Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors  for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0–30 cm) soil samples were collected from the covers 1330 km The information on spatial distribution of soil properties is important to mitigate the problems on food security, soil quality and land degradation ranging from the local, regional and national to the global level –3. PolicThe predictive soil mapping and modeling studies are based on commonly accepted soil formation factors. The empirical deterministic soil formation model introduced by Dokuchaev was formulated by Jenny as climate, organism, relief, parent material and time ,8. The uMachine learning (ML) techniques using spatial variables and measured values of the attribute of interest are very popular in mapping of soil characteristics. The machine learning algorithms based on various approaches as well as geostatistical methods have been used in soil classification and mapping of soil properties –15. ThreThe potential of soils to store organic and inorganic forms of carbon has a greater impact on climate change and global warming than atmospheric carbon . The manThe SOC is a spatially highly variable soil property, while easy to measure. Therefore, estimation of SOC content in areas larger than a single field might be quite difficult. The spatial variation of SOC content is closely related to pedogenic factors, vegetation and topography . ClimateThe objectives of the study are (i) to produce a high spatial resolution SOC map to develop effective policies on problems of land degradation, and soil and food security at a local scale; (ii) to include the most effective and the least number of factors to the model, considering the hierarchical structure of the factors affecting the SOC, without compromising the estimation accuracy; (iii) to compare the spatial SOC estimation success of Inverse Distance Weighting, Ordinary Kriging (OK), Empirical Bayesian Kriging, Multi-Layered Feed-Forward Backpropagation Network (MLP) and MLP-OK Hybrid models; and (iv) to develop a mathematical MLP model to practically estimate the SOC content under similar conditions of the study area.2 land. Long-term (2001–2021) annual average temperature of the study area is 17.7°C and the average precipitation is 636 mm ..75].MLP-OK and RMSEint are the root mean square errors of the multi-layered feed-forward backpropagation artificial neural network and ordinary kriging hybrid approach, and a specific interpolation method used in the study, respectively.In the equation; RMSEDescriptive statistics of the soil properties, climate characteristics and vegetation indices at the sample points used in the modeling are given in Land use type of sampling points with high SOC content was mainly oak, while conventional wheat production with intensive soil tillage using moldboard plough has been used for a long time at low SOC sampling points. Organic carbon losses under long-term conventional tillage systems have been reported in Mediterranean region of Turkey ,79. In aThe distribution of SOC data was slightly skewed, therefore, the median value (1.32 and 1.34%) of the training and testing areas better represented the mean SOC pool compared to the arithmetic mean of the study area . The samThe relationship between soil and environment variables in digital soil mapping can be explained by using linear models with a few standard soil properties and environmental factors. However, the relationship between three or more factors and soil property and environmental variables can only be explained using advanced models such as non-linear machine learning algorithms, ANNs or fuzzy logic due to the non-linear and unstable relationship between soil properties and environmental variables –84. An Mω is the weights of connections between inputs and hidden layer, v is the weights between hidden layer unit and output layer, bh is the bias value for hidden layer neurons . The developed MLP model will be practical to predict the SOC content by determining only the input data in different regions with similar characteristics.In the model equation;  neurons , and bO1The MLP network was established in MATLAB, and the network was trained using the soil properties and environmental variables of the training points. The test data set was used to determine the optimum selection of activation function, number of hidden layers and other parameters of the model. The final structure of the model was determined during the calibration stage, when the MSE (0.012) and RMSE (0.109) values were the most appropriate according to the information obtained from the literature and trial and error method. The graph of the measured and estimated values of the MLP is given in The Mean Absolute Error (MAE) may not always clearly indicate the relative size of the error. Therefore, distinguishing a major error from a minor error may be sometimes difficult. The MAE was calculated as a percentage to deal with this problem . If the After the MLP network has been constructed for the study area, the final SOC content was calculated as the sum of the estimated values of the network, and the residual values estimated by ordinary kriging . The resThe nugget/sill ratio was used to identify spatial dependency classes of SOC content . The nugLikewise, Abdulwaili et al.  reportedPredicted SOC content maps obtained by the interpolation methods used in this study revealed the spatial variation of SOC content in the study area . In partThe samples were divided into 4 elevation groups  to reveal the relationship between the SOC content and the elevation of the sampling points. The difference in SOC content between elevation groups is shown in The indices used to assess the accuracy of the estimated SOC content by all estimation methods and in all test points are given in The Taylor diagram is used to show the quality of model predictions against observed (reference) values . The diaThe methodology significantly improved the accuracy of spatial distribution mapping for SOC content. Other interpolation methods, which could not adequately reflect local variations, either overestimate low values or underestimate high values. This problem was significantly corrected both by using the nonlinear model and by taking the factors influencing the SOC content into account. In addition, the results showed that the spatial distribution of soil properties can be successfully estimated with machine learning algorithms by using remote sensing data and environmental variables. The finding confirmed that, similar to the conclusions of Zhang et al. , ANNs caIn this study, the accuracy of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Feed-Forward Backpropagation Network (MLP) and MLP-OK hybrid estimation models were tested to determine the spatial distribution of soil organic carbon content (SOC) in Besni district of Adiyaman province, Turkey. The accuracy of residual MLP-OK hybrid model to estimate the spatial distribution of SOC content at the regional scale was higher than the other estimation methods tested. The interpolation methods were compared with the MLP-OK model to evaluate the prediction accuracy. An optimal MLP architecture was prepared with three hidden layers, tansig activation function and purelin output function in accordance with the selected pedological and environmental factors for the SOC content estimation. Spatial dependence of MLP residuals was strong. The SOC content map produced with MLP-OK revealed more details than OK, IDW and EBK, especially in the south-central part of the study area, where intensive agricultural practices are carried out.The Taylor Diagram showed that the MLP-OK model had a lower RMSE and a higher Pearson correlation coefficient for the test sampling dataset. The RMSE value improved between 75 and 78% compared to other methods. The results indicated that estimating the MLP residuals with OK improves the spatial estimation accuracy. In addition, the lower and upper limits of the estimated and measured values were more compatible in the SOC map produced by MLP compared to the interpolation models that only consider the spatial variability. Because the MLP uses many factors such as soil properties, topography, and biota in the estimation process. The MLP-OK method reduced the spatial prediction errors at the local scale SOC content mapping, which requires more attention compared to the other soil properties. The result confirms that the MLP-OK hybrid method can be used as an effective method in digital soil mapping and may contribute to digital soil mapping efforts at regional, national, and global scales. In addition, the study showed that data quality and model architecture are of great importance for the ANN to produce successful prediction models. The findings indicate that in SOC model studies, it is possible to reduce the size of input data without negatively affecting model success. In this context, the use of input such as humidity coefficient, drought coefficient and climate coefficient, which represent many environmental factors, may increase the estimation ability of the models.S1 Table(XLSX)Click here for additional data file.


