Age-related macular degeneration (AMD) is a degenerative and progressive disease of the macula, the part of the retina that is responsible for central vision. AMD shares some risk factors with diabetes mellitus (DM), but little is known about the risk of DM in individuals with AMD. With the goal of establishing novel perspectives, this study aimed to investigate the association between AMD and the risk of DM using the Korean Nationwide Health Insurance Database. Individuals aged ≥ 50 years who underwent a national health screening program in 2009 were enrolled. Participants were categorized by the presence of AMD and visual disability (VD). The Cox hazard regression model was used to examine hazard ratios (HRs) of DM with adjustment for potential confounders. Stratified analyses by age, sex, and comorbidities (hypertension or dyslipidemia) were also performed. During a mean follow-up of 8.61 years, there were 403,367 (11.76%) DM incidences among the final 3,430,532 participants. The crude HR ) was 1.16 (1.13–1.20) for AMD. After adjusting for potential confounders, AMD was associated with a 3% decreased risk of DM , but no significant association with the risk of DM was found in AMD with VD . In summary, we did not find an increased risk of DM in individuals with AMD. A 3% decreased risk of DM in patients with AMD is not clinically meaningful. Our study suggests that the association between AMD and the risk of DM is weak, considering the potential confounders. Further studies examining this association are needed to extend our knowledge. Age-related macular degeneration (AMD) is a degenerative and progressive disease of the macula, the part of the retina that is responsible for central vision . Since tAMD is associated with traditional risk factors for cardiovascular disease, such as aging, cigarette smoking, obesity, elevated levels of cholesterol, and hypertension ,6,7,8,9,Notably, AMD is the leading cause of blindness in developed countries, particularly in patients over 60 years of age . Visual The National Health Insurance (NHI) in Korea is a mandatory social health insurance program and provides universal coverage to 97% of the Korean population. Due to their low-income status, the remaining 3% are covered by Medicaid, which is funded by the general Korean tax. The National Health Insurance Service (NHIS) is a public corporation that administers to both NHI enrollees and Medicaid beneficiaries. For every individual aged 40 or above, the NHIS provides a biennial national health screening that consists of a self-questionnaire on health behavior , anthropometric measurements , and laboratory test findings  . Therefon = 576,759); (2) history of type 1 DM  or gestational DM (n = 58); and (3) serum fasting glucose level of 126 mg/dL or above  at health screening in 2009. We also excluded patients who were diagnosed with DM  or died  within 1 year from health screening in 2009. Participants with missing data on at least one variable used in the analysis  were also excluded. As a result, a total of 3,430,532 participants were included in the final analyses.A total of 4,470,729 participants aged  ≥50 years underwent general health screenings in 2009. To extract a homogeneous group of individuals devoid of DM or any glucose metabolism disorders, we excluded participants with DM with the following criteria: (1) history of antidiabetic medication prescription  inhibitor, dipeptidyl peptidase-4 (DPP-4) inhibitor, alpha-glucosidase, and/or insulin) before health screening in 2009 , code for AMD (H353) by an ophthalmologist within 1 year before the health screening examination. This operational definition of AMD has been used in previous epidemiologic studies on AMD ,20.According to the National Disability Registration System (NDRS) in Korea, VD refers to a visual loss or visual field defect. Registration for disability requires submission of validated documentation of the results of disability diagnosis by a specialist physician . For insThe study endpoint was incidence of DM, followed by the period of time analyzed. Newly diagnosed DM was defined as a prescription of antidiabetic medication that was linked to a DM ICD-10 code (E11–E14). The participants were followed from the date of health screening examination in 2009 to the date of DM incidence or death or until the end of the study period (31 December 2019), whichever came first.During the health screening, the participants provided information on lifestyle behaviors using standardized questionnaires . SmokingFasting serum glucose level, serum hemoglobin level, and estimated glomerular filtration rate (eGFR) were also assessed. Hypertension was defined as any of the following: systolic blood pressure ≥ 140  mmHg, diastolic blood pressure ≥ 90 mmHg, or treatment with an antihypertensive medication that was linked to hypertension ICD-10 codes (I10–I13 and I15), and resulted in at least one claim in 1 year. Dyslipidemia was defined as total cholesterol ≥ 240 mg/dL or a history of a lipid-lowering medication that was associated with an ICD-10 code (E78). Finally, the Charlson Comorbidity Index (CCI) was calculated based on the diagnosis code .t-test for continuous variables or the chi-square test for categorical variables. The incidence rates of DM were calculated as incident cases divided by 100,000 person-years. The Cox hazard regression model was used to examine the hazard ratios (HRs) of DM. Multivariable analyses were serially adjusted for age and sex (Model 2); household income, area of residence, BMI, smoking, alcohol consumption, and regular exercise (Model 3); and fasting serum glucose level, serum hemoglobin level, eGFR, hypertension, dyslipidemia, and CCI (Model 4). These variables are based on the method used in previous studies [The comparison of baseline characteristics by the presence of AMD and VD was conducted using Student’s  studies ,25,26. Pp-value < 0.05 was considered statistically significant.Statistical analyses were performed using SAS version 9.4 . A This study was approved by the Institutional Review Board of the Samsung Medical Center (SMC 2022-03-060). Anonymized and de-identified information was used for analyses; therefore, informed consent was not required. The database is open to all researchers whose study protocols are approved by the official review committee.p < 0.001). In addition, the AMD group had lower alcohol consumption, total cholesterol level, and lower income and resided less frequently in urban areas than the non-AMD group .A total of 3,430,532 participants were enrolled in the final analysis. At baseline, 38,726 (1.13%) participants had AMD, and 2723 (7.03% of AMD) had visual disability . The AMDp < 0.001).The AMD with VD group was older with larger proportions of men and current smokers and lower prevalence of hypertension and dyslipidemia compared with the AMD without VD group . After considering the covariates, AMD was associated with a 3% decreased risk of DM incidence , p = 0.041; No significant interactions were found in stratified analyses according to age, sex, and comorbidities (hypertension or dyslipidemia) .Here, we investigated the association between AMD and the risk of DM. The inclusion of more than three million participants from the NHIS database enabled us to adjust for various confounders. Therefore, we had enough data to detect significant differences, even with stratified analyses. Our study revealed that AMD was associated with a 3% decreased risk of DM, which is not clinically important. No significant effect modifiers were found. Therefore, the association between AMD and the risk of DM is weak.p < 0.001) due to the large sample size, it was not clinically important. The smaller proportion of those in the lower 20% income in the AMD without VD group than the AMD with VD group can be explained by the limited access to healthcare service.At baseline, the differences in demographic, socioeconomic, and lifestyle behavior variables between AMD and non-AMD group were noted. Old age, the strongest risk factor of AMD , and higThere are several pathophysiological mechanisms that suggest a possible link between AMD and DM. Chronic inflammation and oxidative stress may explain the link between AMD and the risk of DM. Oxidative stress triggers outer blood retinal barrier degeneration that leads to AMD ,28, and To our knowledge, this is the first study to report DM risk in people with AMD. Several epidemiologic studies have focused on the risk of AMD in DM patients, and the results of those studies are inconsistent ,26,35,36Since the association between AMD and DM was nullified in our final Model 4 in which covariates were serially adjusted from Model 1 (crude), other confounders may explain the association. Therefore, shared risk factors among AMD and DM may explain the association between AMD and DM rather than a common pathophysiology.Another possible explanation is that lifestyle modification  prescribed in individuals with AMD may have preventive effects on the development of DM . HoweverIt is also noteworthy that AMD patients with VD have a slightly higher risk of DM than AMD patients without VD ; aHR 0.97, 95% CI (0.94–1.00), respectively). The strength of our study includes the use of the NDRS in our analysis. VD is associated with an increased risk of DM ; aHR 0.9, regardlSeveral limitations should be considered in interpreting our results. First, AMD might be underdiagnosed, especially during the early stages. Most AMD patients in Korea are diagnosed early without symptoms that can be identified from the claims database. Since we used the operational definition based on an ICD-10 code and not retinal photographs, the association between AMD and DM might be weaker in our findings than the actual association. Therefore, unmeasured confounders could be possible. Second, our results cannot be extended to other ethnic groups, considering the ethnic differences in genetic factors related to AMD ,54. FinaIn summary, we did not find an increased risk of DM in individuals with AMD. Our study suggests that the association between AMD and the risk of DM is weak, considering the potential confounders. Further studies examining this association are needed to extend our knowledge.


