ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Targeting MMP9 in CTNNB1 mutant hepatocellular carcinoma restores CD8+ T cell-mediated antitumour immunity and improves anti-PD-1 efficacy.

OBJECTIVE: The gain of function (GOF) CTNNB1 mutations (CTNNB1 GOF ) in hepatocellular carcinoma (HCC) cause significant immune escape and resistance to anti-PD-1. Here, we aimed to investigate the mechanism of CTNNB1 GOF HCC-mediated immune escape and raise a new therapeutic strategy to enhance anti-PD-1 efficacy in HCC. DESIGN: RNA sequencing was performed to identify the key downstream genes of CTNNB1 GOF associated with immune escape. An in vitro coculture system, murine subcutaneous or orthotopic models, spontaneously tumourigenic models in conditional gene-knock-out mice and flow cytometry were used to explore the biological function of matrix metallopeptidase 9 (MMP9) in tumour progression and immune escape. Single-cell RNA sequencing and proteomics were used to gain insight into the underlying mechanisms of MMP9. RESULTS: MMP9 was significantly upregulated in CTNNB1 GOF HCC. MMP9 suppressed infiltration and cytotoxicity of CD8+ T cells, which was critical for CTNNB1 GOF to drive the suppressive tumour immune microenvironment (TIME) and anti-PD-1 resistance. Mechanistically, CTNNB1 GOF downregulated sirtuin 2 (SIRT2), resulting in promotion of ß-catenin/lysine demethylase 4D (KDM4D) complex formation that fostered the transcriptional activation of MMP9. The secretion of MMP9 from HCC mediated slingshot protein phosphatase 1 (SSH1) shedding from CD8+ T cells, leading to the inhibition of C-X-C motif chemokine receptor 3 (CXCR3)-mediated intracellular of G protein-coupled receptors signalling. Additionally, MMP9 blockade remodelled the TIME and potentiated the sensitivity of anti-PD-1 therapy in HCC. CONCLUSIONS: CTNNB1 GOF induces a suppressive TIME by activating secretion of MMP9. Targeting MMP9 reshapes TIME and potentiates anti-PD-1 efficacy in CTNNB1 GOF HCC.

Characterization of sexual maturity-associated N6-methyladenosine in boar testes.

BACKGROUND: The health and size of the testes are crucial for boar fertility. Testicular development is tightly regulated by epigenetics. N6-methyladenosine (m6A) modification is a prevalent internal modification on mRNA and plays an important role in development. The mRNA m6A methylation in boar testicular development still needs to be investigated. RESULTS: Using the MeRIP-seq technique, we identify and profile m6A modification in boar testes between piglets and adults. The results showed 7783 distinct m6A peaks in piglets and 6590 distinct m6A peaks in adults, with 2,471 peaks shared between the two groups. Enrichment of GO and KEGG analysis reveal dynamic m6A methylation in various biological processes and signalling pathways. Meanwhile, we conjointly analyzed differentially methylated and expressed genes in boar testes before and after sexual maturity, and reproductive related genes (TLE4, TSSK3, TSSK6, C11ORF94, PATZ1, PHLPP1 and PAQR7) were identified. Functional enrichment analysis showed that differential genes are associated with important biological functions, including regulation of growth and development, regulation of metabolic processes and protein catabolic processes. CONCLUSION: The results demonstrate that m6A methylation, differential expression and the related signalling pathways are crucial for boar testicular development. These results suggest a role for m6A modification in boar testicular development and provided a resource for future studies on m6A function in boar testicular development.

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, existing OCSR methods fall far short of our expectations for realistic requirements due to their poor recovery accuracy. In this paper, we developed a deep neural network model named ABC-Net (Atom and Bond Center Network) to predict graph structures directly. Based on the divide-and-conquer principle, we propose to model an atom or a bond as a single point in the center. In this way, we can leverage a fully convolutional neural network (CNN) to generate a series of heat-maps to identify these points and predict relevant properties, such as atom types, atom charges, bond types and other properties. Thus, the molecular structure can be recovered by assembling the detected atoms and bonds. Our approach integrates all the detection and property prediction tasks into a single fully CNN, which is scalable and capable of processing molecular images quite efficiently. Experimental results demonstrate that our method could achieve a significant improvement in recognition performance compared with publicly available tools. The proposed method could be considered as a promising solution to OCSR problems and a starting point for the acquisition of molecular information in the literature.

BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions. RESULTS: We developed BioNet, a deep biological networkmodel with a graph encoder-decoder architecture. The graph encoder utilizes graph convolution to learn latent information embedded in complex interactions among chemicals, genes, diseases and biological pathways. The learning process is featured by two consecutive steps. Then, embedded information learnt by the encoder is then employed to make multi-type interaction predictions between chemicals and genes with a tensor decomposition decoder based on the RESCAL algorithm. BioNet includes 79 325 entities as nodes, and 34 005 501 relations as edges. To train such a massive deep graph model, BioNet introduces a parallel training algorithm utilizing multiple Graphics Processing Unit (GPUs). The evaluation experiments indicated that BioNet exhibits outstanding prediction performance with a best area under Receiver Operating Characteristic (ROC) curve of 0.952, which significantly surpasses state-of-theart methods. For further validation, top predicted CGIs of cancer and COVID-19 by BioNet were verified by external curated data and published literature.

Synthesized 7T MPRAGE From 3T MPRAGE Using Generative Adversarial Network and Validation in Clinical Brain Imaging: A Feasibility Study.

BACKGROUND: Ultra-high field 7T MRI can provide excellent tissue contrast and anatomical details, but is often cost prohibitive, and is not widely accessible in clinical practice. PURPOSE: To generate synthetic 7T images from widely acquired 3T images with deep learning and to evaluate the feasibility of this approach for brain imaging. STUDY TYPE: Prospective. POPULATION: 33 healthy volunteers and 89 patients with brain diseases, divided into training, and evaluation datasets in the ratio 4:1. SEQUENCE AND FIELD STRENGTH: T1-weighted nonenhanced or contrast-enhanced magnetization-prepared rapid acquisition gradient-echo sequence at both 3T and 7T. ASSESSMENT: A generative adversarial network (SynGAN) was developed to produce synthetic 7T images from 3T images as input. SynGAN training and evaluation were performed separately for nonenhanced and contrast-enhanced paired acquisitions. Qualitative image quality of acquired 3T and 7T images and of synthesized 7T images was evaluated by three radiologists in terms of overall image quality, artifacts, sharpness, contrast, and visualization of vessel using 5-point Likert scales. STATISTICAL TESTS: Wilcoxon signed rank tests to compare synthetic 7T images with acquired 7T and 3T images and intraclass correlation coefficients to evaluate interobserver variability. P < 0.05 was considered significant. RESULTS: Of the 122 paired 3T and 7T MRI scans, 66 were acquired without contrast agent and 56 with contrast agent. The average time to generate synthetic images was ~11.4 msec per slice (2.95 sec per participant). The synthetic 7T images achieved significantly improved tissue contrast and sharpness in comparison to 3T images in both nonenhanced and contrast-enhanced subgroups. Meanwhile, there was no significant difference between acquired 7T and synthetic 7T images in terms of all the evaluation criteria for both nonenhanced and contrast-enhanced subgroups (P ≥ 0.180). DATA CONCLUSION: The deep learning model has potential to generate synthetic 7T images with similar image quality to acquired 7T images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

MRI Assessment of Brain Frailty and Clinical Outcome in Patients With Acute Posterior Perforating Artery Infarction.

BACKGROUND: Global brain health has gained increasing attention recently. Imaging markers of brain frailty have been related to functional outcomes in previous studies on anterior circulation; however, little data are available on imaging markers and posterior circulation. PURPOSE: To investigate the impact of brain frailty on functional outcomes in patients with acute perforating artery infarction (PAI) of the posterior circulation. STUDY TYPE: Prospective. POPULATION: One hundred patients (60.78 ± 9.51 years, 72% men) with acute posterior circulation PAI (determined by diffusion-weighted magnetic resonance imaging (MRI)/time-of-flight MR angiography). FIELD STRENGTH/SEQUENCE: T1- and T2-weighted fast spin echo, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted echo planar, gradient echo (susceptibility-weight imaging), and 3D time-of-flight MR angiography sequences at 3.0 T. ASSESSMENT: Periventricular and deep white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS) in the basal ganglia and centrum semiovale area, lacunes, cerebral microbleeds (CMB), and total brain frailty score by calculating the above imaging characters were rated visually by three radiologists with 9, 10, and 11 years of experience and one neuroradiologist with 12. Infarction volume was assessed using baseline diffusion-weighted imaging (DWI) data obtained within 24 hours of symptom onset. A modified Rankin Scale (mRS) score >1 on day 90 defined an adverse functional outcome. Associations between the imaging markers of brain frailty and functional outcomes were assessed. STATISTICAL TESTS: Fisher's exact test, Mann-Whitney U test, and multivariable binary logistic regression. A P value <0.05 was considered statistically significant. RESULTS: Adverse prognoses (mRS > 1) were observed in 34 (34%) patients. Infarction volume, periventricular WMH, deep WMH, basal ganglia EPVS, CMB, and the brain frailty score were significantly associated with adverse functional outcomes. An increased brain frailty score was significantly associated with unfavorable mRS score on day 90 (odds ratio 1.773, 95% confidence interval 1.237-2.541). DATA CONCLUSION: Advanced MRI imaging markers of brain frailty, individually or combined as a total brain frailty score, were associated with worse functional outcomes after acute posterior circulation PAI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Interface Engineering-Modulated Nanoscale Bimetallic CoFe-MIL-88A In-Situ-Grown on 2D V2CTx MXene for Electrocatalytic Nitrogen Reduction.

