Diagnosis Of Disease Research Articles

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

Background: Medical image analysis plays a crucial role in the screening, monitoring, diagnosis, and prognosis of diseases. Hepatocellular Carcinoma (HCC) often remain asymptomatic in their early stages. Timely identification and intervention are crucial to prevent the progression to decompensated liver diseases and advanced-stage HCC, minimizing morbidity and mortality. Methodology: This study examined twenty relevant research articles that met the inclusion criteria. Utilizing the PRISMA criteria, a systematic search was conducted on PubMed/MEDLINE and Google Scholar for studies on HCC screening employing Artificial Intelligence (AI) and Machine Learning (ML). The search focused on the keywords "artificial intelligence" and "hepatocellular carcinoma," with inclusion criteria specifying studies in the English language published in and after 2020. Exclusions were made for histology, animal research, and investigations conducted before 2020. Titles and abstracts were thoroughly reviewed, and any discrepancies were discussed. Results: The comprehensive review reveals the transformative impact of AI and ML on HCC screening, diagnosis, and therapy. ML models demonstrated effectiveness in early HCC diagnosis, distinguishing hepatic lesions, predicting treatment responses, and assessing recurrence risks across various techniques. Integration of mass spectrometry-based technologies, advanced imaging, and real-world data significantly improved diagnostic accuracy and clinical decision support. AI models exhibited diagnostic expertise with potential applications in therapy suggestions, personalized surveillance, and prognostic evaluations. However, further validation and seamless integration into clinical practice are essential for realizing their full potential. Conclusion: This systematic study underscores the progress made in the application of AI and ML in HCC screening, diagnosis, and treatment. Numerous studies highlight the capability of AI and ML systems to enhance hepatocellular carcinoma diagnosis, predict outcomes, and optimize therapy. The enduring and versatile nature of these technologies points towards a revolutionary future in personalized and efficient HCC management.

Diagnosis of Nonalcoholic Fatty Liver Disease via a H2S-Responsive Bioluminescent Probe Combined with Firefly Luciferase mRNA Delivery.

The early detection of nonalcoholic fatty liver disease (NAFLD) through bioluminescent probes is of great significance. However, there remains a challenge to apply them in nontransgenic natural animals due to the lack of exogenous luciferase. To address this issue, we herein report a new strategy for in situ monitoring of endogenous hydrogen sulfide (H2S) in the liver of NAFLD mice by leveraging a H2S-responsive bioluminescent probe (H-Luc) combined with firefly luciferase (fLuc) mRNA delivery. The probe H-Luc was created by installing a H2S recognition moiety, 2,4-dinitrophenol, onto the luciferase substrate (d-luciferin), which is allowed to release cage-free d-luciferin in the presence of H2S via a nucleophilic aromatic substitution reaction. In the meantime, the intracellular luciferase was introduced by lipid nanoparticle (LNP)-mediated fLuc mRNA delivery, rendering it suitable for bioluminescence (BL) imaging in vitro and in vivo. Based on this luciferase-luciferin system, the endogenous H2S could be sensitively and selectively detected in living cells, showing a low limit of detection (LOD) value of 0.72 μM. More importantly, after systematic administration of fLuc mRNA-loaded LNPs in vivo, H-Luc was able to successfully monitor the endogenous H2S levels in the NAFLD mouse model for the first time, displaying a 28-fold higher bioluminescence intensity than that in the liver of normal mice. We believe that this strategy may shed new light on the diagnosis of inflammatory liver disease, further elucidating the roles of H2S.

Primary adrenal insufficiency developed 22 years after the diagnosis of light and heavy chain deposition disease: a case report.

Monoclonal immunoglobulin deposition diseases (MIDDs), including light and heavy chain deposition disease (LHCDD), are rare and heterogeneous disorders associated with underlying B-cell clonal disorders. Adrenal involvement is a potential extrarenal manifestation of MIDDs; however, limited data are available regarding its prevalence and clinical presentation. Herein, the present report describes, for the first time, a case of primary adrenal insufficiency that developed twenty-two years after a diagnosis of LHCDD had been made. A 69year-old woman with a 10year history of hemodialysis suddenly became bedridden after falling down stairs in the absence of focal neurological deficits. Subsequently, she experienced appetite loss, nausea, vomiting, a fever of unknown origin, and unexplained hypotension. Several months later, primary adrenal insufficiency and normal pressure hydrocephalus were diagnosed and successfully managed. The long-term clinical prognosis of MIDDs has not been fully elucidated despite recent advances in the management of the disorders. This report may contribute to improving our understanding of the disease course.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

An ultra-sensitive CRISPR-Cas12a and aptamer-based biosensor utilizing Entropy-driven catalytic DNA networks for precise detection of DNA Methyltransferase 1.

