Method For Diagnosis Research Articles

Diagnostic Role of Non-Invasive Risk Scores in Prediction of Simple Steatosis versus Non-Alcoholic Steatohepatitis

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is currently considered one of the most common liver diseases in the Western world. NAFLD includes a spectrum of diseases from simple fatty liver disease, to non-alcoholic steatohepatitis (NASH), liver cirrhosis and hepatocellular carcinoma (HCC). NASH, the critical stage in NAFLD progression, is the only bridge between NAFLD and liver cirrhosis, and its persistent existence has become one of the leading causes of liver cirrhosis and even HCC. Aim of the Work To assess the value of non-invasive risk scores in differentiation between simple steatosis and NASH Patients and Methods This was prospective cohort study, conducted Internal Medicine and Gastroenterology outpatient clinics and ward in Ain Shams University Hospitals, on 60 patients with NAFLD divided into 2 groups: (Group I): Patients with normal liver enzymes, (Group II): Patients with persistant elevation of liver enzymes, during a period of 6 months. Results There was statistically insignificant difference between the two studied groups and sex and age. There were 12 (40%) of the group I were males and 18 (60.0%) were females. There were 16(53.3%) of the group II were males and 14(46.7%) were females. The mean age of group I was 40.63 ± 10.53 SD with range (25.0 – 59.0). The mean age of group II was 40.83 ± 9.06SD with range (27.0 – 54.0). There was statistically significant difference between the two studied groups and weight, BMI and waist circumference. There was statistically insignificant difference between the two studied groups and height, DM and HTN. There was statistically significant difference between the two studied groups and ALT, AST, GGT, ALP, ALB and Platelets. There was statistically significant difference between the two studied groups and TC, HDL, LDL, TG and AST/ALT ratio. There was statistically insignificant difference between the two studied groups and FBG. There was statistically significant difference between the two studied groups and FLI, NFS, BARD, APRI, FIB-4 and Fibrosis score. There was statistically significant difference between the two studied groups and Ultrasound (Size, Findings and Fibrosis score). The mean size in group I was 10.03 ± 1.56 SD with range (8.0 – 12.0). The mean size in group II was 13.63 ± 1.22SD with range (12.0 – 16.0). Conclusion A combination of Fibroscan, Fib-4, NFS, BARD and APRI provides a valuable non- invasive method in diagnosis of NASH patients, and this can eliminate the need for liver biopsy in patients without clear indication, BMI, platelet count, HDL cholesterol level, ALT level, and the AST/ALT ratio were independent predictors of NAFLD.

P-326 Improving quality of life and awareness of endometriosis patients using personalized web app with indications about non pharmacological support and lifestyle changes

