Hasil untuk "Therapeutics. Pharmacology"

Haoran Liu, Zheni Zeng, Yukun Yan et al.

Molecule generation and optimization is a fundamental task in chemical domain. The rapid development of intelligent tools, especially large language models (LLMs) with powerful knowledge reserves and interactive capabilities, has provided new paradigms for it. Nevertheless, the intrinsic challenge for LLMs lies in the complex implicit relationship between molecular structure and pharmacological properties and the lack of corresponding labeled data. To bridge this gap, we propose DrugR, an LLM-based method that introduces explicit, step-by-step pharmacological reasoning into the optimization process. Our approach integrates domain-specific continual pretraining, supervised fine-tuning via reverse data engineering, and self-balanced multi-granular reinforcement learning. This framework enables DrugR to effectively improve key ADMET properties while preserving the original molecule's core efficacy. Experimental results demonstrate that DrugR achieves comprehensive enhancement across multiple properties without compromising structural similarity or target binding affinity. Importantly, its explicit reasoning process provides clear, interpretable rationales for each optimization step, yielding actionable design insights and advancing toward automated, knowledge-driven scientific discovery. Our code and model checkpoints are open-sourced to foster future research.

Ryan LaRose, Antonios M. Alvertis, Alan Bidart et al.

We evaluate the quantum resource requirements for ATP/metaphosphate hydrolysis, one of the most important reactions in all of biology with implications for metabolism, cellular signaling, and cancer therapeutics. In particular, we consider three algorithms for solving the ground state energy estimation problem: the variational quantum eigensolver, quantum Krylov, and quantum phase estimation. By utilizing exact classical simulation, numerical estimation, and analytical bounds, we provide a current and future outlook for using quantum computers to solve impactful biochemical and biological problems. Our results show that variational methods, while being the most heuristic, still require substantially fewer overall resources on quantum hardware, and could feasibly address such problems on current or near-future devices. We include our complete dataset of biomolecular Hamiltonians and code as benchmarks to improve upon with future techniques.

Jeremy Goldwasser, Addison J. Hu, Alyssa Bilinski et al.

Several key metrics in public health convey the probability that a primary event will lead to a more serious secondary event in the future. These "severity rates" can change over the course of an epidemic in response to shifting conditions like new therapeutics, variants, or public health interventions. In practice, time-varying parameters such as the case-fatality rate are typically estimated from aggregate count data. Prior work has demonstrated that commonly-used ratio-based estimators can be highly biased, motivating the development of new methods. In this paper, we develop an adaptive deconvolution approach based on approximating a Poisson-binomial model for secondary events, and we regularize the maximum likelihood solution in this model with a trend filtering penalty to produce smooth but locally adaptive estimates of severity rates over time. This enables us to compute severity rates both retrospectively and in real time. Experiments based on COVID-19 death and hospitalization data, both real and simulated, demonstrate that our deconvolution estimator is generally more accurate than the standard ratio-based methods, and displays reasonable robustness to model misspecification.

Simon Süwer, Kester Bagemihl, Sylvie Baier et al.

Repurposing approved drugs offers a time-efficient and cost-effective alternative to traditional drug development. However, in silico prediction of repurposing candidates is challenging and requires the effective collaboration of specialists in various fields, including pharmacology, medicine, biology, and bioinformatics. Fragmented, specialized algorithms and tools often address only narrow aspects of the overall problem. Heterogeneous, unstructured data landscapes require the expertise of specialized users. Hence, these data services do not integrate smoothly across workflows. With ChatDRex, we present a conversation-based, multi-agent system that facilitates the execution of complex bioinformatic analyses aiming for network-based drug repurposing prediction. It builds on the integrated systems medicine knowledge graph (NeDRex KG). ChatDRex provides natural language access to its extensive biomedical knowledge base. It integrates bioinformatics agents for network analysis, literature mining, and drug repurposing. These are complemented by agents that evaluate functional coherence for in silico validation. Its flexible multi-agent design assigns specific tasks to specialized agents, including query routing, data retrieval, algorithm execution, and result visualization. A dedicated reasoning module keeps the user in the loop and allows for hallucination detection. By enabling physicians and researchers without computer science expertise to control complex analyses with natural language, ChatDRex democratizes access to bioinformatics as an important resource for drug repurposing. It enables clinical experts to generate hypotheses and explore drug repurposing opportunities, ultimately accelerating the discovery of novel therapies and advancing personalized medicine and translational research. ChatDRex is publicly available at apps.cosy.bio/chatdrex.

