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Timely identification of cognitive decline is critical for effective management of Alzheimer's disease and related dementias (ADRD). Early insights into disease progression can inform preventive strategies and personalized care. This study leverages longitudinal data from the All of Us Research Program to examine comorbidity patterns and progression timing from mild cognitive impairment (MCI) to dementia. Using a concept-name-based SQL pipeline in the All of Us Curated Data Repository (CDRv8), we identified participants with MCI and subsequent dementia diagnoses, classifying cognitive stages and calculating MCI-to-dementia intervals. Diagnosis timelines were aligned with comorbidity data to examine chronic condition distributions before, during, and after the transition. We applied dynamic time warping (DTW)-based hierarchical clustering to comorbidity sequences for pattern discovery. Analyses focused on hypertension, type 2 diabetes, depression, and cardiovascular disease. Cluster validity was assessed using silhouette scores and the Davies-Bouldin index. Associations between comorbidities and conversion timing were evaluated using nonparametric tests. Among 2,011 MCI participants, 281 progressed to dementia. The median time to conversion was 1,132 days (IQR: 594-1,765). Hypertension, diabetes, and depression were the most prevalent comorbidities and were each significantly associated with faster progression (p < 0.01). DTW-based clustering revealed three subgroups: (1) rapid converters with dense cardiometabolic burden near diagnosis, (2) gradual converters with steady comorbidity accumulation, and (3) low-burden profiles. Cluster separation was supported by validation metrics. Findings suggest that abrupt convergence of chronic conditions may signal accelerated transition to dementia. This study demonstrates the feasibility of deriving cognitive trajectories from All of Us data and reveals comorbidity profiles linked to progression speed. Findings support the development of explainable, fairness-aware AI models to improve early risk prediction. Future work will integrate these models into clinical workflows to support timely, targeted dementia care, particularly for groups historically affected by diagnostic delays and limited access.

COVID-19 has introduced new challenges in dementia research, with growing evidence suggesting a potential role in accelerating cognitive decline. However, it remains unclear whether COVID-19 acts as an independent risk factor or amplifies existing vulnerabilities. This study applies AI-enhanced analysis to investigate post-COVID cognitive decline and its associated health patterns. These findings contribute to the development of predictive models that integrate infection history and clinical risk profiles to support early detection and risk stratification. Using retrospective data from 26,952 individuals aged 50 and older with confirmed COVID-19, we defined a study group (n =1,721) with new cognitive impairment diagnoses within 30 days post-infection and a control group (n =25,231) without such diagnoses. In Phase 1, we employed unsupervised learning techniques, including clustering and association rule mining, to identify comorbidity patterns within the study group. Phase 2 applied logistic regression and supervised learning models (Random Forest, XGBoost), incorporating SHAP values for model interpretability. All analyses were adjusted for age, sex, comorbidities, education, and selected social and behavioral factors available in the dataset. COVID-19 severity was independently associated with an increased risk of post-infection cognitive impairment after adjusting for age, sex, comorbidities, and socioeconomic factors (adjusted OR > 2.0, p < 0.001). Clustering analysis identified high-risk subgroups defined by co-occurring cardiometabolic, neuropsychiatric, and inflammatory conditions, which contributed to higher risks of cognitive impairment. Supervised machine learning models achieved strong predictive performance (AUC > 0.85), with key predictors including age, depression, and education level. Differences in risk patterns across the study and control subgroups suggest the utility of adaptable modeling strategies to support individualized risk estimation. COVID-19 appears to be a significant driver of cognitive impairment in older adults, particularly among individuals with specific comorbid profiles. Our findings reinforce the importance of integrating infection history, comorbidities, and individual background factors into models of cognitive decline risk. This study supports the development of explainable AI tools that model varied cognitive trajectories and inform individualized intervention planning across a range of patient profiles. Future work will focus on refining these models for clinical integration and validating their utility in prospective real-world settings.