The electrochemical nitrogen reduction reaction (eNRR) provides a sustainable green development route for the nitrogen-neutral cycle. In this work, bimetallic CoFe-MIL-88A with two active sites (Fe, Co) were immobilized on a 2D V2CTx MXene surface by in situ growth method to achieve the purpose of the control interface. A large number of heterostructures are formed between small CoFe-MIL-88A and V2CTx, which regulate the electron transfer between the catalyst interfaces. The adsorption and activation of nitrogen on the active sites were enhanced, and the NRR reaction kinetics was accelerated. CoFe-MIL-88A is tightly arranged on V2CTx, which makes CoFe-MIL-88A/V2CTx have better hydrophobicity and can significantly inhibit the hydrogen evolution reaction. The synergistic effect of multicatalytic active sites and multi-interface structure of CoFe-MIL-88A/V2CTx MXene is propitious to nitrogen efficiently and stably to convert into ammonia under environmental conditions with superior selectivity and good catalytic activity. The NH3 yield rate is 29.47 µg h-1 mgcat-1 at -0.3 V vs RHE, and the Faradaic efficiency (FE) is 28.86% at -0.1 V vs RHE. The catalytic mechanism was verified to conform to the distal pathway. This work will provide a new way to develop an MXene-based electrocatalyst for eNRR.

Collective effects of rising average temperatures and heat events on oviparous embryos.

Survival of the immobile embryo in response to rising temperature is important to determine a species' vulnerability to climate change. However, the collective effects of 2 key thermal characteristics associated with climate change (i.e., rising average temperature and acute heat events) on embryonic survival remain largely unexplored. We used empirical measurements and niche modeling to investigate how chronic and acute heat stress independently and collectively influence the embryonic survival of lizards across latitudes. We collected and bred lizards from 5 latitudes and incubated their eggs across a range of temperatures to quantify population-specific responses to chronic and acute heat stress. Using an embryonic development model parameterized with measured embryonic heat tolerances, we further identified a collective impact of embryonic chronic and acute heat tolerances on embryonic survival. We also incorporated embryonic chronic and acute heat tolerance in hybrid species distribution models to determine species' range shifts under climate change. Embryos' tolerance of chronic heat (T-chronic) remained consistent across latitudes, whereas their tolerance of acute heat (T-acute) was higher at high latitudes than at low latitudes. Tolerance of acute heat exerted a more pronounced influence than tolerance of chronic heat. In species distribution models, climate change led to the most significant habitat loss for each population and species in its low-latitude distribution. Consequently, habitat for populations across all latitudes will shift toward high latitudes. Our study also highlights the importance of considering embryonic survival under chronic and acute heat stresses to predict species' vulnerability to climate change.