DNA Methyltransferase 1 (DNMT1) serves as a crucial biomarker associated with various diseases and is essential for evaluating DNA methylation levels, diagnosing diseases, and evaluating prognosis. As a result, a convenient, quantitative, and sensitive assay for detecting DNMT1 is in high demand. However, current techniques for DNMT1 detection struggle to balance accuracy, low cost, and high sensitivity, limiting their clinical usefulness. To address this challenge, we have developed a DNMT1 detection method (CAED), which combines aptamer-specific recognition with a highly programmable Entropy-driven catalysis DNA network and is further integrated with the CRISPR-Cas12a system. This innovative approach achieves a detection limit as low as 90.9fmol/L. To demonstrate the clinical applicability and significance of our CAED method, we successfully measured DNMT1 levels in 10 plasma samples 10 cervical tissue samples. These results underscore the potential of our method as an accurate, affordable, and ultra-sensitive tool for evaluating DNMT1 levels. This innovative method offers a potent means for assessing DNMT1 levels and significantly advances disease diagnosis and health risk prediction. Plus, it establishes an innovative design framework for CRISPR-Cas12a-based biosensors, tailored explicitly for enzyme content quantification.

Ultrasonography screening of hepatic cystic echinococcosis in sheep flocks used for evaluating control progress in a remote mountain area of Hejing County, Xinjiang.

Although ultrasonography (US) has been widely used in the diagnosis of human diseases to monitor the progress of cystic echinococcosis (CE) control, the screening method for hepatic CE in sheep flocks requires adjustment. In this study, we used a US scanner to screen sheep flocks and evaluated the efficacy of dosing dogs once a year with praziquantel for 7 years from 2014 to 2021. All sheep in the three flocks were screened using an ultrasound scanner in 2014 and compared with the prevalence of infection in 2021 in Bayinbuluke, Xinjiang, China. Sheep age was determined using incisor teeth. Cyst activity and calcification were determined using US images. The dogs were dewormed with praziquantel once a year to control echinococcosis in the community. Three flocks had 968 sheep in 2014, with 13.22%, 22.62%, 18.7%, 27.27%, 11.88%, and 6.3% of sheep aged 1, 2, 3, 4, 5, and ≥ 6 years old, respectively. US scanning revealed that the overall CE prevalence was 38.43% (372/968), with active cysts and calcified cysts present in 9.40% (91/968) and 29.02% (281/968) of the sheep, respectively. For the young sheep aged 1 and 2 years, the prevalence of active and calcified cysts was: 1.56% and 0.91%, and 10.94% and 18.72%, respectively. Approximately 15.15% and 16.52% of the 4- and 5-year-old sheep, respectively, harbored active cysts. There was no significant difference in the infection rates of sheep between 2014 and 2021 (P > 0.05). US is a practical tool for the field screening of CE in sheep flocks. One-third of the sheep population in the flocks was 1-2 years old, and these sheep played a very limited role in CE transmission, as most of the cysts were calcified. Old sheep, especially culled aged sheep, play a key role in the transmission of CE. Dosing dogs once a year did not affect echinococcosis control.

Effective diameter of the abdominal aorta in children.

Measure out of the standard interval in the aorta diameter is a clue for aortic aneurysm or hypoplasia. Pediatric studies focusing specifically on the normal diameter of the abdominal aorta (AA) were limited in the literature. Therefore, the main goal of this work was to determine changes in the effective diameter of AA in healthy children aged 1-18years for diagnosis of vascular diseases. This retrospective work focused on abdominopelvic computed tomography views of 180 children (sex: 90 males / 90 females, average age: 9.50 ± 5.20years) without any abdominopelvic disease to measure diameters of AA, common iliac artery (CIA), external iliac artery (EIA), and first lumbar vertebra (L1). Vessel and vertebra diameters increased in pediatric subjects between 1 and 18years (p < 0.001). Considering pediatric age periods, vessel diameters increased steadily, but L1 diameter showed an irregular growth pattern between age periods. All parameters were greater in males than females (p < 0.05), except from effective diameters of AA over the coeliac trunk (p = 0.084) and over the renal artery (p = 0.051). The ratios of diameters of vessels to L1 increased depending on ages between 1 and 18years. Considering pediatric age periods, the ratios increased from infancy period to postpubescent period in irregular pattern; however, the ratios for right and left CIA, and AA over the aortic bifurcation did not alter after late childhood period. All ratios for males were similar to females (p > 0.05). Our age-specific ratios may be beneficial for surgeons and radiologists for the diagnosis of vascular disorders such as aortic aneurysm.

