Background: Cervical cancer is one of the main reasons for the death of women’s in the world. It is a major public health problem and it is the second most common cancer in women worldwide which is the leading cause of deaths of women in developing countries. Proper knowledge about the screening and concerned them about its curability if diagnosed in early stage could have a major impact.Objective: To assess the level of knowledge about the screening of cervical cancer for its early detection among women of reproductive age group.Methods: This was a Descriptive type of cross-sectional study. It was conducted from September 2024 to November 2024. A total of 100 participants participated in the study. Data were collected using self-administered structured questionnaire. The data were processed by computer and statistical analysis of data was carried out by using SPSS method.Results: Result showed that among 100 respondents’ majority 23 (23) were 3034 years old. 51 (51) respondents were married. 22 (22) participants got knowledge about screening test of cervical cancer from mass media and 14 (14) knew about different types of screening tests. 27 (27) participants of reproductive age group had the knowledge that cervical cancer is curable if detected in early stage. Conclusion: Unfortunately, the cervical cancer is the second leading cause of deaths in cancer in Bangladesh. There is a huge need to continue with the innovative steps that have been made to overcome the health care barriers crippling this population. The Journal of Ad-din Women's Medical College; Vol. 13 (2), July 2025; p 9-14

Circulating tumor cells （CTC） have emerged as crucial mediators in the metastatic cascade， offering invaluable insights as real-time liquid biomarkers for cancer progression， prognosis， and treatment response. Their exceptionally low concentration in peripheral blood， which typically ranges from a handful to a few dozen cells per milliliter amidst billions of background blood cells， poses formidable challenges for isolation and molecular characterization. Despite this， the efficient and specific capture of CTC holds tremendous potential for revolutionizing early cancer detection， dynamic monitoring of therapeutic efficacy， and guiding personalized treatment strategies. Currently， the primary technologies for CTC enrichment fall into two categories： immunoaffinity-based methods that employ antibodies targeting epithelial surface markers such as epithelial cell adhesion molecule （EpCAM）， and label-free approaches that leverage physical properties including cell size， deformability， and density， exemplified by membrane filtration and centrifugal techniques. However， these conventional methods are hampered by several inherent limitations， including high operational costs， dependence on highly variable surface antigen expression， insufficient capture specificity leading to low purity， and significant interference from heterogeneous blood components such as leukocytes and platelets. Consequently， there is an urgent and growing need to develop novel functional materials and platforms that offer enhanced selectivity， robust stability in physiological conditions， excellent biocompatibility， and improved clinical applicability for the effective isolation and analysis of CTC. In this study， we innovatively integrate cell imprinting technology with a rational amino acid-based affinity strategy to develop a tryptophan-histidine-arginine （WHR） tripeptide-functionalized cell-imprinted hydrogel for highly efficient and selective capture of CTC. The design leverages the unique properties of mesoporous silica nanoparticles （MSN） as carriers， which are first synthesized and then surface-modified with epoxy groups via silane coupling agents. The WHR tripeptide is subsequently grafted onto the MSN surface through a ring-opening reaction， yielding the WHR@SiO₂ composite material. This material demonstrates strong and specific binding affinity toward sialic acid （Neu5Ac） and sialylated glycopeptides （SGP）， which are overexpressed on the surface of many cancer cells. Building on this molecular recognition capability， a three-dimensional cell-imprinted hydrogel is fabricated using poly（ethylene glycol） dimethacrylate （PEGDMA） as the cross-linking backbone via free radical polymerization. The hydrogel is molded against SMMC-7721 template cells to create cavities that complement the target cells in size， shape， and surface topology， thereby enhancing capture efficiency through both physical and biochemical matching. Experimental results demonstrate that the WHR-modified hydrogel achieves a remarkable capture efficiency of up to 94% for SMMC-7721 cells， significantly outperforming hydrogels modified with individual amino acids such as tryptophan， histidine， or arginine alone. The system also exhibits excellent hemocompatibility， with minimal adsorption of human serum albumin （HSA）， below 5%， indicating superior anti-fouling properties in biological environments. In vitro cytotoxicity assessments confirm high biocompatibility， with cell viability exceeding 90% after 48 h of co-culture. Further characterization through scanning electron microscopy （SEM） and atomic force microscopy （AFM） reveals well-defined surface imprints that mirror the morphology of template cells， confirming the successful integration of topographical cues. The synergy between the physical structure of the imprinted cavities and the biochemical affinity of the WHR tripeptide is identified as the key factor contributing to the high capture performance， even at low cell concentrations （as few as 100 cells/mL）. In conclusion， this work presents a robust and efficient platform for CTC capture that combines cell imprinting for morphological recognition with WHR-mediated affinity for sialylated glycoproteins. The hydrogel demonstrates high selectivity， stability， and biocompatibility， offering a promising tool for clinical applications in liquid biopsy and early cancer detection. The modular design of the system also allows for adaptation to other cancer types by altering the peptide sequence or template cells， highlighting its broad potential in cancer research and diagnostics.

