Novel drug-drug salt crystals of metformin with ibuprofen or naproxen: Improved solubility, dissolution rate, and synergistic antinociceptive effects.

As the monotherapy of available analgesics is usually accompanied by serious side effects or limited efficacy in the management of chronic pain, multimodal analgesia is widely used to achieve improved benefit-to-risk ratios in clinic. Drug-drug salts are extensively researched to optimize the physicochemical properties of active pharmaceutical ingredients (APIs) and achieve clinical benefits compared with individual APIs or their combination. New drug-drug salt crystals metformin-ibuprofen (MET-IBU) and metformin-naproxen (MET-NAP) were prepared from metformin (MET) and two poorly water-soluble anti-inflammatory drugs (IBU and NAP) by the solvent evaporation method. The structures of these crystals were confirmed by single crystal and powder X-ray diffraction, Hirshfeld surface, Fourier transform infrared spectroscopy and thermal analysis. Both MET-IBU and MET-NAP showed significantly improved solubility and intrinsic dissolution rate than the pure IBU or NAP. The stability test indicated that MET-IBU and MET-NAP have excellent physical stability under stressing test (10 days) and accelerated conditions (3 months). Moreover, isobolographic analysis suggested that MET-IBU and MET-NAP exerted potent and synergistic antinociceptive effects in λ-Carrageenan-induced inflammatory pain in mice, and both of them had an advantage in rapid pain relief. These results demonstrated the potential of MET-IBU and MET-NAP to achieve synergistic antinociceptive effects by developing drug-drug salt crystals.

Correlation of GABA+ levels in the medial prefrontal cortex and circulating follicular helper T cells in neuromyelitis optica spectrum disorder patients with cognitive impairment.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) associated with cognitive impairment (CI) is acknowledged. However, the underlying pathogenesis and involvement of the immune system remain unclear. OBJECTIVES: This study aimed to investigate the alterations in immune cells, cytokines, and GABA+ levels in NMOSD patients with cognitive deficits. METHODS: Thirty-eight NMOSD patients and 38 healthy controls (HCs) were included. NMOSD patients were stratified as NMOSD-CI and NMOSD-CP groups. The difference in cognitive functions, Tfh and cytokines, and GABA+ levels were assessed, and their correlations were calculated. RESULTS: NMOSD-CI patients showed worse performance on all cognitive tests, and the percentage of circulating follicular helper T cells (cTfh) was significantly elevated. The frequency of cTfh was positively and negatively correlated with Stroop-A and AVLT long-delayed scores, respectively. IL-21 was remarkably higher in NMOSD-CI and NMOSD-CP. The level of GABA+ in medial prefrontal cortex (mPFC) was significantly decreased in NMOSD-CI and was proved positively and negatively correlated with Symbol Digit Modalities Test and the frequency of circulating Tfh cells, respectively. CONCLUSION: In NMOSD-CI patients, all cognitive domains were impacted, , while GABA+ levels in mPFC were decreased. GABA+ levels in NMOSD-CI were negatively correlated with the frequency of cTfh, suggesting the underlying coupling mechanism between immune responses and neurotransmitter metabolism in CI in NMOSD patients.

Metabolomics perspectives into the co-exposure effect of polycyclic aromatic hydrocarbons and metals on renal function: A meet-in-the-middle approach.

