Disease Prognosis Research Articles

UNNECESSARY USE OF ANTIMICROBIALS – COMPARISON OF THE PREDICTIVE PERFORMANCE OF QSOFA AND APACHE SCORE FOR EVALUATION OF DISEASE PROGNOSIS PATTERNS IN CRITICAL ILLNESS

In critical care, accurate prognosis assessment, including antimicrobial therapy, is vital for guiding treatment decisions. The Acute Physiology and Chronic Health Evaluation (APACHE) and quick Sequential Organ Failure Assessment (qSOFA) scores are widely used for predicting patient outcomes. Yet, their relative performance and potential implications for antimicrobial stewardship need further exploration. Objective: To evaluate the disease prognosis patterns in critically ill patients by comparing the predictive performance of APACHE and qSOFA scores and to explore their correlation with antimicrobial misuse. Methods: A comparative study was conducted at the National Hospital and Medical Center in Lahore from January to November 2023. The study included 138 critically ill patients. Demographic information, clinical parameters, and medical history were collected, explicitly focusing on APACHE and qSOFA scores recorded at admission and during follow-up. Statistical analysis, including Pearson's and Spearman's correlation coefficients and logistic regression models, was performed using RStudio to assess the predictive ability of these scores for disease progression and patient outcomes. Results: The analysis revealed a moderate positive correlation (r=0.51) between qSOFA and APACHE-II scores, suggesting that both scores align in assessing the severity of illness in critically ill patients. The predictive models indicated that combining qSOFA and APACHE-II scores enhanced the accuracy of predicting critical outcomes, such as shortness of breath (SOB), with a combined model AUC of 0.659 compared to 0.601 for the APACHE-II model alone. Conclusion: The findings underscore the value of integrating multiple clinical scoring systems in managing critically ill patients. Such integration can aid in more judicious use of antibiotics, potentially mitigating the risk of antimicrobial resistance. The study advocates incorporating these insights into clinical guidelines and decision-making processes in critical care settings.

Using Social Media to Understand Primary Discussions in Gastrointestinal Cancers: Machine Learning Approach.

The incidence and mortality rates of gastrointestinal (GI) cancers are high in the United States as well as worldwide. The widespread use of social media provides unique opportunities to facilitate the dissemination of information, especially in the context of health. We aim to characterize the public's primary discussions, including perceptions, concerns, and interests toward GI cancers, from prevention, diagnosis, and treatment to survivorship care through the social media platform Twitter, using tweets posted by Twitter users. We analyzed 87 860 Twitter posts related to GI cancers. We used machine learning with natural language processing to identify salient topics and themes in the collected tweets. The most common themes across all GI cancer types included cancer risk prevention and awareness outreach programs, risk factors including lifestyles (primarily diet), and cancer survivorship-related discussions (primarily GI symptoms and quality of life). GI symptom-related tweets were prevalent in patients with colorectal and stomach cancers, whereas themes of newer clinical trials, end-of-life trials, palliative care trials, and disease prognosis were common in tweets related to liver/biliary and pancreatic cancers. Our research emphasizes the importance of individualized approaches in managing GI cancers, considering lifestyle and diet, the need for comprehensive survivorship care, raising awareness, delivering information, and improving targeted interventions related to GI cancers. Our study suggests utilizing Twitter data to better understand the real-world interest and concerns about GI cancers among the public, which can guide future patient-centered research in this field.

Utility of Axillary Staging in Older Patients with HER2-Positive Breast Cancer.

The utility of sentinel lymph node biopsy (SLNB) in older patients remains controversial. Advancements in human epidermal growth factor receptor 2 (HER2)-directed therapy have revolutionized disease response rates and prognosis, supporting efforts to re-evaluate the utility of SLNB. We aimed to assess the differences in treatment and overall survival (OS) in older patients with HER2-positive breast cancer based on SLNB. Using the National Cancer Database (2010-2020), patients ≥ 70years of age diagnosed with cT1-2/cN0/M0, HER2-positive breast cancer were identified. Logistic regression assessed associations with SLNB, systemic therapy, and radiation. Cox proportional hazard models were used to identify factors associated with OS. Analyses were stratified by treatment sequence, i.e. upfront surgery or neoadjuvant therapy (NAT) followed by surgery. Of the 17,609 patients included, 94% underwent upfront surgery (n=16,492) and the remaining underwent NAT (n=1117). Those who underwent SLNB were more likely to receive adjuvant therapy, irrespective of nodal status {upfront surgery/systemic therapy (odds ratio [OR] 2.82, 95% confidence interval [CI] 2.17-3.67); upfront surgery/radiation (OR 3.97, 95% CI 3.03-5.21); NAT/radiation (OR 5.69, 95% CI 1.83-17.69)}. The breast pathologic complete response (pCR) rate was highest among the hormone receptor (HR)-negative/HER2-positive subtype (50.0%), of which none were found to be ypN+. Comorbidity burden was associated with significantly lower rates of adjuvant systemic therapy and worse OS. Patients who underwent SLNB, regardless of pN status, were more likely to receive adjuvant therapy. Nodal positivity is exceedingly rare for patients with a breast pCR following NAT, especially among the HR-negative/HER2-positive subtype. It is reasonable to consider omission of SLNB in select subgroups of older patients with HER2-positive breast cancer.

