Cellular communication network factor 1 promotes retinal leakage in diabetic retinopathy via inducing neutrophil stasis and neutrophil extracellular traps extrusion.

BACKGROUND: Diabetic retinopathy (DR) is a major cause of blindness and is characterized by dysfunction of the retinal microvasculature. Neutrophil stasis, resulting in retinal inflammation and the occlusion of retinal microvessels, is a key mechanism driving DR. These plugging neutrophils subsequently release neutrophil extracellular traps (NETs), which further disrupts the retinal vasculature. Nevertheless, the primary catalyst for NETs extrusion in the retinal microenvironment under diabetic conditions remains unidentified. In recent studies, cellular communication network factor 1 (CCN1) has emerged as a central molecule modulating inflammation in pathological settings. Additionally, our previous research has shed light on the pathogenic role of CCN1 in maintaining endothelial integrity. However, the precise role of CCN1 in microvascular occlusion and its potential interaction with neutrophils in diabetic retinopathy have not yet been investigated. METHODS: We first examined the circulating level of CCN1 and NETs in our study cohort and analyzed related clinical parameters. To further evaluate the effects of CCN1 in vivo, we used recombinant CCN1 protein and CCN1 overexpression for gain-of-function, and CCN1 knockdown for loss-of-function by intravitreal injection in diabetic mice. The underlying mechanisms were further validated on human and mouse primary neutrophils and dHL60 cells. RESULTS: We detected increases in CCN1 and neutrophil elastase in the plasma of DR patients and the retinas of diabetic mice. CCN1 gain-of-function in the retina resulted in neutrophil stasis, NETs extrusion, capillary degeneration, and retinal leakage. Pre-treatment with DNase I to reduce NETs effectively eliminated CCN1-induced retinal leakage. Notably, both CCN1 knockdown and DNase I treatment rescued the retinal leakage in the context of diabetes. In vitro, CCN1 promoted adherence, migration, and NETs extrusion of neutrophils. CONCLUSION: In this study, we uncover that CCN1 contributed to retinal inflammation, vessel occlusion and leakage by recruiting neutrophils and triggering NETs extrusion under diabetic conditions. Notably, manipulating CCN1 was able to hold therapeutic promise for the treatment of diabetic retinopathy.

Personalized Hydration Strategy to Improve Fluid Balance and Intermittent Exercise Performance in the Heat.

Sweat rate and electrolyte losses have a large inter-individual variability. A personalized approach to hydration can overcome this issue to meet an individual's needs. This study aimed to investigate the effects of a personalized hydration strategy (PHS) on fluid balance and intermittent exercise performance. Twelve participants conducted 11 laboratory visits including a VO2max test and two 5-day trial arms under normothermic (NOR) or hyperthermic (HYP) environmental conditions. Each arm began with three days of familiarization exercise followed by two random exercise trials with either a PHS or a control (CON). Then, participants crossed over to the second arm for: NOR+PHS, NOR+CON, HYP+PHS, or HYP+CON. The PHS was prescribed according to the participants' fluid and sweat sodium losses. CON drank ad libitum of commercially-available electrolyte solution. Exercise trials consisted of two phases: (1) 45 min constant workload; (2) high-intensity intermittent exercise (HIIT) until exhaustion. Fluids were only provided in phase 1. PHS had a significantly greater fluid intake (HYP+PHS: 831.7 ± 166.4 g; NOR+PHS: 734.2 ± 144.9 g) compared to CON (HYP+CON: 369.8 ± 221.7 g; NOR+CON: 272.3 ± 143.0 g), regardless of environmental conditions (p < 0.001). HYP+CON produced the lowest sweat sodium concentration (56.2 ± 9.0 mmol/L) compared to other trials (p < 0.001). HYP+PHS had a slower elevated thirst perception and a longer HIIT (765 ± 452 s) compared to HYP+CON (548 ± 283 s, p = 0.04). Thus, PHS reinforces fluid intake and successfully optimizes hydration status, regardless of environmental conditions. PHS may be or is an important factor in preventing negative physiological consequences during high-intensity exercise in the heat.

Mitigating the Overheat of Stretchable Electronic Devices Via High-Enthalpy Thermal Dissipation of Hydrogel Encapsulation.

