Bronchial washing fluid sequencing is useful in the diagnosis of lung cancer with necrotic tumor.

BACKGROUND: Early-stage lung cancers detected by low-dose computed tomography (CT) often require confirmation through invasive procedures due to the absence of endobronchial lesions. This study assesses the diagnostic utility of bronchial washing fluid (BW) sequencing, a less invasive alternative, aiming to identify patient characteristics most suited for this approach. METHODS: From June 2017 to March 2018, we conducted a prospective cohort study by enrolling patients with incidental lung lesions suspected of early-stage lung cancer at two independent hospitals, and 114 were diagnosed with lung cancer while 50 were diagnosed with benign lesions. BW sequencing was performed using a targeted gene panel, and the clinical characteristics of patients detected with cancer through sequencing were identified. RESULTS: Malignant cells were detected in 33 patients (28.9 %) through BW cytology. By applying specificity-focused mutation criteria, BW sequencing classified 42 patients (36.8 %) as having cancer. Among the cancer patients who were BW sequencing positive and BW cytology negative, 15 patients (75.0 %) showed necrosis on CT. The sensitivity of BW sequencing was particularly enhanced in patients with necrotic tumors, reaching 75 %. CONCLUSIONS: BW sequencing presents a viable, non-invasive diagnostic option for early-stage lung cancer, especially valuable in patients with necrotic lesions. By potentially reducing the reliance on more invasive diagnostic procedures, this method could streamline clinical workflows, decrease patient burden, and improve overall diagnostic efficiency.

Semen sEV tRF-Based Models Increase Non-Invasive Prediction Accuracy of Clinically Significant Prostate Cancer among Patients with Moderately Altered PSA Levels.

PSA screening has led to an over-diagnosis of prostate cancer (PCa) and unnecessary biopsies of benign conditions due to its low cancer specificity. Consequently, more accurate, preferentially non-invasive, tests are needed. We aim to evaluate the potential of semen sEV (small extracellular vesicles) tsRNAs (tRNA-derived small RNAs) as PCa indicators. Initially, following a literature review in the OncotRF database and high-throughput small RNA-sequencing studies in PCa tissue together with the sncRNA profile in semen sEVs, we selected four candidate 5'tRF tsRNAs for validation as PCa biomarkers. RT-qPCR analysis in semen sEVs from men with moderately elevated serum PSA levels successfully shows that the differential expression of the four tRFs between PCa and healthy control groups can be detected in a non-invasive manner. The combined model incorporating PSA and specific tRFs (5'-tRNA-Glu-TTC-9-1_L30 and 5'-tRNA-Val-CAC-3-1_L30) achieved high predictive accuracy in identifying samples with a Gleason score ≥ 7 and staging disease beyond IIA, supporting that the 5'tRF fingerprint in semen sEV can improve the PSA predictive value to discriminate between malignant and indolent prostate conditions. The in silico study allowed us to map target genes for the four 5'tRFs possibly involved in PCa. Our findings highlight the synergistic use of multiple biomarkers as an efficient approach to improve PCa screening and prognosis.

Harnessing the potential of genomic characterization of mutational profiles to improve early diagnosis of lung cancer.

INTRODUCTION: Lung Cancer (LC) continues to be a leading cause of cancer-related mortality globally, largely due to the asymptomatic nature of its early stages and the limitations of current diagnostic methods such as Low-Dose Computed Tomography (LDCT), whose often result in late diagnosis, highlighting an urgent need for innovative, minimally invasive diagnostic techniques that can improve early detection rates. AREAS COVERED: This review delves into the potential of genomic characterization and mutational profiling to enhance early LC diagnosis, exploring the current state and limitations of traditional diagnostic approaches and the revolutionary role of Liquid Biopsies (LB), including cell-free DNA (cfDNA) analysis through fragmentomics and methylomics. New genomic technologies that allow for earlier detection of LC are scrutinized, alongside a detailed discussion on the literature that shaped our understanding in this field. EXPERT OPINION: Despite the promising advancements in genomic characterization techniques, several challenges remain, such as the heterogeneity of LC mutations, the high cost, and limited accessibility of Next-Generation Sequencing (NGS) technologies. Additionally, there is a critical need of standardized protocols for interpreting mutational data. Future research should focus on overcoming these barriers to integrate these novel diagnostic methods into standard clinical practice, potentially revolutionizing the management of LC patients.