Studies on the dose effects of kidney impairment and metabolomes in co-exposure to polycyclic aromatic hydrocarbons (PAHs) and metals are limited. We aimed to identify overall associations and metabolic perturbations in 130 participants (53 petrochemical workers and 77 controls) exposed to a PAHs-metals mixture in Southern China. The urinary 7 hydroxylated PAHs and 15 metal(loid)s were determined, and serum creatinine, beta-2 microglobulin, and estimated glomerular filtration rate were health outcomes. The liquid chromatography-mass spectrometry-based method was applied to serum metabolomics. Generalized weighted quantile sum (gWQS) regressions were used to estimate the overall dose-response relationships, and pathway analysis, "meet-in-the-middle" approach, and mediation effect analyses were conducted to identify potential metabolites and biological mechanisms linking exposure with nephrotoxic effects. Our results indicated that renal function reduction was associated with a PAHs-metals mixture in a dose-dependent manner, and 1-hydroxynaphthalene and copper were the most predominant contributors among the two families of pollutants. Furthermore, the metabolic disruptions associated with the early onset of kidney impairment induced by the combination of PAHs and metals encompassed pathways such as phenylalanine-tyrosine-tryptophan biosynthesis, phenylalanine metabolism, and alpha-linolenic acid metabolism. In addition, the specifically identified metabolites demonstrated excellent potential as bridging biomarkers connecting the reduction in renal function with the mixture of PAHs and metals. These findings shed light on understanding the overall associations and metabolic mechanism of nephrotoxic effects of co-exposure to PAHs and metals.

Daytime sleep duration during infancy as an indicator for cognitive development at school age: a prospective cohort study.

STUDY OBJECTIVES: The reduction in daytime sleep during early life is considered one of the indicators of the maturation of sleep patterns, which is closely associated with cognitive development. The current study aims to analyze the relationships between daytime sleep duration (DSD) during infancy and cognitive development at 6 and 10 years. METHODS: The study included 262 mothers with their newborns from the Shanghai Sleep Birth Cohort Study, spanning eleven follow-ups from 42 days to 10 years. Sleep parameters were assessed using parent-report questionnaires at each follow-up, and cognitive development was evaluated with the Wechsler Intelligence Scale for Children, 4th edition at 6 and 10 years. RESULTS: Two distinct DSD trajectories in early childhood were identified: "typical DSD" (66.7%) and "infancy excessive DSD" (33.3%). Children in the "infancy excessive DSD" trajectory exhibited lower working memory scores than those in the "typical DSD" trajectory at 6 years (Mean difference=5.90, 95% CI [1.83, 9.96], p=0.005) and 10 years (Mean difference=4.37, 95% CI [0.26, 8.48], p=0.037). Additional analysis in a relatively homogeneous sample consistently showed correlations between DSD trajectories and working memory performance. No consistent significant differences were found in other domains of cognitive development. CONCLUSIONS: Excessive daytime sleep during infancy may serve as an early indicator for poor working memory at school age. These findings raise concerns about the long-term cognitive development of infants with excessive DSD.

The Interface Strengthening of Multi-Walled Carbon Nanotubes/Polylactic Acid Composites via the In-Loop Hybrid Manufacturing Method.

In this study, a new in-loop hybrid manufacturing method is proposed for fabricating multi-walled carbon nanotube (MWCNTs)/polylactic acid (PLA) composites. Molecular dynamics simulations were conducted in conjunction with experiments to reveal the mechanism of the proposed method for improving the interfacial performance of MWCNTs/PLA. The superposed gradients in the PLA chain activity and conformation due to the plasma-actuating MWCNTs promoted intermolecular interaction and infiltration between the MWCNTs and PLA chains, forming an MWCNTs-stress-transfer bridge in the direction perpendicular to the interlayer interface, and finally enhancing the performance of the composites. The experimental results indicated that the interfacial shear strength of the specimen fabricated using the proposed method increased by 30.50% to 43.26 MPa compared to those without the addition of MWCNTs, and this value was 4.77 times higher than that of the traditional manufacturing method, demonstrating the effectiveness of the proposed method in improving the interfacial properties of MWCNTs/PLA composites.

Pregabalin can interact synergistically with Kv7 channel openers to exert antinociception in mice.