Cardiovascular diseases （CVDs） are among the leading cause of global morbidity and mortality. Due to their high prevalence and often asymptomatic progression， there is a pressing need for diagnostic tools that enable the early， accurate， and accessible detection of them. Acute coronary syndrome （ACS）， as a common and severe CVDs with high morbidity and mortality rates， has attracted considerable scientific interest. Various methods have been developed to detect ACS rapidly and accurately. Traditional diagnostic methods relying on antibody-based assays are effective. However， they face significant limitations， including high production costs， poor stability under varying environmental conditions， batch-to-batch variability， and cross-reactivity leading to false positives. These challenges have motivated the search for robust， cost-effective alternatives capable of detecting biomarkers with high sensitivity and specificity. Molecularly imprinted polymers （MIPs） have emerged as a promising alternative solution， offering antibody-like molecular recognition capabilities， superior stability， lower production costs， and resistance to harsh environmental conditions. This review systematically examines the latest advancements in MIP-based sensors for ACS biomarker detection in the last fifteen years， including imprinting strategies for key ACS biomarkers， sensor development and integration， and current challenges along with future perspectives. The first section focuses on the molecular imprinting techniques for essential ACS biomarkers， such as cardiac troponin （cTnI/cTnT）， myoglobin （Myo）， and creatine kinase （CK）. It compares whole-protein imprinting with epitope imprinting， highlighting the advantages of the latter in reducing template costs and enhancing binding specificity. Epitope imprinting using short peptide sequences has demonstrated femtomolar detection limits while overcoming challenges associated with large protein templates， such as structural denaturation and difficult template removal. The review also explores innovative approaches like dummy template imprinting， where structurally similar but cheaper molecules are used to create MIPs for high-cost biomarkers， achieving comparable specificity and sensitivity. The second section discusses the integration of MIPs with advanced biosensing platforms. Electrochemical sensors， using MIP-modified electrodes， have achieved remarkable sensitivity and rapid response times， making them suitable for point-of-care testing （POCT）. Optical sensors， particularly those based on surface-enhanced Raman spectroscopy and surface plasmon resonance， enable label-free， real-time detection with ultra-low detection limits. The review also addresses the integration of MIPs with microfluidic technology， where miniaturized devices facilitate automated， high-throughput biomarker analysis. Examples include paper-based microfluidic sensors that combine capillary action with MIP-SERs tags for multiplexed detection， achieving low detection limits without complex instrumentation. Despite these advancements， the review identifies key challenges hindering widespread clinical adoption of the MIP's based ACS sensor. Although the sensitivity and specificity of MIPs are impressive， they still lag behind those of monoclonal antibodies in some applications， particularly for low-abundance biomarkers. Reproducibility issues arise from variations in polymerization conditions and template removal efficiency. Commercialization barriers include the lack of standardized production protocols and regulatory frameworks for MIP-based diagnostics. The review proposes several strategic directions to address these limitations. Computational modeling and machine learning could optimize monomer selection and polymerization conditions to enhance MIP's performance. The development of hybrid systems combining MIPs with nanomaterials may further improve sensitivity and signal transduction. Multidisciplinary collaborations among chemists， engineers， and clinicians will be essential to translate laboratory innovations into commercially viable diagnostic tools. Additionally， the integration of MIPs with artificial intelligence machine learning algorithms could support the development of personalized diagnosis and treatment strategies. These future perspectives are likely to have a significant impact on the early diagnosis and treatment of cardiovascular diseases. In conclusion， MIP-based sensors represent a promising direction in ACS diagnostics， offering a unique combination of affordability， stability， and precision. By addressing current technical and translational challenges， MIP technology has the potential to revolutionize early disease detection， particularly in resource-limited areas. This review not only summarizes a decade of research progress but also provides a plan for future developments that could make personalized， decentralized cardiovascular diagnostics a widespread reality.

