AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Counter-Doping Effect by Trivalent Cations in Tin-Based Perovskite Solar Cells.

Tin (Sn) -based perovskite solar cells (PSCs) normally show low open circuit voltage due to serious carrier recombination in the devices, which can be attributed to the oxidation and the resultant high p-type doping of the perovskite active layers. Considering the grand challenge to completely prohibit the oxidation of Sn-based perovskites, a feasible way to improve the device performance is to counter-dope the oxidized Sn-based perovskites by replacing Sn2+ with trivalent cations in the crystal lattice, which however is rarely reported. Here, the introduction of Sb3+, which can effectively counter-dope the oxidized perovskite layer and improve the carrier lifetime, is presented. Meanwhile, Sb3+ can passivate deep-level defects and improve carrier mobility of the perovskite layer, which are all favorable for the photovoltaic performance of the devices. Consequently, the target devices yield a relative enhancement of the power conversion efficiency (PCE) of 31.4% as well as excellent shelf-storage stability. This work provides a novel strategy to improve the performance of Sn-based PSCs, which can be developed as a universal way to compensate for the oxidation of Sn-based perovskites in optoelectronic devices.

The potential mechanism of Guizhi Fuling Wan effect in the treatment of cervical squamous cell carcinoma: A bioinformatics analysis investigation.

As a global malignancy with high mortality rate, targeted drug development for Uterine Cervical Neoplasms is an important direction. The traditional formula Guizhi Fuling Wan (GFW) is widely used in gynecological diseases. However, its potential mechanism of action remains to be discovered. We retrieved GFW and cervical squamous cell carcinoma (CSCC) targets from public databases. The protein-protein interaction network was obtained by string computational analysis and imported Cytoscape_v3.9.0 to obtain the core network and the top 10 Hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for enrichment analysis of the core network, and then molecular docking to verify whether the selected signaling pathway binds well to the core node. Finally, clinical prognostic analysis and expression differences of Hub genes were validated using the Cancer Genome Atlas database and R language. Our search yielded 152 common targets for GFW and CSCC. The interleukin-17 signaling pathway, tumor necrosis factor signaling pathway, and Toll-like signaling pathway were then selected for further molecular docking from the hub genes enrichment analysis results, which showed good binding. Among the Hub genes, JUN, VEGFA, IL1B, and EGF had a poor prognosis for CSCC. In conclusion, this study illustrates that GFW can have adjuvant therapeutic effects on CSCC through multiple targets and multiple pathways, providing a basis for further research.

Co-Self-Assembled Monolayers Modified NiOx for Stable Inverted Perovskite Solar Cells.

[4-(3,6-dimethyl-9H-carbazol-9yl)butyl]phosphonic acid (Me-4PACz) self-assembled molecules (SAM) are an effective method to solve the problem of the buried interface of NiOx in inverted perovskite solar cells (PSCs). However, the Me-4PACz end group (carbazole core) cannot forcefully passivate defects at the bottom of the perovskite film. Here, a Co-SAM strategy is employed to modify the buried interface of PSCs. Me-4PACz is doped with phosphorylcholine chloride (PC) to form a Co-SAM to improve the monolayer coverage and reduce leakage current. The phosphate group and chloride ions (Cl-) in PC can inhibit NiOx surface defects. Meantime, the quaternary ammonium ions and Cl- in PC can fill organic cations and halogen vacancies in the perovskite film to enable defects passivation. Moreover, Co-SAM can promote the growth of perovskite crystals, collaboratively solve the problem of buried defects, suppress nonradiative recombination, accelerate carrier transmission, and relieve the residual stress of the perovskite film. Consequently, the Co-SAM modified devices show power conversion efficiencies as high as 25.09% as well as excellent device stability with 93% initial efficiency after 1000 h of operation under one-sun illumination. This work demonstrates the novel approach for enhancing the performance and stability of PSCs by modifying Co-SAM on NiOx.

