Significant Diagnostic Value Research Articles

Computational modeling of tumor invasion from limited and diverse data in Glioblastoma

For diseases with high morbidity rates such as Glioblastoma Multiforme, the prognostic and treatment planning pipeline requires a comprehensive analysis of imaging, clinical, and molecular data. Many mutations have been shown to correlate strongly with the median survival rate and response to therapy of patients. Studies have demonstrated that these mutations manifest as specific visual biomarkers in tumor imaging modalities such as MRI. To minimize the number of invasive procedures on a patient and for the overall resource optimization for the prognostic and treatment planning process, the correlation of imaging and molecular features has garnered much interest. While the tumor mass is the most significant feature, the impacted tissue surrounding the tumor is also a significant biomarker contributing to the visual manifestation of mutations — which has not been studied as extensively. The pattern of tumor growth impacts the surrounding tissue accordingly, which is a reflection of tumor properties as well. Modeling how the tumor growth impacts the surrounding tissue can reveal important information about the patterns of tumor enhancement, which in turn has significant diagnostic and prognostic value. This paper presents the first work to automate the computational modeling of the impacted tissue surrounding the tumor using generative deep learning. The paper isolates and quantifies the impact of the Tumor Invasion (TI) on surrounding tissue based on change in mutation status, subsequently assessing its prognostic value. Furthermore, a TI Generative Adversarial Network (TI-GAN) is proposed to model the tumor invasion properties. Extensive qualitative and quantitative analyses, cross-dataset testing, and radiologist blind tests are carried out to demonstrate that TI-GAN can realistically model the tumor invasion under practical challenges of medical datasets such as limited data and high intra-class heterogeneity.

Diagnostic and prognostic role of NLR in testicular cancer.

To summarize the results of available studies for investigating the role of neutrophil to lymphocyte ratio (NLR) in testicular cancer (tCa). The search was conducted on PubMed, Scopus, and Web of Science up to November 21, 2021. Finally, a total of 31 studies were included in this review. NLR was higher in tCa patients compared to healthy controls and benign testis pathologies, and decreased significantly after orchiectomy. An elevated NLR predicts poor prognosis, advanced stage, presence of nodal or distant metastases, contralateral tumor development, lower time-to-cancer specific death, worse OS, and poorer response to chemotherapy. However, NLR could not differentiate between seminomas and non-seminomatous tCa. NLR has a significant diagnostic and prognostic value in tCa.

Baseline serum glutamate: Implications for diagnosis and prediction in mild cognitive impairment and Alzheimer’s disease of the Alzheimer’s Disease Neuroimaging Initiative

PurposeNumerous studies have highlighted a close link between metabolic imbalances and Alzheimer’s Disease (AD). The advancement of metabolomics has recently enabled the exploration of characteristic metabolic changes associated with AD. Studies indicate that serum glutamate (Glu) levels may correlate with mild cognitive impairment (MCI) and AD. This study aims to further elucidate the characteristics of baseline serum Glu levels in MCI and AD. MethodsThis study included 783 participants from the Alzheimer’s Disease Neuroimaging Initiative-1 (ADNI-1) cohort, categorized into cognitively normal (CN, n = 224), stable MCI (sMCI, n = 181), progressive MCI (pMCI, n = 193), and AD (n = 185). The study aimed to analyze the diagnostic value of baseline serum Glu, to explore its predictive capability for the progression from CN to MCI or AD, and from MCI to AD, and to analyze the relationship between serum Glu and cerebrospinal fluid (CSF) biomarkers and cognitive functions in different diagnostic groups. ResultsCompared to the CN and sMCI groups, the pMCI group showed significantly lower levels of serum Glu, and the AD group also had lower Glu levels compared to the sMCI group. However, serum Glu did not show significant diagnostic value for MCI and AD. Lower levels of serum Glu could predict the progression from MCI to AD. ConclusionSerum Glu levels can predict the progression from MCI to AD, suggesting that it could provide new insights into the pathophysiological mechanisms of AD. However, serum Glu may not be an ideal peripheral biomarker for AD.