Background: Gastric and gastro oesophageal junction cancers remain major contributors to global cancer mortality. In Nigeria, late diagnosis is common, and this pattern continues to drive high cancer related deaths. Materials and Methods: Clinico pathological data were retrieved from four major histopathology laboratories in Anambra State, southeast Nigeria. Statistical analysis was conducted using the Chi square test in SPSS version 25 to assess differences in proportions. Results: Gastric and gastro oesophageal junction cancers were most frequently diagnosed in individuals in their sixth and seventh decades of life, with a median age of 56.7 years. There was no clear sex predilection in the study population. The pylorus and antrum were the most common tumour locations, and intestinal type adenocarcinoma was the predominant histological subtype. Most cases presented at advanced stages, with the majority staged as T3 or T4. Conclusion: Gastric and gastro oesophageal junction cancers in this setting are characterised by late presentation and are predominantly antral adenocarcinomas. These findings highlight the need for a regional screening initiative aimed at earlier detection and improved outcomes.

Tears are an easily accessible biofluid that reflects both emotional states and disease conditions. They are particularly enriched in extracellular vesicles (EVs), which carry proteins and nucleic acids relevant to neurological health. This makes tear EVs a promising source for biomarker discovery. However, limited sample volume and variability pose challenges for identifying reliable biomarkers for clinical diagnosis. We present AI-driven Biomarker Learning for the Early Diagnosis of Neurodegenerative Diseases (ABLEDx), which applies a conditional variational autoencoder (cVAE) to enhance proteomic analysis of tear EVs. This approach effectively addresses sample limitations and improves the identification of disease-associated biomarkers. Our results reveal that tear EVs capture molecular signals along the eye-brain axis, reflecting contributions from both ocular and central nervous system cells. ABLEDx identified clinically relevant protein modules, which were consistently elevated in patients with neurodegenerative diseases. Moreover, we recognize that KRAS is highly expressed in patients with Alzheimer's disease, Parkinson's disease, and ocular myasthenia gravis, and tear-EV-associated LRG1 and HSPG2 exhibit differentiation between Alzheimer's disease and Parkinson's disease. ABLEDx demonstrates the utility of combining AI with tear-EV proteomics for non-invasive biomarker discovery. This strategy enables early and real-time detection of neurodegenerative and ocular diseases, offering new opportunities for clinical diagnostics and translational medicine.

