Long COVID is new or ongoing symptoms at four weeks or more after the start of acute COVID-19. However, the prevalence and factors associated with long COVID are largely unknown in Malaysia. We aim to determine the proportion and factors associated with long COVID among COVID-19 patients in Port Dickson, Malaysia. The positive outcomes of our long COVID active detection initiative were also described.This was a retrospective analysis of long COVID data collected by the Port Dickson District Health Office between 1 September 2021 to 31 October 2021. Monitoring long COVID symptoms was our quality improvement initiative to safeguard residents’ health in the district. The study population was patients previously diagnosed with COVID-19 who resided in Port Dickson. The inclusion criteria were adults aged 18 years and above and were in the fifth week (day 29 to 35) post-COVID-19 diagnosis during the data collection period. We called all consecutive eligible patients to inquire regarding long COVID symptoms. Long COVID was defined as new or ongoing symptoms lasting more than 28 days from the date of positive SARS-CoV-2 by polymerase chain reaction test. Binary multivariate logistic regression was conducted to determine factors associated with long COVID.Among 452 patients, they were predominantly male (54.2%), Malays (68.8%) and aged 18–29 years (58.6%). A total of 27.4% (95% CI [23.4–31.8]) of patients experienced long COVID symptoms and were referred to government clinics. The most frequent long COVID symptoms experienced were fatigue (54.0%), cough (20.2%), muscle pain (18.5%), headache (17.7%) and sleep disturbance (16.1%). Females, patients with underlying cardiovascular disease, asthma and chronic obstructive airway disease, those who received symptomatic care, and patients with myalgia and headaches at COVID-19 diagnosis were more likely to have long COVID. Three patients with suspected severe mental health problems were referred to the district psychologist, and ten patients with no/incomplete vaccination were referred for vaccination.Long COVID is highly prevalent among COVID-19 patients in Port Dickson, Malaysia. Long-term surveillance and management of long COVID, especially among the high-risk groups, are needed as we transition to living with COVID-19. The novel coronavirus SARS-CoV-2 has paved its way into eventually causing the biggest pandemic in the 21st century with almost 520 million coronavirus disease 19 (COVID-19) cases and over 6.1 million deaths worldwide as of 17 May 2022 . EvidencThe long COVID symptoms are highly variable and wide-ranging from respiratory, cardiovascular, neurological, gastrointestinal, musculoskeletal, psychological, ear, nose and throat, dermatological and generalised symptoms . In the Malaysia, an upper-middle-income country, was not spared from the pandemic and had over 4.4 million cases and 35,620 deaths as of 17 May 2022 . An onliBesides the study, the prevalence and factors associated with long COVID symptoms are largely unknown locally. A conceptual framework has been proposed to guide identifying and mitigating long COVID predictors . UnderstThis was a secondary data analysis of the routine long COVID information collected by the Port Dickson District Health Office between 1 September 2021 to 31 October 2021. The data source came from the long COVID active case detection project, one of the quality programmes in our district health office that aims to identify and refer patients with long COVID to nearby government health clinics. In other words, this public health service is unique to Port Dickson District Health Office.Port Dickson district is one of the seven districts in Negeri Sembilan state, Malaysia. The district is located about 90 km south of Kuala Lumpur, the capital city of Malaysia, with an estimated population of 128,689 people in 2020 . The stuThe Port Dickson District Health Office is responsible for healthcare delivery and public health services in the district alongside six government healthcare clinics and six smaller rural/community clinics. The critical functional unit during the current COVID-19 pandemic is the Port Dickson Crisis Preparedness and Response Centre, which leads and coordinates all prevention and control activities.via the National Public Health Laboratory Information System, (ii) public and private healthcare facilities, and (iii) COVID-19 Assessment Centres. We defined a COVID-19 patient as a person with a laboratory confirmation for SARS-CoV-2 by reverse transcriptase-polymerase chain reaction (RT-PCR) test (The centre continuously received notification of new COVID-19 cases through three main channels: (i) the State Health Department of Negeri Sembilan CR) test . It is mCR) test . Our teaA team of eight medical doctors was trained for this long COVID active case detection initiative. We telephoned patients to interview them about long COVID symptoms in the fifth week (day 29 to 35) post-COVID-19 diagnosis. The interviews were guided by a data collection spreadsheet displayed on computers. The interviewer recorded answers that corresponded with the pre-coded responses. When our telephone calls were unanswered three times on different days, we labelled them as a loss of contact.When patients were identified to have long COVID, we referred them to the nearest government health clinics. For patients with psychological symptoms, our doctors assessed their clinical history and referred patients to the district psychologist. Besides that, we identified and referred patients with no or incomplete COVID-19 vaccination to the vaccine administration centre.The primary outcome of this study was to portray the proportion of COVID-19 patients that experienced any long COVID symptoms. We defined long COVID as new or ongoing symptoms lasting more than 28 days from the date of positive SARS-CoV-2 by RT-PCR . The cat2 as one of the criteria for admission to the Low-Risk COVID-19 Quarantine and Treatment Centre  asymptomatic, (ii) symptomatic but did not require oxygen therapy, (iii) symptomatic and required oxygen therapy, and (iv) intubation . All conThe criteria for admission to the Low-Risk COVID-19 Quarantine and Treatment Centre were: (a) patients who fulfilled home monitoring criteria but without suitable home conditions; (b) could ambulate without assistance and self-administered medications and (c) did not have ongoing clinical needs such as haemodialysis . HospitaWe estimated the sample size using online OpenEpi (version 3.01) sample size for frequency in a population . Using aDescriptive analysis was performed for the baseline characteristics and long COVID symptoms. For normally distributed data, the variables were presented in mean ± standard deviation (SD), while median and inter-quartile range (IQR) were presented for skewed data. Both frequency and percentages were reported for categorical variables.P < 0.25 and clinically essential variables. The Omnibus test of model coefficients was used to check the model against the baseline model. The coefficient of determination, R2, represented the amount of variation in the outcome that the model explained. The Hosmer & Lemeshow test indicated if the model fit the data well. The model’s predictive power was assessed using the classification table and area under the Receiver Operating Characteristic (ROC) curve. We reported the adjusted odd ratios with 95% confidence intervals. All the tests were two-tailed and statistical significance was fixed at P < 0.05. All analyses were done using the IBM SPSS Statistical Software, version 23.Univariate binary logistic regression was first conducted to determine individual factors associated with long COVID symptoms. Multivariate logistic regression analysis was carried out for variables with This paper utilised anonymised secondary data from Port Dickson District Health Office. All patients’ identifiers, such as names, identity card numbers, contact numbers, and addresses, were first removed. The Medical Research Ethics Committee (MREC) Ministry of Health Malaysia approved this study (NMRR ID-22-01103-GRF). The MREC waived written informed consent following local legislation and national guidelines because this study was a retrospective analysis of an anonymised dataset.A total of 452 COVID-19 cases were analysed, and patients were predominantly male (54.2%), Malays (68.8%) and were in the 18-29-year age category (58.6%) . It was P values <0.25 were selected to be included in the final model. These variables were age group, sex, ethnicity, smoking status, obesity, cardiovascular disease, asthma and COAD, fever, cough, fatigue, headache, myalgia, sore throat, coryza, dyspnoea, anorexia, nausea, or vomiting, diarrhoea, loss of taste and loss of smell  is the strongest factor associated with long COVID. This could be a new finding because limited studies show direct associations between underlying CVD with long COVID. Pre-existing CVD did not show significant associations with long COVID . NeverthPrevious evidence has indicated that underlying health conditions, including CVD and its risk factors, may lead to severe COVID-19 and mortality . A systeBesides that, we hypothesise that cardiac damages following COVID-19 may worsen in patients with CVD who later develop long COVID. COVID-19 is linked with the new onset of cardiovascular complications . Two cohOur result that females were more likely to have long COVID was consistent with other studies . Middle-The primary target organ for COVID-19 is the respiratory tract. Hence, our finding of an association between underlying asthma with long COVID symptoms is not surprising and has been similarly reported . SubstanWe found that patients who presented with headaches at the onset of COVID-19 diagnosis are three times more likely to develop long COVID symptoms. Although headaches are among the most frequent neurological symptoms of long COVID, the exact causes of headaches in long COVID remain uncertain . PossiblPatients with any viral infections commonly experience myalgia. However, myalgia caused by COVID-19 infection persists longer and is more severe than the usual myalgia of other viral infections. Generalised inflammation and cytokine response inside the musculoskeletal system can cause myalgia . In COVIOur symptomatic patients at disease onset were more likely to develop long COVID than asymptomatic ones. Symptomatic disease is a proxy for more severe COVID-19 and can explain its association with long COVID. Previous studies reported that patients hospitalised for severe COVID-19 frequently suffer long-term symptoms . For insOur initiative to actively detect long COVID has yielded some positive outcomes. Since the natural progression of long COVID remains uncertain, there is a need to investigate, treat, and follow up with the patients . Hence, A study reported a high prevalence of depression, anxiety, and stress symptoms in Malaysia during this COVID-19 pandemic . EvidencThe main strength of this analysis is the use of real-world data to determine the proportion of COVID-19 patients with long COVID and its associated factors. Our findings can add new knowledge to the pool of information on long COVID, especially in Malaysia. Implementing this long COVID surveillance initiative helped improve the quality of public health service and may reduce the long-term disease burden posed by long COVID among COVID-19 patients in our district.We acknowledge several limitations. Firstly, patients were asked to recall their symptoms and experience several weeks after their acute illness and thus might be subjected to recall bias. Secondly, the demographic characteristics of our COVID patients were not representative of the population in Port Dickson and Malaysia. These differences were not unexpected because higher proportions of COVID-19 patients were reported during the period among younger adults in higher education institutions and workplaces. Thus, our results may not be externally generalised to the whole population. Nevertheless, we should emphasise that the findings represent the actual real-life situation in the field.Thirdly, the small sample size for some associated factors was small, and this could cause insufficient study power and wide confidence intervals. Fourthly, our long COVID surveillance by telephone call might not have adequately excluded the alternative explanations for some symptoms and mistakenly assigned them as long COVID. This was unavoidable as our medical doctors could not examine them physically and perform relevant investigations. Nevertheless, we referred them all to government clinics for further management. Finally, we could not follow up with patients further to observe the persistence of long COVID symptoms beyond twelve weeks and the outcomes of our referrals.In conclusion, more than a quarter of our COVID-19 patients in Port Dickson, Malaysia, experienced long COVID symptoms. Females, underlying CVD, asthma and COAD, symptomatic care, myalgia, and headaches at COVID-19 diagnosis were independent factors associated with long COVID. Long-term surveillance and management of long COVID should be considered by policymakers, public health practitioners, and clinicians as we transition to living with COVID-19. Attention can be given to long COVID high-risk groups for targeted intervention. Our field experience showed that long COVID active case detection is feasible using telephone interviews. Sufficient monetary, material, and human resources are needed to implement this initiative.10.7717/peerj.14742/supp-1Supplemental Information 1*anaemia, gastritis, spondylitisClick here for additional data file.


Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson’s disease (PD) and Parkinson’s plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ( MRI improves the diagnostic accuracy and can be used for monitoring disease progression2. Brain MRI can reveal various features that appear in P-plus but not in PD4. For instance, patients with PSP show marked midbrain atrophy5, known as the hummingbird sign. In MSA- Parkinsonian type (MSA-P), the putamen is atrophic, with a flattened lateral border, and shows a hypointense signal on T1-weighted gradient-echo images. Patients with MSA-cerebellar type (MSA-C) show predominant atrophy in the pons and middle cerebellar peduncles, resulting in an increased midbrain-to-pons ratio6 and a decrease in the magnetic resonance Parkinsonism index7. Accordingly, quantitative measures of the volume of these brain structures have also been assessed, showing high sensitivity and specificity in differentiating PD from P-plus8.Parkinson’s disease (PD) diagnosis is primarily based on clinical presentation. However, for atypical symptoms called red flags9, the visual assessment of this area is not quantitative, lacks objectivity, and highly dependent on the physician’s skills or image acquisition. Consequently, diagnoses based on visual assessments have shown a broad spectrum of accuracy, even falling below 80%12. To develop a consistent and quantitative analysis of brain MRI, volumetry of the midbrain area has been used as an optimal predictor for accurate diagnosis14. Thus, brain image segmentation has become an important stage in most downstream analyses based on prediction models or automated machine-learning (ML) methods for volumetry and diagnosis.Although the diagnostic sensitivity and specificity obtained by evaluating the midbrain area are generally high for differentiating between PSP, MSA, and PD16, FreeSurfer (FS)17 can extract brain structures with relatively high accuracy. Therefore, FS has been widely adopted as a non-DL automated segmentation method21.A trained physician’s manual segmentation of brain MRI scans is strenuous and time-consuming, and it requires a highly skilled specialist to correctly identify the brain structures. Various automated techniques using atlas-based or deep-learning (DL) techniques have been developed to overcome these problems. Although automated image segmentation models for the brain show limitations25. In fact, expressing segmentation as an atlas-based registration problem requires considerable time, and FS must be optimized to obtain a coordinate transformation function suitable for the internal atlas model of each test sample.Various automated segmentation methods for brain structures have been developed, but their use in clinical practice is limited, being typically used in one-time studies. This is attributable to the time-consuming and complex process of automated segmentation models compared with physicians’ simple visual assessments of brain MRI scans. For instance, the automated FS for segmentation takes more than 4.5 h per patient to segment the brain captured in an MRI scan. This complexity problem occurs because existing automated segmentation methods use atlas-based registration28, studies for the efficiency and accuracy of segmenting the specific parts of the brain MRI  of specific neurodegenerative diseases is still progressing. Unlike existing non-DL methods, DL may increase the analysis speed by completing segmentation using only forward computations based on learned parameters without requiring optimization processes such as registration. However, it is difficult to predict whether DL shows performance degradation compared with non-DL methods, especially in segmenting brain MRI of neurodegenerative diseases. Our study is significant because it demonstrates the comparative performance of both DL and non-DL methods in segmenting brain MRI and applies them to diagnosing Parkinsonian diseases. In other words, this study took a further step by showing the differential diagnosis of parkinsonian diseases using brain segmentation by AI and non-AI models, not merely comparing the performance of segmentation between AI and non-AI models as previous studies26.An automated model for fast segmentation and diagnosis without involving intricate methods should be developed for clinical use. Although DL segmentation has been developed and used in various fields including brain segmentation29 and UNet transformer (UNETR)30, respectively, were adopted to perform volumetric 3D image segmentation in this study. The DL models were trained to segment brain structures on MRI scans for the diagnosis of neurodegenerative diseases, and their performances were analyzed and compared with an existing non-DL model, FS. Six brain structures that are important in classifying normal, PD, and P-plus cases were segmented: putamen, pallidum, midbrain, pons, caudate, and third ventricle. The volumes of the segmented areas were subsequently used to differentiate between normal, PD, and P-plus cases. As illustrated in Fig. Recent DL segmentation models are classified into convolutional neural network (CNN) and vision transformer (ViT) architectures. Accordingly, a representative model of each framework, V-Netrecon-all pipeline and the complete Brainstem Substructure pipeline. In FS segmentation time, we removed the time consumed for preprocessing  described in Section “Table To compare with FS processing time, we have added time for using CPU in DL models. The CNN-based V-Net and ViT-based UNETR are considerably faster than FS. On average, V-Net took 3.48 s to segment the six brain structures, and UNETR took 48.14 s using GPU and 51.26 s and 1101.82 s using CPU, whereas FS took approximately 15,735 s using CPU, being approximately 307 and 14 times slower than V-Net and UNETR, respectively. Despite calculating the time using CPU, the DL models were faster than FS by 14 to 300 times. Not only GPU-based DL models, but CPU-utilized DL models also demonstrated to have significant performance compared to the non-AI method .Segmentation and prediction results of V-Net, UNETR and FS are illustrated in Fig. 31.Using the estimated volumes, we performed binary classification for cases normal vs. P-plus, normal vs. PD, and PD vs. P-plus, where P-plus comprised PSP, MSA-P, and MSA-C cases. The AUCs of the brain structures for each model were compared, as listed in Table Among the 98 cases (7 cases of binary classification × 2 DL models × 7 cases of brain structures), there was no significant difference in AUC between the DL models and FS, except for 11 cases . In over half of the 11 cases , AUCs of the DL models  were also no less than those of FS. This result demonstrated that the DL model reproduces the performance of the FS model successfully . Furthermore, most of the cases for the CNN-based V-Net showed no lower AUC for disease classification than the cases for the ViT-based UNETR.The highest AUCs in the comparison between the methods were higher in normal or PD vs. MSA-C (0.91–0.94) than in normal or PD vs. PSP (0.75–0.89). Among the brain structures, the midbrain-to-pons ratio showed the best performance in normal vs. MSA-C and PD vs. MSA-C, while the third ventricle and pallidum showed the best performance in normal vs. PSP and PD vs. PSP. The highest AUCs were not significantly different in the classification of normal or PD vs. MSA-P (0.69–0.73) or PD (0.63).Most AUCs of the DL models were not significantly different from those of FS, as listed in Table Table We developed two DL models, V-Net and UNETR, which showed significantly faster brain segmentation than FS and a comparable accuracy. Our DL models shortened the segmentation time by 14 to 300 times compared with FS. Moreover, they showed robust high performance in differential diagnosis between PD and P-plus cases using the volume of segmented brain structures. The DL models were efficient  and effective  in automated brain segmentation and disease diagnosis, even for simultaneous analysis of all brain structures and their individual analyses. Thus, the proposed DL models may promote the application of automated brain segmentation in clinical practice and facilitate efficient and accurate brain research in medicine.16. This is mainly attributable to the complicated and time-consuming process of automated brain segmentation compared with physicians’ qualitative visual assessment of brain MRI scans. Consequently, automated segmentation models have mainly been used in research settings that require quantitative brain measurements. Nevertheless, their application in clinical settings may increase with our DL models, which have shown much faster segmentation than FS with a similar accuracy. The DL models may contribute to improve the accuracy of clinical diagnosis of PD or P-plus cases by providing precise brain image analysis. In addition, clinical trials that require quantitative brain measurement from a large population may be conveniently conducted using our fast and accurate DL models. In the past, methods for brain image analysis were time- and resource-consuming, even with an automated segmentation tool such as FS.Automated tools have scarcely been adopted for brain segmentation in clinical practice despite their high accuracy in the differential diagnosis of patients with Parkinsonismrecon-all pipeline and brainstem substructures pipeline . We excluded those structures owing to the low segmentation accuracy achieved by FS. Also, DL methods learn the coarse features in priority because they are the common region of training data, which are low frequency regions. This results in smoother image than the that of FS, mitigating the images’ minor artifacts on the outer rims. However, in the case of cerebellum where smaller changes are essential, more specific study is needed to know whether our DL methods will be applicable to the segmentation of cerebellum’s small gyri, comparing them to manual segmentation of the cerebellum. Nevertheless, the differential diagnosis of P-plus using only the brain structures included in this study has been reported as reliableAutomated segmentation of brain MRI scans has become an influential method for diagnosing neurodegenerative diseases, including movement disorders. Using the high-performance CNN- and ViT-based models, we significantly shortened the segmentation time of deep brain structures while obtaining comparable accuracy to the conventional FS segmentation. Despite the superior DL performance, no quantitative results of the comparative analysis and evaluation of the performance of DL have been reported to date for the differential diagnosis of neurodegenerative diseases, including PD and P-plus. We found that the cost-effective CNN-based model achieves satisfactory performance in both segmentation and differential diagnosis compared with the most recent ViT-based model. Our DL models may contribute to the development of patient- and clinician-friendly segmentation methods that enable fast and accurate