patients who underwent either seven or eight sessions, all of whom reported pain for more than 6 months. However, symptom duration did not appear to be reflected in pretreatment VAS. The remaining patients, whose symptomologies ranged from 3–6 months, required no more than six sessions. This suggests that patients with chronic symptoms (>6 months) at presentation are likely to require more rESWT sessions.Our analyses found a strong positive correlation between symptom duration and the number of treatment sessions required  comparing rESWT with other management options for coccydynia and comparing different rESWT protocols.Despite its poorly understood mechanism of action, rESWT continues to serve as a safe and effective management option for coccydyniaWe acknowledge some limitations of this study. Firstly, the number of patients included was small because coccydynia is a condition of low incidence. Secondly, our analysis must be interpreted with some degree of caution. Although a single modality was delivered, we cannot be certain that there were no alternative explanations for the effects observed. Additionally, VAS pain levels during treatment were not formally recorded. VAS was also the only measure used to assess patients’ pain tolerance. Employing a second tool to confirm the decrement of intensity may have strengthened our conclusions regarding the efficacy of rESWT. Other outcomes, such as quality of life and disability, have featured in similar research and assessed using scales such as SF-36 and Oswestry Disability Index, respectively.We had identified a high success rate and no recurrence of disease at 12-month follow-up when coccydynia patients were treated with a pain-adapted protocol using rESWT. Our results align with the current evidence base recognising rESWT as a conservative management option for coccydynia and also suggest that adjusting the number of treatment sessions and doses during treatment in line with patients’ pain tolerance may optimise pain management and lower the risk of recurrence. This may also make treatment more comfortable for patients. More RCTs are needed to compare the effect of rESWT on other surgical and non-surgical treatment modalities.Ethical approval was granted by the Thessaloniki Clinic Institutional Review Board (ART No. 2016-09).Written informed consent was obtained from the patient for publication and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal on request.This paper received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors affirm that this manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted and that any discrepancies from the study as planned  have been explained.N.M.: set up and carried out the study; K.S.L. drafted the manuscript. All authors contributed to the writing of the manuscript, revised the draft manuscript, and approved the final version.The authors declare that they have no conflicts of interest.Name of the registry: ClinicalTrials.gov.Unique identifying number or registration ID: NCT05753514.https://clinicaltrials.gov/ct2/show/NCT05753514.Hyperlink to your specific registration (must be publicly accessible and will be checked): ha16657@qmul.ac.uk; Nikos Malliaropoulos, E-mail: contact@sportsmed.gr; Georgios Bikos, E-mail: bikosg77@yahoo.gr; Heinz Lohrer, E-mail: Heinz.Lohrer@esn-ortho.de.Kabir Singh Lota, E-mail: The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.Not applicable.


For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient’s 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis. Accurate diagnosis of interstitial lung disease subtypes and prediction of patient survival rates remains challenging. Here, the authors develop AI algorithms to combine patient’s clinical history and longitudinal CT images to help clinicians diagnose and classify subtypes and dynamically predict disease progression and prognosis. Obtaining a specific diagnosis in cases of ILD is essential to guide patient management and treatment. High-resolution computed tomography (HRCT) plays a significant role in accurately classifying the various subtypes of ILD. According to the American Thoracic Society (ATS) guidelines, accurate diagnosis of ILD subtypes requires a multidisciplinary assessment reviewing clinical history, HRCT, and pathology2. In addition, longitudinal monitoring with CT can assess the progression of Serial CT that can reveal changes in the extent of parenchymal architectural distortion, reticulation, bronchiectasis and honeycombing, allowing for the identification of progressive fibrotic disease which correlates with poorer survival. Mortality is often not feasible as an end-point for diseases with chronic progressive fibrosis (such as IPF); change or lack of change in disease extent on HRCT represents a potential means of assessing treatment response3.Interstitial lung disease (ILD) refers to a group of more than 200 pulmonary conditions which can exhibit varying degrees of lung parenchymal fibrosis4. Interpretation of these difficult exams is challenging and can depend on the expertize of the radiologist.In some cases, diagnosis and classification of ILD types via CT are relatively straightforward. In other cases, the imaging findings can overlap multiple ILD patterns or may have no identifiable pattern, and is thus subject to substantial inter- and intra-observer variation among radiologists5, including detecting abnormal interstitial patterns6, automatic assessment of the extent of systemic sclerosis-related ILD7, and differentiation between nonspecific interstitial pneumonia (NSIP) and usual interstitial pneumonia (UIP)8. However, literature regarding accurate deep learning-aided diagnosis of multiple ILD subtypes as well as prediction of survival rate is limited at this time. The purpose of our study is to develop an AI system that (1) can classify 5 different types of ILD based on initial chest CT scans and relevant clinical history as well as (2) monitor a patient’s disease progression.Prior studies have shown that deep learning can be used to recognize different ILDs on CT imagesFor our study, we collected clinical information retrospectively through a chart review of electronic medical records. Clinical information included age, sex, history of current/former smoking, history of rheumatic disease, home oxygen requirement, history of occupational exposures, pulmonary function test (PFT) values , presence of pulmonary hypertension based on echocardiography or right heart catheterization, and history of lung biopsy. Clinical history was collected longitudinally for every CT scan available for the patient through the course of their treatment to account for changes in exposures or other variables. We collected CT scans and the corresponding clinical history obtained at every clinical encounter. To further predict a patient’s 3-year survival rate, we included medications and other therapeutic information to clinical history Fig. .Fig. 1Ov9 (ViT) to learn image patterns of patients with ILD on the initial chest CT scan. We then used multilayer perceptron10 (MLP), XGBoost11, and support vector machine12 (SVM) classifiers to predict ILD subcategories based on clinical information. Finally, we developed a joint model integrating chest CT characteristics with associated clinical history to predict ILD subtypes. To predict a patient’s survival within 3 years from the initial visit, we created Transformer13 and long-short-term memory14 (LSTM) models to study longitudinal CT scans and longitudinal clinical information. The joint CNN model and the Transformer models showed the best scores on the validation set. Hereafter, the performance of the joint CNN and Transformer models is reported. The performances of other models can be found in Supplementary Figs. For subtype classification, we first created a deep convolutional neural network (CNN) and a vision TransformerThe Mount Sinai Medical Center Research Registry for Interstitial Lung Disease (MSMC-ILD) was established in 2014. Patients enrolled in MSMC-ILD had a consensus diagnosis from radiology, pathology, and pulmonology. 