Type 3c diabetes mellitus (T3cDM) is increasingly recognized with the rising incidence of pancreatitis, yet its risk profile and early diagnostic tools remain insufficient. Identifying independent risk factors and establishing reliable prediction models are essential for improving early detection and clinical management. A retrospective study was conducted on 864 patients with pancreatitis admitted to Zhengzhou Central Hospital from January 2020 to December 2022. They were randomly divided into a training set (n = 604) and a validation set (n = 260) using a 7:3 ratio. Collect baseline data, laboratory indicators, and outcomes of T3cDM through case follow-up. Single factor and multiple factor logistic regression were used to screen potential risk factors, and after excluding collinearity and clinically unrelated variables, a multiple factor logistic regression model was constructed; Evaluate model performance using receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve (DCA); Construct a column chart and dynamic prediction model based on independent risk factors, and use tenfold cross validation to verify the stability of the model. In addition, 172 patients from external hospitals were included for external validation. Furthermore, Kaplan-Meier (K-M) curve analysis was performed to evaluate the survival characteristics of T3cDM in patients with pancreatitis. In 864 patients with pancreatitis, the cumulative incidence rate of T3cDM was 16.67% (144/864). The baseline data of training set and verification set were balanced (P > 0.05), and there was no significant selection bias. Multivariate logistic regression showed that a history of pancreatitis (OR = 4.301, 95%CI: 2.370-7.804, P < 0.001), alcohol consumption factors (OR = 4.542, 95% CI: 1.669-12.360, P = 0.003), body mass index (BMI) (OR = 1.209, 95% CI: 1.038-1.409, P = 0.015), blood potassium (K⁺) (OR = 2.119, 95% CI:1.440-3.938, P = 0.018), maximum blood glucose(Max Glu) (OR = 1.079, 95% CI:1.015-1.146, P = 0.014), glycated hemoglobin (HbA1c%) (OR = 1.401, 95% CI: 1.145-1.716) Triglycerides (TG) (OR = 1.022, 95% CI: 1.006-1.039, P = 0.008), triglyceride glucose index (TyG) (OR = 1.802, 95% CI: 1.248-2.603, P = 0.002) are independent risk factors for T3cDM in patients with pancreatitis. The column chart model constructed based on the above factors has a training set ROC curve area under the curve (AUC) of 0.857 (95% CI: 0.815-0.899), a sensitivity of 80.3%, and a specificity of 80.4%; The AUC of the validation set is 0.773 (95% CI: 0.691-0.855), with a sensitivity of 72.0% and a specificity of 69.0%. The calibration curve shows that the average absolute error of the training set is 0.023, and the validation set is 0.017. The predicted risk is highly consistent with the actual risk; The DCA curve suggests that the net benefit of the model is significantly higher than that of the "full intervention" or "no intervention" strategies within the risk threshold of 0.05 ~ 0.80. External validation further confirmed the robust predictive performance (AUC = 0.904 [95%CI: 0.838-0.971]). Stratified analysis demonstrated that the model exhibited good predictive efficacy across different age and gender groups. Age-stratified analysis demonstrated that the model exhibited favorable performance across three patient cohorts: young adults (18-44years), middle-aged individuals (45-59years), and elderly patients (60-75years), with AUC values of 0.801, 0.956, and 0.903, respectively. Furthermore, the K-M curve showed no significant difference in T3cDM survival curves between the training and validation sets (Log rank P = 0.126, HR = 0.781, 95% CI: 0.545-1.120), and the overall population had a T3cDM free survival rate of approximately 32% at 60months of follow-up. The incidence rate of T3cDM in patients with pancreatitis is high. Previous pancreatitis history, drinking inducement, BMI, K+, Max Glu, HbA1c%, TyG, etc. are independent risk factors for T3cDM; The prediction model and column chart constructed based on the above factors have good discrimination, calibration, and clinical practicality, and can be used as an effective tool for T3cDM risk assessment in patients with pancreatitis. Furthermore, external validation further supports the model's applicability.

We investigated a KPC-2-producing Enterobacterales (KPC-2 CPE) outbreak in a Korean hospital from July to September 2019, which subsided following enhanced surveillance and strict infection control. The study aimed to elucidate transmission dynamics using epidemiological and genomic methods. The study period covered the outbreak and a 9-month post-outbreak observation. Investigations included a matched case-control study and whole-genome sequencing (WGS) of isolates, including long-read sequencing for two isolates. Single nucleotide polymorphism (SNP) analysis (≤ 6 SNPs for clonality, ≤ 15 for relatedness) was used to construct transmission networks. A total of 42 KPC-2 CPE cases were identified: 34 Klebsiella pneumoniae, 4 Escherichia coli, 1 Enterobacter asburiae, and 3 cases co-colonized with K. pneumoniae and E. coli. Among these, 33 were hospital-linked and 9 were imported. Retrospective tracing indicated that covert transmission began a month before the outbreak, and 13 hospital wards were identified as potential acquisition sites. Genomic analysis revealed all but one K. pneumoniae belonged to ST307, cgMLST 439, which grouped into three clades. Clade 1 was linked to a specific hospital ward, supported by the case-control study (adjusted odds ratio, 3.63; 95% confidence interval, 1.36-9.63); Clade 2 was spread between wards via a haemodialysis unit and shared healthcare personnel. Imported cases had the same clones as early hospital-linked cases, suggesting undetected introduction before enhanced surveillance. Additionally, an IncX3 plasmid carrying blaKPC-2 was found in both K. pneumoniae and E. coli, indicating horizontal gene transfer. This study demonstrates that clonal spread of KPC-2 CPE can remain undetected without enhanced active surveillance, underscoring the need for early detection. Genomic analysis clarified ST307 K. pneumoniae transmission through unrecognised epidemiological links and horizontal blaKPC-2 transfer to E. coli.