Dissecting the role of ALK double mutations in drug resistance to lorlatinib with in-depth theoretical modeling and analysis.

Anaplastic lymphoma kinase (ALK) is implicated in the genesis of multiple malignant tumors. Lorlatinib stands out as the most advanced and effective inhibitor currently used in the clinic for the treatment of ALK-positive non-small cell lung cancer. However, resistance to lorlatinib has inevitably manifested over time, with double/triple mutations of G1202, L1196, L1198, C1156 and I1171 frequently observed in clinical practice, and tumors regrow within a short time after treatment with lorlatinib. Therefore, elucidating the mechanism of resistance to lorlatinib is paramount in paving the way for innovative therapeutic strategies and the development of next-generation drugs. In this study, we leveraged multiple computational methodologies to delve into the resistance mechanisms of three specific double mutations of ALKG1202R/L1196M, ALKG1202R/L1198F and ALKI1171N/L1198F to lorlatinib. We analyzed these mechanisms through qualitative (PCA, DCCM) and quantitative (MM/GBSA, US) kinetic analyses. The qualitative analysis shows that these mutations exert minimal perturbations on the conformational dynamics of the structural domains of ALK. The energetic and structural assessments show that the van der Waals interactions, formed by the conserved residue Leu1256 within the ATP-binding site and the residues Glu1197 and Met1199 in the hinge domain with lorlatinib, play integral roles in the occurrence of drug resistance. Furthermore, the US simulation results elucidate that the pathways through which lorlatinib dissociates vary across mutant systems, and the distinct environments during the dissociation process culminate in diverse resistance mechanisms. Collectively, these insights provide important clues for the design of novel inhibitors to combat resistance.

Efficacy of aquatic exercise in chronic musculoskeletal disorders: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Aquatic exercise (AE) is becoming ever more popular as a physical therapy, while it is unclear what precise improvements it will produce and how effective it will be in comparison with other non-surgical therapies. The study aimed to assess whether AE positively impacts chronic musculoskeletal disorder patients in terms of pain, physical function, and quality of life. METHODS: PRISMA guidelines were followed, and our study protocol was published online at PROSPERO under registration number CRD42023417411. We searched PubMed, Embase, Web of Science, and Cochrane library databases for English-language articles published before April 11, 2023, including studies from all relevant randomized controlled trials (RCTs). After screening, we ultimately included 32 RCTs with a total of 2,200 participants. We also performed subgroup analyses for all included studies. This meta-analysis calculated standardized mean difference (SMD) with 95% confidence interval (CI), and the variance was estimated using a random-effects model. The quality of the included studies was assessed by using the Cochrane collaborative "risk of bias" assessment tool (version 2.0). Thus ensuring that the literature included is of high quality. RESULTS: This meta-analysis included 32 trials with 2,200 participants; these patients were all between the ages of 38-80. The study showed that compared to the no exercise (NE) group, patients in the AE group experienced a remarkable reduction in pain (SMD: -0.64, P < 0.001), a significant increase in physical function (SMD: 0.62, P < 0.001), and a statistically significant improvement in quality of life (SMD: -0.64, P < 0.001). When compared to land-based exercise (LE), AE significantly relieves patients' pain (SMD: -0.35, P = 0.03). CONCLUSIONS: This is the first systematic review and meta-analysis to study whether AE could improve chronic musculoskeletal disorders. The evidence suggests that AE benefits pain, physical function, and quality of life in adults with chronic musculoskeletal conditions compared to NE. Furthermore, when compared to LE, AE continues to provide a better improvement in patient pain. More long-term clinical trials are needed to confirm AE's positive effects and improvement mechanisms and the more existential advantages compared to LE.

AI-algorithm-assisted 895-nm praseodymium laser emitting sub-100-fs pulses.