449 patients with 1822 CT scans were collected between September 2014 and April 2021 from 230 centers in the United States. The patient population age ranged from 22–91 years , with 226 males and 223 females. All chest CT scans were obtained using a standard chest CT protocol and were reconstructed using multiple kernels and displayed with a lung window in axial view. A total of 132 patients (29.4%) were diagnosed with UIP, 37 patients (8.2%) with chronic hypersensitivity pneumonitis (CHP), 142 patients (31.6%) with NSIP, 42 patients (9.4%) with sarcoidosis and 96 patients (21.4%) with other various ILD. Of the 449 patients in the MSMC-ILD, 128 who had their initial scan and pulmonary function test performed at the Mount Sinai Hospital (MSH) were used as an external testing set. The remaining 321 patients were randomly split into a training set  and a validation set .p = 0.78). Home oxygen status, history of rheumatic disease, and history of pulmonary hypertension were significant features of CHP (p = 0.91). Patient’s sex and age, FVC, FEV1/FVC ratio, occupational exposures, former smoking history, and history of rheumatic disease were strongly related to NSIP (p = 0.096). Significant predictors of sarcoidosis were FEV1, DLCO, FVC, occupational exposures, history of rheumatic disease, and history of pulmonary hypertension (p = 0.99). Finally, DLCO, FVC, and history of rheumatic disease were key features that correlated with other ILD (p = 0.31).We performed a logistic regression with each ILD subcategory as the outcome and the clinical variables as the predictors to determine whether there existed a correlation between the type of ILD and the clinical history. Detailed descriptions and distributions of clinical history are reported in Fig. We evaluated the AI models on the unseen external test set. The performance of the joint AI model was compared to seven readers who included a senior thoracic radiologist (STR) with 11 years of experience, two junior thoracic radiologists (JTR1 and JTR2) with 5 years of experience and 4 years of experience, respectively, a thoracic radiology fellow (TRF), two senior general radiologists (SGR1 and SGR2) with specialty in musculoskeletal and 10 years of experience and specialty in pediatric radiology and 15 years of experience respectively, and finally a senior pulmonologist (SP) with 10 years of experience. All readers were provided with the same deidentified lung CT scans and clinical information. The area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity were calculated for each ILD category in our study. The performance and comparison of the AI model and human readers are reported in Fig. p < 0.05) and the SP  in sensitivity.For UIP classification, the joint model combining CT scans and clinical information had the highest sensitivity in comparison to all human readers, though the human readers had higher specificities. The joint model had a sensitivity of 82.4%  65.5%, 93.2%), an 68.1% specificity , and an AUROC of 0.828 . Importantly, the joint model outperformed the STR  and significantly better in specificity as compared to both readers .For CHP classification, the joint model displayed the highest specificity. The joint model achieved a 20.0% sensitivity , a 97.5% specificity , and an AUROC of 0.814 , which was equivalent to the STR and SP in sensitivity , an 85.7% specificity , and an AUROC of 0.701 . The SP showed an equivalent 79.6% sensitivity , but was outperformed in specificity .The joint model was more sensitive and specific to classifying and diagnosing NSIP. The joint model achieved a 65.9% sensitivity , a 90.5% specificity , and an AUROC of 0.849 , which was comparable to the STR who had a 54.6% sensitivity , a 99.1% specificity , and an AUROC of 0.674 . The SP had a 35.7% sensitivity , a 100.0% specificity , and an AUROC of 0.679 .For the classification of sarcoidosis, the joint model and human readers had comparable sensitivities and specificities. The joint model achieved a 42.9% sensitivity , a 94.7% specificity , and an AUROC of 0.788 . The STR had a 35.7% sensitivity , a 71.6% specificity , and an AUROC of 0.627 . The SP had a 23.1% sensitivity , a 95.1% specificity , and an AUROC of 0.591 .While human readers tended to be more sensitive than the joint model in classifying other ILD, the joint model was more specific. The joint model achieved a 19.2% sensitivity , an 89.2% specificity , and an AUROC of 0.740 . The STR had a 53.9% sensitivity , 0.632 , 0.801 , and 0.868  evaluated at the initial visit, within 1 year, 2 years, and 3 years respectively. All models remained high, with negative predictive values ranging from 89.66 to 94.55%. The models became more sensitive when more follow up information was available, increasing in sensitivity from 54.55%  to 72.73%  at the end of year 1 and the end of year 3 respectively.We developed models at four endpoints starting from the initial visit to evaluate the patient’s response after treatment. Four Transformer models were developed using the patient’s initial visit information, and the data within 1 year, 2 years, and 3 years. False negatives were minimized. Only negative predictive value and sensitivity are reported hereafter. More details are demonstrated in Fig. 15. Furthermore, quantifying a patient’s response to treatment and disease progression is a second barrier to clinical care16. Walsh17 et al. developed a deep learning model of 1157 high resolution CT scans to classify UIP and non-UIP. It achieved accuracy 79% in classifying 29 UIP cases. Choe18 et al. created a content-based image retrieval method to classify four subtypes of ILD, UIP, NSIP, COP and CHP based on CT scans of 288 patients and showed that their proposed framework can improve radiologists’ ILD classification accuracy from 52.4% to 72.8%. Both studies only used CT images for algorithm development, while in our study we combine CT images with clinical information together to develop a joint model in order to develop a more comprehensive algorithm to study ILD subtype classification. In addition, we conducted a 3 year survival analysis using longitudinal data of patients to monitor patient’s disease progression. Our present study created a joint CNN model by integrating CT images with clinical information. This model accurately predicted five ILD subtypes and outperformed a senior thoracic radiologist and a senior pulmonologist in diagnosing true cases of UIP . Our joint CNN model also performed as well as all human readers in sensitivity when diagnosing CHP, sarcoidosis, NSIP, and other ILD (p > 0.05). In addition to the diagnostic joint CNN model, we created a Transformer model that can predict a patient’s 3-year survival rate after a visit with high sensitivity and negative predictive value while remaining a reasonably high specificity and positive predictive value.Diagnosing, treating, and managing interstitial lung disease and its subtypes remains a complex clinical challenge, often requiring the expertize of highly specialized physicians, such as thoracic fellowship-trained radiologists, and the synthesis of an array of clinical information. Lack of human resources and limited access to clinicians with specialized expertize in ILD is a worldwide barrier in ILD management19 were used as starting points for CNN. The RadImageNet pre-trained model contained CT features such as pulmonary infiltrates. These features shared high-level similarity to our target ILD data, which further improved the CNN performance on CT images. While ViT showed great potential on large natural image datasets9, the ViT model was outperformed by the CNN model using transfer learning due to the small sample size of images from our ILD dataset , JTRs (p < 0.001), SGRs (p < 0.001), and SP (p < 0.001).The joint CNN model showed superior performance in the classification of ILD subtypes. Pretrained weights from the RadImageNet modelsp > 0.05) to all human readers and outperformed the STR (p < 0.01), JTRs (p < 0.001), TRF (p < 0.05), SGR2 (p < 0.01), and SP (p < 0.001) in specificity. Regarding NSIP prediction, the joint model performed equally well in sensitivity as compared to six readers (p > 0.05) and outperformed SGR1 (p < 0.01). Similarly, it demonstrated higher specificity compared to the JTRs (p < 0.001), SP (p < 0.001), and SGR2 (p < 0.001) and performed comparably to the STR (p = 0.48). Both the joint model and human readers performed equally well in reading sarcoidosis (p > 0.05) in sensitivity and specificity, except that the joint model was outperformed by the SP (p < 0.05) in specificity. For the diagnosis of other ILD, the joint model demonstrated similar performance to six human readers (p > 0.05) and was only outperformed by the STR (p < 0.05); the model was significantly more specific than six human readers (p < 0.01) except for the SP (p = 0.11).For the diagnosis of CHP, the joint model achieved equivalent performance in sensitivity (p = 0.153). This shows that response to treatment may require more than one year. After 2 years of treatment, there is high confidence (95%) in predicting the patient’s survival.To analyze the 3-year survival rate, we developed two-time series models, the LSTM and Transformer; both models consisted of multiple factors including quantitative CT information, clinical history, and medication history within 3 years. The average of the Transformer models achieved 7.5% better performance than the average of LSTM models, and the ensemble Transformer model achieved 15.8% better performance than the ensemble LSTM model. Thus, the Transformer algorithm was applied to train patients’ data within 1 year, 2 years, and 3 years. The confidence of 3-year survival prediction via Transformer was increased with more follow-up information. The AUROC was dramatically improved by 31.5% between the evaluation at the initial visit and the end of year 3. There was no difference between the evaluation at the end of year 2 and year 3 (p < 0.05) and significant improvement in specificity for the diagnosis of CHP (p < 0.05), NSIP (p < 0.05), and other ILD (p < 0.05) as compared to human readers. The second barrier in interstitial lung disease management is disease prognosis and progression. It is important to evaluate treatment efficacy and patient prognosis at each visit so that patients may be counseled about their condition