Diagnostic Applications Research Articles

Novel N-doped ZnO and O-doped g-C₃N₄ heterojunction: Enhanced photocatalytic degradation and robust electrochemical biosensing of ascorbic acid

ZnO and g-C₃N₄ are known for their potential in photocatalytic degradation and electrochemical applications, but their limited band gaps and electrical conductivity hinders performance. Doping is a strategy to modify these properties. This study reports the synthesis of nitrogen-doped ZnO (N-ZnO) and oxygen-doped g-C₃N₄ (OCN) nanocomposites using an in-situ hydrothermal process. Structural and compositional analysis confirmed successful synthesis, while FESEM and HRTEM revealed N-ZnO nanorods decorating OCN sheets. Optical analysis indicated a band gap of 2.54 eV, making the material active under visible light. The nanocomposites demonstrated 90 % photodegradation of methylene blue (MB) within 20 min, with a high rate constant (k = 0.1269 min−1), facilitated by a Z-scheme heterojunction. The catalyst remained stable even after 5 cycles. Moreover, electrochemical biosensing of ascorbic acid (AA) showed a regression value of 0.9965 with high anodic current response and small limit of detection (LOD) value, underscoring the potential of nanocomposite material for commercial applications in both photocatalysis and diagnostics.

Advances in aggregation-induced emission luminogens for biomedicine: From luminescence mechanisms to diagnostic applications

Advancements in early detection have demonstrated the significance of biomarkers as indicators of health and disease. Traditional detection methods often face limitations, such as low sensitivity and time consumption. Fluorescence-based techniques are considered promising approaches because of their noninvasiveness and rapid response. However, these conventional methods have some drawbacks, such as low quantum yield, photobleaching, and aggregation-caused quenching. Recently, aggregation-induced emission (AIE) has emerged as a potential alternative, characterized by luminous emission upon aggregation, thus improving detection sensitivity and stability. This review explores the recent advancements in AIE luminogens (AIEgens) in biomedical engineering, with a particular focus on their application in biomarker detection. Here, we discuss the different types of AIE mechanisms and their advantages in disease diagnosis and imaging. In addition, we summarize the development of various AIEgen-based probes for the detection of diverse biomarkers. Finally, we address the remaining challenges and future directions for AIE materials in modern biomedical engineering, emphasizing the potential of AIEgens in biomarker detection and disease diagnosis strategies.

Oxidative Stress and Placental Pathogenesis: A Contemporary Overview of Potential Biomarkers and Emerging Therapeutics.

Oxidative stress (OS) plays a crucial role in placental pathogenesis and pregnancy-related complications. This review explores OS's impact on placental development and function, focusing on novel biomarkers for the early detection of at-risk pregnancies and emerging therapeutic strategies. We analyzed recent research on OS in placental pathophysiology, examining its sources, mechanisms, and effects. While trophoblast invasion under low-oxygen conditions and hypoxia-induced OS regulate physiological placental development, excessive OS can lead to complications like miscarriage, preeclampsia, and intrauterine growth restriction. Promising OS biomarkers, including malondialdehyde, 8-isoprostane, and the sFlt-1/PlGF ratio, show potential for the early detection of pregnancy complications. Therapeutic strategies targeting OS, such as mitochondria-targeted antioxidants, Nrf2 activators, and gasotransmitter therapies, demonstrate encouraging preclinical results. However, clinical translation remains challenging. Future research should focus on validating these biomarkers in large-scale studies and developing personalized therapies to modulate placental OS. Emerging approaches like extracellular vesicle-based therapies and nanomedicine warrant further investigation for both diagnostic and therapeutic applications in pregnancy-related complications. Integrating OS biomarkers with other molecular and cellular markers offers improved potential for the early identification of at-risk pregnancies.

Trop2-Targeted Molecular Imaging in Solid Tumors: Current Advances and Future Outlook.