Efectos colectivos del aumento de las temperaturas promedio y los eventos de calor en embriones ovíparos Resumen La supervivencia de los embriones inmóviles en respuesta al incremento de temperatura es importante para determinar la vulnerabilidad de las especies al cambio climático. Sin embargo, los efectos colectivos de dos características térmicas claves asociadas con el cambio climático (i. e., aumento de temperatura promedio y eventos de calor agudo) sobre la supervivencia embrionaria permanecen en gran parte inexplorados. Utilizamos mediciones empíricas y modelos de nicho para investigar cómo el estrés térmico crónico y agudo influye de forma independiente y colectiva en la supervivencia embrionaria de los lagartos en todas las latitudes. Recolectamos y criamos lagartos de cinco latitudes e incubamos sus huevos en un rango de temperaturas para cuantificar las respuestas específicas de la población al estrés por calor crónico y agudo. Posteriormente, mediante un modelo de desarrollo embrionario parametrizado con mediciones de tolerancia embrionaria al calor, identificamos un impacto colectivo de las tolerancias embrionarias al calor agudo y crónico en la supervivencia embrionaria. También incorporamos la tolerancia embrionaria crónica y aguda al calor en modelos de distribución de especies híbridas para determinar los cambios de distribución de las especies bajo el cambio climático. La tolerancia embrionaria al calor crónico (T­crónico) permaneció constante, mientras que la tolerancia al calor agudo (T­agudo) fue mayor en latitudes altas que en latitudes bajas. La tolerancia al calor agudo ejerció una influencia más pronunciada que la tolerancia al calor crónico. En los modelos de distribución de especies, el cambio climático provocó la pérdida de hábitat más significativa para cada población y especie en su distribución de latitudes bajas. En consecuencia, el hábitat para poblaciones en todas las latitudes se desplazará a latitudes altas. Nuestro estudio también resalta la importancia de considerar la supervivencia embrionaria bajo estrés térmico crónico y agudo para predecir la vulnerabilidad de las especies al cambio climático.

Organic amendments increased Chinese milk vetch symbiotic nitrogen fixation by enriching Mesorhizobium in rhizosphere.

Symbiotic nitrogen fixation of Chinese milk vetch (Astragalus sinicus L.) can fix nitrogen from the atmosphere and serve as an organic nitrogen source in agricultural ecosystems. Exogenous organic material application is a common practice of affecting symbiotic nitrogen fixation; however, the results of the regulation activities remain under discussion. Studies on the impact of organic amendments on symbiotic nitrogen fixation have focused on dissolved organic carbon content changes, whereas the impact on dissolved organic carbon composition and the underlying mechanism remain unclear. In situ pot experiments were carried out using soils from a 40-year-old field experiment platform to investigate symbiotic nitrogen fixation rate trends, dissolved organic carbon concentration and component, and diazotroph community structure in roots and in rhizosphere soils following long-term application of different exogenous organic substrates, i.e., green manure, green manure and pig manure, and green manure and rice straw. Remarkable increases in rate were observed in and when compared with that in green manure treatment, with the greatest enhancement observed in the treatment. Moreover, organic amendments, particularly pig manure application, altered diazotroph community composition in rhizosphere soils, therefore increasing the abundance of the host-specific genus Mesorhizobium. Furthermore, organic amendments influence the diazotroph communities through two primary mechanisms. Firstly, the components of dissolved organic carbon promote an increase in available iron, facilitated by the presence of humus substrates. Secondly, the elevated content of dissolved organic carbon and available iron expands the niche breadth of Mesorhizobium within the rhizosphere. Consequently, these alterations result in a modified diazotroph community within the rhizosphere, which in turn influences Mesorhizobium nodulation in the root and symbiotic nitrogen fixation rate. The results of the present study enhance our understanding of the impact of organic amendments on symbiotic nitrogen fixation and the underlying mechanism, highlighting the key role of dissolved organic carbon composition on diazotroph community composition in the rhizosphere.

Progesterone in Pregnancy: Evidence-Based Strategies to Reduce Miscarriage and Enhance Assisted Reproductive Technology.

The incidence of miscarriage in early pregnancy, between 5-20 weeks, is common, with a prevalence of between 5-22% of all pregnancies. Miscarriage can have physical, social, and mental health impacts on women and their families. In societies such as Taiwan, where the birth rate is falling and life expectancy is increasing, there is concern that factors that reduce birth rates will have detrimental economic and societal effects. Progesterone has a significant role in maintaining early and successful pregnancy to term. Evidence from preclinical and clinical research on the roles of progesterone has supported recent clinical guidelines in obstetrics and gynecology to reduce rates of early miscarriage and improve methods of assisted reproductive technology (ART). This article aims to present an evidence-based review of current recommendations for the use of progesterone in early pregnancy to reduce miscarriage rates and in luteal phase support for ART, including embryo transfer.

The effects of repeated freezing and thawing on bovine sperm morphometry and function.