Praseodymium (Pr) lasers have achieved outstanding pico- and sub-picosecond pulsations covering the near-infrared (NIR) and visible spectral range in recent years. However, it has been a stagnant task for more than two decades to leapfrog into the sub-100 femtosecond (fs) regime as the Pr gain bandwidths are too narrow for their major transition lines. Although the wide tunability at the NIR bands in the Pr:YLF crystals has been explored, the spectral tails in these transitions suffer severely from weak gains for mode locking, combined with the intricate dispersion control to achieve transform-limit formation. In this work, we target the Pr:YLF's 895-nm line with a specially designed edge-pass filter to balance the gain bandwidth and transitional strength. By deploying a symmetric dispersion scheme and tuning with the soft actor-critic artificial intelligence (AI) algorithm, we have achieved the pulse duration down to sub-100-fs in a Pr laser for the first time. This work also enriches the AI-assisted methodology for ultrafast solid-state laser realizations.

Association of cesarean section with asthma in children/adolescents: a systematic review and meta-analysis based on cohort studies.

BACKGROUND: Whether cesarean section (CS) is a risk factor for asthma in offspring is controversial. The purpose of this study was to investigate the association between CS and asthma in children/adolescents. METHODS: Pubmed, Embase, Web of Science, and Cochrane Library electronic databases were searched for cohort studies on the relationship between mode of delivery and asthma in children/adolescents up to February 2023. Birth via CS was considered an exposure factor. Asthma incidence was taken as a result. RESULTS: Thirty-five cohort studies (thirteen prospective and twenty-two retrospective cohort studies) were included. The results showed that the incidence of asthma was higher in CS offspring (odds ratio (OR) = 1.18, P < 0.001) than in the vaginal delivery (VD) group. Partial subgroup analyses showed a higher incidence of asthma in female offspring born via CS (OR = 1.26, P < 0.001) compared with the VD group, while there was no difference in males (OR = 1.07, P = 0.325). Asthma incidence was higher in CS offspring than in the VD group in Europe (OR = 1.20, P < 0.001), North America (OR = 1.15, P < 0.001), and Oceania (OR = 1.06, P = 0.008). This trend was not found in the Asian population (OR = 1.17, P = 0.102). The incidence of atopic asthma was higher in offspring born via CS (OR = 1.14, P < 0.001) compared to the VD group. The CS group had a higher incidence of persistent asthma, but the difference did not reach statistical significance (OR = 1.15, P = 0.063). CONCLUSION: In this meta-analysis, CS may be a risk factor for asthma in offspring children/adolescents compared with VD. The relationship between CS and asthma was influenced by sex and region.

Glyceryl triacetate promotes blood-brain barrier recovery after ischemic stroke through lipogenesis-mediated IL-33 in mice.

BACKGROUND: Lipid metabolism has a crucial role in neural repair in neurodegenerative diseases. We recently revealed that lipogenesis-mediated interleukin-33 (IL-33) upregulation lead to blood-brain barrier (BBB) repair after ischemic stroke. However, manipulating the key enzyme fatty acid synthase (FASN) to enhance lipogenesis was very challenging. Glyceryl triacetate (GTA) was used as a donor of acetate and precursor of acetyl coenzyme A, the key substrate for de novo lipogenesis catalyzed by FASN. Therefore, we hypothesized that GTA would promote lipogenesis the peri-infarct after ischemic stroke and contribute to the BBB repair through IL-33. METHODS: Middle cerebral artery occlusion (MCAO) was performed on C57BL mice and GTA was gavage administrated (4 g/kg) on day 2 and 4 after MCAO. Lipogenesis was evaluated by assessment of the protein level of FASN, lipid droplets, and fatty acid products through liquid chromatography-mass spectrometry in the peri-infarct area on day 3 after MCAO, respectively. BBB permeability was determined by extravasation of Evans blue, IgG and dextran, and levels of tight junction proteins in the peri-infarct area on day 7 after MCAO, respectively. Infarct size and neurological defects were assessed on day 7 after MCAO. Brain atrophy on day 30 and long-term sensorimotor abilities after MCAO were analyzed as well. The inhibitor of FASN, C75 and the virus-delivered FASN shRNA were used to evaluate the role of FASN-driven lipogenesis in GTA-improved BBB repair. Finally, the therapeutic potential of recombinant IL-33 on BBB repair and neurological recovery was evaluated. RESULTS: We found that treatment with GTA increased the lipogenesis as evidenced by lipid droplets level and lauric acid content, but not the FASN protein level. Treatment with GTA increased the IL-33 level in the peri-infarct area and decreased the BBB permeability after MCAO. However, infarct size and neurological defect score were unchanged on day 7 after MCAO, while the long-term recovery of sensorimotor function and brain atrophy were improved by GTA. Inhibition of lipogenesis using C75 or FASN shRNA reversed the beneficial effect of GTA. Finally, exogenous IL-33 improved BBB repair and long-term functional recovery after stroke. CONCLUSION: Collectively, we concluded that treatment with GTA improved the BBB repair and functional recovery after ischemic stroke, probably by the enhancement of lipogenesis and IL-33 expression.

