Hypoxia inducible factor-1α responds to freezing, anoxia and dehydration stresses in a freeze-tolerant frog.

The wood frog, Rana sylvatica (aka Lithobates sylvaticus) is the main model for studies of natural freeze tolerance among amphibians living in seasonally cold climates. During freezing, ∼65% of total body water can be converted to extracellular ice and this imposes both dehydration and hypoxia/anoxia stresses on cells. The current study analyzed the responses of the alpha subunit of the hypoxia-inducible transcription factor (HIF-1), a crucial oxygen-sensitive regulator of gene expression, to freezing, anoxia or dehydration stresses, examining six tissues of wood frogs (liver, skeletal muscle, brain, heart, kidney, skin). RT-PCR revealed a rapid elevation hif-1α transcript levels within 2 h of freeze initiation in both liver and brain and elevated levels of both mRNA and protein in liver and muscle after 24 h frozen. However, both transcript and protein levels reverted to control values after thawing except for HIF-1 protein in liver that dropped to ∼60% of control. Independent exposures of wood frogs to anoxia or dehydration stresses (two components of freezing) also triggered upregulation of hif-1α transcripts and/or HIF-1α protein in liver and kidney with variable responses in other tissues. The results show active modulation of HIF-1 in response to freezing, anoxia and dehydration stresses and implicate this transcription factor as a contributor to the regulation of metabolic adaptations needed for long term survival of wood frogs in the ischemic frozen state.

Identifying barriers to sustainable apple production: A stakeholder perspective.

Apple is one of the most important cash crops in China. However, negative economic, environmental and social impacts are associated with its production. This study aims to apply a holistic systems perspective to understand existing problems associated with apple production in China and use this information to improve its sustainability. A structured survey was administered to farmers (n = 245) in Shandong and Shanxi provinces, combined with semi-structured interviews with apple supply chain stakeholders (n = 25). Themes, dimensions and relationships were identified based on an inductive thematic analysis of interview data, and then triangulated against the survey data. Interpretive Structural Modelling and Cross-Impact Matrix Multiplication Applied to Classification methods were applied to investigate interrelationships and effects of the elicited elements within the system. The results indicated that various environmental, economic and social problems are associated with apple production in China, including environmental and health risks associated with synthetic input applications, yield instability, deterioration of apple quality, farmers' uncertainty about accessing routes to market, and the ageing farming workforce. The interaction of socio-economic and supply chain issues has contributed to the system "lock-in" to unsustainable practices within the apple production system. Existing agricultural policies were ineffective as they did not include policy leverage to mitigate the multiple factors driving lock-in to unsustainable practices within the system. The research has provided evidence to enable policymakers to develop effective and targeted strategies to facilitate sustainable production within the apple production system. In particular, the future policy mix should consider the entirety of the food system including perspectives and requirements of different stakeholders. The three-stage approach applied has demonstrated its feasibility of investigating sustainability issues facing a particular industry within a specific cultural and policy context.

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network.

The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.

A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration.

Synthetic Aperture Radar (SAR) scene classification is challenging but widely applied, in which deep learning can play a pivotal role because of its hierarchical feature learning ability. In the paper, we propose a new scene classification framework, named Feature Recalibration Network with Multi-scale Spatial Features (FRN-MSF), to achieve high accuracy in SAR-based scene classification. First, a Multi-Scale Omnidirectional Gaussian Derivative Filter (MSOGDF) is constructed. Then, Multi-scale Spatial Features (MSF) of SAR scenes are generated by weighting MSOGDF, a Gray Level Gradient Co-occurrence Matrix (GLGCM) and Gabor transformation. These features were processed by the Feature Recalibration Network (FRN) to learn high-level features. In the network, the Depthwise Separable Convolution (DSC), Squeeze-and-Excitation (SE) Block and Convolution Neural Network (CNN) are integrated. Finally, these learned features will be classified by the Softmax function. Eleven types of SAR scenes obtained from four systems combining different bands and resolutions were trained and tested, and a mean accuracy of 98.18% was obtained. To validate the generality of FRN-MSF, five types of SAR scenes sampled from two additional large-scale Gaofen-3 and TerraSAR-X images were evaluated for classification. The mean accuracy of the five types reached 94.56%; while the mean accuracy for the same five types of the former tested 11 types of scene was 96%. The high accuracy indicates that the FRN-MSF is promising for SAR scene classification without losing generality.

Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery.

This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.

A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping.

To analyze the influence factors of hyperspectral remote sensing data processing, and quantitatively evaluate the application capability of hyperspectral data, a combined evaluation model based on the physical process of imaging and statistical analysis was proposed. The normalized average distance between different classes of ground cover is selected as the evaluation index. The proposed model considers the influence factors of the full radiation transmission process and processing algorithms. First- and second-order statistical characteristics (mean and covariance) were applied to calculate the changes for the imaging process based on the radiation energy transfer. The statistical analysis was combined with the remote sensing process and the application performance, which consists of the imaging system parameters and imaging conditions, by building the imaging system and processing models. The season (solar zenith angle), sensor parameters (ground sampling distance, modulation transfer function, spectral resolution, spectral response function, and signal to noise ratio), and number of features were considered in order to analyze the influence factors of the application capability level. Simulated and real data collected by Hymap in the Dongtianshan area (Xinjiang Province, China), were used to estimate the proposed model's performance in the application of mineral mapping. The predicted application capability of the proposed model is consistent with the theoretical analysis.

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery.

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively.

Monitoring Highway Stability in Permafrost Regions with X-band Temporary Scatterers Stacking InSAR.

Interferograms with short wavelength (e.g., X-band) are usually prone to temporal decorrelation in permafrost regions, leading to the unavailability of sufficient high-coherence interferograms for performing conventional time series InSAR analysis. This paper proposes the utilization of temporary scatterers for the stacking InSAR method, thus enabling extraction of subsidence in a permafrost region with limited SAR images and limited high-coherence interferograms. Such method is termed as the temporary scatterers stacking InSAR (TSS-InSAR). Taking the Gonghe-Yushu highway (about 30 km), part of G214 National Highway in Qinghai province (in a permafrost region), as a case study, this TSS-InSAR approach was demonstrated in detail and implemented. With 10 TerraSAR-X images acquired during the period from May 2015 to August 2015, the subsidence along this highway was extracted. In this case the lack of a consistent number of SAR acquisitions limits the possibility to perform other conventional time series InSAR analysis. The results show that the middle part of this highway is in the thermokarst and seasonal frozen soil area, and its accumulated subsidence reach up to 10 cm in 110 days. The thawing phenomena is still the main reason for the instability of highway. The results demonstrate that the TSS-InSAR method can effectively extract the subsidence information in a challenging scenario with limited X-band SAR images and limited high-coherence interferograms, where other time series InSAR-based techniques cannot be applied in a simple way.

Novel Method to Predict In Vivo Liver-to-Plasma Kpuu for OATP Substrates Using Suspension Hepatocytes.

The ability to predict human liver-to-plasma unbound partition coefficient (Kpuu) is of great importance to estimate unbound liver concentration, develop PK/PD relationships, predict efficacy and toxicity in the liver, and model the drug-drug interaction potential for drugs that are asymmetrically distributed into the liver. A novel in vitro method has been developed to predict in vivo Kpuu with good accuracy using cryopreserved suspension hepatocytes in InVitroGRO HI media with 4% BSA. Validation was performed using six OATP substrates with rat in vivo Kpuu data from i.v. infusion studies where a steady state was achieved. Good in vitro-in vivo correlation (IVIVE) was observed as the in vitro Kpuu values were mostly within 2-fold of in vivo Kpuu Good Kpuu IVIVE in human was also observed with in vivo Kpuu data of dehydropravastatin from positron emission tomography and in vivo Kpuu data from PK/PD modeling for pravastatin and rosuvastatin. Under the specific Kpuu assay conditions, the drug-metabolizing enzymes and influx/efflux transporters appear to function at physiologic levels. No scaling factors are necessary to predict in vivo Kpuu from in vitro data. The novel in vitro Kpuu method provides a useful tool in drug discovery to project in vivo Kpuu.

