Diagnostic Analysis Research Articles

AST/ALT Ratio as an indicator of functional severity in chronic heart failure with reduced left ventricular ejection fraction: A prospective cross-sectional study

ObjectiveThe study was carried out to evaluate the role of the AST (Aspartate transaminase)/ALT (Alanine transaminase) ratio as an indicator of the functional severity in people with chronic heart failure (CHF) with reduced left ventricular ejection fraction. MethodsA prospective cross-sectional study was conducted in a tertiary care centre in South India among the individuals who had left ventricular ejection fraction (LVEF) of ≤40 %. The study period was between January 2021 and December 2021. Consecutive patients with the criteria were enrolled in the study. Study participants were grouped based on their AST/ALT ratio value (ratio<1 and ratio≥1). ResultsIn present study of 100 participants, there was a statistically significant difference between two groups with respect to ALT, AST/ALT ratio, and ALP (Alkaline phosphatase). There was a significant correlation between the APRI (AST to platelet ratio index) and FIB-4 (Fibrosis-4) with AST/ALT ratio. Diagnostic analysis of AST/ALT ratio to predict the severity of CHF with reduced EF, the area under the curve (AUC) was 0.547 (p-value = 0.5654) with a 95 % confidence interval of 0.299–0.795 with an optimal cut-off value of 0.6, sensitivity of 96.70 %, and specificity of 33.33 %. ConclusionThe AST/ALT ratio is increased in patients with CHF patients with reduced left ventricular ejection fraction. It is a simple predictor of left ventricular dysfunction in patients with heart failure with reduced ejection fraction.

Model Diagnostic Analysis in a Cold Basin Influenced by Frozen Soils With the Aid of Stable Isotope

AbstractUnderstanding the hydrological processes on the Tibetan Plateau (TP) under climate change is an important scientific question. The frequent multiphase transfer exacerbates the complexity of hydrological processes on the TP, which brings equifinality problem to hydrological models and causes large uncertainties in quantifying the contributions of runoff components. Tracer‐aided hydrological models are helpful for improving model performances and have been adopted in cryospheric regions, but the influence of frozen soil has yet to be considered. This study adopted the Tracer‐aided Tsinghua Representative Elementary Watershed model (THREW‐T) in a typical cold basin with widespread frozen soil on the TP. The model structure was diagnosed with isotope by identifying the influences of frozen soil. A simplified catchment‐scale frozen soil module was incorporated into the model. Results showed that: (a) The THREW‐T model cannot simultaneously simulate baseflow and stream water isotope well. The imbalance of simulations on two objectives could be attributed to the influence of frozen soil, resulting in seasonal variation of soil‐related parameters, which was not considered in the model. (b) Incorporating the frozen soil module significantly improved the balance of baseflow and isotope simulation, simultaneously producing low baseflow and high contribution of subsurface runoff during wet seasons. (c) The frozen soil had little influence on the annual streamflow, but changed the runoff seasonality by reducing baseflow during dry seasons and increasing subsurface runoff during wet seasons. The frozen soil module was still simplified, and further work is needed to improve the physical representation of soil freeze‐thaw process. This study highlights the value of tracer‐aided hydrological modeling method on diagnosing model structure by identifying the influence of specific processes such as frozen soil.

Addressing Heterogeneity in Direct Analysis of Extracellular Vesicles and Their Analogs by Membrane Sensing Peptides as Pan-Vesicular Affinity Probes.

Extracellular vesicles (EVs), crucial mediators of cell-to-cell communication, hold significant diagnostic potential due to their ability to concentrate protein biomarkers in bodily fluids. However, challenges in isolating EVs from biological specimens hinder their widespread use. The preferred strategy involves direct analysis, integrating isolation and analysis solutions, with immunoaffinity methods currently dominating. Yet, the heterogeneous nature of EVs poses challenges, as proposed markers may not be as universally present as thought, raising concerns about biomarker screening reliability. This issue extends to EV-mimics, where conventional methods may lack applicability. Addressing these challenges, the study reports on Membrane Sensing Peptides (MSP) as pan-vesicular affinity ligands for both EVs and their non-canonical analogs, streamlining capture and phenotyping through Single Molecule Array (SiMoA). MSP ligands enable direct analysis of circulating EVs, eliminating the need for prior isolation. Demonstrating clinical translation, MSP technology detects an EV-associated epitope signature in serum and plasma, distinguishing myocardial infarction from stable angina. Additionally, MSP allow analysis of tetraspanin-lacking Red Blood Cell-derived EVs, overcoming limitations associated with antibody-based methods. Overall, the work underlines the value of MSP as complementary tools to antibodies, advancing EV analysis for clinical diagnostics and beyond, and marking the first-ever peptide-based application in SiMoA technology.

