Diagnostic Applications Research Articles

Biology and applications of CRISPR-Cas12 and transposon-associated homologs.

CRISPR-associated Cas12 proteins are a highly variable collection of nucleic acid-targeting proteins. All Cas12 variants use RNA guides and a single nuclease domain to target complementary DNA or, in rare cases, RNA. The high variability of Cas12 effectors can be explained by a series of independent evolution events from different transposon-associated TnpB-like ancestors. Despite basic structural and functional similarities, this has resulted in unprecedented variation of the Cas12 effector proteins in terms of size, domain composition, guide structure, target identity and interference strategy. In this Review, we compare the unique molecular features of natural and engineered Cas12 and TnpB variants. Furthermore, we provide an overview of established genome editing and diagnostic applications and discuss potential future directions.

Nanoarchitectonics in immobilization of DNT/TNT specific binding peptides on laser-induced graphene

Abstract Bio/chemical sensors require high selectivity for specific targets. Carbon nanomaterials, especially graphene-based materials, are preferred in sensors due to their high surface area, superior electrical conductivity, mechanical strength, and flexibility. In this study, DNT-bp (Dinitrotoluene binding peptide) and TNT-bp (Trinitrotoluene binding peptide) were immobilized on LIG (Laser-induced graphene) films through covalent and non-covalent interactions. EDC (1-ethyl-3-3-dimethylaminopropyl carbodiimide hydrochloride) is used to activate carboxylic acid groups, which then react with NHS (N-hydroxysuccinimide) to form NHS esters, facilitating the binding of amine-containing peptides to the LIG surface. XPS analysis of LIG films functionalized with EDC+NHS shows a decrease in COOH (carboxyl) and an increase in C-N/C=N/C-O groups. N1s peak at high-resolution XPS spectrum indicates that DNT-bp immobilization leads to higher elimination of NHS-esters, resulting in fewer N-C=O (amide) functional groups and more protonated nitrogen, which suggests that DNT-bp immobilization is more effective than TNT-bp immobilization. The findings suggest that LIG-based sensors provide a cost-effective platform for the detection of DNT (2,4-dinitrotoluene) and 2,4,6-trinitrotoluene (TNT). Further advancements in LIG-based biosensors may enable their broader application in security, environmental monitoring, and health diagnostics.

Enhanced ePAD-DNA biosensor incorporating ZnO-NRs: Rapid and reliable electrochemical detection of field-isolated Pasteurella multocida

An electrochemical sensor has received much attention due to its importance for early infection identification, hinting at its critical relevance in diagnostic applications. For the detection of field-isolated strains of Pasteurella multocida, this paper reports the development and fabrication of a DNA-based electrochemical biosensor by integrating zinc oxide (ZnO) nanorods (NRs) into an electrochemical paper-based analytical device (ePAD). One significant improvement over the state-of-the-art features of the sensor is the using paper, an economically viable substrate that can be manufactured in large numbers. These sensors, being categorized under precision instruments with an ecologically friendly design, are ideal for mass production of eco-sensitive substrates. The biosensor is more sensitive and specific as compared to the conventional PCR-based sensing techniques. Moreover, the biosensor exploits the inherent properties of ZnO-NRs to amplify the signal output, whereas ePAD limits detection cost. In addition, a probe targeting the KMT gene of P. multocida was first characterized using conventional PCR and then immobilized onto the ZnO nanorods-modified ePAD surface, which improved the biosensor's ability for the rapid and selective genetic material of the pathogen detection. In addition, the sensor was validated using the methods of Cyclic Voltammetry (CV) and Linear Sweep Voltammetry (LSV). The reported biosensor it provides Linear Response of 0.001-10 μg/ml with LOD 0.001 μg/ml within a response time of 30 s. Subsequently, the biosensor reveals fast kinetics of the reaction as a powerful tool for timely and accurate diagnostics, pointing to its contribution to further development in biosensing technologies in the diagnostic area of infectious diseases at hospitals and clinics.

