In relation to the meta-analysis for SCFAs, while there was significant difference (p = 0.02) between the dietary fibre group and placebo with a standardised mean difference of 0.5  regarding total SCFAs, the differences were not significant (p > 0.05) in relation to acetic acid, propionic acid and butyric acid. There was only significant improvement (p = 0.002) with respect to glycated haemoglobin with a mean difference of −0.18  between the dietary fibre group and placebo group. Differences between the two groups were not significant (p > 0.05) in relation to fasting blood glucose and homeostatic model assessment of insulin resistance (HOMA-IR). Furthermore, there were no significant differences between the two groups in subjects who reported adverse events. It is possible that the promotion of SCFA producers in greater diversity and abundance by dietary fibre in this review led to improvement in glycated haemoglobin, partly due to increased glucagon-like peptide-1 (GLP-1) production. In addition, Bifidobacterium lactis has been reported to increase glycogen synthesis, decrease expression of hepatic gluconeogenesis genes, improve translocation of glucose transport-4 and promote glucose uptake. It is also possible that the reduction in body weight of participants in the intervention group compared with control may have contributed to the observed improvement in glycated haemoglobin. Conclusion: This systematic review and meta-analysis have demonstrated that dietary fibre can significantly improve (p < 0.05) the relative abundance of Bifidobacterium, total SCFAs and glycated haemoglobin. However, dietary fibre did not appear to have significant effect (p > 0.05) on fasting blood glucose, HOMA-IR, acetic acid, propionic acid, butyric acid and adverse events.Background: The prevalence of type 2 diabetes is on the increase worldwide, and it represents about 90% of adults who are diagnosed with diabetes. Overweight and obesity, lifestyle, genetic predisposition and gut microbiota dysbiosis have been implicated as possible risk factors in the development of type 2 diabetes. In particular, low intake of dietary fibre and consumption of foods high in fat and sugar, which are common in western lifestyle, have been reported to contribute to the depletion of specific bacterial taxa. Therefore, it is possible that intake of high dietary fibre may alter the environment in the gut and provide the needed substrate for microbial bloom. Aim: The current review is a systematic review and meta-analysis which evaluated the role of dietary fibre in modulating gut microbiota dysbiosis in patients with type 2 diabetes. Methods: This is a systematic review and meta-analysis of randomised controlled trials which relied on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Electronic searches were conducted using EBSCOHost with links to Health Sciences Research Databases, EMBASE and Google Scholar. The reference lists of articles were also searched for relevant studies. Searches were conducted from date of commencement of the database to 5 August 2020. The search strategy was based on the Population, Intervention, Comparator, Outcomes, Studies (PICOS) framework and involved the use of synonyms and medical subject headings (MesH). Search terms were combined with Boolean operators (OR/AND). Results: Nine studies which met the inclusion criteria were selected for the systematic review and meta-analysis, and four distinct areas were identified: the effect of dietary fibre on gut microbiota; the role of dietary fibre on short-chain fatty acids (SCFAs); glycaemic control; and adverse events. There was significant difference ( The prevalence of type 2 diabetes is on the increase globally, and it represents about 90% of adults who are diagnosed with diabetes ,2. The WAn understanding of the pathophysiology of type 2 diabetes and the various management approaches is crucial in reducing the risks of diabetes and its complications. In particular, the use of dietary interventions including dietary fibre has been promoted by researchers and practitioners involved in diabetes care and management. However, there appears to be inconsistencies in what is considered to be a dietary fibre . In 2008There is evidence that diets play a significant role in modulating gut microbiota in terms of its composition and in the production of short-chain fatty acids (SCFAs) . HoweverPoor dietary habits can lead to intestinal dysbiosis, such as alterations in the balance of the different bacterial phyla, including overgrowth of Proteobacteria and/or reduction in Bacteroidetes . There iAlthough an association between higher intake of dietary fibre and reduced incidence of type 2 diabetes has been indicated, the results from different studies are not consistent . FurtherThe current review is a systematic review and meta-analysis which evaluated the role of dietary fibre in modulating gut microbiota dysbiosis in patients with type 2 diabetes.This is a systematic review and meta-analysis of randomised controlled trials which relied on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework .Only randomised controlled studies were included in this review.The participants were people with type 2 diabetes, or in some studies, the control subjects did not have type 2 diabetes.The interventions were dietary fibre including a macrobiotic diet.The following were the outcome measures of interest:Relative abundance of gut microbiota (genera only): Bifidobacterium, Lactobacillus, Roseburia, Bacteroides, Ruminococcus, and Clostridium.Short-chain fatty acids (SCFAs): total SCFA, acetic acid, propionic acid, butyric acid.Glycaemic parameters: glycated haemoglobin (HbA1c), fasting blood glucose (FBG), homeostatic model assessment of insulin resistance (HOMA-IR).Adverse events: total adverse events, diarrhoea, bloating, constipation, abdominal pain.Electronic searches were conducted using EBSCOHost with links to Health Sciences Research Databases . Other electronic databases searched were EMBASE and Google Scholar. The reference lists of articles were also searched for relevant studies. Searches were conducted from date of commencement of database to 5 August 2020. The search strategy was based on the Population, Intervention, Comparator, Outcomes, Studies (PICOS) framework  involvinStudies were included based on a set of inclusion and exclusion criteria and relied on the PRISMA flow chart .Inclusion criteria: studies selected were those involving patients with type 2 diabetes ; above 18 years of age; dietary fibre as intervention; and gut microbiota, glycaemic parameters, short-chain fatty acids and adverse events as outcomes of interest.Exclusion criteria: studies involving participants below 18 years of age, patients with type 1 diabetes, and pre-diabetes or gestational diabetes were excluded. Furthermore, studies involving animal models and probiotics were also excluded.Data from the selected articles were extracted by two researchers  and cross-checked by the other two researchers . For the meta-analysis data, the authors of the selected articles were contacted for the original data where possible. Changes from baseline for the intervention were compared with the control in all the parameters analysed . The EngThe risk of bias for the included studies was assessed using a domain based assessment tool . The domI2 statistic [p < 0.10 was taken as the level of statistical significance of heterogeneity. The fixed effects model and mean difference were used for the meta-analysis. However, for short-chain fatty acids data, these were converted into standardised mean difference (SMD) due to the use of different measurement scales in the outcome of interest and the random effects model was used for the analysis.The meta-analysis was carried out using Review Manager (RevMan) 5.3 software . In additatistic , and p <p < 0.05, and the results of the meta-analysis were presented as forest plots.The overall effect of the intervention in relation to statistical significance was based on Nine studies which met the inclusion criteria were selected for the systematic review and meta-analysis . The chaThe risks of bias in the included studies are shown in Based on the systematic review and meta-analysis, four distinct areas were identified: the effect of dietary fibre on gut microbiota; the role of dietary fibre on short-chain fatty acids; glycaemic control; and adverse events.The effects of dietary fibre on gut microbiota at the genus level are outlined in p = 0.038). Furthermore, there was a higher level for Collinsella spp. in the dietary fibre group compared with control. However, there were no significant differences in alpha or beta diversity [p = 0.045), and the effect was most prominent on operational taxonomic units (OTUs) of Bifidobacterium adolescentis, followed by OTUs of Bacteroides.However, Reimer et al.  found thiversity . Accordiiversity , in the There was no significant effect on total bacteria, Lactobacillus, Roseburia, Enteroacteriaceae, Clostridium leptum or Clostridium coccoides groups following prebiotic fibre treatment in the study conducted by Pedersen et al. .p < 0.01) in the relative abundance of Bifidobacterium with a mean difference of 0.72  between the dietary fibre group compared with placebo , acetic acid (p = 0.02), and propionic acid (p = 0.04) as compared to control group, there was no significant difference in relation to butyric acid between the treatments (p = 0.19) or on the overall microbial diversity [In the study by Birkeland et al. , while tiversity .In terms of the relationship between microbiota and the SCFA , a general trend was that acetic acid was positively related to operational taxonomic units (OTUs) that increased with the prebiotic fibre . On the other hand, the opposite trend was observed for the OTUs that declined with the prebiotic treatment, and the prebiotic affected OTUs of Bifidobacterium adolescentis were negatively related to butyric acid .p = 0.02) between the dietary fibre group and placebo with a standardised mean difference of 0.5  with respect to total SCFAs (p > 0.05) in relation to acetic acid (p = 0.04) for propionic acid and butyric acid when the study of Gona et al. [In relation to the meta-analysis for total SCFAs, two studies were included and involved 95 participants a. In conal SCFAs a, the ditic acid b, propiotic acid c and buttic acid d. Followa et al.  was remop = 0.002) levels than the control group. Furthermore, Soare et al. [According to Candela et al. , when pae et al.  found thp = 0.02); the differences between the