Guanghao Zhu, Zhitian Hou, Zeyu Liu et al.

Multimodal large language models (MLLMs) have shown remarkable potential in various domains, yet their application in the medical field is hindered by several challenges. General-purpose MLLMs often lack the specialized knowledge required for medical tasks, leading to uncertain or hallucinatory responses. Knowledge distillation from advanced models struggles to capture domain-specific expertise in radiology and pharmacology. Additionally, the computational cost of continual pretraining with large-scale medical data poses significant efficiency challenges. To address these issues, we propose InfiMed-Foundation-1.7B and InfiMed-Foundation-4B, two medical-specific MLLMs designed to deliver state-of-the-art performance in medical applications. We combined high-quality general-purpose and medical multimodal data and proposed a novel five-dimensional quality assessment framework to curate high-quality multimodal medical datasets. We employ low-to-high image resolution and multimodal sequence packing to enhance training efficiency, enabling the integration of extensive medical data. Furthermore, a three-stage supervised fine-tuning process ensures effective knowledge extraction for complex medical tasks. Evaluated on the MedEvalKit framework, InfiMed-Foundation-1.7B outperforms Qwen2.5VL-3B, while InfiMed-Foundation-4B surpasses HuatuoGPT-V-7B and MedGemma-27B-IT, demonstrating superior performance in medical visual question answering and diagnostic tasks. By addressing key challenges in data quality, training efficiency, and domain-specific knowledge extraction, our work paves the way for more reliable and effective AI-driven solutions in healthcare. InfiMed-Foundation-4B model is available at \href{https://huggingface.co/InfiX-ai/InfiMed-Foundation-4B}{InfiMed-Foundation-4B}.

Kaixiang Liu, Min Yu, Yangyang He et al.

Background and purposeRenal fibrosis is a common characteristic of chronic kidney disease (CKD). Studies have confirmed the role of ferroptosis in the pathogenesis of various kidney diseases, making it a new research hotspot in the field of renal fibrosis. Monomers of Chinese herbal medicines (CHMs) can improve renal fibrosis by multi-target inhibition of ferroptosis. This review aimed to explore the roles and mechanisms of CHMs in renal fibrosis.MethodsUsing the keywords “ferroptosis”, “chronic kidney disease”, “renal fibrosis”, “Chinese herbal medicine”, “natural products”, “bioactive components”, and “herb”, we conducted an extensive literature search of several databases, including PubMed, Web of Science, CNKI, and Wanfang database, to identify studies reporting the role of CHM monomers in inhibiting ferroptosis and improving renal fibrosis. The names of the plants covered in the review have been checked through MPNS (http://mpns.kew.org). All monomers of CHMs were identified in the Pharmacopoeia of the People’s Republic of China.ResultsIn total, 21 monomers of CHMs were identified in this study, most of which were flavonoids, followed by terpenoids and coumarins. This review showed that monomers of CHMs inhibited ferroptosis and improved renal fibrosis through multi-target mechanisms. They maintained iron homeostasis by acting on NCOA4 and Nrf2 to reduce ferritinophagy. They also inhibited lipid peroxidation and regulated the antioxidant system by modulating ACSL4, NOX4, Nrf2, FSP1, and GPX4 and inhibiting Smad3 to improve renal fibrosis.ConclusionMonomers of CHMs effectively inhibited ferroptosis and prevented renal fibrosis in various animal models and cell models of CKD. However, further in-depth studies with better designs are needed to identify the exact targets of monomers of CHMs and improve the treatment of renal fibrosis and CKD.

Sholpan Askarova, Kseniia Sitdikova, Aliya Kassenova et al.