Non-invasive quantitative assessment of induced component displacement can safely and accurately diagnose tibial component loosening in patients: A prospective diagnostic study.

Aseptic loosening often requires major, expensiveand invasive revision surgery. Current diagnostic modalities merely show indirect signs of loosening. A recent proof of concept study proposed a non-invasive technique for the quantitative and visual assessment of implant movement as a diagnostic aid for tibial component loosening. The primary research question addressed is whether this novel diagnostic modality can safely and effectively aid the diagnosis of aseptic loosening. This clinical study included patients suspected of aseptic total knee arthroplasty (TKA) loosening listed for revision surgery and asymptomatic patients. Safety was evaluated using a numerical rating scale (NRS) for discomfort and by registration of adverse events. Feasibility was assessed by recording the duration and ease of the procedure. Intra- and interrater reliability were evaluated. In symptomatic patients, diagnostic accuracy metrics were evaluated with intra-operative assessment as a reference test. In total, 34 symptomatic and 38 asymptomatic knees with a TKA were analysed. The medianNRSfor discomfort during loading was 6 (interquartile range [IQR]: 3.75-7.00) in symptomatic patients and 2 (IQR: 1.00-3.00) in asymptomatic patients. No adverse events were reported. The majority of users found the use of the loading device easy. The median time spent in the computed tomography roomwas 9 min(IQR: 8.00-11.00). Excellent to good intra- and interrater reliabilities were achieved. Diagnostic accuracy analysis resulted in a sensitivity of 0.91 (95% confidence interval [CI]: 0.72-0.97) and a specificity of 0.72 (95% CI: 0.43-0.90). The proposed diagnostic method is safe, feasible, reliableand accurate in aiding the diagnosis of aseptic tibial component loosening. Level II.

Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress.

Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR) Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed stu dies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicenter data validation, etc Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of artificial intelligence in the diagnosis of oral and maxillofacial diseases.

Why Do Small Earth Dams Deteriorate: Insights from Physical Investigations in the West African Sahel

In West Africa, the construction of small earth dams is common against water scarcity. Burkina Faso, an inland country in West Africa, is home to 1001 dams that serve agricultural and pastoral needs. These embankments are predominantly made of compacted laterite, a cost-effective material abundant in over 2/3 of the country. However, these dams degrade over time, hindering their functionality. This study aims to establish a catalog of typical degradation occurring on small dams in Burkina Faso, which is virtually non-existent in the region while identifying and analyzing the potential causes. The study uses a diagnostic analysis followed up with technical visits on a representative sample of 24 dams in the Centre and Centre-South regions as a basis for future studies. The results reveal that these dams were constructed between 1965 and 2018, with capacities ranging from 150,000 to 4,740,000 m3. 33% of these dams have undergone total failure, likely attributed to factors such as internal erosion, pore overpressures, settlement, and deformation. Although 67% of the dams remain functional, their structural integrity could be improved. Erosion observed in riprap indicates vulnerability during high flood periods. Additionally, the absence of proper maintenance, as shown by the vegetation development weakening embankments, contributes to deterioration. The analysis also suggests that variability in construction techniques and lateritic material properties across time and regions may further exacerbate degradation. These findings inform infrastructure improvements and policy development for sustainable water resource management in Burkina Faso and similar regions.

Rapunzel Syndrome: Diagnostic Challenges and Multidisciplinary Treatment Strategies.

