RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence-structure interactions, we develop an LDM incorporating pre-trained RNA-FM embeddings from a large-scale RNA model. These embeddings capture co-evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion-based methods, cannot effectively handle non-differentiable structural objectives. By contrast, RL excels in this task by using policy-driven reward optimization to navigate complex, non-gradient-based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step-wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single-step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state-of-the-art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.
Recent technological advances have introduced new high-throughput methods for studying host-virus interactions, but testing synergistic interactions between host gene pairs during infection remains relatively slow and labor intensive. Identification of multiple gene knockdowns that effectively inhibit viral replication requires a search over the combinatorial space of all possible target gene pairs and is infeasible via brute-force experiments. Although active learning methods for sequential experimental design have shown promise, existing approaches have generally been restricted to single-gene knockdowns or small-scale double knockdown datasets. In this study, we present an integrated Deep Active Learning (DeepAL) framework that incorporates information from a biological knowledge graph (SPOKE, the Scalable Precision Medicine Open Knowledge Engine) to efficiently search the configuration space of a large dataset of all pairwise knockdowns of 356 human genes in HIV infection. Through graph representation learning, the framework is able to generate task-specific representations of genes while also balancing the exploration-exploitation trade-off to pinpoint highly effective double-knockdown pairs. We additionally present an ensemble method for uncertainty quantification and an interpretation of the gene pairs selected by our algorithm via pathway analysis. To our knowledge, this is the first work to show promising results on double-gene knockdown experimental data of appreciable scale (356 by 356 matrix).
James King, Lewis Cornwall, Andrei Cristian Nica
et al.
Accurate prediction of protein-protein binding affinity is vital for understanding molecular interactions and designing therapeutics. We adapt Boltz-2, a state-of-the-art structure-based protein-ligand affinity predictor, for protein-protein affinity regression and evaluate it on two datasets, TCR3d and PPB-affinity. Despite high structural accuracy, Boltz-2-PPI underperforms relative to sequence-based alternatives in both small- and larger-scale data regimes. Combining embeddings from Boltz-2-PPI with sequence-based embeddings yields complementary improvements, particularly for weaker sequence models, suggesting different signals are learned by sequence- and structure-based models. Our results echo known biases associated with training with structural data and suggest that current structure-based representations are not primed for performant affinity prediction.
Pablo Dorta-González, Alejandro Rodríguez-Caro, María Isabel Dorta-González
This study explores the connection between patent citations and scientific publications across six fields: Biochemistry, Genetics, Pharmacology, Engineering, Mathematics, and Physics. Analysing 117,590 papers from 2014 to 2023, the research emphasises how publication year, open access (OA) status, and discipline influence patent citations. Openly accessible papers, particularly those in hybrid OA journals or green OA repositories, are significantly more likely to be cited in patents, seven times more than those mentioned in blogs, and over twice as likely compared to older publications. However, papers with policy-related references are less frequently cited, indicating that patents may prioritise commercially viable innovations over those addressing societal challenges. Disciplinary differences reveal distinct innovation patterns across sectors. While academic visibility via blogs or platforms like Mendeley increases within scholarly circles, these have limited impact on patent citations. The study also finds that increased funding, possibly tied to applied research trends and fully open access journals, negatively affects patent citations. Social media presence and the number of authors have minimal impact. These findings highlight the complex factors shaping the integration of scientific research into technological innovations.
Djordje Miladinovic, Tobias Höppe, Mathieu Chevalley
et al.
Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks -- from understanding the relationships between biological entities to developing therapeutics. However, these data encompass diverse perturbations and readouts, and the complex dependence of experimental outcomes on their biological context makes it challenging to integrate insights across experiments. Here, we present the Large Perturbation Model (LPM), a deep-learning model that integrates multiple, heterogeneous perturbation experiments by representing perturbation, readout, and context as disentangled dimensions. LPM outperforms existing methods across multiple biological discovery tasks, including in predicting post-perturbation transcriptomes of unseen experiments, identifying shared molecular mechanisms of action between chemical and genetic perturbations, and facilitating the inference of gene-gene interaction networks.