Clinical and radiological variants of the nail–patella syndrome: Why is it important to make a correct diagnosis?

BACKGROUND: Nail–patella syndrome is an autosomal dominant disease caused by LMX1B mutations, characterized by a combination of dermatological and musculoskeletal abnormalities. The classic tetrad of the nail–patella syndrome includes hypoplasia of the nail plates, absence or hypoplasia of the patella, elbow joint contractures, often accompanied by posterior subluxation of the radial head, presence of “iliac horns” on radiographs of the pelvic bones, and renal changes. However, its diagnosis and treatment are not described in sufficient detail in domestic and foreign literature. AIM: This study aimed to analyze the variability of clinical and radiological manifestations in children with nail–patella syndrome. MATERIALS AND METHODS: Nine patients with nail–patella syndrome were examined and treated (aged 4 months to 14 years). All children presented to the H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery with a diagnosis of congenital multiple arthrogryposis. The examination results excluded this diagnosis. The diagnosis of nail–patella syndrome has been verified. RESULTS: Various variants of the nail–patella syndrome have been identified, with lesions to only the elbow joints (n = 3), damage to the upper and lower extremities (n = 5), and damage to only the lower extremities (n = 1). All patients had hypoplasia of fingernail plates. However, the pathognomonic sign – “iliac horns” on radiographs of the pelvic bones – was noted only in 33% of cases. Regarding functional disorders of the kidneys, proteinuria was observed in one patient, which appeared at the age of 15 years. In two cases, the parents suffered from this disease. A genetic examination was performed in three patients: a pathogenic variant in LMX1B was detected in a heterozygous state. All patients underwent conservative treatment for knee and elbow joint contractures and foot deformities. When casting, the presence of dislocation of the radial head or patella was considered. Surgical treatment was performed on five patients. The results of surgery on the lower extremities (knee contractures, foot deformities, and hip dislocation) were good in 87% of the cases. Recurrence of flexion contractures in the elbow joints was observed both after conservative and after surgical treatment in all cases; however, their severity varied. CONCLUSIONS: Clinical observations were considered to attract the attention of doctors of different specialties to a rare pathology. Moreover, a molecular genetic study is recommended for the timely diagnosis of the nail–patella syndrome, determining the treatment techniques and disease prognosis.

Comprehensive analysis of the association between triglyceride-glucose index and coronary artery disease severity across different glucose metabolism states: a large-scale cross-sectional study from an Asian cohort