Fullerenes and fullerenols exhibit antioxidant and anti-inflammatory properties, making them promising candidates for Alzheimer’s disease (AD) therapy. Unlike conventional anti-inflammatory drugs, these compounds have multitargeted effects, including their ability to inhibit amyloid fibril formation. However, few studies have explored their efficacy in high-validity AD models. Female APPswe/PS1E9 (APP/PS1) mice and their wild-type (WT) littermates were orally administered with fullerene C₆₀ (0.1 mg/kg/day) or fullerenol C₆₀(OH)₂₄ (0.15 mg/kg/day) for 10 months starting at 2 months of age. Behavioral assessments were performed at 12 months of age. Amyloid plaque density and size were analyzed in the brain regions using Congo red staining. The expression of genes related to inflammation and plasticity was examined, and an in vitro assay was used to test the toxicity of fullerenol and its effect on amyloid β peptide 42 (Aβ42)-induced reactive oxygen species (ROS) production. Fullerenol reduced the maximum plaque size in the cortex and hippocampus, decreased the small plaque density in the hippocampus and thalamus, and prevented an increase in glial fibrillary acidic protein (GFAP) positive cell density in the mutants. Both treatments improved cognitive and emotional behaviors and reduced <i>Il1β</i> and increased <i>Sirt1</i> expression. In vitro, fullerenol was non-toxic across a range of concentrations and reduced Aβ42-induced ROS production in brain endothelial cells and astrocytes. Long-term administration of fullerene or fullerenol improved behavioral and molecular markers of AD in APP/PS1 mice, with fullerenol showing additional benefits in reducing amyloid burden.

Tatiana Priputnevich, Pavel Denisov, Ksenia Zhigalova et al.

<b><i>Background.</i></b> The establishment and diversity of the gut microbiota during early childhood are fundamental for immune regulation and metabolic processes, with factors such as prematurity, delivery method, antibiotic treatment, and breastfeeding significantly impacting microbiome development and potential health outcomes. <b><i>Objectives/Methods.</i></b> This comparative study examined the gut microbiota composition in children aged 6–8 and 9–12 months, born via spontaneous labor at ≥38 weeks’ gestation, who either did not receive antibacterial therapy or required beta-lactam antibiotics. The composition of the colonic microbiota was analyzed in these fecal samples using a quantitative real-time PCR (qRT-PCR). <b><i>Results.</i></b> Significant differences in microbiota composition were observed between groups. Children treated with antibiotics exhibited a statistically significant reduction in alpha diversity indices (Shannon and Simpson), along with decreased colonization of key functionally important microorganisms, including obligate anaerobic bacteria such as <i>Faecalibacterium prausnitzii</i>, <i>Clostridium leptum</i>, <i>Bacteroides</i> spp., and metabolically active <i>Bifidobacteria</i> (<i>B. bifidum</i>, <i>B. breve</i>, <i>B. longum</i>). <b><i>Conclusions.</i></b> These microbiota alterations may adversely affect child health by diminishing microbial balance and functional potential, especially during this critical period of immune and metabolic development. The decline in anti-inflammatory, short-chain fatty acid-producing bacteria elevates the risk for allergic, atopic, dysbiotic, and metabolic conditions. Recognizing these impacts underscores the importance of strategies to supports microbiota restoration after antibiotic use, such as probiotics, prebiotics, and dietary interventions. Further research should focus on microbiota recovery dynamics to facilitate early intervention and optimize pediatric health outcomes. Overall, understanding antibiotic effects on gut microbiota can guide more judicious treatment approaches, reducing long-term health risks.

Aleksei Dorokhin, Sergei Lipaikin, Galina Ryltseva et al.