Chronic pain is a common public health problem and remains an unmet medical need. Currently available analgesics usually have limited efficacy for the treatment of chronic pain, including neuropathic pain and persistent inflammatory pain, or they are accompanied by many adverse side effects. The voltage-gated calcium channel blocker (pregabalin) and potassium channel openers (flupirtine and retigabine) have been widely used for the management of chronic pain, but their effectiveness in combination is unclear. In this research, we evaluated the antinociceptive effects of pregabalin in combination with flupirtine or retigabine in carrageenan-induced inflammatory pain and paclitaxel-induced peripheral neuropathy in mice using the von Frey test. Isobolographic analysis indicated that pregabalin exerted synergistic antinociceptive effects when combined with flupirtine or retigabine in neuropathic and inflammatory pain models. Furthermore, the antinociceptive effects of pregabalin, flupirtine/retigabine, and their combinations were significantly attenuated by the Kv7 channel blocker XE991. The favored dose ratio between pregabalin and flupirtine/retigabine in combinations was also investigated. Finally, we evaluated the motor coordination of their combinations using the rotarod test, and the outcomes underpinned their safety. Collectively, our results support the potential use of pregabalin in combination with flupirtine or retigabine to alleviate chronic pain.

Flupirtine and antihistamines exert synergistic anti-nociceptive effects in mice.

RATIONALE: Drug combinations are commonly used in pain management, which can produce potent analgesic effects with reduced dosage and adverse effects. OBJECTIVE: This study was designed to evaluate the anti-nociceptive effects and adverse effects of new combinations of flupirtine (a Kv7 potassium channel opener) and antihistamines (promethazine, fexofenadine) on acute and chronic pain in mice, and the possible mechanisms behind the synergistic analgesic effects were preliminarily investigated. METHODS: In acetic acid writhing test, carrageenan-induced inflammatory pain model, and paclitaxel-induced neuropathic pain model, the interaction indexes (γ) between flupirtine and antihistamines were determined by isobolographic analysis. Furthermore, the Kv7 channel blocker XE991 was used to determine whether the effects of single agents and drug combinations on paclitaxel- and carrageenan-induced mechanical allodynia were mediated by Kv7 channels. Finally, hepatotoxicity markers, liver histopathology, and the rotarod test were used to investigate the adverse effects of drugs in combination doses. RESULTS: The interaction indexes of flupirtine-promethazine and flupirtine-fexofenadine in all the above three pain models were lower than 1. The analgesic effects of flupirtine (13 mg/kg), promethazine (5 mg/kg), fexofenadine (20 mg/kg), and their combinations were antagonized significantly by XE991 (3 mg/kg). And the adverse effects of flupirtine and antihistamines in combination doses were not significantly different from the vehicle group. CONCLUSIONS: Flupirtine and antihistamines produced synergistic analgesic effects in all the above pain models. The analgesic effects of antihistamines were partially mediated by Kv7/M channels, and the activation of Kv7/M channels may be partly responsible for the synergistic analgesic effects between flupirtine and antihistamines.

Design, synthesis and evaluation of heterocyclic 2-phenylacetate derivatives as water-soluble rapid recovery hypnotics.

In this work, a series of novel heterocyclic 2-phenylacetate derivatives were designed and synthesized as water-soluble and rapid recovery hypnotic agents. After introducing heterocyclic ring to the amide group of propanidid, the obtained propanidid derivatives showed greatly improved hydrophilicity and good anesthetic activity. In three animal experiments (mice, rats, and rabbits), compounds 13-15 showed potent hypnotic potency (HD50 = 7.6, 6.5, 7.4 mg/kg in rabbits, respectively) and higher therapeutic indexes (TI = 17.3, 16.6, 15.2 in rabbits, respectively) than propanidid (TI = 14.7 in rabbits) or propofol (TI = 5.4 in rabbits). Moreover, the recovery time of compounds 13-15 (time to walk, 96.6, 79.6, 81.4 s in rabbits, respectively) were shorter than that of propanidid (124.5 s in rabbits) or propofol (425.3 s in rabbits). The experimental results suggested the potential of compounds 13-15 as water-soluble anesthetics with rapid recovery profile.

B-cell proliferation characteristics and monitoring significance under the modified reduced-dose rituximab regimen for NMOSD: A real-world case series study.