and what to expect. Accurately predicting patient response and prognosis is extremely challenging but has great value by ultimately improving patient outcomes. Our Transformer model can evaluate the 3-year survival rate at each visit by integrating information from each visit. Moreover, the Transformer model demonstrated significant advancement in predicting a 3-year survival rate when current follow-up information was integrated. Our deep learning system has the potential to be integrated into the daily workflow of pulmonologists, rheumatologists, pathologists, and radiologists, where it could serve as a second opinion for a diagnosis of ILD subtypes and dynamically provide personalized insights regarding current and future treatment efficacy using its 3-year survival prediction feature. Installation of the deep learning models would require cloud computing with the integration of PACS and Epic or other clinical databases, which is relatively easy to achieve in most modern healthcare systems.Our study sought to address two major barriers in interstitial lung disease management. Firstly, the diagnosis of ILD subtypes often requires thoracic fellowship-trained radiologists, and specialists with such expertize are scarce. This potentially limits the timely diagnosis and treatment of persons living with ILD. Thus, with widespread implementation of our deep learning system, we hope to alleviate the burden on these highly specialized clinicians while enhancing patient care. The implementation of our deep learning system could provide a useful diagnostic tool for the general radiologists in the community who infrequently encounter interstitial lung disease. Instead of simply reporting these diverse disease processes using broad terms such as “pulmonary fibrosis”, the general radiologist could use this deep learning system to reach a specific diagnosis. Our diagnostic joint model, for example, showed superior sensitivity in identifying UIP  of the Mount Sinai School of Medicine, in accordance with Mount Sinai’s Federal Wide Assurances to the Department of Health and Human Services (ID# STUDY-14-00584-CR001). Written informed consent has been obtained from patients enrolled in this research registry. A Data and Safety Monitoring Board (DSMB) from Mount Sinai IRB had oversight of the study.We collected chest CT scans and clinical information from 458 patients enrolled in the MSMC-ILD between September 2014 and April 2021. Individuals for participation in Mount Sinai Medical Center Research Registry for Interstitial Lung Disease (MSMC-ILD) included all adult (age > 18 years old) patients who were receiving or seeking medical care for the treatment of interstitial lung disease at Mount Sinai Medical Center, St Luke’s and Beth Israel Medical Centers. Patients with lung fibrosis or other interstitial lung disease were enrolled in the MSMC-ILD and assessing the extent of the disease. MSMC-ILD was established in 2014. The diagnosis of an ILD subtype followed the ATS2018 guidelines. All registry patients had a consensus diagnosis from radiology, pathology, and pulmonology. In this study, occupational exposure or other environmental exposure is included as a clinical feature. It is likely that the patient cohort at MSMC might be different from other patient cohorts. For example, patients at MSMC might be influenced by World Trade Center exposure. There were nine patients excluded due to low image quality resulting in a total of 449 patients with both clinical information and CT images that were included in our ILD diagnosis study. The patient population age ranged from 22 to 91 years , with 226 males and 223 females. A total of 132 patients (29.4%) were diagnosed with usual interstitial pneumonia (UIP), 37 patients (8.2%) with chronic hypersensitivity pneumonitis (CHP), 142 patients (31.6%) with nonspecific interstitial pneumonia (NSIP), 42 patients (9.4%) with sarcoidosis and 96 patients (21.4%) with other various ILD. 234 patients were selected for the 3-year survival analysis , World Trade Center exposure, pulmonary function test (PFT) values , presence of pulmonary hypertension based on echocardiography or right heart catheterization, and history/results of lung biopsy. Clinical information was collected from pulmonology visit notes in the Electronic Medical Record and PFT flowcharts. If data was not available within the 6-month time frame, the data entry for that variable was left blank. Incomplete clinical variables were later filled with values from the nearest visit.We also recorded the medications being used at or about the time of the CT to treat the ILD. There were eight types of medicine used for patients, including azathioprine (immunosuppressant), bosentan (cardiovascular), cyclophosphamide (antineoplastics), mycophenolate (immunosuppressant), nintedanib (unclassified), pirfenidone (unclassified), prednisone (hormone), rituximab (unclassified).The dataset was split by patient ID and hospital. For ILD subtype classification, 128 (28.5%) patients with their initial CT scan and clinical information collected at the Mount Sinai Hospital were used as the external test set. The rest of the 321 (71.5%) patients whose initial data were collected at outside hospitals were used for model development, with 258 (57.5%) patients within the training set and 63 (14.0%) patients into the validation set. For the analysis of the 3-year survival rate, a subset of 234 patients meeting the criteria in Supplementary Fig. The predictions of the joint CNN AI model were compared to seven human readers on the test set. Six board-certified and fellowship-trained radiologists and a pulmonologist, as well as one thoracic radiology fellow, were provided with the same initial CT scan and associated clinical information that were used to develop the AI models. A senior thoracic radiologist (A.J.) with 10 years of post graduate experience, two junior thoracic radiologists (M.C. and A.B.) with 5-years post graduate experience, a thoracic radiology fellow (A.S.), two senior radiologists (M.H. and J.M.) with 10 years of experience in non thoracic radiology specialties, and a senior pulmonologist (S.D.) with 10 years experience each reviewed the 128 studies and associated clinical information from the test set. Their predictions were compared to the predictions of the joint deep learning model and the consensus diagnosis.19 and ViT model (model 2) based on CT images were developed. Second, machine learning models (model 3), including MLP, SVM, and XGBoost, were generated based on the clinical information. Finally, a joint CNN model (model 4) and a joint ViT model (model 5) were developed which integrated both the imaging and clinical data.The consensus diagnosis of UIP, CHP, NSIP, sarcoidosis, and other various ILD was used as the gold standard to develop the AI models in ILD subcategory classification. We created five models using image data and clinical information. First, a CNN model (model 1) using pre-trained weights from the RadImageNetWe used the same optimization strategies for all classification AI models by employing the Adam optimizer with a learning rate of 0.001 and weight decay of 0.0001, except model 4 used a learning rate of 0.0001. Each model was trained with 40 epochs. We used categorical cross-entropy as the objective function.14 and Transformer13, respectively.To predict patients’ 3-year survival rate longitudinal radiological and clinical data were used to create time series models based on each time point  with LSTMWe used the same optimization strategies for all longitudinal AI models by employing the Adam optimizer of learning rate of 0.0001 and weight decay of 0.0001. Both LSTM and the Transformer were developed in two different parameter settings. For each parameter setting, we repeated the simulation 30 times. Each simulation was trained with 100 epochs with a batch size of 64. We used categorical cross-entropy as the objective function. The top two models from each parameter setting with the best performance on the validation dataset were selected for the ensemble model. A total of four models through averaging probability for each patient were then calculated for their performance on the test dataset. The details of parameter settings were reported in later sections.The following clinical data were collected: patients’ sex, age, lung function lab test results , smoking history, occupational exposure, rheumatic disease, hypertension, lung biopsy, and the use of home oxygen. CT imaging data were collected from the study DICOM header. For missing data, we added an unknown class to each categorical variable. The LabelEncoder function in the scikit-learn package was utilized to encode these categorical data into numerical variables. The StandardScaler function in the scikit-learn package was used to normalize each feature to unit variance with the mean set as 0.First, all CT scans were resampled to an isotropic voxel. Next, we generated lung regions for each image in each study. This was achieved by applying a threshold of -400HU to each CT slice to effectively convert the CT image into a binary image consisting of two densities—air and not air. The “not air” periphery of the binary image was removed, and