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Although magnetic resonance spectroscopy (MRS) has provided in vivo measurements of brain chemical profiles in bipolar disorder (BD), there are no data on clinically and therapeutically important onset polarity (OP) and predominant polarity (PP). We conducted a proton MRS study in BD polarity subphenotypes, focusing on emotion regulation brain regions. Forty-one euthymic BD patients stratified according to OP and PP and sixteen healthy controls (HC) were compared. 1H-MRS spectra of the anterior and posterior cingulate cortex (ACC, PCC), left and right hippocampus (LHIPPO, RHIPPO) were acquired at 3.0T to determine metabolite concentrations. We found significant main effects of OP in ACC mI, mI/tNAA, mI/tCr, mI/tCho, PCC tCho, and RHIPPO tNAA/tCho and tCho/tCr. Although PP had no significant main effects, several medium and large effect sizes emerged. Compared to HC, manic subphenotypes (i.e., manic-OP, manic-PP) showed greater differences in RHIPPO and PCC, whereas depressive suphenotypes (i.e., depressive-OP, depressive-PP) in ACC. Effect sizes were consistent between OP and PP as high intraclass correlation coefficients (ICC) were confirmed. Our findings support the utility of MRS in the study of the neurobiological underpinnings of OP and PP, highlighting that the regional specificity of metabolite changes within the emotion regulation network consistently marks both polarity subphenotypes.

Advanced Raman spectroscopy (RS) systems have gained new interest in the field of medicine as an emerging tool for in vivo tissue discrimination. The coupling of RS with artificial intelligence (AI) algorithms has given a boost to RS to analyze spectral data in real time with high specificity and sensitivity. However, limitations are still encountered due to the large amount of clinical data which are required for the pre-training process of AI algorithms. In this study, human healthy and cancerous colon specimens were surgically resected from different sites of the ascending colon and analyzed by RS. Two transfer learning models, the one-dimensional convolutional neural network (1D-CNN) and the 1D–ResNet transfer learning (1D-ResNet) network, were developed and evaluated using a Raman open database for the pre-training process which consisted of spectra of pathogen bacteria. According to the results, both models achieved high accuracy of 88% for healthy/cancerous tissue discrimination by overcoming the limitation of the collection of a large number of spectra for the pre-training process. This gives a boost to RS as an adjuvant tool for real-time biopsy and surgery guidance.

Stroke represents a major cause of mortality and long-term disability among adult populations, leaving a devastating socioeconomic impact globally. Clinical manifestation of stroke is characterized by great diversity, ranging from minor disability to considerable neurological impairment interfering with activities of daily living and even death. Prognostic ambiguity has stimulated the interest for implementing stroke recovery biomarkers, including those provided by structural neuroimaging techniques, i.e., diffusion tensor imaging (DTI) and tractography for the study of white matter (WM) integrity. Considering the necessity of prompt and accurate prognosis in stroke survivors along with the potential capacity of DTI as a relevant imaging biomarker, the purpose of our study was to review the pertinent literature published within the last decade regarding DTI as a prognostic tool for recovery in acute and hyperacute stroke. We conducted a thorough literature search in two databases (MEDLINE and Science Direct) in order to trace all relevant studies published between 1 January 2012 and 16 March 2022 using predefined terms as key words. Only full-text human studies published in the English language were included. Forty-four studies were identified and are included in this review. We present main findings and by describing several methodological issues, we highlight shortcomings and gaps in the current literature so that research priorities for future research can be outlined. Our review suggests that DTI can track longitudinal changes and identify prognostic correlates in acute and hyperacute stroke patients.

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.

Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications.