Background and purpose: Polyhydroxyalkanoates (PHAs) are biodegradable polyesters of bacterial origin that are actively studied as matrices for the preparation of nanoparticulate drug delivery systems. The most significant parameters affecting PHAs nanoparticles (NPs) characteristics are polymer composition and the type of surfactant used to stabilize the emulsion during NPs preparation. However, there are only a few studies in the literature investigating the effect of these factors on the characteristics of PHA NPs. Experimental approach: Blank poly(3-hydroxybutyrate) (P3HB) and poly(3-hydroxybutyrate-co-3-hydroxy­valerate) (P3HBV) NPs were produced and characterized in terms of their size, morphology and zeta potential. Poly(vinyl alcohol) (PVA) with various molecular weights (31-50 and 85-124 kDa), as well as Tween 20 (TW20), Tween 80 (TW80), sodium deoxycholate (SDC) and sodium dodecyl sulphate (SDS) were used as surfactants. For NPs that formed stable aqueous suspensions and had the most desirable characteristics (P3HB/PVA31-50 and P3HBV/PVA31-50), hemolytic activity and cytotoxicity to HeLa and C2C12 cells in vitro were determined. Key results: NPs of both P3HB and P3HBV obtained using PVA with the Mw of 31-50 kDa as a surfactant had regular spherical shape, uniform size distribution, average diameter of about 900 nm and zeta potential of -28.5 and -28.7 mV, respectively. PVA85-124, TW20 and TW80, as well as SDC and SDS as surfactants, did not show satisfactory results due to suspension gelation, formation of hollow NPs with irregular shape and poor resuspension after washing and freeze-drying, respectively. P3HB/PVA31-50 and P3HBV/PVA31-50 NPs did not have hemolytic activity and did not show pronounced cytotoxicity to HeLa and C2C12 cells in the concentration range from 10 to 500 μg mL-1, so these samples were regarded as safe and biocompatible. Conclusion: In this study, the effect of various non-ionic and anionic surfactants on the characteristics of P3HB and P3HBV NPs was investigated. PVA31-50 was found to be effective in producing NPs of both studied polymers with good biocompatibility and favorable characteristics, making them suitable for drug delivery applications. In contrast, other studied surfactants, i.e., PVA85-124, TW20, TW80, SDC and SDS, require further investigation. The obtained findings may promote the development of novel PHA-based nanomedicines.

Guosong Han, Huihui Zhang, Zhixiang Li

IntroductionSpinal cord injury (SCI) represents a severe traumatic disorder of the central nervous system, leading to potential loss of motor and sensory functions. Its intricate pathological mechanism renders its treatment a formidable challenge. Recently, hydrogels have emerged as promising materials for spinal cord repair due to their exceptional biocompatibility and biodegradability, garnering significant attention. Consequently, extensive research on hydrogel applications in spinal cord injuries aims to provide an in-depth understanding of this field’s current state and delineate future research trajectories.MethodsA thorough search was conducted using the Web of Science Core Collection (WoSCC). Bibliometric tools such as CiteSpace, VOSviewer, Scimago Graphica, R and Bibliometrix software were employed to construct a knowledge map regarding the application of hydrogel in SCI.ResultsA bibliometric analysis of 1,015 publications between 2000 and 2025 elucidates the current research landscape, developmental trends, academic impact, and emerging knowledge dissemination patterns in hydrogel applications for SCI. The international collaboration in hydrogels-based SCI research exhibits a China-U.S.-centered network structure: as the top two publishing countries (464 vs. 278 publications), they maintain the closest bilateral collaboration, collectively forming a prominent transnational research network. The journal Biomaterials boasts the highest number of publications with 58 articles. Among prolific authors, Shoichet, Molly S., has authored the most papers, totaling 38 articles. There is a notable collaboration among various countries and institutions, with current research predominantly focusing on inflammation, apoptosis, nanoparticles, and injectable hydrogels. These efforts aim to achieve functionalized hydrogel regulation of microenvironmental changes, emerging as a focal point in contemporary research. This research highlights the latest trend of hydrogels in the treatment of SCI, thus pointing out the direction for new treatment strategies.DiscussionThe current research focus, which include the integration of functionalized hydrogels with biological factors, are setting the stage for subsequent investigative endeavors and the eventual clinical application of hydrogel in the treatment of SCI. This comprehensive analysis not only delineates the current state and emerging frontiers of hydrogel-based treatments for SCI but also provides a roadmap for future innovation.

Xu-Wen Wang, Tong Wang, Yang-Yu Liu

Advancements in artificial intelligence (AI) have transformed many scientific fields, with microbiology and microbiome research now experiencing significant breakthroughs through machine learning applications. This review provides a comprehensive overview of AI-driven approaches tailored for microbiology and microbiome studies, emphasizing both technical advancements and biological insights. We begin with an introduction to foundational AI techniques, including primary machine learning paradigms and various deep learning architectures, and offer guidance on choosing between traditional machine learning and sophisticated deep learning methods based on specific research goals. The primary section on application scenarios spans diverse research areas, from taxonomic profiling, functional annotation \& prediction, microbe-X interactions, microbial ecology, metabolic modeling, precision nutrition, clinical microbiology, to prevention \& therapeutics. Finally, we discuss challenges in this field and highlight some recent breakthroughs. Together, this review underscores AI's transformative role in microbiology and microbiome research, paving the way for innovative methodologies and applications that enhance our understanding of microbial life and its impact on our planet and our health.