Healthcare seeking behaviour among Chinese elderly.

Purpose The Chinese population is rapidly ageing before they are rich. The purpose of this paper is to describe healthcare seeking behaviour and the critical factors associated with healthcare seeking behaviour. Design/methodology/approach Using a purposive sampling method, the authors recruited 44 adults aged 60 years or older from three provinces, representing the developed (Shanghai), undeveloped (Ningxia) regions and the regions in between (Hubei). From July to September 2008, using a semi-structured guide, the authors interviewed participants in focus group discussions. Findings The healthcare needs for chronic and catastrophic diseases were high; however, the healthcare demands were low and healthcare utilizations were even lower owing to the limited accessibility to healthcare services, particularly, in underdeveloped rural areas. "Too expensive to see a doctor" was a prime complaint, explaining substantial discrepancies between healthcare needs, demands and use. Care seeking behaviour varied depending on insurance availability, perceived performance, particularly hospital services, and prescription medications. Participants consistently rated increasing healthcare accessibility as a high priority, including offering financial aid, and improving service convenience. Improving social security fairness was the first on the elderly's wish list. Originality/value Healthcare demand and use were lower than needs, and were influenced by multiple factors, primarily, service affordability and efficiency, perceived performance and hospital service quality.

Determination of Unbound Partition Coefficient and in Vitro-in Vivo Extrapolation for SLC13A Transporter-Mediated Uptake.

Unbound partition coefficient (Kpuu) is important to an understanding of the asymmetric free drug distribution of a compound between cells and medium in vitro, as well as between tissue and plasma in vivo, especially for transporter-mediated processes. Kpuu was determined for a set of compounds from the SLC13A family that are inhibitors and substrates of transporters in hepatocytes and transporter-transfected cell lines. Enantioselectivity was observed, with (R)-enantiomers achieving much higher Kpuu (>4) than the (S)-enantiomers (<1) in human hepatocytes and SLC13A5-transfected human embryonic 293 cells. The intracellular free drug concentration correlated directly with in vitro pharmacological activity rather than the nominal concentration in the assay because of the high Kpuu mediated by SLC13A5 transporter uptake. Delivery of the diacid PF-06649298 directly or via hydrolysis of the ethyl ester prodrug PF-06757303 resulted in quite different Kpuu values in human hepatocytes (Kpuu of 3 for diacid versus 59 for prodrug), which was successfully modeled on the basis of passive diffusion, active uptake, and conversion rate from ester to diacid using a compartmental model. Kpuu values changed with drug concentrations; lower values were observed at higher concentrations possibly owing to a saturation of transporters. Michaelis-Menten constant (Km) of SLC13A5 was estimated to be 24 µM for PF-06649298 in human hepatocytes. In vitro Kpuu obtained from rat suspension hepatocytes supplemented with 4% fatty acid free bovine serum albumin showed good correlation with in vivo Kpuu of liver-to-plasma, illustrating the potential of this approach to predict in vivo Kpuu from in vitro systems.

LSM14A inhibits porcine reproductive and respiratory syndrome virus (PRRSV) replication by activating IFN-ß signaling pathway in Marc-145.