In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted. After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes  was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed. Regarding fibrosis segmentation, the mean boundary F1 score and accuracy were 0.868 and 0.776, respectively. Among 19 subindices of the 4 indices, 73.7% were correlated with the BEI , and 13.2% were correlated with the SCDR (0.406–0.460). The mean subindex of Index 2  It can be visualized using ultrasonography13 and computed tomography (CT)14. Due to the poor capability (29%) of ultrasonography for the visualization of deep tissues in moderate to severe lymphedema 13, CT can serve as an alternative for visualizing pathological changes within deep tissues3. If this internal aggravation can be reliably standardized, patients with lymphedema will be provided with the possibility of early detection and successful management prior to progression into irreversible or nonresponsive stages15. Fewer patients with disabilities (no less than 75% could maintain their jobs) were observed than expected among lymphedema patients undergoing rehabilitation management, which also supports this demand for early standardized detection regardless of disease duration1.In lymphedema, internally persistent deterioration spontaneously progresses in affected soft tissues, starting first at the molecular level mediated by the increased release of proinflammatory cytokines  and leading to macroscopic fibrosis consisting of collagen and extracellular matrix deposition17. Regarding whether CT images could be useful in quantifying histological differences, Geyer et al. reported that significant quasilinear correlations were observed with viscoelastic tissue parameters in patients with lipodermatosclerosis12. Similarly, significant correlations were observed between the histological degree of fibrosis and CT images in cancer patients with lymphedema and in patients with dermal lymphatic invasion18. Furthermore, based on the window width of the absorptive values (Hounsfield units [HUs]) in CT scans19, fibrosis can be two-dimensionally measured as an area value, with the aid of manual differentiation from other soft tissues and water  in cross-sectional images20. In the late 2010s, the development of a manual tracking-assisted method for the 3-dimensional quantification of fibrosis was reported22. However, the HU value-based manual tracking method for the quantification of fibrosis showed no correlation with clinical parameters, such as the three-dimensional volume determined by perometry and the ISL stage, and required a considerable amount of time due to the burdensome manual classification process22. Therefore, the technically promising results and lack of evidence supporting its applicability in clinical practice necessitate the development of novel image-processing methods.On CT, lymphedema-induced fibrosis is visualized in the form of trabecular reticulation with a honeycomb appearance in subcutaneous fat layers23, and Doi at the University of Chicago widened systematic medical image analysis via machine learning for computer-aided diagnosis and reduction of physician labor in the 1980’s24. Since then, machine learning has been extending worldwide into various kinds of medical applications, such as digitized data utilization in healthcare25, predictive algorithms in cardiology26, and AI-mediated interventions in psychiatry27. However, conventional machine learning models have shown insufficient performance and could not satisfy physician demands. To overcome this obstacle, deep learning (DL) has become more common as a solution and involves automatic complex multi-layer neutral network architecture-based learning by converting input data into multiple kinds of abstractions28. Regarding DL-based recognition of medical image pattern, convolutional neural network (CNN), which is the most commonly used DL method, can learn automatically how to extract valid features from the training samples for an assigned task by repetitive backpropagation adjustment of its weights without manual designation of features as input information29. In terms of high efficacy in the detection and differentiation of specific organs or lesions, DL combined with imaging for diagnosis is of great interest; five-minute magnetic resonance imaging (MRI) sequences augmented by DL superresolution could discriminate synovium, bone, cartilage, and meniscal tissue, with high agreement (54–90% sensitivity and 23–91% specificity) with arthroscopic results30. Moreover, combined with positron emission tomography (PET)/CT, DL methods can automatically detect a small nodule within the lungs, even those no less than 2 cm in diameter, with 66.7% sensitivity and 84.5% specificity31. Recently, more advanced DL models, called hybrid methods, have been proposed; for example, hybridization of three DL-based algorithms of concatenation, optimization by minimum redundancy maximum relevance (mPMR), and classification by machine learning classifiers showed very high diagnostic yields (accuracy no less than 96.9%)33.The concept of machine learning, so called artificial intelligence (AI), was introduced at the Dartmouth Conference in the 1950s. In medical fields, the potential applicability of machine learning was first suggested by Fletcher34. Taking this approach one step further, the authors supposed that the DL-based algorithm could automatically recognize fibrosis from other nonfibrotic soft tissues in cross-sectional CT images. To confirm the validity of the DL-based algorithm in lymphedema patients, the authors compared it with gold standard measurements of lymphedema, such as the results of bioelectrophysiological studies  and the standardized circumference ratio of the affected limbs.Based on the finding that HU values of soft tissues are determined by their relative constituents, digital image processing of CT scans can be used to visualize lymphedema-induced fibrosisForty-two patients were screened. Among them, 12 patients were excluded due to insufficient CT images (n = 6), irremovable random noise in the CT images (n = 2), no BEI data (n = 2), no SCDR data (n = 1), and no clinical data on the affected side (n = 1). From the 30 recruited patients, 1920 images were acquired and assigned to the training, validation, and test datasets for the proposed model. Additionally, during image calibration for evaluating diagnosis performance of the trained model, data of 3 patients were excluded due to a lack of CT images under the antecubital fossa (n = 1) and nonsymmetrical limb site images (n = 2). Finally, 2138 images of 27 patients were analyzed to compare the proposed indices with the clinical gold standard ; only one patient (2.5%) was male. The mean weight of the patients was 58.67 ± 1.39 kg. Half of the patients (50%) were affected on their left side, and the other half (50%) were affected on their right side. On average, 106.90 ± 18.07 images were collected from each patient. Regarding underlying diseases, postoperative breast cancer was the most common (66.6%), postoperative gynecological cancer was the second most common (29.2%), and primary lymphedema was the third most common (4.2%) disease. CT scanning was performed at a median of 4 years  after the first swelling presentation. None of the patients classified as having ISL stage I or IIIb were included.Figure 35. The accuracy is the ratio of the number of correctly classified pixels to all the classified pixels of one label. The IoU is the ratio of the number of correctly classified pixels to the union of pixels with ground truth and all the classified pixels of one label. The MeanBFScore is the average of the boundary scores, i.e., how well the boundary is classified. Lastly, the Dice similarity coefficient equivalent with the F1 score is calculated byThe segmentation performance of the trained algorithm was evaluated by means of the accuracy, intersection over union (IoU) , mean boundary F1 score (MeanBFScore), and Dice similarity coefficient (DSC)Air, muscle/water, and skin were fairly well distinguished, with values near 0.9 for all three evaluation parameters, as expected. The fat segmentation accuracy and IoU were less than 0.9. The MeanBFScore of pixels classified as fibrosis was 0.868, but the accuracy, the IoU, and the DSC were 0.776, 0.584, and 0.738, respectively Table .Table 2SFigure Mean of Distal), and the BEI showed the highest correlation (0.875). The mean of Index 1 in the distal part of the limb , the mean of Index 2 in the proximal and distal parts of the limb (Index 2 Mean), the summation of Index 2 in the proximal and distal parts of the limb (Index 2 Sum), the summation of Index 2 in the proximal part of the limb , the summation of Index 2 in the distal part of the limb , and the mean of Index 2 in the distal part of the limb  showed strong significant correlations with the BEI . However, apart from these three subindices , none showed a significant relation with the SCDR. Overall, the calculated values presented a stronger relationship with the BEI than the SCDR.Table (ISL stages II to IIIa). The major findings are as follows: (1) the accuracy and MeanBFScore of the SegNet-based algorithm were 0.776 and 0.868, respectively, showing similar power to previous trials applying a CNN to other diseases; (2) the majority (73.7%) of the 19 subindices of the four indices was significantly correlated with the BEI , and the minority (13.2%) was significantly related with the SCDR ; and (3) the mean value of Index 2 To show the validity of the DL-based algorithm for the automatic quantification of lymphedema-induced fibrosis in CT images, a cross-sectional, observational comparison trial was conducted in chronic lymphedema patients 36; thus, low accuracy and incomprehensive results usually occur. Currently, various DL-based algorithms have been developed worldwide because of their strengths 37. CNNs, successful DL algorithms based on a multilayer hierarchical network, show high analytical performance when applied to the medical images of patients with various kinds of diseases, such as pulmonary tuberculosis38, breast cancer39, brain tumors40, and hepatic diseases41. However, only one study applied a CNN to the quantification of lymphedema-induced fibrosis. Because reliable, repeatable, and highly accurate methods for the