A causal analysis of the relationship between exposure to sunlight and colorectal cancer risk: A Mendelian randomization study.

Several observational studies have found that exposure to sunlight reduces the risk of colorectal cancer (CRC). However, sun exposure remains ambiguous in its relationship to CRC. We carried out a Mendelian randomization (MR) study to explore the potential associations between them. We examined the exposure to sunlight summary statistics of the UK Biobank Consortium using a 2-sample MR analysis. Using data from the FinnGen consortium, we derived summary statistics for CRC. We conducted our analysis with various methods, incorporating inverse variance weighted (IVW) along with 4 other approaches. A Cochran Q statistic was used to measure the heterogeneity of instrumental variables (IVs). We screened 133 single nucleotide polymorphisms (SNPs) (time spent outdoors in summer), 41 SNPs (time spent outdoors in winter), and 35 SNPs (frequency of solarium/sunlamp use) representing sunlight exposure for MR analysis. All selected SNPs had an F-statistic >20, indicating that IVs did not weakly bias the results. The summer outdoor activity trait exhibited significant heterogeneity (Cochran Q statistic = 183.795, P = .002 < 0.05), but we found no horizontal polymorphisms or significant heterogeneity for the other exposure traits. According to IVW estimates, no causal association exists between time spent outdoors in summer and CRC (Odds Ratio, OR = 0.735, 95% confidence interval, CI = 0.494-1.017, P = .128 > 0.017). No causal relationship existed between time spent outdoors in winter and CRC, as indicated by Bonferroni-corrected adjusted p-values. The OR was 0.877 with a 95% CI of 0.334-2.299, and the P value was .789, more significant than the significance threshold of 0.017. The solarium/sunlamp use frequency was not associated with CRC (OR = 1.567, 95%CI = 0.243-10.119, P = .637 > .017). Also, an IVW with random effects was applied to determine the causal relationship between summer outdoor time and CRC. No causal association between summer outdoor time and CRC was found (OR = 0.735, 95% CI = 0.494-1.017, P = .128 > .017). Additionally, 4 additional analyses yielded similar results. The findings of our study suggest that exposure to sunlight may reduce CRC risk, but the causal relationship remains unsolved. There is no evidence to suggest that exposure to sunlight prevents CRC. Randomized, controlled trials are needed to determine whether sunlight exposure protects against CRC.

Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method.

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson's correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

A large deletion conferring pale green leaves of maize.

BACKGROUND: The structural basis of chloroplast and the regulation of chloroplast biogenesis remain largely unknown in maize. Gene mutations in these pathways have been linked to the abnormal leaf color phenotype observed in some mutants. Large scale structure variants (SVs) are crucial for genome evolution, but few validated SVs have been reported in maize and little is known about their functions though they are abundant in maize genomes. RESULTS: In this research, a spontaneous maize mutant, pale green leaf-shandong (pgl-sd), was studied. Genetic analysis showed that the phenotype of pale green leaf was controlled by a recessive Mendel factor mapped to a 156.8-kb interval on the chromosome 1 delineated by molecular markers gy546 and gy548. There were 7 annotated genes in this interval. Reverse transcription quantitative PCR analysis, SV prediction, and de novo assembly of pgl-sd genome revealed that a 137.8-kb deletion, which was verified by Sanger sequencing, might cause the pgl-sd phenotype. This deletion contained 5 annotated genes, three of which, including Zm00001eb031870, Zm00001eb031890 and Zm00001eb031900, were possibly related to the chloroplast development. Zm00001eb031870, encoding a Degradation of Periplasmic Proteins (Deg) homolog, and Zm00001eb031900, putatively encoding a plastid pyruvate dehydrogenase complex E1 component subunit beta (ptPDC-E1-ß), might be the major causative genes for the pgl-sd mutant phenotype. Plastid Degs play roles in protecting the vital photosynthetic machinery and ptPDCs provide acetyl-CoA and NADH for fatty acid biosynthesis in plastids, which were different from functions of other isolated maize leaf color associated genes. The other two genes in the deletion were possibly associated with DNA repair and disease resistance, respectively. The pgl-sd mutation decreased contents of chlorophyll a, chlorophyll b, carotenoids by 37.2%, 22.1%, and 59.8%, respectively, and led to abnormal chloroplast. RNA-seq revealed that the transcription of several other genes involved in the structure and function of chloroplast was affected in the mutant. CONCLUSIONS: It was identified that a 137.8-kb deletion causes the pgl-sd phenotype. Three genes in this deletion were possibly related to the chloroplast development, which may play roles different from that of other isolated maize leaf color associated genes.

