Deep learning has great potential for imaging classification by extracting low to high-level features. Our aim was to train a convolutional neural network (CNN) with single T2-weighted FLAIR sequence to classify different cognitive performances in patients with subcortical ischemic vascular disease (SIVD). In total, 217 patients with SIVD  and 46 matched healthy controls (HCs) underwent MRI scans and neuropsychological assessments. 2D and 3D CNNs were trained to classify VaD, VaMCI, NCI, and HCs based on FLAIR data. For 3D-based model, the loss curves of the training set approached 0.017 after about 20 epochs, while the curves of the testing set maintained at about 0.114. The accuracy of training set and testing set reached 99.7 and 96.9% after about 30 and 35 epochs, respectively. However, the accuracy of the 2D-based model was only around 70%, which performed significantly worse than 3D-based model. This experiment suggests that deep learning is a powerful and convenient method to classify different cognitive performances in SIVD by extracting the shift and scale invariant features of neuroimaging data with single FLAIR sequence. 3D-CNN is superior to 2D-CNN which involves clinical evaluation with MRI multiplanar reformation or volume scanning. As the population keeps aging, the social and family burden of cognitive impairment has been gradually increasing . VasculaMRI scans allow the non-invasive and efficient collection of multimodal/multiparametric information about morphometry, microstructure, function, perfusion, and even metabolism of the brain. MRI has become a one-stop shop for the macro-scale study of the brain . MeanwhiPrevious studies made efforts to address this challenging task by traditional machine learning using multimodal MRI data, including diffusion and morphometry features, and eventually proved the usefulness of machine learning techniques in discriminating HCs from patients suffering from VaD and its prodromal stage: vascular mild cognitive impairment (VaMCI) , which rIn particular, to our knowledge, no studies have been reported using CNN-based deep learning technique to develop a diagnosis model based on single FLAIR sequence for recognizing SIVD from normal subjects, and discriminating different cognitive performances within the SIVD group. Our hypothesis is that 3D-CNN would show ideal performance. This approach enables us to expand our methodology to predict more complicated systems.In total, 217 subjects with SIVD were recruited from patients admitted to the Neurology Department of Renji Hospital from July 2012 to January 2018. 46 matched HCs were recruited from the community through advertising. SIVD can be defined as subcortical WM hyperintensity on T2-weighted images with at least one lacunar infarct, in accordance with the criteria suggested by The exclusion criteria were cerebral hemorrhages, cortical and/or corticosubcortical non-lacunar territorial infarcts and watershed infarcts, specific causes of white matter lesions , neurodegenerative disease (including AD and Parkinson’s disease), and signs of normal pressure hydrocephalus or alcoholic encephalopathy. Patients with a low education level (<6 years), severe depression [Hamilton Depression Rating Scale (HDRS) ≥ 18], other psychiatric comorbidities or severe cognitive impairment , severe claustrophobia, and contraindications to MRI  were also excluded. All the participants had lacunar infarcts, small WMH, and slight atrophy .n = 83), VaMCI group (n = 82), and VaD group (n = 52)]. All the participants were right-handed.Finally, all SIVD patients recruited were subdivided based on cognitive status into subcortical vascular disease with no cognitive impairment group [NCI group  test, auditory verbal learning test , Rey–Osterrieth Complex Figure Test , Boston Naming Test (30 words), Rey–Osterrieth Complex Figure Test (copy), Lawton and Brody’s Activities of Daily Living (ADL) Scale Test, Barthel Index (BI), HDRS, and the Neuropsychiatric Inventory .To assess the subjects’ cognitive statuses, the scores for each measure of normal-aged patients in Shanghai, China, were used as the normal baseline (norms). Cognitive dysfunction was defined as −1.5 SD in at least one neuropsychological test. According to the AHA Statement on Vascular Contributions to Cognitive Impairment and Dementia , VaD diaMRI was performed with the SignaHDxt 3T MRI scanner . An eight-channel standard head coil with foam padding was used to restrict head motion. In addition to conventional brain MRI plain scanning, T2-fluid attenuated inversion recovery sequences (FLAIR) with high resolution were acquired as follows: TE = 150 ms, TR = 9075 ms, TI = 2250 ms, FOV = 256 mm × 256 mm, matrix = 128 × 128, slice thickness = 2 mm, number of slices = 66.The pipeline of the proposed method consists of two major parts, data preprocessing and disease classification with CNN-based deep learning strategy. Data preprocessing is designed to extract Region of Interests (ROI) from raw MRI images since the raw ones consist of too many irrelevant data. Then, the processed images are fed to the CNNs we proposed.∗n∗l∗c, where the m, n, l, and c denote length, width and depth as well as number of color channel, respectively. In this work, the m, n, l, and c are equal to 256, 256, 66, and 1, respectively. Brain Extraction Tool (BET) in the FMRIB Software Library (FSL), which is a comprehensive library of analysis tools for FMRI, MRI, and DTI brain imaging data, is employed to extract the brain tissues from the original images, since non-brain tissues may cause unexpected classification results. As a wildly used brain-extraction tool, FSL-BET can easily remove all non-brain tissues from raw images by optimizing the fractional intensity threshold and reducing image bias as well as residual neck voxels. In this work, we set the threshold at a constant, 0.5. The results are shown in DICOM images of FLAIR were firstly transformed into Mat format with a Matlab program, which stores only meaningful volumes for deep CNN and discards the redundant data. Then, the data in Mat format assume the shape of mNevertheless, even when the non-brain tissues are wiped off from the original MRI images, the data are still not able to be fed to the CNN. As shown in ∗256∗66 to 159∗141∗66, not only it is much easier to train the networks, but the accuracy of classification is greatly improved since the overfitting problem is resolved. The results are shown in First, we defined the region with non-zero values in each slice as a ROI. Different slices have different ROIs. To keep the data as a cubic shape, we found the ROI with maximal are MROI in all slices, whose vertex coordinates are , , , , respectively. Then, each slice was tailored to MROI without changing the positional relationship. With the volume of data reduced from 256The traditional machine leaning method requires manually extracting the features using prior knowledge, and then trying to find the linear or non-linear relationship between these features and target label, which is quite challenging for researchers. Firstly, it requires a very large amount of knowledge to designate the features to be selected. In addition, large numbers of features are time consuming. This problem has been solved thanks to the development of deep learning. Recent studies have shown the great capacity of deep learning in computer vision tasks, such as image classification and object detection. With a deep structure, it is easy to get a larger number of features, which are also are commonly more complex and advanced.∗141, as described above. As a result, the traditional 2D-based convolutional networks, such as AlexNet, VGG, and ResNet, etc., may be not suitable for our task, as such models have no capacity to extract the structural features among the correlative slices. Considering this situation, we proposed a 3D-based convolutional network, as shown in Differently from the traditional image classification task, the data after preprocessing has a 3D construction, which consists of 66 slices and each slice has a shape of 159∗3∗3 means: (1) the output of this convolutional layer contains 32 channels; (2) the filter size is 3∗3∗3, which means the filter has a cubic shape, receiving the data from three adjacent slices. This differs from a traditional 2D-based filter that only receives data from only one slice at a time.∗141 and totally 66 slices are fed to our custom network. Generally, since the features extracted at a deeper layer are more complex and abstract, the number of channels would correspondingly and gradually increase. According to this principle, the channel’s number in convolutional layers in our custom network is gradually increased from 32 to 512. Subsequently, there are eight convolutional layers with different numbers of input and output channels followed by two fully connected (FC) layers activated by linear function and one FC layer activated by softmax. The Rectified Linear Unit (RELU) function, defined as the Eq. 1, is used as the activation function. In addition, the data every two convolutional layers is downsampled by a max-pooling layer with a filter in the size of 2∗2∗2 and stride of 2∗2∗2. The cross-entropy loss function, defined as Eq. 2, is used to minimize the loss function along with the Stochastic Gradient Descent (SGD) optimizer.Preprocessed data with resolution of 159To address the overfitting issues, the batch normalization method is performed before each activation function. Learning rate is lowered as the training process progresses, having been initialized as 1e-2. The decayed learning rate is defined as Eq. 3, where the decay step is equaled to 300.We implemented our model with keras (with TensorFlow as backend). All the experiments were performed on a workstation with Inter Core i7 processor and three NVIDIA 1080 Ti GPUs.We used 75 and 25% of the data for training and testing and the accuracy was employed to evaluate our custom model. In addition, we employed k-fold cross validation for the training set. We divided the training dataset into 10 equal shares. For each training time, nine shares were used for training and the other one was used for validation. Finally, the mean of the results of 10 training sessions represented the accuracy of the training set.In addition to accuracy, we also introduced three other significant metrics vastly applied to evaluate a classification model, including recall (R), precision (P), and F1 score. Using TP, FN, FP, NP to denote the number of the positive samples classified to positive, the positive sample classified as negative, the negative samples classified as positive and the negative samples classified as negative, respectively. Then, the R, P, and F1 score can be defined as:Differently from the binary classification task, our custom model has to figure out four different categories. Therefore, when we calculate these metrics for each category, the positive samples denote the target category and the other three categories would be the negative samples. For example, when it comes to MCI, we remark the MCI as positive samples and the NCI, dementia, and contrast are all remarked as negative samples.For traditional image classification task, 2D convolutional kernels are more widely applied, since we normally regard an image