Microbial- and host immune cell-derived extracellular vesicles in the pathogenesis and therapy of periodontitis: A narrative review.

Periodontitis is a chronic inflammatory disease caused by dysbiotic biofilms and destructive host immune responses. Extracellular vesicles (EVs) are circulating nanoparticles released by microbes and host cells involved in cell-to-cell communication, found in body biofluids, such as saliva and gingival crevicular fluid (GCF). EVs are mainly involved in cell-to-cell communication,and may hold promise for diagnostic and therapeutic purposes. Periodontal research has examined the potential involvement of bacterial- and host-cell-derived EVs in disease pathogenesis, diagnosis, and therapy, but data remains scarce on immune cell- or microbial-derived EVs. In this narrative review, we first provide an overview of the role of microbial and host-derived EVs on disease pathogenesis. Recent studies reveal that Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans-derived outer membrane vesicles (OMVs) can activate inflammatory cytokine release in host cells, while M1 macrophage EVs may contribute to bone loss. Additionally, we summarised current invitro and pre-clinical research on the utilisation of immune cell and microbial-derived EVs as potential therapeutic tools in the context of periodontal treatment. Studies indicate that EVs from M2 macrophages and dendritic cells promote bone regeneration in animal models. While bacterial EVs remain underexplored for periodontal therapy, preliminary research suggests that P. gingivalis OMVs hold promise as vaccine candidates. Finally, we acknowledge the current limitations present in the field of translating immune cell derived EVs and microbial derived EVs in periodontology. It is concluded that microbial and host immune cell-derived EVs have a role in periodontitis pathogenesis and hence may be useful for studying disease pathophysiology, and as diagnostic and treatment monitoring biomarkers.

A Comprehensive Review on Circulating cfRNA in Plasma: Implications for Disease Diagnosis and Beyond

A Comprehensive Review on Circulating cfRNA in Plasma: Implications for Disease Diagnosis and Beyond

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants(three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.

Single‐Walled Carbon Nanotube‐Based Optical Nano/Biosensors for Biomedical Applications: Role in Bioimaging, Disease Diagnosis, and Biomarkers Detection

Single‐Walled Carbon Nanotube‐Based Optical Nano/Biosensors for Biomedical Applications: Role in Bioimaging, Disease Diagnosis, and Biomarkers Detection

Smelly interactions: host-borne volatile organic compounds triggering behavioural responses in mosquitoes, sand flies, and ticks.

Volatile organic compounds (VOCs) are chemicals emitted as products of cell metabolism, which reflects the physiological and pathological conditions of any living organisms. These compounds play a key role as olfactory cues for arthropod vectors such as mosquitoes, sand flies, and ticks, which act in the transmission of pathogens to many animal species, including humans. Some VOCsmay influence arthropod behaviour, e.g.,host preference and oviposition site selection for gravid females. Furthermore, deadly vector-borne pathogens such as Plasmodium falciparum and Leishmania infantum are suggested to manipulate the VOCs profile of the host to make them more attractive to mosquitoes and sand fly vectors, respectively. Under the above circumstances, studies on these compounds have demonstrated their potential usefulness for investigating the behavioural response of mosquitoes, sand flies, and ticks toward their vertebrate hosts, as well as potential tools for diagnosis of vector-borne diseases (VBDs). Herein, we provide an account for scientific data available on VOCs to study the host seeking behaviour of arthropod vectors, and their usefulness as attractants, repellents, or tools for an early diagnosis of VBDs.

Harnessing Graphdiyne for Selective Cu2+ Detection: A Promising Tool for Parkinson's Disease Diagnostics and Pathogenesis.

Cu2+ accelerates the viral-like propagation of α-synuclein fibrils and plays a key role in the pathogenesis of Parkinson's disease (PD). Therefore, the accurate detection of Cu2+ is essential for the diagnosis of PD and other neurological diseases. The Cu2+ detection process is impeded by substances that have similar electrochemical properties. In this study, graphdiyne (GDY), a new kind of carbon allotrope with strong electron-donating ability, was utilized for the highly selective detection of Cu2+ by taking advantage of its outstanding adsorption capacity for Cu2+. Density functional theory (DFT) calculations show that Cu atoms are adsorbed in the cavity of GDY, and the absorption energy between Cu and C atoms is higher than that of graphene (GR), indicating that the cavity of GDY is favorable for the adsorption of Cu atoms and electrochemical sensing. The GDY-based electrochemical sensor can effectively avoid the interference of amino acids, metal ions and neurotransmitters and has a high sensitivity of 9.77 μA·μM-1·cm-2, with a minimum detectable concentration of 200 nM. During the investigating pathogenesis and therapeutic process of PD with α-synuclein as the diagnostic standard, the concentration of Cu2+ in cells before and after L-DOPA and GSH treatments were examined, and it was found that Cu2+ exhibits high potential as a biomarker for PD. This study not only harnesses the favorable adsorption of the GDY and Cu2+ to improve the specificity of ion detection but also provide clues for deeper understanding of the role of Cu2+ in neurobiology and neurological diseases.