Porcine reproductive and respiratory syndrome virus (PRRSV) is considered as a significant contributor to porcine reproductive and respiratory syndrome, one of the most economically important diseases for the pig industry worldwide. Emerging evidence indicates that pattern recognition receptors play key roles in recognizing pathogen-associated molecular patterns. In the present study, we investigated the effects of a novel pattern recognition receptor LSM14A in regulating PRRSV replication. Results in Marc-145 cells and porcine alveolar macrophages (PAMs) indicated that overexpression of porcine LSM14A effectively inhibited the replication of PRRSV, and knockdown of LSM14A by siRNA enhanced the replication of PRRSV. Mechanistically, LSM14A up-regulated the activities of IFN-ß and ISRE promoters, enhanced the production of IFN-ß, RIG-I, and ISGs, and inhibited the production of the inflammatory cytokines of TNF-α and IL-6 mRNA. Additionally, the expression pattern of LSM14A during the infection of PRRSV in Tongcheng and Large White pigs was suppressed by the PRRSV challenge. Taken together, our results suggest that LSM14A is an important PRR that inhibits PPRSV replication by inducing IFN-ß signaling and restraining inflammatory responses. Furthermore, the down-regulation of LSM14A by PRRSV might represent an important mechanism by which PRRSV invades the host. Our study sheds light on the possibility of developing a new strategy to control this disease.

Partial rupture of a locked patch of the Sumatra megathrust during the 2007 earthquake sequence.

The great Sumatra-Andaman earthquake and tsunami of 2004 was a dramatic reminder of the importance of understanding the seismic and tsunami hazards of subduction zones. In March 2005, the Sunda megathrust ruptured again, producing an event of moment magnitude (M(w)) 8.6 south of the 2004 rupture area, which was the site of a similar event in 1861 (ref. 6). Concern was then focused on the Mentawai area, where large earthquakes had occurred in 1797 (M(w) = 8.8) and 1833 (M(w) = 9.0). Two earthquakes, one of M(w) = 8.4 and, twelve hours later, one of M(w) = 7.9, indeed occurred there on 12 September 2007. Here we show that these earthquakes ruptured only a fraction of the area ruptured in 1833 and consist of distinct asperities within a patch of the megathrust that had remained locked in the interseismic period. This indicates that the same portion of a megathrust can rupture in different patterns depending on whether asperities break as isolated seismic events or cooperate to produce a larger rupture. This variability probably arises from the influence of non-permanent barriers, zones with locally lower pre-stress due to the past earthquakes. The stress state of the portion of the Sunda megathrust that had ruptured in 1833 and 1797 was probably not adequate for the development of a single large rupture in 2007. The moment released in 2007 amounts to only a fraction both of that released in 1833 and of the deficit of moment that had accumulated as a result of interseismic strain since 1833. The potential for a large megathrust event in the Mentawai area thus remains large.

InSAR and machine learning reveal new understanding of coastal subsidence risk in the Yellow River Delta, China.

Coastal subsidence is a geological disaster that has devastating consequences. However, an accurate understanding of its risks involves more than simply assessing the amount or rate of land subsidence. The existing methods used to evaluate geological disaster risks depend on extensive data collection, entail substantial workloads, suffer from error estimation challenges, and lack regional adaptability. These limitations prevent us from fully understanding coastal subsidence risks in estuarine deltas. Therefore, in this study, we propose a new subsidence risk assessment method that addresses the challenges of traditional geological risk assessments in terms of spatial coverage, spatiotemporal resolution, and data collection difficulty. First, Sentinel-1 multitemporal interferometric synthetic aperture radar (MT-InSAR) and cluster analysis were used to estimate the subsidence hazards. Subsequently, Landsat-8 imagery and a random forest (RF) classifier were used to obtain land use and land cover (LULC), and the analytic hierarchy process (AHP) was used to obtain settlement vulnerability. Thereafter, subsidence susceptibility was derived from the sediment layer thickness. By combining subsidence hazard, vulnerability, and susceptibility, the first subsidence risk map with a 30 m resolution was generated. The results showed that 4.54 % of the Yellow River Delta (YRD) area was high-risk, 8.75 % was medium-risk, and 10.14 % was low-risk. Notably, the risk map shows a clear overlap between high-risk and saltwater mining areas in the YRD. The proposed method is expected to improve our understanding of the coastal subsidence risk in estuarine deltas. Considering that the risk in high-value economic areas in the YRD is increasing, whereas the risk in low-value economic areas may change owing to human activity, early preventive measures are required.