Limitations and perspectives of the novel salivary test for endometriosis: an open web-based survey study of German gynecologic healthcare providers.

INTRODUCTION: The description of a salivary miRNA signature for endometriosis has led to the development of a non-invasive diagnostic test. Current healthcare provider practices regarding the test remain uncaptured. The application of this test in practice was examined in a web-based survey, with the aim to provide their opinions on it. METHODS: We conducted an open web-based survey study between November 2023 and January 2024. Members of the German society of gynecologic endoscopy (Arbeitsgemeinschaft gynäkologische Endoskopie, AGE), society of endometriosis (Arbeitsgemeinschaft Endometriose, AGEM), and the endometriosis research foundation (Stiftung Endometriose Forschung, SEF) were contacted per e-mail twice. Participants' data were anonymized. Differences in responses based on self-reported expertise in the field (basic knowledge, specialized knowledge, expert) were assessed using the χ2-test or Fisher's exact test. Statistical significance was set as p < 0.05. RESULTS: In total 141 of 190 respondents completely responded to the survey (> 75% of the questions of the survey). Twenty-one physicians reported having experience with the test, while most participants had at least specialized knowledge on the field (112/141). In terms of specific questions, more than 90% found the costs high; almost 85% did not believe that the test replaces standard diagnostic tools (histology, clinical examination, and sonography). Eighty-six providers supported the use of the test in adolescents. Gynecologists with basic knowledge had a more positive attitude compared with more experienced ones in terms of usefulness (Fisher's exact test, p < 0.001). Significant differences were demonstrated between expertise groups regarding (not only) applicability in adolescents (Fisher's exact test, p = 0.004), and using the test for screening purposes (χ2-test, p = 0.002). DISCUSSION: Despite the promising benefits of a salivary test for endometriosis, German healthcare providers would not change current practices. Nevertheless, less experienced colleagues were more positive towards the test.

Diagnostic performance of new BAST score versus FIB-4 index in predicating of the liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease.

BACKGROUND AND AIM: Metabolic dysfunction-associated steatotic liver disease (MASLD) formerly known as non-alcoholic fatty liver disease (NAFLD) is the most common liver condition globally. The FIB-4 test is used to detect fibrosis in fatty liver disease but has limited accuracy in predicting liver stiffness, resulting in high rates of false positives and negatives. The new BAST scoring system, incorporating waist circumference, AST, and BMI, has been developed to assess the presence of fibrosis in NAFLD patients. This study compares the effectiveness of BAST and FIB-4 in predicting liver fibrosis in MASLD patients. PATIENTS AND METHODS: The study included 140 non-diabetic MASLD patients who underwent transient elastography measurement. BAST score and FIB-4 were calculated for each patient. Patients were grouped based on fibrosis severity; F1, F2, and F3-F4. The sensitivity and specificity of the BAST score and FIB-4 were assessed using receiver operating characteristic curves. RESULTS: The BAST score increased significantly with fibrosis progression from F1 to F3-F4. In differentiating advanced fibrosis (F2-F3) from mild/moderate fibrosis (F1-F2), the BAST score at cutoff ≤ - 0.451 showed better diagnostic performance with 90.70% sensitivity, 74.07% specificity, 84.8% PPV and 83.3% NPV compared to FIB-4 that had 60.47% sensitivity, 50.0% specificity, 65.8% PPV and 44.3% NPV. Similarly, for differentiating between F1 and F2 fibrosis, the BAST score at cutoff ≤ - 1.11 outperformed FIB-4, with 80.23% sensitivity, 79.49% specificity, 89.6% PPV and 64.6% NPV, while FIB-4 had 59.30% sensitivity, 51.28% specificity, 72.9% PPV and 36% NPV. CONCLUSIONS: The BAST score is a better predictor of liver fibrosis in MASLD compared to FIB-4, especially in cases of advanced fibrosis or cirrhosis.

Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers.

Changes in plasma and fecal metabolomes in colorectal cancer (CRC) progression (normal-adenoma-CRC) remain unclear. Here, plasma and fecal samples were collected from four independent cohorts of 1,251 individuals (422 CRC, 399 colorectal adenoma [CRA], and 430 normal controls [NC]). By metabolomic profiling, signature plasma and fecal metabolites with consistent shift across NC, CRA, and CRC are identified, including CRC-enriched oleic acid and CRC-depleted allocholic acid. Oleic acid exhibits pro-tumorigenic effects in CRC cells, patient-derived organoids, and two murine CRC models, whereas allocholic acid has opposing effects. By integrative analysis, we found that oleic acid or allocholic acid directly binds to α-enolase or farnesoid X receptor-1 in CRC cells, respectively, to modulate cancer-associated pathways. Clinically, we establish a panel of 17 plasma metabolites that accurately diagnoses CRC in a discovery and three validation cohorts (AUC = 0.848-0.987). Overall, we characterize metabolite signatures, mechanistic significance, and diagnostic potential of plasma and fecal metabolomes in CRC.

[Research Progress of Artificial Intelligence in Prostate Cancer Diagnosis Application].

With the continuous advancement of artificial intelligence in the field of prostate cancer research, numerous studies have shown that AI performance can rival that of physicians. This review examines the recent applications and developments of AI in the early, accurate, and non-invasive diagnosis of prostate cancer, subsequently elucidating its importance, benefits, and limitations. The review emphasizes the exploration of the potential integration of AI with multi-omics and other cutting-edge technologies. Considering the current status of AI in prostate cancer diagnosis, the review summarizes the challenges faced in the clinical adoption of AI technologies and looks forward to improved and enhanced AI-based prostate cancer diagnostic techniques. The goal is to offer a reference for the integration of artificial intelligence into clinical practice.

Integrative metagenomic, transcriptomic, and proteomic analysis reveal the microbiota-host interplay in early-stage lung adenocarcinoma among non-smokers.

BACKGROUND: The incidence of early-stage lung adenocarcinoma (ES-LUAD) is steadily increasing among non-smokers. Previous research has identified dysbiosis in the gut microbiota of patients with lung cancer. However, the local microbial profile of non-smokers with ES-LUAD remains largely unknown. In this study, we systematically characterized the local microbial community and its associated features to enable early intervention. METHODS: A prospective collection of ES-LUAD samples (46 cases) and their corresponding normal tissues adjacent to the tumor (41 cases), along with normal lung tissue samples adjacent to pulmonary bullae in patients with spontaneous pneumothorax (42 cases), were subjected to ultra-deep metagenomic sequencing, host transcriptomic sequencing, and proteomic sequencing. The obtained omics data were subjected to both individual and integrated analysis using Spearman correlation coefficients. RESULTS: We concurrently detected the presence of bacteria, fungi, and viruses in the lung tissues. The microbial profile of ES-LUAD exhibited similarities to NAT but demonstrated significant differences from the healthy controls (HCs), characterized by an overall reduction in species diversity. Patients with ES-LUAD exhibited local microbial dysbiosis, suggesting the potential pathogenicity of certain microbial species. Through multi-omics correlations, intricate local crosstalk between the host and local microbial communities was observed. Additionally, we identified a significant positive correlation (rho > 0.6) between Methyloversatilis discipulorum and GOLM1 at both the transcriptional and protein levels using multi-omics data. This correlated axis may be associated with prognosis. Finally, a diagnostic model composed of six bacterial markers successfully achieved precise differentiation between patients with ES-LUAD and HCs. CONCLUSIONS: Our study depicts the microbial spectrum in patients with ES-LUAD and provides evidence of alterations in lung microbiota and their interplay with the host, enhancing comprehension of the pathogenic mechanisms that underlie ES-LUAD. The specific model incorporating lung microbiota can serve as a potential diagnostic tool for distinguishing between ES-LUAD and HCs.