Early detection in primary care could improve pancreatic cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic Cancer Roadmap developed by Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation. This study aims to assess the acceptability and feasibility of identifying patients with symptoms associated with pancreatic cancer through a CDSS within a simulated environment. We developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data. GPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having cancer. While GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic cancer in primary care.

Congenital hypothyroidism is an endocrine disorder in newborns characterized by thyroid hormone deficiency, which can lead to impaired growth and permanent neurological developmental disorders if not detected and treated early. Congenital hypothyroidism screening through the measurement of neonatal Thyroid Stimulating Hormone (TSH) levels is an important strategy for early detection to prevent these long-term consequences. This study aimed to describe the profile of neonatal TSH levels in the implementation of a congenital hypothyroidism screening program at a private hospital. This study employed a descriptive design with a retrospective approach. The data used were secondary data in the form of neonatal TSH examination results obtained from laboratory records during the period of April–July 2024. The sampling technique used was total sampling, including all data that met the inclusion criteria, with a total sample of 175 newborns. Data analysis was conducted using univariate analysis to describe the distribution of neonatal TSH levels based on the reference values for congenital hypothyroidism screening. The results showed that all neonatal TSH levels were within the normal reference range, with values ranging from 2 to 12 µIU/mL. No neonatal TSH levels ≥20 µIU/mL were found that would indicate suspicion of congenital hypothyroidism. These findings indicate that there were no signs of thyroid dysfunction among the newborns screened during the study period. The implications of this study confirm that the congenital hypothyroidism screening program has been implemented effectively as an early detection effort. Therefore, routine neonatal TSH screening should be maintained to prevent delayed diagnosis and to support optimal growth and development in infants.

Abstract The detection of incipient and minor cracks in prestressed reinforced concrete (RC) structures is crucial in ensuring safety of existing bridges. However, traditional structural health monitoring (SHM) often fails to provide reliable and effective early detection. As a matter of fact, literature SHM applications typically investigated moderate-to-severe cracking conditions and often developed criteria that are likely to depend on investigated scenarios. Aiming at addressing the abovementioned literature gap, this study evaluates the effectiveness of acoustic emission (AE) testing for early crack identification in post-tensioned RC girders. AE tests are carried out during cyclic and monotonic four-point bending tests on different specimens up to failure. Multiple AE analysis methods are systematically implemented considering literature methods and novel method specifications (MSs). The evolution of key AE features is examined, and several indicators are further analyzed through a blind assessment framework. The complete AE dataset of AE data is made publicly available. AE activity trends and their potential correlation with observed mechanical damage are identified and discussed. Among the investigated indicators, relative acoustic entropy shows particular promise for early crack detection. The study systematically compares the application of multiple assessment methods, identifying strengths and weaknesses and outlining potential SHM criteria. The findings demonstrate that AE testing, when combined with suitable MSs and damage criteria, offers a viable path for reliable SHM. This paper lays the groundwork for development of robust damage detection criteria.

Fewer than 2% of eligible patients are screened for primary aldosteronism, despite evidence that early detection and targeted therapy are associated with lower cardiovascular and kidney morbidity. Recent updates to major hypertension and endocrine guidelines reflect growing recognition that primary aldosteronism is far more prevalent than previously understood and that broader, more practical screening approaches are needed. These recommendations increasingly extend screening beyond resistant hypertension to adults with stage 2 hypertension and even to all individuals with hypertension. They also aim to lower barriers to testing through more flexible guidance on antihypertensive medication management, reaffirm the aldosterone-to-renin ratio as the preferred initial test, and provide more standardized criteria for interpretation. Supporting evidence includes epidemiological data demonstrating a continuum of renin-independent aldosterone production across blood pressure categories, strong associations between untreated primary aldosteronism and adverse cardiovascular and kidney outcomes independent of blood pressure, and favorable cost-effectiveness of screening even in lower-risk groups. Implementation remains the principal challenge, with obstacles spanning patient, clinician, and health system levels. Emerging electronic health record strategies, including electronic phenotyping and integrated clinical decision support, have shown early promise in increasing screening uptake and streamlining diagnostic pathways. Collectively, contemporary guideline updates and implementation innovations represent a shift toward earlier and broader detection of primary aldosteronism, with the potential to reduce preventable cardiorenal disease across the hypertensive population.