BackgroundThe triglyceride-glucose (TyG) index is associated with the development and prognosis of coronary artery disease (CAD). However, the impact of the TyG index on CAD severity across different glucose metabolism states exhibits significant disparities in previous research.MethodsThis cross-sectional study comprised 10,433 participants from a prospective cohort. Participants were categorized into four groups based on glucose metabolism state: normal glucose regulation (NGR), prediabetes (pre-DM), diabetes mellitus (DM) without insulin prescribed (Rx), and DM with insulin Rx. The TyG index was determined by the following formula: Ln [TG (mg/dL) × FPG (mg/dL) / 2], where TG is triglycerides and FPG is fasting plasm glucose. Statistical methods such as binary logistic regression, interaction analysis, restricted cubic spline (RCS), and receiver operating characteristic (ROC) were employed to analyze the relationship between the TyG index and CAD severity across the entire population and glucose metabolism subgroups. Mediation analysis was conducted to examine the mediating effects of glycated hemoglobin (HbA1c) on these relationships. Sensitivity analysis was performed to ensure the robustness of the findings.ResultsMultivariable logistic regression analysis revealed a significant positive association between the TyG index and multi-vessel CAD in the entire population (OR: 1.34; 95% CI: 1.22–1.47 per 1-unit increment). Subgroup analysis demonstrated consistent positive associations in the NGR, pre-DM, and DM non-insulin Rx groups, with the highest OR observed in the NGR group (OR: 1.67; 95% CI: 1.3–2.14 per 1-unit increment). No correlation was found in the DM with insulin Rx subgroup. RCS analyses indicated the distinct dose-response relationships across different glucose metabolism subgroups. Including the TyG index in the established model slightly improved the predictive accuracy, particularly in the NGR group. Mediation analyses showed varying mediating effects of HbA1c among different glucose metabolism subgroups. Sensitivity analysis confirmed the robustness of the aforementioned relationships in the new-onset CAD population and in individuals not using antilipidemic medications.ConclusionsThe TyG index positively associated with CAD severity across all glucose metabolism states, except for individuals receiving insulin treatment. Moreover, it might serve as a supplementary noninvasive predictor of CAD severity in addition to established factors, especially in NGR patients.Graphical abstract

Role of serum complement C3 and C4 on kidney outcomes in IgA nephropathy

IgA Nephropathy (IgAN) is the most prevalent glomerular disease worldwide. Complement system activation is crucial in its pathogenesis. Few studies correlated serum C3 and C4 with disease activity and prognosis. This retrospective study investigated the prognostic value of serum complement at the time of diagnosis in patients with IgAN. Specifically we evaluated whether adding serum C3 and C4 levels to established predictive models-one based on variables related to chronic kidney disease (CKD) progression and another incorporating variables from the International IgA Prediction Tool (IntIgAPT)-enhances the accuracy of outcome prediction. A composite renal outcome was defined as 50% decline in eGFR or onset of kidney failure. 101 patients were stratified according to baseline C3 levels in three groups (Low, Medium and High). During a median follow-up of 54 months, the Low group exhibited higher incidence of primary outcome (16.3 events vs 2.9 and 1.7 events × 100 pts/year, p = 0.0026). Model-1 (M1), consisting of CKD progression variables, and Model-3 (M3), comprising IntIgANPT variables, were implemented with baseline C3 and C4 to create Model-2 (M2) and Model-4 (M4), respectively. M2 demonstrated better predictive performance over M1, showing higher discrimination (lower AIC and BIC, higher C-index and NR2). Similarly, M4 outperformed M3, showing enhanced outcome prediction when C3 and C4 levels were added. Implementation of serum C3 and C4 can enhance prediction accuracy of already-validated prognostic models in IgAN. Lower C3 and higher C4 levels were associated with poorer prognosis, highlighting a more 'Complement-Pathic' subset of patients.

Influenza-induced alveolar macrophages protect against death by malaria-associated acute lung injury.

Lower respiratory tract infections are common in malaria-endemic areas, and there is some evidence that co-infections between various bacteria/viruses and Plasmodium may affect disease prognosis. In this study, we report the novel finding that co-infection with influenza/A/X31 protects mice from death by Plasmodium berghei NK65-Edinburgh, a model of severe malarial pulmonary leak which underpins malaria-associated acute lung injury (MA-ALI) and malaria-associated acute respiratory distress (MA-ARDS). Co-infected mice exhibit equivalent parasitemia as mice with malaria only, suggesting that the survival phenotype is due to differences in immune kinetics. We demonstrated that the pulmonary leak typical of Pb E is attenuated in co-infected mice without alteration in CD8 T cell activation and recruitment to the lung. Upon further examination of the immune response to influenza/A/X31 we identified a population of arginase 1-expressing alveolar macrophages that traffic to the lungs early during infection. In vitro these macrophages inhibit CD8 T cell activation and proliferation better than non-arginase expressing cells. Removal of arginase-1 expressing alveolar macrophages in vivo via administration of the antimetabolite gemcitabine removed the protective effects of influenza/A/X31co-infection on MA-ALI. This study opens a route to better understanding of how to modulate the immunopathology observed in pulmonary leak associated with severe malaria, which must be achieved to rationally design therapeutic interventions for MA-ARDS / MA-ALI.

Comprehensive Quality Analysis of Conventional and Novel Biomarkers in Diagnosing and Predicting Prognosis of Coronary Artery Disease, Acute Coronary Syndrome, and Heart Failure, a Comprehensive Literature Review.

