Machine learning study of the extended drug-target interaction network informed by pain related voltage-gated sodium channels.

ABSTRACT: Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.

Machine Learning Study of the Extended Drug-target Interaction Network informed by Pain Related Voltage-Gated Sodium Channels.

Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction (DTI) network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor datasets from a pool of over 1,000 targets in the PPI network. We employ three distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pre-trained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of over 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. Additionally, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.

Machine Learning Methods for Small Data Challenges in Molecular Science.

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

TIDAL: Topology-Inferred Drug Addiction Learning.

Drug addiction is a global public health crisis, and the design of antiaddiction drugs remains a major challenge due to intricate mechanisms. Since experimental drug screening and optimization are too time-consuming and expensive, there is urgent need to develop innovative artificial intelligence (AI) methods for addressing the challenge. We tackle this challenge by topology-inferred drug addiction learning (TIDAL) built from integrating multiscale topological Laplacians, deep bidirectional transformer, and ensemble-assisted neural networks (EANNs). Multiscale topological Laplacians are a novel class of algebraic topology tools that embed molecular topological invariants and algebraic invariants into its harmonic spectra and nonharmonic spectra, respectively. These invariants complement sequence information extracted from a bidirectional transformer. We validate the proposed TIDAL framework on 22 drug addiction related, 4 hERG, and 12 DAT data sets, which suggests that the proposed TIDAL is a state-of-the-art framework for the modeling and analysis of drug addiction data. We carry out cross-target analysis of the current drug addiction candidates to alert their side effects and identify their repurposing potentials. Our analysis reveals drug-mediated linear and bilinear target correlations. Finally, TIDAL is applied to shed light on relative efficacy, repurposing potential, and potential side effects of 12 existing antiaddiction medications. Our results suggest that TIDAL provides a new computational strategy for pressingly needed antisubstance addiction drug development.

Two functionally distinct heme/iron transport systems are virulence determinants of the fish pathogen Flavobacterium psychrophilum.

Bacterial pathogens have a critical impact on aquaculture, a sector that accounts for half of the human fish consumption. Flavobacterium psychrophilum (phylum Bacteroidetes) is responsible for bacterial cold-water disease in salmonids worldwide. The molecular factors involved in host invasion, colonization and haemorrhagic septicaemia are mostly unknown. In this study, we identified two new TonB-dependent receptors, HfpR and BfpR, that are required for adaptation to iron conditions encountered during infection and for virulence in rainbow trout. Transcriptional analyses revealed that their expression is tightly controlled and upregulated under specific iron sources and concentrations. Characterization of deletion mutants showed that they act without redundancy: BfpR is required for optimal growth in the presence of high haemoglobin level, while HfpR confers the capacity to acquire nutrient iron from haem or haemoglobin under iron scarcity. The gene hfpY, co-transcribed with hfpR, encodes a protein related to the HmuY family. We demonstrated that HfpY binds haem and contributes significantly to host colonization and disease severity. Overall, these results are consistent with a model in which both BfpR and Hfp systems promote haem uptake and respond to distinct signals to adapt iron acquisition to the different stages of pathogenesis. Our findings give insight into the molecular basis of pathogenicity of a serious pathogen belonging to the understudied family Flavobacteriaceae and point to the newly identified haem receptors as promising targets for antibacterial development.

Two Robust Isoreticular Metal-Organic Frameworks with Different Interpenetration Degrees Exhibiting Disparate Breathing Behaviors.