Abstract Study question Is it possible to improve quality of life of endometriosis patients using a personalized web app suggesting non-pharmacological interventions and lifestyle changes ? Summary answer A personalized web app is useful to improve awareness and quality of life of endometriosis patients What is known already Our project is based on four main considerations: first, there is a lack of information and awareness about endometriosis symptoms and possible treatments among affected patients; second, there is no definitive cure yet and nor medications nor surgery can be considered as definitive solution; third, web instruments and social media are useful nowadays to spread knowledges about the disease; fourth, non-pharmacological support is a real instrument to better deal with the symptoms and improve quality of life. Our web app is located in this context. Study design, size, duration This is a pilot study structured on one year follow-up of symptoms and quality of life of patients with confirmed endometriosis, giving them non-pharmacological suggestions and lifestyle changes useful to better face the pathology using a personalized web app. So far, we collected data from 50 patients. Preliminary results will be based on six months followup. The project started in December 2023 Participants/materials, setting, methods Participants are patients with diagnosis of endometriosis -ultrasound,mri and surgery - between 24-45 years old, using a personalized web app in which suggestions about non-pharmacological support/lifestyle changes, given by a multidisciplinary team, are contained. Indications received are based on pelvic symptoms - dysmenorrhea, dyspareunia, chronic pelvic pain- and main systemic symptoms - chronic fatigue syndrome, migraine, bowel implications/endobelly, urinary disfunctions. Quality of life before and after the indications received was measured using EHP-30 questionnaire. Main results and the role of chance A multidisciplinary team - specialists in gynaecology, infertility, urology, pelvic floor, nutrition, chronic pain, acupuncture, psychology, yoga/pilates, law- created a web app in which indications about non-pharmacological treatments/lifestyle changes to face endometriosis symptoms are contained. We divided the indications based on main pelvic symptoms of endometriosis - dysmenorrhea, dyspareunia, chronic pelvic pain- and main systemic symptoms - chronic fatigue syndrome, migraine, bowel implications/endobelly, urinary disfunctions. For each protocol, in validation by Italian ministry of health and structured in six months indications to be followed, we gave correct informations about the pathophysiology of endometriosis, related symptoms and how to recognize them, which specialist patients should consult, what kind of supplements are useful to reduce chronic inflammation in addition/without medical therapy, implications on fertility, nutrition indications, psycological support, physical activity, when surgery should be considered or where to refer to have a proper treatment. We also created the first online community formed by specialists and patients: patients web groups are useful to share experiences, but there is risk of misinformation. The presence of specialists in a web-based community with patients could also make the difference. So far, following our indications, 50 patients reported a significant improvement in quality of life Limitations, reasons for caution Limitation in patients selection is the difference in methods of diagnosis, not always performed by specialists. We do not find reasons for caution: non-pharmacological support exists. Correct informations in unique source raise awareness, the role of supplements, pelvic floor treatments, acupuncture, nutrition, psycological support are widely recognized in literature Wider implications of the findings We aim to properly support patients in management of endometriosis. With easily wide-spreading instruments such a web app created by specialists, supported by social media and online community, we also aim to reduce the diagnostic delay. Results so far indicate improvement in quality of life among the participants. Trial registration number not applicable

Evaluation of screening tools for primary ciliary dyskinesia in Egypt: single center study.

Primary ciliary dyskinesia (PCD) is a chronic respiratory illness that places significant strain on the healthcare system due to the complexity and expense of its diagnosis and treatment methods. The diagnostic process typically requires skilled technicians and an assortment of intricate, costly, and time-consuming approaches. Implementing screening tools can enhance efficiency by focusing the diagnostic process on those strongly suspected of having PCD. Tools such as the PCD Rule (PICADAR), North America Criteria Defined Clinical Features (NA-CDCF), the Clinical Index Score (CI), and the newly proposed CInew13 could potentially serve as useful screening tools. This study aims to examine the effectiveness of these tools individually, compare their performance against each other, and assess their results relative to prior research. We conducted a diagnostic accuracy test on 83 Egyptian patients referred to Alexandria University Children's Hospital for potential PCD diagnosis between January 2015 and December 2022. The scores obtained from the screening tools were calculated and assessed. Of the initial group, 10 patients were ruled out because they fit other diagnostic parameters. Forty-three cases received a confirmed diagnosis, while 30 did not. Notably, the confirmed cases consistently scored higher on our screening tools than those that remained unconfirmed (p <.001, for all tested scores). We used receiver operating characteristic curves to assess and compare the effectiveness of each tool. The NA-CDCF had the smallest area under curve 0.736 (95% confiedence interval 0.619-0.832); in contrast, the CI score had the largest 0.898 (95% confidence interval 0.808-0.957). All the tools tested were effective in identifying suitable patients for PCD testing at statistically significant levels. However, the PICADAR and NA-CDCF scores' performance did not significantly differ in the current study. The CI and CInew13 scores, on the other hand, outperformed both.