Automatic Measuring of Finger Joint Space Width on Hand Radiograph using Deep Learning and Conventional Computer Vision Methods.

Hand osteoarthritis (OA) severity can be assessed visually through radiographs using semi-quantitative grading systems. However, these grading systems are subjective and cannot distinguish minor differences. Joint space width (JSW) compensates for these disadvantages, as it quantifies the severity of OA by accurately measuring the distances between joint bones. Current methods used to assess JSW require users' interaction to identify the joints and delineate initial joint boundary, which is time-consuming. To automate this process and offer a more efficient and robust measurement for JSW, we proposed two novel methods to measure JSW: 1) The segmentation-based (SEG) method, which uses traditional computer vision techniques to calculate JSW; 2) The regression-based (REG) method, which is a deep learning approach employing a modified VGG-19 network to predict JSW. On a dataset with 3,591 hand radiographs, 10,845 DIP joints were cut as regions of interest and served as input to the SEG and REG methods. The bone masks of the ROI images generated by a U-Net model were sent as input in addition to the ROIs. The ground truth of JSW was labeled by a trained research assistant using a semi-automatic tool. Compared with the ground truth, the REG method achieved a correlation coefficient of 0.88 and mean square error (MSE) of 0.02 mm on the testing set; the SEG method achieved a correlation coefficient of 0.42 and MSE of 0.15 mm. Results show the REG method has promising performance in automatic JSW measurement and in general, Deep Learning approaches can facilitate the automatic quantification of distance features in medical images.

Exploring the Influence of Hepatitis B Immunoglobulin on the Immune Response to the Hepatitis B Vaccine in a Mouse Model.

Background: The extent to which maternal antibodies against the hepatitis B surface antigen (HBsAb) acquired transplacentally affect the immune responses to the hepatitis B vaccine (HBVac) in infants is still uncertain. Objective: To explore the impact of the HBsAb on the immune response to the HBVac in a mouse model. Methods: According to the doses of the HBVac (2, 5 µg) injected, 267 BALB/c mice were divided into two groups. Each group was subdivided into 3 subgroups based on the doses of the hepatitis B immunoglobulin (HBIG) (0, 25, 50 IU) administered. The HBsAb titers were detected 4 weeks after completing the HepB vaccination. Results: Among all the mice, 40 had an HBsAb titer <100 mIU/mL (non- or low-response to the HBVac). The rates of the HBsAb titer <100 mIU/mL in 0, 25 and 50 IU HBIG groups were 1.1%, 23.1%, and 20.7%, respectively. Multivariate logistic regression analysis showed that the risk factors for low- or non-response to the HBVac were injection with the HBIG, low HBVac dose, and hypodermic injection. The mean HBsAb titers (log10) reduced gradually in the 0, 25 and 50 IU HBIG groups (P<0.001). Conclusion: The HBIG administration has negative impacts on the peak level of the HBsAb and the rate of an effective immune response. This implies that the maternal HBsAb acquired transplacentally might inhibit the immune responses to the HBVac in infants.

Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI.

In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model's performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.

Rapamycin facilitates differentiation of regulatory T cells via enhancement of oxidative phosphorylation.