groups were not statistically significant [However, differences between the intervention and control groups were not significantly different with respect to glucose variables in the study conducted by Medina-Vera et al. . Similarnificant .p = 0.002) with respect to glycated haemoglobin with a mean difference of −0.18  between the dietary fibre group and placebo group (p > 0.05) in relation to fasting blood glucose (p = 0.19) for glycated haemoglobin when the study of Soare et al. [In terms of the meta-analysis, while 6 studies were included for fasting blood glucose with 508 participants a, glycatbo group b. Differ glucose a and HOM glucose c. Followe et al.  was remop > 0.05) between the two groups in subjects who reported adverse events  in glycated haemoglobin, total SCFAs and the relative abundance of Bifidobacterium in the dietary fibre group compared with the control group. In contrast, differences between the two groups were not statistically significant (p > 0.05) in relation to fasting blood glucose, HOMA-IR, acetic acid, propionic acid, butyric acid and adverse events.The results of this systematic review and meta-analysis have shown that there were significant improvements (p > 0.05) between the two groups in relation to fasting blood glucose, HOMA-IR and in the relative abundance of Bifidobacterium. The difference between this current review and the review by Houghton et al. [Some of the findings of this review would appear to confirm the results of an earlier systematic review by Houghton et al.  which asn et al.  with resn et al.  observedn et al.  have alsAlthough several studies have reported the association between dysbiosis and type 2 diabetes, the results are often varied and inconsistent ,33. DysbThe mechanism by which changes in the community of gut microbiota modulates metabolic control is still evolving. However, a number of possible mechanisms have been documented including altering levels of glucagon-like peptide-1, lipopolysaccharides, inflammation and SCFAs . The rolThe gut microbiota has been reported to influence energy levels, blood glucose and the effectiveness of pharmacological interventions . While mIn the study by Candela et al. , which hIn the current review, Gonai et al.  found thThe physico-chemical properties of dietary fibre are significant in influencing the function of the gastrointestinal (GI) tract including digestion, nutrient bio-accessibility, microbial fermentation and glycaemic control . In addiIt would appear that a number of mechanisms are involved in the physiological effects of dietary fibre on macronutrient digestion, and the mechanism that predominates may be determined by a range of factors, including polysaccharide composition, the physical state of the fibre source, whether the fibre has been processed and the presence of other variables in foods such as lipids, which can also influence gut function .Dietary fibres that are water soluble have been shown to reduce fasting plasma cholesterol in human subjects by modifying bile salt metabolism ,46. SoluHowever, a major mechanism that has been identified in the control of blood glucose in patients with type 2 diabetes is the role of dietary fibre in physically encapsulating/entrapping nutrients, thus slowing the rate of digestion of plant tissues and reducing the rise in blood glucose ,49. The The ranking of different carbohydrate foods can also be based on their glycaemic index ,51. TherAlthough nine studies were included in the overall meta-analysis, the studies included in the meta-analysis for SCFAs and gut microbiota were no more than three and two studies, respectively, and this could limit the wider application of the findings. Therefore, more studies are required in this area of research. In addition, despite the use of the random effects model in the analysis of short-chain fatty acids, the high heterogeneity of the studies included may have also affected the results of the meta-analysis.p < 0.05) the relative abundance of Bifidobacterium, total SCFAs and glycated haemoglobin. However, dietary fibre did not appear to have a significant effect (p > 0.05) on fasting blood glucose, HOMA-IR, acetic acid, propionic acid, butyric acid and adverse events.This systematic review and meta-analysis have demonstrated that dietary fibre can significantly improve (


R2 values of 0.8938, 0.8910, and 0.9156 and normalized root mean square error (NRMSE) values of 26.00, 22.07, and 19.94%, outperforming the compared methods for all three growth-related traits. The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno. Generalization tests were conducted by using images of Tiberius from another growing season. The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits, with R2 values of 0.9277, 0.9126, and 0.9251 and NRMSE values of 22.96, 37.29, and 27.60%. The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network (CNN). Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i.e., leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA). To compare the results of the CNN model, widely adopted methods were also used. The results showed that the values estimated by CNN had good agreement with the actual measurements, with  Monitoring the growth of greenhouse lettuce by accurately obtaining growth-related traits  is of great practical significance for improving the yield and quality of lettuce3. The traditional methods for measuring growth-related traits, which are relatively straightforward, can achieve relatively accurate results4. However, the methods require destructive sampling, thus making it time-consuming and laborious7.Growth monitoring is essential for optimizing management and maximizing the production of greenhouse lettuce. Leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA) are critical indicators for characterizing growth10. Specifically, the image-based approaches extract low-level features from digital images and establish the relationship between the low-level features and manually measured growth-related traits, such as LA, LFW, and LDW. Based on this relationship, the image-derived features can estimate the growth-related traits, thus achieving nondestructive growth monitoring. For example, Chen et al.6 proposed method for the estimation of barley biomass. The authors extracted structure properties, color-related features, near-infrared (NIR) signals, and fluorescence-based features from images. Based on the above features, they built multiple models, i.e., support vector regression (SVR), random forest (RF), multivariate linear regression (MLR), and multivariate adaptive regression splines, to estimate barley biomass. The results showed that the RF model was able to accurately estimate the biomass of barley and better quantify the relationship between image-based features and barley biomass than the other methods. Tackenberg et al.11 proposed a method for estimating the growth-related traits of grass based on digital image analysis. Image features, such as the projected area (PA) and proportion of greenish pixels, were extracted, which were then fitted to the actual measured values of the aboveground fresh biomass, oven-dried biomass, and dry matter content by linear regression (LR). The results showed that all the determined coefficients of the constructed models were higher than 0.85, indicating that these features exhibited good linear relationship with growth-related traits. Casadesús and Villegas5 used color-based image features to estimate the leaf area index (LAI), green area index (GAI), and crop dry weight biomass (CDW) of two genotypes of barley. The image features included the H component of the HSI color space, the a* component of the CIEL*a*b* color space, and the U components of the CIELUV color space. In addition, the green fraction and greener fraction were also extracted. The features were linearly fitted to the measured values of LAI, GAI, and CDW at different growth stages. The results showed that the image features based on color had strong correlations with growth-related traits. Fan et al.12 developed a simple visible and NIR (near-infrared) camera system to capture time-series images of Italian ryegrass. Based on the digital number values of the R, G, and NIR channels of the raw images, MLR models for LAI estimation were built. The results showed that the image features derived from segmented images yielded better accuracy than those from non-segmented images, with an R2 value of 0.79 for LAI estimation. Liu and Pattey13 extracted the vertical gap fraction from digital images captured from nadir to estimate the LAI of corn, soybean, and wheat. Prior to the extraction of the canopy vertical gap fraction, the authors adopted the histogram-based threshold method to segment the green vegetative pixels. The results showed that the LAI estimated by the digital images before canopy closure was correlated with the field measurements. Sakamoto et al.14 used vegetation indices derived from digital images, i.e., the visible atmospherically resistant index (VARI) and excess green (ExG), to estimate the biophysical characteristics of maize during the daytime. The results showed that the VARI could accurately estimate the green LAI, and the ExG was able to accurately estimate the total LAI.In recent years, nondestructive monitoring approaches have become a hot research topic. With the development of computer vision technology, image-based approaches have been widely applied to the nondestructive monitoring of crop growth15. Second, the methods greatly rely on manually designed image features, which have large computational complexity. Moreover, the generalization ability of the extracted low-level image features is poor17. Therefore, a more feasible and robust approach should be explored.Although computer vision-based methods for estimating growth-related traits have achieved promising results, they are subject to two issues. First, the methods are susceptible to noise. Since the images are captured under field conditions, noise caused by uneven illumination and cluttered backgrounds is inevitable, which will affect image segmentation and feature extraction, thus potentially reducing the accuracy19. With a sufficient amount of data, CNNs can achieve better precision than conventional methods21. Therefore, CNNs have been used in a wide range of agricultural applications, such as weed and crop recognition23, plant disease diagnosis28, and plant organ detection and counting29. However, despite its extensive use in classification tasks, CNNs have rarely been applied to regression