as 2D matrix without considering the discrete color channel. However, quite differently from a traditional digital image, even without considering the color channel, the MRI image still consists of three dimensions. More than the width and length of a traditional image, we have to take the depth into account. If we just simply apply traditional 2D convolutional kernels, the model will have no ability to extract the spatial features among adjacent slices. To compare the different performances of different models with 2D and 3D convolutional kernels, we also apply the VGG-16, a wildly used and excellent 2D-based CNN model for natural image classification task, to classify the SVCI disease. The results obtained are shown in For our custom 3D-based deep CNNs, we trained the model for a total of 50 epochs with random initialization. Meanwhile, we fine-tuned the 2D-based VGG-16 model, which has been pre-trained with ImageNet. After every epoch, we recorded both the values of accuracy and loss function on the training dataset as well as the testing dataset. Our findings are that the 3D-based model performs much better than 2D-based model. The main reason is that the 2D-based model doesn’t have the ability to retract the structure features among the adjacent slices, as described above. For the 3D-based model, the loss curves of the training set approach 0.017 after about 20 epochs, while the curves of the testing set are stable at about 0.114. The loss curve continues to decline and stabilize, demonstrating that the model works and there is no slightly overfitting. The accuracy of training set and testing set reached 99.7 and 96.9% after about 30 and 35 epochs, respectively. However, the accuracy of 2D-based model is only around 70%, which is much more poorly than the 3D-based model’s performance.In this paper we presented deep learning models for classification of different cognitive performances in SIVD, which overcome the limited feature set extracted by conventional handcrafted methods. Using 3D-CNN, we were able to accurately recognize SIVD from normal subjects, and discriminate within the SVCI group between patients with different cognitive performances, based on single FLAIR sequence. Higher-order deep features were extracted and included in our deep learning model. The accuracy of the training set and the testing set reached 99.7 and 96.9% after about 30 and 35 epochs, respectively. This result is not surprising, as deep features reflect higher-order imaging patterns and capture more imaging heterogeneity compared with low-level shape, intensity, and texture features.Nowadays, neuroimaging is an indispensable part of clinical assessments and has an increasingly important role for early detection of SVCI. Researchers have been devoting their efforts to neuroimaging techniques and computational approaches for the qualitative and quantitative analysis of pathological brain changes related to SIVD. Prior classic machine-learning approaches for linking imaging features to cognitive decline in SIVD patients have relied on human-derived feature extraction. For example, In the medical image processing field, especially for 3D data computing tasks, 3D CNNs hold promising potentials but have not been well explored in SIVD subjects yet. However, 3D CNNs based on MR images have been widely used in the field of other medical imaging, including Alzheimer’s disease , detectiAlthough the proposed method has achieved an appealing performance with a high accuracy, there are still several limitations. First, this is a retrospective study with a relatively small sample size. In the future, large-scale multicenter studies are required to fully assess the generalization ability of the model. Second, the interpretation of the association between the deep features and the cognitive performances remain challenging. It might be related to complex biological processes. Further studies are needed to establish a rationale to explain the correlation between deep imaging features and cognitive performances.We presented an efficient and robust method to automatically recognize SIVD from normal subjects and discriminate within the SIVD group between patients with different cognitive performances by 3D CNN-based deep learning. This provides a global perspective method for clinical evaluation of SIVD from convenient single MRI scan.The datasets generated for this study are available on request to the corresponding author.This study was carried out in accordance with the recommendations of Research Ethics Committee of Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China, with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Research Ethics Committee of Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China.YZ, GZ, and QX conceived and designed the experiments. YW, DT, JD, XH, and YS performed the experiments. YW, DT, and JD analyzed the data and wrote the manuscript. XH and YS contributed materials, analysis tools, and figures. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ipomoea pes-caprae, Wedelia chinensis, Scaevola sericea, Cocos nucifera, and Sesuvium portulacastrum) on the island using real-time quantitative polymerase chain reaction (PCR) and high-throughput amplicon sequencing. Quantitative PCR results showed that fungi and bacteria were ubiquitous and variable among different host plants. Scaevola sericea showed the lowest absolute abundance and highest diversity of fungi and bacteria, while Cocos nucifera had the lowest abundance and the highest diversity of diazotrophs compare to the other four plants. There was a small proportion of shared microorganisms among the five different plants, while unique fungi, bacteria and diazotrophs were significantly enriched for different host plant species in this study (p < 0.05). Some of the most abundant organisms found in the communities of these different host plants are involved in important biogeochemical cycles that can benefit their host, including carbon and nitrogen cycles.The surfaces of a leaf are unique and wide habitats for a microbial community. These microorganisms play a key role in plant growth and adaptation to adverse conditions, such as producing growth factors to promote plant growth and inhibiting pathogens to protect host plants. The composition of microbial communities very greatly amongst different plant species, yet there is little data on the composition of the microbiome of the host plants on the coral island in the South China Sea. In this study, we investigated the abundances and members of a major microbial community  on the leaves of five dominant plant species ( The com6 to 107 cells/cm2 of the leaf [Among the wide variety of microbiomes, bacteria have long been known to be the dominant member inhabiting plant leaves , often bthe leaf . Moleculthe leaf . Bacterithe leaf ,14,15. Tthe leaf .Ipomoea pes-caprae (IP), Wedelia chinensis (WC), Scaevola sericea (SS), Cocos nucifera (CN), and Sesuvium portulacastrum (SP) are the common and abundant plants existed on the island. Among them, Ipomoea pes-caprae and Wedelia chinensis are medicinal plants, Scaevola sericea was mainly used to stabilize the eroding beaches, Sesuvium portulacastrum was widely used for marine ecological remediation, and Cocos nucifera is an extensively cultivated fruit tree. Owing to high humidity and temperature, the leaves of tropical plants typically differ from plant species in temperate to boreal ecosystems. Tropical plant phyllospheres can be very densely colonized by organisms [2 fixation in the phyllosphere is the main mechanism for adding N in humid tropical ecosystems [2 fixation rates in tropical forests are commonly thought to be among the highest of any natural ecosystem, and indeed relatively high symbiotic rates have been suggested for a few tropical sites [Yongxing Island is the biggest coral island in South China Sea. rganisms . Some prrganisms ,18,19. Sosystems . Clevelaosystems  estimateal sites . It is pIpomoea pes-caprae (IP), Wedelia chinensis (WC), Scaevola sericea (SS), Cocos nucifera (CN), and Sesuvium portulacastrum (SP), which mainly grown on Yongxing Island, Hainan province , in the South China Sea. This island has a true tropical maritime monsoon climate, with the annual average precipitation more than 1300 mm. The mean annual temperature is 26~27 °C, and peaks in temperature in May and June. Sampling occurred on a sunny day, and the week before sampling was sunny with an average temperature of 24 °C. Twelve individual plants of each plant species were haphazardly chosen. The sampled IP, WC, SS, and SP were not covered by any plants, including CN. Considering the heterogeneity of the tested leaves, leaf samples were collected from at least 4 plants to form a composite sample from 1.5 m × 1.5 m area, and three composite samples (more than 10 m apart) of phyllosphere were collected for each type of plant. In addition, the collecting area of CN was much bigger owing to the wide space between CN trees (>5 m).On 12 December 2017, we chose phyllosphere samples from five plants of −1 for 20 min at 30 °C. Sonication (frequency 40 kHz) was then continued for a further 3 min. The cell suspension was filtered through a 0.22 µm × 50 mm sterile nylon membrane to separate microbial cells from the leaves. Phyllospheric DNA was directly extracted from each of the collected membranes. All DNA was extracted using a Fast®DNA SPIN Kit  according to manufacturer’s instructions. The DNAs sampleswere stored at −80 °C.Each leaf sample was cut with a pair of sterilized scissors and placed in a Labplas on ice. To standardize conditions as much as possible, we chose only green healthy-looking and intact leaves. All the phyllosphere samples were collected on a day in December 2017 in which they were all under identical weather conditions. The phyllosphere samples were quickly transported to the laboratory and immediately processed for DNA extraction. About each phyllosphere replicate, 30 g leaf samples were placed in 1000 mL sterile Erlenmeyer flask, which was then filled with 500 mL of sterile PBS buffer . To wash the microbial cells off the leaves, sonication (frequency 40 kHz) was performed for 6 min in an ultrasonic cleaning bath, shaking followed at 200 r min16S rRNA gene were performed with primers ITS1/ITS2 and 799F/1115R, with the annealing temperatures of 55 °C and 57 °C, respectively , 0.5 µM of each primer (10 mM), and 1 µL of the DNA template. To obtain the standard curve, a nifH gene fragment was cloned into the pMD19-T vector  and subsequently transformed into Escherichia coli JM109 competent cells. The plasmid DNA was extracted using a Plasmid Purification Kit . The plasmids containing the correct fragment length were selected and verified. These were then used as a template to generate a standard curve. Blanks were run with sterile water as the template instead of the DNA. Specific target gene amplification was confirmed by agarose gel electrophoresis and melting curve analysis. Each DNA sample was determined in triplicate, and amplification efficiencies ranged between 90.1–102.5% with an R2 value of 0.991. The cycling conditions for the three genes were as follows: 40 cycles of 30 s at 95 °C, annealing for 30s at the temperatures in The quantitative PCR (qPCR) thermal profiles of the fungal