Trophoblast cell surface antigen 2 (Trop2), a transmembrane glycoprotein, plays a dual role in physiological and pathological processes. In healthy tissues, Trop2 facilitates development and orchestrates intracellular calcium signaling. However, its overexpression in numerous solid tumors shifts its function toward driving cell proliferation and metastasis, thus leading to a poor prognosis. The clinical relevance of Trop2 is underscored by its utility as both a biomarker for diagnostic imaging and a target for therapy. Notably, the U.S. Food and Drug Administration (FDA) has approved sacituzumab govitecan (SG), a novel Trop2-targeted agent, for treating triple-negative breast cancer (TNBC) and refractory urothelial cancer, highlighting the significance of Trop2 in clinical oncology. Molecular imaging, a powerful tool for visualizing and quantifying biological phenomena at the molecular and cellular levels, has emerged as a critical technique for studying Trop2. This approach encompasses various modalities, including optical imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), and targeted antibodies labeled with radioactive isotopes. Incorporating Trop2-targeted molecular imaging into clinical practice is vital for the early detection, prognostic assessment, and treatment planning of a broad spectrum of solid tumors. Our review captures the latest progress in Trop2-targeted molecular imaging, focusing on both diagnostic and therapeutic applications across diverse tumor types, including lung, breast, gastric, pancreatic, prostate, and cervical cancers, as well as salivary gland carcinomas. We critically evaluate the current state by examining the relevant applications, diagnostic accuracy, therapeutic efficacy, and inherent limitations. Finally, we analyze the challenges impeding widespread clinical application and offer insights into strategies for advancing the field, thereby guiding future research endeavors.

Abstract B026: Glioblastoma multiforme disseminates outside the central nervous system

Abstract Introduction: The liquid biopsy (LB) concept has gained significant attention in the last decade and has the potential to revolutionize cancer care. However, more validation in clinical trials is required on the value of LB in medical settings. Here, we focus on glioblastoma multiforme (GBM), a highly invasive and fatal subtype of the central nervous system malignant tumors. Circulating tumor cells (CTCs) are primary tumor cells released into the peripheral blood and migrating to distant organs. CTC presence is related to tumor spread and recurrence. In GBM, factors like short patient survival, the blood-brain barrier (BBB), and lack of lymphatic drainage in brain tissue make systemic dissemination rare but possible. Thus, noninvasive CTC detection can hold potential for diagnostic, predictive, and prognostic applications. Material and Methods: For cell stability preservation before sample processing, the peripheral blood samples from curatively resected GBM patients were collected preoperatively in Cell-Free DNA BCT® tubes (Streck, Inc.). CTCs were identified using CytoTrack CT11TM (2/C), a semi-automated immunofluorescence microscopy using glial fibrillary acidic protein, vimentin, and CD45 immunostaining, and DAPI counterstaining. The preliminary Kaplan-Maier survival analysis based on CTC positivity was performed in 39 patients with a follow-up longer than two years. Results: We analyzed the CTC presence in 100 GBM patient peripheral blood samples. The CTCs or cancer cell clusters were found in 35% of GBM patients. The Kaplan-Maier survival analysis did not show a correlation between CTC presence and survival. This phenomenon may be associated with the generally short survival of these patients, which is expected to increase with advances in oncology and palliative care and/or a possible suppression of tumor cell growth outside the CNS. Conclusions: We have observed the CTCs in the peripheral blood of GBM patients. The patient enrollment and clinical data recording are still ongoing. Citation Format: Pavel Stejskal, Josef Srovnal, Alona Rehulkova, Ondrej Kalita, Marek Slachta, Marian Hajduch. Glioblastoma multiforme disseminates outside the central nervous system [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B026.

TIA-UNet: transformer-enhanced deep learning for adolescent idiopathic scoliosis spinal x-ray image segmentation

Abstract Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual x-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy. This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models. By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD). TIA-UNet attained a Dice similarity coefficient of 90. 02%, a Mean Intersection over Union (MIoU) of 81. 96%, and a Hausdorff Distance (HD) of 4. 09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floating-point operations (FLOPs). As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning techniques in medical image analysis.

Amphiphilic Oligonucleotide Derivatives-Promising Tools for Therapeutics.

Recent advances in genetics and nucleic acid chemistry have created fundamentally new tools, both for practical applications in therapy and diagnostics and for fundamental genome editing tasks. Nucleic acid-based therapeutic agents offer a distinct advantage of selectively targeting the underlying cause of the disease. Nevertheless, despite the success achieved thus far, there remain unresolved issues regarding the improvement of the pharmacokinetic properties of therapeutic nucleic acids while preserving their biological activity. In order to address these challenges, there is a growing focus on the study of safe and effective delivery methods utilising modified nucleic acid analogues and their lipid bioconjugates. The present review article provides an overview of the current state of the art in the use of chemically modified nucleic acid derivatives for therapeutic applications, with a particular focus on oligonucleotides conjugated to lipid moieties. A systematic analysis has been conducted to investigate the ability of amphiphilic oligonucleotides to self-assemble into micelle-like structures, as well as the influence of non-covalent interactions of such derivatives with serum albumin on their biodistribution and therapeutic effects.