Giant cell tumors (GCT) are benign but locally aggressive neoplasms composed of osteoclast-like giant cells and fusiform to ovoid mononuclear stromal cells. GCT generally comprise 5-10% of all benign bone tumors; they appear most frequently between the second and third decades of life. These tumors are also distributed throughout the vertebral column. Approximately half of all spinal GTCs develop in the sacrum. Many cases remain clinically silent and are discovered incidentally during the study of minor trauma. Symptomatic tumors often mimic other common spinal pathologies. Imaging studies ideal for diagnosis are CT and MRI. The techniques used in the treatment of giant cell tumors are curettage or intralesional surgery, block resection, radiotherapy, and chemotherapy. Herein, we report on a 23-year-old female patient diagnosed with a tumoral mass in the anterior part of the sacrum, suggestive of GCT. The lesion was completely excised in two consecutive surgeries, and lumbopelvic fixation was performed with favorable immediate postoperative results. Careful surgical planning with a multi-disciplinary approach leading to block resection still remains the most viable option for the treatment of vertebral GCT. Giant cell tumors (GCT) are rare and benign tumors of the bone. They are composed of osteoclast-like giant cells and fusiform to ovoid mononuclear stromal cells . GCT genImaging studies ideal for diagnosis are CT and MRI. CT shows an attenuation of the soft parts with defined margins and occasional sclerosis and osteolytic findings . VariablA female patient of 23 years of age with no hereditary family history of importance and an unremarkable personal medical history up-to-date was referred to the department of neurosurgery. She had presented with pelvic pain emanating from the lumbo-sacral region, specifically at night. The pain radiated to the lower left limb, causing an alteration of the gait and a loss of the tip-toe gait. She had a left-sided foot drop and an unintentional weight loss of 14 kg in the last year.A pelvic ultrasound was performed, which revealed an intrapelvic mass. Imaging studies were completed with lumbo-sacral and pelvic CTs and MRIs. The CT revealed a tumoral mass in the sacrum predominantly on the left side, extending superiorly to the left L5 transverse process, laterally to the lateral sacral crest, and obliterating the S1 and S2 neural foraminae  were performed. Approximately 80% of the tumor mass was resected. The excision of the tumor was very laborious and with a significant blood loss (mostly from the tumor bed and the bone margins), requiring a transfusion of two units of erythrocyte suspension. The surgical margins of the anterior pathway were contaminated with tumor rupture and dispersion of the material.After a week, the second surgical step was employed via a posterior lumbo-sacral approach. Through this approach, the remaining tumor was resected. A tumor mass was observed that invaded the left paraspinal muscles, which was resected with a wide surgical margin. A subperiosteal dissection of L3 to S3 was performed to clearly identify spinous processes. For partial sacrectomy, an S1 to S3 laminectomy was performed, and the S1-S2 roots were carefully identified and preserved. There was partial tearing of the dura mater, which was sutured. An L5-S1 discectomy was performed. The void space left, especially in the left sacroiliac joint, was filled with polymethylmethacrylate (PMMA). Bilateral transpedicular polyaxial screws were placed at the L4 and L5 levels. Two rods were placed, which were connected with a bar-to-bar connector to the screws placed in the iliac wing. There were no surgically sacrificed nerve roots.In the immediate postoperative period of the second surgical period, the patient presented paresis in the extension of the knee and dorsiflexion of the left ankle, accompanied by hypoesthesia of the L5-S1 dermatome. Postoperative CT and X-ray analysis Figure  showed tThe patient has consented to her radiological images and results being published for medical use as long as they are anonymized.The surgical treatment of sacral tumors is one of the great challenges for every spinal surgeon. The sacral canal and the pelvis are able to adapt to growing lesions, so sacrococcygeal tumors can be diagnosed after extensive growth . Tumors The need for careful preoperative planning and patient selection cannot be emphasized enough. Conventional radiological studies are usually insufficient in providing the detailed anatomical properties of the tumor and its surroundings. The surgeon must keep in mind that the oversized tumor may have displaced the normal positioning of anatomical structures and destroyed bony landmarks. Thus, all possible modern imaging technologies should be employed to prepare the surgeon for the upcoming challenge. The preoperative planning performed in a multi-disciplinary environment allows for the discussion of different approach strategies and the proper combination of techniques. Each surgical specialty has a different appreciation of the different anatomical structures located in the sacro-coccygeal region that prove beneficial in the treatment of the patient.Total and partial sacrectomy present a high risk of morbidity leading to motor, urinary, bowel, and sexual dysfunctions. The extent of resection of the sacrum can also cause lumbo-pelvic instability. Thus, the amount of resection, determining the surgical borders, and the possibility of root sacrifice are vital in predicting the functional outcome of the patient . The patGiant cell tumors are benign but locally aggressive and highly recurrent lesions, with poor responses to radiation and chemotherapy. The oncological goal of surgery is total excision.The broad resection of the tumor and the decompression of the neural elements associated with instrumented fusion allow for a stable spine, preserve or restore neural function, and prevent tumor recurrence . This caThe clinical presentation, the age of the patients, and the long-term surgical results make the reporting of complex cases treated by multidisciplinary teams extremely valuable. The broad resection of the tumor and the decompression of the neural elements associated with instrumented fusion allow for a stable spine, preserve or restore neural function, and prevent tumor recurrence. Tumor excision followed by fixation can reduce pain, decrease neurological deficits, and maintain or regain normal gait .The primary treatment of most sacral tumors remains surgical as chordoma, chondrosarcoma, and some giant cell tumors are resistant to radiation therapy and chemotherapy. Long-term control or even the cure of the disease can be achieved by a block resection of the tumor with sufficient margins. But due to the complexity of the sacro-coccygeal region and the need for lumbo-pelvic stability, a multi-disciplinary approach with meticulous preoperative planning is a must for sacral tumors. Even the most complex giant cell tumors of the sacrum can be successfully treated, but the patient should always be made aware of the possible complications and their implications for the remaining period of their life while including their views in the decision-making team.


We present the case of an 82-year-old patient who was treated by our liaison psychiatry unit after a suicide attempt through prescription-drug overdose. The patient had been diagnosed with Parkinson’s disease (PD) ten years prior to his admittance and was being treated with carbidopa/levodopa and non-ergot dopamine agonists.Impulse control disorders and depression are the most prevalent neuropsychiatric manifestation of PD. According to several sources, this symptomatology is underdiagnosed and undertreated, causing helplessness and distress to patients and their caregivers. Likewise, the accumulated evidence suggests that certain drugs can contribute to the appearance of the aforementioned symptoms.A case report is presented alongside a review of the relevant literature regarding the neuropsychiatric manifestations in the context of PD and the diagnosis and treatment of these symptoms.During his treatment, ropinirole was removed while quetiapine was progressively administered (up to 150mg/day). Carbidopa/levodopa regime was increased causing visual hallucinations and delusional jealousy. A careful balance between antiparkinsonian and antipsychotic medication needed to be achieved before discharge.Neuropsychiatric manifestations in the context of PD are more prevalent than what was thought in the past. Certain medications, particularly non-ergot dopamine agonists could potentially contribute to the onset of these symptoms. Moreover, these manifestations can be underdiagnosed due to the stigma or social burden imposed upon family and / or caregivers. It is important that recent advances in the understanding of non-motor symptomatology of PD could permeate clinical practice to achieve an adequate identification and treatment of these symptoms.


Upper limb amputation severely affects the quality of life and the activities of daily living of a person. In the last decade, many robotic hand prostheses have been developed which are controlled by using various sensing technologies such as artificial vision and tactile and surface electromyography (sEMG). If controlled properly, these prostheses can significantly improve the daily life of hand amputees by providing them with more autonomy in physical activities. However, despite the advancements in sensing technologies, as well as excellent mechanical capabilities of the prosthetic devices, their control is often limited and usually requires a long time for training and adaptation of the users. The myoelectric prostheses use signals from residual stump muscles to restore the function of the lost limbs seamlessly. However, the use of the sEMG signals in robotic as a user control signal is very complicated due to the presence of noise, and the need for heavy computational power. In this article, we developed motion intention classifiers for transradial (TR) amputees based on EMG data by implementing various machine learning and deep learning models. We benchmarked the performance of these classifiers based on overall generalization across various classes and we presented a systematic study on the impact of time domain features and pre-processing parameters on the performance of the classification models. Our results showed that Ensemble learning and deep learning algorithms outperformed other classical machine learning algorithms. Investigating the trend of varying sliding window on feature-based and non-feature-based classification model revealed interesting correlation with the level of amputation. The study also covered the analysis of performance of classifiers on amputation conditions since the history of amputation and conditions are different to each amputee. These results are vital for understanding the development of machine learning-based classifiers for assistive robotic applications. The upper limb, consisting of the arm, elbow, forearm, wrist, and fingers, is the most dexterous part of the human body that makes activities of daily living possible. The human hand has seven axes along with 27 degrees of freedom of wrist and fingers that helps in the hard and soft manipulation of objects in our dynamic environment . Loss ofSensing and Data acquisition: An active upper limb prosthesis relies on EMG electrodes, an inertial measurement unit (IMU), and an inclinometer for the acquisition of muscle signal, movement of arm, and tilt of the arm, respectively, for understanding the motion intention in the users. These sensors are sensitive to noise making it harder to extract relevant information. Furthermore, the placement of these sensors is not standardized since amputees have different kinds of stump and amputation. Furthermore, bio-signals acquired from amputees are noisier than intact counterparts due to the absent muscle. Thus, data acquisition of bio-signals is challenging, especially in amputees.Data Processing: Since EMG signals obtained are polluted with motion artefacts, electronics interference, electro cardiac signals, ambient noise, etc., there is a need to clearly understand the signals and process them accordingly to make it fit for classification of motion intentions. This stage includes rectification, filtering, and normalization for removing the noise and increasing readability of the data acquired through the sensors.Feature extraction: Features are the usable units of data from raw signals. Feature extraction is a fundamental component to transform the raw data into usable input for the classification algorithm. There are three kinds of features in EMG signals: time domain features, frequency domain features, and time-frequency domain.Time Domain Features: These are features captured from the amplitude of an electric signal in each period. Root Means Square (RMS), Window Length (WL), Slope Sign Change (SSC), Zero Crossing (ZC), Enhanced Mean Absolute Value (EMAV), etc., are some of the time domain features used in relevant works [nt works ,7.Frequency Domain Features: These features are extracted by Fourier frequency decomposition. Short fast Fourier transform (SFFT) and discrete fast Fourier transforms (DFFT) of the signal are some of the features used in previous studies [ studies .Time-Frequency Features: These features are extracted in the complex domain of time and frequency characteristics. Wavelet transforms are one of the features used in previous studies [ studies  ThroughoMotion Intention Classification: The features extracted in the previous step are used to build a motion intention classifier. The conventional myoelectric systems utilize thresholding techniques for classification that is limited due to the variability in signals due to fatigue, change in placements of electrodes, and supporting fewer activity classes. In contrast, machine learning classifiers can learn from the data and capture the variability in the model. Different machine learning algorithms have been used for EMG-based activity classification such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Ensemble Learning, K-Nearest Neighbor (KNN), Random Forest, Artificial Neural Networks (ANN), etc. [N), etc. ,11,12,13Robotic actuation: During this stage, the robot actuators will be activated according to the result of motion intention classification to perform the intended activity with the robotic or prosthetic arm. The motion intentions will translate into specific motion trajectories and applied torques/forces through a kinematic/dynamic model of the robotic arm.The major components of a myoelectric prosthetic system are signal acquisition, data processing, feature extraction, pattern recognition, and actuation. Even though most of these components are similar for any intelligent bio-signal based solutions, the differences amongst amputees who will use such a system pose additional challenges: in amputees, the absent muscle responsible for actuation and the corresponding phantom limb movement which varies across amputees. Hence, the control aspects in EMG-based robotic solutions may vary, which makes each component convoluted and challenging for amputees. Below, each system component is described briefly: Although the given components appear straightforward, there exist a couple of major limitations in the currently available approaches. Most of the research studies focus on EMG classification of activities in intact subjects and directly correlate to the possible success of their technique in amputees . There iLastly, systemic studies of the impact of data processing in performance of the classification model are scarce and the field is still unexplored. Hence, our work sheds light on variations in performance on different data processing and modeling techniques for classifying movement intentions based on EMG sensor data of participating muscles. Developing and comparing machine learning and deep learning models for classification of motion intentions for transradial (TR) amputees.Benchmarking the performance of the motion intention classifiers based on overall generalization across various classes.Systematic study on the impact of sliding window length and time domain features on the performance of classification models.The major contributions of this paper are: Machine learning algorithms have been proven to be a powerful alternative for pattern recognition across multiple disciplines. It is reasonable to view machine learning as a possible means of prediction of motion intention using EMG signals. The earliest EMG classifier was based on SVM, later with the advent of exploration of other learning algorithms, many more algorithms such as Decision tree, KNN, and ANN were used in classification ,13,18 The EMG signals data are modeled as either image (2D array) or time series or combination of both as input to classical machine and deep learning algorithms ,5,6,7. KChen et al.  exploredhttp://ninaweb.hevs.ch/, accessed on 1 September 2020). There are ten datasets of both intact and amputee’s data along with the data from different sensors [It is interesting to note that Coté-Allard et al., Demir et al., and many other researchers used private databases that limited other researchers from reproducing or further exploring other methods to improve the performance of the classifiers ,23. Nina sensors . The ben sensors . M. Atzo sensors . In addition to the selection of the algorithm, sliding window size is also a significant contributor in determining performance of the model in both feature-based and non-feature-based learning. The constraint being 300 ms is maximum delay between intention and actuation of prosthetic solution for smooth and reliable control. A. Ullah et al. explored different sliding window sizes with various classifier algorithms. Furthermore, our work builds on different sliding window sizes and examination of its performance with different classifier algorithms across subjects .Most of the research in machine learning favors average accuracy as a performance of interest ,25,27,28The NinaPro dataset is used as the dataset as its one of the most reliable and well-maintained databases of EMG signals of both intact and amputees. The database serves as an open-source dataset for analysis and improvements in pattern recognition for well-rounded robotic prosthetic solutions for amputees. Since the primary objective of our study is on motion intention recognition of TR amputees through their EMG signals, Database 3 of NinaPro, which contains EMG signals of 11 TR amputees, was used. The dataset was acquired from transradial amputee subjects mimicking through their amputated arm, as far as possible, along with movies of the motion gestures from laptop. The database relies on the phantom limb activity of the amputees that are captured through 12 EMG electrodes placed uniformly around the region of amputation. Through guided visual stimuli, the subjects perform the 50 gestures comprising different finger movements, rotations, force, and grasps . Apart fThe raw EMG data must be processed to reduce the noise and extract relevant features for the classification algorithm. The details of the processing done in our work is described below.Normalization was not required due to absence of maximum amplitude potential and since the data acquisition was performed in single session, it was not needed . Second Different sliding window lengths of 50 ms, 100 ms, 150 ms, 200 ms, and 250 ms were used to arrive at optimum sliding window length with 50% overlap for better performance of the classifier. The study focuses on time-domain features since it is computationally less expensive and faster to compute translating to faster classification. The features used for the study are Root Mean Square (RMS), Enhanced Mean Absolute Value (EMAV), Waveform length (WL), and Variance (VAR). The mathematical representations of each of the features are described in Equations (1)–(4) as: Root Mean Square,Variance,Enhanced Mean absolute Value,ith sample of measurement, and Waveform Length,formance .To analyze the performance of the model across different algorithms and different subjects with transradial amputation, six machine learning/deep learning algorithms were used to build the classifier: Tree algorithm, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Ensemble learning, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). The learning models of this study were selected based on the original classification case study of NinaPro dataset published in ,29. MoreEach of above-mentioned classical machine learning and ANNs were trained on the features extracted from various sliding window lengths and overlaps. This process of feature-based learning is illustrated in 1 score as they elucidated both accuracy and overall learning of each category of classification. These metrics were calculated as: Various combinations of the features were used to train each algorithm to verify the performance of each of the selected algorithms. The performance metrics for the study were average accuracy and average FConvolutional Neural Network is the algorithm used as a non-feature-based learning classifier. The dataset was augmented such that each sliding window (2D matrix input) of the EMG signals was fed to the algorithm. The EMG data were treated as image and the kernels slid over the image to learn more generalized features. This non-feature-based learning approach is described in CNNs are known for capturing features from the dataset. This is made possible by a convolutional layer where the kernel smaller than the image captures meaningful information and the pooling layer provides the statistical summary of the information, making them computationally lighter. Nonlinearity is introduced to the network through the activation function. Since CNNs are known to overfit easily because of millions of parameters encompassed within it, dropouts and batch normalization can be essential to prevent this and generalize the model well. The proposed CNN was built with two convolutional layers followed by a Maxpool layer sandwiched with dropouts (DT), tanh activation function (AF), and batch normalization (BN). The end result was two Fully Connected (FC) layers with dropout in between leading to SoftMax layer . The hypThe performance indexes used were accuracy and F1 score. There was appropriate 10-fold cross-validation and observation of loss function and accuracy on each mini batch that is trained. The learning rate used is 0.001 and the Adam optimizer .Both categories of models were trained across all selected subjects several times and their average performance was recorded. Moreover, several input sliding window lengths and overlaps were experimented with to analyze their impact on the performance of the classifier. The performance metrics were calculated for each of the machine learning models  trained on five different sliding window lengths  across all subjects. The maximum accuracy of each of the algorithm on each sliding window length was analyzed for comparison. It was observed that the accuracy obtained by the dataset is far better than the benchmark metric in Atzori et al. work. This can be attributed to the narrowed activity of interest and hyperparameter optimization. The accuracy of CNN and Ensemble learning-based classifiers were investigated and Ensemble had higher median accuracy than all the algorithms. The accuracy has always been the standard for the performance of EMG-based activity classification. It often leads to an accuracy trap. The average accuracy of the model has failed to convey the overall generalization across all the classes which are crucial for reliable classification. The ANN, CNN, SVM, and Ensemble algorithm, as the applied classification algorithms being the top-four performing models, had varied results on each of the subjects. The sliding windows studied to analyze the relationship between performance of the classifiers and sliding window size for feature extraction-based learning models and featureless models were 50 ms, 100 ms, 150 ms, 200 ms, and 250 ms with 50% overlap. Windows of 300 ms and above were not considered since anything beyond 300 ms along with classification time would impact the real-time behavior of the assistive solutions [Selection of feature is one of the crucial steps in achieving a good performance of the classifier. The performance of our classifier depends on time domain features which are computationally less expensive compared to frequency domain features. Since the causal nature of the signal is being used, selection of the right feature matter even more. The study on features revealed that a combination of features (Q5 combination of all features as given in The aim of the study was to understand the relationship between feature extraction and sliding window length across different classification algorithms with reference to the accuracy and F1 score as a more rounded approach on analyzing the performance. Ensemble learning and deep learning algorithms performed better than other classical machine learning algorithms such as SVM, KNN, LDA, and Tree. Investigating the trend of varying sliding window on feature-based and non-feature-based classification models revealed an interesting correlation with the level of amputation. The study also covered the analysis of performance of classifiers on amputation conditions since the history of amputation and conditions are different to each amputee. These results are vital for understanding the development of machine learning-based classifiers for assistive robotic applications. Learning-based classifiers possess a huge potential in motion intentions classification and development of the assistive robotic systems based on them for amputees. The major limitation of any machine learning and deep learning models is the requirement of huge datasets to build cutting edge, classification models. The dataset used for the research comprises very few subjects and the quantity of data is limited; any generalization on the study of EMG signals needs a bigger, well-maintained dataset. The study undertaken here can serve as the backbone for further analysis on the effect of preprocessing on the dataset over the generalization of classifiers both within and across different subjects, to ensure the robustness and reliability of EMG based amputee solutions. Furthermore, a detailed study on computation can be performed for achieving real time classification for the reliability of solutions. Various assistive systems can be built over the classification model to further uplift subjects’ lives and make them independently able to carry out their activities of daily living.