the two largest “air” regions were kept. The binary mask was then used on the input raw CT image to separate the lung regions. After lung segmentation, a standard lung window (width = 1500HU and level = -400HU) was used to normalize pixel intensities between 0 and 255 for each segmented lung CT slice. GE Centricity Universal Viewer 6.0 was used to review the CT studies. Preprocessed images were used to develop CT-based models in Tensorflow (2.4.0).21. We froze all layers from the pre-trained model and only trained the top10 layers that incorporated high-level features. An average pooling layer and the last dense classifier layers were followed by the last convolutional layer. Using a RadImageNet pre-trained model provided a better starting point than an ImageNet model as the RadImageNet database contains CT lung images and therefore shares higher similarity with the target data.We designed a CT-based convolutional neural network model to diagnose ILD using the CT images. This CT-based CNN model was built via transfer learning using pre-trained weights from a RadImageNet convolutional neural network Inception-ResNet-V2 (IRV2)9. Our ViT model first split the input image into 10 patches and encoded each embedded patch into a self-attention based deep neural network. Then, two fully connected layers with 2048 and 1024 nodes and the final prediction layer were followed by the encoded embedding layers.We trained a CT-based vision Transformer model. ViT model was developed to transfer the success of the self-attention mechanism on NLP tasks into imaging applicationsTo classify ILD subtypes based on clinical information, we applied MLP, SVM, and XGBoost classifiers to build machine learning models. We evaluated the performance of these three classifiers on the validation dataset  was used to preprocess and develop the models.We developed a Transformer time series model to study the temporal information from the time series data of patients’ clinical information and CT image features. The Transformer time series model was developed by stacking 16 Transformer encoders together to evaluate data at each time point. The time-series data were processed via the Transformer encoders and then followed by an average pooling layer and a fully connected layer with 128 nodes. We fine-tuned the hyperparameters of the Transformer model on the training and validation dataset and evaluated the best model on the test dataset. We assessed the number of heads in the Transformer encoder.14. The LSTM time series model was developed to predict living status based on patients’ clinical information and CT image features over time. The time-series input was first passed through two layers of LSTM, which computes the corresponding sequence of input data at different time states and then outputs a sequence of hidden state vectors in forward and reverse directions. Then, the features extracted from LSTM were followed by three fully connected layers and one final classifier layer. We fine-tuned the hyperparameters of the LSTM model on the training and validation dataset and evaluated the best model on the test dataset. We assessed the number of LSTM layers.LSTM is an improved form of a Recurrent Neural Network, designed to solve the problem of vanishing long-term gradients25 in R. A total of 2000 bootstrap permutations were simulated to calculate 95% CIs and p-values. The 95%CIs of sensitivity and specificity for AI models and human readers were calculated by the exact Clopper-Pearson method26. McNemar’s test27 was used to compare sensitivity and specificity. Generalized score statistic test28 was used to calculate p values for negative predictive values and positive predictive values. Two-sided p values were assessed for all statistical analyses, and p-value < 0.05 was defined as statistical significance. We performed logistic regression to evaluate the correlations between clinical variables and each ILD subcategory. The Hosmer–Lemeshow test29 confirmed the goodness of logistic regression. McNemar’s and the generalized score statistic tests were performed in the DTComPair30 package (version 1.0.3) in R 4.1.3.Comparisons of AUROCs were performed by bootstrap in the pROC package (version 1.18.0)Further information on research design is available in the Supplementary InformationPeer review fileReporting Summary


The FIDELIO-DKD and FIGARO-DKD randomized clinical trials (RCTs) showed finerenone, a novel non-steroidal mineralocorticoid receptor antagonist (MRA), reduced the risk of renal and cardiovascular events in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). Using RCT inclusion and exclusion criteria, we analyzed the RCT coverage for patients with T2DM and CKD in routine clinical practice in Germany.2OR eGFR ≥ 60 mL/min/1.73m2 and albuminuria [≥ 30 mg/g]) were included. RCT inclusion and exclusion criteria were then applied, and the characteristics of the two populations compared.German patients from the DPV/DIVE registries who were ≥ 18 years, had T2DM and CKD  Registry patients with CKD were older, less often male, and had a lower eGFR, but more were normoalbuminuric vs the RCTs. Cardiovascular disease burden was higher in the RCTs; diabetic neuropathy, lipid metabolism disorders, and peripheral arterial disease were more frequent in the registry. CKD-specific drugs  were used less often in clinical practice; (2) Due to the RCT’s albuminuric G1/2 to G4 CKD focus, they did not cover 28,147 (43.2%) normoalbuminuric registry patients, 4,519 (6.9%) albuminuric patients with eGFR < 25, and 6,565 (10.1%) patients with microalbuminuria but normal GFR (≥ 90 ml/min); 3) As RCTs required baseline ACEi or ARB treatment, the number of comparable registry patients was reduced to 28,359. Of these, only 12,322 (43.5%) registry patients fulfilled all trial inclusion and exclusion criteria. Registry patients that would have been eligible for the RCTs were more often male, had higher eGFR values, higher rates of albuminuria, more received metformin, and more SGLT-2 inhibitors than patients that would not be eligible.Certain patient subgroups, especially non-albuminuric CKD-patients, were not included in the RCTs. Although recommended by guidelines, there was an undertreatment of CKD-patients with renin-angiotensin system (RAS) blockers. Further research into patients with normoalbuminuric CKD and a wider prescription of RAS blocking agents for CKD patients in clinical practice appears warranted.The online version contains supplementary material available at 10.1186/s12933-023-01840-5. The prevalence of chronic kidney disease (CKD) has increased in recent decades alongside an increase in diabetes and hypertension, the main drivers of CKD . Kidney Renin-angiotensin system (RAS) blockers, such as angiotensin converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs), in the context of a multifactorial risk minimization strategy have been tested for their ability to revert CKD progression or at least prevent its further deterioration and were included in guidelines as a standard therapy for diabetic patients with CKD –12. FurtOne of the potential reasons for the residual renal deterioration is an increase in aldosterone levels which are elevated in up to 50% of patients treated with RAS blockers within a year of initiating treatment, leading to increases in albuminuria and impairment of kidney function . In lineA)Differences between the CKD population documented in DPV/DIVE and the population that was part of the combined finerenone clinical trials (FIDELITY).B)The coverage of the FIDELITY CKD population focusing on albuminuric patients compared with CKD patients included in the registry (DPV and DIVE).C)The impact of the baseline requirement of the prescription of ACEi or ARBs for the study population to be in line with guideline-recommendations AND the coverage of the FIDELITY population for patients with CKD within DPV/DIVE when all the CKD criteria, the requirement for ACEi/ARB treatment at baseline, and all the further inclusion and exclusion criteria were applied.We were interested in the coverage of these two clinical trials for T2DM patients with CKD in Germany. As we recently published data on the prevalence of CKD  and the This analysis used combined data from the DPV and DIVE registries –26. In sThe DIVE registry was established in 2011 . ConsecuA total of 216 specialized diabetes-centers from Germany were included in the present analysis. Patients were sampled in March 2022 and included in the current analysis if they had T2DM, were at least 18 years old, initially registered from 2015 to 2021, and had a clinical diagnosis of CKD , 29.For the current analysis, data regarding age, gender, body mass index (BMI), blood pressure, renal parameters, antidiabetic and antihypertensive drug treatment, and current comorbidities were considered. Data for the most recent treatment year per patient were aggregated and analyzed. For retinopathy, the two most recent treatment years were considered. eGFR was calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula .2OR eGFR ≥ 60 mL/min/1.73m2 and albuminuria (≥ 30 mg/g) . Table S2. Determinants of cardiovascular risk [31]. Table S3. Renal disease—perpetuating factors* [32].


