High Diagnostic Accuracy Research Articles

Functionalized Iron Oxide Nanoparticles for Both Dual-Modal Imaging and Erythropoiesis.

Cancer-related anemia (CRA), a complication of cancer, is considered the primary cause of high mortality for cancer patients. Safe and effective theranostics are desirable for realizing the high diagnostic accuracy of tumors and ameliorating CRA in the clinic. However, the available theranostics do not support dual-modal imaging and the amelioration of CRA at the same time. In this study, we synthesized functionalized iron oxide nanoparticles (Fe3O4 NPs) modified with protoporphyrin IX (PPIX) and folic acid (FA) by a one-step modification strategy (Fe3O4@NH-PPIX&FA NPs) or a step-by-step strategy (Fe3O4@NH-PPIX-FA NPs), aiming at both magnetic resonance imaging/fluorescence imaging (MRI/FI) and erythropoiesis. Fe3O4@NH-PPIX-FA NPs displayed better ability of MRI/FI than Fe3O4@NH-PPIX&FA NPs and had an efficient tumor targeting of 45 min after tail vein injection owing to the reduction of the steric effect and extension of FA groups. Fe3O4@NH-PPIX-FA NPs exhibited satisfactory erythropoiesis with up to 20% elevation of red blood cell (RBC) counts and hemoglobin concentrations in mice with CRA, which provided a safe alternative to RBC transfusions, especially for patients needing recurrent RBC transfusions. With excellent performance in both dual-modal imaging and erythropoiesis, Fe3O4@NH-PPIX-FA NPs could be a powerful tool for the theranostics of cancer patients with anemia.

Reporting Bone Cytopathology-A Proposal Based on a Single Tertiary Centre Experience.

Fine-needle aspiration cytology (FNAC) from bone lesions has been proven to be a useful diagnostic tool but lacks standardisation. The aim of the study was to evaluate the diagnostic utility of FNAC as a basis to propose and test a reporting system for bone reporting cytopathology. This retrospective study is based on patients with bone lesions, that were approached by cytology at Skåne University Hospital, Sweden between 2015 and 2023. The diagnostic performance was measured by sensitivity, specificity and accuracy analyses. All diagnoses were then distributed in six categories: (I) Non-diagnostic, (II) Benign, (III) Atypia, (IV) Bone neoplasm of uncertain significance, (V) Suspicious for malignancy and (VI) Malignant. The risk of malignancy (ROM) in each category was calculated. The final cohort consisted of 721 cases. Bone cytology was able to differentiate between benign and malignant lesion with a sensitivity of 89% and a specificity of 99%. The overall diagnostic accuracy was 65% but varied significantly among different types of lesions. Within the tested diagnostic categories, the ROM was (I) 48%, (II) 6.7%, (III) 69%, (IV) 28%, (V) 93% and (VI) 100%. FNAC from bone lesions is a sensitive and specific diagnostic tool with high diagnostic accuracy among various tumour types. This study provides valuable insights for the development of a standardised reporting system for bone cytopathology.

Comparison of diagnostic performances of ATA guidelines, ACR-TIRADS and EU-TIRADS and Modified K-TIRADS: A single center study of 4238 thyroid nodules.

Several ultrasound (US) -based risk stratification systems (RSS) for managing thyroid nodules have been developed and introduced into clinical practice. However, there are essential differences among these systems. This study aimed to determine and compare the category-based diagnostic performance of four US-based risk stratification systems in the detection of thyroid cancer: ACR-TIRADS, ATA, K-TIRADS, and EU-TIRADS. 4238 nodules with FNA biopsy taken between January 2018 and December 2021 were included in the study. Nodules were classified according to US imaging features and compared with biopsy results. The diagnostic success of the risk stratification systems was evaluated and compared. Of the 4238 nodules, 3861 (%91,1) were benign and 376 (%8,9) were malignant. Malignancy was significantly higher in hypoechoic and marked hypoechoic nodules (p=0.001), and solid nodules (p=0.002). For detection of malignancy, AUCs were 0.862, 0.850, 0.842 and 0.835 for 2017 ACR-TIRADS, EU-TIRADS, for KTA-TIRADS and 2015 ATA guidelines, respectively. EU-TIRADS showed the highest sensitivity (91%), whereas ACR-TIRADS had the highest specificity (87%). Compared to other risk stratification systems, ACR-TIRADS resulted in significantly fewer unnecessary biopsies (p = 0.009). All RSSs show high diagnostic accuracy, and have their own advantages and disadvantages. While deciding which RSS should be chosen, the population, the prevalence of the disease and gender distribution should be considered.