Diffusion tensor imaging (DTI)-tractography and functional magnetic resonance imaging (fMRI) have dynamically entered the presurgical evaluation context of brain surgery during the past decades, providing novel perspectives in surgical planning and lesion access approaches. However, their application in the presurgical setting requires significant time and effort and increased costs, thereby raising questions regarding efficiency and best use. In this work, we set out to evaluate DTI-tractography and combined fMRI/DTI-tractography during intra-operative neuronavigation in resective brain surgery using lesion-related preoperative neurological deficit (PND) outcomes as metrics. We retrospectively reviewed medical records of 252 consecutive patients admitted for brain surgery. Standard anatomical neuroimaging protocols were performed in 127 patients, 69 patients had additional DTI-tractography, and 56 had combined DTI-tractography/fMRI. fMRI procedures involved language, motor, somatic sensory, sensorimotor and visual mapping. DTI-tractography involved fiber tracking of the motor, sensory, language and visual pathways. At 1 month postoperatively, DTI-tractography patients were more likely to present either improvement or preservation of PNDs (p = 0.004 and p = 0.007, respectively). At 6 months, combined DTI-tractography/fMRI patients were more likely to experience complete PND resolution (p < 0.001). Low-grade lesion patients (N = 102) with combined DTI-tractography/fMRI were more likely to experience complete resolution of PNDs at 1 and 6 months (p = 0.001 and p < 0.001, respectively). High-grade lesion patients (N = 140) with combined DTI-tractography/fMRI were more likely to have PNDs resolved at 6 months (p = 0.005). Patients with motor symptoms (N = 80) were more likely to experience complete remission of PNDs at 6 months with DTI-tractography or combined DTI-tractography/fMRI (p = 0.008 and p = 0.004, respectively), without significant difference between the two imaging protocols (p = 1). Patients with sensory symptoms (N = 44) were more likely to experience complete PND remission at 6 months with combined DTI-tractography/fMRI (p = 0.004). The intraoperative neuroimaging modality did not have a significant effect in patients with preoperative seizures (N = 47). Lack of PND worsening was observed at 6 month follow-up in patients with combined DTI-tractography/fMRI. Our results strongly support the combined use of DTI-tractography and fMRI in patients undergoing resective brain surgery for improving their postoperative clinical profile.

The primary objective of this study was to objectively compare the visual performance of patients following premium pseudophakic presbyopia corrections in different light combinations for near- and intermediate-vision activities of daily living (ADLs). This is a prospective, comparative study. A total of 75 patients populated three study groups: G1-patients with bilateral trifocal implantation, G2-patients with bilateral bifocal implantation, and G3-patients with bilateral monofocal implantation. All participants addressed 10 ADLs in nine combinations of light temperature (3000 K, 4000 K, and 6000 K) and light intensity (25 fc, 50 fc, and 75 fc) and declared their subjectively optimal light combination while reading. G2 and G3 had the best total ADL scores in 6000 K/75 fc, while G1 had the best total ADL score in 4000 K/75 fc. Total ADL, easy ADL, and moderate difficulty ADL scores were significantly better in G2, while difficult ADL score was significantly better in G1. The majority of all groups selected 6000 K/75 fc as the most comfortable light combination, and no group selected 3000 K and 25 fc. In conclusion, trifocal patients benefit from intense daylight, while bifocal and monofocal patients benefit from intense, cold lighting. Trifocal patients present superior near-vision capacity in difficult near-vision daily tasks, while bifocal patients present superiority in easy and moderate-difficulty ADLs.

Background: Magnetic resonance spectroscopy (MRS) in amyotrophic lateral sclerosis (ALS) has been overwhelmingly applied to motor regions to date and our understanding of frontotemporal metabolic signatures is relatively limited. The association between metabolic alterations and cognitive performance in also poorly characterised. Material and Methods: In a multimodal, prospective pilot study, the structural, metabolic, and diffusivity profile of the hippocampus was systematically evaluated in patients with ALS. Patients underwent careful clinical and neurocognitive assessments. All patients were non-demented and exhibited normal memory performance. 1H-MRS spectra of the right and left hippocampi were acquired at 3.0T to determine the concentration of a panel of metabolites. The imaging protocol also included high-resolution T1-weighted structural imaging for subsequent hippocampal grey matter (GM) analyses and diffusion tensor imaging (DTI) for the tractographic evaluation of the integrity of the hippocampal perforant pathway zone (PPZ). Results: ALS patients exhibited higher hippocampal tNAA, tNAA/tCr and tCho bilaterally, despite the absence of volumetric and PPZ diffusivity differences between the two groups. Furthermore, superior memory performance was associated with higher hippocampal tNAA/tCr bilaterally. Both longer symptom duration and greater functional disability correlated with higher tCho levels. Conclusion: Hippocampal 1H-MRS may not only contribute to a better academic understanding of extra-motor disease burden in ALS, but given its sensitive correlations with validated clinical metrics, it may serve as practical biomarker for future clinical and clinical trial applications. Neuroimaging protocols in ALS should incorporate MRS in addition to standard structural, functional, and diffusion sequences.