We explored the interplay between energy metabolism and the impact of rapamycin (Rapa) on regulatory T cell (Treg) differentiation. Naïve CD4+ T cells were stimulated under Treg-polarizing conditions with or without Rapa. Rapa promoted Treg induction, as the expression of Foxp3 and Treg phenotypic markers were enhanced. Rapa disrupts glycolysis while favoring mitochondrial metabolism in induced Tregs (iTregs). Metabolic profiling showed reduced glycolytic metabolites in Rapa-treated iTregs, in line with the downregulation of glucose uptake and the expression of glycolytic enzymes. Conversely, Rapa increased the ratios of ATP/ADP and ATP/AMP, the production of mitochondrial ATP, and the expression of ATP5A. Treatment with oxidative phosphorylation inhibitors suppressed Foxp3 expression in Rapa-treated cells. Moreover, Rapa decreased oleic acid and palmitoleic acid levels and increased l-carnitine and acetylcarnitine levels and CPT1A expression in iTregs, indicative of augmented fatty acid oxidation. In conclusion, Rapa induces metabolic reprogramming in Tregs, affecting their differentiation.

Patients' perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data.

Results of radiology imaging studies are not typically comprehensible to patients. With the advances in artificial intelligence (AI) technology in recent years, it is expected that AI technology can aid patients' understanding of radiology imaging data. The aim of this study is to understand patients' perceptions and acceptance of using AI technology to interpret their radiology reports. We conducted semi-structured interviews with 13 participants to elicit reflections pertaining to the use of AI technology in radiology report interpretation. A thematic analysis approach was employed to analyze the interview data. Participants have a generally positive attitude toward using AI-based systems to comprehend their radiology reports. AI is perceived to be particularly useful in seeking actionable information, confirming the doctor's opinions, and preparing for the consultation. However, we also found various concerns related to the use of AI in this context, such as cyber-security, accuracy, and lack of empathy. Our results highlight the necessity of providing AI explanations to promote people's trust and acceptance of AI. Designers of patient-centered AI systems should employ user-centered design approaches to address patients' concerns. Such systems should also be designed to promote trust and deliver concerning health results in an empathetic manner to optimize the user experience.

Nanocomposites of graphene and zirconia for adsorption of organic-arsenic drugs: Performances comparison and analysis of adsorption behavior.

3-Nitro-4-hydroxy-phenylarsonic acid (3-NHPAA), an organic-arsenic compound, as one of widely used antibacterial veterinary drug, has greatly attracted the attention due to its potential threats on ecological environment. A series of the nanocomposites of zirconia nanoparticles with crystal phases (pure monoclinic, pure tetragonal and mixed phase (monoclinic + tetragonal)) anchored on reduced graphene oxide were produced through managing the concentration of triethanolamine solution and the reaction time. The effects of the crystal phases of the zirconia in the structure of the nanocomposites were played a key role in the adsorption performances of the 3-NHPAA. Experiment data identified the nanocomposites with monoclinic phase of zirconia excelled at the adsorption of the 3-NHPAA with a higher adsorption capacity up to 207.2 mg g-1. The uptake of the 3-NHPAA by the three nanocomposites was implemented within 60 min and highly pH-dependent which illustrated electrostatic attraction between them as a main mechanism during the adsorption process. A wider pH range (3.8-8.8) for the uptake of the 3-NHPAA by the nanocomposites with the monoclinic phase of zirconia was obtained compared with the nanocomposites containing tetragonal phase (3.8-5.9) or the mixed phase (3.8-7.1) of zirconia. The adsorption of the 3-NHPAA was well described by the pseudo-second order kinetic and Langmuir equations. The thermodynamic parameters suggested that the adsorption of the 3-NHPAA over the three nanocomposites was endothermic and spontaneous in nature. In summary, the nanocomposites of reduced graphene oxide and monoclinic phase of zirconia nanoparticles as an adsorbent were better to the adsorption of the 3-NHPAA.