internal transcribed spacer (ITS) region and bacterial ectively . Primer bacteria  , 0.5 µL of the reverse primer (20 mM), 23 µL of double distilled water (ddH2O), and 1 µL of the DNA template . Illumina libraries were constructed using the MiSeq Reagent Kit v3 according to manufacturer’s instructions. High-throughput, paired-end sequencing was performed on the Illumina MiSeq PE250 platform. Bacteria, fungi, and nitrogen-fixing bacteria sequencing data were deposited in the SequenceShort Read Archive (SRA) at the National Center for biotechnology information (NCBI) with accession numbers SRP148402, SRP158738, and SRP144329, respectively.Primers ITS1/ITS2, 799F/1115R, and PolF/PolR were used to amplify the region of the ITS1, www.i-sanger.com, Shanghai, China). To calculate community similarities, an OTU-based hierarchical cluster analysis with the unweighted pair group method of arithmetic means, a principal co-ordinates analysis (PCoA) based on Bray-Curtis distance matrices, as well as an analysis of similarity (ANOSIM), were carried out using the vegan package of the R software (Version 3.1.2). Cladograms were drawn using the Huttenhower Galaxy web application  with the LEfSe algorithm . Linear discriminant analysis (LDA) effect size was used to elucidate differences in microbial taxa [16S rRNA gene data, the metagenome of the phyllospheric communities was predicted by Tax4Fun tools, which is connected to the SILVA database [Sequencing data analyses were performed using the free online platform of Majorbio I-Sanger Cloud Platform  and bacteria (3.84 × 106–7.60 × 107 copies per g leaf). The abundance of diazotrophs with nifH gene in phyllosphere ranged from 2.50 × 104 to 9.54 × 104 copies per g leaf. Among the five different phyllosphere samples, CN had the lowest nifH gene abundance and highest copies of ITS region, in contrast, the abundance of nifH gene in SP was the highest, and SS had the extremely low abundance about fungi and bacteria (p < 0.05). In addition, the abundance of bacteria in IP was the higest (p < 0.05).The abundance of bacteria, fungi, and diazotrophs among the phyllosphere samples of IP, WC, SS, CN and SP were detected by performing qPCR assays and found to be different . The qPC16S rRNA gene sequences with 22,438 to 55,137 sequences per sample, and the numbers of OTUs (97% similarity) ranged from 71 to 448 and from 304 to 1780, respectively, depending on p < 0.05). In addition, CN and WC also had the lowest Heip’s evenness index of fungi, and SP had the lowest Heip’s evenness index of bacteria  ranged from 104 to 480, depending on the Given the above, of all determined samples, the phyllosphere of CN showed the highest diazotrophic richness and evenness. Phyllosphere of IP showed the lowest diazotrophic and bacterial richness and evenness compared to other phyllosphere samples. Phyllosphere of SP harbored the lowest fungal richness and evenness, while the phyllosphere of SS both contained the highest fungal and bacterial richness and evenness.p < 0.01, To compare the similarities and differences among phyllosphere microorganism compositions, including fungal, bacterial and diazotrophic community, we used hierarchical cluster analysis, principal coordinates analysis (PCoA) and ANOSIM, based on the OTU composition. These analyses showed that the community compositions of fungi, bacteria and diazotrophs amongst the five tropical phyllosphere were clearly distinct, respectively . The hie16S rRNA gene sequencing, respectively.To acquire comprehensive knowledge of the microorganism community in phyllosphere, we analyzed the composition of total fungi and bacteria, via ITS1 and High-quality ITS1 reads were clustered in to 688 non-singleton operational taxonomic units (OTUs), with a large majority being assigned to the phylum Ascomycota , and Basidiomycota accounted for 2.72% . Among ap < 0.05) dominant among phyllosphere samples of different plant species, respectively  was detected in the SP  dominanted among phyllosphere samples of different plant species, respectively (Pantoea (59.38%) and Bacteroides (12.76%) were dominant in IP, and Pseudomonas, Sphingomonas, Chryseobacterium, Curtobacterium, and Rhizobium were the dominant genera with a relative abundance as high as 42.22% of all reads in WC, in contrast Methylobacterium, Spirosoma and Romboutsia were dominant in SS with a relative abundance of 15.20%. Salinisphaera, Candidatus Uzinura and Actinomycetospora were the dominant genera in CN with a relative abundance of 51.18%, instead, Stenotrophomonas, Sphingobacterium, Halomonas, and Kushneri were the dominant genera with a relative abundance as high as 20.62% of all reads in the phyllosphere SP  dominant among phyllosphere samples of different plant species, respectively  were observed in phyllospheric bacterial community . Almost Ipomoea pes-caprae, Wedelia chinensis, Scaevola sericea, Cocos nucifera and Sesuvium portulacastrum varied greatly, which is associated with the differences of morphology and physiology of different plant species, and in turn likely related to the phylogenetic distance of the five plant species, where Ipomoea pes-caprae belongs to the family Convolvulaceae, Wedelia chinensis belongs to the family Compositae, Scaevola sericea belongs to the family Goodeniaceae, Cocos nucifera belongs to the family Palmae and Sesuvium portulacastrum belongs to the family Aizoaceae.Microbial communities colonize plant surfaces, enabling interactions that are closely related to plant growth and health and play an important role of carbon and nitrogen cycling in the terrestrial ecosystem ,14,29. IOn the whole, the abundance and diversity of microbiome in the five phyllosphere samples was significantly different. SS had the lowest abundance of fungal and bacterial population sizes and this might be related to the waxy leaves known as features of SS , which aAlternaria, Aspergillus, Penicillium, Fusarium, are considered as endophytes supported by various investigations [Mycosphaerella, Eupenidiella, Hortaea, Cladosporium, Ascochyta, Phoma, Alternaria, Devriesia, Pseudopithomyces, and Pyrenochaeta within the Dothidiomycetes, and Gibellulopsis, and Nigrospora within the Sordariomycetes, and Podosphaera within Leotiomycetes, and Aspergillus within Eurotiomycetes, were known as opportunistic pathogens, and their abundance were significantly different among the five plants, indicating that these pathogenic fungi are in particular associated with host plant species and we should pay more attention to the health of these coastal plants. Gibberellins are a big family of isoprenoid plant hormones, some of which are bioactive growth regulators, controlling seed germination, stem elongation, and flowering [Gibberella is a plant growth-promoting fungus producing large amounts of Gibberellins, and was found on the leaves of the detected plant species with relatively high proportions except for the low abundance on CN leaves (0.02% on average). Periglandula ipomoeae was a morning-glory symbiotic fungi [Periglandula detected only on IP leaves (33.1% on average). It might be insufficient to generalize that this genus is IP-specific because we only targeted a small subsection of the plant species diversity of Yongxing Island. However, its distinctive abundance is evidence enough to conclude there is a strong relationship between IP and Periglandula, which may be symbiont.On the phylum level for fungal taxonomic composition, the primarily character of the phyllosphere community in the tropics did not differ greatly from temperate or tropical plants, including relatively high abundances of the phylum Ascomycota, as generally observed from previous studies ,35. Dothigations ,37, whicigations . Dothidiigations . Most oflowering . Gibbereic fungi , interesThe microorganisms on the surface of leaves are subjected to very stressful environments, including exposure to ultraviolet (UV) radiation . There aPantoea, Salinisphaera, Pseudomonas, Bacteroides, Sphingomonas, Actinomycetospora, Methylobacterium, Stenotrophomonas, Sphingobacterium, Chryseobacterium, and so on, accounted for a high percentage of the phyllosphere microbial communities , and not other plant species (excluding CN with relative abundance less than 0.01%)  , which msqualene ,58. Intesqualene , which mAnabaena, Dermocarpa, Chroococcidiopsis, Nostoc, Mastigocladus, Stanieria, Desmonostoc, and Trichormus within Cyanobacteria, and Rhodoplanes, Azospirillum, Skermanella, Bradyrhizobium, Chlorogloeopsis, and Methylocaldum within Alphaproteobacteria, and Methyloversatilis within Betaproteobacteria were found among all the detected plant species. Their abundances were different among the five plants, indicating that the proportions of these common nitrogen fixing bacteria are particularly associated with host plant species. Anabaena is a cosmopolitan genus showing a wide distribution in diverse aquatic and terrestrial habitats, and represents a rich source of bioactive compounds having industrial and agricultural significance [Anabaena were detected on the leaves of SS and SP with the abundance of 16.78% and 14.69%, respectively, next was the IP with the abundance of 2.57%. There are several significant roles of Anabaena, including not only nitrogen fixation, phytohormone-mediated plant growth promotion [Pythium, Fusarium and Rhizoctonia [At the phylum level for taxonomic composition of diazotrophic community structures detected on the five plant species, the character of the phyllosphere community in the tropics were mainly composed of Proteobacteria, as well as Cyanobacteria, as was generally observed from previous studies . Here, mificance . In our romotion  but alsozoctonia ,63.Rhodoplanes (59.58% on average) belonging to Alphaproteobacteria dominates the assemblage on IP phyllosphere sampled here, while a very low abundance of Rhodoplanes found in other phyllosphere samples. Whereas members of this group are generally represented in the communities of roots, compared with leaves [Rhodoplanes have been isolated from pond water [Rhodoplanes only dominated in the specific IP phyllosphere among all the detected tropical phyllosphere samples need to be investigated further.Interestingly, a distinctive pattern in the phyllosphere diazotrophic community composition seen in this study is the fact that the genus h leaves . Represend water  and rhiznd water . Each hond water ,50. The 16S rRNA gene sequences were run through a predictive metagenomics analysis program, revealing that many of the significantly distinct pathways in the different plant-types were associated with amino acid metabolism or transporter proteins (The In conclusion, the abundance, diversity and composition of the phyllospheric microbial communities differed greatly among the five plants. There was a small proportion of shared microorganisms among the five different plants, while unique fungi, bacteria and diazotrophs were enriched for different host plant species in this study. Some of the most abundant organisms found in the communities of these different host plants were involved in important biogeochemical cycles that can benefit their host, including carbon and nitrogen cycles, while some microorganisms are potential pathogens, which need us to pay more attention to the health of these coastal plants.


