i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.

BACKGROUND: 5-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key importance in genetic and pathological studies. However, traditional experimental methods for identifying 5mC sites are time-consuming and costly, so there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites. RESULTS: Deep learning methods have shown great potential in the field of 5mC sites, so we developed a deep learning combinatorial model called i5mC-DCGA. The model innovatively uses the Convolutional Block Attention Module (CBAM) to improve the Dense Convolutional Network (DenseNet), which is improved to extract advanced local feature information. Subsequently, we combined a Bidirectional Gated Recurrent Unit (BiGRU) and a Self-Attention mechanism to extract global feature information. Our model can learn feature representations of abstract and complex from simple sequence coding, while having the ability to solve the sample imbalance problem in benchmark datasets. The experimental results show that the i5mC-DCGA model achieves 97.02%, 96.52%, 96.58% and 85.58% in sensitivity (Sn), specificity (Sp), accuracy (Acc) and matthews correlation coefficient (MCC), respectively. CONCLUSIONS: The i5mC-DCGA model outperforms other existing prediction tools in predicting 5mC sites, and it is currently the most representative promoter 5mC site prediction tool. The benchmark dataset and source code for the i5mC-DCGA model can be found in https://github.com/leirufeng/i5mC-DCGA .

Synthesis and Assembly of Photoresponsive Colloidal Tubes.

Inspired by the sophisticated multicomponent and multistage assembly of proteins and their mixtures in living cells, this study rationally designs and fabricates photoresponsive colloidal tubes that can self-assemble and hybrid-assemble when mixed with colloidal spheres and rods. Time-resolved observation and computer simulation reveal that the assembly is driven by phoretic attraction originating from osmotic pressures. These pressures are induced by the chemical concentration gradients generated by the photochemical reaction caused by colloidal tubes in a H2O2 solution under ultraviolet (UV) irradiation. The assembled structure is dictated by the size and shape of the constituent colloids as well as the intensity of the UV irradiation. Additionally, the resulting assembly can undergo self-propelled motion originating from the broken symmetry of the surrounding concentration gradients. This motion can be steered by a magnetic field and used for microscale cargo delivery. The study demonstrates a facile synthesis method for colloidal tubes and highlights their unique potential for controlled, hierarchical self-assembly and hybrid-assembly.

Biologically active isoquinoline alkaloids covering 2019-2022.

Isoquinoline alkaloids are an important class of natural products that are abundant in the plant kingdom and exhibit a wide range of structural diversity and biological activities. With the deepening of research in recent years, more and more isoquinoline alkaloids have been isolated and identified and proved to contain a variety of biological activities and pharmacological effects. In this review, we introduce the research progress of isoquinoline alkaloids from 2019 to 2022, mainly in the part of biological activities, including antitumor, antimicrobial, antidiabetic, antiviral, anti-inflammatory, antioxidant, neuroprotective, hepatoprotective, analgesic, and other activities. This study provides a clear direction for the rational development and utilization of isoquinoline alkaloids, suggesting that these alkaloids have great potential in the field of drug research.

iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.

The promoter is a key DNA sequence whose primary function is to control the initiation time and the degree of expression of gene transcription. Accurate identification of promoters is essential for understanding gene expression studies. Traditional sequencing techniques for identifying promoters are costly and time-consuming. Therefore, the development of computational methods to identify promoters has become critical. Since deep learning methods show great potential in identifying promoters, this study proposes a new promoter prediction model, called iPro2L-DG. The iPro2L-DG predictor, based on an improved Densely Connected Convolutional Network (DenseNet) and a Global Attention Mechanism (GAM), is constructed to achieve the prediction of promoters. The promoter sequences are combined feature encoding using C2 encoding and nucleotide chemical property (NCP) encoding. An improved DenseNet extracts advanced feature information from the combined feature encoding. GAM evaluates the importance of advanced feature information in terms of channel and spatial dimensions, and finally uses a Full Connect Neural Network (FNN) to derive prediction probabilities. The experimental results showed that the accuracy of iPro2L-DG in the first layer (promoter identification) was 94.10% with Matthews correlation coefficient value of 0.8833. In the second layer (promoter strength prediction), the accuracy was 89.42% with Matthews correlation coefficient value of 0.7915. The iPro2L-DG predictor significantly outperforms other existing predictors in promoter identification and promoter strength prediction. Therefore, our proposed model iPro2L-DG is the most advanced promoter prediction tool. The source code of the iPro2L-DG model can be found in https://github.com/leirufeng/iPro2L-DG.