Relationship between NLR and penile squamous cell carcinoma: a systematic review and meta-analysis

ObjectiveWe conducted this study to summarize the results of studies reporting the role of NLR (neutrophil to lymphocyte ratio) in PSCC (penile squamous cell carcinoma).MethodsThis meta-analysis was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria. A systematic search was conducted on PubMed, Scopus, and web of science up to March 10, 2023. Fourteen studies were included in the review. The NOS (Newcastle–Ottawa Scale) was used to determine the quality of the included studies. This meta-analysis was conducted on the studies reporting the relationship between NLR and survival using HR (hazard ratio) and 95% CI (confidence interval).ResultsThere was a significant association between NLR levels and the prognosis, nodal stage, and anatomical tumor stage of PSCC patients. In the meta-analysis of the association of NLR with survival, NLR level was significantly associated with lower cancer-specific survival (HR = 3.51, 95% CI = 2.07–5.98, p < 0.001) and lower disease-free survival (HR = 2.88, 95% CI = 1.60–5.20, p < 0.001). However, NLR was found to have no association with the stage, grade, location, and size of the tumor.ConclusionNLR has a significant diagnostic and prognostic value in PSCC.

Comprehensive pan-cancer analysis reveals EPHB2 is a novel predictive biomarker for prognosis and immunotherapy response

PurposeRecent studies have increasingly linked Ephrin receptor B2 (EPHB2) to cancer progression. However, comprehensive investigations into the immunological roles and prognostic significance of EPHB2 across various cancers remain lacking.MethodsWe employed various databases and bioinformatics tools to investigate the impact of EPHB2 on prognosis, immune infiltration, genome instability, and response to immunotherapy. Validation of the correlation between EPHB2 expression and M2 macrophages included analyses using bulk and single-cell transcriptomic datasets, spatial transcriptomics, and multi-fluorescence staining. Moreover, we performed cMap web tool to screen for EPHB2-targeted compounds and assessed their potential through molecular docking and dynamics simulations. Additionally, in vitro validation using lung adenocarcinoma (LUAD) cell lines was conducted to confirm the bioinformatics predictions about EPHB2.ResultsEPHB2 dysregulation was observed across multiple cancer types, where it demonstrated significant diagnostic and prognostic value. Gene Set Enrichment Analysis (GSEA) indicated that EPHB2 is involved in enhancing cellular proliferation, invasiveness of cancer cells, and modulation of the anti-cancer immune response. Furthermore, it is emerged as a pan-cancer marker for M2 macrophage infiltration, supported by integrated analyses of transcriptomics and multiple fluorescence staining. In LUAD cells, knockdown of EPHB2 expression led to a decrease in both cell proliferation and migratory activity.ConclusionEPHB2 expression may serve as a pivotal indicator of M2 macrophage infiltration, offering vital insights into tumor dynamics and progression across various cancers, including lung adenocarcinoma, highlighting its significant prognostic and therapeutic potential for further exploration.

Screening of serum markers in patients with resistant hypertension

BackgroundThis study delves into the intricacies of resistant hypertension (RH), a prevalent yet enigmatic chronic cardiovascular ailment that is linked to a myriad of complications. Although its full pathogenesis is still shrouded in mystery, the field of proteomics offers a beacon of hope, with its potential to shed light on the proteins that orchestrate the tapestry of life. Harnessing the power of proteomics is essential for demystifying the pathogenesis of RH, enabling more precise diagnostics and treatments, and ultimately improving prognostic outcomes. MethodsOur approach was to employ rigorous statistical analyses to home in on proteins with significant expression variances between our two cohorts. We complemented this with bioinformatics tools to unravel the intricate functions and pathways of these proteins. By synthesizing these insights with the clinical profiles of our patients, we were able to distill a set of definitive biomarkers with diagnostic potential. In our quest for clarity, we also embarked on a retrospective journey, amassing and scrutinizing clinical data from both RH and hypertension (HTN) patients. We crafted and rigorously assessed risk factor models to evaluate their diagnostic prowess. ResultsOur exploration spanned across 30 blood samples from RH patients and 20 from those grappling with HTN. Our inquiry yielded some compelling revelations: (1) RH patients showcased 29 unique proteins, in contrast to the 59 unique proteins found in HTN patients. A deeper dive into the proteomic data unveiled molecular functions predominantly tied to lipid metabolism, protein networking, and oxidative stress, with a spotlight on pathways such as cholesterol metabolism, coagulation, and the complement cascade. (2) By charting receiver operating characteristic curves and rigorously analyzing the proteomic data, we surfaced 11 proteins with notable diagnostic potential, tightly interwoven with clinical metrics. ConclusionOur research has pinpointed 11 proteins that stand as promising serum biomarkers, endowed with significant diagnostic value. This discovery marks a stride towards a more nuanced understanding and management of resistant hypertension.