Association between statins exposure and risk of skin cancer: an updated meta-analysis.

This study aimed to investigate the relationship between statin (lipophilic statin and hydrophilic statin) exposure and the risk of skin cancer. The incidence of skin cancer under statin exposure was used as the primary outcome, and the relevant studies were screened from Web of Science, PubMed, Cochrane Library, and EBSCO electronic database until September 2022. Ten observational studies and two randomized controlled trials (RCTs) were included. The statistical results indicated that in lipophilic statins, the exposed group had a higher risk of skin cancer than the non-exposed group (OR: 1.09, P = 0.003). However, compared with the non-exposed group, there was no significant difference between hydrophilic statins exposure and the incidence of skin cancer (OR: 1.02, P = 0.341). Further subgroup analysis of the subtypes of statins revealed that compared with the non-exposed group, exposure to lovastatin (OR: 1.18, P = 0.048) or simvastatin (OR: 1.11, P < 0.001) was a risk factor for skin cancer. Besides, subgroup analysis based on the subtypes of skin cancer demonstrated that the risks of melanoma (OR: 1.13, P = 0.009), basal cell carcinoma (BCC) (OR: 1.05, P = 0.036), and squamous cell carcinoma (SCC) (OR: 1.13, P = 0.026) under lipophilic statin exposure were significantly higher than those in the non-exposed group. On the contrary, compared with the non-exposed group, the risk of BCC was significantly reduced under the exposure of hydrophilic statins (OR: 0.93, P = 0.031). This study showed that the relationship between statin exposure and skin cancer risk was affected by the subtypes of statins and skin cancer subtypes.

ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions.

Cracking the entangling code of protein-ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/.

Exploring the mechanism of luteolin by regulating microglia polarization based on network pharmacology and in vitro experiments.

Neuroinflammation manifests following injury to the central nervous system (CNS) and M1/M2 polarization of microglia is closely associated with the development of this neuroinflammation. In this study, multiple databases were used to collect targets regarding luteolin and microglia polarization. After obtaining a common target, a protein-protein interaction (PPI) network was created and further analysis was performed to obtain the core network. Molecular docking of the core network with luteolin after gene enrichment analysis. In vitro experiments were used to examine the polarization of microglia and the expression of related target proteins. A total of 77 common targets were obtained, and the core network obtained by further analysis contained 38 proteins. GO and KEGG analyses revealed that luteolin affects microglia polarization in regulation of inflammatory response as well as the interleukin (IL)-17 and tumor necrosis factor (TNF) signaling pathways. Through in vitro experiments, we confirmed that the use of luteolin reduced the expression of inducible nitric oxide synthase (iNOS), IL-6, TNF-α, p-NFκBIA (p-IκB-α), p-NFκB p65, and MMP9, while upregulating the expression of Arg-1 and IL-10. This study reveals various potential mechanisms by which luteolin induces M2 polarization in microglia to inhibit the neuroinflammatory response.