applications, and there are few reports on how CNNs have been used for the estimation of growth-related traits of greenhouse lettuce. Inspired by Ma et al.18, who accurately estimated the aboveground biomass of winter wheat at early growth stages by using a deep CNN, which is a CNN with a deep network structure, this study intended to adopt a CNN to construct an estimation model for growth monitoring of greenhouse lettuce based on digital images and to compare the results with conventional methods that have been widely adopted to estimate growth-related traits.Convolutional neural networks (CNNs), which is a state-of-the-art deep learning approach, can directly take images as input to automatically learn complex feature representationsThe objective of this study is to achieve accurate estimations of growth-related traits for greenhouse lettuce. A CNN is used to model the relationship between an RGB image of greenhouse lettuce and the corresponding growth-related traits . By following the proposed framework, including lettuce image preprocessing, image augmentation, and CNN construction, this study will investigate the potential of using CNNs with digital images to estimate the growth-related traits of greenhouse lettuce throughout the entire growing season, thus exploring a feasible and robust approach for growth monitoring.The experiment was conducted at the experimental greenhouse of the Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China . Three cultivars of greenhouse lettuce, i.e., Flandria, Tiberius, and Locarno, were grown under controlled climate conditions with 29/24 °C day/night temperatures and an average relative humidity of 58%. During the experiment, natural light was used for illumination, and a nutrient solution was circulated twice a day. The experiment was performed from April 22, 2019, to June 1, 2019. Six shelves were adopted in the experiment. Each shelf had a size of 3.48 × 0.6 m, and each lettuce cultivar occupied two shelves.30. During the image collection, the sensor was mounted on a tripod at a distance of 78 cm to the ground and was oriented vertically downwards over the lettuce canopy to capture digital images and depth images. The original pixel resolutions of the digital images and depth images were 1920 × 1080 and 512 × 424, respectively. The digital images were stored in JPG format, while the depth images were stored in PNG format. The image collection was performed seven times 1 week after transplanting between 9:00 a.m. and 12:00 a.m. Finally, two image datasets were constructed, i.e., a digital image dataset containing 286 digital images and a depth image dataset containing 286 depth images. The number of digital images for Flandria, Tiberius, and Locarno was 96, 94 (two plants did not survive), and 96, respectively, and the number of depth images for the three cultivars was the same.The number of plants for each lettuce cultivar was 96, which were sequentially labeled. Image collection was performed using a low-cost Kinect 2.0 depth sensor31. The brightness of the images was adjusted to 0.8, 0.9, 1.1, and 1.2 times that of the original images to simulate the change in daylight. In total, the training dataset was enlarged by 26 times, resulting in 5954 digital images.Since the original digital images of greenhouse lettuce contained an excess of background pixels, this study manually cropped images to eliminate the extra background pixels, after which images were uniformly adjusted to 900 × 900 pixel resolution. Figure Field measurements of LFW, LDW, and LA were performed simultaneously with image collection. These measurements were conducted at an interval of seven days, specifically on April 29, May 6, May 13, May 20, May 27, May 31, and June 1 of 2019. For the first six measurements, ten plants of greenhouse lettuce were randomly sampled each time for each cultivar. The measurements were obtained using a destructive sampling method. The sample was placed on a balance with a precision of 0.01 g after root removal, and the LFW was measured. The LA of the corresponding sample was obtained by a LA meter . Given the relatively large leaves of lettuce during the late growing season, the sample was sealed in an envelope and oven-dried at 80 °C for 72 h, after which the sample was weighed to obtain the LDW. For the last measurement, all the remaining lettuce plants were harvested, and the measurements were obtained by using the same method.The architecture of the CNN model is shown in Fig. 33 and RF34, were adopted to estimate the growth-related traits of greenhouse lettuce since these two methods have been reported to achieve good performance in crop growth monitoring. According to “Greenhouse lettuce image collection and preprocessing,” there was a large number of background pixels in the captured images of greenhouse lettuce. Therefore, it was necessary to conduct image segmentation to extract the lettuce pixels, thus ensuring that the extracted features in the following step were presenting the lettuce plants. For the digital images of the greenhouse lettuce, since the color contrast between the lettuce plant and the background was very obvious, image segmentation was achieved by using the adaptive threshold method for the color information. Some segmentation results are shown in Fig. To evaluate the performance of the CNN model, tests were performed with the widely adopted estimation methods. Two shallow machine learning classifiers, i.e., SVR35. The color features included the average and standard deviation of 15 color components of five color spaces 36. Based on the color components, the gray level co-occurrence matrix37 was combined to extract the texture features. The texture features included the contrast, correlation, energy, and homogeneity of the 15 color components. The shape features of the greenhouse lettuce that were extracted were area and perimeter in this study. The area was the area enclosed by the outline, and the perimeter was the total length of the blade outline. After extracting the image features, the Pearson coefficient was used to perform correlation analysis between the extracted features and the actual values of the LFW, LDW, and LA of greenhouse lettuce. The features with relatively high correlation values were used to build the shallow machine learning classifiers.To build the shallow machine learning classifiers, feature extraction was performed on the segmented images of greenhouse lettuce. According to the characteristics of the three cultivars of greenhouse lettuce, low-level image features, including color, texture, and shape features, were extracted40. Three LR models using H, PA, and V as the predictor variables  were also used for comparison. Similar to the processing of digital images, image segmentation was also conducted on the depth images, which was achieved by the entropy rate superpixel segmentation method41. The lettuce plant could be extracted using the Euclidean distance to find the superpixel that was closest to the center of the image , were also used to estimate the growth-related traits of the greenhouse lettuceage Fig. . Once thage Fig. . Since tR2) and the normalized root mean square error (NRMSE) were used as the criteria for evaluating the performances of all the estimation models.In this study, the coefficient of determination . The software environment was Windows 10 Professional Edition, the hardware environment was an Intel i7 processor, CPU 3.20 GHz, with 8 GB memory, and the GPU was NVIDIA GeForce GTX1060.R2 and the lowest NRMSE . The performance of the CNN model for LFW and LDW was similar, with R2 values of 0.8983 and 0.8910, respectively, and NRMSE values of 26.00% and 22.07%, respectively. For the lettuce cultivars  and LA from the H of greenhouse lettuce can be inaccurate. In addition, existing research has shown that V and PA have relatively strong correlations with the growth-related traits of crops44. Therefore, the results that the LR models that used V and PA as predictor variables exhibited better estimation accuracy than those using H as the predictor variable were within expectation. Compared with the CNN model . We adopted the same experimental design as in “Greenhouse lettuce image collection and preprocessing,” resulting in a dataset containing 200 images and corresponding growth-related traits covering the entire growing season of the greenhouse lettuce.The evaluation of the performance of the proposed estimation method using images from different growing seasons would strengthen the belief in its validity and generalizationR2 values equal to 0.9277, 0.9126, and 0.9251, respectively, and NRMSE values equal to 22.96%, 37.92%, and 27.60%, respectively. The results revealed that the proposed estimation method had a strong generalization ability. On the other hand, the temperature and humidity in the greenhouse changed with the seasons, which would cause the growth of lettuce to change. Promisingly, the estimation results of the CNN model were still accurate, demonstrating that the proposed estimation method achieved excellent robustness and made a reliable tool for monitoring the growth of greenhouse lettuce.The estimation results are shown in Fig. 46. Direct measurement of growth-related traits is destructive and inefficient. Nondestructive monitoring has emerged and become a hot topic of current research. Computer vision technology has been widely used in nondestructive monitoring, providing great convenience to growth-related trait acquisition by using conventional methods, i.e., SVR, RF, and LR. However, these methods are limited in practical applications. Therefore, with the rapid development of deep learning, CNN has become preferred by researchers for its advantages, such as no need to manually extract features. In this study, we demonstrated that CNN can serve as a convenient and accurate tool to obtain growth-related traits for greenhouse lettuce. In comparison with the conventional methods, the proposed CNN model showed superior estimation performances in estimating the three growth-related traits for multiple cultivars of greenhouse lettuce, as shown in Table R2 values above 0.89 and NRMSE values below 27%. The results demonstrated the advantages of the CNN model in that it was able to automatically learn complex feature representations from digital images, which can be translated to a strong generalization ability47. The obtained results agreed with previous studies by Ma et al.18 and Grinblat et al.23.The close and accurate monitoring of crop growth is critical for the optimized management of