Diabetes can cause hippocampal damage and lead to cognitive impairment. Diabetic cognitive impairment (DCI) is a chronic complication of diabetes associated with a high disability rate; however, its pathogenesis and therapeutic targets are unclear. We aimed to explore the mechanism of hippocampal damage during diabetes and evaluate the potential role of D-pinitol (DP) in protecting hippocampal tissue and improving cognitive dysfunction.DP (150 mg/kg/day) was administered intragastrically to streptozocin-induced aging-accelerated mice for 8 weeks. Hippocampal tissues were examined using tandem mass tag (TMT)-based proteomics and liquid chromatography-mass spectrometry (LC–MS)/MS-based non-targeted metabolomic analysis. Differentially expressed proteins (DEPs) and differentially regulated metabolites (DRMs) were screened for further analysis, and some DEPs were verified using western blotting.Our results showed that 329 proteins had significantly altered hippocampal expression in untreated diabetic mice (DM), which was restored to normal after DP treatment in 72 cases. In total, 207 DRMs were identified in the DM group, and the expression of 32 DRMs was restored to normal post-DP treatment. These proteins and metabolites are involved in metabolic pathways , actin cytoskeleton regulation, oxidative phosphorylation, and Rap1-mediated signaling.Our study may help to better understand the mechanism of diabetic hippocampal damage and cognitive impairment and suggest a potential therapeutic target. There iThe hippocampus, located in the cerebral cortex, is a brain region responsible for learning and memory formation and storage over a long time. At present, many pathogenetic mechanisms underlying hippocampal damage and DCI have been discovered, including insulin resistance, neuroinflammation, oxidative stress, advanced glycation end products, mitochondrial dysfunction, activation of hippocampal neuronal apoptosis, and ferroptosis signaling pathways . HoweverProteomics and metabolomics have been widely used in neuropsychology. Tandem mass tag (TMT) proteomics has shown increasing value for high-throughput and high-precision protein detection and analysis . Therefo2.2.1.DP  and STZ were purchased from Sigma–Aldrich . Acetonitrile and methyl alcohol were purchased from Millipore . TMT 10 plex kit was purchased from Thermo Fisher Scientific . The antibodies for secernin, copine-6, septin-5, profilin 2, and β-actin were all purchased from Proteintech . All other chemical reagents used were of analytical grade.2.2.n = 10), SAMP8 group , STZ-induced SAMP8 group , and DP-treated STZ-induced SAMP8 group .Detailed information on the animal model is provided in the 2.3.Mice were weighed at the end of 8 weeks, and FBG levels were measured using a DVI-1650 Automatic Biochemistry and Analysis instrument . The Morris water maze test (MWM) apparatus was a round pool with a diameter of 120 cm, height of 60 cm, and water depth of 15 cm, and a circular platform with a diameter of 6 cm was placed in the fourth quadrant; the water was added to the pool until the water surface reached 1 cm above the platform, while the temperature was maintained at 21.5°C ± 0.5°C. MWM tests were performed as previously described in previous studies . BrieflyThe step-down test, a passive avoidance response test, was used in this study. The apparatus consisted of a model YLS-3 TB step-down recorder and five mouse step-down compartments of equal size . An insulated high platform was provided at the center of the compartment, and the bottom was paved with an electric copper mesh (3 mA). The experimental procedures were performed as previously described . The ani2.4.The hippocampal tissues of three mice in each group  were fixed with 4% formaldehyde, embedded in paraffin, and sectioned into coronal sections with a thickness of 3 μm. Then, HE and Nissl’s staining were applied.2.5.p < 0.05 at the same time. The proteomic data are available from the Proteome Xchange Consortium (PXD043127).Hippocampal tissues of nine mice in each group  were selected for protein extraction. Three samples were pooled from each group. Briefly, the process of proteomics analysis is as follows: The hippocampal tissue was lysed using the SDT method, followed by quantification using the BCA method. According to the FASP procedure , 300 μg 2.6.p < 0.05 and FC > 1.5 or < 0.67).The hippocampal tissues of nine mice from each group  were selected for metabolite extraction. Briefly, the process of metabolomics analysis is as follows: Samples were weighed and ground using a grinder. Metabolites were extracted using a pre-cooled mixture of methanol, acetonitrile, and water , followed by 1 h of ultrasonic shaking in ice baths. Quality control (QC) samples were prepared by pooling representative aliquots of all samples and used for data normalization. The sample was separated by SHIMADZU-LC30 ultra-high-performance liquid chromatography (UHPLC) and HILIC column. Positive ion (+) and negative ion (−) patterns were detected by electrospray ionization (ESI) in each sample. After UPLC separation, the samples were analyzed by QEPlus mass spectrometer (Thermo Scientific) and ionized by HESI source. The original data were calibrated by MSDIAL software for peak alignment, retention time correction, and peak area extraction. Public databases such as HMDB and MassBank database were searched. Normalizing the total peak area of the positive and negative ion data, integrating the positive and negative ion peaks, and using R software for pattern recognition, the data were preprocessed by unit variance scaling (UV). Multivariate statistical analysis: R (version 4.0.3) was used for all multivariate data analyses and modeling. Data were mean-centered using Pareto scaling. Models were built on principal component analysis (PCA), orthogonal partial least-square discriminant analysis (PLS-DA), and partial least-square discriminant analysis (OPLS-DA). The selection of differential metabolites had both multivariate statistical analysis (VIP > 1) and univariate statistical analysis  and then transferred to polyvinylidene fluoride (PVDF) membranes. The membranes were incubated at room temperature in a closed solution containing 5% skimmed dried milk for 1 h and then incubated with antibodies against secernin, copine-6, septin-5, profilin 2, and β-actin at 4°C overnight. The membranes were washed with TBS-T, incubated with an enzyme-conjugated secondary antibody, and visualized using an enhanced chemiluminescence kit (Thermo Pierce). β-actin was used as an internal reference. Finally, the images were exposed to ECL luminescence and X-ray film  and analyzed using ImageJ 1.8 software.2.9.t-test was used to compare the two groups. The three groups of sample components were compared using a single-factor analysis of variance (ANOVA). The obtained LC–MS/MS raw files were searched using the Sequest HT engine embedded into Proteome Discoverer software . Peptide false discovery rate FDR ≤0.01 GO and KEGG enrichment analyses were performed using Fisher’s exact test, and FDR correction for multiple testing was also performed. The significance level was set to p ≤ 0.05. The calculations were carried out using IBM SPSS 26.0 .The data are expressed as the mean ± standard deviation or standard error. The Student’s 3.3.1.p < 0.01). DP treatment resulted in a significant increase in body weight and decrease in FBG levels in STZ-induced SAMP8 mice . Compared with the DM group, the number of errors decreased and the latent period increased in the DP group . On the 4th day of training, the escape latency of the DP group was shorter than that of the DM group (p < 0.01). During the space exploration period, the number of times the platform was crossed in the DM group was significantly lower than that in the CC group (p < 0.01).The positioning voyage period of MWM is shown in CC group . The num3.2.As shown in 3.3.p < 0.05) were selected for further screening of DEPs. In this screening, 329 DEPs were identified in the DM group compared to the CC group, including 182 downregulated and 147 upregulated proteins . These proteins  and orthogonal partial least square discriminant analysis (OPLS-DA). PCA performed on all samples revealed that the QC samples were tightly cluster together in the PCA score plots , indicat3.6.p < 0.05, and FC > 1.5 or < 0.67, we identified 207 differentially regulated metabolites (DRMs) (108 up-regulated and 99 downregulated) in the DM and CC groups (Metabolic profiling was performed using UPLC-Q-Exactive MS (LC–MS/MS). In total, 832 metabolites were identified. Using a variable importance in projection (VIP) score > 1, C groups , and 199C groups . Of thesabolites RMs , oxidative phosphorylation (n = 10), Alzheimer’s disease (n = 9), protein processing in the endoplasmic reticulum (n = 6), PI3K-Akt signaling pathway (n = 6), MAPK signaling pathway (n = 5), and GABAergic synapses (n = 5) in the DM group. However, in the DP group, these subnetworks were related to metabolic pathways (n = 7), the spliceosome (n = 4), the Rap1 signaling pathway (n = 4), regulation of the actin cytoskeleton (n = 3), and oxidative phosphorylation (n = 3).PPI analysis revealed the protein interaction network of the DEPs in the CC, DM, and DP groups –D. Hsp903.9.Profilin 2, copine-6, septin-5, and secernin-4 were selected for WB validation studies based on the significance of the differences, relationship with the nervous system, and availability of relevant antibodies. Western blot analysis  showed t3.10.p < 0.001). Network analysis showed that the pathways mainly included carbon