Weakly supervised anomaly detection coupled with Fourier transform infrared (FT-IR) spectroscopy for the identification of non-normal tissue.

The detection and classification of histopathological abnormal tissue constituents using machine learning (ML) techniques generally requires example data for each tissue or cell type of interest. This creates problems for studies on tissue that will have few regions of interest, or for those looking to identify and classify diseases of rarity, resulting in inadequate sample sizes from which to build multivariate and ML models. Regarding the impact on vibrational spectroscopy, specifically infrared (IR) spectroscopy, low numbers of samples may result in ineffective modelling of the chemical composition of sample groups, resulting in detection and classification errors. Anomaly detection may be a solution to this problem, enabling users to effectively model tissue constituents considered to represent normal tissue to capture any abnormal tissue and identify instances of non-normal tissue, be it disease or spectral artefacts. This work illustrates how a novel approach using a weakly supervised anomaly detection algorithm paired with IR microscopy can detect non-normal tissue spectra. In addition to incidental interferents such as hair, dust, and tissue scratches, the algorithm can also detect regions of diseased tissue. The model is never introduced to instances of these groups, training solely on healthy control data using only the IR spectral fingerprint region. This approach is demonstrated using liver tissue data from an agrochemical exposure mouse study.

Classification of formalin-fixed bladder cancer cells with laser tweezer Raman spectroscopy.

Bladder cancer is a common cancer that is relatively hard to detect at an early stage because of its non-obvious symptoms. It is known that bladder cells can be found in urine samples which potentially could be used for early detection of bladder cancer. Raman spectroscopy is a powerful non-invasive tool for accessing biochemical information of cells. Combined with laser tweezers, to allow isolation of single cells, Raman spectroscopy has been used to characterise a number of bladder cells that might be found in a urine sample. Using principal component-canonical variates analysis (PC-CVA) and k-fold validation, the results shows that the invasive bladder cancer cells can be identified with accuracy greater than 87%. This demonstrates the potential of developing an early detection method that identifies the invasive bladder cancer cells in urine samples.

Tissue discrimination in head and neck cancer using image fusion of IR and optical microscopy.

A regression-based fusion algorithm has been used to merge hyperspectral Fourier transform infrared (FTIR) data with an H&E image of oral squamous cell carcinoma metastases in cervical lymphoid nodal tissue. This provides insight into the success of the ratio of FTIR absorbances at 1252 cm-1 and 1285 cm-1 in discriminating between these tissue types. The success is due to absorbances at these two wavenumbers being dominated by contributions from DNA and collagen, respectively. A pixel-by-pixel fit of the fused spectra to the FTIR spectra of collagen, DNA and cytokeratin reveals the contributions of these molecules to the tissue at high spatial resolution.

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer.

A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.

The extent of anticholinergic burden across an older Welsh population living with frailty: cross-sectional analysis of general practice records.

BACKGROUND: Anticholinergic medicines are associated with adverse outcomes for older people. However, little is known about their use in frailty. The objectives were to (i) investigate the prevalence of anticholinergic prescribing for older patients, and (ii) examine anticholinergic burden according to frailty status. METHODS: Cross-sectional analysis of Welsh primary care data from the Secure Anonymised Information Linkage databank including patients aged ≥65 at their first GP consultation between 1 January and 31 December 2018. Frailty was identified using the electronic Frailty Index and anticholinergic burden using the Anticholinergic Cognitive Burden (ACB) scale. Descriptive analysis and logistic regression were conducted to (i) describe the type and frequency of anticholinergics prescribed; and (ii) to estimate the association between frailty and cumulative ACB score (ACB-Sum). RESULTS: In this study of 529,095 patients, 47.4% of patients receiving any prescription medications were prescribed at least one anticholinergic medicine. Adjusted regression analysis showed that patients with increasing frailty had higher odds of having an ACB-Sum of >3 compared with patients who were fit (mild frailty, adj OR 1.062 (95%CI 1.061-1.064), moderate frailty, adj OR 1.134 (95%CI 1.131-1.136), severe frailty, adj OR 1.208 (95%CI 1.203-1.213)). CONCLUSIONS: Anticholinergic prescribing was high in this older population. Older people with advancing frailty are exposed to the highest anticholinergic burden despite being the most vulnerable to the associated adverse effects. Older people with advancing frailty should be considered for medicines review to prevent overaccumulation of anticholinergic medications, given the risks of functional and cognitive decline that frailty presents.

