Transplantation of chemically induced pluripotent stem-cell-derived islets under abdominal anterior rectus sheath in a type 1 diabetes patient.

We report the 1-year results from one patient as the preliminary analysis of a first-in-human phase I clinical trial (ChiCTR2300072200) assessing the feasibility of autologous transplantation of chemically induced pluripotent stem-cell-derived islets (CiPSC islets) beneath the abdominal anterior rectus sheath for type 1 diabetes treatment. The patient achieved sustained insulin independence starting 75 days post-transplantation. The patient's time-in-target glycemic range increased from a baseline value of 43.18% to 96.21% by month 4 post-transplantation, accompanied by a decrease in glycated hemoglobin, an indicator of long-term systemic glucose levels at a non-diabetic level. Thereafter, the patient presented a state of stable glycemic control, with time-in-target glycemic range at >98% and glycated hemoglobin at around 5%. At 1 year, the clinical data met all study endpoints with no indication of transplant-related abnormalities. Promising results from this patient suggest that further clinical studies assessing CiPSC-islet transplantation in type 1 diabetes are warranted.

Associations of dinitroaniline herbicide exposure, genetic susceptibility, and lifestyle with glucose dysregulation: A gene-environment interaction study from the Wuhan-Zhuhai cohort.

OBJECTIVE: To assess the association of dinitroaniline herbicides as well as their interactions with genetic susceptibility and lifestyle with glucose dysregulation. METHODS: A total of 4310 Chinese urban adults from the baseline of the Wuhan-Zhuhai Cohort were included in the cross-sectional study. A follow-up panel from the cohort was included in the longitudinal study, including 158 participants with 432 observations. Glucose dysregulation, including fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance (HOMA-IR), type 2 diabetes mellitus (T2DM), and impaired fasting glucose (IFG) were assessed. Serum dinitroaniline herbicides including benfluralin, trifluralin, and pendimethalin were measured. T2DM-related polygenic risk score (PRS) and healthy life scores were constructed. RESULTS: Cross-sectionally, each 2-fold increase in serum benfluralin was associated with a 1.12%, 2.03%, and 9% increase in FPG, HOMA-IR, and IFG risk, respectively. Each 2-fold increase in serum trifluralin was associated with a 0.70% increase in FPG. Each 2-fold increase in serum pendimethalin was associated with a 2.53% and 24% increase in FPG and IFG risk, respectively (all P < 0.05). Positive associations were found between the dinitroaniline herbicide mixture and glucose dysregulation. Longitudinally, serum benfluralin and pendimethalin were associated with the annual increases in FPG and HOMA-IR (P < 0.05). Joint and interaction effect analysis showed that compared with participants with high benfluralin/trifluralin/pendimethalin, high PRS, and unhealthy lifestyle, those with low benfluralin/trifluralin/pendimethalin, low PRS, and healthy lifestyle showed the greatest declines in FPG, i.e., -15.46%, -13.58%, and -10.51% changes, respectively; and the greatest reductions in IFG risks, i.e., 75%, 61%, and 73% reductions, respectively (all P < 0.05). CONCLUSIONS: This study highlighted the importance of controlling dinitroaniline herbicide exposure and following healthy lifestyles in glucose dysregulation prevention, especially among individuals with high genetic risk of T2DM.

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.

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.

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.

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.

Educational attainment and allostatic load in later life: Evidence using genetic markers.

Education is strongly correlated with health outcomes in older adulthood. Whether the impact of education expansion improves health remains unclear due to a lack of clarity over the causal relationship. Previous health research within the social sciences has tended to use specific activities of daily living or self-reported health status. This study uses a broader and objective health measure - allostatic load (AL) - to take into consideration the exposures that accumulate throughout the life course. This paper applies a Mendelian Randomization (MR) approach to identify causality in relation to education on health as measured by AL. Using the Health and Retirement Study 2008 (N=3935), we adopt a polygenic score built from genetic variants associated with years of education. To test whether our analyses violate the exclusion assumption, we further run MR Egger regressions to test for bias from pleiotropy. We also explore the potential pathways between education and AL, including smoking, drinking, marital length, health insurance, etc. Using this genetic instrument, we find a 0.3 unit (19% of a standard deviation) reduction in AL per year of schooling. The effect is mainly driven by BMI and Hba1c. Smoking and marital stability are two potential pathways that also causally influenced by education. If our main and sensitivity analyses are valid, the results find support that a higher level of education is causally related to better health in older adulthood.

Opposing effects of IL-1ß/COX-2/PGE2 pathway loop on islets in type 2 diabetes mellitus.