metabolism , purine metabolism, biosynthesis of amino acids  was significantly correlated with the expression of 78 proteins between the DM and CC groups . Network analysis showed that the pathways mainly included metabolic pathways , regulation of the actin cytoskeleton, oxidative phosphorylation, Rap1 signaling pathway, and others  belongs to a group of cytoplasmic proteins that bind phosphatidylserine at micromolar levels of calcium and promote lipid vesicle aggregation . The CPNormation . Presumaormation . In the ormation . It has ormation . When CPormation . Spine dormation . It may ormation . Since tormation . The cytormation . Howeverormation . In the Veratrum alkaloids can cause DNA damage in the cerebellum and cerebral cortex of mice via oxidative stress (in vivo and in vitro study has shown that combination treatment with melatonin and sitagliptin is superior to monotherapies in treating type 2 diabetes (T2D)  . A recenes (T2D) . In our In this study, syringic acid (SA) was downregulated in the DM group and upregulated by DP treatment. SA is a natural phenolic compound derived from plants . SeveralFinally, in order to further explore the pathogenesis of diabetes-induced hippocampal injury and the mechanism of DP ameliorated diabetes-induced hippocampal injury, we applied TMT-based proteomics and LC–MS-based metabolomics to comprehensively analyze hippocampal damage induced by diabetes and the protective effect of DP on the diabetic hippocampus. Correlation analysis of DEPs-DRMs showed that NADH may be an essential metabolite in the pathogenesis of DCI. NADH is one of the primary molecules of energy transfer in the mitochondrial respiratory chain and is a vital parameter of cellular energy metabolism. The research on NADH and central nervous system diseases is increasing. One study reported that injection of neurotoxic 5, 7-dihydroxytryptamine into the dorsal raphe nucleus in the brainstem resulted in a 50% decrease in NADH fluorescence in the injection area, indicating no recovery of metabolic activity . It has Correlation analysis of DEPs-DRMs showed that melatonin may be a critical target for DP to alleviate DCI. Abnormal melatonin secretion affects blood glucose levels  and circ5.In this study, we applied TMT-based proteomics and LC–MS-based metabolomics to comprehensively analyze hippocampal damage induced by diabetes and the protective effect of DP on the diabetic hippocampus. We found that NADH may be an essential metabolite in the pathogenesis of DCI. Energy metabolism imbalance was the leading cause of diabetes-induced hippocampal damage, including carbon metabolism, purine metabolism, biosynthesis of amino acids, fatty acid degradation and cell death (necroptosis and ferroptosis). Melatonin may be one of the targets of DP in improving diabetic cognitive impairment. DP alleviated diabetes-induced hippocampal damage by rebalancing metabolic pathways , actin cytoskeleton regulation, oxidative phosphorylation, long-term potentiation, and the Rap1 signaling pathway. Our study contributes to an enhanced understanding of the mechanism behind diabetes-induced hippocampal damage and cognitive impairment and provides a potential target for the protective effects of DP.Although our research obtained certain results, there are also some deficiencies. First, the total number of included samples was relatively small, which may have affected the interpretation of results. Second, only four differential proteins were verified by WB; the proportion of verified proteins was relatively low, so there may be some omissions.The original contributions presented in the study are included in the article/The animal study was approved by the Animal Protection and Use Committee of Shandong University. The study was conducted in accordance with the local legislation and institutional requirements.XXL: writing original draft, methodology, obtaining data, bioinformatics analysis of proteomics and metabolomics, and review and editing. YG: writing original draft and methodology. BL: editing-original draft, data analysis, and review and editing. WZ: performed experiments and data analysis. QC, WY, and SZ: data analysis. XLL: review manuscript and design. HG and MC: review manuscript, design, methodology, funding acquisition, and management. All authors read and approved the final manuscript.


Litopenaeus vannamei from the perspective of growth and nonspecific immunity. Five diets were formulated to contain different sterol sources and levels. Two diets were supplemented with 1 g/kg cholesterol (LC (low cholesterol)) or phytosterol (LP (low phytosterol)). Other three experimental diets were supplemented with 2 g/kg cholesterol (HC (high cholesterol)), 2 g/kg phytosterol (HP (high phytosterol)), or mixed sterol source , respectively. A total of 750 healthy and uniform-sized shrimp (0.52 ± 0.008 g) were randomly distributed into 5 groups with 3 replicates and fed with the five experimental diets for 60 days. Results showed that the growth performance of shrimp was influenced by the sterol levels and supplementation with 2 g/kg sterol level facilitated the growth of shrimp. The inclusion of phytosterol has a cholesterol-lowering effect on shrimp, as evidenced by a reduction in hemolymph cholesterol and triglyceride contents in the HP group. Besides, supplementation with 2 g/kg phytosterol or mixed sterol sources had positive effects on the hemolymph superoxide dismutase, phenol oxidase, and lysozyme as well as hepatopancreas alkaline phosphatase activities, demonstrating that the nonspecific immunity and antioxidative capacity were improved. In conclusion, phytosterols could be an appropriate alternative to partially replace dietary cholesterol in shrimp feeds. This study preliminarily revealed the effects of different sterol sources and levels on the growth and nonspecific immunity of shrimp and provided a basis for further exploration of the mechanism of phytosterol.This study is aimed at evaluating the effectiveness of phytosterols as an alternative to cholesterol in practical diets of Pacific white shrimp  However, although crustaceans are considered a rich source of cholesterol, it is generally accepted that shrimp or other crustaceans cannot synthesize cholesterol de novo due to the lack of squalene monooxygenase and lanosterol synthase [Cholesterol, as one of the essential lipid sources for animals, together with phospholipids and fatty acids, forms the integral component of cell membranes . Also, csynthase . Therefoβ-sitosterol, campesterol, and stigmasterol [Litopenaeus vannamei feed [Litopenaeus vannamei [Litopenaeus vannamei [Larimichthys crocea) by activating the activities of catalase and superoxide dismutase [β-defensin, hepcidin antimicrobial peptide 1, interleukin-10, and interleukin-18 of gilthead seabream (Sparus aurata) after the 48 d of feeding trial [As a potential alternative for cholesterol, phytosterols are plant-based sterols and mainly constitute masterol . It has mei feed . In a prvannamei . It is avannamei . Generalvannamei . On the vannamei , 11. Accismutase . Furtherng trial , which iLitopenaeus vannamei, gaining popularity due to its fast growth rate, high survival, unique taste, and abundance of nutrients, has been employed in this study. With the increasing constituent of plant-based ingredients in practical diets such as soybean meal and soybean oil, farmed shrimp may grow slowly due to a lack of cholesterol [Litopenaeus vannamei are still limited. Since shrimp rely mainly on innate immunity rather than adaptive immunity, dietary phytosterols may have different effects on the immune response of shrimp. Therefore, a feeding trial was conducted to demonstrate the effectiveness of phytosterols in practical diets of shrimp by the comparison of different sterols sources and levels. This study sought to preliminarily explore the effect of phytosterol on the growth and nonspecific immunity of shrimp.The objective of this study was to preliminarily evaluate the effect on growth and nonspecific immunity of shrimp after different sterol sources and levels were supplemented in diets. The Pacific white shrimp, lesterol . In addiβ-sitosterol) was provided by Guangdong Wei Lai Biotechnology Co., Ltd. . The main protein sources were composed of meat and bone meal, fishmeal, and soy protein concentrate. The lipid sources were made with fish oil, soybean oil, and soybean lecithin. Based on a previous study, the control diet was formulated to contain 1 g/kg cholesterol (LC (low cholesterol)) [The cholesterol was purchased from Sigma-Aldrich (USA) and the phytosterol ) . The 1.5Juvenile shrimp were obtained from Doumen . The shrimp were fed commercial feed and acclimatized to the experimental conditions before being used. During shrimp distribution, the 750 uniform-sized and healthy shrimp  were divided into 5 groups in triplicate, with 50 shrimp in a 500 L fiberglass tank. Normally, shrimp were fed three times a day at 8 : 30, 13 : 00, and 18 : 00, with a total feeding rate of 3%-8% of shrimp weight per day . In caseAt the end of the feeding trial, shrimp were starved for 24 h to obtain basal metabolic profiles. The shrimp in each tank were counted and weighted to calculate the growth indicators. Eight shrimp from each tank were collected for whole-body composition analysis. The hemolymph was collected from 15 shrimp per tank using a 1 mL sterile syringe and then placed at 4°C for 1 h . After cN × 6.25). Crude lipid was detected by an XT15 extractor  according to the standard of GB/T 6433-2006 in China (weight reduction of feed after extraction by petroleum