Impact of high-dose rate radiotherapy on B and natural killer (NK) cell polarization in peripheral blood mononuclear cells (PBMCs) via inducing non-small cell lung cancer (NSCLC)-derived exosomes.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most commonly diagnosed solid tumor. While it has been established that stereotactic body radiotherapy for NSCLC plays an important role in antitumor immune response, the possible effects of the dose rate on this response has not been fully clarified. METHODS: In vitro, A549 cells were irradiated on a Varian TrueBeam® Linear Accelerator with dose and dose rate escalation using the flattening filter-free (FFF) technique, which was followed by coculturing with peripheral blood mononuclear cells (PBMCs). The exosomes from irradiated A549 cells were isolated and then cocultured with PBMCs. Flow cytometry was performed to analyze the proportion of lymph cell clusters in PBMCs. RESULTS: The proportion of CD3- immune cell clusters in PBMCs was significantly higher in the 10 Gy treatment group than in the nonirradiated group and other lower-dose (2, 6 Gy) treatment groups at the dose rate of 1,000 MU/min. However, no influence was observed on the proportion of CD3+ T cell subsets. Further results showed that both natural killer (NK) and B cell proportions reached peaks in the 14 Gy treatment group when a dose rate of 1,200 MU/min was used. Notably, the peak values of these two cell proportions were reached at a lower radiation dose of 10 Gy when a greater dose rate, ranging from 1,600 to 2,400 MU/min, was used. We further found that a single, high dose of irradiation (10 Gy), as compared with a single, low dose of irradiation (2 Gy), could markedly stimulate the A549-related exosome secretion in a radiation dose rate-dependent manner. The ultrahigh dose rate radiation-derived exosomes contributed to the polarization of B and NK cell subsets in PBMCs. CONCLUSIONS: The optimized radiation regime, which depends on the appropriate radiation dose and dose rate, results in the production of exosomes derived from NSCLC cells and eventually the redistribution of immune cells in PBMCs.

T Cell Metabolism: A New Perspective on Th17/Treg Cell Imbalance in Systemic Lupus Erythematosus.

The Th17/T-regulatory (Treg) cell imbalance is involved in the occurrence and development of organ inflammation in systemic lupus erythematosus (SLE). Metabolic pathways can regulate T cell differentiation and function, thus contributing to SLE inflammation. Increasingly, data have shown metabolism influences and reprograms the Th17/Treg cell balance, and the metabolic pattern of T cells is different in SLE. Notably, metabolic characteristics of SLE T cells, such as enhanced glycolysis, lipid synthesis, glutaminolysis, and highly activated mTOR, all favored Th17 differentiation and function, which underlie the Th17/Treg cell imbalance in SLE patients. Targeting metabolic pathways to reverse Th17/Treg imbalance offer a promising method for SLE therapy.

TGF-ß1 maintains Foxp3 expression and inhibits glycolysis in natural regulatory T cells via PP2A-mediated suppression of mTOR signaling.

Natural regulatory T cells (nTregs) play a dominant role in maintaining immunological homeostasis and they are known to undergo metabolic reprogramming during immune responses. Transforming growth factor-ß1 (TGF-ß1), an anti-inflammatory cytokine, can promote the induction of regulatory T cells. Here, we investigated the effects of TGF-ß1 on the stability and metabolism of nTregs stimulated in vitro. CD4+CD25+ nTregs were isolated from mouse spleens and stimulated with anti-CD3 and anti-CD28 antibodies plus IL-2 in the presence or absence of TGF-ß1. Exposure to TGF-ß1 induced the activation of STAT5 and sustained the expression of the nTregs transcription factor Foxp3. In addition, TGF-ß1 inhibited glycolysis, as shown by reduced lactate production and diminished expression of Glut1, Hk2, Enolase1, and Hif-1α. nTregs treated with TGF-ß1 exhibited downregulated mTORC1 signaling but enhanced activation of the serine-threonine phosphatase PP2A. Moreover, treat with the PP2A inhibitor okadaic acid disrupted the maintenance of Foxp3 expression by TGF-ß1. Thus, TGF-ß1 serves to maintain Foxp3 expression in cultured nTregs, possibly via PP2A activation and suppression of mTORC1-regulated glycolysis.