Neuropathic pain is a chronic condition characterized by damage to and dysfunction of the peripheral or central nervous system. There are currently no effective treatment options available for neuropathic pain, and existing drugs often provide only temporary relief with potential side effects. Multilineage-differentiating stress-enduring (Muse) cells are characterized by high expansion potential, a stable phenotype and strong immunosuppression. These properties make them attractive candidates for therapeutics for neuropathic pain management. In this study, we conducted a series of experiments to evaluate the effect of Muse cells on neuropathic pain. Muse cells from different species demonstrated analgesic potential by reversing CCI-induced neuropathic pain. Protein profiling revealed a high degree of similarity between Muse cells and BMSCs. The intrathecal injection of Muse cells effectively reduced neuropathic pain in various mouse models, resulting in better analgesic effects than the administration of equivalent low doses of BMSCs. Immunohistochemical analysis and qPCR revealed the ability of Muse cells to inhibit spinal cord neuroinflammation caused by SNI. In addition, Transwell and ELISA revealed that Muse cells migrated through the injured dorsal root ganglion (DRG) via the CCR7-CCL21 chemotactic axis. In addition, the secretion of TGF-b and IL-10 by Muse cells was identified as the mechanism underlying the analgesic effect of Muse cells. The capacity of Muse cells to mitigate neuroinflammation and produce analgesic effects via the modulation of TGF-b and IL-10 underscores their potential as promising therapeutic approaches for the treatment of neuropathic pain.
MiRNAs, due to their role in gene regulation, have paved a new pathway for pharmacology, focusing on drug development that targets miRNAs. However, traditional wet lab experiments are limited by efficiency and cost constraints, making it difficult to extensively explore potential associations between developed drugs and target miRNAs. Therefore, we have designed a novel machine learning model based on a multi-layer transformer-based graph neural network, DMAGT, specifically for predicting associations between drugs and miRNAs. This model transforms drug-miRNA associations into graphs, employs Word2Vec for embedding features of drug molecular structures and miRNA base structures, and leverages a graph transformer model to learn from embedded features and relational structures, ultimately predicting associations between drugs and miRNAs. To evaluate DMAGT, we tested its performance on three datasets composed of drug-miRNA associations: ncDR, RNAInter, and SM2miR, achieving up to AUC of $95.24\pm0.05$. DMAGT demonstrated superior performance in comparative experiments tackling similar challenges. To validate its practical efficacy, we specifically focused on two drugs, namely 5-Fluorouracil and Oxaliplatin. Of the 20 potential drug-miRNA associations identified as the most likely, 14 were successfully validated. The above experiments demonstrate that DMAGT has an excellent performance and stability in predicting drug-miRNA associations, providing a new shortcut for miRNA drug development.
Ratna Dewi, Tongku Nizwan Siregar, Amalia Sutriana
et al.
Background: Morinda citrifolia L. possesses antioxidant activity that can ameliorate the decline in semen quality of male rats due to exposure to cigarette smoke. Objectives: This study intend to assess the effectiveness of M._citrifolia leaves extract in ameliorating male infertility associated with oxidative dysregulation induced by exposure to tobacco smoke. Methods: The animals used in the study were evenly and randomly divided into five groups, each containing five rats. Group X1 served as the normal control without any treatment, whereas group X2 comprised rats that were exclusively subjected to cigarette smoke exposure. Groups X3, X4, and X5 were exposed to cigarette smoke and subsequently administered M. citrifolia leaves extract orally via a nasogastric tube at doses of 100, 200, and 300 mg/kg BW, respectively, for a period of 52 days. Twenty-four hours after the final treatment, blood samples were collected to examine FSH, LH, and testosterone levels using ELISA technique. Semen was collected from the cauda epididymis to analyze the quality of spermatozoa. Results: The administration of M. citrifolia leaves extract improved sperm concentration, progressive motility, and viability, while sperm morphological abnormalities were not affected by the extract (P = 0.618). FSH concentration decreased following M. citrifolia leaves extract administration, particularly at dose of 100, and 200 mg/kg BW. LH concentration increased significantly after treatment with 100_mg/kg BWof M. citrifolia leaves extract and testosterone levels improved after treated with leaves extract of M. citrifolia (P <0.001). Conclusions: Methanol extract of M. citrifolia leaves enhance sperm quality and testosterone levels but does not affect FSH and LH concentrations in male rats exposed to cigarette smoke.