Exploring variation in implementation of multifactorial falls risk assessment and tailored interventions: a realist review.

BACKGROUND: Falls are the most common safety incident reported by acute hospitals. In England national guidance recommends delivery of a multifactorial falls risk assessment (MFRA) and interventions tailored to address individual falls risk factors. However, there is variation in how these practices are implemented. This study aimed to explore the variation by examining what supports or constrains delivery of MFRAs and tailored interventions in acute hospitals. METHODS: A realist review of literature was conducted with searches completed in three stages: (1) to construct hypotheses in the form of Context, Mechanism, Outcome configurations (CMOc) about how MFRAs and interventions are delivered, (2) to scope the breadth and depth of evidence available in Embase to test the CMOcs, and (3) following prioritisation of CMOcs, to refine search strategies for use in multiple databases. Citations were managed in EndNote; titles, abstracts, and full texts were screened, with 10% independently screened by two reviewers. RESULTS: Two CMOcs were prioritised for testing labelled: Facilitation via MFRA tools, and Patient Participation in interventions. Analysis indicated that MFRA tools can prompt action, but the number and type of falls risk factors included in tools differ across organisations leading to variation in practice. Furthermore, the extent to which tools work as prompts is influenced by complex ward conditions such as changes in patient condition, bed swaps, and availability of falls prevention interventions. Patient participation in falls prevention interventions is more likely where patient directed messaging takes individual circumstances into account, e.g., not wanting to disturb nurses by using the call bell. However, interactions that elicit individual circumstances can be resource intensive and patients with cognitive impairment may not be able to participate despite appropriately directed messaging. CONCLUSIONS: Organisations should consider how tools can be developed in ways that better support consistent and comprehensive identification of patients' individual falls risk factors and the complex ward conditions that can disrupt how tools work as facilitators. Ward staff should be supported to deliver patient directed messaging that is informed by their individual circumstances to encourage participation in falls prevention interventions, where appropriate. TRIAL REGISTRATION: PROSPERO: CRD42020184458.

Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique.

The visual detection, classification, and differentiation of cancers within tissues of clinical patients is an extremely difficult and time-consuming process with severe diagnosis implications. To this end, many computational approaches have been developed to analyse tissue samples to supplement histological cancer diagnoses. One approach is the interrogation of the chemical composition of the actual tissue samples through the utilisation of vibrational spectroscopy, specifically Infrared (IR) spectroscopy. Cancerous tissue can be detected by analysing the molecular vibration patterns of tissues undergoing IR irradiation, and even graded, with multivariate and Machine Learning (ML) techniques. This publication serves to review and highlight the potential for the application of infrared microscopy techniques such as Fourier Transform Infrared Spectroscopy (FTIR) and Quantum Cascade Laser Infrared Spectroscopy (QCL), as a means to improve diagnostic accuracy and allow earlier detection of human neoplastic disease. This review provides an overview of the detection and classification of different cancerous tissues using FTIR spectroscopy paired with multivariate and ML techniques, using the F1-Score as a quantitative metric for direct comparison of model performances. Comparisons also extend to data handling techniques, with a provision of a suggested pre-processing protocol for future studies alongside suggestions as to reporting standards for future publication.

Analysis of Fixed and Live Single Cells Using Optical Photothermal Infrared with Concomitant Raman Spectroscopy.

This paper reports the first use of a novel completely optically based photothermal method (O-PTIR) for obtaining infrared spectra of both fixed and living cells using a quantum cascade laser (QCL) and optical parametric oscillator (OPO) laser as excitation sources, thus enabling all biologically relevant vibrations to be analyzed at submicron spatial resolution. In addition, infrared data acquisition is combined with concomitant Raman spectra from exactly the same excitation location, meaning the full vibrational profile of the cell can be obtained. The pancreatic cancer cell line MIA PaCa-2 and the breast cancer cell line MDA-MB-231 are used as model cells to demonstrate the capabilities of the new instrumentation. These combined modalities can be used to analyze subcellular structures in both fixed and, more importantly, live cells under aqueous conditions. We show that the protein secondary structure and lipid-rich bodies can be identified on the submicron scale.

Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets.

The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms - AdaBoost and Random Forests - on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.

Image fusion of IR and optical microscopy for mapping of biomolecules in tissue.