Triticum aestivum L.) crop threatens the food security of South Asia. A potential spread of the disease from Bangladesh to India could have devastating impacts on India’s overall food security as wheat is its second most important staple food crop. West Bengal state in eastern India shares a 2,217 km-long border with Bangladesh and has a similar agro-ecology, enhancing the prospects of the disease entering India via West Bengal. The present study explores the possibility of a ‘wheat holiday’ policy in the nine border districts of West Bengal. Under the policy, farmers in these districts would stop wheat cultivation for at least two years. The present scoping study assesses the potential economic feasibility of alternative crops to wheat. Of the ten crops considered, maize, gram (chickpea), urad (black gram), rapeseed and mustard, and potatoes are found to be potentially feasible alternative crops. Any crop substitution would need support to ease the transition including addressing the challenges related to the management of alternative crops, ensuring adequate crop combinations and value chain development. Still, as wheat is a major staple, there is some urgency to support further research on disease epidemiology and forecasting, as well as the development and dissemination of blast-resistant wheat varieties across South Asia.The emergence of wheat-blast in Bangladesh in the 2015–16 wheat ( Puccinia striiformis f. sp. tritici) in Australia in 1978, and the invasion of a different aggressive race of the same pathogen in 2002 [Puccinia graminis) in Africa, the Middle East, the Arabian Peninsula, and parts of Asia [The emergence and spread of virulent crop diseases and pests are continuously threatening global food security , 2. Rece in 2002 , 4; the  of Asia ; Maize L of Asia ; and theMagnaporthe oryzae pathotype triticum (MoT), was officially first reported in 1985 in the Brazilian state of Paraná [Wheat blast, caused by the fungus f Paraná , 14, 15.f Paraná . The disf Paraná , and thef Paraná . In 2012f Paraná , 22.Wheat blast can result in a total wheat crop failure . FungiciThe emergence of this disease in Bangladesh threatens the food security of more than a billion people in South Asia. Wheat consumption in South Asia has a long tradition in north-west India and Pakistan but has been increasing rapidly across South Asia , 31, 32,India shares a 4,096 km- long international border with Bangladesh, including 2,217 km in West Bengal, India alone . BangladSuch potential loss in wheat production in South Asia due to the possible spread of MoT from Bangladesh can have devastating consequences on the already-precarious food security situation in South Asia. For example, despite India’s tremendous success in alleviating abject poverty in the last two and half decades, still nearly 16% of the total Indian population is undernourished (281.4 million), and 44–50% of preschool-age children suffer from micronutrient deficiency . In BangTo avoid any such disaster, as a preventive control, the Indian Council for Agricultural Research (ICAR) has implemented a temporary ‘wheat holiday’ in Murshidabad and Nadia districts of West Bengal for three years , and 34% of the area irrigated . In 1967In pace with the increased production, the yearly per capita wheat consumption in India has also increased over the years , 48, 49.rabi season. According to ICAR-CRIDA [Located in the lower Gangetic Plains, rice is the dominant crop and staple of West Bengal similar to Bangladesh , with thAR-CRIDA , total cAR-CRIDA . The wheThe nine border districts of West Bengal are the major wheat-producing districts among its 23 districts. Altogether, these districts allocated 287 thousand ha of land to wheat, which was nearly 85% of the total land allocated to wheat in West Bengal in TE2015-16 . The borFinally, it is important to note the prevailing food habits and livelihoods in West Bengal, where rice is the major cereal consumed by the households. For example, in 2011–12, in rural India, the monthly average per capita cereal consumption was 11.2kg in which 55% was rice, 39% wheat, and 6% was other cereal. In West Bengal, however, a member of a rural household, on average consumed 12kg of cereals, in which 88% was rice and only 12% was wheat . Rice israbi  crops to examine the economic viability of each crop as a replacement for wheat during the ‘wheat holiday’ period. As alternative crops, we consider ten crops that are all currently grown in the rabi season in West Bengal , WATA is the land area under wheat (ha) in district d (d = 1——9) for TE2015-16, CyieldTA is the district-level yield of the alternate crop c (t/ha), and Pc is the domestic market price in India of the alternate crop c . The average wholesale price/ton of sampled crops were considered and converted into US dollars using the prevailing exchange rate .To simulate alternative scenarios, we assume a complete replacement of the total current wheat area (TE2015-16) of 287 thousand ha  in the nex-ante estimation process, it is thus assumed that (1) the alternative crops can completely replace the current wheat area in the nine sampled border districts; and (2) that the yields and returns of the alternative crops on the entire substituted wheat area in each district would be comparable to those currently achieved in the same district. Based on these assumptions, the expected gross margin for each alternative crop c is calculated as follows:CCc is the production cost of crop c (USD/ha) in West Bengal and WATA is the current wheat area in the sampled nine districts. For production costs/ha, we relied on GoI [urad (USD 214/ha).In the d on GoI , 57, whid on GoI , 57: i.eENMc is the expected net margin from a sampled crop c, GMc is the gross margin of the sampled crop c, and GMw is the wheat gross margin.The expected net margin of each alternate crop in the substitution scenario net of wheat is calculated as follows:urad (USD134/ha); and peas and beans (USD 174/ha). In contrast, the highest labor costs are reported for potato (USD 615 /ha) followed by paddy (USD 524 /ha). In suggesting alternative crops to wheat, in addition to the economic feasibility of the crops, we also need to consider their labor requirement considering wage rates as well as labor calendars.Labor cost is a component in our net margin estimation process of each sampled crop . Still, Note that the present study only intends to provide an indicative scoping of alternative crops based on available secondary data. Based on the initial scope, more rigorous and grounded empirical data are needed to support the transition to the more promising alternatives. The scoping study also does not consider the eventual price and market effects due to the changes in the production and supply associated with the crop transition. The assumption of constant prices is largely warranted as the Indian agricultural sector is integrated into the global commodity markets. The country is a net exporter of rice, wheat, maize, rapeseed, mustard, linseed and potatoes; and imports lentils, and gram from the international market . In an ourad production is taken from GoI [rabi) and summer (kharif) season rice. In our scenario analysis, we only consider a replacement of current wheat areas in nine border districts in West Bengal by rabi rice, which is wheat’s competing crop.from GoI , and thefrom GoI . With a from GoI , India ifrom GoI . The topfrom GoI . India pceteris paribus) and reflects the relatively high production cost of winter rice. The simulation exercise in Considering the production cost of wheat in West Bengal USD 547/ha, and national price USD 257/t , the preth in maize production in the world. The maize area in India increased rapidly in the late 1990s. In 1993–94, the total area under maize was 6 million ha, which was increased to 7.34 million ha in 2003–04. Currently, the total maize area is 8.69 million ha [With 2.5 t/ha yield from 8.69 million ha of land, India produced 21.8MMT of maize in 2015–16 , and thellion ha . KarnataAssuming a complete replacement of 287 thousand ha of wheat area in the sampled districts with maize, the simulation exercise shows that, with the average yield in the sampled districts of 5.5 t/ha, the total additional maize production will be 1.16MMT, resulting in a positive net return of USD 32.5 million . ConsideWith the total production of 1.04MMT from 1.47 million ha of land, India is the second largest lentil producer in the world after Canada Tables  and 4. IThe nine border districts of West Bengal are the major lentil producers in West Bengal. These districts allocate nearly 62 thousand ha of land to lentils, which is more than 88% of the total area in West Bengal . AssuminCicer arietinum) production in the world, as India produces nearly 65% of the total gram in the world  is one of the most popular pulses in India. With an average national yield 0.57 t/ha, India produced 1.87MMT of urad from more than four million ha of land in 2015–16  is an important pulse indigenous to India, is mainly produced in Chhattisgarh, West Bengal, and Bihar. In 2015–16, India produced 282 thousand tons of khesari from 394 thousand ha of land with a national average yield 0.72 t/ha  and non-cereal crops due to the associated investments needed in value chains, such as cold storage for potatoes, credit facilities, and marketing costs of the export-oriented commodities. Among the profitable crops, potato and maize also imply substantially higher labor costs, which may be an issue in the face of labor calendars and increasing labor scarcity in India. For simplicity, the study assumes a complete wheat substitution by the alternate crops, but in reality, combinations may be more realistic and profitable.Applying a scoping Magnaporthe oryzae).A potential ‘wheat holiday’ in the nine border districts in West Bengal may not severely affect India’s total domestic wheat production given its relatively modest share in India’s wheat area and production. However, the proposed wheat holiday policy in West Bengal may not bring the desired outcome, if Bangladesh continues wheat production in its border districts. The wheat-blast host-pathogen systems potentially undermine the feasibility of a potential one-sided ‘wheat holiday’ by India. To generate a desirable outcome, along with India, Bangladesh also would need to introduce a wheat holiday policy in its blast affected and particularly its border districts. Considering the coordination and logistics costs, such inter-country collaboration might be challenging in reality, in addition to the fact that Bangladesh already is a net wheat-importing country. Alarmingly, an implementation of a wheat holiday in West Bengal and Bangladesh may not eliminate wheat blast from the hot spots as alternative crops may harbor this particular pathogen  or variants thereof Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(PDF)Click here for additional data file.S5 File(PDF)Click here for additional data file.S6 File(XLSX)Click here for additional data file.S7 FileData on cost of cultivation and production & related data.(XLSX)Click here for additional data file.