ether). Ash was detected by burning at 550°C according to the standard of GB/T 6438-2007 in China (weight reduction of feed after fully burning) [The moisture of diets and shrimp was determined by oven drying at 105°C according to the standard of GB/T 6435-2014 in China (weight reduction of feed after drying). Crude protein was detected by Primacs100 analyzer  as the standard of GB/T 18868-2002 in China  . The detThe hemolymph biochemical indicators include triglyceride , total cholesterol , low-density lipoprotein cholesterol , high-density lipoprotein cholesterol , and malonaldehyde  were determined . The actThe activities of aspartate aminotransferase , alanine transaminase , and alkaline phosphatase  were determined . The hept is the experimental duration in days.The growth performance was calculated as follows :(1)WeigP < 0.05), Duncan's test was used to compare the mean values between individual treatments. Two-way ANOVA followed by Student-Newman-Keuls (SNK) multiple comparison test was conducted to investigate the interaction effects of sterol sources and levels. A probability value of P < 0.05 was deemed to be statistically significant.The results were presented as the means with SEM. The data were compared between treatments by one-way analysis of variance (one-way ANOVA) using the SPSS 21.0 statistical software . When overall differences were significant (P < 0.05). There were no significant differences in growth performance indicators in shrimp fed with the HP diet compared to those fed with the HC and CP diets (P > 0.05). Also, the feed conversion ratio (FCR) significantly decreased after supplementation of the feed with 2 g/kg sterol sources in the HC, HP, and CP groups (P < 0.05). Survival was not correlated with either sterol source or level and did not differ significantly among the five groups (P > 0.05).As shown in P < 0.05). The type of sterol sources had a significant effect on the crude lipid content, with a decline of which in LP and HP groups compared to the other three groups (P < 0.05 in the two-way ANOVA). The ash content of whole shrimp also increased when supplemented with higher dietary sterol levels, with the ash of shrimp fed the HC, HP, and CP diets significantly increased compared to those fed the LC and LP diets (P < 0.05). Neither the sterol source nor level had significant effects on the condition factor and flesh content of shrimp (P > 0.05).As shown in P < 0.05), while those in shrimp supplemented with dietary phytosterol showed a trend of reduction compared to the LC and HC groups. The sterol source had significant effects on hemolymph high-density lipoprotein cholesterol (HDL-C) content and malonaldehyde (MDA), with the former increased and the latter decreased in shrimp supplemented with dietary phytosterol  (P < 0.05). Neither the sterol source nor the level had significant effects on the hemolymph low-density lipoprotein cholesterol (LDL-C) content of shrimp (P > 0.05).The results of the hemolymph biochemical parameters of shrimp were shown in P < 0.05). Both sterol sources and levels, as well as their interactions, had significant effects on the hemolymph phenol oxidase (PO) activity, which was boosted by dietary phytosterol and a higher level of sterol inclusion (P < 0.05). In hepatopancreas, the alkaline phosphatase (AKP) activity was shown to significantly increase in shrimp supplemented with 2 g/kg sterol level (P < 0.05). The interaction of sterol sources and levels had an impact on the aspartate aminotransferase (AST) activity, which significantly increased in shrimp fed the HC and LC diets compared to the other three groups (P < 0.05).The results of the enzyme activities related to nonspecific immunity and health are shown in P < 0.05). It was also noted that the lipase activity was significantly higher in shrimp fed with the HC diet than that fed the HP and CP diets (P < 0.05). The amylase activity was not affected by the sterol source and level factors in this study (P > 0.05).As shown in β-sitosterol and have an extra hydrophobic carbon chain at the C-22 or C-24 position compared to cholesterol [in vivo but the detailed biochemical process and bioavailability efficiency remains unknown [Larimichthys crocea), Atlantic salmon , European sea bass (Dicentrarchus labrax), and gilthead sea bream (Sparus aurata) [Megalobrama amblycephala), Tilapia (Oreochromis niloticus), and largemouth black bass  [For crustaceans, dietary cholesterol is essential for the synthesis of ecdysteroids and sesquiterpenoids, which regulate life activities including molting, growth, and reproduction . Howeverlesterol . Similarlesterol . Previou unknown , 7. Also unknown , 27–29.  aurata) , 30–32 almoides) –36. Althα) and hepatic fatty acid synthetase (FAS) [Litopenaeus vannamei and how this effect affects the lipid metabolic processes in shrimp still needs further study.Since phytosterols have the function of promoting sterol metabolism, they have predictable potential effects on lipid metabolism and nonspecific immunity in shrimp , 37. In se (FAS) , 40, 41.Litopenaeus vannamei deserves to be studied, as relevant studies are still missing. Different from vertebrates, shrimp fights against pathogens by innate immunity, in which prophenoloxidase, alkaline phosphatase (AKP), and lysozyme (LZM) play a remarkable role [In addition to growth and metabolism, the effect of dietary phytosterols on the nonspecific immunity of ble role , 43. Theble role , 45. Theble role , 47. It ble role –50. In tble role . Furtherble role . In someble role , 32, sugble role . Usuallyble role . In the ble role . A previble role , 56. Notble role . In an eble role . This mable role , 60. In Litopenaeus vannamei. Mixed sterol sources could improve the growth and nonspecific immunity of shrimp. This study revealed the effects of different sterol sources and levels on the growth and nonspecific immunity of shrimp and provided a basis for further exploration of the mechanism of phytosterol.The supplementation of 2 g/kg phytosterols in the diet did not affect the growth of shrimp but resulted in a cholesterol-lowering effect in


Prenatal exposure to air pollution is associated with adverse neurologic consequences in childhood. However, the relationship between in utero exposure to air pollution and neonatal brain development is unclear.2) and particulate matter (PM2.5 and PM10) at postcode level between date of conception to date of birth and studied the effect of prenatal air pollution exposure on neonatal brain morphology in 469  healthy neonates, with gestational age of ≥36 weeks. Infants underwent MR neuroimaging at 3 Tesla at 41.29 (36.71–45.14) weeks post-menstrual age (PMA) as part of the developing human connectome project (dHCP). Single pollutant linear regression and canonical correlation analysis (CCA) were performed to assess the relationship between air pollution and brain morphology, adjusting for confounders and correcting for false discovery rate.We modelled maternal exposure to nitrogen dioxide (NO10 and lower exposure to NO2 was strongly canonically correlated to a larger relative ventricular volume, and moderately associated with larger relative size of the cerebellum. Modest associations were detected with higher exposure to PM10 and lower exposure to NO2 and smaller relative cortical grey matter and amygdala and hippocampus, and larger relaive brainstem and extracerebral CSF volume. No associations were found with white matter or deep grey nuclei volume.Higher exposure to PM2 and PM10. This finding provides further evidence that reducing levels of maternal exposure to particulate matter during pregnancy should be a public health priority and highlights the importance of understanding the impacts of air pollution on this critical development window.Our findings show that prenatal exposure to air pollution is associated with altered brain morphometry in the neonatal period, albeit with opposing results for NO Higher Particulate matter (PM) and traffic-related air pollution (TRAP) exposure can lead to microglial activation, inflammation, mitochondrial dysfunction, and oxidative stress in the brain . Similar2.5 or ultrafine particles was associated with reduced corpus callosum volume  volumes , which have previously been linked to altered brain development in older children.In this study we aimed to examine the relationship between in utero exposure to air pollutants and neonatal brain development using brain MRI data acquired shortly after birth. We hypothesised that prenatal exposure to air pollution is associated with altered neonatal brain morphology. We assessed the impact of prenatal exposure to three ubiquitous air pollutants, PM22.1http://www.developingconnectome.org/), a cohort recruited in Greater London, UK. Research Ethics Committee approval was granted for this project (14/LO/1169) and written informed consent was obtained from parents. The principal inclusion criteria of the neonatal dHCP were infants born between 23- and 44-weeks gestational age, estimated from the mothers last menstrual period and confirmed where possible by early ultrasound scanning (https://www.developingconnectome.org/study-inclusion-and-exclusion-criteria/). Infants were recruited at St Thomas’ Hospital, London and imaged at the Evelina Newborn Imaging Centre, Centre for the Developing Brain, King’s College London, United Kingdom. The subjects in this study were born between the years 2015 to 2020 and were scanned in the neonatal period. 