It is shown that a pixel-level image fusion technique can produce images that combine the spatial resolution of optical microscopy images of haematoxylin and eosin (H&E) stained tissue with the chemical information in Fourier transform infrared (FTIR) images. The fused images show minimal distortion and the higher spatial resolution of the H&E images overcomes the diffraction limit on the spatial resolution of the FTIR images. A consideration of the FTIR spectra of nucleic acids and collagen can explain the changes in contrast between non-cancerous oral epithelium and underlying stroma within fused images formed by combining an H&E stain of oral tissue with FTIR images of the tissue obtained at a number of wavenumbers.

Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM.

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm-1 and 1285 cm-1. The metric yields discriminating sensitivities, specificities and precision of 98.8 ± 0.1%, 99.89 ± 0.01% and 99.78 ± 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 ± 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to ∼4 µm. The image formed from the ratio of the SNOM images obtained at 1252 cm-1 and 1285 cm-1 shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm-1 signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be ∼40 µm wide and the SNOM topography shows that the core centre is raised by ∼1 µm compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are ∼25 µm wide, roughly the width of two cancer cells.

Quality assessment with diverse studies (QuADS): an appraisal tool for methodological and reporting quality in systematic reviews of mixed- or multi-method studies.

BACKGROUND: In the context of the volume of mixed- and multi-methods studies in health services research, the present study sought to develop an appraisal tool to determine the methodological and reporting quality of such studies when included in systematic reviews. Evaluative evidence regarding the design and use of our existing Quality Assessment Tool for Studies with Diverse Designs (QATSDD) was synthesised to enhance and refine it for application across health services research. METHODS: Secondary data were collected through a literature review of all articles identified using Google Scholar that had cited the QATSDD tool from its inception in 2012 to December 2019. First authors of all papers that had cited the QATSDD (n=197) were also invited to provide further evaluative data via a qualitative online survey. Evaluative findings from the survey and literature review were synthesised narratively and these data used to identify areas requiring refinement. The refined tool was subject to inter-rater reliability, face and content validity analyses. RESULTS: Key limitations of the QATSDD tool identified related to a lack of clarity regarding scope of use of the tool and in the ease of application of criteria beyond experimental psychological research. The Quality Appraisal for Diverse Studies (QuADS) tool emerged as a revised tool to address the limitations of the QATSDD. The QuADS tool demonstrated substantial inter-rater reliability (k=0.66), face and content validity for application in systematic reviews with mixed, or multi-methods health services research. CONCLUSION: Our findings highlight the perceived value of appraisal tools to determine the methodological and reporting quality of studies in reviews that include heterogeneous studies. The QuADS tool demonstrates strong reliability and ease of use for application to multi or mixed-methods health services research.

Patients' Perspectives About Factors Affecting Their Use of Electronic Personal Health Records in England: Qualitative Analysis.

BACKGROUND: General practices (GPs) in England have recently introduced a nationwide electronic personal health record (ePHR) system called Patient Online or GP online services, which allows patients to view parts of their medical records, book appointments, and request prescription refills. Although this system is free of charge, its adoption rates are low. To improve patients' adoption and implementation success of the system, it is important to understand the factors affecting their use of the system. OBJECTIVE: The aim of this study is to explore patients' perspectives of factors affecting their use of ePHRs in England. METHODS: A cross-sectional survey was carried out between August 21 and September 26, 2017. A questionnaire was used in this survey to collect mainly quantitative data through closed-ended questions in addition to qualitative data through an open-ended question. A convenience sample was recruited in 4 GPs in West Yorkshire, England. Given that the quantitative data were analyzed in a previous study, we analyzed the qualitative data using thematic analysis. RESULTS: Of the 800 eligible patients invited to participate in the survey, 624 (78.0%) returned a fully completed questionnaire. Of those returned questionnaires, the open-ended question was answered by 136/624 (21.8%) participants. A total of 2 meta-themes emerged from participants' responses. The first meta-theme comprises 5 themes about why patients do not use Patient Online: concerns about using Patient Online, lack of awareness of Patient Online, challenges regarding internet and computers, perceived characteristics of nonusers, and preference for personal contact. The second meta-theme contains 1 theme about why patients use Patient Online: encouraging features of Patient Online. CONCLUSIONS: The challenges and concerns that impede the use of Patient Online seem to be of greater importance than the facilitators that encourage its use. There are practical considerations that, if incorporated into the system, are likely to improve its adoption rate: Patient Online should be useful, easy to use, secure, and easy to access. Different channels should be used to increase the awareness of the system, and GPs should ease registration with the system and provide manuals, training sessions, and technical support. More research is needed to assess the effect of the new factors found in this study (eg, lack of trust, difficulty registering with Patient Online) and factors affecting the continuing use of the system.

