Diagnostic Methods Research Articles

Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework’s effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

Molecular Detection of Respiratory Tract Viruses in Chickens at the Point of Need by Loop-Mediated Isothermal Amplification (LAMP)

Accurate and timely molecular diagnosis of respiratory diseases in chickens is essential for implementing effective control measures, preventing the spread of diseases within poultry flocks, minimizing economic loss, and guarding food security. Traditional molecular diagnostic methods like polymerase chain reaction (PCR) require expensive equipment and trained personnel, limiting their use to centralized labs with a significant delay between sample collection and results. Loop-mediated isothermal amplification (LAMP) of nucleic acids offers an attractive alternative for detecting respiratory viruses in broiler chickens with sensitivity comparable to that of PCR. LAMP’s main advantages over PCR are its constant incubation temperature (∼65 °C), high amplification efficiency, and contaminant tolerance, which reduce equipment complexity, cost, and power consumption and enable instrument-free tests. This review highlights effective LAMP methods and variants that have been developed for detecting respiratory viruses in chickens at the point of need.

Challenges and solutions in developing an objective and structured clinical examination for complementary and integrative medicine: A mixed-method approach.

This study explores the challenges encountered by developers when creating objective and structured clinical examination (OSCE) modules specifically for Korean medicine (KM). The complexity of developing pattern identification (PI) items, due to the lack of standardized materials and ambiguity in KM, was a primary focus. A mixed-method approach was utilized, including a survey, importance-performance analysis, and focus group interviews. Seven developers participated, creating a total of 21 OSCE modules. The main difficulties identified were in developing PI items, selecting appropriate cases, crafting realistic examination situations, determining scoring criteria, setting up checklists, and writing scenarios. Challenges were categorized into "case," "examination situation," "postexamination notes," "checklist," "scenario," "format," and "PI." The importance-performance analysis revealed improvements in module development capabilities with each iteration. For the future development of efficient OSCE modules, standardization of KM diagnostic methods and PI is essential. The study highlights the need for social and academic efforts, as well as support from the KM education community and schools, to address these challenges and enhance the development process.

Viral diarrheas – newer advances in diagnosis and management

Purpose of review Viruses are the most common etiological agents of diarrhea in children. Despite rotavirus vaccine introduction, rotavirus remains as the leading cause of death globally, followed by norovirus, which represents a diagnostic challenge. Here, we describe new advances in the diagnosis and management of viral diarrheas. Recent findings Although immunoassays are widely used for their fast turnaround time and low cost, molecular techniques have become the most reliable diagnostic method due to their high sensitivity and capacity to analyze multiple pathogens in gastrointestinal panels. Isothermal nucleic acid amplification assays (LAMP and RPA) are promising techniques since they do not require sophisticated equipment and can be used as point-of-care testing. CRISPR/Cas nucleic acid detection systems are new diagnostic methods with great potential. Several recent published articles describe the role of human intestinal enteroids to characterize norovirus infection, to test new drugs, and for vaccine development. The interaction between the human gut microbiota and gastrointestinal viral infections has been extensively reviewed and offers some innovative mechanisms for therapeutic and preventive measures. Summary Although important advances have been made, more research is needed to address remaining challenges and further improve diagnostic capabilities and better management strategies for this critical infectious disease.

NMRK2 is an efficient diagnostic indicator for Xp11.2 translocation renal cell carcinoma.

Xp11.2 translocation renal cell carcinomas (tRCC) are a rare and highly malignant type of renal cancer, lacking efficient diagnostic indicators and therapeutic targets. Through the analysis of public databases and our cohort, we identified NMRK2 as a potential diagnostic marker for distinguishing Xp11.2 tRCC from kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) due to its specific upregulation in Xp11.2 tRCC tissues. Mechanistically, we discovered that TFE3 fusion protein binds to the promoter of the NMRK2 gene, leading to its upregulation. Importantly, we established RNA- and protein-based diagnostic methods for identifying Xp11.2 tRCC based on NMRK2 expression levels, and the diagnostic performance of our methods was comparable to a dual-color break-apart fluorescence in situ hybridization assay. Moreover, we successfully identified fresh Xp11.2 tRCC tissues after surgical excision using our diagnostic methods and established an immortalized Xp11.2 tRCC cell line for further research purposes. Functional studies revealed that NMRK2 promotes the progression of Xp11.2 tRCC by upregulating the NAD+/NADH ratio, and supplementation with β-nicotinamide mononucleotide (NMN) or nicotinamide riboside chloride (NR), effectively rescued the phenotypes induced by the knockdown of NMRK2 in Xp11.2 tRCC. Taken together, these data introduce a new diagnostic indicator capable of accurately distinguishing Xp11.2 tRCC and highlight the possibility of developing novel targeted therapeutics. © 2024 The Pathological Society of Great Britain and Ireland.