Covalent organic frameworks (COFs) represent a rapidly expanding class of porous crystalline materials with exceptional potential in cancer diagnosis and therapy. Their ordered π-conjugated backbones, tunable pore architectures, and abundant functional sites provide unique advantages for drug loading, controlled release, and biointerfacing. Unlike conventional porous carriers, COFs exhibit intrinsic optical, electrical, and chemical properties that enable them to act both as delivery scaffolds and as active therapeutic platforms. Recent advances demonstrate their integration into drug delivery systems, photodynamic therapy (PDT), photothermal therapy (PTT), biosensing, and bioimaging. In cancer sensing and imaging, nanoscale COFs improve probe stability, enhance detection sensitivity, and enable responsive diagnostic platforms with reduced signal quenching. Furthermore, COFs can stabilize or directly function as photosensitizers and photothermal agents, thereby facilitating multimodal, imaging-guided therapeutic interventions. Despite these advances, key challenges remain, including scalable synthesis, long-term biocompatibility, precise drug-release control, and overcoming tumor heterogeneity. This review highlights emerging strategies to optimize COF stability, pore design, and functionalization, while exploring their potential applications across oncology. Finally, perspectives on clinical translation underscore the importance of interdisciplinary approaches to position COFs as next-generation platforms for precision cancer medicine, addressing urgent needs in early detection, therapeutic resistance, and metastasis management. Finally, the unique properties of COFs make them promising applicants for improving therapeutic products in cancer treatment.

Osteosarcoma is a highly malignant bone tumor, and a subset of cases is closely associated with hereditary syndromes. These syndrome-related osteosarcomas exhibit unique clinical features, molecular mechanisms, and therapeutic challenges. This review summarizes the current understanding of specific types of syndrome-related osteosarcomas, including those associated with Rothmund-Thomson syndrome, Li-Fraumeni syndrome, secondary osteosarcoma in retinoblastoma survivors, Werner syndrome, and Bloom syndrome. These syndromes are typically characterized by specific gene mutations or chromosomal instability, significantly increasing the risk of osteosarcoma development. However, the rarity and heterogeneity of syndrome-related osteosarcomas pose significant challenges for diagnosis and treatment, including difficulties in early detection, incomplete elucidation of molecular mechanisms, and limitations of conventional therapeutic approaches. This article aims to systematically review the clinical characteristics, molecular mechanisms, and therapeutic challenges of these syndromes, providing a comprehensive reference for clinicians and directions for future research.

Adolescent Idiopathic Scoliosis (AIS) is a three-dimensional deformation of the spine with a frontal plane curvature of 10° or more, measured using Cobb method. It typically gets more severe during pre-puberty and puberty, currently exhibiting unpredictable progression. Severe disease is more prevalent in females, and progression is associated with respiratory and neuromuscular dysfunction, pain, and psychological complications. Management strategies are guided by curve severity and include observation, therapeutic exercises, bracing, and surgery. Despite advances, the cellular and molecular mechanisms driving AIS remain poorly understood. Early detection and reliable progression biomarkers are increasingly recognized as critical to prevent clinical mismanagement. This mini-review summarizes current evidence on circulating biomarkers investigated in AIS, including growth-related hormones, bone metabolism proteins, and more recently non-coding RNAs (ncRNAs) such as microRNAs. In addition, we highlight key methodological limitations and risk-of-bias concerns across existing studies, especially the reliance on single-time-point sampling, underscoring the need for longitudinal prospective cohorts with repeated biomarker measurements. Such designs are critical for capturing dynamic biological changes, distinguishing stable from progressive cases, and validating biomarker trajectories for integration into clinically meaningful prediction models for AIS progression.