The Value of BLUE Protocol in the Diagnosis of Acute Onset of Respiratory Distress in Critically Ill Patient

Abstract To detect the value of BLUE protocol in the diagnosis of acute onset of respiratory distress in critically ill patients This cross-sectional study conducted on 60 patients by performing ultrasonography on consecutive patients admitted to the ICU with acute onset of respiratory distress (dyspnea), comparing lung ultrasonography results on initial presentation with the final diagnosis with conventional method of diagnosis by the ICU team. The BLUE protocol proved its importance, availability, easiness of application, accuracy and its ability to reach a final correct diagnosis in 90%of the patients. The lung U/S showed much better results than the CXR, but still the CT chest was more superior to the lung U/S as the sensitivity of the BLUE protocol in the diagnosis of pneumonia, pulmonary edema, pneumothorax, pleural effusion and COPD was 91.4%, 92.8%, 90%, 88.8% and 78.6 respectively. The lung U/S (BLUE protocol) is a bedside, reliable, dynamic, rapid, and noninvasive technique, these results suggest it could have a significant role in the diagnostic workup of acute onset of respiratory distress in the ICU.

Role of confocal laser endomicroscopy compared to high definition i- scan video bronchoscopy in patients with suspected lung cancer

Abstract Background Lung cancer is the leading cause of cancer-related mortality worldwide. Overall 5-year survival is poor at 15% with little improvement over the last twenty years (1.59 million deaths). Besides effective prevention strategies against the commonest risk factor; tobacco smoking, lung cancer mortality can be reduced if cases are detected and treated early. It is important to detect bronchial carcinoma in situ (CIS) since over 40% of these can develop into invasive cancer. Screening tests using sputum cytology and chest radiograph have been used with limited success Objective to study the diagnostic performance of confocal laser endomicroscopy in comparison to high definition i-scan videobronchoscopy in patients with suspected lung cancer. Patients and Methods The study was performed at Pulmonary diseases hospital in Kobry Elkobba military complex for 2 years since January 2018 to December 2019. The study included 25 patients all had chest CT findings suspicious of lung cancer. Results i-scan findings showed 68% accuracy and 73.3% specificity, and findings of confocal laser endomicroscopy were 92% accuracy and 90% specificity. Conclusion This was the preliminary study establishing patient characteristics in Egypt in the form of a comparison between tow methods of diagnosis of lung cancer. The study showed the higher sensitivity of confocal laser endomicroscopy in the diagnosis of lung cancer approved by the results of the pathological specimens optioned under the visual of confocal laser endomicroscopy in comparison to I-scan method.

Molecular Analysis of Volatile Metabolites Synthesized by Candida albicans and Staphylococcus aureus in In Vitro Cultures and Bronchoalveolar Lavage Specimens Reflecting Single- or Duo-Factor Pneumonia

Current microbiological methods for pneumonia diagnosis require invasive specimen collection and time-consuming analytical procedures. There is a need for less invasive and faster methods to detect lower respiratory tract infections. The analysis of volatile metabolites excreted by pathogenic microorganisms provides the basis for developing such a method. Given the synergistic role of Candida albicans in increasing the virulence of pathogenic bacteria causing pneumonia and the cross-kingdom metabolic interactions between microorganisms, we compare the emission of volatiles from Candida albicans yeasts and the bacteria Staphylococcus aureus using single and mixed co-cultures and apply that knowledge to human in vivo investigations. Gas chromatography–mass spectrometry (GC-MS) analysis resulted in the identification of sixty-eight volatiles that were found to have significantly different levels in cultures compared to reference medium samples. Certain volatiles were found in co-cultures that mainly originated from C. albicans metabolism (e.g., isobutyl acetate), whereas other volatiles primarily came from S. aureus (e.g., ethyl 2-methylbutyrate). Isopentyl valerate reflects synergic interactions of both microbes, as its level in co-cultures was found to be approximately three times higher than the sum of its amounts in monocultures. Hydrophilic–lipophilic-balanced (HLB) coated meshes for thin-film microextraction (TFME) were used to preconcentrate volatiles directly from bronchoalveolar lavage (BAL) specimens collected from patients suffering from ventilation-associated pneumonia (VAP), which was caused explicitly by C. albicans and S. aureus. GC-MS analyses confirmed the existence of in vitro-elucidated microbial VOCs in human specimens. Significant differences in BAL-extracted amounts respective to the pathogen-causing pneumonia were found. The model in vitro experiments provided evidence that cross-kingdom interactions between pathogenic microorganisms affect the synthesis of volatile compounds. The TFME meshes coated with HLB particles proved to be suitable for extracting VOCs from human material, enabling the translation of in vitro experiments on the microbial volatilome to the in vivo situation involving infected patients. This indicates the direction that should be taken for further clinical studies on VAP diagnosis based on volatile analysis.