UDP-glucuronosyltransferases 2A3 as a biomarker for ulcerative colitis and colon cancer

BackgroundUlcerative colitis has a serious impact on the quality of life of patients and is more likely to progress to colon cancer. Therefore, early diagnosis and timely intervention are of considerable importance.MethodsGene expression data of active ulcerative colitis were downloaded from the Gene Expression Omnibus (GEO) database, and genes with significant differential expression were identified. Biochemical markers with diagnostic significance were selected through machine learning methods. The expression differences of the selected markers between colon adenocarcinoma (COAD) and healthy control groups in The Cancer Genome Atlas (TCGA) database were analyzed to evaluate their diagnostic value. In addition, the correlation between the selected markers and clinical indicators, as well as their predictive efficacy for the survival of COAD patients, was explored.ResultsThrough machine learning and LASSO regression analysis, UGT2A3 was finally determined as a diagnostic marker for ulcerative colitis. It demonstrated high diagnostic accuracy in both the training set and the external validation set. Furthermore, UGT2A3 was significantly downregulated in COAD tissues compared to normal control tissues. The ROC curve suggested that UGT2A3 could serve as a diagnostic marker for COAD with excellent performance, achieving an AUC of 0.969. Immune infiltration analysis indicated a significant negative correlation between the expression of UGT2A3 and neutrophils. Correlation analysis suggested a link between UGT2A3 and the pathological classification of colon cancer. Survival analysis showed that UGT2A3 is negatively correlated with OS, PPS, and RFS in colon cancer.ConclusionThe author identified UGT2A3 as a diagnostic marker for ulcerative colitis through bioinformatics methods, and verified its significant downregulation in colon cancer, as well as its predictive role in the survival of COAD patients. These findings suggest that UGT2A3 may serve not only as a diagnostic marker for ulcerative colitis and colon cancer but also as a potential prognostic indicator for colon cancer.

Identification and functional analysis of novel biomarkers related to retinal neovascularization in proliferative diabetic retinopathy

Background Proliferative diabetes retinopathy (PDR) seriously affects the vision of patients. Exploring the key genes of retinal neovascularization is crucial for developing new biomarkers and therapeutic targets. Objective This study aimed to identify key genes associated with retinal neovascularization in Proliferative Diabetic Retinopathy (PDR), intending to develop new biomarkers and therapeutic targets. This would further our understanding of the progression of diabetic retinopathy and improve patient prognosis. Methods The gene data from 36 diabetic retinopathy patient samples and 45 samples from healthy volunteers or diabetic patients were selected from the GEO DataSets (Gene Expression Omnibus), specifically datasets GSE102485 and GSE160310. Utilizing the SVA algorithm to merge datasets and the limma package in R to identify differentially expressed genes (DEGs), we conducted a bioinformatic analysis of diabetic retinopathy. Functional insights were gained through DAVID database analyses, while STRING database-derived Protein-Protein Interaction (PPI) networks visualized in Cytoscape provided further context. Key genes were identified through LASSO regression and SVM analyses, with ROC curves assessing their diagnostic value. Single gene set enrichment analysis (GSEA) enhanced our understanding of the perturbed biological processes and pathways, advancing knowledge of diabetic retinopathy at the genomic level. Results A rigorous bioinformatic analysis yielded a comprehensive list of 1139 differentially expressed genes (DEGs), of which six pivotal genes—KDM5D, AC007040.11, AC015688.3, NLRP2, GYPC, and TMSB4Y—were identified as central to the study. These six genes consistently demonstrated a high diagnostic accuracy, with each exhibiting an area under the receiver operating characteristic (ROC) curve (AUC) exceeding 0.75. Gene Ontology (GO) enrichment analysis elucidated their primary roles in intricate biological processes, including inflammatory and immune responses, T-cell activation, cell apoptosis, and angiogenesis. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed their involvement in crucial signaling cascades such as cytokine-cytokine receptor interactions, cell adhesion molecule pathways, PI3K-Akt signaling, and hematopoietic cell lineage, further substantiating their significance in the pathogenesis of diabetic retinopathy. Conclusions In this research, we conducted a comprehensive gene expression analysis in patients with and without PDR, identifying differentially expressed genes, pivotal biomarkers, and critical pathways potentially involved in PDR progression. Our findings enhance understanding of PDR's molecular dynamics and offer potential avenues for improved prognostication and therapeutic intervention, despite limitations due to sample group heterogeneity.

