High Specificity Research Articles

Three-dimensional telomere profiling predicts risk of progression in smoldering multiple myeloma.

Smoldering multiple myeloma (SMM) is a precursor stage that precedes multiple myeloma (MM). SMM is heterogenous with nearly 40% of patients progressing to MM in the first 5 years. The high rate of progression of SMM patients highlights the need for early intervention, which underscores the importance of identifying SMM patients with the highest risk of progression. Several risk stratification models showed utility in identifying high-risk SMM patients; however, these systems showed limited sensitivity. To date, identifying high-risk SMM patients remains an important clinical need. In this study, we present the 3-dimensional telomere profiling as a structural biomarker capable of stratifying SMM patients as a function of genomic instability. Quantifying telomere dysfunction using the TeloView technology showed utility in risk stratification of cancer patients, particularly hematological malignancies. In this study, we analyzed 168 SMM patients. We report an AUC in ROC analysis of 0.8 using a subset of the patients as a training dataset. We then conducted a blind validation on a different cohort and demonstrated a positive predictive value of 85% and negative predictive value of 73%, with sensitivity and specificity of 83% and 76%, respectively. We examined the correlation between the TeloView prediction and the 20-2-20 scoring system, and cytogenetic abnormalities. We report a correlation of 53% with the 20-2-20 scores and over 60% correlation with cytogenetic abnormalities. The result of this study presents the telomere profiling as an effective biomarker able to stratify SMM patients to their respective risk groups with high sensitivity and specificity.

Test characteristics of glycated albumin in the diagnosis of gestational diabetes mellitus

This study determined the test characteristics of glycated albumin in the diagnosis of gestational diabetes mellitus. The gold standard for diagnosing gestational diabetes mellitus is the oral glucose tolerance test which requires patient preparation, drinking of glucose solution, and multiple sample collections. A possible alternative biomarker for the diagnosis of gestational diabetes is glycated albumin does not require patient preparation and only one sample is collected. Glycated albumin levels are higher among Black Americans than in Caucasians.The study involved 200 pregnant women attending the antenatal clinic at the University of Port Harcourt Teaching Hospital. The diagnosis of gestational diabetes mellitus was made using the World Health Organization 2013 diagnostic criteria. The test characteristics of glycated albumin were determined using the area under the curve of the receiver operator characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. The prevalence of gestational diabetes mellitus was 9.0%. The area under the receiver operator characteristic curve for glycated albumin was 0.8 (95% CI 0.7-0.9; p=0.0001). The sensitivity and specificity of glycated albumin were 83.3% and 86.8% respectively. The positive predictive value was 38.5% and the negative predictive value was 98.1%. Glycated albumin has high sensitivity, specificity, and negative predictive values and therefore, can be used as a preliminary test for gestational diabetes mellitus.

Choriocapillaris reduction accurately discriminates against early-onset Alzheimer's disease.

This study addresses the urgent need for non-invasive early-onset Alzheimer's disease (EOAD) prediction. Using optical coherence tomography angiography (OCTA), we present a choriocapillaris model sensitive to EOAD, correlating with serum biomarkers. Eighty-four EOAD patients and 73 controls were assigned to swept-source OCTA (SS-OCTA) or the spectral domain OCTA (SD-OCTA) cohorts. Our hypothesis on choriocapillaris predictive potential in EOAD was tested and validated in these two cohorts. Both cohorts revealed diminished choriocapillaris signals, demonstrating the highest discriminatory capability (area under the receiver operating characteristic curve: SS-OCTA 0.913, SD-OCTA 0.991; P<0.001). A sparser SS-OCTA choriocapillaris correlated with increased serum amyloid beta (Aβ)42, Aβ42/40, and phosphorylated tau (p-tau)181 levels (all P<0.05). Apolipoprotein E status did not affect choriocapillaris measurement. The choriocapillaris, observed in both cohorts, proves sensitive to EOAD diagnosis, and correlates with serum Aβ and p-tau181 levels, suggesting its potential as a diagnostic tool for identifying and tracking microvascular changes in EOAD. Optical coherence tomography angiography may be applied for non-invasive screening of Alzheimer's disease (AD). Choriocapillaris demonstrates high sensitivity and specificity for early-onset AD diagnosis. Microvascular dynamics abnormalities are associated with AD.