Development and external validation of an artificial intelligence-based method for scalable chest radiograph diagnosis: a multi-country cross-sectional study

Development and external validation of an artificial intelligence-based method for scalable chest radiograph diagnosis: a multi-country cross-sectional study

Comparison of HPV and Cervical Cancer Awareness of Male and Female University Students

Aim: Cervical cancer is one of the most important types of cancer that is caused by the Human Papilloma Virus and affects women. HPV is one of the most common sexually transmitted diseases among young women and men. This study aimed to compare the knowledge of male and female university students studying in the field of health about cervical cancer, HPV infection, and their awareness of the HPV vaccine. Methods: A cross-sectional questionnaire was used, which was appropriate for both male and female students. One hundred university students attending a foundation university were asked to fill out a questionnaire by taking a consent form. Results: It was observed that sexually transmitted diseases, HPV, HPV vaccine, cervical cancer and risk factors, and PAP smear awareness were not very high. Conclusions: These findings showed that it is important to provide education to both male and female university students to increase their awareness about cervical cancer, risk factors, diagnosis and treatment methods, and the HPV vaccine.

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging.

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

Intelligent fault diagnosis of rolling bearings in strongly noisy environments using graph convolutional networks

SummaryRolling bearings often function under complex and non‐stationary conditions, where significant noise interference complicates fault diagnosis by obscuring fault characteristics. This paper presents an innovative fault diagnosis technique using graph convolutional networks (GCN) to address these challenges. Vibration signals are first transformed into the frequency domain through fast Fourier transform (FFT), creating a detailed graph where nodes and edges encapsulate fault signals. The GCN method then extracts complex node features from this graph, enabling a classifier, comprising a fully connected layer and Softmax function, to accurately identify fault types. Experimental results demonstrate the superior performance of the proposed GCN‐based fault diagnosis method, achieving an accuracy of 99.79%. This significantly surpasses traditional machine learning methods (85.4%), deep learning models (92.3%), and other graph neural network approaches (94.1%). Notably, the method shows exceptional resilience to noise, maintaining high accuracy even with 20% added noise, underscoring its robustness for practical industrial applications. The transformation of vibration signals into the frequency domain using FFT, followed by constructing a detailed graph structure, enables the GCN to effectively capture and represent intricate fault characteristics, thus enhancing accurate fault classification. These findings highlight the method's practical applicability and potential for deployment in advanced industrial settings characterized by high noise levels and complexity.

Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm

(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems.

Shedding Light on Breast Cancer, its Development, And Methods of Diagnosis and Prevention

