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PubMed2001

Analysis and comparison of multimodal cancer treatments.

We analyse the sequence in which the three most commonly prescribed cancer treatments--surgery (S), chemotherapy (C) and radiotherapy (R)--should be administered. A system of ordinary differential equations is formulated that captures the various local and systemic effects of the three modes of treatment, as well as the first-order effects of the inter-relationship between the primary tumour and the distant metastatic tumours, including primary tumour shedding and the primary tumour's effect on the rate of angiogenesis in the metastatic tumours. Under a set of stated assumptions on the parameter values, we find the exact cancer cure probability (subject to toxicity constraints) for the six permutation schedules (i.e. SCR, CSR, CRS, SRC, RSC, RCS) and for two novel schedules, SRCR and RSCR, that apply radiotherapy in disjoint, optimally timed portions. We show analytically that SRCR and RSCR are the two best-performing (i.e. highest cure probability) schedules among the eight considered. Further, SRCR is shown to be optimal among all possible schedules, provided a modest condition is satisfied on the delay of initial angiogenesis experienced by the patient's dormant tumours.

Breast Neoplasms↗

PubMed2003

Abnormal variability and distribution of functional maps in autism: an FMRI study of visuomotor learning.

OBJECTIVE: Autism is a neurally based psychiatric disorder, but there is no consensus regarding the underlying neurofunctional abnormalities. Previous functional magnetic resonance imaging (fMRI) studies of simple movement suggested individually variable and scattered functional brain organization in autism. The authors examined whether such abnormalities generalize to multimodal processing (visually driven motor sequence learning). METHOD: Eight male autistic patients and eight comparison subjects matched with the patients on age, gender, and handedness were examined by using fMRI while they performed finger press movements prompted by visually presented repeating six-digit sequences. Hemodynamic responses to the six-digit sequences were statistically compared to responses to single-digit stimuli in one experiment and to regular six-digit sequences in another experiment. RESULTS: Both groups showed activations in bilateral premotor, superior parietal, and occipital cortices in both experiments. Task-by-group interactions showed that superior parietal activations were less pronounced in the autism group, whereas prefrontal cortex and more posterior parietal loci showed greater activation in the autism group than in the comparison group. The distances between Individual subjects' activation peaks and the groupwise peak were greater in the autism group than in the comparison group. CONCLUSIONS: The results support earlier findings of abnormal variability and scatter of functional maps in autism. They are consistent with evidence from other studies suggesting early-onset disturbances in the development of cerebello-thalamo-cortical pathways in autism.

Adolescent↗

PubMed2004

Ultrasonic ranging sensor using simultaneous emissions from different transducers.

In recent applications based on ultrasound, several ultrasonic transducers have been geometrically and electronically associated to constitute a global sensor. There are several different methods used to process the ultrasonic signals obtained from these transducers. In this work, multimode techniques using Golay complementary sequences are proposed for processing the ultrasonic signal. The system increases scan rate, precision, and reliability. It is also capable of echo discrimination, allowing simultaneous measurements to be made and detection of the same obstacle by different transducers without cross-talk problems. The real-time implementation of the algorithm is presented on a field-programmable gate array (FPGA) device.

Algorithms↗

PubMed2026

Integrating Radiogenomics and CSF-Based Liquid Biopsy Sequencing for Precision Neuro-Oncology.

Glioblastoma and diffuse gliomas pose major therapeutic challenges due to marked intratumoral heterogeneity, limited tissue accessibility, and the blood-brain barrier. Tissue-based next-generation sequencing (NGS) remains essential for WHO CNS5 molecular classification, yet it is invasive and poorly suited to serial monitoring. Two complementary non- or minimally invasive approaches have advanced rapidly: radiogenomics, which correlates multiparametric MRI features with genomic alterations, and cerebrospinal fluid (CSF) liquid biopsy sequencing, which detects circulating tumor DNA with high tissue concordance. This review examines the independent progress and synergistic integration of radiogenomics and CSF-NGS. Imaging signatures can non-invasively predict key drivers (IDH1/2, EGFR, TERT, PTEN, TP53) and molecular subtypes, while CSF-ctDNA sequencing enables real-time assessment of clonal evolution, therapy resistance (including post-temozolomide hypermutation), and residual disease. We discuss technical considerations, performance metrics, multimodal artificial-intelligence fusion, and emerging clinical applications for diagnosis, prognosis, treatment selection, and longitudinal surveillance. Critical challenges, standardization, prospective validation, and workflow integration are highlighted. By combining the spatial phenotypic information of radiogenomics with the temporal genomic resolution of CSF sequencing, this multimodal strategy offers a promising path toward precision neuro-oncology and reduced reliance on repeated invasive sampling.