Study of Low Terahertz Radar Signal Backscattering for Surface Identification.

This study explores the scattering of signals within the mm and low Terahertz frequency range, represented by frequencies 79 GHz, 150 GHz, 300 GHz, and 670 GHz, from surfaces with different roughness, to demonstrate advantages of low THz radar for surface discrimination for automotive sensing. The responses of four test surfaces of different roughness were measured and their normalized radar cross sections were estimated as a function of grazing angle and polarization. The Fraunhofer criterion was used as a guideline for determining the type of backscattering (specular and diffuse). The proposed experimental technique provides high accuracy of backscattering coefficient measurement depending on the frequency of the signal, polarization, and grazing angle. An empirical scattering model was used to provide a reference. To compare theoretical and experimental results of the signal scattering on test surfaces, the permittivity of sandpaper has been measured using time-domain spectroscopy. It was shown that the empirical methods for diffuse radar signal scattering developed for lower radar frequencies can be extended for the low THz range with sufficient accuracy. The results obtained will provide reference information for creating remote surface identification systems for automotive use, which will be of particular advantage in surface classification, object classification, and path determination in autonomous automotive vehicle operation.

Optimization Synthesis and Biosensing Performance of an Acrylate-Based Hydrogel as an Optical Waveguiding Sensing Film.

The metal-clad leaky waveguide (MCLW) is an optical biosensor consisting of a metal layer and a low index waveguide layer on a glass substrate. This label-free sensor measures refractive index (RI) changes within the waveguide layer. This work shows the development and optimization of acrylate based-hydrogel as the waveguide layer formed from PEG diacrylate (PEGDA, Mn 700), PEG methyl ether acrylate (PEGMEA, Mn 480), and acrylate-PEG2000-NHS fabricated on a substrate coated with 9.5 nm of titanium. The acrylate-based hydrogel is a synthetic polymer, so properties such as optical transparency, porosity, and hydrogel functionalization by a well-controlled reactive group can be tailored for immobilization of the bioreceptor within the hydrogel matrix. The waveguide sensor demonstrated an equal response to solutions of identical RI containing small (glycerol) and large (bovine serum albumin; BSA) analyte molecules, indicating that the hydrogel waveguide film is highly porous to both sizes of molecule, thus potentially allowing penetration of a range of analytes within the porous matrix. The final optimized MCLW chip was formed from a total hydrogel concentration of 40% v/v of PEGMEA-PEGDA (Mn 700), functionalized with 2.5% v/v of acrylate-PEG2000-NHS. The sensor generated a single-moded waveguide signal with a RI sensitivity of 128.61 ± 0.15° RIU-1 and limit of detection obtained at 2.2 × 10-6 RIU with excellent signal-to-noise ratio for the glycerol detection. The sensor demonstrated RI detection by monitoring changes in the out-coupled angle resulting from successful binding of d-biotin to streptavidin immobilized on functionalized acrylate hydrogel, generating a binding signal of (12.379 ± 0.452) × 10-3°.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study.

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.

Fatty-Acid Uptake in Prostate Cancer Cells Using Dynamic Microfluidic Raman Technology.

It is known that intake of dietary fatty acid (FA) is strongly correlated with prostate cancer progression but is highly dependent on the type of FAs. High levels of palmitic acid (PA) or arachidonic acid (AA) can stimulate the progression of cancer. In this study, a unique experimental set-up consisting of a Raman microscope, coupled with a commercial shear-flow microfluidic system is used to monitor fatty acid uptake by prostate cancer (PC-3) cells in real-time at the single cell level. Uptake of deuterated PA, deuterated AA, and the omega-3 fatty acids docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were monitored using this new system, while complementary flow cytometry experiments using Nile red staining, were also conducted for the validation of the cellular lipid uptake. Using this novel experimental system, we show that DHA and EPA have inhibitory effects on the uptake of PA and AA by PC-3 cells.

Exploiting CELLULOSE SYNTHASE (CESA) Class Specificity to Probe Cellulose Microfibril Biosynthesis.

