Hypoglycemic Effects and Quality Marker Screening of Dendrobium nobile Lindl. at Different Growth Years.

(1) Background: The effect of Dendrobium nobile Lindl. (D. nobile) on hyperglycemic syndrome has only been recently known for several years. Materials of D. nobile were always collected from the plants cultivated in various growth ages. However, regarding the efficacy of D. nobile on hyperglycemic syndrome, it was still unknown as to which cultivation age would be selected. On the other hand, with the lack of quality markers, it is difficult to control the quality of D. nobile to treat hyperglycemic syndrome. (2) Methods: The effects of D. nobile cultivated at year 1 and year 3 were checked on alloxan-induced diabetic mice while their body weight, diet, water intake, and urinary output were monitored. Moreover, levels of glycosylated serum protein and insulin were measured using Elisa kits. The constituents of D. nobile were identified and analyzed by using UPLC-Q/trap. Quality markers were screened out by integrating the data from UPLC-Q/trap into a network pharmacology model. (3) Results: The D. nobile cultivated at both year 1 and year 3 showed a significant effect on hyperglycemic syndrome at the high dosage level; however, regarding the significant level, D. nobile from year 1 showed the better effect. In D. nobile, most of the metabolites were identified as alkaloids and sesquiterpene glycosides. Alkaloids, represented by dendrobine, were enriched in D. nobile from year 1, while sesquiterpene glycosides were enriched in D. nobile from year 3. Twenty one metabolites were differentially expressed between D. nobile from year 1 and year 3. The aforementioned 21 metabolites were enriched to 34 therapeutic targets directly related to diabetes. (4) Conclusions: Regarding the therapy for hyperglycemic syndrome, D. nobile cultivated at year 1 was more recommended than that at year 3. Alkaloids were recommended to be used as markers to control the quality of D. nobile for hyperglycemic syndrome treatment.

Use of novel structural features to identify urinary biomarkers during acute kidney injury that predict progression to chronic kidney disease.

BACKGROUND: A significant barrier to biomarker development in the field of acute kidney injury (AKI) is the use of kidney function to identify candidates. Progress in imaging technology makes it possible to detect early structural changes prior to a decline in kidney function. Early identification of those who will advance to chronic kidney disease (CKD) would allow for the initiation of interventions to halt progression. The goal of this study was to use a structural phenotype defined by magnetic resonance imaging and histology to advance biomarker discovery during the transition from AKI to CKD. METHODS: Urine was collected and analyzed from adult C57Bl/6 male mice at four days and 12 weeks after folic acid-induced AKI. Mice were euthanized 12 weeks after AKI and structural metrics were obtained from cationic ferritin-enhanced-MRI (CFE-MRI) and histologic assessment. The fraction of proximal tubules, number of atubular glomeruli (ATG), and area of scarring were measured histologically. The correlation between the urinary biomarkers at the AKI or CKD and CFE-MRI derived features was determined, alone or in combination with the histologic features, using principal components. RESULTS: Using principal components derived from structural features, twelve urinary proteins were identified at the time of AKI that predicted structural changes 12 weeks after injury. The raw and normalized urinary concentrations of IGFBP-3 and TNFRII strongly correlated to the structural findings from histology and CFE-MRI. Urinary fractalkine concentration at the time of CKD correlated with structural findings of CKD. CONCLUSIONS: We have used structural features to identify several candidate urinary proteins that predict whole kidney pathologic features during the transition from AKI to CKD, including IGFBP-3, TNFRII, and fractalkine. In future work, these biomarkers must be corroborated in patient cohorts to determine their suitability to predict CKD after AKI.

New method for scaling nonpoint source pollution by integrating the SWAT model and IHA-based indicators.