Herein, two robust isoreticular metal-organic frameworks (MOFs), ([Bi(CPTTA)]·[Me2NH2]·2DMF) (JLU-MOF120, H4CPTTA = 5'-(4-carboxyphenyl)-[1,1':3',1″-terphenyl]-3,4″,5-tricarboxylic acid, DMF = N, N- dimethylformamide) and ([In(CPTTA)]·[MeNH3]·2.5H2O·1.5NMF) (JLU-MOF121, NMF = N- methylformamide), with different interpenetration degrees were successfully constructed. According to the hard-soft acid-base (HSAB) theory, high-valent metal ions and carboxylate-based ligands were selected and formed twofold interpenetrated structures with saturated coordinated mononuclear second building units ([M(COO)4], M = Bi, In). Owing to the features of the frameworks, JLU-MOF120 and JLU-MOF121 exhibited excellent stability, which could retain their integrity in water for at least 14 days and aqueous solutions with a pH range of 3-11 for at least 24 h. According to the structural regulation strategy, by changing the torsion angles of the ligand, the degrees of interpenetration for JLU-MOF120 and JLU-MOF121 were different, leading to various gate-opening pressures in CO2 at 195 K. Furthermore, JLU-MOF120 exhibits the scarce potential of C2H2/CO2 separation among Bi-MOF materials at 298 K under 101 kPa, JLU-MOF121 shows high CO2/CH4 selectivity under ambient conditions (11.7 for gas mixtures of 50 and 50% and 16.1 for gas mixtures of 5 and 95%). Moreover, owing to the flexibility of the structure, JLU-MOF121 possesses disparate breathing behaviors for C2H2 and C2H6 at 273 and 298 K, with the differences in uptakes among C2 hydrocarbons resulting in the potentiality of C2H4 purification. Overall, such HSAB theory and the structural regulation strategy could provide a valid method for constructing stable and flexible structures for the application in gas separation.

Meta-Analysis Comparing Steroids and Diuretics in the Treatment of Acute Low-Tone Sensorineural Hearing Loss.

OBJECTIVE: Our objective was to perform a meta-analysis to compare the effectiveness of steroids and diuretics in the treatment of acute low-tone sensorineural hearing loss (ALHL). METHODS: PubMed, Google Scholar, and Sci databases were searched for randomized controlled trials (RCTs) examining the treatment of ALHL with steroids and diuretics. The Cochrane Reviewer's Handbook 5.0 evaluation criteria were used to evaluate the quality of the included RCTs. Meta-analysis was performed using Revman 5.3 software to compare the recovery rate of low-tone hearing levels between patients treated with steroids and diuretics. RESULTS: A total of 3 RCTs were included. There was no heterogeneity between the 3 studies (χ2 = 2.61, P = .27, I2 = 23%); thus, a fixed-effects model of analysis was used. Meta-analysis showed there was no significant difference in the recovery rate of patients treated with steroids and those treated with diuretics (odds ratio = 1.48, 95% confidence interval: 0.64-3.40, P = .36). CONCLUSION: Steroids and diuretics are equally effective for the treatment of ALHL.

Structure, mechanics and material properties of claw cuticle from mole cricket Gryllotalpaorientalis.

Powerful shovel-like forelimbs with special shape, structure and biological materials enable mole cricket to digging efficiently. During digging, the tip of the claw needs to wedge into the soil, and the base needs to withstand considerable anti-shear force. In this study, we analysed the structural characteristics, material composition and mechanical properties of the claw teeth using scanning electron microscopy, plasma atomic emission spectroscopy, nanoindentation and finite element analysis. The results show that the tips of claw teeth have a dense and homogeneous structure and a higher hardness and contents of Mn and Zn compared with the base. The structure of the base of claw teeth has an obvious laminar structure and higher fracture resistance. Moreover, it is speculated from the simulation results that basal position of the claw teeth is tough enough to withstand high stress, and the presence of the ribs effectively improves the mechanical stability and load-bearing capacity of the teeth during excavation. The results of this study can provide inspiration for the design of efficient mechanical components and agricultural implements.

Hyperhomocysteinemia in men with a reproductive history of fetal neural tube defects: Three case reports and literature review.