Follicular dendritic cell sarcoma is a very rare bladder tumor with very few cases that have been reported in the English literature.We report an unusual case of follicular dendritic cell sarcoma that is coexistent with urothelial carcinoma (UC) in the urinary bladder of a 73-year-old man, who first presented with lower abdominal pain. Microscopic examination of the first transurethral resection of bladder tumor (TURBT) sample showed a neoplasm containing spindle or ovoid-shaped cells that were arranged in storiform, nested or swirling patterns. Abundant mitotic Figs. (30 mitoses/10 high-power fields) and apoptotic bodies were present. The tumor cells were positive for CD21 and vimentin, partly positive for CD23, D2–40 and CD35. After 6 weeks, the tumor recurred lately, which surprisingly contained a component of urothelial carcinoma. The first TURBT sample was then reviewed and a coexisting UC mixed with FDCS was identified by examining the deeper levels of the tumor blocks.This case is, to our knowledge, the first time to report the coexistence of FDCS and UC in the urinary bladder of an elderly patient. And these two tumors may share a similar molecular mechanism. Follicular dendritic cell sarcoma (FDCS) is a rare neoplasm involving the proliferation of neoplastic dendritic cells , 2. WhenIn August 2016, a cystoscopically visible protuberant neoplasm of the urinary bladder was found in a 73-year-old man, with clinical manifestation of lower abdominal pain, frequency, urgency and dysuria during urination. Pelvic computed tomography (CT) examination showed a 1.5 cm nodular soft tissue shadow at the left anterior wall of the bladder  was purchased from Roche company.The tumor cells were positive for CD21 and vimentin, partly positive for CD23, D2–40 and CD35. The tumor cells were negative for CK, CK5/6, EMA, CK7, CK20, P63, P40, Uroplakin, Desmin, SMA, S100, TFE-3, HMB45, MelanA, MPO, ALK, CD3, CD5, CD20 and CD30. Ki-67 was expressed in about 30% of the tumor cell nuclei  was lower than that of the first sample. However, the tumor cells were found to infiltrate in muscularis propria. It was surprising that there was also an invasive urothelial carcinoma that was mixed with the FDCS. The UC of bladder infiltrated in mucosa and submucosa. The tumor cells of UC were arranged in nest or cord pattern, the cytoplasm was acidophilic and the nuclear were irregular.  were in keeping with follicular dendritic cell sarcoma. Majority of FDCS are considered low-grade sarcoma, while tumors with larger sizes and more mitotic figures tend to have relapses. In our case, the number of mitosis was high, which might explain the tumor recurrence 6 weeks later and the tumor cells were found to infiltrate into the muscular layer. So far only one case of FDCS was reported in urinary bladder [The etiology of FDCS is not clear. Approximately 10 to 20% of FDCS cases are associated with Castleman disease. Wang et al. reported some cases of Castleman disease contained areas of follicular dendritic cell proliferation, so FDCS was hypothesized to arise from such areas . AdditioThese cytogenetic abnormalities may share mechanisms of tumor genesis with a subset of other tumor types, such as urothelial carcinoma. P16 and P53 genes were altered more prominently in patients with high-grade tumors than low-grade tumors of urothelial carcinoma, which may play significant roles in the progression of bladder cancer . There wIn conclusion, follicular dendritic cell sarcoma can occur with urothelial carcinoma at the same time in the bladder, and these two tumors may share a similar molecular mechanism.


Poor water quality has been reported along with a variety of negative health outcomes in the Saharawi refugee camps in Algeria. We assessed the concentration of elements in drinking water and urine in refugee women and children. Twenty-four samples of distributed public drinking water were collected, along with urine samples from 77 women and 296 children. Using inductively coupled plasma mass spectrometry, we analyzed water and urine for 31 and 10 elements, respectively. In addition, the water samples were analyzed for five anions by ion-exchange chromatography. Data were described according to two areas: zone 1 with purified water and water with naturally better quality, and zone 2 with only partially purified water. Most elements in drinking water had significantly higher concentration in zone 2 compared with zone 1. Sodium, chloride, nitrite, and nitrate were the parameters that exceeded the WHO Guidelines for Drinking Water Quality. Among both women and children, urinary concentration of vanadium, arsenic, selenium, lead, iodine, and uranium exceeded reference values, and most of the elements were significantly higher in zone 2 compared to zone 1. Even though water purification in the Saharawi refugee camps has increased during the last years, some elements are still exceeding the WHO guidelines for drinking water quality. Moreover, urinary exposure of some elements exceeded reference values from the literature. Further effort should be made to improve the water quality among the Saharawi refugees. Although essential for life, water can also be a source of harmful substances. Food, water, and air are the major sources of exposure to potential toxic chemical substances, of which water and food are considered the most important sources when work-related exposure is unaccounted . The susDifferent biomarkers, e.g., urine, serum, or hair, may be used to assess the level of exposure to elements ,6. UrinaIn this study, we have assessed concentrations of selected elements in drinking water and urine from women and children living in the Saharawi refugee camps in the Algerian desert, where the refugees have been situated since 1975. The approximately 165,000 refugees live in four different camps—Boujador/Smara, El Aiun, Auserd, and Dakhla—located within the same area . The refReverse osmosis has been implemented for the water sources with the poorest water quality. However, the capacity of the reverse osmosis plants is not large enough to supply treated water to all camp residents . In 2015Being part of a larger health study from 2013, the sampling selection and procedure have previously been described ,23. In tWater source 1: El Aiun and Auserd. Water is supplied from groundwater wells and reverse osmosis plant for both of the camps. The plant does not have capacity to serve treated water continuously, hence each camp receives treated water for 21 days and raw water for 21 days in different turns. A chlorination step is required for all distributed water.Water source 2: Bojadour/Smara. Water is supplied from ground water wells and the reverse osmosis plant. A chlorination step is required for all distributed water.Water source 3: Dakhla. Water is supplied from groundwater wells followed by chlorination.According to Vivar et al.  and the TM tubes , and stored without addition of any preserving agents at −20 °C.The water from the main water sources are distributed to the respective camps by trucks and kept in water storage containers from which the refugees may retrieve their water. We randomly collected samples of distributed water from each of the camps, in total 24 samples. The water samples were collected during the time span 22 April–2 July 2013. Six samples of distributed public drinking water were collected from each camp—El Aiun, Auserd, Dakhla, and Bojadour/Samara. Previous studies have reported that the water quality in Smara and Dakhla is better  than the water quality in El Aiun and Auserd  ,12,19. TDuring 22 April–5 June 2013 samples of spot urine from women and children were collected using the Vacuette Urine System with a transfer device . The samples were stored at −20 °C pending analysis.4OH (v/v) by means of the Agilent 8800 Triple Quadrupole ICP-MS  using oxygen reaction mode. Iodine was determined on mass 127. 129I was used for correction of non-spectral interferences. Every other elements were determined in an acidic solution (5% nitric acid (v/v)) by means of the Agilent 8800 ICPMS-QQQ using either oxygen reaction mode or helium collision mode, and indium and bismuth as internal standard. The anions fluoride, chloride, nitrate, nitrite, and sulphate were determined by means the Thermo Scientific Dionex ICS-2100 ion chromatography system .In the laboratory, the deep-frozen water samples were thawed and kept dark at 4 °C in a refrigerator. Bromine, chlorine, and iodine in drinking water were quantified in an alkaline solution (5% NHw/v) 1-Butanol, 0.1% (w/v) H4EDTA, 2% (w/v) NH4OH, and 0.1% (w/v) Triton™, X-100, and analyzed for arsenic, bromine, chlorine, iodine, iron, lead, selenium, sulphur, vanadium, and uranium concentrations by means of the Agilent 8800 ICPMS-QQQ using oxygen reaction mode or helium collision mode and indium and bismuth as internal standard. Iodine was determined on mass 127. 129I was used for correction of non-spectral interferences. Reagents of analytical grade or better and deionized water (>18 MΩ) were used throughout. A conformance test between volume and weight of spot urine samples confined concentration of elements to two significant figures.The deep-frozen samples of spot urine were thawed and thoroughly homogenized at room temperature. Subsequently, aliquots of 1.00 mL of urine were diluted to 10.0 mL with an alkaline solution (BENT), containing 4% , the bias was also <5%. In general, trueness is considered satisfactory, however, assessment of trueness for fluoride, sulphate, bromine, and iodine in drinking water is missing due to lack of certified values for these parameters in the certified reference material analyzed.Considering water, accuracy of the analyses was checked by concurrent analysis of standard reference materials (SRM): National Institute of Standards & Technology (NIST) 1640a Trace elements in natural water and Sigma-Aldrich QC3198 Nutrient—WP (Whole volume). Accuracy in the determination of trace elements in urine was examined by analysis of SeronormTM L-1 and L-2 were generally within ±20% of the certified values. Limits of detection and the limits of quantification reported in Table 2 were calculated at three and 10 times the standard deviation (SD) of blank samples, respectively.Regarding elements in urine, the values obtained from concurrent analysis of SeronormSerum samples were collected from 289 children and 76 women and analyzed for serum thyrotropin (TSH), free thyroxine (fT4), free triiodothyronine (fT3), thyroglobulin (Tg), and antibodies to thyroid peroxidase (TPOAb) and thyroglobulin (TgAb). A thorough description of sample collection and analytic procedures can be found in previously published papers ,23 toget2, a BMI = 18.5–24.9 kg/m2, and a BMI ≥ 25.0 kg/m2, respectively [Background data were obtained by interview using pre-coded questionnaires. Body weight of women and children were measured to the nearest 100 g using a UNICEF digital platform scale . Height was measured to the nearest 0.1 cm using a portable UNICEF length board. Body Mass Index (BMI) was used to classify body composition in the women. The following classifications were used: underweight, normal weight, and overweight/obese, defined by a BMI < 18.5 kg/mectively . The chiEthical approval was provided for the original study in 2013 (ref. 2013/192) by the Regional Committees for Medical Health Research in Norway and the Saharawi Ministry of Public Health. Supplementary approval was provided for this specific study in 2015 by the same institutions. Written informed consent was given by the mothers on behalf on themselves and the children included.Data were analyzed using IBM SPSS version 22 . For values below the limit of quantification (LOQ) and limit of detection (LOD), the respective LOQ/LOD cut off divided by two was entered into the dataset. In the descriptives of the data , the higConcentration of the different elements in urine for women and children were checked for association with Zones 1 and 2 in multiple linear regression models. For the women, the models were adjusted for BMI and age (none of the women included were lactating). For children HAZ, WAZ, WHZ, breastfeeding status, and gender were adjusted for. All dependent variables were log (2) transformed due to right skewed distribution. Residuals were checked in each model, and standard residuals above 3 or below−3 were removed from the models.Concentrations of trace elements in urine relevant