The practical application of flexible and stretchable electronics is significantly influenced by their thermal and chemical stability. Elastomer substrates and encapsulation, due to their soft polymer chains and high surface-area-to-volume ratio, are particularly susceptible to high temperatures and flame. Excessive heat poses a severe threat of damage and decomposition to these elastomers. By leveraging water as a high enthalpy dissipating agent, here, a hydrogel encapsulation strategy is proposed to enhance the flame retardancy and thermal stability of stretchable electronics. The hydrogel-based encapsulation provides thermal protection against flames for more than 10 s through the evaporation of water. Further, the stretchability and functions automatically recover by absorbing air moisture. The incorporation of hydrogel encapsulation enables stretchable electronics to maintain their functions and perform complex tasks, such as fire saving in soft robotics and integrated electronics sensing. With high enthalpy heat dissipation, encapsulated soft electronic devices are effectively shielded and retain their full functionality. This strategy offers a universal method for flame retardant encapsulation of stretchable electronic devices.

Skin-Inspired Multi-Modal Mechanoreceptors for Dynamic Haptic Exploration.

Active sensing is a fundamental aspect of human and animal interactions with the environment, providing essential information about the hardness, texture, and tackiness of objects. This ability stems from the presence of diverse mechanoreceptors in the skin, capable of detecting a wide range of stimuli and from the sensorimotor control of biological mechanisms. In contrast, existing tactile sensors for robotic applications typically excel in identifying only limited types of information, lacking the versatility of biological mechanoreceptors and the requisite sensing strategies to extract tactile information proactively. Here, inspired by human haptic perception, a skin-inspired artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli is developed to bridge sensing and action in a closed-loop sensorimotor system for dynamic haptic exploration. A tensor-based non-linear theoretical model is established to characterize the 3D deformation (e.g., tensile, compressive, and shear deformation) of SENS, providing guidance for the design and optimization of multimode sensing properties with high fidelity. Based on SENS, a closed-loop robotic system capable of recognizing objects with improved accuracy (≈96%) is further demonstrated. This dynamic haptic exploration approach shows promise for a wide range of applications such as autonomous learning, healthcare, and space and deep-sea exploration.

Deshielding Anions Enable Solvation Chemistry Control of LiPF6-Based Electrolyte toward Low-Temperature Lithium-Ion Batteries.

Severe capacity decay under subzero temperatures remains a significant challenge for lithium-ion batteries (LIBs) due to the sluggish interfacial kinetics. Current efforts to mitigate this deteriorating interfacial behavior rely on high-solubility lithium salts (e.g., Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), Lithium bis(fluorosulfonyl)imide (LiFSI))-based electrolytes to construct anion participated solvation structures. However, such electrolytes bring issues of corrosion on the current collector and increased costs. Herein, the most commonly used Lithium hexafluorophosphate (LiPF6) instead, to establish a peculiar solvation structure with a high ratio of ion pairs and aggregates by introducing a deshielding NO3 - additive for low-temperature LIBs is utilized. The deshielding anion significantly reduces the energy barrier for interfacial behavior at low temperatures. Benefiting from this, the graphite (Gr) anode retains a high capacity of ≈72.3% at -20 °C, which is far superior to the 32.3% and 19.4% capacity retention of counterpart electrolytes. Moreover, the LiCoO2/Gr full cell exhibits a stable cycling performance of 100 cycles at -20 °C due to the inhibited lithium plating. This work heralds a new paradigm in LiPF6-based electrolyte design for LIBs operating at subzero temperatures.

The profile of genome-wide DNA methylation, transcriptome, and proteome in streptomycin-resistant Mycobacterium tuberculosis.

Streptomycin-resistant (SM-resistant) Mycobacterium tuberculosis (M. tuberculosis) is a major concern in tuberculosis (TB) treatment. However, the mechanisms underlying streptomycin resistance remain unclear. This study primarily aimed to perform preliminary screening of genes associated with streptomycin resistance through conjoint analysis of multiple genomics. Genome-wide methylation, transcriptome, and proteome analyses were used to elucidate the associations between specific genes and streptomycin resistance in M. tuberculosis H37Rv. Methylation analysis revealed that 188 genes were differentially methylated between the SM-resistant and normal groups, with 89 and 99 genes being hypermethylated and hypomethylated, respectively. Furthermore, functional analysis revealed that these 188 differentially methylated genes were enriched in 74 pathways, with most of them being enriched in metabolic pathways. Transcriptome analysis revealed that 516 genes were differentially expressed between the drug-resistant and normal groups, with 263 and 253 genes being significantly upregulated and downregulated, respectively. KEGG analysis indicated that these 516 genes were enriched in 79 pathways, with most of them being enriched in histidine metabolism. The methylation level was negatively related to mRNA abundance. Proteome analysis revealed 56 differentially expressed proteins, including 14 upregulated and 42 downregulated proteins. Moreover, three hub genes (coaE, fadE5, and mprA) were obtained using synthetic analysis. The findings of this study suggest that an integrated DNA methylation, transcriptome, and proteome analysis can provide important resources for epigenetic studies in SM-resistant M. tuberculosis H37Rv.