diagnosis and may provide a meaningful reference for hospitals planning to introduce DL brain segmentation and diagnosis for neurodegenerative diseases.This study focuses on comparing whether AI is more effective in diagnostic performance than the existing representative non-AI method. Therefore, as the subject of this study is to compare techniques, comparison with clinicians was not performed in this study. It would be a promising future study to compare the accuracy of diagnosis between the machine learning methods and clinicians.h, w, d) , where each mask output contained intensities between 0 and 1 . By calculating the absolute volume of each or all the brain structures predicted by FS or DL models, we performed binary classification of PD, MSA-C, MSA-P, PSP, and normal cases, and calculated the area under the curve (AUC) of segmentation.In this section, we describe the brain MRI data Section “”, FS impAll authors of this study confirm that all methods or experiments were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations provided by the policies of the Nature Portfolio journals. This study was approved by the Institutional Review Board of the Samsung Medical Center (IRB number: SMC 2021-07-026). The written informed consent of the patients was waived by the Institutional Review Board of Samsung Medical Center because we used deidentified and retrospective data.39 using [18F] N-(3-fluoropropyl)-2β-carbon ethoxy-3β-(4-iodophenyl) nortropane positron emission tomography, while probable MSA and PSP were diagnosed according to the second consensus diagnosis of MSA40 and movement disorder society clinical diagnostic criteria for PSP41, respectively. MSA cases were further classified as either MSA-P or MSA-C after reaching consensus40. Patients with concomitant or structural brain lesions, including stroke and tumors, which may affect brain MRI scans, were excluded from the study. An age-matched healthy elderly population was included as the control group. Demographic information on age, sex, and disease duration until the brain MRI examination was collected, as listed in Table We retrospectively screened patients from the Neurology Department of Samsung Medical Center between January 2017 and December 2020. Patients diagnosed with PD, probable MSA, or probable PSP were included in this study. The diagnosis for each patient was determined by movement disorder specialists based on the following criteria: PD was determined according to the United Kingdom PD Society Brain Bank criteriaWe applied cross-validation with three outer folds for evaluation to mitigate bias in the validation and test sets and analyze the effect of set composition (combinations of cases in groups). The data were randomly divided into three sections, one for testing and two for training. Each group comprised 35 normal, 35 PD, 23 PSP, 23 MSA-C, and 21 MSA-P cases.Axial brain MRI scans were acquired using a standard protocol for T1-magnetization-prepared rapid acquisition of gradient echo, with repetition/echo time of 11,000/125 ms, inversion time of 2800 ms, field of view of 240 mm, acquisition matrix size of 16. The MRI scans were resized to We included six brain structures that are involved in Parkinsonian syndromes in the gray matter, namely, the midbrain, pons, putamen, pallidum, caudate, and third ventricle. These areas are reported to have the highest sensitivity and specificity for differentiating Parkinsonian syndromes42. NIfTI is a more straightforward format than DICOM and preserves the essential metadata. In addition, it maintains the volume as a single file and uses raw data after a simple header, and NIfTI files can be loaded and processed faster than DICOM files for whole brain images. Therefore, we converted files in the brain MRI DICOM format into files in the NIfTI format using MRIcroGL.The FS accepts Digital Imaging and Communications in Medicine (DICOM) or Neuroimaging Informatics Technology Initiative (NIfTI) files as inputs. DICOM is a compelling and flexible but complex format that provides interoperability between several hardware and software tools. Given its complexity, DICOM format was converted to NIfTI format44. Additionally, FS no longer supports CUDA, thus unable to calculate the time using GPU.The extraction of brain structures obtained using atlas-based automated segmentation are necessary for training and validation before establishing an automated DL segmentation model. In this study, we used these results as DL ground-truth labels and evaluated the validity of DL model for generating the same label. As a representative technology for atlas-based automated segmentation . However, because the Brainstem Substructure pipeline receives pre-processed inputs from the recon-all pipeline, both pipelines should be executed. Therefore, the extraction of the six brain structures through FS can be divided into MRI scan pre-processing in the recon-all pipeline and the remaining segmentation of the recon-all pipeline along with segmentation in the Brainstem Substructure pipeline. These processes are explained in Section “To segment and extract the six brain structures using FS, it sequentially executes the recon-all pipeline of FS mainly consists of (1) motion correction, (2) normalization, and (3) skull stripping. Motion correction is conducted before averaging when various source volumes are used, compensating for small motion variations between volumes. FS constructs cortical surface models and the boundary between white matter and cortical gray matter to automatically match the brain images of patients, using software17. In addition, intensity normalization is applied to the original volume. However, adjusting for intensity fluctuations may hinder intensity-based segmentation. Instead, we scale the intensities of all voxels to the mean value (110) of white matter.The MRI scan pre-processing in the 47 and promote automated brain image segmentation and improve analysis quality. Therefore, brain MRI scans should be pre-processed to isolate the brain from extracranial or nonbrain tissues in a process known as skull stripping48. FS developers devised and applied in-house automated skull-stripping algorithms to isolate intracranial cavities by default.After correcting for motions and normalizing the data, FS removes the skull and provides the skull-stripped brain MRI scan. Removing intracranial brain cavities  may reduce human rater variabilityIn this study, the steps of brain MRI scan pre-processing  took approximately 20 min. We converted the final skull-stripped images to NIfTI files with size of recon-all pipeline and the complete Brainstem Substructure pipeline. After skull stripping, registration-based segmentation proceeds as follows. FS determines and refines the white and gray matter interfaces for both hemispheres. Then, FS searches for the edge of the gray matter, which represents the pial surface. With pial surfaces, FS expands and inflates sulci banks and gyri ridges. Subsequently, it extends again into a sphere and parcellates the cortex. After applying these processes, FS segments the brain. The recon-all pipeline encompasses some brain structures , while the Brainstem Substructure pipeline segments the midbrain and pons.After pre-processing Section “”, FS segrecon-all pipeline) to perform segmentation. For the replacement, we evaluated whether the DL analysis is faster than FS analysis and whether the segmentation result of DL is sufficiently reproducible compared with that of FS. The differences between FS and DL segmentation are illustrated in Fig. In this study, the final segmentation result was assessed with the same input size of entclass1pt{minimarecon-all pipeline, as described in Section “29 and ViT-based UNETR30 using the segmentation results provided by FS as labels  was used as the nonlinear activation function. Convolutions were applied throughout the compression path. The right part of the network learned a residual function similar to that of the left part. V-Net has shown promising segmentation results, and using this model in our application improved performance. The model was adjusted according to the available memory. The proposed architecture is illustrated in Fig. 30 is a transformer architecture for 3D medical-image segmentation. There is a study that used UNETR as brain tumor segmentation52, but no study was held for brain parts segmentation. It uses a transformer as the encoder to learn the sequence representations of the input volume and capture global multi-scale information while adopting U-shaped architectures for the encoder and decoder. The proposed architecture is illustrated in Fig. UNETR53 were applied, and the model was trained for 20,000 iterations. Both models used Python language and were trained using an NVIDIA Tesla V100 DGXS GPU with a batch size of 1 and an initial learning rate of 0.0001. For CPU, Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20Ghz was used.For the DL models, the input comprised a brain mask and the corresponding patient’s segmented brain structures in the MRI scans, which were merged into an array of dimension 54 for each brain structure We evaluated the accuracy of the evaluated models using the Dice score by comparing the expected segmentation with V-Net (or UNETR) and FS outputs. The Dice score measures the overlap between the reference and predicted segmentation masks. A Dice score of 1 indicates perfect spatial correspondence between the two binary pictures, whereas a score of 0 indicates no correlation. We used the Dice loss to determine the performance of the three outer cross-validations on their test sets for the corresponding structures. If We obtained the absolute volumes from the six segmented brain structures  predicted by the DL models  or FS. Based on the absolute volume of the individual brain structures, we calculated the AUC of the binary classification of diseases, normal vs. P-plus, normal vs. PD, and PD vs. P-plus cases. The AUC was computed based on the receiver operating characteristic curve produced by the correlation between the predicted absolute volume of each brain structure and each case.57. XGBoost is a well-established method that produces advanced results among gradient-boosting-based techniques58 . In both methods, we evaluated the AUC obtained by the DL model and FS through threefold cross-validation.Disease binary classification was conducted using the six segmented brain structures individually or collectively. For individual analysis, the AUC was derived through thresholding-based binary classification by obtaining the absolute volume of the individual structures. For a comprehensive analysis of all structures, we additionally considered an ML classification algorithm to perform disease binary classification with the six volumes as inputs. For the classification algorithm, binomial logistic regression (LR) and extreme gradient boosting (XGBoost) were used. LR is a statistical model widely used in ML classificationSupplementary Information.