Construction of a two-dimensional magnetic solid phase extraction microfluidic chip/tandem mass spectrometry platform for the determination of cytarabine in plasma

A novel concept of two-dimensional magnetic solid phase extraction microfluidic chip (2D-MSPE-chip) was proposed, in which two or even more different separation mechanisms of solid phase extraction units were designed and constructed on microfluidic chips in series. The synthesized Fe3O4-UiO-66-C18 was used in the first dimension to retain protein and phospholipid through hydrophobic effect and ZrOP coordination, which could almost eliminate the interference of the matrix effect on the mass spectrum. The second dimensional material utilized Fe3O4-UiO-66-NH2 to enable cytarabine enrichment via π-π and hydrogen bonding. The parameters of 2D MSPE-Chip/tandem MS method such as desorption solvent (5 % acetic acid acetonitrile), loading solvent (20 % acetonitrile), and flow rate for extraction and desorption (10 μL/min) were select. The LOD of the established method for cytarabine in plasma was 0.13 ng/mL, and the linear range was between 2.0 ng/mL and 1000 ng/mL with correlation coefficient (R2 = 0.9991). The matrix effect was −5.0 %. The recovery rate of real samples ranged from 95.5 % to 104.8 %, with RSD ≤ 5.2 %. The chip design was simple and efficient and could be reused, thereby decreasing analysis expenses. It held considerable potential for application in rapid clinical diagnostics and precision medicine.

Phase angle as a marker of muscle quality: A systematic review and meta-analysis

Background & aimsPhase angle (PhA) is a biomarker derived from raw bioelectrical impedance analysis (BIA) values: resistance (R) and reactance (Xc). PhA reflects cellular membrane integrity and, as a result, has been considered a marker of fluid distribution, making it a potential prognostic indicator. A growing body of research demonstrates independent associations between PhA and muscle strength, mass, and composition. In this context, PhA has the extra potential to serve as a marker of muscle quality. However, the evidence supporting its use for this purpose is not well established. This study aimed to investigate the relationship between PhA and markers of muscle quality. MethodsThis systematic review and meta-analysis (Internal Prospective Register of Systematic Reviews – PROSPERO on a registration code: CRD42024507853) focused on observational studies assessing the relationship between PhA and markers of both concepts of muscle quality: the muscle quality index (MQI: strength by a unit of mass) and the muscle composition (i.e., skeletal muscle radiodensity [SMD], muscle echogenicity, muscle fat fraction, inter- and intramuscular adiposity). Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), while the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Meta-analyses with a random-effects model were conducted. ResultsSeventeen studies were included in this systematic review, encompassing 2,710 participants. Meta-analyses demonstrated that PhA had a moderate positive correlation coefficient with SMD (4 studies, 924 participants; r = 0.54, 95% confidence interval (CI) 0.38 to 0.69, heterogeneity (I2) = 92%) and good accuracy (85%) for classifying low SMD (2 studies, 390 participants; Area Under the Curve – AUC pooled 0.85, 95% CI 0.78 to 0.92, I2 = 0%). PhA was inversely-moderately correlated with muscle echogenicity (8 studies, 1,401 participants; r = - 0.42, 95% CI - 0.57 to - 0.24, I2 = 82%) and positively-weakly correlated with MQI (2 studies, 191 participants; r = 0.36, 95% CI 0.21 to 0.49, I2 = 17%). All studies had a higher risk of bias. The certainty of evidence ranged from low to very low. ConclusionDespite technical challenges, this study demonstrates the potential of PhA as a surrogate marker for muscle quality, particularly expressing muscle composition (SMD). Future studies should utilize BIA with standardized protocols to potentially establish specific cutoff values for PhA, thereby enhancing its diagnostic accuracy and clinical applicability. These studies could additionally explore the mechanisms underlying the associations between PhA and muscle quality aspects. In cases where technical factors are not easily controlled, the use of standardized PhA (SPhA), which converts PhA into Z-scores, could be beneficial. Although this warrants investigation, this approach (SPhA) has the potential to account for variables such as age, sex, device differences, and health status.

Theranostic and Combined Approaches Exploiting Multifunctional Gold Nanoclusters in Tumoral Ecosystems: A Paradigm Shift in Precision Oncology.