Multi-band all-silicon TM-pass polarizer based on one-dimensional photonic crystals nanohole array.

We propose an on-chip transverse magnetic (TM)-pass polarizer utilizing one-dimensional photonic crystals for multi-band operation. The TE0 modes in the 1550/2000nm wave band are suppressed by carefully selecting the pitch lengths of the nanoholes, leveraging the bandgap of the nanohole array. Conversely, the TM0 modes remain almost unaffected. The TM-pass polarizer employs a single-etched design on a standard 220 nm SOI platform and has a compact length of ∼ 17.9 µm. The simulated bandwidths (BWs) for polarization extinction ratios (PERs) > 20 dB and > 25 dB are about 210 nm and 195 nm for the 1550 nm wave band, and 265 nm and 240 nm for the 2000nm wave band. Moreover, the insertion losses (ILs) are ∼ 0.5/0.3 dB at wavelengths of 1550/2000nm, respectively. For the fabricated device, the measured BWs for PER > 20 dB and > 25 dB are evaluated to be larger than 100 nm for both 1550/2000nm wave bands. The measured ILs are 1/0.8 dB at wavelengths of 1550/2000nm. This straightforward and compatible design opens possibilities for the development of practical multi-band silicon photonic integrated circuits.

A Leaf-Patchable Reflectance Meter for In Situ Continuous Monitoring of Chlorophyll Content.

Plant wearable sensors facilitate the real-time monitoring of plant physiological status. In situ monitoring of the plant chlorophyll content over days can provide valuable information on the photosynthetic capacity, nitrogen content, and general plant health. However, it cannot be achieved by current chlorophyll measuring methods. Here, a miniaturized and plant-wearable chlorophyll meter for rapid, non-destructive, in situ, and long-term chlorophyll monitoring is developed. The reflectance-based chlorophyll sensor with 1.5 mm thickness and 0.2 g weight (1000 times lighter than the commercial chlorophyll meter), includes a light emitting diode (LED) and two symmetric photodetectors (PDs) on a flexible substrate, and is patched onto the leaf upper epidermis with a conformal light guiding layer. A chlorophyll content index (CCI) calculated based on the sensor shows a better linear relationship with the leaf chlorophyll content (r2 > 0.9) than the traditional chlorophyll meter. This meter can wirelessly communicate with a smartphone to monitor the leaf chlorophyll change under various stresses and indicate the unhealthy status of plants for long-term application of plants under various stresses earlier than chlorophyll meter and naked-eye observation. This wearable chlorophyll sensing patch is promising in smart and precision agriculture.

Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces.

Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2nd harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring.

Protein-rich yeast extract (®fermgard) has potential antioxidant and anti-aging activities.

There existed a deficiency in the research on the nutritional activities of microbial (yeast) active substances in antioxidant and anti-aging activities, although the research objects were concentrated in animals and plants in recent years. In this study, the anti-oxidant and anti-aging activities of protein-rich yeast extract (®fermgard) (YE) were investigated through Caenorhabditis elegans (C. elegans). The results indicated that YE could improve the lifespan and anti-stress ability by up-regulating the activities of antioxidant enzymes in C. elegans. Meanwhile, the mRNA transcriptional level of daf-16, skn-1 and sod-3 was significantly up-regulated. In addition, the composition and level of the gut microbiota and metabolite were modulated. YE exerts antioxidant and anti-aging activities by regulating the expression of anti-oxidation-related mRNA, gut microbiota and metabolites in C. elegans, providing a basis for exploring the deep mechanism of YE improving health. At the same time, it provides new ideas for the development of functional foods.

A computational framework of routine test data for the cost-effective chronic disease prediction.

Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery.

BACKGROUND: The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. RESULTS: Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ). CONCLUSION: Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

EmAtlas: a comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis.