Humans↗

PubMed1993

Locoregionally advanced paranasal sinus carcinoma. Favorable survival with multimodality therapy.

To determine the efficacy of multimodality treatment for stage III and IV, advanced paranasal carcinoma, we have retrospectively reviewed local control rate and disease-free survival in patients treated at the University of Chicago (Ill). Twelve consecutive patients with stage III or IV, newly diagnosed paranasal sinus carcinoma treated between 1984 and 1991 were included in this study. Multimodality therapy was composed of a sequence of fluorouracil-cisplatin-based neoadjuvant chemotherapy (in 12 of 12 patients) followed by standard surgical resection (11 of 12 patients) and radiotherapy (12 of 12 patients, 45 to 73 Gy) with or without concomitant chemotherapy. Eleven patients (92%) are currently alive and free of disease, with a median follow-up of 55 months (range, 13 to 105 months). One patient died of persistent disease. Failure was attributed to incomplete surgical resection. There was only one major irreversible treatment complication (cisplatin ototoxic reaction). Our preliminary data suggest improved local control and survival with multimodality therapy that includes systemic neoadjuvant chemotherapy and standard tumor resection in patients with advanced paranasal sinus carcinoma. These results are superior to the reported 40% survival with bimodal therapy and are better than those achieved in our institution for other head and neck primaries with the same treatment regimens.

Adult↗

PubMed2026

Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.

BACKGROUND: Neoadjuvant therapy (NAT) is recommended for locally advanced gastric cancer (LAGC), but some patients respond poorly. We aimed to construct a multimodal model integrating CT images, transcriptomic sequencing, and clinicopathological data to assess prognosis in LAGC patients receiving NAT. MATERIALS AND METHODS: This multicenter study included 505 LAGC patients who underwent NAT. Radiomic features were extracted from preoperative CT images of 505 patients. RNA-seq was performed on 277 post-NAT specimens, with additional data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (n = 804). Patients were divided into training (168 cases), internal validation (72 cases), and external validation cohorts. Machine learning algorithms identified key radiomic, molecular, and clinical features associated with NAT response, which were then integrated into a multimodal model to predict overall survival (OS) and disease-free survival (DFS). RESULTS: Six radiomic and three molecular features significantly associated with NAT response were selected. Radiomic risk (hazard ratio [HR]: 4.0, P < 0.001) and molecular risk (HR: 7.1, P < 0.001) were independent prognostic factors. By integrating radiomic risk, molecular risk, and clinical characteristics, a multimodal model (MuMo) was constructed.The C-index results (OS, C-index = 0.855; DFS, C-index = 0.786) demonstrated that MuMo outperformed the single-modality models and ypTNM staging.Mechanistic analysis suggested that the efficacy of neoadjuvant therapy was significantly enriched in immune-inflammatory pathways. CONCLUSIONS: MuMo can effectively predict postoperative survival risk in LAGC patients receiving NAT, serving as a powerful tool for optimizing prognostic assessment.

Humans↗

PubMed1998

Strategies for rare-event detection: an approach for automated fetal cell detection in maternal blood.

This article explores the feasibility of the use of automated microscopy and image analysis to detect the presence of rare fetal nucleated red blood cells (NRBCs) circulating in maternal blood. The rationales for enrichment and for automated image analysis for "rare-event" detection are reviewed. We also describe the application of automated image analysis to 42 maternal blood samples, using a protocol consisting of one-step enrichment followed by immunocytochemical staining for fetal hemoglobin (HbF) and FISH for X- and Y-chromosomal sequences. Automated image analysis consisted of multimode microscopy and subsequent visual evaluation of image memories containing the selected objects. The FISH results were compared with the results of conventional karyotyping of the chorionic villi. By use of manual screening, 43% of the slides were found to be positive (>=1 NRBC), with a mean number of 11 NRBCs (range 1-40). By automated microscopy, 52% were positive, with on average 17 NRBCs (range 1-111). There was a good correlation between both manual and automated screening, but the NRBC yield from automated image analysis was found to be superior to that from manual screening (P=.0443), particularly when the NRBC count was >15. Seven (64%) of 11 XY fetuses were correctly diagnosed by FISH analysis of automatically detected cells, and all discrepancies were restricted to the lower cell-count range. We believe that automated microscopy and image analysis reduce the screening workload, are more sensitive than manual evaluation, and can be used to detect rare HbF-containing NRBCs in maternal blood.