Real-world efficacy of a teriparatide biosimilar (RGB-10) compared with reference teriparatide on bone mineral density, trabecular bone score, and bone parameters assessed using quantitative ultrasound, 3D-SHAPER® and high-resolution peripheral computer tomography in postmenopausal women with osteoporosis and very high fracture risk.

To compare the therapeutic efficacy of a teriparatide biosimilar (RGB-10) with reference teriparatide for the treatment of osteoporosis in postmenopausal women at very high fracture risk. A retrospective analysis of 25 postmenopausal female patients treated for osteoporosis with RGB-10 for 24months and a matched cohort of 25 patients treated with reference teriparatide. The following outcomes were assessed at baseline, 12 and 24months: bone mineral density (BMD) at the lumbar spine, femoral neck and total hip using dual-energy x-ray absorptiometry (DXA) and integral, trabecular and cortical volumetric and surface BMD using 3D-SHAPER® imaging, trabecular bone score (TBS), quantitative ultrasound (QUS) measurements, and high-resolution peripheral quantitative computed tomography (HRpQCT) imaging of the radius and tibia. No significant differences were observed between treatment groups in any of the measured parameters of BMD or bone health at baseline as well as in any timepoint when assessed using these various diagnostic methods. Both compounds provided equivalent significant improvements from baseline in measures of osteoporosis and fracture risk. The results of the analysis demonstrate the therapeutic equivalence of the teriparatide biosimilar (RGB-10) to reference teriparatide for the treatment of osteoporosis in postmenopausal women at very high risk of fracture.

GamaNNet: A Novel Plant Pathologist-Level CNN Architecture for Intelligent Diagnosis

Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage of Solanum lycopersicum (tomato). Ten thousand RGB images of leaf tissue were subsampled in training (64%), validation (16%), and test (20%) sets to rank the most suitable CNNs in expediting the diagnosis of plant disease. The study assessed the performance of eight well-known networks under identical hyperparameter conditions. Additionally, it introduced the GamaNNet architecture, a custom-designed model optimised for superior performance on this specific type of dataset. The investigational results were most promising for the innovative GamaNNet and ResNet-152, which both exhibited a 91% accuracy rate, as evidenced by their confusion matrices, ROC curves, and AUC metrics. In comparison, LeNet-5 and ResNet-50 demonstrated lower assertiveness, attaining accuracies of 74% and 69%, respectively. GoogLeNet and Inception-v3 emerged as the frontrunners, displaying diagnostic preeminence, achieving an average F1-score of 97%. Identifying such pathologies as Early Blight, Late Blight, Corynespora Leaf Spot, and Septoria Leaf Spot posed the most significant challenge for this class of problem.

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model’s architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model’s learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model’s ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology.

Development of a new accurate lateral flow immunoassay for diagnosis of human leptospirosis.

The current diagnostic methods for leptospirosis diagnosis are technically complex and expensive, with limited applicability to specialized laboratories. Furthermore, they lack diagnostic accuracy in the acute stage of the disease, which coincides with a period when antibiotics are highly effective. New simple and accurate tests are mandatory to decentralize and improve diagnosis. Here, we introduced a new lateral flow immunoassay (Lepto-LF) for human leptospirosis. We conducted a double-blinded assay using 104 serum samples from patients with confirmed or discarded diagnosis for leptospirosis. The diagnostic performance of Lepto-LF was estimated across different ranges of days from onset of symptoms (dpo), considering the diagnostic algorithm as reference standard. Additionally, it was compared with the screening methods enzyme-linked immunosorbent assay (IgM-ELISA) and the slide agglutination test using temperature-resistant antigen (SATR). Lepto-LF exhibited perfect diagnostic performance with a Youden´s index J = 1 from 6 dpo in the acute phase. IgM-ELISA gave slightly lower accuracy with J = 0.91 and 95.5% of both sensitivity and specificity; while SATR showed a markedly inferior yield (J = 0.41, sensitivity = 95.5%, specificity = 45.5%). The performances remained consistent in the convalescence phase of the disease (> 10 dpo). Lepto-LF was found to be a reliable test for simple, rapid and early diagnosis of leptospirosis, resulting a promising tool for decentralizing leptospirosis diagnosis and enabling timely treatment of patients. In addition, Lepto-LF may be employed as confirmatory test, especially in remote areas and vulnerable contexts where the standard MAT is not available.