early detection of fibrosis (one of the most persistent complications in disease management) would have important clinical significance for the management of lymphedema patients, there is great demand for the establishment of such methods. Positive trials of DL-based image classifiers, such as AlexNet, VGGNet, U-Net, GoogLeNet, SegNet and ImageNet, might lead to fulfillment of this demand. However, in terms of semantic segmentation, U-Net and SegNet are the most popular42. SegNet, an automatic image encoder-decoder, was developed for image classification/image segmentation in 201743. It was chosen here because it has simpler layer structure than the U-Net as well as this investigation required pixel-wise semantic classification in the CT image. The authors have plan to implement the proposed segmentation using U-Net and compare each other.Ensembles of classifiers demand predefinition, such as feature extraction and region of interest (ROI) definitionr of 0.68–0.79, p < 0.01), BEI ratio , and proximal SCDR  and sensitivity of 0.78 and specificity of 0.60 in lymphatic system dysfunction detection21. In terms of CT-based lymphedema fibrosis segmentation, only one report introduced a similar method in which semiautomatic segmentation was conducted by adjusting HU and followed by a convex hull algorithm. Although correlative quantification of fibrosis with a 3-dimensional volume perometry results failed, lateralization of the fibrosis areas was significant in the more-affected limb22. Similarly, Edmunds et al. conducted CT-based lean muscle area segmentation using semiautomatic calculation of the number of voxels with HU value higher than that of fat. This calculation was followed by smoothing and binning by a non-parametric fitting algorithm for quantification of muscle degeneration. They reported high correlation coefficients  with leg strength, timed up-and go test, gait speed44.Prior to discussion of SegNet application for CT-based fibrosis correlation with clinical parameters, comparison of the current semiautomatic baseline method on the thing compositions' segmentation might better justify the effectiveness of the proposed workflow. In our previous trial with the same project, in which the current semiautomatic fibrosis segmentation method was used with the same GUI , 3 types of CT fibrosis index formulated to evaluate their representative capability showed significant correlation with ISL substages , validation , and testing  datasets and input into SegNet, the accuracy, IoU, and MeanBFScore for fibrosis were 0.776, 0.584, and 0.868, respectively. Even though the fibrosis accuracy (0.776) is lower than other labels accuracy, it is comparably higher than its random chance (1/5). It was suspected that similarity between the fibrosis and the fat in shape has made it more difficult.45, in which the dataset contained 420 T1-weighted MRI scans divided into 2 sets for training  and testing . In another study of 375 patients with 517 focal liver lesions , the model showed higher accuracy in the detection of hepatocellular carcinoma and benign noninflammatory focal lesions  than that in the current trial46. However, a different type of CNN, namely, a multiphase convolutional dense network (MP-CDN), was used. In another study using SegNet-based chest X-ray image segmentation (1674 images for training and 199 images for testing), the average accuracy for the detection of a lung nodule and the overlap score were 98.31% and 94.40%, respectively42. A comparison trial of a generative adversarial network (GAN) model with a few CNNs  showed that the SegNet IoU for hepatocellular carcinoma was 78.57, which is higher than the that in the current trial (0.584)47. All of the aforementioned studies, in which stroke lesions, liver masses, or lung nodules were of interest, achieved a higher accuracy and IoU than the current trial, in which fibrotic tissues were of interest. This suggests that the DL-based quantification of lymphedema-induced fibrosis is a greater challenge. Compared to the easy demarcation of oval- or semioval-shaped regions in the abovementioned trials, the difficult demarcation of amorphous fibrotic tissues can explain the lower accuracy and IoU. Moreover, the differences in demographic factors, such as medical diseases, sex, and age, and the relatively small sample size of the current trial should be considered.Using the same CNN application, the accuracy of the SegNet-based segmentation model for stroke lesion detection ranged from 85 to 87% in the brain MRI images of stroke patients33. In a CNN-based trial of cystourethrography on pediatric vesicoureteral reflux (1228 images), features were extracted by hybridization of Googlenet, MobilenetV2, and Densenet201 models and concatenated. They then were optimized by mPMR and classified by SVM and KNN and showed very high accuracy of vesicoureteral reflux grades  ranging from 95.5 to 96.9%32. Regarding the relatively low accuracy in the current trial, the recent hybrid DL models might be a potential solution to increase the accuracy.Meanwhile, hybrid DL models have shown very successful results in diagnostic accuracy since the early 2020s; in a CNN-based trial of Alzheimer’s disease brain MRI (2560 sections), features were extracted by hybridization of Darknet53, InceptionV3, and Resent101 models and concatenated. Then, they were optimized by mPMR and classified by support vector machine (SVM) and k-nearest neighbors (KNN) and showed very high accuracy of Alzheimer’s disease grades  ranging from 94.4 to 99.1%In addition to the aforementioned limited accuracy, it is uncertain that the authors measured pure fibrosis. As shown in Figs. 10. As shown in previous reports17, fibrosis can be viewed on conventional-resolution CT at a 512 × 512 pixel matrix size and an element size of 0.5 mm (0.25 mm in super-high resolution)48. Thus, fibrosis is measurable by current DL-based segmentation. Meanwhile, exudate fluid in pure lymphedema without fibrosis can induce reticulation in CT scanning49. So the reticulation in the subcutaneous fat compartment usually contains both water and fibrosis. HU value for suprafascial lymphedema is known to range from − 120 HU to − 100 HU50. However, the HU was measured a few times at selective areas within subcutaneous fat compartments with no discrimination of lymphedema from the fat in such reports. Furthermore, fat infiltration, an inevitable complication in lymphedema10, cannot be helped but included as well in such assessments. As the results, both background fat and fat infiltration cause shifting of the selected ROI value down toward that of fat (− 70 HU to − 190 HU)34, similar to Edmunds et al.’s report44 and Aubrey et al.’s report51. However, if this affection is abolished with the aid of the method in the current study, value of the selected ROI will return to the original value of soft tissue (30 HU to 60 HU)51. In addition, muscles in general show CT value of 30 HU to 60 HU in healthy young population51. However, low attenuation of muscles (− 29 HU to 29 HU) can happen in case of muscle-affecting focal/systemic diseases: for instance, denervated muscles and muscles in the elderly. In such cases, fat infiltration is the most acceptable factor as an underlying cause51. Similar to that, it is well known that fat infiltration occurs inside the affected muscles as the results of lymphedema-induced inflammatory cascade. Considering the aforementioned two findings, authors selected pixels of HU from − 34 to 26 to remove the fat-infiltrated, low attenuated muscles and interstitial water (HU: 0) inside the lymphedema, and then reticulation pixels were differentiated, via k-means clustering method, into either of the subcutaneous fat and the fibrosis in the current study.The most significant problem is that the authors cannot discriminate between lymphedema and fibrosis. Fibrosis involves macroscopic change that can be visualized on histo-pathology53, the authors expect advances in CT technology to allow visualization of lymphedema.However, lymphedema is defined as accumulation of tissue fluid in the extracellular, interstitial space and can be observed only at a microscopic level. CT, including super-high-resolution CT, cannot yet visualize a microscopic scale. Following the pattern of improved MRI resolution to 7.0 T and 11.74 T21. The 0.45 correlation coefficient with the SCDR is similar to that in the current trial . However, the correlation coefficient (0.875) with the 1-kHz-based BEI ratio in the current trial is much higher than that (0.46) in the former trial. Considering that the purpose of the current trial is to develop a DL-based algorithm as an alternative to traditional filter-based algorithms demanding manual predefinition and feature extraction, this advancement suggests the superiority of CNN over traditional methods in automatic lymphedema-induced fibrosis recognition.In contrast to fibrosis segmentation performance, the correlation of the calculated indices with the clinical gold standard measurements showed more advanced outcomes Table ; previou56, lymphoproliferative inflammation will continue to worsen over time. This will result in macroscopic fibrosis consisting of collagen and extracellular matrix deposition11. Lymphedema-induced fibrosis takes place internally between the skin and muscle layers through replacement of normal anatomical components by fibrotic tissues rather than outward expansion of the limb volume11. Moreover, this reconstitution change occurs dominantly in subcutaneous fat layers57 in the form of a spatiotemporal cascade22. Therefore, a simple measurement of the external circumference cannot precisely represent this kind of inner fibrotic replacement. Furthermore, adiposity can be affected by anatomical location and sex58; the larger is the area of the involved subcutaneous fat compartment in an individual patient, the greater can be the discrepancy between the inner change and external limb circumference. Taking the above findings into consideration, a lower correlation of the four indices with the SCDR might be expected. Moreover, the low-frequency direct current used in BEI analysis is mainly conducted via interstitial fluid60; in other words, the impedance value measured by BEI analysis, which is dependent on the relative ratio of connective tissue, in which electrical current