The development of a rapid, sensitive, and straightforward detection method of prostate‐specific antigen (PSA) is indispensable for the early diagnosis of prostate cancer (PCa). This work relates an electrochemical method using functionalized single‐stranded DNA aptamer to diagnose PCa and benign prostate hyperplasia. The sensing platform relies on PSA recognition by aptamer/Au/GO‐nanohybrid‐modified glassy carbon electrode. Besides ferrocyanide TiO2/carbon quantum dots (CQDs) probe is used to investigate the effect of nanoparticle‐containing electrolyte. Optimization of incubation time of aptamer/Au/GO‐nanohybrid and volume fraction of nafion were done using Design Expert 10 software reporting 42.4 h and 0.095% V/V, respectively. In ferrocyanide medium, PSA detection as low as 3, 2.96, and 0.85 ng mL−1 was achieved with a dynamic range from 0.5 to 7 ng ml−1, in accord with clinical values, using cyclic voltammetry, square wave voltammetry, and electrochemical impedance spectroscopy, respectively. Moreover, this sensor exhibited conspicuous performance in TiO2/CQDs‐containing medium with different pH values of 5.4 and 8 to distinguish total PSA and free PSA, resulting in very low limit of detections, 0.028, and 0.007 ng ml−1, respectively. The results manifested the proposed system as a forthcoming sensor in a clinical and point of care analysis of PSA.

Purpose: Recently, bone tissue engineering as a new strategy is used to repair and replace bone defects due to limitations in allograft and autograft methods. In this regard, we prepared nanofibrous scaffolds composed of polycaprolactone and magnesium oxide nanoparticles using the electrospinning technique for possible bone tissue engineering applications. Methods: The fabricated composites were characterized via scanning electron microscopy imaging of scaffolds and seeded cells, water contact angle, DAPI staining, and MTT assay. Then osteogenic differentiation of adipose-derived mesenchymal stem cells cultured on this composite scaffold was determined by standard osteogenic marker tests, including alkaline phosphatase activity, calcium deposition, and expression of osteogenic differentiation genes in the laboratory conditions. Results: The Scanning electron microscopy analysis demonstrated that the diameter of nanofibers significantly decreased from 1029.25±209.349 µm to 537.83+0.140 nm, with the increase of MgO concentration to 2% (p<0.05). Initial adhesion and proliferation of the adipose-derived mesenchymal stem cells on magnesium oxide/polycaprolactone scaffolds were significantly enhanced with the increasing of magnesium oxide concentration (p<0.05). The 2% magnesium oxide/polycaprolactone nanofibrous scaffold showed significant increase in ALP activity (p<0.05) and osteogenic-related gene expressions (Col1a1 and OPN) (p<0.05) in compared to pure polycaprolactone and (0, 0.5 and 1%) magnesium oxide/polycaprolactone scaffolds. Conclusion: According to the results, it was demonstrated that magnesium oxide/polycaprolactone composite nanofibers have considerable osteoinductive potential, and taking together adipose-derived mesenchymal stem cells-magnesium oxide/polycaprolactone composite nanofibers can be a proper bio-implant to usage for bone regenerative medicine applications. Future in vivo studies are needed to determine this composite therapeutic potential.

Regenerative endodontics seeks to restore the vascularized pulp–dentin complex following conventional root canal therapy, yet reliable neovascularization within the constrained root canal remains a key challenge. This study investigates the development of an injectable, dual-curing hydrogel based on methacrylated decellularized amniotic membrane (dAM-MA) and compares its performance to a conventional gelatin methacryloyl (GelMA). The dAM-MA platform was designed for biphasic release, incorporating both free vascular endothelial growth factor (VEGF) for an initial burst and matrix-metalloproteinase-cleavable VEGF conjugates for sustained delivery. The dAM-MA hydrogel achieved shape-fidelity via thermal gelation at 37 °C and possessed tunable stiffness (0.5–7.8 kPa) after visible-light irradiation. While showing high cytocompatibility comparable to GelMA (>125% hDPSC viability), the dAM-MA platform markedly outperformed the control in promoting endothelial tube formation (up to 800 µm total length; 42 branch points at 96 h). The biphasic VEGF release from dAM-MA matched physiological injury kinetics, driving both early chemotaxis and late vessel maturation. These results demonstrate that dAM-MA hydrogels combine native extracellular matrix complexity with practical, dual-curing injectability and programmable VEGF kinetics, offering a promising scaffold for minimally invasive pulp–dentin regeneration.