Rapunzel syndrome, a rare yet complex condition, poses diagnostic and therapeutic challenges. Trichobezoars, stemming from trichotillomania and pica, manifest as hair conglomerates within the gastrointestinal tract, often necessitating surgical intervention. This review synthesizes literature on symptomatology, diagnostic methods, and treatment modalities, emphasizing the multidisciplinary approach essential for effective management. Psychological interventions, including cognitive-behavioral therapy, complement surgical measures in addressing underlying psychiatric factors. Diagnostic imaging, endoscopic examinations, and histopathological analysis aid in an accurate diagnosis. Enhanced awareness among healthcare providers regarding the association between psychological disorders and gastrointestinal complications is crucial for timely intervention and improved outcomes in individuals with Rapunzel syndrome.

“Codex 4D” Project: Interdisciplinary Investigations on Materials and Colors of De Balneis Puteolanis (Angelica Library, Rome, Ms. 1474)

This paper sheds light on the manufacturing processes, techniques, and materials used in the splendid illuminations of the oldest surviving copy of De Balneis Puteolanis, preserved at the Angelica Library in Rome (Ms. 1474). The codex is one of the masterpieces of mid-13th-century Italian-Southern illumination, traditionally referred to as the commission of Manfredi, son of Frederick II. The findings reported in the article result from the interdisciplinary study conducted in 2021–2023 in the framework of “Codex 4D: journey in four dimensions into the manuscript”, a multidisciplinary project involving many competences and dealing with art-historical studies on manuscripts, diagnostic and conservative analyses, scientific dissemination, storytelling, and public engagement. The considerations we present aims at increasing the knowledge of book artefacts while respecting their extraordinary complexity; data from non-invasive diagnostic investigations (X-ray fluorescence, Vis-NIR reflectance and Raman spectroscopies, hyperspectral imaging, and multi-band imaging techniques as ultraviolet, reflectography, and thermography), carried out in situ with portable instruments on the book, have been integrated with observations resulting from the historical-artistic study, and the reading of some ancient treatises on the production and use of the pigments and dyes employed in illumination.

Applicability Study of Euler–Lagrange Integration Scheme in Constructing Small-Scale Atmospheric Dynamics Models

The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s.

#1723 Genetic landscape of polycystic kidney disease in a Chinese cohort – experience using whole genome sequencing in a tertiary renal unit

Abstract Background and Aims Genetic testing has increasingly been employed to provide definitive diagnoses and prognostic information in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). We aim to explore the genetic landscape of ADPKD in a Chinese cohort utilizing whole genome sequencing and study its real-world clinical utility. Method From 1st January 2022 to 31st March 2023, we recruited 50 adult Chinese patients who had the clinical diagnosis of ADPKD followed up at Queen Mary Hospital and consented to the study protocol. The study was approved by the University Institution Review Board and local ethics committees. Genomic DNA was isolated from samples obtained from patients per standard protocol. Whole-genome sequencing was performed and analysed through the germline analysis pipeline. Diagnostic analysis was performed per American College of Medical Genetics (ACMG) guidelines and ClinGen variant curation expert panel specifications. Diagnostic variants were defined as those classified as ‘pathogenic’ or ‘likely pathogenic’. Potential clinical utility of genetic findings were studied case-by-case based on the Kidney Disease Improving Global Outcomes (KDIGO) 2023 Clinical Practice Guideline for the Evaluation, Management and Treatment of ADPKD. Results Among the 50 unrelated probands with ADPKD, half were males and the mean age at presentation was 38. 38% patients had incidental finding of kidney cysts on abdominal imaging performed for unrelated symptoms or health screening, 32% presented for screening due to positive family history and only 30% presented with typical symptoms of ADPKD. Cystic complications were present in 30%, with cyst haemorrhage being the most common, followed by kidney stones, cyst rupture and infection. 80% patients also had liver cysts and none had a history of cerebral aneurysms. The majority of our cohort (72%) had a family history of ADPKD. We identified diagnostic variants in 35 individuals, reaching a diagnostic yield of 70%. Diagnostic variants were identified in PKD1 (20/35), PKD2 (11/35), IFT140 (3/35) and ALG9 (1/35). The majority of diagnostic variants were protein-truncating (32/35), while non-truncating variants consisted of 1 in-frame deletion and 2 missense variants in PKD1. We identified 3 copy number variation:NC_000016.10:g.2110587_2111851del,NC_000016.10:g.2132339_2137621del,NC_000016.10:g.2036427_2101117del. The genetic findings allowed physicians to make definitive diagnosis in 9 patients with negative family history of ADPKD. The genetic findings provided important prognostic information as protein-truncating variants in PKD1 or PKD2 were associated with more severe disease compared to non-truncating variants. For the 3 patients harbouring heterozygous pathogenic variants in IFT140 (NM_014714.4:c.2659G&amp;gt;T, NM_014714.4:c.3780del, NM_014714.4:c.1035_1036del), they demonstrated a milder PKD phenotype with stable kidney function, normal kidneys size and absence of liver cysts 10 years after the diagnosis. The patient with heterozygous likely pathogenic ALG9 variant (NM_024740.2:c.1225del) also had milder PKD phenotype with normal kidney function and normal sized kidneys on USG 5 years after the diagnosis. In another male patient with young hypertension who was classified as Mayo Class 1C according to kidney imaging, identifying the pathogenic protein-truncating variant in PKD1 (NM_001009944.3:c.7288C&amp;gt;T) would render him eligible to tolvaptan treatment in view of the high PROPKD score (7). Furthermore, the genetic findings enabled an earlier diagnosis in a number of family members by cascade testing and genetic results were important to identify suitable living-related kidney donors. Conclusion Genetic testing utilizing whole genome sequencing has multiple clinical utility in ADPKD patients. It could provide a definitive diagnosis in those with atypical presentation or without family history, inform prognosis, guide treatment, determine living-related donor suitability and allow earlier diagnosis in other family members.