TDDAM: transformer based deep domain adaptation methodology for lithium-ion battery prognosis

The status of health (SOH) is a vital indicator to characterize the remaining life of lithium-ion batteries (LIBs), and precise prognosis of the SOH is of great importance for battery management systems. In order to prognosis the SOH of LIBs, this paper proposed a Transformer based deep domain adaptation methodology (TDDAM). This paper applies the transformer model, which is widely used in natural language processing and other fields, to the prediction of LIBs. Meanwhile in order to solve the problem of model matching in different types of batteries or different environments, this paper combines domain adaptation method based on the maximum mean discrepancy. Firstly, we extract the data features of LIBs through position encoding and processing of the encoder structure with the multi-head self-attention mechanism as the core. Then, based on the maximum mean discrepancy index, the target domain data and the source domain data features are aligned, and the decoder part of the original transformer model is replaced with a fully connected layer for the prediction of SOH of LIBs in the target domain. This is the first time that a Transformer has been combined with the maximum mean discrepancy to be applied to LIBs prediction. Comprehensive experiments on two CALCE LIBs data showed that the TDDAM achieved smaller prognostic prediction errors over popular SOH diagnostic methods, indicating its great potential as a generic backbone for LIBs prognosis.

MiR-182, miR-221 and miR-222 are potential urinary extracellular vesicle biomarkers for canine urothelial carcinoma

Current diagnostic methods for canine urothelial carcinoma (UC) are technically challenging or can lack specificity, hence there is a need for novel biomarkers of UC. To this end, we analysed the microRNA (miRNA) cargo of extracellular vesicles (EVs) from urine samples of dogs with UC to identify candidate miRNA biomarkers. Urine was fractionated using ultrafiltration combined with size-exclusion chromatography and small RNA sequencing analysis was performed on both the EV enriched and (EV free) protein fractions. A greater number of candidate miRNA biomarkers were detected in the EV fraction than the protein fraction, and further validation using droplet digital PCR (ddPCR) was performed on the EV enriched fraction of a second cohort of dogs with UC which indicated that miR-182, miR-221 and miR-222 were significantly overrepresented in dogs with UC when compared with healthy dogs and dogs with urinary tract infections. Pathway analysis confirmed that these three miRNAs are involved in cancer. In addition, their potential downstream gene targets were predicted and PIK3R1, a well-known oncogene is likely to be a shared target between miRNA-182 and miRNA-221/222. In summary, this study highlights the potential of urinary EV-associated miRNAs as a source of biomarkers for the diagnosis of canine UC.

Clinical Characteristics, Prognosis Factors and Metagenomic Next-Generation Sequencing Diagnosis of Mucormycosis in patients With Hematologic Diseases.

New diagnostic methods and antifungal strategies may improve prognosis of mucormycosis. We describe the diagnostic value of metagenomic next⁃generation sequencing (mNGS) and identify the prognostic factors of mucormycosis. We conducted a retrospective study of hematologic patients suffered from mucormycosis and treated with monotherapy [amphotericin B (AmB) or posaconazole] or combination therapy (AmB and posaconazole). The primary outcome was 84-day all-cause mortality after diagnosis. Ninety-five patients were included, with "proven" (n = 27), "probable" (n = 16) mucormycosis confirmed by traditional diagnostic methods, and "possible" (n = 52) mucormycosis with positive mNGS results. The mortality rate at 84 days was 44.2%. Possible + mNGS patients and probable patients had similar diagnosis processes, overall survival rates (44.2% vs 50.0%, p = 0.685) and overall response rates to effective drugs (44.0% vs 37.5%, p = 0.647). Furthermore, the median diagnostic time was shorter in possible + mNGS patients than proven and probable patients (14 vs 26 days, p < 0.001). Combination therapy was associated with better survival compared to monotherapy at six weeks after treatment (78.8% vs 53.1%, p = 0.0075). Multivariate analysis showed that combination therapy was the protective factor (HR = 0.338, 95% CI: 0.162-0.703, p = 0.004), though diabetes (HR = 3.864, 95% CI: 1.897-7.874, p < 0.001) and hypoxemia (HR = 3.536, 95% CI: 1.874-6.673, p < 0.001) were risk factors for mortality. Mucormycosis is a life-threatening infection. Early management of diabetes and hypoxemia may improve the prognosis. Exploring effective diagnostic and treatment methods is important, and combination antifungal therapy seems to hold potential benefits.