Nonpoint source (NPS) pollution shows spatial scaling effects because it is affected by topography, river networks, and many other factors. Currently, the lack of an integrated methodology for quantifying the scaling effect has become a crucial barrier in evaluating NPS pollution. In this study, a new method was proposed for scaling NPS pollution by integrating hydrological model and hydrological alteration indicators. Nested catchments were delineated by eight-direction algorithm, and a semidistributed hydrological model was used to simulate the interannual process within the drainage area and to obtain data series of runoff, sediment, and total phosphorus (TP) at different spatial scales. In addition, the average, the extrema, the change rate and feature variables of each type of indicators were proposed to quantitatively describe the pattern of NPS pollution at different spatial scales. The results show the coefficients of variation (CVs) of most runoff and TP indicators are 0.6-0.8, while those of sediment vary greatly from 0.4 to 1.6 with the threshold of those indicators being 0.33. With the increase in drainage area, the NPS load-related indicators show an increasing trend, while load intensity indicators show a decreasing trend and their changing patterns are affected by the heterogeneity of topographic or hydrological information included. Based on logarithmic variance of the change rate, 825 km2 was identified as the turning point for scaling transformation where the slope changes dramatically. The proposed methodology comprehensively describes features of the NPS scaling effect that could be utilized for targeted monitoring and control of NPS pollution in other watersheds.

Image analysis techniques to map pyramids, pyramid structure, glomerular distribution, and pathology in the intact human kidney from 3-D MRI.

Kidney pathologies are often highly heterogeneous. To comprehensively understand kidney structure and pathology, it is critical to develop tools to map tissue microstructure in the context of the whole, intact organ. Magnetic resonance imaging (MRI) can provide a unique, three-dimensional view of the kidney and allows for measurements of multiple pathological features. Here, we developed a platform to systematically render and map gross and microstructural features of the human kidney based on three-dimensional MRI. These features include pyramid number and morphology as well as the associated medulla and cortex. In a subset of these kidneys, we also mapped individual glomeruli and glomerular volumes using cationic ferritin-enhanced MRI to report intrarenal heterogeneity in glomerular density and size. Finally, we rendered and measured regions of nephron loss due to pathology and individual glomerular volumes in each pyramidal unit. This work provides new tools to comprehensively evaluate the kidney across scales, with potential applications in anatomic and physiological research, transplant allograft evaluation, biomarker development, biopsy guidance, and therapeutic monitoring. These image rendering and analysis tools could eventually impact the field of transplantation medicine to improve longevity matching of donor allografts and recipients and reduce discard rates through the direct assessment of donor kidneys.NEW & NOTEWORTHY We report the application of cutting-edge image analysis approaches to characterize the pyramidal geometry, glomerular microstructure, and heterogeneity of the whole human kidney imaged using MRI. This work establishes a framework to improve the detection of microstructural pathology to potentially facilitate disease monitoring or transplant evaluation in the individual kidney.

Magnetic resonance imaging accurately tracks kidney pathology and heterogeneity in the transition from acute kidney injury to chronic kidney disease.

Acute kidney injury (AKI) increases the risk for chronic kidney disease (CKD). However, there are few tools to detect microstructural changes after AKI. Here, cationic ferritin-enhanced magnetic resonance imaging (CFE-MRI) was applied to examine the heterogeneity of kidney pathology in the transition from AKI to CKD. Adult male mice received folic acid followed by cationic ferritin and were euthanized at four days (AKI), four weeks (CKD-4) or 12 weeks (CKD-12). Kidneys were examined by histologic methods and CFE-MRI. In the CKD-4 and CKD-12 groups, glomerular number was reduced and atubular cortical lesions were observed. Apparent glomerular volume was larger in the AKI, CKD-4 and CKD-12 groups compared to controls. Glomerular hypertrophy occurred with ageing. Interglomerular distance and glomerular density were combined with other MRI metrics to distinguish the AKI and CKD groups from controls. Despite significant heterogeneity, the noninvasive (MRI-based) metrics were as accurate as invasive (histological) metrics at distinguishing AKI and CKD from controls. To assess the toxicity of cationic ferritin in a CKD model, CKD-4 mice received cationic ferritin and were examined one week later. The CKD-4 groups with and without cationic ferritin were similar, except the iron content of the kidney, liver, and spleen was greater in the CKD-4 plus cationic ferritin group. Thus, our study demonstrates the accuracy and safety of CFE-MRI to detect whole kidney pathology allowing for the development of novel biomarkers of kidney disease and providing a foundation for future in vivo longitudinal studies in mouse models of AKI and CKD to track nephron fate.

PPARα is one of the key targets for dendrobine to improve hepatic steatosis in NAFLD.