Coronary artery disease (CAD), acute coronary syndrome (ACS), and heart failure (HF) are major global health issues with high morbidity and mortality rates. Biomarkers like cardiac troponins (cTn) and natriuretic peptides (NPs) are crucial tools in cardiology, but numerous new biomarkers have emerged, proving increasingly valuable in CAD/ACS. These biomarkers are classified based on their mechanisms, such as fibrosis, metabolism, inflammation, and congestion. The integration of established and emerging biomarkers into clinical practice is an ongoing process, and recognizing their strengths and limitations is crucial for their accurate interpretation, incorporation into clinical settings, and improved management of CVD patients. We explored established biomarkers like cTn, NPs, and CRP, alongside newer biomarkers such as Apo-A1, IL-17E, IgA, Gal-3, sST2, GDF-15, MPO, H-FABP, Lp-PLA2, and ncRNAs; provided evidence of their utility in CAD/ACS diagnosis and prognosis; and empowered clinicians to confidently integrate these biomarkers into clinical practice based on solid evidence.

A Comprehensive Framework for Pathology Classification Bridging Precision and Interpretability

Pathology classification is an indispensable component of medical diagnostics, facilitating accurate disease identification, prognosis determination, and treatment planning. However, the increasing complexity and heterogeneity of pathological manifestations pose significant challenges to traditional classification methodologies. This abstract presents a novel framework that integrates advanced machine learning techniques with domain-specific expertise to enhance the precision and interpretability of pathology classification. Our framework adopts a multi-modal approach, leveraging diverse data sources including histopathological images, clinical records, genomic profiles, and molecular biomarkers. Through feature fusion and dimensionality reduction techniques, we effectively capture intricate patterns and latent relationships embedded within the data, enabling robust classification across diverse pathological conditions. Furthermore, interpretability is prioritized through the incorporation of explainable AI methodologies, facilitating the identification of salient features and decision rationales underlying classification outcomes. This ensures transparency and trustworthiness in the diagnostic process, empowering clinicians to make informed decisions and refine treatment strategies. Validation of our framework across various pathological contexts demonstrates superior performance compared to conventional approaches, exhibiting high accuracy, sensitivity, and specificity. Moreover, its modular architecture facilitates customization and scalability, accommodating evolving diagnostic needs and emerging technological advancements. In conclusion, our proposed framework represents a significant advancement in pathology classification, offering a synergistic blend of computational sophistication and clinical relevance. By seamlessly integrating cutting-edge technologies with domain knowledge, it holds promise for revolutionizing diagnostic practices and improving patient outcomes in the realm of precision medicine.

Applications of artificial intelligence in computed tomography imaging for phenotyping pulmonary hypertension.

Pulmonary hypertension is a heterogeneous condition with significant morbidity and mortality. Computer tomography (CT) plays a central role in determining the phenotype of pulmonary hypertension, informing treatment strategies. Many artificial intelligence tools have been developed in this modality for the assessment of pulmonary hypertension. This article reviews the latest CT artificial intelligence applications in pulmonary hypertension and related diseases. Multistructure segmentation tools have been developed in both pulmonary hypertension and nonpulmonary hypertension cohorts using state-of-the-art UNet architecture. These segmentations correspond well with those of trained radiologists, giving clinically valuable metrics in significantly less time. Artificial intelligence lung parenchymal assessment accurately identifies and quantifies lung disease patterns by integrating multiple radiomic techniques such as texture analysis and classification. This gives valuable information on disease burden and prognosis. There are many accurate artificial intelligence tools to detect acute pulmonary embolism. Detection of chronic pulmonary embolism proves more challenging with further research required. There are numerous artificial intelligence tools being developed to identify and quantify many clinically relevant parameters in both pulmonary hypertension and related disease cohorts. These potentially provide accurate and efficient clinical information, impacting clinical decision-making.

The role of HDAC3 in inflammation: mechanisms and therapeutic implications.

