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PubMed2026

Hypernetwork-guided fusion with intra-class MixUp for breast cancer subtyping.

Accurate breast cancer subtyping guides treatment selection, yet histopathology captures morphology without molecular state, while genomic profiling captures molecular signatures without spatial context. Existing fusion methods rely on concatenation, or on attention applied only after each modality is encoded independently. This work identifies a scale-dependent asymmetry in the direction of cross-modal conditioning: the direction that performs best under limited samples is not the one that holds at scale, and the reversal is traced to the capacity of the modulation pathway rather than to the fusion principle. The comparison is carried out within a hypernetwork-guided framework in which an auxiliary network maps one modality to conditioning parameters that modulate the other's feature representation, shaping features at the parametric level rather than the decision stage; modulation is patient-specific rather than patch-specific. Both directions are instantiated-gene-to-image (HyperG2I) and image-to-gene (HyperI2G) - and trained under a label-aware MixUp strategy that interpolates within-class samples across both modalities, preserving the hard binary labels clinical decisions require. The framework is evaluated on two paired TCGA-BRCA cohorts-one limited-sample, one independently assembled at scale-under a single protocol spanning two whole-slide representations, multiple visual backbones, and both conditioning directions. On the limited-sample cohort, gene-to-image conditioning at its optimal augmentation setting exceeds early fusion and both unimodal baselines, giving the highest recall on the aggressive Basal/HER2 class of any configuration evaluated, and an ablation favours intra-class over inter-class mixing. At scale this ordering does not hold: image-to-gene conditioning sustains its performance whereas gene-to-image does not, recovering only partially under the full tissue bag and isolating the capacity of the modulation pathway as the binding constraint. Direction and capacity of cross-modal conditioning, rather than fusion depth alone, therefore govern how such frameworks scale.

Breast Neoplasms↗
PubMed1980

Two relative efficiencies of polymorphic enzymes for characterizing cell lines, detecting contaminations, and monitoring transplants.

A new calculation of the relative efficiency of polymorphic enzyme markers, called the REB, was determined and compared with one of Fisher's determinations of the relative efficiency called REA here. The REA estimates the chance of failing, and 1-REA of succeeding, to show a phenotypic difference between two randomly selected persons or cultured cell lines (Case 1). In this study it was shown that the REA also estimates the chance of detecting a cell line mislabeling or similar mixup (Case 2) and a cell line cross-contamination leading to the complete replacement of an original line by contaminating line (Case 3). The new REB determines the probability of failing, and 1 - REB of succeeding, to detect a contamination of an original line by another line leading to their coexistence, or at least a sufficiently long period of transitional coexistence before one overgrows the other. The REA and REB also apply to determining the efficiency of polymorphic markers in detecting donor and recipient cells in tissue transplants.

Alleles↗
PubMed1983

The invisible dermatoses.

It is understandable that clinically normal skin may show abnormalities when examined with the light microscope, but paradoxical that biopsy of a clinically significant skin disorder may show a histologic picture that looks like normal skin. From the perspective of the dermatopathologist, the invisible dermatoses are clinically evident skin diseases that show a histologic picture resembling normal skin. A strategy for approaching the problem of the invisible dermatoses is to first examine the epidermis for fungi, cornoid lamellae (disseminated superficial actinic porokeratosis), and absence of the granular layer (dominant ichthyosis vulgaris). The cutis is then studied for hyalin deposition (macular amyloidosis), mast cells, microfilaria, dermal melanocytosis, silver granules, and absence of sweat glands (anhidrotic ectodermal dysplasia). Special stains may be required to uncover conditions like anetoderma and nevus elasticus. Comparison of the specimen with normal skin may disclose atrophoderma, lipoatrophy, vitiligo, or café au lait spot. Finally, technical problems should be considered, including sampling errors and mixup of specimens, either by the clinician or the laboratory.

Biopsy↗
PubMed2025

DNABERT-S: pioneering species differentiation with species-aware DNA embeddings.

SUMMARY: We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e. DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 28 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. AVAILABILITY AND IMPLEMENTATION: Model, codes, and data are publically available at https://github.com/MAGICS-LAB/DNABERT_S.

Sequence Analysis, DNA↗
PubMedDate not supplied

Good manufacturing practices and clinical supplies.

Quality characteristics must be assured through adherence to good manufacturing practices in the production, control, and testing of drug products intended for investigational as well as commercial use. A draft guideline on the preparation of investigational new drug products, soon to be available in final form, addresses questions that have been raised regarding acceptable practices and procedures to facilitate compliance with the CGMP regulations as applied to clinical supplies. Inspections of sterile clinical supplies production can be expected to include the areas most likely to influence product safety, quality, and uniformity in the same manner as would be expected regarding the manufacture of commercial batches. Some areas of particular significance in the manufacture of parenteral clinical supplies include validation of terminal sterilization, aseptic processing, and oxygen exclusion. The validation of the aseptic handling during lyophilization requires special attention. Other CGMP concerns include the provision of a quality control unit, avoiding packaging mixups, and being prepared for an amendment to the CGMP regulations regarding terminal sterilization.

Drugs, Investigational↗
PubMed2024

DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings.

We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e., DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 23 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. Model, codes, and data is publicly available at https://github.com/MAGlCS-LAB/DNABERT_S.

Journal Article↗
PubMedDate not supplied

Misdiagnosed HIV infection in pregnant women: implications for clinical care.

Out of nearly 900 women in a research study of human immunodeficiency virus infection in pregnancy, 8 were subsequently found not to be infected. Misdiagnoses could have resulted from (a) laboratory errors or specimen mixups; (b) failure to follow the testing algorithm recommended by the Centers for Disease Control and Prevention to confirm results; (c) women perceiving they were infected by high-risk behavior in the absence of testing, despite the receipt of negative test results, or based on screening results only; or (d) factitious disorder, HIV Munchausen syndrome, or malingering. Because of the potentially devastating impact of an HIV diagnosis and the toxicity of HIV therapies, health care providers should obtain independent confirmation of the diagnosis before initiating treatment or followup for HIV based on patient report or provider referral. Quality test interpretation and counseling must be ensured. Therapeutic interventions may be indicated for persons intentionally and falsely presenting themselves as HIV-infected.

Adult↗