The utilization of blood serum ATR-FTIR spectroscopy for the identification of gastric cancer

Gastric cancer represents a significant public health challenge, necessitating advancements in early diagnostic methodologies. This investigation employed attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to conduct a multivariate analysis of human serum. The study encompassed the examination of blood samples from 96 individuals diagnosed with gastric cancer and 96 healthy volunteers. Principal component analysis (PCA) was utilized to interpret the infrared spectral data of the serum samples. Specific spectral bands exhibiting intensity variations between the two groups were identified. The infrared spectral ranges of 3500 ~ 3000 cm⁻1, 1700 ~ 1600 cm⁻1, and 1090 ~ 1070 cm⁻1 demonstrated significant diagnostic value for gastric cancer, likely attributable to differences in protein conformation and nucleic acids. By employing machine learning algorithms to differentiate between gastric cancer patients (n = 96) and healthy controls (n = 96), we achieved a sensitivity of up to 89.7% and a specificity of 87.2%. Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.901. These findings underscore the potential of our serum-based ATR-FTIR spectroscopy examination method as a straightforward, minimally invasive, and reliable diagnostic test for the detection of gastric cancer.

Predicting gestational diabetes mellitus risk at 11–13 weeks’ gestation: the role of extrachromosomal circular DNA

BackgroundGestational diabetes mellitus (GDM) significantly impacts maternal and infant health both immediately and over the long term, yet effective early diagnostic biomarkers are currently lacking. Thus, it is essential to identify early diagnostic biomarkers for GDM risk screening. Extrachromosomal circular DNA (eccDNA), being more stable than linear DNA and involved in disease pathologies, is a viable biomarker candidate for diverse conditions. In this study, eccDNA biomarkers identified for early diagnosis and assessment of GDM risk were explored.MethodsUsing Circle-seq, we identified plasma eccDNA profiles in five pregnant women who later developed GDM and five matched healthy controls at 11–13 weeks of gestation. These profiles were subsequently analyzed through bioinformatics and validated through outward PCR combined with Sanger sequencing. Furthermore, candidate eccDNA was validated by quantitative PCR (qPCR) in a larger cohort of 70 women who developed GDM and 70 normal glucose-tolerant (NGT) subjects. A ROC curve assessed the eccDNA’s diagnostic potential for GDM.Results2217 eccDNAs were differentially detected between future GDM patients and controls, with 1289 increased and 928 decreased in abundance. KEGG analysis linked eccDNA genes mainly to GDM-related pathways such as Rap1, MAPK, and PI3K-Akt, and Insulin resistance, among others. Validation confirmed a significant decrease in eccDNA PRDM16circle in the plasma of 70 women who developed GDM compared to 70 NGT women, consistent with the eccDNA-seq results. PRDM16circle showed significant diagnostic value in 11–13 weeks of gestation (AUC = 0.941, p < 0.001).ConclusionsOur study first demonstrats that eccDNAs are aberrantly produced in women who develop GDM, including PRDM16circle, which can predict GDM at an early stage of pregnancy, indicating its potential as a biomarker.Trial registration ChiCTR2300075971, http://www.chictr.org.cn. Registered 20 September 2023.

Meta-analysis of the value of dual-energy computed tomography in the diagnosis of anterior cruciate ligament injuries of the knee