Engineering of a Baeyer-Villiger monooxygenase to improve substrate scope, stereoselectivity and regioselectivity.

Baeyer-Villiger monooxygenases belong to a family of flavin-binding proteins that catalyze the Baeyer-Villiger (BV) oxidation of ketones to produce lactones or esters, which are important intermediates in pharmaceuticals or sustainable materials. Phenylacetone monooxygenase (PAMO) from Thermobifida fusca with moderate thermostability catalyzes the oxidation of aryl ketone substrates, but is limited by high specificity and narrow substrate scope. In the present study, we applied loop optimization by loop swapping followed by focused saturation mutagenesis in order to evolve PAMO mutants capable of catalyzing the regioselective BV oxidation of cyclohexanone and cyclobutanone derivatives with formation of either normal or abnormal esters or lactones. We further modulated PAMO to increase enantioselectivity. Crystal structure studies indicate that rotation occurs in the NADP-binding domain and that the high B-factor region is predominantly distributed in the catalytic pocket residues. Computational analyses further revealed dynamic character in the catalytic pocket and reshaped hydrogen bond interaction networks, which is more favorable for substrate binding. Our study provides useful insights for studying enzyme-substrate adaptations.

Assessment of the phenotypic severity of hemophilia A: using rotational thromboelastometry (ROTEM) and APTT-clot waveform analysis

BackgroundHemophilia A (HA) is an X-linked inherited bleeding disorder caused by reduced factor VIII (FVIII) levels. Approximately 10–15% of patients with severe HA (SHA) do not present with the anticipated bleeding pattern. Here, we assessed the phenotypic severity of hemophilia A using rotational thromboelastometry (ROTEM) and activated partial thromboplastin time-clot waveform analysis (APTT-CWA).MethodsPatients diagnosed with hemophilia A were enrolled. Clinical phenotype assignment was performed according to the published literature, and patients were classified into four phenotypic subgroups. The whole blood sample was first run on ROTEM in INTEM mode using platelet-poor plasma, APTT was run, and the APTT-CWA graph was simultaneously recorded.ResultsA total of 66 patients were recruited for this study. Statistically significant differences were observed between the four phenotypically categorized groups using ROTEM and APTT-CWA. On comparing patients with mild/moderate-to-severe phenotypes (Group II) with SHA without inhibitors (Group IV), no significant difference was found for all parameters of ROTEM or APTT-CWA. The MCF, MA30, MAXV, and Alpha angle values using ROTEM were found to be the lowest in patients with SHA with inhibitors, which helped differentiate them from those with SHA without inhibitors. However, these two groups could not be differentiated using the APTT-CWA parameters.ConclusionROTEM can be used to distinguish patients with SHA with inhibitors from those with SHA without inhibitors using a combination of parameters with high sensitivity and specificity. However, APTT-CWA cannot be used to differentiate these patient groups.

Opportunities and challenges in accurate early diagnosis and treatment of lung cancer/pulmonary nodules

Lung cancer remains the most prevalent and lethal malignancy in our country. Early diagnosis and treatment are crucial for improving patient prognosis in lung cancer/pulmonary nodules. Recent advancements in non-invasive/minimally invasive liquid biopsy, multi-omics, and artificial intelligence technologies have significantly enhanced the accuracy of early lung cancer/pulmonary nodule diagnosis. However, an early diagnostic method with both high sensitivity and specificity is yet to be established. Furthermore, addressing the methods and extent of early precision surgery, local precision therapy, perioperative combined treatment, and postoperative recurrence and metastasis monitoring are urgent challenges in the early management of lung cancer/pulmonary nodules. Integrating the advantages of various treatment strategies and formulating personalized and precise treatment plans is key to further improving patient survival. In the future, while exploring new therapeutic strategies, it is necessary to continuously search for biomarkers to identify the population that will benefit from the treatment effectively. Additionally, large-sample randomized controlled clinical studies should be conducted to investigate the benefits of long-term patient survival under a diverse range of treatment strategies.

