Corrigendum: Association between napping and type 2 diabetes mellitus.

[This corrects the article DOI: 10.3389/fendo.2024.1294638.].

Association between napping and type 2 diabetes mellitus.

As the incidence of type 2 diabetes mellitus (T2DM) is increasing rapidly and its consequences are severe, effective intervention and prevention, including sleep-related interventions, are urgently needed. As a component of sleep architecture, naps, alone or in combination with nocturnal sleep, may influence the onset and progression of T2DM. Overall, napping is associated with an increased risk of T2DM in women, especially in postmenopausal White women. Our study showed that napping >30 minutes (min) increased the risk of T2DM by 8-21%. In addition, non-optimal nighttime sleep increases T2DM risk, and this effect combines with the effect of napping. For nondiabetic patients, napping >30 min could increase the risks of high HbA1c levels and impaired fasting glucose (IFG), which would increase the risk of developing T2DM later on. For diabetic patients, prolonged napping may further impair glycemic control and increase the risk of developing diabetic complications (e.g., diabetic nephropathy) in the distant future. The following three mechanisms are suggested as interpretations for the association between napping and T2DM. First, napping >30 min increases the levels of important inflammatory factors, including interleukin 6 and C-reactive protein, elevating the risks of inflammation, associated adiposity and T2DM. Second, the interaction between postmenopausal hormonal changes and napping further increases insulin resistance. Third, prolonged napping may also affect melatonin secretion by interfering with nighttime sleep, leading to circadian rhythm disruption and further increasing the risk of T2DM. This review summarizes the existing evidence on the effect of napping on T2DM and provides detailed information for future T2DM intervention and prevention strategies that address napping.

Trajectories tracking of maternal and neonatal health in eastern China from 2010 to 2021: A multicentre cross-sectional study.

Background: China's fertility policy has dramatically changed in the past decade with the successive promulgation of the partial two-child policy, universal two-child policy and three-child policy. The trajectories of maternal and neonatal health accompanied the changes in fertility policy are unknown. Methods: We obtained data of 280 203 deliveries with six common pregnancy complications and thirteen perinatal outcomes between 2010 and 2021 in eastern China. The average annual percent change (AAPC) was calculated to evaluated the temporal trajectories of obstetric characteristics and adverse outcomes during this period. Then, the autoregressive integrated moving average (ARIMA) models were constructed to project future trend of obstetric characteristics and outcomes until 2027. Results: The proportion of advanced maternal age (AMA), assisted reproduction technology (ART) treatment, gestational diabetes mellitus (GDM), anaemia, thrombocytopenia, thyroid dysfunction, oligohydramnios, placental abruption, small for gestational age (SGA) infants, and congenital malformation significantly increased from 2010 to 2021. However, the placenta previa, large for gestational age (LGA) infants and stillbirth significantly decreased during the same period. The AMA and ART treatment were identified as independent risk factors for the uptrends of pregnancy complications and adverse perinatal outcomes. The overall caesarean section rate remained above 40%. Importantly, among multiparas, a previous caesarean section was found to be associated with a significantly reduced risk of hypertensive disorders of pregnancy (HDP), premature rupture of membranes (PROM), placenta previa, placental abruption, perinatal asphyxia, LGA infants, stillbirths, and preterm births. In addition, the ARIMA time series models predicted increasing trends in the ART treatment, GDM, anaemia, thrombocytopenia, postpartum haemorrhage, congenital malformation, and caesarean section until 2027. Conversely, a decreasing trend was predicted for HDP, PROM, and placental abruption premature, LGA infants, SGA infants, perinatal asphyxia, and stillbirth. Conclusions: Maternal and neonatal adverse outcomes became more prevalent from 2010 to 2021 in China. Maternal age and ART treatment were independent risk factors for adverse obstetric outcomes. The findings offered comprehensive trajectories for monitoring pregnancy complications and perinatal outcomes in China, and provided robust intervention targets in obstetric safety. The development of early prediction models and the implementation of prevention efforts for adverse obstetric events are necessary to enhance obstetric safety.

A neutrophil extracellular trap-related risk score predicts prognosis and characterizes the tumor microenvironment in multiple myeloma.