782 infants were included in the dHCP project at the time of this study. Infants were excluded if their mothers smoked during pregnancy (n = 16), gestational age (GA) at birth was 36 weeks or less (n = 94), or 44 weeks or more (n = 0), or there was evidence of major focal lesions on MRI (n = 29). In cases of multiple pregnancies (n = 46), only one infant of the twin pair was included. In addition, 128 infants were excluded as the mother’s residential address was outside of the air pollution model domain. The final sample consisted of 469 infants. Index of multiple deprivation (IMD) was calculated from postcode at birth for all infants using an online tool (https://tools.npeu.ox.ac.uk/imd/). IMD is a composite measure of socioeconomic status in England encompassing factors such as income, employment, education, health and crime and will be expressed in quintiles throughout this paper .This study is based on a sample of neonates participating in the Developing Human Connectome Project . All infants were scanned during natural sleep without sedation using a dedicated protocol as previously described by our group , includihttps://www.developingconnectome.org/; (3). All images were reviewed by a perinatal neuroradiologist.T2-weighted, T1-weighted, and diffusion-weighted MR images were acquired using the dHCP protocol optimized for neonatal scanning  me.org/; ). T2-wei2.33 isotropic T2-weighted images  using the method described in Whitehouse et al . All volumes were adjusted for intracranial volume (ICV) to remove the effect of individual differences in head size and will be referred to as “relative” volumes throughout this paper. The relationship between prenatal pollution exposure and brain morphology was first explored using single pollutant linear regression analysis, adjusting for postmenstrual age (PMA) at scan, gestational age (GA) at birth, sex, and IMD. Results were false discovery rate corrected using Benjamini-Hochberg false discovery rate method  . CCA isn 1.2.1, .r was greater than the 95th percentile of random permutation tests .33.1The study sample comprised 469 infants  born at a median (range) GA of 40.14 (36.14–43.57) weeks and imaged at 41.29 (36.71–45.14) weeks PMA . The med3.22 and PM10 were non-normally distributed, summary statistics are therefore presented as median and range. A map of annual mean PM10 and NO2 concentrations across Greater London in 2016 is included in the supplement  to illustrate spatial exposure gradients.Descriptive statistics of subjects’ pollution exposure are detailed below . NO2 and3.3Descriptive statistics of subject’s brain volumes are detailed below . Total b3.3.13.43.4.1CCA of exposure to air pollution over the entire gestational period identified three modes. Only mode 1 exceeded the 95th percentile of random permutations .Fig. 2 during pregnancy and increased cerebellar size in children age 9–12, and an association between increased exposure to PM2.5 and smaller cerebellar volume in their multipollutant model, though this latter finding was not statistically significant in their single-pollutant model (To date, studies assessing prenatal exposure to air pollutants and subsequent brain development provide conflicting results. Lubczynska nt model . A studynt model . These f10, and decreased exposure to NO2. In mice, Allen et al detected an association between increased ultrafine particle exposure during pregnancy and male-specific ventriculomegaly. Ventriculomegaly occurred in concert with inflammation and microglial activation, aberrant WM development and hypomyelination in the corpus callosum (et al also related higher gestational exposure to concentrated ambient particles in mice with ventriculomegaly (10. Ventriculomegaly could reflect an expansion of the ventricles to fill available space due to cell loss and/or altered brain development (We found larger relative ventricle volumes in relation to increased exposure to PMcallosum . Klocke lomegaly . In our elopment .2 concentrations are related to larger relative ventricle size is unexpected. One other study reported opposing effects of NO2 and PM2.5, in which the third mode of CCA linked higher concentrations of PM2.5 to lower concentrations of NO2, and was related to lower area and volumes across the cortex as well as white matter indices in school-aged children (2 may affect the developing brain in several ways, and prenatal exposure could harm the fetal brain by inducing inflammation, oxidative stress, DNA methylation, and vascular injury (2 increases nitrite in the blood stream and lowers blood pressure (et al, and there is no proven link between long-term exposure to NO2 and lower blood pressure.The finding that lower NOchildren . NO2 mayr injury . Howeverpressure . In this10 and lower exposure to NO2 was related to larger relative cerebellar volume in our study. The mechanisms relating increased PM exposure in utero and increased cerebellar size are not clear. In rat brains, direct exposure of the cerebellum to PM2.5 in solution was shown to cause oxidative stress (et al examined young mice and found that gestational exposure to ambient PM2.5 was related to hypermyelination in the cerebellum of male mice (et al, may be a response to injury.Higher gestational exposure to PMe stress , and inhe stress . Again, ale mice . This la10 and lower exposure to NO2 was related to smaller relative cGM and amygdala and hippocampus volumes, and larger relative brainstem and extracerebral CSF volumes. Some of these findings are comparable to previous studies in children. Guxens et al observed that higher pregnancy and childhood exposure to multiple air pollutants was related to smaller hippocampus and larger amygdala in 12-year-olds with no associations found for cGM (Air pollution exposure explained a small amount of variance in cGM, amygdala & hippocampus, brainstem, and extracerebral CSF volumes. Higher gestational exposure to PM for cGM . Beckwit for cGM .10 may contribute to the smaller relative cGM, hippocampal and amygdala volumes we observe in our study.In mice, decreased volume of the hippocampus was associated with prenatal exposure to ultra-fine particles . Woodwaret al, demonstrated a relationship between amygdala volumes in the neonatal period and behavioural scores in 5-year-old children born preterm.There have been few studies analysing brain volumes in healthy term infants in the neonatal period . However2.5 (Air pollution concentrations modelled for our London-based cohort are comparable to those found in other studies examining pregnancy cohorts in European cities . Pregnan2.5 . Thus, o2.5 significantly associated with any of the brain volumes studied. Further, we did not observe any significant associations between exposure over the entire gestational period and WM or deep grey nuclei volume, despite previous studies showing associations in children aged 6–14 years old (We did not observe any associations between individual trimester exposure and brain volumes, nor was prenatal exposure to PMears old . It is pStrengths and limitations.A strength of this study is the large sample size for a neonatal cohort, which is comparable to cohorts in studies with MRI taken in childhood. Further, bespoke imaging methods were used to obtain state of the art neonatal imaging, and air pollution data was modelled using a validated high-quality ambient air pollution model. Finally, the statistical method used allowed incorporation of three ubiquitous yet highly correlated pollutants.Our study has some limitations. Firstly, IMD does not include individual level variables of socioeconomic status, as it is modelled at postcode level. It therefore does not fully adjust for factors that have been shown to influence fetal brain development, such as maternal education. Ambient air pollution concentrations were also modelled on postcode level. There may be heterogeneity within postcodes, even if postcodes in the UK are often of small size. Additionally, no information was available on spatiotemporal subject mobility, which could lead to exposure misclassification. However, people spend most of their time at home, and this is likely especially the case for pregnant women . Subject52 exposure is related to larger relative ventricular and cerebellar size is more difficult to interpret, and requires further study.This study shows for the first time in humans that prenatal air pollution exposure affects neonatal brain morphology, particularly relative cerebellar and ventricle size. The brain is especially vulnerable during this critical period of development and altered brain development in utero may have adverse consequences throughout the life course. This finding is an important addition to the growing body of evidence that gestational exposure to pollution affects health in early life and shows that reducing particulate matter concentrations and exposure during fetal life should be a public health priority. Our finding that lower NOBrendan Bos: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Ben Barratt: Methodology, Writing – review & editing, Supervision, Project administration. Dafnis Batalle: Methodology, Formal analysis, Writing – review & editing. Oliver Gale-Grant: Methodology, Formal analysis, Writing – review & editing. Emer J. Hughes: Investigation, Data curation. Sean Beevers: Software. Lucilio Cordero-Grande: Software, Writing – review & editing. Jana Hutter: Methodology, Writing – review & editing. Joseph V. Hajnal: Funding acquisition, Methodology. Frank J. Kelly: Writing – review & editing. A. David Edwards: Funding acquisition, Writing – review & editing. Serena J. Counsell: Conceptualization, Methodology, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.