Monitoring Disease Progression Research Articles

Posttraumatic osteoarthritis after athletic knee injury: A narrative review of diagnostic imaging strategies.

Intraarticular knee injuries and subsequent posttraumatic arthritis (PTOA) are common in athletes. Unfortunately, PTOA may significantly affect performance and overall function, but this condition remains difficult to characterize. In this review, we provide an overview of imaging modalities used to evaluate PTOA among athletes and physically active individuals following knee injury, with the goal to discuss the strengths and limitations of their application in this population. A literature search was performed to identify clinical studies focusing of knee injuries in athletes and athletic persons, specifically using imaging for diagnosis or monitoring disease progression. A total of 81 articles were identified, and 23 were included for review. Studies on plain radiographs (n = 8) and magnetic resonance imaging (MRI) assessed arthritic burden (n = 13), with MRI able to depict the earliest cartilage changes. Few studies (n = 2) leveraged ultrasound. Challenges persist, particularly regarding standardization and reliability across different radiographic grading systems. Additionally, further research is needed to establish the clinical significance of techniques to assess cartilage composition on MRI, including ultrashort echo-time enhanced T2*, T1rho and T2 imaging. Addressing these challenges through standardized protocols and intensified research efforts will enhance the diagnostic utility of imaging modalities in musculoskeletal medicine and enable high-quality prospective studies.

Possible alteration in serum ferritin level in malaria infected HIV seropositive individuals in Nauth, Nnewi, Nigeria

The role of Ferritin in monitoring disease progression in immune compromised HIV individuals with an underlying active infection like malaria is a subject of growing research. Ferritin being an acute phase protein, plays important role in assessing the range of damage due to inflammation in immunosuppressed patients leaving in highly endemic malaria regions. The levels of serum ferritin in malaria infected HIV positive individuals in Nnamdi Azikiwe University teaching hospital (NAUTH), Nnewi, Anambra State, Nigeria, was assessed. Questionnaire was used to obtain the demographic details of the participants, and to rule out other inflammatory infections. 88 participants of the age group 18 - 65 years, comprising 24 with HIV infection, 22 with HIV and Malaria co-infection, 22 with Malaria infection, and a control group of 22 individuals with neither HIV nor malaria infection were randomly recruited. Ferritin level was determined using enzyme linked immunosorbent assay technique and a cross sectional prospective study design was used. A significantly high mean serum ferritin level was observed in HIV infected individuals with and/ or without malaria co-infection than in malaria positive and control group (p<0.05 respectively). Serum ferritin level was significantly higher in female participants than in male counterparts (p < 0.05). Serum ferritin level was significantly higher in individuals with CD4 count >500 than in CD4 count ≤ 500 (p < 0.05). Blood pressure was significantly higher in HIV infected and malaria positive individuals when compared with controls. The increased serum ferritin level and higher blood pressure in HIV infected participants suggests active inflammatory process, reduced immunity and hypertension which may have worsened by malaria co-infection. This may subsequently lead to vascular and endothelia damage causing disease severity.

A mediation approach in resting-state connectivity between the medial prefrontal cortex and anterior cingulate in mild cognitive impairment

Mild cognitive impairment (MCI) is recognized as the prodromal phase of dementia, a condition that can be either maintained or reversed through timely medical interventions to prevent cognitive decline. Considerable studies using functional magnetic resonance imaging (fMRI) have indicated that altered activity in the medial prefrontal cortex (mPFC) serves as an indicator of various cognitive stages of aging. However, the impacts of intrinsic functional connectivity in the mPFC as a mediator on cognitive performance in individuals with and without MCI have not been fully understood. In this study, we recruited 42 MCI patients and 57 healthy controls, assessing their cognitive abilities and functional brain connectivity patterns through neuropsychological evaluations and resting-state fMRI, respectively. The MCI patients exhibited poorer performance on multiple neuropsychological tests compared to the healthy controls. At the neural level, functional connectivity between the mPFC and the anterior cingulate cortex (ACC) was significantly weaker in the MCI group and correlated with multiple neuropsychological test scores. The result of the mediation analysis further demonstrated that functional connectivity between the mPFC and ACC notably mediated the relationship between the MCI and semantic fluency performance. These findings suggest that altered mPFC-ACC connectivity may have a plausible causal influence on cognitive decline and provide implications for early identifications of neurodegenerative diseases and precise monitoring of disease progression.