crop productionAlthough the proposed method has been shown to be accurate and efficient, there are still limitations that we need to take into account. One limitation is that images were acquired from only the top view, indicating that the error may increase if there are too many overlaps between the leaves. Another limitation is the fixed H during image collection. If the H changes, the estimated results may be biased.Future studies will continue to collect more images to enlarge our dataset, such as images of other lettuce cultivars. To improve the efficiency of the method, we will explore the growth-related traits of multiple lettuce plants in a single image. In addition, the factors that may influence the performance of the CNN model, such as stress and H for image collection, will also be explored.Growth monitoring can indicate the status of greenhouse lettuce, which is critical for intelligent field management to control the greenhouse environment and establish nutrition strategies. The proposed estimation method allowed us to estimate LFW, LDW, and LA for multiple cultivars of greenhouse lettuce by using digital images, which are low cost and easy to use. The method has great potential for being used in the field when combined with mobile devices or when integrated into other automatic platforms since its input images can be captured by low-cost digital cameras.R2 values of ~0.9 and NRMSE values of ~20%. Furthermore, the performance of the proposed method was superior to that of the conventional methods that are widely adopted to estimate growth-related traits. The obtained results showed that the proposed method in this study achieved better estimation performance for Flandria and Tiberius cultivars than Locarno. After another batch of images were acquired of the Tiberius cultivar that was planted in Season 2 for verification, the results reinforced that the proposed estimation method had a strong generalization ability, as well as robust estimation performance despite the seasonal factors. It can be concluded that the proposed method is a reliable tool for estimating the growth-related traits of greenhouse lettuce and has excellent potential in the application of growth monitoring. Furthermore, the accurate monitoring of growth-related traits can provide support for scientific management decision-making.In this study, a method for the estimation of growth-related traits of multiple cultivars of greenhouse lettuce was proposed by using digital images and CNN, which could provide support for growth monitoring. The estimated growth-related traits had good agreement with the actual measurements, with


Peripheral arterial disease (PAD) is a common manifestation of generalized atherosclerosis, which affects more than 200 million patients worldwide. Currently, there is no ideal biomarker for PAD risk stratification and diagnosis. The goal of this research was to investigate the levels of inflammation biomarkers and cystatin C and to explore their utility for the diagnosis of PAD. The study included 296 participants, distributed in two groups: 216 patients diagnosed with PAD and 80 patients without PAD as controls. All studied biomarker levels  were significantly higher in the PAD group and indirectly correlated with the ankle–brachial index (ABI). The final logistic regression model included an association of neopterin, fibrinogen, and cystatin C as the most efficient markers for the prediction of PAD diagnosis. When comparing the area under the curve (AUC) for all biomarkers, the value for neopterin was significantly higher than those of all the other analyzed biomarkers. In agreement with previous studies, this research shows that markers such as fibrinogen, CRP, ESR, B2-MG, and cystatin C have significant value for the diagnosis of PAD, and also clearly underlines the accuracy of neopterin as a leading biomarker in PAD prediction. Peripheral artery disease (PAD) is a common vascular disease involving major structural and functional changes of the lower limbs’ arterial circulation, leading to disability, a marked decline in quality of life, and high mortality with a substantial economic burden ,4,5,6,7.Atherosclerosis represents the main pathophysiological substrate of PAD . AtherosVarious studies have identified the “classical” inflammatory biomarkers as vascular inflammation markers and potential candidates for PAD prediction : C-reactIn recent decades, the “novel” biomarkers panel has gained particular interest. Neopterin, a member of the pteridine family, is an indicator of macrophage activation ,27. SeveAs a result, some biomarkers have already found their application in current clinical practice, but several still require more detailed studies to include them in the diagnosing algorithm of PAD ,33,34,35Sf. Spiridon Iasi, Romania. In this prospective observational study, both PAD and control patients were all consecutively enrolled. A diagnosis of PAD was established in patients with (1) an ankle–brachial index (ABI) value of ≤0.90 and (2) intermittent claudication, or (3) lower extremity arterial revascularization. Individuals who had experienced acute inflammatory diseases in the last 30 days, malignancy, autoimmune diseases, immunodeficient conditions, and neuropsychiatric disorders were excluded from this study.This study evaluated patients admitted between January 2017 and February 2018 to the Second Department of Internal Medicine and the Department of Cardiology of the Emergency Clinical Hospital Medical history data were obtained from the patients’ medical hospital records. Diagnoses of cardio-vascular diseases, diabetes mellitus, and dyslipidemia were previously documented or established in current hospitalization. Hypertension was defined as a systolic/diastolic blood pressure ≥140/90 mmHg or the current use of anti-hypertensive drugs. CAD was determined as a positive history of angina pectoris or prior documentation of ischemic heart disease illustrated by exercise test/echocardiography contractility disorders. Chronic heart failure diagnosis was based on major and minor Framingham criteria. Diabetes mellitus was defined as HbA1c ≥6.5%, a fasting blood glucose level ≥126 mg/dL, or current hypoglycemic medication used for diabetes control. Dyslipidemia was defined as total cholesterol >200 mg/dL, low-density lipoprotein cholesterol (LDLc) >130 mg/dL, triglycerides >150 mg/dL, or a history of treatment for dyslipidemia. The body mass index (BMI) was calculated as the ratio of weight (kilograms) divided by the square of height (meters).The ankle-brachial index (ABI) was assessed in all patients. The blood pressures of the four limbs were measured using a handheld continuous wave Doppler probe . The ABI was calculated using the ratio of the systolic arterial blood pressure on the posterior tibial and dorsalis pedis arteries divided by the systolic arterial pressure on the brachial arteries.Doppler ultrasonography was performed using a Samsung Ultrasound System H60 LS22EMU1HS . The patients were divided according to the number of arteries that presented various stenosis degrees  .Upon patient admission, blood samples were collected to determine the levels of inflammatory biomarkers and cystatin C. In the laboratory, the serum/plasma samples were isolated by centrifugation and then tested, or frozen at −20 °C for up three months, until the samples were analyzed. The neopterin and B2-MG concentrations were measured using the ELISA method , CRP was analyzed using the immune turbidimetric assay on the ARCHITECT c16000 automate analyzer , fibrinogen was evaluated using a fully automated coagulation analyzer (ACL TOP 750) , and ESR was determined by standard routine laboratory procedures using the SRS100/II analyzer . The cystatin C level was detected by spectrophotometry in the ARCHITECT c4000 automated chemistry analyzer . Normal and abnormal controls of known concentrations were included in each assay performed. For cystatin C level determinations, grossly lipemic samples and samples with very high triglyceride concentrations were diluted as one part sample and four parts isotonic saline to decrease nonspecific light scattering.The following cut-off values were used as normal interpretation levels: Neopterin (<10 nmol/L), B2-MG , fibrinogen (<400 mg/dL), CRP (<0.5 mg/dL), ESR (<10 mm/1 h), and cystatin C .Continuous variables were tested for a normal distribution using the Kolmogorov–Smirnov test and were presented as the median with an interquartile range (IQR). Categorical variables were presented as frequencies and percentages. Differences between the control and PAD groups were compared using parametric (independent sample t test) or non-parametric (Mann–Whitney U) tests, as appropriate. Spearman’s test was used to investigate the correlations between variables. In certain cases, the logarithmic transformation of data was performed.The analysis of covariance (ANCOVA) was applied in the attempt to clarify if the decrease of ABI in the PAD group was directly a result of the increase in cystatin C, or more likely due to the inflammation/progression of PAD (and therefore leading to higher levels of cystatin C). ANCOVA was used to control for the effects that continuous variables (biomarkers) such as cystatin C may have over the output of ABI between PAD and non-PAD groups.Receiver operating characteristic (ROC) analysis was used to investigate the diagnostic performance for biomarker levels in PAD, and the areas under the curve (AUCs) were compared .To assess the performance of candidate biomarkers as prediction factors for PAD, a binary logistic regression was executed for each variable that demonstrated significant differences between the two groups. The significant variables in the univariate analysis were included in the final multivariable logistic regression model.The method used to estimate the coefficients of the logistic regression was maximum likelihood estimation (MLE) and a Wald test was used to evaluate the statistical significance of each coefficient. Odds ratios (OR) with confidence intervals (CI) were calculated . Internap-value < 0.05 was considered statistically significant.Data analysis was performed using IBM SPSS Statistics for Windows (version 19) and MedCalc Statistical Software version 19.4.0 . All tests were two-tailed and a The Research Ethics Committee of the Grigore T. Popa University of Medicine and Pharmacy and the Ethics Committee of the Emergency Clinical Hospital Sf. Spiridon board approved this study, and it was conducted according to the ethical guidelines of the 