Automation↗

PubMed1995

Integration of information-seeking skills and activities into a problem-based curriculum.

Recent trends in medical education include a shift from the traditional, didactic, lecture-oriented approach to a more student-driven, problem-based approach to learning. This trend provides librarians with an opportunity to develop programs to teach information-gathering skills that support and are integrated into problem-based learning (PBL). In 1992, the University of Pittsburgh School of Medicine implemented the initial phase of a curriculum revision that emphasizes PBL. Since that time, Falk Library of the Health Sciences has provided a large-scale, intensive program integrating information-seeking skills and activities into the first-year Patient-Doctor Relationship course, a sequence that initiates medical school. A multimodal approach to information seeking and sources is emphasized, utilizing print and audiovisual materials, computerized resources, and subject experts. The Falk Library program emphasizes the gathering and use of information as central to both PBL and student skills development. An informal, post-course evaluation was conducted to gauge which information resources were used and valued most by students. This article presents evaluation results, including data on the use of information sources and services, and student perceptions of the librarian's role in the PBL sessions.

Curriculum↗

PubMed2025

STEMIN transcription factor drives selective chromatin remodeling for gene activation within a relaxed chromatin during reprogramming in the moss Physcomitrium patens.

Land plants exhibit remarkable cellular plasticity, readily reprogramming differentiated cells into stem cells in response to internal and external stimuli. While chromatin remodeling is crucial for cellular reprogramming, its interplay with gene expression during reprogramming into stem cells remains elusive. In the moss Physcomitrium patens, wounding induces reprogramming of leaf cells facing wounded cells to change into chloronema apical stem cells through the activation of the AP2/ERF transcription factor STEMIN. In this study, we employed multimodal single-nuclei RNA and ATAC sequencing to explore the interplay between gene expression and chromatin dynamics during STEMIN-mediated reprogramming. Profiling 20 883 single-nuclei from gametophores, protonemata, and cut leaves, we identified 11 distinct cell types including reprogramming leaf cells. Our analysis revealed that reprogramming leaf cells exhibit a partly relaxed chromatin landscape and STEMIN transcription factors selectively enhance accessibility at specific genomic loci essential for stem cell formation. Thus, our results indicate that wounding initiates a broad chromatin relaxation, creating a permissive environment and specific transcription factors act to refine this permissive state by specifically relaxing chromatin regions critical for reprogramming.

Bryopsida↗

PubMed2026

TCRspec: A Recognition Interface-Informed Multimodal Method for TCR-pMHC Specificity Prediction.

Specific recognition between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to adaptive immunity, yet accurate prediction of TCR-pMHC specificity remains challenging. Existing models mainly rely on sequence features or isolated molecular structures, limiting their ability to capture interface-level determinants within the ternary recognition complex. Here, we constructed the multimodal TCR-pMHC ternary complex (MM-TCR) data set, integrating paired TCR-pMHC sequences, V/J gene annotations, and modeled TCR-pMHC complex structures refined by short molecular dynamics-based relaxation. Based on MM-TCR, we developed TCRspec, an interpretable multimodal framework combining sequence embeddings, gene-usage features, and complex-level structural representations. Under a stringent CD-HIT TCR-cluster-disjoint split, TCRspec achieved an average AUROC of 0.896 and AUPRC of 0.882 across seven antigen-specific test data sets, outperforming representative baseline models. Cross-validation and ablation analyses confirmed the contribution of ternary complex structural information and MD-refined structures. In independent OOD peptide-TCR systems, TCRspec retained discriminative performance and identified model-inferred peptide positions associated with TCR recognition, providing a structure-informed framework for TCR specificity prediction.

Receptors, Antigen, T-Cell↗

PubMed2026

Multimodal computational framework resolves B cell maturation in autoimmunity and ageing.