ETHNOPHARMACOLOGICAL RELEVANCE: Dendrobium nobile Lindl. (DNL) is a traditional Chinese ethnobotanical herb. Dendrobine (DNE) has been designated as a quality indicator for DNL in the Chinese Pharmacopoeia. DNE exhibits various pharmacological activities, including the reduction of blood lipids, regulation of blood sugar levels, as well as anti-inflammatory and antioxidant properties. AIM OF THE STUDY: The objective of this study is to explore the impact of DNE on lipid degeneration in nonalcoholic fatty liver disease (NAFLD) liver cells and elucidate its specific mechanism. The findings aim to offer theoretical support for the development of drugs related to DNL. MATERIALS AND METHODS: We utilized male C57BL/6J mice, aged 6 weeks old, to establish a NAFLD model. This model allowed us to assess the impact of DNE on liver pathology and lipid levels in NAFLD mice. We investigated the mechanism of DNE's regulation of lipid metabolism through RNA-seq analysis. Furthermore, a NAFLD model was established using HepG2 cells to further evaluate the impact of DNE on the pathological changes of NAFLD liver cells. The potential mechanism of DNE's improvement was rapidly elucidated using HT-qPCR technology. These results were subsequently validated using mouse liver samples. Following the in vitro activation or inhibition of PPARα function, we observed changes in DNE's ability to ameliorate pathological changes in NAFLD hepatocytes. This mechanism was further verified through RT-qPCR and Western blot analysis. RESULTS: DNE demonstrated a capacity to enhance serum TC, TG, and liver TG levels in mice, concurrently mitigating liver lipid degeneration. RNA-seq analysis unveiled that DNE primarily modulates the expression of genes related to metabolic pathways in mouse liver. Utilizing HT-qPCR technology, it was observed that DNE markedly regulates the expression of genes associated with the PPAR signaling pathway in liver cells. Consistency was observed in the in vivo data, where DNE significantly up-regulated the expression of PPARα mRNA and its protein level in mouse liver. Additionally, the expression of fatty acid metabolism-related genes (ACOX1, CPT2, HMGCS2, LPL), regulated by PPARα, was significantly elevated following DNE treatment. In vitro experiments further demonstrated that DNE notably ameliorated lipid deposition, peroxidation, and inflammation levels in NAFLD hepatocytes, particularly when administered in conjunction with fenofibrate. Notably, the PPARα inhibitor GW6471 attenuated these effects of DNE. CONCLUSIONS: In summary, DNE exerts its influence on the expression of genes associated with downstream fat metabolism by regulating PPARα. This regulatory mechanism enhances liver lipid metabolism, mitigates lipid degeneration in hepatocytes, and ultimately ameliorates the pathological changes in NAFLD hepatocytes.

The cumulative effects of cascade reservoirs control nitrogen and phosphorus flux: Base on biogeochemical processes.

The reservoir serves as a water source, a flood control structure, a navigational aid, and also impacts the downstream ecosystem as well as the reservoir zone. However, debate exists about effectiveness of cascade reservoirs in controlling the transportation of nutrients, particularly in the Yangtze River basin, which has been significantly affected by reservoir development. This research develops a new model X-NPSEM (X with Nitrogen and Phosphorus Steady-state Reservoir Model) based on biogeochemical processes of nitrogen and phosphorus reaction for investigating the dynamic storage capacity of cascade reservoirs at both reservoir- and watershed scales. Then the cumulative effects of cascade reservoirs and the related mechanism were investigated in Fujiang watershed, China. Based on the results, cascade reservoirs retained 16.3 % of nitrogen fluxes and 37.6 % of phosphorus fluxes annually. Downstream reservoirs have higher retention rates of phosphorus (0.48/d) compared to upstream reservoirs (0.10/d), mainly due to inflow sediment. Nitrogen retention rates show seasonal variations: wet season (0.21/d) and dry season (0.17/d). These fluctuations in nitrogen retention are primarily influenced by changes in temperature rather than other factors such as operation period, nitrogen and phosphorus concentration, or the nitrogen/phosphorus ratio. In upstream, the concentration of sediment entering the reservoir plays a decisive role in the transformation of P retention from sink to source. The X-NPSRM coupler model could be used for global reservoir operation and watershed management.

NBS1 rs1805794G>C polymorphism is associated with decreased risk of acute myeloid leukemia in a Chinese population.