Structural and mechanistic insights into Quinolone Synthase to address its functional promiscuity

Quinolone synthase from Aegle marmelos (AmQNS) is a type III polyketide synthase that yields therapeutically effective quinolone and acridone compounds. Addressing the structural and molecular underpinnings of AmQNS and its substrate interaction in terms of its high selectivity and specificity can aid in the development of numerous novel compounds. This paper presents a high-resolution AmQNS crystal structure and explains its mechanistic role in synthetic selectivity. Additionally, we provide a model framework to comprehend structural constraints on ketide insertion and postulate that AmQNS’s steric and electrostatic selectivity plays a role in its ability to bind to various core substrates, resulting in its synthetic diversity. AmQNS prefers quinolone synthesis and can accommodate large substrates because of its wide active site entrance. However, our research suggests that acridone is exclusively synthesized in the presence of high malonyl-CoA concentrations. Potential implications of functionally relevant residue mutations were also investigated, which will assist in harnessing the benefits of mutations for targeted polyketide production. The pharmaceutical industry stands to gain from these findings as they expand the pool of potential drug candidates, and these methodologies can also be applied to additional promising enzymes.

A comprehensive evaluation of serum circCSPP1 as a novel diagnostic and prognostic biomarker for gastric cancer

PurposeGastric cancer (GC) has high incidence and mortality due to its low early screening efficiency. Circular RNAs (CircRNAs) are a new class of non-coding RNAs which is closely related to GC. Nevertheless, the clinical application value of circRNAs in GC are largely unknown. Therefore, we studied the role of a novel circRNA named circCSPP1 in patients with GC. MethodsCircRNA sequencing was performed to screen out the target molecule. Real-time fluorescent quantitative PCR (RT-qPCR) was utilized to detect the expression level of circCSPP1 in GC tissues, cells, and serum. Gel and Sanger sequencing were utilized to verify the ring structure of circCSPP1. RNase R enzyme digestion experiment and actinomycin D experiment were verifed the advantage of circCSPP1 as a diagnostic biomarker in patients with GC when that compared with linear RNA. The correlation between the expression level of serum circCSPP1 and clinicopathological data of GC patients was further analyzed. Receiver operating characteristic curve (ROC) and the area under ROC curve (AUC) were utilied to evaluate the diagnostic performance. ResultsCircCSPP1 has a circular structure which with resistance to RNA exonuclease digestion and long half-life compared with linear RNA. In our study, circCSPP1 was first found up-regulated in patients with GC. Serum circCSPP1 level was decreased significantly after surgical resection whereas increased after recurrence. High expression of circCSPP1 was associated with poor survival rates. The expression level of circCSPP1 was significantly correlated to tumor size, T stage, lymph node metastasis, and TNM stage. The AUC of serum circCSPP1 was 0.834, with high sensitivity and specificity in discriminating patients with GC from healthy donors. More importantly, the combined diagnosis of circCSPP1, CEA, and CA19–9 achieved the superior AUC of 0.882, with the highest specificity. ConclusionSerum circCSPP1 may prove to be a potential non-invasive auxiliary diagnostic biomarker for patients with GC.

Diagnostic performance of volatile organic compounds analysis and electronic noses for detecting colorectal cancer: a systematic review and meta-analysis