Identification of the origin of pathogenic immune cells is crucial for therapeutic interventions and diagnosis but pseudotime methods struggle to trace immune cells accurately. Current trajectory inference methods for B cell development and response in health and disease either ignore or underutilize antigen receptor sequence information, limiting their ability to resolve developmental pathways, particularly for pathogenic populations. Widely used methods such as Monocle 3 reconstruct developmental paths from transcriptomic similarity alone, discarding the features from immune receptors. Dandelion has combined the immune receptor features with transcriptomics but it struggles to simulate the trajectory path of B cells. Here we present ClonoTrace, a computational framework that integrates BCR sequence features with transcriptomic trajectory inference through gated fusion of multimodal embeddings. In fetal B cell development and germinal centre development, ClonoTrace demonstrates closer concordance with the canonical reference ordering than Monocle 3 and Dandelion. Applied to systemic lupus erythematosus, ClonoTrace indicates a memory B cell extrafollicular maturation route alongside the naïve B cell route, accompanied by induction of ZEB2 with a concomitant decline of BACH2 along the trajectory, as a candidate alternative route to pathogenic double negative 2 B cells (DN2) in systemic lupus erythematosus (SLE) patients. In healthy ageing, ClonoTrace resolved three candidate age-related B cell maturation routes, from naïve, IgM+ memory and switched-memory B cells, each passing through a DN2-associated transcriptional state that is ordered before age-associated B cells along the inferred trajectory. ClonoTrace's fate probability algorithm indicated that IgM+ memory B cell to ABC transition as the leading candidate age-associated transition, which may be distinct from SLE DN2 maturation. ClonoTrace provides a generalizable framework for receptor-informed trajectory inference, describing candidate developmental routes of pathogenic B cell populations in autoimmunity and ageing.

Humans↗

PubMedDate not supplied

Spatio-temporal pattern discrimination in cats with insular-temporal lesions.

Previous studies have demonstrated that cats with bilateral insular-temporal lesions are impaired in their ability to perform temporal pattern discriminations of the general form A--B--A vs B--A--B. This deficit has been seen to occur when A and B are made up of two different auditory, visual, or vibrotractile stimuli. These data suggest that insular-temporal cortex is a multimodal area concerned with the perception of temporal sequences of stimuli. The present study extends these earlier observations by testing insular-temporal lesioned cats on a spatio-temporal pattern discrimination. A spatio-temporal pattern is defined as one in which the same stimulus is presented to the animal sequentially from different spatial locations. The data indicate that insular-temporal lesions disrupt a spatio-temporal pattern discrimination just as they do auditory, visual, or vibrotactile temporal pattern discriminations. Insular-temporal cortex appears to be critical for certain higher order perceptual abilities in the cat.

Animals↗

PubMed2000

Photodynamic therapy in lung cancer.

Photodynamic therapy (PDT) involves the use of photosensitizing agents that are selectively retained within tumor cells. The agents remain inactive until exposed to light of the proper wavelength. When activated by light, these compounds generate toxic oxygen radicals that result in tumor necrosis. In lung cancer, PDT can be used for both carcinoma in situ and for the treatment of unresectable disease with endobronchial obstruction. For patients with advanced disease, careful patient selection and integration of PDT with other interventional techniques are critical. Limited data suggest that PDT is comparable in efficacy to neodymium-yttrium-aluminum garnet (Nd-YAG) laser therapy, and some evidence indicates that it may be superior in terms of duration of response. For PDT to be used effectively, it should be integrated into a multimodality approach with chemotherapy and radiation. The optimal sequencing of these treatment modalities remains an area for further investigation.

Carcinoma in Situ↗

PubMedDate not supplied

Visceral clear cell sarcoma of soft tissue with confirmation by EWS-ATF1 fusion detection.

Clear cell sarcoma of soft tissue (CCS-ST) is a rare malignant neoplasm characterized by a tumor-defining translocation [t(12;22) (q13;q12)], resulting in the EWS-ATF1 gene fusion. An extremely limited number of visceral CCS-ST cases have been reported in the literature. Here the authors report a visceral CCS-ST in a Hispanic adolescent male with a large infiltrative mass involving the small bowel. The tumor was evaluated by light microscopy, immunocytochemistry, electron microscopy, cytogenetics, and molecular genetics. The tumor cells were strongly positive for S-100 protein, but negative for HMB-45. Rare premelanosomes were identified only after an extensive search with electron microscopy. Cytogenetics showed a characteristic t(12;22)(q13;q12) for CCS-ST with isochromosome 18q and trisomy 22. An EWS exon 8 sense primer and an antisense ATF1 primer were employed for detection of the CCS-ST tumor-defining EWS-ATF1 translocation, using reverse transcriptase-polymerase chain reaction techniques (RT-PCR), and the fusion gene breakpoint underwent DNA sequencing. This tumor is exceptional, because it is the first visceral CCS-ST that has been confirmed by RT-PCR and DNA sequencing. This case also illustrates the necessity of a multimodal approach to tumor diagnosis, and the utility of cytogenetics and molecular pathology in confirming the diagnosis of CCS-ST and eliminating conventional metastatic or primary visceral malignant melanoma as a consideration.