Biosensor Based on Zif-8-905% Metal-organic Nanocomposite and Carbon Nanotubes Associated with Concanavalin a for Detection of Alpha-fetoprotein

Introduction:: Lung carcinoma presents an aggressive evolution, with its carriers having reduced survival. Late diagnosis is one of the main factors of death. In the neoplasia in question, there is an established correlation with increases in Alpha-Fetoprotein (AFP) serum concentrations. Methos: Commonly used diagnostic methods are invasive or inaccessible. Therefore, a low-cost, non-invasive method would be extremely promising, and biomarkers can be used to achieve this goal. Electrochemical biosensors are a promising approach for detecting analytes of clinical interest using innovative bioreceptors. In this work, we obtained an electrochemical biosensor based on a hybrid ligand metal-organic structure (ZIF-8-905%) and functionalized carbon nanotubes (MWCNTs- COOH) in association with the lectin Concanavalin A (ConA), as a biorecognition element for detecting AFP in human serum from patients with lung carcinoma. Cyclic Voltammetry (CV), Square Wave Voltammetry (SWV), and Electrochemical Impedance Spectroscopy (EIS) were used to characterize the development of this biosensor. Microscopic analysis through Atomic Force Microscopy (AFM) revealed the formation of ConA-AFP complexes, pointing out the sensor's ability to identify the target analyte. Results: The blocking electron transfer effect in the electrode-redox pair interface assessed AFP detection. The ZIF-8-905%/MWCNTs-COOH/ConA platform exhibited a limit of detection (LOD) of 7.98 ng/mL, and a limit of quantification (LOQ) of 23.78ng/mL was also estimated. In addition, the biosensor showed excellent selectivity towards interfering biomolecules. Conclusion:: Therefore, the biosensor represents an efficient form of detection, contributing to research that aims to detect tumor biomarkers and ensure better prognoses.

Localization and etiological stratification of non-neoplastic small bowel bleeding via CT imaging: a 10-year study

ObjectivesThe purpose of this study is to assess the diagnostic efficacy of contrast-enhanced CT scans for small bowel bleeding.MethodsThis retrospective study evaluated patients diagnosed with non-neoplastic small intestinal bleeding (including duodenum) who underwent abdominal CT at our institution from December 2013 to March 2023. Patients were categorized into diverticulum and non-diverticulum groups based on the cause of bleeding. Active bleeding was defined on the CT images as extravasation of contrast material in the intestinal lumen during the arterial phase and/or progressive accumulation of contrast material during the venous phase. We have documented the original report (extracted from the medical record system and additional consultation opinions from senior radiologists), including the presence of active bleeding and its potential bleeding location. Furthermore, two radiologists reassessed the CT images, seeking consensus on the diagnosis between them.ResultsThe study included 165 patients, predominantly male, with a median age of 30 years. Active bleeding was identified in 48.3% of patients. Notably, all identified bleeding diverticula in the diverticulum group exhibited cul-de-sac termination. Among the identified causes of bleeding, Crohn’s disease was most prevalent (46.7%, N of causes = 64). Significant differences were observed in the diagnostic methods between the diverticulum and non-diverticulum groups, with surgery predominantly applied in the diverticulum group, and endoscopy in the non-diverticulum group (n = 49 vs n = 15, p = 0.001). Contrast agent extravasation was significantly higher in the diverticulum group (n = 54 vs n = 16, p = 0.001), and Meckel’s diverticulum cases appearing tubular were significantly higher than in other diverticulum cases (n = 25 vs n = 3, p < 0.001).ConclusionCT allows for a higher detection rate of diverticular bleeding, even if asymptomatic, guiding classification into multiple potentially clinically relevant categories.Critical relevance statementContrast-enhanced CT imaging is effective in determining the location and cause of non-neoplastic small bowel bleeding, especially diverticular bleeding. Therefore, the use of enhanced CT should be prioritized in the diagnosis and management of small bowel bleeding.Key PointsCT has potential value in the diagnosis of small bowel bleeding.CT imaging suggests possible surgical intervention for active bleeding detection.CT diagnoses and localizes small bowel bleeding, aiding in treatment and prioritizing in guidelines.Graphical