Accuracy of cell-free fetal DNA in detecting chromosomal anomalies in women experiencing miscarriage: systematic review and meta-analysis.

To report the diagnostic accuracy of cell-free fetal DNA (cfDNA) in detecting fetal chromosomal anomalies in women experiencing miscarriage. PubMed, MEDLINE, EMBASE and Cochrane databases were searched from inception to June 2024. The inclusion criteria were women experiencing miscarriage (defined as pregnancy loss before 20 weeks of gestation) who underwent cfDNA screening for trisomies 21, 18and13, other autosomal aneuploidies, sex-chromosome aneuploidies and/or copy-number variants (CNVs). The index test was cfDNA screening for each of the chromosomal anomalies listed. The reference standard was cytogenetic analysis of pregnancy tissue. The quality of the studies was assessed using the revised tool for the quality assessment of diagnostic accuracy studies. Summary estimates of sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio were computed using the hierarchical summary receiver-operating-characteristics model. Seven studies (887 women) were included in the systematic review and meta-analysis. cfDNA had a sensitivity and specificity of 100% (95% CI, 81.5-100%) and 100% (95% CI, 99.1-100%), respectively, for trisomy 21, 100% (95% CI, 54.1-100%) and 100% (95% CI, 99.0-100%), respectively, for trisomy 18, and 88.9% (95% CI, 51.8-99.7%) and 100% (95% CI, 99.1-100%), respectively, for trisomy 13. The respective values for other autosomal trisomies were 75.8% (95% CI, 65.7-84.2%) and 99.4% (95% CI, 97.9-99.9%), while those for CNVs were 60.0% (95% CI, 14.7-94.7%) and 100% (95% CI, 97.4-100%). Failure of cfDNA testing was reported in 7.3% (95% CI, 5.7-9.2%) of cases. cfDNA has high diagnostic accuracy in detecting fetal trisomies 21, 18 and 13 in women experiencing miscarriage, while its accuracy for other autosomal aneuploidies and CNVs is only moderate. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Does Colonoscopy as a First Screening Test Still Make Sense?-Counterpoint.

Colonoscopy has been widely regarded as the gold standard for its high diagnostic accuracy and preventive potential. However, its invasive nature, high cost, and suboptimal participation rates limit its utility at the population level. Non-invasive screening tests, notably the fecal immunochemical test (FIT) and multitarget stool DNA tests, present promising alternatives that may improve screening participation and reduce barriers to participation. Among these, FIT has demonstrated a consistent advantage in enhancing participation, which subsequently contributes to better long-term outcomes in CRC prevention. FIT-based two-step screening offers several practical advantages, including cost-effectiveness, non-invasiveness, and greater flexibility. Moreover, the quantitative nature of FIT allows for adjustable sensitivity thresholds and the ability of risk stratification, making it adaptable across diverse populations and scenarios. Through serial testing, FIT can increase cumulative detection rates over time. This approach facilitates the identification of high-risk individuals, allowing for more judicious use of colonoscopy resources and reducing unnecessary invasive procedures, especially among low-risk populations. Notably, evidence indicates that participation to FIT-based screening is consistently higher than to colonoscopy, which enhances the detection of early-stage cancers and advanced adenomas in the long run. Given the constraints of limited endoscopic capacity, the aging population, and the recent lowering of the recommended screening age due to the rising incidence of early-onset CRC, FIT emerges as a practical, flexible solution. The role of two-step FIT screening in improving participation and enabling risk-stratified, personalized approaches to CRC prevention is pivotal, advocating for its expanded integration into future screening paradigms.

Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging

Rationale and ObjectivesThe management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imaging may provide additional useful information.Materials and MethodsA total of 322 patients with Bosniak II-IV cysts were included in the study from January 2010 to December 2019. Contrast-enhanced CT scans were performed on all patients. ITK-snap was used for segmentation, and the PyRadiomics 3.0.1 package was used for feature extraction. The radiomics features were screened via the least absolute shrinkage and selection operator (LASSO) regression method. After feature selection, a logistic regression (LR) model, support vector machine (SVM) model and random forest (RF) model were constructed.ResultsIn the present study, 217 benign renal cysts (67.4%) and 105 cystic renal cell carcinomas (32.6%) were identified. According to the Bosniak classification, the sample included 179 (55.6%) Bosniak II cysts, 38 (11.8%) Bosniak IIF cysts, 44 (13.7%) Bosniak III cysts and 61 (18.9%) Bosniak IV cysts. A total of 1334 radiomics features were extracted from both unenhanced and cortical CT scans. After LASSO regression, all the models (LR, SVM and RF) showed satisfactory discrimination and reliability in both unenhanced and cortical CT scans (AUC > 0.950). In the Bosniak IIF-III subgroup analysis, the diagnostic accuracy of the LR model was very low for both the unenhanced and cortical scans. In contrast, the SVM model and RF model showed excellent and stable performance in classifying Bosniak IIF-III cysts. The AUCs of the models were all > 0.85, with a maximum of 0.941. The sensitivity, specificity, accuracy, and AUC of the RF model were 0.889, 0.913, 0.902, and 0.941, respectively.ConclusionOur data indicate that radiomics models can effectively distinguish between cystic renal cell carcinoma (cRCC) and complex renal cysts (Bosniak II-IV). Radiomics models may still have high diagnostic accuracy even for Bosniak IIF-III cysts that are clinically difficult to distinguish. However, external validation of these findings is still needed.

A decision tree analysis to evaluate the optimal approach to screen allergic bronchopulmonary aspergillosis in asthmatic patients

ABSTRACT Background Various methods are available to screen for allergic bronchopulmonary aspergillosis (ABPA) in asthma, but their comparative performance remains uncertain. Objectives To identify the optimal screening algorithm for ABPA in asthmatic patients and evaluate the crude cost of various diagnostic approaches. Methods We performed a post hoc analysis of prospectively collected data from consecutive adult asthmatic patients evaluated for ABPA. The diagnosis was based on the revised International Society for Human and Animal Mycology ABPA Working Group criteria. Initial evaluations included measurements of serum Aspergillus fumigatus-IgE (≥0.35 kUA/L), serum total IgE (≥500 IU/mL), serum A. fumigatus-IgG (≥27 mgA/L), blood eosinophil count (BEC ≥500 cells/μL), and chest CT findings. A decision tree was manually constructed using recursive partitioning to identify the most effective diagnostic pathway. Results Among 543 adult asthmatics, 106 were diagnosed with ABPA. Serum A. fumigatus-IgE was positive in 221 (40.7%) patients, while serum total IgE was elevated (≥500 IU/mL) in 300 (55.3%) patients. The serum total IgE-based approach required 196 additional tests during screening, compared to 115 in the A. fumigatus-IgE method. The BEC-based strategy missed 28 cases of ABPA. Although the CT-directed protocol had the fewest false positives, it required 437 additional screening radiographic procedures and missed eight ABPA cases. The A. fumigatus-IgE pathway emerged as the most cost-effective, whereas imaging-based strategies were the most expensive. Conclusions Serum A. fumigatus-IgE is the optimal screening test for ABPA in asthma. It minimizes unnecessary testing while maintaining high diagnostic accuracy, making it a preferable approach in clinical practice.

Assessment of Renal Function in Women with Preeclampsia by Measuring of Serum Cystatin C Level

Abstract Background: Preeclampsia (PE) is a major health problem that increases the risk of renal impairment during pregnancy. Studies have described the utility of Cystatin C as a better indicator of kidney function during pregnancy compared to urea, creatinine, or uric acid. Objective: The objective of the study was to evaluate cystatin C as a diagnostic tool for renal impairment in women with severe PE and compare it to standard renal function tests. Patients and Methods: A case–control study was conducted at Al Yarmouk Teaching Hospital, Bagdad. From March 1, 2022, to September 1, 2022, 94 3rd-trimester pregnant women with newly diagnosed or a history of PE were included in the study and interviewed and subsequently divided into three groups. The GraphPad Prism Software was used for data analysis and graphical presentations in addition to SPSS version 28; 0.05 was selected as a cutoff point for statistical significance. Results: The mean of cystatin C for control 0.39 ± 0.15 was significantly lower than for severe PE 1.12 ± 0.53, P < 0.001. The mean cystatin C for mild PE 0.52 ± 0.13 was considerably lower than for severe PE 1.12 ± 0.53, P < 0.001. Conclusions: Patients with severe PE had higher serum cystatin C than patients with mild PE and healthy normotensive pregnant females. Serum cystatin C at a cutoff value of >0.61 had higher diagnostic accuracy than the other standard kidney tests (urea, creatinine, and uric acid), with a sensitivity and specificity of 90.32% and 93.75%, respectively.