BackgroundThis meta-analysis assessed the efficacy of dual-energy computed tomography (DECT) in the diagnosis of anterior cruciate ligament (ACL) injuries.MethodsThe literature search was performed up to December 8, 2023, and included a comprehensive examination of several databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP. Diagnostic metrics sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and a summary receiver operating characteristic (SROC) were determined using a bivariate model analysis. Heterogeneity within the data was explored through subgroup analyses, which considered variables including geographical region, use of magnetic resonance imaging (MRI), arthroscopy, and study design.ResultsThe analysis included ten studies encompassing 544 patients. DECT demonstrated substantial diagnostic utility for ACL injuries of the knee, with a sensitivity of 0.91 (95% confidence interval [CI]: 0.88–0.94), a specificity of 0.90 (95% CI: 0.81–0.95), a PLR of 9.20 (95% CI: 4.50–19.00), a NLR of 0.10 (95% CI: 0.06–0.14), a DOR of 97.00 (95% CI: 35.00–268.00), and an area under the curve (AUC) of 0.95 (95% CI: 0.93–0.97). The subgroup analyses consistently showed high diagnostic precision for ACL injuries across Asian population (sensitivity: 0.91, specificity: 0.91, PLR: 9.90, NLR: 0.09, DOR: 105.00, AUC: 0.96), in MRI subgroup (sensitivity: 0.85, specificity: 0.94, PLR: 9.57, NLR: 0.18, DOR: 56.00, AUC: 0.93), in arthroscopy subgroup (sensitivity: 0.92, specificity: 0.89, PLR: 8.40, NLR: 0.09, DOR: 94.00, AUC: 0.95), for prospective studies (sensitivity: 0.92, specificity: 0.88, PLR: 7.40, NLR: 0.09, DOR: 78.00, AUC: 0.95), and for retrospective studies (sensitivity: 0.91, specificity: 0.93, AUC: 0.93).ConclusionDECT exhibits a high value in diagnosing ACL injuries. The significant diagnostic value of DECT provides clinicians with a powerful tool that enhances the accuracy and efficiency of diagnosis and optimizes patient management and treatment outcomes.

Biomarkers and Related Factors for the Diagnosis, Risk of Coronary Artery Lesions, and Resistance to Intravenous Immunoglobulin in Kawasaki Disease: An Umbrella Review of Meta-Analyses.

Kawasaki disease (KD) is a self-limited febrile disease predominantly affecting infants and children under 5 years old. Coronary artery lesions (CAL) are a prevalent complication, highlighting the necessity for swift diagnosis and treatment. A comprehensive review of biomarkers applicable for the diagnosis and treatment of Kawasaki disease (KD) in clinical settings is imperative. To provide a comprehensive review and analysis of biomarkers for diagnosis of KD, incidence of CAL, and intravenous immunoglobulin (IVIG) resistance. The data included in our study were sourced from searches conducted in PubMed/MEDLINE, Embase, EBSCO, and Google Scholar until March 15, 2024. Studies investigating the association with KD or evaluating diagnostic value were included in our study. Eligibility was independently assessed by two authors, with conflicts resolved through discussion. Data extraction was performed by 2 independent authors, following Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guideline. Data were pooled using a random-effects model. We assess biomarkers relevant to KD, categorizing them into three groups: diagnostic, associated with CAL incidence, and linked to IVIG resistance. For studies focusing solely on association, we present standardized mean differences (SMD). For those reporting sensitivity and specificity as diagnostic measures, we calculate the diagnostic odds ratio (DOR) to compare their efficacy. We identified 14 meta-analyses on biomarkers related to KD. 11 biomarkers exhibited diagnostic value for KD, while 21 were associated with its progression. Four biomarkers, including non-codingRNAs (DOR, 19.35 [95% CI, 13.58-27.56]), Serumferritin (DOR, 24.90 [11.67-53.12]), NterminalproBNP (DOR, 21.03 [9.03-49.00]), and microRNAs (DOR, 45.28 [6.30-325.52]), have significant diagnostic value for the diagnosis of KD. Seven biomarkers showed significant association with the incidence of CAL. Twenty biomarkers were for the prediction of IVIG resistance, including prognostic nutritional index (DOR, 7.72 [95% CI, 2.37-25.09]), non-codingRNAs (DOR, 14.63 [3.24-66.14]), neutrophil to lymphocyte ratio (DOR, 6.62 [4.05-10.81]), platelet to lymphocyte ratio (DOR, 3.30 [2.10-5.19]), and Creactive protein (DOR, 6.58 [3.69-11.74]). Based on the evidence, we have proposed various biomarkers associated with KD. Our aim is for these biomarkers to have wide applicability in both diagnostic and therapeutic settings.

Quantitative measurements of dual-energy CT parameters in the diagnosis of osteoporosis - a highly sensitive and specific technique: An observational study.