Adolescent↗

PubMed2006

Multimodal imaging of mouse development: tools for the postgenomic era.

With the sequence of the mouse genome known, it is now possible to create or identify mutations in every gene to determine the molecules necessary for normal development. Consequently, there is a growing need for advanced phenotyping tools to best understand defects produced by altering gene function. Perhaps nothing is more satisfying than to directly observe a process in action; to disturb it and see for ourselves how the process changes before our very eyes. No doubt, this desire is what drove the invention of the very first microscopes and continues to this day to fuel progress in the field of biological imaging. Because mouse embryos are small and develop embedded within many tissue layers within the nurturing environment of the mother, directly observing the dynamic, micro- and nanoscopic events of early mammalian development has proven to be one of the greater challenges for imaging scientists. Here, I will review some of the imaging methods being used to study mouse development, highlighting the results obtained from imaging.

Animals↗

PubMed2024

Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Deep Learning↗

PubMed2026

Cell-free DNA genomic and fragmentomic features for early outcome prediction in large B cell lymphoma.

Curative-intent immunochemotherapy fails in ∼30% of patients with large B cell lymphoma (LBCL), yet no validated molecular tool enables early identification of high-risk individuals to guide treatment intensification. Using shallow whole-genome sequencing (sWGS) of plasma cell-free DNA from 190 LBCL patients, we develop and validate the ACT score (aberrations, composition of fragments, and terminal motif analyses), a composite classifier integrating genomic and fragmentomic features from a single post-cycle-1 sample. ACT-positive patients have worse 2-year outcomes versus ACT-negative patients: time-to-progression 29% vs. 83% (hazard ratio [HR]: 4.4, 95% confidence interval [CI]: 1.9-10.0; p = 1.5 × 10-4) and overall survival 47% vs. 93% (HR: 8.7, 95% CI: 3.0-25.4; p = 1.8 × 10-6). The ACT score is independently prognostic of the International Prognostic Index, and their combination identifies the highest risk patients. Unlike mutation-based approaches, this assay requires neither tumor tissue, germline control, nor a baseline plasma sample. Built on open-source tools and sWGS, the ACT score offers a feasible, scalable strategy for early risk stratification in aggressive LBCL.

Humans↗

PubMed2026

Deep generative models in biological sequence and structure analysis and design.

Deep generative models have transformed biological sequence modeling from predictive analysis toward increasingly controllable design. Early biological applications of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) established latent representation learning and sequence synthesis, while recent advances in transformer-based language models, discrete diffusion, flow-matching, and multimodal generative frameworks have substantially expanded the scope of biological design. This review examines generative models for DNA, RNA, and protein sequence design, emphasizing how different model classes represent biological constraints, operate over discrete and continuous spaces, and integrate sequence, structure, and function. We compare VAEs, GANs, autoregressive and masked language models, diffusion models, and flow-based approaches across genomics, transcriptomics, and proteomics, with particular attention to controllability, long-range dependency modeling, structural grounding, generalization, and experimental utility. We further examine evaluation strategies, out-of-distribution generalization, and closed-loop design-build-test-learn workflows that connect in silico generation with empirical validation. We distinguish fundamental modality-dependent constraints including sequence discreteness, context length, structural coupling, and physical or thermodynamic requirements from architecture-dependent advantages that reflect the current state of the field. Current studies suggest that long-context models are particularly useful for genome-scale representation and sequence modeling, whereas structure-aware diffusion, flow-based, and inverse-folding approaches provide better frameworks for geometry-constrained RNA and protein design. This perspective provides a critical framework for understanding the present capabilities, limitations, and convergence of generative approaches toward reliable and experimentally grounded biological design.

Biological sequence analysis↗