Magnetic Resonance images (MRIs) are employed in brain Stereotactic Radiosurgery and Radiotherapy (SRS/SRT) for target and/or critical organ localization and delineation. However, MRIs are inherently distorted, which also impacts the accuracy of the Magnetic Resonance Imaging/Computed Tomography (MRI/CT) co-registration process. In this phantom-based study, geometric distortion is assessed in 3T T2-weighted images (T2WIs), while the efficacy of an MRI distortion correction technique is also evaluated. A homogeneous polymer gel-filled phantom was CT-imaged before being irradiated with 26 4-mm Gamma Knife shots at predefined locations (reference control points). The irradiated phantom was MRI-scanned at 3T, implementing a T2-weighted protocol suitable for SRS/SRT treatment planning. The centers of mass of all shots were identified in the 3D image space by implementing an iterative localization algorithm and served as the evaluated control points for MRI distortion detection. MRIs and CT images were spatially co-registered using a mutual information algorithm. The inverse transformation matrix was applied to the reference control points and compared with the corresponding MRI-identified ones to evaluate the overall spatial accuracy of the MRI/CT dataset. The mean image distortion correction technique was implemented, and resulting MRI-corrected control points were compared against the corresponding reference ones. For the scanning parameters used, increased MRI distortion (>1mm) was detected at areas distant from the MRI isocenter (>5cm), while median radial distortion was 0.76mm. Detected offsets were slightly higher for the MRI/CT dataset (0.92mm median distortion). The mean image distortion correction improves geometric accuracy, but residual distortion cannot be considered negligible (0.51mm median distortion). For all three datasets studied, a statistically significant positive correlation between detected spatial offsets and their distance from the MRI isocenter was revealed. This work contributes towards the wider adoption of 3T imaging in SRS/SRT treatment planning. The presented methodology can be employed in commissioning and quality assurance programmes of corresponding treatment workflows.

Epidemics and pandemics are a field of scientific research since ancient times. The intensity of the repeated phenomena demonstrates their cyclicality in time. The ongoing COVID-19 pandemic, also known as the coronavirus pandemic, confirmed observations made in previous disease outbreaks. Epidemics are mainly characterized by two factors: (a) the population dynamics and (b) the nature of the disease. This article uses continuous mathematical models, on the basis of a scalable compartmental approach, characterized by systems of ordinary differential equations under the condition that individuals can freely move from one compartment to another. Numerous experiments were carried out to examine the impact of quarantine and vaccination policies, separately or in combination, on cumulative viral load, a measure adopted to reflect the cumulative viral burden of an infected population for a given time period. Current findings demonstrate that quarantine may play a crucial role in controlling an epidemic at its early stages, as well as the importance of early and widespread implementation of a vaccination program. The suggested approach may be utilized to study specific quarantine and vaccination scenarios, by manipulating various parameters such as the duration and extent of social distancing measures or the effectiveness and compliance to vaccination policies, and thus assist in decision making.

MR scans of low-gamma X-nuclei, low-concentration metabolites, or standard imaging at very low field entail a challenging tradeoff between resolution, signal-to-noise, and acquisition duration. Deep learning (DL) techniques, such as UNets, can potentially be used to improve such “low-quality” (LQ) images. We investigate three UNets for upscaling LQ MRI: dense (DUNet), robust (RUNet), and anisotropic (AUNet). These were evaluated for two acquisition scenarios. In the same-subject High-Quality Complementary Priors (HQCP) scenario, an LQ and a high quality (HQ) image are collected and both LQ and HQ were inputs to the UNets. In the No Complementary Priors (NoCP) scenario, only the LQ images are collected and used as the sole input to the UNets. To address the lack of same-subject LQ and HQ images, we added data from the OASIS-1 database. The UNets were tested in upscaling 1/8, 1/4, and 1/2 undersampled images for both scenarios. As manifested by non-statically significant differences of matrices, also supported by subjective observation, the three UNets upscaled images equally well. This was in contrast to mixed effects statistics that clearly illustrated significant differences. Observations suggest that the detailed architecture of these UNets may not play a critical role. As expected, HQCP substantially improves upscaling with any of the UNets. The outcomes support the notion that DL methods may have merit as an integral part of integrated holistic approaches in advancing special MRI acquisitions; however, primary attention should be paid to the foundational step of such approaches, i.e., the actual data collected.