Extracellular Vesicles in Lung Cancer: Implementation in Diagnosis and Therapeutic Perspectives.

Lung cancer represents the leading cause of cancer-related mortality worldwide, with around 1.8 million deaths in 2020. For this reason, there is an enormous interest in finding early diagnostic tools and novel therapeutic approaches, one of which is extracellular vesicles (EVs). EVs are nanoscale membranous particles that can carry proteins, lipids, and nucleic acids (DNA and RNA), mediating various biological processes, especially in cell-cell communication. As such, they represent an interesting biomarker for diagnostic analysis that can be performed easily by liquid biopsy. Moreover, their growing dataset shows promising results as drug delivery cargo. The aim of our work is to summarize the recent advances in and possible implications of EVs for early diagnosis and innovative therapies for lung cancer.

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the stateoftheart in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.

Groundwater model diagnostic calibration and uncertainty analysis using information theory

ABSTRACT This study aims, as a first attempt, to establish a framework for multi-objective optimization diagnostic model calibration and uncertainty analysis based on information theory, including mutual information (MI) and variation of information (VI). Moreover, a global sensitivity analysis was performed using the information coefficient of correlation to determine the incremental contribution of each model parameter. The applicability of the proposed framework was tested in an arid region of Oman with complex hydrogeological conditions and hardrock-alluvial aquifer systems. The findings highlight the capability of the information theory approach to simultaneously calibrate the model and assess uncertainty, while providing valuable diagnostics for identifying areas of the model that require further refinement. Furthermore, the results indicate that the proposed framework successfully reproduced 96% of the observed data, demonstrating its ability to reduce parameter uncertainty and ensure an accurate match between the simulated and observed data.

Dark energy nature of logarithmic f(R,Lm)-gravity models with observational constraints

This work explores the dark energy nature of logarithmic [Formula: see text]-gravity models with observational constraints, where [Formula: see text] represents the matter Lagrangian for perfect fluid and R is the Ricci-scalar curvature. With a matter Lagrangian [Formula: see text], a flat FLRW space–time metric and an arbitrary function [Formula: see text], where [Formula: see text] is a positive model parameter, we have derived the field equations of this model. By using the Hubble function, we have been able to solve the energy conservation equation and create a relationship between [Formula: see text], [Formula: see text] and [Formula: see text]. Using the most recent two observational datasets as 32 [Formula: see text] and 1048 Pantheon SNe Ia datasets, we conducted MCMC analysis. We have obtained best fit values of model parameters with [Formula: see text] errors and using these values, we have explored the cosmological properties of the model. We have performed om diagnostic analysis for the model and estimated the present age of the universe. Thus our derived model presents a transit phase decelerating to accelerating model without dark energy term [Formula: see text]. We found that the effective equation of state parameter is in the range [Formula: see text] over [Formula: see text] with present value [Formula: see text].