Multiple myeloma (MM) is a distinguished hematologic malignancy, with existing studies elucidating its interaction with neutrophil extracellular traps (NETs), which may potentially facilitate tumor growth. However, systematic investigations into the role of NETs in MM remain limited. Utilizing the single-cell dataset GSE223060, we discerned active NET cell subgroups, namely neutrophils, monocytes, and macrophages. A transcriptional trajectory was subsequently constructed to comprehend the progression of MM. Following this, an analysis of cellular communication in MM was conducted with a particular emphasis on neutrophils, revealing an augmentation in interactions albeit with diminished strength, alongside abnormal communication links between neutrophils and NK cells within MM samples. Through the intersection of differentially expressed genes (DEGs) between NET active/inactive cells and MM versus healthy samples, a total of 316 genes were identified. This led to the development of a 13-gene risk model for prognostic prediction based on overall survival, utilizing transcriptomics dataset GSE136337. The high-risk group manifested altered immune infiltration and heightened sensitivity to chemotherapy. A constructed nomogram for predicting survival probabilities demonstrated encouraging AUCs for 1, 3, and 5-year survival predictions. Collectively, our findings unveil a novel NET-related prognostic signature for MM, thereby providing a potential avenue for therapeutic exploration.

Tracking the Dynamic Neural Connectivity via Conjugate Gradient Optimization.

Neural connectivity describes how neuron populations coordinate and create cognitive and behavioral functions. Neural connectivity performs dynamics where its population spiking responses to stimuli or intention change over time. Brain-machine interface (BMI) provides a framework for studying dynamical neural connectivity. In BMI, point process is a powerful technique in analyzing the single neuronal tuning. And generalized linear mode (GLM) as an encoding model can incorporate the tuning in kinematics and the neural connectivity. Quantification and tracking of dynamic neural connectivity can contribute to the elucidation of the generation of brain functions in a computational way. However, most of the previous work focused on single neuronal adaptation to kinematics. When a neuron is significantly modulated by some other neurons in some tasks, the shape of the log likelihood function for single neuronal observations can be narrowed in some dimensions. And the existing gradient-based methods are not able to reach the optimum in a fast and adaptive searching way. In this work, to maximize the likelihood of observations and obtain the dynamic neural connectivity tuning parameters, we proposed a conjugate gradient-based encoding model (CGE). We illustrate CGE for likelihood function using the real experimental data under manual control and brain control. The results show that the proposed CGE has better performance in tracking the dynamic neural connectivity tuning parameters and modeling neural encoding.Clinical Relevance- Not directly related.

A novel telomere-related genes model for predicting prognosis and treatment responsiveness in diffuse large B-cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous disease with diverse clinical and molecular features. Telomere maintenance is widely present in tumors, but there is a lack of relevant reports on the role of telomere-related genes (TRGs) in DLBCL. In this study, we used consensus clustering based on TRGs expression to identify two molecular clusters with distinct prognoses and immune cell infiltration. We developed a TRGs scoring model using univariate Cox regression and LASSO regression in the GSE10846 training cohort. DLBCL patients in the high-risk group had a worse prognosis than those in the low-risk group, as revealed by Kaplan-Meier curves. The scoring model was validated in the GSE10846 testing cohort and GSE87371 cohort, respectively. The high-risk group was characterized by elevated infiltration of activated DCs, CD56 dim natural killer cells, myeloid-derived suppressor cells, monocytes, and plasmacytoid DCs, along with reduced infiltration of activated CD4 T cells, Type 2 T helper cells, γδ T cells, NK cells, and neutrophils. Overexpression of immune checkpoints, such as PDCD1, CD274, and LAG3, was observed in the high-risk group. Furthermore, high-risk DLBCL patients exhibited increased sensitivity to bortezomib, rapamycin, AZD6244, and BMS.536924, while low-risk DLBCL patients showed sensitivity to cisplatin and ABT.263. Using RT-qPCR, we found that three protective model genes, namely TCEAL7, EPHA4, and ELOVL4, were down-regulated in DLBCL tissues compared with control tissues. In conclusion, our novel TRGs-based model has great predictive value for the prognosis of DLBCL patients and provides a promising direction for treatment optimization.

The status of virtual simulation experiments in medical education in China: based on the national virtual simulation experiment teaching Center (iLAB-X).