Combined microfluidic enrichment and staining workflow for single-cell analysis of circulating tumor cells in metastatic prostate cancer patients

Circulating tumor cells (CTCs) are precursors of cancer in the blood and provide an attractive source for dynamic monitoring of disease progression and tumor heterogeneity. However, the scarcity of CTCs in the bloodstream has limited their use in clinical practice. In this study, we present a workflow for easy detection of CTCs by cytokeratin staining using the FDA-cleared Parsortix device for size-based microfluidic enrichment. To minimize sample handling, the isolated cells are stained inside the separation cassette and harvested for subsequent single cell isolation and whole genome copy-number analysis. We validated the workflow on a panel of four prostate cancer cell lines spiked into healthy donor blood collected in CellRescue or EDTA tubes, resulting in mean recoveries of 42% (16–69%). Furthermore, we evaluated the clinical utility in a cohort of 12 metastatic prostate cancer patients and found CTCs in 67% of patients ranging from 0 to 1172 CTCs in 10 mL blood. Additionally, we isolated single patient-derived CTCs and identified genomic aberrations associated with treatment response and clinical outcome. Thus, this workflow provides a readily scalable strategy for analysis of single CTCs, applicable for use in monitoring studies to identify genomic variations important for guiding clinical therapy decision.

Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient’s disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.g., ixazomib, lenalidomide, dexamethasone (IRd) vs lenalidomide, dexamethasone (Rd). Using TOURMALINE trial data, we trained and internally validated our model on newly diagnosed myeloma patients (N = 703) and externally validated it on relapsed and refractory myeloma patients (N = 720). Our model achieved superior performance to a risk model based on the multiple myeloma international staging system (ISS) (p < 0.001, Bonferroni corrected) and comparable performance to survival models trained separately on each task, but unable to forecast biomarkers. Our approach outperformed state-of-the-art deep learning models, tailored towards forecasting, on predicting key disease biomarkers (p < 0.001, Bonferroni corrected). Finally, leveraging our model’s capacity to estimate individual-level treatment effects, we found that patients with IgA kappa myeloma appear to benefit the most from IRd. Our study suggests that a holistic assessment of a patient’s myeloma course is possible, potentially serving as the foundation for a personalized decision support system.

Long-term survival outcomes of 'watch and wait' in patients with bronchus-associated lymphoid tissue lymphoma: a multicenter real-world data analysis in Korea.

Bronchus-associated lymphoid tissue (BALT) is a rare cause of extranodal marginal zone lymphoma (MZL). Although most patients with BALT lymphoma (BALToma) show an indolent clinical course and are monitored without treatment, there are limited real-world data on the long-term outcome of "watch-and-wait' strategy in comparison with other treatments.The survival outcomes of patients newly diagnosed with BALToma at three tertiary hospitals in Korea undergoing two treatment strategies were analyzed: group A, patients who were monitored without any treatment or received only radiotherapy after diagnosis; and group B, patients receiving any kind of systemic chemotherapy after diagnosis, regardless of their history of any local treatment such as surgery or radiotherapy.Of the 67 patients included in our analysis, the 10-year progression-free survival (PFS) and 10-year overall survival (OS) rates were 65.3% and 83.2%, respectively. The 10-year PFS rates for observation or localized treatment and systemic chemotherapy were 78.7% and 56.9%, respectively (p = 0.044). Ten-year OS rates for observation or localized treatment and systemic chemotherapy were 100% and 71.7%, respectively (p = 0.016). Multivariate analysis showed that bilateral lung (HR 2.462, p = 0.047) and extrapulmonary organ (HR 4.485, p = 0.004) involvement were the only significant factors associated with poor PFS. Prognostic factor analysis for OS did not yield significant results.Patients with BALToma show a favorable prognosis, suggesting that observation or localized therapy alone may be effective for patient management. However, patients with bilateral lung or extrapulmonary involvement may require careful monitoring for disease progression and more aggressive treatment approaches.