Malignant tumors pose a significant threat to human life and well-being because of their rising occurrence and size. The current treatment methods and diagnostic techniques employed in clinical practice are inadequate for effectively treating tumors. Fluorescence, photothermal effects, radiosensitization, and biocompatibility are only a few instances of the unique photonic and physicochemical properties exhibited. Gold nanoclusters (AuNCs) are nanomaterials that possess modest dimensions, typically measuring approximately 3 nm, and are composed of a limited number of particles. AuNCs have three primary functions in practical applications: serving as imaging agents, drug transporters, and therapeutic agents. This article discusses nanosystems. The text emphasizes the promise of AuNCs for tumor theranostic and combination treatment while also acknowledging any existing limitations. Lastly, it is anticipated that the information presented here will serve as a valuable tool for researchers in this sector, resulting in novel perspectives and, ultimately, a wider adoption of AuNCs in pharmaceuticals. This study focuses on the expansion of diagnostic applications in cancer therapy by utilizing AuNC-based devices, made possible by the use of dynamic or passive tumor targeting techniques. The utilization of AuNCs has been thoroughly investigated for their prospective applicability as light-activated and radiation agents. Furthermore, they have been investigated as nanocarriers for transporting anticancer drugs. The medications can either bind to the closure receptor or be linked to the AuNCs through various techniques, showcasing their extensive potential for therapeutic applications.

Iron oxide nanozymes as versatile analytical tools: an overview of their application as detection technique.

Iron oxide nanozymes (IONzymes) have become fundamental components in various analyte detection methodologies such as colorimetric, electrochemistry, fluorescence and luminescence. Their tunability, stability and the possibility of modification, alongside their ability to mimic the catalytic properties of natural enzymes like peroxidase, render them invaluable in analytical chemistry. This review explores the diverse applications of IONzymes across analytical chemistry, with a particular highlighting on their roles in different detection techniques and their potential in biomedical and diagnostic applications. This information would be valuable for researchers and practitioners in the fields of analytical chemistry, biochemistry, biotechnology and materials science who are interested in applying IONzymes in their work. In essence, this review article on iron oxide nanozymes in analytical chemistry would serve as a valuable resource for researchers, educators and industry professionals, offering insights, guidance and inspiration for further study and application of this promising class of nanomaterials.

Hydrogel-Based Microdroplet Ensembles Encapsulating Multiplexed EXPAR Assays for Trichromic Digital Profiling of MicroRNAs and in-Depth Classification of Primary Urethral Cancers.

The primary challenge in microarray-based biological analysis lies in achieving the sensitive and specific detection of single-molecule targets while ensuring high reproducibility. A user-friendly digital imaging platform has been developed for the encoded trichromic profiling of circulating microRNAs (miRNAs). This platform replaces the traditional exponential polymerase amplification reaction (EXPAR) conducted on the microliter scale with a system that confines the amplification process within thousands of femtoliter-sized microdroplet reactors, cross-linked from tetra-armed poly(ethylene glycol) acrylate (Tetra-PEGA) and poly(ethylene glycol) dithiol (HS-PEG-SH), thus offering significant advantages, including minimal sample input, enhanced reactivity, and simplified analytical procedures. The quantitative analysis relies on digital counting of fluorescently positive microdroplets, each containing an individual miRNA sequence. This approach significantly reduces nonspecific amplification and improves sensitivity by over 2 orders of magnitude. The system has shown great potential in differentiating between subtypes of primary urethral carcinoma, suggesting its practical application in routine cancer diagnostics through simple urinalysis.

A novel dual-staining method for cost-effective visualization and differentiation of microbial biofilms

Microbial biofilms are intricate communities that pose significant challenges in clinical and microbiological settings due to their resistance to antibiotics and immune responses. Advanced microscopy techniques, such as scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), and fluorescence microscopy, are often employed to visualize and differentiate between these biofilms. However, these methods are not feasible in all laboratories because of their high cost and complexity. In contrast, simpler techniques like crystal violet and Congo red staining fail to differentiate bacterial cells from the biofilm matrix. This study introduces a novel dual-staining method using Maneval’s stain for microbial biofilm detection and differentiation. This simple, cost-effective method requires only basic equipment and minimal reagents, making it suitable for routine use across various settings. We applied the dual-staining method to various microbial species, including Staphylococcus aureus, Enterococcus faecalis, Candida albicans, Escherichia coli, and Pseudomonas aeruginosa. When compared with the microtiter plate assay, results showed strong agreement, with the dual-staining method effectively differentiating between bacterial cells and the surrounding biofilm matrix, displaying a distinctive blue polysaccharide layer surrounding the magenta‒red bacterial cells. This technique offers a viable alternative to more expensive and complex biofilm detection methods, with potential applications in clinical diagnostics and biofilm research.

