GR-pKa: a message-passing neural network with retention mechanism for pKa prediction.

During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors.

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Comparative effectiveness of traditional Chinese medicine and angiotensin converting enzyme inhibitors, angiotensin receptor blockers, and sodium glucose cotransporter inhibitors in patients with diabetic kidney disease: A systematic review and network meta-analysis.

Angiotensin converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), and sodium glucose cotransporter inhibitors (SGLT2i) are commonly used to treat diabetic kidney disease (DKD). Currently, increasing evidence also suggests traditional Chinese medicine (TCM) as an effective strategy. We assessed the efficacy of ACEI, ARB, SGLT2i, and TCM on major renal outcomes. We searched the electronic literature published up to March 2021 from CNKI, VIP, WanFang, SinoMed, PubMed, Embase, Cochrane Library, Web of Science, and clinicaltrials.gov; a total of 56 studies and 5464 participants were included. We found that TCM plus ACEI, TCM plus ARB, and TCM alone are very effective treatment methods compared with ACEI, ARB, and the placebo in reducing 24-h urine protein, serum creatinine, and blood urea nitrogen. TCM plus ACEI was the most effective treatment (TCM plus ACEI vs. the placebo in 24-h urine protein [mean difference (MD) - 757.18, 95% confidence interval-1177.41 to - 353.31], serum creatinine [MD - 25.81, 95% confidence interval - 35.51 to - 16.03], and blood urea nitrogen [MD - 3.48, 95% confidence interval - 5.04 to - 1.90]). Although the incidence of end-stage renal disease while receiving an TCM plus ARB compared with a placebo was not statistically significant, the treatment ranking showed this combination therapy to have the greatest probability (72.8%) of reducing end-stage renal disease mortality, followed by SGLT2i (68%). Our analyses showed that combining TCM with conventional treatments for patients with DKD can improve renoprotective effects and superiority, and ACEI plus TCM may be the most effective option for treating DKD.

Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes.

Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.

Multi-omics characterization of type 2 diabetes mellitus-induced gastroenteropathy in the db/db mouse model.

Objective: Gastrointestinal dysfunction are often associated with type 2 diabetes mellitus (T2DM), a complicated metabolic illness. Contributing factors have been proposed, including genetic predisposition, gene environmental, and lifestyle interactions, but the pathophysiology remains unknown. Methods: We aim to explore the possible causes behind gastrointestinal dysfunction caused by type 2 diabetes in this study. A comprehensive analysis of the gastric sinus metabolome, transcriptome, and proteome in db/db mice with gastrointestinal dysfunction was conducted. Results: The model group of mice had considerably lower small intestine propulsion and gastric emptying rates, higher blood glucose levels, and were significantly obese compared to the control group. We identified 297 genes, 350 proteins, and 1,001 metabolites exhibiting significant differences between db/db and control mice (p < 0.05). Moreover, multi-omics analysis revealed that the genes, proteins, and metabolites in the T2DM-induced gastroenteropathy mice group were involved in arachidonic acid metabolism, glycerophospholipid metabolism and vitamin digestion and absorption. Specifically, Cbr3, Etnppl, and Apob were the major mRNAs associated with T2DM-induced gastrointestinal dysfunction, while Cyp2b10, Cyp2b19, Pgs1, Gpat3, Apoa4, and Tcn2 were the major proteins associated with T2DM-induced gastrointestinal injury, and 16(R)-HET, 5-HETE, LysoPC (22:0), and Pantothenic acid were the major metabolites associated with T2DM-induced gastrointestinal disorders. Conclusion: The mechanism of action of diabetic gastroenteropathy may be related to vitamin digestion and absorption, glycerophospholipid metabolism, and arachidonic acid metabolism.

The MTHFR C677T/A1298C polymorphism is associated with increased risk of microangiopathy in type 2 diabetes mellitus: A systematic review and meta-analysis.