Neuregulin-1 promotes mitochondrial biogenesis, attenuates mitochondrial dysfunction, and prevents hypoxia/reoxygenation injury in neonatal cardiomyocytes.

Neuregulin-1 (NRG-1)/erythroblastic leukaemia viral oncogene homologues (ErbB) pathway activation plays a crucial role in regulating the adaptation of the adult heart to physiological and pathological stress. In the present study, we investigate the effect of recombined human NRG-1 (rhNRG-1) on mitochondrial biogenesis, mitochondrial function, and cell survival in neonatal rat cardiac myocytes (NRCMs) exposed to hypoxia/reoxygenation (H/R). The results of this study showed that, in the H/R-exposed NRCMs, mitochondrial biogenesis was impaired, as manifested by the decrease of the expression of peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) and mitochondrial membrane proteins, the inner membrane (Tim23), mitofusin 1 (Mfn1), and mitofusin 2 (Mfn2). RhNRG-1 pretreatment effectively restored the expression of PGC-1α and these membrane proteins, upregulated the expression of the anti-apoptosis proteins Bcl-2 and Bcl-xL, preserved the mitochondrial membrane potential, and attenuated H/R-induced cell apoptosis. Blocking PGC-1 expression with siRNA abolished the beneficial role of rhNRG-1 on mitochondrial function and cell survival. The results of the present study strongly suggest that NRG-1/ErbB activation enhances the adaption of cardiomyocytes to H/R injury via promoted mitochondrial biogenesis and improved mitochondrial homeostasis. SIGNIFICANCE OF THE STUDY: The results of this research revealed for the first time the relationship between neuregulin-1 (NRG-1)/erythroblastic leukaemia viral oncogene homologues (ErbB) activation and mitochondrial biogenesis in neonatal cardiomyocytes and verified the significance of this promoted mitochondrial biogenesis in attenuating hypoxia/reoxygenation injury. This finding may open a new field to further understand the biological role of NRG-1/ErbB signalling pathway in cardiomyocyte.

Spin transport in insulators without exchange stiffness.

The discovery of new materials that efficiently transmit spin currents has been important for spintronics and material science. The electric insulator Gd3Ga5O12 (GGG), a standard substrate for growing magnetic films, can be a spin current generator, but has never been considered as a superior conduit for spin currents. Here we report spin current propagation in paramagnetic GGG over several microns. Surprisingly, spin transport persists up to temperatures of 100 K [Formula: see text] Tg = 180 mK, the magnetic glass-like transition temperature of GGG. At 5 K and 3.5 T, we find a spin diffusion length λGGG = 1.8 ± 0.2 µm and a spin conductivity σGGG = (7.3 ± 0.3) × 104 Sm-1 that is larger than that of the record quality magnet Y3Fe5O12 (YIG). We conclude that exchange stiffness is not required for efficient spin transport, which challenges conventional models and provides new material-design strategies for spintronic devices.

The effect of a comprehensive care and rehabilitation program on enhancing pelvic floor muscle functions and preventing postpartum stress urinary incontinence.

This study was to investigate the incidence and the risk factors of postpartum stress urinary incontinence (SUI), and the effect of comprehensive care and rehabilitation program (CCRP) on preventing postpartum SUI.In stage I, 479 puerperae were recruited within 1 week postpartum, then the postpartum SUI incidence at 8th week and its risk factors were investigated. In stage II, 240 vaginal delivery puerperae were enrolled within 1 week postpartum and randomly assigned to CCRP group or control group as 1:1 ratio. The postpartum SUI incidence and pelvic floor muscle function indexes were evaluated at 8th week.In stage I, the postpartum SUI incidence was 25.7%, and SUI puerperae presented with higher body mass index (BMI), vaginal delivery rate, newborn weight, and larger newborn head diameter compared with non-SUI puerperae. Besides, the vaginal delivery, the elevated age and BMI were independent risk factors for postpartum SUI. In stage II, the postpartum SUI incidence in CCRP group was decreased compared with control group, and the vaginal resting pressure, vaginal squeezing pressure, and vaginal contraction duration were increased in CCRP group compared to control group at 8th week postpartum.The incidence of postpartum SUI is 25.7%, and the vaginal delivery, increased age, and BMI are independent risk factors for postpartum SUI. More importantly, CCRP strengthens pelvic floor muscle functions and decreases postpartum SUI incidence in puerperae.