Application of genetic testing for the diagnosis of von Willebrand disease.

von Willebrand disease (VWD) is the most frequent inherited bleeding disorder with an estimated symptomatic prevalence of 1 per 1,000 in the general population. VWD is characterized by defects in the quantity, quality, or multimeric structure of von Willebrand factor (VWF), a glycoprotein being hemostatically essential in circulation. It is classified into three principal types; Low VWF/type 1 with partial quantitative deficiency of VWF, type 3 with virtual absence of VWF, and type 2 with functional abnormalities of VWF, being classified as 2A, 2B, 2M and 2N. A new VWD type has been recently discussed by the joint ASH/ISTH/NHF/WFH 2021 guidelines (i.e., type 1C) indicating patients with quantitative deficiency due to an enhanced VWF clearance. With the advent of next-generation sequencing technologies, the process of genetic diagnosis has substantially changed and improved accuracy. Therefore, nowadays patients with type 3 and severe type 1 VWD can benefit from genetic testing as much as type 2. Specifically, genetic testing can be used to confirm or differentiate a VWD diagnosis, as well as to provide genetic counseling. The focus of this manuscript is to discuss the current knowledge on VWD molecular pathophysiology and the application of genetic testing for VWD diagnosis.

Organic Nanoparticles in Progressing Cardiovascular Disease Treatment and Diagnosis

Organic Nanoparticles in Progressing Cardiovascular Disease Treatment and Diagnosis

DNA tetrahedral molecular sieve for size-selective fluorescence sensing of miRNA 21 in living cells.

In situ monitoring of intracellular microRNAs (miRNAs) often encounters the challenges of surrounding complexity, coexistence of precursor miRNAs (pre-miRNAs) and the degradation of biological enzyme in living cells. Here, we designed a novel probe encapsulated DNA tetrahedral molecular sieve (DTMS) to realize the size-selective detection of intracellular miRNA 21 that can avoid the interference of pre-miRNAs. In such strategy, quencher (BHQ-1) labeled probe DNA (S6-BHQ 1) was introduced into the inner cavity of fluorophore (FAM) labeled DNA tetrahedral scaffolds (DTS) to prepare DTMS, making the FAM and BHQ-1 closely proximate, and resulting the sensor in a "signal-off" state. In the presence of miRNA 21, strand displacement reaction happened to form more stable DNA double-stranded structure, accompanied by the release of S6-BHQ 1 from the inner cavity of DTMS, making the sensor in a "signal-on" state. The DTMS based sensing platform can then realized the size-selective detection of miRNA 21 with a detection limit of 3.6 pM. Relying on the mechanical rigidity of DTS and the encapsulation of DNA probe using DTMS, such proposed method achieved preferable reproducibility and storage stability. Moreover, this sensing system exhibited good performance for monitoring the change of intracellular miRNA 21 level during the treatment with miRNA-related drugs, demonstrating great potential for biological studies and accurate disease diagnosis.

A High-Avidity, Thermostable, and Low-Cost Synthetic Capture for Ultrasensitive Detection and Quantification of Viral Antigens and Aerosols.

Lateral flow assays (LFAs) are currently the most popular point-of-care diagnostics, rapidly transforming disease diagnosis from expensive doctor checkups and laboratory-based tests to potential on-the-shelf commodities. Yet, their sensitive element, a monoclonal antibody, is expensive to formulate, and their long-term storage depends on refrigeration technology that cannot be met in resource-limited areas. In this work, LCB1 affibodies (antibody mimetic miniproteins) were conjugated to bovine serum albumin (BSA) to afford a high-avidity synthetic capture (LCB1-BSA) capable of detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and virus like particles (VLPs). Substituting the monoclonal antibody 2B04 for LCB1-BSA (stable up to 60 °C) significantly improved the thermal stability, shelf life, and affordability of plasmonic-fluor-based LFAs (p-LFAs). Furthermore, this substitution significantly improved the sensitivity of p-LFAs toward the spike protein and VLPs with precise quantitative ability over 2 and 3 orders of magnitude, respectively. LCB1-BSA sensors could detect VLPs at 100-fold lower concentrations, and this improvement, combined with their robust nature, enabled us to develop an aerosol sampling technology to detect aerosolized viral particles. Synthetic captures like LCB1-BSA can increase the ultrasensitivity, availability, sustainability, and long-term accuracy of LFAs while also decreasing their manufacturing costs.

AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.

Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English, this manuscript focuses on predicting disease categories automatically from symptoms documented in the Afaan Oromo language, employing various classification algorithms. This study encompasses machine learning techniques such as support vector machines, random forests, logistic regression, and Naïve Bayes, as well as deep learning approaches including LSTM, GRU, and Bi-LSTM. Due to the unavailability of a standard corpus, we prepared three data sets with different numbers of patient symptoms arranged into 10 categories. The two feature representations, TF-IDF and word embedding, were employed. The performance of the proposed methodology has been evaluated using accuracy, recall, precision, and F1 score. The experimental results show that, among machine learning models, the SVM model using TF-IDF had the highest accuracy and F1 score of 94.7%, while the LSTM model using word2vec embedding showed an accuracy rate of 95.7% and F1 score of 96.0% from deep learning models. To enhance the optimal performance of each model, several hyper-parameter tuning settings were used. This study shows that the LSTM model verifies to be the best of all the other models over the entire dataset.

Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis.

Electronic stethoscope used to detect cardiac sounds that contains essential clinical information is a primary tool for diagnosis of various cardiac disorders. However, the linear electro-mechanical constitutive relation makes conventional piezoelectric sensors rather ineffective to detect low-intensity, low-frequency heart acoustic signal without the assistance of complex filtering and amplification circuits. Herein, we find that triboelectric sensor features superior advantages over piezoelectric one for micro-quantity sensing originated from the fast saturated constitutive characteristic. As a result, the triboelectric sensor shows ultrahigh sensitivity (1215mV/Pa) than the piezoelectric counterpart (21mV/Pa) in the sound pressure range of 50 - 80dB under the same testing condition. By designing a trumpet-shaped auscultatory cavity with a power function cross-section to achieve acoustic energy converging and impedance matching, triboelectric stethoscope delivers 36dB signal-to-noise ratio for human test (2.3 times of that for piezoelectric one). Further combining with machine learning, five cardiac states can be diagnosed at 97% accuracy. In general, the triboelectric sensor is distinctly unique in basic mechanism, provides a novel design concept for sensing micromechanical quantities, and presents significant potential for application in cardiac sounds sensing and disease diagnosis. This article is protected by copyright. All rights reserved.

Mpox (Monkeypox) Virus and Its Co-Infection with HIV, Sexually Transmitted Infections, or Bacterial Superinfections: Double Whammy or a New Prime Culprit?

Epidemiologic studies have established that mpox (formerly known as monkeypox) outbreaks worldwide in 2022–2023, due to Clade IIb mpox virus (MPXV), disproportionately affected gay, bisexual, and other men who have sex with men. More than 35% and 40% of the mpox cases suffer from co-infection with HIV and sexually transmitted infections (STIs) (e.g., Chlamydia trachomatis, Neisseria gonorrhoeae, Treponema pallidum, and herpes simplex virus), respectively. Bacterial superinfection can also occur. Co-infection of MPXV and other infectious agents may enhance disease severity, deteriorate outcomes, elongate the recovery process, and potentially contribute to the morbidity and mortality of the ensuing diseases. However, the interplays between MPXV and HIV, bacteria, other STI pathogens and host cells are poorly studied. There are many open questions regarding the impact of co-infections with HIV, STIs, or bacterial superinfections on the diagnosis and treatment of MPXV infections, including clinical and laboratory-confirmed mpox diagnosis, suboptimal treatment effectiveness, and induction of antiviral drug resistance. In this review article, we will discuss the progress and knowledge gaps in MPXV biology, antiviral therapy, pathogenesis of human MPXV and its co-infection with HIV, STIs, or bacterial superinfections, and the impact of the co-infections on the diagnosis and treatment of mpox disease. This review not only sheds light on the MPXV infection and co-infection of other etiologies but also calls for more research on MPXV life cycles and the molecular mechanisms of pathogenesis of co-infection of MPXV and other infectious agents, as well as research and development of a novel multiplex molecular testing panel for the detection of MPXV and other STI co-infections.