Background: Ferredoxin reductase (FDXR) has already been reported as a promising biomarker for estimating radiation doses in radiotherapy. This study aimed to investigate the responsiveness of FDXR on pediatric population exposed to ionizing radiation (X-rays) during pediatric interventional cardiology (IC) procedures. Patients and Methods: Peripheral blood was collected by venipuncture from 24 pediatric donors before and 24 hours after the IC procedure. To estimate the effective dose, demographic data and Air Kerma-Area Product (PKA) were recorded for each patient. The relative quantification (RQ) of the FDXR gene in irradiated patient blood samples compared to the non-irradiated blood samples was determined using qPCR analysis. The relative values of FDXR were log- transformed. Results: The effective dose ranged from 0.002 mSv to 8.004 mSv. Over this radiation exposure range, the FDXR gene expression varied randomly with the effective dose. Up-regulation in FDXR expression was observed in 17 patients and down-regulation in 7 patients. Conclusions: Further studies in a larger cohort of pediatric patients along with the record of clinical data are needed to determine whether FDXR gene expression is an effective biomarker for radiation exposure estimation in pediatric imaging.

Magnetic resonance spectroscopy (MRS) has contributed important academic insights in motor neuron diseases (MNDs), particularly in amyotrophic lateral sclerosis (ALS). Over the past three decades momentous methodological advances took place, including the emergence of high-field magnetic resonance imaging (MRI) platforms, multi-voxel techniques, whole-brain protocols, novel head-coil designs, and a multitude of open-source imaging suites. Technological advances in MRS are complemented by important conceptual developments in MND, such as the recognition of the importance of extra-motor brain regions, multi-timepoint longitudinal study designs, assessment of asymptomatic mutation carriers, description of genotype-associated signatures, and the gradual characterisation of non-ALS MND phenotypes. We have conducted a systematic review of published MRS studies in MND to identify important emerging research trends, key lessons from pioneering studies, and stereotyped shortcomings. We also sought to highlight notable gaps in the current literature so that research priorities for future studies can be outlined. While MRS remains relatively underutilised in MND compared to other structural, diffusivity and functional imaging modalities, our review suggests that MRS can not only advance our academic understanding of MND biology, but has a multitude of practical benefits for clinical and pharmaceutical trial applications.

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

TBI results in significant cognitive impairments and in altered brain functional connectivity. However, no studies explored so far, the relationship between global functional connectivity and cognitive outcome in chronic moderate-severe TBI. This proof of principle study employed the intrinsic connectivity contrast, an objective voxel-based metric of global functional connectivity, in a small sample of chronic moderate-severe TBI participants and a group of healthy controls matched on gender (males), age, and education. Cognitive tests assessing executive functions, verbal memory, visual memory, attention/organization, and cognitive reserve were administered. Group differences in terms of global functional connectivity maps were assessed and the association between performance on the cognitive measures and global functional connectivity was examined. Next, we investigated the spatial extent of functional connectivity in the brain regions found to be associated with cognitive performance, using traditional seed-based analyses. Global functional connectivity of the TBI group was altered, compared to the controls. Moreover, the strength of global functional connectivity in affected brain areas was associated with cognitive outcome. These findings indicate that impaired global functional connectivity is a significant consequence of TBI suggesting that cognitive impairments following TBI may be partly attributed to altered functional connectivity between brain areas involved in the specific cognitive functions.

The overall prognosis for patients with ß-thalassemia has improved considerably during the past decades mainly due to regular blood transfusions, improvements in chelation therapy, and enhanced surveillance with imaging studies examining iron overload and other clinical complications. However, the prolonged survival of these patients leads to the development of other health problems including degenerative diseases such as arthropathies, which require further attention since they have a significant impact on the quality of life. In the current case report, we present a 45-year-old white man with ß-thalassemia complaining of non-traumatic pain and restriction in the range of motion of both knees. Magnetic resonance imaging (MRI) revealed a tear in the medial meniscus of the left knee as well as iron deposits in both knees. Histological findings confirmed the presence of hemosiderin in both joints. To our knowledge, this is the first reported case of macroscopically documented iron deposits in the knee joints of a patient with ß-thalassemia using MRI.