Background COVID‐19 has introduced new challenges in dementia research, with growing evidence suggesting a potential role in accelerating cognitive decline. However, it remains unclear whether COVID‐19 acts as an independent risk factor or amplifies existing vulnerabilities. This study applies AI‐enhanced analysis to investigate post‐COVID cognitive decline and its associated health patterns. These findings contribute to the development of predictive models that integrate infection history and clinical risk profiles to support early detection and risk stratification. Methods Using retrospective data from 26,952 individuals aged 50 and older with confirmed COVID‐19, we defined a study group (n =1,721) with new cognitive impairment diagnoses within 30 days post‐infection and a control group (n =25,231) without such diagnoses. In Phase 1, we employed unsupervised learning techniques, including clustering and association rule mining, to identify comorbidity patterns within the study group. Phase 2 applied logistic regression and supervised learning models (Random Forest, XGBoost), incorporating SHAP values for model interpretability. All analyses were adjusted for age, sex, comorbidities, education, and selected social and behavioral factors available in the dataset. Results COVID‐19 severity was independently associated with an increased risk of post‐infection cognitive impairment after adjusting for age, sex, comorbidities, and socioeconomic factors (adjusted OR > 2.0, p < 0.001). Clustering analysis identified high‐risk subgroups defined by co‐occurring cardiometabolic, neuropsychiatric, and inflammatory conditions, which contributed to higher risks of cognitive impairment. Supervised machine learning models achieved strong predictive performance (AUC > 0.85), with key predictors including age, depression, and education level. Differences in risk patterns across the study and control subgroups suggest the utility of adaptable modeling strategies to support individualized risk estimation. Conclusion COVID‐19 appears to be a significant driver of cognitive impairment in older adults, particularly among individuals with specific comorbid profiles. Our findings reinforce the importance of integrating infection history, comorbidities, and individual background factors into models of cognitive decline risk. This study supports the development of explainable AI tools that model varied cognitive trajectories and inform individualized intervention planning across a range of patient profiles. Future work will focus on refining these models for clinical integration and validating their utility in prospective real‐world settings.

Background Timely identification of cognitive decline is critical for effective management of Alzheimer’s disease and related dementias (ADRD). Early insights into disease progression can inform preventive strategies and personalized care. This study leverages longitudinal data from the All of Us Research Program to examine comorbidity patterns and progression timing from mild cognitive impairment (MCI) to dementia. Methods Using a concept‐name–based SQL pipeline in the All of Us Curated Data Repository (CDRv8), we identified participants with MCI and subsequent dementia diagnoses, classifying cognitive stages and calculating MCI‐to‐dementia intervals. Diagnosis timelines were aligned with comorbidity data to examine chronic condition distributions before, during, and after the transition. We applied dynamic time warping (DTW)‐based hierarchical clustering to comorbidity sequences for pattern discovery. Analyses focused on hypertension, type 2 diabetes, depression, and cardiovascular disease. Cluster validity was assessed using silhouette scores and the Davies–Bouldin index. Associations between comorbidities and conversion timing were evaluated using nonparametric tests. Results Among 2,011 MCI participants, 281 progressed to dementia. The median time to conversion was 1,132 days (IQR: 594–1,765). Hypertension, diabetes, and depression were the most prevalent comorbidities and were each significantly associated with faster progression (p < 0.01). DTW‐based clustering revealed three subgroups: (1) rapid converters with dense cardiometabolic burden near diagnosis, (2) gradual converters with steady comorbidity accumulation, and (3) low‐burden profiles. Cluster separation was supported by validation metrics. Findings suggest that abrupt convergence of chronic conditions may signal accelerated transition to dementia. Conclusion This study demonstrates the feasibility of deriving cognitive trajectories from All of Us data and reveals comorbidity profiles linked to progression speed. Findings support the development of explainable, fairness‐aware AI models to improve early risk prediction. Future work will integrate these models into clinical workflows to support timely, targeted dementia care, particularly for groups historically affected by diagnostic delays and limited access.