It is a complex task to find out a specific reason of breast cancer which are based on multiple factors. Nevertheless, hormones, especially estrogens, are the ones that are mostly responsible for hampering unrestricted division of cancerous cells in the mammary gland. Fully knowing their role is pivotal for matching preventive and therapeutic plans. Chromosomal anomalies are abundant in this inborn module but they are next to BRCA1 and BRCA2, the errors that increase the risk. The diagnostic technologies which include HER2/neu testing and gene expression profiling are made to personalize the therapy practice with the intention to enhance the outcomes. The amalgam of environmental, hormonal, and genetic influences results in the creation of an individuality. The effectiveness of rehabilitation can be drastically boosted through the early identification of a disease process. A mammogram, the top screening tool for breast cancer built-in, can spot a cancer even before it shows any symptoms. In the matter of particles the breast cancer treatment has been changed greatly. Distressed tablets like trastuzumab and aromatase inhibitors in the event when accompanied by traditional chemotherapy, which in return produce efficacy with lesser side effects. Preventative policies encompass the realization of lifestyle changes as well as medical intervention. Maintaining a healthy weight, doing some extra movements, and limiting alcohol intake are all good examples of what you can do to reduce your chances of complications. Successful chemoprevention among high-risk subjects, via drugs like raloxifene and tamoxifene, has been founded. Immunotherapy is a novel approach of curing cancer by triggering immune response of the body.A review of its mechanism of operation, and preventive measures. The precision of staging is improved because of other supplementary information that is available in additional techniques like MRI and ultrasound. Such individuals can be candidates for preventive procedures such as oophorectomy or mastectomy.

Intelligent diagnosis method of torque-angle dynamometer cards for beam pumping units based on transfer learning

Remote monitoring and intelligent diagnosis of operating conditions are two critical aspects for achieving cloud intelligent diagnosis of the beam pumping unit. However, the current manual detection method restricts real-time diagnostic efficiency. Due to poor sensor real-time performance and high maintenance costs, the technical conditions of the dynamometer card for remote monitoring are inferior to those of motor power. This paper establishes a torque-angle dynamometer card fault diagnosis method based on transfer learning to diagnose working conditions effectively based on motor power. Firstly, based on the existing production data and mechanism model, the torque-angle dynamometer card dataset was built by converting motor parameters and dynamometer cards. Secondly, five convolutional neural networks (AlexNet, VGG16, ResNet_V2, MobileNet_V3_large, and DenseNet) were constructed, and three pre-training models (ResNet_V2, MobileNet_V3_large, and DenseNet) based on ImageNet dataset were developed based on the transfer learning. Finally, utilizing the dataset above, the recognition models were trained. The overall recognition performances of the models and the recognition performance under each working condition were evaluated and compared. The results indicated that, excluding MobileNet_V3_large, the recognition accuracies of these base models achieved 90%, while the recognition performance of each working condition generally fell below 80%. This highlighted the significant impact of the dataset quality on the recognition performance of convolutional neural networks. In contrast, the pre-trained models based on transfer learning achieved a recognition performance of 95% overall and 90% for each working condition. The recognition accuracy, speed, and generalization performance of the pre-trained models were significantly better than the base models.

Regeneration diagnosis for the service lifespans of residential buildings on a circular economy perspective

As urbanized areas progress into the era of stock, an immense number of existing residential buildings (RBs) are destined to undergo replacement and renovation. The irrational decision of urban renewal led to serious economic waste and environmental unsustainability. In this regard, this study proposes and validates a method for regeneration diagnosis and optimal regeneration of RBs based on circular economy (CE) theory. A hybrid diagnostic tool of life cycle cost (LCC)-life cycle assessment (LCA) model to systematically evaluate the economic and environmental impact of RBs throughout the circular lifecycle was proposed. On this basis, with the minimum average annual economic and environmental impact as the objective function, the optimal dynamic regeneration cycle of RBs is obtained using a multi-objective optimization method. The results of the case study show that: (1) The discount rate and deterioration rate changes the development tendency of economic and environmental data, which brings a new regenerating time point, (2) the economic impact of RBs’ regeneration tends to be more pronounced during the intermediate stages, while the environmental impact becomes more significant in the later stages, (3) the environmental benefits of regeneration outweigh the economic benefits, (4) the regeneration of all building elements at the local optimal level leads to enhanced comprehensive benefits compared to multiple regenerations of a single building element. The feasibility of the method employed in this study has been corroborated by practical case studies. This approach transcends the limitations of quantitative analysis in assessing the regeneration of RBs in a circular lifecycle and offers profound implications for the development of urban renewal and sustainable strategies.