RATIONALE: Hereditary hyperhomocysteinemia results from a polymorphism in the methylenetetrahydrofolate reductase (MTHFR) gene that reduces folate metabolism. Mutations in the MTHFR gene are common in parents who have given birth to children with neural tube defects (NTDs). Most research has focused on the risk for fetal NTDs in women with hyperhomocysteinemia and MTHFR gene mutations. Studies investigating the association between hyperhomocysteinemia, MTHFR gene mutations, and the risk for fetal NTDs in men are scarce. PATIENT CONCERNS: Here, we report on 3 men with hyperhomocysteinemia and the MTHFR C677T homozygous TT genotype that have reproductive histories of fetal NTDs. DIAGNOSIS: these 3 men were diagnosed as hyperhomocysteinemia and MTHFR C677T homozygous TT genotype. INTERVENTIONS: Three men received homocysteine-lowering therapy. OUTCOMES: The first man's wife became pregnant, and a healthy infant was spontaneously delivered at term, the other 2 men's wives are still not pregnant. LESSONS: Findings from this case reports and published literature imply that hereditary hyperhomocysteinemia in men affects sperm quality and sperm DNA methylation and causes epigenetic modifications that can result in fetal NTDs. We recommend monitoring homocysteine and folate levels in men before conception and supplementing with folate as needed, especially in men with a reproductive history of fetuses with neural tube or other birth defects.

Complete Azoospermia Factor b Deletion of Y Chromosome in an Infertile Male With Severe Oligoasthenozoospermia: Case Report and Literature Review.

OBJECTIVE: To report on a male patient with complete deletion of azoospermia factor b (AZFb) who presented with severe oligoasthenozoospermia, but who successfully fathered a child via intracytoplasmic sperm injection (ICSI). MATERIALS AND METHODS: Karyotype analysis of peripheral blood lymphocytes was performed by standard G-banding. Y chromosome microdeletions were detected by multiplex polymerase chain reaction amplification using AZF-specific, sequence-tagged site markers. The ICSI procedure was performed using ejaculated motile spermatozoa. RESULTS: Cytogenetic analysis of the patient revealed a normal male karyotype, 46,XY. Multiplex polymerase chain reaction screening showed complete deletion of AZFb demonstrated by the absence of specific sequence-tagged site markers sY121, sY127, sY134, and sY143. Following successful ICSI, an ultrasound scan of the patient's partner revealed a single pregnancy with cardiac activity. A healthy boy was born by cesarean section at 38 weeks of gestation. Genetic testing 2 years later revealed that the infant had inherited his father's AZFb deletion. CONCLUSION: Evidence from this case supports the fact that carriers of AZFb deletions can sometimes produce spermatozoa and father a son with the same AZFb deletion. This possibility reinforces the need for genetic counseling in patients with Y chromosome microdeletions.

Genome-wide identification, characterization and expression analysis of the auxin response factor gene family in Vitis vinifera.

KEY MESSAGE: Our study has identified and analyzed the VvARF gene family that may be associated with the development of grape berry and other tissues. Auxin response factors (ARFs) are transcription factors that regulate the expression of auxin responsive genes through specific binding to auxin response elements (AuxREs). The ARF genes are represented by a large multigene family in plants. Until now, many ARF families have been characterized based on genome resources. However, there is no specialized research about ARF genes in grapevine (Vitis vinifera). In this study, a comprehensive bioinformatics analysis of the grapevine ARF gene family is presented, including chromosomal locations, phylogenetic relationships, gene structures, conserved domains and expression profiles. Nineteen VvARF genes were identified and categorized into four groups (Classes 1, 2, 3 and 4). Most of VvARF proteins contain B3, AUX_RESP and AUX_IAA domains. The VvARF genes were widely expressed in a range of grape tissues, and fruit had higher transcript levels for most VvARFs detected in the EST sources. Furthermore, analysis of expression profiles indicated some VvARF genes may play important roles in the regulation of grape berry maturation processes. This study which provided basic genomic information for the grapevine ARF gene family will be useful in selecting candidate genes related to tissue development in grapevine and pave the way for further functional verification of these VvARF genes.