Genes of DLK1-DIO3 Locus and miR-379/656 Cluster is a Potential Diagnostic and Prognostic Marker in Patients With Hepatocellular Carcinoma: A Systems Biology Study

BackgroundHepatocellular carcinoma is the sixth most common malignancy reported globally. This highlights the need for reliable biomarkers that can be employed for diagnostic and prognostic applications. The present study aimed to classify and characterize the clinical potential of DLK1-DIO3 and miR-379/656 cluster genes in hepatocellular carcinoma. MethodsWe extensively studied the clinical potential of DLK1-DIO3 genes through a comprehensive systems biology approach and assessed the diagnostic and prognostic potential of the genes associated with the region. Additionally, we have predicted the gene targets of the miR-379/656 cluster associated with the locus and have identified the gene ontology, pathway, and disease associations. ResultsWe report this region as a potential biomarker for hepatocellular carcinoma. About thirty clustered miRNAs, a long-non-coding RNA, and two coding genes of the region were underexpressed in tumors. ROC analysis identified 11 clustered miRNAs with diagnostic potential. Survival analyses identified MEG3 and the miR-379/656 cluster as prognostically significant. Further, the Random-Forest model predicted that the miRNA cluster classifies patients according to TNM staging. Furthermore, overrepresentation analysis identified several key pathways, molecular functions, and biological processes associated with the cluster gene targets. ConclusionOur study suggests that DLK1-DIO3 genes, miR-379/656 cluster, and its target gene network might be potential diagnostic and prognostic markers for HCC management and therapy.

Delivery Methods of Radiopharmaceuticals: Exploring Global Strategies to Minimize Occupational Radiation Exposure.

In the world of nuclear medicine, health care professionals face the challenge of safeguarding themselves and their patients from occupational radiation exposure. As the field experiences exponential growth, driven by the surge in approvals of radiopharmaceuticals for diagnostic and therapeutic applications, it becomes vital to delve into the delivery methods of radiopharmaceuticals. Health care professionals take precautions during radiopharmaceutical administration, including maintaining distance from radioactive sources, using shielding, limiting exposure time, and monitoring radiation levels with badges. Regular evaluations provide compliance with recommended exposure limits, yet concerns persist, especially regarding the cumulative radiation exposure from manual injections over time. Understanding the long-term effects of radiation exposure has spurred the development of cutting-edge medical device technologies, such as autoinjectors, designed to administer radiopharmaceuticals accurately while minimizing total radiation dose to health care professionals. The U.S. Pharmacopeia 825 regulation refers to these devices as "direct infusion systems." Nuclear medicine technologists commonly refer to them as "autoinjectors," whereas device manufacturers may use terms such as injection system, radiopharmaceutical injector, or infusion system. Despite variations in terminology, these devices hold a pivotal role in shaping the future of radiopharmaceutical delivery. In an era of escalating demand for PET procedures worldwide, skilled health care professionals ensure the safe and precise dosing of radiopharmaceuticals. This article explores the state-of-the-art medical devices in radiopharmaceutical delivery, spotlighting transformative medical devices currently revolutionizing the nuclear medicine landscape in the global market.

CNSC-34. SURGICAL INTERVENTION ASSOCIATION WITH THE DEVELOPMENT OF SUBSEQUENT DISSEMINATION IN CHILDHOOD DIFFUSE INTRINSIC PONTINE GLIOMAS (DIPG)

Abstract BACKGROUND Diffuse intrinsic pontine glioma (DIPG) is one of the most aggressive pediatric central nervous system tumors, and currently, there are no curative treatment options available. With the increasing application of molecular diagnostics, more and more DIPG patients are recommended to undergo surgery; however, its correlation with dissemination needs to be better understood. Hence, we conducted this cohort to analyze the predictors of subsequent metastasis in pediatric DIPGs. METHODS The final analysis included 142 pediatric (age ≤ 12) DIPG patients treated at Sanjiu Brain Hospital between January 2017 and September 2023. The data were retrospectively collected following the initial diagnosis. Patients were followed up for 6 months. The primary endpoint was the occurrence of subsequent metastasis. Univariable and multivariable logistic regression analyses were utilized to identify statistically significant predictors of subsequent dissemination. All statistical assessments were two-tailed, and only P < 0.05 was deemed to be statistically significant. RESULTS Among 142 patients, 14(9.9%) developed metastasis within the follow-up time. There were 77(54.2%) males and 65(45.8%) females. The median age was 7 years (ranging 1 - 12 years). The overall cohort demonstrated that surgery was the only predictor of dissemination both in univariable (OR 4.567, 95%CI 1.351 – 20.901, P = 0.024) and multivariable (OR 5.501, 95%CI 1.560 – 26.105, P = 0.014) analyses. However, subgroup analysis involving patients who received surgical intervention showed that biopsy was not superior nor inferior to resection in terms of relative risk for subsequent dissemination (16.7% vs. 15.8%). CONCLUSION Surgical intervention increases the relative risk of dissemination occurrence to four times more than those who did not receive surgery. Surgical intervention is crucial given the potential of molecular analysis leading to improved therapeutic options. Nonetheless, patients should be informed about the risks associated with surgery, including subsequent metastasis.