Erratum: [Corrigendum] Relationship between changes in mitochondrial function and hippocampal neuronal apoptosis after recurrent convulsion during developmental stage.

[This corrects the article DOI: 10.3892/etm.2018.6147.].

Chronic smoke exposure induces rheumatoid factor and anti-heat shock protein 70 autoantibodies in susceptible mice and humans with lung disease.

The impact of cigarette smoke (CS), a risk factor for rheumatoid arthritis (RA), on sauto-antibody production was studied in humans and mice with and without chronic lung disease (LD). Rheumatoid factor (RF), anti-cyclic citrullinated peptides (CCPs), and anti-HSP70 autoantibodies were measured in several mouse strains and in cohorts of smokers and nonsmokers with and without autoimmune disease. Chronic smoking-induced RFs in AKR/J mice, which are most susceptible to LD. RFs were identified in human smokers, preferentially in those with LD. Anti-HSP70 auto-antibodies were identified in CS-exposed AKR/J mice but not in ambient air exposed AKR/J controls. Whereas inflammation could induce anti-HSP70 IgM, smoke exposure promoted the switch to anti-HSP70 IgG autoantibodies. Elevated anti-CCP autoantibodies were not detected in CS-exposed mice or smokers. AKR/J splenocytes stimulated in vitro by immune complexes (ICs) of HSP70/anti-HSP70 antibodies produced RFs. The CD91 scavenger pathway was required as anti-CD91 blocked the HSP70-IC-induced RF response. Blocking Toll-like receptors did not influence the HSP70-IC-induced RFs. These studies identify both anti-HSP70 and RFs as serological markers of smoke-related LD in humans and mice. Identification of these autoantibodies could suggest a common environmental insult, namely CS, in a number of different disease settings.

The T-cell immunoglobulin and mucin domain (Tim) gene family in asthma, allergy, and autoimmunity.

The T-cell immunoglobulin and mucin domain (Tim) gene family is a relatively newly discovered group of molecules with a conserved structure and important immunologic functions. Tim molecules express on many types of immune cells including T cells, B cells, dendritic cells, macrophages, and mast cells that have been shown to be involved in asthma, allergic rhinitis, food allergy, and autoimmunity. Tim-1-Tim-4 interaction promotes Th2 cytokine responses, and blocking this interaction can decrease airway inflammation in asthma and in allergic rhinitis. Tim-3 stimulates mast cells to produce Th2 cytokines, and anti-Tim-3 is able to dampen asthmatic inflammation. The Tim-3 ligand was shown to be greatly enhanced on intestinal epithelial cells in patients with food allergy and Tim-4 may play a role in maintaining oral tolerance and prevention of food allergy. Tim-3 deregulation plays a role in the pathogenesis of multiple sclerosis. Increased Tim-1 expression has been shown in mononuclear cells from systemic lupus erythematosus patients and Tim-3 may be involved in a protective role in rheumatoid arthritis.

Asthma in the geriatric population.

Asthma is associated with significant morbidity and mortality in the geriatric population. Despite the rising incidence of asthma in people >65 years of age, the diagnosis is frequently missed in this population. Factors that contribute to this include respiratory changes caused by aging, immunosenescence, lack of symptoms, polypharmacy, clinician unawareness, and lack of evidence-based guidelines for diagnosis and management that target this population. This literature review addresses the current state of research in this area. Age-related changes influence the pathophysiology and role of allergy in elderly asthmatic patients. Specific obstacles encountered in caring for these patients are discussed. Asthma in the elderly and younger population are compared. We conclude with a broad set of goals to guide future management driven by a multidiscipline approach.

Relationship of binding-site occupancy, transthyretin stabilisation and disease modification in patients with tafamidis-treated transthyretin amyloid cardiomyopathy.