1975 Declaration of Helsinki Principles. All patients gave written informed consent to participate in this study.The study included 296 participants, distributed in two groups: 216 patients diagnosed with PAD and 80 patients without PAD as controls.Baseline characteristics for study participants, in total and by group as well as comparisons between study participants positively diagnosed with PAD and those without PAD are presented in p < 0.05).The biomarker profiles in total and by group as well as comparisons between groups are displayed in As the levels for all biomarkers considered were significantly higher in the PAD group, the next step was to quantify the correlations between these biomarkers and various relevant clinical and sonographic measures of PAD . All correlations between biomarkers and ABI  or the CAll biomarkers had significant positive and direct correlations with each other. The correlation coefficient values were especially high for the association between neopterin and the five remaining biomarkers .p = 0.000) between groups remained significant . The adjusted ABI means and variability (standard error) by group, once controlling for differences in cystatin C as a covariate, were as follows: PAD 0.589 ± 0.13, and non-PAD 1.055 ± 0.22.ANCOVA results indicated that after controlling for the differences in cystatin C between PAD and non-PAD patients, the differences in ABI (F = 328.179 (1291), p < 0.05)  .p < 0.0001)  .p < 0.0001), B2-MG (p = 0.0024), CRP (p = 0.033), and ESR (p < 0.0001)  .p = 0.0002) and ESR (p = 0.0002). AUC for B2-MG was significantly higher than AUC values for cystatin C (p = 0.01) and ESR (p = 0.04)  .p < 0.05). All markers significant in the univariate analysis were tested in a multivariate logistic model.The results after running a univariate binary logistic regression for each of the biomarkers are presented in Although all variables were predictive for morbidity in univariate analysis, ultimately, some were excluded from the final multivariable logistic regression model, because they did not prove to be statistically significant, namely, B2-MG (mg/L), ESR (mm/1 h) and CRP (mg/dL).The multivariable analysis indicates that the PAD probability can be best predicted by using the following three biomarkers: cystatin C (mg/L), neopterin (nmol/L), and fibrinogen (mg/dL) .p < 0.001 for all of the prediction variables. The C-statistic was 0.998 , validating the discriminatory power of the final model.The bootstrap method validates the model, where One of the main features of peripheral artery disease is the tendency to present constant atherosclerosis progression. The fundamental role of inflammation in the development of atherosclerosis is well-established and many previous studies have thoroughly researched the subject. Nevertheless, there is currently no ideal specific biomarker for PAD, neither for the purpose of risk stratification, nor for a positive diagnosis of PAD. Therefore, this study focused on the relationship between PAD and inflammatory biomarkers as well as PAD and cystatin C in an attempt to reveal a practical hierarchy in their use.This study represents an accurate model of the daily clinical practice as its control group includes patients without PAD, but with various cardio-vascular disorders. PAD is often associated with other comorbidities such as hypertension, diabetes, and dyslipidemia, and it is important for a biomarker to be related to PAD, even in such a constellation of diseases that are also connected with atherosclerosis, inflammation, and its biomarkers.In recent decades, many studies in the field of atherosclerosis have used “classical” biomarkers of inflammation separately or as a reference to test the “novel” biomarkers panel. CRP, fibrinogen, and ESR have an especially important significance for PAD diagnosis and risk stratification.Fibrinogen is an inflammation biomarker of interest, an acute phase reactant, and a determinant of blood viscosity, which has received much attention in the context of PAD ,22. The CRP has an important role in several aspects of the inflammatory process and quickly responds to its appearance . This CRESR is an indirect marker of the acute phase reaction ,44,45. IWhen analyzing the association between these “classical” inflammation markers, CD and ABI, this research showed a strong indirect correlation between the levels of fibrinogen, CRP, ESR, and CD/ABI, demonstrating that higher values of inflammatory markers are associated with a significant decrease in CD/ABI . MoreoveAccording to certain research reports, B2-MG is a biomarker that may be associated with vascular inflammation and arterial stiffness. This study found significantly elevated B2-MG plasma levels in PAD patients compared with the control non-PAD group. B2-MG also indirectly and strongly correlated with CD and ABI. These results are in agreement with similar studies, which demonstrated the same relationship between high B2-MG concentrations and the presence of PAD ,51,52,53Given its documented advantages in identifying subclinical kidney disease, cystatin C has constantly been used as a marker associated with cardiovascular events ,30,56,57In this context, when investigating cystatin C, significantly increased levels were found for the PAD patients. More importantly, cystatin C was indirectly correlated with CD and ABI, and an increase in its concentration was associated with significant decrease in both CD and ABI values.In order to attempt clarification of the relationship between ABI and cystatin C in the presence of vascular damage and inflammation, the study tried to establish if the differences observed in ABI between PAD and non-PAD groups were due to the presence of inflammation associated with PAD and not solely and directly to cystatin C. To this end, an analysis of covariance (ANCOVA) was used to control for the differences in cystatin C levels between PAD and non-PAD patients, and to assess whether PAD patients still had decreased ABI values when compared with non-PAD patients.Controlling the cystatin C impact over ABI showed that the decrease in ABI between PAD and non-PAD patients was maintained and still significant. Partial eta square was 0.53, showing that the variable group (PAD vs non-PAD) was responsible for 53% in the variability of ABI.This could constitute an argument for the fact that the increase in cystatin C levels in PAD patients is probably secondary to the presence of inflammation and inflammatory markers, and not the main trigger for the ABI decrease.It is yet to be proven if a causal relation exists between cystatin C and vascular damage. Since the atherosclerosis development and progression involves the inflammatory mechanisms from the early stages, it becomes highly relevant whether the same inflammatory biomarkers used for the diagnosis and prognosis of PAD can, in fact, trigger and promote the endothelial dysfunction ,60,61.Interestingly, the inhibiting capacity of increased circulating cystatin C levels over the regenerative properties of the endothelial cells seems to alter the process of angiogenesis and induce endothelial dysfunction. The possible involvement of cystatin C as a negative regulatory mechanism modulating the vascular development has been proposed even from the late embryonic period. Thus, it bears that intricate patterns generate the atherosclerotic plaque burden ,60,62.Neopterin is a pteridine produced by activated macrophages and dendritic cells and is able to induce oxidative stress activity ,27. Prevp < 0.01), and as such, the strongest association with the variation of both the CD and ABI. The observed inverse correlation between the biomarker levels and both the CD and ABI values indicate that in PAD, there is a strong association between tissue ischemia  and high levels of inflammation markers  or cystatin C.All six biomarkers studied were shown to be indirectly correlated with CD and ABI. Neopterin had the highest value for the correlation coefficient , as measured on the Doppler ultrasound , which identified these two markers as the most efficient for discerning PAD diagnosis. When comparing the AUC for all biomarkers, the value for neopterin was found to be significantly higher than all of the other markers analyzed. It became rapidly evident that neopterin had by far the strongest diagnostic performance in pointing to PAD risk among the group of markers considered .Logistic regression was used to predict the diagnosis of PAD based on a group of biomarkers associated more or less directly with inflammation. The final model included an association of neopterin, fibrinogen, and cystatin C as the most efficient markers for the prediction of PAD diagnosis. The odds ratio for neopterin was the highest among all of the markers studied, with a high degree of significance. An increase in neopterin serum levels by one unit increased the risk of PAD by 113 times. Therefore, the logistic regression demonstrates that, among these biomarkers, neopterin was the best important performing predictor for PAD disease .With certainty, an important element in PAD pathophysiology is the endothelial damage . There aThis study has certain possible limitations. The study population was representative for the northeastern region of Romania and the patients were enrolled from a single tertiary center. Therefore, the population for this study was exclusively Caucasian, and the accuracy when applying these results to populations that include non-Caucasian ethnic groups is limited.In PAD, biomarkers are tested mainly in establishing risk of progression, to select individuals for diagnostic vascular examination, for prognosis, or in determining the response to therapy.Unfortunately, biomarkers could not represent a replacement for the physical examination in PAD patient diagnosis and every physician, even in an environment of decreasing time, should perform additional measures (ABI) as part of a physical examination. Further studies are needed in order to identify the most specific biomarker for PAD diagnosis with the same value as, for instance, troponin or brain natriuretic peptide used in other pathology.In conclusion, over the last two decades, an increasing body of evidence has pointed toward the essential role of biomarkers in evaluating PAD risk. In agreement with previous studies, this research shows that markers such as fibrinogen, CRP, ESR, B2-MG, and cystatin C have significant value for the presence of PAD, and it also clearly underlines the accuracy of neopterin as a leading biomarker in PAD prediction.