Histone deacetylases (HDACs) are critical regulators of inflammatory gene expression, and the efficacy of pan-HDAC inhibitors has been implicated in various disease conditions. However, it remains largely unclear how HDACs precisely regulate inflammation. To this end, evaluating the isoform-specific function of HDACs is critical, and the isoform-specific targeting could also circumvent the off-target effects of pan-HDAC inhibitors. This review provides an overview of the roles of HDAC3, a class I HDAC isoform, in modulating inflammatory responses and discusses the molecular mechanisms by which HDAC3 regulates inflammation associated with brain pathology, arthritis, cardiovascular diseases, lung pathology, allergic conditions, and kidney disorders. The articles also identify knowledge gaps in the field for future studies. Despite some conflicting reports, the selective inhibition of HDAC3 has been demonstrated to play a beneficial role in various inflammatory pathologies. Exploring the potential of HDAC3 inhibition to improve disease prognosis is a promising avenue requiring further investigation.

Liver fibrosis analysis using digital pathology.

Digital pathology has enabled the noninvasive quantification of pathological parameters. In addition, the combination of digital pathology and artificial intelligence has enabled the analysis of a vast amount of information, leading to the sharing of much information and the elimination of knowledge gaps. Fibrosis, which reflects chronic inflammation, is the most important pathological parameter in chronic liver diseases, such as viral hepatitis and metabolic dysfunction-associated steatotic liver disease. It has been reported that the quantitative evaluation of various fibrotic parameters by digital pathology can predict the prognosis of liver disease and hepatocarcinogenesis. Liver fibrosis evaluation methods include 1 fiber quantification, 2 elastin and collagen quantification, 3s harmonic generation/two photon excitation fluorescence (SHG/TPE) microscopy, and 4 Fibronest™.. In this review, we provide an overview of role of digital pathology on the evaluation of fibrosis in liver disease and the characteristics of recent methods to assess liver fibrosis.

A Fully Biodegradable and Ultra‐Sensitive Crack‐Based Strain Sensor for Biomechanical Signal Monitoring

AbstractA fully biodegradable, ultra‐sensitive, and soft strain sensor is pivotal for temporary, real‐time monitoring of microdeformations, crucial in disease diagnosis, surgical precision, and prognosis of muscular, and vascular conditions. Nevertheless, the strain sensitivity of previous biodegradable sensors, denoted by gauge factor (GF) up to ≈100, falls short of requirements for complex biomedical monitoring scenarios, specifically monitoring cardio‐cerebrovascular diseases with microscale variations in vascular surface strain. Here, a fully biodegradable, ultra‐sensitive crack‐based flexible strain sensor is introduced achieving GF of 1355 at 1.5% strain through integration of molybdenum (Mo) film, molybdenum trioxide (MoO3) adhesion layer, and polycaprolactone (PCL) substrate. Analysis of crack morphology of biodegradable thin‐film metals, including Mo, tungsten (W), and magnesium (Mg), reveals material‐dependent sensitivity and repeatability of crack‐based strain sensors. The effect of the adhesion layer and polymer substrate is also investigated. Overall morphological studies on the sensor present a comprehensive understanding of metal film cracking behavior and corresponding performance characterization, showing significant potential for highly sensitive sensors. A hybrid membrane composed of candelilla wax (Cw), beeswax (Bw), and polybutylene adipate‐co‐terephthalate (PBAT) is introduced to provide hydrophobic, yet flexible encapsulation. In vivo, short‐term (≈3 days) monitoring of vascular pulsatility underscores the potential of the sensing tool for rapid, accurate, and temporal disease diagnosis and treatment.

Research progress of neuron-specific enolase in cognitive disorder: a mini review.

Numerous studies have demonstrated that neuron-specific enolase (NSE) serves as a distinctive indicator of neuronal injury, with its concentration in blood reflecting the extent and magnitude of nervous system damage, and the expression of serum NSE is correlated with cognitive dysfunction. The assessment of NSE holds significant importance in diagnosing cognitive dysfunction, assessing disease severity, predicting prognosis, and guiding treatment. In this review, the research progress of NSE in cognitive dysfunction was reviewed, and the value of serum NSE level in predicting disease severity and prognosis of patients with cognitive dysfunction was discussed.

Pathway-Based Mendelian Randomization for Pre-Infection IL-6 Levels Highlights Its Role in Coronavirus Disease.