Kaihua Yu,1,* Yunfei Gu,2,* Ying Yao,1 Jianchun Li,3 Suheng Chen,1 Hong Guo,4 Yulan Li,5 Jian Liu1,6 1The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, People’s Republic of China; 2Anesthesiology Department, Gansu Provincial Maternity and Child-Care Hospital (Gansu Provincial Center Hospital), Lanzhou, Gansu, People’s Republic of China; 3Department of Intensive Care Unit, Suzhou Science and Technology City Hospital, Nanjing, Jiangsu, People’s Republic of China; 4Department of Anesthesiology, Inner Mongolia Hospital of Peking University Cancer Hospital, Hohhot, Inner Mongolia, People’s Republic of China; 5Department of Anesthesiology, First Hospital of Lanzhou University, Lanzhou, Gansu, People’s Republic of China; 6Gansu Provincial Maternity and Child-Care Hospital (Gansu Provincial Center Hospital), Lanzhou, Gansu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian Liu; Yulan Li, Email medecinliujian@163.com; liyul@lzu.edu.cnBackground: Oxygen supplementation is essential for patients with a multitude of diseases but can cause severe hyperoxia-induced lung injury (HLI), necessitating the identification of therapeutic targets to improve clinical outcomes. Cuproptosis, a novel copper-dependent form of cell death characterized by proteotoxic stress resulting from lipoylated protein aggregation and loss of iron-sulfur cluster proteins, is distinct from other forms of cell death. However, the role of cuproptosis in HLI remains unclear.Methods: We established an HLI model in MLE-12 cells and C57BL/6 mice to investigate the involvement of cuproptosis in hyperoxia-induced toxicity.Results: We observed a time-dependent increase in the cuproptosis-related gene Fdx1 under hyperoxia. Moreover, hyperoxia activated the membrane-associated copper transporter SLC31A1 and significantly elevated copper levels in MLE-12 cells, as well as in the serum and lung tissue of C57BL/6 mice. Further analysis revealed that hyperoxia significantly altered the expression of cuproptosis-related genes without affecting DLAT levels, but significantly increased lipoylated-DLAT levels. ELISA, CCK-8 assays, HE staining, lung wet-to-dry weight ratio, and bronchoalveolar lavage fluid analysis demonstrated that treatment with the cuproptosis inhibitor TTM reduced pro-inflammatory cytokines (TNF-α and IL-1β) and alleviated hyperoxia-induced injury in both MLE-12 cells and C57BL/6 mice.Conclusion: Our study identifies the involvement of cuproptosis in HLI, providing new insights into the pathogenesis of hyperoxic lung injury and potential therapeutic strategies.Keywords: hyperoxia, lung injury, hyperoxia-induced lung injury, cuproptosis, copper, FDX1
Muhammad Mazher Irshad , Kausar Noor, Muhammad Imran Khan
et al.
Background: Laparoscopic surgery is increasingly preferred for various abdominal procedures due to its minimally invasive nature. However, port site complications, particularly wound infections, remain a significant concern. The method of umbilical access—either intra-umbilical or periumbilical—may influence the rate of postoperative infections, yet evidence comparing the two remains limited. The study aimed to compare the frequency of wound infection between intra-umbilical and periumbilical incisions in patients undergoing laparoscopic appendectomy or cholecystectomy.