Refreezing the remaining genetic resources after in vitro fertilization (IVF) can conserve genetic materials. However, the precise damage inflicted by repeated freezing and thawing on bovine sperm and its underlying mechanism remain largely unexplored. Thus, this study investigates the impact of repeated freeze-thaw cycles on sperm. Our findings indicate that such cycles significantly reduce sperm viability and motility. Furthermore, the integrity of the sperm plasma membrane and acrosome is compromised during this process, exacerbating the advanced apoptosis triggered by oxidative stress. Additionally, transmission electron microscopy exposed severe damage to the plasma membranes of both the sperm head and tail. Notably, the "9 + 2" structure of the tail was disrupted, along with a significant decrease in the level of the axonemal protein DNAH10, leading to reduced sperm motility. IVF outcomes revealed that repeated freeze-thaw cycles considerably impair sperm fertilization capability, ultimately reducing the blastocyst rate. In summary, our research demonstrates that repeated freeze-thaw cycles lead to a decline in sperm viability and motility, attributed to oxidative stress-induced apoptosis and DNAH10-related dynamic deficiency. As a result, the utility of semen is compromised after repeated freezing.

Development of a single nucleotide polymorphism-based strain-identified method for Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047 using pan-genomics analysis.

The health benefits conferred by probiotics is specific to individual probiotic strains, highlighting the importance of identifying specific strains for research and production purposes. Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047 are exceedingly valuable for commercial use with an excellent mixed-culture fermentation. To differentiate these 2 strains from other S. thermophilus and L. delbrueckii ssp. bulgaricus, a specific, sensitive, accurate, rapid, convenient, and cost-effective method is required. In this study, we conducted a pan-genome analysis of S. thermophilus and L. delbrueckii ssp. bulgaricus to identify species-specific core genes, along with strain-specific SNPs. These genes were used to develop suitable PCR primers, and the conformity of sequence length and unique SNPs was confirmed by sequencing for qualitative identification at the strain level. The results demonstrated that SNPs analysis of PCR products derived from these primers could distinguish CICC 6038 and CICC 6047 accurately and reproducibly from the other strains of S. thermophilus and L. delbrueckii ssp. bulgaricus, respectively. The strain-specific PCR method based on SNPs herein is universally applicable for probiotics identification. It offers valuable insights into identifying probiotics at the strain level that is fit-for-purpose in quality control and compliance assessment of commercial dairy products.

Prevalence and clinical outcomes in subtypes of metabolic associated fatty liver disease.

BACKGROUND/PURPOSE: In 2020, metabolic Associated Fatty Liver Disease (MAFLD) was proposed to replace non-alcoholic fatty liver disease (NAFLD) with new diagnostic criteria. The prevalence and clinical outcomes of MAFLD subtypes remained unclear. METHODS: The participants from Taiwan bio-bank cohort were included. MAFLD was defined as the presence of fatty liver, plus any of the following three conditions: overweight/obesity, diabetes mellitus (DM), or metabolic dysfunction. The patients with positive HBsAg or anti-HCV were considered as chronic HBV or HCV infection. NAFLD fibrosis score (NFS) > 0.676 plus fibrosis 4 (FIB-4) score > 2.67 was defined as advanced liver fibrosis. Atherosclerosis was diagnosed as having carotid plaques on duplex ultrasounds. The clinical outcomes were assessed among four subtypes of MAFLD including DM, obesity, chronic HBV infection, and chronic HCV infection. RESULTS: A total of 21,885 participants (mean age 55.34 ± 10.31; 35.69% males) were included in the final analysis. Among them, 38.83% were diagnosed with MAFLD. The prevalence of MAFLD was 66.95% in DM patients, 65.07% in obese participants, 33.74% in chronic HBV patients, and 30.23% in chronic HCV patients. Logistic regression analysis showed that the subtypes of DM and chronic HCV infection were associated with an increased risk of advanced liver fibrosis in MAFLD patients. Additionally, the subtypes of DM and lean were associated with an increased risk of atherosclerosis, but a decreased risk of atherosclerosis in the subtype of chronic HBV infection. CONCLUSION: This population-based study proves the concept that subtypes of MAFLD can help risk stratification of clinical outcomes.

The association between trajectory of serum cholesterol, statin dosage, and the risk of recurrent biliary stone diseases.