An electrochemical aptasensor is described for the voltammetric determination of lipopolysaccharide (LPS) from Escherichia coli 055:B5. Aptamer chains were immobilized on the surface of a glassy carbon electrode (GCE) via reduced graphene oxide and gold nanoparticles (RGO/AuNPs). Fast Fourier transform infrared, X-ray diffraction and transmission electron microscopy were used to characterize the nanomaterials. Cyclic voltammetry, square wave voltammetry and electrochemical impedance spectroscopy were used to characterize the modified GCE. The results show that the modified electrode has a good selectivity for LPS over other biomolecules. The hexacyanoferrate redox system, typically operated at around 0.3 V (vs. Ag/AgCl) is used as an electrochemical probe. The detection limit is 30 fg·mL −1 . To decrease the electrochemical potential for detection of LPS, Mg/carbon quantum dots were used as redox active media. They decrease the detection potentialto 0 V and the detection of limit (LOD) to 1 fg·mL −1 . The electrode was successfully used to analyze serum of patients and healthy persons. Graphical abstract Schematic representation of the modification of reduced graphene oxide gold nanoparticles with aptamer chains to immobilize on the glassy carbon electrode surface for electrochemical detection of lipopolysaccharides.

Developing smart scaffolds with drug release capability is one of the main approaches to bone tissue engineering. The current study involves the fabrication of novel gelatin (G)–hydroxyapatite (HA)-/vitamin D (VD)-loaded graphene oxide (GO) scaffolds with different concentrations through solvent-casting method. Characterizations confirmed the successful synthesis of HA and GO, and VD was loaded in GO with 36.87 ± 4.87% encapsulation efficiency. Physicochemical characterizations showed that the scaffold containing 1% VD-loaded GO had the best mechanical properties and its porosity percentage and density was in the range of natural spongy bone. All scaffolds were degraded after 1-month, subjecting to phosphate buffer saline. The release profile of VD did not match any mathematical kinetics model, porosities and the degradation rate of the scaffolds were dominant controlling factors of release behavior. Studies on the bioactivity of scaffolds immersed in simulated body fluid indicated that VD and HA could encourage the formation of secondary apatite crystals in vitro. Buccal fat pad-derived stem cells (BFPSCs) were seeded on the scaffolds, MTT assay, alkaline phosphatase activity as an indicator of osteoconductivity, and cell adhesion were conducted in order to evaluate in vitro biological responses. All scaffolds highly supported cell adhesion, MTT assay indicated better cell viability in 0.5% VD-loaded GO containing scaffold, and the scaffold enriched with 2% VD-loaded GO performed the most ALP activity. The results demonstrated the potential of these scaffolds to induce bone regeneration.

The current design requirement of cutaneous wound healing is an ideal wound dressing via biodegradable property through which fibroblasts can support, migrate and proliferate. This study aimed to evaluate the effect of the synthesized hybrid scaffold based on chitosan polymer and a natural source of calcium (Ca) and vitamin D on wound healing. To achieve this purpose, three scaffolds include in chitosan (CS), eggshell + chitosan (ES/CS) and chitosan + eggshell + vitamin D (CS/ES/Vit D) were fabricated using the freeze-drying approach. Synthesized scaffolds were characterized by field emission scanning electron microscopy (FESEM), Fourier transformed infrared (FTIR), X-ray diffraction (XRD) and MTT assay. Histological examinations of the wound healing were performed using hematoxylin–eosin (H&E) and Masson's trichrome staining methods on different days in the rat full-thickness skin wound model. Finally, the effect of the synthesized scaffold was evaluated on the TGF-β1 gene expression. The results of the scaffold characterization showed that scaffolds have the homogeneity structure. The MTT assay indicated that the cultured fibroblasts on the ES/CS and Vit D/ES/CS scaffold had viability higher than those cultured on CS. Also, histological studies demonstrated an increased effect on epithelialization and collagen production and accelerated wound healing using designed scaffolds. The TGF-β1 gene expression in all scaffolds was not significantly different between test groups and controls. In general, it can be concluded that the synthesized scaffolds have the potential to accelerate wound healing and can be used as a suitable scaffold in wound management and skin regeneration. Graphic abstract