OBJECTIVE: To explore the B-cell proliferation characteristics and monitoring significance under the modified reduced-dose rituximab (mRTX) regimen for neuromyelitis optica spectrum disorder (NMOSD). METHODS: NMOSD patients treated with mRTX were recruited, and the percentages of total CD19+ B cells and CD27+ memory B cells were dynamically detected by flow cytometry. The annualized relapse rate (ARR) and expanded disability status scale (EDSS) scores were compared before and after mRTX treatment, and the differences in B-cell values were compared between groups. RESULTS: A total of 34 patients with NMOSD were ultimately enrolled. The EDSS score decreased from 2.5 (1.5, 3.0) to 1.3 (1.0, 2.0), and the ARR decreased from 1.0 (0, 2.0) to 0 (0, 0) (p < 0.001). Relapses occurred in 6 patients, with total CD19+ B-cell percentages of 3.25% (2.7%, 3.7%) and CD27+ memory B-cell percentages of 0.3% (0.2%, 0.3%) at initial relapse. Twenty-eight patients (82.4%) remained relapse-free with 84 doses of mRTX. Before 56 repeated doses, the total CD19+ B cells and CD27+ memory B cells were 4.00% (3.14%, 5.32%) and 0.26% (0.17%, 0.40%), respectively. The mean dosing interval was 9.2 months. Both total CD19+ B cells and CD27+ memory B cells proliferated over time after mRTX use, with significantly faster proliferation rates in the later stages. In 28 relapse-free patients, the mean time to reach 1% for total CD19+ B cells was 210 days, and the mean time to reach 3% was 240 days, with the mean interval from 1% to 3% of 65 days. Twenty-five relapse-free patients had no significant differences in maximum, minimum, and mean B-cell values compared to those of 6 patients with relapse. CONCLUSION: The high rate of B-cell proliferation under the mRTX regimen indicates that closer dynamic B-cell monitoring is required to guide repeated mRTX dosing. Sustained depletion of total CD19+ B cells targeting < 3% of lymphocytes may be feasible, enabling extended dosing intervals.

Programmable Thermo-Responsive Self-Morphing Structures Design and Performance.

Additive manufacturing (AM), also known as 3D printing, was introduced to design complicated structures/geometries that overcome the manufacturability limitations of traditional manufacturing processes. However, like any other manufacturing technique, AM also has its limitations, such as the need of support structures for overhangs, long build time etc. To overcome these limitations of 3D printing, 4D printing was introduced, which utilizes smart materials and processes to create shapeshifting structures with the external stimuli, such as temperature, humidity, magnetism, etc. The state-of-the-art 4D printing technology focuses on the "form" of the 4D prints through the multi-material variability. However, the quantitative morphing analysis is largely absent in the existing literature on 4D printing. In this research, the inherited material anisotropic behaviors from the AM processes are utilized to drive the morphing behaviors. In addition, the quantitative morphing analysis is performed for designing and controlling the shapeshifting. A material-process-performance 4D printing prediction framework has been developed through a novel dual-way multi-dimensional machine learning model. The morphing evaluation metrics, bending angle and curvature, are obtained and archived at 99% and 93.5% R2, respectively. Based on the proposed method, the material and production time consumption can be reduced by around 65-90%, which justifies that the proposed method can re-imagine the digital-physical production cycle.

Neuromyelitis optica spectrum disorders associated with AQP4-positive-cancer-A case series.

Neuromyelitis optica spectrum disorders (NMOSD) are autoimmune, astrocytopathic diseases affecting the central nervous system(CNS), especially the central optic nerve and spinal cord. Aquaporin 4-immunoglobulin G (AQP4-IgG) is the dominant pathogenic antibody and can be detected in about 80% of patients with NMOSD. Although only a few cases were reported on NMOSD associated with cancer, they demonstrated the potential paraneoplastic link between cancer and NMOSD. In the present study, we report three NMOSD cases associated with cancer, which are teratoma and lung adenocarcinoma, teratoma, and transverse colon adenocarcinoma, respectively. Pathological staining of tumor sections revealed a high AQP4 expression. After tumor removal, all cases were stable and suffered no further relapses, which revealed the potential paraneoplastic mechanism between cancer and NMOSD. One of our patient's serum AQP4-IgG was transiently slightly elevated even though AQP4 was highly expressed in tumor cells, which indicates that AQP4 is not the main pathogenic antibody but might be induced by other underlying pathogenic antibody-antigen reactions.