Investigating the clinical significance of OAS family genes in breast cancer: an in vitro and in silico study

BackgroundBreast cancer is the most common malignancy among women worldwide, characterized by complex molecular and cellular heterogeneity. Despite advances in diagnosis and treatment, there is an urgent need to identify reliable biomarkers and therapeutic targets to improve early detection and personalized therapy. The OAS (2′-5′-oligoadenylate synthetase) family genes, known for their roles in antiviral immunity, have emerged as potential regulators in cancer biology. This study aimed to explore the diagnostic and functional relevance of OAS family genes in breast cancer.MethodologyBreast cancer cell lines and controls were cultured under specific conditions, and DNA and RNA were extracted for downstream analyses. RT-qPCR, bisulfite sequencing, and Western blotting were employed to assess gene expression, promoter methylation, and knockdown efficiency of OAS family genes. Functional assays, including CCK-8, colony formation, and wound healing, evaluated cellular behaviors, while bioinformatics tools (UALCAN, GEPIA, HPA, OncoDB, cBioPortal, and others) validated findings and explored correlations with clinical data.ResultsThe OAS family genes (OAS1, OAS2, OAS3, and OASL) were found to be significantly upregulated in breast cancer cell lines and tissues compared to normal controls. This overexpression was strongly associated with reduced promoter methylation. Receiver operating characteristic (ROC) analysis demonstrated high diagnostic accuracy, with area under the curve (AUC) values exceeding 0.93 for all four genes. Increased OAS expression correlated with advanced cancer stages and poor overall survival in breast cancer patients. Functional analysis revealed their involvement in critical biological processes, including immune modulation and oncogenic pathways. Silencing OAS genes in breast cancer cells significantly inhibited cell proliferation and colony formation, while unexpectedly enhancing migratory capacity. Additionally, correlations with immune cell infiltration, molecular subtypes, and drug sensitivity highlighted their potential roles in the tumor microenvironment and therapeutic response.ConclusionThe findings of this study established OAS family genes as potential biomarkers and key players in breast cancer progression, offering promise as diagnostic biomarkers and therapeutic targets to address unmet clinical needs.

Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation

BackgroundDiabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therapeutic targets. This study aims to identify differentially expressed PANoptosis-related genes (DE-PRGs) in DR, offering insights into the disease’s pathogenesis and potential diagnostic tools.MethodsDR datasets were obtained from the Gene Expression Omnibus (GEO) database, while PANoptosis-related genes were sourced from the GeneCards database. Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. Three machine learning algorithms—LASSO regression, Random Forest, and SVM-RFE—were employed to identify hub genes. A diagnostic nomogram was constructed and its performance assessed via ROC analysis. The CIBERSORT algorithm analyzed immune cell infiltration. Hub genes were validated through RT-qPCR, Western blotting, immunohistochemistry, and publicly available datasets. Additionally, the impact of FASN and PLSCR3 knockdown on HUVECs behavior was validated through in vitro experiments.ResultsDifferential expression analysis identified 1,418 DEGs in the GSE221521 dataset, with 39 overlapping DE-PRGs (29 upregulated, 10 downregulated). Functional enrichment indicated that DE-PRGs are involved in apoptosis, signal transduction, and inflammatory responses, with key pathways such as MAPK and TNF signaling. Machine learning algorithms identified six PANoptosis-related hub genes (BEX2, CASP2, CD36, FASN, OSMR, and PLSCR3) as potential biomarkers. A diagnostic nomogram based on these hub genes showed high diagnostic accuracy. Immune cell infiltration analysis revealed significant differences in immune cell patterns between control and DR groups, especially in Activated CD4 Memory T Cells and Monocytes. Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. Furthermore, experiments demonstrated that knocking down FASN and PLSCR3 impacted HUVECs behavior.ConclusionThis study provides valuable insights into the molecular mechanisms of DR, particularly highlighting PANoptosis-related pathways, and identifies potential biomarkers and therapeutic targets for the disease.

Role of the Lymphocyte Profile in Mediastinal Lymph Nodes in the Differential Diagnosis of Sarcoidosis and Tuberculous Lymphadenitis Patients Undergoing EBUS-TBNA.