Fractal Geometry in Vertebrate Respiratory Systems: A Comparative Analysis of Branching Patterns Across Species

This study investigates the application of fractal geometry to the analysis of vertebrate respiratory systems, focusing on the branching patterns of the bronchial tree across diverse species. Using a combination of mathematical modeling and computational visualization, we explore how fractal dimensions and branching characteristics reflect evolutionary adaptations to various habitats and physiological demands. The research encompasses a range of vertebrates, including humans, horses, dolphins, chickens, iguanas, and bullfrogs, representing diverse taxonomic groups and ecological niches. Our analysis reveals a clear gradient of complexity in respiratory structures, correlating strongly with metabolic rates and environmental adaptations. Mammals, particularly those adapted for high‐performance activities like horses, demonstrate the most complex fractal patterns, indicating highly efficient gas exchange systems. In contrast, ectothermic species such as iguanas and bullfrogs exhibit simpler structures, reflecting their lower metabolic demands and alternative respiratory strategies. The study employs fractal dimension calculations, L‐system modeling, and lacunarity analysis to quantify and visualize these differences. Our findings suggest that fractal analysis provides valuable insights into the relationship between form and function in vertebrate respiratory systems, offering a novel perspective on comparative physiology and evolutionary biology. This research not only enhances our understanding of respiratory system evolution but also has potential applications in biomimetic design, medical diagnostics, and ecological physiology. It demonstrates the power of interdisciplinary approaches, combining advanced mathematics with biological principles to uncover fundamental patterns in nature.

Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications.

Colorectal cancer is the third most common malignancy in developed countries. Diagnosis strongly depends on the pathologist's expertise and laboratory equipment, and patient survival is influenced by the cancer's stage at detection. Non-invasive spectroscopic techniques can aid early diagnosis, monitor disease progression, and assess changes in physiological parameters in both heterogeneous samples and advanced platforms like Organ-on-Chip (OoC). In this study, Raman microspectroscopy combined with Machine Learning was used to analyse structural and biochemical changes in a Caco-2 cell-based intestinal epithelial model before and after treatment with a calcium chelating agent. The Machine Learning (ML) algorithm successfully classified different epithelium damage conditions, achieving an accuracy of 91.9% using only 7 features. Two data-splitting approaches, "sample-based" and "spectra-based," were also compared. Further, Raman microspectroscopy results were confirmed by TEER measurements and immunofluorescence staining. Experimental results demonstrate that this approach, combined with supervised Machine Learning, can investigate dynamic biomolecular changes in real-time with high spatial resolution. This represents a promising non-invasive alternative technique for characterizing cells and biological barriers in organoids and OoC platforms, with potential applications in cytology diagnostics, tumor monitoring, and drug efficacy analysis.

BRET Nano Q-Body: A Nanobody-Based Ratiometric Bioluminescent Immunosensor for Point-of-Care Testing.

We developed a nanobody-based homogeneous bioluminescent immunosensor to achieve a one-pot detection for point-of-care testing (POCT). This immunosensor was named BRET nano Q-body as its emission color changes via bioluminescence resonance energy transfer (BRET) upon antigen addition. NanoLuc luciferase and a cysteine-containing tag were fused to the N-terminus of the nanobody, which was labeled with a fluorescent dye via thiol-maleimide Michael addition. The nanobody employed in this proof-of-principle experiment recognizes methotrexate (MTX), a chemotherapeutic agent. After optimizing the fluorescent dye and linker, the BRET nano Q-body dose-dependently exhibited a greater than 7-fold increase in emission ratio (TAMRA/NanoLuc). Moreover, we found its superior thermostability endurance in organic solvents, reducing agents, and detergents due to the robust structure of nanobody, as well as accommodation in biological fluids, such as milk, serum, and whole blood without dilution, with limits of detection of 0.50, 1.6, and 3.7 nM, respectively. Furthermore, the BRET nano Q-body was subjected to lyophilization and fabricated into a paper device, which markedly improved its portability and enabled more than one month of storage at 25 °C. The paper device also performed appropriate functions in the biological fluids without any dilution and can be used for on-site therapeutic drug monitoring of MTX. Altogether, we developed a powerful tool, the BRET nano Q-body, for POCT, and demonstrated its applicability in several biological fluids. In addition, we confirmed the feasibility of paper devices, which are expected to be transformative for in situ detection in therapeutic, diagnostic, and environmental applications.