The cyclooxygenase2 (COX-2) enzyme catalyzes the first step of prostanoid biosynthesis, and is known for its crucial role in the pathogenesis of several inflammatory diseases including type 2 diabetes mellitus (T2DM). Although a variety of studies revealed that COX-2 played a role in the IL-1ß induced ß cell dysfunction, the molecular mechanism remains unclear. Here, using a cDNA microarray and in silico analysis, we demonstrated that inflammatory responses were upregulated in human T2DM islets compared with non-diabetic (ND) islets. COX-2 expression was significantly enhanced in human T2DM islets, correlated with the high inflammation level. PGE2, the catalytic product of COX-2, downregulated the functional gene expression of PDX1, NKX6.1, and MAFA and blunted the glucose induced insulin secretion of human islets. Conversely, inhibition of COX-2 activity by a pharmaceutical inhibitor prevented the ß-cell dysfunction induced by IL-1ß. COX-2 inhibitor also abrogated the IL-1ß autostimulation in ß cells, which further resulted in reduced COX-2 expression in ß cells. Together, our results revealed that COX-2/PGE2 signaling was involved in the regulation of IL-1ß autostimulation, thus forming an IL-1ß/COX-2/PGE2 pathway loop, which may result in the high inflammation level in human T2DM islets and the inflammatory impairment of ß cells. Breaking this IL-1ß/COX-2/PGE2 pathway loop provides a potential therapeutic strategy to improve ß cell function in the treatment of T2DM patients.

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.

Cross-sectional and longitudinal associations of PAHs exposure with serum uric acid and hyperuricemia among Chinese urban residents: The potential role of oxidative damage.

A few studies found polycyclic aromatic hydrocarbons (PAHs) were associated with serum uric acid (SUA) or hyperuricemia (HUA). However, the longitudinal study is vacant, and the underlying mechanisms remain unclear. We aimed to assess the cross-sectional and longitudinal associations of urinary PAHs metabolites with SUA levels and HUA risk, and explore the mediating effects of oxidative stress and inflammation. 10 urinary mono-hydroxylated PAHs metabolites and SUA levels were measured among 4047 Chinese urban residents at baseline and 1496 individuals at 6-year follow-up. Biomarkers of oxidative damage and inflammation in urine/plasma were determined at baseline. We adopted generalized linear mixed models and logistic regression to assess the associations of PAHs metabolites with SUA and HUA, weighted quantile sum regression and adaptive elastic net regression to evaluate the overall effects of multi-PAHs mixture, and mediation analysis to estimate the mediating roles of the biomarkers. In the cross-sectional study, each 1-unit increase in the ln-transformed values of 2-OHNa, 2-OHFlu, 4-OHPh, 9-OHPh, 3-OHPh, 2-OHPh, ΣOHNa, ΣOHPh, and ΣOHPAHs was associated with a 4.10-, 3.90-, 6.42-, 7.33-, 4.85-, 5.43-, 4.47-, 7.67-, and 5.22-µmol/L increase in SUA, respectively. Meanwhile, each 1-unit increase in the ln-transformed values of 1-OHNa, 2-OHNa, 4-OHPh, 9-OHPh, 3-OHPh, 2-OHPh, ΣOHNa, ΣOHPh, and ΣOHPAHs was associated with a 17, 14, 15, 22, 14, 19, 18, 27, and 21% increment in HUA risk, respectively. After 6 years, individuals with persistent high level of 9-OHPh had a 12.5 µmol/L increase in SUA compared with those with persistent low level. The overall effects of multi-PAHs mixture on SUA and HUA remain positive. 8-hydroxy-deoxyguanosine mediated the associations of PAHs metabolites with SUA and HUA, and the mediated proportion ranged from 5.39% to 15.34%. PAHs exposure was associated with the elevated SUA levels and increased HUA risk, and oxidative DNA damage may be one of the underlying mechanisms.

Associations of personal PM2.5-bound heavy metals and heavy metal mixture with lung function: Results from a panel study in Chinese urban residents.

BACKGROUND: There are a few reports on the associations between fine particulate matter (PM2.5)-bound heavy metals and lung function. OBJECTIVES: To evaluate the associations of single and mixed PM2.5-bound heavy metals with lung function. METHODS: This study included 316 observations of 224 Chinese adults from the Wuhan-Zhuhai cohort over two study periods, and measured participants' personal PM2.5-bound heavy metals and lung function. Three linear mixed models, including the single constituent model, the PM2.5-adjusted constituent model, and the constituent residual model were used to evaluate the association between single metal and lung function. Mixed exposure models including Bayesian kernel machine regression (BKMR) model, weighted quantile sum (WQS) model, and Explainable Machine Learning model were used to assess the relationship between PM2.5-bound heavy metal mixtures and lung function. RESULTS: In the single exposure analyses, significant negative associations of PM2.5-bound lead, antimony, and cadmium with peak expiratory flow (PEF) were observed. In the mixed exposure analyses, significant decreases in forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC), maximal mid-expiratory flow (MMF), and forced expiratory flow at 75% of the pulmonary volume (FEF75) were associated with the increased PM2.5-bound heavy metal mixture. The BKMR models suggested negative associations of PM2.5-bound lead and antimony with lung function. In addition, PM2.5-bound copper was positively associated with FEV1/FVC, MMF, and FEF75. The Explainable Machine Learning models suggested that FEV1/FVC, MMF, and FEF75 decreased with the elevated PM2.5-bound lead, manganese, and vanadium, and increased with the elevated PM2.5-bound copper. CONCLUSIONS: The negative relationships were detected between PM2.5-bound heavy metal mixture and FEV1/FVC, MMF, as well as FEF75. Among the PM2.5-bound heavy metal mixture, PM2.5-bound lead, antimony, manganese, and vanadium were negatively associated with FEV1/FVC, MMF, and FEF75, while PM2.5-bound copper was positively associated with FEV1/FVC, MMF, and FEF75.

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.

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.

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.

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.

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.

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.