BACKGROUND: Virtual simulation experiments have been rapidly applied to medical education curricula in recent years. China constructed a national virtual simulation experimental teaching center (iLAB-X), and this platform covered almost all of the virtual simulation experiment curricula of domestic colleges or universities. We aimed to comprehensively assess the characteristics and usages of virtual simulation experiments in medical education based on iLAB-X. METHODS: A total of 480 virtual simulation experiment courses had been constructed on iLAB-X (https://www.ilab-x.com/) by December 20, 2022, and the curriculum level, type and design were all searched in this platform. We also conducted an evaluation of curriculum usage and online tests, including the page view, frequency of participation, number of participants, duration of experimental learning and passing rate of the experimental test. RESULTS: The national and provincial high-quality virtual simulation experiment curricula accounted for 33.5% (161/480) and 35.8% (172/480), respectively. The curricula were mainly set as basic practice experiments (46.5%) and synthetic designing experiments (48.8%). Significantly, forensic medicine (100%), public health and preventive medicine (83%) and basic medical sciences (66%) focused on synthetic design experiments. In terms of usage experiments, the average duration of experimental learning was 25 minutes per course, and the average number of participants was just 1257. The average passing (score ≥60) rate of online tests was 80.6%, but the average rate of score ≥ 85 was only 58.5%. In particular, the average page views, the number of participants, the duration of learning and the test passing rate of clinical medicine were relatively low. CONCLUSIONS: The curriculum design features, construction level and utilization rate varied in different medical majors. Virtual simulation experiments are particularly underutilized in clinical medicine. There is a long way for virtual simulation experiments to go to become a supplement or alternative for traditional medical education in the future.

Efficacy and safety of venetoclax combined with hypomethylating agents for relapse of acute myeloid leukemia and myelodysplastic syndrome post allogeneic hematopoietic stem cell transplantation: a systematic review and meta-analysis.

BACKGROUND: Currently, there is no standard treatment for managing relapse in patients with acute myeloid leukemia and myelodysplastic syndrome (AML/MDS) after allogeneic hematopoietic cell transplantation. Venetoclax-based therapies have been increasingly used for treating post-transplantation relapse of AML. The aim of this systematic review and meta-analysis was to evaluate the efficacy and adverse events of Venetoclax combined with hypomethylating agents (HMAs) for AML/MDS relapse post-transplantation. METHODS: We searched PubMed, Web of Science, Excerpta Medica Database, Cochrane Library, and Clinical. gov for eligible studies from the inception to February 2022. The Methodological Index for Non-Randomized Studies was used to evaluate the quality of the included literatures. The inverse variance method calculated the pooled proportion and 95% confidence interval (CI). RESULTS: This meta-analysis included 10 studies involving a total of 243 patients. The pooled complete response and complete response with incomplete blood count recovery rate of Venetoclax combined with HMAs for post-transplantation relapse in AML/MDS was 32% (95% CI, 26-39%, I2 = 0%), with an overall response rate of 48% (95% CI, 39-56%, I2 = 37%). The 6-month survival rate was 42% (95% CI, 29-55%, I2 = 62%) and the 1-year survival rate was 23% (95% CI, 11-38%, I2 = 78%). CONCLUSION: This study demonstrated a moderate benefit of Venetoclax in combination with HMAs for patients with relapsed AML/MDS post-transplantation (including those who have received prior HMAs therapy), and may become one of treatment options in the future. Large-scale prospective studies are needed to confirm the potential benefit from venetoclax combined with HMAs.

Comprehensive landscape of the T and B-cell repertoires of newly diagnosed gestational diabetes mellitus.

This study conducted a high-throughput sequencing analysis of the T- and B- repertoires in the newly diagnosed GDM patients and evaluated the association between abnormal adaptive immunity and GDM. The unique TCR CDR3 clonotypes were mildly decreased in GDM patients, and the similarity of TCR V-J distributions was higher in the GDM group. Moreover, the usages of the V gene and V-J pair and the frequency distributions of some CDR3 amino acids (AAs) both in BCR and TCR were significantly different between groups. Moreover, the cytokines including IL-4, IL-6, IFN-γ and IL-17A were synchronously elevated in the GDM cases. Our findings provide a comprehensive view of BCR and TCR repertoires at newly diagnosed GDM patients, revealing the mild reduction in unique TCRB CDR3 sequences and slight alteration of the V gene, V-J combination and CDR3 (AA) usages of BCR and TCR. This work provides deep insight into the mechanism of maternal adaptive immunity in GDM and provides novel diagnostic biomarkers and potential immunotherapy targets for GDM.

The irreplaceable role of medical massive open online courses in China during the COVID-19 pandemic.