Quality Quantification and Control via Novel Self-Growing Process-Quality Model of Parts Fabricated by LPBF Process.

Laser Powder Bed Fusion (LPBF) presents a more extensive allowable design complexity and manufacturability compared with the traditional manufacturing processes by depositing materials in a layer-wised manner. However, the process variability in the LPBF process induces quality uncertainty and inconsistency. Specifically, the mechanical properties, e.g., tensile strength, are hard to be predicted and controlled in the LPBF process. Much research has recently been reported exploring the qualitative influence of single/two process parameters on tensile strength. In fact, mechanical properties are comprehensively affected by multiple correlated process parameters with unclear and complex interactions. Thus, the study on the quantitative process-quality model of the metal LPBF process is urgently needed to provide an enough-strength component via the metal LPBF process. Recent progress in artificial intelligence (AI) and machine learning (ML) provides new insight into quality prediction in terms of computational accuracy and speed. However, the predictive model quality through the traditional AL/ML is heavily determined by the training data size, and the experimental analysis can be expansive on LPBF. This paper explores the comprehensive effect of the tensile strength of 316L stainless-steel parts on LPBF and proposes a valid quantitative predictive model through a novel self-growing machine-learning framework. The self-growing framework can autonomously expand and classify the growing dataset to provide a high-accuracy prediction with fewer input data. To verify this predictive model of tensile strength, specimens manufactured by the LPBF process with different group process parameters (laser power, scanning speed, and hatch spacing) are collected. The experimental results validate the predicted tensile strengths within a less than 3% deviation.

Combination strategies to optimize the efficacy of chimeric antigen receptor T cell therapy in haematological malignancies.

Chimeric antigen receptor (CAR) T cell therapy has revolutionized the therapeutic landscape of haematological malignancies. However, resistance and relapse remain prominent limitations, and they are related to the limited persistence and efficacy of CAR T cells, downregulation or loss of tumour antigens, intrinsic resistance of tumours to death signalling, and immune suppressive microenvironment. Rational combined modality treatments are regarded as a promising strategy to further unlock the antitumor potential of CAR T cell therapy, which can be applied before CAR T cell infusion as a conditioning regimen or in ex vivo culture settings as well as concomitant with or after CAR T cell infusion. In this review, we summarize the combinatorial strategies, including chemotherapy, radiotherapy, haematopoietic stem cell transplantation, targeted therapies and other immunotherapies, in an effort to further enhance the effectiveness of this impressive therapy and benefit more patients.

MICER: a pre-trained encoder-decoder architecture for molecular image captioning.

MOTIVATION: Automatic recognition of chemical structures from molecular images provides an important avenue for the rediscovery of chemicals. Traditional rule-based approaches that rely on expert knowledge and fail to consider all the stylistic variations of molecular images usually suffer from cumbersome recognition processes and low generalization ability. Deep learning-based methods that integrate different image styles and automatically learn valuable features are flexible, but currently under-researched and have limitations, and are therefore not fully exploited. RESULTS: MICER, an encoder-decoder-based, reconstructed architecture for molecular image captioning, combines transfer learning, attention mechanisms and several strategies to strengthen effectiveness and plasticity in different datasets. The effects of stereochemical information, molecular complexity, data volume and pre-trained encoders on MICER performance were evaluated. Experimental results show that the intrinsic features of the molecular images and the sub-model match have a significant impact on the performance of this task. These findings inspire us to design the training dataset and the encoder for the final validation model, and the experimental results suggest that the MICER model consistently outperforms the state-of-the-art methods on four datasets. MICER was more reliable and scalable due to its interpretability and transfer capacity and provides a practical framework for developing comprehensive and accurate automated molecular structure identification tools to explore unknown chemical space. AVAILABILITY AND IMPLEMENTATION: https://github.com/Jiacai-Yi/MICER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Discovery of a novel class of benzoxazole derivatives as histamine H3 receptor ligands for the treatment of neuropathic pain.