Sishen Yuan, Guang Li, Baijia Liang et al.

Capsule endoscopes, predominantly serving diagnostic functions, provide lucid internal imagery but are devoid of surgical or therapeutic capabilities. Consequently, despite lesion detection, physicians frequently resort to traditional endoscopic or open surgical procedures for treatment, resulting in more complex, potentially risky interventions. To surmount these limitations, this study introduces a chained flexible capsule endoscope (FCE) design concept, specifically conceived to navigate the inherent volume constraints of capsule endoscopes whilst augmenting their therapeutic functionalities. The FCE's distinctive flexibility originates from a conventional rotating joint design and the incision pattern in the flexible material. In vitro experiments validated the passive navigation ability of the FCE in rugged intestinal tracts. Further, the FCE demonstrates consistent reptile-like peristalsis under the influence of an external magnetic field, and possesses the capability for film expansion and disintegration under high-frequency electromagnetic stimulation. These findings illuminate a promising path toward amplifying the therapeutic capacities of capsule endoscopes without necessitating a size compromise.

Julieana Moon, Naimul Khan

Within the evolving field of digital intervention, serious games emerge as promising tools for evidence-based interventions. Research indicates that gamified therapy, whether employed independently or in conjunction with online psychoeducation or traditional programs, proves more efficacious in delivering care to patients. As we navigate the intricate realm of serious game design, bridging the gap between therapeutic approaches and creative design proves complex. Professionals in clinical and research roles demonstrate innovative thinking yet face challenges in executing engaging therapeutic serious games due to the lack of specialized design skills and knowledge. Thus, a larger question remains: How might we aid and educate professionals in clinical and research roles the importance of game design to support their innovative therapeutic approaches? This study examines potential solutions aimed at facilitating the integration of gamification design principles into clinical study protocols, a pivotal aspect for aligning therapeutic practices with captivating narratives in the pursuit of innovative interventions. We propose two solutions, a flow chart framework for serious games or a comprehensive reference document encompassing gamification design principles and guidelines for best design practices. Through an examination of literature reviews, it was observed that selected design decisions varied across studies. Thus, we propose that the second solution, a comprehensive reference design guide, is more versatile and adaptable.

Maximilian G. Schuh, Davide Boldini, Stephan A. Sieber

The success of drug discovery and development relies on the precise prediction of molecular activities and properties. While in silico molecular property prediction has shown remarkable potential, its use has been limited so far to assays for which large amounts of data are available. In this study, we use a fine-tuned large language model to integrate biological assays based on their textual information, coupled with Barlow Twins, a Siamese neural network using a novel self-supervised learning approach. This architecture uses both assay information and molecular fingerprints to extract the true molecular information. TwinBooster enables the prediction of properties of unseen bioassays and molecules by providing state-of-the-art zero-shot learning tasks. Remarkably, our artificial intelligence pipeline shows excellent performance on the FS-Mol benchmark. This breakthrough demonstrates the application of deep learning to critical property prediction tasks where data is typically scarce. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to help streamline the identification of novel therapeutics.

Agnij Bhattacharyya, Debadatta Chakrabarty, Anindya Mukherjee et al.