BACKGROUND: Massive open online courses (MOOCs) have become innovative open-learning approach in medical education. This study aimed to evaluate the dynamic changes in the construction and application of medical MOOCs before and after the coronavirus disease 2019 (COVID-19) pandemic in China. METHODS: The dynamic changes of usages about medical MOOCs before and after 2020 were mainly searched on the Smart Education of China Higher Education platform, and the detailed learning profiles and outcome indicators were further analyzed using 40 national first-class medical MOOCs from 'zhihuishu' platform. RESULTS: A total of 2,405 medical MOOCs were exported from the Smart Education of China Higher Education platform, of which 1,313 (54.6%) were launched since 2020. The total and average numbers of participants of 141 national first-class medical MOOCs peaked during the initial spread of COVID-19 in 2020. We further analyzed the dynamic usage of MOOCs from 2018 to 2022 based on 40 national first-class medical MOOCs launched on the 'Zhihuishu' platform. The findings revealed that the number of registered learners (3,240 versus 2,654), questions and answers (27,005 versus 5,116) and students taking the final examination (2,782 versus 1,995) per semester were significantly higher since 2020 compared to these before 2020. Especially, the number of registered learners, registered schools, questions and answers, and students participating in online discussion, taking the unit quiz, taking final examinations and passing final examinations all peaked in the 2020 spring-summer semester. Pearson's correlation analysis found that the number of questions and answers and the number of learners who participated in online discussion were both positively correlated with the number of students who passed the final examination, and the correlation was especially strong since 2020. Moreover, the number of publications on medical MOOC research has soared since 2020 and has maintained a continuous upward trend. CONCLUSIONS: High-quality medical MOOCs have been launched rapidly since the COVID-19 pandemic in China. The number of participants and online interactions of medical MOOCs peaked during the initial spread of COVID-19 in 2020. MOOCs are reliable and valid digital sources that facilitate medical higher education and play irreplaceable roles in emergency management.

Molecular Mechanisms of the Melatonin Receptor Pathway Linking Circadian Rhythm to Type 2 Diabetes Mellitus.

Night-shift work and sleep disorders are associated with type 2 diabetes (T2DM), and circadian rhythm disruption is intrinsically involved. Studies have identified several signaling pathways that separately link two melatonin receptors (MT1 and MT2) to insulin secretion and T2DM occurrence, but a comprehensive explanation of the molecular mechanism to elucidate the association between these receptors to T2DM, reasonably and precisely, has been lacking. This review thoroughly explicates the signaling system, which consists of four important pathways, linking melatonin receptors MT1 or MT2 to insulin secretion. Then, the association of the circadian rhythm with MTNR1B transcription is extensively expounded. Finally, a concrete molecular and evolutionary mechanism underlying the macroscopic association between the circadian rhythm and T2DM is established. This review provides new insights into the pathology, treatment, and prevention of T2DM.

The melatonin receptor 1B gene links circadian rhythms and type 2 diabetes mellitus: an evolutionary story.

Disturbed circadian rhythms have been a risk factor for type 2 diabetes mellitus (T2DM). Melatonin is the major chronobiotic hormone regulating both circadian rhythm and glucose homeostasis. The rs10830963 (G allele) of the melatonin receptor 1B (MTNR1B) gene has the strongest genetic associations with T2DM according to several genome-wide association studies. The MTNR1B rs10830963 G allele is also associated with disturbed circadian phenotypes and altered melatonin secretion, both factors that can elevate the risk of diabetes. Furthermore, evolutionary studies implied the presence of selection pressure and ethnic diversity in MTNR1B, which was consistent with the "thrifty gene" hypothesis in T2DM. The rs10830963 G risk allele is associated with delayed melatonin secretion onset in dim-light and prolonged duration of peak melatonin. This delayed melatonin secretion may help human ancestors adapt to famine or food shortages during long nights and early mornings and avoid nocturnal hypoglycemia but confers susceptibility to T2DM due to adequate energy intake in modern society. We provide new insight into the role of MTNR1B variants in T2DM via disturbed circadian rhythms from the perspective of the "thrifty gene" hypothesis; these data indicate a novel target for the prevention and treatment of susceptible populations with the thrifty genotype.

Ambient air pollution and gestational diabetes mellitus: An updated systematic review and meta-analysis.