for thyroid function were explored in women and children with detected thyroid disturbances, and we tested for differences with the Mann Whitney U test.Characteristics of women and children are shown in In Concentration of elements and anions in distributed drinking water from Zones 1 and 2 are shown in Concentration of elements in urine among women and children are shown in , associations between urinary concentrations of the different elements and Zones 1 and 2 are shown. For both women and children, all elements were significantly associated with zones in adjusted models, except from iron and arsenic. For the elements sulphur, chlorine, vanadium, bromine, selenium, iodine, and uranium, there was a positive and significant association with zones, where the urinary concentrations were higher in Zone 2 for all the mentioned elements except from lead, where the association was negative. Vanadium and iodine were the two elements with most variance explained by zones, 60% and 49% for vanadium and 83% and 43% for iodine among women and children, respectively.In Table 6Overall, the prevalence of thyroid disturbances was high in the population—31% in women and 14% in children. We have previously reported data regarding thyroid function in relation with iodine status among these women and children ,21,23. IPrevious studies have revealed poor drinking water quality in the Saharawi refugee camps. Even though water purification systems have been implemented in the camps, the concentrations of sodium, chloride, nitrite, and nitrate still exceeded the WHO guideline values. Further on, for most elements and anions, the values were significantly higher in drinking water from Zone 2 compared to Zone 1. Urinary concentrations of vanadium, arsenic, bromine, selenium, and uranium among women and children were found to be higher than reported in relevant literature studies.The elements and anions that exceeded WHO guidelines, were sodium, chloride, nitrite, and nitrate, in Zone 2 see . In lineThe median vanadium concentration in Zone 2 was 55 µg/L, significantly higher than in Zone 1. No guideline value has been established by the WHO, even though toxic effects have been reported in human studies . Italy hIn both women and children, urinary excretion of most of the elements were significantly associated with the zone they lived in . ConsideConsidering women, concentrations of vanadium, arsenic, bromine, selenium, and uranium in urine in Zone 2 exceeded the values of the reference studies. The urinary lead excretion in women living in Zone 1 was higher than reference values. Regarding the children, the urinary concentration of vanadium, arsenic, selenium, lead, and uranium in both Zone 1 and Zone 2 were higher than the respective reference values reported in the German reference study . HoweverIn general, considering a non-exposed population, urinary concentrations of vanadium are between 0.1–0.2 µg/L ,29,30,38High iodine concentration in urine among women and children has previously been examined and published ,23,39. TThe urinary uranium concentrations for the women and children were substantially higher than what was measured in the reference studies ,29,30. CUrinary selenium usually does not exceed 30 µg/L , in mostUrinary arsenic concentrations are associated with concentrations of inorganic arsenic in drinking water but may also originate from dietary sources such as seafood or rice . ExposurUrinary lead excretion was higher than reported in reference studies. Interestingly, the urinary lead excretion was highest in Zone 1, in contrast to the other measured elements. For children, the urinary lead concentration was approximately three times higher than in the reference study, in both zones, and for the women in Zone 1, it was approximately five times higher than the reference studies ,29,30. IIodine is essential for thyroid hormone metabolism, and the relation between thyroid function and iodine status from these data has previously been examined. No relation between UIC and thyroid dysfunction were found among women and children ,21,23. HPrevious studies have found poor drinking water quality including high concentrations of some trace elements in the Saharawi refugee camps. In recent years, an effort has been made to improve the water quality using purification by reverse osmosis. In the areas with purified water and raw water of better quality (Zone 1), the concentrations of elements seemed to be within acceptable levels. However, in the areas with partially purified water (Zone 2), some elements exceeded the WHO guideline values. Additionally, urinary concentration of selected elements was higher in Zone 2 than Zone 1 and exceeded values from reference studies. Further effort should be made to improve the water quality by increasing the share of purified water distributed in order to ensure the population access to clean and safe drinking water, fulfilling their right to safe drinking water.


Whey protein, a by-product of the cheese industry, can be putatively used as a functional food due to its beneficial health properties. The main objective of the present study was to assess in vivo the effect of a sheep/goat whey protein on the plasma amino acid profile and mammalian target of rapamycin (mTOR), a regulator of skeletal myogenesis. A control group was fed with a standard commercial diet while the experimental group received a standard commercial diet plus sheep/goat whey protein for 28 days. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was conducted to determine plasma amino acid levels while the expression of p70-S6 Kinase 1 (p70-S6K1) in liver and quadriceps muscles was quantified and used as a biomarker of mTOR activity. The results obtained showed a decrease in the levels of essential and branched-chain amino acids (BCAAs) in the experimental group. Furthermore, p70-S6K1 expression was decreased in the liver of rats consumed whey protein. In conclusion, the reduction of amino acid levels and the concomitant inactivation of mTOR imply that whey could potentially act protectively against disorders induced by mTOR overactivation. Intriguingly, this mode of action mimics fasting, an approach with established advantageous health effects. Proteins are fundamental biomolecules that are constructed by amino acids and perform a wide range of function, both from the technological ,2 and nu+) as well as OH• and O2•− that normally exist in the organisms  and a cocktail of protease inhibitor tablet  was added. The homogenate was vigorously vortexed and a brief sonication treatment on ice was applied. The homogenate was then centrifuged  software (SCN 882). The quantitative determination of endogenous compounds was performed by an external calibration; details on the method have been previously published . All tesp < 0.05 with the alpha level set at 0.025. All results are expressed as mean ± SEM.Orthogonal Partial Least Squared Discriminant Analysis (OPLS-DA) was used for modeling differences between the metabolic profiles of plasma samples from rats subjected to whey feeding conditions and for statistical evaluation of the models. Additionally, one-way ANOVA was applied in order to compare the means between the control and the whey protein feeding group. Differences were considered significant at p-value of the Cross Validated ANOVA Analysis  was 0.007, indicating the statistical significance of the investigated models. Additional statistical characteristics of the model R2Xcum and R2Ycum were 0.752 and 0.983, respectively, showing how well the model explains the variation in X and Y, respectively. The Q2Y that represents the quality and predictive power of the model was 0.95.In total, 22 amino acids were detected in both the control and whey protein fed rats in the present study in order to evaluate the relationship between amino acid profile and mTOR activity after the administration of sheep/goat whey protein in vivo. Chromatographic peak areas were used for multivariate statistical analysis in order to find any differentiation between the studied groups. Discrimination of the sample groups was already revealed by principal component analysis (PCA) (data not shown), however, the partial least discriminant analysis was further performed to maximize the group differentiation and to identify potential biomarkers related to mTOR activity after different feeding conditions. The OPLS-DA provided a clear separation between plasma samples from control and whey protein fed rats. In Additionally, an unpaired student’s t-test was performed to establish and confirm the significance of important variables indicated by OPLS-DA. With regard to the amino acid contribution to that differentiation, reduced levels were observed for the majority of them in the whey protein fed group compared to the control. Specifically, 3-methylhistidine, arginine, asparagine, aspartic acid, glutamine, isoleucine, leucine, lysine, methionine, ornithine, phenylalanine, threonine, tryptophan, tyrosine and valine levels were reduced significantly by 37, 31, 33, 29, 37, 38, 34, 24, 35, 48, 40, 49, 45, 52 and 22%, respectively. On the contrary, no significant alterations were observed in the levels of alanine, glutamic acid, glycine, proline, sarcosine, serine and taurine between the two groups .With respect to liver p70-S6K expression, it was reduced by 32.5% in the experimental group compared to the control group. On the contrary, no significant effect of sheep/goat whey protein on muscle p70-S6K expression was observed .This is the first study reporting a decrease in plasma amino acid levels after the administration of whey protein. Specifically, we report that sheep/goat whey protein administration in rats for 28 days led to decreased levels of 3-methylhistidine, arginine, asparagine, aspartic acid, glutamine, lysine, methionine, ornithine, phenylalanine, threonine, tryptophan, tyrosine and the BCAAs . Moreover, sheep/goat whey protein decreased liver—but not muscle—p70-S6K (Thr389) expression. This is a substrate that is phosphorylated by activated-mTORC1, leading to translation and protein synthesis, whereas its extended phosphorylation induced by mTOR overactivation leads to several pathologies.Milk components have been recognized as functional foods suggesting that their use has direct and measurable health effects . Milk prAmino acids are important regulators of mTORC1 through Rag GTPases. When amino acids are present, Rag GTPases are converted into an active configuration and then interact with the raptor, promoting the clustering of mTORC1 on the lysosomal surface ,71,72. TThe present study describes the outstanding effect of whey protein along with its molecular background that has not been previously reported again. Intriguingly, it reduces the amino acid profile of plasma and it partially prevents the p70-S6K1 Thr389) expression, thus leading to the putative inhibition of the adverse, disease-related effects induced by mTOR overactivation. This beneficial action of whey protein administration could be aptly parallelized with the mode of action of intermittent fasting . With re expressiAmino acids are significant activators of mTORC1, an important factor that regulates cell growth and the metabolism. However, its overactivation leads to metabolic disorders, several cancer types and aging. Administration of sheep/goat whey protein in rats reduces amino acid levels in plasma and inactivates mTORC1 in the liver, therefore, it appears that it acts protectively. Specifically, the levels of 22 plasma amino acids were examined and 15 of them were reduced significantly in the experimental group compared to the control group. The reduction percentage ranged from 29% to 52%. Among them, the BCAAs  whose elevated levels are implicated in IR probably through the upregulation of mTOR complex 1 are included. Furthermore, sheep/goat whey protein in liver reduced the p70-S6K1 (Thr389) expression by 32.5% in the experimental group compared to the control group, thus leading to the putative inhibition of the adverse, disease-related effects induced by mTOR overactivation. The mode of its action appears to mimic the beneficial impact of fasting on health. This is an intriguing piece of evidence indicating that sheep/goat whey protein could potentially be used as an alternative and promising nutritional intervention for the prevention or alleviation of diseases associated with homeostasis and growth disturbance.