Extensive case-control association studies have been conducted over the past few decades to investigate the relationship between MTHFR polymorphism and type 2 diabetes mellitus (T2DM) microangiopathy. However, the strength of the evidence and clinical significance are unclear. Consequently, a meta-analysis was performed to examine the correlations between two prevalent MTHFR single nucleotide polymorphisms, MTHFR C677T and A1298C, and T2DM microangiopathy. Randomized controlled trials were systematically searched in PubMed, Cochrane, Embase, Web of Science, CNKI, VIP database, China Biology Medicine, and Wanfang until August 2023. A total of 42 studies were included. Random-effect models were utilized to estimate odds ratios (ORs) with 95% confidence intervals (CIs) to assess the association between MTHFR polymorphisms and T2DM microangiopathy susceptibility. T2DM microangiopathy was significantly associated with the MTHFR C677T polymorphism in the overall population (T vs C, OR = 1.43, 95% CI = 1.25-1.64; TT + CT vs CC: OR = 1.56, 95% CI = 1.30-1.88; TT vs CT + CC: OR = 1.66, 95% CI = 1.38-1.99; TT vs CC: OR = 2.03, 95% CI = 1.58-2.60). Additionally, the dominant model revealed that the MTHFR A1298C polymorphism was associated with T2DM microangiopathy (OR = 1.27, 95% CI: 1.09-1.47). This meta-analysis revealed that MTHFR may be involved in the pathogenesis of T2DM microangiopathy, providing a reference for early diagnosis and treatment of T2DM.

Mitochondrial regulation of diabetic endothelial dysfunction: Pathophysiological links.

Micro- and macrovascular complications frequently occur in patients with diabetes, with endothelial dysfunction playing a key role in the development and progression of the complications. For the early diagnosis and optimal treatment of vascular complications associated with diabetes, it is imperative to comprehend the cellular and molecular mechanisms governing the function of diabetic endothelial cells. Mitochondria function as crucial sensors of environmental and cellular stress regulating endothelial cell viability, structural integrity and function. Impaired mitochondrial quality control mechanisms and mitochondrial dysfunction are the main features of endothelial damage. Hence, targeted mitochondrial therapy is considered promising novel therapeutic options in vascular complications of diabetes. In this review, we focus on the mitochondrial functions in the vascular endothelial cells and the pathophysiological role of mitochondria in diabetic endothelial dysfunction, aiming to provide a reference for related drug development and clinical diagnosis and treatment.

Advances in secondary prevention mechanisms of macrovascular complications in type 2 diabetes mellitus patients: a comprehensive review.

Type 2 diabetes mellitus (T2DM) poses a significant global health burden. This is particularly due to its macrovascular complications, such as coronary artery disease, peripheral vascular disease, and cerebrovascular disease, which have emerged as leading contributors to morbidity and mortality. This review comprehensively explores the pathophysiological mechanisms underlying these complications, protective strategies, and both existing and emerging secondary preventive measures. Furthermore, we delve into the applications of experimental models and methodologies in foundational research while also highlighting current research limitations and future directions. Specifically, we focus on the literature published post-2020 concerning the secondary prevention of macrovascular complications in patients with T2DM by conducting a targeted review of studies supported by robust evidence to offer a holistic perspective.

Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus.

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

The role of machine learning in advancing diabetic foot: a review.

Background: Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies. Objective: This review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics. Methods: A meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as "Machine Learning," "Diabetic Foot," "Diabetic Foot Ulcers," "Diabetic Foot Care," "Artificial Intelligence," and "Predictive Modeling." This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved. Results: ML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care. Conclusion: The review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.

The durable effect of acupuncture for episodic migraine: a systematic review and meta-analysis.

Background: Migraine is a common and recurrent type of headache. Avoiding trigger factors is not often successful in reducing headache frequency, duration, and severity. Prophylactic medications may be effective but are limited by strict indications and daily medication intake. This review aimed to investigate the durable effect of acupuncture on episodic migraine. Methods: Seven databases including Medline, Embase, PubMed, etc., were searched for English and Chinese literature from their inception to 23 November 2022. Two independent reviewers screened the retrieved studies and extracted the data. Primary outcomes were monthly migraine days, monthly migraine attacks, and VAS score at 3 months post-treatment. The risk of bias in included studies was assessed using the Cochrane Risk of Bias 2.0 tool. Meta-analysis was conducted where applicable. Results: Fifteen studies were included in this review. Acupuncture reduced the number of migraine attacks (MD -0.68; 95% CI -0.93, -0.43; p < 0.001), the number of days with migraine (MD -0.86; 95% CI -1.18, -0.55; p < 0.001), and VAS score (MD -1.01; 95% CI -1.30, -0.72; p < 0.001) to a greater degree than sham acupuncture at 3 months after treatment. Significant differences in reducing pain intensity of migraine in favor of acupuncture compared with waitlist (MD -1.84; 95% CI -2.31, -1.37; p < 0.001) or flunarizine (MD -2.00; 95% CI -2.35, -1.65; p < 0.001) at 3 months after treatment were found, and the differences reached the minimal clinically important difference (MCID). Conclusion: This review found that the durable effect of acupuncture for episodic migraine lasted at least 3 months after treatment. More high-quality studies with longer follow-up periods in the future are needed to confirm the findings.