Previous literature has reported that patients with diabetes in pregnancy (DIP) are at risk of developing antepartum depression but the results have been inconsistent in cohort studies. We conducted a systematic review and performed a meta-analysis to quantify the association between DIP and risk of antepartum depression in cohort studies. Medline, Cinahl, and PubMed databases were searched for studies investigating DIP involving pregnant women with pre-existing diabetes and gestational diabetes mellitus and their risk of antepartum depression that were published in journals from inception to 27 December 2019. We derived the summary estimates using a random-effects model and reported the findings as pooled relative risks (RR) and confidence interval (CI). Publication bias was assessed using a funnel plot and was quantified by Egger and Begg’s tests. Ten studies, involving 71,036 pregnant women were included in this meta-analysis. The pooled RR to develop antepartum depression was  among women with gestational diabetes mellitus. Combining pregnant women with pre-existing diabetes mellitus and gestational diabetes mellitus, they had a significant increased risk of developing antepartum depression  compared with those without it. In comparison, we found no association between pre-existing diabetes mellitus in pregnancy  and the risk of developing antepartum depression. This study has a few limitations: first, different questionnaire and cut-off points were used in evaluation of depression across the studies. Second, there was a lack of data on history of depression prior to pregnancy, which lead to confounding bias that could not be solved by this meta-analysis. Third, data were dominated by studies in Western countries; this is due to the studies from Eastern countries failing to meet our inclusion criteria for statistical analysis. Women with gestational diabetes mellitus have an increased risk of developing antepartum depression compared to those without the disease. Therefore, more attention on the mental health status should be given on pregnant women diagnosed with pre-existing diabetes mellitus and gestational diabetes mellitus. Depression is a common illness worldwide ,2 and woBased on the existing systematic review conducted among the Indian women, Arora and Aeri in 2019 summarized that the significant risk factors for antenatal depression are unplanned pregnancy, being a multigravida, having a history of abortion, advancing age, lower socio-economic status, lower educational status, unemployment, bad relationship with her in-laws, male gender preference, and excessive demand for dowry ,26,27,28On the other hand, most of the existing literature focused on the association between DIP and postpartum depression, for example a meta-analysis by Arafa and Dong  that repWomen with GDM have an increased risk of developing type 2 diabetes (with a RR of 7.43) compared to pregnant women who are normo-glycemic . As it iThe present study was registered with the National Medical Research Register, Ministry of Health Malaysia (registration number: NMRR-20-674-53879). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria when conducting this meta-analysis and reporting its results .Two investigators (K.W.L. and S.C.C.) independently searched Medline, Cinahl, and PubMed databases for potential studies published in journals from inception to 27 December 2019. We considered any relevant studies in the search as long as it was published before or on 27 December 2019. We also did not impose a limitation on the years of publication on the studies identified from reverse–forward citation tracking. We used a combination of search terms:  AND  for related studies. The search strategies are shown in Firstly, relevant articles identified through the databases were imported into Endnote program X5 version and any duplicate publications were removed. This step was performed by two investigators (K.W.L. and S.Y.L.) independently. Secondly, two investigators (K.W.L. and S.Y.L.) independently screened the titles and abstracts of those articles for suitability based on the search strategies mentioned above. Thirdly, full-text articles were assessed based on the inclusion criteria mentioned below by two investigators (K.W.L. and S.Y.L.) independently. Any disagreements were resolved by discussion before commencing the quantitative analysis. In addition, we manually performed reverse–forward citation tracking of the identified studies. This step was also performed by two investigators (K.W.L. and S.Y.L.) independently.Cohort studies were eligible for quantitative analysis if the study’s participants consisted of those with and without DIP. The studies were also required to present data of antepartum depression screened or diagnosed at either the second or third trimester as a primary or secondary outcome. The studies must have been published in an English peer-reviewed journal. Studies were excluded if the samples size was less than 100 or there was no information on which trimester the depression assessment was conducted. We also excluded studies that did not show any data relevant to a correlation between DIP and antepartum depression; these studies were categorized as “insufficient data” in PRISMA flowchart.The following data were extracted by two reviewers (K.W.L. and S.Y.L.): the last name of the first author, year of publication, country, ethnic origin, mean age or median of participants, number of participants with diagnosis in pregnancy among those with or without depression symptoms, study tool for assessment of depression, cut-off value for diagnosis of depression, and trimester where depression assessment was done. Data extraction was conducted independently, and the results of data extraction were compared between the two reviewers to ensure no errors.Data regarding exposure to pre-existing DM are referred as pre-gestational diabetes (type 1 or type 2 DM diagnosed before pregnancy) . GDM is Measures of exposure for DIP were derived in three ways. First, the number of DIPs was calculated by summing the number of pregnant women with pre-existing DM and GDM if both data were available separately in original articles. Second, if pre-existing DM and GDM were presented as diabetic pregnant women in original articles in which differentiating the data between pre-existing DM and GDM could never be possible, we used the number of diabetic pregnant women in calculation of the number of DIPs. Third, if the articles indicated the numbers of pregnant women with either pre-existing DM and GDM, the data of available groups (either pre-existing DM or GDM) were used in calculation for the numbers of DIPs and its risk for antepartum depression.The outcome was the presence of antepartum depression, where it could be determined either with a confirmed diagnosis or use of screening tools. Any diagnostic guidelines or screening cut-off value for depression was acceptable for data synthesis in the meta-analysis. Measures of outcomes were the number of pregnant women with antepartum depression.2 index , that indicated the total per cent of discrepancy due to studies variation [We used the relative risk (RR) and the corresponding 95% confidence interval (CI) to quantify the association between DIP and depression for observational studies. The meta-analyses were performed using a random-effects model of DerSimonian and Laird, which incorporates both within and between-study variability, as we anticipated between-study heterogeneity. Heterogeneity across studies was assessed using the Iariation .The quality of the individual studies was determined using the checklist Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) . Two inv2 index  [http://www.cebm.brown.edu/openmeta/index.html [A random-effects (DerSimonian and Laird Method) meta-analysis was used throughout the analysis to compute the pooled RRs and their 95% CI ,46. We adex.html .n = 44), a total of 824 studies were retrieved for review of title and abstract. After screening for its suitability through title and abstract, 50 studies were subjected to full-text assessment for inclusion criteria. After careful evaluation of the 50 articles, 10 studies were eligible for quantitative analysis in this study.Our literature search identified 868 articles in the initial screening as shown in The characteristics of the included studies are summarized in In terms of screening for depression, Edinburg Postnatal Depression Score was the most commonly used assessment tool ,26,27. IHalf of the included studies provided data of respondent ethnicity composition. As we could see that most of the respondents comprised of Caucasians. The mean age range was within 29 to 30.2 years old, however these values merely came from two studies that reported this value ,26, and Eight studies reporting on the association between risk of antepartum depression and GDM, three studies for pre-existing DM, and nine studies for DIP were included for the overall analysis. The pooled RR using random-effect models are presented in 2 = 18.8, p = 0.281). The funnel plot, Egger’s test (p = 0.882) and Begg’s test (p = 0.621) suggested that there was no publication bias  but not on funnel plots and Egger’s test  and the test for the small-study effect suggested that this study [p = 0.076) but not in Begg’s test (p = 1.000). Sensitivity analysis of all studies had substantial influences on the overall relative risk, which cause variation in pooled RRs ranging from 1.300 to 6.778 [Our study showed that pregnant women with pre-existing DM, compared with those without diabetes, had an insignificant pooled RR of 1.300 (95% CI: 0.736–2.297) and Iis study  may haveto 6.778 ,17,20.2 = 82.5, p <0.001 for heterogeneity). Funnel plot and Egger’s test (p = 0.091) but not Begg’s test (p = 0.655) indicated that the study by Cripe et al. [2 reduced to 50.0% (p = 0.043). We also did not find any evidence of publication bias when we reassessed the funnel plot, Egger’s (p = 0.462) and Begg’s test .Similar observation was also seen in the association between DIP and the risk of antepartum depression, in which the pooled RRs also declined (from pooled RR = 1.601 to pooled RR = 1.431 by excluding the study by Cripe et al.  and hetee et al.  could haTo our knowledge, this is the first meta-analysis to comprehensively summarize the association between DIP and the risk of antepartum depression. This study aimed to assess whether pregnant women with pre-existing DM or GDM are indeed at a higher risk of developing antepartum depression.In the present meta-analysis, it was demonstrated that the presence of GDM correlated with a 43% increased risk of having antepartum depression, and DIP presence also indicated a similar result in its association with antepartum depression. Even though this meta-analysis showed significant increased risk of antepartum depression in women with GDM, it would be too premature to say that DIP and antepartum depression have a strong association due to the individual biases of the studies and limitation of this study. Therefore, more studies are needed to look into this association. Having said that, a possible physiologic mechanism for this significant association could be linked to the secretion of cortisol and expression of certain inflammation markers in pregnancy , that arSurprisingly, our study found a no association between pre-existing DM in pregnancy and the risk of developing antepartum depression. The statistical power could be limited by the mere inclusion of three studies having high heterogeneity for the current meta-analysis. Nevertheless, the lack of association could also be explained by the possibility that women with pre-existing DM were more aware of the importance of physical activity and weight control through various psycho-education or lifestyle modification programs they were exposed to prior to conceiving. This may also help in improving the insulin resistance and preventing the emergence of depressive symptoms . In addiA prior meta-analysis estimated that GDM significantly increased the risk of postpartum depression by a pooled RR ranging from 1.32 to 1.59 ,30. MeanThe co-existence of DIP and depression could be a lethal combination. Therefore, it is important to be able to identify pregnant women with diabetes at risk of developing antepartum depression earlier for various reasons. For the mother, antepartum depression is known to cause detrimental effects such as poor self-care, impaired quality of life, increased risk of suicide, and postnatal depression . For theFor healthcare providers, this indicates the need to screen for depression in woman with DIP. For policymakers, the latest clinical practice guidelines should clearly outline the need for those providing antepartum services to do this important screening.In view of the fact that antepartum depression is a negative psychological effect of DIP, and both conditions are associated with an increased possibility of both maternal and neonatal adverse outcome ,61, it iThis is the first systematic review and meta-analysis that reported a significant association between GDM, DIP, and antepartum depression, in which it estimated the relative risk based on 10 cohort studies comprising of more than 71,000 pregnant women.Our meta-analysis has a number of potential limitations. First, the questionnaire used in evaluation of depression varied across studies. Furthermore, we notice that different cut-off points at 12 and 13 were used in the Edinburgh Postnatal Depression Score, which differed across three studies. In a one of the studies the questionnaire used for assessment of depression was not even mentioned, which may have increased the risk of information bias. Second, data on the history of depression prior to pregnancy have been particularly helpful for subgroup analysis. However, the availability of these data was very limited leading to an increased risk of confounding bias that could not be solved by this meta-analysis. Third, data of the current study were dominated by studies in Western countries, and less so from Asian and African countries. Therefore, the finding of this study should be interpreted with caution. In fact, there are studies on the correlation between DIP and antepartum depression that have been reported from Eastern countries, but they were conducted with a cross-sectional design. We choose cohort study design as one of our inclusion criteria because it demonstrates a more causal relationship than cross-sectional study design. Therefore, many studies from Eastern countries were not eligible for statistical analysis.In summary, our study suggested that the presence of GDM significantly increased the risk of developing antepartum depression. Given the appreciable relative risk of antepartum depression among those with DIP, more attention on the mental health status should be given to pregnant women diagnosed with pre-existing DM and GDM. Furthermore, more studies from Eastern countries with a well-designed prospective study design are needed to confirm our study findings.