Endometrial cancer (EC) is one of the most common gynecologic tumors, with a high incidence in developed countries. Although the overall prognosis is good, some women have invasive tumors, the risk of recurrence, and death is high. The common surgical methods used in EC are total-abdominal hysterectomy (TAH), total-vaginal hysterectomy (TVH), laparoscopic-assisted vaginal hysterectomy (LAVH), and total-laparoscopic hysterectomy (TLH) including both conventional and robotically assisted.The literature search was performed in The Cochrane Central Register of Controlled Trials, PubMed, Web of Science, and Embase. The randomized controlled trials (RCTs) will be included. The search date is until June 2019. The risk of bias of included RCTs was assessed by 2 investigators according to the Cochrane Collaboration's tool. Network meta-analysis will be conducted by R software.This study is ongoing and the results will be submitted to a peer-reviewed journal for publication.This network meta-analysis will provide clinical staff with current and reliable information on the best surgical approach for EC. Ethical approval is not applicable, since this is a network mate-analysis based on published articles. The protocol has been registered on PROSPERO under the number CRD42019128094. Traditionally, almost all ECs were performed by TAH. The treatment framework for EC has undergone major changes during these years, and traditional treatment models are challenged by new treatments.,18,19 TAH was associated with an increased risk of perioperative complications compared with other surgical procedures, but it is simpler, faster.,21 Since its publication in 1989, LAVH has been increasingly used in clinical practice and is currently considered a safe and viable technology for the treatment of benign uterine diseases, it has been recommended as an alternative to TAH for early EC.,23 TVH had beginnings in 1507,,28 It has been used effectively for benign hysterectomies, but it often comes with higher costs.,30Regardless of surgical staging,,33,34 and it is also the main source of key information for scientific researchers.,36 In this study, we will use NMA to determine the best surgical approach for EC.In the last decade, network meta-analysis (NMA) has been introduced.22.1https://www.crd.york.ac.uk/PROSPERO/#myprospero), and the registration number is CRD42019128094.This protocol has been registered on the international prospective register of a systematic review (PROSPERO)  will screen the titles and abstracts of literature independently, then read the full text to assess literature according to the inclusion and exclusion criteria, any disagreements will be resolved by a 3rd reviewer (LZ).2.4.2To avoid bias, 2 reviewers  independently extracted data using the same data recording form, which includes the following contents:General characteristics of included studies: title, name of the 1st author, publication time, study period.Detail of participants: gender, age, country, body mass index.Information about study design: total sample size, allocation sequence concealment, blinding.Intervention-related characteristics: name of experimental or control interventions.Outcomes: main outcomes and additional outcomes.2.5The risk of bias of included RCTs was assessed by 2 reviewers  according to the Cochrane Collaboration's tool. This tool included 6 items: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and anything else ideally prespecified. In the assessment, a judgment of “yes,” “no,” or “unclear,” each domain was assigned to designate, respectively, a low, high, or unclear risk of bias. Any disagreement between the reviewers on the risk of bias will be resolved by discussion and, if necessary, by consulting a 3rd reviewer (LZ).2.62.6.1Statistical analyses will be performed using Stata 15.0 and R  software. For continuous variables, weighted mean differences with 95% confidence interval (CI) will be indicated, and for dichotomous variables, risk ratios with 95% CI will be used. The NMA will be conducted in a Bayesian framework. Data analysis will be performed using R software. The result of direct comparisons will be acquired through the traditional meta-analysis. If the available data are not suitable for synthesis, we will perform a narrative review and summarize the evidences.2.6.2I-square (I2) values, if I2 > 50%, significant heterogeneity is considered, then a subgroup analysis is needed to determine the source of heterogeneity. If there is missing data in the included study, we will contact the author by email or phone to get the missing data.We can reflect the feasibility of meta-analysis by evaluating the heterogeneity of the included studies. According to the guideline of Cochrane Handbook, heterogeneity between RCTs can be quantified using 2.6.3If the evidence is sufficient, we will conduct a subgroup analysis to determine the difference between normal and overweight, over 60 years old and <60 years old, etc.2.6.4Sensitivity analysis is used to assess the stability and reliability of the results, and explore the sources of heterogeneity, we can use random effects models or fixed effect models, relative risk, or odds ratio changes to assess if there is a change in meta-analysis results. If the meta-analysis results in a fundamental change, the meta-analysis results are less stable and reliable.2.6.5In the meta-analysis, publication bias is a very important part that directly affects the validity of the conclusion.2.7Two reviewers  will use the Grading of Recommendations Assessment, Development and Evaluation method to assess the quality of evidence of included studies. The evidence levels classified into 4 levels: high, moderate, low, or very low.3Endometrial cancer is a common gynecologic cancer; the treatment for EC is primarily surgical in operable patients, and surgery is usually curative and is the mainstay of initial treatment for most patients with EC, hysterectomy and bilateral salpingo-oophorectomy is usually the gold standard for treatment, but different surgical methods have different advantages and disadvantages. Overall, this network meta-analysis will be the 1st to assess the impact of 5 surgical approaches to EC on patients with cancer; the results of this NMA may provide practical guidance for the clinic and provide new research ideas for researchers.Data curation: Yitong Cai.Methodology: Yitong Cai.Software: Yitong Cai.Writing – original draft: Li Zhou.Writing – review & editing: Min Yin.


Data standardization is essential in electronic health records (EHRs) for both clinical practice and retrospective research. However, it is still not easy to standardize EHR data because of nonidentical duplicates, typographical errors, or inconsistencies. To overcome this drawback, standardization efforts have been undertaken for collecting data in a standardized format as well as for curating the stored data in EHRs. To perform clinical big data research, the stored data in EHR should be standardized, starting from laboratory results, given their importance. However, most of the previous efforts have been based on labor-intensive manual methods.We aimed to develop an automatic standardization method for eliminating the noises of categorical laboratory data, grouping, and mapping of cleaned data using standard terminology.pos +, 250 4+ , and reddish . SALT-C consists of five steps. First, it applies data cleaning rules to categorical laboratory data. Second, it categorizes the cleaned data into 5 predefined groups . Third, all data in each group are vectorized. Fourth, similarity is calculated between the vectors of data and those of each value in the predefined value sets. Finally, the value closest to the data is assigned.We developed a method called standardization algorithm for laboratory test–categorical result (SALT-C) that can process categorical laboratory data, such as ** and _^), SALT-C mapped unique raw data to the correct reference value for each group with accuracy of 97.6% , 97.5% , 95% , 99.68% , and 99.61% .The performance of SALT-C was validated using 59,213,696 data points  generated over 23 years from a tertiary hospital. Apart from the data whose original meaning could not be interpreted correctly (eg, The proposed SALT-C successfully standardized the categorical laboratory test results with high reliability. SALT-C can be beneficial for clinical big data research by reducing laborious manual standardization efforts. As the volume of digitized medical data generated from real-world clinical settings explosively increases owing to the wide adoption of electronic health records (EHRs), there are mounting expectations that such data offer an opportunity to find high-quality medical evidence and improve health-related decision making and patient outcomes -6. EHR dIndeed, significant promising values are expected from using EHR. However, a substantial number of studies have mentioned that clinical data in EHR may not be of sufficient quality for research -27. CompSeveral EHR data standardization guidelines and tools for laboratory test name have been published -32, but To resolve this drawback, there is a growing demand for data processing guidance and mapping tools for categorical laboratory data. In this study, we proposed a new automatic standardization algorithm for categorical laboratory results data, called standardization algorithm for laboratory test—categorical results (SALT-C). This algorithm was designed to help data curators by minimizing human intervention.The original laboratory data used in this study are extracted from the clinical data warehouse (CDW) of Samsung Medical Center in Korea. The CDW contains deidentified clinical data of over 3,700,000 patients, including inpatient, outpatient, and emergency room patients, since 1994. The target dataset consists of 59,574,124 categorical laboratory results from 817 laboratory tests. This study focused on categorical data generated by machines; observation data, such as from health examination and allergy tests, were excluded even if sorted in categorical values.Before developing SALT-C, 5 value sets were predefined as a reference. The value sets were defined as follows. First, we analyzed the distribution of laboratory tests with their results. Second, from the most frequent laboratory tests, we defined the value set of each laboratory test by consulting physicians and referring to SNOMED value sets. Finally, we identified 5 common value sets by combing the value sets with similar values . The valThe overall procedure of SALT-C is described in - to rh – and + to rh + in Rh blood type laboratory data to distinguish it from the other - data of other laboratory tests, (3) formatting the urinalysis data. For example, results of urinalysis 4+ need to be converted into ++++, which has SNOMED concept identifier 260350009.First, SALT-C extracts categorical laboratory data from a database or a comma-separated values format. Second, it preprocesses the extracted data with several methods: (1) applying the general data cleaning rules , (2) correcting the abbreviation of SALT-C creates a distribution table for each laboratory test to extract the representative values. The distribution table is implemented in the following order: classify the data for each laboratory test, calculate the frequency of the data, and organize them in descending order. After the creation of the distribution table, the main values of each test are extracted. Only the data with a cumulative frequency of 99.5% or more are extracted as main values.In performing the experiments by changing the cumulative frequency, 99.5% seemed the most reasonable threshold, empirically. If there are less than 5 values in a laboratory test, then all the values are extracted as main values because the categorizer may not work properly if too few values are extracted as main values.