Graphene-Based ESD Protection for Future ICs.

On-chip electrostatic discharge (ESD) protection is required for all integrated circuits (ICs). Conventional on-chip ESD protection relies on in-Si PN junction-based device structures for ESD. However, such in-Si PN-based ESD protection solutions pose significant challenges related to ESD protection design overhead, including parasitic capacitance, leakage current, and noises, as well as large chip area consumption and difficulty in IC layout floor planning. The design overhead effects of ESD protection devices are becoming unacceptable to modern ICs as IC technologies continuously advance, which is an emerging design-for-reliability challenge for advanced ICs. In this paper, we review the concept development of disruptive graphene-based on-chip ESD protection comprising a novel graphene nanoelectromechanical system (gNEMS) ESD switch and graphene ESD interconnects. This review discusses the simulation, design, and measurements of the gNEMS ESD protection structures and graphene ESD protection interconnects. The review aims to inspire non-traditional thinking for future on-chip ESD protection.

Iron metabolism and ferroptosis in type 2 diabetes mellitus and complications: mechanisms and therapeutic opportunities.

The maintenance of iron homeostasis is essential for proper endocrine function. A growing body of evidence suggests that iron imbalance is a key factor in the development of several endocrine diseases. Nowadays, ferroptosis, an iron-dependent form of regulated cell death, has become increasingly recognized as an important process to mediate the pathogenesis and progression of type 2 diabetes mellitus (T2DM). It has been shown that ferroptosis in pancreas ß cells leads to decreased insulin secretion; and ferroptosis in the liver, fat, and muscle induces insulin resistance. Understanding the mechanisms concerning the regulation of iron metabolism and ferroptosis in T2DM may lead to improved disease management. In this review, we summarized the connection between the metabolic pathways and molecular mechanisms of iron metabolism and ferroptosis in T2DM. Additionally, we discuss the potential targets and pathways concerning ferroptosis in treating T2DM and analysis the current limitations and future directions concerning these novel T2DM treatment targets.

Comparison of the effects of different percentages of soy protein in the diet on patients with type 2 diabetic nephropathy: systematic reviews and network meta-analysis.

Background: Dietary soy protein (SP) is a potential intervention for protecting the kidneys and improving glucose and lipid metabolism. However, whether this effect is related to the percentage of SP intake remains unclear. Objective: This study aims to review and analyze the results of randomized clinical trials (RCTs) in patients with type 2 diabetic nephropathy (T2DN) who received diets with different percentages of SP. Methods: The databases: PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, China National Knowledge Infrastructure (CNKI), Chinese BioMedical Literature Database (CBM), WanFang, Weipu (VIP), and ClinicalTrials.gov were searched until February 2023, for RCTs on T2DN and SP. Results: A total of six studies comprising 116 participants were included. The interventions were classified as 0% SP, 35% SP, and 100% SP. To improve serum creatinine (Scr), blood urea nitrogen (BUN), 24-h urine total protein (24hUTP), and glomerular filtration rate (GFR), a 35% SP diet was the most effective, compared to a 0% SP diet, which showed a mean difference of -154.00 (95% confidence interval: -266.69, -41.31) for 24hUTP. Although it had significant benefits for 24hUTP, great heterogeneity was observed. To improve the glycolipid metabolism-related markers such as cholesterol (CHO), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FPG), and weight, the 35% SP diet demonstrated superior efficacy compared to the 0% SP diet. Specifically, the mean difference for CHO was -0.55 (95% confidence interval: -1.08, -0.03), and for LDL-C, it was -17.71 (95% confidence interval: -39.67, -4.24). The other indicators were not statistically significant. Most studies had concerns regarding the risk of bias. Conclusion: The findings of this study demonstrate that both 35% and 100% SP diets are more effective than a diet with no SP in improving renal function and glucolipid metabolism in patients with T2DN. As a result, a diet incorporating 35% SP may be the optimal choice for individuals with T2DN. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=352638, identifier CRD42022352638.