The incidence of multimorbidity (MM) and its correlates among older adults remain poorly understood. This study aimed to examine the socio-demographic and lifestyle factors associated with MM in New Zealand.People aged 55-70 years were invited to participate in a population-based cohort study, the Health Work and Retirement Study, in 2006. Those who accepted the invitation and completed the baseline questionnaire were followed up on a biennial basis. Data on socio-demographic factors, health and lifestyle behaviours, and diagnoses of chronic diseases were obtained from baseline and 6 waves of follow-up. Generalised estimating equations (GEE) adjusted for both time-constant and time-varying factors were used to model factors associated with the onset of MM.A total of 1,673 participants (with 0 or 1 chronic condition) contributed to an overall 8,616 person-years of observation. There were 590 new cases of MM over 10 years of follow-up, corresponding to an overall incidence of 68.5 per 1,000 person-years. The results of the age- and sex-adjusted GEE analysis showed that age, ethnicity, living alone, obesity, hypertension, and having 1 chronic condition at baseline were significant predictors of MM onset. Higher education, income, physical activity, and regular alcohol consumption were protective factors. In a fully adjusted model, marital status , hypertension  and having 1 chronic condition at baseline  remained significant.The higher incidence of MM among Māori people, socioeconomically disadvantaged groups, those with low physical activity, and obese individuals highlights the importance of targeted prevention strategies. Multimorbidity (MM), defined as the “coexistence of 2 or more chronic diseases in the same individual,” is a current health concern due to population ageing . The comPublications on this topic using a prospective or longitudinal approach are scarce. A Swedish study  includinAlthough these studies provide important information about the predictors of MM, limitations in the literature to date include a focus on people over 78 years of age , an operWe aimed to investigate the incidence of MM (in a population without MM at baseline) and to examine its associations with important socio-demographic, lifestyle, and clinical factors based on data from 6 waves of a nationwide longitudinal study on ageing in New Zealand.The Health, Work and Retirement Study is a biennial prospective cohort study of community-dwelling older adults. The questionnaire form assesses domains of health and wellbeing, family and social support, work and retirement, financial wellbeing, and cultural identity. The study commenced in 2006 as a postal survey of a representative national sample of community-dwelling adults aged 55-70, who were randomly selected from the New Zealand electoral roll. The original survey had a response rate of 53% , of whom 2,632 consented to be invited to participate in subsequent waves. Of this subsample, 1,609 (41%) were lost to follow-up over the subsequent 5 waves . For the current analysis, the baseline study population (at-risk population) were participants who both consented to follow-up and did not meet the criteria for MM at baseline. The age and sex distribution of the original responders, baseline cohort, and the study participants were similar.Information on a range of demographic, socioeconomic, lifestyle, and clinical factors obtained from core survey measures were examined in the current analysis.The outcome variable was the onset of MM based on participants’ responses to a question asking, ‘Has a doctor, nurse or other healthcare worker told you that you have any of the following health problems?’, which preceded a list of chronic conditions. Nine groups of diseases assessed in all survey waves were included in the current analysis: heart diseases, stroke, other neurologic diseases , musculoskeletal , diabetes mellitus, respiratory diseases , chronic liver conditions (cirrhosis), cancer, and mental disorders . In all waves, MM was defined as reporting a positive diagnosis for 2 or more groups of chronic diseases.Age, which was provided as a continuous variable, was also categorised as <65 years and ≥65 years. Marital status was dichotomised as married/living with a partner or divorced/separated/single/widow; ethnicity was classified as the priority ethnic groups in New Zealand, Māori or non-Māori . Socioeconomic status indicators included the highest educational qualification  and annual personal income . For multivariate statistical modelling, annual income was categorised into 3 levels .Smoking was defined by a question that asked respondents to identify themselves as regular smokers or not. Alcohol consumption was measured by a question that assessed the frequency of drinking, which was classified into 2 categories: regular alcohol consumption (≥2 drink/wk) and non-regular alcohol drinking (≤1 drink/wk). PA was measured by the frequency of moderate/brisk walking or vigorous activity in the last 7 days and categorised into 2 levels: ≥2 times/wk (sufficient) or 1 times/wk/none (insufficient).2), overweight (25.0-29.9 kg/m2), and obese (≥30.0 kg/m2).Hypertension was considered as a dichotomous variable (yes/no) based on participants’ responses to a question about diagnosed health problems. Body mass index  was measured in the 2008 survey wave and was categorised as normal weight  and 95% confidence intervals (CIs) of MM per 1,000 person-years were calculated with stratification by age, sex, and ethnicity. Generalized estimating equations (GEEs) with an exchangeable correlation matrix and robust standard errors were used to assess the factors associated with the onset of MM. We calculated the odds ratios (ORs) and 95% CIs for MM by socio-demographic and lifestyle factors, and constructed 3 models: crude, adjusted for age and sex, and fully adjusted. The fixed covariables were sex, ethnicity, education, income, and BMI. All other variables were considered as time-varying.All estimates were reported with 95% CIs and a significance level of 0.05. All analyses were performed using the Stata version 14 , except the IR calculations, which were run using OpenEpi [The study procedures were approved by the Massey University Human Ethics Committee (MUHEC Southern B 09/70).Of the 2,632 participants who responded to the baseline questionnaire and agreed to participate in follow-up, 957 met the criteria for MM at baseline and were excluded from the analysis. There were 1,673 respondents who reported either no disease (n=776) or 1 disease (n=897), of whom a total of 590 developed MM over the follow-up period .A total of 1,673 participants (with 0 or 1 chronic condition) contributed to an overall 8,616 person-years of observation. There were 590 new cases of MM over 10 years of follow-up, which resulted in an overall incidence of 68.5 per 1,000 person-years. The incidence was higher among older participants than among their younger counterparts , but did not differ significantly by sex. However, Māori participants had a higher incidence of MM than non-Māori participants  . The incThe results of the GEE analysis adjusting for age and sex  indicateThis study examined the incidence of MM and its socioeconomic and lifestyle predictors using data from a prospective cohort study of older New Zealand adults. The overall incidence of MM over 10 years of follow-up was 68.5 per 1,000 person-years. This rate is higher than the reported IR of 59 per 1,000 person-years based on data from different waves of the ELSA . HoweverFor example, MM may have been less frequently observed in the current study than in the Swedish study by Melis et al. , becauseThe major strength of the present study is that it examined socio-demographic, health behaviour, and lifestyle factors associated with the onset of MM in an older New Zealand population sample. The analyses of the present study indicate that the incidence of MM was higher among older participants than among their younger counterparts , which is in line with evidence that the incidence of MM is higher among older adults . In the Among the lifestyle factors that were investigated, obesity and insufficient PA were significant predictors of MM onset in age and sex-adjusted models. We found that regular alcohol consumption was a protective predictor of MM onset. No significant association between smoking and MM was found in our analysis. The current finding regarding the protective effects of PA was similar to the findings of other studies -13,15 , The value of this study lies in its longitudinal nature and the provision of additional insights into the burden of MM and its correlates. However, we acknowledge that our results might be particularly vulnerable to biases associated with the assessment and operational definition of MM. This is a particular threat to comparison between studies. The current study employed a list of doctor/health professional–diagnosed self-reported chronic conditions to identify MM. While this is a common method to ascertain MM in the literature, heterogeneity in this definition and in the number of chronic conditions included remains an issue. In addition, in this analysis, only current smoking and alcohol consumption were considered, since we did not have further details on these variables.This project is the first longitudinal study providing evidence on the incidence of MM and its associations with socioeconomic and lifestyle factors in New Zealand, and the results may be of particular interest for public health policy. The higher incidence of MM among Māori, socioeconomically disadvantaged groups, those with inadequate PA, and obese individuals highlights the importance of targeted information and associated prevention strategies. The strong association between hypertension and having 1 chronic condition at baseline with the incidence of MM should be considered as part of care and management strategies.Further research is recommended using a better definition of MM and including more details on smoking and alcohol consumption to be able to show to what extent quitting smoking or alcohol consumption might be related to the incidence of MM. In addition, the role of hypertension in the development of MM might of interest for further research, as we found a significant association between the incidence of MM and hypertension. Therefore, it would also be useful to explore the pattern of chronic conditions related to hypertension.


