Liraglutide Protects Pancreatic Islet From Ischemic Injury by Reducing Oxidative Stress and Activating Akt Signaling During Cold Preservation to Improve Islet Transplantation Outcomes.

BACKGROUND: Islet transplantation is a promising therapy for patients with type 1 diabetes. However, ischemic injury to the donor islets during cold preservation leads to reduced islet quality and compromises transplant outcome. Several studies imply that liraglutide, a glucagon-like peptide-1 receptor agonist, has a positive effect on promoting islet survival, but its impact on islet cold-ischemic injury remains unexplored. Therefore, the aim of this study was to investigate whether liraglutide can improve islet transplantation efficacy by inhibiting cold-ischemic injury and to explore the underlying mechanisms. METHODS: Liraglutide was applied in a mouse pancreas preservation model and a human islets cold-preservation model, and islet viability, function, oxidative stress levels were evaluated. Furthermore, islet transplantation was performed in a syngeneic mouse model and a human-to-nude mouse islet xenotransplantation model. RESULTS: The supplementation of liraglutide in preservation solution improved islet viability, function, and reduced cell apoptosis. Liraglutide inhibited the oxidative stress of cold-preserved pancreas or islets through upregulating the antioxidant enzyme glutathione levels, inhibiting reactive oxygen species accumulation, and maintaining the mitochondrial membrane integrity, which is associated with the activation of Akt signaling. Furthermore, the addition of liraglutide during cold preservation of donor pancreas or donor islets significantly improved the subsequent transplant outcomes in both syngeneic mouse islet transplantation model and human-to-nude mouse islet xenotransplantation model. CONCLUSIONS: Liraglutide protects islets from cold ischemia-related oxidative stress during preservation and hence improved islet transplantation outcomes, and this protective effect of liraglutide in islets is associated with the activation of Akt signaling.

A Novel Approach to the Technique of Lung Region Segmentation Based on a Deep Learning Model to Diagnose COVID-19 X-ray Images.

BACKGROUND: The novel coronavirus pandemic has caused a global health crisis, placing immense strain on healthcare systems worldwide. Chest X-ray technology has emerged as a critical tool for the diagnosis and treatment of COVID-19. However, the manual interpretation of chest X-ray films has proven to be inefficient and time-consuming, necessitating the development of an automated classification system. OBJECTIVE: In response to the challenges posed by the COVID-19 pandemic, we aimed to develop a deep learning model that accurately classifies chest X-ray images, specifically focusing on lung regions, to enhance the efficiency and accuracy of COVID-19 and pneumonia diagnosis. METHODS: We have proposed a novel deep network called "FocusNet" for precise segmentation of lung regions in chest radiographs. This segmentation allows for the accurate extraction of lung contours from chest X-ray images, which are then input into the classification network, ResNet18. By training the model on these segmented lung datasets, we sought to improve the accuracy of classification. RESULTS: The performance of our proposed system was evaluated on three types of lung regions in normal individuals, COVID-19 patients, and those with pneumonia. The average accuracy of the segmentation model (FocusNet) in segmenting lung regions was found to be above 90%. After reclassification of the segmented lung images, the specificities and sensitivities for normal, COVID-19, and pneumonia were excellent, with values of 98.00%, 99.00%, 99.50%, and 98.50%, 100.00%, and 99.00%, respectively. ResNet18 achieved impressive area under the curve (AUC) values of 0.99, 1.00, and 0.99 for classifying normal, COVID-19, and pneumonia, respectively, on the segmented lung datasets. Moreover, the AUC values of the three groups increased by 0.02, 0.02, and 0.06, respectively, when compared to the direct classification of unsegmented original images. Overall, the accuracy of lung region classification after processing the datasets was 99.3%. CONCLUSION: Our deep learning-based automated chest X-ray classification system, incorporating lung region segmentation using FocusNet and subsequent classification with ResNet18, has significantly improved the accuracy of diagnosing respiratory lung diseases, including COVID-19. The proposed approach has great potential to revolutionize the diagnosis of COVID-19 and other respiratory lung diseases, offering a valuable tool to support healthcare professionals during health crises.