Blood pressure is one of the basic physiological parameters of human physiology. Frequent and repeated measurement of blood pressure along with recording of environmental or other physiological parameters when measuring blood pressure may reveal important cardiovascular risk factors that can predict occurrence of cardiovascular events. Currently, wearable non-invasive blood pressure measurement technology has attracted much research attention. Several different technical routes have been proposed to solve the challenge between portability or continuity of measurement methods and medical level accuracy of measurement results. The accuracy of blood pressure measurement technology based on auscultation and oscillography has been clinically verified, while majority of other technical routes are being explored at laboratory or multi-center clinical demonstration stage. Normally, Blood pressure measurement based on oscillographic method outside the hospital can only be measured at intervals. There is a need to develop techniques for frequent and high-precision blood pressure measurement under natural conditions outside the hospital. In this paper, we discussed the current status of blood pressure measurement technology and development trends of blood pressure measurement technology in different scenarios. We focuses on the key technical challenges and the latest advances in the study of miniaturization devices based on oscillographic method at wrist and PTT related method at finger positions as well as technology processes. This study is of great significance to the application of high frequency blood pressure measurement technology. Blood pressure (BP) is one of the basic physiological parameters of human physiology. Currently, in the main international guidelines, ambulatory blood pressure monitoring (ABPM), and/or home blood pressure monitoring (HBPM) are recommended for the diagnosis of hypertension. They are mainly carried out using electronic sphygmomanometer based on oscillometry. ABPM can complement HBPM when there is uncertainty around threshold values or there is disagreement. Studies have shown that daily BP monitoring can help to predict mortality and routine management of BP of most known or recessive hypertensive patients –3. ContiRegarding limitation of the measurement accuracy of the current biomedical sensing measurement technology of human movement, this paper divides the scenes of the human body from rest to intense movement into four levels as displayed in When an individual is sleeping at night (Scene 1), BPM technique based on the principle of volume clamp method and pulse wave velocity (PWV) method achieves continuous BP monitoring while the sleep quality is high. A meta-analysis by Saugel demonstrated that, in addition to surgical or critical patients, the use of non-invasive fingertip cuff-based constant volume methods to measure arterial BP and cardiac output parameters is interchangeable with invasive standard methods . It normWhile we mainly consider the situation that people need sedentary or ambush work (scene 2). The duration of sedentary is related to the decrease of cardiac autonomic regulation function –14. SedeWhen an individual is walking (scene 3) in a mall/park, statistical methods can be used to analyze the trend of BP changes in the quiet state of the activity gap . Ningsihvia auscultation or oscillometry , exercise-induced cardiac output increases compared to scene 3, this is associated with increased SBP and DBP . The sigllometry , 22. Nonllometry , 23. Ronllometry  proposedllometry  developeResearchers have studied and developed various BPM technologies suitable for different scenarios as Specifically, from the perspective of the universality of applicable scenes in natural conditions, the applanation tonometry and volume clamp method are generally used for continuous BP monitoring in hospitals or beat- to-beat BP monitoring. These methods usually require professional medical staff to operate. To improve the user experience based on volume clamp method, the control algorithms are improving. Chen and Chen  proposedIn conclusion, the technology maturity degree of auscultatory, applanation tonometry, volume clamp method are mature, and new control methods of volume clamp method are developing. As to oscillometry, the latest wrist-cuff systems are attractive and more clinical validations are performing. PTT related method is one of the hot spots of current researches, it is low cost and has the potential to be applied in all the scenes mentioned in this paper after calibration to achieve medical level instrument accuracy. This paper focuses on the key technical challenges and the latest advances in the study of miniaturization devices based on oscillographic method at wrist and PTT related method at finger positions as well as technology processes –32.The accuracy of HBPM using wrist devices has not been conclusively determined . In the The American Heart Association guidelines suggest that BP cuffs should be 80% of the circumference of the user’s arm and 40% of the width . Due to Generally, studies on structural sizes of the airbag are based on hydrodynamic pressures , assuminAn important basis for implementation of oscillographic BPM techniques is that the artery is at zero transmural pressure, and blood volume pulsation is at its maximum. At this time, it is assumed that extravascular pressure is equal to cuff pressure, while the cuff pressure corresponding to the maximum cuff pressure oscillation is equal to mean pressure , 63. A gvia clinical studies to improve BPM accuracy from a statistical perspective. Jun and Kim , weighted k-nearest neighbor (WkNN) as well as Bagged tree algorithm to classify them. A machine learning BP estimation method that is based on waveform classification was proposed, the direction of algorithm development is to model individual parameters based on the population statistical model.With advances in computing abilities of electronic systems, the significance of the algorithm model has been evaluated. Forouzanfar elucidated on the oscillographic BP estimation algorithm, including the traditional maximum amplitude and derivative oscillation methods, learning algorithm, model-based algorithm, and the algorithm based on pulse shapes and pulse passing time analysis to classify and describe various underlying algorithms . Tian etAlthough hands and wrists occupy a small part of spatial volumes in the human body, they are human body carriers that enable the completion of fine movements. Relatively, the finger has an abundant capillary network, the original signal of the detected volume wave has higher signal-to-noise ratio and perfusion index, and the smaller balloon structure can be used to detect pressure waves. Detection of pulse wave signals in PTT at this site has broad application prospects in BPM in various scenarios , 70.Blood pressure measurement based on the constant volume method and its evolution method at the fingertip is characterized with small balloon volume and high consistency with the average pressure measured by the upper arm. Various studies on the fingertip have been performed , 72. GizThe above frontier studies were based on various aspects, such as physiological bases, theoretical models as well as engineering realization, and discussed the feasibility of designing a miniaturized system of finger ring, finger clip or finger sleeve type based on the constant volume method or improved method at the finger end and the accuracy of BPM, which made a useful exploration for the development of wearable devices that are suitable for multiple scenes and multiple populations.The methods associated with pulse waves and propagation speed discussed in this paper includes: i. Modeling method, which features extracts from the pulse wave morphology or the entire pulse wave as an input signal; ii. Polynomial regression modeling method, in which the pulse wave transfer time is calculated based on the peak value or other characteristic sites of the pulse wave and single-lead ECG; iii. Propagation time, which is based on pulse waves and other signals other than single-channel ECG . A BPM tvia two tasks of mental arithmetic stress and Valsalva’s manoeuvre test, and detected BP changes using the PTT regression model. They reported an improved measurement accuracy. These studies simulated different daily life scenarios, monitored the corresponding BP fluctuations, and predicted blood pressures based on simple linear polynomial and non-linear regression models, which verified the feasibility of the technology in laboratory environments.Chen et al.  and Poonvia the deep learning model based on the seq2seq structure and by adding attention mechanisms using the pulse wave waveform. Without allowing data from the same subjects to be trained and tested, mean absolute error (MAE) of DBP met the requirements of AAMI with MAE of SBP being larger. These studies used public databases in model training to evaluate the accuracy of BP prediction in cases of multiple populations and samples. Machine learning methods include SVM, regression tree (RT), adaptive boosting (AdaBoost), and artificial neural network (ANN). Deep learning methods include convolution neural network (CNN), recursive neural network (RNN), and long-short-term memory (LSTM). Through overall processing of feature extraction and model establishment, it provides an opportunity for the use of single-channel PPG signals of the finger to achieve accurate BP prediction, and during this process calibration technology or strategy is needed in the current researches to measure the absolute BP.Xin et al. , ChowdhuIn addition to main BPM technologies summarized in Advances in material science have provided new opportunities for miniaturization of BPM technologies. Liu et al.  developeIn summary, various BPM technologies in forms of wrist watches or finger rings are accurate in BPM on the wrist or fingers. These technologies have significant effects from the perspective of scientific research and engineering implementation, and can be used for BPM in various scenes under natural conditions in daily life. Although there have been significant advances in development and applications of remote non-invasive BPM technologies, some of the limitations in the existing finger-end BPM technologies include: i. Hand temperatures significantly affect signal quality, leading to measurement errors; ii. For users with peripheral arterial diseases, the reliability of measurement result is low; iii. Measurement errors can not negligible as a result of exercise interference and heart rate abnormalities. The challenges associated BPM technologies in the wrist are: i. The volume pulse wave perfusion ratio and signal-to-noise ratio of the wrist are low, and the stability is poor; ii. Pressure pulse wave detection of wrist radial artery and ulnar artery is susceptible to interference, which requires a high accuracy of detection position; iii. Affected by physiological structures, closure