Heart failure (HF) is a growing major public health and economic concern in the United States and worldwide. Heart failure mortality rates can be as high as 75% despite advances in therapies. HF is expected to be the fastest growing among all cardiovascular diseases, with HF-associated direct medical costs projected to nearly double over the next 10 years. Hospital admissions, re-admission, and medical cost are a huge burden to the healthcare system, and this is estimated to have increased gradually over the past decades despite the available advances in HF treatment and prevention. Many heart failure therapies have shown improvement in terms of mortality, morbidity, and symptomatic management. Guideline-directed medical therapy (GDMT) for heart failure has proven its ability to reduce morbidity and mortality by 66%. GDMT is recommended to be used among all HF patients when appropriate. In recent years, two new drug classes, angiotensin receptor-neprilysin inhibitor (ARNi) and sodium-glucose cotransporter-2 (SGLT-2) inhibitors, were approved by the United States Food and Drug Administration (US FDA) for the management of heart failure. The exact mechanism by which the SGLT-2 inhibitors attenuate the inflammatory process remains unclear. Several mechanisms have been suggested related to the cardiovascular benefit of SGLT-2 inhibitors, including a reduction in inflammation, improvement in natriuresis/diuresis, and promotion of the use of ketones as a secondary energy source. Clinical data showed that SGLT-2 inhibitors have morbidity and mortality benefits within 30 days of initiation. Studies have proven that clinical pharmacists practicing in HF inpatient and outpatient settings resulted in a reduction of HF hospitalization and an increase in the uptake of GDMT by initiating or up-titrating GDMT agents as well as providing patient education. Heart failure (HF) is a complicated clinical syndrome in which the body is not able to maintain adequate metabolic supply to organs and tissues due to structural and/or functional myocardial dysfunction . In clinThe American College of Cardiology and the American Heart Association (ACC/AHA) established treatment guidelines for the management of heart failure, which were recently updated in 2022 from 2016 . The ACCIn recent decades, the pharmacological management of heart failure has become a true success story of modern medicine. Evidence from high-quality randomized controlled studies has been adopted as a basis of pharmacological management that improves both mortality and symptoms . In factClinical Pharmacists practicing in HF care settings as part of the multidisciplinary team have been able to show a significant reduction in HF hospitalization and an increase in the uptake of GDMT by initiating or up-titrating GDMT agents as well as providing patient education ,12. FurtHistorically, SGLT-2 inhibitors were originally approved and utilized exclusively for glycemic control in patients with diabetes ,15,16,17Although the exact mechanism by which SGLT-2 inhibitors help reduce mortality and morbidity among heart failure patients remains elusive, results reported by many clinical trials established the clear cardiovascular benefits when SGLT-2 inhibitors are used among heart failure patients ,19. One SGLT-2 inhibitors are the first class of antidiabetic agents to be granted FDA approval for the treatment of heart failure with reduced ejection fraction (HFrEF) ,18. The Moreover, the use of SGLT-2 inhibitors with a proven heart failure benefit—empagliflozin, dapagliflozin, canagliflozin, and ertugliflozin—for the treatment of diabetes is recommended by The American Diabetes Association and The Kidney Disease: Improving Global Outcomes (KDIGO) guidelines among diabetic patients with confirmed HF or at high risk of HF ,20.2 with continuation up to but excluding dialysis [When used outside of antihyperglycemic management, the dose of dapagliflozin is 10 mg by mouth daily without regard to meals and without need for dose titration ,17. In pdialysis ,17.Adverse effect monitoring should include pharmacovigilance for hypotension secondary to hypovolemia and volume contraction, euglycemic ketoacidosis, acute kidney injury, genital mycotic infections , hypoglycemia when combined with insulin, Fournier gangrene, and pyelonephritis ,22. Routp = 0.005) & reduced HF hospitalization (HR 0.73 (0.61–0.88)) [The first observations of the benefits of SGLT-2 inhibitors on Heart Failure were noted in the EMPA-REG (2015), CANVAS trial (2017), and Declare-TIMI 58 (2018) cardiovascular outcomes trials (CVOTs) ,24. Thesp < 0.001 [The subsequent landmark trial DAPA-HF (2019) further validated dapagliflozin’s effects on HF and was the first of the SGLT-2 inhibitors trials in HF . DAPA-HF < 0.001 . In a pr < 0.001 . What ra < 0.001 . Time to < 0.001 .2 [p < 0.001 [The DELIVER trial published in late 2022 followed the AHA/ACC/HFSA 2022 guideline publication but corroborated the findings of EMPEROR-preserved in HFmrEF and HFpEF . DELIVER2 . It show < 0.001 . These o < 0.001 .2 [Unlike when it is used for hyperglycemia management, the empagliflozin HF FDA-approved dose is 10 mg by mouth daily ,18. Empa2 ,18. EmpaEmpagliflozin has several adverse effects to be monitored for, including hypotension, ketoacidosis, acute kidney injury, genital mycotic infections, hypoglycemia when combined with insulin, a rare but serious Fournier gangrene, and pyelonephritis . Neverth2 [2, urine albumin-to-creatinine ratio >300 mg/g, and not on insulin therapy while receiving empagliflozin [EMPA-REG OUTCOME was the first randomized clinical trial to report a statistically significant lower death from cardiovascular causes, non-fatal myocardial infarction, or non-fatal stroke with empagliflozin among high-risk diabetic patients . Additio2 . Based oliflozin . Death fliflozin . The EMPliflozin . EMPA-TRliflozin . Among Hliflozin .Two major landmark clinical trials, EMPEROR-Reduced and EMPEROR-Preserved, further investigated the role of empagliflozin among heart failure patients ,29. EMPEEMPULSE is the largest clinical trial to date examining the use of empagliflozin initiation during an acute hospital admission for heart failure . Over 50AHA/ACC/HFSA guidelines do not specify how best to perform therapy sequencing and when to initiate guideline-directed treatment . HoweverPrior to the FDA approval of SGLT-2 inhibitors, the historical initiation of the therapy and sequencing focused on maximizing beta-blockers and renin-angiotensin-aldosterone system inhibitors (RAASi) to target doses before mineralocorticoid receptor antagonists (MRA) or SGLT-2 inhibitors initiation. This could ultimately result in unnecessary delays in initiating crucial GDMT therapies. Based on the landmark trials in HF, SGLT-2 inhibitors have proven efficacy independent of the presence or absence of other HFrEF therapies . MoreoveA major analysis of the DAPA-HF trial sustained the benefit of dapagliflozin over placebo whether patients were optimized on guideline-recommended doses or not . DespiteThe cumulative evidence supports the initiation of all GDMT as early as possible, as their benefits were found to be independent of each other . SGLT-2 Although medication dispensing represents a core role of hospital system pharmacists, modern medicine and clinical pharmacy have expanded the pharmacists’ roles and provided the opportunity for them to be an active part of the medical rounds at the bedside and to perform patient education ,35. ThisA meta-analysis has found that pharmacists’ interventions at transitions of care estimated a 37% reduction in medication errors and a decrease in emergency department visits after hospital discharge . FurtherWith the fast-growing evidence and as the use of SGLT-2 inhibitors becomes more popular in patients with or without diabetes, it is crucial to understand the benefits of these medications and to communicate them to the patient and health care providers ,42. PharCardiovascular diseases such as heart failure are the leading cause of death in the United States and worldwide, and their management costs more than two-thirds of global healthcare expenditure ,41. ThusThis was a review article and included experts’ opinions. Any review article comes with the risk of potential bias and is subject to systematic and random errors. Due to the nature of this study, no statistical analysis was performed. Moreover, we only discussed SGLT-2 inhibitors currently approved for HF in the US. To date, no randomized clinical trial has evaluated the safety and efficacy of empagliflozin versus dapagliflozin among HF patients. Thus, we could not collate and contrast empagliflozin and dapagliflozin in clinical practice.Evidence-based practices for the management of HF patients are rapidly growing. SGLT-2 inhibitors are currently approved by the FDA for treating HFrEF and demonstrated morbidity and mortality benefits within 30 days of initiation. The exact mechanism by which SGLT-2 inhibitors attenuate the inflammatory process remains unknown. SGLT-2 inhibitors were found to be safe and effective even with early initiation and alongside other concomitant GDMT. Pharmacists play a major role in patients’ selection, early initiation, and adherence to HF guidelines-directed medical therapies, including SGLT-2 inhibitors.


