Garima Sharma, Ashish Ranjan Sharma, M. Bhattacharya
et al.
At present, the idea of genome modification has revolutionized the modern therapeutic research era. Genome modification studies have traveled a long way from gene modifications in primary cells to genetic modifications in animals. The targeted genetic modification may result in the modulation (i.e., either upregulation or downregulation) of the predefined gene expression. Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated nuclease 9 (Cas9) is a promising genome-editing tool that has therapeutic potential against incurable genetic disorders by modifying their DNA sequences. In comparison with other genome-editing techniques, CRISPR-Cas9 is simple, efficient, and very specific. This enabled CRISPR-Cas9 genome-editing technology to enter into clinical trials against cancer. Besides therapeutic potential, the CRISPR-Cas9 tool can also be applied to generate genetically inhibited animal models for drug discovery and development. This comprehensive review paper discusses the origin of CRISPR-Cas9 systems and their therapeutic potential against various genetic disorders, including cancer, allergy, immunological disorders, Duchenne muscular dystrophy, cardiovascular disorders, neurological disorders, liver-related disorders, cystic fibrosis, blood-related disorders, eye-related disorders, and viral infection. Finally, we discuss the different challenges, safety concerns, and strategies that can be applied to overcome the obstacles during CRISPR-Cas9-mediated therapeutic approaches.
Silvia Crescioli, Hélène Kaplon, Alicia Chenoweth
et al.
The Antibodies to Watch article series provides annual updates on commercial late-stage clinical development, regulatory review, and marketing approvals of antibody therapeutics. Since the first article was published in 2010, the late-stage pipeline has grown from 26 antibody therapeutics to over 200, while during the same time numerous molecules in late-stage studies either transitioned to regulatory review and were approved or were terminated. In this installment of the series, we recap first marketing approvals granted to 19 antibody therapeutics in 2025, discuss 26 molecules currently in regulatory review, including the bispecific antibody-drug conjugate izalontamab brengitecan, and predict which molecules of the 209 currently in the commercial late-stage pipeline might transition to regulatory review by the end of 2026. Most antibody therapeutics in the latter category are for non-cancer indications (16/21, 76%) and have a conventional format (13/21, 62%), but the category also includes numerous antibody-oligo or -drug conjugates, such as delpacibart etedesiran, delpacibart zotadirsen, zeleciment rostudirsen, sonesitatug vedotin, trastuzumab pamirtecan, and ifinatamab deruxtecan, as well as the bispecific petosemtamab. As antibody therapeutics development is a global enterprise, we also discuss trends in annual first approvals granted to antibody therapeutics in any country since 2010, stratified by the antibody’s country of origin, documenting the notable increases in the total number of first approvals and those approved first in China. Finally, to benchmark the time typically required for clinical development and regulatory review, we calculated this period for recently approved antibody therapeutic products stratified by their therapeutic area, mechanism of action, format, and country of origin. Our data show that the development and approval period were typically ~6 years, but on average this period was shorter for China-originated products.
Zilal Eiz AlDin, John Wu, Jeffrey Paul Fung
et al.
Despite rare diseases affecting 1 in 10 Americans, their differential diagnosis remains challenging. Due to their impressive recall abilities, large language models (LLMs) have been recently explored for differential diagnosis. Existing approaches to evaluating LLM-based rare disease diagnosis suffer from two critical limitations: they rely on idealized clinical case studies that fail to capture real-world clinical complexity, or they use ICD codes as disease labels, which significantly undercounts rare diseases since many lack direct mappings to comprehensive rare disease databases like Orphanet. To address these limitations, we explore MIMIC-RD, a rare disease differential diagnosis benchmark constructed by directly mapping clinical text entities to Orphanet. Our methodology involved an initial LLM-based mining process followed by validation from four medical annotators to confirm identified entities were genuine rare diseases. We evaluated various models on our dataset of 145 patients and found that current state-of-the-art LLMs perform poorly on rare disease differential diagnosis, highlighting the substantial gap between existing capabilities and clinical needs. From our findings, we outline several future steps towards improving differential diagnosis of rare diseases.