conditions of wrist arteries are more challenging than those of brachial arteries. Studies on new detection technologies to overcome these limitations and challenges will promote the development of daily multi-scene and multi-frequency BPM technologies, and provide the basis for realization of long-term personal BP management strategies.The BP calculation method that is based on PTT or pulse wave morphological characteristics is mainly founded on the fluid mechanics or statistical models, in which determination of model parameters is mostly dependent on empirical values. Therefore, measurement accuracy should be clinically improved. Due to the limited number of model coefficients, establishment of coefficients that are suitable for the population through population data and adjustment of the coefficient to be suitable for a single individual through several calibrations is a major challenge. Although machine learning/deep learning model-based methods can reduce the detection signals of a channel on the basis of PTT, its calculation is large. To achieve better test accuracy in current laboratory research, it is necessary to set a strict standard for data selection. The signal quality of the original data should be high, however, in actual scenes, meeting these requirements are challenging.Therefore, we postulate that: i. Medical-level oscillographic detection technologies should be designed based on characteristics of the detection site in the miniaturization process. A new type of airbag/cuff structure is designed or a flexible pressure sensor is used as a reference pressure source. Another signal channel that characterizes blood vessel states, such as volume pulse wave, should be introduced as a control quantity during pressure changes of the oscillographic method to achieve the goal of detecting oscillographic BP at the wrist or fingertip. The advantage of this technical method is that the low-load design of the device can integrate BPM into various daily life scenes, which is helpful to achieve high-precision BP monitoring with a long life cycle. ii. The PTT method is one of the hot spots in current research. It has obvious advantages in BPM in various scenes of daily life. However, its accuracy and physiological basis should be fully evaluated. By using the existing public datasets with BP calibration per blog, construction of reasonable and interpretable BP models will improve measurement accuracy and realize scientific prediction as well as tracking of changes in BP trends. In this process, construction of clinically calibrated datasets that can reflect BP changes in various scenarios in daily life will be of great significance in development of this method.YH and HW supervised the project. ZL and YH wrote the first draft of the manuscript. All authors contributed to writing and finalizing the manuscript and approved the submitted version.


Pregnant women are considered a “high-risk” group with limited access to health facilities in urban slums in India. Barriers to using health services appropriately may lead to maternal and child mortality, morbidity, low birth weight, and children with stunted growth. With the increase in the use of artificial intelligence (AI) and machine learning in the health sector, we plan to develop a predictive model that can enable substantial uptake of maternal health services and improvements in adverse pregnancy health care outcomes from early diagnostics to treatment in urban slum settings.The objective of our study is to develop and evaluate the AI-guided citizen-centric platform that will support the uptake of maternal health services among pregnant women seeking antenatal care living in urban slum settings.We will conduct a cross-sectional study using a mixed methods approach to enroll 225 pregnant women aged 18-44 years, living in the urban slums of Delhi for more than 6 months, seeking antenatal care, and who have smartphones. Quantitative and qualitative data will be collected using an Open Data Kit Android-based tool. Variables gathered will include sociodemographics, clinical history, pregnancy history, dietary history, COVID-19 history, health care facility data, socioeconomic status, and pregnancy outcomes. All data gathered will be aggregated into a common database. We will use AI to predict the early at-risk pregnancy outcomes  depending on the needs of the beneficiaries translating into effective service-delivery improvements in enhancing the use of maternal health services among pregnant women seeking antenatal care. The proposed research will help policy makers to prioritize resource planning, resource allocation, and the development of programs and policies to enhance maternal health outcomes. The academic research study has received ethical approval from the University Research Ethics Committee of Dehradun Institute of Technology (DIT) University, Dehradun, India.The study was approved by the University Research Ethics Committee of DIT University, Dehradun, on July 4, 2021. Enrollment of the eligible participants will begin by April 2022 followed by the development of the predictive model by October 2022 till January 2023. The proposed AI-guided citizen-centric tool will be designed, developed, implemented, and evaluated using principles of human-centered design that will help to predict early at-risk pregnancy outcomes.The proposed internet-enabled AI-guided prediction model will help identify the potential risk associated with pregnancies and enhance the uptake of maternal health services among those seeking antenatal care for safer deliveries. We will explore the scalability of the proposed platform up to different geographic locations for adoption for similar and other health conditions.PRR1-10.2196/35452 The World Health Organization has defined maternal mortality as the annual number of female deaths from any cause related to or aggravated by the pregnancy or its management  during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, which is estimated to be 295,000 globally each year . Of the There have been various factors contributing to maternal mortality found between the onset of obstetric complications and its outcome. If prompt, adequate treatment is provided, the outcome will usually be satisfactory; therefore, the outcome is most adversely affected by delayed treatment. A review of delayed decisions to seek care and delay in accessing adequate care can provide the actual picture to address the satisfactory utilization of maternal health care services in the context of low- and middle-income countries (LMICs) .There are various national maternal health programs initiated by the Government of India under National Health Mission. Some of them are as follows: (1) Janani Shishu Suraksha Yojana, which is a new approach to health care that emphasizes entitlements and elimination of out-of-pocket expenses for both pregnant women  and sick infants ; (2) JanThe application of citizen-centric design in this study will primarily keep pregnant women as the beneficiary by understanding user characteristics, needs, and preferences that are critical for developing a human-centered artificial intelligence (AI)–guided predictive model. Analysis of demographic parameters such as gestational age, medical history, immunization, education, and socioeconomic status as well as prior exposure knowledge, practice, and attitude toward the use of antenatal care (ANC) services plays a key role in ensuring optimal use of maternal health services in slums .In urban slums, pregnant women are a “high-risk” group with limited access to health facilities. Barriers to utilization of health services are well documented in a few studies where the urban poor cannot be treated as homogeneous entities because of strong evidence on important sociodemographic variations within the urban poor population with their use of services and the barriers faced in service usage ,9,10. OnMost of the urban slums comprise immigrant families bearing their area-specific social customs, rituals, and taboos. Depending on these factors, the slums may differ from one another in their health indicators, which acts as a barrier to timely and appropriate utilization of maternal health care services under the national programs ,13.The identified barriers in the utilization of maternal health care services at a local level may differ significantly between slums that need to be recognized, identified, and addressed in the district-level planning for health . Poor faResearch shows that AI, automation, and data science can support overburdened health systems and health workers when responsibly deployed. AI-enabled platforms can be used to communicate timely information to the public, support self-care, and communicate initial recommended preventive options and even psychosocial care -20. HencThe research hypotheses are as follows:Is there an association between sociodemographic factors, health behaviors, health care access, clinical factors, and use of maternal health care services such as ANC services?Is the design and development of an internet-enabled AI-guided predictive model using a human-centered approach a good fit for enhancing the uptake of maternal health services among pregnant women living in urban slum settings?Can the proposed AI-guided predictive model be useful to enhance maternal health services to improve pregnancy-related health outcomes?The study has three aims:Identify the factors that affect the uptake of maternal health services such as ANC among pregnant women living in urban slum settings of South DelhiUse community data to design and develop a comprehensive AI-guided citizen-centric tool for improved utilization of maternal health services among pregnant womenEvaluate the usefulness and effectiveness of the proposed AI-guided citizen-centric tool to enhance the uptake of maternal health services for ANC servicesThe objective of our study is to develop and evaluate the AI-guided citizen-centric platform that will support the uptake of maternal health services among pregnant women seeking ANC and living in urban slum settings.A cross-sectional mixed methods study will be conducted to gather both qualitative and quantitative data on an android-based electronic platform. The study will be conducted in the urban slums of South Delhi, and participants will be enrolled from the Anganwadi centers. The Anganwadi workers will assist in mobilizing the eligible participants seeking ANC services to participate in the study. The inclusion criteria are as follows:Eligible married pregnant women aged between 18 and 44 years regardless of pregnancy orderPregnant women residing in urban slum settings for the last 6 monthsParticipants who gave written/e-informed consentParticipants having a smartphoneParticipants who are not willing to participate are not included in the study.Eligible pregnant women seeking ANC and aged between 18 and 44 years will be enrolled in the study.A convenience sampling approach will be employed in selected urban