Effects and Mechanistic Role of Mulberry Leaves in Treating Diabetes and its Complications.

Diabetes mellitus (DM) has become a surge burden worldwide owing to its high prevalence and range of associated complications such as coronary artery disease, blindness, stroke, and renal failure. Accordingly, the treatment and management of DM have become a research hotspot. Mulberry leaves (Morus alba L.) have been used in Traditional Chinese Medicine for a long time, with the first record of its use published in Shennong Bencao Jing (Shennong's Classic of Materia Medica). Mulberry leaves (MLs) are considered highly valuable medicinal food homologs that contain polysaccharides, flavonoids, alkaloids, and other bioactive substances. Modern pharmacological studies have shown that MLs have multiple bioactive effects, including hypolipidemic, hypoglycemic, antioxidation, and anti-inflammatory properties, with the ability to protect islet [Formula: see text]-cells, alleviate insulin resistance, and regulate intestinal flora. However, the pharmacological mechanisms of MLs in DM have not been fully elucidated. In this review, we summarize the botanical characterization, traditional use, chemical constituents, pharmacokinetics, and toxicology of MLs, and highlight the mechanisms involved in treating DM and its complications. This review can provide a valuable reference for the further development and utilization of MLs in the prevention and treatment of DM.

Effectiveness and safety of traditional Chinese medicine decoction for diabetic gastroparesis: A network meta-analysis.

BACKGROUND: Diabetic gastroparesis (DGP) is a prevalent complication of diabetes that impairs people's quality of life and places a significant financial burden on them. The gastrointestinal symptoms of DGP patients can be improved by several Traditional Chinese Medicine (TCM) decoctions that have been shown to be effective in treating the disease. There are still many unanswered questions regarding the identification of appropriate therapeutic agents for the treatment of DGP in clinical practice. AIM: To analyze the efficacy of several TCM decoctions in the treatment of DGP using Bayesian network meta-analysis for reference. METHODS: PubMed, EMBASE, Cochrane Library, Web of Science, China National Kno-wledge Infrastructure, The China Biology Medicine DVD, Wanfang, and CQVIP were searched from inception to September 17, 2022, to collect randomized controlled trials (RCTs) about TCM decoctions for DGP. Clinical effects and symptom scores were the primary outcomes. Additionally, we assessed motilin (MOT), somatostatin (SS), gastrin (GAS), gastric emptying rate, gastric emptying time, and adverse drug events as secondary outcomes. RESULTS: A total of 67 eligible RCTs involving 4790 DGP patients and 7 TCM decoctions were included. The results of network meta-analysis (NMA) and surface under the cumulative ranking curve showed that with western medicine (WM) as a common control, the Banxia Xiexin Decoction (BXXD) + WM was most effective in clinical effects and enhancing early satiety scores; the Simo decoction (SMD) + WM was most effective in improving nausea and vomiting scores and anorexia scores, bloating scores; the Chaishao Liujunzi Decoction (CSLJD) was most effective in MOT, the Zhishi Xiaopi Decoction (ZSXPD) was most effective in SS and upgrading emptying rate; the Jianpi Xiaozhi Decoction was most effective in GAS; the CSLJD + WM was most effective in improving gastric emptying time. CONCLUSION: These NMA results suggest that the BXXD + WM and SMD + WM may be one of the potential optimal treatments. Due to various limitations, further large-sample, double-blind, multi-center randomized RCTs are needed.

Advances in multi-omics study of biomarkers of glycolipid metabolism disorder.

Glycolipid metabolism disorder are major threats to human health and life. Genetic, environmental, psychological, cellular, and molecular factors contribute to their pathogenesis. Several studies demonstrated that neuroendocrine axis dysfunction, insulin resistance, oxidative stress, chronic inflammatory response, and gut microbiota dysbiosis are core pathological links associated with it. However, the underlying molecular mechanisms and therapeutic targets of glycolipid metabolism disorder remain to be elucidated. Progress in high-throughput technologies has helped clarify the pathophysiology of glycolipid metabolism disorder. In the present review, we explored the ways and means by which genomics, transcriptomics, proteomics, metabolomics, and gut microbiomics could help identify novel candidate biomarkers for the clinical management of glycolipid metabolism disorder. We also discuss the limitations and recommended future research directions of multi-omics studies on these diseases.