Rapamycin Modulate Treg/Th17 Balance via Regulating Metabolic Pathways: A Study in Mice.

A regulatory T (Treg) cell/T helper 17 (Th17) cell imbalance is involved in many autoimmune diseases. Rapamycin (Rapa), a clinically used immunosuppressive drug, has been shown to inhibit Th17 cell differentiation but promote Treg cell generation. In this study, we aimed to study the mechanism of Rapa acting on Treg and Th17 cell differentiation. Purified mouse CD4+CD25- T cells were stimulated and polarized in vitro to generate Th17 or Treg cells in the presence or absence of Rapa. We first confirmed that Rapa inhibited the differentiation of Th17 cells and greatly promoted Treg cell generation in vitro. As metabolic pathways play a key role in T cell differentiation, we then detected the metabolic programs in Rapa-treated T cells. We found that Rapa blocked glycolysis in induced Th17 cells, evidenced by reduced glucose uptake, and inhibited expression of glucose transporter 1 and the rate-limiting enzyme HK2. In addition, the expression of c-Myc and of HIF-1α transcription factor, which regulate many genes involved in glycolysis, were inhibited by Rapa. Conversely, Rapa promoted fatty acid oxidation (FAO) metabolism in differentiated Treg cells, with the elevation of FAO product ß-hydroxybutyrate, and increased expression of ATGL and CPT1A, the key enzymes of FAO in differentiated Treg cells. The expression of phospho-AMPKα, the key signal in the regulation of FAO, was also promoted in Rapa-treated induced Treg cells. Together, these findings indicated that Rapa abrogated glycolysis in Th17 cells but facilitated FAO in induced Treg cells, which may underlie the mechanism by which Rapa regulates the Treg/Th17 balance.

Impact of obesity on the efficacy of different biologic agents in inflammatory diseases: A systematic review and meta-analysis.

OBJECTIVE: Obesity is a worldwide epidemic and a growing body of evidence suggests that it may affect the body's response to biologic agents. We investigated the influence of obesity on the efficacy of different biologic agents used to treat inflammatory diseases. METHODS: Medline, EMBASE and the Cochrane Database were searched using relevant MeSH and keyword terms for obesity and bDMARDs. Articles were selected if they reported a clinical response in obese subjects relative to other BMI categories. Response and remission outcomes were assessed using meta-analysis and all other reported outcomes were summarized. RESULTS: Among the 3850 records retrieved, 24 articles met the inclusion criteria, including 10 on rheumatoid arthritis (RA), 4 on axial spondyloarthritis (axSpA), 4 on Crohn's disease (CD), 4 on psoriasis (Ps) and 2 on psoriasic arthritis (PsA). Four biological disease-modifying anti-rheumatic drugs (bDMARDs) - anti-TNF agents, T cell co-stimulation inhibitor (abatacept), IL-6 inhibitor (tocilizumab), and B-cell depletion therapy (rituximab) - were involved. The meta-analysis showed that the odds to reach a good response or achieve remission were lower in obese (BMI>30kg/m2) than non-obese (BMI≤30kg/m2) patients who were treated with anti-TNF agents (good responder % in RA: OR 0.34, 95% CI 0.18-0.64; remission% in RA: OR 0.36, 95% CI 0.21-0.59; BASDAI50% in axSpA: OR 0.41, 95% CI 0.21-0.83), but no significant difference between obese and non-obese was found in patients treated with abatacept (good responder % in RA: OR 0.75, 95% CI 0.42-1.36; remission% in RA: OR 0.84, 95% CI 0.65-1.09) and tocilizumab (good responder % in RA: OR 1.08, 95% CI 0.44-2.63; remission% in RA: OR 0.91, 95% CI 0.50-1.66). CONCLUSION: Obesity hampered the effect of anti-TNF agents, but not those of abatacept and tocilizumab, suggesting that a personalized treatment strategy should be considered for obese patients with inflammatory diseases.