To discover effective analgesics, we summarize the synthesis, optimization, and pharmacological anti-nociceptive effects of a novel series of benzoxazole derivatives targeting H3 receptor (H3R). The new benzoxazoles were assayed in vitro for histamine H3R and H1R binding affinity. The best compound 8d (2-methyl-6-(3-(4-methylpiperazin-1-yl)propoxy)benzo[d]oxazole) exhibited high affinity for H3R (Ki = 19.7 nM), high selectivity for ten other off-target receptors, and negligible effects on human ether-a-go-go-related gene (hERG, cardiac ion channel). In rodent animals, compound 8d dose-dependently reversed formalin-evoked pain (Phase I, ED50 = 6.0 mg/kg; Phase II, ED50 = 7.8 mg/kg) and CCI-induced neuropathic pain (chronic constriction injury, ED50 = 15.6 mg/kg). Furthermore, compound 8d showed an excellent safety profile in acute toxicity test (LD50 > 2000 mg/kg) with a therapeutic index (TI = LD50/ED50) > 250 and showed a desirable drug-like pharmacokinetic profile. Above characteristics indicate that compound 8d represents a promising candidate analgesic for the treatment of neuropathic pain.

Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr.

Separation of Cs/Sr is one of many coordination-chemistry-centered processes in the grand scheme of spent nuclear fuel reprocessing, a critical link for a sustainable nuclear energy industry. To deploy a crystallizing Cs/Sr separation technology, we planned to systematically screen and identify candidate ligands that can efficiently and selectively bind to Sr2+ and form coordination polymers. Therefore, we mined the Cambridge Structural Database for characteristic structural information and developed a machine-learning-guided methodology for ligand evaluation. The optimized machine-learning model, correlating the molecular structures of the ligands with the predicted coordinative properties, generated a ranking list of potential compounds for Cs/Sr selective crystallization. The Sr2+ sequestration capability and selectivity over Cs+ of the promising ligands identified (squaric acid and chloranilic acid) were subsequently confirmed experimentally, with commendable performances, corroborating the artificial-intelligence-guided strategy.

Two-dimensional Zr/Hf-hydroxamate metal-organic frameworks.

Novel two-dimensional kagome metal-organic frameworks with mononuclear Zr4+/Hf4+ nodes chelated by benzene-1,4-dihydroxamate linkers were synthesized. The MOFs, namely SUM-1, are chemically robust and kinetically favorable, as confirmed by theoretical and experimental studies. SUM-1(Zr) can be readily made into large (∼100 µm) single crystals and nanoplates (∼50 nm), constituting a versatile MOF platform.

Quality Prediction and Control in Wire Arc Additive Manufacturing via Novel Machine Learning Framework.

Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.

Mechanisms of cytokine release syndrome and neurotoxicity of CAR T-cell therapy and associated prevention and management strategies.

Chimeric antigen receptor (CAR) T-cell therapy has yielded impressive outcomes and transformed treatment algorithms for hematological malignancies. To date, five CAR T-cell products have been approved by the US Food and Drug Administration (FDA). Nevertheless, some significant toxicities pose great challenges to the development of CAR T-cell therapy, most notably cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). Understanding the mechanisms underlying these toxicities and establishing prevention and treatment strategies are important. In this review, we summarize the mechanisms underlying CRS and ICANS and provide potential treatment and prevention strategies.

Mining microbe-disease interactions from literature via a transfer learning model.

BACKGROUND: Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe-disease interactions are hidden in the biomedical literature. The structured databases for microbe-disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe-disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information. RESULTS: Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe-disease interactions for curation. Moreover, we proposed a text mining framework for microbe-disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug-target interactions or drug-drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe-disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading. CONCLUSIONS: Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average [Formula: see text]-score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/.