IntroductionThe detection of Volatile Organic Compounds (VOCs) could provide a potential diagnostic modality for the early detection and surveillance of colorectal cancers. However, the overall diagnostic accuracy of the proposed tests remains uncertain.ObjectiveThis systematic review is to ascertain the diagnostic accuracy of using VOC analysis techniques and electronic noses (e-noses) as noninvasive diagnostic methods for colorectal cancer within the realm of clinical practice.MethodsA systematic search was undertaken on PubMed, EMBASE, Web of Science, and the Cochrane Library to scrutinize pertinent studies published from their inception to September 1, 2023. Only studies conducted on human subjects were included. Meta-analysis was performed using a bivariate model to obtain summary estimates of sensitivity, specificity, and positive and negative likelihood ratios. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was deployed for quality assessment. The protocol for this systematic review was registered in PROSPERO, and PRISMA guidelines were used for the identification, screening, eligibility, and selection process.ResultsThis review encompassed 32 studies, 22 studies for VOC analysis and 9 studies for e-nose, one for both, with a total of 4688 subjects in the analysis. The pooled sensitivity and specificity of VOC analysis for CRC detection were 0.88 (95% CI, 0.83-0.92) and 0.85 (95% CI, 0.78-0.90), respectively. In the case of e-nose, the pooled sensitivity was 0.87 (95% CI, 0.83-0.90), and the pooled specificity was 0.78 (95% CI, 0.62-0.88). The area under the receiver operating characteristic analysis (ROC) curve for VOC analysis and e-noses were 0.93 (95% CI, 0.90-0.95) and 0.90 (95% CI, 0.87-0.92), respectively.ConclusionThe outcomes of this review substantiate the commendable accuracy of VOC analysis and e-nose technology in detecting CRC. VOC analysis has a higher specificity than e-nose for the diagnosis of CRC and a sensitivity comparable to that of e-nose. However, numerous limitations, including a modest sample size, absence of standardized collection methods, lack of external validation, and a notable risk of bias, were identified. Consequently, there exists an imperative need for expansive, multi-center clinical studies to elucidate the applicability and reproducibility of VOC analysis or e-nose in the noninvasive diagnosis of colorectal cancer.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023398465.

Advancements in skin cancer classification: a review of machine learning techniques in clinical image analysis

AbstractEarly detection of skin cancer from skin lesion images using visual inspection can be challenging. In recent years, research in applying deep learning models to assist in the diagnosis of skin cancer has achieved impressive results. State-of-the-art techniques have shown high accuracy, sensitivity and specificity compared with dermatologists. However, the analysis of dermoscopy images with deep learning models still faces several challenges, including image segmentation, noise filtering and image capture environment inconsistency. After making the introduction to the topic, this paper firstly presents the components of machine learning-based skin cancer diagnosis. It then presents the literature review on the current advance in machine learning approaches for skin cancer classification, which covers both the traditional machine learning approaches and deep learning approaches. The paper also presents the current challenges and future directions for skin cancer classification using machine learning approaches.

Lateral Flow Assay Biotesting by Utilizing Plasmonic Nanoparticles Made of Inexpensive Metals─Replacing Colloidal Gold.

Nanoparticles (NPs) can be conjugated with diverse biomolecules and employed in biosensing to detect target analytes in biological samples. This proven concept was primarily used during the COVID-19 pandemic with gold-NP-based lateral flow assays (LFAs). Considering the gold price and its worldwide depletion, here we show that novel plasmonic NPs based on inexpensive metals, titanium nitride (TiN) and copper covered with a gold shell (Cu@Au), perform comparable to or even better than gold nanoparticles. After conjugation, these novel nanoparticles provided high figures of merit for LFA testing, such as high signals and specificity and robust naked-eye signal recognition. Since the main cost of Au NPs in commercial testing kits is the colloidal synthesis, our development with the Cu@Au and the laser-ablation-fabricated TiN NPs is exciting, offering potentially inexpensive plasmonic nanomaterials for various bioapplications. Moreover, our machine learning study showed that biodetection with TiN is more accurate than that with Au.

An Intelligent Telediagnosis of Acute Lymphoblastic Leukemia using Histopathological Deep Learning

Leukemia, a global health challenge characterized by malignant blood cell proliferation, demands innovative diagnostic techniques due to its increasing incidence. Among leukemia types, Acute Lymphoblastic Leukemia (ALL) emerges as a particularly aggressive form affecting diverse age groups. This study proposes an advanced mechanized system utilizing Deep Neural Networks for detecting ALL blast cells in microscopic blood smear images. Achieving a remarkable accuracy of 97% using MobileNetV2, our system demonstrates high sensitivity and specificity in identifying multiple ALL sub-types. Furthermore, we introduce cutting-edge telediagnosis software facilitating real-time support for clinicians in promptly and accurately diagnosing various ALL subtypes from microscopic blood smear images. This research aims to enhance leukemia diagnosis efficiency, which is crucial for the timely intervention and managing this life-threatening condition.