Oculomotor and Vestibular Deficits in Friedreich Ataxia - Systematic Review and Meta-Analysis of Quantitative Measurements.

Disease-specific oculomotor assessments play a crucial role in the early diagnosis of hereditary cerebellar ataxias. Whereas several studies have reported on quantitative oculomotor and vestibular measurements in Friedreich's Ataxia (FRDA), the value of specific oculomotor paradigms remains unclear. We aimed to address this knowledge gap through a systematic literature review and providing disease-specific recommendations for a tailored set of eye-movement recordings in FRDA. MEDLINE and Embase were searched for studies reporting on quantitative oculomotor and/or vestibular measurements in FRDA-patients. Data on oculomotor and vestibular parameters were extracted and correlations with a range of clinical parameters were sought. Included studies (n = 17) reported on 185 patients. Abnormalities observed included the presence of saccadic intrusions (143/161) such as square-wave jerks (SWJ, 90/109) and ocular flutter (21/43), impaired eccentric gaze-holding (40/104), abnormal pursuit (81/93) and angular vestibulo-ocular reflex (aVOR) deficits (39/48). For visually-guided saccades (VGS), we frequently observed increases in saccade latency (27/38) and dysmetric saccades (71/93), whereas saccade velocity was more often preserved (37/43). Augmented anti-saccade (AS) latency, downbeat nystagmus and frequent macro-SWJ correlated with disease duration. Increased AS-latency and VGS-latency, frequent macro-SWJ, reduced aVOR-gain and augmented aVOR peak-latency correlated with disease severity. A broad range of oculomotor and vestibular deficits are documented in the literature. Impairments in pursuit, saccades and aVOR-responses are most commonly reported, and as such, should be prioritized as disease markers. Quantitative oculomotor testing in FRDA may facilitate early diagnosis and prove valuable in monitoring disease progression and treatment response.

Based on machine learning, CDC20 has been identified as a biomarker for postoperative recurrence and progression in stage I &amp; II lung adenocarcinoma patients

ObjectiveBy utilizing machine learning, we can identify genes that are associated with recurrence, invasion, and tumor stemness, thus uncovering new therapeutic targets.MethodsTo begin, we obtained a gene set related to recurrence and invasion from the GEO database, a comprehensive gene expression database. We then employed the Weighted Gene Co-expression Network Analysis (WGCNA) to identify core gene modules and perform functional enrichment analysis on them. Next, we utilized the random forest and random survival forest algorithms to calculate the genes within the key modules, resulting in the identification of three crucial genes. Subsequently, one of these key genes was selected for prognosis analysis and potential drug screening using the Kaplan-Meier tool. Finally, in order to examine the role of CDC20 in lung adenocarcinoma (LUAD), we conducted a variety of in vitro and in vivo experiments, including wound healing assay, colony formation assays, Transwell migration assays, flow cytometric cell cycle analysis, western blotting, and a mouse tumor model experiment.ResultsFirst, we collected a total of 279 samples from two datasets, GSE166722 and GSE31210, to identify 91 differentially expressed genes associated with recurrence, invasion, and stemness in lung adenocarcinoma. Functional enrichment analysis revealed that these key gene clusters were primarily involved in microtubule binding, spindle, chromosomal region, organelle fission, and nuclear division. Next, using machine learning, we identified and validated three hub genes (CDC45, CDC20, TPX2), with CDC20 showing the highest correlation with tumor stemness and limited previous research. Furthermore, we found a close association between CDC20 and clinical pathological features, poor overall survival (OS), progression-free interval (PFI), progression-free survival (PFS), and adverse prognosis in lung adenocarcinoma patients. Lastly, our functional research demonstrated that knocking down CDC20 could inhibit cancer cell migration, invasion, proliferation, cell cycle progression, and tumor growth possibly through the MAPK signaling pathway.ConclusionCDC20 has emerged as a novel biomarker for monitoring treatment response, recurrence, and disease progression in patients with lung adenocarcinoma. Due to its significance, further research studying CDC20 as a potential therapeutic target is warranted. Investigating the role of CDC20 could lead to valuable insights for developing new treatments and improving patient outcomes.