OBJECTIVE: We aimed to evaluate the relationship between the composition of particulate matter (PM) and gestational diabetes mellitus (GDM) by a comprehensively review of epidemiological studies. METHODS: We systematically identified cohort studies related to air pollution and GDM risk before February 8, 2023 from six databases (PubMed, Embase, Web of Science Core Collection, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and Chongqing VIP Chinese Science and Technology Periodical databases). We calculated the relative risk (RR) and its 95% confidence intervals (CIs) to assess the overall effect by using a random effects model. RESULTS: This meta-analysis of 31 eligible cohort studies showed that exposure to PM2.5, PM10, SO2, and NO2 was associated with a significantly increased risk of GDM, especially in preconception and first trimester. Analysis of the components of PM2.5 found that the risk of GDM was strongly linked to black carbon (BC) and nitrates (NO3-). Specifically, BC exposure in the second trimester and NO3- exposure in the first trimester elevated the risk of GDM, with the RR of 1.128 (1.032-1.231) and 1.128 (1.032-1.231), respectively. The stratified analysis showed stronger correlations of GDM risk with higher levels of pollutants in Asia, except for PM2.5 and BC, which suggested that the specific composition of particulate pollutants had a greater effect on the exposure-outcome association than the concentration. CONCLUSIONS: Our study found that ambient air pollutant is a critical factor for GDM and further studies on specific particulate matter components should be considered in the future.

Benzodiazepine-Receptor Agonist Utilization in Outpatients with Anxiety Disorder: A Retrospective Study Based on Electronic Healthcare Data from a Large General Tertiary Hospital.

Benzodiazepine-receptor agonists (BZRAs), including benzodiazepines (BZDs) and drugs related to BZDs (Z-drugs), are commonly used for anxiety, but often have side effects. We retrospectively investigated the utilization and prescription characteristics of BZRAs for patients with anxiety disorders in a large tertiary care general hospital between 2018 and 2021, based on electronic healthcare records. We also examined the pattern of simultaneous consumption of multiple BZRA drugs, and the diseases coexisting with anxiety that are associated with this. The numbers of patients and BZRA prescriptions increased over the 4 years. Moreover, 7195 prescriptions from 694 patients contained two or more BZRAs, of which 78.08% contained both BZDs and Z-drugs, 19.78% contained multiple BZDs, and 2.14% contained multiple Z-drugs. For anxiety patients with concomitant Alzheimer's disease or Parkinson's disease, and dyslipidemia, they were more likely to consume multiple BZRAs simultaneously, whereas patients with concomitant insomnia, depression, hypertension, diabetes, or tumors were less likely to consume multiple BZRAs (all p < 0.05). Furthermore, older patients who consume multiple BZRAs simultaneously may have higher probabilities of long-term drug use. Better interventions supporting standardized BZD utilization may be needed to minimize the side effects of inappropriate BZRA administration.

Adaptive Neural Network Control of an Uncertain 2-DOF Helicopter With Unknown Backlash-Like Hysteresis and Output Constraints.

An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.

Robust Adaptive Fault-Tolerant Control for a Riser-Vessel System With Input Hysteresis and Time-Varying Output Constraints.

Recently, with the development of the marine economy, marine risers have garnered increasing attention as they present facile and reliable methods for oil and gas transportation. However, these risers are susceptible to vibrations, which can lead to system performance degradation and fatigue damage. Therefore, effective vibration control strategies are required to address this issue. In this study, a novel adaptive fault-tolerant control (FTC) strategy is adopted to suppress the vibrations of a 3-D riser-vessel system against the effects of actuator failures, backlash-like hysteresis, and external disturbances. A barrier-based Lyapunov function is merged to eliminate the time-varying output constraints of the system. Adaptive FTC laws with projection mapping operators are designed to compensate for parameter uncertainties and consider input nonlinearities to improve system robustness. Finally, a rigorous Lyapunov analysis and numerical simulations are performed to verify the validity of the proposed controller and guarantee uniformly bounded stability of the system.

The prevalence of gestational diabetes mellitus before and after the implementation of the universal two-child policy in China.

Background: After the universal two-child policy has been fully implemented, challenges regarding pregnancy complications seemed to be more severe in China. This study aimed to evaluate the prevalence of gestational diabetes mellitus (GDM) and the main risk factors for GDM before and after the implementation of the universal two-child policy in China. Methods: A retrospective study was performed with 128,270 pregnant women who delivered at Ningbo Women & Children's Hospital from January 2010 to December 2020. Univariate and multivariate logistic regression analysis was applied to estimate the risk factors associated with GDM prevalence. Segmented regression analyses of interrupted time series (ITS) were conducted to assess the effect of the universal two-child policy on the trends of GDM. Results: The prevalence of GDM increased remarkably from 4% in 2010 to 21% in 2020. ITS analysis presented that the prevalence of GDM increased by 0.190% (ß1) per month from 2010 to 2016 (P<0.05), and by 0.044% (ß1+ß3) per month after the implementation of the universal two-child policy; the rate of elevation of GDM slowed down significantly (ß3=-0.146, P=0.004). Advanced maternal age (>30 years), multigravidity, multiparity, multiple gestation and gestational hypertension were significantly associated with GDM. Advanced age remained an independent risk factor for GDM even after cross stratification with gravidity and parity. The proportion of women with advanced maternal age (>30 years) increased by 0.161% per month before the implementation of the universal two-child policy and increased by 5.25% during the policy took effect month, and gradually increased by 0.124% (ß1+ß3) per month after then. Conclusions: The prevalence of GDM has sharply increased in the past decade. The growth rate of GDM slowed down after the implementation of the universal two-child policy in China, but the rate would maintain at a high plateau. The rise in the proportion of older pregnant women could increase the GDM rate. We recommend having children at a relatively optimal reproductive age when encouraging childbearing.