BACKGROUND: Tafamidis inhibits progression of transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) by binding TTR tetramer and inhibiting dissociation to monomers capable of denaturation and deposition in cardiac tissue. While the phase 3 ATTR-ACT trial demonstrated the efficacy of tafamidis, the degree to which the approved dose captures the full potential of the mechanism has yet to be assessed. METHODS: We developed a model of dynamic TTR concentrations in plasma to relate TTR occupancy by tafamidis to TTR stabilisation. We then developed population pharmacokinetic-pharmacodynamic models to characterise the relationship between stabilisation and measures of disease progression. RESULTS: Modelling individual patient data of tafamidis exposure and increased plasma TTR confirmed that single-site binding provides complete tetramer stabilisation in vivo. The approved dose was estimated to reduce unbound TTR tetramer by 92%, and was associated with 53%, 56% and 49% decreases in the rate of change in NT-proBNP, KCCQ-OS, and six-minute walk test disease progression measures, respectively. Simulating complete TTR stabilisation predicted slightly greater reductions of 58%, 61% and 54%, respectively. CONCLUSIONS: These findings support the value of TTR stabilisation as a clinically beneficial treatment option in ATTR-CM and the ability of tafamidis to realise nearly the full therapeutic benefit of this mechanism. CLINICALTRIALS.GOV IDENTIFIER: NCT01994889.

Correlation analysis between plasma fibrinogen and nerve electrophysiological changes in type 2 diabetic peripheral neuropathy.

INTRODUCTION: The aim of the study was to investigate the pathogenesis of diabetic peripheral neuropathy (DPN) and the value of fibrinogen (FIB) in the early diagnosis of DPN. MATERIAL AND METHODS: A total of 121 patients with type 2 diabetes mellitus (T2DM) and DPN hospitalized in the Endocrinology Department of the 923 Hospital of the People's Liberation Army of China were randomly selected between May and October 2020 and divided into a T2DM asymptomatic (no peripheral neuropathy-related symptoms) group (66 cases) and a T2DM symptomatic group (55 cases) according to the presence or absence of clinical neurological symptoms and signs. Forty healthy volunteers were selected as a normal control group. In addition to plasma FIB and nerve electrophysiological tests, all included subjects were electrophysiologically tested for nerve conduction velocity (NCV), terminal motor latency (DML), sensory nerve action potential (SNAP) amplitude, and compound muscle action potential (CMAP) amplitude. RESULTS: Compared with the control group, NCV was slowed down in T2DM patients, DML was prolonged, and the amplitude of CMAP and SNAP were decreased. Compared with asymptomatic T2DM patients, symptomatic patients had slower NCV, longer DML, lower CMAP amplitude of median nerve, ulnar nerve and tibial nerve, and significantly lower SNAP amplitude of median nerve and ulnar nerve. CMAP amplitudes were decreased, and median and ulnar nerve SNAP amplitudes were also significantly decreased ( p < 0.05). The plasma FIB concentration of asymptomatic patients with T2DM was higher than that of the control group, and the plasma FIB concentration of symptomatic patients with T2DM was higher than that of asymptomatic patients with T2DM ( p < 0.01). The NCV and DML of asymptomatic patients with T2DM slowed down and prolonged as the FIB level increased; the NCV of T2DM symptomatic patients also slowed down as FIB increased, and median and ulnar nerve DML increased as FIB increased. There was no correlation between NCV and DML and the plasma FIB level in the control group. SNAP amplitudes of symptomatic and asymptomatic patients with T2DM decreased as plasma FIB increased, while CMAP amplitudes of the tibial nerve and the T2DM symptomatic ulnar nerve decreased as FIB increased in the control group. CONCLUSIONS: FIB may be a contributing factor for diabetic neuropathy and could be used as an indicator in the early screening and diagnosis of peripheral neuropathy in patients with T2DM.