An automatic measure for speech intelligibility in dysarthrias—validation across multiple languages and neurological disorders

IntroductionDysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework.MethodsThe study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants’ speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed.ResultsVerification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure's discriminative capability.DiscussionThe ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation.

Drone Technology for Crop Disease Resistance: Innovations and Challenges

Drones have been used for diverse application purposes in precision agriculture and new ways of using them are being explored. Many drone applications have been developed for different purposes such as pest detection, crop yield prediction, crop spraying, yield estimation, water stress detection, land mapping, identifying nutrient deficiency in plants, weed detection, livestock control, protection of agricultural products and soil analysis. Drones can create georeferenced maps that pinpoint the exact location of disease outbreaks within a field. These maps help farmers and agronomists monitor disease progression and plan targeted interventions. Drone operations are highly dependent on weather conditions. High winds, rain, and fog can hinder drone flights and affect the quality of images captured. Addressing technical limitations, regulatory and safety concerns, economic barriers, and data management issues will be crucial for the widespread adoption of drones in agriculture. By overcoming these challenges, drone technology can become a vital tool in sustainable and effective crop disease management.

DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries.

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.

Gold Nanoparticles: An Emerging Novel Technology for Targeted Delivery System for Site-Specific Diseases

Abstract: Gold Nanoparticles (GNPs) have emerged as a novel technology in the field of targeted delivery systems, offering promising solutions for site-specific disease treatment. These nanoparticles possess unique physicochemical properties, such as controlled size, shape, and surface chemistry, which enable precise manipulation for enhanced therapeutic efficacy. The biocompatibility and ease of functionalization of GNPs facilitate the conjugation with various biomolecules, including drugs, peptides, and nucleic acids, thereby improving their targeted delivery capabilities. Recent advancements in nanotechnology have leveraged GNPs for the treatment of a range of diseases, particularly in oncology, cardiology, and neurology. In cancer therapy, GNPs can be engineered to target tumor cells selectively, minimizing damage to healthy tissues and reducing side effects. This is achieved through the conjugation of GNPs with tumor-specific ligands, antibodies, or aptamers, which direct the nanoparticles to malignant cells, allowing for localized drug release and improved therapeutic outcomes. Moreover, GNPs exhibit remarkable potential in diagnostic imaging and photothermal therapy. Their unique optical properties, such as surface plasmon resonance, enable their use as contrast agents in imaging techniques, providing high-resolution and real-time monitoring of disease progression. In photothermal therapy, GNPs convert light energy into heat, effectively destroying targeted cells with minimal invasiveness. The development of GNP-based delivery systems also addresses significant challenges in drug resistance and bioavailability. By overcoming biological barriers and enhancing cellular uptake, GNPs improve the pharmacokinetics and pharmacodynamics of therapeutic agents. However, despite these advancements, the clinical translation of GNPs faces challenges such as potential toxicity, long-term stability, and regulatory hurdles. In conclusion, gold nanoparticles represent a cutting-edge approach in targeted delivery systems, offering significant potential for site-specific disease treatment. Continued research and innovation are essential to overcome existing challenges and fully realize the clinical applications of GNPs, ultimately revolutionizing precision medicine. Gold nanoparticles exhibit unique physicochemical and optical properties. The gold nanoparticles have advanced techniques to cure different chronic diseases. Today, gold nanoparticles are aided by photodynamic therapy and radiation therapy as drug carriers. Due to this, researchers now focus on medical sciences to treat various diseases and therapeutic applications. This review provides all the aspects of gold-based nanoparticles, methods, and their pharmacological benefits in different fields of medical sciences. We also discuss various preparation methods and their advantages in pharmaceutical formulations.