A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods.

This paper explored the hidden biomedical information from knee magnetic resonance (MR) images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (CDI) information from 36 informative locations on tibiofemoral cartilage compartment from 3-D MR imaging and used principal component analysis (PCA) analysis to process the feature set. Four machine learning methods (artificial neural network (ANN), support vector machine, random forest, and naïve Bayes) were employed to predict the progression of OA, which was measured by the change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on Medial compartment (JSM) grade, and Joint Space Narrowing on Lateral compartment (JSL) grade. To examine the different effects of medial and lateral informative locations, we have divided the 36-D feature set into a 18-D medial feature set and a 18-D lateral feature set and run the experiment on four classifiers separately. Experiment results showed that the medial feature set generated better prediction performance than the lateral feature set, while using the total 36-D feature set generated the best. PCA analysis is helpful in feature space reduction and performance improvement. For KL grade prediction, the best performance was achieved by ANN with AUC = 0.761 and F-measure = 0.714. For JSM grade prediction, the best performance was achieved by random forest with AUC = 0.785 and F-measure = 0.743, while for JSL grade prediction, the best performance was achieved by ANN with AUC = 0.695 and F-measure = 0.796. As experiment results showing that the informative locations on medial compartment provide more distinguishing features than informative locations on the lateral compartment, it could be considered to select more points from the medial compartment while reducing the number of points from the lateral compartment to improve clinical CDI design.

Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods.

Diabetic retinopathy (DR) is an eye abnormality caused by long-term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR. In this paper, we analyzed MA detectability using small 25 by 25 pixel patches extracted from fundus images in the DIAbetic RETinopathy DataBase - Calibration Level 1 (DIARETDB1). Raw pixel intensities of extracted patches served directly as inputs into the following classifiers: random forest (RF), neural network, and support vector machine. We also explored the use of two techniques (principal component analysis and RF feature importance) for reducing input dimensionality. With traditional machine learning methods and leave-10-patients-out cross validation, our method outperformed a deep learning-based MA detection method, with AUC performance improved from 0.962 to 0.985 and F-measure improved from 0.913 to 0.926, using the same DIARETDB1 database. Furthermore, we validated our method on a different dataset-retinopathy online challenge (ROC) data set. The performance of the three classifiers and the pattern with different percentage of principal components are consistent on the two data sets. Especially, we trained the RF on DIARETDB1 and applied it to ROC; the performance is very similar to that of the RF trained and tested using cross validation on ROC data set. This result indicates that our method has the potential to generalize to different datasets.

Inducing ferromagnetism and Kondo effect in platinum by paramagnetic ionic gating.

Electrically controllable magnetism, which requires the field-effect manipulation of both charge and spin degrees of freedom, has attracted growing interest since the emergence of spintronics. We report the reversible electrical switching of ferromagnetic (FM) states in platinum (Pt) thin films by introducing paramagnetic ionic liquid (PIL) as the gating media. The paramagnetic ionic gating controls the movement of ions with magnetic moments, which induces itinerant ferromagnetism on the surface of Pt films, with large coercivity and perpendicular anisotropy mimicking the ideal two-dimensional Ising-type FM state. The electrical transport of the induced FM state shows Kondo effect at low temperature, suggesting spatially separated coexistence of Kondo scattering beneath the FM interface. The tunable FM state indicates that paramagnetic ionic gating could serve as a versatile method to induce rich transport phenomena combining field effect and magnetism at PIL-gated interfaces.