Evidence from Finland and Sweden on the relationship between early-life diseases and lifetime childlessness in men and women.

The percentage of people without children over their lifetime is approximately 25% in men and 20% in women. Individual diseases have been linked to childlessness, mostly in women, yet we lack a comprehensive picture of the effect of early-life diseases on lifetime childlessness. We examined all individuals born in 1956-1968 (men) and 1956-1973 (women) in Finland (n = 1,035,928) and Sweden (n = 1,509,092) to the completion of their reproductive lifespan in 2018. Leveraging nationwide registers, we associated sociodemographic and reproductive information with 414 diseases across 16 categories, using a population and matched-pair case-control design of siblings discordant for childlessness (71,524 full sisters and 77,622 full brothers). The strongest associations were mental-behavioural disorders (particularly among men), congenital anomalies and endocrine-nutritional-metabolic disorders (strongest among women). We identified new associations for inflammatory and autoimmune diseases. Associations were dependent on age at onset and mediated by singlehood and education. This evidence can be used to understand how disease contributes to involuntary childlessness.

Prepayment meters strongly associated with multiple types of deprivation and emergency respiratory hospital admissions: an observational, cross-sectional study.

BACKGROUND: Prepayment meters (PPMs) require energy to be paid in advance. Action groups and media contend that PPMs are concentrated in the most vulnerable groups, prone to run out of credit and experience financial burden. This led to forced installation for those over age 85 being banned in April 2023 and a 'prepayment premium' scrapped in July 2023. Yet, we lack empirical evidence of which groups PPMs are concentrated. This ecological study examines the extent to which PPMs are associated with multiple measures of structural social, economic and health deprivation to establish evidence-based policy. METHODS: Combining multiple regional data and census estimates at the Lower Layer Super Output Area and the Middle Layer Super Output Area level from England and Wales, we use Spearman's rank correlation, Pearson correlation and multivariate linear regression to empirically establish associations between PPMs and multiple types of deprivation. RESULTS: Higher PPM prevalence is strongly associated with: lower income, receipt of employment benefits, ethnic minorities, lower education and higher health deprivation. Higher PPM prevalence is strongly associated with higher income deprivation affecting children, the elderly and social rental properties. PPMs are significantly associated with emergency hospital admissions for respiratory diseases in England, even after controlling for confounders (coefficient=1.81; 95% CI 1.51 to 2.11). CONCLUSIONS: We found empirical evidence that PPM users are concentrated among the population who already experience multiple disadvantages. Furthermore, PPM concentrated areas are associated with higher emergency hospital admissions for respiratory diseases.

Characteristics of research-focused human islet preparations from organ donors with type 2 diabetes.

Type 2 diabetes mellitus (T2D) affects 463 million individuals worldwide. ß-cell dysfunction and relatively inadequate ß-cell mass has been implicated in the pathogenesis of T2D. Primary human islets from T2D patients can reveal the islet dysfunction and the underlying mechanisms and thus have become valued resources for diabetes research. Our center (Human Islet Resource Center, China) has prepared a number of batches of human islets from T2D organ donors. The present study aims to characterize islet isolation processes, islet yields, and qualities of T2D pancreases by comparing with non-diabetic (ND) ones. Overall, 24 T2D and 80 ND pancreases were obtained with informed research consents. The digestion time, islet purity, yield, size distribution, islet morphology score, viability, and function in each islet preparation were analyzed. We found that at digestion stage, T2D pancreases need significantly longer digestion duration and have worse digestion rates and lower gross islet yields. At purification stage, T2D pancreases have poorer purity, purification rate, morphology score, and islet yields after purification. Functional evaluation by GSI assay showed that the human T2D islets have significantly lower glucose stimulated insulin secretion ability. In conclusion, the features of longer digestion duration, lower yields and quality, and impaired insulin secretion in T2D group are consistent with the pathological condition of this disease. Both islet yields and islet function evaluation results did not support human T2D islets as clinical transplantation resources. However, they could serve as good research models for T2D disease studies and promote the advancement of diabetes research.