Background: The acceptance of the coronavirus disease 2019 (COVID-19) vaccination by the community was largely affected by information or misinformation spreading through various channels. Hence, in the process of deploying vaccines, it became important to explore the community&apos;s knowledge and attitude toward such intervention. Aims and Objectives: The aim of this study was to assess the knowledge, attitude, and practice toward COVID-19 vaccination among the general population of Kolkata and to compare knowledge, attitude, and practice toward COVID-19 vaccination among different groups based on age, gender, socioeconomic status, educational qualifications, and occupation. Materials and Methods: A descriptive cross-sectional study was carried out with 389 participants among the patients attending general outpatient department of a tertiary care hospital in Kolkata using a pre-tested questionnaire having sections on knowledge, attitude, and practices toward COVID-19 vaccination. Knowledge, attitude, and practice scores were then calculated and compared among different groups based on age, gender, socioeconomic status, educational qualifications, occupation, and history of COVID-19 infection. Results: Knowledge was found to differ significantly with respect to education, occupation, socioeconomic status, and history of COVID-19 infection. Attitude showed no variation to these variables, while practice scores were found to be significantly associated with education, socioeconomic status, and history of COVID-19 infection. The unavailability of slots for vaccination was not the reason for anyone remaining unvaccinated. Financial reasons were one of the factors determining vaccine hesitancy in parts of the world before vaccines were available. This study found that 72.8% of participants found vaccines to be inexpensive. Of the participants not having received even a single dose of vaccine, the most common reason was pregnancy and lactation-related issues. Conclusion: The reason behind such associations can be further explored in bigger multicentric studies. Improvement studies may also be carried out to assess the effectiveness of various channels of communication which may aid in figuring out lacunae and planning of similar vaccination drives in the future. [Natl J Physiol Pharm Pharmacol 2024; 14(1.000): 92-98]

Yujin Cho, Mingeon Kim, Seojin Kim et al.

This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents. With the rapid advancement of artificial intelligence, particularly in natural language processing, LLMs present a novel opportunity to augment traditional psychological counseling methods. This research primarily focuses on evaluating the LLM's ability to engage in empathetic, adaptable, and contextually appropriate interactions within a therapeutic setting. A comprehensive evaluation was conducted by a panel of clinical psychologists and psychiatrists using a specially developed scorecard. The assessment covered various aspects of the LLM's performance, including empathy, communication skills, adaptability, engagement, and the ability to establish a therapeutic alliance. The study avoided direct testing with patients, prioritizing privacy and ethical considerations, and instead relied on simulated scenarios to gauge the LLM's effectiveness. The results indicate that LLMs hold significant promise as supportive tools in therapy, demonstrating strengths in empathetic engagement and adaptability in conversation. However, challenges in achieving the depth of personalization and emotional understanding characteristic of human therapists were noted. The study also highlights the importance of ethical considerations in the application of AI in therapeutic contexts. This research provides valuable insights into the potential and limitations of using LLMs in psychological counseling for autistic adolescents. It lays the groundwork for future explorations into AI's role in mental health care, emphasizing the need for ongoing development to enhance the capabilities of these models in therapeutic settings.

Anton Bushuiev, Roman Bushuiev, Petr Kouba et al.

Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we construct PPIRef, the largest and non-redundant dataset of 3D protein-protein interactions, enabling effective large-scale learning. Second, we leverage the PPIRef dataset to pre-train PPIformer, a new SE(3)-equivariant model generalizing across diverse protein-binder variants. We fine-tune PPIformer to predict effects of mutations on protein-protein interactions via a thermodynamically motivated adjustment of the pre-training loss function. Finally, we demonstrate the enhanced generalization of our new PPIformer approach by outperforming other state-of-the-art methods on new, non-leaking splits of standard labeled PPI mutational data and independent case studies optimizing a human antibody against SARS-CoV-2 and increasing the thrombolytic activity of staphylokinase.

Xu S, Fu Y, Xu D et al.

Shuang Xu,1 Yi Fu,2 Dan Xu,1 Shuang Han,1 Mingzhi Wu,3 Xinrong Ju,1 Meng Liu,1 De-Sheng Huang,4,5 Peng Guan4,6 1Library of China Medical University, Shenyang, Liaoning, People’s Republic of China; 2School of Health Management, China Medical University, Shenyang, Liaoning, People’s Republic of China; 3Library of Shenyang Pharmaceutical University, Shenyang, Liaoning, People’s Republic of China; 4Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, Liaoning, People’s Republic of China; 5Department of Intelligent Computing, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, People’s Republic of China; 6Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, People’s Republic of ChinaCorrespondence: De-Sheng Huang; Peng Guan, China Medical University, Shenyang, Liaoning, 110122, People’s Republic of China, Email dshuang@cmu.edu.cn; pguan@cmu.edu.cnBackground: Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH).Methods: PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots.Results: Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected.Conclusion: This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis.Keywords: multidrug-resistant tuberculosis, pulmonary tuberculosis, medication trends, specific semantic types, MeSH tree, pubMR