Accurate classification of glomerular diseases by hyperspectral imaging and transformer.

BACKGROUND AND OBJECTIVE: In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsistent results. There is a pressing need for innovative diagnostic tools that enhance traditional methods, streamline processes, and ensure accurate and consistent disease detection. METHODS: In this study, we present an innovative Convolutional Neural Networks-Vision Transformer (CVT) model leveraging Transformer technology to refine glomerular disease diagnosis by fusing spectral and spatial data, surpassing traditional diagnostic limitations. Using interval sampling, preprocessing, and wavelength optimization, we also introduced the Gramian Angular Field (GAF) method for a unified representation of spectral and spatial characteristics. RESULTS: We captured hyperspectral images ranging from 385.18 nm to 1009.47 nm and employed various methods to extract sample features. Initial models based solely on spectral features achieved a accuracy of 85.24 %. However, the CVT model significantly outperformed these, achieving an average accuracy of 94 %. This demonstrates the model's superior capability in utilizing sample data and learning joint feature representations. CONCLUSIONS: The CVT model not only breaks through the limitations of existing diagnostic techniques but also showcases the vast potential of non-invasive, high-precision diagnostic technology in supporting the classification and prognosis of complex glomerular diseases. This innovative approach could significantly impact future diagnostic strategies in renal disease research. CONCISE ABSTRACT: This study introduces a transformative hyperspectral image classification model leveraging a Transformer to significantly improve glomerular disease diagnosis accuracy by synergizing spectral and spatial data, surpassing conventional methods. Through a rigorous comparative analysis, it was determined that while spectral features alone reached a peak accuracy of 85.24 %, the novel Convolutional Neural Network-Transformer (CVT) model's integration of spatial-spectral features via the Gramian Angular Field (GAF) method markedly enhanced diagnostic precision, achieving an average accuracy of 94 %. This methodological innovation not only overcomes traditional diagnostic limitations but also underscores the potential of non-invasive, high-precision technologies in advancing the classification and prognosis of complex renal diseases, setting a new benchmark in the field.

Smell cancer by machine learning-assisted peptide/MXene bioelectronic array.

Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In this work, biosensors with remarkable gas-sensing characteristics were developed using a self-assembly method consisting of peptides and MXene. Based on these biosensors, a mimetic biosensor array (MBA) was fabricated and integrated into a real-time testing platform (RTP). In addition, machine learning (ML) algorithms were introduced to improve the RTP's detection and identification capabilities of exhaled gas signals. The synthesized biosensor, with the ability to specifically bind to targeted gas molecules, demonstrated higher performance than the pristine MXene, with a response up to 150% greater. Besides, the MBA successfully detected 15 odor molecules affiliated with five categories of alcohols, ketones, aldehydes, esters, and acids by pattern recognition algorithms. Furthermore, with the ML assistance, the RTP detected the breath odor samples from volunteers of four categories, including healthy populations, patients of lung cancer, upper digestive tract cancer, and lower digestive tract cancer, with accuracies of 100%, 94.1%, 90%, and 95.2%, respectively. In summary, we have developed a cost-effective and precise model for non-invasive tumor diagnosis. Furthermore, this prototype also offers a versatile solution for diagnosing other diseases like nephropathy, diabetes, etc.