Topology-Based and Conformation-Based Decoys Database: An Unbiased Online Database for Training and Benchmarking Machine-Learning Scoring Functions.

Machine-learning-based scoring functions (MLSFs) have gained attention for their potential to improve accuracy in binding affinity prediction and structure-based virtual screening (SBVS) compared to classical SFs. Developing accurate MLSFs for SBVS requires a large and unbiased dataset that includes structurally diverse actives and decoys. Unfortunately, most datasets suffer from hidden biases and data insufficiency. Here, we developed topology-based and conformation-based decoys database (ToCoDDB). The biological targets and active ligands in ToCoDDB were collected from scientific literature and established datasets. The decoys were generated and debiased by using conditional recurrent neural networks and molecular docking. ToCoDDB is presently the largest unbiased database with 2.4 million decoys encompassing 155 targets. The detailed information and performance benchmark for each target are provided, which are beneficial for training and evaluating MLSFs. Moreover, the online decoys generation function of ToCoDDB further expands its application range to any target. ToCoDDB is freely available at http://cadd.zju.edu.cn/tocodecoy/.

NUSAP1-LDHA-Glycolysis-Lactate feedforward loop promotes Warburg effect and metastasis in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by hypoxia and hypovascular tumor microenvironment. Nucleolar and spindle associated protein 1 (NUSAP1) is a microtubule-associated protein that is known to be involved in cancer biology. Our study aimed to investigate the role of NUSAP1 in glycolytic metabolism and metastasis in PDAC. Expression and prognostic value of NUSAP1 in PDAC and common gastrointestinal tumors was evaluated. The function of NUSAP1 in PDAC progression was clarified by single-cell RNA-seq and further experiments in vitro, xenograft mouse model, spontaneous PDAC mice model and human tissue microarray. The downstream genes and signaling pathways regulated by NUSAP1 were explored by RNA-Seq. And the regulation of NUSAP1 on Lactate dehydrogenase A (LDHA)-mediated glycolysis and its underlying mechanism was further clarified by CHIP-seq. NUSAP1 was an independent unfavorable predictor of PDAC prognosis that playing a critical role in metastasis of PDAC by regulating LDHA-mediated glycolysis. Mechanically, NUSAP1 could bind to c-Myc and HIF-1α that forming a transcription regulatory complex localized to LDHA promoter region and enhanced its expression. Intriguingly, lactate upregulated NUSAP1 expression by inhibiting NUSAP1 protein degradation through lysine lactylated (Kla) modification, thus forming a NUSAP1-LDHA-glycolysis-lactate feedforward loop. The NUSAP1-LDHA-glycolysis-lactate feedforward loop is one of the underlying mechanisms to explain the metastasis and glycolytic metabolic potential in PDAC, which also provides a novel insights to understand the Warburg effect in cancer. Targeting NUSAP1 would be an attractive paradigm for PDAC treatment.

Metformin attenuates sevoflurane-induced neurogenesis damage and cognitive impairment: involvement of the Nrf2/G6PD pathway.