The emerging importance of embryonic development research rapidly increases the volume for a professional resource related to multi-omics data. However, the lack of global embryogenesis repository and systematic analysis tools limits the preceding in stem cell research, human congenital diseases and assisted reproduction. Here, we developed the EmAtlas, which collects the most comprehensive multi-omics data and provides multi-scale tools to explore spatiotemporal activation during mammalian embryogenesis. EmAtlas contains data on multiple types of gene expression, chromatin accessibility, DNA methylation, nucleosome occupancy, histone modifications, and transcription factors, which displays the complete spatiotemporal landscape in mouse and human across several time points, involving gametogenesis, preimplantation, even fetus and neonate, and each tissue involves various cell types. To characterize signatures involved in the tissue, cell, genome, gene and protein levels during mammalian embryogenesis, analysis tools on these five scales were developed. Additionally, we proposed EmRanger to deliver extensive development-related biological background annotations. Users can utilize these tools to analyze, browse, visualize, and download data owing to the user-friendly interface. EmAtlas is freely accessible at http://bioinfor.imu.edu.cn/ematlas.

Wet-Adhesive On-Skin Sensors Based on Metal-Organic Frameworks for Wireless Monitoring of Metabolites in Sweat.

Metal-organic frameworks (MOFs) with well-defined porous structures and tailored functionalities have been widely used in chemical sensing. However, the integration of MOFs with flexible electronic devices for wearable sensing is challenging because of their low electrical conductivity and fragile mechanical properties. Herein, a wearable sweat sensor for metabolite detection is presented by integrating an electrically conductive Ni-MOF with a flexible nanocellulose substrate. The MOF-based layered film sensor with inherent conductivity, highly porous structure, and active catalytic properties enables the selective and accurate detection of vitamin C and uric acid. More importantly, the lightweight sensor can conformably self-adhere to sweaty skin and exhibits high water-vapor permeability. Furthermore, a wireless epidermal nutrition tracking system for the in situ monitoring of the dynamics of sweat vitamin C is demonstrated, the results of which are comparable to those tested by high-performance liquid chromatography. This study opens a new avenue for integrating MOFs as the active layer in wearable electronic devices and holds promise for the future development of high-performance electronics with enhanced sensing, energy production, and catalytic capabilities through the implementation of multifunctional MOFs.

mbtD and celA1 association with ethambutol resistance in Mycobacterium tuberculosis: A multiomics analysis.

Ethambutol (EMB) is a first-line antituberculosis drug currently being used clinically to treat tuberculosis. Mutations in the embCAB operon are responsible for EMB resistance. However, the discrepancies between genotypic and phenotypic EMB resistance have attracted much attention. We induced EMB resistance in Mycobacterium tuberculosis in vitro and used an integrated genome-methylome-transcriptome-proteome approach to study the microevolutionary mechanism of EMB resistance. We identified 509 aberrantly methylated genes (313 hypermethylated genes and 196 hypomethylated genes). Moreover, some hypermethylated and hypomethylated genes were identified using RNA-seq profiling. Correlation analysis revealed that the differential methylation of genes was negatively correlated with transcription levels in EMB-resistant strains. Additionally, two hypermethylated candidate genes (mbtD and celA1) were screened by iTRAQ-based quantitative proteomics analysis, verified by qPCR, and corresponded with DNA methylation differences. This is the first report that identifies EMB resistance-related genes in laboratory-induced mono-EMB-resistant M. tuberculosis using multi-omics profiling. Understanding the epigenetic features associated with EMB resistance may provide new insights into the underlying molecular mechanisms.

Nicotinamide mononucleotide improves spermatogenic function in streptozotocin-induced diabetic mice via modulating the glycolysis pathway.

Spermatogenic dysfunction is one of the major secondary complications of diabetes; however, the underlying mechanisms remain ill-defined, and there is no available drug or strategy for the radical treatment of diabetic spermatogenic dysfunction. Therefore, the objective of this study is to investigate the protective effects of nicotinamide mononucleotide (NMN) on testicular spermatogenic function in streptozotocin (STZ)-induced diabetic mice. The results show that oral administration of NMN significantly increases the body and testis weight and the number of sperms. Moreover, the abnormal sperm count and the rate of sperm malformation are significantly decreased compared with the saline-treated diabetic mice. Histological analysis reveals that NMN treatment significantly increases the area and diameter of seminiferous tubules, accompanied by an increased number of spermatogenic cells and sperms. Immunohistochemistry and qRT-PCR results show that NMN increases Bcl-2 expression and decreases Bax expression in the testis. NMN also increases the protein expression of Vimentin and the mRNA expressions of WT1 and GATA4. In addition, qRT-PCR, western blot analysis and immunohistochemistry results also show that NMN increases the expressions of glycolysis-related rate-limiting enzymes including HK2, PKM2, and LDHA. In summary, this study demonstrates the protective effects of NMN on the testis in an STZ-induced diabetic mice model. NMN exerts its protective effects via reducing spermatogenic cell apoptosis by regulating glycolysis of Sertoli cells in diabetic mice. This study provides an experimental basis for the future clinical application of NMN in diabetes-induced spermatogenic dysfunction.

iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences.