The metabolites mainly composed of lipids in tongue coating are non-invasive potential biomarkers for chronic gastritis.

The changes in tongue coating metabolites in patients with chronic gastritis (CG) under different gastroscopy indicators were analyzed, and these metabolites were screened for potential non-invasive biomarkers to assist in the diagnosis of chronic gastritis. The technology of gas chromatography and liquid chromatography combined with mass spectrometry has been used to more comprehensively detect tongue coating metabolites of 350 CG patients. Spearman correlation analysis and random forest algorithm were used to screen metabolites that can serve as potential biomarkers. Compared with healthy individuals, CG group showed significant changes in the content of 101 metabolites, with an increase in the content of 54 metabolites and a decrease in the content of 47 metabolites. These differential metabolites are mainly composed of 47 lipids and lipid like substances. 1 metabolite was associated with bile reflux, 1 metabolite was associated with gastric mucosal erosion, 10 metabolites were associated with atrophy, 10 metabolites were associated with intestinal metaplasia, and 3 metabolites were associated with Helicobacter pylori infection. The ROC model composed of 5 metabolites can distinguish between CG group and healthy individuals, with an accuracy of 95.4%. The ROC model composed of 5,6-Dihydroxyindole can distinguish between chronic superficial gastritis group and chronic atrophic gastritis group, with an accuracy of 75.3%. The lipids and lipid like metabolites were the main abnormal metabolites in patients with chronic gastritis. It was worth noting that the content of Sphinganine 1-phase, 4-Ipomenol, and Nervonic acid in tongue coating increased, and the content of 1-Methyladenosine and 3-Hydroxycapric acid decreased, which helped to identify CG patients. The decrease in the content of 5,6-dihydroxyindole reminded patients that the development trend of CG was shifting from superficial to atrophic or even intestinal metaplasia. The detection of these metabolic markers of tongue coating was expected to be developed as a non-invasive and convenient technology in the future to assist us in monitoring and diagnosing the occurrence and development of CG.

Application of Quantitative Ultrasonography and Artificial Intelligence for Assessing Severity of Fatty Liver: A Pilot Study.

Non-alcoholic fatty liver disease (NAFLD), prevalent among conditions like obesity and diabetes, is globally significant. Existing ultrasound diagnosis methods, despite their use, often lack accuracy and precision, necessitating innovative solutions like AI. This study aims to validate an AI-enhanced quantitative ultrasound (QUS) algorithm for NAFLD severity assessment and compare its performance with Magnetic Resonance Imaging Proton Density Fat Fraction (MRI-PDFF), a conventional diagnostic tool. A single-center cross-sectional pilot study was conducted. Liver fat content was estimated using an AI-enhanced quantitative ultrasound attenuation coefficient (QUS-AC) of Barreleye Inc. with an AI-based QUS algorithm and two conventional ultrasound techniques, FibroTouch Ultrasound Attenuation Parameter (UAP) and Canon Attenuation Imaging (ATI). The results were compared with MRI-PDFF values. The intraclass correlation coefficient (ICC) was also assessed. Significant correlation was found between the QUS-AC and the MRI-PDFF, reflected by an R value of 0.95. On other hand, ATI and UAP displayed lower correlations with MRI-PDFF, yielding R values of 0.73 and 0.51, respectively. In addition, ICC for QUS-AC was 0.983 for individual observations. On the other hand, the ICCs for ATI and UAP were 0.76 and 0.39, respectively. Our findings suggest that AC with AI-enhanced QUS could serve as a valuable tool for the non-invasive diagnosis of NAFLD.

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.

Fecal metagenomic and metabolomic analyses reveal non-invasive biomarkers of Flavobacterium psychrophilum infection in ayu (Plecoglossus altivelis).