Application of active piezoresistive cantilevers in high-eigenmode surface imaging

Abstract One of the most important limitations of the atomic force microscopy (AFM) is scanning speed, whose high values are required for contemporary high-resolution, long-range diagnostic applications. The measurement bandwidth of an AFM depends on several factors, but usually results from the time constant of the oscillating cantilever, which is correlated with its resonance frequency and quality factor. We propose a method to overcome this problem by performing the surface measurements when the cantilever is vibrating in higher eigenmodes. In this paper we demonstrate the application of active piezoresistive cantilevers operating in this mode. The active piezoresistive cantilever comprises a piezoresistive deflection sensor, a deflection actuator and a nanotip. It is a complete micro-electro-mechanical system, ensuring the highest reliability of cantilever vibration control and detection. Higher eigenmode operations are usually difficult to implement as they usually result in lower deflection and lower sensitivity of the probe vibration deflection. Here we present an experimental modification of the structure of an active piezoresistive cantilever using focused ion beam machining that mitigates both weaknesses. This has enabled the cantilever to scan the surface at a scanning rate of 10 lines s−1 with a maximum speed of 500 μm s−1 and a data acquisition rate of 10 kS s−1, when the probe is vibrating at 380 kHz in the second eigenmode. We also describe a traceable calibration routine (based on analysis of the response of the piezoresistive detector, the output of the HeNe interferometer and precise control of the deflection actuator), together with the cantilever modification process and the development of the measurement setup. We show measurement results of dedicated calibration samples and silicon carbide crystal lattice references.

MICROWAVE REFLECTOMETRY CIRCUITS INTEGRATION WITH COAXIAL PROBE FOR INITIAL BREAST TUMOR DETECTION

A six-port reflectometry (SPR) system was developed to predict the dielectric properties of both tumor and normal breast tissue, intended for medical diagnostic applications. Ensuring precise measurements, the SPR underwent calibration using a well-established four-step procedure, which will be briefly outlined. Afterward, the investigated coaxial probe was connected to the SPR through the calibrated measurement port. Subsequently, the exposed end of the probe aperture was immersed into synthetic samples representing both healthy and cancerous breast tissue to assess the dielectric constant, εrʹ and loss factor, εrʺ across frequencies ranging from 1.5 GHz to 3.3 GHz. The dielectric constant, εrʹ and loss factor, εrʺ were derived from the measured reflection coefficient using a closed-form equation associated with the coaxial probe. An examination was undertaken to compare the performance of a commercially available vector network analyzer (VNA) outfitted with a Keysight 85070E dielectric probe against an SPR-probe system. The comparison was based on analyzing the reflection coefficient magnitude, phase shift, dielectric constant, and loss factor of synthetic breast tissue samples. The study revealed maximum absolute errors of 0.01, 1.07°, 1.12, and 0.75 for the measured reflection coefficient magnitude, phase shift, dielectric constant, and loss factor, respectively. The calibrated reflection coefficient and predicted relative permittivity, εr can be effectively utilized to distinguish between normal (εrʹ < 50) and tumor (εrʹ > 50) breast tissue.

Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods.

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI's ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as 'artificial intelligence', 'machine learning', 'sports injury', and 'risk prediction'. While AI's predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.