cannot propagate as efficiently as in the interstitial space, could be more suitable for representing inner fibrotic changes than the external circumference61. Considering the above findings, the high correlation of the four indices with the BEI might be expected. However, further comparison studies involving direct histological findings, such as those of tissue biopsies, might be required for more definite validation.In lymphedema, inflammatory fibrosis is first triggered at the cellular-molecular level via increased transcription of IL-6, IL-8, TNF-α, and TGF-β1. If lymphatic system dysfunction does not spontaneously recover or is well compensated following this trigger in the acute period62, use of the relative ratio rather than the absolute ratio is the preferred data standardization technique. Therefore, Index 2 calculated using the above standardization method might demonstrate higher classification power than Indices 1, 3, and 4, such that it would show the highest relationship with the BEI, similar to a calculation formula used in another image segmentation-based AI lymphedema study21.All the subindices of Index 2 showed moderate to strong significant correlations with the BEI (0.630–0.875). However, only half of the Index 1, 3, and 4 subindices showed moderate significant correlations (0.420–0.701). This discrepancy was remarkable. Because the pixels classified as fibrosis in the affected limb were subtracted from the pixels classified as fibrosis in the unaffected limb, some segmentation errors in the affected limb might have been eliminated, potentially contributing to the stronger correlation. In image-based medical trials, due to common phenomena of anatomical variation among individuals63. Moreover, if the sample size is small, this phenomenon can be stronger. As a widely reported DL-related issue, overfitting (good fit on the training dataset but poor performance on a new test dataset) should be considered64. Because images from 30 patients (1920 CT images) were utilized for the model development in the current trial, actions to avoid overfitting, such as normalized layer generation, dropout layer insertion, and data augmentation to compensate for data variations46, had to be conducted. Because images can be influenced by CT scanning parameters or acquisition protocols at individual medical centers, using the current model with a single CT scanner at a single center could lead to data and/or model bias. Another limitation is that female patients made up the vast majority of the study population 65. Additionally, the study followed a cross-sectional rather than prospective design. Although lymphedema-induced subfascial muscular fibrosis66 and subsequent muscular change44 have been reported, they were not included here. Future studies are needed to support the current findings in a larger number of subjects.Instead of an intention-to-treat analysis, a per-protocol analysis was used in the current trial because the protocol was violated in 3 patients. The effect size can be overvalued in per-protocol analyses67. Such systems would use cross-sectional CT images, leading to a greater chance of early detection with standardized classification in the future.To show the validity of the DL-based algorithm for identifying lymphedema-induced fibrosis in CT scans, a cross-sectional, observational cohort trial was conducted, and the proposed algorithm was compared with the two gold standard measurements of lymphedema: the BEI and SCDR. In total, 1920 images of 30 lymphedema patients were assigned to a training dataset (65%), validation dataset (15%), and test dataset (20%). The accuracy and MeanBFScore of the SegNet-based fibrosis segmentation were 0.776 and 0.868, respectively. The 4 calculated indices showed a significant strength of correlation (0.420–0.875) with the BEI in patients with chronic moderate-to-severe lymphedema. Among the 4 calculated formulae, the subtraction-type formula is the most promising for this estimation and could act as a foundation for development of automatic recognition systems for fibrotic changes resulting from various inflammation-provoking diseases, such as cellulitis and generalized edemawww.clinicaltrials.gov . All methods were performed in accordance with the relevant guidelines and regulations. The trial conformed to the tenets of the Declaration of Helsinki. Patients were included if they were clinically diagnosed with unilateral limb lymphedema and had undergone BEI analysis and CT scanning. The subjects provided written informed consent for publication of the case details. Data were collected as close to the CT scanning date as possible. Patients who were diagnosed with deep vein thrombosis, bilateral limb involvement, vascular disease, or local infection were excluded.A cross-sectional, observational cohort trial was conducted from January 2018 to March 2019 at a teaching university hospital/tertiary medical center. The protocol of this study was approved by the University Hospital Institutional Review Board (2018-04-009) and was registered at the Protocol Registration and Results System (PRS), 60. Regarding accuracy, the lower is the frequency, the more reliable is the measurement59. However, a frequency of 0 kHz is not applicable, so BEI analysis was conducted at the lowest appropriate frequency (1 kHz)60. To minimize variation in the interstitial hydrostatic pressure68, patients were requested to maintain their regular diet the day before the BEI analysis and to rest in a supine position without movement for 20 min before the analysis. The data were collected using the direct segmental measurement BEI analysis method. The value on the affected side was subtracted from the value on the unaffected side, and that result was divided by the value on the unaffected side.Multifrequency BEI analysis and BEI spectroscopy are frequently used to verify the interstitial fluid of patients with lymphedemaAfter the patients were instructed to lie in the supine position, the limb circumference 5 cm below and 5 cm above the midpopliteal fossa or midantecubital crease was measured on both sides with nonelastic 1-mm polyvinyl chloride fiberglass tape. The difference between the affected and unaffected sides was divided by the circumference of the unaffected side.69. Among them, images of the patellae were manually excluded because their composition prohibits fibrosis. The CT images were acquired by a single 64-channel, multi-detector plain CT , parameters of which are 120 kVp, pitch of 0.984 : 1, 64 × 0.625 collimator configuration, slice thicknesses of 3.8–5.7 mm, and 120 mA. The data were preprocessed at a 512 × 512 pixel matrix size and element size of no larger than 0.5 mm.In this trial, an automatic segmentation method using the DL algorithm was applied. The patients were instructed to keep their body in an anatomically neutral position with both arms in full extension if necessary. Both limbs from the wrist crease to the distal tip of the clavicle or from 5 cm above the lateral malleolus to the point of lower limb separation were simultaneously scanned using CTThe collected CT images were labeled semiautomatically using a lab-developed MATLAB  GUI, as shown in Fig. As a DL model for pixelwise segmentation, SegNet was chosen and trained using 1920 CT images. It is one of the most common models used for semantic segmentation in digital images. As the input, the original gray-scale CT images were cropped to 256 by 256 pixels. Among the 1920 cropped images, the proportions of images selected for the training dataset, validation dataset, and test dataset were 65% (1252), 15% (290), and 20% (378), respectively. All the images from one patient were assigned to the same dataset. By this method, images from one patient were not used for training and testing simultaneously. The training and validation datasets were augmented with translation, reflection, and rotation. Every image in the training dataset was translated from − 3 to 3 pixels along the X or Y direction, reflected to the horizontal direction, and rotated between − 30 degrees and 30 degrees at an interval of one degree. By this augmentation approach, the collective training and validation datasets were increased to 4,684,596 images. Using the augmented images, the SegNet-based model was trained with an encoder depth of 3, 55 epochs, learning rate of 0.002, a minibatch size of 16, the Adam optimizer, and shuffling after every epoch. The encoder depth of 3 indicated 3 sets of one convolution layer and one ReLu and one max pooling layer with 2 strides for down sampling. After encoding, 3 sets of up sampling layers were arranged. A commercial mathematical package, MATLAB , in a graphics processing unit environment  was used to train the SegNet-based model.17, pixels classified as fibrosis were present in the unaffected limb and might be representative of subcutaneous fat septa or superficial fascia67. However, for notation uniformity, these pixels are denoted as Based on the segmentation results of the trained algorithm, four types of indices were defined and calculated for every cross-sectional CT image for comparison with the clinical gold standards, i.e., the BEI and SCDR. The first index was defined as the ratio between the affected and unaffected limbs of the summation of the fat- and fibrosis-classified pixel numbers, as shown in Eq. The second index was defined as the difference between the number of pixels classified as fibrosis between the affected and unaffected limbs divided by the number of pixels in the unaffected limb, as shown in Eq. .3\documeThe third index was defined as the ratio of the number of pixels classified as fibrosis in the affected and unaffected limbs, as shown in Eq. .4\documeThe fourth index was defined as the sum of all the pixels classified as fibrosis in the affected limb of one patient, as shown in Eq. .5\documehttps://www.ncss.com/software/pass/). For two correlation analyses, a power of 0.80, an α of 0.05, an R0 (baseline correlation) of 0, and an R1  of 0.5 were chosen based on data reported by Koo et al.21. The estimated minimum sample size was 2970. Considering that image processing-based trials are well controlled, the likelihood of midterm dropout was expected to be very low. Allowing for a dropout ratio of 5%, 30 patients were required. Two-sided statistical analyses were conducted using the Statistical Package for the Social Sciences, version 24 . Normality verification was performed using the Kolmogorov–Smirnov test. To remove the linear effect derived from interstitial fluid (BEI) or limb swelling (SCDR), the four indices were compared with the BEI and SCDR using partial correlation analysis. If a normal distribution was not verified, the data were analyzed after log transformation. A small positive number was added prior to the log transformation if the values were negative71.The sample size was calculated using Power Analysis and Sample Size Software, version 11.0. (PASS, NCSS Statistical Software, Kaysville, UT, USA:


Stenotrophomonas acidaminiphila, the most recent reported species in genus Stenotrophomonas, is a relatively rare bacteria and is an aerobic, glucose non-fermentative, Gram-negative bacterium. However, little information of S. acidaminiphila is known to cause human infections. In this research, we firstly reported a multidrug-resistant strain S. acidaminiphila SINDOREI isolated from the blood of a patient with sepsis, who was dead of infection eventually. The whole genome of strain SINDOREI was sequenced, and genome comparisons were performed among six closely related S. acidaminiphila strains. The core genes  and strain-specific genes were identified, respectively, to know about the strain-level diversity in six S. acidaminiphila stains. The presence of a unique gene (narG) and essential genes involved in biofilm formation in strain SINDOREI are important for the pathogenesis of infections. Strain SINDOREI was resistant to trimethoprim/sulfamethoxazole, ciprofloxacin, ofloxacin, cefepime, ceftazidime, and aztreonam. Several common and specific antibiotic resistance genes were identified in strain SINDOREI. The presence of two sul genes and exclusive determinants GES-1, aadA3, qacL, and cmlA5 is responsible for the resistance to multidrug. The virulence factors and resistance determinants can show the relationship between the phenotype and genotype and afford potential therapeutic strategies for infections. Stenotrophomonas, which was first described with the type species Stenotrophomonas maltophilia  reactor  was considered as the first-line therapy , but wastibility . The strsul1 gene, sul2 gene and dfrA gene are well known to be responsible for resistance to TMP/SMX  on March 16, 2020, complaining of intermittent fever and full-body pain for more than 1 month without obvious causes. Based on physical examination, laboratory test results and other related examinations including bone marrow (BM) aspirate smear, flow cytometry and RT-PCR, etc., a diagnosis of sepsis was made. The antibiotic treatment did not improve his condition. The patient had a continuous high fever (39.5°C) with some new symptoms such as chills, high fever, appetite, fatigue, and shortness of breath and eventually died for multiple organ failure. The detailed medical record of the patient was shown in Antimicrobial susceptibility testing was performed by VITEK2 system  for minimum inhibitory concentration (MIC) according to the manufacturer’s instructions. MIC breakpoint was determined referring to the clinical and laboratory standards institute (CLSI) guidelines -based methods. Then genomic DNA was randomly fragmented and was selected to average size of 200–400 bp. Adaptors were ligated to the ends of fragments by PCR assay. PCR products were processed into the sequencing library. The qualified BGI-seq libraries were sequenced on a BGISEQ-500 platform with read length of PE100. The Nanopore libraries were prepared and sequenced according to the manufacturer’s instructions  and sequenced on MinION with flowcell version of R9.4.1. The short reads and nanopore long reads were assembled using software Unicycler via GGDC 3.0 using Formula 2.Average nucleotide identity (ANI) values between any two genomes were calculated using fastANI . The digS. acidaminiphila strains, including a type strain and other five cultured stains, were downloaded from the NCBI database  was used . The ratio of specific genes in strain ZAC14D2_NAIMI4_2, SUNEO, T0-18, JAM 13310T, SINDOREI, and T25-65 was 8.2%, 6.4%, 6.2%, 6.1%, 4.5%, and 4.5%, respectively. 159 genes were exclusive to strain SINDOREI, and functional analysis based on COG category revealed that these genes were mainly assigned to signal transduction mechanisms  category, including motility (flagella), adherence (Type IV pili and non-pilus adhesins), biofilm formation, immune modulation , antimicrobial activity/competitive advantage, stress survival, nutritional/metabolic factor, regulation, exotoxin, effector delivery system, and others. The virulence genes of S. acidaminiphila genomes was shown in S. acidaminiphila strains included the same VF categories, but the presence and distribution of genes were different. It is worth noting that the narGH operon only found in strain SINDOREI is responsible for nitrate reductase, which is identified as an important virulence factor for many bacterial infections, such as Mycobacterium tuberculosis and Pseudomonas aeruginosa  and efflux pumps (10 genes) contribute to the multidrug resistance in strain SINDOREI. The 24 genes are involved in resistance to TMP/SMX  in Strain SINDOREI. The amino acid sequences of homologues to the sul genes in Stenotrophomonas genomes were employed to construct the phylogenetic tree. Four distinct groups were generated  was also found. However, narH gene was only found in strain JCM 13310T, T25-65, T0-18 and ZAC14D2_NAIMI4_2. As reported previously, the nitrate reductase enzyme is important in the pathogenesis of many bacteria and a narG knockout mutant can cause reduced virulence and reduce lung damage in severe combined immunodeficiency mice , qacL (antibiotic efflux of disinfecting agents), and cmlA5 (antibiotic efflux of phenicol antibiotic) associated with the resistance to β-lactam, aminoglycoside, disinfecting agents and phenicol are exclusive to strain SINDOREI. The phylogenetic analysis of homologues to sul genes indicated that there are two types of sul genes (sul1 and sul2) in strain SINDOREI. The presence of redundant genes (sul1 and sul2) in strain SINDOREI, should be responsible for the increased resistance to TMP/SMX. The presence of IS6 and Tn3 family transposase located upstream and downstream of the sul2, suggesting that acquisition of resistance genes is a relevant mechanism for strain SINDOREI antibiotic resistance.Investigation of the presence of virulence genes is important to explain the genetic mechanisms of multidrug resistance. In MDR strain SINDOREI, 24 genes involved in the resistance to a broad array of antimicrobial agents were analyzed. Strain SINDOREI has all the target genes mainly encoding antibiotic inactivating enzymes and multidrug efflux pumps, which is an important reason for the resistance to antibiotics. Meanwhile, the unique and redundant resistance genes occurred in strain SINDOREI. GES-1 (GES-type beta-lactamase), S. acidaminiphila and obtained the high-quality genome of clinical isolated MDR strain SINDOREI. The comparative genomes analysis suggested a unique gene (narG) and key genes involved in biofilm formation in strain SINDOREI played an important role in pathogenesis of infections. Antimicrobial susceptibility test revealed that stain SINDOREI were resistant to TMP/SMX, ciprofloxacin, ofloxacin, cefepime, ceftazidime, and aztreonam. The presence of redundant Sul1 and Sul2 from two distinct groups and the exclusive determinants GES-1, aadA3, qacL and cmlA5 exist in SINDOREI, which can explain the mechanisms of strain’s multidrug resistance and afford potential therapeutic strategies for pathogen infections.In this study, we reported the world’s first case of fatal infection with https://ngdc.cncb.ac.cn/) and accession numbers is PRJCA009493, as mentioned in The genome sequence data of strain SINDOREI has been deposited in National Genomics Data Center  for the publication of any potentially identifiable images or data included in this article.YZ, PX, and WC made contributions to the acquisition of clinical data. QY, MZ, AZ, and WW contributed to laboratory work. DW, HX, and YX conducted the clinical work. YZ and DL drafted the manuscript. ZJ revised the manuscript critically for important intellectual content and had given final approval of the version to be published. All authors contributed to the article and approved the submitted version.This paper was supported by the Key Research and Development program of the Hunan Provincial Science and Technology Department (grant no. 2022SK2005).DL was employed by Hugobiotech Co. Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used in practical applications. We used convolutional neural network (CNN) to achieve a satisfying accuracy of the Fitzpatrick skin-type classification by using a simple self-developed device in 2018. Leveraging on the hardware, firmware, and software app-developing experience, a low-cost miniature skin spectrum measurement system (LMSSMS) using deep neural network (DNN) technology was further studied, and the feasibility of the system is verified in this paper. The developed LMSSMS is divided into three parts: (1) miniature skin spectrum measurement device (MSSMD), (2) DNN model, and (3) mobile app. The MSSMD was developed with innovative low-cost MSSC, 3D printing, and a simple LED light source. The DNN model is designed to enhance measurement accuracy. Finally, the mobile app is used to control and show the measurement results. The developed app also includes a variety of skin-spectrum-related applications, such as erythema index and melanin index (EI/MI) measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, and body-fat measurement. In order to verify the feasibility of LMSSMS, we used the standard instrumentation device as a reference. The results show that the accuracy of the LMSSMS can reach 94.7%, which also confirms that this development idea has much potential for further development. Skin spectrometry has many applications worth exploring in the fields of medicine, cosmetology, and personal healthcare. In medicine, it can be used for preliminary physical health testing ,2. For e® Mx 18  [However, currently available skin tone or skin spectrum measuring devices present different problems for individual daily use. Although professional