Validity of dynamic contrast-enhanced magnetic resonance imaging of the breast versus diffusion-weighted imaging and magnetic resonance spectroscopy in predicting the malignant nature of non-mass enhancement lesions

BackgroundBreast cancer is the commonest cancer affecting women worldwide. So, it is important to accurately detect and classify different breast lesions. Noninvasive methods for tissue characterization have increased interest, particularly for early diagnosis. Non-mass enhancement (NME) breast lesions are described in magnetic resonance imaging (MRI) as the presence of enhancement without space-occupying lesions. Several studies have described that certain characteristics can be used as new indicators of malignancy in breast NME lesions. We aimed to study the role of multiparametric-MRI (Mp-MRI) as diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) in assessment of NME lesions and to suggest which one offers the greatest diagnostic accuracy.MethodsThis retrospective study was conducted from March 2017 to December 2023 on 220 NME breast lesions. All lesions were analyzed to study the features of benign and malignant NME lesions using different MRI techniques including dynamic contrast-enhanced MRI (DCE-MRI), DWI, and MRS. Breast MRI was performed at 1.5 Tesla, findings were correlated with histopathological results of all cases.ResultsPatients’ mean age was 46.56 years with 220 NME breast lesions (54 were benign and 166 were malignant). Invasive ductal carcinoma with ductal carcinoma in situ was the most malignant type representing 93 cases. We found that segmental distribution, heterogeneous enhancement, type III curve, restricted diffusion, lower apparent diffusion coefficient, and positive choline peak were more with malignancy (P = 0.008, 0.02, 0.004, 0.001, and < 0.001). We detected that Mp-MRI has higher diagnostic accuracy than DCE-MRI and combined other functional sequences (DWI, MRS), it was 91.2% with sensitivity 89.9%, specificity 87.8%, positive predictive value 89.2%, and negative predictive value 82.2%.ConclusionsFunctional MRI techniques, such as DWI and MRS, can provide helpful information in assessment of NME lesions. They have high diagnostic accuracy, sensitivity, and specificity in characterizing NME breast lesions as benign or malignant. However, DCE-MRI is mandatory for lesion characterization and delineation of its nature and cannot be replaced by them alone in cases of lesion visualization. So, multiparametric-MRI can improve the diagnostic accuracy of NME breast lesions when combined with dynamic contrast-enhanced MRI and can help in reducing negative biopsy rates.

Diagnostic Role of Serum Adenosine Deaminase In Smear-negative Pulmonary Tuberculosis

Background: Diagnosis of tuberculosis is not always easy, particularly if it is a case of sputum smear-negative pulmonary tuberculosis (SNPTB). Patients with respiratory symptoms resembling SNPTB are difficult to differentiate based on clinical features, chest X-ray, and Xpert MTB/RIF negativity. So, additional diagnostic test with high sensitivity and specificity is needed to increase the yield of the ongoing diagnostic strategy for SNPTB. Adenosine deaminase (ADA) is now being widely used for the diagnosis of TB particularly in effusion fluids due to its simplicity, low cost, and quick available results, it is not always possible to access effusion fluids and therefore, it would be helpful to take advantage of serum levels. Therefore, the purpose of the study was to assess the role of serum ADA in the diagnosis of SNPTB. Methods: This cross-sectional analytical study was conducted in Dhaka Medical College &amp; Hospital, Dhaka from March 2019 to September 2021. A total of 140 patients were included in this study and divided into two groups according to selection criteria: Group I (SNPTB, n=62), and Group II (non-TB pulmonary diseases, n=78). ADA estimation was carried out using the sensitive colorimetric method described by Guisti and Galanti with a BIOSIC kit. After the collection of all the required data, analysis was done by SPSS 24.0. Results: The mean age of the study patients was 48.02 ± 9.60 years (23-73 years) with male predominance in both Group I and Group II (71 % and 60.3%, respectively, p&gt;0.05). Non-TB pulmonary cases were significantly older than SNPTBpatients (52 ± 8.56 vs 43.02 ± 8.49 years, p&lt;0.001). SNPTB patients had a significantly higher frequency of cough, fever, and weight loss compared to non-TB pulmonary cases (p&lt;0.05). In contrast, chest pain and shortness of breath were more frequent in Group II than in Group I (p&lt;0.05). Serum ADA was significantly higher among SNPTB patients compared to non-TB pulmonary cases (48.16 ± 12.13 vs 18.64 ± 7.85 IU/L, p&lt;0.001). ROC analysis of serum ADA in the diagnosis of patients with SNPTB found an AUC of 0.9850 (95% CI, 0.969-1.00) which was statistically significant (p&lt;0.001). A cut-off value of serum ADAe”33 IU/L showed sensitivity, specificity, NPV, PPV, and accuracy of 93.55%, 94.87%, 94.87%, 93.55%, and 94.29%, respectively to correctly diagnose SNPTB cases. Conclusion. This study finding stated that serum ADA may be a useful marker to distinguish SNPTB from non-TB respiratory diseases. However, further study with a more generalized study population is recommended. Bangladesh J Medicine 2024; 35: 61-69