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.

TPX2 upregulates MMP13 to promote the progression of lipopolysaccharide-induced osteoarthritis.

This study seeks to identify potential clinical biomarkers for osteoarthritis (OA) using bioinformatics and investigate OA mechanisms through cellular assays. Differentially Expressed Genes (DEGs) from GSE52042 (four OA samples, four control samples) were screened and analyzed with protein-protein interaction (PPI) analysis. Overlapping genes in GSE52042 and GSE206848 (seven OA samples, and seven control samples) were identified and evaluated using Gene Set Enrichment Analysis (GSEA) and clinical diagnostic value analysis to determine the hub gene. Finally, whether and how the hub gene impacts LPS-induced OA progression was explored by in vitro experiments, including Western blotting (WB), co-immunoprecipitation (Co-IP), flow cytometry, etc. Bioinformatics analysis of DEGs (142 up-regulated and 171 down-regulated) in GSE52042 identified two overlapping genes (U2AF2, TPX2) that exhibit significant clinical diagnostic value. These genes are up-regulated in OA samples from both GSE52042 and GSE206848 datasets. Notably, TPX2, which AUC = 0.873 was identified as the hub gene. In vitro experiments have demonstrated that silencing TPX2 can alleviate damage to chondrocytes induced by lipopolysaccharide (LPS). Furthermore, there is a protein interaction between TPX2 and MMP13 in OA. Excessive MMP13 can attenuate the effects of TPX2 knockdown on LPS-induced changes in OA protein expression, cell growth, and apoptosis. In conclusion, our findings shed light on the molecular mechanisms of OA and suggested TPX2 as a potential therapeutic target. TPX2 could promote the progression of LPS-induced OA by up-regulating the expression of MMP13, which provides some implications for clinical research.

Grammatical correction and morphological productivity tasks as potential identifiers of developmental language disorder in Spanish-Catalan bilingual children: a pilot study

ABSTRACT A critical problem for Catalan-Spanish bilinguals is the lack of assessment tools to conduct valid and accurate oral language evaluations. The purpose of this preliminary study was to provide pilot data for a new potential assessment tool in Catalan. We examined the possibility that two novel tasks, a grammatical correction (GramCorr) and morphological productivity (MP; nonword and word subtasks), could differentiate between Catalan-speaking children with and without developmental language disorder (DLD) with good diagnostic accuracy. Twenty-seven school-aged children with DLD (M age = 9;1 years) and twenty-seven age/sex-matched typically developing (TD) children (M age = 9;0 years) participated in the study. Group differences and diagnostic accuracy analyses (sensitivity and specificity and likelihood ratios) were conducted. Results showed that the overall accuracy of children with DLD was significantly poorer than that of the TD controls in the two tasks. The preliminary diagnostic accuracy results suggested that the overall test (GramCorr+MP) could be useful in identifying children with DLD (cut-off point 62.1%; sensitivity (.929)/specificity (.893); +LH (8.67)/-LH (0.80). Moreover, the overall MP task (nonword + word subtasks; cut-off point 60%; sensitivity (.897)/specificity (.931), +LH (13.00)/-LH (0.111)) appears to provide adequate information to help in detecting DLD. The nature of both tasks, their usefulness for practitioners and future steps in the design of valid tools for the identification of Catalan speaking children with DLD are discussed.

Application of Data Mining Techniques in Biopsy Interpretation and Staging of Carcinoma Cancer Disease: A Case Study of Northeastern Nigeria

This paper introduces an innovative framework tailored for carcinoma cancer staging in northeastern Nigeria, employing data mining techniques and machine learning algorithms, with analysis conducted using the WEKA toolkit. Given the challenges in healthcare systems, especially in cancer diagnosis and treatment, this study aims to enhance diagnostic precision thorough analysis of biopsy reports. By assessing Support Vector Machine (SVM) and decision tree algorithms using datasets from six tertiary health institutions, the framework demonstrates promising outcomes in accurately determining cancer malignancy stage. SVM exhibits high accuracies in staging breast and prostate cancer, while decision tree algorithms show notable accuracy rates in staging skin cancer. These results highlight the potential of machine learning in advancing cancer diagnosis and treatment planning, particularly in resource-constrained settings, and pave the way for the development of computer-aided diagnostic tools tailored for carcinoma cancer staging in northeastern Nigeria.