An online human-robot collaborative grinding state recognition approach based on contact dynamics and LSTM.

Collaborative state recognition is a critical issue for physical human-robot collaboration (PHRC). This paper proposes a contact dynamics-based state recognition method to identify the human-robot collaborative grinding state. The main idea of the proposed approach is to distinguish between the human-robot contact and the robot-environment contact. To achieve this, dynamic models of both these contacts are first established to identify the difference in dynamics between the human-robot contact and the robot-environment contact. Considering the reaction speed required for human-robot collaborative state recognition, feature selections based on Spearman's correlation and random forest recursive feature elimination are conducted to reduce data redundancy and computational burden. Long short-term memory (LSTM) is then used to construct a collaborative state classifier. Experimental results illustrate that the proposed method can achieve a recognition accuracy of 97% in a period of 5 ms and 99% in a period of 40 ms.

Vertical GaN Power MOSFET with Integrated Fin-Shaped Diode for Reverse Conduction.

A vertical GaN power MOSFET featuring an integrated fin-shaped non-junction diode (FDMOS) is proposed to improve reverse conduction and switching characteristics. Its static and dynamic characteristics are studied and analyzed by Sentaurus TCAD simulation. Compared with the conventional MOSFET (Con. MOS) with a body diode as a freewheeling diode (FWD), the FDMOS uses the integrated fin-shaped diode to reverse conduction, and thus, a low reverse turn-on voltage VON of 0.66 V is achieved, with a decreasing of 77.9%. Moreover, the Qrr of the FDMOS is reduced to 1.36 µC from 1.64 µC of the Con. MOS, without the minority carrier injection. The gate charge (QGD) of the FDMOS is significantly reduced because the fin structure reduces the gate area and transforms some part of CGD to CGS, and thus, a low switching loss is realized. The QGD, the turn-on loss (Eon) and the turn-off loss (Eoff) of the FDMOS are decreased by 56.8%, 33.8% and 53.8%, respectively, compared with those of the Con. MOS. In addition, the FDMOS is beneficial to reduce the parasitic inductance and the total chip area compared with the conventional method of using an externally connected Schottky diode as an FWD.

Shared and specific biological signalling pathways for diabetic retinopathy, peripheral neuropathy and nephropathy by high-throughput sequencing analysis.

OBJECTIVES: We aimed to explore the shared and specific signalling pathways involved in diabetic retinopathy (DR), diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN). METHODS: Differentially expressed mRNAs and lncRNAs were identified by high-throughput sequencing. Subsequently, functional enrichment analysis, protein-protein interaction (PPI) analysis and lncRNAs-mRNAs networks were conducted to determine the pathogenic mechanisms underlying DR, DPN and DN. RESULTS: Twenty-six biological pathways were shared among DR, DPN and DN groups compared to the type 2 diabetes mellitus (T2DM) group without complications, and most of the shared pathways and core proteins were involved in immune and inflammatory responses of microvascular damage. Cytokine‒cytokine receptor interactions and chemokine signalling pathway were the most significant and specific pathways for DR, and the lncRNA‒mRNA regulatory networks affected DR by targeting these pathways. Sphingolipid metabolism and neuroactive ligand-receptor pathways were found to be specific for the pathogenesis of DPN. Moreover, multiple amino acid metabolic pathways were involved in the occurrence and progression of DN. CONCLUSIONS: Diabetic retinopathy, DPN and DN exhibited commonality and heterogeneity simultaneously. The shared pathologic mechanisms underlying these diabetic complications are involved in diabetic microvascular damage via immune and inflammatory pathways. Our findings predict several biomarkers and therapeutic targets for these diabetic complications.