slums across 4 geographic zones of South Delhi where only 10% of the households in each slum site will be selected from each zone. Very few studies reported ANC utilization in urban slums in Delhi. Using a reference of this indicator for urban Delhi considering an expected proportion of 30% of participants completing at least 3 ANC visits, with 95% confidence levels, 6% of absolute precision, and a nonresponse rate of 10%, the estimated sample size of the study will be 225.Data collection will be done at the Anganwadi centers using prevalidated instruments adapted from the National Family Household Survey (NFHS). The data will be collected using the electronic data collection tool  focusingAgeAge of the women at the time of data collection during pregnancyDuration of pregnancyThe current duration of pregnancy from the last menstrual period until the date of data collectionEducationThe literacy level of the participant to understand the importance of antenatal careBirth orderThe order a child is born in their familyAbortion historyHistory of medical termination of pregnancyHistory of diabetesA family history that can make women with a family history of diabetes more at risk for developing the diseaseHypertensionFamily history of high blood pressure inducing the pregnancyThyroidFamily history of hypo- or hyperthyroidism inducing the pregnancyNumber of visitsFrequency of seeking antenatal care services through Anganwadi centersImmunizationImmunization against tetanus toxoid during pregnancySupplementsIron folic acid supplements are prescribed during pregnancyUltrasonographyUltrasonography for fetus growth, development, and monitoring of pregnancyRelated complicationsPregnancy-related complications during the prepartum, antepartum, and postpartum periodCOVID-19 history and its related complicationsHistory of COVID-19 during pregnancy and its related complicationsImmunizationVaccinations against COVID-19 infectionHeightHeight of women during pregnancy at the time of data collectionWeightWeight of women during pregnancy at the time of data collection about the duration of pregnancyBlood pressureMonitoring blood pressure of women during pregnancyPregnancy BMIBody mass divided by the square of the body heightKnowledge of national maternal and child health programs and benefitsKnowledge about various maternal and child health schemes under the Government of IndiaAntenatal care accessibilityWomen seeking antenatal care from health care providers and distance from their residenceWe will develop a machine learning predictive model for early at-risk pregnancy outcomes using citizen-centric design to enhance the uptake of maternal health services among the beneficiaries seeking ANC. This includes some common parameters based on which the model will be developed .The data pool will be structured following well-established common data models (CDM), which will facilitate harmonizing the data set providing access to relevant data concurrently for ease of consistent and accurate analysis. The CDM, identified at this stage, will include the Observational Medical Outcomes Partnership (OMOP) for clinical data extracted from sources and the customized Community-Based Demographic model for data extracted from communities . The OMOThe participants will be followed every 3 months from the date of enrollment until their pregnancy outcomes through telephonic calls. The data collected from the first 100 participants will help to develop the AI-guided tool, and the same will be evaluated for its potential competitiveness in the next 125 participants’ data .The data will be collected in bilingual languages (English and Hindi) electronically. Source data verification and quality control will be managed by the team of researchers. The anonymized data set for the entire study will be stored in Amazon Web Server (cloud server) and will be used in the model.Data will be analyzed using a multivariate regression model that may account for the clustered nature of the pooled data. Uptake in the utilization of maternal health services especially during the antenatal period and pregnancy outcomes  will be tested against all independent variables: maternal morbidity, pregnancy outcome, complications of previous pregnancy, literacy, and availability of health care facility using the SPSS statistical tool. Predictive analysis will be compared for accurate future outcomes through the supplied data sets using machine learning to further optimize the model.The anticipated outcome from this study will be more focused on the epidemiology of maternal health in terms of a better quality of life through the uptake and proper utilization of maternal health services in terms of ANC service utilization efficiently through behavior change communication, advocacy, and health monitoring defining the aspects other than the clinical side in urban slums. Different parameters based on which the efficacy of this proposed platform will be assessed are as follows:Monitoring the trend of the utilization of maternal health services by pregnant women seeking ANC through local service providers (Anganwadi centers) in urban slumsRisk identification during the antenatal periodMapping of health care facilities (Anganwadi centers)Evaluating the competencies of the proposed platform in predicting early at-risk pregnancy outcomes.Three levels of data security will be implemented, namely participant level, data transmission level, and server level, ensuring no harm to study participants  while collecting primary data. For secondary data access from individuals or institutions, prior permissions from respective data owners unless the aforesaid data are in the public domain are obtained (if applicable).The academic research study has received ethical approval from the University Research Ethics Committee (UREC) of the Dehradun Institute of Technology (DIT) University, Dehradun, India, bearing the approval letter number Protocol DITU/UREC/2021/07/4 dated July 4, 2021. The results will be disseminated at various presentations, seminars, and conferences; as a part of a PhD thesis in Population Health Informatics at DIT University, Dehradun; and in a manuscript that will be submitted to peer-reviewed journals only. The evaluation of the proposed outcome areas will be completed by January 2023.The study was approved by the University Research Ethics Committee of DIT University, Dehradun, on July 4, 2021. Enrollment of the eligible participants will begin by April 2022 followed by the development of the predictive model by October 2022 until January 2023. The proposed AI-guided citizen-centric tool will be designed, developed, implemented, and evaluated using principles of human-centered design that will help to predict early at-risk pregnancy outcomes. The results of the study will identify the factors that affect the proper use of maternal health services during the antenatal period. The study results will also intend to make an important contribution by evaluating the usefulness and effectiveness of the proposed predictive model, further evaluating the model for accuracy.In India, slums have been identified in 2613 towns including 20 census towns  irrespective of their population size in the Census 2011 report, wherein the slum population was enumerated as 22.4% of the total slum population in cities/towns and 17.4% of the total urban population of all the states and union territories -27. The Operational challenges unique to the COVID-19 pandemic, in the form of barriers due to administrative lockdown, restrictions in field mobility in containment zones, and outbreaks at the study site, are anticipated in the study. Hesitance on the part of potential participants toward commonplace data collection at Anganwadi centers and a guarded attitude toward disclosing any health issues due to COVID-19–associated stigma are expected in some communities. We expect that functional linkages with the local health system will be established at each Anganwadi from where the potential participants can be enrolled and benefit from this research study. The study tools adopted from the NFHS will assist in collecting reliable data that are a part of the Indian census enumeration program. The NFHS is a large-scale, multiround survey conducted every 10th year in a representative sample of households throughout India to provide state and national information on fertility, infant and child mortality, the adoption rate of family planning, maternal and child health, reproductive health, nutrition, anemia, and utilization and quality of health and family planning services . Thus, tJhuggi Basti.The recruitment sites represent the residents living in the periurban area, generally defined as slum dwellers/This study will establish the clinical need for a robust prediction model for adverse event–related pregnancy outcomes during the antenatal period to support therapeutic decision-making and continuum of care. Engagement with stakeholders in the model design stage will improve the clinical acceptability of the model and will also support future implementation efforts in the field through the references to a systematic review.There are a few limitations of this study, such as difficulty in enrollment due to the COVID-19 pandemic, the potential loss to follow-up given the current increase in COVID-19 cases and its complications, participants of the first trimester to be followed until the third trimester down the line to strengthen the system, and results cannot be generalized as the study is limited to one geographic setting with a small sample size.In this study, no possible risk is anticipated as of now other than the exposure of participants to an infected individual who could be a point of transmission for COVID-19 (considering mega vaccination drives in India) and the emergence of a new variant (Omicron) in the community.The design of the study will enable us to understand the dynamics of the community, household, and individual due to the inclusion of basic demographic parameters and the socioeconomic status of the family, which will help to analyze the root cause of the underuse of maternal health services and leading complications related to pregnancy outcomes. Subsequent follow-up with the participants will help to evaluate and optimize the model.The proposed internet-enabled AI-guided predictive model will help to identify the potential risk associated with pregnancies and enhance the uptake of maternal health services among those seeking ANC for safer deliveries. This study will help to get an insight into various sociodemographic factors other than clinical aspects for underutilization of maternal health services in the community, which will motivate pregnant women to use the government-facilitated national maternal health care program more efficiently, thereby reducing maternal mortality and morbidity during pregnancy. We will explore the scalability of the proposed platform to different geographic locations for use with similar and other health conditions.