A rare case of successful treatment of peritoneal dialysis patient with Serratia marcescens peritonitis without catheter removal: case report and literature review.

Serratia marcescens, as a Gram-negative opportunistic pathogen, is a rare cause of peritonitis and has worse clinical outcomes than Gram-positive peritonitis. In this case report, we describe a case of Serratia marcescens associated peritonitis that was successfully cured without catheter removal. A 40-year-old male patient with peritoneal dialysis who worked in the catering industry was admitted to the hospital for 16 hours after the discovery of cloudy peritoneal dialysate and abdominal pain. Ceftazidime and cefazolin sodium were immediately given intravenously as an empirical antibiotic regimen. After detecting Serratia marcescens in the peritoneal diasate culture, the treatment was switched to ceftazidime and levofloxacin. The routine examination of peritoneal dialysate showed a significant decrease in white blood cells, the peritoneal dialysate became clear, and the peritoneal dialysis catheter was retained. The patient was treated for 2 weeks and treated with oral antibiotics for 1 week. It is necessary to further strengthen the hygiene of work environment to prevent Serratia marcescens infection in peritoneal dialysis patients. We recommend that patients with Serratia marcescens associated peritonitis should be treated with a combination of antibiotics as early as possible empirically, and at the same time, the peritoneal dialysis fluid culture should be improved, and the antibiotic regimen should be timely adjusted according to the drug sensitivity results. For patients with clinical symptoms for more than 3 days, considering the strong virulence of Serratia marcescens, whether to use meropenem directly or not can provide a reference for clinical decision-making. Further clinical studies are needed to achieve more precise anti-infective treatment.

Risk factors of neuropathic pain in multiple sclerosis: a retrospective case-cohort study.

Background: Pain is a common symptom in multiple sclerosis (MS), especially neuropathic pain, which has a significant impact on patients' mental and physical health and quality of life. However, risk factors that related to neuropathic pain, still remain unclear. Objective: The study aimed to explore the risk factors of neuropathic pain among MS patients. Materials and methods: This retrospective study examined the consecutive patients diagnosed with MS in the Department of Neurology of Guangdong Provincial Hospital of Chinese Medicine between August 2011 and October 2022. Neuropathic pain was defined as "pain arising as a direct consequence of a lesion or disease affecting the somatosensory system". Demographic and clinical features were obtained from the electronic system of the hospital. Results: Our cohort revealed that the prevalence of patients with neuropathic pain in MS was 34.1%. The results indicated that the longer the spinal lesions, the greater the neuropathic pain risks (2-4: OR, 13.3(2.1-82), >5: OR, 15.2(2.7-86.8), p for tread: 0.037). Meanwhile, multivariate regression analysis showed that cervical and thoracic lesions (OR 4.276, 95% CI 1.366-13.382, P = 0.013), upper thoracic lesions (T1-T6) (OR 3.047, 95% CI 1.018-9.124, P = 0.046) were positively correlated with neuropathic pain, while basal ganglia lesions (OR 0.188, 95% CI 0.044-0.809, P = 0.025) were negatively correlated with neuropathic pain among MS patients. Conclusion: Extended spinal lesions (≥3 spinal lesions), cervical and thoracic lesions, upper thoracic lesions were independent risk factors of neuropathic pain among MS patients. Furthermore, our study found that the longer the spinal lesions, the greater the neuropathic pain risks.