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins.

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures advances our understanding about how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Importantly, both technologies also have a profound impact on drug discovery as any therapeutic protein target can now be modelled allowing the de novo design of peptide binders or the identification of small molecule ligands through the in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design enabled by this transformative shift in protein science.

Efficiency of Artificial Intelligence for Interpretation of Chest Radiograms in the Republic of Tajikistan

The article presents data from recent publications and own data on screening studies with interpretation of chest radiographs using artificial intelligence CAD (Computer-Assisted Diagnosis), which, according to WHO recommendations, provides more accurate clinical thresholds for deciding who needs to take a sputum test. Another aspect of the WHO recommendations is the cost-effectiveness of CAD as a tool for triaging patients with tuberculosis symptoms in low-income countries with a high incidence of tuberculosis. Compared with smear microscopy and GeneXpert, without preliminary sorting, the use of mobile digital X-ray machines equipped with a CAD tool reduces costs, allowing sorting of individuals suspected of having tuberculosis for testing on GeneXpert, while reducing the time to start tuberculosis treatment. Thus, conducting a study using portable X-ray machines using a CAD program is a low-cost and easy-to-implement method, does not require large funds, does not require separate rooms, is highly effective, has good image quality, allows you to quickly clarify individuals suspected of having tuberculosis, differentiating it from other pathological changes in the lungs. Our experience shows that machine analysis of chest computed tomography data, due to the higher resolution capabilities of the method and the absence of fundamental disadvantages of radiography, including the effect of shadow summation, the presence of “blind” zones, etc., is finding increasing application in both diagnostics and screening of respiratory diseases. Our use of this tool allowed us to identify additional new cases of phthisio-onco-pulmonary diseases in field conditions.

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures advances our understanding about how life’s molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine‐learning based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Importantly, both technologies also have a profound impact on drug discovery as any therapeutic protein target can now be modelled allowing the de novo design of peptide binders or the identification of small molecule ligands through the in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design enabled by this transformative shift in protein science.

An Application of Machine-Learning-Oriented Radiomics Model in Clear Cell Renal Cell Carcinoma (ccRCC) Early Diagnosis.

Aims/Background Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of kidney cancer, where early diagnosis is crucial for improving prognosis and treatment outcomes. Radiomics, which utilizes machine learning techniques, presents a promising approach in medical imaging for the early detection and characterization of such conditions. This study aims to explore the clinical utility of a machine-learning-based radiomics model in the early diagnosis of ccRCC. Methods Case data and abdominal computed tomography (CT) tumour images of patients with ccRCC were obtained from The Cancer Imaging Archive (TCIA) database. The dataset included 31 cases in the training set (19 males and 12 females, with an average age of 58.1 years) and 13 cases in the validation set (8 males and 5 females, with an average age of 69.6 years). The volume of interest (VOI) was manually delineated, slice by slice, along the tumour's edge in cross-sectional images of ccRCC. Radiomics features were extracted from each region of interest (ROI) using the "PyRadiomics" plug-in in 3D Slicer software (version 5.1.0, Massachusetts Institute of Technology and Brigham and Women's Hospital, Boston, MA, USA). Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, followed by 10-fold cross-validation. The selected radiomics features were then used to construct prediction models based on two different supervised machine learning algorithms: logistic regression and random forest. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curves and calibration curves. Finally, clinical data were integrated with the radiomics features to enhance the prediction model. Results A total of 44 radiomics features were ultimately selected to establish the prediction model based on the training set results. Among the two machine learning models, the logistic regression model demonstrated superior diagnostic performance. An evaluation of model establishment, considering both individual radiomics features (DifferenceVariance, JointEnergy.1, JointEntropy.2, MeanAbsoluteDeviation.7, SmallAreaHighGrayLevelEmphasis.7) and clinical data, indicated that the logistic regression model was stable and exhibited strong diagnostic performance, good calibration, and clinical applicability in patients with ccRCC. When clinical data were combined with radiomics features in the model, the area under the curve (AUC) reached 0.969, with an optimal threshold of -2.290, and sensitivity and specificity values of 89.3% and 95.2%, respectively. The calibration curve also confirmed that the logistic regression model had high calibration accuracy and greater clinical application value. Conclusion This machine-learning-based radiomics prediction model demonstrated significant value in the early diagnosis of clear cell renal cell carcinoma (ccRCC).