Bipolar disorder or manic-depressive illness is a mental disorder which consists of abnormal and long-lasting changes in a person’s mood, energy, and ability to function. The ongoing COVID-19 pandemic restrictions precipitate the condition of those with bipolar affective disorder.We searched for significant differences before and during the pandemic by analyzing socio-demographicdata.We carried out a research activity at the I Psychiatry Clinic of the Clinical Hospital of Neuropsychiatry Craiova. We formed two groups of hospitalized patients during 2019 and during 2020, when the pandemic broke out. The inclusion criterion was the presence of bipolar affective disorder as a primary diagnosis.The number of cases and the total number of hospitalization days was higher during the pandemic, 101 cases versus 94 cases, 1667 days versus 1184 days. We identified a predominance of females during the pandemic, whereas in the previous year the distribution by sex was approximately equal. Regarding environment, the number of patients from urban and rural areas was approximately equal in 2019, while during the pandemic those in urban areas predominated, possibly due to easier access to psychiatric services. The ages of patients maintained a Gaussian distribution with a concentration of cases between 35-55 years.While other psychiatric disorders were less present in the clinic during the pandemic, the number of bipolar affective disorder cases increased. Bipolar affective disorder is a major challenge due to the wide range of symptoms which cross with comorbidities that increase the likelihood of a SARS-CoV-2 infection.No significant relationships.


We present the first three-dimensional (3D) concordance maps of cyto- and fiber architecture of the human brain, combining histology, immunohistochemistry, and 7-T quantitative magnetic resonance imaging (MRI), in two individual specimens. These 3D maps each integrate data from approximately 800 microscopy sections per brain, showing neuronal and glial cell bodies, nerve fibers, and interneuronal populations, as well as ultrahigh-field quantitative MRI, all coaligned at the 200-μm scale to the stacked blockface images obtained during sectioning. These unprecedented 3D multimodal datasets are shared without any restrictions and provide a unique resource for the joint study of cell and fiber architecture of the brain, detailed anatomical atlasing, or modeling of the microscopic underpinnings of MRI contrasts. A 200-μm scale 3D reconstruction of the human brain was created based on ultrahigh-field quantitative MRI and light microscopy. An exhaustive map of the human brain has been a long-sought goal of neuroanatomists. From the outset, the need to create systematic descriptions of the human brain has been clear. Entire research groups dedicated themselves to these endeavors, which led to the development of neuroanatomical methodology  and the creation of classical maps by Brodmann . We have included all available sections for staining, such as the BigBrain reconstruction , a general-purpose medical imaging visualization suite  were obtained from the body donation program of the University of Maastricht following a whole-body perfusion, for which written consent was obtained during life, and in accordance with the Dutch Burial and Cremation Act. Quantitative MRI maps of relaxation rates  and proton density  In each specimen, consecutive sections located rostral and caudal to the corpus callosum were processed for Nissl , silver staining (Bielschowsky for labeling nerve fibers), and parvalbumin staining (immunolabeling of interneurons). We would like to note that Luxol fast blue staining did not provide satisfactory results in 200-μm sections for the visualization of white matter tracts. We therefore labeled nerve fibers using silver staining. In sections containing the corpus callosum, sampling intervals were increased to provide additional calretinin and calbindin labeling in both the cortex and subcortex. Calretinin and calbindin immunolabeling provide additional contrast, e.g., to facilitate the identification of individual thalamic nuclei  and labeTo optimize tissue contrast for Nissl-stained sections, images were acquired using a polarization filter. Color information was discarded from the images in the reconstructions to reduce data size without notable loss of information since the color did not carry any meaningful anatomical information. Individual color channels red-green-blue (RGB) contributed equally to the grayscale image. Original color information is available through the original .tiff images .Restacked and background-masked blockface images defined the common space, as they provided both a high-resolution 3D anatomical map and correspondence to the individual microscopy slices. Coregistration was performed using a forward-backward approach across slices, aligning individual stained contrasts not only to the blockface but also to previously coregistered neighboring slices across microscopy contrasts and feature maps outlining the contours of the brain and the position of blood vessels . AlignmeAlthough simple linear registration of the blockface and MR images gave reasonable results, a more careful nonlinear alignment was needed to match the MRI and blockface contrasts as closely as possible. We performed a series of three coregistrations with ANTs using decreasing amounts of regularization to obtain the best results. Direct nonlinear registration with low regularization was not successful since differences resulting from some artifacts in the MRI and heterogeneity of the ventricles in the blockface images were not adequately handled by the mutual information–based coregistration. The appearance of the ventricles in the blockface images was variable because of the uneven filling with Tissue-Tek during the freezing procedure. In addition, the ventricles are subject to deformation during the mounting of the sections on glass slides for microscopy. This could be partially resolved by tailoring of the segmentation procedure to explicitly model this artifact.1/2 for the neighboring slide of the same staining and corresponding boundary map, and 1/4 for the two nearest neighboring slides of any staining and their boundary maps . Even with optimized 2D coregistration, the smoothness of the 3D reconstruction was still limited . To overmaps see .Coregistration of multimodal data with extensive amounts of nonlinear in-slice distortions is inherently challenging, and small registration errors may remain despite our efforts. With the data provided, fine-tuning of the coregistration in problematic cases can be done, for instance, using manual or semiautomated methods to define boundaries of interest to further optimize . This anIn the reconstructed 3D microscopy of specimen no. 15-2017, staining-to-staining intensity differences are still visible in axial and sagittal views. In this work, rather than introduce nonlinear distortions of the staining intensity by inhomogeneity correction or nonlinear intensity matching, we resorted to linearly matching the intensity across consecutive slices of the same contrast. As a result, a small intensity drift from frontal to occipital slices can be seen in some of the contrasts, but the original dynamic range of each staining is preserved. Despite these intensity variations, the 3D reconstructions of microscopy could be used successfully to label subcortical structures with the MASSP algorithm. The automated parcellation of whole-brain structures and cortical surfaces was performed on blockface images after retuning intensity priors to match the appearance of the blockface data. Subcortical parcellations were obtained from automatized contrast mapping across the MASSP atlas and the five microscopy contrasts as well as the blockface image, following the approach described previously for parcellation on the data of the Human Connectome Project data (


In response to Coronavirus disease 2019 (COVID-19) global pandemic, various COVID-19 vaccines were rapidly administered under emergency use authorization. Rare outcomes associated with COVID-19 vaccines might be less likely to be captured in clinical trials, leading to a knowledge gap in real-world vaccine safety. In contrast with high-income countries, many low-to-middle income countries have limited capacity to conduct active surveillance, owing to the absence of large and fully-integrated health information databases. This paper describes the study protocol, which aims to investigate risk of prespecified adverse events of special interests following COVID-19 vaccination in a partially integrated health information system with non-shareable electronic health records. The SAFECOVAC study is a longitudinal, observational retrospective study of active safety surveillance using case-based monitoring approach. This involves linkage of several administrative databases and hospitalization data monitoring to identify adverse events of special interests following administration of COVID-19 vaccines in Malaysia. The source population comprises of all individuals who received at least one dose of COVID-19 vaccine. Self-controlled design and vaccinated case-coverage design will be employed to assess risk of adverse events of special interests and determine the association with vaccine exposure. Data on vaccination records will be obtained from the national COVID-19 vaccination register to identify the vaccination platforms, doses and the timing of vaccinations. The outcome of this study is hospitalization for the adverse events of special interests between March 2021 and June 2022. The outcomes will be obtained through linkage with hospital admission database and national pharmacovigilance database. Findings will provide analysis of real-world data which can inform deliberations by government and public health decision makers relative to the refinement of COVID-19 vaccination recommendations. In response to the Coronavirus disease 2019 (COVID-19) global pandemic, various COVID-19 vaccines that are either in prelicensure clinical trials or that have been authorized by emergency use authorization were rapidly administered worldwide. Data from clinical trials are inherently limited in providing full safety profile of COVID-19 vaccines, as limited sample size and strict inclusion criteria might limit the generalisability of the safety data, given that trial subjects might differ from actual recipients of the vaccines . FurtherWith the roll-out of the COVID-19 vaccination programs worldwide, public concerns over the safety of novel COVID-19 vaccines are anticipated . In ordeThe type and scope of vaccine safety monitoring activities in each