Modulation of Gut Microbiota and Metabolites by Berberine in Treating Mice With Disturbances in Glucose and Lipid Metabolism.

Introduction: Glucose and lipid metabolism disturbances has become the third major disease after cancer and cardio-cerebrovascular diseases. Emerging evidence shows that berberine can effectively intervene glucose and lipid metabolism disturbances, but the underlying mechanisms of this remain unclear. To investigate this issue, we performed metagenomic and metabolomic analysis in a group of normal mice (the NC group), mice with disturbances in glucose and lipid metabolism (the MC group) and mice with disturbances in glucose and lipid metabolism after berberine intervention (the BER group). Result: Firstly, analysis of the clinical indicators revealed that berberine significantly improved the blood glucose and blood lipid of the host. The fasting blood glucose level decreased by approximately 30% in the BER group after 8 weeks and the oral glucose tolerance test showed that the blood glucose level of the BER group was lower than that of the MC group at any time. Besides, berberine significantly reduced body weight, total plasma cholesterol and triglyceride. Secondly, compared to the NC group, we found dramatically decreased microbial richness and diversity in the MC group and BER group. Thirdly, LDA effect size suggested that berberine significantly altered the overall gut microbiota structure and enriched many bacteria, including Akkermansia (p < 0.01), Eubacterium (p < 0.01) and Ruminococcus (p < 0.01). Fourthly, the metabolomic analysis suggested that there were significant differences in the metabolomics signature of each group. For example, isoleucine (p < 0.01), phenylalanine (p < 0.05), and arbutin (p < 0.05) significantly increased in the MC group, and berberine intervention significantly reduced them. The arbutin content in the BER group was even lower than that in the NC group. Fifthly, by combined analysis of metagenomics and metabolomics, we observed that there were significantly negative correlations between the reduced faecal metabolites (e.g., arbutin) in the BER group and the enriched gut microbiota (e.g., Eubacterium and Ruminococcus) (p < 0.05). Finally, the correlation analysis between gut microbiota and clinical indices indicated that the bacteria (e.g., Eubacterium) enriched in the BER group were negatively associated with the above-mentioned clinical indices (p < 0.05). Conclusion: Overall, our results describe that the changes of gut microbiota and metabolites are associated with berberine improving glucose and lipid metabolism disturbances.

Selective Overview of 3D Heterogeneity in CMOS.

As the demands for improved performance of integrated circuit (IC) chips continue to increase, while technology scaling driven by Moore's law is becoming extremely challenging, if not impractical or impossible, heterogeneous integration (HI) emerges as an attractive pathway to further enhance performance of Si-based complementary metal-oxide-semiconductor (CMOS) chips. The underlying basis for using HI technologies and structures is that IC performance goes well beyond classic logic functions; rather, functionalities and complexity of smart chips span across the full information chain, including signal sensing, conditioning, processing, storage, computing, communication, control, and actuation, which are required to facilitate comprehensive human-world interactions. Therefore, HI technologies can bring in more function diversifications to make system chips smarter within acceptable design constraints, including costs. Over the past two decades or so, a large number of HI technologies have been explored to increase heterogeneities in materials, technologies, devices, circuits, and system architectures, making it practically impossible to provide one single comprehensive review of everything in the field in one paper. This article chooses to offer a topical overview of selected HI structures that have been validated in CMOS platforms, including a stacked-via vertical magnetic-cored inductor structure in CMOSs, a metal wall structure in the back end of line (BEOL) of CMOSs to suppress global flying noises, an above-IC graphene nano-electromechanical system (NEMS) switch and nano-crossbar array electrostatic discharge (ESD) protection structure, and graphene ESD interconnects.

Akt: A Potential Drug Target for Metabolic Syndrome.

The serine/threonine kinase Akt, also known as protein kinase B (PKB), is one of the key factors regulating glucose and lipid energy metabolism, and is the core focus of current research on diabetes and metabolic diseases. Akt is mostly expressed in key metabolism-related organs and it is activated in response to various stimuli, including cell stress, cell movement, and various hormones and drugs that affect cell metabolism. Genetic and pharmacological studies have shown that Akt is necessary to maintain the steady state of glucose and lipid metabolism and a variety of cellular responses. Existing evidence shows that metabolic syndrome is related to insulin resistance and lipid metabolism disorders. Based on a large number of studies on Akt-related pathways and reactions, we believe that Akt can be used as a potential drug target to effectively treat metabolic syndrome.