Advancing Diagnostics: The Role of Ultrasound and MR Imaging in Evaluating Musculotendinous Pathologies of the Shoulder Joint – A Comprehensive Literature Review

Musculotendinous pathologies of the shoulder joint are a common source of pain and functional impairment, often requiring precise imaging for accurate diagnosis and management. This review explores the roles of ultrasound (US) and magnetic resonance imaging (MRI) in evaluating these conditions, focusing on their diagnostic strengths, limitations, and clinical applications. Ultrasound offers dynamic, real-time assessment with high accuracy for superficial structures, making it a cost-effective option for evaluating rotator cuff tears and tendinopathies. MRI, on the other hand, is the gold standard for comprehensive imaging of deep soft tissues, labral injuries, and subtle intra-articular abnormalities. Recent advances in both modalities, including AI-powered ultrasound and faster MRI sequences, have enhanced diagnostic capabilities. This review synthesizes evidence from the past decade, comparing the sensitivity, specificity, and cost-effectiveness of US and MRI while discussing scenarios where their integration can provide the most benefit. The findings emphasize the need for modality selection tailored to clinical presentations, the expertise of practitioners, and healthcare resource availability. Future research should aim to standardize imaging protocols and explore innovative multimodal approaches to improve diagnostic accuracy further.

Evaluation of circANKLE2 & circL3MBTL4 -RNAs Expression in Fertile and Infertile Men.

There are many factors that affect male fertility such as chronic health problems, psychological factors, and illnesses. Male infertility can be caused abnormal sperm function, low sperm production or even blockages that prevent the delivery of sperm. The aim of the work is to determine the expression pattern of the circularANKLE2 and circularL3MBTL4 RNA in spermatozoa from fertile and infertile males, as well as the relationship between these circRNA transcripts and sperm quality. The study involved two groups: a control group comprising 40 healthy, fertile men and an experimental group of 90 infertile males. Semen samples were collected and processed for analysis using computer-assisted semen analysis. Following RNA extraction from sperm samples, reverse transcription and real-time PCR were performed to assess the levels of circular ANKLE2 and circular L3MBTL4 RNA. There was a significant up-regulation of circularANKLE2 RNA expression (p < 0.05), and a significant down-regulation of circularL3MBTL4 RNA expression (p < 0.05) in asthenozoospermia, astheno-teratozoospermia, and oligo-astheno-teratozoospermia groups, as well as, in immature spermatozoa separated from normozoospermic samples. Moreover, the altered expression of both circular L3MBTL4 and circular ANKLE2 RNA showed significant correlations with the associated sperm parameters. In conclusion, the expression of circular ANKLE2 RNA and circular L3MBTL4 RNA may play a significant role in male fertility and could serve as potential biomarkers of sperm quality, warranting further investigation for their application in infertility diagnostics.

Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method

Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural Network (CNN) designs to tackle the complexity and unpredictability present in brain MRI data. Methods: The methodology commences with an extensive preparation phase that includes image resizing, grayscale conversion, Gaussian blurring, and the delineation of the brain region for preparing the MRI images for analysis. The Multi-verse Optimizer (MVO) is utilized to optimize data augmentation parameters and refine the configuration of trainable layers in VGG16 and ResNet50. The model’s generalization capabilities are significantly improved by the MVO’s ability to effectively balance computational cost and performance. Results: The amalgamation of VGG16 and ResNet50, further refined by the MVO, exhibits substantial enhancements in classification metrics. The MVO-optimized hybrid model demonstrates enhanced performance, exhibiting a well-calibrated balance between precision and recall, rendering it exceptionally trustworthy for medical diagnostic applications. Conclusions: The results highlight the effectiveness of MVO-optimized CNN models for classifying brain tumors in MRI data. Future investigations may examine the model’s applicability to multiclass issues and its validation in practical clinical environments.