Anesthetics such as sevoflurane are commonly administered to infants and children. However, the possible neurotoxicity caused by prolonged or repetitive exposure to it should be a concern. The neuroprotective effects of metformin are observed in many models of neurological disorders. In this study, we investigated whether metformin could reduce the developmental neurotoxicity induced by sevoflurane exposure in neonatal rats and the potential mechanism. Postnatal day 7 (PND 7) Sprague-Dawley rats and neural stem cells (NSCs) were treated with normal saline or metformin before sevoflurane exposure. The Morris water maze (MWM) was used to observe spatial memory and learning at PND 35-42. Immunofluorescence staining was used to detect neurogenesis in the subventricular zone (SVZ) of the lateral ventricle and the subgranular zone (SGZ) of the dentate gyrus at PND 14. MTT assays, immunofluorescence staining, and TUNEL staining were used to assess the viability, proliferation, differentiation, and apoptosis of NSCs. Western blotting and ELISA were used to assess the protein expression of cleaved caspase-3, nuclear factor erythroid 2-related factor 2 (Nrf2), and glucose-6-phosphate dehydrogenase (G6PD) pathway-related molecules. Exposure to sevoflurane resulted in late cognitive defects, impaired neurogenesis in both the SVZ and SGZ, reduced NSC viability and proliferation, increased NSC apoptosis, and decreased protein expression of G6PD in vitro. Metformin pretreatment attenuated sevoflurane-induced cognitive functional decline and neurogenesis inhibition. Metformin pretreatment also increased the protein expression of Nrf2 and G6PD. However, treatment with the Nrf2 inhibitor, ML385 or the G6PD inhibitor, dehydroepiandrosterone (DHEA) reversed the protective effect of metformin on sevoflurane-induced NSC damage in vitro. Our findings suggested that metformin could reduce sevoflurane-induced neurogenesis damage and neurocognitive defects in the developing rat brain by influencing the Nrf2/G6PD signaling pathways.

Identification and integration analysis of a novel prognostic signature associated with cuproptosis-related ferroptosis genes and relevant lncRNA regulatory axis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a highly prevalent malignancy worldwide, and its clinical prognosis assessment and treatment is a major research direction. Both ferroptosis and cuproptosis are novel forms of cell death and are considered to be important factors involved in cancer progression. To further understand the correlation between the cuproptosis-related ferroptosis genes (CRFGs) and the prognosis of LUAD, we explore the molecular mechanisms related to the development of the disease. We constructed a prognostic signature containing 13 CRFGs, which, after grouping based on risk score, revealed that the LUAD high-risk group exhibited poor prognosis. Nomogram confirmed that it could be an independent risk factor for LUAD, and ROC curves and DCA validated the validity of the model. Further analysis showed that the three prognostic biomarkers (LIFR, CAV1, TFAP2A) were significantly correlated with immunization. Meanwhile, we found that a LINC00324/miR-200c-3p/TFAP2A regulatory axis could be involved in the progression of LUAD. In conclusion, our report reveals that CRFGs are well correlated with LUAD and provide new ideas for the construction of clinical prognostic tools, immunotherapy, and targeted therapy for LUAD.

Lactate modulates microglial inflammatory responses after oxygen-glucose deprivation through HIF-1α-mediated inhibition of NF-κB.

Metabolic adaption drives microglial inflammatory responses, and lactate shapes immunological and inflammatory states. However, whether lactate was involved in the regulation of microglial inflammatory responses after cerebral ischemia remains unclear. In this study, the expression of iNOS, arginase-1, phosphorylated NF-κB p65 and IκB-α, and HIF-1α in BV2 cells after oxygen-glucose deprivation (OGD) were detected by western blotting and immunofluorescence. The mRNA levels of microglial responsive markers and inflammatory factors were assessed by real-time-qPCR. The effect of lactate-treated BV2 cells on the survival of primary neurons was observed using transwell co-culture. The results showed that the protein levels of iNOS and arginase-1, the ratio of mRNA levels of iNOS/CD206, CD86/Ym1, IL-6/IL-10, TNF-α/IL-10 and the mRNA levels of IL-6 and TNF-α, as well as the protein levels of phosphorylated NF-κB p65 and IκB-α, were increased after OGD. Lactate treatment inhibited the OGD-induced increase in the protein levels of iNOS, phosphorylated NF-κB p65 and IκB-α, as well as iNOS/CD206, CD86/Ym1, IL-6/IL-10, TNF-α/IL-10, IL-6 and TNF-α mRNA levels in BV2 cells, while promoted arginase-1 protein expression as well as IL-10 and TGF-ß mRNA level. Interestingly, lactate activated HIF-1α and the HIF-1α inhibitor YC-1 reversed the effect of lactate on levels of microglial responsive markers and phosphorylated NF-κB p65 and IκB-α in BV2 cells. Moreover, knockdown of HIF-1α by lentivirus-delivered shRNA also reversed the effect of lactate on phosphorylated NF-κB p65 and IκB-α in BV2 cells after OGD. Finally, and importantly, lactate-treated BV2 microglia increased the viability and decreased the apoptosis of neurons after OGD. These findings revealed that lactate inhibited NF-κB pathway and skewed BV2 microglia toward the protective response through activation of HIF-1α after OGD, thereby improving neuronal survival.