With the aging of the population in our society, osteoporosis (OP) has become one of the chronic diseases that seriously threaten the physical health of the elderly, leading to a heavy burden on healthcare. In recent years, with the continuous development of dual-energy CT (DECT) technology, quantitative measurements of DECT parameters, which is highly sensitive to OP, provides accurate results, is convenient and cost-effective, and is expected to be widely used in bone density testing. This study was aimed to explore the value of quantitative measurements of DECT parameters in diagnosing OP, in order to better guide clinical judgments and treatment. A total of 187 patients who underwent dual-energy X-ray and DECT examinations at Tianjin hospital between January 2022 and June 2023 were included as participants in this study. The bone mineral density (BMD) values of the lumbar spine (L1-L4) were determined using dual-energy X-ray absorptiometry. Simultaneously, CT scans of the lumbar spine (L1-L4) were conducted to measure the CT values of contrast media (CM), mixed-energy image CT values (regular CT value [rCT]), calcium concentration (CaD), as well as fat fraction (FF). Pearson correlation analysis was used to examine the relationship between the quantitative measurements of L1 to L4 vertebral bodies obtained from DECT and BMD. The values of CM, rCT, and CaD in the OP group were all lower than those in the non-OP group with statistical significance (P < .001). Conversely, the fat fraction parameter value in the OP group was significantly higher in contrast with the non-OP group (P = .004); there was a positive correlation between CM, rCT, CaD, and BMD values (R = 0.579, P < .001; R = 0.604, P < .001; R = 0.563, P < .001); CM, rCT, and CaD had high diagnostic value for OP, as evidenced by AUCs of 0.935 (95% CI: 0.900-0.971), 0.956 (95% CI: 0.925-0.987), and 0.926 (95% CI: 0.858-0.954), respectively, all with P values < .001. Quantitative measurement of DECT parameters showed a high sensitivity as well as a high specificity in the diagnosis of OP. It is also highly feasible and holds significant clinical diagnostic value, making it a suitable candidate for widespread application.

Cost-Effectiveness of Plasma Microbial Cell-Free DNA Sequencing When Added to Usual Care Diagnostic Testing for Immunocompromised Host Pneumonia.

Immunocompromised host pneumonia (ICHP) is an important cause of morbidity and mortality, yet usual care (UC) diagnostic tests often fail to identify an infectious etiology. A US-based, multicenter study (PICKUP) among ICHP patients with hematological malignancies, including hematological cell transplant recipients, showed that plasma microbial cell-free DNA (mcfDNA) sequencing provided significant additive diagnostic value. The objective of this study was to perform a cost-effectiveness analysis (CEA) of adding mcfDNA sequencing to UC diagnostic testing for hospitalized ICHP patients. A semi-Markov model was utilized from the US third-party payer's perspective such that only direct costs were included, using a lifetime time horizon with discount rates of 3% for costs and benefits. Three comparators were considered: (1) All UC, which included non-invasive (NI) and invasive testing and early bronchoscopy; (2) All UC & mcfDNA; and (3) NI UC & mcfDNA & conditional UC Bronch (later bronchoscopy if the initial tests are negative). The model considered whether a probable causative infectious etiology was identified and if the patient received appropriate antimicrobial treatment through expert adjudication, and if the patient died in-hospital. The primary endpoints were total costs, life-years (LYs), equal value life-years (evLYs), quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio per QALY. Extensive scenario and probabilistic sensitivity analyses (PSA) were conducted. At a price of $2000 (2023 USD) for the plasma mcfDNA, All UC & mcfDNA was more costly ($165,247 vs $153,642) but more effective (13.39 vs 12.47 LYs gained; 10.20 vs 9.42 evLYs gained; 10.11 vs 9.42 QALYs gained) compared to All UC alone, giving a cost/QALY of $16,761. NI UC & mcfDNA & conditional UC Bronch was also more costly ($162,655 vs $153,642) and more effective (13.19 vs 12.47 LYs gained; 9.96 vs 9.42 evLYs gained; 9.96 vs 9.42 QALYs gained) compared to All UC alone, with a cost/QALY of $16,729. The PSA showed that above a willingness-to-pay threshold of $50,000/QALY, All UC & mcfDNA was the preferred scenario on cost-effectiveness grounds (as it provides the most QALYs gained). Further scenario analyses found that All UC & mcfDNA always improved patient outcomes but was not cost saving, even when the price of mcfDNA was set to $0. Based on the evidence available at the time of this analysis, this CEA suggests that mcfDNA may be cost-effective when added to All UC, as well as in a scenario using conditional bronchoscopy when NI testing fails to identify a probable infectious etiology for ICHP. Adding mcfDNA testing to UC diagnostic testing should allow more patients to receive appropriate therapy earlier and improve patient outcomes.