A compact review of progress and prospects of deep learning in drug discovery.

BACKGROUND: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. RESULTS: This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.

Gene-x-environment analysis supports protective effects of eveningness chronotype on self-reported and actigraphy-derived sleep duration among those who always work night shifts in the UK Biobank.

Previous research has linked having an eveningness chronotype with a higher tolerance for night shift work, suggesting the ability to work nights without health consequences may partially depend upon having a circadian clock optimized for these times. As chronotypes entrain over time to environmental cues, it remains unclear whether higher relative eveningness among healthy night workers reflects a moderating or mediating effect of chronotype on health. We address these concerns conducting a genome-wide association study and utilizing a polygenic score (PGS) for eveningness as a time-invariant measure of chronotype. On a sample of 53 211 workers in the UK Biobank (2006-2018), we focus on the effects of night shift work on sleep duration, a channel through which night shift work adversely affects health. We ask whether a higher predisposition toward eveningness promotes night shift work tolerance. Results indicate that regular night shift work is associated with a 13-minute (3.5%) reduction in self-reported sleep per night relative to those who never work these hours (95% confidence interval [CI] = -17:01, -8:36). We find that eveningness has a strong protective effect on night workers: a one-SD increase in the PGS is associated with a 4-minute (28%) reduction in the night shift work sleep penalty per night (CI = 0:10, 7:04). This protective effect is pronounced for those working the longest hours. Consistent patterns are observed with an actigraphy-derived measure of sleep duration. These findings indicate that solutions to health consequences of night shift work should take individual differences in chronotype into account.

Internet use, depression, and cognitive outcomes among Chinese adolescents.

This study provides new evidence on how the growingly significant digital life shapes Chinese adolescents' cognitive and mental health outcomes based on their gender, parental education, and geographical location. Using the China Education Panel Survey, a nationally representative survey following 12-15-year-old students in 2013 and 2014, and individual fixed-effect models, we find that more time spent on the Internet is associated with higher self-reported depression scores. This negative impact on mental health is more substantial for girls, those with less-educated parents, and those living outside the city center. The link between Internet use and cognitive development is almost null. Time spent online negatively affects Chinese young adolescents' subjective well-being but has little impact on their cognitive development. The link between Internet use time and subjective well-being also depends on gender, parental education, and the geographical location of those adolescents. The heterogeneous impacts of Internet use time offer crucial new evidence to the multiple dimensions of the digital divide among adolescents in China.

A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials.

Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.

Gender differences in sleep disruption during COVID-19: cross-sectional analyses from two UK nationally representative surveys.

OBJECTIVE: COVID-19 related measures have impacted sleep on a global level. We examine changes in sleep problems and duration focusing on gender differentials. DESIGN: Cross-sectional analyses using two nationally representative surveys collected during the first and second month after the 2020 lockdown in the UK. SETTING AND PARTICIPANTS: Participants (age 17 years and above) from the first wave of the Understanding Society COVID-19 Study are linked to the most recent wave before the pandemic completed during 2018 and 2019 (n=14 073). COVID-19 Survey Data was collected from 2 to 31 May 2020 (n=8547) with participants drawn from five nationally representative cohort studies in the UK. ANALYSIS: We conducted bivariate analyses to examine gender gaps in change in sleep problems and change in sleep duration overall and by other predictors. A series of multivariate ordinary least squares (OLS) regression models were estimated to explore predictors of change in sleep problems and change in sleep time. RESULTS: People in the UK on average experienced an increase in sleep loss during the first 4 weeks of the lockdown (mean=0.13, SD=0.9). Women report more sleep loss than men (coefficient=0.15, 95% CI 0.11 to 0.19). Daily sleep duration on average increased by ten minutes (mean=-0.16, SD=1.11), with men gaining eight more minutes of sleep per day than women (coefficient=0.13, 95% CI 0.09 to 0.17). CONCLUSION: The COVID-19 related measures amplified traditional gender roles. Men's sleep was more affected by changes in their financial situation and employment status related to the crisis, with women more influenced by their emotional reaction to the pandemic, feeling anxious and spending more time on family duties such as home schooling, unpaid domestic duties, nurturing and caregiving. Based on our findings, we provide policy advice of early, clear and better employment protection coverage of self-employed and precarious workers and employer recognition for parents.