Exploration of Fuzheng Yugan Mixture on COVID-19 based on network pharmacology and molecular docking.

After the World Health Organization declared coronavirus disease 2019 (COVID-19), as a global pandemic, global health workers have been facing an unprecedented and severe challenge. Currently, a mixturetion to inhibit the exacerbation of pulmonary inflammation caused by COVID-19, Fuzheng Yugan Mixture (FZYGM), has been approved for medical institution mixturetion notification. However, the mechanism of FZYGM remains poorly defined. This study aimed to elucidate the molecular and related physiological pathways of FZYGM as a potential therapeutic agent for COVID-19. Active molecules of FZYGM were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), while potential target genes of COVID-19 were identified by DrugBank and GeneCards. Compound-target networks and protein-protein interactions (PPI) were established by Cytoscape_v3.8.2 and String databases, respectively. The gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Finally, a more in-depth study was performed using molecular docking. Our study identified 7 active compounds and 3 corresponding core targets. The main potentially acting signaling pathways include the interleukin (IL)-17 signaling pathway, tumor necrosis factor (TNF) signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation, and coronavirus disease-COVID-19. This study shows that FZYGM can exhibit anti-COVID-19 effects through multiple targets and pathways. Therefore, FZYGM can be considered a drug candidate for the treatment of COVID-19, and it provides good theoretical support for subsequent experiments and clinical applications of COVID-19.

Altered functional connectivity of anterior cingulate cortex in chronic insomnia: A resting-state fMRI study.

The aim of our present study was to explore the connectivity pattern change between the anterior cingulate cortex (ACC) and the voxels from the whole brain in chronic insomnia (CI). With region of interest (ROI)-based functional connectivity, a two-sample t-test was performed on individual FC correlation maps from two groups based on the resting-state fMRI data acquired from 57 CI patients and 46 healthy controls (GRF correction, voxel-level P < 0.001 and cluster-level P < 0.001). A correlation analysis was performed to evaluate the relationship between the clinical features and the abnormal FC. Compared to the healthy controls, the CI patients show increased connectivity between the ACC and the right middle frontal gyrus, with decreased connectivity between the ACC and the bilateral precuneus gyrus. Correlation analysis indicated that the decreased connectivity showed positive correlations with Self-Rating Anxiety Scale (SAS) scores. Our study shows the alterations of CI patients in the level of functional integration and may indicate the dysfunction of communication within brain regions of the default mode network (DMN). These changes and their correlation with negative emotions may provide additional evidence to understand the possible neural mechanisms of CI.

Learning on topological surface and geometric structure for 3D molecular generation.

Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes. The omission of these fine details leads to many models having difficulty in outputting reasonable molecules for a variety of therapeutic targets. Here, to address this challenge, we formulate a model, called SurfGen, that designs molecules in a fashion closely resembling the figurative key-and-lock principle. SurfGen comprises two equivariant neural networks, Geodesic-GNN and Geoatom-GNN, which capture the topological interactions on the pocket surface and the spatial interaction between ligand atoms and surface nodes, respectively. SurfGen outperforms other methods in a number of benchmarks, and its high sensitivity on the pocket structures enables an effective generative-model-based solution to the thorny issue of mutation-induced drug resistance.