Cancer therapy with antibodies.

The greatest challenge in cancer therapy is to eradicate cancer cells with minimal damage to normal cells. Targeted therapy has been developed to meet that challenge, showing a substantially increased therapeutic index compared with conventional cancer therapies. Antibodies are important members of the family of targeted therapeutic agents because of their extraordinarily high specificity to the target antigens. Therapeutic antibodies use a range of mechanisms that directly or indirectly kill the cancer cells. Early antibodies were developed to directly antagonize targets on cancer cells. This was followed by advancements in linker technologies that allowed the production of antibody-drug conjugates (ADCs) that guide cytotoxic payloads to the cancer cells. Improvement in our understanding of the biology of T cells led to the production of immune checkpoint-inhibiting antibodies that indirectly kill the cancer cells through activation of the T cells. Even more recently, bispecific antibodies were synthetically designed to redirect the T cells of a patient to kill the cancer cells. In this Review, we summarize the different approaches used by therapeutic antibodies to target cancer cells. We discuss their mechanisms of action, the structural basis for target specificity, clinical applications and the ongoing research to improve efficacy and reduce toxicity.

Development of a time-resolved immunochromatographic test strip for rapid and quantitative determination of GFAP in serum.

Glial fibrillary acidic protein (GFAP) in serum has been shown as a biomarker of traumatic brain injury (TBI) which is a significant global public health concern. Accurate and rapid detection of serum GFAP is critical for TBI diagnosis. In this study, a time-resolved fluorescence immunochromatographic test strip (TRFIS) was proposed for the quantitative detection of serum GFAP. This TRFIS possessed excellent linearity ranging from 0.05 to 2.5 ng/mL for the detection of serum GFAP and displayed good linearity (Y = 598723X + 797198, R2 = 0.99), with the lowest detection limit of 16 pg/mL. This TRFIS allowed for quantitative detection of serum GFAP within 15 min and showed high specificity. The intra-batch coefficient of variation (CV) and the inter-batch CV were both < 4.0%. Additionally, this TRFIS was applied to detect GFAP in the serum samples from healthy donors and patients with cerebral hemorrhage, and the results of TRFIS could efficiently discern the patients with cerebral hemorrhage from the healthy donors. Our developed TRFIS has the characteristics of high sensitivity, high accuracy, and a wide linear range and is suitable for rapid and quantitative determination of serum GFAP on-site.

ClassyPose: A Machine‐Learning Classification Model for Ligand Pose Selection Applied to Virtual Screening in Drug Discovery

Determining the target‐bound conformation of a drug‐like molecule is a crucial step in drug design, as it affects the outcome of virtual screening (VS), and paves the way for hit‐to‐lead and lead optimization. While most docking programs usually manage to produce at least a near‐native pose for a bioactive molecule inside its binding pocket, their integrated classical scoring functions (SFs) generally fail to prioritize this pose. Many studies have been carried out to tackle this SF problem, offering multiple pose refinement and/or classification methods, albeit with limitations. This study presents a new support vector machine model for pose classification, called “ClassyPose”, which predicts the probability that a receptor‐bound ligand conformation could be near‐native, without any additional pose optimization step. Trained on protein‐ligand extended connectivity features extracted from over 21 600 crystal and docking poses of diverse ligands, this model outperformed other machine‐learning algorithms and three existing SFs in terms of docking power, identifying the native ligand pose as top‐ranked solution for more than 90% of entries in two test sets. It also achieved high specificity (above 0.96), and improved VS performance when used for pose selection. This efficient, user‐friendly tool and all related data are available at https://github.com/vktrannguyen/Classy_Pose.