Lactic acid bacteria consortia are commonly present in food, and some of these bacteria possess probiotic properties. However, discovery and experimental validation of probiotics require extensive time and effort. Therefore, it is of great interest to develop effective screening methods for identifying probiotics. Advances in sequencing technology have generated massive genomic data, enabling us to create a machine learning-based platform for such purpose in this work. This study first selected a comprehensive probiotics genome dataset from the probiotic database (PROBIO) and literature surveys. Then, k-mer (from 2 to 8) compositional analysis was performed, revealing diverse oligonucleotide composition in strain genomes and apparently more probiotic (P-) features in probiotic genomes than non-probiotic genomes. To reduce noise and improve computational efficiency, 87 376 k-mers were refined by an incremental feature selection (IFS) method, and the model achieved the maximum accuracy level at 184 core features, with a high prediction accuracy (97.77%) and area under the curve (98.00%). Functional genomic analysis using annotations from gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Rapid Annotation using Subsystem Technology (RAST) databases, as well as analysis of genes associated with host gastrointestinal survival/settlement, carbohydrate utilization, drug resistance and virulence factors, revealed that the distribution of P-features was biased toward genes/pathways related to probiotic function. Our results suggest that the role of probiotics is not determined by a single gene, but by a combination of k-mer genomic components, providing new insights into the identification and underlying mechanisms of probiotics. This work created a novel and free online bioinformatic tool, iProbiotics, which would facilitate rapid screening for probiotics.

Caveolin-1 Is Essential for the Improvement of Insulin Sensitivity through AKT Activation during Glargine Treatment on Diabetic Mice.

Insulin treatment was confirmed to reduce insulin resistance, but the underlying mechanism remains unknown. Caveolin-1 (Cav-1) is a functional protein of the membrane lipid rafts, known as caveolae, and is widely expressed in mammalian adipose tissue. There is increasing evidence that show the involvement of Cav-1 in the AKT activation, which is responsible for insulin sensitivity. Our aim was to investigate the effect of Cav-1 depletion on insulin sensitivity and AKT activation in glargine-treated type 2 diabetic mice. Mice were exposed to a high-fat diet and subject to intraperitoneal injection of streptozotocin to induce diabetes. Next, glargine was administered to treat T2DM mice for 3 weeks (insulin group). The expression of Cav-1 was then silenced by injecting lentiviral-vectored short hairpin RNA (shRNA) through the tail vein of glargine-treated T2DM mice (CAV1-shRNA group), while scramble virus injection was used as a negative control (Ctrl-shRNA group). The results showed that glargine was able to upregulate the expression of PI3K and activate serine phosphorylation of AKT through the upregulation of Cav-1 expression in paraepididymal adipose tissue of the insulin group. However, glargine treatment could not activate AKT pathway in Cav-1 silenced diabetic mice. These results suggest that Cav-1 is essential for the activation of AKT and improving insulin sensitivity in type 2 diabetic mice during glargine treatment.

Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.

Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI. Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis. Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

Association of the Monocyte-to-High-Density Lipoprotein Cholesterol Ratio With Diabetic Retinopathy.

Background: Chronic inflammation in type 2 diabetes mellitus (T2DM) is an essential contributor to the development of diabetic retinopathy (DR). The monocyte-to-high-density lipoprotein cholesterol (HDL-C) ratio (MHR) is a novel and simple measure related to inflammatory and oxidative stress status. However, little is known regarding the role of the MHR in evaluating the development of DR. Methods: A total of 771 patients with T2DM and 607 healthy controls were enrolled in this cross-sectional study. MHR determination and eye examination were performed. The association of MHR with the prevalence of DR in T2DM patients was analyzed. Results: The MHR in patients with DR was significantly higher than that in both non-DR diabetic patients (P < 0.05) and healthy controls (P < 0.01). No significance was observed in the MHR of different DR severity grades. Moreover, the MHR was similar between patients with non-macular oedema and those with macular oedema. Logistic regression analysis demonstrated that MHR was independently associated with the prevalence of DR in diabetic patients [odds ratio (OR) = 1.438, 95% confidence interval (CI): 1.249-1.655, P < 0.01]. After additional stratification by HbA1c level and diabetic duration, the MHR was still independently associated with the prevalence of DR. Conclusions: Our study suggests that the MHR can be used as a marker to indicate the prevalence of DR in patients with T2DM.