Monitoring mouse papillomavirus-associated cancer development using longitudinal Pap smear screening.

Papanicolaou (Pap) smear has saved millions of women's lives as a valuable early screening tool for detecting human papillomavirus (HPV) cervical precancers and cancer. However, more than 200,000 women in the United States alone remain at risk for cervical cancer due to pre-existing HPV infection-induced precancers, as there are currently no effective treatments for HPV-associated precancers and cancers other than invasive procedures including a loop electrosurgical excision procedure (LEEP) to remove abnormal tissues. In the current study, we validated the use of Pap smears to monitor disease progression in our recently established mouse papillomavirus model. To the best of our knowledge, this is the first study that provides compelling evidence of applying Pap smears from cervicovaginal swabs to monitor disease progression in mice. This HPV-relevant cytology assay will enable us to develop and test novel antiviral and anti-tumor therapies using this model to eliminate HPV-associated diseases and cancers.

Mechanical rheological model on the assessment of elasticity and viscosity in tissue inflammation: A systematic review.

Understanding the extent of inflammation is crucial for early disease detection, monitoring disease progression, and evaluating treatment responses. Over the past decade, researchers have demonstrated the need to understand the extent of inflammation through qualitative or quantitative characterization of tissue viscoelasticity using different techniques. In this scientific review, an examination of research on the association between elasticity and Viscosity in diseases, particularly as tissue inflammation progresses, is conducted. A review of utilizing mechanical rheological models to characterize quantitative viscoelastic parameters of normal and inflamed tissues is also undertaken. Based on inclusion and exclusion criteria, we identified 14 full-text studies suitable for review out of 290 articles published from January 2000 to January 2024. We used PRISMA guidelines for the systematic review. In the review, three studies demonstrated the criterion used by the researchers in identifying the best rheological model. Eleven studies showed the clinical application of the rheological model in quantifying the viscoelastic properties of normal and pathological tissue. The review quantified viscoelastic parameters for normal and pathological tissue across various soft tissues. It evaluated the effectiveness of each viscoelastic property in distinguishing between normal and pathological tissue stiffness. Furthermore, the review outlined additional viscoelastic-related parameters for researchers to consider in future stiffness classification studies.

Parotid saliva and blood biomarkers in juvenile idiopathic arthritis in relation to temporomandibular joint magnetic resonance imaging findings.

Juvenile idiopathic arthritis (JIA) often affects the temporomandibular joint (TMJ) caused by an abnormal immune system that includes overactive inflammatory processes. Salivary biomarkers may be a powerful tool that can help establishing diagnosis, prognosis and monitor disease progress. The objective was to investigate biomarkers in parotid saliva and blood plasma in relation to temporomandibular joint (TMJ) magnetic resonance imaging (MRI) findings in patients with JIA and healthy individuals. Forty-five children aged 6 to 16 years with JIA and 16 healthy age- and sex-matched controls were included. Unstimulated parotid saliva samples and venous blood were collected. Biochemical analyses were performed for the cytokine biomarkers. The participants underwent MR imaging of the TMJs, where changes in the inflammatory and the damage domains were assessed. In the JIA patients, lower concentrations of IL-6R and gp130 were found in parotid saliva than in plasma. Higher concentrations of IL-6 were found in parotid saliva than in plasma. IL-6, IL-6R and gp130 in parotid saliva explained the presence of bone marrow oedema and effusion in the JIA patients. This study suggests that the IL-6 family in parotid saliva is associated with TMJ bone marrow oedema and effusion in patients with JIA, suggesting that IL-6 has promising properties as a parotid saliva biomarker for TMJ inflammatory activity.

Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging.

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson's disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.