The value of lymphocyte profiling (LP) in mediastinal lymph nodes for the differential diagnosis of sarcoidosis has not been extensively studied, and existing literature presents mixed results. This was a prospective study of patients with intrathoracic lymphadenopathy who underwent endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). LP in lymph node puncture fluid (LNPF) was evaluated using flow cytometry. The results of LP in sarcoidosis patients were compared with tuberculous lymphadenitis (TBLA) patients. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-offs of the statistically significant parameters for screening for sarcoidosis. Based on the optimal cut-offs and the final diagnosis of sarcoidosis and TBLA, the sensitivity, specificity, and accuracy of every statistically significant parameter and different combinations of the above three parameters were calculated for the diagnosis of sarcoidosis. Forty-five cases of sarcoidosis and 33 cases of TBLA were enrolled in this study. Compared with the LP in TBLA patients, in sarcoidosis patients, the proportion of CD4 T cells and CD4/CD8 ratio increased, and the proportion of CD8 T cells and natural killer (NK) cells decreased. Among all single parameters, the CD4/CD8 ratio had high diagnostic sensitivity (84.4%), specificity (81.8%), and accuracy (83.3%) for sarcoidosis. Among all the combinations of three parameters, the combination of CD4, CD8, and NKT/NK ratio had high diagnostic sensitivity (91.1%), specificity (84.8%), and accuracy (87.2%) for sarcoidosis. Assessment of LP in LNPF may improve the differential diagnostic accuracy of sarcoidosis from TBLA and further strengthen the importance of LP in LNPF in the diagnostic workup of sarcoidosis.

General practitioners’ experiences with chronic abdominal symptoms and a faecal calprotectin guided referral strategy in children: A Dutch qualitative study

Background General practitioners (GPs) often struggle to distinguish functional gastrointestinal disorders (FGID) from organic disorders in children with chronic abdominal symptoms. A referral strategy guided by faecal calprotectin (FCal) testing may help. Objective This study explores GPs’ experiences with these children and the strategy. Methods GPs were sampled purposively to data saturation. Ultimately, we conducted one focus group session and 13 semi-structured interviews with 17 Dutch GPs who had been involved in a randomised controlled trial evaluating an FCal-testing strategy. The online focus group and interviews were recorded, transcribed verbatim, and subject to thematic content analysis. Results Four themes arose: diagnostic confidence, fear of missing something severe, reassurance and managing FGID in primary care. Although GPs typically felt confident during the diagnostic process, they did fear missing somatic or psychosocial conditions. They felt more diagnostically confident due to FCals clear indications, high diagnostic accuracy, and non-invasiveness. Reassurance was considered crucial in children with FGID, either by labelling symptoms, providing explanatory models, or offering medical interventions (e.g. FCal testing). When helping children with FGID proved too difficult, GPs referred to specialist care. Besides the integration of FCal during reassurance, the testing strategy did not help GPs manage children with FGID. Conclusion While the FCal-strategy improved diagnosis according to GPs, they found the primary challenge to be managing children with FGID. Nevertheless, they found the FCal-strategy beneficial, likely due to its integration into reassurance strategies. Further research focusing on enhancing communication and interventions for paediatric FGID in primary care is warranted.

Identification of PANoptosis-Related Genes in Community-Acquired Pneumonia Diagnosis.

This study aimed to identify and characterize novel PANoptosis biomarkers for community-acquired pneumonia (CAP) diagnosis. Transcriptomic data from training set GSE196399 and validation sets GSE94916 and GSE202947 were utilized. A PANoptosis gene set was identified by intersecting DEGs linked to CAP, WGCNA hub genes, and PANoptosis-related genes. GO and KEGG analyses were conducted for enrichment analysis. PANoptosis scores were calculated via ssGSEA. Feature genes were identified using SVM-RFE, LASSO regression, and RF methods. Diagnostic performance was assessed via ROC analysis. Immune cell infiltration was evaluated using CIBERSORT. A PPI network was constructed, and a nomogram was developed for CAP prediction. Drug-gene interactions were investigated. qRT-PCR was conducted to confirm feature gene alterations in clinical samples. We identified 7555 DEGs associated with CAP from the GSE196399 dataset. Through WGCNA, a PANoptosis gene set of 39 genes was found, showing significant enrichment in pathways related to apoptosis and inflammation. CAP patients exhibited significantly reduced PANoptosis scores compared to healthy controls, with a marked upregulation in the majority of the PANoptosis gene set in high-score individuals. Four feature genes (ZNF304, AKT3, MAPK8, and ARHGAP10) were identified as potential biomarkers, exhibiting high diagnostic accuracy with AUCs generally above 0.8. These genes also showed significant correlations with M0 macrophages and neutrophils. Drug-gene interaction analysis revealed potential therapeutic agents targeting MAPK8 and AKT3. Validation in clinical samples confirmed gene expression alterations in CAP patients. The identified PANoptosis feature genes demonstrate high diagnostic accuracy for CAP, serving as potential biomarkers and therapeutic targets for CAP.