Epidemiological investigation and diagnostic analysis of osteonecrosis of the femoral head in three northeastern provinces of China

BackgroundIn this retrospective case investigation, we analysed the data of patients with osteonecrosis of the femoral head (ONFH) to reveal demographic and clinical diagnostic features of ONFH in three northeastern provinces of China and provide a reference for its prevention, diagnosis, and treatment.MethodsWe collected data from patients in Beijing Orthopaedic Hospital of Liaoning, focusing on the aetiology and diagnosis of ONFH. Medical records and self-designed questionnaires were used to collect information for statistical analysis, including age, aetiology, reason for glucocorticoid use, hospital level at first visit, and diagnosis.ResultsIn total, 906 patients with complete medical records were included in the analysis. The mean patient age was 47.65 ± 12.12 years. The peak age distribution was in the 40s for men and the 50s for women. Among the total cohort, 72 patients (7.95%; 40 men and 32 women) had traumatic ONFH, 198 (21.85%; 131 men and 67 women) had steroid-induced ONFH, 230 (25.39%; 121 men and 109 women) had idiopathic ONFH, and 406 (44.81%; 397 men and 9 women) had alcohol-induced ONFH. Six hundred and twenty patients were diagnosed with ONFH at the first visit, while 286 patients were misdiagnosed, with a diagnosis rate of 68.43%. The diagnosis rate at the first visit in tertiary hospitals was 76.14%. The diagnosis rate at the first visit in second-class hospitals was 52.07%.ONFH was most likely to be misdiagnosed as lumbar disc herniation.ConclusionsMost patients with ONFH in three northeastern provinces of China were middle-aged, male, and had alcohol-induced ONFH. The misdiagnosis rate of ONFH at the first visit was very high, especially for misdiagnosis of lumbar disc herniation, indicating that the diagnosis of ONFH requires further improvement.

Incineration-source fingerprints and emission spectrums of dioxins with diagnostic application

Despite increasing waste-to-energy (WtE) capacities, there remain deficiencies in comprehension of 136 kinds of tetra- through octa-chlorinated dibenzo-p-dioxin and dibenzofurans (136 PCDD/Fs) originating from incineration sources. Samples from twenty typical WtE plants, encompassing coal-fired power plants (CPP), grate incinerators (GI), fluidized bed incinerators (FBI), and rotary kilns (RK), yielded extensive PCDD/F datasets. Research was conducted on fingerprint mapping, formation pathways, emission profiles, and diagnostic analysis of PCDD/Fs in WtE plants. Fingerprints revealed a prevalence of TCDF, followed by PeCDF, while CPP and RK respectively generated more PCDD and HxCDD. De novo synthesis was the predominant formation pathway except one plant, where CP-route dominated. DD/DF chlorination also facilitated PCDD/F formation, showing general trends of FBI > GI > CPP > RK. The PCDD/F emission intensities emitted in air pollution control system inlet (APCSI) and outlet (APCSO) followed the statistical sequence of RK > FBI > GI > CPP, with the average I-TEQ concentrations in APCSO reaching 0.18, 0.08, 0.11, and 0.04 ng I-TEQ·Nm−3. Emission spectrum were accordingly formed. Four clusters were segmented for diagnosis analysis, where PCDD/Fs in GI and FBI were similar, grouped as a single cluster. PCDD/Fs in CPP and RK demonstrated distinctive features in TCDD, HxCDD, and HxCDF. The WtE plants exceeding the limit value tended to generate and retain fewer TCDD and TCDF yet had higher fractions of HxCDD and HxCDF. The failure of APCS coupled with the intrinsic source strength of PCDD/Fs directly led to exceedance, highlighting safe operational practices. This study motivated source tracing and precise evaluation of 136 PCDD/Fs based on the revealed fingerprint profiles for WtE processes.