As a key encoding protein gene of MRN (MRE11-RAD50-NBS1) complex, NBS1 plays a crucial role in maintaining genomic stability and preventing cell apoptosis, inflammation and tumorgenesis. Single nucleotide polymorphisms (rs2735383 and rs1805794) in NBS1 have been frequently studied in some cancers with discordant results in previous case-control studies. However, the relationship between these two functional polymorphisms and the susceptibility to acute myeloid leukemia (AML) in Chinese population has not been investigated. We performed a case-control study with 428 patients and 600 controls to detect the association between the two polymorphisms of NBS1 and the risk of AML in a Chinese population. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was carried out to determine the genotypes of potential functional SNPs in NBS1 gene. The results showed that compared with the homozygous carriers rs1805794CC, rs1805794GC genotype was significantly associated with decreased risk of AML in total subjects (adjusted odds ratio (OR) = 0.50; 95% CI = 0.37-0.67), the risk decreased even further in those carrying rs1805794GG genotype (OR = 0.23; 95% CI = 0.16-0.34). No significant association was found between rs2735383C>G polymorphism and the risk of AML (OR = 0.93; 95% CI = 0.71-1.22 for GC; OR = 0.78; 95% CI = 0.53-1.13 for CC, P = 0.152). These findings indicated that rs1805794G/C polymorphism in NBS1 may play a protective role in mediating the risk of AML.

Microbiological Quality and Safety of Pizza Held Out of Temperature Control in University Dining Halls.

Pizza is a popular food consumed around the world every day. Hot food temperatures were obtained from 19,754 nonpizza samples and 1,336 pizza temperatures were taken from dining facilities operated by Rutgers University between 2001 and 2020. These data showed that pizza was more frequently out of temperature control than many other foods. A total of 57 pizza samples that were out of temperature control were collected for further study. Pizza was tested for total aerobic plate count (TPC), Staphylococcus aureus, Bacillus cereus, Lactic acid bacteria, coliforms, and Escherichia coli. Water activity of pizza and surface pH of each individual pizza component (topping, cheese, bread) were measured. Predictions for the growth of four relevant pathogens were made for select pH and water activity values using ComBase. Rutgers University dining hall data show only about 60% of all foods that are pizza are held at the appropriate temperature. When pizza contained detectable microorganisms (∼70% of samples), average TPC ranged from 2.72 log CFU/g to 3.34 log CFU/g. Two pizza samples contained detectable S. aureus (∼50 CFU/g). Two other samples contained B. cereus (∼50 and 100 CFU/g). Five pizza samples contained coliforms (4-9 MPN/g), and no E. coli were detected. Correlation coefficients (R2 values) for TPC and pickup temperature are quite low (<0.06). Based on the pH and water activity measurements, most (but not all) of the pizza samples would be considered to potentially require time temperature control for safety. The modeling analysis shows that the organism most likely to pose a risk would be S. aureus, and the largest magnitude increase predicted is 0.89 log CFU at 30°C, pH 5.52, and water activity 0.963. The overall conclusion from this study is that while pizza represents a theoretical risk, the actual risk would likely only manifest for pizza samples that are held out of temperature control for time periods of more than eight hours.

BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.

Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.

Impacted molar upright with self-made helical spring.

With a case of mesial impaction of maxillary first and second molar, the mechanical analysis and clinical applications of a self-made helical spring for the uprighting treatment of mesial impacted molars was introduced.

Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales.

Recent advances in medical imaging technology bring great promises for medicine practices. Imaging biomarkers are discovered to inform disease diagnosis, prognosis, and treatment assessment. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap among the blobs. This research proposes a Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector to uncover the relationship between the U-Net threshold and the Difference of Gaussian (DoG) scale to derive a multi-threshold, multi-scale small blob detector. With lower and upper bounds on the probability thresholds from U-Net, two binarized maps of the distance are rendered between blob centers. Each blob is transformed to a DoG space with an adaptively identified local optimum scale. A Hessian convexity map is rendered using the adaptive scale, and the under-segmentation typical of the U-Net is resolved. To validate the performance of the proposed BTCAS, a 3D simulated dataset (n = 20) of blobs, a 3D MRI dataset of human kidneys and a 3D MRI dataset of mouse kidneys, are studied. BTCAS is compared against four state-of-the-art methods: HDoG, U-Net with standard thresholding, U-Net with optimal thresholding, and UH-DoG using precision, recall, F-score, Dice and IoU. We conclude that BTCAS statistically outperforms the compared detectors.

Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities through the large number of layers and trainable parameters. In this research, we propose a new architecture of residual inception encoder-decoder neural network (RIED-Net) to learn the nonlinear mapping between the input images and targeting output images. To evaluate the validity of the proposed approach, it is compared with two models from the literature: synthetic CT deep convolutional neural network (sCT-DCNN) and shallow CNN, using both an institutional mammogram dataset from Mayo Clinic Arizona and a public neuroimaging dataset from the Alzheimer's Disease Neuroimaging Initiative. Experimental results show that the proposed RIED-Net outperforms the two models on both datasets significantly in terms of structural similarity index, mean absolute percent error, and peak signal-to-noise ratio.

AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction.

The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have been developed to serve the purpose. With extensive methodology development efforts on neuroimaging, an emerging field is deep learning research. One great challenge facing deep learning is the limited medical imaging data available. To address the issue, researchers explore the use of transfer learning to extend the applicability of deep models on neuroimaging research for AD diagnosis and prognosis. Existing transfer learning models mostly focus on transferring the features from the pre-training into the fine-tuning stage. Recognizing the advantages of the knowledge gained during the pre-training, we propose an AD-NET (Age-adjust neural network) with the pre-training model serving two purposes: extracting and transferring features; and obtaining and transferring knowledge. Specifically, the knowledge being transferred in this research is an age-related surrogate biomarker. To evaluate the effectiveness of the proposed approach, AD-NET is compared with 8 classification models from literature using the same public neuroimaging dataset. Experimental results show that the proposed AD-NET outperforms the competing models in predicting the MCI patients at risk for conversion to the AD stage.

Improved small blob detection in 3D images using jointly constrained deep learning and Hessian analysis.

Imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. The development of these biomarkers requires advances in both image acquisition and analysis. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap between the blobs. The Difference of Gaussian (DoG) detector has been used to overcome these challenges in blob detection. However, the DoG detector is susceptible to over-detection and must be refined for robust, reproducible detection in a wide range of medical images. In this research, we propose a joint constraint blob detector from U-Net, a deep learning model, and Hessian analysis, to overcome these problems and identify true blobs from noisy medical images. We evaluate this approach, UH-DoG, using a public 2D fluorescent dataset for cell nucleus detection and a 3D kidney magnetic resonance imaging dataset for glomerulus detection. We then compare this approach to methods in the literature. While comparable to the other four comparing methods on recall, the UH-DoG outperforms them on both precision and F-score.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.

In situ immobilization of cadmium in soil by stabilized biochar-supported iron phosphate nanoparticles.

The potential for nanoscale phosphate amendments to remediate heavy metal contamination has been widely investigated, but the strong tendency of nanoparticles to form aggregates limits the application of this technique in soil. This study synthesized a composite of biochar-supported iron phosphate nanoparticle (BC@Fe3(PO4)2) stabilized by a sodium carboxymethyl cellulose to improve the stability and mobility of the amendment in soil. The sedimentation test and column test demonstrated that BC@Fe3(PO4)2 exhibited better stability and mobility than iron phosphate nanoparticles. After 28 days of simulated in situ remediation, the immobilization efficiency of Cd was 60.2 %, and the physiological-based extraction test bioaccessibility was reduced by 53.9 %. The results of sequential extraction procedures indicated that the transformation from exchangeable (EX) Cd to organic matter (OM) and residue (RS) was responsible for the decrease in Cd leachability in soil. Accordingly, the pot test indicated that Cd uptake by cabbage mustard was suppressed by 86.8 %. Compared to tests using iron phosphate nanoparticles, the addition of BC@Fe3(PO4)2 to soil could reduce the Fe uptake of cabbage mustard. Overall, this study revealed that BC@Fe3(PO4)2 could provide effective in situ remediation of Cd in soil.

[The mechanics analysis and clinical application of molar distalization with micro-implant combining with utility arch].

Combined with a case, the mechanical analysis and clinical application of micro-implant combining with utility arch were introduced in this paper.