With the rapid growth of inland aquaculture worldwide, side effects such as the discharge of nutrients and antibiotics pose a threat to the global environments. A sustainable future for aquaculture requires an effective management system, including the early detection of disease through the monitoring of specific biomarkers in aquaculture tanks. To this end, we investigated whether fish feces in aquaculture tanks could be used for non-invasive health monitoring using ayu (Plecoglossus altivelis) infected with Flavobacterium psychrophilum, which causes bacterial cold-water disease worldwide. Feces that were subsequently produced in the tanks were used for metagenomic and metabolomic analyses. The relative abundances of the genera Cypionkella (0.6% ± 1.0%, 0.1% ± 0.2%), Klebsiella (11.2% ± 10.0%, 6.2% ± 5.9%), and F. psychrophilum (0.5% ± 1.0%, 0.0% ± 0.0%) were significantly higher in the feces of the infection challenge test tanks than in those of the control tanks. The abundances of cortisol, glucose, and acetate in the feces of the infection challenge test tanks were 2.4, 2.4, and 1.3 times higher, respectively, than those of the control tanks. Metagenome analysis suggested that acetate was produced by microbes such as Cypionkella. The abundances of indicated microbes or metabolites increased after day 4 of infection at the earliest, and were thus considered possible biomarkers. Our results suggest that feces produced in aquaculture tanks can potentially be used for non-invasive and holistic monitoring of fish diseases in aquaculture systems. IMPORTANCE: The aquaculture industry is rapidly growing, yet sustainability remains a challenge. One crucial task is to reduce losses due to diseases. Monitoring fish health and detecting diseases early are key to establishing sustainable aquaculture. Using metagenomic and metabolomic analyses, we found that feces of ayu infected with Flavobacterium psychrophilum contain various specific biomarkers that increased 4 days post-challenge, at the earliest. Our findings are the first step in establishing a novel, non-invasive, and holistic monitoring method for fish diseases in aquaculture systems, especially in ayu, which is an important freshwater fish species in Asia, promoting a sustainable future.

Non-invasive imaging techniques for diagnosis of pelvic deep endometriosis and endometriosis classification systems: an International Consensus Statement.

The International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) and International Deep Endometriosis Analysis (IDEA) group, the European Endometriosis League (EEL), the European Society for Gynaecological Endoscopy (ESGE), ESHRE, the International Society for Gynecologic Endoscopy (ISGE), the American Association of Gynecologic Laparoscopists (AAGL) and the European Society of Urogenital Radiology (ESUR) elected an international, multidisciplinary panel of gynecological surgeons, sonographers, and radiologists, including a steering committee, which searched the literature for relevant articles in order to review the literature and provide evidence-based and clinically relevant statements on the use of imaging techniques for non-invasive diagnosis and classification of pelvic deep endometriosis. Preliminary statements were drafted based on review of the relevant literature. Following two rounds of revisions and voting orchestrated by chairs of the participating societies, consensus statements were finalized. A final version of the document was then resubmitted to the society chairs for approval. Twenty statements were drafted, of which 14 reached strong and three moderate agreement after the first voting round. The remaining three statements were discussed by all members of the steering committee and society chairs and rephrased, followed by an additional round of voting. At the conclusion of the process, 14 statements had strong and five statements moderate agreement, with one statement left in equipoise. This consensus work aims to guide clinicians involved in treating women with suspected endometriosis during patient assessment, counselling, and planning of surgical treatment strategies.

Challenges of prenatal diagnosis in obese pregnant women.

Obesity rates are increasing world-wide with most of the increase in women of the reproductive age group. While recognised as an important contributor to non-communicable diseases, pregnant women with obesity are particularly at risk of not only maternal and pregnant complications but also have an increased risk of congenital malformations. Furthermore, pregnant obese women are more likely to be older and therefore at a greater risk of aneuploidy. Prenatal diagnosis in these women especially those who are morbidly obese is challenging due not only to their weight but the implications of the increase adiposity on biochemical markers of aneuploidy. In this review we discuss the current challenges in providing prenatal diagnosis for these women including those related to the ergonomics of ultrasound and those inherent in them because of their obesity. Appropriate counselling for these women should include the lower sensitivity of the tests, the difficulties in performing some of the procedures (imaging and invasive testing) as well as the increased risk of structural abnormalities related to their obesity.