Interleukin 6 (IL-6) levels at hospital admission have been suggested for disease prognosis, and IL-6 antagonists have been suggested for the treatment of patients with severe COVID-19. However, less is known about the relationship between pre-COVID-19 IL-6 levels and the risk of severe COVID-19. To fill in this gap, here we extensively investigated the association of genetically instrumented IL-6 pathway components with the risk of severe COVID-19. We used a two-sample Mendelian randomization study design and retrieved genetic instruments for blood biomarkers of IL-6 activation, including IL-6, soluble IL-6 receptor, IL-6 signal transducer, and CRP, from respective large available GWASs. To establish associations of these instruments with COVID-19 outcomes, we used data from the Host Genetics Initiative and GenOMICC studies. Our analyses revealed inverse associations of genetically instrumented levels of IL-6 and its soluble receptor with the risk of developing severe disease (OR = 0.60 and 0.94, respectively). They also demonstrated a positive association of severe disease with the soluble signal transducer level (OR = 1.13). Only IL-6 associations with severe COVID-19 outcomes reached the significance threshold corrected for multiple testing (p < 0.003; with COVID-19 hospitalization and critical illness). These potential causal relationships for pre-COVID-19 IL-6 levels with the risk of developing severe symptoms provide opportunities for further evaluation of these factors as prognostic/preventive markers of severe COVID-19. Further studies will need to clarify whether the higher risk for a severe disease course with lower baseline IL-6 levels may also extend to other infectious diseases.

The intratumoral microbiota biomarkers for predicting survival and efficacy of immunotherapy in patients with ovarian serous cystadenocarcinoma

BackgroundOvarian serous cystadenocarcinoma, accounting for about 90% of ovarian cancers, is frequently diagnosed at advanced stages, leading to suboptimal treatment outcomes. Given the malignant nature of the disease, effective biomarkers for accurate prediction and personalized treatment remain an urgent clinical need.MethodsIn this study, we analyzed the microbial contents of 453 ovarian serous cystadenocarcinoma and 68 adjacent non-cancerous samples. A univariate Cox regression model was used to identify microorganisms significantly associated with survival and a prognostic risk score model constructed using LASSO Cox regression analysis. Patients were subsequently categorized into high-risk and low-risk groups based on their risk scores.ResultsSurvival analysis revealed that patients in the low-risk group had a higher overall survival rate. A nomogram was constructed for easy visualization of the prognostic model. Analysis of immune cell infiltration and immune checkpoint gene expression in both groups showed that both parameters were positively correlated with the risk level, indicating an increased immune response in higher risk groups.ConclusionOur findings suggest that microbial profiles in ovarian serous cystadenocarcinoma may serve as viable clinical prognostic indicators. This study provides novel insights into the potential impact of intratumoral microbial communities on disease prognosis and opens avenues for future therapeutic interventions targeting these microorganisms.

Surrogate Biomarker to Identify Obesity and Predict Cardiovascular Disease Risk: A Systematic Review

The study investigates the relationship between obesity and cardiovascular disease (CVD) risk, comparing the use of body mass index (BMI) versus other biomarkers in predicting CVD risk among obese individuals. It conducts a systematic literature review following the PRISMA guidelines, searching databases for relevant articles published from 2017-2022. The review analyzes 12 eligible articles and finds that factors beyond just BMI, such as genetics, physical activity, metabolic disorders, previous heart disease history, nutrition, fat distribution, and changes in BMI, can significantly impact the prognosis of heart disease in obese individuals. Importantly, the study shows that measures of fat distribution, like waist-to-height ratio, waist circumference, log-transformed body shape index (LBSIZ), and the ratio of visceral adipose tissue (VAT) to subcutaneous adipose tissue (SAT), are superior to BMI in predicting CVD risk among those with obesity. The key takeaway is that while obesity is strongly linked to CVD risk, BMI alone often fails to predict that risk accurately. Fat distribution measures may be a more effective tool for identifying obesity status and predicting associated CVD risk compared to relying solely on BMI.

TICRR as a potential prognostic biomarker for lung adenocarcinoma: A comprehensive analysis using TCGA database.