Effect of framework and complementarity determining region H1 charge on the human VH-B1a domain expression, folding, stability, and solubility

Many difficulties related to using antibodies in diagnostic and therapeutic applications can sometimes be circumvented by using smaller, less complex single domain antibodies based on variable heavy chain (VH) domain constructs such as camelid VHH domains. However, VH domains have their own limitations, including an increased tendency to aggregate. VH domains often contain hydrophobic residues within their complementarity-determining regions (CDRs) that facilitate binding to target antigens but can also mediate VH domain aggregation, which is a concern for therapeutic applications since this can trigger immune responses. In this study, we engineered the human VH-1Ba domain to improve its stability and solubility by introducing charged amino acids in the VH domain framework region and CDRH1. We followed two strategies to improve the stability and solubility of VH domains. First, we introduced positive and negatively charged amino acids in the framework region of an autonomous human VH domain (VH–B1a) and observed the effect of framework net charge on VH domain refolding, stability, and solubility. Introducing positive charge into the VH-B1a framework increased its thermostability but slightly lowered its refolding ability and solubility. We were not able to obtain correctly folded negatively charged (-VH) VH domains. Second, we introduced a series of positive and negatively charged amino acids in the CDRH1 loop of near-neutral (VH–B1a) and positively charged (+VH) VH domains, and observed their effect on expression, refolding, stability, and solubility. For both the VH-B1a and +VH domains, we observed a decrease in melting temperature (Tm) and room temperature solubility as more negative or positive charged amino acids were added to the CDRH1. The VH-B1a domain had higher room temperature solubility for negative and slightly positive CDRH1 net charges. The + VH domain had higher Tms for all CDRH1 net charges and was better able to tolerate the adverse effects of adding positive charge to CDRH1. We observed a similar response in refolding and solubility of VH-B1a and +VH in response to changes in CDRH1 net charge after temperature-induced denaturation for negative and neutral CDRH1s. We observed a positional effect with a single Lys (31K) and double Lys (31, 32 KK) substitutions in CDRH1, which promoted VH-B1a aggregation and was partially overcome by the +VH domain. In summary, this study provides information for designing VH domains with improved biophysical properties and a +VH domain that will be useful for applications where positive surface charge and CDRH1 are desirable.

Transfer Learning Approaches for Brain Metastases Screenings.

In this study, we examined the effectiveness of transfer learning in improving automatic segmentation of brain metastases on magnetic resonance imaging scans, with potential applications in preventive exams and remote diagnostics. We trained three deep learning models on a public dataset from the ASNR-MICCAI Brain Metastasis Challenge 2024, fine-tuned them on a small private dataset, and compared their performance to models trained from scratch. Results showed that models using transfer learning performed better than scratch-trained models, though the improvement was not statistically substantial. The custom Tversky and Binary Cross-Entropy loss function helped manage class imbalance and reduce false negatives, limiting missed tumor regions. Medical experts noted that, while fine-tuned models worked well with larger, well-defined tumors, they struggled with tiny, scattered tumors in complex cases. This study highlights the potential of transfer learning and tailored loss functions in medical imaging, while also pointing out the models' limitations in detecting very small tumors in challenging cases.

An mRNA Display Approach for Covalent Targeting of a Staphylococcus aureus Virulence Factor.

Staphylococcus aureus ( S. aureus ) is an opportunistic human pathogen that causes over one million deaths around the world each year. We recently identified a family of serine hydrolases termed fluorophosphonate binding hydrolases (Fphs) that play important roles in lipid metabolism and colonization of a host. Because many of these enzymes are only expressed in Staphylococcus bacteria, they are valuable targets for diagnostics and therapeutics. Here we developed and screened highly diverse cyclic peptide libraries using mRNA display with a genetically encoded oxadiazolone (Ox) electrophile that was previously shown to potently and covalently inhibit multiple Fph enzymes. By performing multiple rounds of counter selections with WT and catalytic dead FphB, we were able to tune the selectivity of the resulting selected cyclic peptides containing the Ox residue towards the desired target. From our mRNA display hits, we developed potent and selective fluorescent probes that label the active site of FphB at single digit nanomolar concentrations in live S. aureus bacteria. Taken together, this work demonstrates the potential of using direct genetically encoded electrophiles for mRNA display of covalent binding ligands and identifies potent new probes for FphB that have the potential to be used for diagnostic and therapeutic applications.

Smartphone microscopy: Design and implementation of a dual magnification system

This paper presents a dual magnification smartphone microscope designed for versatile imaging, enabling low magnification for wide-field viewing and high magnification for detailed biological sample analysis and automatic control of the working distance. It offers a cost-effective alternative to traditional lab microscopes, particularly in low-resource settings. Simulated, optimized, and designed using Ansys Zemax OpticStudio, the microscope was fabricated with 3D printing technology. Dual magnification (150x and 1000x) was achieved with two optical paths, improving resolution to 1 µm at higher and 1.5 µm at lower magnifications. The design provides clear and accurate images. The smartphone integration enhances usability, allowing easy magnification switching, image capture, and potential use of diagnostic applications.