instruments such as CM-700d/CM-600d  [Germany)  are easyGermany)  and SKINGermany)  have emeIt is worth noting that in recent years, with the advancement of semiconductor manufacturing processes and micro electromechanical systems (MEMS) technology, new miniature spectrum sensing chips (MSSCs) have emerged increasingly, such as C12666MA  and so oThe organizational structure of this paper is as follows. The structure of the LMSSMS is shown in The MSSMD is mainly divided into four parts for design and development: (1) miniature spectrum measurement element, (2) measurement light source, (3) optical mechanism, and (4) control and communication interface. They are respectively explained as follows:The traditional spectrum measurement element needs to complete the dispersion by a large volume and precise optical mechanism for the measurement. Although the measurement is more accurate, it is difficult to reduce the cost and achieve miniaturization. Therefore, this study focused on using a novel MSSC NSP32 developed by nanoLambda as a chip for skin spectrometry. The NSP32-W1 spectrum chip uses a 1024-pixel, 32 × 32 nano-optical filter array to measure the light spectrum, based on a technological breakthrough, the plasmonic filter. The 100 μm-high sensor die is assembled in an advanced ball grid array (BGA) package with an optical window for only 500 μm height. The other optical parts, the lens and the diffuser, are assembled in a 5.7 mm–high module. The module is very small overall, at just 6 mm × 6 mm × 5.3 mm. Spectrum measurement range is wide from 400 nm to 1000 nm. Therefore, it is very suitable for the use in developing miniature spectrum measurement systems.The skin spectrum is the spectrum of light reflected from the light impinging on the skin. Therefore, a full wavelength light source is required to illuminate the skin during measurement. LEDs are the best choice for miniature and low-cost light sources. In this study, two SunLike LEDs and two NIR LEDs developed by Seoul Semiconductor were selected as measurement light sources. The specifications and configurations are shown in The design of the optical mechanism will affect the optical path during measurement, and this, in turn, affects the accuracy of the spectrum measurement results. In order to reduce the cost, the 3D design software Sketch Up 2018 was used in this study to design a simple, low-cost, available optical path and compact optical mechanism, as shown in The miniature skin spectrometry device has a very compact design. In order to control the device for skin spectrum measurement, process the measured data, receive the measurement command, and return the measurement result, we designed the control circuit and communication interface of the MSSMD, using the microcontroller STM32F411 and the Bluetooth chip nRF52832. The STM32F411 is mainly responsible for processing the spectral-measurement-related commands received by nRF52832, controlling the light source switch, capturing NSP32 spectral signals, and returning the measured spectral data to nRF52832. The nRF52832 is mainly responsible for receiving measurement commands and returning measurement results, using Bluetooth.Finally, the power supply of the system adopts USB power supply and lithium battery. The device housing is designed according to the internal space requirements, such as the volume of individual components. The final size is set to 38.5 mm × 36 mm × 30 mm. The finished device is shown in Due to the simplicity and low cost of MSSMD design and components, spectral signal accuracy still needs to be improved. This study uses the DNN technique of VDSR  to enhanN, the receptive field size is (2N + 1) × (2N + 1). Therefore, the receptive field size in this study is 41 × 41. The activation function uses ReLu and uses zero padding to keep the input and output the same size. The complete spectrum measurement accuracy improvement process is shown in The VDSR architecture parameters used in this study are set as follows: The number of convolutional layers is 20. The first layer operates on the input image. The last layer of the convolutional layer is used to reconstruct the image with only 1 filter. Each of the remaining convolutional layers has 64 filters. The kernel size is 3 × 3. The size of the receptive field is related to the number of convolutional layers. If the number of convolutional layers is In order to build an accurate spectrum enhancement model for VDSR, we need accurate spectral data, which is required for training. In this study, X-rite i1pro was selected as the reference spectral measuring instrument. The i1pro offers the advantages of a full-spectrum LED light source, accurate spectral measurements, and ease of use. The I1pro is hereinafter referred to as RS, which is the reference spectrometer. About the training spectral data, we invited 16 individuals to measure their skin spectrum at 9 parts, using the MSSMD and the RS, respectively. The 9 parts are the back of the left hand, the left arm, the back of the right hand, the right arm, the inner side of the left arm, the inner side of the right arm, the left cheek, the right cheek, and the forehead. Both devices collected 902 pieces of skin spectral data for training VDSR. Finally, the trained VDSR model is implanted into the system.We used Android Studio and Java to design a mobile application to control the miniature skin spectrum measurement device, as shown in EI and MI can be calculated by (1) and (2), based on the research of Yoshimura et al. [a et al. :(1)EI=50ITA°) algorithm. The calculation method is shown in (3):In terms of the Fitzpatrick type, according to the research of Bino and Bernerd , the humThe classification results are shown in CIELAB ΔE* is used to express the difference between two colors. The smaller the CIELAB ΔE*, the more similar the colors are. Therefore, we calculate the CIELAB ΔE* between the measured skin tone and each numbered color in the Pantone SkinTone Guide in the app. The number corresponding to the smallest CIELAB ΔE* is the Pantone SkinTone Guide number of the subject’s skin tone.TRSVD) and the recommended maximum sun exposure time (RMSET) are as follows, (4) and (5), where SDD is the standard vitamin D dose. UV irradiance is obtained from the current Bureau of Meteorology data. Depending on the type of skin tone, the energy required is shown in According to the research of Webb et al.  and Terul1 is the reflected light intensity of 937 nm, l2 is the reflected light intensity of 947 nm, W is the weight (lbs), H is the height (inches), S is the gender , and EL is the level of exercise . K0 is an intercept error constant; KA2 and KB2 are the slopes of the curves, respectively, representing the two wavelengths being measured; and K3–K6 are for the respective body parameters. Moreover, K0–K6 are determined by a multiple regression technique. A set of example K values is K0 = 84.2, KA2 = −16.3, KB2 = −6.2, K3 = 8.1, K4 = −13.7, K5 = −124.2, and K6 = −81.4:According to the patent of Robert D. Rosenthal , the bodThe LMSSMS developed in this study uses a low-cost MSSC NSP32, LED light source, 3D print housing, and control circuit to develop a low-cost MSSMD. The developed LMSSMD is shown in The app user interface is shown in To verify the accuracy of the developed LMSSMS, we compare the measurement results of the disabled-VDSR LMSSMS, the enabled-VDSR LMSSMS, and RS. The experiments are described as follows: The study invited 16 people to measure the spectrum of the skin, using the developed the disabled-VDSR LMSSMS, the enabled-VDSR LMSSMS, and RS, respectively. Each device collects 386 skin spectral data. ® Mx 18  [Germany)  are highGermany)  are easiGermany)  and SKINGermany)  have higIn this study, we developed and verified the proposed LMSSMS with DNN technology, which can implement accurate skin spectrum and achieve many applications, such as EI/MI measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, body-fat measurement, etc. In this study, the LMSSMS was divided into three parts: (1) MSSMD: The goal was to develop a low-cost, lightweight, portable skin spectrum measurement device, which is mainly divided into four parts: (i) Miniature spectrum measurement element: In this study, a NSP32 was innovatively used as the skin spectrum measurement element. The chip is currently the smallest chip with a low cost and can measure the spectrum in a wide wavelength range. (ii) Measurement light source: We used SunLike LEDs to provide the light source required for NSP32 measurements. The light source emitted by the LED can cover the spectral measurement range of the spectrum with uniform light intensity. (iii) Optical mechanism: In order to develop a compact and portable optical mechanism with NSP32 and SunLike LED as the core, a 3D design software Sketch Up and a 3D printer were used to create a simple optical mechanism. (iv) Control and communication interface: In order to process measurement data, receive measurement commands, and return measurement results, the microcontroller STM32F411 and Bluetooth chip nRF52832 were used to design the control circuit and communication interface of the MSSMD. (2) DNN: To achieve accurate skin spectrum measurements with a low-cost light source, low-cost sensor, and low-complexity optical mechanism, we proposed a DNN technique, VDSR, to improve measurement accuracy. (3) Mobile app: In order to allow the users to control and understand the measurement results clearly, a mobile app was designed. In addition to controlling and showcasing results, this app integrates advanced skin-spectrum-related applications, including the EI/MI measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, body-fat measurement, etc. Finally, the skin spectra collected by the developed LMSSMS were compared with standard instruments. The comparison results show that the measurement accuracy of the LMSSMS can reach 94.7%. This result has verified the feasibility of developing a miniature and reliable skin spectroscopy measurement system that uses a low-cost micro-spectroscopy chip combined with DNN technology. In this study, the experimental results show that the currently used DNN technology, VDSR, performs quite well. However, in the future, we can further explore what kind of DNN technology can make the measurement results of the LMSSMS more accurate.