Applications of nanostructured materials for severe acute respiratory syndrome-CoV-2 diagnostic

There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infections will continue to rise, and there is now no safe and effective vaccination available to prevent a pandemic. This has increased the need for rapid, sensitive, and highly selective diagnostic techniques for coronavirus disease (COVID-19) detection to levels never seen before. Researchers are now looking at other biosensing techniques that may be able to detect the COVID-19 infection and stop its spread. According to high sensitivity, and selectivity that could provide real-time results at a reasonable cost, nanomaterial show great promise for quick coronavirus detection. In order to better comprehend the rapid course of the infection and administer more effective treatments, these diagnostic methods can be used for widespread COVID-19 identification. This article summarises the current state of research into nanomaterial-based biosensors for quick SARS‑CoV‑2 diagnosis as well as the prospects for future advancement in this field. This research will be very useful during the COVID-19 epidemic in terms of establishing rules for designing nanostructure materials to deal with the outbreak. In order to predict the spread of the SARS-CoV-2 virus, we investigate the advantages of using nano-structure material and its biosensing applications.

Surgical approaches for pudendal nerve entrapment: insights from a systematic review and meta-analysis.

Pudendal nerve entrapment (PNE) is an underdiagnosed condition affecting a spectrum of pelvic functions, primarily pain, as outlined by Nantes diagnostic criteria. Although numerous surgical decompression techniques are available for its management, consensus on efficacy and safety is lacking. This study conducts a systematic review and meta-analysis to assess the efficacy and complication rates of the main surgical decompression techniques. A comprehensive literature search was conducted in PubMed®, Embase®, Web of Science®, and ClinicalTrails.gov® on 19th of April 2023. Initial screening involved title and abstract evaluation, with subsequent retrieval and assessment of abstracts and full-text articles. Studies assessing pain outcomes before and after surgical release of the pudendal nerve were included. Studies without full-text, focusing on diagnostic methods or with outcomes relating solely to LUTS, digestive symptoms, or sexual dysfunction, were excluded. Risk of bias assessement was conducted using the National Institute of Health (NIH) Study Quality Assessment tool. Studies were categorized based on three surgical techniques: perineal, transgluteal, and laparoscopic transperitoneal. Random-effects meta-analysis with subgroup analysis were used. Meta-regression analyses were conducted to investigate the influence of covariates on the observed outcomes. Nineteen studies, comprising 810 patients, were included. The overall significant pain relief rate across all techniques was estimated at 0.67 (95% CI 0.54 to 0.78) with considerable heterogeneity (I2 = 80.4%). Subgroup analysis revealed success rate for different techniques: laparoscopic (0.91, 95% CI 0.64 to 0.98), perineal (0.69, 95% CI 0.52 to 0.82), and transgluteal (0.50, 95% CI 0.37 to 0.63). The laparoscopic technique exhibited a complication rate of 16.0%. Meta-regression indicated that patient age and median follow-up significantly influenced outcomes. While comparing surgical techniques is challenging, this meta-analysis highlights important outcome differences. The laparoscopic technique appears most promising for pain improvement. However, the study also emphasizes the need for further robust, long-term research due to significant heterogeneity across studies and prevelent risk of bias. PROSPERO database: CRD42023496564.

Single and Combined Serum Proteins Expressed in TB Infection are Candidates for Point-of-care Diagnostic Testing of Active TB Patients in Lambaréné, Gabon.