Plant disease is a critical factor affecting agricultural production. Traditional manual recognition methods face significant drawbacks, including low accuracy, high costs, and inefficiency. Deep learning techniques have demonstrated significant benefits in identifying plant diseases, but they still face challenges such as inference delays and high energy consumption. Deep learning algorithms are difficult to run on resource-limited embedded devices. Offloading these models to cloud servers is confronted with the restriction of communication bandwidth, and all of these factors will influence the inference's efficiency. We propose a collaborative inference framework for recognizing plant diseases between edge devices and cloud servers to enhance inference speed. The DNN model for plant disease recognition is pruned through deep reinforcement learning to improve the inference speed and reduce energy consumption. Then the optimal split point is determined by a greedy strategy to achieve the best collaborated inference acceleration. Finally, the system for collaborative inference acceleration in plant disease recognition has been implemented using Gradio to facilitate friendly human-machine interaction. Experiments indicate that the proposed collaborative inference framework significantly increases inference speed while maintaining acceptable recognition accuracy, offering a novel solution for rapidly diagnosing and preventing plant diseases.
Afsana Ahsan Jeny, Masum Shah Junayed, Md Robel Mia
et al.
Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L*a*b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods.
ObjectiveIn malignant tumors, a hypercoagulable state is frequently observed and is intricately intertwined with cancer development and the remodeling of the immune microenvironment. Recently, the coagulation-related genes (CRGs) signature has emerged as highly significant for the prognosis and immunotherapy of patients with various cancers. Nevertheless, their application in esophageal squamous cell carcinoma (ESCC) remains uninvestigated. Here, our objective is to explore the role of the CRGs signature in forecasting prognosis and predicting patient’s response to immunotherapy.MethodsAccording to the prognostic CRGs, consensus clustering was utilized to stratify ESCC patients in the GSE53625 cohort into two subgroups. Subsequently, difference analysis and univariate cox analysis were conducted on the two subgroups, and a CRGs signature was constructed by leveraging these genes. Next, multiple RNA transcriptome cohorts were utilized to validate the signature. Moreover, functional enrichment, tumor mutation burden (TMB), tumor infiltration, immune function, and immunotherapy response of this signature were investigated.ResultsA CRGs signature composed of six genes (PTX3, CILP, CFHR4, SULT1B1, IL5RA, and FAM151A) was constructed. This signature serves as an independent and reliable prognostic factor. Additionally, when compared with the 32 prognostic signatures previously reported, the CRGs signature exhibited superior performance in the ESCC prognostic cohorts. Additionally, we found that high-risk ESCC exhibited higher immune infiltration, lower TMB, higher TIDE, and a lower proportion of immunotherapy response. In vitro experiments have shown that the gene SULT1B1, which exhibits the highest accuracy in predicting tumor status, significantly inhibited the proliferation and metastasis.ConclusionsWe constructed and validated a robust CRGs signature. Moreover, as one of the model CRGs, the tumor-suppressive role of SULT1B1 in ESCC was experimentally verified in vitro. These results provide novel insights into enhancing the prognosis of ESCC and formulating treatment strategies.
Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with limited responses to immune checkpoint blockade (ICB) therapies in most patients. Increasing evidence indicates that the tumor immune microenvironment (TIME) plays a crucial role in immunotherapy outcomes. Among various metabolic abnormalities in the TIME, dysregulated lipid metabolism has emerged as a critical determinant of immune cell fate, differentiation, and function. In this review, we comprehensively summarize the current understanding of the immune landscape in GC, focusing on how altered lipid metabolism reshapes immune cell populations—including tumor-associated macrophages (TAMs), dendritic cells (DCs), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cytotoxic CD8+ T cells. We highlight key metabolic pathways such as fatty acid oxidation(FAO), cholesterol homeostasis, and lipid uptake that impact immune cell activity, contributing to immune evasion and therapeutic resistance. Importantly, we explore emerging therapeutic strategies targeting lipid metabolism, including inhibitors of cluster of differentiation 36 (CD36), fatty acid synthase (FASN), and sterol regulatory element-binding protein 1 (SREBP1) and discuss their synergistic potential when combined with ICB therapies. In conclusion, lipid metabolic reprogramming represents a promising yet underexplored axis in modulating antitumor immunity in GC. Integrating metabolic intervention with immunotherapy holds potential to overcome current treatment limitations and improve clinical outcomes. Future studies incorporating spatial omics and single-cell profiling will be essential to elucidate cell-type specific metabolic dependencies and foster translational breakthroughs.
