Biology‐Related Research Relies Too Much on the Problematic Method of Significance Testing, Whereas There Are Good Alternatives

In biology, as in many other empirical sciences, drawing conclusions is often based on statistical analyses. Still, by far, the most common approach for this is null hypothesis significance testing, ....

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Recent Advancements in Cancer Diagnosis via Spectroscopic Analytical Techniques

Spectroscopic methods offer vital information on the molecular makeup, interactions, and structure of biological tissues by examining the relationship between matter and electromagnetic radiation. ...

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Spectroscopic Signature of Apoptosis Induced by Venetoclax in Philadelphia-Positive Acute Lymphoblastic Leukemia

Abstract. Understanding why some leukemia cells undergo apoptosis upon venetoclax (VEN) treatment while others remain refractory is essential for improving

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Automatic Identification of Paraffin Pixels on FTIR Images Acquired on FFPE Human Samples

Abstract. The transfer of mid-infrared spectral histopathology to the clinic will be possible provided that its application in clinical practice is simple.

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Simple Estimation of Errors and Multi-Pixel Signal-to-Noise Ratio for Gaussian, Lorentzian, and Pseudo-Voigt functions Fit to Noisy Spectral Data - Ryan S. Jakubek, Rohit Bhartia, Marc D. Fries, 2026

Quantification of band parameters in spectral data typically requires iteratively fitting the spectroscopic feature to Gaussian, Lorentzian, or pseudo-Voigt fun...

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From unlabeled to labeled: self-supervised deep learning in computational pathology - BioData Mining

Purpose The advancement of decision support systems for pathology and their implementation in clinical practice have been limited by the necessity for extensive, manually annotated datasets. Self-supervised learning (SSL) automates the extraction and interpretation of histopathological features from unannotated images, facilitating efficient model development without dependence on expert labeling. In this study, we introduce the SSL-HistoNet model that learns disease-relevant morphological representations from histopathological images through self-supervised learning. Materials and methods We applied it to WGA-stained skeletal muscle tissues from mouse models of amyotrophic lateral sclerosis (ALS) and Type I diabetes to explore its ability to capture pathological muscle phenotypes in an annotation-free setting. Following pretraining on unlabeled data, the SSL encoder was further integrated with an attention-guided classifier to evaluate its capacity to identify pathological muscle alterations. Results SSL-HistoNet achieved a precision of 0.98, a recall of 0.98, and an AUC of 0.98, demonstrating performance comparable to or outperforming state-of-the-art supervised models. Alongside high discriminative performance, exploratory feature analyses demonstrated consistent class-level changes in morphology-related patterns identified through representation learning. Conclusion These findings highlight the capability of SSL-HistoNet as an annotation-free framework for outlining disease-specific tissue structures, reducing manual labeling demands and mitigating inter- and intra-observer variability in histological processes. Clinical trial number Not applicable

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Causal responsibility based explainable AI for vibrational spectroscopy applied to oral FTIR and oesophageal Raman diagnostics - Scientific Reports

Scientific Reports - Causal responsibility based explainable AI for vibrational spectroscopy applied to oral FTIR and oesophageal Raman diagnostics

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An Integrated Raman Platform with Embedded AI for Intraoperative Real-Time Cancer Detection

Raman spectroscopy workflows are often fragmented across proprietary tools and ad hoc data-processing scripts, which slow decision-making and limit reproducibility and auditability. We present an integrated Raman platform with a Python-based GUI application that interfaces with the optical system(s) to unify instrument control, automate data acquisition, noise removal, signal processing, and run an embedded AI model for real-time classification of experimental samples. The system automatically archives intermediate and final outputs for auditability. We validated the platform’s output for two types of experimental specimens: Tylenol and peritumoral biological samples from human laryngeal tissues. We compared the results with other studies, commercial software, and previous data-processing algorithms. The results obtained were consistent across all comparators. To demonstrate native analytics within the same workflow, we also developed and embedded a 1D convolutional neural network (CNN) tailored for biological Raman spectra. The multilayered CNN was trained on ex vivo human laryngeal tissue Raman spectra and was evaluated across 50 independent runs. The model achieved a mean test accuracy of 0.8929 ± 0.0213, a sensitivity of 0.9086 ± 0.0321, a specificity of 0.8698 ± 0.0434, and a mean AUC of 0.9506 ± 0.0142. The average latency across 50 runs was 2.82 s from signal acquisition to prediction, with device acquisition accounting for most of the time. The key innovation is not only the embedded AI but also an end-to-end, auditable workflow that unifies device control, acquisition, signal processing, visualization, and the archival of intermediate and final outputs, with optional real-time inference within a single platform.

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FAIRaman: making clinical Raman spectroscopy data FAIR through biobanking and NeXus standards | ChemRxiv

Raman spectroscopy is increasingly applied to biomedical and diagnostic research, generating spectral datasets that could support the development of robust artificial-intelligence models for disease diagnosis, prognosis, and treatment guidance. However, ...

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IR Map of the Human Cell

Classic infrared (IR) microscopy is limited by the diffraction limit, which obscures subcellular heterogeneity, and by the complex overlap of vibrational bands, which complicates precise molecular assignment. This study presents a comprehensive “IR map of the cell” that provides a standardized framework for label-free chemical identification of subcellular compartments across various human cell lines. By integrating Fourier transform infrared (FTIR) spectroscopy with submicron-resolution optical photothermal infrared (OPTIR) microscopy (∼0.3 μm), the cellular landscape was mapped with improved spatial specificity beyond that achievable by conventional FTIR imaging. Advanced chemometric tools were employed to segment the nucleus, cytoplasm, and regions rich in lipids and glycogen, each characterized by a definitive “IR barcode”. Furthermore, in silico modeling validated spectral assignments by simulating cellular fingerprints from reference biocompounds, while detailed spectroscopic characterization of subcellular compartments defined marker bands for proteins, lipids, carbohydrates, and nucleic acids. The modeling supported the interpretation that experimental spectra can be approximated as a linear combination of biomolecular classes, helping to constrain spectral band overlap and refine the proposed “IR barcode” for cellular identification. The developed IR map provides a robust, standardized foundation for the label-free interpretation of cellular chemistry. This study demonstrates the utility of IR microscopy as a powerful diagnostic and analytical tool for monitoring metabolic shifts and cellular status at the micrometric level.

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AirNet: A Deep Learning-Driven Auto Baseline Correction Algorithm Balancing Global Smoothness and Local Fidelity

Baseline correction is a critical preprocessing step to eliminate non-Raman scattering backgrounds in the Raman/SERS spectrum, ensuring accurate peak positions and intensities for qualitative and quan...

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SORS and SESORS: deep Raman spectroscopy in biomedical analysis and disease diagnosis

Spatially offset Raman spectroscopy (SORS) and its surface-enhanced variant, SESORS, have emerged as transformative techniques in analytical chemistry and biomedical diagnostics, enabling non-invasive...

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