Methods: A descriptive study was conducted over six months in the Department of Surgery at Khyber Teaching Hospital, Peshawar. A total of 201 patients undergoing laparoscopic surgeries were enrolled using a non-probability consecutive sampling technique. Patients were divided into Group A (intra-umbilical incision, n=101) and Group B (periumbilical incision, n=100). Baseline demographics, comorbidities, and type of surgery were recorded. Postoperative wound infections were assessed within two weeks based on predefined clinical criteria. Data were analyzed using SPSS v25, with Chi-square and Fisher's exact tests applied where appropriate.
Results: The overall wound infection rate was 11.4%, with 8 infections (7.9%) in Group A and 15 infections (15%) in Group B. The infection rate was nearly double in the periumbilical group compared to the intra-umbilical group. Other variables, including comorbidities and type of surgery, were comparable between the groups.
Conclusion: Intra-umbilical incisions were associated with a lower incidence of wound infections compared to periumbilical incisions in laparoscopic procedures. This method may offer a safer and cosmetically superior alternative for initial port access in routine laparoscopic surgeries
Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder described as progressive cognitive decline and neuronal dysfunction, affecting millions globally. While current pharmacological treatments provide symptomatic relief and modestly slow disease progression, they fail to address the underlying pathophysiology and are often accompanied by severe adverse effects. This underscores the urgent need for innovative, multi-target therapeutic strategies that can effectively step in AD’s complex pathogenesis. Emerging evidence highlights the therapeutic potential of natural products, particularly herbal medicines, as versatile modulators of key pathogenic processes in AD. These compounds exert neuroprotective effects by mitigating oxidative stress, suppressing neuroinflammation, inhibiting tau hyperphosphorylation, and reducing amyloid-beta aggregation. Additionally, they strengthen synaptic plasticity and stabilize mitochondrial function, offering a holistic approach to disease control. This comprehensive review synthesizes findings from network pharmacology, in vitro and in vivo studies, and clinical trials to evaluate the role of natural products in AD treatment. Advances in bioinformatics and systems biology facilitate the mapping of intricate protein-protein interactions, the identification of potential biomarkers, and the clarification of molecular mechanisms underlying AD progression. Integrating phytochemicals with conventional AD medications may improve therapeutic efficacy through synergistic mechanisms; however, pharmacokinetic interactions and safety considerations must be rigorously assessed. Notably, clinical trials investigating compounds such as curcumin, resveratrol, and ginsenosides suggest promising adjunctive benefits when incorporated into established treatment regimens. Furthermore, the convergence of herbal therapeutics with modern pharmacology presents an avenue for customized and integrative AD management. This review also emphasizes advancements in experimental models, including brain organoids and transgenic animals, which serve as crucial platforms for mechanistic studies and therapeutic validation. Ongoing trials on plant-derived compounds continue to pave the way for translational applications, reinforcing the viability of natural product-based interventions. By advocating a multidisciplinary framework that merges traditional medicine, modern pharmacology, and precision medicine, this work contributes to reshaping the AD landscape of therapy. It provides a roadmap for future research, fostering novel treatment paradigms that prioritize efficacy, safety, and sustainability in combating this disastrous disorder.
Liangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang
et al.