Invasive freshwater turtles are major drivers of biodiversity loss, underscoring the importance of early detection and management. However, it is challenging for experts to manually monitor a broad geographic area, necessitating support tools. Deep learning-based object detection models have displayed high performance in automating wildlife monitoring tasks. Furthermore, hyperparameter optimization, including optimizer selection and hyperparameter tuning, might further enhance performance by optimizing training settings to the dataset. In this study, an optimized model was developed to apply hyperparameter optimization to detect and classify six invasive turtle species in Korea from images. The optimized model was compared to a default model trained using the default optimizer and hyperparameters. The optimized model outperformed the default model, as indicated by the evaluations of mean average precision using a fixed intersection over union threshold of 0.5 (0.973 vs. 0.959) and a range of thresholds ranging from 0.5 to 0.95 (0.841 vs. 0.815). The classification accuracy of the optimized model reached 92.7%, exceeding that of the default model (89.9%). These findings highlight the utility of hyperparameter optimization and suggest that the proposed approach can support the early detection of invasive turtles, thereby enhancing to invasive species management.

Cardiometabolic diseases remain the leading causes of global morbidity and mortality, with early detection often hindered by nonspecific symptoms and reliance on systemic biomarkers. The retinal nerve fiber layer (RNFL), a microvascular and neurodegenerative biomarker, is highly sensitive to systemic metabolic and vascular insults. Artificial intelligence (AI)-driven metabolomics integrates high-resolution RNFL imaging with circulating metabolite profiling, enabling precise risk stratification for mortality and cardiometabolic disease. By combining optical coherence tomography data with machine learning algorithms, this approach deciphers complex biochemical signatures and correlates them with systemic outcomes. Recent studies demonstrate that AI-powered RNFL metabolomics achieves superior sensitivity and specificity compared to conventional diagnostic tools, with applications extending to diabetes, hypertension, neurodegenerative disorders, and chronic kidney disease. However, challenges such as dataset bias, limited accessibility of metabolomic assays, and regulatory hurdles remain. Synthesizing current evidence, AI-driven RNFL metabolomics represents a transformative innovation in precision medicine, offering a scalable, noninvasive pathway for early detection, personalized care, and improved survival outcomes in cardiometabolic disease.

Vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is typically diagnosed using CT or catheter angiography, both of which involve radiation exposure, iodinated contrast agent administration, and the transfer of critically unwell patients to the radiology department. Transcranial Doppler (TCD) ultrasound offers a bedside radiation-free alternative, but concerns regarding diagnostic performance have limited widespread use. The aim of this study was to assess whether routine TCD monitoring in patients with aSAH influenced the incidence of delayed cerebral ischemia (DCI) and long-term functional outcomes. This retrospective single-institution study analyzed patients who were treated for aSAH before (2019-2020) and after (2021-2022) the introduction of routine TCD monitoring. Clinicodemographic data were analyzed, with propensity score matching and multivariable regression used to control for confounders. The primary outcome was the modified Rankin Scale (mRS) score at 6 months, with DCI incidence as a secondary outcome. Of 466 patients (305 female, mean age 56.6 years) included in this analysis, 239 were in the pre-TCD group and 227 were in the post-TCD group. Baseline demographics and comorbidities were similar between the groups, as was the 6-month favorable functional outcome (mRS scores 0-2; 74.5% vs 73.6%, p = 0.906). However, following propensity score matching, routine TCD monitoring was found to be associated with reduced odds of poor outcome (matched OR 0.50, p = 0.010). The incidence of DCI was lower in the post-TCD cohort (18.1% vs 25.5%) and multivariable analysis confirmed a protective effect of routine TCD monitoring on the incidence of DCI (OR 0.48, p = 0.004). Counterfactual analysis indicated an absolute risk reduction for clinical DCI of 5.3% (p = 0.026) and relative risk reduction of 18.7% in association with the use of TCD. The implementation of routine TCD monitoring was associated with improved functional outcomes and a reduced incidence of DCI in patients with aSAH. These findings support routine TCD use for early vasospasm detection and timely intervention, but require prospective multicenter validation.