Diabetic retinopathy is a diabetic microvascular complication. Pyroptosis, as a way of inflammatory death, plays an important role in the occurrence and development of diabetic retinopathy, but its underlying mechanism has not been fully elucidated. The purpose of this study is to identify the potential pyroptosis-related genes in diabetic retinopathy by bioinformatics analysis and validation in a diabetic retinopathy model and predict the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) interacting with them. Subsequently, the competing endogenous RNA (ceRNA) regulatory network is structured to explore their potential molecular mechanism.in vitro model of diabetic retinopathy. Western blot was performed to measure the protein levels of gasdermin D (GSDMD), dasdermin E (GSDME) and cleaved caspase-3 in the cells. Moreover, the aforementioned genes were further confirmed with the validation set. Finally, the ceRNA regulatory network was structured, and the miRNAs and lncRNAs which interacted with CASP3, TLR4, and GBP2 were predicted.We obtained mRNA expression profile dataset GSE60436 from the Gene Expression Omnibus (GEO) database and collected 51 pyroptosis-related genes from the PubMmed database. The differentially expressed pyroptosis-related genes were obtained by bioinformatics analysis with R software, and then eight key genes of interest were identified by correlation analysis, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein–protein interaction (PPI) network analysis. Then, the expression levels of these key pyroptosis-related genes were validated with quantitative real-time polymerase chain reaction (qRT-PCR) in human retinal endothelial cells with high glucose incubation, which was used as an in vitro model of diabetic retinopathy was consistent with the results of the bioinformatics analysis of mRNA chip. Among them, CASP4, GBP1, CASP3, TLR4, and GBP2 were further validated in the GSE179568 dataset. Finally, 20 miRNAs were predicted to target three key genes—CASP3, GBP2, and TLR4, and 22 lncRNAs were predicted to potentially bind to these 20 miRNAs. Then, we constructed a key ceRNA network that is expected to mediate cellular pyroptosis in diabetic retinopathy.A total of 13 differentially expressed pyroptosis-related genes were screened from six proliferative diabetic retinopathy patients and three RNA samples from human retinas, including one downregulated gene and 12 upregulated genes. A correlation analysis showed that there was a correlation among these genes. Then, KEGG pathway and GO enrichment analyses were performed to explore the functional roles of these genes. The results showed that the mRNA of these genes was mainly related to inflammasome complex, interleukin-1 beta production, and NOD-like receptor signaling pathway. In addition, eight hub genes—CASP3, TLR4, NLRP3, GBP2, CASP1, CASP4, PYCARD, and GBP1—were identified by PPI network analysis using Cytoscape software. High glucose increased the protein level of GSDMD and GSDME, as critical effectors of pyroptosis, in retinal vascular endothelial cells. Verified by qRT-PCR, the expression of all these eight hub genes in the Through the data analysis of the GEO database by R software and verification by qRT-PCR and validation set, we successfully identified potential pyroptosis-related genes involved in the occurrence of diabetic retinopathy. The key ceRNA regulatory network associated with these genes was structured. These findings might improve the understanding of molecular mechanisms underlying pyroptosis in diabetic retinopathy. Diabetic retinopathy is an irreversible blindness disease characterized by retinal microvascular dysfunction . As one Pyroptosis is a new way of programmed cell death mediated by gasdermin . DiffereIshikawa K et al. generated the dataset GSE60436 . They anhttps://www.ncbi.nlm.nih.gov/geo/). The dataset contains three active FVMs and three inactive FVMs, which were obtained from proliferative diabetic retinopathy patients, and the RNAs from human retinas were purchased and were extracted from normal retinas pooled from 99 Caucasians. All samples were analyzed by GPL6884 platform (IlluminaHumanWG-6v3.0 expression beadchip), and R software (version 4.1.0) was used for data quality control, normalization, background correction, and subsequent analysis. The RNA-seq-based dataset GSE179568 was also obtained from GEO, including seven human retinal neovascularization (RNV) membranes, 10 macular pucker, and seven macular hole samples as controls. The search for pyroptosis-related genes was mainly carried out in the PubMed database (https://pubmed.ncbi.nlm.nih.gov/). Finally, 51 genes were collected  database  was used to construct the PPI network of DEG-encoded proteins  with experimental methods to verify the relationship, and uses this tool to predict the miRNAs of the pyroptosis-related genes of interest. All the lncRNA–miRNA interaction data were obtained in the starbase database (https://starbase.sysu.edu.cn/), and the target lncRNA was screened according to clipExpNum >7. Finally, visualization was carried out in Cytoscape software.The “multiMiR” package in R software combines 14 databases, including the mirTarbase database  for 48 h, and the cells cultured in normal medium containing 5 mM glucose were used as control under the same conditions. In order to ensure the accuracy and stability of the results, 8–10 generations of cells were selected for follow-up study.Human retinal endothelial cells (HRECs)  were cultured in endothelial Complete Classic Medium  containing 10% fetal bovine serum . The culture medium was changed every 2 days. When the cells were 85–90% confluent, it was passaged at a ratio of 1:3. The −ΔΔCt method. β-actin mRNA was selected as the internal control.According to the manufacturer’s plan, the total RNA of the experimental group and the control group was extracted by TRIzol reagent . The concentration and the purity of RNA were measured by NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific). Then, cDNA was reverse-transcribed from total RNA using UEIris II RT-PCR System  in strict accordance with the manufacturer’s instructions, and qRT-PCR was performed using 2× SYBR Green qPCR Master Mix . The sequence of the qRT-PCR primers is shown in HRECs were lysed with RIPA lysis buffer  containing protease inhibitors and phosphatase inhibitors, and the protein concentrations were determined using the BCA kit . The samples were separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis at 140 V and transferred to polyvinylidene membranes. Subsequently, the membranes were placed in 5% blocking buffer  to block for 1 h, incubated with primary antibodies against CASP3 , GSDMD , GSDME , and GAPDH  at 4˚C overnight, followed by horseradish peroxidase-labeled secondary antibodies  for 1 h at room temperature, and finally developed with an enhanced chemiluminescence kit for visualization. The collected images were examined using ImageJ software .in vitro experiment was repeated at least three times. Statistical analysis was performed using GraphPad Prism software (version 8.2.1). On the basis of the homogeneity of the variance test, Student’s t-test was performed, and P <0.05 was considered significant and statistically significant.Each P-value <0.05 and |log2 fold change| >0.5 as thresholds, including 1,640 upregulated genes and 1,548 downregulated genes as shown in the volcano plot  or normal-glucose medium. High-glucose-exposed HRECs were used as an in vitro model of diabetic retinopathy. Similar to the results of mRNA chip in the GSE60436 dataset, high glucose treatment significantly increased the mRNA expression levels of CASP3, TLR4, GBP2, CASP4, and GBP1 in HRECs. In addition, the expression levels of NLRP3, CASP1, and PYCARD were also increased  were increased in human retinal endothelial cells after high glucose incubation, suggesting the occurrence of pyroptosis  can be found in the article/NW and LD analyzed the data and drafted the manuscript. DL, XX, QZ, and GZ edited and provided comments to improve the manuscript. SX designed this experiment and reviewed and revised the manuscripts. All the authors contributed to the article and approved the final manuscript.This study was financially supported by the National Natural Science Foundation of China (no. 81974137 to SX), the National Natural Science Foundation of China (no. 82070966 to LD), the Natural Science Foundation of Hunan Province (no. 2019JJ40507 to SX), and the Science and Technology Innovation Program of Hunan Province (no. 2021RC3026 to LD).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We used these variables to develop a predictive model with an AUROC of 0.926 , which was significantly higher than that of the WHO COVID severity classification, 0.853  , showing that our model can accurately predict mortality of hospitalized COVID-19 patients.Predicting the mortality risk of patients with Coronavirus Disease 2019 (COVID-19) can be valuable in allocating limited medical resources in the setting of outbreaks. This study assessed the role of a chest X-ray (CXR) scoring system in a multivariable model in predicting the mortality of COVID-19 patients by performing a single-center, retrospective, observational study including consecutive patients admitted with a confirmed diagnosis of COVID-19 and an initial CXR. The CXR severity score was calculated by three radiologists with 12 to 15 years of experience in thoracic imaging, based on the extent of lung involvement and density of lung opacities. Logistic regression analysis was used to identify independent predictive factors for mortality to create a predictive model. A validation dataset was used to calculate its predictive value as the AUROC. A total of 628 patients  were included in this study. Age ( The unprecedented spread of Coronavirus Disease 2019 (COVID-19) infection across the world has strained our society with significant morbidity and mortality. Its clinical manifestations range from minor flu-like symptoms to severe pneumonia that can lead to acute respiratory distress syndrome, multiple organ dysfunction syndrome, and death ,4,5, facOne of the main problems with using imaging modalities as diagnostic tools for COVID-19 is that all