Deep adversarial transition learning using cross-grafted generative stacks.

As a common approach of deep domain adaptation in computer vision, current works have mainly focused on learning domain-invariant features from different domains, achieving limited success in transfer learning. In this paper, we present a novel "deep adversarial transition learning" (DATL) framework that bridges the domain gap by generating some intermediate, transitional spaces between the source and target domains through the employment of adjustable, cross-grafted generative network stacks and effective adversarial learning between transitions. Specifically, variational auto-encoders (VAEs) are constructed for the domains, and bidirectional transitions are formed by cross-grafting the VAEs' decoder stacks. Generative adversarial networks are then employed to map the target domain data to the label space of the source domain, which is achieved by aligning the transitions initiated by different domains. This results in a new, effective learning paradigm, where training and testing are carried out in the associated transitional spaces instead of the original domains. Experimental results demonstrate that our method outperforms the state-of-the-art on a number of unsupervised domain adaptation benchmarks.

HIF-1α/FOXO1 axis regulated autophagy is protective for ß cell survival under hypoxia in human islets.

ß cells suffer from hypoxia due to the rapid metabolic rate to supply insulin production. Mechanistic study of ß cell survival under hypoxia may shed light on the ß cell mass loss in type 2 diabetes mellitus (T2DM). Here, we found that the expressions of LC3 and p62/SQSTM1, two key autophagy regulators, were significantly higher in ß cells than that in non-ß endocrine cells in both non-diabetic and T2DM human pancreases, and the autophagy process was accelerated upon Cobalt Chloride (CoCl2) treatment in ex vivo cultured primary human islets. Meanwhile, CoCl2 induced the upregulation of FOXO1 in human islets, where HIF-1α played a key role. CoCl2 treatment caused the increase of ß cell apoptosis, yet inhibiting autophagy by Chloroquine or by FOXO1 knockdown further aggravated apoptosis, suggesting that FOXO1-regulated autophagy is protective for ß cell survival under hypoxia. Immunofluorescence staining showed that LC3 and p62/SQSTM1 expressions were significantly decreased in T2DM patients and negatively correlated with HbA1c, indicating that the autophagy capacity of ß cells is impaired along with the progression of the disease. Our study revealed that HIF-1α/FOXO1 regulated autophagy benefits ß cell survival under hypoxia and autophagy dysregulation may account for ß cell mass loss in T2DM. BRIEF SUMMARY: Our study revealed that HIF-1α/FOXO1 regulated autophagy benefits ß cell survival under hypoxia and autophagy dysregulation may account for ß cell mass loss in T2DM.

Factors affecting adherence to non-pharmaceutical interventions for COVID-19 infections in the first year of the pandemic in the UK.

OBJECTIVE: Non-pharmaceutical interventions (NPIs), including wearing face covering/masks, social distancing and working from home, have been introduced to control SARS-CoV-2 infections. We provide individual-level empirical evidence of whether adherence reduces infections. SETTING AND PARTICIPANTS: The COVID-19 Infection Study (CIS) was used from 10 May 2020 to 2 February 2021 with 409 009 COVID-19 nose and throat swab tests nested in 72 866 households for 100 138 individuals in the labour force aged 18-64. ANALYSIS: ORs for a positive COVID-19 test were calculated using multilevel logistic regression models, stratified by sex and time, by an index of autonomy to abide by NPIs, adjusted for various socioeconomic and behavioural covariates. RESULTS: Inability to comply with NPIs predicted higher infections when individuals reported not wearing a face covering outside. The main effect for inability to comply was OR 0.79 (95% CI 0.67 to 0.92), for wearing face covering/masks was OR 0.29 (95% CI 0.15 to 0.56) and the interaction term being OR 1.25 (95% CI 1.07 to 1.46). The youngest age groups had a significantly higher risk of infection (OR 1.52, 95% CI 1.28 to 1.82) as did women in larger households (OR 1.04, 95% CI 1.02 to 1.06). Effects varied over time with autonomy to follow NPIs only significant in the pre-second lockdown May-November 2020 period. Wearing a face covering outside was a significant predictor of a lower chance of infection before mid-December 2020 when a stricter second lockdown was implemented (OR 0.44, 95% CI 0.27 to 0.73). CONCLUSION: The protective effect of wearing a face covering/mask was the strongest for those who were the most unable to comply with NPIs. Higher infection rates were in younger groups and women in large households. Wearing a face covering or mask outside the home consistently and significantly predicted lower infection before the 2020 Christmas period and among women.