Nonlinearity, baseline drift, humidity interference, and selectivity are among the primary challenges associated with direct current measurements in semiconducting gas sensors today. This study demonstrates that integrating PtSnO2 and MXene-based sensors enables high-performance gas sensing by effectively mitigating humidity-induced interference. Impedance measurements across multiple frequencies were employed, yielding sensors with tunable gas responses, low noise, extended dynamic range, enhanced baseline stability, and minimal humidity cross-sensitivity. PtSnO2 was optimized for simultaneous NH3 and relative humidity (RH) detection, while MXene served as a dedicated RH sensor. A multilayer perceptron was trained on the impedance dataset to deconvolute and accurately predict RH and NH3 concentrations. The proposed sensor system and analytical framework were benchmarked against commercial DHT22 (humidity) and DFrobot (NH3) sensors, demonstrating superior performance and sensitivity. This methodology is extendable to other material systems, including metal oxides and transition metal dichalcogenides, for advanced gas-sensing applications. These findings advance gas-sensing technology by improving detection accuracy and robustness in industrial safety, environmental monitoring, and biomedical diagnostics, including early disease detection.

The inflammatory response contributes to the progression and prognosis of depression. The C-reactive protein-to-lymphocyte ratio (CLR) is increasingly recognized as a promising indicator of systemic inflammatory activity. Yet the relationship between CLR and depression is still uncertain. The conducted study aimed to investigate the potential link between CLR and depression. We analyzed 12,578 participants aged ≥20 years from National Health and Nutrition Examination Survey 1999 to 2010. CLR ([C-reactive protein mg/L]/[lymphocyte × 103/µL]) was calculated from fasting blood. Depression was defined as Patient Health Questionnaire-9 ≥ 10. Five sequential logistic models adjusted for demographics, lifestyle and comorbidities; quartile and trend analyses were performed, followed by stratified assessments. Overall depression prevalence was 8.53%. Each 0.1-unit CLR increment was associated with 11% higher odds of depression in the fully adjusted model (odds ratio = 1.11; 95% confidence intervals 1.01-1.22). Participants in the highest CLR quartile showed 36% increased risk versus the lowest quartile (odds ratio = 1.36; 95% confidence intervals 1.10-1.66; P-trend = .004). Findings were consistent across sex, age, education, smoking and metabolic subgroups. Higher CLR, reflecting combined innate activation and adaptive suppression, is significantly and dose-dependently associated with depression. This inexpensive, routinely available biomarker may aid early risk detection and guide anti-inflammatory treatment strategies; prospective studies are needed to confirm causality.

Alzheimer’s disease (AD) is marked by amyloid plaques, hyperphosphorylated TAU proteins, and neuroinflammation. The APP/PS1 mouse model is widely used to study AD pathogenesis. In this study, we investigated the expression of chemokines and their receptors, which may play a role in AD’s pathological mechanisms, using brain cortex tissue from female APP/PS1 mice aged 20–21 months. We analyzed several chemokine receptors (CCR1, CCR2, CCR3, CCR4, CCR6, CCR7, CCR9, and CCR10) by Western blot and focused on CCR6, CCR7, and CCR10 using RT-PCR. Additionally, we quantified the levels of chemokines (CCL6, CCL8, CCL19, CCL20, CCL24, and CCL27) by RT-PCR. Our results showed a significant decrease in CCL8 and CCL19, along with their respective receptors, in the APP/PS1 mice compared to controls. On the other hand, we observed a notable increase in CCL6, CCL24, CCL20, CCL27, and their receptors. Chemokines like CCL8 and CCL20, involved in inflammatory responses, may reveal how neuroinflammation contributes to AD. CCL19 and CCL27 are linked to immune cell trafficking, which may help explain immune cell interactions with amyloid plaques and TAU tangles in the CNS. Overall, the altered expression of chemokines such as CCL24 could serve as biomarkers for early AD detection and monitoring disease progression. These findings suggest potential therapeutic targets to modulate immune responses and reduce neuroinflammation in AD.