Empowered chaotic local search-based differential evolution algorithm with entropy-based hybrid objective function for brain tumor segmentation

In neuro-oncology, the precise segmentation of brain tumors from Magnetic Resonance Images is crucial for diagnosis, treatment planning, and monitoring disease progression. Accurate segmentation helps determine the tumor’s size, location, and growth potential, which is essential for formulating effective treatment strategies. In response to this challenge, we developed a novel approach using Chaotic Local Search-Enhanced Differential Evolution (CJADE). CJADE, particularly its variant CJADE-M, which employs chaotic maps selected through a probability-based approach, has proven effective in optimizing brain tumor segmentation. Our study shows that CJADE-M outperforms traditional metaheuristic algorithms on various evaluation metrics. We further enhanced CJADE-M with an entropy-based hybrid objective function, which improved accuracy and reduced computational time in tumor segmentation compared to conventional methods like Minimum Cross-Entropy and Kapur. This makes our method suitable for real-time medical imaging analysis. Our findings indicate that CJADE-M, equipped with the hybrid objective function, achieves superior segmentation performance for both benign lobulated and malignant irregular tumors across metrics such as PSNR, FSIM, QILV, and HPSI. By providing a more accurate and efficient tool, our approach can significantly enhance the outcomes of brain tumor diagnosis and treatment, improving patient care in neuro-oncology.

Associations between Serum Metabolites and Female Cancers: a Bidirectional Two-sample Mendelian Randomization Study

Female cancers, especially breast, ovarian, cervical and endometrial cancers, constitute a major threat to women's health worldwide. In view of the complex genetic background of cancers cannot be fully explained with current genetic information, we used a bidirectional two-sample mendelian randomization approach to explore the causal associations between serum metabolites and four major female cancers—breast, ovarian, cervical and endometrial cancers. We analyzed the metabolites dataset from the Canadian Longitudinal Study of Aging and cancer datasets from the 10th round of the Finngen project. Replication analyses was performed with Cancer Association Consortium and Leo’s studies. Instrumental variables were analyzed using methods including the Wald ratio, inverse-variance weighted, MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane’s Q, Egger’s intercept, MR-PRESSO, and leave-one-out methods. After meticulous analysis, we obtained levels of 3-hydroxyoleoylcarnitine, hexadecanedioate, tetradecanedioate and carnitine C14 with robust causal associations with breast cancer, levels of 5alpha-androstan-3alpha,17beta-diol monosulfate (1), androstenediol (3beta,17beta) monosulfate (1), androsterone sulfate, and 5alpha-androstan-3beta,17beta-diol disulfate causal associations with endometrial cancer. The reverse analysis showed that breast, ovarian, and endometrial cancer and survival of breast and ovarian cancer were found to have causal relationships with 8, 5, 2, 6, and 3 metabolites, respectively. These insights underscore the potential roles of specific metabolites in the etiology of female cancers, providing new biomarkers for early detection, risk stratification, and disease progression monitoring. Further research could elucidate how these metabolites influence specific pathways in cancer development, offering theoretical foundations for prevention and treatment strategies.

Value of Mac-2 Binding Protein Glycosylation Isomer (M2BPGi) in Assessing Liver Fibrosis in Metabolic Dysfunction-Associated Liver Disease: A Comprehensive Review of its Serum Biomarker Role.

Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) is a broad condition characterized by lipid accumulation in the liver tissue, which can progress to fibrosis and cirrhosis if left untreated. Traditionally, liver biopsy is the gold standard for evaluating fibrosis. However, non-invasive biomarkers of liver fibrosis are developed to assess the fibrosis without the risk of biopsy complications. Novel serum biomarkers have emerged as a promising tool for non-invasive assessment of liver fibrosis in MAFLD patients. Several studies have shown that elevated levels of Mac-2 binding protein glycosylation isomer (M2BPGi) are associated with increased liver fibrosis severity in MAFLD patients. This suggests that M2BPGi could serve as a reliable marker for identifying individuals at higher risk of disease progression. Furthermore, the use of M2BPGi offers a non-invasive alternative to liver biopsy, which is invasive and prone to sampling errors. Overall, the usage of M2BPGi in assessing liver fibrosis in MAFLD holds great promise for improving risk stratification and monitoring disease progression in affected individuals. Further research is needed to validate its utility in clinical practice and establish standardized protocols for its implementation.

Exploring explainable AI features in the vocal biomarkers of lung disease

This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.