Identification and Validation of Serum Biomarkers to Improve Colorectal Cancer Diagnosis.

The pressing need for reliable biomarkers in colorectal cancer (CRC) diagnosis and prognosis is a major global health concern. Current diagnostic methods rely heavily on invasive procedures like colonoscopy, and existing biomarkers such as Carbohydrate Antigen 19-9 (CA19-9) and Carcinoembryonic Antigen (CEA) exhibit limitations in accuracy and specificity. This study aims to identify and validate novel biomarkers that can enhance the early detection and diagnostic precision of CRC while overcoming the shortcomings of conventional biomarkers. Leveraging advancements in genomic and proteomic technologies, gene expression datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified differentially expressed genes (DEGs) and conducted further analyses, including Gene Ontology (GO) enrichment and Protein-Protein Interaction (PPI) network construction. Five promising biomarkers-INHBA, MMP7, PSAT1, SLC7A5, and TGFBI-were selected based on their robust performance in Receiver Operating Characteristic (ROC) curve analysis. Serum concentrations of these biomarkers were measured in 200 CRC patients and 100 healthy controls. The study revealed significantly elevated expression levels of the selected biomarkers in CRC tissues compared to normal tissues. Additionally, serum concentrations of INHBA, MMP7, PSAT1, SLC7A5, and TGFBI were notably higher in CRC patients than in healthy individuals, with Area Under the Curve (AUC) values ranging from 0.8361 to 0.9869 indicating high diagnostic accuracy. Optimal cutoff values for diagnosis ranged from 38.9 pg/mL to 280.7 pg/mL, yielding sensitivity and specificity values between 74.5% and 92.9%. The findings underscore the potential of INHBA, MMP7, PSAT1, SLC7A5, and TGFBI as effective non-invasive biomarkers for CRC detection. Their elevated serum concentrations and robust discriminatory abilities highlight their promise in improving diagnostic accuracy and patient outcomes compared to traditional biomarkers. The identification and validation of these novel biomarkers represent a significant advancement in CRC diagnosis and management. Further studies are required to validate their clinical applicability in larger cohorts and to elucidate their functional roles in CRC pathogenesis, ultimately enhancing diagnostic strategies and personalized treatment approaches.

Surgeon-Led Point-Of-Care UltraSound-guided THORacic (POCUS-THOR) Biopsy: A New Paradigm in Efficient Diagnosis and Resource-Sparing Care

Tissue diagnosis through a variety of interventional approaches guides thoracic cancer management, but often introduces delay to definitive treatment and can be resource intensive. We introduced a thoracic surgeon-led, point-of-care ultrasound-guided biopsy program to provide rapid diagnosis for patients with thoracic cancers. We assessed the diagnostic yield and adverse events with this approach. A prospective cohort study was performed of consecutive patients undergoing ultrasound-guided biopsies performed by 5 thoracic surgeons from June 2021 to April 2024 at a tertiary Canadian thoracic surgery institution. Using a bedside ultrasound, 20-gauge tissue cores were obtained using multiple passes with a standard spinal needle. Descriptive univariable statistics were used. 160 patients underwent bedside biopsy for lung (n=101), liver (n=20), chest wall/pleural (n=20), mediastinal (n=18), or other (n=1) lesions. Tissue diagnosis was obtained in 86.3% of patients (n=138), and diagnostic yield was similar for high and low-volume providers and over time. All liver biopsies were diagnostic. Non-diagnostic biopsies were more likely to occur with benign pathology, chest wall/pleural lesions, or extensive necrosis; diagnosis was achieved with other modalities in most cases. There was 1 post-procedure pneumothorax (adverse event rate 0.6%). Thoracic surgeon-led ultrasound-guided biopsies are safe in an outpatient clinic setting, and have high diagnostic accuracy. This results in reduced time to diagnosis by an estimated 28-35 days, and frees up endoscopic and radiology resources for other patients. This low-cost procedure can be adopted as part of comprehensive thoracic malignancy assessment, and can accelerate patient access to cancer treatment.