Drug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance to occur. Some of the rarely occurred DDI types are often high risk and could be life-critical if overlooked, exemplifying the long-tailed distribution problem. Existing models falter against this distribution challenge and overlook the multi-faceted nature of drugs in DDI prediction. In this paper, a novel multi-modal deep learning-based framework, namely TFDM, is introduced to leverage multiple properties of a drug to achieve DDI classification. The proposed framework fuses multimodal features of drugs, including graph-based, molecular structure, Target and Enzyme, for DDI identification. To tackle the challenge posed by the distribution skewness across categories, a novel loss function called Tailed Focal Loss is introduced, aimed at further enhancing the model performance and address gradient vanishing problem of focal loss in extremely long-tailed dataset. Intensive experiments over 4 challenging long-tailed dataset demonstrate that the TFMD outperforms the most recent SOTA methods in long-tailed DDI classification tasks. The source code is released to reproduce our experiment results: https://github.com/IcurasLW/TFMD_Longtailed_DDI.git
O-GlcNAcylation, a subtype of glycosylation, has the potential to be an important target for therapeutics, but methods to reliably predict O-GlcNAcylation sites had not been available until 2023; a 2021 review correctly noted that published models were insufficient and failed to generalize. Moreover, many are no longer usable. In 2023, a considerably better recurrent neural network (RNN) model was published. This article creates improved models by using a new loss function, which we call the weighted focal differentiable MCC. RNN models trained with this new loss display superior performance to models trained using the weighted cross-entropy loss; this new function can also be used to fine-tune trained models. An RNN trained with this loss achieves state-of-the-art performance in O-GlcNAcylation site prediction with an F$_1$ score of 38.88% and an MCC of 38.20% on an independent test set from the largest dataset available.
Seyedeh Mahboobeh Hosseini Zare, Jafar Babapour, Mehdi Basakha
et al.
Objective Insurance deductions are among the most important causes of hospital resource waste. Insurance deductions cause financial problems for hospitals and create tension between insurance organizations and hospitals. This study, done by the University of Social Welfare and Rehabilitation Sciences, investigates the reasons for deductions of inpatient bills covered by Social Security Insurance in Rofeideh Rehabilitation Hospital
Materials & Methods The study was conducted cross-sectionally on 776 cases admitted to Rofeidah Rehabilitation Hospital in 2021. All invoices for inpatient services sent to Social Security Insurance were reviewed using the census method. To collect data, invoices sent to Social Security, inpatient records and checklists, and a 31-question questionnaire of Mohammadkhani et al. were used. The collected data were analyzed by descriptive (frequency and percentage) and analytical statistics (the Spearman correlation coefficient) in SPSS software, version 23.
Results According to the findings, the highest frequency of inpatient prescriptions was related to November 2021 (7.48%). The most frequent deductions of inpatients’ bills were related to medicine and consumables to the amount of 310815448 Rails and surgeon’s fees in 187728448 Rails. Also, the most common reasons were requesting a surcharge and wrong coding. In this study, there was a significant relationship between the documentation of nurses and doctors and the amount of deductions (P<0.0001).
Conclusion Multiple causes affect the reduction of insurance deductions. By teaching insurance rules and the book on the relative value of health services to medical and nursing staff groups, electronic documents to Social Security Insurance and creating warning mechanisms in it, continuous interaction with Social Security Insurance to justify the managers of the organization regarding the way of providing services to rehabilitation patients and the reason for the prolonged hospitalization time of such patients could be the potential solutions for preventing patients records deductions and help hospitals achieve financial goals.
Nicotine readily crosses the placenta to reach fetuses. However, membrane transporters, e.g., organic cation transporters (OCTs) play a role in the clearance of nicotine from the fetal to the maternal side, and this is rarely investigated clinically. In this work, we use an in silico model to simulate an ex vivo placenta perfusion experiment, which is the gold standard for measuring the transplacental permeability of compounds, including nicotine. The model consists of a system of seven ordinary differential equations (ODEs), where each equation represents the nicotine concentration in compartments that emulate the ex vivo experiment setup. The transport role of OCTs is simulated bi-directionally at the placenta’s basal membrane (the fetal side). We show that the model can not only reproduce the actual ex vivo experiment results, but also predict the likely maternal and fetal nicotine concentrations when the OCT transporters are inhibited, which leads to a ∼12% increase in fetal nicotine concentration after 2 hours of OCT modulated nicotine perfusion. In conclusion, a first in silico model is proposed in this paper that can be used to simulate some subtle features of trans-placental properties of nicotine.