Causal intervention graph neural network for fault diagnosis of complex industrial processes

With the development of industry and manufacturing, the mechanical structures of equipment have become intricate and complex. Due to the interaction between components, once a failure occurs, it will propagate through the industrial processes, resulting in multiple sensor anomalies. Identifying the root causes of faults and eliminating interference from irrelevant sensor signals are critical issues in enhancing the stability and reliability of intelligent fault diagnosis. The components of industrial processes and their interactions can be represented by a structural attribute graph. The causal subgraph formed by fault signals determines the fault mode, while irrelevant sensor signals constitute a non-causal subgraph. The structure of non-causal subgraphs is relatively simple, and graph neural networks tend to use this part as a shortcut for prediction, leading to a significant decrease in prediction accuracy. To address this issue, a causal intervention graph neural network (CIGNN) framework is proposed. First, the sensor signals are constructed into structural attribute graphs using an attention mechanism. Due to causal and confounding features are highly coupled in graphs, explicitly decoupling them is almost impossible. Then, we design an instrumental variable to implement causal intervention to mitigate the confounding effect. Experimental results on two complex industrial datasets demonstrate the reliability and effectiveness of the proposed method in fault diagnosis.

Causal intervention graph neural network for fault diagnosis of complex industrial processes

With the development of industry and manufacturing, the mechanical structures of equipment have become intricate and complex. Due to the interaction between components, once a failure occurs, it will propagate through the industrial processes, resulting in multiple sensor anomalies. Identifying the root causes of faults and eliminating interference from irrelevant sensor signals are critical issues in enhancing the stability and reliability of intelligent fault diagnosis. The components of industrial processes and their interactions can be represented by a structural attribute graph. The causal subgraph formed by fault signals determines the fault mode, while irrelevant sensor signals constitute a non-causal subgraph. The structure of non-causal subgraphs is relatively simple, and graph neural networks tend to use this part as a shortcut for prediction, leading to a significant decrease in prediction accuracy. To address this issue, a causal intervention graph neural network (CIGNN) framework is proposed. First, the sensor signals are constructed into structural attribute graphs using an attention mechanism. Due to causal and confounding features are highly coupled in graphs, explicitly decoupling them is almost impossible. Then, we design an instrumental variable to implement causal intervention to mitigate the confounding effect. Experimental results on two complex industrial datasets demonstrate the reliability and effectiveness of the proposed method in fault diagnosis.

Microarray-Based Detection of Critical Overexpressed Genes in the Progression of Hepatic Fibrosis in Non-Alcoholic Fatty Liver Disease: A Protein-Protein Interaction Network Analysis.