Hereditary multiple exostosis (HME) often involves forearm deformities. The aim of this study was to present the clinical results of 37 children who underwent ulnar lengthening with two different types of unilateral external fixators and to investigate the risk factors of complications.We evaluated 37 children with forearm deformities caused by HME treated in our hospital from January 2008 to July 2019. The surgical procedures included resection of exostosis, osteotomy of the ulna, and gradual lengthening of the ulna with a unilateral external fixator. According to the type of fixator they received, the children were divided into two groups: group A received monorail fixators and group B received multi-joint fixators. Radiographic and functional parameters were assessed. Complications were recorded.p < 0.05). However, the ulnar deviation was observed in 4 cases in group B and no cases in group A. According to logistic regression, the difference was only related to age (p < 0.05) and the type of external fixator (p < 0.05).All patients were followed-up for an average of 4.6 years (3.0 to 6.5). In both group A and group B, the ulna shortening (US), radial articular angle (RAA), carpal slip (CS), elbow flexion, forearm pronation, supination, and Mayo Elbow Performance Score (MEPS) values improved significantly from preoperatively to postoperatively (Ulnar lengthening with unilateral external fixation is a safe and effective procedure for the treatment of HME. Regarding complications, deviation of the ulna axis was more likely to occur in older children with multi-joint external fixators. Hereditary multiple exostosis (HME) is a type of autosomal dominant inheritance-induced skeletal dysplasia, with an incidence of approximately 1/50,000 .Most forearm deformities in patients with HME are caused by ulna shortening –4. The dThe optimal timing and surgical treatment for forearm deformity caused by HME remain controversial –8. The mThirty-seven children with forearm deformities caused by HME were enrolled in this study, and they underwent the surgical procedure of ulnar lengthening with two different types of single-arm external fixators. The aim of this study was to retrospectively analyze and present the clinical results and compare the two types of external fixators in terms of the complications that occur postoperatively.Thirty-seven patients  with forearm deformities caused by HME were treated in our hospital from January 2008 to July 2019. All of the deformities were unilateral. The age at operation ranged from 4.5 to 12.5 years (average 7.4 years). The forearm deformities were classified according to the Masada Classification system , which iThe inclusion criteria for surgery included worsening of the deformity or impairments in daily activities, such as ulnar shortening (US) by at least 15 mm or 8% of the length of the ulna , associaAll surgical procedures were performed by the same senior surgeon. Under full general anesthesia, each patient was placed in the supine position. A tourniquet (180–200 mmHg for a maximum of 60 min) and hand table were used. A longitudinal incision was made according to the location and size of the exostosis. The exostosis, as well as the surrounding periosteum, was bluntly separated and completely exposed. Adjacent vessels, nerves, and epiphyses were protected. Four Schanz pins  were placed on the ulna in parallel to each other and on the same plane, avoiding the plane of maximum curvature on the back of the ulnar arch. The osteotomy was performed using a sharp drill and a chisel whilst preserving the periosteum. The preferred location for osteotomy was the proximal part of the ulna near the epiphysis, avoiding the middle segment of the ulna and leaving enough space for Schanz pins. Then, a monorail single-arm external fixator  was applied. For complex deformities, when the Schanz pins cannot be placed on the same plane, a multi-joint fixator was applied. In patients with dislocation of the radial head, no special procedures were performed for the radial head.According to Fogel et al. , the radMany methods are available to estimate the lengthening length. To avoid the recurrence of deformity, most scholars suggest that the length of the ulna should be over-lengthened by 4–10 mm more than the length of the contralateral ulna , 13, 18.SPSS 26.0  software and the R programming language  were used to perform all the statistical analyses. For both group A and group B, variables that were measured preoperatively and at the final follow-up, such as the US, RAA, CS, MEPS, and ranges of elbow flexion, forearm pronation, and supination, were assessed by the Wilcoxon test. The improvements in each variable listed above were assessed by Spearman correlation analysis adjusted for variables such as age, sex, type of HME, follow-up time, and increase in the length of the ulna. The differences in complications between the two groups were assessed by two logistic regression models using the R programming language.All 37 patients were followed-up. According to the type of external fixator the patients received, they were divided into two groups, and the basic information is shown in Table p < 0.05). According to Spearman correlation analysis, although changes in RAA and CS were associated with improved length of the ulna (IL) (p < 0.05), all the other improvements were not associated with age, sex, the type of HME, follow-up time, or IL (p > 0.05). Pin site infections were found in 3 patients, and all patients recovered after oral antibiotics. No other complications were recorded. A typical case is shown in Fig. In group A, as shown in Table p < 0.05), except for that of supination (p > 0.05). The improvements were not associated with age, sex, the type of HME, follow-up time, or IL (p > 0.05), according to Spearman correlation analysis. The reduction of the radial head was satisfying in the process of ulna lengthening, and no redislocation was reported. One patient presented with pin site infection and recovered after oral antibiotics. Deviation of the ulna axis and poor bone formation was found in 4 patients, two typical cases are shown in Fig. During the process of the gradual extension of the ulna, the reduction of the radial head was satisfying. In group B, as shown in Table p < 0.05) and the type of external fixator (p < 0.05) but not to sex (p > 0.05), the follow-up time (p > 0.05), or the type of HME (p > 0.05). The positive estimate values suggested that ulna axis deviation was more likely to occur in older children with multi-joint external fixators.Differences in complications between the two groups were assessed by two logistic regression models using the R programming language Table . For theThe aim of this study was to present the clinical results of 37 children who underwent ulnar lengthening with two different types of unilateral external fixators and to investigate the risk factors of complications.p < 0.05). Only supination in group B did not significantly change, which might be attributed to the small sample size. The appearance and function of the upper limb significantly improved in the two groups, and the effect of unilateral external fixation on ulnar lengthening was obvious.In this study, two types of unilateral external fixations were selected: the monorail (group A) and multi-joint fixators (group B). Tables For complications, no fractures, neurovascular problems, or recurrences of exostoses were observed in the two groups. Pin site infections  easily recovered. For radial head dislocation in patients with type IIa and type IIb deformities, no special procedures were performed during the operation, all cases self-reduced during ulna lengthening, and no cases of redislocation were found. This result suggests that it may be best not to treat radial head dislocation. The interosseous membrane can transmit the forces leading to lengthening, and the reduction of the radial head can be reached gradually .Remarkably, ulna deviations and poorly regenerated bone formation were more likely to occur in group B than in group A. Differences between the two groups were assessed by two logistic regression models using the R programming language Table , which sOlder age was one risk factor for ulna deviations and nonunion in this study. The regenerate healed more slowly in older age patients than that in younger ones; therefore, it was easy to be affected by other factors in the process of lengthening and lead to nonunion. It may suggest that surgical intervention should be performed at an earlier age. The optimal timing of surgical intervention is controversial. One view , 21, 22 Another risk factor for ulna deviations and nonunion was the use of multi-joint external fixator. In some cases of complex deformities, the pins of the fixator were not orthogonal and not suitable for mono-rail fixator, so we used a multi-joint fixator to adapt to the divergence angle of pins. However, 4 out of 10 cases who were lengthened by the multi-joint fixator resulted in poor bone formation. It suggested that instability may be a potential defect of a multi-joint fixator. Although multi-joint fixations were stable and reliable in the surgical treatment of fractures, there was still instability throughout the process of ulnar lengthening, whether human factors or instrumental factors. On the other hand, for complex malformations, when the four pins were not located in the same plane, lateral torque will be generated during the ulna lengthening, resulting in ulna deviation. Therefore, we suggest that for complex deformities, more stable fixators, such as the Ilizarov fixator , 9, may The occurrence of nonunion may also be related to the position of the osteotomy and direction of the nail. It has been reported that  the diamA patient with type IIb deformity is shown in Fig. Whether exostoses should be removed is still debated. Akita  et al. fOur study presents some limitations. This study is a retrospective study, with a small sample size and a short follow-up time. The clinical efficacy and complications remain to be further verified by a long-term prospective randomized controlled study.Ulnar lengthening with unilateral external fixation is a safe and effective procedure for the treatment of HME. Older age and multi-joint external fixator are two risk factors of complications. Monorail fixators are more reliable than multi-joint fixators.