ASSESSEMENT IN PERFORMANCE OF LI-RADS ALGORITHM IN DETECTING TREATMENT RESPONSE OF HEPATOCELLULAR CARCINOMA AFTER TRANS-ARTERIAL CHEMOEMBOLIZATION

Background:Trans-arterial hemoembolization (TACE) usually indicated for unresectable hepatocellular carcinoma (HCC) on purpose as a bridging, downstaging or palliative treatment. Liver Imaging Reporting and Data Systems Treatment Response (LR-TR) was created to evaluate each lesion post-TACE, which has localized and is suitable for determining whether the tumor is still viable or Non-viable. Purpose: To assess the completion of LI-RADS version 2018 on computer tomography in the evaluation of HCC after TACE. Materials and Methods: A retrospective cross-sectional study involved 58 patients with hepatocellular carcinoma (HCC) who underwent treatment base TACE from June 2021 – September 2022. The clinical situation, AFP levels, and computer tomography (CT) of the patient after treatment were analyzed. The Radiologist evaluated pre- and post-treatment CT findings using LR-TR category, appropriately. The pooled sensitivity, specificity, and accuracy correclated between LR-TR with standard reference. Results: A total 58 patients with HCC (M/F: 13:1, mean age 56.9±11.0 years). For demonstrating the treatment response after first TACE on CT, Non-viable resulted in 32.8%(19/58), and LR-TR Viable was 67.2%(39/58), sensitivity value of 90% (36/40), specificity value of 83.3% (15/18), positive predictor value of 16.7% (3/18), a negative predictor value of 10% (4/40), accuracy value of 87.9% (51/58). The performance status response- AFP response- imaging response correlation was not statistically different, p= 0.552 and p=0.647. Conclusion: Using LR-TR on CT detects treatment response of HCC post-TACE was useful, and high sensitivity, specificity, and accuracy.

On-Site and Visual Detection of the H5 Subtype Avian Influenza Virus Based on RT-RPA and CRISPR/Cas12a

Avian influenza viruses (AIVs) of the H5 subtype rank among the most serious pathogens, leading to significant economic losses in the global poultry industry and posing risks to human health. Therefore, rapid and accurate virus detection is crucial for the prevention and control of H5 AIVs. In this study, we established a novel detection method for H5 viruses by utilizing the precision of CRISPR/Cas12a and the efficiency of RT-RPA technologies. This assay facilitates the direct visualization of detection results through blue light and lateral flow strips, accurately identifying H5 viruses with high specificity and without cross-reactivity against other AIV subtypes, NDV, IBV, and IBDV. With detection thresholds of 1.9 copies/μL (blue light) and 1.9 × 103 copies/μL (lateral flow strips), our method not only competes with but also slightly surpasses RT-qPCR, demonstrating an 80.70% positive detection rate across 81 clinical samples. The RT-RPA/CRISPR-based detection method is characterized by high sensitivity, specificity, and independence from specialized equipment. The immediate field applicability of the RT-RPA/CRISPR approach underscores its importance as an effective tool for the early detection and management of outbreaks caused by the H5 subtype of AIVs.

Glycan-based scaffolds and nanoparticles as drug delivery system in cancer therapy

Glycan-based scaffolds are unique in their high specificity, versatility, low immunogenicity, and ability to mimic natural carbohydrates, making them attractive candidates for use in cancer treatment. These scaffolds are made up of glycans, which are biopolymers with well biocompatibility in the human body that can be used for drug delivery. The versatility of glycan-based scaffolds allows for the modulation of drug activity and targeted delivery to specific cells or tissues, which increases the potency of drugs and reduces side effects. Despite their promise, there are still technical challenges in the design and production of glycan-based scaffolds, as well as limitations in their therapeutic efficacy and specificity.