E and BCG-revaccination, could differ in Delhi and Gujarat under varying delivery strategies.India has the largest tuberculosis burden globally, but this burden varies nationwide. All-age tuberculosis prevalence in 2021 ranged from 747/100,000 in Delhi to 137/100,000 in Gujarat. Previous modelling has demonstrated the benefits and costs of introducing novel tuberculosis vaccines in India overall. However, no studies have compared the potential impact of tuberculosis vaccines in regions within India with differing tuberculosis disease and infection prevalence. We used mathematical modelling to investigate how the health and economic impact of two potential tuberculosis vaccines, M72/AS01E and BCG-revaccination scenarios varying target ages and vaccine characteristics. We estimated cumulative cases, deaths, and disability-adjusted life years averted between 2025–2050 compared to the no-new-vaccine scenario and compared incremental cost-effectiveness ratios to three cost-effectiveness thresholds.We applied a compartmental tuberculosis model separately for Delhi  and Gujarat , and projected epidemiological trends to 2050 assuming no new vaccine introduction. We simulated M72/AS01E averted a higher proportion of tuberculosis cases than BCG-revaccination in both regions  and had higher vaccination costs . M72/AS01E in Delhi could be cost-effective, or even cost-saving, for all modelled vaccine characteristics. M72/AS01E could be cost-effective in Gujarat, unless efficacy was assumed only for those with current infection at vaccination. BCG-revaccination could be cost-effective, or cost-saving, in both regions for all modelled vaccine scenarios.M72/AS01E and BCG-revaccination could be impactful and cost-effective in Delhi and Gujarat. Differences in impact, costs, and cost-effectiveness between vaccines and regions, were determined partly by differences in disease and infection prevalence, and demography. Age-specific regional estimates of infection prevalence could help to inform delivery strategies for vaccines that may only be effective in people with a particular infection status. Evidence on the mechanism of effect of M72/AS01E and its effectiveness in uninfected individuals, which were important drivers of impact and cost-effectiveness, particularly in Gujarat, are also key to improve estimates of population-level impact.M72/AS01 In the National Tuberculosis (TB) Prevalence survey conducted from 2019–2021, the estimated tuberculosis prevalence was 312 per 100,000 for all ages in India overall.2 As of July 2023, there were sixteen tuberculosis vaccine candidates in clinical trials. Results are eagerly anticipated from an upcoming Phase III trial of the vaccine candidate M72/AS01E and the ongoing confirmatory Phase IIb trial for BCG-revaccination, as both products have demonstrated promising results in previous Phase IIb trials.4Tuberculosis elimination is a key focus for the Indian government, and prevention strategies, including tuberculosis vaccines and preventive treatment, are considered within the National Strategic Plan for Elimination of Tuberculosis 2017–2025.10 and in India.15 However, it is unknown how or if the impact of tuberculosis vaccines will vary regionally within India, given the varying burdens of disease. The Indian government is set to undertake a study to investigate the impact of delivering BCG to household contacts aged 6–18 compared to offering preventive therapy.16 Variation in disease and infection prevalence may influence the impact of these interventions by region.Earlier modelling studies have found that the introduction of new tuberculosis vaccines could have a positive impact worldwide E and BCG-revaccination could vary between high- and low-tuberculosis burden areas of India—represented by Delhi and Gujarat—under varying delivery strategies.We used mathematical modelling to investigate how the health impact and cost-effectiveness of M72/AS011 the India TB Report 2022 and 2023,17 and Ni-kshay—an online tuberculosis reporting and surveillance system developed by the National TB Elimination Programme.18 We combined available demographic data and extrapolated to obtain single age and year projections of population size for each region.19Data to inform calibration was obtained from the National TB Prevalence survey in India,11 We employed history matching with emulation using the ‘hmer’ R package to calibrate the model to each region.20 We fit each model to three targets to represent the higher tuberculosis burden in Delhi, and the lower tuberculosis burden in Gujarat. We assumed a uniform distribution between lower and upper bounds, and adjusted each target as described in the 1, the 2021 notification rate per 100,000 17, and the 2020 proportion of active tuberculosis that was subclinical [0.564 (interquartile range = 0.428–0.685)]21. The model for Gujarat was also fit to the estimated adult tuberculosis prevalence in 2011 .22We adapted a tuberculosis natural history model structure and parameterisation from previous studies.i.We used the calibrated models for Delhi and Gujarat to project baseline epidemiology to 2050 in each setting, assuming the coverage and quality of non-vaccine tuberculosis services continued at 2019 levels, with no new vaccine introduction.ii.Basecase vaccine scenario for each vaccine product. Basecase vaccine characteristics were informed by trial characteristics and expert opinion, and we assumed that each vaccine would be delivered to an age group aligned with the clinical-trial-eligible ages.4 The Basecase M72/AS01E scenario assumed a 50% efficacy prevention of disease vaccine effective with any infection status at vaccination and ten years average protection, introduced in 2030 routinely to those aged 15 (achieving 80% coverage over five years) and as a campaign for ages 16–34 (achieving 70% coverage over five years) in 2030 and 2040. The Basecase BCG-revaccination scenario assumed a 45% efficacy prevention of infection vaccine effective in individuals with no current infection at the time of vaccination and ten years average protection, introduced in 2025 routinely to those aged 10 (achieving 80% coverage over five years) and as a campaign for ages 11–18 (achieving 80% coverage over five years), in 2025, 2035, and 2045.We established a Policy Scenarios for both vaccine products. We met with in-country partners in the Government of India to discuss preferred ages to target for tuberculosis vaccine delivery. We ensured that our modelled scenarios captured this information to provide the most useful estimates to decision makers. The Older Ages: M72/AS01E scenario assumed routine delivery to those aged 17 and a campaign for ages 18–55, and the Older Ages: BCG-revaccination scenario assumed routine delivery to those aged 15 and a campaign for ages 16–34. For both vaccine products, we evaluated an All-Adults scenario with routine delivery for those aged 18 and a campaign for everyone aged 19 and older.We evaluated age-targeting Vaccine Characteristic and Coverage Scenarios by varying individual features of the vaccine profile from the Basecase  and $0.17 per dose for BCG-revaccination (assuming one dose per course). For vaccine campaigns, we included a one-time vaccine introduction cost of $2.40 (1.20–4.80) per individual in the targeted age group to represent non-recurring start-up costs.We assumed vaccine delivery costs of $2.50 (1.00–5.00) per dose, supply chain costs of $0.11 (0.06–0.22) per dose and a vaccine price of $2.50 per dose for M72/AS01We estimated the cumulative number of tuberculosis cases and deaths averted between vaccine introduction and 2050 for each scenario compared to the predicted numbers in the no-new-vaccine baseline. We estimated incidence and mortality rate reductions in 2050 for each scenario compared to the estimated rates in 2050 for the no-new-vaccine baseline. We calculated incremental vaccination, diagnostic, and treatment costs for each scenario compared to the no-new-vaccine baseline in 2020 US dollars.Policy Scenarios for each vaccine product and region. Costs and benefits were discounted to 2025 at 3% per year as per guidelines.23 We estimated incremental costs and disability-adjusted life years (DALYs) averted for each scenario between 2025–2050, using the disability weight for tuberculosis from the Global Burden of Disease 2019 study,24 and India-specific life expectancy estimates from the United Nations Development Programme.25 We calculated incremental cost-effectiveness ratios (ICERs) as mean incremental costs divided by mean incremental DALYs averted for each scenario. We evaluated the resulting ICERs against three cost-effectiveness thresholds: 1 times gross domestic product (GDP) per capita for India , and two opportunity cost thresholds defined by Ochalek et al: the country-level upper (US$443) and lower (US$328) bounds.26We performed cost-effectiveness analysis comparing the Vaccine Characteristic and Coverage Scenarios compared to the no-new-vaccine baseline. We assumed each vaccine product was delivered using the Basecase age-targeting assumptions.To investigate if the decision to introduce a vaccine would change based on the assumed vaccine characteristics, we calculated ICERs for the Calibrated trends for Delhi and Gujarat are shown in Basecase M72/AS01E scenario averted 655 (587–730) thousand cases, or 16.0% of the total predicted cases, and 77 (49–112) thousand deaths, or 14.4% of the total predicted deaths between 2025 and 2050 in Delhi (Basecase M72/AS01E scenario averted 186 (155–228) thousand cases  and 16 (7–27) thousand deaths  in Gujarat between 2025–2050  thousand cases  and 44 (29–65) thousand deaths  in Delhi, and 113 (91–143) thousand cases  and 10 (5–17) thousand deaths  in Gujarat between 2025–2050 (Older Ages and AllAdults scenarios) decreased the number of cases and deaths averted in Delhi, but increased the impact in Gujarat compared to the Basecase BCG-revaccination scenario  in Delhi and was US$366m (248–536) in Gujarat, compared to the BCG-revaccination Basecase vaccination total cost of US$27m (12–49) in Delhi and US$97m (42–178) in Gujarat  and in Gujarat was US$332m (213–505) (Basecase BCG-revaccination scenario led to cost-savings of US$38m (58–13) in Delhi (Basecase BCG-revaccination scenario in Gujarat was US$77m (21–158) in Gujarat  , S10.8. in Delhi . The tot Gujarat .E was potentially cost-effective for all Policy Scenarios. The Basecase M72/AS01E scenario (ICER = US$4), Older Ages scenario (ICER = US$126) and AllAdults scenario (ICER = US$317) were cost-effective at the country-level upper and lower bounds, and the 1xGDP threshold (Basecase M72/AS01E scenario was US$5m (minus 37–63), averting 1.5m (1.0–2.1) DALYs between 2025–2050 compared to the no-new-vaccine baseline   . The cos025–2050 .Older Ages and All-Adults BCG-revaccination scenarios were dominated by the Basecase BCG-revaccination scenario. The Basecase BCG-revaccination scenario was considered cost-effective at all thresholds (ICER = cost-saving), with cost-savings of US$37m and averted 938 thousand DALYs between 2025–2050 compared to the no-new-vaccine baseline. In Gujarat, the Basecase BCG-revaccination scenario was cost-effective at the country-level upper bound (ICER = US$351), with an incremental cost of US$77m compared to the no-new-vaccine baseline and averted 219 thousand DALYs between 2025–2050. The Older Ages scenario was cost-effective at 1xGDP per capita (ICER = US$868)  .Vaccine Characteristic and Coverage Scenarios in Delhi, regardless of the assumed product characteristics, introducing M72/AS01E routinely to those aged 15 and as a campaign for ages 16–34 could be cost-effective, and in some cases, cost-saving, at the country-level lower bound . As we modelled a higher infection prevalence for all ages in Delhi, and assumed that BCG-revaccination would only be effective if administered to people who were uninfected, there was a higher proportion of the population who were uninfected and would receive protection from the vaccine in Gujarat than in Delhi.Several findings related to the lower infection prevalence modelled in Gujarat compared to Delhi. For M72/AS01E and BCG-revaccination were likely to be cost-effective (and even cost-saving) in Delhi compared to the thresholds evaluated. In Gujarat, M72/AS01E could be cost-effective unless efficacy was restricted to those with current infection, and BCG-revaccination was likely to be cost-effective regardless of the modelled characteristics. Understanding the mechanism of effect of M72/AS01E, and confirming whether it works in all populations is a key area for future research, particularly in Gujarat and other areas with a low prevalence of infection.Across the range of assumptions examined for vaccine product characteristics, M72/AS01E was predicted to have higher vaccination costs than BCG-revaccination in both regions: 4.4 times as high in Delhi (US$118m vs US$27m) and 3.5 times as high in Gujarat (US$366m vs US$97m), due to the higher price per dose for M72/AS01E, ($2.50 per dose vs $0.17 per dose for BCG) and assuming two doses per course. Higher costs for both products were predicted in Gujarat compared to Delhi due to the larger population size.M72/AS0111 If this was an incorrect assumption, or if initial assumptions on the national India model prior ranges were incorrect, our projections may inaccurately represent Delhi and Gujarat.There are limitations associated with this work. Firstly, this is a mathematical modelling study, and therefore limitations associated with models apply. We represented tuberculosis natural history with a compartmental model accounting for multiple infection states. If our assumptions around how the latency structure or aspects such as subclinical tuberculosis interact with vaccines were incorrect, we may have over- or under-protected the population, leading to incorrect impact estimates. We assumed bounds of certain natural history parameters would not vary between regions in India, and therefore used national India posterior ranges as priors for Delhi and Gujarat calibration.Our model included an on-treatment compartment but assumed the only people treated were those with disease. The reported notification rate in Gujarat was greater than the prevalence estimate, implying more people were treated per year than those with prevalent disease. While Gujarat has excellent tuberculosis treatment services, only 35% of reported notifications in 2021 were bacteriologically confirmed. Therefore, there could be treatment of individuals who did not have tuberculosis, which we did not represent, but could be investigated with future adaptations to the model.A key limitation of this work was the availability of region-specific data to inform calibration. The National TB Prevalence Survey in India provided estimates of the tuberculosis prevalence for each region for one year, allowing us to model a higher burden of tuberculosis in Delhi compared to Gujarat, but this did not allow us to incorporate a data-driven time trend. There were no region-specific calibration targets to constrain mortality, and therefore we found large uncertainty on the number of cumulative deaths averted due to large uncertainty around trends in mortality. Additionally, there were no region-specific estimates of infection prevalence, which was a key determiner of vaccine impact. We assumed that differences in mortality and infection prevalence between Delhi and Gujarat would align with the differences observed in disease prevalence, and modelled a higher mortality rate and infection prevalence in Delhi. To continue modelling subnational regions, more region-specific data to inform model predictions is urgently needed.We represented population size and age structure for Delhi and Gujarat by utilising all available demographic data and projections for the regions and extrapolated forward to 2050 where no data was available. As the risk of tuberculosis is age-dependent, if we incorrectly represented the demographic structure of the regions we may have over or underestimated the health impact possible with new vaccines.The no-new-vaccine baseline assumed that the current quality and coverage of services would continue. We did not consider improvements in social determinants which may occur over the time-period. If the burden projected in the no-new-vaccine baseline was higher than reality, we may be overestimating the health benefit and cost-effectiveness of vaccines. We introduced vaccines into the population independently, and did not integrate with other available services, such as tuberculosis preventive therapy, which may alter future outcomes.E and BCG-revaccination are likely to be impactful and cost-effective if introduced in Delhi and Gujarat. Delhi and Gujarat were selected as the modelled regions to represent a high and low burden setting respectively. There were differences in vaccine impact between regions, which were only revealed through subnational modelling and considering differences in disease and infection prevalence. While national models are beneficial to demonstrate potential impact overall, if there are distinct epidemiological differences within the country the impact can vary.Our study has demonstrated that M72/AS01E, and confirming effectiveness in uninfected individuals, which was an important driver of impact and cost-effectiveness in Gujarat. Further research to reduce vaccine characteristic uncertainty and generate subnational models for additional regions is needed to maximise success of vaccine delivery in India.Our results support the need for more infection prevalence surveys. We discovered how important the modelled infection prevalence of each region was to determine the likely impact if vaccines may only work in those who are uninfected or those who are infected. Age-specific regional estimates of infection prevalence would help to inform delivery strategies for vaccines only effective in people with a particular infection status, and improve estimates of vaccine impact. Another key area for future research is investigating the mechanism of effect of M72/AS01Supplement 1


Developmental coordination disorder (DCD) is characterised by poor motor coordination, which interferes with the ability to execute activities of daily living (ADLs). Combined action observation and motor imagery (AOMI) involves observing movement videos whilst imagining simultaneously the sensations of executing the same movement. Laboratory-based research indicates that AOMI can help improve movement coordination in children with DCD, but no previous research had investigated the efficacy of AOMI interventions for learning ADLs. This study investigated the efficacy of a home-based, parent-led, AOMI intervention for learning ADLs in children with DCD. Children with confirmed (n = 23) or suspected (n = 5) DCD , aged 7–12 years, were assigned to either an AOMI intervention or a control intervention (both n = 14). Participants attempted the following ADLs at pre-test (week 1), post-test (week 4), and retention test (week 6): shoelace tying, cutlery use, shirt buttoning, and cup stacking. Task completion times and movement techniques were recorded. The AOMI intervention produced significantly faster task completion times than the control intervention at post-test for shoelace tying, and significantly improved movement techniques for shoelace tying and cup stacking. Importantly, for children who could not tie shoelaces at pre-test (n = 9 per group), 89% of those following the AOMI intervention learnt the skill successfully by the end of the study, compared to only 44% of those following the control intervention. The findings indicate that home-based, parent-led, AOMI interventions can aid the learning of complex ADLs in children with DCD, and may be particularly effective for facilitating the learning of motor skills that do not currently exist within these children’s motor repertoire. Developmental coordinating disorder (DCD) is a lifelong neurodevelopmental condition prevalent in 5–6% of children . Key indConventional treatments for DCD often emphasise repetitive physical practice of tasks or components of these tasks . It has It has been suggested that these mental training strategies may enhance movement outcomes through refining internal models . A promiRecently it has been proposed that the simultaneous use of AO and MI  could further enhance motor function for children with DCD compared to either technique alone , 28. AOMWhile this evidence showing benefits of AOMI training in children with DCD is encouraging, to date research has focused on abstract motor tasks and only examined acute training effects within a single testing session. No research has yet investigated the potential benefits of AOMI on activities that are essential for daily living, nor explored the longitudinal effect of AOMI training on motor learning in children with DCD. The current study, therefore, investigated the efficacy of a home-based and parent-led AOMI intervention involving the