country depend on the availability of the resources related with pharmacovigilance surveillance systems . For insThe study aims to assess the safety of COVID-19 vaccination on the risk of AESIs within prespecified time windows following immunization among population in Malaysia. This study will also evaluate the risks of AESI between different vaccine platforms and in specific subgroups of interest.Program Imunisasi COVID-19 Kebangsaan; PICK) is a national vaccination campaign implemented by the Malaysian government as an approach in curbing the spread of COVID-19 in Malaysia (TM (CanSinoBio), and 7) COVILO (Sinopharm).The National COVID-19 Immunization Program  of the Ministry of Health (MOH), with a mobile application, MySejahtera, which was developed by the government to assist in managing and mitigating the COVID-19 outbreaks in the country . MySejahThe SAFECOVAC study is a longitudinal, observational study using retrospective data collected in administrative registers and health databases. The study population comprised all individuals who received at least one dose of COVID-19 vaccine. This study adapted the research protocol from the vaCcine COVID-19 monitoring readinESS (ACCESS) project to monitor COVID-19 vaccines post-introduction , as wellUsing self-controlled designs, each subject will serve as his/her own control and implicitly adjusting for fixed confounders . Events The SCCS method compares incidence of events across different risk periods relative to the exposure with incidence during a baseline or control period. In SCCS, observation period will be anchored based on calendar time during the ongoing COVID-19 vaccination program in Malaysia and inclusion of period before and after vaccination.SCRI approach is a subtype of SCCS design and utilizes comparison of incidence of outcome of interest in risk period with that in the control period. Only vaccinated subjects with events occurring during these periods are informative for the analysis. Vaccination date will be used as the index date to define the risk and control intervals. The follow-up period will begin at the date of vaccination and complete at the end of pre-specified duration of the control period.For the vaccinated case-coverage design, the observed odds of vaccination in the exposure window for each case is compared with the expected odds of vaccination for each case during the same time period as the case’s exposure window in an external reference population . The refThis study is currently plan to cover the period from February 2021, following availability of COVID-19 vaccines in the country, until June 2022.Exposure is defined as receipt of any COVID-19 vaccine dose. For vaccines administered in multiple doses, each individual dose will be evaluated separately. Individual-level data on vaccination will be obtained from the MyVAS database which has nationwide coverage for COVID-19 vaccination status of populations in Malaysia, including individuals vaccinated abroad. The database provides information on vaccine administered including date of vaccination and vaccine type.The primary outcome is hospitalization for the specified medical events as listed in Information on the hospitalizations will be sourced from the Malaysian Health Data Warehouse (MyHDW), a centralized health data repository that collects data from all healthcare facilities across Malaysia including episodes of inpatient care at public and private hospitals. The database provides information on dates of admission and discharge, diagnoses, discharge status, and patients’ demographic. Diagnoses were coded using the ICD-10 coding system; all data within MyHDW are monitored and verified by the Health Informatics Centre, Ministry of Health Malaysia . The MyHSecondary outcome is all-cause mortality in the vaccinated population. Death will be ascertained using death registrations from the National Registration Department of Malaysia. The death registry covers all deaths in Malaysia including deaths of Malaysian citizens abroad. It provides the date and cause of death based on the death certificate issued.Data collection at the sentinel sites will utilise electronic case report form from Research Electronic Data Capture (REDCap) 10.0.31  hosted a. If shared identifiers are not available, an alternative approach is to use patient-matching algorithms to determine whether two sets of information belong to separate patients or the same patient. However, the matching algorithms requires the availability of some basic data elements such as patient name-first and last, birth date, gender, address and hospital. In situation where data custodians do not allow transfer of individual-level data to the study coordinating centre, the data linkage and analysis will be performed at their sites. Data extraction and linkage will be performed by the research team or data custodians.Each dataset from different repositories will be pooled and linked deterministically with the use of a unique, common patient identifier . This isThe estimated number of cases  needed for a self-controlled design to have 80% power under the range of assumed effect sizes and the proportion of the observation period in the risk interval are shown in A cohort of patients hospitalized and vaccinated during the study period will be constructed. For patients with multiple hospitalizations for the same diagnosis, only the first hospitalization during the study period will be included in analysis. The cohort will be linked with records of hospital admissions from the previous years to exclude patients who had previous admissions for the same diagnosis within the last 2 consecutive years. COVID-19 infection status will be ascertained by linking the cohort with the database of confirmed cases from the national COVID-19 surveillance database. Patients with a positive COVID-19 test during or within 30 days prior to the admission for the outcome events will be excluded from analysis. Similar method will be applied for mortality outcome. All outcome events that occurred during the study period and fulfilled the criteria will be identified and categorized according to defined time intervals.Baseline characteristics of the study population will be summarized using descriptive statistics. Categorical data will be reported as counts and percentage while continuous data will be reported as means with standard deviation or medians with range.The risk and control period for each outcome event were determined based on review of literature and consultation with subject matter experts  Smeeth . The intThe self-controlled case-series models will be fitted using a conditional Poisson regression model with logarithm of time in each interval as the offset . Rates wFor vaccination case-coverage method, association between vaccination and outcomes of interest will be calculated as the odds ratio for vaccination in the cases compared with the matched population. This will be computed using logistic regression with the outcome indicating whether vaccination occurred in the case’s exposure or comparison window . The logStratified models will be used to estimate the risk for subgroups, including but not limited to age and sex stratification. Additional analyses will be undertaken by varying the risk periods and using adjacent risk periods to assess for any clustering of events during a specific period.Study data will be handled and stored responsibly and in accordance with applicable data protection and privacy laws with password protected folders and data files. Data protection and privacy regulations will be observed in collecting, forwarding, processing, and storing of data. No patient-level data will be shared outside of the Institute for Clinical Research, Malaysia or investigation team and the data will not be stored in files that do not meet the requirements of integrity and security. Deidentification of dataset will be performed for statistical analysis. Confidentiality and security protocols will be in place to restrict access to information as appropriate. Study data will be retained for at least five years after completion of the research, archived in a secure server, and destroyed after the specified storage period. Study results will be published following guidelines, including those for authorship, established by the International Committee of Medical Journal Editors . When reTo the best of our knowledge, this study will be among the few from low- and middle-income countries and Asian population on COVID-19 vaccine safety evaluation in real-world environment. Findings from this study will be useful for monitoring development of anticipated AESIs potentially related to COVID-19 vaccination to complement or reaffirm results provided by clinical trials. Furthermore, the findings will provide analysis of real-world data which can inform deliberations by government and public health decision makers relative to the refinement of COVID-19 vaccination recommendations. As the number of vaccinated people around the world increases, growing data from clinical studies including real-world surveillance are important for long-term monitoring of vaccine’s safety and effective profiles in the population . These dFor this study, the case-based monitoring approach addresses the potential limitation of underreporting in the existing passive surveillance system to detect AESIs in the population. It consists of active surveillance to find cases rather than relying on voluntary reporting for vaccine safety assessment. Combination of passive and active safety surveillance systems is recommended for monitoring vaccine safety after licensure, but the latter is often time- and resource-consuming . HoweverThere are several plausible limitations in this study that we acknowledge. First, there is potential misclassification of study variables which is unavoidable in studies utilising secondary data resources. Next, the statistical power to analyse rare outcomes might be limited, given the potential variation in follow-up time periods. Data coverage in the MyHDW hospital admission database for private hospitals is lower compared to public hospitals; however, admission to public hospitals account for nearly 70% of total hospital admissions in the country . The useIn summary, findings from this study will provide insights on the AESIs occurred following COVID-19 vaccination in a real-world setting and contributes to the global data on vaccine safety for comparison. The study protocol provides a framework of a coordinated approach in performing vaccine safety assessment via linkage of several secondary data sources, despite the challenges pertaining to the integration of heterogenous data sources.