Non-alcoholic fatty liver disease (NAFLD) is a prevalent cause of chronic liver disease and encompasses a broad spectrum of disorders, including simple steatosis, steatohepatitis, fibrosis, cirrhosis, and liver cancer. However, due to the global epidemic of NAFLD, where invasive liver biopsy is the gold standard for diagnosis, it is necessary to identify a more practical method for early NAFLD diagnosis with useful therapeutic targets; as such, molecular biomarkers could most readily serve these aims. To this end, we explored the hub genes and biological pathways in fibrosis progression in NAFLD patients. Raw data from microarray chips with GEO accession GSE49541 were downloaded from the Gene Expression Omnibus database, and the R package (Affy and Limma) was applied to investigate differentially expressed genes (DEGs) involved in the progress of low- (mild 0-1 fibrosis score) to high- (severe 3-4 fibrosis score) fibrosis stage NAFLD patients. Subsequently, significant DEGs with pathway enrichment were analyzed, including gene ontology (GO), KEGG and Wikipathway. In order to then explore critical genes, the protein-protein interaction network (PPI) was established and visualized using the STRING database, with further analysis undertaken using Cytoscape and Gephi software. Survival analysis was undertaken to determine the overall survival of the hub genes in the progression of NAFLD to hepatocellular carcinoma. A total of 311 significant genes were identified, with an expression of 278 being upregulated and 33 downregulated in the high vs. low group. Gene functional enrichment analysis of these significant genes demonstrated major involvement in extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, and the AGE-RAGE signaling pathway. The PPI network was constructed with 196 nodes and 572 edges with PPI enrichment using a p-value < 1.0 e-16. Based on this cut-off, we identified 12 genes with the highest score in four centralities: Degree, Betweenness, Closeness, and Eigenvector. Those twelve hub genes were CD34, THY1, CFTR, COL3A1, COL1A1, COL1A2, SPP1, THBS1, THBS2, LUM, VCAN, and VWF. Four of these hub genes, namely CD34, VWF, SPP1, and VCAN, showed significant association with the development of hepatocellular carcinoma. This PPI network analysis of DEGs identified critical hub genes involved in the progression of fibrosis and the biological pathways through which they exert their effects in NAFLD patients. Those 12 genes offer an excellent opportunity for further focused research to determine potential targets for therapeutic applications.

A new and fast method for diabetes and dyslipidemia diagnosis using FTIR-MIR, spectroscopy and multivariate data analysis: a proof of concept

Diabetes and dyslipidemia are well-established risk factors for cardiovascular disease, which is the primary cause of death both in Brazil and globally. Fourier-transform mid-infrared spectroscopy (FTIR-MIR) generates spectral fingerprints of biomolecules, allowing for correlation with metabolic changes, while remaining a rapid, non-invasive, and non-destructive method. The study provided a proof of concept for the effectiveness of FTIR-MIR in screening diabetes, pre-diabetes, hypercholesterolemia, hypertriglyceridemia, and mixed dyslipidemia in blood serum. After acquiring mid-infrared spectra of 60 human serum samples, both unsupervised and supervised analysis models were developed. Principal component analysis (PCA) was used for pattern recognition and to determine how closely related the samples were based on their spectral profiles. The results obtained by the supervised models showed a clear discriminative ability to distinguish both diabetic and dyslipidemic samples from healthy subjects by multivariate analysis performed on FTIR-MIR spectra. High accuracy rates of more than 90% were achieved for diabetes and dyslipidemia diagnosis with PLS-DA. Dyslipidemia type discrimination could be attributed mainly to the amide I region [1720-1600 cm-1, (ν(C=O)] and altered lipid concentration in the 3000-2800 cm-1 region, whereas the discrimination of diabetes and prediabetes was primarily due to the altered conformational protein in the Amides I [1720-1600 cm-1, ν(C=O)] and Amide II [1570-1480 cm-1, δ(N=H) + ν(CH)] range.

Clinical Profile of Adults with Inherited Bone Marrow Failure Syndromes: Results of an Ambispective Clinical Single-Center Study