Biomaterial Promotes Triboelectric Nanogenerator for Health Diagnostics and Clinical Application

With the growing demand for personalized healthcare services, biomaterial-based triboelectric nanogenerators (BM-TENGs) have gained widespread attention due to their non-toxicity, biocompatibility, and biodegradability. This review systematically examines the working principles, material choices, biomimetic designs, and clinical application scenarios of BM-TENGs, with a focus on the use of natural biomaterials, biocomposites, hydrogels, and other materials in health diagnostics. Biomaterials show significant potential in enhancing TENG performance, improving device flexibility, and expanding application ranges, especially in early disease detection, health monitoring, and self-powered sensing devices. This paper also addresses the current challenges faced by BM-TENG technology, including performance optimization, biocompatibility, and device durability. By integrating existing research and technological advancements, this review aims to deeply analyze the development of BM-TENG technology, propose corresponding solutions, and explore its practical application prospects in the medical field.

DNA-guided CRISPR/Cas12 for RNA targeting.

CRISPR-Cas nucleases are transforming genome editing, RNA editing, and diagnostics but have been limited to RNA-guided systems. We present ΨDNA, a DNA-based guide for Cas12 enzymes, engineered for specific and efficient RNA targeting. ΨDNA mimics a crRNA but with a reverse orientation, enabling stable Cas12-RNA assembly and activating trans-cleavage without RNA components. ΨDNAs are effective in sensing short and long RNAs and demonstrated 100% accuracy for detecting HCV RNA in clinical samples. We discovered that ΨDNAs can guide certain Cas12 enzymes for RNA targeting in cells, enhancing mRNA degradation via ribosome stalling and enabling multiplex knockdown of multiple RNA transcripts. This study establishes ΨDNA as a robust alternative to RNA guides, augmenting the potential of CRISPR-Cas12 for diagnostic applications and targeted RNA modulation in cellular environments.

Design and Validation of Specific Oligonucleotide Probes on Planar Magnetic Biosensors.

Planar DNA biosensors employ surface-tethered oligonucleotide probes to capture target molecules for diagnostic applications. To improve the sensitivity and specificity of biosensing, hybridization affinities should be enhanced, and cross-hybridization with off-targets must be minimized. To this end, assays can be designed using the thermodynamic properties of hybridization between probes and on-targets or off-targets based on Gibbs free energies and melting temperatures. However, the nature of heterogeneous hybridization between the probes on the surface and the targets in a solution imposes challenges in predicting precise hybridization affinities and the degree of cross-hybridization due to indeterminable thermodynamic penalties induced by the solid surface and its status. Herein, we suggest practical and convenient guidelines for designing oligonucleotide probes based on data obtained from planar magnetic biosensors and thermodynamic properties calculated by using easily accessible solution-phase prediction. The suggested requirements comprised Gibbs free energy ≥ -7.5 kcal mol-1 and melting temperature ≤10 °C below the hybridization temperature, and we validated for the absence of cross-hybridization. Additionally, the effects of secondary structures such as hairpins and homodimers were investigated for better oligonucleotide probe designs. We believe that these practical guidelines will assist researchers in developing planar magnetic biosensors with high sensitivity and specificity for the detection of new targets.

State of Practice on Transcranial MR-Guided Focused Ultrasound: A Report from the ASNR Standards and Guidelines Committee and ACR Commission on Neuroradiology Workgroup.

Transcranial focused ultrasound (FUS) is a versatile, MR-guided, incisionless intervention with diagnostic and therapeutic applications for neurologic and psychiatric diseases. It is currently FDA-approved as a thermoablative treatment of essential tremor and Parkinson disease. However, other applications of FUS including BBB opening for diagnostic and therapeutic applications, sonodynamic therapy, histotripsy, and low-intensity focused ultrasound neuromodulation are all in clinical trials. While FUS targeting for essential tremor and Parkinson disease has classically relied on an indirect, landmark-based approach, development of novel, advanced MR imaging techniques such as DTI tractography and fast gray matter acquisition T1 inversion recovery has the potential to improve individualized targeting and thus potentially enhance treatment response, decrease treatment times, and avoid adverse effects. As the technology advances and the number of clinical applications increases, the role of the neuroradiologist on a multidisciplinary team will be essential in pairing advanced structural and functional imaging to further this image-guided procedure via a precision medicine approach. This multi-institutional report, written by an experienced team of neuroradiologists, neurosurgeons, and neurologists, summarizes current practices, the use of advanced imaging techniques for transcranial MR-guided high-intensity FUS, recommendations for clinical implementation, and emerging clinical indications.