BACKGROUND: Statins may reduce the risk of recurrent gallstone disease by decreasing bile cholesterol saturation and pathogenicity. However, limited studies have investigated this issue. This study aimed to assess whether statin doses and serum cholesterol levels were associated with a decreased risk of recurrent biliary stone diseases after the first event index, with a follow-up time of 15 years. METHODS: Based on the Chang Gung Research Database (CGRD) between January 1, 2001, and December 31, 2020, we enrolled 68,384 patients with the International Classification of Diseases, Ninth and Tenth Revision codes of choledocholithiasis. After exclusions, 32,696 patients were divided into non-statin (<28 cDDD, cumulative defined daily doses) (n = 27,929) and statin (≥28 cDDD) (n = 4767) user groups for analysis. Serum cholesterol trajectories were estimated using group-based trajectory modeling (n = 8410). RESULTS: The statin users had higher Charlson Comorbidity Index (CCI) scores than the non-statin users. Time-dependent Cox regression analysis showed that statin use >365 cDDD was associated with a significantly lower risk of recurrent biliary stones (adjusted hazard ratio [aHR] = 0.28, 95% CI, 0.24-0.34; p < 00.0001), acute pancreatitis (aHR = 0.24, 95% CI, 0.17-0.32, p < 00.0001), and cholangitis (aHR = 0.28, 95% CI, 0.25-0.32, p < 00.0001). Cholecystectomy was also a protective factor for recurrent biliary stones (aHR = 0.41, 95% CI, 0.37-0.46; p < 00.0001). The higher trajectory serum cholesterol group (Group 3) had a lower risk trend for recurrent biliary stones (aHR = 0.79, p = 0.0700) and a lower risk of cholangitis (aHR = 0.79, p = 0.0071). CONCLUSION: This study supports the potential benefits of statin use and the role of cholecystectomy in reducing the risk of recurrent biliary stone diseases.

Interaction of vascular endothelial cells with hydrophilic fullerene nanoarchitectured structures in 2D and 3D environments.

The interaction between diverse nanoarchitectured fullerenes and cells is crucial for biomedical applications. Here, we detailed the preparation of hydrophilic self-assembled fullerenes by the liquid-liquid interfacial precipitation (LLIP) method and hydrophilic coating of the materials as a possible vascularization strategy. The interactions of vascular endothelial cells (ECs) with hydrophilic fullerene nanotubes (FNT-P) and hydrophilic fullerene nanowhiskers (FNW-P) were investigated. The average length and diameter of FNT-P were 16 ± 2 µm and 3.4 ± 0.4 µm (i.e. aspect ratios of 4.6), respectively. The average length and diameter of FNW-P were 65 ± 8 µm and 1.2 ± 0.2 µm (i.e. aspect ratios of 53.9), respectively. For two-dimensional (2D) culture after 7 days, the ECs remained viable and proliferated up to ~ 420% and ~ 400% with FNT-P and FNW-P of 50 µg/mL, respectively. Furthermore, an optimized chitosan-based self-healing hydrogel with a modulus of ~400 Pa was developed and used to incorporate self-assembled fullerenes as in vitro three-dimensional (3D) platforms to investigate the impact of FNT-P and FNW-P on ECs within a 3D environment. The addition of FNW-P or FNT-P (50 µg/mL) in the hydrogel system led to proliferation rates of ECs up to ~323% and ~280%, respectively, after 7 days of culture. The ECs in FNW-P hydrogel displayed an elongated shape with aligned morphology, while those in FNT-P hydrogel exhibited a rounded and clustered distribution. Vascular-related gene expressions of ECs were significantly upregulated through interactions with these fullerenes. Thus, the combined use of different nanoarchitectured self-assembled fullerenes and self-healing hydrogels may offer environmental cues influencing EC development in a 3D biomimetic microenvironment, holding promise for advancing vascularization strategy in tissue engineering.

Self-assembled fullerenes with large aspect ratios modulate the morphology and gene expression of endothelial cells within a soft biomimetic 3D microenvironment, representing a promising new vascularization strategy in tissue engineering.

CD226 deficiency exacerbated intestinal immune dysregulation in mice with dinitrochlorobenzene-induced atopic dermatitis.