Design, synthesis and antimicrobial activity of novel berberine derivatives.

BACKGROUND: The threats to the safety of humans and the environment and the resistance of agricultural chemicals to plant pathogenic fungi and bacteria highlight an urgent need to find safe and efficient alternatives to chemical fungicides and bactericides. In this study, a series of Berberine (BBR) derivatives were designed, synthesized and evaluated for in vitro and in vivo antimicrobial activity against plant pathogenic fungi and bacteria. RESULTS: Bioassay results indicated that compounds A11, A14, A20, A21, A22, A25, A26, E1, E2, E3, Z1 and Z2 showed high inhibitory activity against Sclerotinia sclerotiorum and Botrytis cinerea. Especially, A25 showed a broad spectrum and the highest antifungal activity among these compounds. Its EC50 value against Botrytis cinerea was 1.34 µg mL-1. Compound E6 possessed high inhibitory activity against Xanthomonas oryzae and Xanthomonas Campestris, with MIC90 values of 3.12 µg mL-1 and 1.56 µg mL-1. A Topomer CoMFA model was generated for 3D-QSAR studies based on anti-B. cinerea effects, with high predictive accuracy, showed that the addition of an appropriate substituent group at the para-position of benzyl of BBR derivatives could effectively improve the anti-B. cinerea activity. In addition, compound A25 could significantly inhibit the spore germination of Botrytis cinerea at low concentration, and compound F4 exhibited remarkable curative and protective efficiencies on rice bacterial leaf blight. CONCLUSION: This study indicates that the BBR derivatives are hopeful for further exploration as the lead compound with novel antimicrobial agents. © 2024 Society of Chemical Industry.

im5C-DSCGA: A Proposed Hybrid Framework Based on Improved DenseNet and Attention Mechanisms for Identifying 5-methylcytosine Sites in Human RNA.

BACKGROUND: 5-methylcytosine (m5C) is a key post-transcriptional modification that plays a critical role in RNA metabolism. Owing to the large increase in identified m5C modification sites in organisms, their epigenetic roles are becoming increasingly unknown. Therefore, it is crucial to precisely identify m5C modification sites to gain more insight into cellular processes and other mechanisms related to biological functions. Although researchers have proposed some traditional computational methods and machine learning algorithms, some limitations still remain. In this study, we propose a more powerful and reliable deep-learning model, im5C-DSCGA, to identify novel RNA m5C modification sites in humans. METHODS: Our proposed im5C-DSCGA model uses three feature encoding methods initially-one-hot, nucleotide chemical property (NCP), and nucleotide density (ND)-to extract the original features in RNA sequences and ensure splicing; next, the original features are fed into the improved densely connected convolutional network (DenseNet) and Convolutional Block Attention Module (CBAM) mechanisms to extract the advanced local features; then, the bidirectional gated recurrent unit (BGRU) method is used to capture the long-term dependencies from advanced local features and extract global features using Self-Attention; Finally, ensemble learning is used and full connectivity is used to classify and predict the m5C site. RESULTS: Unsurprisingly, the deep-learning-based im5C-DSCGA model performed well in terms of sensitivity (Sn), specificity (SP), accuracy (Acc), Matthew's correlation coefficient (MCC), and area under the curve (AUC), generating values of 81.0%, 90.8%, 85.9%, 72.1%, and 92.6%, respectively, in the independent test dataset following the use of three feature encoding methods. CONCLUSIONS: We critically evaluated the performance of im5C-DSCGA using five-fold cross-validation and independent testing and compared it to existing methods. The MCC metric reached 72.1% when using the independent test, which is 3.0% higher than the current state-of-the-art prediction method Deepm5C model. The results show that the im5C-DSCGA model achieves more accurate and stable performances and is an effective tool for predicting m5C modification sites. To the authors' knowledge, this is the first time that the improved DenseNet, BGRU, CBAM Attention mechanism, and Self-Attention mechanism have been combined to predict novel m5C sites in human RNA.