training of four ADLs which children with DCD are known to struggle executing; shoelace tying, shirt buttoning, cutlery use and cup stacking , 37. Basfs = 0.47) and α set at 0.05, 6 participants would be required per group  to detect significant behavioural differences with a power of 90%. Subsequent recruitment was conducted to ensure analyses were adequately powered and to accommodate any potential dropout across the longitudinal intervention.A power analysis was initially conducted using the repeated measures, within-between interaction test function in G*Power 3.1. Estimates were based on previous motor learning research by Marshall et al.  where chth percentile on the MABC-2 were classified as confirmed DCD and those without a therapist-confirmed DCD diagnosis but who scored below the 5th percentile were classified as suspected DCD. Ethical approval was granted by the Health and Education Research Ethics and Governance Committee at Manchester Metropolitan University , and both parents/guardians and children provided their written informed consent and assent to participate, respectively. In addition to the final sample of 28 participants, four participants initially recruited and enrolled to the intervention  were excluded due to their non-completion of the study (insufficient number of training sessions completed during the training phase for three participants [< 75% of training sessions] and one non-completion of the retention phase).Twenty-eight children aged 7 to 12 years  with confirmed (n = 23) or suspected (n = 5) DCD were recruited from England, UK  completed pre-test, post-test and retention test measurements to determine the efficacy of a home-based AOMI intervention for children with DCD. Interventions were delivered at the participants’ home by parents after receiving training in their respective intervention. After the pre-test, children were randomly allocated to either an AOMI group or a control group (both n = 14) via minimisation , a recogThe ADLs selected for the current study were shoelace tying, cutlery use, shirt buttoning and a cup stacking task see . The choParticipants first completed the full age-appropriate version of the MABC-2, involving the measurement of manual dexterity ability, catching and throwing, and balance ability. Following this, children completed the MIQ-C  to assesAOMI group. Children and parents in the AOMI group were informed that the aim of the study was to investigate whether the implementation of AOMI alongside physical practice may facilitate the learning of ADLs. Accordingly, both parents and their children were introduced to the concept of AOMI, and the parents were trained on how to deliver an AOMI intervention at home using a tablet computer (10.1-inch Samsung Tab A) with the AOMI intervention uploaded .Adherence and monitoring. To ensure adherence throughout the intervention, participants in both groups maintained contact with the research team via weekly video calls and email. This provided ample opportunities to discuss progress and answer questions about their respective interventions. Furthermore, both children and parents received a booklet tailored to their respective intervention. The children’s booklets, titled ‘The Young Scientist’s Diary’, gave a brief and simplified overview of their intervention. Children were asked to collect data as the young scientist by reporting how much they enjoyed each training session, using a Likert-type scale developed using emojis . Participants self-initiated each trial by starting the timer and then stopped the timer after finishing each trial. If children were unable to complete one trial of an ADL, incompletion times were imputed to allow analysis of these trials  and retention , compared to pre-test . There was no significant difference between post-test and retention performance times (p = 0.24). Importantly, there was a significant interaction which indicated that at post-test the AOMI group  performed significantly faster than the control group . This difference, however, was not retained at the retention phase (p = 0.162), despite a visual trend in that direction. See As expected, all children performed significantly faster at both post-test  = 4.5, p = 0.018, M = 44.14, SD = 24.04) and retention , compared to the control group .Interestingly, at pre-test only 10 out of 28 children were capable of performing the shoelace tying task (5 children per group), with a sub-sample of 18 children (9 per group) unable to complete this task successfully. Within this sub-sample, 89% (eight out of nine) of children following the AOMI intervention were able to complete this task successfully by the post-test, while only 44% (four out of nine) of those following the control intervention were able to perform the task after training. An exploratory analysis was conducted including data only from those unable to complete the shoelace tying task successfully at pre-test See  Panel C.M = 3.6, SD = 1.58, p = 0.002) and retention , compared to pre-test . Comparison of post-test and retention test performances revealed no difference (p = 0.671). A significant interaction was revealed at post-test (p = 0.002), with the technique used by the AOMI group  found to be significantly better than the technique used by the control group . This effect was also evident at retention (p = 0.011), where the technique of those in the AOMI group  was significantly better than that of the control group . See For the ratings of movement technique, all children significantly improved at post-test (p = 0.08) or retention (p = 0.18), see Further analysis of participants unable to tie shoelaces at pre-test revealed no significant group differences for movement technique at post-test  and retention  compared to pre-test . Post hoc testing revealed no differences between post-test and retention performance times (p = 0.947). Furthermore, no group differences were revealed at post-test or retention phases (ps > 0.05). See Children showed significant improvements in performance at both post-test  and retention , when compared to the pre-test . Comparison of post-test and retention techniques were not significant (p = 0.99). As shown in M = 3.4, SD = 1.29) had a better technique than the control group . This group difference was also present at retention (p = 0.01), where the technique of the AOMI group  was significantly better than the control group .Analysis of technique ratings showed that all children improved techniques across post-test  and retention  compared to the pre-test . As can been seen in the data profiles of p = 0.906). Furthermore, no differences were identified between groups at any of the test phases (ps > 0.05).All children showed significant improvements at post-test  and post-test  technique rating scores was identified, where techniques were better at post-test . No technique differences were revealed between the post-test and retention test (p = 0.972). There were no significant interactions between groups and test phase (ps > 0.05).A significant difference between pre-test  and retention  compared to the pre-test . No differences between post-test and retention performances were found (p = 0.148) and no significant group differences were revealed across test phases  to post-test  and pre-test to retention . No significant difference was found between post-test and retention phases (p = 0.71). Comparisons of groups across test phases revealed no significant interactions (ps > 0.05).As shown in The purpose of the present study was to investigate the effect of a 4-week AOMI intervention on the performance and learning of four ADLs in children with DCD. Based on previous AOMI research in this population , 36, it The finding that AOMI improvements only occurred in the shoelace tying and cup stacking tasks may be explained by the relative complexity of the four ADLs. Motor skill complexity refers to the number of different components involved in a skill, with higher complexity skills comprising more individual components . The shoThere are two, not mutually exclusive, explanations for the beneficial AOMI effects reported in this study. The first explanation relates to the correspondence between the brain regions reportedly hypoactive in children with DCD and those active during AOMI. Motor areas most frequently identified to have supressed activations during movement execution and motor simulation in DCD populations include the inferior frontal gyrus (IFG), precentral gyrus and cerebellum. These areas are important for movement planning, execution and coordination , 10. AltExamination of the technique data for the shoelace tying and cup stacking tasks supports the argument that AOMI interventions can contribute to improved imitation ability in children with DCD  in the AOMI group could tie their laces successfully, yet only four out of nine (44%) in the control group could complete this task. In addition, the statistical analysis confirmed that task performance was significantly better in the AOMI group at post-test and this difference was maintained at retention. Given the inability of these participants to complete this task at pre-test, this represents a true motor learning benefit for AOMI, as opposed to motor refinement in those who could already perform the task . This coThe current study aimed to expand on previous findings showing beneficial effects of acute AOMI training in abstract motor tasks for children DCD see –36), thr, thr36])combined  with motor imagery (AOMI) on complex, bimanual movement tasks. This is the first study to show beneficial effects of home-based AOMI interventions for developing performance on fundamental ADLs in children with DCD over a longitudinal period. While both groups improved their performances, AOMI was more effective in supporting the more complex ADLs, and the greatest benefits were found for children with the lowest proficiency in these tasks. These findings align with recent research reporting benefits for AOMI training in children with DCD [This study adds to the expanding body of literature evidencing benefits for simulation-based interventions in children with DCD , 19, 26.with DCD , 36. MorS1 FileThese scales allowed children to record their enjoyment and allowed parents to record training dates, times and their child’s motivation and progress.(DOCX)Click here for additional data file.S2 FileThese scales were used to measure movement technique for each activity of daily living.(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.