Intestinal mucosal immunity plays a pivotal role in host defence. In this study, we found that cluster of differentiation 226 (CD226) gene knockout (KO) led to more severe atopic dermatitis (AD)-related skin pathologies and bowel abnormalities in a 2,4-dinitrochlorobenzene (DNCB)-induced AD-like mouse model. Following DNCB administration, the expression of CD226 was elevated in intestinal mucosal tissues, including group 3 innate lymphoid cells (ILC3s) and CD4+ T cells of Peyer's patches (PPs). CD226 deficiency led to an overactive intestinal immune response in the AD-like mice, as evidenced by increased inflammation and Th1/Th2-related cytokine levels as well as increased Paneth cell numbers and antimicrobial peptide (AMP) expression, which was likely due to the higher interleukin (IL)-22 production in the lamina propria. Additionally, CD226 deficiency increased the production of IL-4 and IL-17 in mesenteric lymph nodes as well as the number of PPs and expression of immunoglobulin (Ig) A in B cells. Moreover, insufficient expression of CD226 affected the characterization of intraepithelial and lamina propria lymphocytes in the intestinal mucosa. Finally, the number of PPs was increased in CD4+ T cell-specific CD226 KO and regulatory T (Treg) cell-specific CD226 KO mice; thus, loss of CD226 in Treg cells resulted in impaired Treg cell-suppressive function. Therefore, our findings indicate that CD226 deficiency alters intestinal immune functionality in inflammatory diseases.

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular input line entry specification (SMILES) strings, which contain all the composition and structure information of molecules. However, current models usually suffer from the scarcity of labeled data. This results in a low generalization ability of SMILES-based DL models, which prevents them from competing with the state-of-the-art computational methods. In this study, we utilized the BiLSTM (bidirectional long short term merory) attention network (BAN) in which we employed a novel multi-step attention mechanism to facilitate the extracting of key features from the SMILES strings. Meanwhile, SMILES enumeration was utilized as a data augmentation method in the training phase to substantially increase the number of labeled data and enlarge the probability of mining more patterns from complex SMILES. We again took advantage of SMILES enumeration in the prediction phase to rectify model prediction bias and provide a more accurate prediction. Combined with the BAN model, our strategies can greatly improve the performance of latent features learned from SMILES strings. In 11 canonical absorption, distribution, metabolism, excretion and toxicity-related tasks, our method outperformed the state-of-the-art approaches.

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction.

MOTIVATION: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Nevertheless, when applied to molecular property prediction, AI models usually suffer from the scarcity of labeled data and show poor generalization ability. RESULTS: In this study, we proposed molecular graph BERT (MG-BERT), which integrates the local message passing mechanism of graph neural networks (GNNs) into the powerful BERT model to facilitate learning from molecular graphs. Furthermore, an effective self-supervised learning strategy named masked atoms prediction was proposed to pretrain the MG-BERT model on a large amount of unlabeled data to mine context information in molecules. We found the MG-BERT model can generate context-sensitive atomic representations after pretraining and transfer the learned knowledge to the prediction of a variety of molecular properties. The experimental results show that the pretrained MG-BERT model with a little extra fine-tuning can consistently outperform the state-of-the-art methods on all 11 ADMET datasets. Moreover, the MG-BERT model leverages attention mechanisms to focus on atomic features essential to the target property, providing excellent interpretability for the trained model. The MG-BERT model does not require any hand-crafted feature as input and is more reliable due to its excellent interpretability, providing a novel framework to develop state-of-the-art models for a wide range of drug discovery tasks.

Trace photoacoustic SO2 gas sensor in SF6 utilizing a 266†nm UV laser and an acousto-optic power stabilizer.

A sulfur dioxide (SO2) gas sensor based on the photoacoustic spectroscopy technology in a sulfur hexafluoride (SF6) gas matrix was demonstrated for SF6 decomposition components monitoring in the power system. A passive Q-switching laser diode (LD) pumped all-solid-state 266 nm deep-ultraviolet laser was exploited as the laser excitation source. The photoacoustic signal amplitude is linear related to the incident optical power, whereas, a random laser power jitter is inevitable since the immature laser manufacturing technology in UV spectral region. A compact laser power stabilization system was developed for better sensor performance by adopting a photodetector, a custom-made internal closed-loop feedback controller and a Bragg acousto-optic modulator (AOM). The out-power stability of 0.04% was achieved even though the original power stability was 0.41% for ∼ 2 hours. A differential two-resonator photoacoustic cell (PAC) was designed for weak photoacoustic signal detection. The special physical constants of SF6 buffer gas induced a high-Q factor of 85. A detection limit of 140 ppbv was obtained after the optimization, which corresponds to a normalized noise equivalent absorption coefficient of 3.2 × 10-9 cm-1WHz-1/2.