Point-of-care testing using nonsputum samples like serum or plasma proteins can improve tuberculosis (TB) patients access to a definitive diagnosis, especially in resource-constrained and remote areas. Recently, approximately 400 proteins were identified as playing a role in the pathogenesis of TB, offering a translational clinical research repository for TB. In a previous manuscript, we proved the potential use of these proteins for point-of-care testing for active TB diagnosis. The present work aims to confirm the performance of single and combination proteins to select the best candidate biomarkers for further development as a diagnostic testing tool for active TB. Seventy-four participants were assessed on the diagnostic performance of 17 single proteins and combinations of 2 to 4 proteins to diagnose active TB. The selection criteria included differential expression of the proteins between active TB and community-acquired pneumonia (CAP) and a performance rate ≥70% for active TB. SULT4A1, WASPF3, SPTLC1, FAM107B, SORCS2, and CYTOb561 were differentially expressed in TB compared to CAP patients. Two single proteins, SULT4A1 and WASPF3, performed ≥70% to discriminate active TB from CAP patients. The diagnostic performance of 3 protein-based combinations of active TB was 81% after leave-one-out cross-validation. Single proteins and 3 protein-based combinations are candidate biomarkers for diagnosing active TB disease. A large and prospective study will confirm their performance as complementary diagnostic tools to rapid diagnostic methods for detecting active TB.

Nanotechnology-based Strategies for Molecular Imaging, Diagnosis, and Therapy of Organ Transplantation.

Organ transplantation is the preferred paradigm for patients with end-stage organ failures. Despite unprecedented successes, complications such as immune rejection, ischemia-reperfusion injury, and graft dysfunction remain significant barriers to long-term recipient survival after transplantation. Conventional immunosuppressive drugs have limited efficacy because of significant drug toxicities, high systemic immune burden, and emergence of transplant infectious disease, leading to poor quality of life for patients. Nanoparticle-based drug delivery has emerged as a promising medical technology and offers several advantages by enhancing the delivery of drug payloads to their target sites, reducing systemic toxicity, and facilitating patient compliance over free drug administration. In addition, nanotechnology-based imaging approaches provide exciting diagnostic methods for monitoring molecular and cellular changes in transplanted organs, visualizing immune responses, and assessing the severity of rejection. These noninvasive technologies are expected to help enhance the posttransplantation patient survival through real time and early diagnosis of disease progression. Here, we present a comprehensive review of nanotechnology-assisted strategies in various aspects of organ transplantation, including organ protection before transplantation, mitigation of ischemia-reperfusion injury, counteraction of immune rejection, early detection of organ dysfunction posttransplantation, and molecular imaging and diagnosis of immune rejection.

A comprehensive review of early detection of COVID-19 based on machine learning and deep learning models

This paper reviews the use of machine learning (ML) and deep learning (DL) for early coronavirus disease (COVID-19) detection, highlighting their potential to overcome the limitations of traditional diagnostic methods such as long processing times and high costs. We analyze studies applying ML and DL to imaging, clinical, and genomic data, assessing their performance in terms of accuracy, sensitivity, specificity, and efficiency. The review discusses the advantages, limitations, and challenges of these models, including data quality, generalizability, and ethical considerations. It also suggests future research directions for improving model efficacy, such as integrating multi-modal data and developing more interpretable models. This concise review serves as a guide for researchers, healthcare practitioners, and policymakers on the advancements and prospects of ML and DL in early COVID-19 detection, promoting further innovation and collaboration in this vital public health domain.

Coagulation disorders in liver cirrhosis - Diagnostics and management

Patients with liver cirrhosis often exhibit complex alterations in their hemostatic system that can be associated with both bleeding and thrombotic complications. While prophylactic correction of abnormal coagulation parameters should be avoided, an individualized approach is recommended prior to invasive procedures, whereby specific preventive measures to stabilize hemostasis should be based on the periprocedural bleeding risk. While the haemostatic system of patients with compensated cirrhosis is often in a rebalanced haemostatic state due to a parallel decline in both pro- and anti-haemostatic factors, a decompensation of liver cirrhosis can lead to destabilization of this fragile equilibrium. Since conventional coagulation tests do not adequately capture the complex changes in the hemostatic system in cirrhosis, functional analysis methods such as viscoelastic tests or thrombin generation assays can be used for evaluating the coagulation status. This review describes the underlying pathophysiological changes in the hemostatic system in liver cirrhosis, provides an overview of diagnostic methods and discusses therapeutic measures in case of bleeding and thrombotic complications.