Increased frequency of ß cells with abnormal NKX6.1 expression in type 2 diabetes but not in subjects with higher risk for type 2 diabetes.

BACKGROUND: NKX6.1 is a transcription factor for insulin, as well as a marker for ß cell maturity. Abnormal NKX6.1 expression in ß cells, such as translocation from the nucleus to cytoplasm or lost expression, has been shown as a marker for ß cell dedifferentiation. METHODS: We obtained pancreatic sections from organ donors and immunofluorescence staining with NKX6.1 and insulin was performed to characterize NKX6.1 expression in subjects with or without type 2 diabetes mellitus (T2DM). RESULTS: Our results showed that cells with insulin expression but no nucleic NKX6.1 expression (NKX6.1Nuc-Ins+), and cells with cytoplasmic NKX6.1 expression but no insulin expression (NKX6.1cytIns-) were significantly increased in T2DM subjects and positively correlated with glycated hemoglobin (HbA1c), indicating the elevated ß cell dedifferentiation with NKX6.1 inactivation in T2DM. To investigate whether ß cell dedifferentiation has initiated in subjects with higher risks for T2DM, we next analyzed the association between ß-cell dedifferentiation level in ND subjects with different ages, body mass index, and HbA1c. The results showed the absolute number and percentage of dedifferentiated ß cells with NKX6.1 inactivation did not significantly change in subjects with advanced aging, obesity, or modest hyperglycemia, indicating that the ß cell dedifferentiation might mainly occur after T2DM was diagnosed. CONCLUSION: Our results suggested that NKX6.1 expression in ß cells was changed in type 2 diabetic subjects, evidenced by significantly increased NKX6.1Nuc-Ins+ and NKX6.1cytIns- cells. This abnormality did not occur more frequently in subjects with a higher risk for T2DM, suggesting that ß cell dedifferentiation might be secondary to the pathological changes in T2DM.

Adipose Tissue-Derived Stem Cells from Type 2 Diabetics Reveal Conservative Alterations in Multidimensional Characteristics.

BACKGROUND AND OBJECTIVES: Adipose tissue-derived mesenchymal stem cells (ASCs) are recognized as an advantaged source for the prevention and treatment of diverse diseases including type 2 diabetes mellitus (T2DM). However, alterations in characteristics of ASCs from the aforementioned T2DM patients are still obscure, which also hinder the rigorous and systematic illumination of progression and pathogenesis. METHODS AND RESULTS: In this study, we originally isolated peripancreatic adipose tissue-derived mesenchymal stem cells from both human type 2 diabetic and non-diabetic donors (T2DM-ASCs, ND-ASCs) with the parental consent, respectively. We noticed that T2DM-ASCs exhibited indistinguishable immunophenotype, cell vitality, chondrogenic differentiation and stemness as ND-ASCs. Simultaneously, there's merely alterations in migration and immunoregulatory capacities in T2DM-ASCs. However, differing from ND-ASCs, T2DM-ASCs exhibited deficiency in adipogenic and osteogenic differentiation, and in particular, the delayed cell cycle and different cytokine expression spectrum. CONCLUSIONS: The conservative alterations of T2DM-ASCs in multifaceted characteristics indicated the possibility of autologous application of ASCs for cell-based T2DM treatment in the future.

Dynamic Change of ß to α Ratio in Islets of Chinese People With Prediabetes and Type 2 Diabetes Mellitus.