To investigate the role of TopBP1-interacting checkpoint and replication regulator (TICRR) in the tumorigenesis and prognosis of lung adenocarcinoma (LUAD) patients. Wilcoxon signed-rank test and logistic regression were utilized to analyze the relationship between clinical characteristics and TICRR expression in LUAD from TCGA dataset. Kaplan-Meier plots and Cox regressions were used to assess the impact of TICRR impact on prognosis. ROC curves and nomograms were generated to further evaluate the relationship between TICRR expression and the risk of LUAD. Gene set enrichment analysis (GSEA) was conducted on TCGA dataset, and ssGSEA was employed to investigate the association between TICRR and immune infiltrates. The results showed that high TICRR expression was significantly associated with various clinical factors including gender, age, pathological stage, T stage, N stage, M stage, outcome of primary therapy and smoking status. ROC curves also demonstrated that TICRR was a promising biomarker for molecular pathology diagnosis in LUAD patients (AUC = 0.952). Further analysis using gene ontology (GO) term enrichment and GSEA revealed an abnormal correlation between TICRR expression and cell division. Interestingly, ssGSEA analysis showed that TICRR expression correlated with multiple immune cell types, such as Th2 cell, TFH cell, mast cell, iDC, eosinophils, and dendritic cell. Lastly, the KM-plotters indicated that LUAD patients with high TICRR expression obtained worse life expectancy (P < .001). TICRR has proven to be a valuable tool in predicting disease progression and prognosis in patients with LUAD, thereby establishing itself as a fitting biomarker for forecasting overall survival (OS) of LUAD patients.

Relationship between nailfold capillaroscopy findings and the etiology and prognosis of interstitial lung disease

ObjectivesConnective tissue-associated interstitial lung diseases (CTD-ILD) are believed to be caused by microvascular damage. The objective of this study was to assess the nailfold capillaroscopy (NFC) pattern in patients diagnosed with both CTD-ILD and non-CTD-ILD to identify microvascular changes and determine the relation between capillaroscopic parameters, clinical variables, and disease-related measurements.Patients and methodsThis cross-sectional study included 95 patients with interstitial lung disease who applied to our Rheumatology and Chest Clinics between September 2021 and July 2023. The patients were divided into two groups based on their diagnosis: non-CTD-ILD (group 1) and CTD-ILD (group 2). Nailfold capillaroscopy was performed.ResultsNinety-five patients, 49 (51% female, mean age 62.31 ± 11.027 years) in group 1 and 46 (69.6% female, mean age 62.09 ± 10.887 years) in group 2, were included in the study. Abnormal capillary morphologies were both detected in the CTD-ILD group and the non-CTD-ILD groups. In patients with a usual interstitial pneumonia (UIP) pattern on chest computed tomography (CT), tortuosity was higher than in patients with non-specific interstitial pneumonia (NSIP) (P = 0.041), and the proportion of tortuosity increased significantly as the duration of the disease increased (P = 0.016).ConclusionOur study highlights capillaroscopic abnormalities alone may not be sufficient to differentiate CTD-ILD (other than systemic sclerosis) from non-CTD-ILD. The presence of NFC abnormalities in non-CTD-ILD may suggest that fibrotic lung disease could potentially play a role in the deterioration of the microvascular structure or abnormal angiogenesis. Our study demonstrated that a multidisciplinary approach, incorporating clinical, morphological, pathological, and serological evaluations, is necessary for interpreting ILD.Key Points• Capillaroscopic abnormalities can also be seen in non-CTD-ILD.• Capillaroscopy findings do not distinguish the non-Ssc etiology of ILD.• Nailfold capillaroscopy may have the potential to serve as a useful tool in predicting prognosis and monitoring the disease progression in patients with idiopathic pulmonary fibrosis (IPF).

HTERT Epigenetics Provides New Perspectives for Diagnosis and Evidence-Based Guidance of Chemotherapy in Cancer.

Strong epigenetic pan-cancer biomarkers are required to meet several current, urgent clinical needs and to further improve the present chemotherapeutic standard. We have concentrated on the investigation of epigenetic alteration of the hTERT gene, which is frequently epigenetically dysregulated in a number of cancers in specific developmental stages. Distinct DNA methylation profiles were identified in our data on early urothelial cancer. An efficient EpihTERT assay could be developed utilizing suitable combinations with sequence-dependent thermodynamic parameters to distinguish between differentially methylated states. We infer from this data set, the epigenetic context, and the related literature that a CpG-rich, 2800 bp region, a prominent CpG island, surrounding the transcription start of the hTERT gene is the crucial epigenetic zone for the development of a potent biomarker. In order to accurately describe this region, we have named it "Acheron" (Ἀχέρων). In Greek mythology, this is the river of woe and misery and the path to the underworld. Exploitation of the DNA methylation profiles focused on this region, e.g., idiolocal normalized Methylation Specific PCR (IDLN-MSP), opens up a wide range of new possibilities for diagnosis, determination of prognosis, follow-up, and detection of residual disease. It may also have broad implications for the choice of chemotherapy.