radiological findings in this disease are generic and have significant overlap with other infectious processes. Findings such as reticulonodular or ground-glass opacities, patchy infiltration, and consolidation can be identified on CT scans as early as the fifth day following onset of symptoms, while changes in CXRs are expected to appear on days 10 to 12 . MultiplConsidering our unique patient population in the South Bronx is predominantly composed of minorities with the highest prevalence of chronic diseases such as hypertension, diabetes, obesity, and COPD among the five boroughs of New York City , we aimeThis single-center, retrospective, observational study was reviewed and approved by the institutional review board (IRB# 20-007), and the requirement for obtaining written consent from the participants was waived. Consecutive adult patients admitted to the hospital from 5 March to 16 April 2020 with a positive RT-PCR test for SARS-CoV-2 on nasopharyngeal swab samples  and a chest X-ray performed in the emergency room were included in the study. Radiological findings better explained by non-COVID-19 causes such as acute congestive lung markings, effusion, abscess, white-out lung, or pneumothorax were excluded from our study. Subjects with documented pulmonary diseases such as interstitial lung disease, sarcoidosis, tuberculosis, lung cancer or metastasis to lung, or prior pneumonia within the last 3 months were also excluded .2), fraction of inspired oxygen (FiO2), the SpO2/FiO2 ratio (S/F ratio), and the need for mechanical ventilation. Sepsis syndrome on admission was assessed based on the quick Sequential Organ Failure Assessment (qSOFA) score [Baseline characteristics, comorbidities, clinical data on admission, and clinical course were collected from electronic medical records for each patient. Collected data included age, gender, race, body mass index, smoking, medical comorbidities, symptoms on admission , and the number of days from the onset of symptoms to admission. Oxygen requirement data were taken from the first encounter in the emergency department, including pulse oximetric saturation (SpOA) score . COVID-1A) score , intubatChest radiographs acquired in the anteroposterior (AP) view using a portable X-ray machine during their emergency department stay were examined retrospectively. These were read by three radiologists with 12, 15, and 15 years of experience in thoracic imaging, who were blinded to the patients’ clinical history and condition. They were asked to give a consensus reading of two radiologic features: distribution and characteristics of the lung opacities. They assigned a score from 0 to 4 for the distribution of lung opacities: 0 for normal; 1 for unilateral–unilobar; 2 for unilateral–multilobar; 3 for bilateral–not diffuse; and 4 for diffuse bilateral . They alCategorical variables were summarized as frequency (percentage) and continuous variables were presented as either mean (standard deviation—SD) or median (interquartile range—IQR) according to their distribution.Binary logistic regression analysis was used to assess the correlation between possible confounders and outcome at discharge to identify independent predictive factors for in-hospital mortality in these patients. The results are presented as odds ratios (ORs) and 95% confidence intervals. The dataset was then randomly split into derivation and validation datasets with a ratio of 70:30, respectively. Binary logistic regression analysis was used on the derivation dataset to develop a model with the identified factors to predict in-hospital mortality. The model was then tested on the validation dataset. Its predictive value was calculated as the area under the receiver operating characteristic curve (AUROC) and then compared to that of the COVID severity using Hanley and McNeil’s method , acknowlp-value less than 0.05 was considered statistically significant.All the analyses were performed using IBM SPSS statistical software version 25 . A A total of 628 patients admitted with a confirmed diagnosis of COVID-19 were included in this study. Patients’ baseline characteristics, underlying comorbidities, and clinical course are shown in Abnormal CXR findings on admission were observed in 70.6% (479) of our cohort. Common CXR findings included hazy or interstitial infiltrates in 74.5% (357), while dense consolidation was seen in 22.5% (122). In terms of distribution, diffuse bilateral infiltrates were predominant and seen in 69.1% (331), followed by unilateral–unilobar in 13.4% (64), and bilateral—not diffuse in 13.2% (63) of cases. The median CXR severity score for our cohort was 3 . More than half of the patients (53.4%) had diffuse bilateral changes with hazy opacities, which corresponds to a CXR score of 4, followed by diffuse bilateral with dense opacities in 15.7% (75), which corresponds to a CXR score of 8. The distribution and frequency of radiographic findings are shown in p < 0.001), sepsis on admission (p < 0.001), S/F ratio (p < 0.001), mechanical ventilation on admission (p < 0.001), and the CXR severity score (p = 0.005) to be independent predictive factors for the in-hospital mortality of patients.Binary logistic regression analysis was used to identify independent predictors for in-hospital mortality in our cohort. The results of this analysis, presented in N = 439) and validation (N = 189) datasets with a ratio of 70:30, respectively. According to the binary logistic regression analysis results, a model was developed on the derivation dataset and included the following variables: age, sepsis syndrome on admission, S/F ratio, mechanical ventilation on admission, and CXR severity score. The specifics of the model are presented in N = 189) as 0.853 . Hanley and McNeil’s method was used in a one-tailed setting, testing to see if the AUROC of our predictive model was more significant than that of the WHO COVID severity. The results showed our model to have a significantly higher predictive value compared to COVID severity, with a one-tailed p-value of 0.028 (The dataset was then randomly split into two derivation (of 0.028 . AlthougN = 189), yielding an AUROC of 0.926  for predicting mortality in hospitalized COVID-19 patients.Considering the limited medical resources available during the COVID-19 pandemic surge, the accurate prediction of patients with COVID-19 who are particularly at risk for poor outcomes can be very valuable in determining how to best allocate resources and optimize care. Accordingly, numerous studies have used clinical data, lab results, and information obtained from imaging to develop statistical and machine-learning models to predict the mortality of COVID-19 patients ,6,7,8. IN = 21) compared to the surviving subjects (N = 69) [Although prior studies have introduced comparable CXR scoring systems, to our knowledge, this study is one of the first to have incorporated a CXR severity scoring system in a multivariable model to provide an accurate predictive tool that can be used at bedside on admission. For instance, Toussie et al., designed a scoring method in which each lung was divided into three zones and each zone was given a score of 0 if opacity was absent or 1 if opacity was present. The sum of these scores constituted the final score, with scores greater than 2 associated with hospitalization and those greater than 3 associated with intubation. In their cohort of 338 patients, only 7% expired throughout the study and—although most of these patients had more extensive lung opacification—a meaningful relationship could not be demonstrated . In anot(N = 69) . Joseph (N = 69) . Neitherp < 0.001) [Later, Balbi et al., used the Brixia score in their study conducted on a sample of 340 COVID-19 patients. They ran a binary logistic regression analysis and found that the Brixia score, age, PaO2/FiO2 ratio, and cardiovascular diseases were independent predictive factors for death in these patients . They di< 0.001) . In compTo summarize the strengths of our work compared to other similar studies, we had a larger sample population, we developed an accurate predictive model for in-hospital mortality in COVID-19 patients and validated it in a smaller sample of patients admitted to our center, we used easily accessible clinical variables along with information obtained from CXRs making it suitable for bedside use, and our model had a considerably high predictive value for the outcome, which was superior or comparable to other statistical and machine-learning models ,30,31,32There were also several limitations to this work. Our prediction model was only internally validated, and it is based on a retrospective cohort of patients from one hospital providing healthcare to a particular population with a high prevalence of baseline comorbidities contributing to a higher death rate due to COVID-19, which limits its generalizability. Additionally, the data for this study were collected during the first wave of the COVID-19 pandemic, and since then, multiple new variants have emerged, which raises concerns about the performance of our model on new COVID-19 patients afflicted with the new variants. However, the variables we included in our model are conceptually general indicators of poor outcome in any acute respiratory disease, or even non-respiratory acute conditions. Another limitation was that all clinical parameters and laboratory values known to be associated with worse outcomes were not included in our model, mainly because they were not available for all patients. Moreover, the three radiologists reading the CXRs reached a consensus and provided us with a final score, which does not reflect the actual clinical workflow, and consequently, inter-rater variability could not be assessed in the setting of our study.In summary, we found that basic clinical information and a simple assessment of lung involvement on a CXR—which are available in the first few hours of hospital admission—can provide complementary information for the prognosis of hospitalized patients with COVID-19. We demonstrated that a simple model composed of age, sepsis syndrome on admission, S/F ratio, mechanical ventilation on admission, and a CXR severity score were predictive of in-hospital mortality among these patients.