+ and - data as their main values and can be misclassified if they are not ordered.Once the main values are extracted in the previous step, they are used to categorize the laboratory tests into 5 groups according to a rule-based categorizer, as in - and + data to rh− and rh+ when they related to blood type tests, (2) classification of tests that have ++, +++, and ++++ as main values in advance so that + data would not affect the subsequent classification, and (3) classification of blood type–related tests as a subsequent step; the remaining tests are classified into the presence-finding or pathogenesis category.We included the following when designing the laboratory test categorizer, to prevent laboratory tests from being assigned to incorrect categories: (1) correction of In SALT-C, we choose the character-level vectorization to represent laboratory data. By vectorizing, only a limited number of alphabets of laboratory data are used, instead of laboratory test names. The scheme consists of alphabets (a-z) and special characters . All data are represented as vectors with the number of characters corresponding to the scheme features. This process is described in After all of the words are vectorized, a similarity score is calculated between a laboratory data point and each of the values in the standardized value set, and then the most similar value is selected. As a method of measuring similarity, we used and compared cosine similarity measure, Euclidean distance, and a hybrid method. The hybrid method was used to select the most similar value calculated by Euclidean distance when there are 2 or more same cosine similarity values.We performed manual validation by adjudicating a total of 167,936 laboratory unique values that SALT-C predicted as labels. We examined the accuracy of the predicted labels calculated by the similarity measure. Three medical providers were recruited to manually verify data. Two of them examined the total data set and another person was involved to determine the final adjudication in the case of a discrepancy. The mean of the similarity scores for correct, incorrect, and unclassified data were identified.Treponema pallidum latex agglutination , HIV , and hepatitis B blood test . Other tests had a rate of less than 0.5%. Additional results are described in A total of 817 categorical laboratory tests and 59,574,124 test results were selected from the source database. The most frequent laboratory test was urinalysis , followed by hepatitis B blood , ABO/Rh blood type , hepatitis C blood , rapid plasma reagin , venereal disease research laboratory , Frequency distribution tables for laboratory data were created for the 817 laboratory tests. Representative distribution tables for each of the 5 categories are described in Straw, accounting for 69.43%, followed by Yellow (16.97%), and Amber (11.88%). Other data comprised less than 1%. Straw, Yellow, Amber, and Brown were extracted as main values according to the criterion that only data with a cumulative frequency of 99.5% or less are extracted as main values. The main values had various synonyms or typos and abbreviations. For example, the number of different notations that should be corrected as Straw was 151, for example, Starw, ]traw, Strow, Strwa, traw, ]Straw, and steaw.In the color test of urinalysis , there wtrace had 29 such notation variations: 10 tr, 25 tr, tr -, 5 tr, tr, 10 trace, and 10 trt. The most common value was neg -, accounting for 52.32%, followed by 10 tr (13.73%), 25 + (11.73%), 250 ++++ (6.89%), 50 ++ (6.60%), and 150 +++ (4.16%). Other data comprised less than 1%. Items neg -, 10 tr,25 +,250 ++++, and 50 ++ were extracted as main values.As for the blood detection test in urinalysis , there wA, accounting for 34.17%, followed by O (27.42%), B (27.08%), and AB (11.15%). Other data consisting of blood group variant  comprised less than 1%. A, O, B, and AB were extracted as main values.In ABO blood type laboratory tests , there wNEG (2.00), accounting for 11.66%, followed by POS (>1000) (11.09%), NEGATIVE (10.14%), and NEG (0.01) (1.81%). Other data comprised less than 1%. NEG (2.00), POS (>1000), NEGATIVE, and NEG (0.01) were extracted as main values. Laboratory tests belonging to this category usually had data composed of numbers and letters; thus, the number of unique values was far higher compared with other categories.As the representative case of the presence-finding tests category, the antihepatitis B surface antibody laboratory test  had 1,19NON-REACT, accounting for 67.64%, followed by NON-REACTIVE (31.05%). Other data comprised less than 1%. NON-REACT, NON-REACTIVE, W-REACT, REACTIVE, and WEAKLY-REACTIVE were extracted as main values.As the representative case of the pathogenesis tests category, the venereal disease research laboratory test had 551,980 data points, of which 130 were unique values before preprocessing . The mosOverall, 5 categories and common value sets were created, and 480 laboratory tests were categorized into their corresponding group by the categorizer . A totalAs shown in We examined 3 similarity measures, namely, cosine similarity, Euclidean distance, and hybrid method. For the mapping results of values, the hybrid method showed a 97.82% accuracy compared with cosine similarity (93.20%) and Euclidean distance (97.64%). For the mapping results of data, the hybrid method, with 99.99% accuracy, was also the most accurate compared with cosine similarity (93.78%) and Euclidean distance (99.96%), as shown in The primary goal for this study was to find the way to efficiently map raw data to international standard terms. The first thing we did was to find standard value sets or code lists related to categorical laboratory test results. There are some value sets publicly available at SNOMED, LOINC, and Value Set Authority Center, but these were scattered, requiring an integrated dictionary to identify the spectrum of categorical laboratory data. Without an integrated reference dictionary, it is hard for researchers to convert their data into standard codes systemically, given that these data contain many synonyms, typos, and abbreviations. Such a situation has impeded easy organization and aggregation into standard terminology, as medical providers’ help is needed.In this study, we identified 5 common value sets for categorical laboratory results by analyzing the distribution of laboratory tests with their results, by consulting with medical doctors, and by referring to laboratory tests’ SNOMED child codes. We found that 99.39% of the categorical test values fell into these value sets. As most of the categorical laboratory results were urinalysis data and data related to positive, negative, reactive, and nonreactive findings, and given that many researchers struggle with urinalysis data processing, we designed the value sets to handle as much urinalysis data as possible. The value sets developed in this study can be used for EHR interoperability, such as using Fast Health Interoperable Resources and Clinical Document Architecture. We continue to expand the values of value sets by applying SALT-C to several EHR databases internationally; furthermore, we are registering categorical laboratory value sets at Value Set Authority Center.negative and positive data were converted to neg – and posi to reduce similarity. We also attempted other string-matching methods, such as K-means clustering and Levenshtein distance; however, these 2 methods performed poorly. We demonstrated that the combination of cosine similarity and Euclidean distance method could give the best accuracy for laboratory test data, exceeding the performance of other measures. This hybrid model was complementary: the cosine similarity method selects the standard term with the most similar vector direction, and if the most similar vector direction is more than one, then the model adopts the closest value using the Euclidean distance method. For example, +++ 6 data have the same cosine similarity scores for +, ++, +++, and ++++, respectively, but Euclidean distance indicates +++ is the closest value. Usually, the cosine similarity is more accurate than Euclidean distance because it is less sensitive to the length or character order of terms; in some cases, the cosine similarity can be more accurate when combined with the Euclidean distance method. If there is a predefined code list table, it is more accurate to find the closest standard term by measuring the distance between the standard values and the data to be corrected; otherwise, the K-means method can be an alternative.The laboratory data categorizer  measuresA1, A2, A3, Ax, Am, Ael, and Aend into A, blood group B variants such as B1, B2, B3, Bx, Bm, Bel, and Bend into B, and cis-AB into AB. However, it is more accurate to categorize blood group variants into subgroup [Our study has a number of limitations to be considered. First, we validated SALT-C through one institution; thus, it may not be generalizable to other institutions’ data. However, our manual validation of 167,936 data points proved the high performance of SALT-C. When it comes to applying this algorithm to other institutions, the framework suggested in this study can be used to process categorical laboratory data, and the accuracy of the algorithm can be increased by adding more values to the value sets. Second, we only targeted the diagnostic test results from devices, whereas data from observational health examinations, such as past history, family history, and manual allergy test results, were excluded. In the case of processing allergy test results, it is much more efficient to treat it as a regular expression method, so we did not include it in the algorithm. We believe that observational health examination data should be managed using a different table ; the terms and structure of reporting these data are not well standardized, and as such, we were unable to include them in this study. Third, meaningless data or data that do not correspond to any values in the value sets were assigned standard values randomly. In this case, we suggest 2 solutions: (1) if the similarity scores measured by cosine similarity or Euclidean distance between the actual data and each of the standard values in the value set are the same, then these data need manual mapping; (2) as these data do not take up much of the total dataset, the rate of manual mapping will decrease by selecting the dataset corresponding to 95% of the cumulative frequency from the beginning. Fourth, we grouped blood group A variants such as subgroup . We recoFor the short-to-medium term, we plan to validate SALT-C algorithm with multiple institutions and add more values sets that covers more laboratory tests. In addition, as a series of SALT algorithm, we aim to develop standardization algorithm for laboratory test—allergy (SALT-A) that handles allergy data and standardization algorithm for laboratory test—blood culture (SALT-BC) that deals with semistructuralized blood culture results.We developed SALT-C, an algorithm that supports mapping of categorical laboratory data to the SNOMED-clinical terms (CT), and applied it to a large, long-period EHR system database. Previous studies on laboratory data processing have focused on the automatic mapping of laboratory test names or the standardization of numeric laboratory data -32,38; hThere is an increasing need to aggregate and standardize EHR data to aid discovery of high-quality medical evidence and improve health-related decision making and patient outcomes. However, guidelines and automated methods for systemically converting disparate categorical laboratory data to standard terminology have been left to future work. The value sets and automated method suggested in this study may improve data interoperability and could be used for implementing standardized clinical data warehouse while reducing the manual effort of converting data. We plan to validate SALT-C through applying it at multisite institutions as well as expanding the value sets.