A retrospective study of 29 fatal cases of insulin overdose

PurposeTo summarize recent cases of fatal insulin poisoning both domestically and internationally, thereby offering valuable insights for the forensic identification of insulin overdose cases. MethodsLiterature published since 2000 on fatal insulin overdose were systematically searched and screened. Data encompassing variables such as year, age, sex, cause of death, scene conditions, occupations, medical histories of victims and perpetrators, autopsy timing, dosage and administration methods, forensic pathology, and toxicological analysis, were compiled for rigorous statistical analysis. ResultsAmong the 29 fatal cases of insulin poisoning, suicides and homicides accounted for 55.2 % and 41.4 %, respectively. Precisely 34.5 % of victims or perpetrators were associated with the medical industry, 27.6 % had diabetes, and 24.1 % had mental illnesses such as depression. Intravenous injection resulted in quicker death than did subcutaneous injection. In some cases, immunohistochemical staining of insulin and protamine at injection sites yielded positive results. The average molar ratio of insulin to C-peptide in post-mortem blood was 13.76 ± 5.167, indicating a significant diagnostic value for insulin poisoning. ConclusionAssessment of cases of fatal insulin overdose should be thorough, incorporating case investigation, scene examination, medical records review, autopsy findings, pathological examinations, and laboratory tests, alongside considering the condition of the body and timing of death autopsy. Using mass spectrometry to detect insulin proves valuable, particularly in cases of poor body preservation.

Comprehensive bioinformatics analysis of integrator complex subunits: expression patterns, immune infiltration, and prognostic signature, validated through experimental approaches in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a common gastrointestinal malignancy with a high incidence and poor prognosis. The subunits of the integrator complex (INTS1-14) play a crucial role in regulating genes dependent on RNA Polymerase II, which may be associated with cancer. However, the role of INTSs in HCC remains unclear. This study aims to comprehensively analyze the clinical value and potential role of INTS family genes in HCC through systematic bioinformatics analysis.MethodsWe employed various public databases, including UALCAN, HPA, Kaplan–Meier Plotter, GEPIA2, TNMplot, STRING, TIMER, and TISIDB, to investigate the expression levels, clinicopathological correlations, diagnostic and prognostic value, genetic alterations, co-expression network, molecular targets, and immune infiltration of INTSs in HCC. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to investigate the biological functions of genes associated with INTSs. Furthermore, Western blot, real-time fluorescence quantitative reverse transcription polymerase chain reaction (RT-qPCR), and immunohistochemistry techniques were employed to assess the expression of relevant proteins and genes. The proliferation of HCC cells was evaluated using the CCK8 assay.ResultsWe found that in HCC, there was a significant upregulation of INTSs at the transcriptional level, particularly INTS1, INTS4, INTS7, and INTS8. Additionally, the protein levels of INTS1 and INTS8 were notably elevated. The overexpression of these INTSs was strongly correlated with tumor stages in HCC patients. INTS1, INTS4, INTS7, and INTS8 exhibited significant diagnostic and prognostic value in HCC. Moreover, their expression was associated with immune infiltrations and activated status, including B cells, CD8 + T cells, CD4 + T cells, NK cells, macrophages, and dendritic cells. Functional predictions indicated that INTS1, INTS4, INTS7, and INTS8 were involved in various cancer-related signaling pathways, such as TRAIL, IFN-gamma, mTOR, CDC42, Apoptosis, and the p53 pathway. Furthermore, we observed a significant upregulation of INTS1, INTS4, INTS7, and INTS8 expression in HCC cell lines compared to normal liver cell lines. The level of INTS1 protein was higher in cancerous tissues compared to adjacent non-cancerous tissues (n = 16), and the suppression of INTS1 resulted in a significant decrease in the proliferation of Huh7 cells.ConclusionThese findings indicate the potential of INTS family genes as diagnostic biomarkers and therapeutic targets in HCC. Further research is needed to understand the underlying mechanisms and explore clinical applications.