Time-segment-wise feature fusion transformer for multi-modal fault diagnosis

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Recently, deep learning based fault diagnosis methods have achieved remarkable advancements with monitored data from a single sensor. However, obtaining satisfactory diagnostic results based on a single sensor is often difficult because the complementary information between different sensors is ignored. Extracting comprehensive fault features from multi-modal data is a problem that remains to be solved. To address these challenges, a time-segment-wise feature fusion Transformer (FFTR) is proposed in this paper. First, the signals from various modalities as multiple channels are normalized channel-by-channel and form a multi-modal sample. Second, a time-segment-wise feature learning network is designed to transform a multi-modal sample into several fusion features through the sequential processes of sample segmentation, segment-level feature extraction and time-aligned feature fusion. Finally, a Transformer network is employed for comprehensive multi-modal feature analysis and fault classification. In addition, a joint loss function is designed to comprehensively train the end-to-end FFTR. The comparison experiment with other baseline methods is conducted on two multi-modal datasets. The experimental results show that FFTR achieves 3.69% and 3.93% higher diagnostic accuracy than baseline on two datasets respectively and can address real-world problems effectively.

CENPE is a Diagnostic and Prognostic Biomarker for Cervical Cancer

Cervical squamous cell carcinoma (CESC) is a common cancer in women. Despite advancements in early diagnosis through high-risk human papillomavirus (HPV) screening, challenges remain in predicting and treating the disease. Hence, the identification of novel biomarkers for prognosis and therapeutic targets is crucial.CENPE, a microtubule-end directed motor protein that accumulates during the G2 phase, is recognized for its involvement in promoting cancer growth and progression. However, its specific role in CESC remains unclear. This research investigated the expression of CENPE in CESC utilizing data from The Cancer Genome Atlas (TCGA), which was further validated through gene expression profiles, the Human Protein Atlas (HPA), and clinical data. The study utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis to elucidate the role of CENPE in CESC. Additionally, Protein-protein interaction (PPI) networks and competing endogenous RNA (CeRNA) networks involving CENPE and its differentially expressed genes were established. Furthermore, Kaplan-Meier survival analysis was conducted to evaluate the impact of CENPE on patient prognosis.Our study revealed an upregulation of CENPE expression in cervical cancer tissues, which promotes the progression of CESC through IL-6-mediated PI3K-Akt and MAPK signaling pathways. The significant associations with ACNG3, LY6H, and SLC6A7 suggest that CENPE may play a role in tumor growth and metastasis, potentially involving the nervous system. Moreover, the correlations with ARIH1, KDM1A, KDM5B, and NSD3 indicate that CENPE could be a promising target for drug development. Our analysis of the ROC curve demonstrated a high diagnostic accuracy of CENPE in CESC (AUC: 0.997, CI: 0.990-1.000). Subgroup analysis highlighted substantial effects in patients under 50 years old, those with a height under 160 cm, individuals in peri- and post-menopausal stages, and patients in clinical stages 1 and 4. Additionally, COX regression analysis indicated that older age, lower BMI, and higher CENPE expression are associated with decreased 1-year, 3-year, and 5-year survival rates.In conclusion, CENPE emerges as a crucial factor in the initiation and advancement of cervical cancer, showing potential as a novel target for therapeutic interventions.

ProtoMed: Prototypical networks with auxiliary regularization for few-shot medical image classification

Although deep learning has shown impressive results in computer vision, the scarcity of annotated medical images poses a significant challenge for its effective integration into Computer-Aided Diagnosis (CAD) systems. Few-Shot Learning (FSL) opens promising perspectives for image recognition in low-data scenarios. However, applying FSL for medical image diagnosis presents significant challenges, particularly in learning disease-specific and clinically relevant features from a limited number of images. In the medical domain, training samples from different classes often exhibit visual similarities. Consequently, certain medical conditions may present striking resemblances, resulting in minimal inter-class variation. In this paper, we propose a prototypical network-based approach for few-shot medical image classification for low-prevalence diseases detection. Our method leverages meta-learning to use prior knowledge gained from common diseases, enabling generalization to new cases with limited data. However, the episodic training inherent in meta-learning tends to disproportionately emphasize the connections between elements in the support set and those in the query set, which can compromise the understanding of complex relationships within medical image data during the training phase. To address this, we propose an auxiliary network as a regularizer in the meta-training phase, designed to enhance the similarity of image representations from the same class while enforcing dissimilarity between representations from different classes in both the query and support sets. The proposed method has been evaluated using three medical diagnosis problems with different imaging modalities and different levels of visual imaging details and patterns. The obtained model is lightweight and efficient, demonstrating superior performance in both efficiency and accuracy compared to state-of-the-art. These findings highlight the potential of our approach to improve performance in practical applications, balancing resource limitations with the need for high diagnostic accuracy.