Cellulose microfibrils are the basic units of cellulose in plants. The structure of these microfibrils is at least partly determined by the structure of the cellulose synthase complex. In higher plants, this complex is composed of 18 to 24 catalytic subunits known as CELLULOSE SYNTHASE A (CESA) proteins. Three different classes of CESA proteins are required for cellulose synthesis and for secondary cell wall cellulose biosynthesis these classes are represented by CESA4, CESA7, and CESA8. To probe the relationship between CESA proteins and microfibril structure, we created mutant cesa proteins that lack catalytic activity but retain sufficient structural integrity to allow assembly of the cellulose synthase complex. Using a series of Arabidopsis (Arabidopsis thaliana) mutants and genetic backgrounds, we found consistent differences in the ability of these mutant cesa proteins to complement the cellulose-deficient phenotype of the cesa null mutants. The best complementation was observed with catalytically inactive cesa4, while the equivalent mutation in cesa8 exhibited significantly lower levels of complementation. Using a variety of biophysical techniques, including solid-state nuclear magnetic resonance and Fourier transform infrared microscopy, to study these mutant plants, we found evidence for changes in cellulose microfibril structure, but these changes largely correlated with cellulose content and reflected differences in the relative proportions of primary and secondary cell walls. Our results suggest that individual CESA classes have similar roles in determining cellulose microfibril structure, and it is likely that the different effects of mutating members of different CESA classes are the consequence of their different catalytic activity and their influence on the overall rate of cellulose synthesis.

Live single cell analysis using synchrotron FTIR microspectroscopy: development of a simple dynamic flow system for prolonged sample viability.

Synchrotron radiation Fourier transform infrared microspectroscopy (SR-microFTIR) of live biological cells has the potential to provide far greater biochemical and morphological detail than equivalent studies using dehydrated, chemically-fixed single cells. Attempts to measure live cells using microFTIR are complicated by the aqueous environment required and corresponding strong infrared absorbance by water. There is also the additional problem of the limited lifetime of the cells outside of their preferred culture environment. In this work, we outline simple, cost-effective modifications to a commercially available liquid sample holder to perform single live cell analysis under an IR microscope and demonstrate cell viability up to at least 24 hours. A study using this system in which live cells have been measured at increasing temperature has shown spectral changes in protein bands attributed to α-ß transition, consistent with other published work, and proves the ability to simultaneously induce and measure biochemical changes. An additional study of deuterated palmitic acid (D31-PA) uptake at different timepoints has made use of over 200 individual IR spectra collected over ∼4 hours, taking advantage of the ability to maintain viable cell samples for longer periods of time in the measurement environment, and therefore acquire greatly increased numbers of spectra without compromising on spectral quality. Further developments of this system are planned to widen the range of possible experiments, and incorporate more complex studies, including drug-cell interaction.

Anticancer drug impact on DNA - a study by neutron spectroscopy coupled with synchrotron-based FTIR and EXAFS.

Complementary structural and dynamical information on drug-DNA interplay has been achieved at a molecular level, for Pt/Pd-drugs, allowing a better understanding of their pharmacodynamic profile which is crucial for the development of improved chemotherapeutic agents. The interaction of two cisplatin-like dinuclear Pt(ii) and Pd(ii) complexes with DNA was studied through a multidisciplinary experimental approach, using quasi-elastic neutron scattering (QENS) techniques coupled with synchrotron-based extended X-ray absorption fine structure (SR-EXAFS) and Fourier-Transform Infrared Spectroscopy-Attenuated Total Reflectance (SR-FTIR-ATR). DNA extracted from drug-exposed human triple negative breast cancer cells (MDA-MB-231) was used, with a view to evaluate the effect of the unconventional antineoplastic agents on this low prognosis type of cancer. The drug impact on DNA's dynamical profile, via its hydration layer, was provided by QENS, a drug-triggered enhanced mobility having been revealed. Additionally, an onset of anharmonicity was detected for dehydrated DNA, at room temperature. Far- and mid-infrared measurements allowed the first simultaneous detection of the drugs and their primary pharmacological target, as well as the drug-prompted changes in DNA's conformation that mediate cytotoxicity. The local environment of the absorbing Pd(ii) and Pt(ii) centers in the drugs' adducts with adenine, guanine and glutathione was attained by EXAFS.