Clinical, pathological and gene expression profiling of estrogen receptor discordance in breast cancer.

Breast cancer (BC) is the world's largest tumor species in which hormone receptor-positive patients have relatively good prognosis. However, majority of patients will develop late resistance, one of the important factors is due to the loss of the original estrogen receptor (ER) expression. We conducted this study in 115 patients with BC who experienced second biopsy at Jiangsu Province Hospital (JSPH) and divided patients into two subgroups ER + to - and ER + to + . First, clinicopathological characteristics between two groups were evaluated. Second, we explored candidate genes related to BC ER intratumor heterogeneity by applying next-generation sequencing (NGS) in 42 patients. Multi-omics integrative analysis of tumor transcriptomic, cancer-related pathway, diagnostic and prognostic value and immune profile were conducted. Besides, preliminary assay were also used to evaluate the correlation between KMT2C and ERα (ESR1) expression. The CCK-8, 5-Ethynyl-2'-deoxyuridine (EdU) assays, Transwell assays and the wound scratch tests were applied to explore the cellular interactions between KMT2C and BC. We find the histological type (p = 0.008) and disease-free survival (DFS) (p = 0.004) were significantly different in two subgroups. In Cox survival analysis, metastasis (Hazard ratio (HR) > 1, p = 0.007) and neo-adjuvant (HR < 1, p < 0.001) are independent prognostic factors of DFS. Besides, by analyzing NGS results, we found four genes KMT2C, FGFR19, FGF1 and FGF4 were highly mutated genes in ER + to - subgroup. Furthermore, the gene KMT2C displayed significant diagnostic value and prognostic value in BC and pan-cancer. In addition, a positive correlation between KMT2C expression and immune infiltrating levels of T cell CD4 + , macrophage and neutrophil was found. In the end, Western blot and RT-qPCR assay were used and found KMT2C and ERα (ESR1) expressions are strongly positive correlated in mRNA and protein level. Inhibition of KMT2C significantly reduced proliferation, invasion, and migration of MCF7 cells. People in two cohorts from JSPH presented different clinical characteristics and prognosis. The gene KMT2C may affect the progression of BC by regulating the molecular, epigenetic activity and immune infiltration. It may also serve as a novel prognostic biomarker for BC patients who underwent ER status converted from positive to negative.

Is conventional magnetic resonance imaging superior to radiography in the functional integrity evaluation of anterior cruciate ligament in patients with knee osteoarthritis?

BackgroundThe functional integrity of the anterior cruciate ligament (ACL) influences surgical decision-making in patients with knee osteoarthritis (KOA). This study aimed to compare the diagnostic value of radiography and magnetic resonance imaging (MRI) in determining the functional status of ACL.MethodsWe analyzed 306 knees retrospectively using preoperative hip-to-ankle anteroposterior standing (APS) radiographs, anteroposterior (AP) and lateral knee radiographs, AP valgus stress (VS) force radiographs, and standard orthogonal MRI. Based on the intraoperative visualization, the knees were grouped into ACL functionally-intact and ACL functionally-deficient (ACLD) groups. The diagnostic validity and reliability were calculated based on the radiograph parameters such as hip-knee-ankle angle (HKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), posterior tibial slope (PTS), sagittal tibiofemoral subluxation (STFS), coronal tibiofemoral subluxation (CTFS), joint line convergence angle (JLCA), the maximum wear point of the proximal tibia plateau (MWPPT%), and MRI parameters including ACL grades and MWPPT%.ResultsHKA, MPTA, PTS, STFS, JLCA, and CTFS on APS and AP radiographs, and MWPPT% on radiographs and MRI showed a significant diagnostic value (P < 0.05). There were no statistically significant differences in the single parameters from radiographs and MRI. After constructing the logistic regression models, MRI showed higher sensitivity, specificity, and accuracy, reaching 96.8%, 79.9%, and 83.3%, respectively (P < 0.001).ConclusionsIn patients with KOA, the diagnostic value of single radiographic or MRI parameter in assessing the functional integrity of the ACL are equivalent. However, by constructing predictive models, MRI could significantly improve diagnostic validity compared with radiography.