BACKGROUND. Inherited bone marrow failure syndromes (IBMFS) is a heterogenous group of rare genetically determined diseases with variable hematologic and nonhematologic manifestations. The implementation of highly specific methods of genetic diagnosis advanced the understanding of IBMFS and allowed its application also beyond pediatrics. That presupposes an awareness of clinical features and reference points for recognizing IBMFS in adults. AIM. To describe the clinical profile of adult IBMFS patients. MATERIALS &amp; METHODS. This ambispective single-center study enrolled 35 patients (10 women and 25 men) with IBMFS. Patients were aged 18–51 years (median 26 years). The following IBMFS were identified: congenital dyskeratosis (n = 10; 28 %), Diamond-Blackfan anemia (n = 9; 26 %), Fanconi anemia (n = 7; 20 %), GATA2 deficiency (n = 3; 8 %), Shwachman-Diamond syndrome (n = 1; 3 %), GATA2 deficiency (n = 1; 3 %), amegakaryocytic thrombocytopenia (n = 1; 3 %), bone marrow failure syndrome type 3 (n = 1; 3 %), severe congenital neutropenia (n = 1; 3 %), bone marrow failure with SAMD9 mutation (n = 1; 3 %). These diseases were analyzed in terms of hematologic and nonhematologic manifestations as well as main diagnosis stages and factors that contribute to recognizing IBMFS. RESULTS. Monolinear cytopenia, bilinear cytopenia, and pancytopenia were identified at hematologic onset in 18 (52 %), 6 (17 %), and 11 (31 %) patients, respectively. The median age of patients by hematologic onset was 15 years (range 0–43 years), in 14 (40 %) patients cytopenia was newly diagnosed at the age of &gt; 18 years. In 23 (63 %) patients hypocellular bone marrow was reported, 7 (20 %) and 5 (14 %) patients had pure red cell aplasia and multilineage myelodysplasia, respectively. Chromosomal aberrations were identified in 2 patients. Paroxysmal nocturnal hemoglobinuria clone was detected in none of 27 examined patients. In 12 (34 %) patients, the criteria for non-severe aplastic anemia were met. Temporary partial or complete spontaneous hematologic recovery was observed in 6 (17 %) patients. Abnormalities with partial or complete organ dysfunctions were identified in 14 patients, whereas all patients showed minor congenital defects. All 7 Fanconi anemia patients and 9 out of 10 congenital dyskeratosis patients demonstrated organ damage specific to these diseases. Family history predominantly showing malignant neoplasms in relatives was reported in 15 (43 %) patients. Initial hematological examination yielded suspect of IBMFS in 12 (34 %) patients with the median time to diagnosis of 6 months. In 23 (66 %) patients, hematologic defects with cytopenia were erroneously accounted for by various acquired diseases, which led to a delayed correct diagnosis (median 7 years). The key factors in suspecting IBMFS were organ abnormalities and positive family history. The IBMFS diagnosis was verified by the next-generation sequencing (NGS) in 29 (83 %) patients and by other specific methods in 4 (11 %) patients. In 2 patients, the diagnosis was established on the basis of complete clinical criteria alone. CONCLUSION. IBMFS is a matter of current concern and a difficult-to-recognize clinical challenge in adult hematology patients. Differential diagnosis of acquired and congenital bone marrow failure needs to be performed irrespective of patient’s age. A detailed physical examination of patients, family history, and critical analysis of clinical profile and disease course allow for early suspicion of IBMFS. Suspected IBMFS is an indication for referral of patients to specialized centers and performing genetic diagnostics including NGS.

Diagnoses of flow behaviors in gas-liquid-solid circulating fluidized beds using vibration acceleration signals

Gas-liquid-solid circulating fluidized bed is an important multi-phase reactor. However, due to the complexity of gas-liquid-solid flow and the limitations of flow measuring techniques, the flow behaviors of gas bubbles, liquid phase and solid particles in such a system is still little understood and its industry design and scale-up still relies on empirical methods. In this work, the method of vibration acceleration signal diagnosis together with high-speed image analysis was applied in the first time to investigate the collision behaviors of gas bubbles and solid phases with the walls of the riser in a three-phase circulating fluidized bed. The effects of the superficial gas and liquid velocities, type and holdup of solid particles on the characteristics of the original acceleration signals were studied in the time and frequency domains with the help of statistical analysis, spectrum density analysis, wavelet analysis, short Fourier transform and other methods. The comparison analyses of the acceleration signals with the images were emphasized. The results show that the acceleration signal is a good indicator in identifying the collision origins of gas bubbles and solid particles with the walls of the beds and the multiscale flow behaviors, such as flow pattern transition and aggregation of cluster of solid particles in the gas-liquid-solid circulating fluidized bed.