Currently, large populations have been vaccinated against COVID-19. The whole inactivated Sinopharm COVID-19 vaccine has been the main available COVID-19 vaccine in Iran. Ocular inflammatory reactions have been reported following vaccination. The present case reports aim to introduce four cases of uveitis after the Sinopharm vaccine administration.Our first reported case is a 38-year-old woman with a positive medical history of inactive ulcerative colitis. Active uveitis had developed following the second dose of the COVID-19 vaccination. The remaining three cases were healthy individuals who developed the first episode of uveitis, after the COVID-19 vaccine administration. Vogt-Koyanagi-Harada syndrome was the final diagnosis in one of the aforementioned cases. All four patients demonstrated favorable responses to corticosteroid treatment.These observations are in line with incoming reports from all around the world and raise concerns about the possibility of post-vaccination uveitis development, especially in cases with a previous history of auto-immune systemic diseases or inactive uveitis. COVID-19 is a contagious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Vaccination is one of the most effective ways to control its spread. The whole inactivated COVID-19 Sinopharm vaccine has been the main available and widely used COVID-19 vaccine in Iran.There have been reports of inflammatory eye diseases following Sinopharm vaccination. El Sheikh et al. reported an 18-year-old female case of juvenile idiopathic arthritis with bilateral anterior uveitis after receiving a second dose of the Sinopharm vaccine . Goyal eA 38-year-old woman with a history of ulcerative colitis for 20 years was referred to the emergency department of Khatam-Al-Anbia Eye Hospital, affiliated with Mashhad University of Medical Sciences, Mashhad, Iran. According to the patient’s medical history, she experienced inactive systemic disease for the last four years without any ocular manifestations albeit not taking any medications. She had complaints of blurred vision, ocular pain, and photophobia in her left eye six days after inoculation with the second dose of the Sinopharm vaccine. No other ocular or systemic symptoms were observed. The best-corrected visual acuity (BCVA) was 20/32 for her left eye. The anterior segment examination of the left eye showed posterior synechia, ciliary injection, fine keratic precipitates (KPs), 4 + anterior chamber (AC) cells and flare, and 1 + vitreous cell, according to SUN Working Group classification [A 15-year-old female who developed bilateral signs and symptoms including red-eye, photophobia, and blurred vision three days after inoculation with the first dose of the Sinopharm vaccine was referred to our medical center. We examined the patient two days after the disease onset. The patient’s BCVA was 20/25 for both eyes, and slit-lamp examination revealed bilateral ciliary injection, symmetrical 4 + AC cell, and 2 + flare in both eyes. The past medical, ocular, and drug histories were unremarkable, and systemic workup results  were inconclusive. Macular OCT and fluorescein angiography (FA) showed no other pathological findings (Fig. A 65-year-old male was referred to our clinic with complaints of blurred vision and photophobia 12 days after inoculation with the second dose of the Sinopharm vaccine. The patient reported malaise and anorexia after the injection of the second vaccine dose. Eye redness, pain, photophobia, and blurred vision occurred six days later. Slit-lamp examination showed 3 + AC cells and 1 + vitreous cells in the right eye and 2 + AC cells and no vitreous cells in the left eye. Fundus examination revealed disc hyperemia and two small patches of retinitis measuring less than two-disc diameters in the posterior pole of the right eye (Fig. A 23-year-old female patient with no previous medical or medication history was referred to our clinic for evaluation of progressive vision loss. She had received a second dose of the Sinopharm vaccine two weeks prior and reported episodes of malaise and fever for one week. One week after inoculation, the patient experienced severe headaches and the vision in both eyes started to decline. Topical steroid drops had been administered to her for bilateral anterior uveitis with no effect, and she was then referred to our clinic for further evaluation. BCVA was 20 /100 for the right eye and 20/630 for the left eye. Slit-lamp examinations revealed fine KPs, 3 + AC cells, 1 + flare, and 1–2 + vitreous cells in both eyes. Fundus examination showed bilateral disc hyperemia and multifocal posterior pole serous retinal detachment in both eyes. FAF, FA, and indocyanine angiography images are presented in Fig. At the time of the last follow-up three weeks after treatment, BCVA was 20/20 and 20/30 in the right and left eye, respectively Fig. .Fig. 6MaVaccine-related uveitis is a well-established and well-documented phenomenon and has been reported about all available vaccines. Although exact pathogenesis has remained unclear, molecular mimicry, antigen-specific cell, and antibody-mediated hypersensitivity reactions have been proposed as possible hypotheses .Reports of post-COVID-19 immune-mediated diseases like uveitis, Guillain–Barre syndrome, or systemic lupus erythematosus have been published. It is postulated that COVID-19, the same as other viruses, can disturb self-tolerance and incite autoimmune responses through cross-reactivity with host cells. The same immunologic processes may occur following vaccination .In the current article, we presented four cases of uveitis, including a case of VKH syndrome, after inoculation with inactivated COVID-19 Sinopharm vaccine. The first case was a patient with unilateral anterior uveitis and reactivated ulcerative colitis six days after receiving the second dose of the Sinopharm vaccine. The patient’s underlying disease had been inactive for four years without any medication. Ocular manifestations responded well to topical and systemic steroids, and systemic medications were initiated for treating active ulcerative colitis. In case 2, we observed the onset of bilateral anterior uveitis in an otherwise healthy female with no ocular history. Inflammation completely resolved after topical treatment and findings in our systemic workup were unremarkable. The third case developed panuveitis with two foci of posterior polar retinitis patches in the right eye and isolated anterior uveitis in the left eye. The patient responded to the combination of oral and topical steroids with complete resolution of uveitis and retinitis patches. Regarding the normal results of local and systemic evaluations in this patient, as well as the appropriate clinical response to the corticosteroid treatment, it can be concluded that the uveitis was a non-infectious immune-mediated type possibly triggered by the COVID vaccine.The last case in this series was a young female diagnosed with probable VKH syndrome after inoculation with the second dose of inactivated vaccine. VKH is a multisystem immune-mediated disease presenting with bilateral, non-necrotizing granulomatous panuveitis, and serous retinal detachment associated with neurological, auditory, and/or dermatological disorders . This coThere have been multiple reports of VKH syndrome following vaccination for influenza , yellow It has been suggested that dysregulation of the immune system and molecular mimicry are possible etiologies of uveitis following vaccination. Xin Le Ng et al. reviewed the ocular side effects of the COVID-19 vaccination. Regarding the overlap between the ocular manifestations of the vaccination and COVID-19 itself, researchers suggested a possible similar mechanism .The appropriate response to corticosteroid treatment is the common denominator of all the introduced cases. The prompt response confirms that inflammation responds well to treatment without any significant vision-threatening complications, regardless of the type of uveitis. Therefore, ophthalmologists’ awareness of complications of the COVID-19 vaccine is important for reassuring patients regarding the benign course of inflammation and conducting specific research to identify the possible stimulating particle of the COVID-19 vaccine shortly. One possible mechanism of vaccine immunogenicity may be related to the adjuvant particles of vaccines that trigger immunologic reaction via multiple mechanisms, including antigen-presenting-cells activation, cytokine release, and sustained release of antigen due to depo effect of adjuvants, antibody production, and cellular recruitment .Another responsible mechanism might be related to the existence of specific viral particles in vaccines, including the COVID-19 vaccine, which causes an immunologic reaction in the body, involving uveal tissues as potent immunogenic tissue in the eye.It should be noted that no definite causal relationship can be established based on the cases in this report, and the possible ocular side effects are rare despite the large scale of worldwide vaccination. However, our cases and similar reports around the world raise concerns about the possibility of uveal inflammations in cases with a history of auto-immune systemic diseases or inactive uveitis after the COVID-19 vaccination.