Non-invasive diagnosis of non-alcoholic fatty liver disease: Current status and future perspective.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease throughout the world. Hepatocellular carcinoma (HCC) and liver cirrhosis can result from nonalcoholic steatohepatitis (NASH), the severe stage of NAFLD progression. By some estimates, NAFLD affects almost one-third of the world's population, which is completely new and serious public health issue. Unfortunately, NAFLD is diagnosed by exclusion, and the gold standard for identifying NAFLD/NASH and reliably measuring liver fibrosis remains liver biopsy, which is an invasive, costly, time-consuming procedure and involves variable inter-observer diagnosis. With the progress of omics and imaging techniques, numerous non-invasive serological assays have been generated and developed. On the basis of these developments, non-invasive biomarkers and imaging techniques have been combined to increase diagnostic accuracy. This review provides information for the diagnosis and assessment of NAFLD/NASH in clinical practice going forward and may assist the clinician in making an early and accurate diagnosis and in proposing a cost-effective patient surveillance. We discuss newly identified and validated non-invasive diagnostic methods from biopsy-confirmed NAFLD patient studies and their implementation in clinical practice, encompassing NAFLD/NASH diagnosis and differentiation, fibrosis assessment, and disease progression monitoring. A series of tests, including 20-carboxy arachidonic acid (20-COOH AA) and 13,14-dihydro-15-keto prostaglandin D2 (dhk PGD2), were found to be potentially the most accurate non-invasive tests for diagnosing NAFLD. Additionally, the Three-dimensional magnetic resonance imaging (3D-MRE), combination of the FM-fibro index and Liver stiffness measurement (FM-fibro LSM index) and the machine learning algorithm (MLA) tests are more accurate than other tests in assessing liver fibrosis. However, it is essential to use bigger cohort studies to corroborate a number of non-invasive diagnostic tests with extremely elevated diagnostic values.

Ultrasound-guided initial diagnosis and follow-up of pediatric idiopathic intracranial hypertension.

BACKGROUND: Idiopathic intracranial hypertension in children often presents with non-specific symptoms found in conditions such as hydrocephalus. For definite diagnosis, invasive intracranial pressure measurement is usually required. Ultrasound (US) of the optic nerve sheath diameter provides a non-invasive method to assess intracranial pressure. Transtemporal US allows imaging of the third ventricle and thus assessment for hydrocephalus. OBJECTIVE: To investigate whether the combination of US optic nerve sheath and third ventricle diameter can be used as a screening tool in pediatric idiopathic intracranial hypertension to indicate elevated intracranial pressure and exclude hydrocephalus as an underlying pathology. Further, to analyze whether both parameters can be used to monitor treatment outcome. MATERIALS AND METHODS: We prospectively included 36 children with idiopathic intracranial hypertension and 32 controls. Using a 12-Mhz linear transducer and a 1-4-Mhz phased-array transducer, respectively, optic nerve sheath and third ventricle diameters were determined initially and during the course of treatment. RESULTS: In patients, the mean optic nerve sheath diameter was significantly larger (6.45±0.65 mm, controls: 4.96±0.32 mm) and the mean third ventricle diameter (1.69±0.65 mm, controls: 2.99±1.31 mm) was significantly smaller compared to the control group, P<0.001. Optimal cut-off values were 5.55 mm for the optic nerve sheath and 1.83 mm for the third ventricle diameter. CONCLUSIONS: The combined use of US optic nerve sheath and third ventricle diameter is an ideal non-invasive screening tool in pediatric idiopathic intracranial hypertension to indicate elevated intracranial pressure while ruling out hydrocephalus. Treatment can effectively be monitored by repeated US, which also reliably indicates relapse.