ABSTRACT Objective This study aimed to elucidate the underlying mechanisms by which lipopolysaccharide (LPS) influences differential ovarian responses to controlled ovarian stimulation at the metabolite and gene levels. Materials and Methods A total of 16 female mice were randomly allocated into LPS and control group. Each mice underwent controlled ovarian stimulation. Oocytes were gathered and quantified. Ovarian tissue samples from 8 mice and blood samples from 16 mice were collected. Metabolomics analysis was conducted for both ovary tissues and blood samples. Transcriptome sequencing analysis was used for ovary tissues. Spearman's correlation analysis was performed to identify the metabolites and their corresponding differential transcripts genes within the key signaling pathway. Results The number (86 vs. 122) and rate (49.7% vs. 78.2%) of metaphase II (MII) oocytes were markedly lower in LPS group (p < 0.001). Compared with control group, metabolites related to carbohydrates and lipid were decreased in ovary tissue of LPS group while metabolites related to amino acid and fatty acid beta‐oxidation were decreased in serum of LPS group. Within the category of biological processes, terms associated with the regulation of lipid metabolic process and steroid metabolic, biosynthetic process were downregulated in the LPS group. Correlation analysis indicated five genes (Lcn2, S100a9, S100a8, Muc5b, Muc5ac) in IL‐17 signaling pathway were positively related to the catabolites of polyunsaturated fatty acids and negatively related to dihydroxyacetone phosphate. Conclusions This study provided evidence that inflammation has damaging effect on ovary response to controlled ovarian stimulation, linked to dysfunctions in lipid, carbohydrate, and amino acid metabolism. IL‐17 signal pathways may play a critical role in this process.
IntroductionChronic nonhealing wounds are one of the most serious complications of diabetes mellitus (DM), with limited treatment options. Hydrogen sulfide (H2S) plays a protective role against multiple inflammatory diseases. This study aimed to explore the effects of H2S on diabetic skin wound healing and its underlying mechanisms.MethodsA streptozotocin-induced diabetic rat model was established, and the rats were randomly divided into control, DM, and DM + NaHS (a donor of H2S) groups. Full-thickness wounds were made on the dorsal skin of the rats. H2S levels and H2S-synthesizing enzyme expression were evaluated in the wound tissue. Wound healing, histological changes, inflammasome activation, fibroblast pyroptosis, and phosphorylation of signaling components of nuclear factor kappa B (NF-κB) pathway were assessed.ResultsThe results showed that NaHS administration effectively restored H2S levels and promoted skin wound healing, as evidenced by the amelioration of histological changes and increased collagen deposition in diabetic rats. Meanwhile, NaHS treatment inhibited macrophage M1 polarization and decreased the levels of pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6) in diabetic wound tissues, notably, suppressing NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome activation and fibroblast pyroptosis. In addition, NaHS treatment was able to inhibit the activation of NF-κB pathway in the wound tissues.ConclusionTaken together, these results show that H2S promotes skin wound healing in diabetic rats and may be involved in the restoration of H2S levels, inhibition of NLRP3 inflammasome activation, and fibroblast pyroptosis, suggesting that it may be a promising therapeutic agent for treating diabetic skin wounds.
Stephanie K. Lathrop, Jordan J. Clark, Karthik Siram
et al.
AbstractMany different platforms have been used to develop highly protective vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans. However, protection has eroded over time due to the emergence of antigenically diverse viral variants, especially the Omicron subvariants. One successful platform for the generation of SARS-CoV-2 vaccines are recombinant spike protein vaccines, of which two are licensed in the United States and Europe. Typically, purified recombinant protein antigens are poorly immunogenic and adjuvants must be included in the formulation. Here, we adjuvanted recombinant ancestral SARS-CoV-2 Wuhan-Hu-1 spike proteins with an emulsion formulation combined with synthetic Toll-like receptor (TLR) 4 and 7/8 agonists. This combination led to the induction of a Th1-skewed immune response that included high titers of antibodies against Wuhan-Hu-1 spike. These serum antibodies included neutralizing and cross-reactive antibodies that recognized the spike from multiple SARS-CoV-2 variants, as well as the receptor binding domain (RBD) from SARS-CoV-1. Despite an absence of robust cross-neutralization, vaccination against Wuhan-Hu-1 spike in the context of TLR-containing emulsions provided complete cross-protection against disease from a lethal challenge with XBB.1 in a stringent K18-hACE2 mouse model. We believe that the combination of recombinant spike antigens with TLR agonist-based emulsion formulations could lead to the development of next-generation SARS-CoV-2 vaccines that provide significant protection from future emerging variants.
Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients.
This study, our main topic is to devlop a new deep-learning approachs for plant leaf disease identification and detection using leaf image datasets. We also discussed the challenges facing current methods of leaf disease detection and how deep learning may be used to overcome these challenges and enhance the accuracy of disease detection. Therefore, we have proposed a novel method for the detection of various leaf diseases in crops, along with the identification and description of an efficient network architecture that encompasses hyperparameters and optimization methods. The effectiveness of different architectures was compared and evaluated to see the best architecture configuration and to create an effective model that can quickly detect leaf disease. In addition to the work done on pre-trained models, we proposed a new model based on CNN, which provides an efficient method for identifying and detecting plant leaf disease. Furthermore, we evaluated the efficacy of our model and compared the results to those of some pre-trained state-of-the-art architectures.
Mateusz Daniol, Daria Hemmerling, Jakub Sikora
et al.
Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Clinical relevance - Developing a non-invasive biomarker for Parkinson's disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear.
Bibandhan Poudyal, David Soriano Panõs, Gourab Ghoshal
Non-pharmaceutical interventions (NPIs) aimed at limiting human mobility have demonstrated success in curbing the transmission of airborne diseases. However, their effectiveness in managing vector-borne diseases remains less clear. In this study, we introduce a framework that integrates mobility data with vulnerability matrices to evaluate the differential impacts of mobility-based NPIs on both airborne and vector-borne pathogens. Focusing on the city of Santiago de Cali in Colombia, our analysis illustrates how mobility-based policies previously proposed to contain airborne disease can make cities more prone to the spread of vector-borne diseases. By proposing a simplified synthetic model, we explain the limitations of the latter policies and exploit the synergies between both types of diseases to find new interventions reshaping the mobility network for their simultaneous control. Our results thus offer valuable insights into the epidemiological trade-offs of concurrent disease management, providing a foundation for the design and assessment of targeted interventions that reshape human mobility.
In the general domain, large multimodal models (LMMs) have achieved significant advancements, yet challenges persist in applying them to specific fields, especially agriculture. As the backbone of the global economy, agriculture confronts numerous challenges, with pests and diseases being particularly concerning due to their complexity, variability, rapid spread, and high resistance. This paper specifically addresses these issues. We construct the first multimodal instruction-following dataset in the agricultural domain, covering over 221 types of pests and diseases with approximately 400,000 data entries. This dataset aims to explore and address the unique challenges in pest and disease control. Based on this dataset, we propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system. To accelerate progress in this field and inspire more researchers to engage, we design a diverse and challenging evaluation benchmark for agricultural pests and diseases. Experimental results demonstrate that Agri-LLaVA excels in agricultural multimodal conversation and visual understanding, providing new insights and approaches to address agricultural pests and diseases. By open-sourcing our dataset and model, we aim to promote research and development in LMMs within the agricultural domain and make significant contributions to tackle the challenges of agricultural pests and diseases. All resources can be found at https://github.com/Kki2Eve/Agri-LLaVA.
Saurav Bharadwaj, Akshita Midha, Shikha Sharma
et al.
Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.
The emerging research shows that lncRNA has crucial research value in a series of complex human diseases. Therefore, the accurate identification of lncRNA-disease associations (LDAs) is very important for the warning and treatment of diseases. However, most of the existing methods have limitations in identifying nonlinear LDAs, and it remains a huge challenge to predict new LDAs. In this paper, a deep learning model based on a heterogeneous network and convolutional neural network (CNN) is proposed for lncRNA-disease association prediction, named HCNNLDA. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair is constructed according to various biological premises about lncRNAs, diseases, and miRNAs. Then, the low-dimensional feature representation is fully learned by the convolutional neural network. In the end, the XGBoot classifier model is trained to predict the potential LDAs. HCNNLDA obtains a high AUC value of 0.9752 and AUPR of 0.9740 under the 5-fold cross-validation. The experimental results show that the proposed model has better performance than that of several latest prediction models. Meanwhile, the effectiveness of HCNNLDA in identifying novel LDAs is further demonstrated by case studies of three diseases. To sum up, HCNNLDA is a feasible calculation model to predict LDAs.