Muscle texture features on preoperative MRI for diagnosis and assessment of severity of congenital muscular torticollis

ObjectivesTo develop an objective method based on texture analysis on MRI for diagnosis of congenital muscular torticollis (CMT).Material and methodsThe T1- and T2-weighted imaging, Q-dixon, and T1-mapping MRI data of 38 children with CMT were retrospectively analyzed. The region of interest (ROI) was manually drawn at the level of the largest cross-sectional area of the SCM on the affected side. MaZda software was used to obtain the texture features of the T2WI sequences of the ROI in healthy and affected SCM. A radiomics diagnostic model based on muscle texture features was constructed using logistic regression analysis. Fatty infiltration grade was calculated by hematoxylin and eosin staining, and fibrosis ratio by Masson staining. Correlation between the MRI parameters and pathological indicators was analyzed.ResultsThere was positive correlation between fatty infiltration grade and mean value, standard deviation, and maximum value of the Q-dixon sequence of the affected SCM (correlation coefficients, 0.65, 0.59, and 0.58, respectively, P < 0.05).Three muscle texture features—S(2,2)SumAverg, S(3,3)SumVarnc, and T2WI extreme difference—were selected to construct the diagnostic model. The model showed significant diagnostic value for CMT (P < 0.05). The area under the curve of the multivariate conditional logistic regression model was 0.828 (95% confidence interval 0.735–0.922); the sensitivity was 0.684 and the specificity 0.868.ConclusionThe radiomics diagnostic model constructed using T2WI muscle texture features and MRI signal values appears to have good diagnostic efficiency. Q-dixon sequence can reflect the fatty infiltration grade of CMT.

A retinal vessel segmentation network approach based on rough sets and attention fusion module

The morphological changes of retinal vessels are of significant diagnostic value for early ophthalmic diseases and can aid in distinguishing other conditions such as diabetes and cardiovascular diseases. However, precise segmentation poses a challenge due to the complex structure of retinal vessels. To address these issues, we propose a Rough Attention Fusion Module (RAFM). This module employs max-pooling and average-pooling to define the upper and lower bounds of feature significance, introducing upper and lower weight matrices to obtain more reasonable attention coefficients. This enables the model to more accurately focus on important features in retinal images. Additionally, we integrate the RAFM into the GTS U-Net model, a simplified version of the GT U-Net model, which enhances the segmentation accuracy while reducing computational complexity. Ultimately, we construct a retinal vessel segmentation network based on the RAFM along with Group Transformer. Finally, the network structure is tested on the public DRIVE color fundus image dataset, achieving an Accuracy, F1 score, and AUC of 0.9641, 0.8506, and 0.9820, respectively. In contrast to prevalent retinal vessel segmentation networks in the mainstream, our proposed network demonstrates certain strengths.

Relationship of immature granulocytes with disease activity in rheumatoid arthritis.

To determine whether the IG count (#) and IG percentage (%) are associated with disease activity in rheumatoid arthritis (RA). This retrospective study included 65 RA patients and 65 healthy controls. Clinical and demographic characteristics of controls and RA patients (at active period and when the patients achieved remission) were obtained from medical records. Disease activity was defined by disease activity score 28 (DAS28). Furthermore, the clinical disease activity index (CDAI), and simple disease activity index (SDAI) were calculated. For the differential diagnosis of RA patients from healthy controls, the cut-off value was estimated by making receiver-operator curves (ROC). In active RA patients, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), IG#, and IG% levels were significantly higher compared to the healthy controls (p < .001, for all). When the patients achieved remission, DAS28, CDAI, SDAI, ESR, CRP, IG#, and IG% values were significantly decreased (p < .001, for all). IG# and IG% were significantly positively correlated with DAS28, CDAI, SDAI, ESR, and CRP (p = .024, p = .008, p = .003, p < .001, p < .001, respectively). According to ROC curve analysis, IG% and IG# were the biomarkers to have a significant diagnostic value for RA with the area under the curve of 0.853 and 0.865 (p < .001, for all). The present study demonstrated that two novel inflammatory markers, IG# and IG%, can be useful for monitoring RA patients' disease activity. Furthermore, IG# and IG% can also be used as fast, inexpensive, and easily available complementary diagnostic markers to diagnose RA patients.

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.