Ultrasensitive sensing urinary cystatin C via an interface-engineered graphene extended-gate field-effect transistor for non-invasive diagnosis of chronic kidney disease.

Early chronic kidney disease (CKD) has strong concealment and lacks an efficient, non-invasive, and lable-free detection platform. Cystatin C (Cys C) in urine is closely related to the progress of CKD (especially at the early stage), which is an ideal endogenous marker to evaluate the impairment of renal function. Thus, the accurate detection of urinary Cys C (u-Cys C) is great significant for early prevention and treatment and delaying the course of the disease of CKD patients. Herein, we developed an extended-gate field-effect transistor (EG-FET) sensor for ultrasensitive detection of u-Cys C, which consists of a monolithic interface-engineered graphene EG electrode array and a commercially available MOSFET. Laser-induced graphene (LIG) loaded with sputtered Au NPs in the presence of adhesive Cr (Au NPs/Cr/LIG) boosts the electrical performance of the EG electrode. Meanwhile, Au NPs also serve as linkers to immobilize papain that can selectively form protein complexes with Cys C. Supported by the synergistic effect of multilevel interface-engineered graphene, our sensor exhibits a good linear correlation within the u-Cys C concentration range of 5 ag/µL to 50 ng/µL with low detection limit of 0.05 ag/µL. Our work makes accurate, specific and rapid detection of u-Cys C feasible and promising for early screening for CKD.

Comprehensive study of clinicopathological and immune cell infiltration and lactate dehydrogenase expression in patients with thymic epithelial tumours.

BACKGROUND: Lactate dehydrogenase (LDH) has emerged as a promising biomarker for cancer. However, the current understanding of LDH and circulating LDH expression in thymic epithelial tumour (TET) is lacking. METHODS: A comprehensive literature review and meta-analysis were performed to evaluate the clinical significance of circulating LDH levels in patients with TET. Circulating LDH levels were measured using a laboratory analyser (Cobas8000, Roche, Basel, Switzerland). The maximum standardised uptake value (SUVmax) was determined in patients who underwent whole-body 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Multiplex immunohistochemistry (IHC) was performed using a commercially available kit (Opal 6-plex Detection Kit, Akoya Biosciences, Marlborough, MA, USA) and slide scanner (Slideview VS200, Olympus, Tokyo, Japan). All statistical analyses were performed using SPSS (IBM Corp., Armonk, NY, USA) and Prism version 9.0 (GraphPad Inc., San Diego, CA, USA). Differences with p < 0.05 were considered to be statistically significant. RESULTS: Meta-analysis revealed that elevated circulating serum levels of LDH predicted poor prognosis in patients with TET. Circulating levels of LDH were analysed in the serum of 313 patients with TET and 87 with benign mediastinal mass. The mean circulating LDH level in patients with thymic carcinoma (TC) was significantly higher than that in those with thymoma (TM) and the benign group (p < 0.001). Expression levels of circulating LDH were significantly reduced in postoperative samples compared with that in preoperative samples (p < 0.05). Receiver operating characteristic (ROC) curve analysis for diagnosing TC yielded an area under the curve of 0.74, with a sensitivity of 54 % and specificity of 86 %. Furthermore, patients with TC exhibited higher 18F-FDG PET/CT SUVmax values compared to those with TM. Correlation analysis demonstrated a positive association between SUVmax values and circulating LDH levels. In addition, the percentages of LDH-positive cells in TC and type B1 TM tissues were higher than those in other subtypes of TM, and a significant positive correlation between the percentages of LDH-positive and CD20-positive cells was detected in patients with TET (p < 0.05). CONCLUSION: Circulating serum LDH level may serve as a non-invasive biomarker for the diagnosis and prognosis of TET. The relationship between LDH expression and immune cell infiltration merits further regarding its application in companion diagnosis for immunotherapy.