OBJECTIVES: The present study aimed to investigate the dynamic change of α cells and ß cells, and their ratios in prediabetes and type 2 diabetes in the Chinese population. METHODS: Pancreata from 27 nondiabetic (ND), 8 prediabetic (PreD), and 19 type 2 diabetic (T2D) organ donors were subjected to immunofluorescence staining with insulin and glucagon. RESULTS: The ß to α ratio in islets (ß/α) in PreD was significantly higher than that in ND, resulting from an increase of ß cells and a decrease of α cells per islet, but that in T2D was significantly lower than that in ND, resulting from a decrease of ß cells and an increase of α cells per islet. The ß-cell percentage and ß/α ratio positively correlated and α-cell percentage negatively correlated with HbA1c (glycated hemoglobin) in ND and PreD, but these correlations disappeared when T2D subjects were included. CONCLUSIONS: The islet ß to α ratio increased in PreD individuals because of a relative α-cell loss and ß-cell compensation and decreased after T2D onset because of both ß-cell loss and α-cell reexpansion.

Demographic science aids in understanding the spread and fatality rates of COVID-19.

Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.

Mesenchymal stem cells ameliorate ß cell dysfunction of human type 2 diabetic islets by reversing ß cell dedifferentiation.

BACKGROUND: A physiological hallmark of patients with type 2 diabetes mellitus (T2DM) is ß cell dysfunction. Despite adequate treatment, it is an irreversible process that follows disease progression. Therefore, the development of novel therapies that restore ß cell function is of utmost importance. METHODS: This study aims to unveil the mechanistic action of mesenchymal stem cells (MSCs) by investigating its impact on isolated human T2DM islets ex vivo and in vivo. FINDINGS: We propose that MSCs can attenuate ß cell dysfunction by reversing ß cell dedifferentiation in an IL-1Ra-mediated manner. In response to the elevated expression of proinflammatory cytokines in human T2DM islet cells, we observed that MSCs was activated to secret IL-1R antagonist (IL-1Ra) which acted on the inflammed islets and reversed ß cell dedifferentiation, suggesting a crosstalk between MSCs and human T2DM islets. The co-transplantation of MSCs with human T2DM islets in diabetic SCID mice and intravenous infusion of MSCs in db/db mice revealed the reversal of ß cell dedifferentiation and improved glycaemic control in the latter. INTERPRETATION: This evidence highlights the potential of MSCs in future cell-based therapies regarding the amelioration of ß cell dysfunction.

Hypoxia-inducible factor-1α mediates the expression of mature ß cell-disallowed genes in hypoxia-induced ß cell dedifferentiation.

Hypoxia affects the function of pancreatic ß cells, and the molecular mechanism underlying hypoxia-related ß cell dysfunction in human type 2 diabetes mellitus (T2DM) remains to be elucidated. In this study, by comparing the gene expression profiles of islets from nondiabetic and T2D subjects using gene chip array, we aimed to elucidate that hypoxia signaling pathways are activated in human T2DM islets. CoCl2 treatment, which was employed to mimic hypoxic stimulation in human islets, decreased insulin secretion, insulin content, and the functional gene expression of human islets. In parallel, the expression of mature ß cell-disallowed genes was upregulated by CoCl2, including progenitor cell marker NGN3, ß cell differentiation marker ALDH1A3, and genes that are typically inhibited in mature ß cells, namely, GLUT1 and LDHA, indicating that CoCl2-mimicked hypoxia induced ß cell dedifferentiation of human islets. This finding in human islets was confirmed in mouse ß cell line NIT-1. By using Dimethyloxalylglycine (DMOG) to activate hypoxia-inducible factor-1α (HIF-1α) or siRNAs to knockdown HIF-1α, we found that HIF-1α was a key regulator of hypoxia-induced dedifferentiation of ß cells by upregulating mature ß cell-disallowed genes. Our findings suggested that HIF-1α activation might be an important contributor to ß cell dedifferentiation in human T2DM islets, and HIF-1α-targeted therapies may have the potential to reverse ß cell dedifferentiation of human T2DM islets.